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Environmental sustainability assessment comparing through the means of lifecycle assessment the potential environmental impacts of the use of alternative feedstock (biomass, recycled plastics, CO2) for plastic articles in comparison to using current feedstock (oil and gas)

Draft report for stakeholder consultation (part 1):

- Meta-analysis of selected existing studies

- Draft method for LCA of plastics

Authors: Nessi S., Bulgheroni C., Konti A., Sinkko T., Tonini D., Pant R. (project leader)

Deadline for consultation comments: December 19, 2018

EUR XXXXX XX

Comparative LCA of alternative feedstock for plastic production – DRAFT FOR CONSULTATION Part I

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and 1 knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The 2 scientific output expressed does not imply a policy position of the European Commission. Neither the European 3 Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this 4 publication. 5 6 7 Ispra: European Commission, 2018 8 9 © European Union, 2018 10 11 The reuse policy of the European Commission is implemented by Commission Decision 2011/833/EU of 12 December 12 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Reuse is authorised, provided the source 13 of the document is acknowledged and its original meaning or message is not distorted. The European Commission shall 14 not be liable for any consequence stemming from the reuse. For any use or reproduction of photos or other material 15 that is not owned by the EU, permission must be sought directly from the copyright holders. 16 17 All content © European Union, 2018 18 19 20 How to cite this report: Author(s), Title, EUR (where available), Publisher, Publisher City, Year of Publication, ISBN 21 978-92-79-XXXXX-X (where available), doi:10.2760/XXXXX (where available), JRCXXXXXX 22 23

Administrative Arrangement

JRC. 34854-2017

DG GROW N SI2.762599

"Environmental sustainability assessment comparing through the means of lifecycle assessment the potential environmental impacts of the use of alternative

feedstock (biomass, recycled plastics, CO2) for plastic articles in comparison to using current feedstock (oil and gas)"

Draft report for stakeholder consultation (part I):

- Meta-analysis of selected existing studies - Draft method for LCA of plastics

Status: November 20, 2018

Deadline for consultation comment: December 19, 2018

Authors: Nessi S., Bulgheroni C., Konti A., Sinkko T., Tonini D., Pant R. (project leader)


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Contents

Acronyms and Abbreviations .................................................................................................... 10

Definitions ............................................................................................................................... 11

1. Context and objectives ......................................................................................................... 22

2. Meta-analysis of selected studies, approaches and information ............................................ 23

2.1 Introduction ...................................................................................................................................................................... 23

2.2 Approach ............................................................................................................................................................................ 23

2.2.1 Screening assessment ..................................................................................................................... 24

2.2.2 Selection of relevant studies for in-depth assessment .................................................................. 25

2.2.3 In-depth assessment....................................................................................................................... 29

2.3 Results ................................................................................................................................................................................. 29

2.3.1 Most relevant aspects of the screening assessment (all studies) .................................................. 29

2.3.2 Challenging methodological aspects (in-depth assessment) ......................................................... 33

2.4 Conclusions ........................................................................................................................................................................ 49

2.5 References .......................................................................................................................................................................... 51

3. Methodology for LCA of plastic articles ................................................................................. 57

3.1 Target audience ............................................................................................................................................................... 57

3.2 Relationship to other methods and standards ................................................................................................... 58

3.3 Terminology used: shall, should and may ............................................................................................................ 59

3.4 How to Use this Document .......................................................................................................................................... 59

3.5 Principles for LCA studies ........................................................................................................................................... 59

3.6 Phases of a LCA study .................................................................................................................................................... 60

4. Goal and Scope Definition .................................................................................................... 62

4.1 Defining the goal of the LCA study ........................................................................................................................... 62

4.2 Defining the Scope of the LCA Study ....................................................................................................................... 63

4.2.1 Description/characteristics of the studied product ....................................................................... 63

4.2.2 Functional unit and reference flow ................................................................................................ 65

4.2.3 System boundary ............................................................................................................................ 67

4.2.4 Selecting Impact Categories and Assessment Methods ................................................................. 69

4.2.5 Selecting additional technical and environmental information to be included in the LCA ............ 72

4.2.6 Assumptions/limitations ................................................................................................................. 74

5. Life Cycle Inventory .............................................................................................................. 75


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5.1 Screening step (recommended) ................................................................................................................................ 76

5.2 Life Cycle Stages .............................................................................................................................................................. 76

5.2.1 Raw Material Acquisition and Pre-processing (Cradle-to- Gate).................................................... 77

5.2.2 Agricultural production .................................................................................................................. 77

5.2.3 Capital goods .................................................................................................................................. 78

5.2.4 Production ...................................................................................................................................... 78

5.2.5 Product Distribution and Storage ................................................................................................... 78

5.2.6 Use stage ........................................................................................................................................ 78

5.2.7 End-of-Life ...................................................................................................................................... 80

5.3 Nomenclature for the Life Cycle Inventory .......................................................................................................... 80

5.4 Handling multi-functional processes ...................................................................................................................... 81

5.5 Modelling requirements ............................................................................................................................................... 82

5.5.1 Agricultural production .................................................................................................................. 83

5.5.2 Animal husbandry ........................................................................................................................... 95

5.5.3 Capital goods (infrastructures and equipment) ........................................................................... 112

5.5.4 Logistics and transport ................................................................................................................. 112

5.5.5 Packaging ...................................................................................................................................... 119

5.5.6 Use stage ...................................................................................................................................... 122

5.5.7 Electricity use ................................................................................................................................ 126

5.5.8 End-of-life stage ............................................................................................................................ 131

5.5.9 Extended product lifetime ............................................................................................................ 168

5.5.10 Greenhouse gas emissions and removals................................................................................... 169

5.5.11 (Temporary) carbon storage and delayed emissions ................................................................. 171

5.5.12 Land Use Changes ....................................................................................................................... 172

5.6 Data collection ................................................................................................................................................................180

5.6.1 Company-specific data ................................................................................................................. 180

5.6.2 Secondary data ............................................................................................................................. 181

5.6.3 Data gaps ...................................................................................................................................... 182

5.6.4 Sampling procedure...................................................................................................................... 182

5.6.5 Cut off ........................................................................................................................................... 186

5.6.7 Data collection: summary of requirement and relation to the next methodological phases in a LCA study ............................................................................................................................................... 187

5.7 Data quality assessment and requirements .......................................................................................................189


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5.7.1 Data quality criteria ...................................................................................................................... 189

5.7.2 Semi-quantitative assessment of data quality ............................................................................. 191

5.7.3 Data quality assessment of company-specific datasets ............................................................... 194

5.7.4 Data quality assessment of secondary datasets ........................................................................... 198

5.7.5 The Data Quality Rating (DQR) of the study ................................................................................. 198

5.7.6 Data quality requirements ............................................................................................................ 199

6. Life Cycle Impact Assessment ............................................................................................. 200

6.1 Classification and Characterisation (mandatory) ............................................................................................200

6.1.1 Classification of Life Cycle Inventory Flows .................................................................................. 200

6.1.2 Characterisation of Life Cycle Inventory Flows ............................................................................ 201

6.2 Normalisation and Weighting (recommended/optional) ............................................................................205

6.2.1 Normalisation of Life Cycle Impact Assessment Results (recommended) ................................... 205

6.2.2 Weighting of Environmental Footprint Impact Assessment Results (recommended) ................. 206

6.3 Assessment of biodiversity impacts ......................................................................................................................206

7. Interpretation of the LCA results ......................................................................................... 208

7.1 Assessment of the robustness of the LCA model .............................................................................................208

7.2 Identification of Hotspots: most relevant impact categories, life cycle stages, processes and elementary flows ..................................................................................................................................................................209

7.2.1 Procedure to identify the most relevant impact categories ........................................................ 209

7.2.2 Procedure to identify the most relevant life cycle stages ............................................................ 209

7.2.3 Procedure to identify the most relevant processes ..................................................................... 210

7.2.4 Dealing with negative numbers .................................................................................................... 210

7.2.5 Summary of requirements............................................................................................................ 210

7.2.6 Example ........................................................................................................................................ 211

7.3 Conclusions, Recommendations and Limitations ............................................................................................212

8. Reporting ........................................................................................................................... 213

8.1 General ..............................................................................................................................................................................213

8.2 Reporting elements ......................................................................................................................................................213

8.2.1 First element: Summary ............................................................................................................... 213

8.2.2 Second element: Main Report ...................................................................................................... 214

8.2.3 Third element: Annex ................................................................................................................... 216

8.2.4 Fourth element: Confidential Report ........................................................................................... 216

9. References ......................................................................................................................... 217


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Annex A: Full list of studies screened in the meta-analysis ...................................................... 227

Annex B: List of normalisation and weighting factors .............................................................. 241

Annex C: Example of rating criteria for semi-quantitative assessment of data-quality ............. 245

Annex D: Default loss rates per type of product ...................................................................... 247

Annex E: List of default values for A, R1, R2, R3 and Qs/Qp ..................................................... 251

Annex F: Background information to calculate R2 for packaging materials .............................. 252

Annex G: Identifying Appropriate Nomenclature and Properties for Specific Flows ................. 254

Annex H: EF-compliant datasets ............................................................................................. 258

H.1 List of all technical requirements to be fulfilled by datasets to be recognised as EF compliant .258

H.1.1 Documentation ............................................................................................................................ 258

H.1.2 Nomenclature .............................................................................................................................. 258

H.1.3 Review .......................................................................................................................................... 258

H.1.4 Methodological requirements ..................................................................................................... 259

H.2 Aggregation .....................................................................................................................................................................260

H.3 Data quality criteria and scores ..............................................................................................................................261

ANNEX I: Definition of product types for marine litter accounting ........................................... 265


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Acronyms and Abbreviations 1 A Article 2 ADEME Agence de l'Environnement et de la Maîtrise de l'Energie 3 B2B Business to Business 4 B2C Business to Consumer 5 Bio-PE (Bio-based) PolyEthylene 6 Bio-PET (Bio-based) PolyEthylene Terephatalate 7 Bio-PP (Bio-based) PolyPropylene 8 Bio-PVC (Bio-based) PolyVinyl Chloride 9 Bio-PUR (Bio-based) PolyURethane 10 BSI British Standards Institution 11 C Cradle 12 CF Characterisation Factor 13 CFCs Chlorofluorocarbons 14 CPA Statistical Classification of Products by Activity 15 dLUC direct Land Use Change 16 DQR Data Quality Rating 17 EC European Commission 18 EIA Environmental Impact Assessments 19 ELCD European Reference Life Cycle Database 20 EF Environmental Footprint 21 EMAS Eco-Management and Audit Schemes 22 EMS Environmental Management Schemes 23 EoL End-of-Life 24 EPD Environmental Product Declaration 25 FG Factory Gate 26 GHG Greenhouse Gas 27 GR Grave 28 GRI Global Reporting Initiative 29 I Intermediate 30 ILCD International Reference Life Cycle Data System 31 iLUC Indirect Land Use Change 32 IPCC Intergovernmental Panel on Climate Change 33 ISIC International Standard Industrial Classification 34 ISO International Organization for Standardization 35 IUCN International Union for Conservation of Nature and Natural Resources 36 LCA Life Cycle Assessment 37 LCI Life Cycle Inventory 38 LCIA Life Cycle Impact Assessment 39 LCT Life Cycle Thinking 40 M Monomer 41 NACE Nomenclature Générale des Activités Economiques dans les Communautés 42

Européennes 43 OEF Organisation Environmental Footprint 44 P Polymer 45 PAS Publicly Available Specification 46 PBAT Polybutylene adipate co-terephthalate 47 PBS Polybutylene succinate 48


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PBSA Polybutylene succinate adipate 1 PCR Product Category Rule 2 PEF Polyethylene furanoate 3 PEFCR Product Environmental Footprint Category Rule 4 PHAs Polyhydroxyalkanoates 5 PLA Polylactic acid 6 PTT Polytrimethylene terephthalate 7 TPS Thermoplastic starch 8 WRI World Resources Institute 9 WBCSD World Business Council for Sustainable Development 10

Definitions 11

Acidification – Impact category that addresses impacts due to acidifying substances in the environment. 12 Emissions of NOx, NH3 and SOx lead to releases of hydrogen ions (H+) when the gases are mineralised. The 13 protons contribute to the acidification of soils and water when they are released in areas where the buffering 14 capacity is low, resulting in forest decline and lake acidification. 15

Activity data - This term refers to information which is associated with processes while modelling Life Cycle 16 Inventories (LCI). In the PEF Guide it is also called “non-elementary flows”. The aggregated LCI results of the 17 process chains that represent the activities of a process are each multiplied by the corresponding activity 18 data (WRI, 2011a) and then combined to derive the environmental footprint associated with that cess (See 19 Figure 1). Examples of activity data include quantity of kilowatt-hours of electricity used, quantity of fuel 20 used, output of a process (e.g. waste), number of hours equipment is operated, distance travelled, floor area 21 of a building, etc. In the context of PEF the amounts of ingredients from the bill of material (BOM) shall always 22 be considered as activity data. 23

Additional Environmental Information – Impact categories and other environmental indicators that are 24 calculated and communicated alongside LCA results. 25

Aggregated dataset - This term is defined as a life cycle inventory of multiple unit processes (e.g. material or 26 energy production) or life cycle stages (cradle-to-gate), but for which the inputs and outputs are provided 27 only at the aggregated level. Aggregated datasets are also called "LCI results", “cumulative inventory” or 28 “system processes” datasets. The aggregated dataset can have been aggregated horizontally and/or 29 vertically. Depending on the specific situation and modelling choices a "unit process" dataset can also be 30 aggregated. See Figure 1. 31


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Figure 1: Definition of a unit process dataset and an aggregated process dataset (UNEP, 2016) 2

Allocation – An approach to solving multi-functionality problems. It refers to “partitioning the input or output 3 flows of a process or a product system between the product system under study and one or more other 4 product systems” (ISO 14040:2006). 5

Application specific – It refers to the generic aspect of the specific application in which a material is used. 6 For example, the average recycling rate of PET in bottles. 7

Attributional – Refers to process-based modelling intended to provide a static representation of average 8 conditions, excluding market-mediated effects. 9

Average Data – Refers to a production-weighted average of specific data. 10

Background processes – Refers to those processes in the product life cycle for which no direct access to 11 information is possible. For example, most of the upstream life-cycle processes and generally all processes 12 further downstream will be considered part of the background processes. 13

Benchmark – A standard or point of reference against which any comparison can be made. In the context of 14 PEF, the term ‘benchmark’ refers to the average environmental performance of the representative product 15 sold in the EU market. A benchmark may eventually be used, if appropriate, in the context of communicating 16 environmental performance of a product belonging to the same category. 17

Bill of materials – A bill of materials or product structure (sometimes bill of material, BOM or associated list) 18 is a list of the raw materials, sub-assemblies, intermediate assemblies, sub-components, parts and the 19 quantities of each needed to manufacture an end product. 20

Biodegradation rate (biodegradability) – Is determined according to testing procedures recommended in 21 compostability standards. Normally refers to the percentage of carbon converted to CO2 during the 22 biodegradation process. 23


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Business to Business (B2B) – Describes transactions between businesses, such as between a manufacturer 1 and a wholesaler, or between a wholesaler and a retailer. 2

Business to Consumers (B2C) – Describes transactions between business and consumers, such as between 3 retailers and consumers. According to ISO 14025:2006, a consumer is defined as “an individual member of 4 the general public purchasing or using goods, property or services for private purposes”. 5

Characterisation – Calculation of the magnitude of the contribution of each classified input/output to their 6 respective EF impact categories, and aggregation of contributions within each category. This requires a linear 7 multiplication of the inventory data with characterisation factors for each substance and EF impact category 8 of concern. For example, with respect to the impact category “Climate Change”, CO2 is chosen as the 9 reference substance and kg CO2-equivalents as the reference unit. 10

Characterisation factor – Factor derived from a characterisation model which is applied to convert an 11 assigned Life Cycle Inventory result to the common unit of the impact category indicator (based on ISO 12 14040:2006). 13

Classification – Assigning the material/energy inputs and outputs tabulated in the Resource and Emissions 14 Profile to EF impact categories according to each substance’s potential to contribute to each of the EF impact 15 categories considered. 16

Co-function - Any of two or more functions resulting from the same unit process or product system. 17

Commissioner of the LCA study - Organisation (or group of organisations) that finances the LCA study in 18 accordance with the LCA Guide (definition adapted from ISO 14071/2014, point 3.4). 19

Company-specific data – It refers to directly measured or collected data from one or multiple facilities (site-20 specific data) that are representative for the activities of the company. It is synonymous to “primary data”. 21 To determine the level of representativeness a sampling procedure can be applied. 22

Comparative Assertion - An environmental claim regarding the superiority or equivalence of one product 23 versus a competing product that performs the same function (adapted from ISO 14025:2006). 24

Co-product – Any of two or more products resulting from the same unit process or product system (ISO 25 14040:2006). 26

Cradle to Gate – A partial product supply chain, from the extraction of raw materials (cradle) up to the 27 manufacturer’s “gate”. The distribution, storage, use stage and end-of-life stages of the supply chain are 28 omitted. 29

Cradle to Grave – A product’s life cycle that includes raw material extraction, processing, distribution, 30 storage, use, and disposal or recycling stages. All relevant inputs and outputs are considered for all of the 31 stages of the life cycle. 32

Critical review – Process intended to ensure consistency between a LCA study and the principles and 33 requirements of this Guide (based on ISO 14040:2006). 34

Data Quality – Characteristics of data that relate to their ability to satisfy stated requirements (ISO 35 14040:2006). Data quality covers various aspects, such as technological, geographical and time-related 36 representativeness, as well as completeness and precision of the inventory data. 37


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Data Quality Rating (DQR) - Semi-quantitative assessment of the quality criteria of a dataset based on 1 Technological representativeness, Geographical representativeness, Time-related representativeness, and 2 Precision. The data quality shall be considered as the quality of the dataset as documented. 3

Delayed emissions - Emissions that are released over time, e.g. through long use or final disposal stages, 4 versus a single emission at time t. 5

Direct elementary flows (also named elementary flows) – All output emissions and input resource use that 6 arise directly in the context of a process. Examples are emissions from a chemical process, or fugitive 7 emissions from a boiler directly onsite. See Figure 2. 8

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Figure 2: An example of a partially aggregated dataset, at level 1. The activity data and direct 10 elementary flows are to the left, and the complementing sub-processes in their aggregated form are to 11 the right. The grey text indicates elementary flows 12

Direct Land Use Changes (dLUC) – The transformation from one land use type into another, which takes place 13 in a unique land area and does not lead to a change in another system. 14

Direct substitution - Occurs e.g. when manure nitrogen is applied to agricultural land, directly substituting 15 an equivalent amount of the specific fertiliser nitrogen that the farmer would otherwise have applied. In this 16 case, the animal husbandry system from which the manure is derived is credited for the displaced fertiliser 17 production and use (taking into account differences in transportation, handling, and emissions). 18

Directly attributable – Refers to a process, activity or impact occurring within the defined system boundary. 19

Downstream – Occurring along a product supply chain after the point of referral. 20

Ecotoxicity – Impact category that addresses the toxic impacts on an ecosystem, which damage individual 21 species and change the structure and function of the ecosystem. Ecotoxicity is a result of a variety of different 22 toxicological mechanisms caused by the release of substances with a direct effect on the health of the 23 ecosystem. 24

Elementary flows – In the Life Cycle Inventory, elementary flows include “material or energy entering the 25 system being studied that has been drawn from the environment without previous human transformation, or 26


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material or energy leaving the system being studied that is released into the environment without subsequent 1 human transformation” (ISO 14040:2006, 3.12). Elementary flows include, for example, resources taken from 2 nature or emissions into air, water, soil that are directly linked to the characterisation factors of the impact 3 categories. 4

Environmental aspect – An element of an organisation’s activities or products or services that interacts or 5 can interact with the environment (ISO 14001:2015). 6

Environmental impact – Any change to the environment, whether adverse or beneficial, that wholly or 7 partially results from an organisation’s activities, products or services (EMAS regulation). 8

Environmental mechanism – System of physical, chemical and biological processes for a given impact 9 category linking the Life Cycle Inventory results to category indicators (based on ISO 14040:2006). 10

Environmental profile – The quantified results of a LCA study. It includes the quantification of the impacts 11 for the various impact categories and the additional environmental information considered necessary to be 12 reported. 13

Eutrophication – Nutrients (mainly nitrogen and phosphorus) from sewage outfalls and fertilised farmland 14 accelerate the growth of algae and other vegetation in water. The degradation of organic material consumes 15 oxygen resulting in oxygen deficiency and, in some cases, fish death. Eutrophication translates the quantity 16 of substances emitted into a common measure expressed as the oxygen required for the degradation of dead 17 biomass. 18

External Communication – Communication to any interested party other than the commissioner or the 19 practitioner of the study. 20

Extrapolated Data – Refers to data from a given process that is used to represent a similar process for which 21 data is not available, on the assumption that it is reasonably representative. 22

Flow diagram – Schematic representation of the flows occurring during one or more process stages within 23 the life cycle of the product being assessed. 24

Foreground elementary flows - Direct elementary flows (emissions and resources) for which access to 25 primary data (or company-specific information) is available. 26

Foreground Processes – Refer to those processes in the product life cycle for which direct access to 27 information is available. For example, the producer’s site and other processes operated by the producer or 28 its contractors (e.g. goods transport, head-office services, etc.) belong to the foreground processes. 29

Functional unit – The functional unit defines the qualitative and quantitative aspects of the function(s) 30 and/or service(s) provided by the product being evaluated; the functional unit definition answers the 31 questions “what?”, “how much?”, “how well?”, and “for how long?” 32

Generic Data – Refers to data that is not directly collected, measured, or estimated, but rather sourced from 33 a third-party life-cycle-inventory database or other source that complies with the data quality requirements 34 of the PEF method. 35

Global Warming Potential – Capacity of a greenhouse gas to influence radiative forcing, expressed in terms 36 of a reference substance (for example, CO2-equivalent units) and specified time horizon (e.g. GWP 20, GWP 37 100, GWP 500, for 20, 100, and 500 years respectively). It relates to the capacity to influence changes in the 38


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global average surface-air temperature and subsequent change in various climate parameters and their 1 effects, such as storm frequency and intensity, rainfall intensity and frequency of flooding, etc. 2

Human Toxicity – cancer – Impact category that accounts for the adverse health effects on human beings 3 caused by the intake of toxic substances through inhalation of air, food/water ingestion, penetration through 4 the skin insofar as they are related to cancer. 5

Human Toxicity – non-cancer – Impact category that accounts for the adverse health effects on human 6 beings caused by the intake of toxic substances through inhalation of air, food/water ingestion, penetration 7 through the skin insofar as they are related to non-cancer effects that are not caused by particulate 8 matter/respiratory inorganics or ionising radiation. 9

Impact Assessment – Phase of the LCA analysis aimed at understanding and evaluating the magnitude and 10 significance of the potential environmental impacts for a product system throughout the life cycle of the 11 product (based on ISO 14044:2006). The impact assessment methods provide impact characterisation factors 12 for elementary flows in order to aggregate the impact to obtain a limited number of midpoint and/or damage 13 indicators. 14

Impact Assessment Method – Protocol for quantitative translation of Life Cycle Inventory data into 15 contributions to an environmental impact of concern. 16

Impact Category – Class of resource use or environmental impact to which the Life Cycle Inventory data are 17 related. 18

Impact category indicator – Quantifiable representation of an impact category (based on ISO 14040:2006). 19

Indirect Land Use Changes (iLUC) – Occur when a demand for a certain land use leads to changes, outside 20 the system boundary, i.e. in other land use types. These indirect effects can be mainly assessed by means of 21 economic modelling of the demand for land or by modelling the relocation of activities on a global scale. The 22 main drawbacks of such models are their reliance on trends, which might not reflect future developments. 23 They are commonly used as the basis for political decisions. 24

Indirect substitution - Occurs when a product is substituted but you don’t know by which products exactly 25 (i.e. more technically, when a co-product is assumed to displace a marginal or average market-equivalent 26 product via market-mediated processes). For example, when animal manure is packaged and sold for use in 27 home gardening, the animal husbandry system from which the manure is derived is credited for the 28 production and use of the market-average home gardening fertiliser that is assumed to have been displaced 29 (taking into account differences in transportation, handling, and emissions). 30

Input – Product, material or energy flow that enters a unit process. Products and materials include raw 31 materials, intermediate products and co-products (ISO 14040:2006). 32

Intermediate product – An intermediate product is a product that requires further processing before it is 33 saleable to the final consumer. 34

Ionising Radiation – Impact category that accounts for the adverse health effects on human health caused 35 by radioactive releases. 36

Land Use – Impact category related to use (occupation) and conversion (transformation) of land area by 37 activities such as agriculture, roads, housing, mining, etc. Land occupation considers the effects of the land 38 use, the amount of area involved and the duration of its occupation (changes in quality multiplied by area 39


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and duration). Land transformation considers the extent of changes in land properties and the area affected 1 (changes in quality multiplied by the area). 2

LCA report – Document that summarises the results of the LCA study. In case the commissioner of the LCA 3 study decides to communicate the results of the LCA study (independently from the communication vehicle1 4 used), the LCA report shall be made available for free through the commissioner’s website. The LCA report 5 shall not contain any information that is considered as confidential by the commissioner, however the 6 confidential information shall be provided to the verifier(s). 7

LCA study – Term used to identify the totality of actions needed to calculate the LCA results. It includes the 8 modelisation, the data collection, and the analysis of the results. 9

Life cycle – Consecutive and interlinked stages of a product system, from raw material acquisition or 10 generation from natural resources to final disposal (ISO 14040:2006). 11

Life-Cycle Approach – Takes into consideration the spectrum of resource flows and environmental 12 interventions associated with a product from a supply-chain perspective, including all stages from raw 13 material acquisition through processing, distribution, use, and end-of-life processes, and all relevant related 14 environmental impacts (instead of focusing on a single issue). 15

Life-Cycle Assessment (LCA) – Compilation and evaluation of the inputs, outputs and the potential 16 environmental impacts of a product system throughout its life cycle (ISO 14040:2006). 17

Life-Cycle Impact Assessment (LCIA) – Phase of life cycle assessment that aims at understanding and 18 evaluating the magnitude and significance of the potential environmental impacts for a system throughout 19 the life cycle (ISO 14040:2006). The LCIA methods used provide impact characterisation factors for 20 elementary flows to in order to aggregate the impact to obtain a limited number of midpoint and/or damage 21 indicators. 22

Life Cycle Inventory Analysis (LCI) – Phase of LCA involving the compilation and quantification of inputs and 23 outputs for a product throughout its life cycle (ISO 14040:2006). 24

Life Cycle Inventory (LCI) dataset - A document or file with life cycle information of a specified product or 25 other reference (e.g. site, process), covering descriptive metadata and quantitative life cycle inventory. A LCI 26 dataset could be a unit process dataset2, partially aggregated or an aggregated dataset. 27

Life Cycle Inventory results – Outcome of a Life Cycle Inventory that catalogues the flows crossing the system 28 boundary and provides the starting point for the life cycle impact assessment. 29

1 Communication vehicles includes all the possible ways that can be used to communicate the results of the LCA study to the stakeholders. The list of communication vehicles includes, but it is not limited to, labels, environmental product declarations, green claims, websites, infographics, etc. 2 Unit process dataset is a smallest element considered in the life cycle inventory analysis for which input and output data are quantified (ISO 14040:2006). In LCA practice, both physically not further separable processes (such as unit operations in production plants, then called “unit process single operation”) and also whole production sites are covered under "unit process", then called “unit process, black box” (ILCD Handbook (EC-JRC 2010a)).


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Loading rate – Ratio of actual load to the full load or capacity (e.g. mass or volume) that a vehicle carries per 1 trip. 2

Material-specific – It refers to a generic aspect of a material. For example, the recycling rate of PET. 3

Multi-functionality – If a process or facility provides more than one function, i.e. it delivers several goods 4 and/or services ("co-products"), it is “multifunctional”. In these situations, all inputs and emissions linked to 5 the process must be partitioned between the product of interest and the other co-products in a principled 6 manner. 7

Non-elementary (or complex) flows – In the Life Cycle Inventory, non-elementary flows include all the inputs 8 (e.g. electricity, materials, transport processes) and outputs (e.g. waste, by-products) in a system that need 9 further modelling efforts to be transformed into elementary flows. 10

Normalisation – After the characterisation step, normalisation is an optional step in which the life cycle 11 impact assessment results are multiplied by normalisation factors that represent the overall inventory of a 12 reference unit (e.g. a whole country or an average citizen). Normalised life cycle impact assessment results 13 express the relative shares of the impacts of the analysed system in terms of the total contributions to each 14 impact category per reference unit. When displaying the normalised life cycle impact assessment results of 15 the different impact topics next to each other, it becomes evident which impact categories are affected most 16 and least by the analysed system. Normalised life cycle impact assessment results reflect only the 17 contribution of the analysed system to the total impact potential, not the severity/relevance of the respective 18 total impact. Normalised results are dimensionless, but not additive. 19

Output – Product, material or energy flow that leaves a unit process. Products and materials include raw 20 materials, intermediate products, co-products and releases (ISO 14040:2006). 21

Ozone Depletion – Impact category that accounts for the degradation of stratospheric ozone due to 22 emissions of ozone-depleting substances, for example long-lived chlorine and bromine containing gases (e.g. 23 CFCs, HCFCs, Halons). 24

Particulate Matter/Respiratory Inorganics – Impact category that accounts for the adverse health effects on 25 human health caused by emissions of Particulate Matter (PM) and its precursors (NOx, SOx, NH3) 26

Photochemical Ozone Formation – Impact category that accounts for the formation of ozone at the ground 27 level of the troposphere caused by photochemical oxidation of Volatile Organic Compounds (VOCs) and 28 carbon monoxide (CO) in the presence of nitrogen oxides (NOx) and sunlight. High concentrations of ground-29 level tropospheric ozone damage vegetation, human respiratory tracts and manmade materials through 30 reaction with organic materials. 31

Population - Any finite or infinite aggregation of individuals, not necessarily animate, subject to a statistical 32 study. 33

Practitioner of the LCA study – Individual, organisation or group of organisations that performs the LCA study 34 in accordance with the LCA Guide. The practitioner of the LCA study can belong to the same organisation as 35 the commissioner of the LCA study (adapted from ISO 14071/2014, point 3.6). 36


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Primary data - This term refers to data from specific processes within the supply-chain of the company 1 applying the LCA Guide. Such data may take the form of activity data, or foreground elementary flows3 (life 2 cycle inventory). Primary data are site-specific, company-specific (if multiple sites for the same product) or 3 supply-chain-specific. Primary data may be obtained through meter readings, purchase records, utility bills, 4 engineering models, direct monitoring, material/product balances, stoichiometry, or other methods for 5 obtaining data from specific processes in the value chain of the company applying the LCA Guide (WRI, 6 2011a). In this Guidance, primary data is synonym of "company-specific data" or "supply-chain specific data". 7

Product – Any goods or services (ISO 14040:2006). 8

Product category – Group of products (or services) that can fulfil equivalent functions (ISO 14025:2006). 9

Product Category Rules (PCR) – Set of specific rules, requirements and guidelines for developing Type III 10 environmental declarations4 for one or more product categories (ISO 14025:2006). 11

Product Environmental Footprint Category Rules (PEFCRs) – Product-category-specific, life-cycle-based rules 12 that complement general methodological guidance for PEF studies by providing further specification at the 13 level of a specific product category. PEFCRs can help to shift the focus of the PEF study towards those aspects 14 and parameters that matter the most, and hence contribute to increased relevance, reproducibility and 15 consistency of the results by reducing costs versus a study based on the comprehensive requirements of the 16 PEF guide. 17

Product flow – Products entering from or leaving to another product system (ISO 14040:2006). 18

Product system – Collection of unit processes with elementary and product flows, performing one or more 19 defined functions, and which models the life cycle of a product (ISO 14040:2006). 20

Raw material – Primary or secondary material that is used to produce a product (ISO 14040:2006). 21

Reference Flow – Measure of the outputs from processes in a given product system required to fulfil the 22 function expressed by the functional unit (based on ISO 14040:2006). 23

Refurbishment – It is the process of restoring components to a functional and/or satisfactory state to the 24 original specification (providing the same function), using methods such as resurfacing, repainting, etc. 25 Refurbished products may have been tested and verified to function properly. 26

Releases – Emissions to air and discharges to water and soil (ISO 14040:2006). 27

Representative sample – A representative sample with respect to one or more variables is a sample in which 28 the distribution of these variables is exactly the same (or similar) as in the population from which the sample 29 is a subset 30

3 Foreground elementary flows are direct elementary flows (emissions and resources) for which access to primary data (or company-specific information) is available. 4 An environmental declaration providing quantified environmental data using predetermined parameters and, where relevant, additional environmental information (ISO 14025:2006). The predetermined parameters are based on the ISO 14040 series of standards, which is made up of ISO 14040 and ISO 14044.


20

Resource Depletion – Impact category that addresses use of natural resources, either renewable or non-1 renewable, biotic or abiotic. 2

Sample – A sample is a subset containing the characteristics of a larger population. Samples are used in 3 statistical testing when population sizes are too large for the test to include all possible members or 4 observations. A sample should represent the whole population and not reflect bias toward a specific 5 attribute. 6

Secondary data - It refers to data not from specific process within the supply-chain of the company applying 7 the PEFCR. This refers to data that is not directly collected, measured, or estimated by the company, but 8 sourced from a third party life-cycle-inventory database or other sources. Secondary data includes industry-9 average data (e.g., from published production data, government statistics, and industry associations), 10 literature studies, engineering studies and patents, and can also be based on financial data, and contain proxy 11 data, and other generic data. Primary data that go through a horizontal aggregation step are considered as 12 secondary data. (WRI, 2011a) 13

Sensitivity analysis – Systematic procedures for estimating the effects of the choices made regarding 14 methods and data on the results of a PEF study (based on ISO 14040: 2006). 15

Site-specific data – It refers to directly measured or collected data from one facility (production site). It is 16 synonymous to “primary data”. 17

Soil Organic Matter (SOM) – Is the measure of the content of organic material in soil. This derives from plants 18 and animals and comprises all of the organic matter in the soil exclusive of the matter that has not decayed. 19

Specific Data – Refers to directly measured or collected data representative of activities at a specific facility 20 or set of facilities. Synonymous with “primary data.” 21

Subdivision – Subdivision refers to disaggregating multifunctional processes or facilities to isolate the input 22 flows directly associated with each process or facility output. The process is investigated to see whether it 23 can be subdivided. Where subdivision is possible, inventory data should be collected only for those unit 24 processes directly attributable to the products/services of concern. 25

Sub-population – In this document this term indicates any finite or infinite aggregation of individuals, not 26 necessarily animate, subject to a statistical study that constitutes a homogenous sub-set of the whole 27 population. Sometimes the word "stratum" can be used as well. 28

Sub-processes - Those processes used to represent the activities of the level 1 processes (=building blocks). 29 Sub-processes can be presented in their (partially) aggregated form (see Figure 2). 30

Sub-sample - In this document this term indicates a sample of a sub-population. 31

Supply-chain – It refers to all of the upstream and downstream activities associated with the operations of 32 the company applying the PEFCR, including the use of sold products by consumers and the end-of-life 33 treatment of sold products after consumer use. 34

Supply-chain specific – It refers to a specific aspect of the specific supply-chain of a company. For example 35 the recycled content value of an aluminium can produced by a specific company. 36


21

System Boundary – Definition of aspects included or excluded from the study. For example, for a “cradle-to-1 grave” EF analysis, the system boundary should include all activities from the extraction of raw materials 2 through the processing, distribution, storage, use, and disposal or recycling stages. 3

System boundary diagram – Graphic representation of the system boundary defined for the PEF study. 4

Temporary carbon storage - happens when a product “reduces the GHGs in the atmosphere” or creates 5 “negative emissions”, by removing and storing carbon for a limited amount of time. 6

Uncertainty analysis – Procedure to assess the uncertainty introduced into the results of a PEF study due to 7 data variability and choice-related uncertainty. 8

Unit process – Smallest element considered in the Life Cycle Inventory for which input and output data are 9 quantified (based on ISO 14040:2006). 10

Upstream – Occurring along the supply chain of purchased goods/services prior to entering the system 11 boundary. 12

Waste – Substances or objects which the holder intends or is required to dispose of (ISO 14040:2006). 13

Weighting – Weighting is an additional, but not mandatory, step that may support the interpretation and 14 communication of the results of the analysis. PEF results are multiplied by a set of weighting factors, which 15 reflect the perceived relative importance of the impact categories considered. Weighted EF results can be 16 directly compared across impact categories, and also summed across impact categories to obtain a single-17 value overall impact indicator. Weighting requires making value judgements as to the respective importance 18 of the EF impact categories considered. These judgements may be based on expert opinion, social science 19 methods, cultural/political viewpoints, or economic considerations. 20

21


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1. Context and objectives 1 This project is developed under the framework of the European Strategy for Plastics in a Circular Economy 2 (COM(2018 28 final), adopted by the European Commission on January 2018. This strategy proposes a vision 3 where innovative materials and alternative feedstocks for plastic production are developed and used where 4 evidence clearly shows that they are more sustainable compared to traditional non-renewable alternatives. 5 Moreover, the Strategy also urges the identification of those applications where the use of plastics with 6 biodegradable properties provides clear environmental benefits. Therefore, the Commission has engaged to 7 investigate the environmental impacts of alternative feedstocks for plastic production, as well as to develop 8 life cycle assessment studies to identify the conditions under which the use of biodegradable or compostable 9 plastics is beneficial. 10

In this framework, DG JRC has been entrusted by DG GROW with the project “Environmental sustainability 11 assessment comparing through the means of lifecycle assessment the potential environmental impacts of the 12 use of alternative feedstocks (biomass, recycled plastics, C02) for plastic articles in comparison to using current 13 feedstocks (oil and gas)”. The main purpose of this project is to: i) elaborate a consistent and appropriate 14 LCA-based method to evaluate the potential environmental impacts of the use of alternative feedstocks for 15 plastic articles production in comparison to using current fossil-based feedstocks, and ii) to apply the 16 developed methodological framework to a number of full LCA case studies for specific plastic articles. The 17 project is articulated into the following main steps: 18

- A meta-analysis of selected existing studies related to LCA in the field of plastics; 19

- The development of a draft methodology to be applicable to LCA for plastics, including indirect 20 effects associated with the use of biomass for plastic production; 21

- The selection of relevant plastic articles for LCA; 22

- Conduction of 5 screening case studies to test the draft methodology; 23

- A technical stakeholder consultation; 24

- Refinement and finalisation of the draft method, accounting for the outcome of the consultation; 25

- Detailed LCA analysis of 10 specific plastic articles. 26

This document reports on the outcome of the first to steps, i.e. the meta-analysis of selected studies and the 27 draft methodology for comparative LCA of plastic articles. The selection of relevant articles and the five 28 screening case studies are addressed in a separate document. 29

30


23

2. Meta-analysis of selected studies, approaches and information 1

2.1 Introduction 2

As a first step of the project, a meta-analysis of selected existing studies developed at the European and 3 international level related to LCA in the field of plastics was performed. The purpose of the meta-analysis 4 was to: 5

- identify key methodological aspects and challenges; 6 - review the methodological choices and approaches commonly applied to address such aspects, 7

focusing on the most challenging and controversial ones; 8 - map the base of existing case studies to understand the availability of life cycle inventory data on the 9

covered products, polymers and key intermediates; 10 - highlight possible knowledge gaps and research needs. 11

The outcome of the assessment was used as input to the next stages of methodology development (i and ii), 12 as well as of selection of relevant articles to be assessed and subsequent case study development (iii). 13

2.2 Approach 14

First of all, potentially relevant peer-reviewed scientific studies published during the timeframe 2008-2018 15 were searched from Scopus using the following keywords: 16

- (bioplastic OR bio-plastic) AND (LCA OR life cycle assessment); 17 - (biobased plastic OR bio-based plastic) AND (LCA OR life cycle assessment); 18 - biodegradable plastic AND (LCA OR life cycle assessment); 19 - (biopolymer OR bio-polymer) AND (LCA OR life cycle assessment); 20 - (biobased OR bio-based polymer) AND (LCA OR life cycle assessment); 21 - (recycled or recovered) AND plastics AND (LCA OR life cycle assessment) 22 - (CO2 or CO2-based) AND (plastic or polymer) AND (LCA OR life cycle assessment). 23

Potentially relevant technical reports were then searched through traditional web search engines using the 24 same keywords. Cross-check searches were also performed via Scopus, considering the names of relevant 25 bio-polymers, bio-monomers and related key intermediates currently available on the market, within the 26 search key: name of polymer/monomer/key intermediate AND (LCA or life cycle assessment). The following 27 bio-polymers were specifically considered (with both full name and respective acronym): bio-PE 28 (PolyEthylene), bio-PET (PolyEthylene Terephatalate), bio-PP (PolyPropylene), bio-PVC (PolyVinyl Chloride), 29 bio-PUR (PolyURethane), PEF (PolyEthylene Furanoate), starch blends, TPS (thermoplastic starch), PLA 30 (PolyLactic Acid), (PHAs) PolyHydroxyAlkanoates, PBS (PolyButylene Succinate), PBSA (PolyButylene 31 Succinate Adipate), PBAT (PolyButylene Adipate co-Terephthalate), PTT (PolyTrimethylene Terephthalate). 32 For bio-monomers and key intermediates, the search included bio-ethylene, bio-ethylene glycol, bio-33 butylene, furandicarboxylic acid, lactic acid, hyrdroxyalcanoic acids; succinic acid, 1,4-butanediol, adipic acid, 34 and 1,3-propanediol. 35

Overall, 171 documents were collected through this procedure (see full list in Annex A), subdivided into the 36 following main categories: 37

- Monomers/Intermediates (34); 38


24

- Polymers (58); 1 - Plastic articles (65); 2 - End-of-life-related studies (14). 3

CEN standards have been analysed in details and have been properly taken into account in the draft 4 methodology development, but because of their nature they have not been considered for the purpose of 5 the meta-analysis, thus are not included in the 171 documents categorized above. In particular, we focused 6 on the following EN standards: 7

- EN 16760(2015) Bio-based products – Life Cycle Assessment, 8 - CEN TR 16957(2016) Bio-based products – Guidelines for Life Cycle Inventory (LCI) for the End-of-life 9

phase, 10 - EN 13432(2000) Packaging - Requirements for packaging recoverable through composting and 11

biodegradation - Test scheme and evaluation criteria for the final acceptance of packaging, 12 - EN 14995(2006) Plastics - Evaluation of compostability - Test scheme and specifications, 13 - EN 17033(2018) Plastics - Biodegradable mulch films for use in agriculture and horticulture - 14

Requirements and test methods. 15 16 Finally, to avoid missing relevant information a “call for data and information” has been sent to relevant 17 stakeholders (more than 450 addresses) to provide the project team with existing data and technical 18 information on fossil-based, bio-based, CO2-based polymers and articles (both biodegradable and non-19 biodegradable). The request referred to existing Life Cycle Inventory (LCI) and Life Cycle Impact Assessment 20 (LCIA) data and complete Life Cycle Assessment (LCA) studies, as well as to data and technical information 21 helpful for the purpose of developing the methodology and the screening LCA case studies. We received 22 feedback from 56 stakeholders categorized as follows: 23

- Industries: 23; 24 - Consultancies: 7; 25 - Industry associations: 7; 26 - Academia: 7; 27 - Research institutes: 7; 28 - National bodies: 2; 29 - Others: 2. 30

31 Relevant inputs have been taken into account as appropriate in the screening LCA case studies. However, a 32 thorough analysis of the feedback provided is foreseen as a next step, to allow a proper use of all the amount 33 of information provided by the stakeholders. 34

2.2.1 Screening assessment 35

A screening assessment of all retrieved documents was performed first, in order to collect basic information 36 useful for classification and selection of relevant studies for a more in-depth assessment of methodological 37 choices and data availability. The following information was specifically collected: 38

- Goal of the study; 39 - Analysed product(s) and related polymer(s); 40


25

- Comparative or non-comparative study; 1 - Type of feedstock (including the type of biomass used, if available); 2 - Geography of reference; 3 - Analysed scenarios (if any); 4 - Type of LCA approach (attributional, consequential or not specified); 5 - System boundary (cradle-to-grave, cradle-to-gate + grave, cradle-to-gate, gate-to-gate or end-of-life 6

comparison); 7 - Functional unit; 8 - Number of midpoint impact categories assessed; 9 - Availability of characterised midpoint impact assessment results (Y/N); 10 - Availability of supplementary material or background report with detailed modelling approach and 11

LCI data description (Y/N); 12 - ISO-compliance of the study (Y/N); 13 - Peer-review of the study according to ISO standards (Y/N). 14

15

2.2.2 Selection of relevant studies for in-depth assessment 16

To limit the scope of the in-depth assessment to studies with a minimum level of quality and detail, a range 17 of selection criteria were applied: 18

- A comparison between at least 2 alternatives is performed; 19 - At least one midpoint environmental impact category is assessed (giving priority to studies covering 20

a broad range of categories; studies considering only energy demand were excluded); 21 - Characterised midpoint LCIA results (expressed in physical units) are provided; 22 - A background report or supplementary information adequately detailing the modelling approach and 23

LCI data used is available5 ; 24 - Overall relevance for the purposes of the project (in terms of goals and scope). 25

26 After applying the listed criteria, 32 studies were selected for the in-depth assessment (Table 1), 27 subdivided as follow: 28

- Monomers/Intermediates (4); 29 - Polymers (13); 30 - Plastic articles (13); 31 - End-of-life-related studies (2). 32

33

5 Or extensive description of methods and data is at least provided in the study


26

Table 1: List of screened studies selected for in depth assessment 1

N. Reference Product category (1)

Scope

1 Daful et al., 2016 M/I Lactic acid (LA) production from sugarcane bagasse and leaves in an existing South African sugar mill and comparison with fossil based LA

2 Forte et al., 2016 M/I Bio-based BDO (1,4-butanediol) production via direct fermentation of renewable sugars from lignocellulosic feedstock (wheat straw) and comparison with its fossil counterpart

3 Liptow et al., 2013 M/I Ethylene production from sawmill chips at the industrial scale, with focus on the identification of possible environmental key contributors

4 Parajuli et al., 2017

M/I Comparison of two standalone biorefinery systems with an integrated biorefinery plant using winter wheat straw and alfalfa as raw materials

5 Alvarenga et al., 2013a

P PVC resin production based on sugarcane bioethanol and comparison with fossil-based PVC (attributional approach)

6 Alvarenga et al., 2013b

P PVC resin production based on sugarcane bioethanol and comparison with fossil-based PVC (consequential approach)

7 Belboom & Léonard, 2016

P HDPE produced from sugar beet or wheat and comparison with conventional HDPE (including also EoL)

8 Groot & Borén, 2010

P L-lactide, D-lactide, PLLA, and two PLLA/PDLA blends made from cane sugar in Thailand and comparison with fossil-based polymers

9 Hansen et al., 2015

P GPPS and HIPS production from sugarcane ethanol in Brazil and comparison with their entirely fossil-based counterparts (including also EoL)

10 Hottle et al., 2017 P Drop-in polymers from sugarcane (bio-PET, bio-HDPE, bio-LDPE) and compostable biopolymers from corn (PLA, TPS) in comparison with traditional fossil-based polymers (PET, HDPE, LDPE), including also EoL

11 Kendall, 2012 P PHB polymer production from the cellulose in the organic fraction of material recovery facility (MRF) residuals and comparison with PHB from a dedicated crop -corn- (including also EoL)

12 Kim & Dale, 2008 P Corn-starch-based PHB production in an real PHB facility in Iowa

13 Liptow & Tillman, 2012

P Comparison of sugarcane-based LDPE and fossil-based LDPE (including also EoL)


27


Scope

14 Posen et al., 2016 P Comparison of PLA, PHB and bioethylene-based plastics from corn or switchgrass and comparison with the 8 highest-volume thermoplastics

15 Shen et al., 2012 P Comparison of bio-based PET, recycled PET, PLA and man-made cellulosic fibre from wood pulp, sugarcane or maize (considering also EoL)

16 Tsiropoulos et al., 2015

P Fully bio-based HDPE and partially bio-based PET from Brazilian and Indian sugarcane ethanol in comparison with the production of their petrochemical counterparts

17 Van Uytvanck et al., 2014

P Biomass-based PET production from sugarcane- or willow-derived ethylene compared to fossil-based PET

18 Arnold & Alston, 2012

A Manufacture, use and disposal/degradation of PP tree shelters, including a comparison with shelters made from corn-based starch and PLA

19 Chen et al., 2016 A Comparison of traditional petroleum-based PET bottles and partially or fully bio-based PET bottles from different feedstock (corn, switchgrass or wheat straw for ethylene glycol; corn stover or forest residues for terephthalic acid)

20 Deng et al., 2013 A Wheat-gluten-based powder production and wheat-gluten-based packaging film compared to corn-based PLA packaging film and LDPE packaging film

21 Razza et al., 2015 A Bio-based and biodegradable foamed packaging for protecting washing machines made of tapioca, potato or corn starch and comparison with conventional oil-based (EPS) cushioning packaging

22 van der Harst et al., 2014

A Comparison of disposable beverage cups made of petro-plastic (PS), bioplastic (PLA) and paperboard coated with bioplastic (PLA)

23 Bisinella et al., 2018

A Production, use and disposal of grocery carrier bags of different types and materials including LDPE, r-LDPE, PP, r-PET, polyester, starch-polyester biopolymer, paper, cotton and composite (jute, PP, cotton)

24 Detzel & Krüger, 2006

A Comparison of clam shells made from corn-based PLA,PET, PP and oPS

25 Gérand & Roux, 2014

A Partly recycled PET bottle (75 cl) compared to glass bottle, including the life cycle of packaged wine

26 Parker & Edwards, 2012

A Comparison of a conventional HDPE bag, an oxo-biodegradable HDPE bag and a bio-based (starch-polyester) bag for use as carrier bags and bread packaging

27 Markwardt et al., 2017

A Comparison of Tetra Pak carton packages and several alternative packages (based on PET, HDPE and glass) for beverage in the Nordic markets. Compared packaging alternatives also include bio-based HDPE from sugarcane ethanol and partially recycled PET


28


Scope

28 Müller, 2012 A Comparison of compostable (ecovio®)(2) bags with PE and paper bags for transportation of staple goods, reuse and disposal of organic waste

29 Müller & Müller, 2017

A Comparison of fruit and vegetable bags made of different materials (HDPE, paper, compostable ecovio® polymer(2))

30 Müller & Müller, 2015

A Comparison of biodegradable (ecovio®) (2) and conventional (PE) mulch film for cotton growing in China

31 Guo et al., 2013 EoL Comparison of alternative waste management options (landfill, anaerobic digestion, industrial composting and home composting) for a display board made of starch-PVOH biopolymer foam from three different feedstock (wheat, potato, maize)

32 Rossi et al., 2015 EoL Comparison of end-of-life options for biodegradable PLA and TPS packaging made of corn (mechanical recycling , industrial composting, anaerobic digestion, direct fuel substitution in industrial facilities, municipal incineration with heat recovery, landfilling)

(1) The following abbreviations are used: M/I (monomer/intermediate), P (polymers), A (article), EoL (End of Life); 1 (2) ecovio® is the commercial name for a compostable polymer blend of PLA and PBAT copolyester (1,4-butanediol, adipic acid and terephthalic acid) 2


29

2.2.3 In-depth assessment 1

In the in-depth assessment, information was collected about the methodological choices performed in each 2 study with respect to aspects considered relevant in the field of LCA for plastics. The focus was on most 3 challenging and/or controversial aspects. Regarding feedstock supply, addressed aspects include: 4

- modelling approach for agricultural production processes (with a special focus on fertiliser and 5 pesticide emissions); 6

- direct and indirect land use change (dLUC/iLUC) and related modelling approach; 7 - range of emissions covered in the modelling of dLUC and iLUC (e.g. only GHGs or also other 8

emissions); 9 - biogenic carbon accounting; 10 - biogenic carbon storage and timing effects of carbon emissions; 11 - handling of the use of waste or residual bio-based feedstock; 12 - handling of multifunctionality at the biorefinery level; 13 - inclusion of plastic additives and related impacts. 14

Regarding EoL modelling, the following aspects were instead considered: 15

- modelling of biodegradation during biological treatment or in-situ (use of product-specific or generic 16 degradation rates, overall modelling approach, etc.) 17

- consequences of biodegradable plastic use on organic waste management (e.g. reduction of 18 impurities, increased separate collection, etc.) 19

- inclusion of littering impacts and related modelling approach; 20 - inclusion of microplastic impacts and related modelling approach. 21

Finally, general methodological aspects were addressed, including: 22

- LCA software used; 23 - database used for secondary/background data; 24 - impact assessment level; 25 - considered impact categories; 26 - impact assessment method. 27

28

2.3 Results 29

This section summarises the main findings of the meta-analysis. An overview of the most relevant aspects 30 analysed in the screening assessment and related to the scope of the studies is presented first. This is 31 followed by a description of the most challenging and/or controversial methodological aspects considered in 32 the in-depth assessment. 33

2.3.1 Most relevant aspects of the screening assessment (all studies) 34

2.3.1.1 Products 35

A huge variety of products is treated in the screened studies due to the inclusion of many product categories 36 in the analysis (i.e. monomers, polymers, etc.). Most of the studies about monomers referred to a specific 37 product: only ethylene, succinic acid, lactic acid and propylene glycol were treated in more than one study 38


30

(3, 3, 2 and 2 respectively). Among polymers, the most addressed ones were poly-lactic acid (PLA) (14 studies, 1 2 of them on Ingeo PLA), followed by poly-hydroxy-alkanoate (PHAs) and poly-hydroxy-butyrates (PHBs) who 2 were treated in 10 studies (7 and 3 respectively). The studies on plastic articles mostly addressed PLA (20 3 studies) based products (e.g. bottles, cups, packaging, etc.) confirming the relevance of PLA. Five studies 4 specifically referred to BASF Ecovio® or Ecoflex® products (e.g. mulching film). Overall, 111 of 171 studies 5 included biodegradable products, while 49 studies addressed non-biodegradable products (in the remaining 6 11 studies, any explicit mention to biodegradability was done). It has been noticed that the entire set of End-7 of-Life studies (14 studies) focused on biodegradable products. 8

2.3.1.2 Feedstock 9

Considering the feedstocks, corn (starch, stover, etc.) is the most addressed one in the studies, being PLA the 10 main products derived by it. 11

2.3.1.3 System boundary 12

Regarding the system boundaries applied in the studies, the overall picture is shown in Figure 3 and in 13


31

Table 2. 1

2

Figure 3: Total number of studies defining different system boundaries applied in the Life Cycle 3 Assessment. (C-FG = Cradle-to-Factory Gate; C-FG+GR = Cradle-to-Factory Gate + Grave (use stage is 4 no included in the system); C-GR = Cradle-to-Grave; EOL comparison = different End-of-Life options 5 analysed in the system); G-G = Gate to Gate 6

7

8


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Table 2: Number of studies (per product category) applying different system boundaries in the Life Cycle 1 Assessment. (Note: n.a includes reviews, non-LCA studies and those studies where System Boundaries 2 are not specified) 3

System boundaries

Monomers/ Intermediates

Polymers Articles EOL TOTAL

C-FG 32 37 11 1 81 C-FG + GR 1 11 21 6 39 C-GR 1 2 21 2 26 EOL comparison - - 9 4 13 G-G - 3 - - 3 n.a. - 5 3 1 9

TOTAL 34 58 65 14 171

4

For most of the screened studies (33) dealing with monomers the system boundaries were defined as Cradle-5 to-Gate. Among those, two studies considered the use stage or the End-of-Life stage: Adom at al. (2017) 6 considered Cradle-to-Gate results for PGLA and ethyl lactate, but analysed biogenic carbon emissions with a 7 Cradle-to-Grave approach (making assumptions on the bio-product End-of-Life and the consequent carbon 8 fate). Tao et al. (2014) compared cellulosic iso-butanol with cellulosic ethanol and n-butanol under a Cradle-9 to-Grave perspective. 10

Also the literature about polymers is mostly based on Cradle-to-Gate approach (48 studies). Among those, 11 eleven studies included also the “Grave” in the system boundaries, while excluding the Use stage. Two 12 studies applied the full Cradle-to-Grave approach: Renouf et al (2013) to poly-lactic acid (PLA) from 13 sugarcane, Roes & Patel (2007) to poly-trimethylene terephthalate (PTT), polyhydroxyalka-noates (PHA), 14 polyethylene terephthalate (PET), and polyethylene (PE). Three studies restricted the system boundary to 15 the manufacturing phase in a Gate-to-Gate approach to test (i) the potential environmental impact of a novel 16 protocol for the extraction of poly-hydrobutyrate (PHB) based on dimethyl carbonate (DMC) (Righ et al., 17 2017), (ii) the energy demand of PLA, PHBV, Bio-PE and PP in three different processes (Schulze et al., 2017) 18 and (iii) the environmental impact of mixed culture PHA production, in comparison with other biodegradable 19 polymers and petrochemical polymers production methods. 20

The analysis of the literature on plastic articles shows the importance of the End-of-Life stage in the LCA of 21 such articles: two thirds of the studies (42 over 65 studies) set the boundaries as Cradle-to-Gate plus Grave 22 (i.e. including the End-of-Life stage, but not the Use of the article, in the system) or as a full Cradle-to-Grave 23 approach, while nine studies focused on the comparison of different options for plastic articles End-of-Life. 24 Eleven studies defined the system boundary as Cradle-to-Gate and three of them considered the distribution 25 of the plastic article to the consumer as part of the system. 26

Four of the studies selected specifically because of their End-of-Life focus compared alternative options of 27 End-of-Life stage for a number of biodegradable materials like PLA (Cosate de Andrade et al, 2016), MaterBi, 28 PBAT and PLA (Hermann et al., 2011) and PHA carrier bags in comparison with conventional bags (Khoo & 29 Tan, 2010), being the other mostly Cradle-to-Grave (8 over 14), excluding the Use stage. 30


33

2.3.2 Challenging methodological aspects (in-depth assessment) 1

Unless stated otherwise, the figures in this chapter refer to the 32 studies selected for the in depth analysis. 2 However, in some cases relevant information for one or the other challenging aspects was retrieved also in 3 studies, that were not selected for the in depth analysis. In those cases, this information was added to achieve 4 a more comprehensive picture and to not disregard relevant information. 5

Figure 4 presents the number of studies addressing different methodological aspects. The most addressed 6 aspect was handling/modelling of biodegradation in the end of life stage (28 studies including also studies 7 which were not included in the in-depth analysis). The least addressed aspect was littering, which was 8 discussed or included only in four studies. More details related to different methodological aspects are 9 presented in the following sections. 10

11

Figure 4: Number of studies addressing different methodological aspects; dLUC = direct Land Use 12 Change; iLUC = indirect Land Use Change 13

2.3.2.1 Biogenic carbon balance, CO2 storage and timing of CO2 emissions 14

19 studies, out of 32 studies included in the in-depth analysis, included biogenic carbon in their study on 15 some level. Only four studies reported all three aspects, i.e. biogenic carbon balance, CO2 storage and timing 16 of the CO2 emissions (Figure 5) (Rossi et al., 2015; Posen et al., 2016; Corbière-Nicollier et al., 2001; Razza et 17 al., 2015). Kendall et al. (2012) included CO2 storage and timing of CO2 emissions only in the scenario analysis. 18 Nine studies included carbon uptake at plant growth or carbon sequestered in the product and release of the 19 CO2 at the end of life stage without taking into account the timing of storage (or at least they did not report 20 that). However, e.g. Hottle et al. (2017) took into account that under some conditions biopolymer do not 21 degrade, and this carbon is permanently sequestered. Five studies included carbon uptake at plant growth 22 or carbon sequestered in the product, but they did not include end of life stage in their study, i.e. they 23 included only benefits of carbon sequestration, but not emissions when the carbon is released. Only seven 24 studies presented inventory of the biogenic carbon, others just included it into the results. Parajuli et al. 25 (2017) accounted carbon sequestration in the shape of soil organic carbon. Forte et al. (2016), in contrast, 26 argued that CO2 uptake was not included in the study because the long-term storage of carbon is highly 27 dependent on the final use and durability of the biopolymer. 28

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34

1

Figure 5: Number of studies addressing different aspects related to biogenic carbon in their study 2

2.3.2.2 Direct Land Use Change 3

Impact of direct land use change (dLUC) was addressed in eight out of the 32 studies included in the in-depth 4 analysis, as reported in Table 3 (Liptow and Tillman, 2012; Alvarenga et al., 2013a,b; Hansen et al., 2015; 5 Razza et al., 2015; Tsiropoulos et al., 2015; Posen et al., 2016; Markwardt et al., 2017). Only GHG emissions 6 potentially associated to dLUC are taken into account in those studies. 7

In Liptow and Tillman (2012), dLUC is considered only when a consequential approach is applied and 8 expansion to virgin areas of Brazil takes place. In this case, a release of 1040 kg CO2 eq. per m3 of ethanol is 9 assumed, based on literature (Zuurbier and Van de Vooren, 2008). Alvarenga et al. (2013a) modelled dLUC 10 based on historical literature data about the type of land affected by the expansion of sugarcane cultivation 11 in the state of Sao Paulo (Brazil) between 2003 and 2009 (taken from Rudorff et al., 2010), considering that 12 dLUC occurs only in 15.5% of the total sugarcane area in 2009 (the rest being areas already cultivated before 13 2003). GHG emissions from dLUC were distributed over 20 years. In contrary, in Alvarenga et al. (2013b), who 14 apply a consequential approach, sugarcane cultivation takes place only in new land, which was previously 15 pasture land, since this is the assumed trend for the future. In both studies, emission factors are estimated 16 based on the approach recommended in the ILCD Handbook (EC-JRC-IES, 2010) and account for changes in 17 soil organic carbon content. Hansen et al. (2015) calculated C release from dLUC (land transformation) based 18 on the difference between C stocks before and after the installation of the crop using IPCC (2006) Tier 1 19 approach, in accordance with the EC guidelines supporting the RED (EC, 2010). Tsiropoulos et al. (2015) 20 included additional GHG emissions due to dLUC, based on the range of emission factors provided in the 21 review of land use change models by Wicke et al. (2012; e.g. 3-46 g CO2 eq./MJethanol). Posen et al. (2016) 22 used the Carbon Calculator tool for Land Use Change (CCLUB) tool included in the GREET model (Wang, 2014), 23 which is based on a general equilibrium model to predict global land changes (seemingly both direct and 24 indirect) from biofuel production (GTAP model). The authors assumed that the contribution of bio-based 25 plastics to LUC would be similar to that of biofuel production (per unit of feedstock diverted). A range of LUC 26 emissions from predicted land use changes is estimated based on a combination of different agroecosystem 27 and crop models (e.g. CENTURY). Razza et al. (2015) assessed the impact of dLUC in sensitivity analysis for 28 best and worst case scenarios, when previous land use was either grassland (best case) or rainforest (worst 29


35

case). GHG emission factors were taken from PAS 2050 (BSI, 2011), and factors for Malaysia were used as 1 proxy for those of Thailand. Finally, Markwardt et al. (2017) performed a sensitivity analysis applying the LCI 2 for bio-based PE developed by Braskem, where the effects of both dLUC and iLUC are addressed. For dLUC, 3 both a current and a future scenario is modelled, based on land types converted in the two preceding seasons 4 or that are expected to be converted in the near future, respectively. The corresponding emissions are 5 estimated considering the EC guidelines for the calculation of land carbon stocks (EC, 2010). 6

At least three studies not included in the in-depth analysis also addressed dLUC, at least in a sensitivity 7 analysis (Ekman and Börjesson, 2011; Ziem et al., 2013; Gironi & Piemonte, 2010). Ekman and Börjesson 8 (2011) considered dLUC in sensitivity analysis taking into account additional land area needed to produce 9 sugar for propionic acid production. Based on current Swedish conditions, the land area was assumed to be 10 taken from previous grassland leading to additional biogenic CO2-emissions from the soil, equivalent to 350–11 450 kg C/ha and year (Börjesson and Tufvesson, 2011, Cherubini et al., 2009a, Cherubini et al., 2009b). Ziem 12 et al. (2013) assessed dLUC according to the already mentioned EC guidelines for the purpose of RED (EC, 13 2010). Gironi and Piemonte (2010) used the average of the values reported by Searchinger et al. (2008; 351 14 t CO2 eq/ha of land converted to cropland), and Righelato and Spracklen (2007; 305 t CO2 eq/ha). Table 3 15 summarises the approaches or emission factors chosen to model dLUC emissions in the assessed studies. 16

Table 3: Summary of approaches applied in the assessed studies to model GHG emissions from dLUC 17

Approach/Emission factor (source) n. of studies Reference(s)

EC guidelines (EC, 2010) + IPCC (2006; where appropriate)

3 (2 from in-depth assessment)

Hansen et al. (2015)

Markwardt et al. (2017) Ziem et al. (2013) (1)

ILCD Handbook (EC-JRC-IES, 2010) 2 Alvarenga et al. (2013a) Alvarenga et al. (2013b)

PAS 2050 (BSI, 2011) 1 Razza et al. (2015)

CCLUB tool in GREET model (Wang, 2014) 1 Posen et al. (2016)

Literature values (Wicke et al., 2012; Zuurbier and Van de Vooren, 2008; Searchinger et al., 2008 Righelato and Spracklen, 2007); Börjesson and Tufvesson, 2011; Cherubini et al., 2009a and 2009b)


Liptow and Tillman (2012)

Tsiropoulos et al. (2015)

Gironi & Piemonte (2010) (1)

Ekman & Börjesson (2011) (1) (1) Study not included in the in-depth assessment. Due to the limited number of selected studies addressing dLUC, this 18 overview table has been extended also to studies that otherwise were not assessed in-depth. 19

2.3.2.3 Indirect Land Use Change 20

Indirect land use change (iLUC) was addressed in 7 studies included in the in-depth analysis, as detailed in 21 Table 4 (Kendall et al., 2012; Liptow and Tillman, 2012; Shen et al., 2012; Alvarenga et al., 2013b; Tsiropoulos 22 et al., 2015; Posen et al.; 2016; Parajuli et al., 2017). In all these studies, only GHG emissions potentially 23 arising from iLUC are considered. 24

Kendall et al. (2012) applied the iLUC factor from Argonne National Lab’s GREET model (Wang, 2009) 25 equalling 15.97 g CO2eq/kg glucose. This is, however, a very low figure compared to what is available in the 26 literature and is the result of older studies relying on GREET. Liptow and Tillman (2012) used a range for their 27 iLUC impact on climate change, considering both a “low impact value” of zero (i.e. no impact) and a “high 28


36

impact value” equal to 46 g CO2/MJ sugarcane-derived ethanol (taken from California EPA, 2009). Shen et al. 1 (2012) applied standard emission factors extracted from the same source, which at the time was the first one 2 providing government-adopted iLUC emission factors (30 g CO2 eq./MJ maize-based ethanol and 46 g CO2 3 eq./MJ sugarcane-based ethanol). In Alvarenga et al. (2013b), the calculation was based on the assumption 4 that pasture lands displaced by sugarcane expansion (as part of dLUC) would move to areas with natural 5 vegetation, i.e. Amazon Forest. The annualized area of land transformed was calculated considering a 6 distribution period of 20 years, while the corresponding CO2 emissions (accounting for changes in the content 7 of SOC) were calculated using the method proposed by ILCD (EC-JRC-IES, 2010). Impacts from forest clearing 8 were included as well, through an ecoivent dataset specifically related to deforestation in the Amazon forest 9 (Provision, stubbed land, BR). Tsiropoulos et al. (2015) estimates an upper and a lower bound for the climate 10 change impact from iLUC based on emissions factors taken from the literature (review by Wicke et al., 2012). 11 Posen et al. (2016) applied emission factors calculated through the already mentioned CCLUB tool included 12 in the GREET model (Wang, 2014), accounting for both direct and indirect LUC. Finally, Parajuli et al. (2017) 13 calculated GHG emissions from iLUC differently depending on the applied LCA approach (attributional or 14 consequential). In Consequential LCA (C-LCA), the authors account for both the emissions from feedstock 15 cultivation in a productive land in Denmark, and avoided emissions due to the displacement of agriculture 16 products from biorefinery co-products. In Attributional LCA (A-LCA), no substation is applied and no avoided 17 iLUC emissions are thus accounted for. Emission factors for iLUC related to feedstock cultivation are different 18 in CLCA and ALCA approaches and are in both cases taken from the literature (Schmidt and Muños, 2014 and 19 Fritsche et al., 2010, respectively). 20

Table 4: Summary of approaches applied in the assessed studies to model GHG emissions from iLUC 21

Approach/Emission factor (source) n. of studies Reference(s)

ILCD Handbook (EC_JRC_IES, 2010) 1 Alvarenga et al. (2013b)

GREET model (Wang 2009; 2014) 2 Kendall et al. (2012)

Posen et al. (2016)

California EPA Air Resource Board (California EPA, 2009)

2 Liptow and Tillman (2012)

Shen et al. (2012)

Causal-descriptive modelling approach by E4tech 1 Ziem et al. (2013) (1)

Literature values (Wicke et al., 2012; Schmidt and Muños, 2014; Fritsche et al., 2010; Searchinger et al., 2008; LCFS; Tyner et al., 2010; Nassar et al., 2010; Fargione et al., 2008; Piemonte & Gironi, 2011)


Tsiropoulos et al. (2015)

Parajuli et al. (2017) Piemonte and Gironi (2011; 2012) (1)

Eerhart et al. (2012) (1)

Kikuchi et al. (2013) (1)

Suwanmanee et al. (2013) (1)

Ganne-Chédeville and Diederichs (2015) (1)

Not reported 1 Leejarkpai et al. (2016)(1)

(1) Study not included in the in-depth assessment. Due to the limited number of selected studies addressing iLUC, this 22 overview table has been extended also to studies that otherwise were not assessed in-depth. 23

24


37

There were eight additional studies not included in the “detailed analysis”, which addressed iLUC impacts as 1 a baseline or as a sensitivity analysis (Piemonte and Gironi, 2011 and 2012; Eerhart et al., 2012; Kikuchi et al., 2 2013; Suwanmanee et al., 2013; Ziem et al., 2013; Ganne-Chédeville and Diederichs (2015); Leejarkpai,et al., 3 2016). Piemonte and Gironi (2011, 2012) included iLUC impact on climate change based on the result 4 reported by Searchinger et al. (2008). On this basis, an emission value of 351 metric tons of CO2 eq. per 5 hectare was used, assuming an amortization time (in order to calculate an annual emission value) of 30y. In 6 Eerhart et al. (2012), the climate change impact of iLUC is explored in a sensitivity analysis, applying a range 7 of literature data (7, 14 and 30 g CO2 eq./MJethanol). Kikuchi et al (2013) adapted their iLUC factor on the basis 8 of the earlier result from Fargione et al. (2008), i.e. 165 t CO2/ (50y∙ha cleared). This implies the initial clearing 9 of the land is estimated to release 165 t CO2/ha, while an amortization period equal to 50 years is considered, 10 which is somehow arbitrarily chosen (e.g. IPCC suggests 20 years). In Suwanmanee et al. (2013), the average 11 value of 328 t CO2 eq. per hectare of land converted to cropland adopted in the abovementioned study by 12 Piemonte and Gironi (2011) is applied. Ziem et al. (2013) used the causal-descriptive modelling approach by 13 E4tech (http://www.apere.org/doc/1010_e4tech.pdf). Ganne-Chédeville and Diederichs (2015) used the 14 results summarized by Wicke et al. (2012) to assess iLUC. Finally, also Leejarkpai et al. 2016 included iLUC in 15 their study, albeit the approach was not transparently detailed in their analysis. 16

2.3.2.4 Handling of waste or residual bio-based feedstock 17

Different types of waste and residual biomass can be used as feedstock materials for (bio-plastic production, 18 including wheat straw, corn stover, wood bark and chips, sugarcane bagasse and wastewater, just to mention 19 a few. Therefore, the agricultural, forestry or industrial processes generating residues or by-product, as well 20 as the (biorefinery) processes receiving waste as input material, are essentially multifunctional. The use of 21 waste or residual feedstock is considered in 30 studies included in this meta-analysis (7 of which are included 22 in the in-depth assessment), where this multi-functionality is addressed differently ( 23


38

Table 5)6. 1

In several studies (7 out of 30), a cut-off approach is applied, i.e. no upstream production burdens are 2 allocated to the feedstock, but only those associated with any collection/harvesting and/or recovery 3 operation. This is mostly the case of studies where industrial waste flows or wastewater flows are used as 4 feedstock, such as starch-rich wastewater from potato processing (Broeren et al., 2017), whey from dairy 5 industries (Koller et al., 2013), orange peel (Günkaya and Banar, 2016) and shrimp shells (Leceta et al., 2013, 6 2014). However, in one screening study (Detzel et al., 2013), this approach is also applied to maize straw, 7 where no burdens from corn cultivation are assigned to the straw, according to the specifications provided 8 by the Renewable Energy Directive (EC, 2009). 9

Similarly, a couple of studies focusing on PHA production from municipal or industrial wastewater (Gurieff 10 and Lant, 2007; Morgan-Sagastume et al., 2016) assigns no upstream burdens to wastewater entering the 11 system, according to the so-called "zero-burden approach". These studies compare different process 12 configurations for wastewater treatment, including the integration of PHA production, with reference to 13 functional unit related to the wastewater treatment service rather than to polymer production. 14

Economic allocation is frequently applied (in seven studies), to attribute a share of the burdens from 15 agriculture, forestry or processing activities to forest residues (Aryapratama and Janssen, 2017; Liptow et al, 16 2015), wheat straw (Forte et al., 2016; Parajuli et al., 20177), sugarcane bagasse (Daful et al., 2016; Daful and 17 Görgens, 2017; Mandegari et al., 2017); and sawmill chips (Liptow et al., 2013, 2015). Conversely, mass 18 allocation is applied only in three studies for corn stover (Yu and Chen, 2008), forest residues (Chen et al., 19 2016), and pulp mill side streams (Gontia and Janssen, 2016). Allocation based on mass is considered by some 20 inappropriate for agricultural and forestry residues like straw and bark (Ahlgren et al., 2015), as these are 21 usually produced in a fixed proportion with the main product (grains or debarked wood) and thus a mass-22 based allocation would not reflect any underling physical relationship among co-products. 23

When agricultural residues like wheat straw and corn stover are used as feedstock, system expansion is often 24 applied (4 studies), to account for the consequences of redirecting this feedstock to the biorefinery, in line 25 with a more consequential approach (Adom and Dunn, 2017; Parajuli et al., 20178; Tao et al., 2014; Dunn et 26 al., 2015). According to this approach, only the burdens from harvesting and transport of the residues are 27 accounted for, along with the potential consequences of removing it from the field. In particular, all 28 mentioned studies include the replacement of removed residues with mineral fertilisers for nutrient supply, 29 while only Parajuli et al. (2017) also account for the (nitrogen) emissions associated with such replacement, 30 and with potential changes in the levels of soil organic carbon. Changes in SOC from straw removal are also 31 accounted for in Kim and Dale (2005), who however do not specify how the residual feedstock is handled. 32

Finally, Kendall et al. (2012) applied system expansion to a case study where cellulosic material recovery 33 facility (MRF) residues are used as a feedstock. As in the previous cases, no upstream burdens are assigned 34

6 Due to the relevance of this methodological issue, it is here discussed with reference to the whole set of studies included in the meta-analysis and not only those selected for in-depth assessment. 7 In this study, two LCA approaches (attributional and consequential) are compared for the assessment of the same products. Allocation is only applied in the attributional approach. 8 In this study, two LCA approaches (attributional and consequential) are compared for the assessment of the same products. System expansion is only applied in the consequential approach.


39

to the residues, but the consequences of diverting them from their alternative fate (in this case landfilling) 1 are taken into account and factored in the model as avoided burdens from landfilling. 2

3


40

Table 5: Summary of the approaches adopted in the assessed studies to handle multifunctionality 1 associated with the use of residual or waste feedstock for bio-based plastic production 2

Approach n. of studies Reference(s)

Cut-off 7 Broeren et al. (2017)

Detzel et al. (2013)

Günkaya & Banar (2016) Koller et al. (2013)

Leceta et al. (2014)

Leceta et al. (2013)

Sun et al. (2015)

Zero burden approach 2 Gurieff and Lant (2007)

Morgan-Sagastume et al. (2016)

System expansion* 5 Adom and Dunn (2017)

Dunn et al. (2015)

Kendall (2012)

Kim and Dale (2005)

Tao et al. (2014)

Mass allocation 3 Chen et al. (2016)

Gontia and Janssen (2016)

Yu and Chen, 2008

Economic allocation 7 Aryapratama and Janssen (2017) Daful and Görgens, 2017

Daful et al. (2016)

Forte et al. (2016) Liptow et al. (2015)

Liptow et at. (2013)

Mandegari et al. (2017)

Different methods 1 Parajuli et al., 2017

Not specified / Default approach from corresponding dataset 5 Akanuma et al. (2014)

Guo & Crittenden (2011)

Karka et al. (2017)

Kim & Dale (2015)

Kikuchi et al. (2018)

*Here it is intended as the accounting of the consequences of re-directing the waste/residual feedstock to the 3 biorefinery. 4

2.3.2.5 Handling of multi-functionality at biorefinery 5

Regarding the handling of multifunctionality at the biorefinery, it was observed that 25 out of 32 studies 6 included in the in-depth analysis, specify the method/s used to handle multifunctionality (Figure 6). 7 Conversely, 7 studies do not specify/give further details on this issue, or simply applies as such ecoinvent v2 8 datasets for biopolymers (at the time based on cut off, e.g. the PLA dataset from NatureWorks). Most studies 9


41

(19) apply only one method, or a combination of methods, for a given process, while in 6 studies alternative 1 methods are tested as sensitivity analysis (3 studies), or as part of a comparison of different methods (3 2 studies). Excluding sensitivity analyses those studies comparing different methods, mass-based allocation is 3 the most applied method (8 studies; Figure 6), followed by system expansion through substitution (6 4 studies). Economic allocation is applied in three studies, while other three studies applied a hybrid method 5 combining different methods for different co-products (i.e. substitution for energy products and allocation 6 for physical products). A couple of studies applied allocation based on other physical relationships, i.e. exergy 7 (Alvarenga et al., 2013a) or energy content (Belboom and Léonard, 2016). Regarding those studies comparing 8 different methods, two of them applied both economic allocation and system expansion (Parajuli et al., 2017; 9 Tsiropoulos et al., 2015), while Liptow and Tillman (2012) compared results with system expansion and 10 energy-based allocation. 11

12

13

Figure 6: Summary of methods used in the assessed studies to handle multifunctionality at the 14 biorefinery 15

2.3.2.6 Additives: modelling and impacts 16

Several additives are normally used in plastic production, including, for instance, mineral fillers, plasticisers, 17 flame retardants, impact modifiers, reinforcing agents, heat stabilisers and colourants. Additives were 18 explicitly considered only in the studies by Broeren et al. (2017 and 2016), both not included in the in-depth 19 assessment, but reported here to cover the challenge of how to address additives. According to the latter, 20 additives account for about 10% of the weight of plastics on average9, thus being potentially relevant for 21 environmental assessment. However, the quantities and specific substances used as additives are rarely 22 revealed, representing an uncertainty that is typically ignored in LCA studies for final products (Broeren et 23 al., 2016). For starch-based plastics, additives such as compatibilisers, plasticisers, processing aids and fillers 24

9 Based on overall additive use and total plastic production in 2005, which amounted to about 22 and 225 million tonnes, respectively (van Oers et al., 2012; PlasticsEurope, 2013).


42

are usually added, to achieve a favourable balance between technical properties, processability, and cost. 1 They can account for about one third of the weight of starch plastics, and could thus strongly affect the overall 2 environmental performance (Broeren et al., 2017). 3

In the screening study by Broeren et al. (2016), four families of additives are distinguished for each of the 4 nine plastic grades assessed: mineral fillers, plasticisers, flame retardants and others/unknown. The focus on 5 additive types, rather than on the specific substances used, avoided the collection of full composition data, 6 which are normally hardly retrievable, at least at the screening level. Depending on the plastic grade, 7 additives accounted for different ranges (from 0-10% to 31-40% by weight), although quantities were not 8 explicitly reported. To overcome the lack of detailed LCI data for many substances, representative ranges of 9 non-renewable energy use and GHG emissions associated with the production of each family of substances 10 were derived from the literature, and used in the assessment to calculate overall impact ranges for each 11 plastic type. Additive burdens in other lifecycle stages other than production (e.g. from release during use or 12 EoL) were not considered. For plastic grades including flame retardants, additive production was found to 13 account for 5-25% to 10-45% of cradle-to-grave GHG emissions, depending on the range of emission factor 14 considered. For non-flame retardants grades, the contribution was lower (up to 5%) instead. 15

The six grades of starch-polyester plastic studied in Broeren et al. (2017) include 16% to 32% wt. of additives, 16 distinguished between compatibilisers and other additives (mineral fillers, plasticisers, processing aids). Their 17 production was modelled by combining confidential data with LCI datasets from commercial databases, and 18 no further detail is provided. Additives were shown to account for up to 46% of non-renewable energy use 19 and GHG emissions for those plastic grades using larger amounts of such substances (e.g. starch/PLA). When 20 smaller amounts of additives are used in combination with relatively energy-intensive polyesters (e.g. 21 starch/PBS), additives only account for 9% of those indicators. Additives also provide a non-negligible 22 contribution to eutrophication and agricultural land use, when they are bio-based. 23

In the study of Lorite et al. (2017), PLA-based packaging with different additives was prepared and their 24 performance was compared with PET and pristine PLA. Studied PLA-based packages were (i) PLA and 25 nanoclay, (ii) PLA, nanoclay and surfactant, and (iii) PLA and nanowhiskers. Inventory data related to 26 production of additives was based on laboratory tests. All additives were assumed to extend the fruits’ 27 lifetime equally long. The results showed that PLA-based packaging with nanowhiskers additives requires 28 more energy and materials and presented the largest impacts in all impact categories compared to the other 29 PLA based solutions. However, the additives had only a minor influence on the materials compared to pristine 30 PLA and PET pellets. Although PLA-based packaging with additives require more energy for their production, 31 results clearly showed that PLA-based packaging with nanoclay and surfactant is very competitive with PET 32 for environmental impact. In addition, PLA-based packaging revealed to cause lower impact in human health 33 compared to PET packaging. In addition, the results indicated that PLA-based packaging with additives can 34 reduce food loss through extending the shelf-life of the fruits. 35

Petrucci et al. (2017) assessed the environmental impact of a limonene (20% wt.) plasticized PLA film 36 containing 1% wt. of cellulose nanocrystals (PLA/1CNC/20limonene) and compared it to the PLA film 37 containing 3% wt. of organo-modified montmorillonite (OMMT) plasticized with acetyl tributyl citrate (ATBC) 38 at the 16.5% wt. Data related to the extraction of cellulose nanocrystals was collected at laboratory scale. 39 Data regarding the production of limonene, used as a plasticizer in the PLA/CNC based system, was collected 40 from a specific process carried out in a 200,000 ton/yr biorefinery. According to results, even if the cellulose 41 nanocrystals amount is relatively low, its contribution to the LCA score appears to be relevant. This is mainly 42


43

due to the use of a wide range of chemical species (i.e., sodium hydroxide, sodium dichlorite, sodium 1 bisulphate, acetic acid, etc.) necessary for the extraction from Phormium fibres. Limonene contribution to 2 the environmental impact was not significant, except in climate impact its contribution was 13%. Slightly 3 better environmental performance was evidenced for the limonene plasticized nanocomposite based on 4 cellulose nanocrystals with respect of ATBC plasticized system containing organomodified montmorillonite. 5

2.3.2.7 Modelling of biodegradation at end-of-life 6

Thirteen studies included in the in-depth analysis address biodegradation at the End of Life (EoL) stage. This 7 includes either biodegradation during biological waste treatment processes such as composting and 8 anaerobic digestion (11 studies), or in-situ/on-field degradation (2 studies). Landfilling is excluded from the 9 scope of this section, as not being a treatment intended to promote biodegradation, but rather a disposal 10 option. 11

All these studies apply product-specific (or better, polymer-specific) degradation rates, rather than average 12 degradation efficiencies typically achieved for generic bio-waste. However, in most cases (5 studies), 13 literature values from other LCA or experimental studies are used (Hottle et al., 2017; Posen et al., 2016; van 14 der Hast et al., 2014; Guo et al., 2013; Rossi et al., 2015). Assumptions of complete or partial degradation are 15 also frequently performed (4 studies, including Kendall, 2012; Arnold and Alston, 2012; Deng et al., 2013; and 16 Detzel and Krüger, 2006). Only a couple of studies rely, at least partially, on results from laboratory tests 17 (Razza et al., 2015; Müller, 2012), while in one case study on mulch film (Müller and Müller, 2015), the 18 minimum biodegradation level required by developing biodegradability standards for plastic material in soil 19 is assumed to be achieved (90% over 2 years). 20

As a result, degradation rates considered for a given material-treatment combination frequently vary over a 21 quite wide range, especially for industrial composting ( 22


44

Table 6). For instance, degradation of PLA in industrial composting varied between 44% (Posen et al., 2016) 1 and 95% (Detzel and Krüger, 2006), similarly to that of PHB (44-100%). For starch blends (starch/copolymers) 2 and TPS, the range is more restricted, albeit still appreciable (60-80% for TPS and 75-91% for starch blends). 3 A similar situation is observed for anaerobic digestion, with a more marked variation for PLA (60-98%) and 4 less pronounced for starch blends (68-75%). Regarding in-situ degradation, Arnold and Alston (2012) report 5 interesting assumptions for PP and PLA/starch biopolymer used in tree shelters, as detailed in 6


45

Table 6. 1

Among the studies not included in the in-depth analysis, it is worth mentioning the one by Hermann et al. 2 (2011), where average degradation rates for a number of biopolymers in industrial composting plants are 3 determined based on an extensive survey of the literature available at the time. Surveyed biopolymers 4 include TPS, PLA, a starch blend (MaterBi), PBAT, PHB and PHBV. 5

6


46

Table 6: Summary of (bio)degradation rates considered in the assessed studies for different plastic 1 materials and EoL options. Values refer only to degradation during the relevant process. 2

Polymer

EoL option

Source Industrial composting

Home composting

Anaerobic digestion

In-situ (bio-) degradation

PLA 44-95% - 60-98% -

Hottle et al. (2017); Posen et al. (2016); Detzel & Krüger (2006); Rossi et al. (2015)

PLA/copolymer (1) 100% - - 90% (2) Müller (2012); Müller & Müller (2015)

TPS 60-80% - 90% - Hottle et al. (2017); Rossi et al. (2015)

Starch/copolymers 75-91% 86-90% 68-75% - Razza et al. (2015); Guo et al. (2013)

Starch/PLA - - - 50% (3) Arnold & Alston (2012)

PHB 44-100% - - - Kendall (2012); Posen et al. (2016)

Wheat gluten 100% - - - Deng et al. (2013)

PP - - - 50% (4) Arnold & Alston (2012)

(1) Refer to a blend of PLA and PBAT (also traded under the commercial name of ecovio®). 3 (2) The remaining 10% is assumed to be bound in the non-toxic form in clay as soil organic matter. 4 (3) The remaining 50% is assumed to be degraded to sugars without any additional environmental impact. Pigments and 5 stabilisers do not degrade and are considered as emitted to soil. 6 (4) 50% is assumed to be lost as CO2 and water, the remaining 50% is degraded by photo-oxidation to aldehydes and 7 ketones (50%) organic acids (20%), esters (20%) and alcohols (10%). Pigments and stabilisers do not degrade and are 8 considered as emitted to soil. 9

10

Regarding the overall modelling of biodegradation in biological waste treatments (composting and anaerobic 11 digestion), the focus is mostly on the fate of carbon and (any) nitrogen included in the biodegradable plastics 12 during treatment and subsequent land application of the residual composted or digested material (if 13 performed). Hence, biodegradation is mostly addressed in terms of carbon (CO2 and CH4) and nitrogen (NH4 14 and N2O) emissions taking place during these operations, as well as of any credits associated with the carbon 15 and nitrogen still included in the residual composted or digested material after land application and further 16 degradation in soil. The modelling of biodegradation after land application is frequently not reported across 17 the set of studies, and, in general, no common and consistent approach could be identified. However, it can 18 be reasonably inferred that the fate and impacts of any non-degradable element or compound in the 19 biodegradable plastics (e.g. metals and additives) is usually not addressed. Information about full product 20 composition and use of additives are indeed hardly availbale for accounting in LCA studies. 21

As for the overall modelling of biological waste treatments, and especially aerobic composting, the majority 22 of the studies selected for the in-depth assessment apply an approach that can be assimilated to the 23 "individual perspective" described by Hermann et al. (2011). According to this approach, the biodegradable 24


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plastic is considered in isolation from any other processed organic waste, and only the burdens and benefits 1 associated with its treatment are taken into account. This means that only the emissions directly related to 2 the degradation of the biodegradable plastic are considered, based on its specific composition and 3 degradation rate (e.g. no nitrogen emissions are considered if nitrogen is not in the product composition). 4 Similarly, any benefits from compost utilisation are calculated considering the quantity and composition of 5 the compost that can be realistically obtained from biodegradable plastic composting (i.e. no benefits from 6 replacing mineral fertilisers are credited if the biodegradable plastic does not contain any nutrients that can 7 be transferred to the compost). Finally, process-specific burdens associated with waste handling and 8 treatment operations (e.g. from electricity and fuel consumption) are taken into account, normally in 9 proportion to the mass of biodegradable plastic waste to be treated. 10

Conversely, in Razza et al. (2015), composting of biodegradable plastic is modelled according to a variant of 11 the "combined perspective" of Herman et al. (2011). A product-specific biodegradation rate (75%) and the 12 corresponding carbon emissions are considered, as in the "individual perspective" above. However, a share 13 of nitrogen emissions (NH3, N2O) from the overall process are assigned to the biodegradable plastic, based 14 on average emissions from composting process. Moreover, benefits from compost utilisation are calculated 15 considering that the compost obtained from the bio-plastic has the average characteristics of compost 16 obtained from bio-waste (thus allowing for the replacement of mineral fertilisers, other than for peat 17 substitution and carbon sequestration). Detzel and Krüger (2006), only mention that compost from PLA 18 degradation is assumed to serve as soil amendment and to displace mineral fertilisers and peat. However, no 19 sufficient information is provided to understand how the overall modelling was handled 20

2.3.2.8 Potential consequences of using biodegradable plastic bags and cutlery on organic waste 21 management 22

The potential consequences of introducing biodegradable plastic bags on the management of other waste 23 streams (somehow very closely linked to) such as food/organic waste are included, through consequential 24 thinking approach, in two studies carried out on behalf of BASF (Müller, 2012; Müller and Müller, 2017). In 25 Müller (2012), the benefits of a potential increase in separate collection of organic waste thanks to the use 26 of biodegradable (compostable) carrier bags are accounted for. A 2% increase is considered compared to the 27 use of conventional, non-degradable PE bags10 and paper bags, according to the results of a 3 months pilot 28 project with around 21,000 households from two districts in Berlin, which in September 2011 shifted to the 29 use of biodegradable bags. At the end of the three months, a 10% increase was observed compared to the 30 project start, while a 30% increase was measured in comparison to districts not using biodegradable bags. 31 An average 20% increase per user was thus assumed, along with a usage rate of 10% of total German 32 population (corresponding to an overall 2% increase nationwide). The effect of this increase on the LCA (eco-33 efficiency) results was only marginal, though. The study also claims potential benefits from a possible 34 reduction in the level of non-biodegradable plastic impurities (from conventional PE bags) in the collected 35 organic waste and subsequent composting treatment, according to the results of the pilot project above11 36

10 Note that non-degradable PE bags are only used for organic waste collection by the user, while the subsequent delivery to the municipal collection system is made through bins without the use of bags (which are sent to incineration), and the cleaning of bins is included in the assessment. 11 A reduction of PE impurities ranging from 37% to 67% was observed, depending on the district.


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and the discussion by Razza and degli Inncocenti (2012). However, this effect was not taken into account in 1 the study. 2

Also the second study for BASF (Müller and Müller, 2017), which focuses more specifically on fruit and 3 vegetable bags, accounts for an increase in organic waste capture rate when compostable bags are used 4 (+36% compared to non-degradable bags and +14.5% compared to paper bags). On the other hand, the 5 analysed type of biodegradable bag has the potential to reduce food waste thanks to increased shelf life of 6 packed food. This is taken into account in three sensitivity scenarios considering alternative types of 7 vegetables (tomato and lettuce) and different consumption patterns (linear consumption over different 8 timeframes). The modelling is based on the results of shelf-life tests carried out for BASF, which showed a 9 four-fold increase in tomato shelf life compared to (closed) PE bags, and a seven-fold increase for lettuce. 10 The study also mentions that a higher biogas production potential can be achieved (up to 50% compared to 11 collection without compostable bags), because of better conservation of food waste, which prevent 12 methanisation processes from starting already at home when biodegradable bags are used. However, it is 13 not clear whether this effect has eventually been taken into account in the assessment or not. 14

Finally, a couple of studies (Fieschi and Pretato, 2017; Razza et al., 2009) accounts for the effects of using 15 compostable plastic cutlery or tableware on the collection and management of waste from events and quick 16 service restaurants and catering. Thanks to compostable items, a homogenous waste stream including 17 discarded cutlery/tableware and food residues can indeed be separately collected for composting, rather 18 than being routed to incineration or disposal as mixed residual waste. To account for this potentially 19 beneficial effect, the management of the overall waste generated from meal supply is included in the 20 functional unit of the study, considering both discarded cutlery/tableware and the associated amount of food 21 residues (assumed to be 0.15 kg per meal). A different collection and management scenario is then 22 considered, depending on the type of cutlery or tableware used (conventional or compostable). 23

2.3.2.9 Other consequences/indirect effects associated with the use of bioplastics 24

Other consequences associated with the use of biodegradable plastics are explored for mulch film in a study 25 carried out for BASF (Muller, 2015). In this study, the effects of biodegradable and conventional film on cotton 26 yield are taken into account. In the case of PE mulch film, 8.5% mean yield decrease per year due to land 27 contamination of long term use of PE mulch film (CAAS, 2013) was taken into account in the study, which 28 was avoided by using biodegradable mulch film. 29

Other effects associated with the use of biodegradable plastics are related to substitution of mineral 30 fertilizers, i.e. after processing biodegradable plastics at end of life stage in the biogas process, the digestate 31 can be used as fertilizer (Parajuli et al., 2017). Also, after composting of biodegradable plastics, the compost 32 can serve as soil amendment and displace mineral fertilizers and peat (Detzel & Krüger, 2006). However, 33 Müller (2012) study argued that credits for using compost as a substitute for mineral fertilizers cannot be 34 assigned to the bags, because bags are assumed to completely decompose to CO2 and water. 35

2.3.2.10 Littering 36

Littering was semi-quantitatively addressed only in one study included in the in-depth analysis, i.e. Parker 37 and Edwards (2012), who assessed the impact of plastic bag littering at two different levels. First of all, the 38 impacts from degradation of littered bag in the open environment were assessed, using LCI data for disposal 39 of bag material(s) in sanitary landfill as a proxy. Default data were adjusted to account for carbon emissions 40


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from aerobic degradation, assuming specific degradation rates, equal to 91% for oxo-degradable bags and 1 50% for biodegradable bags, while no degradation was assumed for conventional PE bags. In addition, an 2 attempt is made to quantify the visual impact of littering, by introducing an additional indicator termed “litter 3 aesthetics”, which was initially developed by ExcelPlas Australia et al (2004). The indicator is expressed as 4 the area of the littered product multiplied by the time spent in the environment before degrading (m2∙y), 5 both considered relevant variables in relation to aesthetic impacts. The quantification of all these impacts 6 requires a prior estimation of the rate and quantity of littered product, which is conducted by combining data 7 from a beach survey and on overall littering in the reference country, with the total amount of product (bag) 8 consumed during the same year of the survey in the same country. An estimated share of littered bags equal 9 to 0.75% was ultimately calculated based on this procedure. 10

In the original study by ExcelPlas Australia (2004), a second indicator termed “litter marine biodiversity” is 11 proposed in the attempt to quantify the potential impact of littering on marine organisms, but it is not taken 12 into account in Parker and Edwards (2012). As reported in Dilli (2007), a study excluded from the in-depth 13 assessment, the indicator is based on the time over which the littered product has the potential for ingestion 14 or entanglement of marine fauna (kg∙y), and is mostly affected by the propensity of the material to float or 15 sink. In this study, 0.5% of used bags were assumed to enter the litter stream at EoL, based on existing data 16 related to single-use HDPE bags. However, only a qualitative assessment of the indicator is performed, and 17 not sufficient detail is provided to make this approach transparent or even reproducible. 18

In Müller (2012), littering is addressed through qualitative statements, highlighting that BASF does not see 19 the use of biodegradable bags in itself as a solution to marine littering, which can only be solved by means of 20 education. 21

2.4 Conclusions 22

Through a preliminary literature search, 171 studies concerning LCA and plastics were found. 32 of them 23 were selected for an in-depth meta-analysis. The selection, apart from the overall relevance of the study for 24 the purposes of the project, was based on the following criteria: being a comparative LCA study, existence of 25 at least two alternatives, assessment of at least one midpoint environmental impact category, availability of 26 characterised midpoint LCIA results (expressed in physical units) as well as availability of a background report 27 or supplementary information adequately detailing the modelling approach and LCI data used 28

The meta-analysis was performed through a two-phases screening of the selected studies and revealed a 29 significant variety of approaches applied to handle the methodological aspects evaluated in the analysis. 30 More specifically: 31

Biogenic carbon balance is addressed in many studies, however the level of detail varies significantly, 32 with CO2 storage being taken into account most often, while the timing of CO2 emissions were rarely 33 taken into account; 34

Indirect Land Use Change (iLUC) is addressed only in a few studies (7/32), applying different emission 35 factors and approaches, basically highlighting the lack of an commonly accepted and consistent 36 method for iLUC accounting in the LCA community; 37

The use of waste/residual bio-based feedstock is addressed with different methods (different types 38 of allocation/substitution), potentially leading to different results; 39


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Additives are only included, limited to production impacts, only in few studies, by means of emission 1 (impact) ranges for similar class of substances; 2

Biodegradation rates of materials during biological treatment or in-situ degradation vary in a wide 3 range across case studies; 4

Other potential consequences of the use of biodegradable plastics, e.g. bags for collection of 5 biowaste and mulch film, were investigated only in a few cases. Those studies do not necessarily 6 present comprehensive or conclusive and robust insights, especially for taking those aspects into 7 account in a quantitative manner in LCA; 8

Littering is almost always neglected. It is semi-quantitatively addressed only in one case study, 9 through the introduction of an indicator attempting to capture the aesthetic impact of littered 10 product. 11

12


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Rossi, V., Cleeve-Edwards, N., Lundquist, L., Schenker, U., Dubois, C., Humbert, S., Jolliet, O. (2015). Life cycle 33 assessment of end-of-life options for two biodegradable packaging materials: Sound application of the 34 European waste hierarchy. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2014.08.049 35

Rudorff, B.F.T., Aguiar, D.A., Silva, W.F., Sugawara, L.M., Adami, M., Moreira, M.A. (2010). Studies on the 36 Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data. Remote 37 Sens., 2, 1057-1076. 38

Schmidt, J.H., Muños, I. (2014). The Carbon Footprint of Danish Production and Consumption: Literature 39 Review and Model Calculations. Energistyrelsen, Copenhagen, Denmark (2014), pp. 1-119. 40 http://vbn.aau.dk/files/196725552/_dk_carbon_footprint_20140305final.pdf 41

Schulze, C., Juraschek, M., Herrmann, C., Thiede, S. (2017). Energy Analysis of Bioplastics Processing. Procedia 42 CIRP, 61, 600–605. http://doi.org/10.1016/j.procir.2016.11.181 43


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Shen, L., Worrell, E., Patel, M. K. (2012). Comparing life cycle energy and GHG emissions of bio-based PET, 4 recycled PET, PLA, and man-made cellulosics. Biofuels, Bioproducts and Biorefining, 6(6), 625–639. 5 http://doi.org/10.1002/bbb.1368 6

Suwanmanee, U., Varabuntoonvit, V., Chaiwutthinan, P., Tajan, M., Mungcharoen, T., Leejarkpai, T. (2013). 7 Life cycle assessment of single use thermoform boxes made from polystyrene (PS), polylactic acid, (PLA), and 8 PLA/starch: Cradle to consumer gate. International Journal of Life Cycle Assessment. 9 https://doi.org/10.1007/s11367-012-0479-7 10

Tao, L., Tan, E.C.D., Mccormick, R., Zhang, M., Aden, A., He, X., Zigler, B.T. (2014). Techno-economic analysis 11 and life-cycle assessment of cellulosic isobutanol and comparison with cellulosic ethanol and n-butanol. 12 Biofuel Bioprod. Biorefining 8, 30-48. 13

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3. Methodology for LCA of plastic articles 1 This chapter provides methodological guidance on how to conduct life cycle assessment (LCA) studies to 2 evaluate the use of alternative feedstocks (biomass, recycled material, CO2) for the production of plastic 3 articles, in comparison with the use of traditional fossil-based feedstocks (oil and natural gas). Moreover, the 4 method specified in this section can also be applied to biodegradable plastic articles, regardless of the type 5 of feedstock used for their production (bio-based or not). 6

Life cycle assessment is a method to calculate the potential environmental impacts of products from a supply 7 chain perspective, i.e. by accounting for the impacts associated with the emissions and resource 8 consumptions taking place throughout the whole supply chain (life cycle) of a product, from raw material 9 acquisition through processing, distribution, use and final waste management (end-of-life). Moreover, a 10 range of environmental impacts, health effects and resource-related threats are accounted for. This is in 11 contrast with focusing only on site-specific impacts or on single environmental impacts, in order to reduce 12 the risk of unintended shifting of environmental impacts (“burden shifting”) from one stage of the supply 13 chain to another, from one impact category to another, between impacts and resource efficiency, and/or 14 between countries. 15

The method presented in this section is based on the general structure, requirements and recommendations 16 of the Product Environmental Footprint (PEF) method (EC, 2013)12 and of the related technical guidance for 17 the development of product category rules (PEFCR Guidance; EC, 2018). Product Environmental Footprint is 18 a method based on the life-cycle-approach, providing a multi-criteria measure of the environmental-19 performance of a good or service throughout its life cycle. It has been developed by the European 20 Commission, which in 2013 recommended it as a common method to measure and communicate the life 21 cycle environmental performance of products. The method is complemented by a guidance to develop 22 product category rules, which includes additional modelling requirements and recommendations for specific 23 processes and life cycle stages. 24

Compared to the PEF guide and the PEFCR guidance, the method presented in this section provides further 25 guidance to address relevant aspects for plastic articles and the related variety of feedstocks. Additional 26 aspects covered include, for instance, the assessment of potential environmental impacts from indirect land 27 use change (iLUC), as well as the modelling of end-of-life options for biodegradable and non-biodegradable 28 plastics. 29

3.1 Target audience 30 This technical guidance is primarily aimed at technical experts who need to develop comparative or non-31 comparative LCA studies for plastic articles and possible non-plastic counterparts. A minimum background in 32 environmental assessment methods is needed to use this guide for conducting a LCA study. 33

12 The Product Environmental Footprint (PEF) is a multi-criteria measure of the environmental performance of a good or service throughout its life cycle. PEF information is produced for the overarching purpose of helping to reduce the environmental impacts of goods and services. The Product Environmental Footprint (PEF) was recommended by the European Commission in 2013 as a standardised method to measure and communicate the environmental performance of products throughout their life cycle.


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3.2 Relationship to other methods and standards 1 In line with the PEF guide, the guidelines provided in this method have been partly developed by taking into 2 account the requirements and recommendations of similar, widely recognised environmental accounting 3 methods and guidance documents. Specifically, the following documents were considered: 4

PEF Guide, Annex to Commission Recommendation 2013/179/EU on the use of common methods to 5 measure and communicate the life cycle environmental performance of products and organisations 6 (EC, 2013b); 7

ISO standards13: 8 o ISO 14040:2006 Environmental management — Life cycle assessment —Principles and 9

framework, 10 o ISO 14044:2006 Environmental management — Life cycle assessment —Requirements and 11

guidelines, 12 o ISO 14046:2014 Environmental management -- Water footprint -- Principles, requirements 13

and guidelines, 14 o ISO/TS 14067:2018 Greenhouse gases -- Carbon footprint of products -- Requirements and 15

guidelines for quantification and communication, 16 o ISO 14025:2006 Environmental labels and declarations – Type III environmental declarations 17

– Principles and procedures (ISO), 18 o ISO 14020:2000 Environmental labels and declarations – General principles, 19 o ISO TS 14071:2014 Environmental management – Life Cycle Assessment – Critical reviewer 20

competences: additional requirements and guidelines to ISO 14044:2006; 21 EN standards: 22

o EN 16760:2015 Bio-based products – Life Cycle Assessment, 23 o CEN TR 16957:2016 Bio-based products – Guidelines for Life Cycle Inventory (LCI) for the End-24

of-life phase, 25 o EN 13432:2000 Packaging - Requirements for packaging recoverable through composting 26

and biodegradation - Test scheme and evaluation criteria for the final acceptance of 27 packaging, 28

o EN 14995:2006 Plastics - Evaluation of compostability - Test scheme and specifications, 29 o EN 17033:2018 Plastics - Biodegradable mulch films for use in agriculture and horticulture - 30

Requirements and test methods; 31 ILCD (International Reference Life Cycle Data System) Handbook (EC-JRC, 2010a); 32 Ecological Footprint Standards14; 33 Greenhouse Gas Protocol - Product Life Cycle Accounting and Reporting Standard (WRI, 2011b); 34

13 Available online at http://www.iso.org/iso/iso_catalogue.htm 14 “Ecological Footprint Standards 2009” – Global Footprint Network. Available online at http://www.footprintnetwork.org/images/uploads/Ecological_Footprint_Standards_2009.pdf


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BP X30-323-0:2011 - General principles for an environmental communication on mass market 1 products (AFNOR, 2011)15; 2

PAS 2050:2011 Specification for the assessment of the life cycle greenhouse gas emissions of goods 3 and services (BSI, 2011); and 4

ENVIFOOD PROTOCOL - Food SCP RT (2013), ENVIFOOD Protocol, Environmental Assessment of 5 Food and Drink Protocol, European Food Sustainable Consumption and Production Round Table 6 (SCP RT), Working Group 1, Brussels, Belgium. 7

Whereas existing methods may provide several alternatives for a given methodological decision point, the 8 intention of this guidance is (wherever feasible and in line with the approach adopted in the PEF framework) 9 to identify a single requirement for each decision point, or to provide additional guidance that will support 10 more consistent, robust and reproducible LCA studies. Thus, comparability is given priority over flexibility. 11

3.3 Terminology used: shall, should and may 12 This guide uses precise terminology to indicate the requirements, the recommendations and options that 13 may be chosen when developing a LCA study in accordance with this method. 14

The term “shall” is used to indicate what is required in order for a LCA study to be in conformance with the 15 method specified in this Guide. 16

The term “should” is used to indicate a recommendation rather than a requirement. Any deviation from a 17 “should” requirement has to be justified when developing a LCA study and made transparent. 18

The term “may” is used to indicate an option that is permissible. Whenever options are available, the LCA 19 study shall include adequate argumentation to justify the chosen option. 20

3.4 How to Use this Document 21 This guide provides methodological guidance to conduct a consistent and robust LCA study. Information in 22 this guide is presented in a sequential manner, in the order of the methodological phases that shall be 23 completed when conducting a LCA study. Each section normally includes a first general description of the 24 corresponding methodological phase or issue, followed by the methodological requirements and/or 25 recommendations that “shall / should” be fulfilled in order to comply with the methodology described in this 26 document. Supporting examples and “Tips” are also provided where appropriate. “Tips” describe non-27 mandatory but recommended best practices. 28

3.5 Principles for LCA studies 29 To produce consistent, robust and reproducible LCA studies, a core suite of analytical principles shall be 30 strictly adhered to. They provide overarching guidance in the application of the present method, and shall be 31 considered with respect to each phase of LCA studies (see the next section). 32

15 Replaced by new standard: http://www.base-impacts.ademe.fr/gestdoclist/download?url=/documents/Environmentallabelling0Generalprinciplesandmethodologicalframework.pdf


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The following principles shall be observed by users of this method in conducting a LCA study: 1

(1) Relevance 2

All methods used and data collected for the purpose of the LCA study shall be as relevant to the study 3 as possible. 4

(2) Completeness 5

LCA studies shall include all environmentally relevant material/energy flows and other environmental 6 burdens as required for adherence to the defined system boundary, the data requirements, and the 7 impact assessment methods employed. 8

(3) Consistency 9

Strict conformity to this guide shall be observed in all steps of the LCA study so as to ensure internal 10 consistency and comparability with similar analyses. 11

(4) Accuracy 12

All reasonable efforts shall be taken to reduce uncertainties in product system modelling and the 13 reporting of results. 14

(5) Transparency 15

LCA information shall be disclosed in such a way as to provide intended users with the necessary 16 basis for decision making, and for stakeholders to assess its robustness and reliability. 17

3.6 Phases of a LCA study 18 A number of phases shall be completed in carrying out a LCA study in line with this method (Figure 7): Goal 19 Definition, Scope Definition, Life Cycle Inventory, Life Cycle Impact Assessment, Interpretation of LCA results 20 and Reporting. 21

22


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1

Figure 7: Phases of a LCA study (based on ISO 14040: 2006) 2

3

Define the goals and scope of the LCA study

Compile the Life Cycle Inventory

Conduct the Life Cycle Impact Assessment

Interpretation and Reporting

Critical review


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4. Goal and Scope Definition 1

4.1 Defining the goal of the LCA study 2 Goal definition is the first step of a LCA study, and sets the overall context for the study itself. The purpose 3 of clearly defining goals is to ensure that the analytical aims, methods, results and intended applications are 4 optimally aligned, and that a shared vision is in place to guide participants in the study. Therefore, it is 5 important to take the time to carefully consider and articulate goals in order to ensure the success of the LCA 6 study. 7

In defining goals, it is important to identify the intended applications and the degree of analytical depth and 8 rigour of the study. This should also be reflected in the defined study limitations (scope definition phase, 9 section 4.2.6). In particular, the goal definition of the LCA study shall address the following aspects, which 10 are further exemplified in Table 7: 11

Intended application(s); 12

Reasons for carrying out the study and decision context; 13

Target audience; 14

Whether comparisons and/or comparative assertions are to be disclosed to the public; 15

Commissioner of the study; 16

Review procedure (if applicable). 17

Table 7: Example of goal definition (LCA of disposable shopping bags) 18

Aspect Detail

Intended application(s): Compare the lifecycle environmental impacts of a new type of biodegradable and partly bio-based shopping bags with those of conventional non-biodegradable bags

Reasons for carrying out the study and decision context:

Understand the environmental implications of material substitution by a bag producer

Target audience: External technical audience, business-to-business

Comparisons or comparative assertions intended to be disclosed to the public:

Yes, the results of the comparative study will be disclosed to the public

Commissioner of the study: Bags&Bags limited

Review: Independent external reviewer (Mr Name Surname)

19

20


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4.2 Defining the Scope of the LCA Study 1 In defining the scope of the LCA study, the system to be evaluated and the associated analytical specifications 2 are described in detail. 3

The scope definition of the LCA study shall be in line with the defined goals of the study itself and shall include 4 the following elements (see subsequent sections for a more detailed description): 5

• Description and characteristics of the studied product; 6

• Functional unit and reference flow; 7

• System boundary; 8

• Assessed impact categories and related impact assessment methods; 9

• Assumptions/Limitations. 10

Moreover, a general description of the studied product and of the respective life cycle (supply chain, use 11 stage and end of life) should be provided. 12

4.2.1 Description/characteristics of the studied product 13 A general description of the studied product(s) and of its relevant function(s) shall be included in the study. 14 Moreover, the chemical and physical properties reported in 15


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Table 8 shall be specified for all the analysed products, along with the respective source. This ensure 1 transparency and enable comparability and reproducibility, as the reported parameters are essential for a 2 proper modelling. 3

4


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Table 8. Chemical and physical properties of the analysed product(s) that shall be specified in the LCA 1 study 2

Parameter Recommended unit Energy content -as lower heating value (LHV)-

MJ/kg

Water content % Volatile Solids (VS) % Ash % Density kg/m3 Compostability (Y/N) specifying the standard of reference Biodegradability -on-land- (Y/N) specifying any standard of reference Biodegradability -marine- (Y/N) specifying any standard of reference Main constituents Main polymer

g/kg*

Co-polymer 1 Co-polymer 2 Co-polymer n Additive 1 (e.g. plasticisers, fillers, flame retardants, stabilisers) Additive 2 (e.g. plasticisers, fillers, flame retardants, stabilisers) Additive n (e.g. plasticisers, fillers, flame retardants, stabilisers) Chemical composition Carbon -fossil- (C)

g/kg*

Carbon -biogenic- (C) Hydrogen (H) Oxygen (O) Nitrogen (N) Phosphorus (P) Potassium (K) Sulphur (S) Chlorine (Cl) Fluorine (F) Arsenic (As) Cadmium (Cd) Cobalt (Co) Chromium (Cr) Copper (Cu) Lead (Pb) Manganese (Mg) Mercury (Hg) Nickel (Ni) Zinc (Zn) Other elements (e.g. Se and Mo)

(*) On total weight 3

4.2.2 Functional unit and reference flow 4 Users of this guide are required to define the functional unit and reference flow for the LCA study. 5

The functional unit (FU) is the quantified performance of a product system, to be used to express the 6 potential environmental impacts of the product. For instance, the FU of a wall paint could be described as 7 providing protection of 1 m2 of substrate for 20 years with a minimum 98% opacity. Meaningful comparisons 8 can only be made when products can fulfil the same function. 9


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The functional unit of the LCA study should describe qualitatively and quantitatively the function(s) and 1 duration of the product, according to the four following aspects (further exemplified in Table 9): 2

- The function(s)/service(s) provided: “what”; 3

- The extent of the function or service: “how much”; 4

- The expected level of quality: “how well”; 5

- The duration/life time of the function or service: “how long”. 6

Note: the “how well” attribute (expected level of quality) may not always be possible to incorporate in the 7 FU. The “how long” attribute (duration/life time of the product) shall be quantified if technical standards or 8 agreed procedures exist at sectoral level. 9

For food packaging, the FU should be defined at the product consumption level, in order to account for 10 potential wastage of food at the use stage (and preceding ones). Any omission of the functions of the product 11 in the definition of the functional unit shall be explained and documented. Moreover, in case applicable 12 standards exist when defining the FU, they shall be used and cited in the LCA study. 13

Table 9: Example of functional unit definition (LCA of shopping bags) 14

Aspect Example

“What” (function(s) or service(s) provided)

Carrying of shopping from supermarket to home

“How much” (extent of the function(s) or service(s))

An average volume of 22 litres and an average weight of 12 kg

“How well” (expected level of quality)

Without breaking during the shopping trip

“How long” (duration/lifetime of the function or service)

Ten times

15

Note: For intermediate products, the FU is more difficult to define because they can often be converted into 16 a variety of final products, fulfilling multiple functions. Therefore, a declared unit should be applied in this 17 case, and based, for example, on mass (kilogram) or volume (cubic meter). 18

Some products may perform more than one function over the life cycle. Additional functions may be included 19 in the FU, provided that full functional equivalence is ensured among the compared alternatives or scenarios. 20 For instance, biodegradable and/or compostable shopping bags could also be used for organic waste 21 collection after their first use for the transport of goods. This additional function may be included in the FU, 22 provided that an equivalent additional function is also provided in the compared scenarios where 23


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conventional, non-degradable bags are used. If the use of non-degradable shopping bags for organic waste 1 collection is permitted in the reference country or region, this additional function is directly provided by the 2 non-degradable bags themselves. Conversely, it will have to be provided by the alternative type of bag that 3 would otherwise be used in the absence of biodegradable plastic bags (e.g. paper bags), whose life cycle have 4 to be included into the analysed system. 5

The reference flow is the amount of product needed in order to fulfil the defined functional unit. All input 6 and output flows in the analysis quantitatively relate to it. An appropriate reference flow shall be determined 7 in relation to the functional unit. The quantitative input and output data collected in support of the analysis 8 shall be calculated in relation to this flow. 9

Example of reference flow (LCA of shopping bags): number of bags required to carry the quantity of goods 10 reported in the functional unit. 11 12 The LCA study shall describe (i) how each aspect of product performance reflected in the functional unit can 13 affect the environmental performance of the product, (ii) how to include this effect in the LCA calculations 14 and (iii) how an appropriate reference flow shall be calculated. 15

For example, the type of packaging might affect the shelf-life of the packed food product and the amount of 16 food (e.g. salad) wasted at retail and at the use stage. As a consequence, the type of packaging affects the 17 amount of salad (and thus packaging) which is needed to fulfil the “how long” and “how much” attributes of 18 the FU. The LCA study report shall describe the potential effects of the type of packaging on food waste and 19 provide a table with the % of salad wasted, at each stage and as a whole, per packaging type applied. Finally, 20 the report shall describe how the % of salad waste from the table is integrated in the reference flow needed 21 to fulfil the FU of, e.g., 1 kg of salad consumed. All quantitative input and output data collected in the analysis 22 shall be calculated in relation to this reference flow of 1 kg consumed salad + X kg salad waste. 23

4.2.3 System boundary 24 The system boundary defines which parts of the product life cycle and which associated processes belong to 25 the analysed system (i.e. are required for carrying out its function as defined by the functional unit). 26 Therefore, the system boundary must be clearly defined for the product system to be evaluated, specifying 27 the considered life cycle stages and the processes assigned to each stage. 28

The system boundary shall be defined following general supply-chain logic, including all stages from raw 29 material extraction through processing, production, distribution, storage, use and end-of-life of the product 30 (i.e. cradle-to-grave), as appropriate to the intended application of the study. The system boundary shall 31 include all processes linked to the product supply chain relative to the functional unit. Any deviation from 32 the default cradle-to-grave approach shall be explicitly specified and justified (e.g. exclusion of the unknown 33 use-stage or end-of-life of intermediate products). 34


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The processes included in the system boundary shall be divided into foreground processes (i.e. core processes 1 in the product life cycle for which direct access to information is available16) and background processes (i.e. 2 those processes in the product life cycle for which no direct access to information is possible17). 3

4.2.3.1 System boundary diagram 4

A system boundary diagram, or a flow diagram, is a schematic representation of the analysed system. It 5 details which parts of the product life cycle are included or excluded from the analysis. A system boundary 6 diagram can be a useful tool in defining the system boundary and organising subsequent data collection 7 activities. 8

The preparation of a system boundary diagram is highly recommended and should be included in the scope 9 definition, as it will help to define and structure the analysis. 10

4.2.3.2 Indirect effects 11

Indirect effects due to the introduction in the market of the investigated product may take place. We intend 12 these as the expected or potential consequences of our product on other product systems. In the case of 13 plastic products, these may include effects on the collection and management of other (closely linked) waste 14 streams (e.g. organic waste). Examples in this respect include i) a potential reduction of impurities associated 15 with the presence of non-biodegradable plastics in separately collected organic waste, ii) a possible increase 16 in the separate collection rate of household organic waste when biodegradable bags are used in place of non-17 biodegradable ones, and iii) on the other hand, a possible reduction in recycling efficiencies and/or in the 18 quality of conventional plastic materials due to the presence of extraneous biodegradable plastics that 19 cannot be properly sorted out. In addition, indirect effects on crop production systems affected by the use 20 of plastic articles may take place. For instance, in those situations where proper collection of mulch film 21 cannot be ensured, the use of a biodegradable material may prevent a possible yield decrease of the affected 22 crop, compared to a non-biodegradable film that may accumulate into soil over the years. 23

These indirect effects may be included in the system boundary, provided that clear evidence is available in 24 this respect. Related assumptions shall be clearly specified and documented in the LCA study report, and 25 implications on the LCA results shall be clearly discussed. 26

4.2.3.3 Offsets 27

The term “offset” is frequently used with reference to third-party greenhouse gas mitigation activities, e.g. 28 regulated schemes in the framework of the Kyoto Protocol (UNFCC, 2007), or voluntary schemes. Offsets are 29 discrete greenhouse gas (GHG) reductions used to compensate for (i.e. offset) GHG emissions elsewhere, for 30 example to meet a voluntary or mandatory GHG target or cap. Offsets are calculated relative to a baseline 31 hypothetical scenario considering the emissions that would have released in the absence of the mitigation 32

16 For example, the producer’s site and other processes operated by the producer or its contractors such as goods transport, head-office services, etc. 17 For example, e.g. most of the upstream life cycle processes – such as infrastructures, buildings - and generally all processes further downstream


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project that generates the offsets. Examples of offset emissions are carbon off-setting by the Clean 1 Development Mechanism, carbon credits, and other system-external off-sets. 2

Offsets shall not be included in the LCA study, but may be reported separately as “Additional Environmental 3 Information.” 4

4.2.4 Selecting Impact Categories and Assessment Methods 5 Impact categories refer to specific environmental issues of concern considered in a LCA study. These are 6 generally related to resource use and emissions of environmentally damaging substances (e.g. greenhouse 7 gases and toxic chemicals), which may as well affect human health. Impact assessment methods use models 8 for quantifying the causal relationships between the material/energy inputs and emissions associated with 9 the product life cycle (compiled in the Life Cycle Inventory) and the potential environmental impact in each 10 impact category considered. Each category hence refers to a certain stand-alone impact assessment model, 11 which is used to calculate a specific impact category indicator quantitatively representing the category. 12

During life cycle impact assessment (section 6), Life Cycle Inventory data are grouped and aggregated 13 according to the respective contributions to each impact category. This subsequently provides the necessary 14 basis for interpretation of the LCA results relative to the goals of the LCA study (for example, identification 15 of supply chain “hotspots” and “options” for improvement). The selection of impact categories should 16 therefore be comprehensive in the sense that they cover all relevant environmental issues related to the 17 product supply chain of interest. 18

19


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Table 10 provides a default list of impact categories and related impact assessment methods that shall be 1 entirely applied in a LCA study.18 Further instructions on how to calculate these impacts are described in 2 Section 6. 3

4

18 For more information on environmental impact categories and assessment methods, reference is made to the ILCD Handbook “Framework and requirements for LCIA models and indicators” (EC-JRC, 2010b), “Analysis of existing Environmental Assessment methodologies for use in LCA” (EC-JRC, 2011) and “Recommendation for life cycle impact assessment in the European context” (EC-JRC, 2010c). These are available online at http://eplca.jrc.ec.europa.eu/


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Table 10: Default impact categories (with respective category indicators) and impact assessment models 1 to be considered and applied in LCA studies 2

Impact Category Impact Category indicators

Unit Impact Assessment Model19

Robustness

Climate Change20 Increase of radiative forcing as Global Warming Potential (GWP100)

kg CO2 eq Baseline model of the IPCC over a 100 year time horizon (IPCC, 2013)21

I

Ozone Depletion Increase of stratospheric ozone breakdown as Ozone Depletion Potential (ODP)

kg CFC-11 (**) eq

Steady- state model of the World Meteorological Organization over an infinite time horizon (WMO, 1999)

I

Human Toxicity –cancer effects*

Comparative Toxic Unit for humans (CTUh)

CTUh USEtox model (Rosenbaum et al., 2008)

III/interim

Human Toxicity –non-cancer effects*

Comparative Toxic Unit for humans (CTUh)

CTUh USEtox model (Rosenbaum et al., 2008)

III/interim

Particulate Matter Impact on human health Disease incidence

PM method recomended by UNEP (UNEP, 2016)

I

Ionising Radiation –human health

Human exposure efficiency relative to U235

kBq U235 eq Human Health effect model (Dreicer et al., 1995)

II

Photochemical Ozone Formation

Tropospheric ozone concentration increase

kg NMVOC (***) eq

LOTOS-EUROS model (Van Zelm et al., 2008) as implemented in ReCiPe

II

Acidification Accumulated Exceedance (AE) of the critical load

mol H+ eq Accumulated Exceedance model (Seppälä et al., 2006; Posch et al., 2008)

II

Eutrophication –terrestrial

Accumulated Exceedance (AE) of the critical load

mol N eq Accumulated Exceedance model (Seppälä et al., 2006; Posch et al., 2008)

II

Eutrophication – freshwater

Fraction of nutrients (P) reaching freshwater end compartment

Kg P eq EUTREND model (Struijs et al., 2009) as implemented in ReCiPe

II

Eutrophication –marine

Fraction of nutrients (N) reaching marine end compartment

Kg N eq EUTREND model (Struijs et al., 2009) as implemented in ReCiPe

II

19 The full list of characterization factors for each default impact assessment model is available at the link http://eplca.jrc.ec.europa.eu/LCDN/developer.xhtml. 20 Including three sub-categories (as described in section 5.5.10): Climate change – fossil; Climate change – biogenic; and Climate Change – land use and land transformation. The contribution of each of these sub-categories shall be reported separately when the respective contribution to the total impact is larger than 5%, as also specified in section 5.5.10. 21 With carbon feedbacks. The CFs for VOC emissions is without carbon feedback and should be noted as an inconsistency. CF = 4.23 CO2 eq, from 2002 and scaled by 0.94 to account for updated values for the reference gas CO2 (IPCC 2013, Table 8.A.5)


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Impact Category Impact Category indicators

Unit Impact Assessment Model19

Robustness

Ecotoxicity –freshwater*

Comparative Toxic Unit for ecosystems (CTUe)

CTUe USEtox model, (Rosenbaum et al, 2008)

III/interim

Water use User deprivation potential (deprivation-weighted water consumption)

m3 world eq Available WAter REmaining (AWARE) as recommended by UNEP, 2016

III

Resource use – minerals and metals

Abiotic resource depletion (ADP, based on ultimate reserves)

Kg Sb eq CML 2002 (Guinée et al., 2002) as updated in Van Oers et al. (2002)

III

Resource use –fossils

Abiotic resource depletion –fossil fuels (ADP-fossil)22

MJ CML 2002 (Guinée et al., 2002) as updated in Van Oers et al. (2002)

III

Land Use • Soil quality index23 • Biotic production • Erosion resistance • Mechanical filtration • Groundwater

replenishment

• Dimension less (pt)

• kg biotic production

• kg soil • m3 water • m3

groundwater

Soil quality index based on LANCA (Beck et al., 2010 and Bos et al., 2016)

III

* Long-term emissions (occurring beyond 100 years) shall be excluded from the toxic impact categories. Toxicity emissions to this sub-compartment have a characterisation factor set to 0 in the EF LCIA (to ensure consistency). If included by the applicant in the LCI modelling, the sub-compartment 'unspecified (long-term)' shall be used.

** CFC-11 = Trichlorofluoromethane, also called freon-11 or R-11, is a chlorofluorocarbon.

*** NMVOC = Non-Methane Volatile Organic Compounds

1

4.2.5 Selecting additional technical and environmental information to be included in the LCA 2 Relevant potential environmental impacts of a product may go beyond the widely accepted life-cycle-based 3 impact categories. It is important to consider these environmental impacts whenever feasible. For example, 4 biodiversity impacts due to land use changes may occur in association with a specific site or activity. This may 5 require the application of additional impact categories that are not included in the default list provided in 6 this guide (Section 4.2.4), or even additional qualitative descriptions where impacts cannot be linked to the 7 product supply chain in a quantitative manner. Such additional methods should be viewed as complementary 8 to the default list of impact categories. 9

22 In the ILCD flow list, and for the current recommendation, Uranium is included in the list of energy carriers, and it is measured in MJ. 23 This index is the result of the aggregation, performed by JRC, of the 4 indicators provided by LANCA model as indicators for land use.


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Some products might be produced in companies, which are located close to the sea. Their emissions might 1 therefore directly impact marine water instead of fresh water. Because the default set of impact categories 2 only include ecotoxicity resulting from emissions to fresh water, it is important to also consider emissions 3 that are made directly into marine water. These need to be included at the elementary flow (i.e. at the 4 inventory) level because no impact assessment model is currently available for such emissions. 5

Additional environmental information may include (non-exhaustive list): 6

(a) Bill-of-materials data; 7 (b) Information about disassemblability, recyclability, recoverability, and reusability of the product; 8 (c) Information about the use of secondary materials (and more in general about the overall product 9

efficiency); 10 (d) Information on the use of hazardous substances; 11 (e) Information on the disposal of hazardous/non-hazardous waste; 12 (f) Information on energy consumption (e.g. use of renewable vs non-renewable energy and/or fuels); 13 (g) Information on the use of fresh water resource; 14 (h) Life-cycle energy consumption by primary energy source, separately accounting for “renewable” 15

energy use; 16 (i) Direct energy consumption by primary energy source, separately accounting for “renewable” 17

energy use; 18 (j) Total weight of waste by type and disposal method; 19 (k) Weight of transported, imported, exported, or treated waste deemed hazardous under the terms 20

of the Basel Convention Annexes I, II, III, and VIII, and percentage of transported waste shipped 21 internationally. 22

(l) Other relevant environmental impacts for the product category; 23 (m) Environmental impacts calculated by means of alternative impact assessment methods to default 24

ones (e.g. when characterisation factors (CFs) in the default method are not available for certain 25 substance flows from the Life Cycle Inventory, such as groups of chemicals); 26

(n) Information on local/site-specific impacts, e.g. local impacts on acidification, eutrophication and 27 biodiversity; 28

(o) Description of significant impacts of activities, products, and services on biodiversity in protected 29 areas and in areas of high biodiversity value outside protected areas; 30

(p) For gate-to-gate phases, number of IUCN Red List species and national conservation list species 31 with habitats in areas affected by operations, by level of extinction risk; 32

(q) Environmental indicators or product responsibility indicators (as per the Global Reporting Initiative 33 (GRI)); 34

(r) Other relevant environmental information on the activities and/or sites involved, as well as on the 35 product output. 36

(s) Qualitative or semi-quantitative information related to the potential impact form littering of the 37 product 38

If the default set of impact categories or the default impact assessment models do not properly cover all the 39 potential environmental impacts or aspects of the product being evaluated, any relevant 40 (qualitative/quantitative) environmental impacts and aspects that may be missing shall be additionally 41


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included in the study. These shall be reported separately from the life-cycle-based LCA results, under 1 “additional environmental information”. It has to be noted that potential impacts calculated through 2 alternative models shall not substitute those calculated through the mandatory assessment models of the 3 default impact categories. The supporting models of any additional impact categories shall be clearly 4 referenced and documented with the corresponding indicators. 5

Additional environmental information shall be: 6

Based on information that is substantiated and has been reviewed or verified in accordance with the 7 requirements of ISO 14020:2000 and Clause 5 of ISO 14021:2016 8

Specific, accurate and not misleading; 9

Relevant to the particular product category. 10

Overall emissions of substances released directly into marine water shall be included in the additional 11 environmental information (at the inventory flow level and separately for each substance). 12

If additional environmental information is used to support the interpretation phase of a LCA study, then all 13 data needed to produce such information shall meet the same quality requirements established for the data 14 used to calculate the LCA results (see section 5.7). 15

Additional environmental information shall only be related to environmental issues. Information and 16 instructions (e.g. product safety sheets), which are not directly related to the environmental performance of 17 the product shall not be part of a LCA study. Similarly, information related to legal requirements shall not be 18 included. 19

4.2.6 Assumptions/limitations 20 In LCA studies, several limitations to carrying out the analysis may arise and therefore assumptions need to 21 be made. For example, secondary data may not completely represent the reality of the product analysed and 22 may be adapted for better representation. 23

All limitations and assumptions made to overcome such limitations shall be transparently reported in the LCA 24 study. 25

26

27


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5. Life Cycle Inventory 1

An inventory of all material/energy resource inputs/outputs and emissions into air, water and soil for the 2 product supply chain shall be compiled as a basis for calculating the potential environmental impacts of the 3 analysed product. This is called Life Cycle Inventory. 4

Ideally, the inventory modelling the product supply chain would be constructed using facility- or product-5 specific data (i.e. modelling the exact life cycle depicting the supply chain, use, and end-of-life stages as 6 appropriate). In practice, and as a general rule, directly collected, facility-specific inventory data should be 7 used wherever possible. For processes where no direct access to (company-) specific data is possible (i.e. 8 background processes related to energy and material supply), secondary data will typically be used. However, 9 it is good practice to access data collected directly from suppliers of the most relevant products when 10 possible, unless secondary data are more representative or appropriate. 11

The Life Cycle Inventory shall adopt the following classifications of the flows included: 12

Elementary flows, which are (ISO 14040:2006, 3.12) “material or energy entering the system being 13 studied that has been drawn from the environment without previous human transformation, or 14 material or energy leaving the system being studied that is released into the environment without 15 subsequent human transformation.” Elementary flows are, for example, resources extracted from 16 nature or emissions into air, water, soil (which are directly linked to the characterisation factors of 17 impact assessment models); 18

Non-elementary (or complex) flows, which are all the remaining inputs (e.g. electricity, materials, 19 transport processes) and outputs (e.g. waste, by-products) in a system that require further modelling 20 efforts to be transformed into elementary flows. 21

All non-elementary flows in the Life Cycle Inventory shall be transformed into elementary flows. For example, 22 waste flows shall not only be reported as kg of household waste or hazardous waste, but shall also include 23 the emissions into water, air and soil and resource consumption due to the treatment of the solid waste. This 24 is necessary for the comparability of LCA studies. The compilation of the Life Cycle Inventory is therefore 25 completed when all flows are expressed as elementary flows. 26

To summarise, all resource use and emissions associated with the life-cycle stages included in the defined 27 system boundary shall be included in the Life Cycle Inventory. The flows shall then be grouped into 28 “elementary flows” and “non-elementary (i.e. complex) flows”. Finally, all non-elementary flows in the Life 29 Cycle Inventory shall be transformed into elementary flows. 30

Compiling the Life Cycle Inventory in a LCA study may be completed following a 2-step procedure, as 31 explained in 32

Figure 8. The first step (“screening-level” assessment) is not mandatory, but is highly recommended, because 33 it helps focussing data collection activities and data quality priorities for the final Life Cycle Inventory. Further 34 requirements on how to conduct the screening step are provided in section 5.1 below. 35


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1

Figure 8: Two-step procedure to compile the Life Cycle Inventory 2

3

TIP: Documenting the data collection process is useful for improving the data quality over time, preparing for critical review, and revising future product inventories to reflect changes in production practices.

4

5.1 Screening step (recommended) 5 An initial “screening-level” assessment, referred to as the screening step, is highly recommended because it 6 helps focussing data collection activities and data quality priorities for the actual Life Cycle Inventory. 7

If a screening step is conducted (highly recommended), readily available specific and/or secondary data shall 8 be used fulfilling the data quality requirements as defined in Section 5.7. All processes and activities to be 9 considered in the Life Cycle Inventory shall be included in the screening step. Any exclusion of supply-chain 10 stages shall be explicitly justified and submitted to the review process, and their influence on the final results 11 shall be discussed. 12

For supply-chain stages for which a quantitative life cycle impact assessment is not intended, the screening 13 step shall refer to existing literature and other sources in order to develop qualitative descriptions of 14 potentially environmentally significant processes. Such qualitative descriptions shall be included in the 15 additional environmental information. 16

5.2 Life Cycle Stages 17 18 All resource use and emissions associated with the life-cycle stages included in the defined system boundary 19 shall be included in the Life Cycle Inventory. 20

The following life cycle stages and elements shall be considered for inclusion in the Life Cycle Inventory: 21 ● Raw material acquisi on and pre-processing; 22

Screening step

Completing the Life Cycle Inventory

Use readily available specific or generic data to populate the Life Cycle Inventory

Apply the life cycle impact assessment methods

Ensure that the data collected meet the data quality requirements and, where necessary, collect better data

Transform any remaining non- elementary flows into elementary flows

Life Cycle Inventory

Two steps for carrying out the Life Cycle Inventory

1.

2.


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● Agricultural production; 1 ● Capital goods; 2 ● Produc on; 3 ● Product distribu on and storage; 4 ● Use stage; 5 ● Logis cs; 6 ● End-of-life. 7 8

5.2.1 Raw Material Acquisition and Pre-processing (Cradle-to- Gate)24 9 The raw material acquisition and pre-processing stage starts when resources are extracted from nature and 10 ends when the product components enter (through the gate of) the product’s production facility. Processes 11 that may occur in this stage include: 12

Mining and extraction of resources; 13 Pre-processing of all material inputs to the studied product, such as: 14

o Forming metals into ingots; 15 o Cleaning coal; 16

Conversion of recycled material; 17 Photosynthesis for biogenic materials; 18 Cultivation and harvesting of trees or crops; 19 Transportation within and between extraction and pre-processing facilities, and to the production 20

facility. 21

5.2.2 Agricultural production 22 The agricultural production stage is an essential part of the life cycle of food, feed and other bio-based 23 products (e.g. biopolymers). The following input and output flows and activities shall be considered, where 24 applicable, when developing Life Cycle Inventories for agricultural production processes: 25

Seeds and/or seedlings; 26 Fertilisers (synthetic and organic); 27 Peat; 28 Lime; 29 Pesticides; 30 Mulch film and its fate after use; 31 Irrigation (and associated water and energy input); 32 Use of agricultural machinery (and associated fuel consumption and emissions); 33 Input N from crop residues that stay on the field or are burned; 34 Field emissions of N, P and heavy metals from fertiliser and pesticide application; 35 Emissions from burning of residues; 36

24 This section builds upon the Greenhouse Gas Protocol Product Life Cycle Accounting and Reporting Standard (WRI, 2011b) – Chapter 7.3.1


78

Drying and storage of products. 1 2

Further detail on how to model agricultural production is provided in Section 5.5.1. 3

5.2.3 Capital goods 4 Examples of capital goods that shall be included are: 5

Machinery used in production processes; 6 Buildings; 7 Office equipment; 8 Transport vehicles; 9 Transportation infrastructure. 10

5.2.4 Production 11 The production stage begins when the product components enter the production site and ends when the 12 finished product leaves the production facility. Examples of production-related activities include: 13

Chemical processing; 14 Manufacturing; 15 Transport of semi-finished products between manufacturing processes; 16 Assembly of material components; 17 Packaging; 18 Treatment of waste; 19 Employee transport (if relevant); 20 Business travel (if relevant). 21

5.2.5 Product Distribution and Storage 22 Products are distributed to users and may be stored at various points along the supply chain. Examples of 23 processes related to distribution and storage that shall be included are (non-exhaustive list): 24

Energy inputs for warehouse lighting and heating; 25 Use of refrigerants in warehouses and transport vehicles; 26 Fuel use by vehicles. 27

The transport from factory to final client (including consumer transport) shall be included in the distribution 28 stage of LCA studies. This helps fair comparisons between products delivered through traditional shops as 29 well as delivered at home. 30

5.2.6 Use stage 31 The use stage describes how the product is expected to be used by the end user (e.g. the consumer). It begins 32 when the end user starts using the product and ends when the used product leaves its place of use and enters 33 the end-of-life stage (e.g. it is collected for recycling disposal). 34 The use stage includes all activities and products that are needed for a proper use of the product (i.e. such 35 that the provision of its original function is kept throughout its lifetime, see Figure 9). Examples of use-stage 36 processes to be included are: 37

Refrigeration at the location of use; 38 Preparation for use (e.g. provision of tap water and wastewater treatment when cooking pasta); 39


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Resource and product consumption during use (e.g. detergent, energy and water use for washing 1 machine; manufacturing, distribution and waste management of paper filters for coffee); 2

Manufacturing, distribution and waste management of materials needed for maintenance, repair or 3 refurbishment of the product during the use stage (e.g. spare parts needed to repair the product, the 4 coolant production and waste management due to losses). 5 6

The following additional requirements shall be followed: 7

(i) The waste of the product in use (e.g. food waste, primary packaging, or the product left at its end 8 of use) is excluded from the use stage and shall be part of the End-of-Life stage of the product. 9

(ii) If a product is reused, the processes needed to collect the product and make it ready for the new 10 use cycle are excluded from the use stage (e.g. the impacts from collection and cleaning reusable 11 bottles). 12

(iii) Transport from retail to consumer home shall be excluded from the use stage and may be 13 included in the distribution stage. 14

15

Figure 9: Processes included and excluded from the use stage 16

The use scenario also needs to reflect whether or not the use of the analysed products might lead to changes 17 in the systems in which they are used. For example, energy-using products might affect the energy needed 18 for heating/cooling in a building, while the weight of a car battery might affect the fuel consumption of the 19 car. 20

The following sources of technical information on the use scenario should be taken into account (non-21 exhaustive list): 22

Published international standards that specify guidance and requirements for the development of 23 scenarios for the use stage and scenarios for (i.e. estimation of) the service life of the product; 24

Published national guidelines for the development of scenarios for the use stage and scenarios for 25 (i.e. estimation of) the service life of the product; 26

Published industry guidelines for the development of scenarios for the use stage and scenarios for 27 (i.e. estimation of) the service life of the product; 28

Market surveys or other market data. 29

NOTE: The manufacturer’s recommended method to be applied in the use stage (e.g. cooking in an oven at 30 a specified temperature for a specified time) might provide a basis for determining the use stage of a product. 31


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The actual usage pattern may, however, differ from those recommended and should be used if this 1 information is available. 2

Where no method for determining the use stage of products has been established in accordance with the 3 techniques specified in this guide, the approach taken in determining the use stage of products shall be 4 established by the organisation carrying out the study. The actual usage pattern may, however, differ from 5 those recommended and should be used if this information is available. 6

Documentation of methods and assumptions shall be provided. All relevant assumptions for the use stage 7 shall be documented. 8

The use stage shall be excluded for intermediate products. 9

5.2.7 End-of-Life25 10 The end-of-life stage begins when the used product is discarded by the user and ends when the product is 11 returned to nature as a waste product or enters another product’s life cycle (i.e. as a recycled input). 12 Examples of end-of-life processes that shall be included in the LCA study include: 13

Collection and transport of end-of-life products and packages; 14 Dismantling of components; 15 Shredding and sorting; 16 Conversion into recycled material; 17 Composting or other organic-waste-treatment methods; 18 Littering; 19 Incineration and disposal of bottom ash; 20 Landfilling and landfill operation and maintenance; 21 Transport required to all end-of-life treatment facilities. 22

23 For intermediate products, the End-of-Life of the main product in scope shall be excluded. 24

5.3 Nomenclature for the Life Cycle Inventory 25 Developers of LCA studies shall check the documented nomenclature and properties for a given flow in the 26 Life Cycle Inventory against the nomenclature and properties of the International Reference Life Cycle Data 27 System (ILCD) (EC-JRC, 2010). 28

All relevant resource use and emissions associated with the life cycle stages included in the defined system 29 boundary shall be documented using the International Reference Life Cycle Data System (ILCD) nomenclature 30 and properties, as described in Annex G. 31

If nomenclature and properties for a given flow are not available in the ILCD, the practitioner shall create an 32 appropriate nomenclature and document the flow properties. 33

25 This section builds upon the Greenhouse Gas Protocol’s Product Life Cycle Accounting and Reporting Standard (WRI, 2011b) – Chapter 7.3.1


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5.4 Handling multi-functional processes 1 If a process or facility provides more than one function, i.e. it delivers several goods and/or services ("co-2 products"), it is “multifunctional”. In these situations, all inputs and emissions linked to the process must be 3 partitioned between the product of interest and the other co-products in a principled manner. Systems 4 involving multi-functionality of processes shall be modelled in accordance with the following general decision 5 hierarchy. However, for activities at farm, and activities at slaughterhouse, and electricity use the allocation 6 approach to be used shall be the one described in sections 5.5.1.1, 5.5.2, and 5.5.7 respectively. 7

Decision hierarchy 8

I) Subdivision or system expansion 9

Wherever possible, subdivision or system expansion should be used to avoid allocation. Subdivision refers to 10 disaggregating multifunctional processes or facilities to isolate the input flows directly associated with each 11 process or facility output. System expansion refers to expanding the system by including additional functions 12 related to the co-products. It shall be investigated first whether the analysed process can be subdivided or 13 expanded. Where subdivision is possible, inventory data should be collected only for those unit processes 14 directly attributable to the goods/services of concern. If the system can be expanded, the additional functions 15 shall be included in the analysis with results communicated for the expanded system as a whole rather than 16 on an individual co-product level. If this is not compatible with the scope of the assessment, the system can 17 be modelled using direct substitution if a product can be identified that is directly substituted. 18

Can a direct substitution-effect be robustly modelled? This can be demonstrated by proving that (1) there is 19 a direct, empirically demonstrable substitution effect, AND (2) the substituted product can be modelled and 20 the life cycle inventory data subtracted in a directly representative manner: 21

If yes (i.e. both conditions are verified), model the substitution effect. 22

If no direct substitutions can be identified, the system can be modelled using indirect substitution. 23

Can an indirect substitution effect be identified? AND can the substituted product be modelled and the 24 inventory subtracted in a reasonably representative manner? 25

If yes (i.e. both conditions are verified), model the indirect substitution effect. 26

II) Allocation based on a relevant underlying physical relationship 27

Where subdivision or system expansion cannot be applied, allocation should be applied: the inputs and 28 outputs of the system should be partitioned between its different products or functions in a way that reflects 29 relevant underlying physical relationships between them. (ISO 14044:2006, 14) 30

Allocation based on a relevant underlying physical relationship refers to partitioning the input and output 31 flows of a multi-functional process or facility in accordance with a relevant, quantifiable physical relationship 32 between the process inputs and co-product outputs (for example, a physical property of the inputs and 33 outputs that is relevant to the function provided by the co-product of interest). 34

Can input/output flows be allocated based on some other relevant underlying physical relationship that 35 relates the inputs and outputs to the function provided by the system? This can be demonstrated by proving 36


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that a relevant physical relationship can be defined by which to allocate the flows attributable to the 1 provision of the defined function of the product system: 2

If yes, allocate based on this physical relationship. 3

III) Allocation Based on Some Other Relationship 4

Allocation based on some other relationship may be possible. For example, economic allocation refers to 5 allocating inputs and outputs associated with multi-functional processes to the co-product outputs in 6 proportion to their relative market values. The market price of the co-functions should refer to the specific 7 condition and point at which the co-products are produced. Allocation based on economic value shall only 8 be applied when options (I) and (II) are not possible. In any case, a clear justification for having discarded 9 options (I) and (II) and for having selected a certain allocation rule in step (III) shall be provided, to ensure 10 the physical representativeness of the LCA results as far as possible. 11

12 Dealing with multi-functionality of products is particularly challenging when recycling or energy recovery of 13 one (or more) of these products is involved as the systems tend to get rather complex. The Circular Footprint 14 Formula (see section 5.5.8.10) provides an approach that shall be used to estimate the overall emissions 15 associated to a certain process involving recycling and/or energy recovery. These moreover also relate to 16 waste flows generated within the system boundary. 17

In short, the following multi-functionality decision hierarchy shall be applied for resolving multi-functionality 18 problems: (1) subdivision or system expansion (including direct substitution or indirect substitution); (2) 19 allocation based on a relevant underlying physical relationship; (3) allocation based on some other 20 relationship. 21

All choices made to address multi-functionality (chosen solution, corresponding assumptions and 22 parameters, e.g. allocation factors) shall be reported and justified with respect to the overarching goal of 23 ensuring physically representative, environmentally relevant results. 24

For multi-functionality of products in recycling or energy recovery situations, the equation described in 25 Section 5.5.8.10 shall be applied. The abovementioned decision process also applies for end-of-life multi-26 functionality. 27

5.5 Modelling requirements 28 This section provides detailed guidance and requirements on how to model specific stages, processes and 29 other aspects of the product life cycle, in order to compile the Life Cycle Inventory. Covered aspects include: 30

Agricultural production; 31 Animal husbandry; 32 Capital goods (infrastructures and equipment); 33 Logistics and transport; 34 Packaging; 35 Use stage; 36 Electricity use; 37


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End-of-life stage; 1 Climate change-related aspects (carbon emissions, removals and storage). 2

5.5.1 Agricultural production 3 The modelling guidelines in this chapter shall be followed by the user of this method when modelling and 4 creating a new dataset for agricultural production processes. 5

5.5.1.1 Handling multi-functional processes 6

The rules described in the LEAP Guideline (FAO, 2015) shall be followed. They are reported below for the 7 sake of convenience, with a few adjustments introduced where appropriate for consistency. 8

Handling multi-functionality at farms and factories26 9

The ISO 14044 step-by-step approach (decision hierarchy) is applied on three aggregate stages (Figure 10): 10

• Stage 1 identifies the processes that can be directly allocated to the co-products. This corresponds to the 11 step 1a of ISO 14044: avoid allocation by subdivision (Box 1, Figure 10). 12

• Stage 2 applies the subsequent steps 1b, 2 and 3 of ISO 14044 to allocate inputs and emissions from 13 farm/factory level to production unit level (Box 2, Figure 10). 14

• Stage 3 applies the steps 1b, 2 and 3 of ISO 14044 to allocate inputs and emissions from production unit 15 level to co-products level (Box 3, Figure 10). 16

A production unit is defined here as a group of activities (along with the necessary inputs, machinery and 17 equipment) in a factory or a farm, needed to produce one or more co-products. Examples include the crop 18 fields in an arable farm, or the production lines in a manufacturing factory. 19

In the process of defining the most suitable approach to handle multi-functionality in an LCA of agricultural 20 products, decisions often need to be made as to which allocation method to apply and where. Moreover, it 21 is necessary to identify the market supplied by each co-product, and the function of the product on this 22 market. Grouping can also be made of co-products from the same production unit when they have the same 23 function and the downstream application is not affected by the differences between the products. Figure 10 24 presents the detailed decision tree and principles recommended in the application of the process to handle 25 multi-functionality at farms and factories. Examples on the application of the decision tree are provided in 26 Section 11 of the LEAP Guidelines (FAO, 2015). 27

28

26 This section also applies to industrial fishing for fishmeal and fish oil.

Comparative LCA of alternative feedstock for plastic – DRAFT FOR CONSULTATION Part I

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1 Figure 10: Decision tree to handle multi-functionality for agricultural production processes (adapted from FAO, 2015)2


85

Stage 1: Avoid allocation by subdividing processing system 1

‘ISO step 1a subdivision’, all processes, activities and inputs of a farm/factory are divided into three 2 categories: 3

Flow 1.a. Inputs/activities that can be directly assigned to a single co-product. These should be assigned 4 to that co-product (e.g. storage and drying operations that can be assigned to one specific product only, 5 or drying of oil seed meals after separation). 6

Flow 1.b. Inputs/activities that can be assigned to single production units, which may provide single or 7 multiple co-products (e.g. input of pesticides for specific crop at a multi-crop farm, fertilizers used for 8 corn in a farm with multiple crops; energy inputs of field operations for a specific crop at a multi-crop 9 farm; feed intake for a specific animal type at a multi-type-animal farm; or energy inputs in a (pre) 10 separation process, such as crushing or milling). It should be noted that the application of lime, fertilizers 11 and soil improvement products or operations that are applied to, or performed for, a specific crop may 12 reduce the need for such inputs to other crops. These inputs may therefore be subdivided among the 13 different crops in proportion to the requirements of each crop for the specific inputs. 14

Flow 1.c Inputs/activities of a generic nature in a farm or factory. Some general inputs, such as internal 15 transport, capital goods and office overheads, that cannot be directly attributed to specific production 16 units, but are nevertheless necessary for the operation of all production units, can normally be assigned 17 to each production unit in proportion to the causal relationship that determines increased need for each 18 input. Examples of such relationships include weight, volume, or area (transport, roads, buildings) or 19 revenue (office and accounting). 20

All three of these flows are relevant for the feed life cycle. The inputs and activities of flow 1c should be 21 further assigned to single production units in Step 2, while inputs and activities of flow 1b (and assigned flows 22 from Step 2) shall be further assigned to single co-products. 23

Stage 2: Attribute combined production to separate production units. 24

System expansion( ISO step 1b): As part of the harmonization effort behind these guidelines, the application 25 of system expansion (by means of substitution) is limited to situations in which this is acceptable within the 26 goal and scope of the study, and only to outputs that unambiguously avoid external production (e.g. energy 27 delivered to grid). For these outputs, the avoided burdens (emissions) from the actually replaced products or 28 service are calculated and subtracted from the inventory. 29

Allocation based on relevant (bio-)physical relationships (ISO step 2) or other relationships (ISO step 3): 30 When system expansion is not possible, the second question is whether a (bio-)physical allocation is possible. 31 If inputs/outputs can be divided on the basis of a (bio-)physical mechanism/parameter that explains 32 attribution to single production units (e.g. the plant or animal need for a given nutrient), then (bio-)physical 33 allocation shall be applied. If this is not the case, economic allocation shall be applied. 34

In farming systems, allocation is relevant for the following three situations: 35

a) inputs at farm level for basic operations that cannot be unambiguously attributed to specific 36 crops (e.g. capital goods and infrastructure, such as concrete pavements, fences, sheds, or 37 electricity use for offices and sheds); 38


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b) inputs to the field that are meant to maintain overall soil quality and benefit all the crops 1 (e.g. manure and other organic fertilisers that provide minerals to the subsequent crops after 2 being applied to the initial crop); 3

c) complex multiple cropping systems where plants are cultivated alongside one another in an 4 intercropping system, i.e. in a single field. 5

If inputs in a multiple crop production system benefit all crops and are not specifically assigned to single 6 production units, the allocation to specific crop production units shall be based on the nutrient requirements 7 of the crop (e.g. nitrogen, phosphorus or potassium), if sufficient information is available. Otherwise, 8 allocation shall be based on the economic value of the crop-production units, except for crop rotations in 9 open field cultivation that is area-based (flow 2b, Figure 10). 10

Application of organic fertilisers (e.g. animal manure, peat products, compost) in agricultural production 11 systems result in emissions that occur within one year and delayed emissions that occur afterward. Assuming 12 a steady state situation, these delayed emissions are divided among the crop production units in the crop 13 rotation scheme, i.e. those planted and harvested in the year of application. An alternative method is to 14 divide the emissions into: 15

• emissions that occur in the same year that the organic fertiliser is applied, which should be fully 16 allocated to the crop of application. 17

• emissions that occur after one year from the organic fertilizer application; which should be allocated 18 to all crops that grow in the year following application. 19

Note 1: The minimum period of data collection for open field cultivation is three years. The calculation and 20 allocation of delayed emissions per crop shall be done per year and then averaged over three years. 21

Note 2: If there are multiple yields of a crop within one year, a correction shall be made on the total area in 22 the allocation by multiplying the area used for sequential cropping by the number of cropping cycles. 23

Similar to cultivation, some of the activities in processing cannot be simply assigned to the production units 24 (e.g. climate control, lighting, infrastructure). Normally, these activities do not have a large contribution and 25 neglecting them may not significantly affect the results. However, when a relevant contribution is expected, 26 data should be collected and a choice for an allocation method needs to be made. Generally, it is possible to 27 select a physical property among the flow of products being produced for attribution of the generic impacts. 28

If inputs in a multiple production system benefit all products and cannot be specifically assigned to a single 29 production unit, allocation should be based on a physical property (flow 2b in Figure 10). 30

Stage 3: Split single production units into single co-products 31

Regarding system expansion (ISO step 1b), the rule described above for attribution to single production units 32 applies. Only in unambiguous situations of avoidance, such as electricity supply to the grid, should system 33 expansion be applied. 34

The next step is to define whether the outputs should be considered as residues. Outputs of a production 35 process are considered as residues (flow 3f) if: 36


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• they are sold in the condition as it appears in the process (before drying and other modifications) and 1 contribute very little to the turnover of the company (value of the total flow less than 1 percent); and 2

• they are included in upstream and production process that produce the output and are not deliberately 3 modified for these outputs. 4

Co-products27 classified as residues shall not be considered as ‘waste’ because they are part of a processing 5 or production process. ‘Waste’ is material that is intended for disposal (e.g. incineration and landfill). 6

After residues and waste have been separated from co-products, practitioners should base their decision as 7 to whether physical allocation is possible and logical on the underlying mechanism or properties of the co-8 products. 9

In most cases, however, there is no consistent physical model available that can be used to attribute 10 environmental impacts to specific co-products. First, in contrast with dairy production, where energy 11 requirements for milk and meat can be separated (IDF, 2015), the inputs in crop production cannot be 12 attributed to crop/plant components, nor to components that are separated in a processing industry. Second, 13 the physical characteristics for which co-products are used for nutrition (e.g. feed) vary greatly. For example 14 some products are used for their energy content, while others for their protein content or even specific 15 amino acids. 16

One could thus consider developing a physical allocation rule for each category of feed (e.g. energy-rich, 17 protein-rich). This, however, would lead to inconsistencies between the attribution rules used for different 18 feed materials, something which is against the ISO recommendations. 19

In parallel, the price of feed materials seems to be generally correlated to their nutritional value, and in 20 particular with their energy and protein content. Unless the complex physical relationship can be captured 21 in a physical model, economic allocation is the preferred method, as it seems to provide the best option to 22 allocate the environmental burdens in a consistent manner and on the basis of meaningful relationships. The 23 average economic value of a product should be estimated over 5-year time frame. For external 24 communication or comparison, several alternative allocation options shall be compared as part of a process 25 of sensitivity analysis. 26

Economic allocation can be applied on several levels of aggregation. Often, groupings of products that have 27 similar applications is done so that the basket of co-products is reduced to a few product groups for which 28 an average value can be determined. One example is the dry milling of wheat, where an average value for 29 the brans is derived from average sales prices instead of defining bran qualities per batch of flour milling. 30

The slaughtering process also generates a great number of diverse co-products that enter different markets. 31 In practice, these co-products are often grouped together on the basis of the level of legally allowable 32 applications: material, feed and food. When it comes to fresh products that enter the food market, prices are 33

27 Co-products of processing, having a very low value at the moment they arise in the production process, are usually wet by-products (e.g. wet cassava pulp, wet whey, wet citrus pulp, wet potato pulp and potato peels, disposed fruit and vegetables, wet distillers’ grain and wet beet pulp).


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to a great extent determined by consumer perception. However, how meaningful is it to distinguish among 1 different meat cuts or between different quality apples? In PAS 2050-1:2012 (BSI, 2012), it is recommended 2 not to differentiate beyond a level that exceeds basic functionality and a level that is related exclusively to 3 consumer preferences. 4

Grouping of co-products should be conducted on the basis of their essential functionality. 5

The allocation process as described above and as visualized in Figure 10 may result eventually in the flows 3a 6 to 3f. A number of examples of economic allocation are given in Section 11.3.5 of the LEAP guidelines (FAO, 7 2015). 8

Allocation of manure 9

Manure links the animal and the plant production systems on different levels. An allocation problem arises 10 when the manure leaves the animal farm to be then applied in a plant production system. A comprehensive 11 approach for defining the allocation procedure for manure is given in the LEAP animal production guidelines 12 (FAO, 2016). For the agricultural production, only the application and decomposition of manure in cultivation 13 falls within the system boundaries. At this point, the most important issue is defining the upstream life cycle 14 of manure in a way that is in line with the abovementioned animal production guidelines. 15

3.5.1.2 Crop type specific and country-region-or-climate specific data 16

Crop type specific and country-region-or-climate specific data should be used for the following parameters 17 (per hectare and per year): yield, water and land use, land use change, fertiliser (synthetic and organic) 18 amount applied (N, P amount), and pesticide amount applied (per active ingredient). 19

3.5.1.3 Averaging data 20

When company-specific data are used, cultivation data shall be collected over a period of time sufficient to 21 develop an average life cycle inventory of the inputs and outputs of cultivation, which will offset fluctuations 22 due to seasonal differences. This shall be undertaken as described in the LEAP guidelines (FAO, 2015), set out 23 below: 24

● For annual crops, an assessment period of at least three years shall be used (to level out differences 25 in crop yields related to fluctuations in growing conditions over the years such as climate, pests and 26 diseases, etc.). Where data covering a three-year period is not available i.e. due to starting up a new 27 production system (e.g. a new greenhouse, newly cleared land, shift to other crop), the assessment 28 may be conducted over a shorter period, but shall not be less than 1 year. Crops/plants grown in 29 greenhouses shall be considered as annual crops/plants, unless the cultivation cycle is significantly 30 shorter than a year and another crop is cultivated consecutively within that year. Tomatoes, peppers 31 and other crops which are cultivated and harvested over a longer period through the year are 32 considered as annual crops. 33


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● For perennial plants (including entire plants and edible portions of perennial plants) a steady state 1 situation (i.e. where all development stages are proportionally represented in the studied time 2 period) shall be assumed and a three-year period shall be used to estimate the inputs and outputs28. 3

● Where the different stages in the cultivation cycle are known to be disproportional, a correction shall 4 be made by adjusting the crop areas allocated to different development stages in proportion to the 5 crop areas expected in a theoretical steady state. The application of such correction shall be justified 6 and recorded. The life cycle inventory of perennial plants and crops shall not be undertaken until the 7 production system actually yields output. 8

● For crops that are grown and harvested in less than one year (e.g. lettuce produced in 2 to 4 months) 9 data shall be gathered in relation to the specific time period for production of a single crop, from at 10 least three recent consecutive cycles. Averaging over three years can best be done by first gathering 11 annual data and calculating the life cycle inventory per year and then determine the three years 12 average. 13

5.5.1.4 Pesticide emissions 14

Pesticide emissions shall be modelled as specific active ingredients. The default life cycle impact assessment 15 model for toxicity-related impact categories (USEtox, 16

28 The underlying assumption in the cradle-to-gate life cycle inventory of horticultural products is that the inputs and outputs of the cultivation are in a ‘steady state’, which means that all development stages of perennial crops (with different quantities of inputs and outputs) shall be proportionally represented in the time period of cultivation that is studied. This approach gives the advantage that inputs and outputs of a relatively short period can be used for the calculation of the cradle-to-gate life cycle inventory from the perennial crop product. Studying all development stages of a horticultural perennial crop can have a lifespan of 30 years and more (e.g. in case of fruit and nut trees).


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Table 10) has a built-in multimedia fate model that simulates the fate of the pesticides starting from the 1 different emission compartments. Therefore, default emission fractions are needed in the LCI modelling, to 2 split the amount of pesticides applied on the field among the different environmental emission 3 compartments (Rosenbaum et al., 2015). As default approach, the pesticides applied on the field shall be 4 modelled as 90% emitted to the agricultural soil compartment, 9% emitted to air and 1% emitted to water 5 (based on expert judgement due to current limitations29). More specific data may be used if available, and 6 shall be adequately justified and documented. 7

A robust model to assess the link between the amount of pesticides applied on the field and the amount 8 ending up in the different emission compartments is still missing today. The PESTLCI model might fill in this 9 gap in the future, but is currently still under testing. 10

3.5.1.5 Fertiliser emissions 11

Fertiliser (and manure) emissions shall be differentiated per fertiliser type and cover as a minimum: 12

● NH3, to air (from N-fertiliser application) 13 ● N2O, to air (direct and indirect) (from N-fertiliser application) 14 ● CO2, to air (from lime, urea and urea-compounds application) 15 ● NO3, to water unspecified (leaching from N-fertiliser application) 16 ● PO4, to water unspecified or freshwater (leaching and run-off of soluble phosphate from P-fertiliser 17

application) 18 ● P, to water unspecified or freshwater (runoff of soil particles containing phosphorous, from P-19

fertiliser application). 20

The default impact assessment model for freshwater eutrophication can start (i) when P leaves the 21 agricultural field (via leaching or run off) or (ii) from manure or fertiliser application on agricultural field. 22 However, within LCI modelling, the agricultural field (soil) is often seen as belonging to the technosphere and 23 thus included in the LCI model. This aligns with approach (i) where the impact assessment model starts after 24 leaching or run-off, i.e. when P leaves the agricultural field. Therefore, the LCI should be normally modelled 25 as the amount of P emitted to water after leaching or run-off, and the emission compartment 'water' shall 26 be used. When this amount is not available, the LCI may be modelled as the amount of P directly applied on 27 the agricultural field (through manure or fertilisers) and the emission compartment 'soil' shall be used. In this 28 case, the run-off and leaching from soil to water is part of the impact assessment method and included in the 29 provided CF for soil. 30

The default impact assessment model for marine eutrophication starts after N leaves the field (soil). 31 Therefore, N emissions shall not be modelled as bare emissions to (agricultural) soil. Conversely, the amount 32

29 Several databases consider a 100% emitted to soil out of simplification (e.g. Agribalyse and Ecoinvent). It is recognized that emissions to freshwater and air do occur. However, emission fractions vary significantly depending on the type of pesticide, the geographical location, time of application and application technique (ranging from 0% to 100%). Especially the % emitted to water can be strongly debated, however, overall it seems that 1% indicates a reasonable average (e.g. WUR-Alterra 2016: Emissies landbouwbestrijdingsmiddelen). Please note that these are temporary values until future modelling fills this gap.


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of nitrogen ending up in the different air and water end compartments per amount of fertilisers applied on 1 the field shall be modelled within the LCI. Nitrogen emissions shall be calculated from nitrogen applications 2 by the farmer on the field, excluding any external sources (e.g. rain deposition). 3

To avoid strong inconsistencies among different LCA studies, a number of emission factors are fixed, by 4 following a simplified approach. For nitrogen-based fertilisers, the Tier 1 emissions factors of IPCC (2006) 5 (Tables 2-4) should be used, as presented in 6


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Table 11. Note that the values provided shall not be used to compare different types of synthetic fertilisers. 1 More detailed modelling shall be used for that purpose. In case better data is available, a more 2 comprehensive nitrogen field model may be used by the LCA developer, provided (i) it covers at least the 3 emissions requested above, (ii) N shall be balanced in inputs and outputs and (iii) it shall be described in a 4 transparent way. 5

6


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Table 11: Tier 1 emission factors of IPCC for nitrogen emissions from fertilisers applications (adapted 1 from IPCC, 2006) 2

Substance Compartment Emission factor to be applied

N2O (synthetic fertilisers and manure; direct and indirect)

Air 0.022 kg N2O/ kg N fertiliser applied

NH3 (synthetic fertilisers) Air kg NH3= kg N * FracGASFf30= 1*0.1* (17/14)= 0.12 kg NH3/

kg N fertiliser applied

NH3 (manure) Air kg NH3= kg N*FracGASFm31= 1*0.2* (17/14)= 0.24 kg NH3/

kg N manure applied

NO3- (synthetic fertilisers and manure) Water kg NO3

- = kg N*FracLEACH32 = 1*0.3*(62/14) = 1.33 kg NO3

-/ kg N applied

3

It is recognized that the above nitrogen field model has its limitations and shall be improved in the future. 4 Therefore, the following alternative approach shall be tested. The N-balance is calculated using the 5 parameters in Table 12 and the formula reported below. The latter calculates the total NO3-N emission to 6 water (which is considered a variable) as: 7

“Total NO3-N emission to water” = “NO3- base loss” + “additional NO3-N emissions to water”, where 8

“Additional NO3-N emissions to water” = “N input with all fertilisers” + “N2 fixation by crop” – “N-removal 9 with the harvest” – “NH3 emissions to air” – “N2O emissions to air” – “N2 emissions to air” -“NO3- base 10 loss”. 11

If in certain low-input schemes the value for “additional NO3-N emissions to water” is negative, the value is 12 to be set to “0”. Moreover, in such cases the absolute value of the calculated “additional NO3-N emissions to 13 water” is to be inventoried as additional N-fertiliser input into the system, using the same combination of N-14 fertilisers as employed to the analysed crop. This serves to avoid regarding fertility-depleting schemes by 15 capturing the N-depletion by the analysed crop that is assumed to lead to the need for additional fertiliser 16 later on to keep the same soil fertility level. 17

30 FracGASFf: fraction of nitrogen contained in synthetic fertilisers that is emitted as ammonia to air. 31 FracGASFm: fraction of nitrogen contained in manure that is emitted as ammonia to air. 32 FracLEACH: fraction of nitrogen contained in synthetic fertilisers and/or manure that is leached as nitrate to groundwater.


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Table 12: Alternative approach to nitrogen modelling 1

Emission Compartment Value to be applied

NO3- base loss (synthetic

fertiliser and manure) Water kg NO3

-= kg N*FracLEACH33= 1*0.1*(62/14) = 0.44 kg NO3-/ kg N

applied

N2O (synthetic fertiliser and manure; direct and indirect)

Air 0.022 kg N2O/ kg N fertilizer applied

NH3 - Urea (synthetic fertiliser) Air kg NH3= kg N * FracGASFu34= 1*0.15* (17/14)= 0.18 kg NH3/ kg N

fertilizer applied

NH3 - Ammonium nitrate (synthetic fertiliser)

Air kg NH3= kg N * FracGASFan35= 1*0.1* (17/14)= 0.12 kg NH3/ kg N

fertilizer applied

NH3 - others (synthetic fertiliser) Air kg NH3= kg N * FracGASFf36= 1*0.02* (17/14)= 0.024 kg NH3/ kg

N fertilizer applied

NH3 (manure) Air kg NH3= kg N*FracGASFm37= 1*0.2* (17/14)= 0.24 kg NH3/ kg N

manure applied

N2-fixation by crop For crops with symbiotic N2-fixation: the fixed amount is assumed to be identical to the N-content in the harvested crop

N2 Air 0.09 kg N2 / kg N applied

5.5.1.6 Heavy metal emissions 2

Heavy metal emissions from field inputs shall be modelled as emission to soil and/or leaching or erosion (run-3 off) to water. The inventory to water shall specify the oxidation state of the metal (e.g., Cr+3, Cr+6). As crops 4 assimilate part of the heavy metal emissions during their cultivation, clarification is needed on how to model 5 crops that act as a sink. Two different modelling approaches are allowed, with a preference for option 1: 6

1. The final fate (emission compartment) of the heavy metal elementary flows is considered within the 7 system boundary: the inventory does account for the final emissions of the heavy metals in the 8 environment and therefore shall also account for the uptake of heavy metals by the crop. For 9 example, heavy metals in agricultural crops cultivated for feed will mainly end up in the animal 10 digestion and used as manure back on the field where the metals are released in the environment 11 and their impacts captured by the impact assessment methods. Therefore, the inventory of the 12

33 FracLEACH: fraction of nitrogen contained in synthetic fertilisers and/or manure that is leached as nitrogen to groundwater. 34 FracGASFu: fraction of nitrogen contained in urea that is emitted as ammonia to air. 35 FracGASFan: fraction of nitrogen contained in ammonium nitrate that is emitted as ammonia to air. 36 FracGASFf: fraction of nitrogen contained in other types of synthetic fertilise that is emitted as ammonia to air. 37 FracGASFm: fraction of nitrogen contained in manure that is emitted as ammonia to air.


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agricultural stage shall account for the uptake of heavy metals by the crop. A limited amount ends 1 up in the animal (=sink), which may be neglected for simplification. 2

2. The final fate of the heavy metals elementary flows are not further considered within the system 3 boundary: the inventory does not account for the final emissions of the heavy metals and therefore 4 shall not account for the uptake of heavy metals by the crop. For example, heavy metals in 5 agricultural crops cultivated for human consumption end up in the plant. Within this context human 6 consumption is not modelled, the final fate is not further modelled and the plant acts as a heavy 7 metal sink. Therefore, the uptake of heavy metals by the crop shall not be modelled. 8

5.5.1.7 Rice cultivation 9

Methane emissions from rice cultivation shall be included based on the calculation rules of IPCC (2006) 10 (Volume 4, Chapter 5.5, page 44-53). 11

5.5.1.8 Peat soils 12

Drained peat soils shall include carbon dioxide emissions based on a model that relates the drainage levels 13 to annual carbon oxidation. 14

5.5.1.9 Other activities 15

The following activities shall be included in agricultural modelling, if applicable: 16

● Input of seed material (kg/ha), 17 ● Input of synthetic and organic fertilisers (kg/ha + nutrient content), 18 ● Input of peat to soil (kg/ha + C/N ratio), 19 ● Input of lime (kg /ha, type), 20 ● Input of pesticides (kg/ha + composition as active substance), 21 ● Mulch film (input + fate after use), 22 ● Irrigation (water + any relating energy inputs), 23 ● Machinery use (hours, type), 24 ● Input N from crop residues that stay on the field or are burned (kg residue/ha + N content). Including 25

emissions from residues burning. Drying and storage of products (shall always be included, unless its 26 exclusion is clearly justified in the study). 27 28

Unless it is clearly documented that agricultural operations are carried out manually, they shall be accounted 29 for through total fuel consumption and resulting airborne emissions, or through more comprehensive 30 datasets that model the respective burdens. Similarly, irrigation shall be modelled through the respective 31 water and energy/fuel consumption, or through a more comprehensive specific dataset. Transport to/from 32 the field shall be accounted for where relevant. 33

5.5.2 Animal husbandry 34

This section provides instructions on how to address specific issues related to farm, slaughterhouse and 35 rendering modelling for cattle, pig, sheep and goat. In particular, instructions will be provided on: 36

1. Allocation of upstream burdens at farm level among outputs leaving the farm 37


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2. Allocation of upstream burdens (linked to live animals) at slaughterhouse among outputs leaving the 1 slaughterhouse. 2

5.5.2.1 Allocation within the farm module 3

At farm module, subdivision shall be used for processes that can be directly attributed to certain outputs 4 (e.g. energy use and emissions related to milking processes). When the processes cannot be subdivided due 5 to the lack of separate data or because technically impossible, the upstream burden, e.g. feed production, 6 shall be allocated to farm outputs using a biophysical allocation method. Default values to perform allocation 7 are provided in the following sections for each type of animal and these default values shall be applied in LCA 8 studies unless company-specific data are collected. The change of allocation factors is allowed only when 9 company-specific data are collected and used for the farm module. In case secondary data are used for the 10 farm module, no change of allocation factors is allowed and the ones included in this document shall be used. 11

5.5.2.2 Allocation within the farm module for cattle 12

The IDF (2015) allocation method between milk, cull cows and surplus calves shall be used. Dead animals and 13 all the products coming from dead animals shall be regarded as waste and the Circular Footprint Formula 14 (CFF, 5.5.8.10) shall be applied. In this case, however, the traceability of the products coming from dead 15 animals shall be granted in order for this aspect to be taken into consideration into LCA studies. 16

Manure exported to another farm shall be considered as: 17

o Residual (default option): when manure does not have an economic value at the farm gate, 18 it is regarded as residual without allocation of an upstream burden. The emissions related to 19 manure management up to farm gate are allocated to the other outputs of the farm where 20 manure is produced. 21

o Co-product: when exported manure has economic value at farm gate, an economic 22 allocation of the upstream burden shall be used for manure by using the relative economic 23 value of manure compared to milk and live animals at the farm gate. Biophysical allocation 24 based on IDF rules shall nevertheless be applied to allocate the remaining emissions between 25 milk and live animals. 26

o Manure as waste: when manure is treated as waste (e.g. landfilled), the Circular Footprint 27 Formula shall be applied. 28

The allocation factor (AF) for milk shall be calculated using the following equation: 29

𝐴𝐹 = 1 − 6.04 ∗ [Equation 1] 30

Where Mmeat is the mass of live weight of all animals sold including bull calves and culled mature animals per 31 year and Mmilk is the mass of fat and protein corrected milk (FPCM) sold per year (corrected to 4% fat and 32 3.3% protein). The constant 6.04 describes the causal relationship between the energy content in feed in 33 relation to the milk and live weight of animals produced. The constant is determined based on a study that 34 collected data from 536 US dairy farms (Thoma et al., 2013). Although based on US farms, IDF considers that 35 the approach is applicable to the European farming systems. 36


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The FPCM (corrected to 4% fat and 3.3% protein) shall be calculated by using the following equation: 1

𝐹𝑃𝐶𝑀𝑘𝑔

𝑦𝑟= 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛

𝑘𝑔

𝑦𝑟∗ (0.1226 ∗ 𝑇𝑟𝑢𝑒 𝐹𝑎𝑡 % + 0.0776 ∗ 𝑇𝑟𝑢𝑒 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 %2

+ 0.2534) 3

[Equation 2] 4

When a default value of 0.02 kgmeat/kgmilk for the ratio of live weight of animals and milk produced in Equation 5 1 is used, the equation yields default allocation factors of 12% to live weight of animals and 88% to milk 6 (Table 13). These values shall be used as default values for allocating the upstream burdens to milk and live 7 weight of animals for cattle when secondary datasets are used. When company-specific data are collected 8 for the farming stage, the allocation factors shall be changed using the equations included in this section. 9

Table 13: Default allocation factors for cattle at farming 10

Co-product Allocation factor

Animals, live weight 12%

Milk 88%

5.5.2.3 Allocation within the farm module for the sheep and goat 11

A biophysical approach shall be used for the allocation of upstream burdens to the different co-products for 12 sheep and goat. The 2006 IPCC guidelines for national greenhouse gas inventories (IPCC, 2006) contain a 13 model to calculate energy requirements that shall be used for sheep and, as a proxy, for goats. This model is 14 applied in the present document. 15

Dead animals and all the products coming from dead animals shall be regarded as waste and the Circular 16 Footprint Formula (CFF, 5.5.8.10) shall be applied. In this case, however, the traceability of the products 17 coming from dead animals shall be granted in order for this aspect to be taken into consideration into LCA 18 studies. 19

The use of the default allocation factors included in this document is mandatory whenever secondary 20 datasets are used for the life cycle stage of farming for sheep and goat. If company specific data are used for 21 this life cycle stage, then the calculation of the allocation factors with the company specific data shall be 22 performed using the equations provided. 23

The allocation factors shall be calculated as follows38: 24

% 𝑤𝑜𝑜𝑙 = [ ( )]

[( ( ) ( ) ( )] [Equation 3] 25

38 The same naming as used in IPCC (2006) is used.


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% 𝑚𝑖𝑙𝑘 = [ ( )]

[( ( ) ( ) ( )] [Equation 4] 1

% 𝑚𝑒𝑎𝑡 = [ ( )]

[( ( ) ( ) ( )] [Equation 5] 2

For the calculation of energy for wool (NEwool), energy for milk (NEl) and energy for meat (NEg) with company 3 specific data, the equations included in IPPC (2006) and reported below shall be used. In case secondary data 4 are used instead, the default values for the allocation factors provided in this document shall be used. 5

Energy for wool, NEwool 6

𝑁𝐸 =( ∙ )

[Equation 6] 7

8 NEwool = net energy required to produce wool, MJ day-1 9 EVwool = the energy value of each kg of wool produced (weighed after drying but before scouring), MJ 10 kg-1. A default value of 157 MJ kg-1 (NRC, 2007) shall be used for this estimate.39 11 Productionwool = annual wool production per sheep, kg yr-1 12

Default values to be used for the calculation of NEwool and the resulting net energy required are reported in 13 Table 14. 14

Table 14: Default values to be used for the calculation of NEwool for sheep 15

Parameter Value Source

𝑬𝑽𝒘𝒐𝒐𝒍 - sheep 157 MJ kg-1 NRC, 2007

𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏𝒘𝒐𝒐𝒍 - sheep 7.121 kg Average of the four values provided in Table 1 of "Application of LCA to sheep production systems: investigating co-production of wool and meat using case studies from major global producers. Wiedemann et al. (2015).

𝑵𝑬𝒘𝒐𝒐𝒍 - sheep 3.063 MJ/d Calculated using Eq. 6

𝑵𝑬𝒘𝒐𝒐𝒍 - goat 2.784 MJ/d Calculated from NEwool – sheep using Eq. 9

16

17

39 The default value of 24 MJ kg-1 originally included in the IPPC (2006) document has been modified into 157 MJ kg-1 following the indication of FAO (2014).


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Energy for milk, NEl 1

𝑁𝐸 = 𝑀𝑖𝑙𝑘 ∙ 𝐸𝑉 [Equation 7] 2

NEl = net energy for lactation, MJ day-1 3 Milk = amount of milk produced, kg of milk day-1 4 EVmilk = the net energy required to produce 1 kg of milk. A default value of 4.6 MJ/kg (AFRC, 1993) shall be 5 used which corresponds to a milk fat content of 7% by weight. 6 7 Default values to be used for the calculation of NEl and the resulting net energy required are reported in 8 Table 15. 9

Table 15: Default values to be used for the calculation of NEl for sheep 10


𝑬𝑽𝒎𝒊𝒍𝒌 - sheep 4.6 MJ kg-1 AFRC, 1993

𝑴𝒊𝒍𝒌 - sheep 2.08 kg/d Estimated milk production 550 lbs of sheep milk per year (average value), milk production estimated for 120 days in one year.

𝑵𝑬𝒍 - sheep 9.568 MJ/d Calculated using Eq. 7

𝑵𝑬𝒍 - goat 8.697 MJ/d Calculated from NEl – sheep using Eq. 9

11 Energy for meat, NEg 12 13

𝑁𝐸 = 𝑊𝐺 ∙.

[Equation 8] 14 15 NEg = net energy needed for growth, MJ day-1 16 WGlamb = the weight gain (BWf – BWi), kg yr-1 17 BWi = the live bodyweight at weaning, kg 18 BWf = the live bodyweight at 1-year old or at slaughter (live-weight) if slaughtered prior to 1 year of age, kg 19 a, b = constants as described in Table 16. 20

Note that lambs will be weaned over a period of weeks as they supplement a milk diet with pasture feed or 21 supplied feed. The time of weaning should be taken as the time at which they are dependent on milk for half 22 their energy supply. The NEg equation used for sheep includes two empirical constants (a and b) that vary by 23 animal species/category (Table 16). 24


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Table 16: Constants for use in calculating NEg for sheep40 1

Animal species/category a (MJ kg-1) b (MJ kg-2)

Intact males 2.5 0.35

Castrates 4.4 0.32

Females 2.1 0.45

2 In case company specific data are used for the farming stage, the allocation factors shall be recalculated. In 3 this case, the parameter "a" and "b" shall be calculated as weighted average when more than one animal 4 category is present. 5

Default values to be used for the calculation of NEg are reported in Table 17. 6

Table 17: Default values to be used for the calculation of NEg for sheep 7


WGlamb - sheep 26.2-15=11.2 kg Calculated

BWi - sheep 15 kg It is assumed that the weaning happens at six weeks. Weight at six weeks taken from Figure 1 in Johnson et al. (2015).

BWf - sheep 26.2 kg Average of the values for weight at slaughter, sheep as provided in Appendix 5, FAO (2014).

a - sheep 3 Average of the three values provided in Table 16

b - sheep 0.37 Average of the three values provided in Table 16

NEg - sheep 0.326 MJ/d Calculated using Eq. 8

NEg - goat 0.296 MJ/d Calculated from NEg – sheep using Eq. 9

8 The default allocation factors to be used in LCA studies for sheep and goat are reported in Table 18 together 9 with the calculations. The same equations41 and default values used for the calculation of the energy 10 requirements for sheep are used for the calculation of the energy requirements for goats after application of 11 a correction factor. 12

Net energy requirement, goat = [(goat weight) / (sheep weight)]0.75 • Net energy requirement,sheep 13 14 [Equation 9] 15

40 This table corresponds to Table 10.6 in IPCC (2006). 41 Page 10.24 of IPCC (2006).


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Sheep weight: 64.8 kg, average of male and female sheeps for different regions in the world (data from 1 Appendix 5, FAO, 2014). 2 3 Goat weight: 57.05 kg, average of male and female goats for different regions in the world (data from 4 Appendix 5, FAO, 2014). 5

Therefore: 6

Net energy requirement, goat = [(57.05) / (64.8)]0.75 • Net energy requirement, sheep 7 8

Table 18: Default allocation factors to be used in LCA studies for sheep and goat at farming stage 9

Sheep Goat42

Allocation factor, meat % 𝒎𝒆𝒂𝒕 = [(𝑵𝑬𝒈)]

[(𝑵𝑬𝒘𝒐𝒐𝒍) (𝑵𝑬𝒍) (𝑵𝑬𝒈)] = 2.52% 2.51 %

Allocation factor, milk % 𝒎𝒊𝒍𝒌 = [(𝑵𝑬𝒍)]

[(𝑵𝑬𝒘𝒐𝒐𝒍) (𝑵𝑬𝒍) (𝑵𝑬𝒈)] = 73.84% 73.85%

Allocation factor, wool % 𝒘𝒐𝒐𝒍 = [ (𝑵𝑬𝒘𝒐𝒐𝒍)]

[(𝑵𝑬𝒘𝒐𝒐𝒍) (𝑵𝑬𝒍) (𝑵𝑬𝒈)] = 23.64% 23.64%

10

5.5.2.4 Allocation within the farm module for pig 11

Allocation at farming stage between piglets and sows shall be made applying economic allocation. The default 12 allocation factors to be used are reported in Table 19 (data from the screening PEF study conducted in the 13 framework of the “meat pilot”). 14

Table 19: Allocation at farming stage between piglets and sows 15

Unit Price Allocation factors

Piglets 24.8 p 0.95 €/kg live weight 92.63%

Sow to slaughter 84.8 kg 40.80 €/pig 7.37%

5.5.2.5 Allocation within the slaughterhouse 16

Slaughterhouse and rendering processes produce multiple outputs going to the food and feed chain or to 17 other non-food or feed value chains as the leather industry or chemical or energy recovery chains. 18

At the slaughterhouse and rendering module, subdivision shall be used for those process flows that can be 19 directly attributed to certain outputs. When the processes cannot be subdivided, the remaining flows (e.g. 20 excluding those already allocated to milk for milk producing systems or to wool for wool producing systems) 21 shall be allocated to slaughterhouse and rendering outputs using economic allocation. Default allocation 22

42 Allocation factors for goat are calculated starting from the net energy requirements for goat estimated from the net energy requirements for sheep and considering: sheep weight= 64.8 kg and goat weight= 57.05 kg.


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factors are provided in the following sections for cattle, pigs and small ruminants (sheep, goat) and these 1 default values shall be used in LCA studies. No change of allocation factors is allowed. 2

5.5.2.6 Allocation within the slaughterhouse for cattle 3

At the slaughterhouse, the allocation factors are established for the five product categories reported in 4


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Table 20. If allocation factors to subdivide the impact of the carcass among the different cuts are desired, 1 they shall be directly defined in the LCA study and appropriately justified. 2

The by-products from slaughterhouse and rendering can be classified in three categories: 3 Category 1: Risk materials, e.g. infected/contaminated animals or animal by-products 4

o Disposal and use: incineration, co-incineration, landfill, used as biofuel for combustion, 5 manufacture of derived products 6

Category 2: Manure and digestive tract content, products of animal origin unfit for human 7 consumption 8

o Disposal and use: incineration, co-incineration, landfill, fertilisers, compost, biofuels, 9 combustion, manufacture of derived products 10

Category 3: Carcases and parts of animals slaughtered and which are fit for human consumption but 11 are not intended for human consumption for commercial reasons, including skins and hides going for 12 leather industry (note that hides and skins can also belong to other categories depending on the 13 condition and nature that is determined by the accompanying sanitary documentation) 14

o Disposal and use: incineration, co-incineration, landfill, feed, pet food, fertilisers, compost, 15 biofuels, combustion, manufacture of derived products (e.g. leather), oleo-chemicals and 16 chemicals 17

The upstream burdens to slaughterhouse and rendering outputs shall be allocated as follows: 18 Food grade materials: product with allocation of upstream burdens 19 Cat 1 material: default no allocation of upstream burdens as it is seen as animal by-product treated 20

as waste according to the CFF 21 Cat 2 material: default no allocation of upstream burdens as it is seen as animal by-product treated 22

as waste according to the CFF 23 Cat 3 material going the same way as cat 1 and cat 2 (for fat – to be burned, or bone and meat meal) 24

and does not have an economic value at the slaughterhouse gate: default no allocation of upstream 25 burdens as it is treated as waste according to the CFF 26

Cat 3 skins and hides (unless they are classified as waste and/or following the same way as cat 1 and 27 cat2): product with allocation of a upstream burdens 28

Cat 3 materials, not included in previous categories: product with allocation of an upstream burden 29

The default values in 30


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Table 20 shall be used in LCA studies. The change of allocation factors is not allowed. 1

2


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Table 20: Economic allocation ratios for beef 1

Mass faction (F), % Price (P), €/kg

Economic allocation (EA), %

Allocation ratio* (AR)

a) Fresh meat and edible offal 49.0 3.00 92.943 1.90

b) Food grade bones 8.0 0.19 1.0 0.12

c) Food grade fat 7.0 0.40 1.8 0.25

d) Cat. 3 slaughter by-products 7.0 0.18 0.8 0.11

e) Hides and skins 7.0 0.80 3.5 0.51

f) Cat 1/2 material and waste 22.0 0.00 0.0 0.00

*Allocation ratios (AR) have been calculated as ‘Economic allocation’ divided by ‘Mass fraction’ 2

Allocation ratios (AR) shall be used to calculate the environmental impact of a unit of product by using 3 Equation 10. 4

𝐸𝐼 = 𝐸𝐼 ∗ 𝐴𝑅 [Equation 10] 5

Where, EIi is the environmental impact per mass unit of product i , (i = a slaughterhouse output listed in 6


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Table 20), EIw is the environmental impact of the whole animal divided by live weight mass of the animal and 1 ARi is the allocation ratio for product i (calculated as economic value of i divided by mass fraction of i). 2

EIw shall include upstream impacts, slaughterhouse impacts that cannot be directly attributed to any specific 3 products and impacts from the management of slaughterhouse waste (cat. 1/2 material and waste in 4


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Table 20). The default values for ARi as shown in 1


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Table 20 shall be used for LCA studies to represent the European average situation. 1

5.5.2.7 Allocation within the slaughterhouse for pigs 2



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Table 21 shall be used in LCA studies dealing with allocation within the slaughterhouse for pigs. The change 1 of allocation factors based on company-specific data is not allowed. The mass fractions and the prices are 2 taken from the screening PEF study developed in the framework of the “meat pilot”. 3

4


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Table 21: Economic allocation ratios for pigs (from the screening PEF study conducted in the “meat 1 pilot”) 2

Mass fraction (F), % Price (P),

€/kg Economic allocation (EA), %


a) Fresh meat and edible offal 67.044 1.08 98.6745 1.54


c) Food grade fat 3.0 0.02 0.09 0.03


e) Hides and skins (categorized in cat.3 products)

0.0 0.00 0 0

Total 100.0

100.0

*Allocation ratios (AR) have been calculated as ‘Economic allocation’ divided by ‘Mass fraction’ 3

5.5.2.8 Allocation within the slaughterhouse for sheep and goat 4


44 The data in the screening do not sum up to 100%, but to 96%. We have recalculated the percentages to arrive at 100%.


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Table 22 shall be used in LCA studies dealing with allocation within the slaughterhouse for sheep and goat. 1 The change of allocation factors based on company-specific data is not allowed. The mass fractions and the 2 prices are taken from the screening PEF study carried out in the framework of the “meat pilot”. Until more 3 reliable data on mass fractions and price for goats are made available, the same allocation factors as for 4 sheep shall be used also for goat. 5 6


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Table 22: Economic allocation ratios for sheep (from the screening PEF study conducted in the “meat 1 pilot”). The same allocation factors shall be used also for goat 2

Mass fraction (F), %

Price (P), €/kg

Economic allocation (EA), %


% €/kg %

a) Fresh meat and edible offal 44.0 7 97.846 2.22


c) Food grade fat 6.0 0.01 0.0190 0.0032


e) Hides and skins (categorized in cat.3 products)

14.0 0.35 1.6 0.11

f) cat ½ material and waste 19 0 0 0

100

100

5.5.3 Capital goods (infrastructures and equipment) 3 As a general rule, the modelling of capital goods shall be based on linear depreciation (i.e. the respective 4 environmental burdens shall be evenly distributed throughout the useful life of the good). The expected 5 service life of capital goods shall be taken into account (and not the time to evolve to an economic book value 6 of 0). 7

5.5.4 Logistics and transport 8 Important parameters that shall be taken into account when modelling transport include: 9

1. Transport type: The type of transport, e.g. by land (truck, rail, pipe), by water (boat, ferry, barge), or air 10 (airplane); 11

2. Vehicle type & fuel consumption: The type of vehicle by transport type, as well as the fuel consumption 12 when fully loaded and empty. An adjustment shall be applied to the consumption of a fully-loaded 13 vehicle according to loading rate; 14

3. Loading rate: Environmental impacts are directly linked to the actual loading rate, which shall therefore 15 be considered; 16

4. Number of empty returns: The number of empty returns (i.e. the ratio of the distance travelled to 17 collect the next load after unloading the product to the distance travelled to transport the product), 18


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when applicable and relevant. The kilometres travelled by the empty vehicle shall be allocated to the 1 product. Specific values shall be developed by country and by type of transported product; 2

5. Transport distance: Average transport distances specific to the context being considered shall be 3 applied and documented; 4

6. Calculation of impacts from transport: A fraction of the impacts from transportation activities shall be 5 allocated to the considered product based on the load-limiting factor. The following modelling principles 6 should be considered: 7 Goods transport: time or distance AND mass or volume (or in specific cases: pieces/pallets) of the 8

transported good: 9 a) If the maximum authorised weight is reached before the vehicle has reached its maximum 10 physical load at 100% of its volume (high density products), then allocation shall be based on 11 the mass of transported products; 12 b) If the vehicle is loaded at 100% of the volume but it does not reach the authorised maximum 13 weight (low density products), then allocation shall be based on the volume of the transported 14 products; 15

Personal transport: time or distance; 16 Staff business travel: time, distance or economic value; 17

7. Fuel production: Default values for fuel production can be found, for example, in the European 18 Reference Life Cycle Database (ELCD)47; 19

Additional parameters that should be taken into account when modelling transport include: 20

8. Infrastructure: The transport infrastructure, that of road, rail and water; 21

9. Resources and tools: The amount and type of additional resources and tools needed for logistic 22 operations such as cranes and transporters. 23

The impacts of transport activities shall be expressed in the default reference units, i.e. t∙km for goods, and 24 Vehicle∙km for passenger transport. Any deviation from these default reference units shall be justified and 25 reported. 26

The environmental impact due to transport within the product life cycle shall be calculated by multiplying 27 the impact per reference unit of each vehicle type by: 28

a) for goods: the distance and load (mass for high density products or volume for low density 29 products); 30 b) for persons: the distance and number of persons based on the defined transport scenarios. 31 32

47 For more information, please refer to: http://eplca.jrc.ec.europa.eu/ELCD3/index.xhtml?stock=default


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In case no company-specific and supply chain-specific data is available, the default transport scenarios and 1 values outlined in the sections below shall be used. Replacement of the default values below with case-2 specific values shall be clearly mentioned and justified in the LCA study. 3

The (final and intermediate) client of the product shall also be defined in the LCA study48. The final client may 4 be a consumer (i.e. a person who purchases goods and services for personal use) or a company that uses the 5 product for final use, such as restaurants, professional painters, or a construction site. Re-sellers and 6 importers are intermediate clients and not final clients. 7

5.5.4.1 How to calculate transport burdens 8

Truck transport 9

EF-compliant LCI datasets for truck transport refer to a functional unit of 1 tkm (tonne*km), expressing the 10 environmental impact for 1 tonne of product that covers a distance of 1km in a truck with a certain load. The 11 transport payload (=maximum mass allowed) is indicated in the dataset. For example, a truck of 28-32t has 12 a payload of 22t. The corresponding LCI dataset for 1tkm (fully loaded) expresses the environmental impact 13 for 1 ton of product that covers 1km within a 22t loaded truck. The overall transport burdens are allocated 14 to the reference unit of 1 tkm based on the payload, so that only 1/22 share of the overall burdens of the 15 truck are assigned to it. 16

When the mass of a full freight in the product life cycle is lower than the load capacity of the truck (e.g. 10t), 17 the transport of the product may be considered volume limited. In this case, the truck has less fuel 18 consumption per total load transported and the environmental impact per ton of product is 1/10 share of 19 the total burdens of the volume limited truck. Within the EF-compliant transport datasets available at 20 http://lcdn.thinkstep.com/Node/, the transport payload is modelled in a parameterised way through the 21 utilisation ratio. The utilisation ratio is calculated as the kg real load divided by the kg payload and shall be 22 adjusted upon the use of the dataset. In case the real load is 0 kg, a real load of 1 kg shall be used to allow 23 the calculation. Note that default truck volumes cannot be provided as this strongly depends on the type of 24 material transported. In case truck volumes are needed to calculate the volume limited transport load, case-25 specific data should be used. 26

The following utilisation ratio shall be used in LCA studies: 27

● If the load is mass limited: a default utilisation ratio of 64%49 shall be used. This utilisation ratio 28 includes empty return trips. Therefore, empty returns shall not be modelled separately. However, 29 the user shall check and possibly adapt the utilisation factor as appropriate. 30

● If the load is volume limited and the full volume is used: the LCA study report shall indicate the 31 company-specific utilisation ratio calculated as the kg real load/kg payload of the dataset and 32 indicate how empty returns are modelled. 33

48 A clear definition of the final client facilitates a correct interpretation of the LCA study, which will enhance the comparability of results. 49 Eurostat 2015 indicates that 21% of the kms truck transport are driven with empty load and 79% are driven loaded (with an unknown load). In Germany only, the average truck load is 64%.


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● If the load is delicate (e.g. flowers): the full truck volume might not be used. The most appropriate 1 load factor to be applied shall be evaluated on a case-by-case basis. 2

● Bulk transport (e.g. gravel transport from mining pit to concrete plant) shall be modelled with a 3 default utilisation ratio of 50% (100% loaded outbound and 0% loaded inbound). 4

● Reusable products and packaging shall be modelled with case-specific utilisation ratios. The default 5 value of 64% (including empty return) cannot be used because the return transport is modelled 6 separately for reusable products. 7

8 When default data cannot be used, the LCA study report shall specify the utilisation ratio used for each 9 truck transport modelled, as well clearly indicate whether the utilisation ratio includes empty return trips. 10

Van transport 11

Vans are often used for home delivery products like books and clothes or home delivery from retailers. For 12 vans the mass is usually not the limiting factor, but rather the volume, where often the van is half empty. If 13 no specific information is available to perform the LCA study, a lorry of <1.2t with a default utilisation ratio 14 of 50% shall be used. In case no datasets of a lorry of <1.2t are available, a lorry of <7.5t shall be used as 15 approximation, with an utilisation ratio of 20%. A lorry of <7.5t with a payload of 3.3t and an utilisation ratio 16 of 20%, comes to the same load as a van with payload of 1.2t and utilisation ratio of 50%. 17

Consumer transport 18

EF-compliant LCI datasets for consumer transport (typically, passenger car) refer to a reference unit of 1 km. 19 In this context, the allocation of the car impact shall be based on volume. The maximum volume to be 20 considered for consumer transport is 0.2 m3 (around 1/3 of a trunk of 0.6 m3). For products larger than 0.2 21 m3 the full car transport impact shall be considered. For products sold through supermarkets or shopping 22 malls, the product volume (including packaging and empty spaces such as between fruits or bottles) shall be 23 used to allocate the transport burdens over the product transported. The allocation factor shall be calculated 24 as the volume of the product transported divided by 0.2 m3. For simplification, all other types of consumer 25 transport (like buying in specialised shops or using combined trips) shall be modelled as through 26 supermarket. The LCA study report shall specify the default allocation value to be used. 27

5.5.4.2 From supplier to factory 28

If no company-specific and supply chain-specific data are available, the default data provided below shall be 29 used to determine the transport distance for the transport of product from supplier to factory. 30

31


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For suppliers located within Europe: 1

For packaging materials from manufacturing plants to filler plants (beside glass; values based on Eurostat 2 201550), the following scenario shall be used: 3

● 230 km by truck (>32 t, EURO 4; UUID 938d5ba6-17e4-4f0d-bef0-481608681f57), case-specific 4 utilisation ratio; and 5

● 280 km by train (average freight train; UUID 02e87631-6d70-48ce-affd-1975dc36f5be); and 6 ● 360 km by ship (barge; UUID 4cfacea0-cce4-4b4d-bd2b-223c8d4c90ae). 7

For transport of empty bottles (communication from FEVE (2016)51), the following scenario shall be used: 8



For all other products from supplier to factory (values based on Eurostat 201552), the following scenario shall 13 be used: 14



For all suppliers located outside Europe, the following scenario shall be used: 19

● 1000 km by truck (>32 t, EURO 4; UUID 938d5ba6-17e4-4f0d-bef0-481608681f57), for the sum of 20 distances from harbour/airport to factory outside and inside Europe. case-specific utilisation ratio; 21 and 22

● 18000 km by ship (transoceanic container; UUID 6ca61112-1d5b-473c-abfa-4accc66a8a63) or 10’000 23 km by plane (cargo; UUID 1cc5d465-a12a-43da-aa86-a9c6383c78ac). 24

● If producers country (origin) is known: the adequate distance for ship and airplane should be 25 determined using http://www.searates.com/services/routes-explorer or 26 https://co2.myclimate.org/en/flight_calculators/new 27

50Calculated as the mass weighted average of the goods categories 06, 08 and 10 using the Ramon goods classification for transport statistics after 2007. The category 'non-metallic mineral products' are excluded as they can double count with glass.

51 Based on the peer reviewed LCA study of the European container glass, FEVE 2016. Primary data collected among 84% of the European container glass manufactures.

52 Calculated as the mass weighted average of the goods of all categories.


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● In case it is unknown if the supplier is located within or outside Europe, the transport shall be 1 modelled as supplier being located outside Europe. 2

5.5.4.3 From factory to final client 3

In case no company-specific and supply chain-specific information is available to define a transport scenario, 4 the default scenario outlined below shall be used as a basis (see Figure 11) together with a number of supply 5 chain-specific values: 6

● Ratio between products sold through retail, distribution centre (DC) and directly to the final client; 7 ● From factory to final client: Ratio between local, intracontinental and international supply chains; 8 ● From factory to retail: distribution between intracontinental and international supply chains. 9

10

11

Figure 11: Default transport scenario 12

13

(1) X% (supply chain-specific) from factory to final client: 14

● X% (supply chain-specific) local supply chain: 1'200 km by truck (>32 t, EURO 4; UUID 938d5ba6-17e4-15 4f0d-bef0-481608681f57), case-specific utilisation ratio. 16

● X% (supply chain-specific) intracontinental supply chain: 3'500 km by truck (>32 t, EURO 4; UUID 17 938d5ba6-17e4-4f0d-bef0-481608681f57), case-specific utilisation ratio. 18

● X% (supply chain-specific) international supply chain: 1'000 km by truck (>32 t, EURO 4; UUID 19 938d5ba6-17e4-4f0d-bef0-481608681f57), case-specific utilisation ratio and 18'000 km by ship 20 (transoceanic container; UUID 6ca61112-1d5b-473c-abfa-4accc66a8a63). Note that for specific 21 cases, plane or train may be used instead of ship. 22

(2) X% (supply chain-specific) from factory to retail/DC: 23

● X% supply-chain specific) local supply chain: 1'200 km by truck (>32 t, EURO 4; UUID 938d5ba6-17e4-24 4f0d-bef0-481608681f57), case-specific utilisation ratio. 25

● X% (supply chain-specific) intracontinental supply chain: 3'500 km by truck (>32 t, EURO 4; UUID 26 938d5ba6-17e4-4f0d-bef0-481608681f57) (Eurostat 2014), case-specific utilisation ratio. 27


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● X% (supply chain-specific) international supply chain: 1'000 km truck (>32 t, EURO 4; UUID 938d5ba6-1 17e4-4f0d-bef0-481608681f57), case-specific utilisation ratio and 18’000 km by ship (transoceanic 2 container; UUID 6ca61112-1d5b-473c-abfa-4accc66a8a63). Note that for specific cases, plane or 3 train may be used instead of ship. 4

(3) X% (supply chain-specific) from DC to final client: 5

● 100% Local: 250 km round trip by van (lorry <7.5t, EURO 3, utilisation ratio of 20%; UUID aea613ae-6 573b-443a-aba2-6a69900ca2ff) 7

(4) X% (supply chain-specific) from retail to final client: 8

● 62%: 5 km, by passenger car (average; UUID 1ead35dd-fc71-4b0c-9410-7e39da95c7dc), case-specific 9 allocation 10

● 5%: 5 km round trip, by van (lorry <7.5t, EURO 3 with utilisation ratio of 20%6; UUID aea613ae-573b-11 443a-aba2-6a69900ca2ff) 12

● 33%: no impact modelled 13

Note that for reusable products the return transport from retail/DC to factory shall be modelled in addition 14 to the transport needed to go to retail/DC. The same transport distances as from product factory to final 15 client shall be used (see above), however the truck utilisation ratio might be volume limited depending on 16 the type of product. The utilisation ratio to be used for the return transport shall be case-specific. 17

5.5.4.4 From EOL waste collection to EOL treatment 18

The transport from collection place to EOL treatment is included in the landfill, incineration and recycling 19 datasets provided by the EC. However, there are some cases, where additional default data might be needed 20 by the practitioner. The following values shall be used in case no better data is available: 21

Consumer transport from home to sorting place: 1 km by passenger car (UUID 1ead35dd-fc71-22 4b0c-9410-7e39da95c7dc )53 23

Transport from collection place to methanisation: 100 km by truck (>32 t, EURO 4; UUID 938d5ba6-24 17e4-4f0d-bef0-481608681f57) 25

Transport from collection place to composting: 30 km by truck (lorry <7.5t, EURO 3 with UUID 26 aea613ae-573b-443a-aba2-6a69900ca2ff) 27

5.5.4.5 Transport processes for cooled and frozen product 28

Note that the transport processes from factory to final client, DC and retail suggested above are for products 29 at ambient temperature only. Products frozen or cooled are to be transported in freezers or coolers. These 30 datasets are available at http://lcdn.thinkstep.com/Node/. 31

53 Assumption (Justification: 75% of households do not need to "move" their waste, or can simply do it by walking. However 25% of the households do about 4 km by car to bring their waste to a local collection place (whether for trash or for recycling), which corresponds in average for all waste to 1 km by car).


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5.5.5 Packaging 1 This section provides guidance on: (i) datasets to be used for the modelling of packaging used throughout 2 the life cycle of the main product in scope; (ii) how to calculate reuse rates for returnable packaging (used 3 both as main product and as ancillary product); and (iii) recommended reuse rates to be considered for 4 specific types of packaging when they are not the main product in scope. 5

5.5.5.1 Packaging datasets 6

A large number of EF-compliant packaging related datasets are available on the node 7 (http://lcdn.thinkstep.com/Node). These European average packaging datasets shall be used in case no 8 company-specific data or supplier-specific information is available, or the packaging is not relevant within 9 the product life cycle. For some multi-material packaging, additional information are however needed to 10 perform a correct modelling. This is the case for, e.g., beverage cartons and bag-in-box packaging. 11

Beverage cartons are made out of LDPE film and liquid packaging board, with or without aluminium foil. The 12 amount of LDPE film, board and foil (also called the bill of material of beverage cartons) depends on the 13 application of the beverage carton and shall be defined on a case-by-case basis in the LCA study (e.g. wine 14 cartons, milk cartons). Beverage cartons shall be modelled by combining the prescribed amounts of material 15 datasets with the beverage carton conversion dataset. 16

Bag in box is made out of corrugated board and packaging film. The LCA study should define the amount of 17 corrugated board, as well as the amount and type of packaging film. If this information is not available, the 18 default dataset for bag-in-box shall be used. 19

5.5.5.2 Packaging reuse rates 20

Reuse rate is the number of times a packaging material is used (e.g. filled) at the factory. This is often also 21 called trip rate, reuse time or number of rotations. This may be expressed as the absolute number of reuse 22 cycles or as % reuse rate. For example: a reuse rate of 80% equals 5 reuse cycles. Equation 11 describes the 23 conversion: 24

Number of reuse cycles= % %

[Equation 11] 25

The number of reuse cycles applied here refers to the total number of uses during the life of a packaging. It 26 includes both the first use and all the following reuses. 27

A packaging return system can be organized by the company owning the packaging material (company owned 28 pools) or at a higher level by a third party, e.g. the government or a pooler (third party operated pools). This 29 may have an influence on the lifetime of the material as well as the data source to be used. Therefore, it is 30 important to separate these two return systems. 31

For company owned packaging pools the reuse rate shall be calculated using supply-chain-specific data. 32 Depending on the data available within the company, two different calculation approaches may be used 33 (see Options a and b presented below). Returnable glass bottles are used as an example, but the 34 calculations also apply for other types of company owned reusable packaging. 35


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Option a: Use of supply-chain-specific data, based on accumulated experience over the lifetime of the 1 previous glass bottle pool. This is the most accurate way to calculate the reuse rate of bottles for the previous 2 bottle pool and can be a proper estimate for the current bottle pool. The following supply-chain-specific data 3 is collected: 4

● Number of bottles filled during the lifetime of the bottle pool (#Fi) 5 ● Number of bottles at initial stock plus purchased over the lifetime of the bottle pool (#B) 6

7

Reuse rate of the bottle pool =#

# [Equation 12] 8

The net glass use (kg glass/l beverage) =# ×( / )

# [Equation 13] 9

This calculation option shall be used: 10

i. With data of the previous bottle pool when the previous and current bottle pool are comparable. 11 This implies the same product category, similar bottle characteristics (e.g. size), comparable return 12 systems (e.g. way of collection, consumer group and outlet channels), etc. 13

ii. With data of the current bottle pool when future estimations/extrapolations are available on (i) the 14 bottle purchases, (ii) the volumes sold, and (iii) the lifetime of the bottle pool. 15

The data shall be supply-chain-specific and shall be verified by an external verification, including the 16 reasoning of this method choice. 17

Option b: When no real data is tracked, the calculation shall be done partly based on assumptions. This option 18 is less accurate due to the assumptions made and therefore conservative/safe estimates shall be used. The 19 following data is needed: 20

● Average number of rotations of a single bottle, during one calendar year (if not broken). One loop 21 consists of filling, delivery, use, back to brewer for washing (#Rot) 22

● Estimated lifetime of the bottle pool (LT, in years) 23 ● Average percentage of loss per rotation. This refers to the sum of losses at consumer and the bottles 24

scrapped at filling sites (%Los) 25 26

Reuse rate of the bottle pool = ( ×% )

#

[Equation 14] 27

This calculation option shall be used when option a) is not applicable (e.g. the previous pool is not usable as 28 reference). The data used shall be verified by an external verification, including the reasoning of this 29 method choice. 30

Average reuse rates for company owned pools 31

The following average reuse rates shall be applied when company owned reusable packaging pools are used, 32 unless data of better quality is available: 33


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● Glass bottles: 20 trips for beer and water bottles54, 2 trips for wine55 1 ● Plastic crates for bottles: 30 trips56 2 ● Plastic pallets: 30 trips (Nederlands Instituut voor Bouwbiologie en Ecologie, 201457) 3

4

If other values are used, they shall be clearly justified and the data source shall be provided. In case a specific 5 packaging type is not present in the list above, sector-specific data shall be used. New values shall be subject 6 to review, if applicable. 7

Average reuse rates for third party operated pools 8

Average reuse rates provided by literature vary a lot, are not usable as such or are too country specific. Some 9 data sources are outdated (more than 15 years old) and thus not representative for the current situation (EC, 10 1999). Some others are biased due to a significant change in consumer behaviour. For example, the return 11 rate of beer bottles in Denmark is higher than 100% due to a decrease of this packaging in sales (Årsrapport, 12 2013). One recent study is valid for Germany only and provides reuse rates for reusable glass bottles in third 13 party operated pools and company owned pools (Deloitte, 2014). 14

The following reuse rates shall be used by LCA studies that have third party operated reusable packaging 15 pools in scope, unless data of better quality is available: 16

● Glass bottles: 30 trips for beer and water58, 5 trips for wine59 17 ● Plastic crates for bottles: 30 trips60 18

54 Agreement from packaging working group members (including beer and packed water pilot).

55 Estimation: http://ec.europa.eu/environment/waste/studies/packaging/belgium.pdf

56 Technical approximation as no data source could be found. Technical specifications guarantee a lifetime of 10 years. A return of 3 times per year (between 2 to 4) is taken as first approximation.

57 Most conservative number is used.

58The reuse rates for third party operated glass bottle pools was largely discussed within the packaging working group. Literature provides values between 5 and 50 reuse rates, but is mainly outdated. The study of Deloitte (2014) is most recent but provides values within the German context only. It can be questioned if these results are directly applicable for the European context. However, the study provides results for both company owned pools (23 trips, considering all foreign bottles as exchanged) and third party operated pools (36 trips, considering all foreign bottles as exchanged). It shows that the reuse rates for third party operated pools are ±1.5 times higher than for company owned pools. As first approximation the packaging working group proposes to use this ratio to extrapolate the average reuse rates for company owned pools (20 trips) towards average reuse rates for third party operated pools (20*1.5= 30 trips).

59Assumption based on monopoly system of Finland. http://ec.europa.eu/environment/waste/studies/packaging/finland.pdf

60 Technical approximation as no data source could be found. Technical specifications guarantee a lifetime of 10 years. A return of 3 times per year (between 2 to 4) is taken as first approximation.


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● Plastic pallets: 50 trips (Nederlands Instituut voor Bouwbiologie en Ecologie, 2014)61 1 ● Wooden pallets: 25 trips (Nederlands Instituut voor Bouwbiologie en Ecologie, 2014)62 2

If other values are used in the study, they shall be clearly justified and the data source shall be provided. In 3 case a specific packaging type is not present in the list above, sector-specific data shall be collected and 4 applied. New values shall be subject to review, if applicable. 5

5.5.6 Use stage 6 The use stage is a life cycle stage that can result in a high overall environmental contribution for many product 7 categories. As the use stage is generally calculated based on many modelling assumptions, the real 8 contribution is affected by potentially very high uncertainties. 9

In cradle-to-grave LCA studies, the use stage shall always be included for final products by following the 10 guidelines outlined below. The use stage shall be excluded for intermediate products. 11

5.5.6.1 Types of use stage processes 12

The use stage often involves multiple processes (see section 5.2.6). A distinction shall be made between (i) 13 product independent and (ii) product dependent processes. 14

(i) Product independent processes have no relationship with the way the product is designed or distributed. 15 The use stage process impacts will remain the same for all products in this product (sub) category even if the 16 producer changes the product's characteristics. Therefore, they don’t contribute to any form of 17 differentiation between two products or might even hide the difference. Examples are the use of a glass for 18 drinking wine (considering that the product doesn’t determine a difference in glass use); frying time when 19 using olive oil; energy use for boiling one litre of water to be used for preparing coffee made from bulk instant 20 coffee; the washing machine used for heavy laundry detergents (capital good). 21

(ii) Product dependent processes are directly or indirectly determined or influenced by the product design or 22 are related to instructions for use of the product. These processes depend on the product characteristics and 23 therefore contribute to differentiation between two products. All instructions provided by the producer and 24 directed towards the consumer (through labels, websites or other media) shall be considered as product 25 dependent. Examples of instruction are indications on how long the food must be cooked, how much water 26 must be used, or in the case of drinks the recommended serving temperature and storage conditions. An 27 example of a direct dependent process is the energy use of electric equipment when used in normal 28 conditions. 29

5.5.6.2 Main function approach or Delta approach 30

Modelling of the use stage may be done in different ways. Very often the related impacts and activities are 31 modelled fully. For example, the total electricity consumption when using a coffee machine, or the total 32 cooking time and related gas consumption when boiling pasta. In these cases, the use stage processes for 33

61 The less conservative number is used.

62 Half of plastic pallets is used as approximation.


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drinking coffee or eating pasta are related to the main function of the product (referred to as "main function 1 approach"). 2

In some cases, the use of one product can influence the environmental impact of another product. Some 3 examples are: 4

i. A toner cartridge is not held responsible for the paper it prints. But if remanufactured toner cartridge 5 works less efficiently and causes more paper loss compared to an original cartridge, the additional 6 paper loss should be considered. In that case, the paper loss is a dependent process of the use stage 7 of a remanufactured cartridge. The use stage involves processes and activities which are not 100% 8 related to the product. 9

ii. The energy consumption during the use stage of the battery/charger system is not related to the 10 amount of energy stored and released from the battery. It only refers to the energy loss in each 11 loading cycle. That energy loss can be caused by the loading system or the internal losses in the 12 battery. 13

In these cases, only the additional activities and processes should be allocated to the product (e.g. paper and 14 energy of remanufactured toner cartridge and battery). The method to deal with multifunctionality consists 15 in taking all associated products in the system (here paper and energy), and allocating the excess 16 consumption of these associated products to the product which is considered responsible for this excess. 17 This requires a reference consumption to be defined for each associated product in the LCA study (e.g. of 18 energy and materials). The reference consumption refers to the minimum consumption that is essential for 19 providing the function. The consumption above this reference (the delta) will then be allocated to the 20 product. This approach is also named "Delta approach" by ADEME63, and should only be used for increasing 21 impacts and to account for additional consumptions above the reference. To define the reference situation, 22 the following source shall be considered when existing: 23

● Regulations applicable to the product category 24 ● Standards or harmonised standards 25 ● Recommendations from manufacturers or manufacturers' organisations 26 ● Use agreements established by consensus in sector-specific working groups. 27

It is up to the practitioner to decide the approach to be taken (main function approach or Delta approach), 28 which shall be described in the LCA study report. 29

5.5.6.3 Modelling requirements 30

For all processes belonging to the use stage, the practitioner shall decide and describe in the LCA report 31 whether the main function approach or Delta approach shall be applied. 32

63 Specifications for drafting and revising product category rules (10.12.2014), ADEME.


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If the main function approach is applied, the developed or used dataset shall reflect as much as possible the 1 reality of market situations. In case the Delta approach is applied, a reference consumption to be used shall 2 be provided. 3

Table 23 and Table 24 provide default data and assumptions to be used by the practitioner to model use 4 stage activities that might be crosscutting for several product categories. Better data may be used, but shall 5 be justified in the LCA report. 6

Table 23: Default data to model crosscutting use stage activities for several product categories (note: 7 data based on assumptions, except if specified otherwise) 8

Product Use stage assumptions per product category

Meat, fish, eggs Chilled storage. Cooking: 10 minutes in frying pan (75% on gas and 25% electricity), 5 gram sunflower oil (incl. its life cycle) per kg product. Dishwashing of frying pan.

Milk Chilled storage, drunk cold in 200 ml glass (i.e., 5 glasses per L milk), incl. glass life cycle and dishwashing.

Frozen dishes Frozen storage. Cooked in oven 15 minutes at 200°C (incl. a fraction of a stove, a fraction of a baking sheet). Baking sheet rinsing: 5 L water.

Beer Cooling, drunk in 33 cl glass (i.e., 3 glasses per L beer), glass production, end-of-life and dishwashing.

Bottled water Chilled storage. Storage duration: 1 day. 2.7 glasses per L water drunk, 260 gram glass production, end-of-life and dishwashing.

Laundry detergent Use of a washing machine (see T-shirt data for washing machine model). 70 ml laundry detergent assumed per cycle, i.e., 14 cycles per kg detergent.

Automotive oil 10% losses during use assessed as hydrocarbons emissions to water.

9

Table 24: Default data to model storage during the use stage (note: data based on assumptions, except 10 if specified otherwise) 11

Product Assumptions common for several product categories

Ambient storage (at home) Ambient storage at home is considered, for the sake of simplification, as having no impact.

Chilled storage (in a fridge, at home)

Storage time: product dependent. As default 7 days storage in fridge (ANIA and ADEME 2012).

Storage volume: assumed to be 3x the actual product volume


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Product Assumptions common for several product categories

Energy consumption: 0.0037 kWh/L (i.e., “the storage volume”) - day (ANIA and ADEME 2012).

Fridge production and end-of-life considered (assuming 15 years of lifetime).

Chilled storage (at the pub/restaurant)

The fridge at the pub is assumed to consume 1400 kWh/ yr (Communication with Heineken green cooling expert, 2015). 100% of this energy consumption is assumed to be for the cooling of beer. The throughput of the fridge is assumed to be 40hl/ yr. This means 0.035 kWh/ l for pub / supermarket cooling for the full storage time.

Fridge production and end-of-life considered (assuming 15 years of lifetime).

Frozen storage (in a freezer, at home)

Storage time: 30 days in freezer (based on ANIA and ADEME 2012).

Storage volume: assumed to be 2x the actual product volume.

Energy consumption: 0.0049 kWh/L (i.e., “the storage volume”) - day (ANIA and ADEME 2012).

Freezer production and end-of-life considered (assuming 15 years of lifetime): assumed similar to fridge.

Cooking (at home) Cooking: 1 kWh/h use (derived from consumptions for induction stove (0.588 kWh/h), ceramic stove (0.999 kWh/h) and electric stove (1.161 kWh/h) all from (ANIA and ADEME 2012).

Backing in oven: electricity considered: 1.23 kWh/h (ANIA and ADEME 2012).

Dishwashing (at home) Dishwasher use: 15 L water, 10 g soap and 1.2 kWh per washing cycle (Kaenzig and Jolliet 2006).

Dishwasher production and end-of-life considered (assuming 1500 cycle per lifetime).

When dishwashing is done by hand, one assumes an equivalent of 0.5 L of water and 1 g of soap for the value above of 2.5% (with a scaling in terms of water use and soap, using the % above). The water is assumed to be warmed by natural gas, considering a delta T of 40 °C and an efficiency of energy from natural gas heating to water heat of 1/1.25 (meaning that to heat the 0.5 L of water one needs to use 1.25 * 0.5 * 4186 * 40 = 0.1 MJ of “Heat, natural gas, at boiler”).

1


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5.5.7 Electricity use 1 This section provides guidelines on how to model electricity use from processes and activities included in the 2 system boundary. 3

5.5.7.1 General guidelines 4

The following electricity mix shall be used, in hierarchical order, to model electricity from the grid consumed 5 within the defined system boundary: 6

(i) Supplier-specific electricity product64 shall be used if: 7 (a) available, and 8 (b) the set of minimum criteria to ensure the contractual instruments are reliable is 9

met. 10 (ii) The supplier-specific total electricity mix shall be used if: 11

(i) available, and 12 (ii) the set of minimum criteria to ensure the contractual instruments are reliable is 13

met. 14 (iii) As a last option, the 'country-specific residual grid mix, consumption mix' shall be used (available 15

at http://lcdn.thinkstep.com/Node/). Country-specific means the country in which the life cycle 16 stage occurs. This may be an EU country or non-EU country. The residual grid mix characterizes 17 the unclaimed, untracked or publicly shared electricity. This prevents double counting with the 18 use of supplier-specific electricity mixes in (i) and (ii). 19

Note: if for a country, there is a 100% tracking system in place, case (i) shall be applied. 20

The environmental integrity of the use of supplier-specific electricity mix depends on ensuring that 21 contractual instruments (for tracking) reliably and uniquely convey claims to consumers. Without this, the 22 LCA study lacks the accuracy and consistency necessary to drive product/corporate electricity procurement 23 decisions and accurate consumer (buyer of electricity) claims. Therefore, a set of minimum criteria that relate 24 to the integrity of the contractual instruments as reliable conveyers of environmental footprint information 25 has been identified (see the following sections). They represent the minimum features necessary to use 26 supplier-specific mix within LCA studies. 27

If (part of) the electricity delivered by a specific supplier is renewable, it shall be guaranteed that no double 28 counting of such electricity and of the associated impacts occurs. A statement of the supplier shall be included 29 as an annex to the LCA report, guaranteeing that the electricity supplied to the organisation to produce the 30 product is effectively generated using renewable sources and is not put into the grid to be used by other 31 consumers or sold to any other organisation (e.g. Guarantee of Origin for production of renewable electricity 32 (EU, 2009)). 33

64 Electricity delivered to the grid by a specific supplier (see ISO 14067).


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5.5.7.2 Set of minimal criteria to ensure contractual instruments from suppliers 1

A supplier-specific electricity product/mix shall only be used when the applicant ensures that any contractual 2 instrument meets the criteria specified below. If contractual instruments do not meet the criteria, then 3 country-specific residual electricity consumption-mix shall be used in the modelling. 4

The proposed list of criteria below is based on the criteria from the Sotos (2015). A contractual instrument 5 used for electricity modelling shall: 6

Criterion 1: Convey attributes 7

● Convey the energy type mix associated with the unit of electricity produced. 8 ● The energy type mix shall be calculated based on delivered electricity, incorporating certificates 9

sourced and retired on behalf of its customers. Electricity from facilities for which the attributes have 10 been sold off (via contracts or certificates) shall be characterised as having the environmental 11 attributes of the country residual consumption mix where the facility is located. 12

Criterion 2: Be a unique claim 13

● Be the only instruments that carry the environmental attribute claim associated with that quantity 14 of electricity generated. 15

● Be tracked and redeemed, retired, or cancelled by or on behalf of the company (e.g. by an audit of 16 contracts, third party certification, or may be handled automatically through other disclosure 17 registries, systems, or mechanisms). 18

19

Criterion 3: Be as close as possible to the period to which the contractual instrument is applied 20

Table 25 gives guidance on how to fulfil each criterion. 21

Table 25: Minimal criteria to ensure contractual instruments from electricity suppliers 22

Criterion 1 CONVEY ENVIRONMENTAL ATTRIBUTES AND GIVE EXPLANATION ABOUT THE CALCULATION METHOD

Convey the energy type mix (or other related environmental attributes) associated with the unit of electricity produced.

Give explanation about the calculation method used to determine this mix

Context Each program or policy will establish their own eligibility criteria and the attributes to be conveyed. These criteria specify energy resource type and certain energy generation facility characteristics, such as type of technologies, facility ages, or facility locations (but differ from one program/policy to another one). These attributes specify the energy resource type and sometimes some energy generation facility characteristics.

Conditions for satisfying the criterion

1) Convey the energy mix: If there is no energy type mix specified in the contractual instruments, ask your supplier to receive this information or other environmental attributes (GHG emission rate…). If no answer is received, use the 'country-specific residual grid mix, consumption mix'. If an answer is received, go to step 2).

2) Give explanation about the calculation method used: Ask your supplier to receive calculation method details in order to ensure he follow the above principle. If no information is received, apply


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the supplier-specific electricity mix, include the information received and document it was not possible to check for double counting.

Criterion 2 UNIQUE CLAIMS Be the only instrument that carry the environmental attribute claim associated with that

quantity of electricity generation. Be tracked and redeemed, retired, or cancelled by or on behalf of the company (e.g. by an

audit of contracts, third party certification, or may be handled automatically through other disclosure registries, systems, or mechanisms).

Context Certificates generally serve four main purposes, including (i) supplier disclosure, (ii) supplier quotas for the delivery or sales of specific energy sources, (iii) tax exemption, (iv) voluntary consumer programs. Each program or policy will establish their own eligibility criteria. These criteria specify certain energy generation facility characteristics, such as type of technologies, facility ages, or facility locations (but differ from one program/policy to another one). Certificates must come from facilities meeting these criteria in order to be eligible for use in that program. In addition, individual country markets or policy-making bodies may accomplish these different functions using a single certificate system or a multi-certificate system.

Conditions for satisfying the criterion

1. Is the plant located in a country with no tracking system? Consult RE-DISS II (2015) Table 2: - If yes, use the 'country-specific residual grid mix, consumption mix' - If no, go to the second question

2. Is the plant located in a country with a part of untracked consumption > 95%? - If yes, use the 'country-specific residual grid mix, consumption mix' as the best data

available to approximate the residual consumption mix - If no, go to the 3rd question

3. Is the plant located in a country with a single certificate system or a multi-certificate system? Consult Draeck (20090. Then:

- If the plant is located in a region/country with a single certificate system the unique claim criteria is met. Use energy type mix mentioned on the contractual instrument.

- If the plant is located in a region/country with a multi-certificate system, the unique claim is not ensured. Contact the country-specific Issuing Body (The European organization which governs the European Energy Certificate System, http://www.aib-net.org) to identify if there is a need to ask for more than one contractual instrument(s) to be sure there is no risk of double counting

o If more than one contractual instruments is needed, request all contractual instruments at the supplier to avoid double counting

o If it is not possible to avoid double counting, report this risk of double counting in the LCA study and use the 'country-specific residual grid mix, consumption mix'.

Criteria 3 Be issued and redeemed as close as possible to the period of electricity consumption to which the contractual instrument is applied.

5.5.7.3 How to model 'country-specific residual grid mix, consumption mix' 1

Datasets for residual grid mix, per energy type, per country and per voltage have been purchased by the 2 European Commission and are available in the dedicated node (http://lcdn.thinkstep.com/Node/). In case 3 the necessary dataset is not available, an alternative dataset shall be chosen according to the procedure 4 described in section 5.6.2. If no dataset is available, the following approach may be used: 5


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Determine the country consumption mix (e.g. X% of MWh produced with hydro energy, Y% of MWh produced 1 with coal power plant) and combine it with LCI datasets per energy type and country/region (e.g. LCI dataset 2 for the production of 1MWh hydro energy in Switzerland): 3

○ Activity data related to non-EU country consumption mix per detailed energy type shall be 4 determined based on: 5

- Domestic production mix per production technologies 6 - Import quantity and from which neighbouring countries 7 - Transmission losses 8 - Distribution losses 9 - Type of fuel supply (share of resources used, by import and / or domestic supply) 10

These data may be found in the publications of the International Energy Agency (IEA). 11

○ Available LCI datasets per fuel technologies in the node (http://lcdn.thinkstep.com/Node/). 12 ○ The LCI datasets available are generally specific to a country or a region in terms of: 13

- fuel supply (share of resources used, by import and / or domestic supply), 14 - energy carrier properties (e.g. element and energy contents), 15 - technology standards of power plants regarding efficiency, firing technology, flue-16

gas desulphurisation, NOx removal and de-dusting. 17

5.5.7.4 A single location with multiple products and more than one electricity mix 18

How to proceed if only a part of the electricity use is covered by a supplier-specific mix or by on-site electricity 19 generation? And how to attribute the electricity mix among products produced at the same location? 20

In general, the subdivision of electricity supply used among multiple products is based on a physical 21 relationship (e.g. number of pieces or kg of product). If the consumed electricity comes from more than one 22 electricity mix, each mix source shall be used in terms of its proportion in the total kWh consumed. For 23 example, if a fraction of this total kWh consumed is coming from a specific supplier, a supplier-specific 24 electricity mix shall be used for this part. See below for on-site electricity generation (section 5.5.7.7). 25

A specific electricity type may be allocated to one specific product in the following conditions: 26

a. The production (and related electricity consumption) of a product occurs in a separate site (building) 27 of the same facility; the energy type physically related to this separated site may be used. 28

b. The production (and related electricity consumption) of a product occurs in a shared space with 29 specific energy metering or purchase records or electricity bills; the product specific information 30 (measure, record, bill) may be used. 31

c. All the products produced in the specific plant are supplied with a public available LCA study. The 32 company who wants to make the claim shall make all LCA studies available. The allocation rule 33 applied shall be described in the LCA study, consistently applied in all LCA studies connected to the 34 site and verified. An example is the 100% allocation of a greener electricity mix to a specific product. 35

5.5.7.5 Multiple locations producing one product 36

In case a product is produced in different locations or sold in different countries, the electricity mix shall 37 reflect the ratios of production or ratios of sales between EU countries/regions. To determine the ratio, a 38


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physical unit shall be used (e.g. number of pieces or kg of product). If such data are not available, the average 1 EU residual consumption mix (EU-28 +EFTA), or region representative residual mix, shall be used. The same 2 general guidelines mentioned above shall be applied. 3

5.5.7.6 Electricity use at the use stage 4

For electricity consumed during the use stage of products, the consumption grid mix shall be used. The 5 electricity mix shall reflect the ratios of sales between EU countries/regions. To determine the ratio, a 6 physical unit shall be used (e.g. number of pieces or kg of product). Where such data are not available, the 7 average EU consumption mix (EU-28 +EFTA), or region representative consumption mix, shall be used. 8

5.5.7.7 How to deal with on-site electricity generation? 9

If on-site electricity production is equal to the site own consumption, two situations apply: 10 ○ No contractual instruments have been sold to a third party: the applicant shall model its own 11

electricity mix (combined with LCI datasets per production technology). 12 ○ Contractual instruments have been sold to a third party: the applicant shall use 'country-13

specific residual grid mix, consumption mix' (combined with LCI datasets per production 14 technology). 15

If electricity is produced in excess of the amount consumed on-site within the defined system boundary and 16 is sold to, for example, the electricity grid, this system can be seen as a multifunctionality situation. The 17 system will provide two functions (e.g. product function + electricity provision) and the following rules shall 18 be followed: 19

o If possible, apply subdivision. 20 o Subdivision applies either to (i) separate electricity productions, or (ii) to a common electricity 21

production process where you may allocate the related upstream and direct emissions to on-site 22 consumption and to the share that is sold out of the company based on the corresponding amount of 23 electricity consumed or sold. For instance, if a company has a wind mill on its production site and 24 export 30% of the produced electricity, upstream and direct emissions related to 70% of produced 25 electricity should be accounted in the LCA study. 26

o If not possible, direct substitution shall be used. The country-specific residual consumption electricity 27 mix shall be used as substitution65. 28

o Subdivision is considered as not possible when upstream impacts or direct emissions are closely related 29 to the product itself. 30

31 Note that any amount of renewable electricity provided to, for example, the electricity grid, shall only be 32 credited to the assessed product if the credit has not already been taken into account in other schemes. 33 Documentation (e.g. Guarantee of Origin for production of renewable electricity) is required to explain 34 whether the credit is considered in the calculation. 35 36

65 For some countries, this option is a best case rather than a worst case.


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5.5.8 End-of-life stage 1 A general differentiation can be made between: (i) the end-of-life the main product in scope once it has 2 reached its end-of-life and has been discarded by the user; and (ii) the end-of-life of the waste flows 3 generated across the different stages of the life cycle (i.e. during the manufacturing, distribution, retail and 4 use stage of the main product). 5

The end-of-life of the waste flows generated during the manufacturing, distribution, retail, and use stage 6 shall be included in the overall modelling of the life cycle of the product. Overall, it should be modelled and 7 reported at the life cycle stage where the waste occurs. For example, the end-of-life of the wastes generated 8 during manufacturing should be modelled and reported at the manufacturing life cycle stage. The end-of-life 9 of product losses shall also be included in the modelling and attributed to the life cycle stage where these 10 occur. Default loss rates per type of product during distribution and at consumer are provided in Annex C. 11 These values shall be used in case no supply chain-specific information is available. 12

The end-of-life of the main product in scope is mostly to be modelled in the End-of-Life stage of the life cycle. 13 The End-of-Life stage is a life cycle stage that, in general, includes the waste of the product in scope (such as 14 the food waste) the product left at its end of use, and the primary packaging of the product. For intermediate 15 products, the End-of-Life of the product in scope shall be excluded. 16

All waste flows arising from processes included in the system boundary (and belonging to both the 17 abovementioned categories) shall be modelled to the level of elementary flows. This means that waste flows 18 shall not represent, per se, an emission to the environment, while the emissions and resource consumption 19 resulting from their end-of-life management shall be modelled in the Life Cycle Inventory. 20

The following sections provide provisions and recommendations for the modelling of End of Life scenarios 21 and specific End of Life options applicable to bot non-biodegradable and biodegradable plastic materials. The 22 general guidelines reported in CEN TR 16957 (2016)66 were taken into account and used as inspiration in the 23 drafting of these sections. 24

5.5.8.1 End-of-life scenarios 25

As it is often not known exactly what will happen at the end-of-life of a product, end-of-life scenarios shall 26 be defined. These scenarios shall be based on most recent (year of analysis) practice, technology and data. 27

Frequently, a combination of end-of-life options is applied, especially to the main product in scope, when it 28 is delivered to an advanced waste management system including separate collection of recoverable 29 materials. As a consequence, an appropriate end-of-life scenario should be modelled, based on most recent 30 European, national or regional waste statistics (depending on the geographical scope of the study), and/or 31 data supplied by the operators of extended producer responsibility schemes (e.g. packaging material 32 consortia). If this information is not available, alternative end-of-life scenarios covering the different viable 33 end-of-life options for the product should be modelled in the study. At least an expected best-case and worst-34 case scenario should be considered in this situation. 35

66 CEN TR 16957 (2016) Bio-based products – Guidelines for Life Cycle Inventory (LCI) for the End-of-life phase.


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End-of-life scenarios should also account for the share of product littered on-land and (directly or indirectly) 1 into the marine and/or riverine environment. Few data are currently available in this respect. However, for 2 plastic products, estimates can be attempted based on aggregated results from beach count campaigns (e.g. 3 Hanke, 2016) and the quantity of product introduced into the market over an appropriate timeframe (e.g. 4 the 5 years preceding the counting period. Further initial hints and recommendations on how this estimate 5 can be approached are provided in Section 5.5.8.8. 6

5.5.8.2 Waste-specific parameters relevant for EOL modelling 7

The physico-chemical properties of the modelled waste shall reflect those of the analysed product(s) as 8 defined in the scope definition phase (section 4.2.1). The physical carbon content in (bio-based) products 9 shall be considered for calculations related to the modelling of the EoL stage, even if any allocation is 10 performed upstream in the life cycle of the product. 11

5.5.8.3 Modelling of mechanical recycling processes 12

In mechanical recycling, waste material is reclaimed in order to enable the use of the material in manufacture 13 of a new product. During mechanical recycling, waste is for example ground, cleaned and eventually recycled 14 (e.g. plastics are recycled into flakes or pellets). The quality of the recycled materials differs depending on 15 original material properties and recycling processes applied. 16

This waste treatment pathway can also be applied to bio-based materials. Prerequisite for a valuable 17 mechanical recycling of bio-based material is, as for any other material, a source-separated waste collection 18 and subsequent sorting. Recycled bio-based material maintains the CO2 fixed from the atmosphere during 19 plant growth within the recycled material until (after one or more recycling “loops”) it ends up in final disposal 20 (e.g. incineration). 21

The key parameters for modelling mechanical recycling are listed in Table 26. Additional guidance on how to 22 handle multi-functionality of recycling is provided in Section 5.5.8.10 (Circular Footprint Formula). For that 23 purpose, additional parameters on the quality of the recycled material and of the replaced (primary) material 24 are needed, as better specified in Section 5.5.8.11. 25

Table 26: Parameters required for modelling mechanical recycling processes 26

Parameter Unit Recycling efficiency % of input waste ultimately recycled Energy demand -electricity- kWh/kg input waste Energy demand -thermal- MJ/kg input waste Energy demand -mechanical- (e.g. fuel consumption) MJ/kg input waste Water m3/kg input waste Ancillary materials (e.g. detergents, chemicals) kg/kg input waste Amount of rejects (non-recycled material) -and respective fate- kg/kg input waste

27

5.5.8.4 Modelling of composting processes 28

Composting is a biological waste treatment process where biodegradable waste is typically converted, under 29 aerobic conditions, into carbon dioxide, methane, water, Non-Methane Volatile Organic Compounds 30 (NMVOC) and a residual solid fraction (the compost), which is the main output of the process. The compost 31


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produced can serve as a soil amendment, maintaining soil carbon stock and possibly replacing mineral 1 fertilisers. 2

Composting can be classified into a) industrial composting and b) home composting. Industrial composting is 3 a managed process, typically consisting of two stages (main oxidation and a maturation phase), where the 4 conditions (temperature, humidity, oxygen level, availability of microorganisms and residence time), are 5 controlled. The composting performance is thus stable and guaranteed. Moreover, process emissions are 6 normally controlled (e.g. through bio-filters in encapsulated systems). Conversely, home composting is a 7 simple, one-stage, open-pile composting process where operating parameters can vary widely, resulting in a 8 process where the average composting rate and performance is less predictable. Moreover, no emission 9 control is carried out in home composting. 10

Often, such as in the case of biodegradable polymer materials, the degradation process starts with the 11 chemical/physical degradation (e.g. hydrolysis) of the material into simpler and smaller compounds (e.g. 12 monomers), which are then subject to biodegradation by microorganisms present in the composting 13 environment. 14

Prerequisite for a composting treatment is the compostability of the material (or better of the product) under 15 study, which can be assessed according to specific standards, such as EN 13432 (for packaging) and EN 14995 16 (for plastics in generals). 17

The modelling of the composting process of suitable materials or products shall be carried out by reflecting, 18 as far as possible, the specific scope of the LCA study in terms of analysed material/product, geography and 19 reference period. The actual biodegradation rate (biodegradability) of the product in scope shall be 20 considered, while the use of generic, product-unspecific data shall be avoided. The biodegradation rate 21 determined in accordance with the standardised testing methods (e.g. ISO 14855-1:2012) recommended by 22 the abovementioned European standards on compostability shall be preferably used, if available. 23 Alternatively, a 90% biodegradation rate67 shall be considered as default value, according to the minimum 24 biodegradability required by such standards. If composting is considered as an end of life option, it is 25 expected that the material complies with such requirement. 26

Process emissions, compost production (if any) and its composition shall be determined taking into account 27 the actual elemental composition of the product and the biodegradation rate considered in the study. 28 Emissions of substances which are not contained in the product shall not be assigned to the composting 29 process (e.g. nitrogen emissions shall not be considered if the nitrogen content of the product in scope is 30 null). Similarly, any credits for replacing mineral fertilisers shall not be assigned to the composting process if 31 no nutrients that can be potentially transferred to the compost (if produced) are included in the composition 32 of the product in scope. 33

67 The biodegradation rate (biodegradability) determined according to testing procedures recommended in compostability standards normally refers to the percentage of carbon converted to CO2 during the biodegradation test. However, for modelling purposes, such rate can also be considered a reasonable approximation of the overall percentage of degradation of the material (i.e. of the volatile solids content in the material).


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Table 27 specifies a list of relevant parameters for composting modelling and, where appropriate, provides 1 requirements or recommendations on how they shall be determined. 2

Table 27: Main parameters and data required to model industrial composting processes of suitable 3 plastic materials and requirements and recommendations for their determination in LCA studies of plastic 4 articlesa 5

Parameter Unit Requirement / recommendation

Type of composting technology(ies) considered

- Shall reflect the relevant technology (or mix of technologies) for the geography and time period in scope

Biodegradation rate % of C (and VS) in the waste

Shall be product-specific and preferably determined in accordance with the testing methods recommended in compostability standardsb. Alternatively, a 90% biodegradation rate shall be considered (according to the minimum biodegradability required in EN 13432/EN 14995)

Compost (residual material) production

Kg/kg waste ww Shall be consistent with the considered biodegradation rate for VS (e.g. Compost = non-degraded VS + Initial Ash & Water content)

Energy demand (waste handling, aeration, etc.)

Electricity kWh/kg waste ww Shall be based on process-specific consumption for the reference technology (or mix of technologies) Fuel (e.g. diesel) l/kg waste ww

Water consumption m3/kg waste ww Shall be based on process-specific consumption for the reference technology (or mix of technologies)

Emissions to air

CO2 (biogenic/fossil) kg/kg waste ww 99.99% of degraded carbon in the material/product

CH4 (biogenic/fossil) kg/kg waste ww 0.01% of degraded carbon in the material/product

NH3 kg/kg waste ww 98.5% of N content in the material/product

N2O kg/kg waste ww 1.4% of N content in the material/product

N kg/kg waste ww 0.1% of N content in the material/product

H2S

kg/kg waste ww Should be based on process-specific emissions for the reference technology (or mix of technologies) Terpenes

NMVOC

Leachate production (if any) m3/kg waste ww Should be based on process-specific production for the reference technology (or mix of technologies)

Emissions to water (if any)


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BOD kg/kg waste ww Should be calculated as % of non-degraded carbon, or based on process-specific emission for the reference technology (or mix of technologies)c

N kg/kg waste ww Should be calculated as % of N/P content in the material/product, or based on process-specific emission for the reference technology (or mix of technologies)c P kg/kg waste ww

Compost characteristics and fertilising value (C, N, P, K and water content)

g/kg DM Shall be based on the specific product composition, and take into account the considered biodegradation rate and the amount of substances emitted to the environment during the composting process

(a) For instance EN 13432 for plastic packaging and EN 14995 for plastic materials in general. 1 (b) Not all the listed parameters may be relevant across all composting technologies. 2 (c) Net of any removal at wastewater treatment facilities. 3

5.5.8.5 Modelling of anaerobic digestion processes 4

Anaerobic digestion is a biological treatment process where biodegradable feedstock is converted, under 5 anaerobic conditions, into biogas, water, and a residual fraction called digestate. Biogas is typically a mixture 6 of methane, carbon dioxide, NMVOC, N2, H2S and NH3, depending on the composition of the input waste. 7 Carbon dioxide and methane are, however, the main components. Due to its high greenhouse gas potential, 8 biogas needs to be properly managed in order to avoid its release to the atmosphere. Energy may be 9 recovered from the generated biogas, through combustion in cogeneration units, after removal of water 10 vapour and acid gases. Biogas can also be upgraded to bio-methane for use together with natural gas, as fuel 11 for vehicles, or for electricity generation. The digestate may undergo a subsequent (aerobic) composting 12 process, where it is converted to soil conditioner (in this case, the provisions reported in Section 5.5.8.4 are 13 also valid for composting of digestate). Alternatively, the digestate may be directly applied on field (as such 14 or after dehydration). Abatement of air emissions in encapsulated digestion plants may be achieved by 15 biofilters and scrubbers. 16

Prerequisite for a digestion treatment is at least the anaerobic biodegradability of the bio-based waste. The 17 property can be assessed by compostability standards covering also anaerobic treatability, such as EN 13432 18 (for packaging) and EN 14995 (for plastics in general). 19

Anaerobic digestion of suitable materials or products shall be modelled by reflecting, as far as possible, the 20 specific scope of the LCA study in terms of analysed material/product, geography and reference period. The 21 actual biodegradation rate (biodegradability) of the product in scope under anaerobic conditions shall be 22 considered, as well as the corresponding biogas production. The biodegradation rate and corresponding 23 biogas production determined according to the standardised testing methods (e.g. ISO 15985 and ISO 14853) 24 recommended by the abovementioned European standards shall be preferably used, if available. 25 Alternatively, a 50% biodegradation rate shall be considered as a default value, according to the minimum 26 percentage of biodegradation required by such standards, which expresses the share of anaerobically 27 gasified carbon. However, this value should be reduced to account for the fact that it refers to laboratory 28 conditions, and may be hardly achieved in real, full-scale plants. The typical conversion yield of anaerobically 29 biodegradable carbon in organic waste to biogas is 70% (as the average of the range 50-90%; Angelidaki and 30 Batstone, 2010). Therefore, in the absence of more specific data, the application of this conversion factor is 31


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recommended also for biodegradable plastics, which would imply considering an overall biodegradation rate 1 equal to 35% (i.e. 70% of the degradation rate achievable under ideal laboratory conditions). 2

Any direct process emissions (e.g. fugitive emissions), digestate production (if any) and its composition shall 3 be determined by taking into account the actual elemental composition of the product and the considered 4 biodegradation rate. For instance, no nitrogen emissions shall be assigned to the digestion process if the 5 nitrogen content of the product is null. Similarly, no fertilising value shall be assigned to the digestate if the 6 composition of the product in scope does not include nutrients that can be transferred to digestion output. 7

Table 28 specifies a list of relevant parameters for the modelling of anaerobic digestion and, where 8 appropriate, provides requirements or recommendations on how they shall be determined. 9

Table 28: Main parameters and data required to model anaerobic digestion processes of suitable plastic 10 materials and requirements and recommendations for their determination in LCA studies of plastic articles 11


Type of anaerobic digestion technology(ies) considered

- Shall reflect the relevant technology (or mix of technologies) for the geography and time period in scope

Biodegradation rate (theoretical)

% of C in the waste

Shall be product-specific and preferably determined in accordance with the testing methods recommended in compostability/biodegradability standards*. Alternatively, a 50% biodegradation rate shall be considered (according to the minimum percentage of biodegradation required in EN 13432 and EN 14995).

Biogas production (total) Nm3 CH4/kg waste

ww Shall be based on the considered biodegradation rate and should be calculated considering a conversion efficiency of carbon to biogas equal to 70% of the (theoretical) biodegradation rate

Biogas composition (CH4, CO2, N2, H2S, NH3, NMVOC)

% Should be based on average content of CH4 and CO2 in organic waste biogas (e.g. 63% CH4 and 37% CO2)

Digestate (residual material) production

kg/kg waste ww Shall be consistent with the considered biodegradation rate, and should be calculated based on a ratio VSdegraded/Cdegraded equal to 1.89 (e.g. Digestate = non-degraded VS + Initial Ash & Water content)

Energy demand (waste handling, capture equipment, etc.)

Electricity kWh/kg waste ww Shall be based on process-specific consumption for the reference technology (or mix of technologies)

Thermal energy MJ/kg waste ww

Fuel (e.g. diesel) l/kg waste ww

Water consumption m3/kg waste ww Shall be based on process-specific consumption for the reference technology (or mix of technologies)


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Energy recovery

Electricity generation kWh/kg waste ww Shall be based on the actual energy content of the generated biogas and on the energy efficiency of utilisation units for the relevant geography Heat generation MJ/kg waste ww

Direct emissions to air (e.g. fugitive emissions)

CH4, CO2, N2O, NH3, etc. g/kg waste ww Shall be consistent with the actual composition of the product (no emissions related to substances excluded from the product composition shall be included)

Leachate production (if any) m3/kg waste ww Should be based on process-specific production for the reference technology (or mix of technologies)

Emissions to water (if any)

BOD g/kg waste ww Should be calculated as % of non-degraded carbon, or based on process-specific emission for the reference technology (or mix of technologies)c

N g/kg waste ww Should be calculated as % of N/P content in the material/product, or based on process-specific emission for the reference technology (or mix of technologies)c P

Digestate characteristics and fertilising value (C, N, P, K and water content)

g/kg DM Shall be based on the specific product composition and take into account the considered degradation rate and the amount of substances emitted to the environment during the digestion process

(*) For instance EN 13432 for plastic packaging and EN 14995 for plastic materials in general. 1

5.5.8.6 Modelling of incineration processes 2

During incineration the waste material or product undergoes a combustion (oxidation) process where it is 3 fully or partially converted into a number of combustion products including CO2, water vapor, SOx (if the 4 material contains sulphur), NOx (either from nitrogen in the material or in the combustion air, or both), etc. 5 If the waste material also includes metals, those are also released in the combustion process. Inert fractions 6 of the waste material end up in a residual fraction consisting of ash and, possibly, slag. Flue gases with 7 combustion products normally undergo a number of cleaning steps, where air emissions are abated with 8 efficiencies depending on the type of device used. Energy contained in the waste material (i.e. its lower 9 heating value) is generally recovered as electricity, heat, or both. 10

The modelling of the incineration process of waste materials or products shall be carried out by reflecting, as 11 far as possible, the specific scope of the LCA study in terms of analysed product, geography and reference 12 period. 13

The actual chemical composition and energy content (Lower Heating Value; LHV) of the product in scope 14 shall be taken into account to determine process emissions and energy recovery. Emissions of substances 15


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that are not contained in the product (and that cannot be originated from other sources -e.g NOx-) shall not 1 be attributed to the incineration process. However, the ultimate emissions of substances that are subject to 2 abatement (e.g. NOx, HCl, SO2, particulate matter, metals, dioxins and other organic compounds, etc.) 3 depend on the efficiency of flue gas cleaning devices, other than on product composition, and shall thus be 4 modelled as process-specific, taking into account the considered reference technology. 5

Table 29Table 29 specifies a list of relevant parameters for incineration modelling and, where appropriate, 6 provides requirements or recommendations on how they shall be determined. 7

Table 29: Main parameters and data required to model incineration processes and requirements and 8 recommendations for their determination in LCA studies of plastic articles 9


Energy recovery

Electricity* kWh/kg waste ww Shall be based on the specific energy content (LHV) of the product and on process-specific energy efficiencies (%LHVww) for the reference technology Heat* MJ/kg waste ww

Air emissions

CO2 (fossil, biogenic) kg/kg waste ww Shall be calculated based on the C content of the product

CO kg/kg waste ww Shall be based on process-specific emissions for the reference technology

Dioxins

NOx (thermal)

Particulate matter

HCl kg/kg waste ww

Shall be based on process-specific emissions for the reference technology, consistently with product composition (i.e. no emission shall be accounted if the substance is not included in the product composition)

HF

SO2

Metals (e.g. As, Cd, Cr, Hg, Pb, Zn)

kg/kg waste ww Shall be based on product composition and respective transfer coefficients to air

Production of residues

Bottom Ash Kg/kg waste ww Shall be based on the ash content of the product or on process-specific production for the reference technology

Fly ash Shall be based on process-specific production for the reference technology

Slag

(*) Net amount exported from the incineration plant. 10


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5.5.8.7 Modelling of in-situ biodegradation 1

In-situ biodegradation represents a possible end-of-life option for biodegradable agricultural products such 2 as biodegradable mulching film and pots, which after use are left to biodegrade on or into the field and 3 possibly incorporated into land during the next ploughing. Also portions of non-biodegradable products 4 (especially of mulch film) may be left on the field due to difficult collection conditions (e.g. after tear of 5 thinner film). In this case, in-situ degradation is not an appropriate end-of-life option, but rather represents 6 a littering phenomenon, the modelling of which is addressed in a separate section (section 5.5.8.8). 7

In principle, a complete (100%) biodegradation of the product could be assumed within the 100 year 8 timeframe covered in the assessment. This value shall be considered if no better information is available. For 9 mulch film, a standard specifying the biodegradability requirements and testing method is available (EN 10 17033). The biodegradation rate (percentage) determined in accordance with the specified testing method 11 (ISO 17556) shall be considered, if quantified. Alternatively, a 90% biodegradation rate shall be considered, 12 according to the minimum percentage of conversion of organic carbon to CO2 required by EN 17033. 13 Degraded carbon can be reasonably assumed to be entirely converted into CO2, although CH4 production 14 may also take place after the product is ploughed back into the soil. Any amount of carbon not degraded 15 within 100 years from application shall be considered as stored into soil (in accordance with the overall 16 approach for the modelling of carbon emissions and removals described in section 5.5.10). 17

Non-biodegradable elements present in the material composition, including metals, shall be assumed to be 18 entirely emitted to the soil. 19

Table 30 summarises the main parameters relevant for the modelling of in-situ degradation and provides, 20 where appropriate, requirements and recommendations on how they shall be determined. 21

Table 30: Main parameters required for modelling in-situ degradation and requirements and 22 recommendations for their determination in LCA studies 23


Biodegradation rate % of C (and VS) in the waste

For mulch film: the percentage of biodegradation determined according to testing method recommended in EN 17033 shall be used (if available). Otherwise, a 90% biodegradation rate shall be considered (according to the minimum requirement in EN 17033)

For products with no biodegradability standards of reference: a 100% biodegradation rate can be assumed in the absence of more specific information.

Air emissions

CO2 (biogenic/fossil) kg/kg waste ww 100% of degraded carbon

Soil emissions

Metals (e.g. Cd, Cr, Cu, Hg, Ni, Pb, Zn)

kg/kg waste ww 100% of the amount of substance contained in the product


140

5.5.8.8 Framework for future accounting of marine litter generation (not covered in the screening LCAs) 1

Marine litter is arising as an environmental pressure that can generate impacts to human health and 2 biodiversity (Deudero & Alomar, 2015; Kim et al., 2018). Macroplastics and microplastics can be released 3 along the life cycle of products due to direct (e.g., beverage bottles on the beach) or indirect actions (e.g., 4 littering from mismanaged landfill), leading to an emission to the marine environment. Furthermore, 5 macroplastics can eventually become microplastics due to partial degradation and break-down into smaller 6 pieces, thereby generating secondary microplastics. These macro- and microplastics can have a negative 7 environmental impact to the marine biodiversity and environment as well as to humans through the food 8 chain (e.g., microplastics in sea food). 9

10

Figure 12: Generation of macroplastics and microplastics along the life cycle of products 11

This framework aims to account for the generation of macro- and microplastics at the life cycle inventory 12 level in order to set the basis for an indicator addressing marine litter. Therefore, the following two indicators 13 are evaluated: 14

Cumulative macroplastics generation (CMaG) (kg) 15

Cumulative microplastics generation (CMiG) (kg) 16

17

Macroplastics generation 18

The generation of macroplastics can be estimated by employing littering rates to the marine environment 19 (Edwards & Parker, 2012). Littering rates are not available for the context of Europe and an estimation 20 pathway is proposed in this framework based on the ratio between beach counting and product 21 consumption. Marine littering rates (ML, %) can be calculated by product, specific temporal framework and 22 geographical boundaries using Equation 15. 23


141

𝑴𝑳 (%) =𝑳𝒊𝒕𝒕𝒆𝒓𝒆𝒅 𝒑𝒓𝒐𝒅𝒖𝒄𝒕 (𝒌𝒈)

𝑪𝒐𝒏𝒔𝒖𝒎𝒆𝒅 𝒑𝒓𝒐𝒅𝒖𝒄𝒕 (𝒌𝒈)1

=𝑨𝒗𝒆𝒓𝒂𝒈𝒆 𝒃𝒆𝒂𝒄𝒉 𝒄𝒐𝒖𝒏𝒕

𝒖𝒌𝒎

𝒙 𝑷𝒓𝒐𝒅𝒖𝒄𝒕 𝒕𝒚𝒑𝒆 𝒔𝒉𝒂𝒓𝒆 (%) 𝒙 𝑨𝒗𝒆𝒓𝒂𝒈𝒆 𝒎𝒂𝒔𝒔 𝒌𝒈𝒖

𝒙 𝑪𝒐𝒂𝒔𝒕 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆 (𝒌𝒎)

𝑪𝒐𝒏𝒔𝒖𝒎𝒆𝒅 𝒑𝒓𝒐𝒅𝒖𝒄𝒕 (𝒌𝒈)2

+ 𝑨𝒗𝒆𝒓𝒂𝒈𝒆 𝒎𝒂𝒓𝒊𝒏𝒆 𝒅𝒆𝒃𝒓𝒊𝒔

𝒖𝒌𝒎𝟐 𝒙 𝑷𝒓𝒐𝒅𝒖𝒄𝒕 𝒕𝒚𝒑𝒆 𝒔𝒉𝒂𝒓𝒆 (%) 𝒙 𝑨𝒗𝒆𝒓𝒂𝒈𝒆 𝒎𝒂𝒔𝒔

𝒌𝒈𝒖

𝒙 𝑴𝒂𝒓𝒊𝒏𝒆 𝒂𝒓𝒆𝒂 (𝒌𝒎𝟐)

𝑪𝒐𝒏𝒔𝒖𝒎𝒆𝒅 𝒑𝒓𝒐𝒅𝒖𝒄𝒕 (𝒌𝒈) 3

[Equation 15] 4

Where Average beach count is the average of data regarding items per coastline distance from beach count 5 reports, Average marine debris is the average of data regarding items per marine surface from marine debris 6 reports, Product type share is the share of the product type under assessment among the beach count items 7 in the reported data, Coast distance refers to the distance of the coast of the geographical area under 8 assessment, Marine area refers to the area of the marine environment of the geographical area under 9 assessment and Consumed product is the amount of consumed product in the geographical and temporal 10 boundaries considering trade (i.e., production plus imports minus exports). Formula 15 shows the 11 parameters and units employed for calculating marine litter shares regarding mass. However, when the 12 reference unit of the consumed product is expressed in items rather than mass, the parameter Average mass 13 should be suppressed. 14

As example of this calculation, we here present the data for the estimation of the marine litter based on the 15 beach count of plastic bottles for EU-28: 16

𝑴𝑳𝒑𝒍𝒂𝒔𝒕𝒊𝒄 𝒃𝒐𝒕𝒕𝒍𝒆𝒔 (%) =𝟒, 𝟔𝟒𝟓

𝒊𝒕𝒆𝒎𝒔𝒌𝒎

𝒙 𝟏𝟕 (%) 𝒙 𝟏𝟒𝟖, 𝟗𝟐𝟒. 𝟔 (𝒌𝒎)

𝟏𝟎𝟒, 𝟐𝟔𝟗, 𝟔𝟓𝟐, 𝟓𝟑𝟕 (𝐢𝐭𝐞𝐦𝐬)= 𝟎. 𝟏𝟏% 17

Average beach count and Product type share data were obtained from Hanke (2016), Coast distance was 18 calculated for all the EU-28 countries and Consumed product type value for 2017 was extracted from the 19 PRODCOM database of Eurostat (2018). 20

Microplastics generation 21

The generation of microplastics can be related to both primary and secondary microplastics. On the one 22 hand, Boucher & Friot (2017) estimated the global generation of microplastics from seven different sources, 23 which have different relevance in the life cycle stages of products ( 24


142

Table 31). 1

2


143

Table 31: Sources of microplastics and relation to the life cycle stages of products (Based on Boucher & 1 Friot, 2017) 2

Materials extraction Manufacturing Transportation Use End of life

Plastic pellets X X X

Tyres X

Marine coatings X

Road markings X

Synthetic textiles X

Personal care

products

X

City dust X

3

For the assessment of plastic products, it should be further explored how to account for the generation of 4 primary microplastics from plastic pellets (MiGp), tyres (MiGt), marine coatings (MiGmc), road markings 5 (MiGrm) and synthetic textiles (MiGst), as well as of secondary microplastics. 6

𝑪𝑴𝒊𝑮𝒕 (𝒌𝒈) = 𝑷𝒓𝒊𝒎𝒂𝒓𝒚 𝒎𝒊𝒄𝒓𝒐𝒑𝒍𝒂𝒔𝒕𝒊𝒄𝒔 𝒈𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒐𝒏 + 𝑺𝒆𝒄𝒐𝒏𝒅𝒂𝒓𝒚 𝒎𝒊𝒄𝒓𝒐𝒑𝒍𝒂𝒔𝒕𝒊𝒄𝒔 𝒈𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒐𝒏7 = 𝑴𝒊𝑮𝒑 (𝒌𝒈) + 𝑴𝒊𝑮𝒕 (𝒌𝒈) + 𝑴𝒊𝑮𝒎𝒄 (𝒌𝒈) + 𝑴𝒊𝑮𝒓𝒎 (𝒌𝒈) + 𝑴𝒊𝑮𝒔𝒕 (𝒌𝒈) + 𝑺𝑴𝒊𝑮 (𝒌𝒈) 8

[Equation 16] 9

a) Microplastics generation due to plastic pellets (MiGp) 10

Plastic pellets can be emitted during the life cycle of plastics, particularly during the manufacturing, transport 11 and end of life stage (Boucher & Friot, 2017). The microplastics production due to plastic pellets can be 12 calculated using Equation 17. 13

𝑴𝒊𝑮𝒑 (𝒌𝒈) = 𝑻𝒓,𝒘𝒊 + 𝟎, 𝟗 • 𝑻𝒓,𝒘 • 𝜼𝒕 + 𝑻𝒐 • 𝑳𝒓(%) • 𝒎𝒊,𝒋(𝒌𝒈)

𝒏

𝒊 𝟎

𝒏

𝒋 𝟎

14

[Equation 17] 15

where Tr,wi is the transfer rate from the road to the wind, Tr,w is the transfer rate from the road to the water, 16 To is the transfer rate to the ocean, 𝜂 is the water treatment efficiency, Lr is the loss rate of plastic pellets and 17 mi,j is the mass of the plastic element (i) in each of the life cycle stages considered (j). 18

While the mass depends on the LCI of the system under assessment, the other parameters can use the 19 following default values (Table 32). 20


144

Table 32: Default values for the parameters in Equation 17 1

Variable Value Source

Tr,wi 0.1 Boucher & Friot (2017)

Tr,w 0.035, for rural areas

0.5, for urban areas

Boucher & Friot (2017)

To 1, for maritime transport Boucher & Friot (2017)

𝜂 0.85 Boucher & Friot (2017)

Lr 0.00003%, optimistic scenario

0.0001%, central scenario

0.001%, pessimistic scenario

Cole G & Sherrington C (2016) in Boucher & Friot (2017)

2

b) Microplastics generation due to tyre abrasion (MiGt) 3

There is a release of plastic from the abrasion of tyres due to road transportation that is released to ocean. 4 Such microplastic emissions can occur in the transportation stages of the life cycle of a product (e.g., materials 5 transportation, product distribution, end of life transportation). The microplastics production due to tyre 6 abrasion can be calculated as the sum of the microplastics generation due to tyre abrasion in each 7 transportation taking place in the life cyce of the product (i) using Equation 18. 8

𝑀𝑖𝐺 (𝑘𝑔) = (0.1 • 𝑇 , + 0,9 • 𝑇 , • (1 − 𝜂 )) • 𝑅(%) • 𝐿𝑘𝑔

𝑘𝑚• 𝑑 (𝑘𝑚) • 𝑚 (𝑘𝑔) 9

[Equation 18] 10

where Tr,wi is the transfer rate from the road to the wind, Tr,w is the transfer rate from the road to the water, 11 𝜂 is the water treatment efficiency, R is the share of rubber in the tyre, Lr is the loss rate of the tyre, di is the 12 transport distance, mi is the mass of the transported element and i is any transportation taking place during 13 the life cycle. 14

While the distance and mass depends on the LCI of the system under assessment, the other parameters can 15 use the following default values ( 16


145

Table 33). 1

2


146








R 25, passenger car

14.5, commercial truck

(averages for EU and US values)

Kazantzidis (2011) and ETRMA (2012) in Samolada & Zabaniotou (2012)

Lr 0.033, passenger car

0.051, light commercial

0.178, commercial

Lassen et al. (2015)

2

c) Microplastics generation due to marine coatings (MiGmc) 3

There is a release of plastic from the erosion of marine coatings during maritime transportation. The 4 microplastics production due to marine coatings can be calculated using Equation 19. 5

𝑴𝒊𝑮𝒕 (𝒌𝒈) = 𝑻𝒐 • 𝑳𝒓(%) • 𝒎𝒎𝒄(𝒌𝒈) [Equation 19] 6

where To is the transfer rate to the ocean, Lr is the loss rate of the marine coating and mmc is the mass of 7 marine coating employed in the life cycle of the product. 8

While the mass of the marine coating depends on the LCI of the system under assessment, the other 9 parameters can use the following default values (Table 34). 10



To 1 Boucher & Friot (2017)

Lr 6% OECD (2009)

12

d) Microplastics generation due to road marking (MiGrm) 13

There is a release of plastic from the erosion of road markings associated to road transportation. The 14 microplastics production due to road markings can be calculated using Equation 20. 15


147

𝑴𝒊𝑮𝒕 (𝒌𝒈) = ∑ ∑ (𝟎, 𝟏 • 𝑻𝒓,𝒘𝒊 + 𝟎, 𝟗 • 𝑻𝒓,𝒘 • (𝟏 − 𝜼𝒕) + 𝑻𝒐) • 𝑳𝒓(%) • 𝒎𝒓𝒎(𝒌𝒈)𝒏𝒊 𝟎

𝒏𝒋 𝟎 [Equation 20] 1

where Tr,wi is the transfer rate from the road to the wind, Tr,w is the transfer rate from the road to the water, 2 To is the transfer rate to the ocean, 𝜂 is the water treatment efficiency, Lr is the loss rate of the road marking 3 and mrm is the mass of road marking employed in the life cycle of the product. 4

While the mass depends on the LCI of the system under assessment, the other parameters can use the 5 following default values (Table 35). 6







To 1, for maritime transport Boucher & Friot (2017)


Lr 23%, optimistic scenario

43%, central scenario

100%, pessimistic scenario

Lassen et al. (2015)

8

e) Microplastics generation due to synthetic textiles (MiGst) 9

When synthetic textiles are included in the life cycle of the product under assessment, the use and 10 maintenance phase of these should be included. There is a release of plastic during the laundry of synthetic 11 textiles that is emitted to the wastewater treatment pathway. The microplastics production due to synthetic 12 textiles can be calculated using Equation 21: 13

𝑴𝒊𝑮𝒔𝒕 (𝒌𝒈) = (𝟏 − 𝜼𝒕) • 𝑳𝒓(%) • 𝒎(𝒌𝒈) [Equation 21] 14

where 𝜂 is the water treatment efficiency, Lr is the loss rate of the synthetic textile and m is the mass of the 15 product. 16

While the mass depends on the LCI of the system under assessment, the other parameters can use the 17 following default values ( 18


148

Table 36). 1

2


149




Lr 0.74%, optimistic scenario

2%, central scenario

5%, pessimistic scenario

Essel et al. (2015) in Boucer & Friot (2017)

2

Secondary microplastics generation 3

The generation of secondary microplastics (SMiG) are calculated based on the estimated generation of 4 macroplastics of the life cycle of de product by product type (i), according to a product type-specific 5 degradation rate (Dr). The product types (13) were defined by mapping with the product groups employed in 6 different reference documents (including EU policy, JRC technical reports and scientific publications) (Annex 7 I). 8

𝑺𝑴𝒊𝑮 (𝒌𝒈) = ∑ ∑ 𝑫𝒓,𝒊𝒏𝒊 𝟎 • 𝑴𝒂𝑮𝒊

𝒏𝒋 𝟎 [Equation 22] 9

where Dr,i is degradation rate for the product type i and MaGi is the amount of macroplastics generated of 10 the product type i. 11

In the case of the degradation rate, three scenarios were defined considering different timeframes ( 12


150

Table 37). 1

2


151


Scenario Timeframe Modeling Dr

Low Product life cycle

timeframe (10y)

No secondary microplastics generation 0

Central Intermediate

timeframe (100y):

Secondary microplastic generation rates are defined by product type*:

Food container including fast food packaging

Fishing gear

Industrial packaging

Cigarette buds

Cotton buds stick

Sanitary application device

Other plastics

0

Drink bottle, caps and lids

Plastic bags

Crisp packets / sweet wrappers

Sanitary towels/ wet wipes

Cutlery, straws and drink stirrers

Drink cups and cup lids

Baloons and baloon sticks

100

High Infinite timeframe All macroplastics become microplastics 100

*Note: For the central scenario, dichotomic degradability rates were proposed for the product types regarding the 2 probability to suffer physico-chemical degradation to become plastics due to lack of specific data in the literature. 3

Impact assessment of macroplastics and microplastics generation 4

Further steps in the framework going beyond the inventory phase could deal with the impact assessment 5 phase of LCA regarding the impacts of marine litter to human health and biodiversity. In the literature, 6 macroplastics are related to direct impact on marine biodiversity (e.g., Deudero & Alomar, 2015), while 7 microplastics are associated to potential impacts to human health through accumulation in the trophic chain 8 (e.g., Kim et al., 2018). 9

However, due to the lack of basic underlying data required as input to a Life Cycle Impact Assessment of 10 marine litter, e.g. physico-chemical, fate and effect data for the calculation of characterization factors for 11 toxicity, the inclusion of marine litter in an impact assessment method for LCA is not feasible. 12


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5.5.8.9 Handling multi-functionality in reuse, recycling and energy recovery 1

The current PEF Guide (Recommendation 2013/179/EU) requires the use of a formula, commonly known as 2 End-of-Life (EoL) formula, available in the Annex V of the PEF Guide (EC, 2013a), to deal with multi-3 functionality in recycling, re-use and energy recovery situations. 4

The initial feedbacks received during the EF pilot phase (2013-2018) and the further experience gathered 5 during five years of pilot phase, led the Commission to re-consider the EoL formula available in the Annex V 6 and, together with interested stakeholders, to come up with an alternative proposal. 7

The new formula has been renamed to “Circular Footprint Formula” (CFF) and shall be used in this -context 8 instead of the original "End-of-Life" formula. The formula applies both to products including recycled material 9 and to products that are recycled at their end of life (or a combination of both). 10

The following subsections (5.5.8.10 to 3.5.19) describe the formula and parameters to be used, while the last 11 subsections describe how the formula and parameters shall be applied to intermediate products (section 12 5.5.8.20) and to intermediate construction products (see section 5.5.8.21). 13

5.5.8.10 The Circular Footprint Formula 14

The CFF is a combination of "material recovery + energy recovery+ disposal", i.e.: 15

Material recovery (𝟏 − 𝑹𝟏)𝑬𝑽 + 𝑹𝟏 × 𝑨𝑬𝒓𝒆𝒄𝒚𝒄𝒍𝒆𝒅 + (𝟏 − 𝑨)𝑬𝑽 ×𝑸𝑺𝒊𝒏

𝑸𝒑+ (𝟏 − 𝑨)𝑹𝟐 × 𝑬𝒓𝒆𝒄𝒚𝒄𝒍𝒊𝒏𝒈𝑬𝒐𝑳 − 𝑬𝑽

∗ ×𝑸𝑺𝒐𝒖𝒕

𝑸𝑷 16

Energy recovery (𝟏 − 𝑩)𝑹𝟑 × (𝑬𝑬𝑹 − 𝑳𝑯𝑽 × 𝑿𝑬𝑹,𝒉𝒆𝒂𝒕 × 𝑬𝑺𝑬,𝒉𝒆𝒂𝒕 − 𝑳𝑯𝑽 × 𝑿𝑬𝑹,𝒆𝒍𝒆𝒄 × 𝑬𝑺𝑬,𝒆𝒍𝒆𝒄) 17

Disposal (𝟏 − 𝑹𝟐 − 𝑹𝟑) × 𝑬𝑫 18

[Equation 23] 19

20

The modular form of the Circular Footprint Formula 21

The CFF can be arranged in a modular way, to fit for example the structure of the EN 15804 standard. 22

Equation 24 is the CFF re-arranged in different modules. The acronym for this formula is CFF-M. 23

24


153

Production burdens (𝟏 − 𝑹𝟏)𝑬𝑽 + 𝑹𝟏 × 𝑬𝒓𝒆𝒄𝒚𝒄𝒍𝒆𝒅 Cradle-to-gate

Burdens and benefits related to secondary materials input

−(𝟏 − 𝑨)𝑹𝟏 × 𝑬𝒓𝒆𝒄𝒚𝒄𝒍𝒆𝒅 − 𝑬𝑽 ×𝑸𝑺𝒊𝒏

𝑸𝑷

Addi

tiona

l inf

orm

atio

n fr

om th

e Eo

L st

age

Burdens and benefits related to secondary materials output

(𝟏 − 𝑨)𝑹𝟐 × 𝑬𝒓𝒆𝒄𝒚𝒄𝒍𝒊𝒏𝒈𝑬𝒐𝑳 − 𝑬𝑽∗ ×

𝑸𝑺𝒐𝒖𝒕

𝑸𝑷

Energy recovery (𝟏 − 𝑩)𝑹𝟑 × 𝑬𝑬𝑹 − 𝑳𝑯𝑽 × 𝑿𝑬𝑹,𝒉𝒆𝒂𝒕 × 𝑬𝑺𝑬,𝒉𝒆𝒂𝒕

− 𝑳𝑯𝑽 × 𝑿𝑬𝑹,𝒆𝒍𝒆𝒄 × 𝑬𝑺𝑬,𝒆𝒍𝒆𝒄

Disposal (𝟏 − 𝑹𝟐 − 𝑹𝟑) × 𝑬𝑫

[Equation 24] 1

5.5.8.11 The parameters of the Circular Footprint Formula (CFF and CFF-M) 2

A: Allocation factor of burdens and credits between supplier and user of recycled materials. 3

B: Allocation factor of energy recovery processes: it applies both to burdens and credits. 4

Qsin: Quality of the ingoing secondary material, i.e. the quality of the recycled material at the point of 5 substitution. 6

Qsout: Quality of the outgoing secondary material, i.e. the quality of the recyclable material at the point of 7 substitution. 8

Qp: Quality of the primary material, i.e. quality of the virgin material. 9

R1: The proportion of material in the input to the production that has been recycled from a previous system. 10

R2: The proportion of the material in the product that will be recycled (or reused) in a subsequent system. R2 11 shall therefore take into account the inefficiencies in the collection and recycling (or reuse) processes. R2 12 shall be measured at the output of the recycling plant. 13

R3: The proportion of the material in the product that is used for energy recovery at EoL. 14

Erecycled (Erec): Specific emissions and resources consumed (per functional unit) arising from the recycling 15 process of the recycled (reused) material, including collection, sorting and transportation process. 16

ErecyclingEoL (ErecEoL): Specific emissions and resources consumed (per functional unit) arising from the recycling 17 process at EoL, including collection, sorting and transportation process. 18

Ev: Specific emissions and resources consumed (per functional unit) arising from the acquisition and pre-19 processing of virgin material. 20


154

E*v: Specific emissions and resources consumed (per functional unit) arising from the acquisition and pre-1 processing of virgin material assumed to be substituted by recyclable materials. 2

EER: Specific emissions and resources consumed (per functional unit) arising from the energy recovery process 3 (e.g. incineration with energy recovery, landfill with energy recovery, …). 4

ESE,heat and ESE,elec: Specific emissions and resources consumed (per functional unit) that would have arisen 5 from the specific substituted energy source, heat and electricity respectively. 6

ED: Specific emissions and resources consumed (per functional unit) arising from disposal of waste material 7 at the EoL of the analysed product, without energy recovery. 8

XER,heat and XER,elec: The efficiency of the energy recovery process for both heat and electricity. 9

LHV: Lower Heating Value of the material in the product that is used for energy recovery. 10

5.5.8.12 The A factor 11

The A factor allocates burdens and credits from recycling and primary material production between two life 12 cycles (i.e. the one supplying and the one using recycled material) and it aims to reflect market realities. 13

The A factor values shall be in the range 0.2 ≤ A ≤ 0.8, to always capture both aspects of recycling (recycled 14 content and recyclability at end-of-life). 15

The driver to determine the values of the A factor is the analysis of the market situation. This means: 16

● A=0.2. Low offer of recyclable materials and high demand: the formula focus on recyclability at End-17 of-Life. 18

● A=0.8. High offer of recyclable materials and low demand: the formula focus on recycled content. 19 ● A=0.5. Equilibrium between offer and demand: the formula focuses both on recyclability at EoL and 20

recycled content. 21 The list of A values is available in Annex D. Proposals to include new or updated values of A will be evaluated 22 by the EC. The list of A values in the Annex D will be periodically reviewed and updated by the European 23 Commission; practitioners are thus invited to check and use the most updated values. 24

The following procedure shall be applied to select the value of A to be used in the study: 25

● Check in the list in Annex D the availability of an application specific A value which fits the analysed 26 product, 27

● If an application specific A value is not available, the material specific A value in the list in Annex D 28 shall be used, 29

● If a material specific A value is not available, the A value shall be set equal to 0.5. 30 31

5.5.8.13 The B factor 32

The B factor is used as an allocation factor of energy recovery processes. It applies both to burdens and 33 credits. Credits refer to the amount of heat and electricity sold, not to the total produced, taking into account 34 relevant variations over a 12-months period, e.g. for heat. 35

The B value shall be equal to 0 as default. 36


155

To avoid double-counting between the current and the subsequent system (i.e. between the system 1 producing energy from waste incineration and the one using such energy) in case of energy recovery, the 2 subsequent system shall model its own energy use (electricity mix) as primary energy. 3

Proposals to include new or updated values of B in Annex D will be evaluated by the Commission. The list of 4 B values will be periodically reviewed and updated by the European Commission; the practitioner is thus 5 invited to check and use the most updated values. 6

5.5.8.14 The point of substitution 7

It is necessary to determine the point of substitution to apply the “material” part of the formula. The point 8 of substitution corresponds to the point in the supply chain where secondary materials substitute primary 9 materials. 10

The point of substitution shall be identified in correspondence to the process where input flows are coming 11 from 100% primary sources and 100% secondary sources (level 1 in Figure 13). This corresponds to, e.g. metal 12 scrap, glass cullet and pulp. However, in some cases, the point of substitution may be identified after some 13 mixing of primary and secondary material flows has occurred (level 2 in Figure 13), which corresponds to e.g. 14 metal ingots, glass and paper. The point of substitution at level 2 may be applied only if the datasets used to 15 model e.g. Erec and Ev take into account the real (average) flows regarding primary and secondary materials: 16 for example, if Erec corresponds to the “production of 1 t of secondary material” (see Figure 13) and it has an 17 average input of 10% from primary raw materials, the amount of primary materials, together with their 18 environmental burdens, shall be included in the Erec dataset. 19 20

21

Figure 13: Point of substitution at level 1 and at level 2 22

Figure 13 is a schematic representation of a generic situation (flows are 100% primary and 100% secondary). 23 In practice in some situations, more than one point of substitution can be identified at different steps in the 24 value chain, as represented in Figure 14, where e.g. scrap of two different qualities is processed at different 25 steps. 26


156

1

Figure 14: Example of point of substitutions at different steps in the value chain 2

3

5.5.8.15 The quality ratios: Qsin/Qp and Qsout/Qp 4

Two quality ratios are used in the CFF, to take into account the quality of both the ingoing and the outgoing 5 recycled materials. 6

Two further cases can be distinguished: 7

a) If Ev=E*v the two quality ratios are needed: Qsin/Qp associated to the recycled content, and Qsout/Qp 8 associated to recyclability at EoL; the quality factors are there to capture down cycling of a material compared 9 to the original primary material and, in some cases, may capture the effect of multiple recycling loops. 10

b) If Ev≠E*v one quality ratio is needed: Qsin/Qp associated to the recycled content. In this case E*v refers to 11 the functional unit of the material substituted in a specific application. For example, plastic recycled to 12 produce a bench modelled via substitution of cement, shall take into account also how much, how long, how 13 well. Therefore, the E*v parameter indirectly integrates the Qsout/Qp parameter, and therefore the Qsout and 14 Qp parameters are not part of the CFF. 15

The quality ratios shall be determined at the point of substitution and per application or material. The quality 16 ratios are material specific and for packaging materials the values in section 3.5.8.23 shall be applied. 17

The quantification of the quality ratios shall be based on: 18

● Economical aspects: i.e. price ratio of secondary compared to primary materials at the point of 19 substitution. In case the price of secondary materials is higher than the primary ones, the quality 20 ratios shall be set equal to 1. 21

● When economic aspects are less relevant than physical aspects, the latter may be used. 22


157

5.5.8.16 Recycled content (R1) 1

The R1 values applied shall be supply-chain or application specific. The R1 value shall be set to 0% when no 2 application-specific data is available. Material-specific values based on supply market statistics are not 3 accepted as a proxy. 4

The applied R1 values shall be subject to verification, if applicable. 5

Guidelines when using supply-chain specific R1 values 6

When using supply-chain specific R1 values other than 0, traceability throughout the supply chain is necessary. 7 The following general guidelines shall be followed when using supply-chain specific R1 values: 8

● The supplier information (through e.g. statement of conformity or delivery note) shall be maintained 9 during all stages of production and delivery at the converter; 10

● Once the material is delivered to the converter for production of the end products, the converter 11 shall handle information through their regular administrative procedures; 12

● The converter for production of the end products claiming recycled content shall demonstrate 13 through his management system the [%] of recycled input material into the respective end 14 product(s). 15

● The latter demonstration shall be transferred upon request to the user of the end product. 16 ● Industry- or company-owned traceability systems may be applied as long as they cover the general 17

guidelines outlined above. If not, they shall be supplemented with the general guidelines above. 18 19

Guidelines when using default R1 values 20

Default R1 values are available in Annex D and are application specific. Default R1 values shall be used if there 21 is an application specific value available in Annex D. If no application-specific value is available in Annex D, 22 the R1 value shall be set equal to 0. 23

Guidelines on how to deal with pre-consumer scrap 24

When dealing with pre-consumer scrap, two options may be applied: 25

● Option 1: The impacts to produce the input material that lead to the pre-consumer scrap in question 26 have to be allocated to the product system that generated this scrap. Scrap is claimed as pre-27 consumer recycled content. Process boundaries and modelling requirements applying the Circular 28 Footprint Formula are shown in Figure 15. 29

30


158

1

Figure 15: Modelling option when pre-consumer scrap is claimed as pre-consumer recycled content 2

3

● Option 2: Any material that circulates within a process chain or pool of process chains is excluded 4 from being defined as recycled content and it is not included in R1. Scrap is not claimed as pre-5 consumer recycled content. Process boundaries and modelling requirements applying the Circular 6 Footprint Formula are shown in Figure 16. 7

8


159

1

Figure 16: Modelling option when pre-consumer scrap is not claimed as pre-consumer recycled content 2

3

5.5.8.17 Recycling output rate (R2) 4

Default R2 values are available in Annex D and shall be used by the applicant in case no company-specific 5 values are available. If an R2 value is not available for a specific application in Annex D, material-specific values 6 shall be used (e.g. materials average). In case no R2 values are available in Annex D, R2 shall be set equal to 0 7 or new statistics may be generated in order to assign an R2 value. Proposals to include new or updated values 8 of R2 in Annex D will be evaluated by the Commission. The list of R2 values in the Annex D will be periodically 9 reviewed and updated by the Commission; the practitioner is thus invited to check and use the most updated 10 values. 11

The following procedure shall be followed by the applicant to select the right R2 value: 12

● Company-specific values shall be used when available. 13 ● If no company-specific values are available and the criteria for evaluation of recyclability are fulfilled 14

(see below), application-specific R2 values shall be used as listed in Annex D, 15 ○ If an R2 value is not available for a specific country, then the European average shall be 16

used. 17 ○ If an R2 value is not available for a specific application, the R2 values of the material shall 18

be used (e.g. materials average). 19 ○ In case no R2 values are available, R2 shall be set equal to 0 or new statistics may be 20

generated in order to assign an R2 value in the specific situation. 21 ● The applied R2 values shall be subject to the LCA study verification. 22

A visual representation of the output recycling rate is given in Figure 17. Often, values are available for point 23 8 in Figure 17, therefore such values shall be corrected to the actual output recycling rate (point 10). In Figure 24 17 the output recycling rate (R2) is in correspondence of point 10. 25


160

1

Figure 17: Simplified collection recycling scheme of a material 2

3

The product design and composition will determine if the material in the specific product is actually suitable 4 for recycling and thus falls within the values available in Annex D. Therefore, before selecting the appropriate 5 R2 value, an evaluation for recyclability of the material shall be done and the LCA study shall include a 6 statement on the recyclability of the materials/products. 7

The statement on the recyclability shall be provided together with an evaluation for recyclability that includes 8 evidence for the following three criteria (as described by ISO 14021:1999, section 7.7.4 'Evaluation 9 methodology'): 10

1. The collection, sorting and delivery systems to transfer the materials from the source to the recycling 11 facility are conveniently available to a reasonable proportion of the purchasers, potential purchasers 12 and users of the product; 13

2. The recycling facilities are available to accommodate the collected materials; 14 3. Evidence is available that the product for which recyclability is claimed is being collected and 15

recycled. 16

Points 1 and 3 can be proven by recycling statistics (country specific) derived from industry associations or 17 national bodies. Approximation to evidence at point 3 can be provided by applying for example the design 18


161

for recyclability evaluation outlined in EN 13430 Material recycling (Annexes A and B) or other sector-specific 1 recyclability guidelines if available68. 2

Following the evaluation for recyclability, the appropriate R2 values (supply-chain specific or provided in 3 Annex D) shall be used. 4

If one criterion is not fulfilled or the sector-specific recyclability guidelines indicate a limited recyclability: an 5 R2 value of 0% shall be applied. 6

5.5.8.18 Erecycled and ErecyclingEoL 7

The system boundary of Erec and ErecEoL shall consider all the emissions and resources consumed starting from 8 collection up to the defined point of substitution. 9

If the point of substitution is identified at “level 2” Erec and ErecEoL shall be modelled using the real input flows. 10 Therefore, if a portion of the input flows are from primary raw materials, they shall be included in the 11 datasets used to model Erec and ErecEoL. 12

In some cases Erec can correspond to ErecEoL, for example in cases where close loops occurs. 13

5.5.8.19 The E*v 14

When E*v equals Ev, it is assumed that a recyclable material at end-of-life is replacing the same virgin material 15 as the one the recyclable material is produced from (at input side). 16

In some cases, E*v will be different from Ev, when evidence is provided that a recyclable material is 17 substituting a different virgin material than the one the recyclable material is produced from. 18

When E*v ≠ Ev, E*v refers to the actual amount of virgin material substituted by the recyclable material. In 19 such cases E*v is not multiplied by Qsout/Qp, because this parameter is indirectly taken into account when 20 calculating the “actual amount” of virgin material substituted: such amount shall be calculated taking into 21 account that the virgin material substituted and the recyclable material shall fulfil the same function, in terms 22 of “how long” and “how well”. E*v shall be determined based on evidence of actual substitution of the 23 selected virgin material. 24

3.5.8.20 How to apply the formula to intermediate products (cradle-to-gate studies) 25

In cradle-to-gate LCA studies for intermediate products, the end-of-life of the product (i.e. recyclability at 26 end-of-life, energy recovery, disposal) shall not be accounted for, unless additional information from the EoL 27 stage are to be calculated. 28

Therefore, when the formula is applied in LCA cradle-to-gate studies with intermediate products: 29

● End-of-life shall be excluded by setting the parameters R2, R3, and Ed equal to 0, for the products in 30 scope. 31

● Results shall be calculated and reported considering two A values for the product in scope: 32

68 E.g. the EPBP design guidelines (http://www.epbp.org/design-guidelines), or Recyclability by design (http://www.recoup.org/)


162

○ Setting A = 1: to be used as default in the LCA calculation. 1 ○ Setting A = the application- or material-specific default values as listed in Annex D 2

3 These results shall be reported as 'additional technical information'. 4

5.5.8.21 How to deal with specific aspects 5

Biogenic carbon 6

When modelling bio-based products, biogenic carbon shall be modelled according to the requirements listed 7 in section 5.5.10.2. 8

Recovery bottom ashes/slag from incineration 9

Recovery of bottom ashes/slag shall be included in the R2 value (recycling output rate) of the original 10 product/material. Emissions from their treatment is within the ErecEoL. 11

Landfill and incineration with energy recovery 12

Whenever a process, such as landfill with energy recovery or municipal solid waste incineration with energy 13 recovery, is leading to an energy recovery, it shall be modelled under the “energy” part in Equation 15 (CFF). 14 The credit is calculated based on the amount of output energy that is sold. 15

Municipal solid waste 16

Default values per country are provided in Annex D and shall be used to quantify the share to landfill and the 17 share to incineration, unless supply-chain specific values are available. 18

Compost and anaerobic digestion/sewage treatment 19

Compost, including digestate coming out of the anaerobic digestion, shall be treated in the “material” part 20 (Equation 23) like a recycling with A = 0.5. The energy part of the anaerobic digestion shall be treated as a 21 normal process of energy recovery under the “energy” part of Equation 23 (CFF). 22

Waste materials used as a fuel 23

When a waste material is used as a fuel (e.g. waste plastic used as fuel in cement kilns), it shall be treated as 24 an energy recovery process under the “energy” part of Equation 23 (CFF). 25

Modelling complex products 26

When considering complex products (e.g. printed wiring boards PWB) with complex end-of-life management, 27 the default values of the parameters shall refer to the ones in Annex D. The Bill of Material (BoM) should be 28 taken as a starting point for calculations if no default data is available. 29

Reuse and refurbishment 30

If the reuse/refurbishment of a product results into a product with different product specifications (providing 31 another function), this shall be considered as part of the CFF, as a form of recycling (see section 5.5.8.9). Also, 32 old parts that have been changed during refurbishment shall be modelled under the CFF. 33

In this case, reuse/refurbishment activities are part of the ErecEoL parameter, while the alternative function 34 provided (or the avoided production of parts or components) falls under the E*v parameter. 35


163

5.5.8.22 Packaging 1

Qs/Qp values for packaging 2

Packaging materials used by industry are often the same within different sectors and product groups. 3 Therefore, consistency is also needed in the quality ratios used within the CFF. Annex D provides one 4 worksheet with Qs/Qp values applicable to packaging materials, which shall be used in LCA studies. Any 5 changes to default values shall be adequately justified. These values are based on user experience and have 6 no literature references. 7

Recycled content (R1) for packaging 8

When using supply-chain-specific R1 values, traceability throughout the supply chain is necessary and 9 supplementary information is required. For the packaging industry, the following industry-specific guidelines 10 are recommended: 11

● For the container glass industry (FEVE - The European Container Glass Federation): the European 12 Commission regulation no 1179/2012 (EC, 2012). This regulation requests a statement of conformity 13 delivered by the cullet producer. 14

● For the paper industry: European Recovered Paper Identification System (CEPI, 2008). This document 15 prescribes rules and guidance on necessary information and steps, with a delivery note that shall be 16 received at the reception of the mill. 17

● For beverage cartons no recycled content is used so far and thus sector specific rules are redundant 18 so far. However, if needed, the same guidelines as paper shall be used as being most suitable 19 (beverage cartons are covered by a recovered paper grade category under EN643). 20

● For the plastics industry: EN standard 15343:2007. This standard prescribes rules and guidelines on 21 traceability. The supplier of the recyclate is requested to provide specific information. 22

Recycling output rate (R2) for packaging 23

Background information used to calculate R2 values for packaging is reported in Annex E. It presents, per 24 packaging application, the corresponding material and default R2 data source to be used, as available in 25 Annex D. The R2 values may only be used after making an evaluation for recyclability based on three criteria 26 (as described by ISO 14021:1999 and in section 3.5.8.17). Sector-specific recyclability guidelines may be used 27 to show that a certain product is collected and recycled. For PET bottles the EPBP guidelines should be used 28 (epbp.org/design-guidelines), while for generic plastics the recyclability by design should be used 29 (www.recoup.org). 30


164

Table 38 specifies, for most common packaging applications, which data source reported in Annex D shall be 1 considered to determine R2. 2

3


165

Table 38: Data source for R2 per packaging application 1

Packaging application Material Data source R2 (see Annex D)

Bag in Box - High barrier EVOH Packaging film Generic plastic packaging

Bag in Box - High barrier EVOH HDPE tap PET bottle

Bag in Box - High barrier EVOH Corrugated board Paper and cardboard

Aseptic beverage carton Aluminium foil Aluminium, Liquid beverage carton

Aseptic beverage carton LDPE film Generic plastics, Liquid beverage carton

Aseptic beverage carton Liquid Packaging Board Paper and cardboard, Liquid beverage carton

Beverage carton LDPE film Generic plastics, Liquid beverage carton

Beverage carton Liquid Packaging Board Paper and cardboard, Liquid beverage carton

Closure - Plastic cap PP PP granulates Generic plastic packaging

Closure - Plastic cap HDPE HDPE granulates PET bottle

Closure - Alu-Ring pull Aluminium sheet Aluminium cans

Closure - Alu-Screw cap Aluminium foil Aluminium cans

Closure - Tin plated steel Tin plated steel (ETP) Steel for packaging

Closure - ESSC steel-Pry off Tin free steel (ECCS) Steel for packaging

Closure - plastic cork stopper LDPE cork Generic plastic packaging

Crates - Plastic, HDPE HDPE granulates Generic plastic packaging

Crates - Plastic, PP PP granulates Generic plastic packaging

Packaging film - High barrier PET/ALU/PE film Generic plastic packaging

Packaging film - Medium barrier PP film PP film

Generic plastic packaging

Packaging film - Low barrier PP film PP film


Packaging film - High barrier PE/EVOH/PE

PE film EVOH film LDPE film


Flexible paper packaging Kraft paper - uncoated Paper and cardboard


166


Glass bottle, unspecified colour Glass, unspecified colour Container glass, unspecified colour

Glass bottle, colourless (flint) Glass, unspecified colour Container glass, colourless (flint)

Glass bottle, green colour Glass, unspecified colour Container glass, green colour

Glass bottle, amber colour Glass, unspecified colour Container glass, amber colour

Label - Plastic self adhesive PP film PET bottle

Label - Plastic wrap around OPP film PET bottle

Label - Alu label Neck Foil Aluminium foil Aluminium cans

Label - Paper Kraft paper - uncoated Paper and cardboard

Label - Plastic PE film Generic plastic packaging

Plastic - Shrink Sleeve PET PET film PET bottle

Plastic - Shrink Sleeve PVC PVC film PET bottle

Plastic - Shrink Sleeve OPS PS film PET bottle

Can beverage - sanitary end aluminium Aluminium sheet Aluminium cans

Can beverage - body aluminium Aluminium sheet Aluminium cans

Can beverage - body steel Tin plated steel (ETP) Steel for packaging

Can Food - sanitary end aluminium Aluminium sheet Aluminium cans

Can Food - sanitary end tin plated steel Tin plated steel (ETP) Steel for packaging

Can Food - body ESSC Tin free steel (ECCS) Steel for packaging

Can Food - body aluminium Aluminium sheet Aluminium cans

Can Food - body tin plated steel Tin plated steel (ETP) Steel for packaging

Can - body ECCS PET coated Tin free steel (ECCS) Steel for packaging

Can - sanitary end ECCS PET coated Tin free steel (ECCS) Steel for packaging

Can non food - body tin plated steel - coated

Tin plated steel (ETP) Steel for packaging

Can non food - sanitary end tin plated steel

Tin plated steel (ETP) Steel for packaging

Can non food - body tin plated steel Tin plated steel (ETP) Steel for packaging

Aluminium tray Aluminium foil Aluminium cans


167


Pallet - Plastic, 80x120 HDPE granulates Generic plastic packaging

Pallet - Plastic, 100x120 HDPE granulates Generic plastic packaging

Pallet - Plastic, half HDPE granulates Generic plastic packaging

Paper sack Sack kraft paper Paper, Paper sack

Paper bag Kraft paper - uncoated Paper, Paper bag

Carton - box / inserts Cartonboard Paper, Carton - box / inserts

Solid board box Solid board Paper, Solid board box

Solid board box - bleached Solid bleached board Paper, Solid board box - bleached

Corrugated - pads / box / inserts Corrugated board Paper, Corrugated - pads / box / inserts

PET bottle transparent PET granulates, bottle grade

PET bottle

PET Preform transparent PET granulates, bottle grade

PET bottle

Plastic film - PET PET film Generic plastic packaging

Plastic film - PE PE film Generic plastic packaging

Plastic film - PP PP film Generic plastic packaging

Plastic film - OPP PP film Generic plastic packaging

Plastic film - PP strapping PP film Generic plastic packaging

Plastic film - PE wrapping PE film Generic plastic packaging

Plastic - Shrink wrap LDPE film Generic plastic packaging

Plastic - Stretch film LLDPE film Generic plastic packaging

Plastic bag - PE bag PE film Generic plastic packaging

Plastic bag - Dry food PP film Generic plastic packaging

Plastic bag - Dry food LDPE film Generic plastic packaging

Slipsheet / Plastic divider LDPE granulates Generic plastic packaging

Plastic Can - body PP PP granulates Generic plastic packaging

Plastic Can - sanitary end PP PP granulates Generic plastic packaging


168


Plastic Can - body HDPE HDPE granulates Generic plastic packaging

Plastic Can - sanitary end HDPE HDPE granulates Generic plastic packaging

Plastic tray - Polypropylene PP granulates Generic plastic packaging

Corner foam - polyethylene LDPE granulates Generic plastic packaging

Corner foam - polystyrene EPS beads Generic plastic packaging

HDPE tap HDPE granulates Generic plastic packaging

1

5.5.9 Extended product lifetime 2 Extended product lifetime, due to reuse or refurbishment of a product, can be split into two situations: 3

1. Reuse/refurbishment into a product with original product specifications (providing the same 4 function) 5

2. Reuse/refurbishment into a product with different product specifications (providing another 6 function) 7

In situation 1, the product lifetime is extended into a product with original product specifications (providing 8 the same function) and shall be included in the FU and reference flow. The practitioner shall describe how 9 reuse or refurbishment is included in the calculations of the reference flow and full life cycle model, taking 10 into account the “how long” of the FU. 11

In situation 2, the reuse/refurbishment of a product results into a product with different product 12 specifications (providing another function). This shall be considered as part of the CFF, as a form of recycling 13 (see section 5.5.8.10). Also, old parts that have been changed during refurbishment shall be modelled under 14 the CFF. 15

5.5.9.1 Reuse rates 16

Reuse rate is the number of times a material is used at the factory. This is often also called trip rates, reuse 17 time or number of rotations. This may be expressed as the absolute number of reuse or as % of reuse rate. 18 For example: a reuse rate of 80% equals 5 reuses. Equation 25 describes the conversion: 19

Number of reuse = % %

[Equation 25] 20

The number of reuse applied here refers to the total number of uses during the life of the material. It includes 21 both the first use and all the following reuses. 22

Specific calculation rules for reusable packaging as well as average reuse rates for company or third party 23 operated packaging pools can be found in section 5.5.5.2. 24


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5.5.9.2 How to apply 'reuse rate' 1

The number of times a material is reused affects the environmental profile of the product at different life 2 cycle stages. The following 5 steps explain how the different life cycle stages with reusable materials shall be 3 modelled, using packaging as an example: 4

1) Raw material acquisition: The reuse rate determines the quantity of packaging material consumed per 5 product sold. The raw material consumption shall be calculated by dividing the actual weight of the packaging 6 by the number of times this packaging is reused. For example: A 1l glass bottle weights 600 grams and is 7 reused 10 times. The raw material use per litre is 60 gram (= 600 gram per bottle / 10 reuses). 8

2) Transport from packaging manufacturer to the product factory (where the products are packed): The reuse 9 rate determines the quantity of transport that is needed per product sold. The transport impact shall be 10 calculated by dividing the one-way trip impact by the number of times this packaging is reused. 11

3) Transport from product factory to final client and back: additional to the transport needed to go to the 12 client, the return transport shall also be taken into account. To model the total transport, section 5.5.4 on 13 modelling transport shall be followed. 14 15 4) At product factory: once the empty packaging is returned to the product factory, energy and resource use 16 shall be accounted for cleaning, repairing or refilling (if applicable). 17

5) Packaging End-of-Life: the reuse rate determines the quantity of packaging material (per product sold) to 18 be treated at End-of-Life. The amount of packaging treated at End-of-Life shall be calculated by dividing the 19 actual weight of the packaging by the number of times this packaging was reused. 20

5.5.10 Greenhouse gas emissions and removals 21 Three main categories of greenhouse (GHG) emissions and removals can be distinguished, each contributing 22 to a specific sub-category of the impact category 'climate change': 23

1. Fossil GHG emissions and removals (contributing to the sub-category ‘Climate change – fossil’); 24 2. Biogenic carbon emissions and removals (contributing to the sub-category ‘Climate change – 25

biogenic’); 26 3. Carbon emissions from land use and land transformation (contributing to the sub-category ‘Climate 27

change – land use and land transformation’). 28 29

The contribution of each of the three sub-categories to the total climate change impact indicator shall be 30 reported separately if it is larger than 5%69. 31

69For example, if 'Climate change - biogenic' contributes with 7% to the total climate change impact and 'Climate change – land use and land transformation' contributes with 3%, only the former contribution ('Climate change – biogenic') shall be reported, along with the total value of the climate change impact.


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5.5.10.1 Sub-category 1: Climate change – fossil 1

This category covers GHG (carbon) emissions to any media originating from the oxidation and/or reduction 2 of fossil fuels by means of their transformation or degradation (e.g. combustion, landfilling, etc). This 3 category also includes emissions from peat mineralisation and calcination/carbonation of limestone. 4

Modelling requirements: The flows falling under this definition should be modelled consistently with the most 5 updated ILCD list of elementary flows70. The names ending with '(fossil)' (e.g., 'carbon dioxide (fossil)'' and 6 'methane (fossil)') shall be used if available. 7

5.5.10.2 Sub-category 2: Climate change – biogenic 8

This sub-category covers carbon emissions to air (CO2, CO and CH4) originating from the oxidation and/or 9 reduction of aboveground biomass by means of its transformation or degradation (e.g. combustion, 10 digestion, composting, landfilling), as well as CO2 uptake from the atmosphere through photosynthesis during 11 biomass growth71. Carbon exchanges from native forests72 shall be modelled under sub-category 3 (including 12 connected soil emissions, derived products or residues). 13

Modelling requirements: The flows falling under this definition shall be modelled consistently with the most 14 updated ILCD list of elementary flows and using the flow names ending with '(biogenic)'. A mass allocation 15 shall be applied to model the biogenic carbon flows. A simplified modelling approach is often applied, where 16 only those flows that influence the climate change impact results (namely biogenic methane emissions) are 17 modelled. This is, for instance, the case of food LCAs as it avoids modelling human digestion while arriving 18 eventually at a zero balance. However, this approach may be limiting for (bio)plastics products, as it prevents 19 the possibility to quantify the effects of any (temporary) storage of carbon in the product itself. Therefore, 20 all biogenic carbon emissions and removals shall be preferably modelled in the inventory, including biogenic 21 CO2 uptakes and releases. Note, however, that the default characterisation factors for biogenic CO2 uptakes 22 and release are set to zero (0) in this method (see section 6.1.2.1 for further detail on the characterisation 23 factors to be used for GHG emissions and removals). 24

5.5.10.3 Sub-category 3: Climate change – land use and land transformation 25

This sub-category accounts for carbon uptakes and emissions (CO2, CO and CH4), as well as other GHG 26 emissions (e.g. N2O), originating from carbon stock changes caused by land use and land use change. This 27 sub-category includes biogenic carbon exchanges from deforestation, road construction or other soil 28 activities (including soil carbon emissions). All CO2 emissions related to the conversion of native forests are 29

70 http://eplca.jrc.ec.europa.eu/EF-node/elementaryFlowList.xhtml?stock=default

71 CO2 uptake from the atmosphere contributes to define the carbon content of products, biofuels and aboveground plant residues such as litter and dead wood. 72Native forests – represents native or long-term, non-degraded forests. Definition adapted from table 8 in Annex V C(2010)3751 to Directive 2009/28/EC. In principle, this definition excludes short-term forests, degraded forests, managed forest, and forests with short-term or long-term rotations.


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included and modelled under this sub-category (including connected soil emissions, products derived from 1 native forest73 and residues), while their CO2 uptake is excluded. 2

Modelling requirements: The flows falling under this definition shall be modelled consistently with the most 3 updated ILCD list of elementary flows and using the flow names ending with '(land use change)'. Biogenic 4 carbon uptakes and emissions have to be inventoried separately for each unit process. 5

Detailed guidelines on how to model land use change and related emissions are provided in section 5.5.12. 6

Carbon emissions from changes in soil carbon stock shall be included and modelled under this sub-category 7 (e.g. from rice fields). Soil carbon emissions derived from aboveground residues (except from native forest) 8 shall be modelled under sub-category 2, such as the application of non-native forest residues or straw. Soil 9 carbon uptake (accumulation) shall be excluded from the LCA results, as it is highly questionable how the 10 long term uptakes (beyond 100 years) can be guaranteed in practice. For example, from grasslands or 11 improved land management through tilling techniques or other management actions taken in relation to 12 agricultural land. Soil carbon storage may be included in the LCA study as additional environmental 13 information when proof is provided. For example, when legislation has different modelling requirements for 14 the sector, such as the EU greenhouse gas accounting directive from 2013 (Decision 529/2013/EU) which 15 indicate carbon stock accounting. 16

5.5.11 (Temporary) carbon storage and delayed emissions 17 Temporary carbon storage takes place when a product removes carbon from the atmosphere and stores it 18 for a limited amount of time. A consequence of this storage is delayed emissions, i.e. emissions that are 19 released over time (e.g. through long-use or final disposal phases), compared to instantaneous emissions at 20 a specific time t close to the uptake. For instance, let us assume the case of timber furniture with an estimated 21 life span of 120 years (starting from the year of harvest/production), at the end of which it is disposed of 22 through incineration. The CO2 is taken up by the plant used for the production of the timber furniture, is 23 stored in the furniture itself for 120 years, and is released only when the furniture is incinerated at its end of 24 life. Related CO2 emissions are thus delayed by 120 years with respect to the harvest/production year, as 25 they occur only at the end of the product life span instead of at very early stage from uptake, which could be 26 the case if that wood was instead (harvested and) used for energy purposes. 27

Credits associated with temporary carbon storage and/or delayed emissions shall not be considered as a 28 baseline in the inventory model and in the calculation of the climate change impact. This means that all 29 emissions occurring over the product life cycle shall be accounted for as emitted at year 0, regardless of their 30 time of uptake, and therefore assuming no discounting of emissions within the time horizon assumed for the 31 scope. This is also in line with ISO14067:2018). However, the effects of temporary carbon storage and delayed 32 carbon emissions may be calculated according to the approach described in section 5.5.11.1, and included in 33 the LCA study report as “additional environmental information”. 34

73 Following the instantaneous oxidation approach in IPCC 2013 (Chapter 2).


172

No permanent carbon storage (carbon sequestration) shall be considered in the assessment, regardless of 1 the storage duration (or product lifetime). For both cradle–to-grave assessments of final products and cradle-2 to-gate assessments of intermediate products (whose lifetime is unknown) with a lifetime below 100 years, 3 no carbon credits shall thus be modelled. 4

For intermediate products, the biogenic carbon content at factory gate (physical content and allocated 5 content) shall always be reported as 'additional technical information'. 6

5.5.11.1 Modelling guidelines for temporary carbon storage and delayed emissions 7

5.5.12 Land Use Changes 8 Section 5.5.12.1 provides a definition of direct and indirect land use changes (dLUC/iLUC). While the 9 methodology followed to quantify dLUC conforming to PAS2050-1:2012 (BSI, 2012) is reported in section 10 5.5.12.2, the inclusion of aspects of indirect land use change (iLUC) is one of the deliberate deviations of this 11 methodology for conducting plastics LCAs compared to the Product Environmental Footprint (PEF), in which 12 iLUC is excluded from calculation of the baseline as there is no agreed method on how to calculate iLUC 13 effects. Therefore, in this chapter, some more background and discussion is provided compared to other 14 chapters of this document: in this respect, section 5.5.12.3 illustrates the different models available for 15 quantification of iLUC and applicable to LCA, highlighting pros and cons. Section 5.5.12.4 describes the iLUC 16 approach applied in the screening. 17

5.5.12.1 Land use changes: direct and indirect (dLUC/iLUC) 18

Different but somehow aligned definitions of dLUC and iLUC exist in the literature. ISO (2013) defines direct 19 land use change (dLUC) as a “change in human use or management of land within the boundaries of the 20 product system being assessed” while iLUC as “change in the use or management of land which is a 21 consequence of direct land use change, but which occurs outside the product system being assessed”. 22 Another study by Schmidt et al. (2015) defines dLUC as ”those changes that occur on the same land as the 23 land use” and iLUC as “the upstream life cycle consequences of the land use regardless of the purpose of the 24 land use”, i.e. a change in land use caused indirectly as an upstream consequence of a dLUC but taking place 25 somewhere else in the World. Marelli et al. (2015) define a land use change to be direct when "the demanded 26 crops are grown on uncultivated land" while indirect "when the demanded crops are grown on already 27 cultivated or used land". In the scientific literature, dLUC has also been defined as “all changes in above- and 28 below-ground flows of carbon, nitrogen and phosphorus flows on a particular site, as one land use takes 29 place instead of another” (e.g. Hamelin et al., 2012; Tonini et al., 2012). In a nutshell, dLUC refers to the 30 changes occurring on the same land where the land use for the product under assessment takes place. The 31 iLUC refers instead to market-driven consequences incurred (somewhere else) by the dLUC taking place in 32 the very first place Figure 18. The point of departure for ILUC to occur is when arable land, already-in-use for 33 cropping or grazing activities, is used for supplying the feedstock under assessment. In other words, iLUC 34 arises as changes in overall land demand occur. The pre-condition for iLUC to occur is that the global 35 agricultural area is still expanding because of increased population, GDP increase of some countries, etc. and 36 that its capacity is inherently limited/constrained. For example, if the feedstock needed for a bio-based 37 product or biofuel is cultivated at the expense of another crop, the service this formerly-cultivated crop 38 provided to the food/feed market will still be demanded on the World’s market. The main underlying 39 postulate of iLUC is that this relative drop in supply is likely to cause a relative increase in agricultural prices, 40


173

which in turn provides incentives to increase production elsewhere. This in principle can incur: i) agricultural 1 land expansion (at the expenses of nature), ii) production intensification and iii) crop-displacement 2 mechanisms (reduced consumption). The latter is supported by some studies arguing that in the short-to-3 medium term not 100% of the displaced feedstock may need to be compensated by increased production as 4 reduced consumption may also occur (e.g. Edwards et al., 2010). This hypothesis is however contrasted by 5 other authors (e.g. Schmidt et al., 2015) arguing that this effect should not be included in LCAs, since it is the 6 long-term effect of the demand that should be guiding for decisions (Weidema et al., 2013). According to 7 these authors, the supply of goods and services should be assumed fully elastic, i.e. an increase in demand is 8 to be met by a corresponding (1:1) increase in supply. 9

10

Figure 18. Schematic representation of direct and indirect land use changes. Adapted from CE Delft 11 2010) 12

5.5.12.2 Methodology for direct land use change (dLUC) 13

Carbon emissions and removals from dLUC are modelled following the guidelines of PAS 2050:2011 (BSI, 14 2011) and the supplementary document PAS2050-1:2012 (BSI, 2012) for horticultural products. Following 15 PEFCR guidelines (v6.3), soil carbon uptake (accumulation) shall be excluded from the footprint results as it 16 is highly questionable how the long term uptakes (beyond 100 years) can be guaranteed in practice. In 17 PAS2050:2011 (see Appendix C of the PAS2050:2011), it can be seen that two main types of previous land 18 use are considered for dLUC: i) transformation from grassland (to annual or perennial crop) and ii) 19 transformation from forest land (to annual or perennial crop). The GHG emissions and removals arising from 20 dLUC shall be assessed for any input to the life cycle of the product originating from land and shall be included 21 in the assessment of GHG emissions. The emissions arising from the product shall be assessed on the basis 22 of the default land use change values provided in PAS 2050:2011 Appendix C, unless better data is available. 23 For countries and land use change types not covered in Appendix C, the emissions arising from the product 24 shall be assessed using the included GHG emissions and removals occurring as a result of direct land use 25 change in accordance with the relevant sections of the IPCC Guidelines for National Greenhouse Gas 26 Inventories (IPCC, 2006). 27

The assessment of the impact of land use change shall include all direct land use change occurring not more 28 than 20 years, or a single harvest period, prior to undertaking the assessment (whichever is the longer). The 29 total GHG emissions and removals arising from dLUC over the period shall be included in the quantification 30 of GHG emissions of products arising from this land on the basis of equal allocation to each year of the 31

Cultivated system

Natural or close-to-natural land

Cultivated system


Cultivated system

Cultivated system

Biofuel production



Cultivated system

expansion

Direct land use change Indirect land use change

Biofuel production


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period74. Where it can be demonstrated that the land use change occurred more than 20 years prior to the 1 assessment being carried out, no emissions from land use change should be included in the assessment, i.e. 2 dLUC should be set to zero. Where the timing of land use change cannot be demonstrated to be more than 3 20 years, or a single harvest period, prior to making the assessment (whichever is the longer), it shall be 4 assumed that the land use change occurred on 1 January of either: 5

● The earliest year in which it can be demonstrated that the land use change had occurred; or 6 ● On 1st of January of the year in which the assessment of GHG emissions and removals is being carried 7

out. 8 The following hierarchy shall apply when determining the GHG emissions and removals arising from land use 9 change occurring not more than 20 years or a single harvest period, prior to making the assessment 10 (whichever is the longer): 11

I. Where the country of production is known and the previous land use is known, the GHG 12 emissions and removals arising from land use change shall be those resulting from the change in land 13 use from the previous land use to the current land use in that country (additional guidelines on the 14 calculations can be found in PAS 2050-1:2012); 15

II. Where the country of production is known, but the former land use is not known, the GHG 16 emissions arising from land use change shall be the estimate of the average emissions from the land 17 use change for the specific crop under assessment in that country (additional guidelines on the 18 calculations can be found in PAS 2050-1:2012); 19

III. Where neither the country of production nor the former land use is known, the GHG 20 emissions arising from land use change shall be the weighted average of the average land use change 21 emissions of that commodity in the countries in which it is grown. 22

23 Knowledge of the prior land use can be demonstrated using a number of sources of information, such as 24 satellite imagery and land survey data. Where records are not available, local knowledge of prior land 25 use can be used. Countries in which a crop is grown can be determined from import statistics, and a cut-26 off threshold of not less than 90% of the weight of imports may be applied. Data sources, location and 27 timing of land use change associated with inputs to products shall be reported. 28

5.5.12.3 Overview of models available for quantification of iLUC 29

A number of approaches and models have been proposed in recent years to quantify iLUCs in LCA, but a 30 broad consensus on what to apply still needs to be reached (Warner et al., 2014). According to De Rosa et al. 31 (2016) the main challenges are: i) the identification of the affected (in consequential LCA often referred as to 32 marginal) land; ii) establishing the relationship between the demand for agricultural products and the 33 occurring land use changes; iii) including the effect of by-products; iv) the overall level of uncertainty caused 34 by the multiple modelling assumptions involved as highlighted in Marelli et al. (2011). The LUC models are 35 typically distinguished into 3 types (De Rosa et al., 2016): economic equilibrium models (EEMs), causal-36 descriptive models (CDMs) and role-based normative models (NMs). 37

74 In case of variability of production over the years, a mass allocation should be applied.


175

EEMs are based on the theory of economic equilibrium: changes in supply and demand induce fluctuations 1 of the price of goods until a new equilibrium “supply=demand” is reached, with a new price. Any variation in 2 goods demand incurs changes in land requirement and occupation from which LUC between the former and 3 new equilibriums can be estimated. Two main types of EEMs models exist: partial equilibrium models (PE), 4 restricting the modelling to selected sectors of the economy (e.g. CAPRI, 2012), and computable general 5 equilibrium models (CGE) striving to include and link all the sectors of the global (or regional) economy (e.g. 6 GTAP, IMAGE, LEITAP; among others see Britz and Hertel, 2011). 7

CDMs (sometimes also referred to as biophysical or deterministic) are simpler and conceptually easier than 8 EEMs (Nassar et al., 2011), reducing computational efforts and data needs. They describe future states of a 9 system establishing cause-effect relationships. These can be determined from a combination of biological 10 and physical land characteristics, own and cross-price elasticities, statistical data, etc. (De Rosa et al., 2016). 11 CDMs do not exclude economic aspects that drive the supply-demand patterns; rather, they forecast future 12 production and consumption patterns based on current or historical market trends and assumptions on 13 agriculture supply-demand trajectories. Based on this, future land uses and their geographic origin (i.e. land 14 areas affected by a change in demand/supply) can be identified. Recent examples of CDMs are Bauen et al. 15 (2010), Schmidt et al. (2015), and Tonini et al. (2016). 16

Normative models attempt to establish assumptions or LUC factors based on statistical metadata (Audsley 17 et al., 2009). Often, they de facto exclude iLUC, avoiding the most controversial aspect, and only focus on the 18 quantification of dLUCs GHG emissions; an example is the approach proposed in the PAS 2050 (BSI, 2011) 19 also used by EC (2016), and that from Flynn et al. (2012). Another NM is that proposed by (Fritsche et al., 20 2010), where the bioproduct is assumed to come by 25% from set-free land with no iLUC risk and by 75% 21 from new land incurring iLUCs. The iLUC GHG factors reported in EU 2015/1513 (EC 2015) can also be 22 classified under this category. In EU 2015/1513 a number of default iLUC GHG factors are provided for 23 biofuels obtained from sugar-, starch-, and oil-rich crops. The figures were derived from a meta-analysis of 24 the iLUC GHG factors reported in the scientific literature and are originally reported as g CO2-eq. MJ-1, as 25 intended to be applied to biofuels assessment studies. An overview of the main differences between EEMs 26 (e.g. Valin et al., 2015) and CDMs (e.g. Schmidt et al., 2015) is reported in 27


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Table 39. 1

2


177

Table 39: Main differences between EEMs (economic equilibrium models) and CDMs (causal descriptive 1 models) 2

EEMs (e.g. Valin et al., 2015) CDMs (e.g. Schmidt et al., 2015; Tonini et al., 2016; Bauen et al., 2010)

Model type EEM (Global equilibrium model) targeted to economic analyses

CDM (causal-effect/deterministic) specifically targeted to application in LCA

Type of iLUC factor Crop- and location-specific

Global, independent on crop type and location of occupation

What iLUC includes? iLUC + all the substitution of co-products at farm and biorefinery level (in terms of land avoided)

iLUC only

Land suppliers Transformation, intensification, reduced consumption

Transformation, intensification

Identification of affected lands (i.e. marginal)

Estimated using price and price elasticities that are implemented as model functions

Estimated with historical patterns using transformation matrices that can be changed/updated (mainly FAO data)

Intensification effects

Accounted for as reduced land needs, but without accounting for associated GHG impacts due to increased fertilizers use

Accounted for both as reduced land needs and as GHG emissions due to increased fertiliser use

GW assessment of LUC-deforestation GHGs

Annual amortisation of the initial C-emissions using a 20yr period

Schmidt et al. (2015): After 1-year of occupation, land is released back to other product systems (uses). This equals to speed up the emission by one year. Follows using Bern C-Cycle and IPCC-GWP (Forster et al., 2007) to calculate the change in radiative forcing. Tonini et al. (2016)/Bauen et al. (2010): Annual amortisation

Previous applications

A number of studies on biofuels (e.g. Valin et al., 2015)

A number of Danish Energy Agency, Danish EPA, private companies and peer-review studies on food, biofuels, and bioproducts

3

Pros and cons of these models, in the endeavour of their application to LCA, have been highlighted in two 4 recent reviews by Warner et al. (2014) and De Rosa et al. (2016). In particular, De Rosa et al. (2016) performed 5 a pairwise comparison, based on a number of criteria, between: a PE model (CAPRI, 2012), a CGE model 6 (GTAP-AEZ), a hybrid CGE/PE model developed by JRC (integrating data from the CGE model IFPRI-MIRAGE 7 and the PE model AGLINK-COSIMO), a NM (BSI, 2011), and two CDMs (Bauen et al., 2010; Schmidt et al., 8 2015). The criteria of the comparison were: i) completeness of scope, ii) impact assessment relevance, iii) 9 scientific robustness and certainty, and iv) transparency, reproducibility and applicability. The main results 10 of the analysis are summarised herein: 11

Completeness of scope: generally, GTAP-AEZ and JRC have more complete datasets and land classification 12 maps to derive the origin of the affected (marginal) land compared to CDMs. Yet, the uncertainty around this 13 is high as it depends upon the assumptions regarding the competition for land; this is described by the 14 function of elasticity of land transformation σ (Hertel et al., 2009). Land transformation elasticity distributes 15 the productivity-adjusted land to its alternative uses. Regarding the distribution of the GHG emissions, only 16 Schmidt et al. (2015) model suggests a methodology for handling this. Other EEMs leave this aspect to the 17 users or consider the issue outside the scope of LUC modelling. EEMs have usually a (rough) national level of 18 GHG data aggregation. A common limit of all LUC models, regardless of the approach and resolution of GHG 19


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emissions, is the amortisation of the emission over an arbitrary period of time, generally 20 years (Fritsche 1 et al., 2010; Valin et al., 2015) or 30 years (Bauen et al., 2010). Schmidt et al. (2015) propose an alternative 2 approach that avoids in toto amortisation. 3

Impact assessment relevance: all these models only focus on GHG emissions and lack a more broad coverage 4 of the iLUC environmental effects. 5

Scientific robustness and certainty: all these models do not assess nor propagate uncertainties. Regarding 6 updates, GTAP-AEZ and CAPRI are constantly updated. Schmidt et al. (2015) is updated with a biannual 7 frequency. Bauen et al. (2010) and JRC provide suggestions for future developments, but are not regularly 8 updated. PAS 2050 has not been updated. PAS 2050 and Bauen et al. (2010) are the only ones not been peer-9 reviewed. 10

Transparency, reproducibility, and applicability: All are well documented. CAPRI and JRC have a limited focus 11 on agriculture, their analyses mostly focus on biofuels, a regional scope (EU) only is available. Bauen et al. 12 (2010) is limited to biofuels analyses. In contrast, GTAP-AEZ, Schmidt et al. (2015) and PAS 2050 models are 13 designed to be applied regardless of location and economic sector, thus having a larger applicability. It should 14 be borne in mind that in LCA it is common practice to assume long-term full elasticity of supply under a 15 competitive unconstrained market. In this respect, EEMs-derived results may instead reflect fluctuations of 16 market prices due to a short-term inelastic supply where a sudden demand increase (or supply decrease) 17 induces a higher price (new equilibrium) and vice versa. This may ultimately generate incongruences in the 18 LCA. 19

The main conclusions from the reviews of Warner et al. (2014) and De Rosa et al. (2016), can be summarised 20 as follows: 21

CDMs specifically developed for application to LCAs may be more suitable than applying more 22 complex and computation-wise intensive EEMs. 23

EEMs, however, appear useful for identifying the affected (i.e. marginal) land. 24 EEMs address iLUC by attempting to capture all crop displacements at the specific crop and country 25

level (including intensification). EEMs identify the affected crops by price and price elasticity 26 information, and specific crop markets are assumed (e.g. rapeseed displaced in one country is 27 compensated with the quantity of another crop producing an equivalent amount of oil, using 28 elasticities of substitutions). This leads to iLUC factor crop- and country-specific. 29

EEMs describe land competition and transformation through mathematical functions (elasticities), 30 difficult to calibrate with real data. When calibration is made, it is not free from uncertainties and 31 may ultimately only reflect case-specific scenarios. 32

EEMs may include the effect of co-products, potentially incurring double counting in LCAs; these 33 should not be included in LUC models for LCAs, as they belong to a part of the product system not 34 related to the actual provision of land. 35

The iLUC factors derived with EEMs represent the sum of direct and indirect effects; in principle, 36 double counting (e.g. for dLUC) should be avoided. 37

A careful combination of the two modelling approaches, whenever possible, is ultimately 38 encouraged. 39

Time allocation of GHG emissions over an arbitrary period of time (e.g. 20yr) should be avoided. 40


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To avoid arbitrary allocation, alternative time-dynamic formulations exist (among the others see: 1 (Cherubini et al., 2011, 2016; Kloverpris and Mueller, 2013; Schmidt et al., 2015). 2

Incongruences may be generated when using EEMs, as their results reflect prices fluctuations 3 following short-term inelasticity of supply, while LCA typically assumes long-term full elasticity of 4 supply. 5

5.5.12.4 Applied methodology for quantification of iLUC in the screening LCAs on plastic articles 6

In this screening LCA study, the iLUC contribution was calculated only for GHG emissions derived from land 7 clearing applying the iLUC factors proposed in the EU 2015/1513, annex V and VIII (EC, 2015), following a 8 normative-based approach. These iLUC factors were originally reported per type of crop required (differing 9 between starch-rich, sugar-rich, and oil-rich) as gCO2-eq. MJ-1 and intended to be applied to the assessment 10 of energy-rich products, e.g. biofuels. In order to apply these figures to the non-energy products investigated 11 in this study, it was necessary to first recalculate these factors as kg CO2-eq. ha-1 a-1. This was done using 12 typical yields for each individual crop-type based on the figures reported in a recent study by (Valin et al., 13 2015). The results are reported in Table 40. Once this was done, the agricultural (mostly arable) land 14 demanded by the (fully or partly) bio-based plastic article in each individual LCA scenario was quantified as 15 ha∙a∙FU-1 (i.e. this value is scenario-specific, depending on the amount and type of crop used as feedstock). 16 By multiplying the land demanded per FU with the appropriate iLUC factors derived after EU 2015/1513 the 17 iLUC GHG contribution can be finally quantified (Equation 26). 18

land demand [ℎ𝑎 ∙ 𝑎 ∙ 𝐹𝑈 ] ∙ iLUC [𝑘𝑔𝐶𝑂 ∙ ℎ𝑎 ∙ 𝑎 ] = iLUC contribution [𝑘𝑔𝐶𝑂2 ∙ 𝐹𝑈 ] 19

[Equation 26] 20

Table 40: iLUC GHG contribution recalculated on the basis of the figures in EU 2015/1513 (EC 2015) 21

Unit Starch-rich Sugar-rich Oil-rich

iLUC factor (energy basis)

gCO2-eq. MJ-1 12 13 55

Yield MJ ha-1 a-1 51000 135000 37000

iLUC factor (land basis)

kgCO2-eq. ha-1 a-1 612 1755 2035

Amortisation time a 20 20 20

iLUC factor (land basis, non-amortised)

kgCO2-eq ha-1 12240 35100 40700

22

23


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5.6 Data collection 1 This section addresses the data sources to be used to compile the life cycle inventory of processes included 2 in the system boundary, the corresponding data collection procedures, how to fill any data gaps and how to 3 apply cut-off. 4

5.6.1 Company-specific data 5 This section describes the collection of company-specific Life Cycle Inventory data, which are data directly 6 measured or collected at a specific facility or set of facilities, and representative of one or more activities or 7 processes in the system boundary. The data should include all known inputs and outputs for the processes. 8 Inputs are (for example) use of energy, water, materials, etc. Outputs are the products, co-products, and 9 emissions. Emissions can be divided into four categories: emissions to air, to water, to soil, and solid waste 10 flows Company-specific emission data can be collected, measured or calculated using company-specific 11 activity data75 and related emission factors (e.g. litre of fuel consumption and emission factors for 12 combustion in a vehicle or boiler). It should be noted that emission factors may be derived from secondary 13 data subject to data quality requirements. 14

Data collection - measurement and tailored questionnaires 15

The most representative sources of data for specific processes are measurements directly performed on the 16 process, or obtained from operators via interviews or questionnaires. The data may need scaling, aggregation 17 or other forms of mathematical treatment to bring them in line with the functional unit and reference flow 18 of the process. 19

Typical specific sources of company-specific data are: 20

• Process- or plant-level consumption data; 21

• Bills and stock/inventory changes of consumables; 22

• Emission measurements (amounts and concentrations of emissions from flue gas and wastewater); 23

• Composition of products and waste; 24

• Procurement and sale department(s)/unit(s). 25

Company-specific data76 shall be obtained for all foreground processes and for background processes, where 26 appropriate. However, if secondary data are more representative or appropriate than specific data for 27 foreground processes (to be justified and reported), secondary data shall also be used for the foreground 28 processes. 29

75 Activity data are data that are specific to the process being considered, as opposed to secondary data. 76 Including average data representing multiple sites. Average data refers to a production-weighted average of specific data.


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5.6.2 Secondary data 1 Secondary data refers to data that are not based on direct measurements or calculation of the respective 2 processes in the system boundary. Secondary data can be either sector-specific, i.e. specific to the sector 3 being considered for the LCA study, or multi-sector. Examples of secondary data include: 4

Data from literature or scientific papers; 5 Industry-average life-cycle data from life-cycle-inventory databases, industry association reports, 6

government statistics, etc. 7

Secondary data should be used only for processes in the background system, unless (secondary data) are 8 more representative or appropriate than company-specific data for foreground processes. In this case, 9 secondary data shall also be used for processes in the foreground system. When available, sector-specific 10 secondary data shall be used instead of multi-sector secondary data. All secondary data shall fulfil the data 11 quality requirements specified in this document (section 5.7). The sources of the data used shall be clearly 12 documented and reported in the LCA report. 13

Sourcing secondary data 14

The following rules shall be followed, in hierarchical order, to select secondary datasets for those processes 15 and activities to be modelled based on secondary data: 16

Use an EF-compliant dataset available on one of the following nodes: 17 o http://eplca.jrc.ec.europa.eu/EF-node 18 o http://lcdn.blonkconsultants.nl 19 o http://ecoinvent.lca-data.com 20 o http://lcdn-cepe.org 21 o https://lcdn.quantis-software.com/PEF/ 22 o http://lcdn.thinkstep.com/Node 23

Use an EF-compliant dataset available in a free or commercial source/database; 24 Use another EF-compliant dataset that can be considered a good proxy for the relevant process or 25

activity. In such case this information shall be included in the "limitation" section of the LCA study 26 report (section 8.2.2). 27

Use an ILCD-entry level-compliant dataset. In such case this information shall be included in the "data 28 gap" section of the LCA study report (section 8.2.2). 29

30 Note: among the EF tendered datasets, integrated modelling inconsistencies may occur (e.g. the glass 31 default dataset uses the 50/50 allocation approach at the input side to model input scrap, but then applies 32 the CFF at output side; while plastics is fully modelled by applying the CFF). The aim for consistency in 33 modelling approach within the LCA study is preferred. An ILCD-entry level-compliant dataset or proper 34 modelling proxy may thus be chosen above an EF-compliant dataset to achieve consistency. This shall be 35 explicitly reported and justified in the LCA study report. 36

If no secondary dataset can be selected based on the above hierarchy, it should then preferentially be 37 sourced from: 38

Databases provided by international governmental organisations (for example FAO, UNEP); 39


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Country-specific national governmental LCI database projects (for data specific to the host 1 country’s database); 2

National governmental LCI database projects; 3 Other third-party LCI databases; 4 Peer-reviewed literature. 5

6 Other potential sources of secondary data can also be found, e.g. in the Resource Directory of the European 7 Platform on LCA77. If the necessary data cannot be found in the above-listed sources, other sources may be 8 used. 9

5.6.3 Data gaps 10 Data gaps exist when there is no company-specific or secondary data available that is sufficiently 11 representative of the given process in the system boundary. For most processes where data may be missing, 12 it should be possible to obtain sufficient information to provide a reasonable estimate of the missing data. 13 Therefore, there should be few, if any, data gaps in the final Life Cycle Inventory. Missing information can be 14 of different types and have different characteristics, each requiring separate resolution approaches. 15

Data gaps may exist when: 16

Data does not exist for a specific input/output or product, or 17 Data exists for a similar process but: 18

o The data refer to a different region; 19 o The data refer to a different technology; 20 o The data refer to a different time period. 21

Any data gaps shall be filled using the best available secondary or extrapolated data. The contribution of such 22 data (including gaps in secondary data) shall not account for more than 10% of the overall contribution to 23 each impact category considered. This is reflected in the data quality requirements (section 5.7), according 24 to which 10% of the data can be chosen from the best available data (without any further data quality 25 requirements). 26

5.6.4 Sampling procedure 27 In some cases, a sampling procedure is needed in order to limit the collection of specific data only to a 28 representative sample of plants/farms etc. Examples of cases when the sampling procedure may be needed 29 are in case multiple production sites are involved in the production of the same Stock Keeping Unit (SKU). 30 For instance, in case the same raw material/input material comes from multiple sites or in case the same 31 process is outsourced to more than one subcontractor/supplier. 32

Different procedures exist to derive a representative sample. In this context, a stratified sample shall be used, 33 i.e. a sample ensuring that sub-populations (strata) of a given population are each adequately represented 34 within the whole sample of the study. With this type of sampling, it is guaranteed that subjects from each 35

77 http://eplca.jrc.ec.europa.eu/ResourceDirectory/


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sub-population are included in the final sample, whereas simple random sampling does not ensure that sub-1 populations are represented equally or proportionately within the sample. 2

Using a stratified sample will always achieve greater precision than a simple random sample, provided that 3 the sub-populations have been chosen so that the items of the same sub-population are as similar as possible 4 in terms of the characteristics of interest. In addition, a stratified sample guarantees better coverage of the 5 population. The practitioner has control over the sub-populations that are included in the sample, whereas 6 simple random sampling does not guarantee that sub-populations (strata) of a given population are each 7 adequately represented within the final sample. However, one main disadvantage of stratified sampling is 8 that it can be difficult to identify appropriate sub-populations for a population. 9

The following procedure shall be applied in order to select a representative sample as a stratified sample: 10

1) define the population 11 2) define homogenous sub-populations (stratification) 12 3) define the sub-samples at sub-population level 13 4) define the sample for the population starting from sub-samples at sub-population level. 14

5.6.4.1 How to define homogenous sub-populations (stratification) 15

Stratification is the process of dividing members of the population into homogeneous subgroups (sub-16 populations) before sampling. The sub-populations should be mutually exclusive: every element in the 17 population shall be assigned to only one sub-population. 18

Aspects at least to be taken into consideration in the identification of the sub-populations are: 19

- Geographical distribution of sites 20 - Technologies/farming practices involved 21 - Production capacity of the companies/sites taken into consideration 22

Additional aspects to be taken into consideration may be added for a specific product category. 23

The number of sub-populations may be identified as: 24

𝑁𝑠𝑝 = 𝑔 ∗ 𝑡 ∗ 𝑐 [Equation 27] 25

o Nsp: number of sub-populations 26 o g : number of countries in which the sites/plants/farms are located 27 o t : number of technologies/farming practices 28 o c : number of classes of production capacity of companies 29

In case additional aspects are taken into account, the number of sub-populations is calculated using Equation 30 27 and multiplying the result with the numbers of classes identified for each additional aspect (e.g. those 31 sites which have an environmental management or reporting systems in place). 32

33


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Example 1 1

Identify the number of sub-populations for the following population: 2

350 farms located in the same region in Spain, all the farms have more or less the same annual production 3 and apply the same farming techniques. 4

In this case: 5

g=1 : all the farmers are located in the same country 6 t=1 : all the farmers are using the same cultivation techniques 7 c=1 : the capacity of the companies is almost the same (i.e. they have the same annual production) 8

𝑁𝑠𝑝 = 𝑔 ∗ 𝑡 ∗ 𝑐 = 1 ∗ 1 ∗ 1 = 1 9

Only one sub-population may be identified, which coincides with the (main) population. 10

Example 2 11

350 farms are distributed in three different countries (100 in Spain, 200 in France and 50 in Germany). Two 12 different harvesting techniques are used overall, which differ in a relevant way (Spain: 70 technique A, 30 13 technique B; France: 100 technique A, 100 technique B; Germany: 50 technique A). The capacity of the farms 14 in terms of annual production varies between 10.000 t and 100.000 t. According to expert 15 judgement/relevant literature, it has been estimated that farmers with an annual production lower than 16 50.000 t are completely different in terms of efficiency compared to the farmers with an annual production 17 higher than 50000 t. Two classes of companies are thus defined, based on the annual production: class 1, if 18 production is lower than 50.000 t and class 2, if production if higher than 50.000 t (Spain: 80 class 1, 20 class 19 2; France: 50 class 1, 150 class 2; Germany: 50 class 1). Table 41 summarises the details of the population. 20

Table 41: Features of the population for Example 2 21

Sub-population

Country Technology Capacity

1 Spain

100

Technique A 70

Class 1 50 2 Spain Technique A Class 2 20 3 Spain Technique B

30 Class 1 30

4 Spain Technique B Class 2 0 5 France

200

Technique A 100

Class 1 20 6 France Technique A Class 2 80 7 France Technique B

100 Class 1 30

8 France Technique B Class 2 70 9 Germany

50

Technique A 50

Class 1 50 10 Germany Technique A Class 2 0 11 Germany Technique B

0 Class 1 0

12 Germany Technique B Class 2 0 22

In this case: 23

g=3 : three countries 24


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t=2 : two different harvesting techniques are identified 1 c=2 : two classes of production are identified 2

𝑁𝑠𝑝 = 𝑔 ∗ 𝑡 ∗ 𝑐 = 3 ∗ 2 ∗ 2 = 12 3

It is thus possible to identify maximum 12 sub-populations, which are summarized in Table 42. 4

Table 42: Summary of the sub-populations for example 2 5

Sub-population Country Technology Capacity Number of companies in the sub-population

1 Spain Technique A Class 1 50 2 Spain Technique A Class 2 20 3 Spain Technique B Class 1 30 4 Spain Technique B Class 2 0 5 France Technique A Class 1 20 6 France Technique A Class 2 80 7 France Technique B Class 1 30 8 France Technique B Class 2 70 9 Germany Technique A Class 1 50

10 Germany Technique A Class 2 0 11 Germany Technique B Class 1 0 12 Germany Technique B Class 2 0

6

5.6.4.2 How to define sub-sample size at sub-population level 7

Once the sub-populations have been identified, the size of sample shall be calculated for each sub-population 8 (the sub-sample size). Two approaches are possible: 9

1) based on the total production of the sub-population 10 2) based on the number of sites /farms/plants involved in the sub-population 11

The chosen approach shall be specified in the LCA study. The same approach shall be used for all the sub-12 populations selected. 13

First approach 14

In case the first approach is chosen, the unit of measure for the production (t, m3, m2, value) shall be 15 established. The percentage of production to be covered by each sub-population shall be also identified, and 16 shall not be lower than 50%, expressed in the relevant unit. This percentage determines the sample size 17 within the sub-population. 18

Second approach 19

In case the second approach is chosen: 20

The required sub-sample size shall be calculated using the square root of the sub-population size. 21

𝑛 = 𝑛 [Equation 28] 22


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o nSS: required sub-sample size 1 o nSP: sub-population size 2

An example of the approach is provided in Table 43. 3

Table 43: Example – how to calculate the number of companies in each sub-sample based on sub-4 population size 5

Sub-population Country Technology Capacity Number of companies in the sub-population (size)

Number of companies in the sample (sub-sample size, [nSS])

1 Spain Technique A Class 1 50 7 2 Spain Technique A Class 2 20 5 3 Spain Technique B Class 1 30 6 4 Spain Technique B Class 2 0 0 5 France Technique A Class 1 20 5 6 France Technique A Class 2 80 9 7 France Technique B Class 1 30 6 8 France Technique B Class 2 70 8 9 Germany Technique A Class 1 50 7

10 Germany Technique A Class 2 0 0 11 Germany Technique B Class 1 0 0 12 Germany Technique B Class 2 0 0

5.6.4.3 How to define the sample for the population 6

The representative sample of the population corresponds to the sum of the sub-samples at sub-population 7 level. 8

5.6.4.4 What to do in case rounding is necessary 9

In case rounding is necessary, the general rule used in mathematics shall be applied: 10

If the number you are rounding is followed by 5, 6, 7, 8, or 9, round the number up. 11 If the number you are rounding is followed by 0, 1, 2, 3, or 4, round the number down. 12

5.6.5 Cut off 13

Any cut-off shall be avoided in LCA studies. However, if a screening assessment is performed (see section 14 5.1), it may be possible to identify the processes that could be excluded from the modelling by applying a 1% 15 cut-off for all impact categories based on environmental significance. The 1% cut-off is additional to the cut-16 off already included in the background datasets. To calculate a 1% cut-off order the processes starting from 17 the less relevant to the most relevant one, in terms of contribution to the overall impact in each category. 18 The processes that in total account for less than 1% of the environmental impact for each impact category 19 may be excluded from the LCA study (starting from the less relevant). In case the cut-off rule is applied, the 20 LCA study report shall list the processes that are excluded based on the cut-off. This rule is valid for both 21 intermediate and final products. 22

Only the processes identified following this procedure may be excluded according to the cut off rule. No 23 additional cut-offs are allowed. 24


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5.6.7 Data collection: summary of requirement and relation to the next methodological phases 1 in a LCA study 2

Figure 19 summarises the “shall/should/may” requirements for the collection of both specific and generic 3 data when developing a LCA study. Moreover, the figure illustrates the link between the data collection step 4 and the development of the Life Cycle Inventory and the subsequent Life Cycle Impact Assessment phases. 5


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1 Figure 19: Relationship between data collection, Life Cycle Inventory and Life Cycle Impact Assessment 2

3

4

DATA COLLECTION

Specific data • Shall be obtained for all foreground processes and for background processes, where

appropriate. • Shall fulfill the data quality requirements specified in this document. • Should include all known inputs and outputs for the processes. Inputs include, e.g. use of

energy, water, material. Outputs include products, co -products and emissions. • May be collected, measured or calculated using activity data and related emission factors.

Emission factors may derive from secondary data subject to data quality requirements. e.g. for the energy sector, a specific data of “x” kWh electricity consumed may need to be combined with a generic data like “y” kg CO2 / kWh electricity, so that a flow of “x*y”

kg CO2 can be included in the resource use and emissions profile.

Secondary data • Should be used only for processes in the background system. When available, sector -

specific secondary data shall be used instead of multi -sector secondary data. • Shall fulfill the data quality requirements specified in this document. • Should , where available, be sourced following the data sources provided in this document.

LIFE CYCLE INVENTORY

As data collection is completed, an inventory of all input and output flows relative to the LCA systemboundary is built: kg CO2, kg H2S, kg Pb, etc.

IMPACT ASSESSMENT (mandatory steps) • Classification, i.e. assigning each data point within the life cycle inventory to the relevant impact

categories.

• Characterisation, i.e. applying characterisation factors to each input and output flows in order to

obtain aggregated impacts within each impact category.


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5.7 Data quality assessment and requirements 1 This section describes how the data quality shall be assessed, as well as data quality requirements. Data 2 quality requirements are established according to the “materiality” approach, which aims at “focusing on 3 where it really matters”. This means that most relevant lifecycle processes, leading the environmental profile 4 of a product, shall be modelled by using data with higher quality compared to less relevant processes 5 (regardless of where these processes take place in the life cycle of the product). 6

Within this context, a semi-quantitative assessment of data quality shall be performed and reported for the 7 processes or activities (i.e. the respective inventory datasets) accounting for at least 70% of contributions to 8 each considered impact category. The data quality of the overall LCA study shall also be calculated and 9 reported. Data quality shall be evaluated against four quality criteria (section 5.7.1), according to the semi-10 quantitative assessment method described in section 5.7.2. 11

Note that discussion on data quality requirements is still ongoing, so that the provisions and 12 recommendations in this section may be subject to refinement and changes, compared to this draft version 13 of the method. 14

5.7.1 Data quality criteria 15 Data quality shall be evaluated against four quality criteria, including: (i) Technological-Representativeness 16 (TeR), (ii) Geographical-Representativeness (GR), (iii) Time-Representativeness (TiR), and (iv) Parameter 17 uncertainty (P). The representativeness (technological, geographical and time-related) characterises to what 18 degree the processes and products selected are adequately depicting the system analysed, while the 19 precision indicates the way the data is derived and related level of uncertainty. 20

Besides these criteria, three additional aspects are included in the quality assessment, i.e. documentation 21 (compliance with the ILCD format), nomenclature (compliance with ILCD nomenclature), and review. The 22 latter three are not included within the semi-quantitative assessment of the data quality as described in the 23 following paragraphs, but shall however be fulfilled. 24

25


190

Table 44 summarises data quality criteria and data quality aspects relevant for LCA studies. 1

2


191

Table 44: Data quality criteria, documentation, nomenclature and review 1

Data quality criteria

Technological representativeness78 Geographical representativeness79 Time-related representativeness80 Parameter uncertainty81

Documentation Compliant with ILCD format

Nomenclature Compliant with ILCD nomenclature (e.g. use of ILCD reference elementary flows for IT compatible inventories)

Review Review by "Qualified reviewer” Separate review report

5.7.2 Semi-quantitative assessment of data quality 2

A semi-quantitative assessment of the quality level associated with the four data quality criteria shall be 3 performed first, according to the rating criteria reported in Table 45 (note that some criteria are context-4 specific and further guidance is provided in section 3.7.3 and 3.7.4 for company-specific and secondary 5 datasets respectively). An example of rating criteria for semi-quantitative assessment is reported in Annex B. 6 The overall data quality of the dataset (Data Quality Rating; DQR) shall then be calculated by summing up the 7 achieved quality rating for each of the quality criteria, divided by the total number of criteria (i.e. four), as 8 reported in Equation 29. Finally, the Data Quality Rating (DQR) result is used to identify the corresponding 9 quality level, as specified in Table 46. 10

𝐷𝑄𝑅 = [Equation 29] 11

DQR : Data Quality Rating of the dataset 12

TeR: Technological Representativeness 13

GR: Geographical Representativeness 14

TiR: Time-related Representativeness 15

P: Parameter uncertainty 16

17

Equation 29 shall be used to identify the overall data quality level according to the achieved data quality 18 rating. 19

78 The term “technological representativeness” is used throughout this Guide instead of “technological coverage” used in ISO14044. 79 The term “geographical representativeness” is used throughout this Guide instead of “geographical coverage” used in ISO14044. 80 The term “time-related representativeness” is used throughout this Guide instead of “time-related coverage” used in ISO14044.


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Table 45: Rating criteria for semi-quantitative assessment of overall data quality of Life Cycle Inventory datasets used in the LCA study 1

Quality level

Quality rating

Definition Technological representativeness

Geographical representativeness

Time representativeness Parameter uncertainty

Degree to which the dataset reflects the true population of interest regarding technology, including for included background datasets, if any.

Comment: i.e. of the technological characteristics including operating conditions.

Degree to which the dataset reflects the true population of interest regarding geography, including background datasets, if any.

Comment: i.e. of the given location / site, region, country, market, continent, etc.

Degree to which the dataset reflects the specific conditions of the system being considered regarding the time / age of the data, and including background datasets, if any.

Comment: i.e. of the given year (and, if applicable, of intra-annual or intra-daily differences).

Qualitative expert judgement or relative standard deviation as a % if a Monte Carlo simulation is used.

Comment: The uncertainty assessment is related to the resource use and emission data only; it does not cover the life cycle impact assessment.

Very good

1 Meets the criterion to a very high degree, without need for improvement.

Context–specific Context–specific Context–specific Very low uncertainty

Very low uncertainty ( 10%)

Good 2 Meets the criterion to a high degree, with little significant need for improvement.

Context–specific Context–specific Context–specific Low uncertainty

Low uncertainty (10% to 20%]

Fair 3 Meets the criterion to an acceptable degree, but merits improvement.

Context–specific Context–specific Context–specific Fair uncertainty

Fair uncertainty (20% to 30%]


193

Quality level

Quality rating

Definition Technological representativeness


Time representativeness Parameter uncertainty

Poor 4 Does not meet the criterion to a sufficient degree. Requires improvement.

Context–specific Context–specific Context–specific High uncertainty

High uncertainty (30% to

50%]

Very poor

5 Does not meet the criterion. Substantial improvement is necessary OR:

This criterion was not judged / reviewed or its quality could not be verified / is unknown.

Context–specific Context–specific Context–specific Very high uncertainty

Very high uncertainty ( 50%)

1


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Table 46: Overall data quality level of the LCI datasets according to the achieved data quality rating 1

Overall data quality rating (DQR) Overall data quality level

1.6 “Excellent quality”

1.6 to 2.0 "Very good quality"

2.0 to 3.0 “Good quality”

3.0 to 4.0 "Fair quality"

>4.0 “Poor quality”

2

Table 47: Example for determining the data quality rating of LCI datasets 3

Component Achieved quality level Corresponding quality rating

Technological representativeness (TeR) good 2

Geographical representativeness (GR) good 2

Time-related representativeness (TiR) fair 3

Parameter uncertainty (P) good 2

4

DQR =TeR + GR + TiR + P

4=

2 + 2 + 3 + 2

4= 2.2

5

6 A DQR of 2.2 corresponds to an overall “good quality” rating. 7 8 The data quality requirements for technological, geographical and time-related representativeness shall be 9 subject to review as part of the LCA study, if appropriate. 10

5.7.3 Data quality assessment of company-specific datasets 11

Data quality of company-specific datasets accounting for at least 70% of contributions to each considered 12 impact category shall be separately assessed for (i) the company-specific activity data (type and magnitude 13 of non-elementary flows), (ii) the company-specific emission data (type and magnitude of elementary flows) 14 and (iii) the secondary sub-processes used to model non-elementary flows. The DQR of the newly developed 15 dataset shall be calculated as follow: 16

1) Select the most relevant sub-processes and direct elementary flows that account for at least 80% of the 17 total (weighted) environmental impact of the company-specific dataset, listing them from the most 18 contributing to the least contributing one. 19

2) Calculate the data quality (DQR) criteria TeR, TiR, GR and P for each most relevant sub-process and each 20 most relevant direct elementary flow. The value of each DQR criteria shall be assigned based on Table 48 21 (see section 5.7.3.1). 22


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2.a) Each most relevant elementary flow consists of the amount and elementary flow naming (e.g. 1 40 g carbon dioxide). For each most relevant elementary flow, the practitioner shall evaluate the 4 2 DQR criteria named TeR-EF, TiR-EF, GR-EF, PEF (where EF stands for elementary flow). For example, the 3 practitioner shall evaluate the timing of the flow measured, for which technology the flow was 4 measured and in which geographical area. 5

2.b) Each most relevant process is a combination of activity data (quantifying the magnitude of each 6 non-elementary flow) and the secondary dataset used to model such flows. For each most relevant 7 process, the DQR is calculated by the practitioner as a combination of the 4 DQR criteria for activity 8 data and the corresponding secondary dataset: (i) TiR and P shall be evaluated at the level of the 9 activity data (named TiR-AD, PAD) and (ii) TeR, TiR and GR shall be evaluated at the level of the secondary 10 dataset used (named TeR-SD , TiR-SD and GR-SD). As TiR is evaluated twice, the arithmetic average of TiR-11 AD and TiR-SD represents the TiR of the most relevant process. 12

3) Calculate the contribution of each most-relevant sub-process and direct elementary flow to the total 13 (weighted) environmental impact of all most-relevant processes and elementary flows of the dataset, in %. 14 For example, if the newly developed dataset has only two most relevant processes, contributing in total to 15 80% of the total environmental impact of the dataset, and: 16

Process 1 carries 30% of the total dataset environmental impact. The contribution of this process to 17 the total of 80% is 37.5% (30/0.8). 18

Process 2 carries 50% of the total dataset environmental impact. The contribution of this process to 19 the total of 80% is 62.5% (50/0.8). 20

The calculated values (37.5% and 62.5%) are the weight to be used in the following point 4. 21

4) Calculate the TeR, TiR, GR and P criteria of the newly developed dataset as the weighted average of each 22 criteria of the most relevant processes and direct elementary flows. The weight is the relative contribution 23 (in %) of each most relevant process and direct elementary flow calculated in step 3. 24

5) Calculate the total DQR of the newly developed dataset using Equation 30, where 𝑇𝑒 , 𝐺 , 𝑇𝚤 , 𝑃 are the 25 weighted average calculated as specified in point (4). 26

𝐷𝑄𝑅 = [Equation 30] 27

NOTE: in case the newly developed dataset has most relevant sub-processes filled in by non-EF compliant 28 datasets (and thus without DQR), then these datasets cannot be included in step 4 and 5 of the DQR 29 calculation. In this situation: (1) The weight of step 3 shall be recalculated for the EF-compliant datasets only. 30 Calculate the environmental contribution of each most-relevant EF compliant process and elementary flow 31 to the total environmental impact of all most-relevant EF compliant processes and elementary flows, in %. 32 Continue with step 4 and 5. (2) The weight of the non-EF compliant dataset (calculated in step 3) shall be 33 used to increase the DQR criteria and total DQR accordingly. For example: 34

Process 1 carries 30% of the total dataset environmental impact and is ILCD entry level compliant. 35 The contribution of this process to the total of 80% is 37.5% (the latter is the weight to be used). 36

Process 1 carries 50% of the total dataset environmental impact and is EF compliant. The contribution 37 of this process to all most-relevant EF compliant processes is 100%. The latter is the weight to be 38 used in step 4. 39


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After step 5, the parameters Te , G , Tı , P and the total DQR shall be multiplied with 1.375. 1

5.7.3.1 DQR tables for processes with company-specific data 2

To assess the value of the DQR criteria of processes for which company-specific data are used (i.e. company-3 specific datasets), the scoring criteria reported in Table 48 shall be used. Only the reference years criteria TiR 4 (TiR-EF and TiR-AD and TiR-SD) might be adapted by the practitioner. It is not allowed to modify the text for the other 5 criteria. 6


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Table 48: How to assign the values to DQR criteria when using company-specific datasets 1

Value PEF and PAD TiR-EF and TiR-AD TiR-SD TeR-EF and TeR-SD GR-EF and GR-SD

1 Measured/calculated and externally verified

The data refers to the most recent annual administration period with respect to the timing of the LCA study

The LCA study is carried out within the time validity of the dataset

The elementary flows and the secondary dataset reflect exactly the technology of the newly developed dataset

The data(set) reflects the exact geography where the process modelled in the newly created dataset takes place

2 Measured/calculated and internally verified, plausibility checked by reviewer

The data refers to maximum 2 annual administration periods with respect to the timing of the LCA study

The LCA study is carried out not later than 2 years beyond the time validity of the dataset

The elementary flows and the secondary dataset is a proxy of the technology of the newly developed dataset

The data(set) partly reflects the geography where the process modelled in the newly created dataset takes place

3 Measured/calculated/literature and plausibility not checked by reviewer OR Qualified estimate based on calculations plausibility checked by reviewer

The data refers to maximum three annual administration periods with respect to the timing of the LCA study

Not applicable Not applicable Not applicable

4-5 Not applicable Not applicable Not applicable Not applicable Not applicable

PEF: Precision for elementary flows; PAD: Precision for activity data; TiR-EF: Time Representativeness for elementary flows; TiR-AD: Time representativeness for activity data; TiR-SD: Time 2 representativeness for secondary datasets; TeR-EF: Technology representativeness for elementary flows; TeR-SD: Technology representativeness for secondary datasets; GR-EF: Geographical 3 representativeness for elementary flows; GR-SD: Geographical representativeness for secondary datasets. 4


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5.7.4 Data quality assessment of secondary datasets 1 To assess the value of context-specific data quality (DQR) criteria TeR, TiR and GR for most relevant processes 2 modelled based on secondary datasets, the scoring criteria reported in Table 49 shall be used. Only the 3 reference year for criteria TiR might be adapted by the practitioner, per process. It is not allowed to modify 4 the text for the other criteria. 5

Table 49: How to assign the values to DQR criteria when using secondary datasets 6

TiR TeR GR

1 The LCA study is conducted within the time validity of the dataset

The technology used in the LCA study is exactly the same as the one in scope of the dataset

The process modelled in the LCA study takes place in the country the dataset is valid for

2 The LCA study is conducted not later than 2 years beyond the time validity of the dataset

The technologies used in the LCA study is included in the mix of technologies in scope of the dataset

The process modelled in the LCA study takes place in the geographical region (e.g. Europe) the dataset is valid for


The technologies used in the LCA study are only partly included in the scope of the dataset

The process modelled in the LCA study takes place in one of the geographical regions the dataset is valid for


The technologies used in the LCA study are similar to those included in the scope of the dataset

The process modelled in the LCA study takes place in a country that is not included in the geographical region(s) the dataset is valid for, but sufficient similarities are estimated based on expert judgement.

5 The LCA study is conducted later than 6 years after the time validity of the dataset

The technologies used in the LCA study are different from those included in the scope of the dataset

The process modelled in the LCA study takes place in a different country than the one the dataset is valid for

TiR: Time representativeness; TeR: Technology representativeness; GR: Geographic representativeness. 7

5.7.5 The Data Quality Rating (DQR) of the study 8 The DQR of the LCA study (i.e. of the overall dataset related to the analysed product) shall be calculated and 9 reported in the LCA study report. 10

In order to calculate the DQR of the LCA study, the applicant shall calculate separately the TeR, TiR, GR and 11 P for the LCA study as the weighted average of the values of TeR, TiR and GR related to all most relevant 12 processes82. Weighting factors shall be based on the relative environmental contribution (in %) of each 13 process to the total weighted impact (single score) of all most relevant processes. The detailed DQR 14 calculation rules of section 5.7.3 shall be followed. 15

82 Most relevant processes are those that collectively contribute with at least 80% to any of the considered impact categories.


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5.7.6 Data quality requirements 1 Data quality requirements specified below shall be met by LCA studies intended for external 2 communication, i.e. B2B and B2C. For LCA studies (claiming to be in line with this method) intended for in-3 house applications, the specified data quality requirements should be met (i.e. are recommended, but are 4 not mandatory). Any deviations from the requirements shall be documented. Data quality requirements 5 apply to both company-specific83 and secondary data84. 6

In the optional screening step (section 5.1) a minimum “fair quality” level (i.e. data quality rating ranging 7 from 3 to 4) is required for datasets contributing to at least 90% of the impact estimated for each impact 8 category, as assessed via a qualitative expert judgement. 9

In the final Life Cycle Inventory, for the processes or activities accounting for at least 70% of contributions 10 to each impact category, both company-specific and generic data shall achieve at least an overall “good 11 quality” level. At least 2/3 of the remaining 30% (i.e. 20% to 30%) shall be modelled with at least “fair 12 quality” data. Data of less than “fair quality” level shall not account for more than 10% contributions to 13 each impact category. Note that the 70% threshold is chosen to balance the goal of achieving a robust 14 assessment with the need to keep it feasible and accessible. 15

16

83 Most relevant processes are those that collectively contribute with at least 80% to any of the considered impact categories. 84 Refers to data that is not directly collected, measured, or estimated, but rather sourced from a third-party life-cycle-inventory database or other source that complies with the data quality requirements of the PEF method.


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6. Life Cycle Impact Assessment 1 Once the Life Cycle Inventory has been compiled, the life cycle impact assessment shall be undertaken to 2 calculate the environmental performance of the product, using the selected impact categories and models 3 (section 4.2.4). Life cycle impact assessment includes two mandatory and two optional steps. The Life Cycle 4 Impact Assessment does not intend to replace other (regulatory) tools that have a different scope and 5 objective such as (Environmental) Risk Assessment ((E)RA), site specific Environmental Impact Assessment 6 (EIA) or Health and Safety regulations at product level or related to safety at the workplace. Especially, the 7 Life Cycle Impact Assessment has not the objective to predict if at any specific location at any specific time 8 thresholds are exceeded and actual impacts occur. In contrast it describes the existing pressures on the 9 environment. Thus, the Life Cycle Impact Assessment is complementary to other well-proven tools, adding 10 the life cycle perspective. 11

6.1 Classification and Characterisation (mandatory) 12 As a requirement for any LCA study, life cycle impact assessment shall include a classification and 13 characterisation of the life cycle inventory flows. 14

6.1.1 Classification of Life Cycle Inventory Flows 15 Classification requires assigning the material/energy inputs and outputs compiled in the Life Cycle Inventory 16 to the relevant impact category. For example, during the classification phase, all inputs/outputs that result 17 in greenhouse gas emissions are assigned to the Climate Change category. Similarly, those that result in 18 emissions of ozone-depleting substances are classified accordingly to the Ozone Depletions category. In some 19 cases, an input/output may contribute to more than one impact category (for example, chlorofluorocarbons 20 (CFCs) contribute to both Climate Change and Ozone Depletion). An example of classification is reported 21 below. 22

All inputs/outputs inventoried during the compilation of the Life Cycle Inventory shall be assigned to the 23 impact categories to which they contribute (“classification”), using the classification data available at 24 http://eplca.jrc.ec.europa.eu/LCDN/. 25

As part of the classification of the Life Cycle Inventory, data should be expressed in terms of constituent 26 substances for which characterisation factors (see Section 6.1.2) are available. 27

For example, data for a composite NPK fertiliser should be disaggregated and classified according to its N, P, 28 and K fractions, because each constituent element will contribute to different impact categories. In practice, 29 much of the Life Cycle Inventory data may be drawn from existing public or commercial life-cycle-inventory 30 databases, where classification has already been implemented. In such cases, it must be assured, for example 31 by the provider, that the classification and linked impact assessment pathways correspond to the 32 requirements of this guide. 33

Example: classification of life cycle inventory data 34

Classification of data in the climate change impact category: 35 CO2 Yes 36 CH4 Yes 37 SO2 No 38 NOx No 39


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Classification of data in the acidification impact category: 1 CO2 No 2 CH4 No 3 SO2 Yes 4 NOx Yes 5

6.1.1.1 Classification for the climate change impact category 6

The 'climate change' impact category normally consists of three main sub-categories, each one referring to a 7 specific category of greenhouse gas (GHG) emissions and removals: 8

1. ‘Climate change –fossil’, accounting for fossil GHG emissions and removals; 9 2. ‘Climate change – biogenic’, accounting for biogenic carbon emissions and removals; 10 3. ‘Climate change – land use and land transformation’, accounting for carbon emissions associated 11

with land use and land transformation. 12 13

The contribution of each sub-category to the total climate change impact shall be reported separately if it is 14 larger than 5%85. 15

6.1.2 Characterisation of Life Cycle Inventory Flows 16 Characterisation refers to the calculation of the magnitude of the contribution of each classified input/output 17 to their respective impact categories, and aggregation of the contributions within each category. This is 18 carried out by multiplying the values in the Life Cycle Inventory by the relevant characterisation factor for 19 each impact category. 20

The characterisation factors are substance- or resource- specific. They represent the impact intensity of a 21 substance relative to a common reference substance for an impact category (impact category indicator). For 22 example, in the case of calculating climate change impacts, all greenhouse gas emissions inventoried in the 23 Life Cycle Inventory are weighted in terms of their impact intensity relative to carbon dioxide, which is the 24 reference substance for this category. This allows for the aggregation of impact potentials and expression in 25 terms of a single equivalent substance (in this case, CO2 equivalents) for each impact category. For example, 26 the CF expressed as global warming potential for methane equals 25 CO2 – equivalents and its impact on 27 global warming is thus 25 times higher than of CO2 (i.e. CF of 1 CO2-equivalent). An example of 28 characterisation is reported below. 29

All classified inputs/outputs in each impact category shall be assigned characterisation factors representing 30 the contribution per unit of input/output to the category, using the provided characterisation factors 31 available online at http://eplca.jrc.ec.europa.eu/EF-node/LCIAMethodList.xhtml?stock=default. Life cycle 32 impact assessment results shall subsequently be calculated for each impact category by multiplying the 33 amount of each input/output by its characterisation factor and summing the contributions of all 34

85For example, if 'Climate change - biogenic' contributes with 7% (using absolute values) to the total climate change impact and 'Climate change – land use and land transformation' contributes with 3% , the Total climate change impact and the 'Climate change – biogenic' shall be reported..


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inputs/outputs within each category in order to obtain a single measure expressed in the appropriate 1 reference unit. 2

If characterisation factors (CFs) from the default model are not available for certain flows (e.g. a group of 3 chemicals) of the Life Cycle Inventory, then other approaches may be used for characterising these flows. In 4 such circumstances, this shall be reported under “additional environmental information”. The 5 characterisation models shall be scientifically and technically valid, and based upon distinct, identifiable 6 environmental mechanisms86 or reproducible empirical observations. 7

Example: calculation of life cycle impact assessment results 8

Global warming 9

CF 10

CO2 g 5.132 x 1 = 5.132 kg CO2eq 11

CH4 g 8.2 x 25 = 0.205 kg CO2eq 12

SO2 g 3.9 x 0 = 0 kg CO2eq 13

NOx g 26.8 x 0 = 0 kg CO2eq 14

Total = 5.337 kg CO2eq 15

Acidification 16

CF 17

CO2 g 5.132 x 0 = 0 Mol H+ eq 18

CH4 g 8.2 x 0 = 0 Mol H+ eq 19

SO2 g 3.9 x 1.31 = 0.005 Mol H+ eq 20

NOx g 26.8 x 0.74 = 0.019 Mol H+ eq 21

Total = 0.024kg Mol H+ eq 22

23

6.1.2.1 Characterisation factors for the climate change impact category 24

The global warming potentials (GWPs) of the Fifth Assessment Report of IPCC (IPCC, 2013) are applied. GWPs 25 including climate-change carbon feedbacks for both CO2 and non-CO2 substances shall be specifically used 26 (following the UNEP/SETAC recommendations of the Pellston Workshop, January 2016). The values with 27 feedbacks are applied to ensure consistency, as feedbacks are already included for CO2. The GWPs of well-28 mixed GHGs can be found in chapter 8 of the Scientific basis report, Tables 8.7 and 8.SM.16. The GWPs for 29 near term GHGs are not recommended for use due to their complexity and high uncertainty. Near term GHGs 30 refer to substances that are not well-mixed once emitted to the atmosphere because of their very rapid 31 decay (black carbon, organic carbon, nitrogen oxides, sulphur oxides, volatile organic compounds, and carbon 32 monoxide). 33

The third assessment IPCC report (2007) estimated the global warming potential for methane at 25 for a time 34 period of 100 years. This value accounts for the indirect climate effects of methane emissions (such as the 35

86 An environmental mechanism is defined as a system of physical, chemical and biological processes for a given EF impact category linking the Life Cycle Inventory results to EF category indicators. (based on ISO 14040:2006)


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positive feedback on the methane lifetime and on the concentrations of ozone and stratospheric water 1 vapour) but excludes the oxidation of methane into carbon dioxide. The Fifth assessment report of IPCC 2 (2013) reports a global warming potential for methane at 34, still with the exclusion of methane oxidation 3 into carbon dioxide and which is valid for biogenic methane only (IPCC 2013, Table 8.7). IPCC (2013) refers to 4 Boucher et al. (2009) to add the methane oxidation for fossil methane, resulting in a GWP of 36. The added 5 value of +2 includes only a partial oxidation of methane into CO2. Boucher et al. (2009) calculated an upper 6 limit of +2.5 when considering that all methane is converted into CO2 and up to +2.75 with a longer time 7 horizon. Within the context of this method, a simple stoichiometric calculation is used to compensate the 8 avoided CO2 uptake within the released methane (+2.75). It can be discussed which correction factor should 9 be applied, (i) +2 following IPCC, (ii) +2.5 following the upper margin of Boucher et al. (2009) for a time 10 horizon of 100 years or (iii) +2.75 using the stoichiometric balance (all emissions happens "now"). The last 11 approach is chosen, as a GWP of 36.75 ensures the same outcome between a detailed modelling (modelling 12 all biogenic carbon uptakes and releases) and a simplified modelling approach (only modelling the biogenic 13 CH4 release). In the context of this method, the same result between a detailed modelling approach or the 14 proposed simplified modelling approach is considered to be essential. This means that for fossil methane a 15 GWP of 36.75 shall be used. 16

For biogenic carbon modelling, the list of ILCD elementary flows and CFs shall be applied. 17

18


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Table 50 summarises CFs to be applied for fossil and biogenic carbon emissions. 1

2


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Table 50: CFs (in CO2-equivalents, with carbon feedbacks) 1

Substance Compartment GWP100

Carbon dioxide (fossil) Air emission 1

Methane (fossil) Air emission 36.75

Carbon monoxide (fossil) Air emission 1.57 87

Carbon dioxide (biogenic) Resources from air 0

Carbon dioxide (biogenic) Air emission 0

Methane (biogenic) Air emission 34

Carbon monoxide (biogenic) Air emission 1.57

Carbon dioxide (land use change) Resources from air -1

Carbon dioxide (land use change) Air emission 1

Methane (land use change) Air emission 36.75

Carbon monoxide (land use change) Air emission 1.57

2

6.2 Normalisation and Weighting (recommended/optional) 3 Following the two mandatory steps of classification and characterisation, the life cycle impact assessment 4 may be complemented with normalisation and weighting, which are recommended/optional steps. 5

6.2.1 Normalisation of Life Cycle Impact Assessment Results (recommended) 6 Normalisation is not a required, but recommended step in which the life cycle impact assessment results are 7 multiplied by normalisation factors in order to calculate and compare the magnitude of their contributions 8 to the impact categories relative to a reference unit (typically the pressure related to that category caused 9 by the emissions over one year of a whole country or an average citizen). As a result, dimensionless, 10 normalised life cycle impact assessment results are obtained. These reflect the burdens attributable to a 11 product relative to the reference unit, such as per capita for a given year and region. This allows the relevance 12 of the contributions made by individual processes to be compared to the reference unit of the impact 13 categories considered. For example, life cycle impact assessment results may be compared to the same life 14 cycle impact assessment results for a given region such as the EU-27 and on a per-person basis. In this case 15 they would reflect person-equivalents relative to the emissions associated with the EU-27. Normalised 16 environmental footprint results do not, however, indicate the severity/relevance of the respective impacts. 17

87 The effects of near term climate forcers are uncertain and therefore excluded (following the UNEP/SETAC recommendations of the Pellston Workshop, January 2016). The GWP presented here represents only the effects from degradation of CO into CO2 (stoichiometric calculation).


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Normalisation is not a required, but recommended step for plastic LCA studies. If normalisation is applied, 1 the set of normalisation factors reported in Annex B shall be employed. 2

Normalised results shall not be simply aggregated as this implicitly applies weighting. Results from the life 3 cycle impact assessment prior to normalisation shall be reported alongside the normalised results. 4

6.2.2 Weighting of Environmental Footprint Impact Assessment Results (recommended) 5 Weighting is not a required, but recommended step that may support the interpretation and communication 6 of the results of the analysis. In this step, life cycle impact assessment results, for example normalised results, 7 are multiplied by a set of weighting factors, which reflect the perceived relative importance of the impact 8 categories considered. Weighted life cycle impact assessment results can then be compared to assess their 9 relative importance. They can also be aggregated across impact categories to obtain several aggregated 10 values or a single overall impact indicator. 11

Weighting requires making value judgements as to the respective importance of the impact categories 12 considered. These judgements may be based on expert opinion, cultural/political viewpoints, or economic 13 considerations.88 14

Weighting is not a required, but optional step for LCA studies. If weighting is applied, the set of weighting 15 factors reported in Annex B shall be employed. Weighted impact assessment results shall be reported under 16 “additional environmental information, along with life cycle impact assessment results prior to weighting. 17

The application of normalisation and weighting steps in LCA studies shall be consistent with the defined goals 18 and scope of the study, including the intended applications.89 19

6.3 Assessment of biodiversity impacts 20 The default set of impact categories adopted in this method includes no impact category named 21 "biodiversity”. However, the method includes at least 7 impact categories that have an effect on biodiversity 22 (i.e., climate change, acidification, terrestrial eutrophication, freshwater eutrophication, marine 23 eutrophication, freshwater ecotoxicity, and land use). 24

As biodiversity is an important topic on the political agenda, an assessment of the potential impact on 25 biodiversity can be performed, if considered relevant for the analysed product(s). The chosen approach to 26 the assessment shall be adequately described in the LCA study report. The results of the biodiversity 27 assessment shall be reported under “additional environmental information”. 28

While the practitioner is free to determine how biodiversity shall be assessed and reported (if relevant), the 29 following suggestions are offered: 30

88 For more information on existing weighting approaches in Life Cycle Impact Assessment, please refer to the reports developed by the JRC and CML entitled “Background review of existing weighting approaches in LCIA” and “Evaluation of weighting methods for measuring the EU-27 overall environmental impact”. These are available online at http://lct.jrc.ec.europa.eu/assessment/publications 89 It should be noted that ISO 14040 and 14044 do not permit the use of weighting in support of comparative assertions intended to be disclosed to the public.


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● To express the (avoided) impact on biodiversity as the percentage of material that comes from 1 ecosystems that have been managed to maintain or enhance conditions for biodiversity (e.g. such 2 that less than 15% of species richness is lost due to disturbance, but the practitioner may set an 3 alternative level provided this is well justified). Fulfilment of the established threshold shall be 4 demonstrated by regular monitoring and reporting of biodiversity levels and respective gains or 5 losses. The assessment should refer to materials that end up in the final products and to materials 6 that have been used during the production process. For example, charcoal that is used in steel 7 production processes, or soy that is used to feed cows that produce dairy etc. 8

● To report additionally the percentage of materials for which no chain of custody or traceability 9 information can be found. 10

● To use a certification system as a proxy. A certification schemes that provide sufficient evidence for 11 ensuring biodiversity maintenance should be selected and the criteria used shall be described. A 12 useful overview of standards can be found on http://www.standardsmap.org/. 13

14 15


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7. Interpretation of the LCA results 1 Interpretation of the results of the LCA90 study serves two purposes: 2

The first is to ensure that the LCA model corresponds to the goals of the study and fulfil its quality 3 requirements. In this sense, result interpretation may inform iterative improvements of the LCA 4 model until all goals and requirements are met; 5

The second purpose is to derive robust conclusions and recommendations from the analysis, for 6 example in support of environmental improvements. 7

To meet these objectives, the result interpretation phase shall include four key steps, as outlined in this 8 chapter: (i) assessment of the robustness of the LCA model; (ii) identification of hotspots; (ii) estimation of 9 uncertainty; and (iv) conclusions, limitations and recommendations. 10

7.1 Assessment of the robustness of the LCA model 11

The assessment of the robustness of the LCA model assesses the extent to which methodological choices 12 such as the system boundary, data sources, allocation choices, and coverage of impact categories influence 13 the analytical outcomes. 14

Tools that should be used to assess the robustness of the LCA model include: 15

Completeness checks: assess the Life Cycle Inventory data to ensure that it is complete relative to 16 the defined goals, scope, system boundary and quality criteria. This includes completeness of process 17 coverage (i.e. all processes at each supply-chain stage considered have been included) and 18 input/output coverage (i.e. all relevant material and/or energy inputs and emissions associated with 19 each process have been included). 20

21 Sensitivity checks: assess the extent to which the results are determined by specific methodological 22

choices, and the impact of implementing alternative choices where these are identifiable. It is useful 23 to structure sensitivity checks for each phase of the LCA study, including goal and scope definition, 24 the Life Cycle Inventory, and the life cycle impact assessment. 25

26 Consistency checks: assess the extent to which assumptions, methods, and data quality 27

considerations have been applied consistently throughout the LCA study. 28 29

Any issues flagged in this evaluation may be used to inform iterative improvements to the LCA study. 30

Within the present method, the assessment of the robustness of the LCA model shall include a sensitivity 31 check to assess the extent to which methodological choices (made in accordance with the requirement of 32 this guide) influence the results. Other tools that should be used to assess the robustness of the LCA model 33 are completeness checks and consistency checks. 34

90 The term “life cycle interpretation” is used in ISO 14044 to refer to this stage.


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7.2 Identification of Hotspots: most relevant impact categories, life cycle stages, 1

processes and elementary flows 2 Once it has been ensured that the LCA model is robust and conforms to all aspects defined in the goal and 3 scope definition phases, the next step is to identify the main contributing elements to the LCA results. This 4 step may also be referred to as “hotspot” or “weak point” analysis. Contributing elements may be specific 5 life-cycle stages, processes, or individual material/energy inputs/outputs associated with a given stage or 6 process in the product supply chain. These are identified by systematically reviewing the LCA study results. 7 Graphical tools may be particularly useful in this context. Such analyses provide the necessary basis to 8 identify improvement potentials of the environmental performance of the product, associated with specific 9 management interventions. 10

In the interpretation phase, the performer of a LCA study shall identify the most relevant: 11

1. Impact categories, 12

2. Life cycle stages 13

3. Processes 14

The procedure that shall be followed to identify the most relevant impact categories, life cycle stages, 15 processes and direct elementary flows is described in the following sections. 16

7.2.1 Procedure to identify the most relevant impact categories 17 The identification of the most relevant impact categories shall be based on the normalised and weighted 18 results. The most relevant impact categories shall be identified as all impact categories that cumulatively 19 contribute to at least 80% to the total environmental impact. This should start from the largest to the smallest 20 contributions. At least three relevant impact categories shall be identified as most relevant ones. The 21 performer of the LCA study may add more impact categories to the list of the most relevant ones but none 22 shall be deleted. 23

7.2.2 Procedure to identify the most relevant life cycle stages 24 The most relevant life cycle stages are the ones that together contribute to at least 80% to any of the most 25 relevant impact categories identified. This should start from the largest to the smallest contributions. The 26 performer of the LCA study may add more life cycle stages to the list of the most relevant ones but none shall 27 be deleted. As a minimum, the following life cycle stages shall be considered: 28

● Raw material acquisition and pre-processing (including production of parts and unspecific 29 components); 30

● Production of the main product; 31 ● Product distribution and storage; 32 ● Use stage (if in scope); 33 ● End-of-life (including product, recovery / recycling, if in scope). 34

35

If the use stage accounts for more than 50% of the total impact then the procedure shall be re-run by 36 excluding the use stage. In this case, the list of most relevant life cycle stages shall be those selected through 37 the latter procedure plus the use stage. 38


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7.2.3 Procedure to identify the most relevant processes 1 Each most relevant impact category shall be further investigated to identify the most relevant processes used 2 to model the product life cycle. Similar/identical processes taking place in different life cycle stages (e.g. 3 transportation, electricity use) shall be accounted for separately. They shall be reported in the LCA study 4 report together with the respective life cycle stage or multiple life cycle stages if relevant. The identification 5 of the most relevant processes shall be done according to Table 51. 6

Table 51: Criteria to select at which life cycle stage level to identify the most relevant processes 7

Contribution of the use stage to the total impact Most relevant processes identified at the level of

≥ 50% ∙ Whole life cycle excluding use stage, and ∙ Use stage

< 50% ∙ Whole life cycle

8

The most relevant processes are those that collectively contribute to at least 80% to any of the most 9 relevant impact categories identified. The performer of the LCA study may add more processes to the list of 10 the most relevant ones but none shall be delete. 11

7.2.4 Dealing with negative numbers 12 When identifying the percentage impact contribution for any process or flow, it is important that absolute 13 values are used (i.e. the minus sign is ignored). This allows the relevance of any credits (e.g., from recycling) 14 to be identified. In case of flows with a negative impact score, (i) you should consider those flows to have a 15 plus sign, namely a positive score, (ii) the total impact score needs to be recalculated including the converted 16 negative scores, (iii) the total impact score is set to 100% and (iv) the percentage impact contribution for any 17 life cycle stage, process or flow is assessed to this new total. 18

7.2.5 Summary of requirements 19 In Table 52 the requirements to define most relevant contributions are summarized. 20

Table 52: Summary of requirements to define most relevant contributions 21

Item At what level does relevance need to be identified?

Threshold

Most relevant impact categories

Normalised and weighted results

Impact categories cumulatively contributing at least 80% of the total environmental impact (excluding toxicity related impact categories)

Most relevant life cycle stages

For each most relevant impact category

All life cycle stages contributing cumulatively more than 80% to that impact category

Most relevant processes

For each most relevant impact category

All processes contributing cumulatively (along the entire life cycle) more than 80% to that impact category

22

23


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7.2.6 Example 1 What follows is a fictitious example, not based on any specific LCA study results. 2

Most relevant impact categories 3

Table 53: Contribution of different impact categories based on normalised and weighted results 4

Impact category Contribution to the total impact (%)

Climate change 21.5

Ozone depletion 3.0

Human toxicity, cancer 6.0

Human toxicity, non-cancer 0.1

Particulate matter 14.9

Ionizing radiation, human health 0.5

Photochemical ozone formation, human health 2.4

Acidification 1.5

Eutrophication, terrestrial 1.0

Eutrophication, freshwater 1.0

Eutrophication, marine 0.1

Ecotoxicity, freshwater 0.1

Land use 14.3

Water use 18.6

Resource use, minerals and metals 6.7

Resource use, fossils 8.3

Total most relevant Impact Categories 84.3

Based on the normalised and weighted results, and excluding the toxicity related impacts, the most relevant 5 impact categories are: climate change, water use, land use, and resource use (minerals and metals and fossils) 6 for a cumulative contribution of 87.4% of the total impact. 7

8


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Most relevant life cycle stages 1

Table 54: Contribution of different life cycle stages to the Climate Change impact category (based on the 2 characterised inventory results) 3

Life cycle stage (LCS) Contribution (%)

Raw material acquisition and pre-processing 46.3

Production of the main product 21.2

Product distribution and storage 16.5

Use stage 5.9

End-of-Life 10.1

Total most relevant LCS 88.0

The three life cycle stages in yellow will be the ones identified as "most relevant" for climate change as they 4 are contributing to more than 80%. Ranking shall start from the highest contributors. 5

This procedure shall be repeated for all the selected most relevant impact categories. 6

Most relevant processes 7

Table 55: Contribution of different processes to the Climate Change impact category (based on the 8 characterised inventory results) 9

Life cycle stage Unit process Contribution (%)

Raw material acquisition and pre-processing Process A 4.9

Process B 41.4

Production of the main product Process C 18.4

Process D 2.8

Product distribution and storage Process E 16.5

Use stage Process F 5.9

End-of-Life Process G 10.1

Total most relevant processes 86.4

According to the proposed procedure the processes B, C, E and G shall be selected as “most relevant”. 10

This procedure shall be repeated for all the selected most relevant impact categories. 11

12

7.3 Conclusions, Recommendations and Limitations 13 The final step of the result interpretation phase is to draw conclusions based on the analytical results, answer 14 the questions posed at the outset of the LCA study (goal definition), and advance recommendations 15


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appropriate to the intended audience and context while explicitly taking into account any limitations to the 1 robustness and applicability of the results. The LCA study needs to be seen as complementary to other 2 assessments and instruments such as site-specific environmental impact assessments or chemical risk 3 assessments. 4

Potential improvement options can also be identified, such as cleaner technology or techniques, changes in 5 product design, implementation of environmental management systems (e.g. Eco-Management and Audit 6 Scheme (EMAS) or ISO 14001), or other systematic approaches. 7

Conclusions, recommendations and limitations shall be described in accordance with the defined goals and 8 scope of the LCA study. LCA studies intended to support comparative assertions to be disclosed to the public 9 (i.e. claims about the environmental superiority or equivalence of the product) shall fulfil the requirement of 10 this guide. Where appropriate, the conclusions should include a summary of identified supply chain 11 “hotspots” and the potential improvement in the environmental performance of the product(s) associated 12 with management interventions. 13

8. Reporting 14

8.1 General 15 A LCA report provides a relevant, comprehensive, consistent, accurate, and transparent account of the study 16 and of the calculated environmental impacts associated with the analysed product(s). It reflects the best 17 possible information in such a way as to maximise its usefulness to intended current and future users, whilst 18 honestly and transparently communicating limitations. Effective LCA reporting requires that several criteria, 19 both procedural (report quality) and substantive (report content), are met. 20

Any LCA study intended for external communications shall include a LCA study report, which shall provide a 21 robust basis for assessing, tracking, and seeking to improve the environmental performance of the analysed 22 product(s) over time. The LCA study report shall include, as a minimum, the three main elements specified in 23 section 8.2 (Summary, Main Report and Annex) and all the respective sub-elements. Any additional 24 supporting information may also be included, for example a Confidential Report (section 8.2.4). 25

8.2 Reporting elements 26 A LCA report consists of at least three elements: a Summary, the Main Report, and an Annex. Confidential 27 and proprietary information can be documented in a fourth element – i.e. a complementary Confidential 28 Report that will not be disclosed to the public. Review reports are either annexed or referenced. 29

8.2.1 First element: Summary 30 The Summary shall be able to stand alone without compromising the results and 31 conclusions/recommendations (if included). The Summary shall fulfil the same criteria about transparency, 32 consistency, etc. as the detailed report. The Summary shall, as a minimum, include: 33

Key elements of the goal and scope of the study with relevant limitations and assumptions; 34

A description of the system boundary; 35

The main results from the Life Cycle Inventory and the life cycle impact assessment components: 36 these shall be presented in such a way as to ensure the proper use of the information; 37


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If applicable, environmental improvements compared to previous periods; 1

Relevant statements about data quality, assumptions and value judgements; 2

A description of what has been achieved by the study, any recommendations made and conclusions 3 drawn; 4

Overall appreciation of the uncertainties of the results. 5

8.2.2 Second element: Main Report 6 The Main Report91 shall, as a minimum, include the following components: 7

Goal of the study: 8 Mandatory reporting elements include, as a minimum: 9 10

o Intended application(s) of the study; 11 o Reasons for carrying out the study; 12 o Methodological or LCIA impact category limitations; 13 o Target audience ; 14 o Whether the study is intended for comparison or for comparative assertions to be disclosed 15

to the public; 16 o Commissioner of the study (if any). 17

• Scope of the study: 18

The Scope of the study shall identify the analysed system in detail and address the overall approach 19 used to establish the system boundary. The Scope of the study shall also address data quality 20 requirements. Finally, the Scope shall specify which impact categories are included, describe the 21 methods applied for assessing potential environmental impacts, and specify which normalisation and 22 weighting criteria are possibly used. 23

Mandatory reporting elements include, as a minimum: 24

o Functional unit and reference flow; 25 o The system boundary, including any omissions of life-cycle stages, processes or 26

quantification of energy and material inputs and outputs, as well as performed assumptions 27 about electricity production, use and end-of-life stages; 28

o The reasons for and potential significance of any exclusions; 29 o All assumptions, value judgements, and limitations along with justifications for the 30

assumptions made; 31 o Data representativeness, appropriateness of data, and types/ sources of required data and 32

information; 33 o Assessed impact categories and related models and indicators; 34 o Normalisation and weighting factors (if used); 35

91 The Main Report, as defined here, is insofar as possible in line with ISO 14044 requirements on reporting for studies which do not contain comparative assertions to be disclosed to the public.


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o Treatment of any multi-functionality issues encountered in the LCA modelling activity. 1

• Compiling and recording the Life Cycle Inventory: 2

Mandatory reporting elements include, as a minimum: 3

o Description and documentation of all unit process data collected; 4 o Data collection procedures; 5 o Sources of published literature considered for data collection; 6 o Data gaps 7 o Information on any use and end-of-life scenarios considered in downstream stages; 8 o Calculation procedures; 9 o Validation of data, including documentation and justification of any allocation procedures; 10 o If a sensitivity analysis has been conducted, this shall be reported. 11

• Calculating life cycle impact assessment results: 12

Mandatory reporting elements include: 13

o The life cycle impact assessment procedure, calculations and results of the LCA study; 14 o Limitation of the LCA results relative to the defined goal and scope of the LCA study; 15 o The relationship of the life cycle impact assessment results to the defined goal and scope; 16 o If any exclusion from the default list of impact categories has been made, the justification for 17

the exclusion(s) shall be reported; 18 o If any deviation from the default impact assessment methods has been made (which shall be 19

justified and included under additional environmental information), then the mandatory 20 reporting elements shall also include: 21

o Impact categories and impact category indicators considered, including a rationale 22 for their selection and a reference to their source; 23

o Description of, or reference to all characterisation models, characterisation factors 24 and methods used, including all assumptions and limitations; 25

o Description of, or reference to all value-choices used in relation to impact categories, 26 characterisation models, characterisation factors, normalisation, grouping, 27 weighting and a justification for their use and their influence on the results, 28 conclusions and recommendations of the study; 29

o A statement and justification of any grouping of the impact categories; 30 o Any analysis of the indicator results, for example sensitivity and uncertainty analysis 31

on the use of alternative impact categories or additional environmental information, 32 including any implication for the results; 33

o Additional environmental information, if any; 34 o Information on carbon storage in products; 35 o Information on delayed (carbon) emissions; 36 o Data and indicator results calculated prior to any normalisation; 37 o If included, normalisation and weighting factors and corresponding results. 38

• Interpretation of LCA results: 39

Mandatory reporting elements include: 40


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o Assessment of data quality; 1 o Full transparency of value choices, rationale and expert judgements; 2 o Identification of environmental hotspots; 3 o Uncertainty (at least a qualitative description); 4 o Conclusions, recommendations, limitations, and improvement potentials. 5

8.2.3 Third element: Annex 6 The Annex serves to document supporting elements to the main report, which are of a more technical nature. 7 It shall include: 8

Descriptions of all assumptions, including those assumptions that have been shown to be 9 irrelevant; 10

Life Cycle Inventory (optional if considered sensitive and communicated separately in the 11 Confidential Report, see below); 12

Critical review report, including (where applicable) the name and affiliation of reviewer or review 13 team, a critical review, responses to recommendations (if any). 14

8.2.4 Fourth element: Confidential Report 15 The Confidential Report is an optional reporting element that, if produced, shall contain all those data 16 (including raw data) and information that are confidential or proprietary and cannot be made externally 17 available. It shall be made available confidentially to the critical reviewers. 18

19


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Annex A: Full list of studies screened in the meta-analysis 1 The following lists provide the reference to all the studies collected in the meta-analysis and considered in 2 the initial screening assessment. A separate list is reported for each identified category 3 (Monomers/Intemediates, Polymers, Plastic Articles, End-of-life-related studies). 4

Monomers/Intermediates 5

1. Adom, F.K., Dunn, J.B., 2017. Life cycle analysis of corn-stover-derived polymer-grade l-lactic acid 6 and ethyl lactate: greenhouse gas emissions and fossil energy consumption. Biofuel Bioprod. 7 Biorefining 11, 258-268. 8

2. Alvarenga, R.A.F., Dewulf, J., 2013. Plastic vs. fuel: Which use of the Brazilian ethanol can bring 9 more environmental gains? Renew. Energy 59, 49-52. 10

3. Aryapratama, R., Janssen, M., 2017. Prospective life cycle assessment of bio-based adipic acid 11 production from forest residues. J. Clean. Prod. 164, 434-443. 12

4. Cespi, D., Passarini, F., Vassura, I., Cavani, F., 2016. Butadiene from biomass, a life cycle perspective 13 to address sustainability in the chemical industry. Green Chem. 18, 1625-1638. 14

5. Cok, B., Tsiropoulos, I., Roes, A.L., Patel, M.K., 2014. Succinic acid production derived from 15 carbohydrates: An energy and greenhouse gas assessment of a platform chemical toward a bio-16 based economy. Biofuel Bioprod. Biorefining 8, 16-29. 17

6. Daful, A.G., Görgens, J.F., 2017. Techno-economic analysis and environmental impact assessment 18 of lignocellulosic lactic acid production. Chem. Eng. Sci. 162, 53-65. 19

7. Daful, A.G., Haigh, K., Vaskan, P., Görgens, J.F., 2016. Environmental impact assessment of 20 lignocellulosic lactic acid production: Integrated with existing sugar mills. Food Bioprod. Process. 21 99, 58-70. 22

8. Dros, A.B., Larue, O., Reimond, A., De Campo, F., Pera-Titus, M., 2015. Hexamethylenediamine 23 (HMDA) from fossil- vs. bio-based routes: an economic and life cycle assessment comparative 24 study. Green Chem. 17, 4760-4772. 25

9. Dunn, J. B., Adom, F., Sather, N., Han, J., & Snyder, S. (2015). Life-cycle Analysis of Bioproducts and 26 Their Conventional Counterparts in GREET TM. Argoone National Laboratory. 27

10. Ekman, A., & Börjesson, P. (2011). Environmental assessment of propionic acid produced in an 28 agricultural biomass-based biorefinery system. Journal of Cleaner Production. 29 https://doi.org/10.1016/j.jclepro.2011.03.008. 30

11. Fernández-Dacosta, C., Van Der Spek, M., Hung, C. R., Oregionni, G. D., Skagestad, R., Parihar, P., 31 Ramirez, A. (2017). Prospective techno-economic and environmental assessment of carbon capture 32 at a refinery and CO2 utilisation in polyol synthesis. Journal of CO2 Utilization. 33 https://doi.org/10.1016/j.jcou.2017.08.005 34

12. Fridrihsone-Girone, A., 2015. Preliminary life cycle inventory of rapeseed oil polyols for 35 polyurethane production. J. Renew. Mater. 3, 28-33. 36


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13. Forte, A., Zucaro, A., Basosi, R., Fierro, A., 2016. LCA of 1,4-butanediol produced via direct 1 fermentation of sugars from wheat straw feedstock within a territorial biorefinery. Mater. 9. 2

14. Garcia Gonzalez, M. N., Levi, M., & Turri, S. (2017). Development of polyester binders for the 3 production of sustainable polyurethane coatings: Technological characterization and life cycle 4 assessment. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2017.06.190 5

15. Gargalo, C. L., Cheali, P., Posada, J. A., Carvalho, A., Gernaey, K. V., & Sin, G. (2016). Assessing the 6 environmental sustainability of early stage design for bioprocesses under uncertainties: An analysis 7 of glycerol bioconversion. Journal of Cleaner Production. 8 https://doi.org/10.1016/j.jclepro.2016.08.156. 9

16. Gonzalez-Garay, A., Gonzalez-Miquel, M., Guillen-Gosalbez, G., 2017. High-Value Propylene Glycol 10 from Low-Value Biodiesel Glycerol: A Techno-Economic and Environmental Assessment under 11 Uncertainty. ACS Sustainable Chem. Eng. 5, 5723-5732. 12

17. Hong, J., Zhang, Y., Xu, X., Li, X., 2014. Life cycle assessment of corn- and cassava-based ethylene 13 production. Biomass Bioenergy 67, 304-311. 14

18. Isola, C., Sieverding, H. L., Raghunathan, R., Sibi, M. P., Webster, D. C., Sivaguru, J., & Stone, J. J. 15 (2017). Life cycle assessment of photodegradable polymeric material derived from renewable 16 bioresources. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2016.10.177 17

19. Lammens, T., Josepotting, Sanders, J. M. M., & Deboer. (2011). 18 EnvironmentalComparisonofBiobasedChemicalsfromGlutamic 19 AcidwithTheirPetrochemicalEquivalents. |Environ.Sci.Technol, 45, 8521–8528. 20

20. Liptow, C., Tillman, A.-., Janssen, M., 2015. Life cycle assessment of biomass-based ethylene 21 production in Sweden — is gasification or fermentation the environmentally preferable route? Int. 22 J. Life Cycle Assess. 20, 632-644. 23

21. Liptow, C., Tillman, A.-., Janssen, M., Wallberg, O., Taylor, G.A., 2013. Ethylene based on woody 24 biomass - What are environmental key issues of a possible future Swedish production on industrial 25 scale. Int. J. Life Cycle Assess. 18, 1071-1081. 26

22. Mandegari, M.A., Farzad, S., van Rensburg, E., Görgens, J.F., 2017. Multi-criteria analysis of a 27 biorefinery for co-production of lactic acid and ethanol from sugarcane lignocellulose. Biofuel 28 Bioprod. Biorefining 11, 971-990. 29

23. Morales, M., Dapsens, P.Y., Giovinazzo, I., Witte, J., Mondelli, C., Papadokonstantakis, S., 30 Hungerbühler, K., Pérez-Ramírez, J., 2015. Environmental and economic assessment of lactic acid 31 production from glycerol using cascade bio- and chemocatalysis. Energy Environ. Sci. 8, 558-567. 32

24. Moussa, H.I., Elkamel, A., Young, S.B., 2016. Assessing energy performance of bio-based succinic 33 acid production using LCA. J. Clean. Prod. 139, 761-769. 34

25. Parajuli, R., Knudsen, M.T., Birkved, M., Djomo, S.N., Corona, A., Dalgaard, T., 2017. Environmental 35 impacts of producing bioethanol and biobased lactic acid from standalone and integrated 36


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biorefineries using a consequential and an attributional life cycle assessment approach. Sci. Total 1 Environ. 598, 497-512. 2

26. Pommeret, A., Yang, X., Kwan, T.H., Christoforou, E.A., Fokaides, P.A., Lin, C.S.K., 2017. Techno-3 economic study and environmental assessment of food waste based biorefinery, in Anonymous 4 Food Waste Reduction and Valorisation: Sustainability Assessment and Policy Analysis, pp. 121-146. 5

27. Susterra. (2016). Life Cycle Analysis Overview – Susterra ® Propanediol. 6

28. Tao, L., Tan, E.C.D., Mccormick, R., Zhang, M., Aden, A., He, X., Zigler, B.T., 2014. Techno-economic 7 analysis and life-cycle assessment of cellulosic isobutanol and comparison with cellulosic ethanol 8 and n-butanol. Biofuel Bioprod. Biorefining 8, 30-48. 9

29. Tufvesson, P., Ekman, A., Sardari, R. R. R., Engdahl, K., & Tufvesson, L. (2013). Economic and 10 environmental assessment of propionic acid production by fermentation using different renewable 11 raw materials. Bioresource Technology. https://doi.org/10.1016/j.biortech.2013.09.049 12

30. Urban, R.A., Bakshi, B.R., 2009. 1,3-Propanediol from fossils versus biomass: A life cycle evaluation 13 of emissions and ecological resources. Ind Eng Chem Res 48, 8068-8082. 14

31. Van Duuren, J.B.J.H., Brehmer, B., Mars, A.E., Eggink, G., dos Santos, V.M., Sanders, J.P.M., 2011. A 15 limited LCA of bio-adipic acid: Manufacturing the nylon-6,6 precursor adipic acid using the benzoic 16 acid degradation pathway from different feedstocks. Biotechnol. Bioeng. 108, 1298-1306. 17

32. Vlysidis, A., Binns, M., Webb, C., Theodoropoulos, C., 2010. An integrated biorefinery framework 18 for the coproduction of biofuels and chemicals: Experimental analysis, detailed modelling, 19 optimization and life cycle analysis. Chem. Eng. Trans. 21, 1165-1170. 20

33. von der Assen, N., & Bardow, A. (2014). Life cycle assessment of polyols for polyurethane 21 production using CO2 as feedstock: insights from an industrial case study. Green Chem. 22 https://doi.org/10.1039/C4GC00513A 23

34. Zucaro, A., Forte, A., Fierro, A., 2017. Greenhouse gas emissions and non-renewable energy use 24 profiles of bio-based succinic acid from Arundo donax L. lignocellulosic feedstock. Clean Technol. 25 Environ. Policy 19, 2129-2143. 26

Polymers 27

1. Akanuma, Y., Selke, S.E.M., Auras, R., 2014. A preliminary LCA case study: Comparison of 28 different pathways to produce purified terephthalic acid suitable for synthesis of 100 % bio-29 based PET. Int. J. Life Cycle Assess. 19, 1238-1246. 30

2. Alvarenga, R. A., Dewulf, J., De Meester, S., Wathelet, A., Villers, J., Thommeret, R., & Hruska, Z. 31 (2013). Life cycle assessment of bioethanol-based PVC: Part 1: Attributional approach. Biofuels, 32 Bioproducts and Biorefining, 7(4), 386–395. http://doi.org/10.1002/bbb.1405 33

3. Alvarenga, R. A., Dewulf, J., De Meester, S., Wathelet, A., Villers, J., Thommeret, R., & Hruska, Z. 34 (2013). Life cycle assessment of bioethanol-based PVC: Part 2: Consequential approach. 35 Biofuels, Bioproducts and Biorefining, 7(4), 396–405. http://doi.org/10.1002/bbb.1398 36


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4. Belboom, S., & Léonard, A. (2016). Does biobased polymer achieve better environmental 1 impacts than fossil polymer? Comparison of fossil HDPE and biobased HDPE produced from 2 sugar beet and wheat. Biomass and Bioenergy, 85, 159–167. 3 http://doi.org/10.1016/j.biombioe.2015.12.014 4

5. Boonniteewanich, J., Pitivut, S., Tongjoy, S., Lapnonkawow, S., & Suttiruengwong, S. (2014). 5 Evaluation of carbon footprint of bioplastic straw compared to petroleum based straw 6 products. Energy Procedia, 56(C), 518–524. http://doi.org/10.1016/j.egypro.2014.07.187 7

6. Bos, H. L., Meesters, K. P., Conijn, S. G., Corré, W. J., & Patel, M. K. (2012). Accounting for the 8 constrained availability of land: A comparison of bio-based ethanol, polyethylene, and PLA with 9 regard to non-renewable energy use and land use. Biofuels, Bioproducts and Biorefining, 6(2), 10 146–158. http://doi.org/10.1002/bbb.1320 11

7. Bos, H. L., Meesters, K. P. H., Conijn, S. G., Corré, W. J., & Patel, M. K. (2016). Comparing 12 biobased products from oil crops versus sugar crops with regard to non-renewable energy use, 13 GHG emissions and land use. Industrial Crops and Products, 84, 366–374. 14 http://doi.org/10.1016/j.indcrop.2016.02.013 15

8. Braskem. (2017). I’m greenTM PE Life Cycle Assessment. 16

9. Broeren, M. L. M., Kuling, L., Worrell, E., & Shen, L. (2017). Environmental impact assessment of 17 six starch plastics focusing on wastewater-derived starch and additives. Resources, 18 Conservation and Recycling, 127(September), 246–255. 19 http://doi.org/10.1016/j.resconrec.2017.09.001 20

10. Chinnawornrungsee, R., Malakul, P., & Mungcharoen, T. (2013). Life cycle energy and 21 environmental analysis study of a model biorefinery in Thailand. Chemical Engineering 22 Transactions, 32, 439–444. http://doi.org/10.3303/CET1332074 23

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12. Dietrich, K., Dumont, M. J., Del Rio, L. F., & Orsat, V. (2017). Producing PHAs in the bioeconomy 26 — Towards a sustainable bioplastic. Sustainable Production and Consumption, 9(August 2016), 27 58–70. http://doi.org/10.1016/j.spc.2016.09.001 28

13. Dornburg, V., Lewandowski, I., & Patel, M. (2003). Comparing the land requirements, energy 29 savings, and greenhouse gas emissions reduction of biobased polymers and bioenergy. Journal 30 of Industrial …, 7(3–4), 93–116. http://doi.org/10.1162/108819803323059424 31

14. Eerhart, A. J. J. E., Faaij, A. P. C., & Patel, M. K. (2012). Replacing fossil based PET with biobased 32 PEF; process analysis, energy and GHG balance. Energy & Environmental Science, 5(4), 6407. 33 http://doi.org/10.1039/c2ee02480b 34

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18. Groot, W. J., & Borén, T. (2010). Life cycle assessment of the manufacture of lactide and PLA 4 biopolymers from sugarcane in Thailand. International Journal of Life Cycle Assessment, 15(9), 5 970–984. http://doi.org/10.1007/s11367-010-0225-y 6

19. Guo, M., Stuckey, D. C., & Murphy, R. J. (2013). Is it possible to develop biopolymer production 7 systems independent of fossil fuels? Case study in energy profiling of polyhydroxybutyrate-8 valerate (PHBV). Green Chemistry, 15(3), 706. http://doi.org/10.1039/c2gc36546d 9

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35. Mattila, T., Kujanpää, M., Dahlbo, H., Soukka, R., & Myllymaa, T. (2011). Uncertainty and Sensitivity 15 in the Carbon Footprint of Shopping Bags. Journal of Industrial Ecology, 15(2), 217–227. 16 https://doi.org/10.1111/j.1530-9290.2010.00326.x 17

36. McDevitt, J. E., & Grigsby, W. J. (2014). Life Cycle Assessment of Bio- and Petro-Chemical Adhesives 18 Used in Fiberboard Production. Journal of Polymers and the Environment. 19 https://doi.org/10.1007/s10924-014-0677-4 20

37. Meyer, D. E., & Katz, J. P. (2016). Analyzing the environmental impacts of laptop enclosures using 21 screening-level life cycle assessment to support sustainable consumer electronics. Journal of 22 Cleaner Production. https://doi.org/10.1016/j.jclepro.2015.05.143 23

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49. Potting, J., & van der Harst, E. (2015). Facility arrangements and the environmental performance of 16 disposable and reusable cups. International Journal of Life Cycle Assessment. 17 https://doi.org/10.1007/s11367-015-0914-7 18

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51. Razza, F., & Cerutti, A. K. (2017). Life Cycle and Environmental Cycle Assessment of Biodegradable 22 Plastics for Agriculture. https://doi.org/10.1007/978-3-662-54130-2_7 23

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Annex B: List of normalisation and weighting factors 1

Table A1: Normalisation factors for use in LCA studies 2

Impact category Model Unit Global NFs Person NF

Robustness of ILCD for the

impact assessment

Inventory coverage

completeness

Inventory robustness

Comment

Climate change IPCC, 2013 kg CO2 eq 5.35E+13 7.76E+03 I II I

Ozone depletion

World Metereological Organisation (WMO), 1999

kg CFC-11 eq 1.61E+08 2.34E-02 I III II

Human toxicity, cancer

USEtox (Rosenbaum et al., 2008)

CTUh 2.66E+05 3.85E-05 II/III III III

Human toxicity, non-cancer


CTUh 3.27E+06 4.75E-04 II/III III III

Particulate matter UNEP, 2016 Disease incidence 4.39E+06 6.37E-04 I I/II I /II

NF calculation takes into account the emission height both in the emission inventory and in the impact assessment.

Ionising radiation, human health

Frischknecht et al., 2000

kBq U235-eq 2.91E+13 4.22E+03 II II III

Photochemical ozone formation

Van Zelm et al., 2008, as applied in ReCiPe, 2008

kg NMVOC eq 2.80E+11 4.06E+01 II III I/II


242

Acidification Posch et al., 2008 mol H+ eq 3.83E+11 5.55E+01 II II I/II

Eutrophication, terrestrial

Posch et al., 2008 mol N eq 1.22E+12 1.77E+02 II II I/II

Eutrophication, freshwater

Struijs et al., 2009 kg P eq 1.76E+10 2.55E+00 II II III

Eutrophication, marine

Struijs et al., 2009 kg N eq 1.95E+11 2.83E+01 II II II/III

Land use Bos et al., 2016 (based on) pt 9.20E+15 1.33E+06 III II I I

The NF is built by means of regionalised CFs.

Ecotoxicity, freshwater


CTUe 8.15E+13 1.18E+04 II/III III III

Water use AWARE 100 (based on; UNEP, 2016)

m3 world eq 7.91E+13 1.15E+04 III I II

The NF is built by means of regionalised CFs.

Resource use, fossils

ADP fossils (van Oers et al., 2002)

MJ 4.50E+14 6.53E+04 III

I II

Resource use, minerals and metals

ADP ultimate reserve (van Oers et al., 2002)

kg Sb eq 3.99E+08 5.79E-02 III

1

2


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Table A2: Weighting factors for LCA studies 1

WITH TOXICITY CATEGORIES

Aggregated weighting set Robustness factors

Calculation Final weighting factors

(50:50) (scale 1-0.1)

A B C=A*B C scaled to 100% Climate change 12.9 0.87 11.18 21.06% Ozone depletion 5.58 0.6 3.35 6.31% Human toxicity, cancer 6.8 0.17 1.13 2.13% Human toxicity, non-cancer 5.88 0.17 0.98 1.84%

Particulate matter 5.49 0.87 4.76 8.96%

Ionizing radiation, human health 5.7 0.47 2.66 5.01% Photochemical ozone formation 4.76 0.53 2.54 4.78% Acidification 4.94 0.67 3.29 6.2% Eutrophication, terrestrial 2.95 0.67 1.97 3.71% Eutrophication, freshwater 3.19 0.47 1.49 2.8% Eutrophication, marine 2.94 0.53 1.57 2.96% Ecotoxicity, freshwater 6.12 0.17 1.02 1.92% Land use 9.04 0.47 4.22 7.94% Water use 9.69 0.47 4.52 8.51% Resource use, minerals and metals 6.68 0.6 4.01 7.55% Resource use, fossils 7.37 0.6 4.42 8.32%

2

3


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1

WITHOUT TOXICITY CATEGORIES

Aggregated weighting set

Robustness factors Calculation Final weighting factors

(50:50) (scale 1-0.1)

A B C=A*B C scaled to 100% Climate change 15.75 0.87 13.65 22.19% Ozone depletion 6.92 0.6 4.15 6.75% Particulate matter 6.77 0.87 5.87 9.54% Ionizing radiation, human health 7.07 0.47 3.3 5.37% Photochemical ozone formation 5.88 0.53 3.14 5.1% Acidification 6.13 0.67 4.08 6.64% Eutrophication, terrestrial 3.61 0.67 2.4 3.91% Eutrophication, freshwater 3.88 0.47 1.81 2.95% Eutrophication, marine 3.59 0.53 1.92 3.12% Land use 11.1 0.47 5.18 8.42%

Water use 11.89 0.47 5.55 9.03%

Resource use, minerals and metals 8.28 0.6 4.97 8.08%

Resource use, fossils 9.14 0.6 5.48 8.92% 2

3 4


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Annex C: Example of rating criteria for semi-quantitative assessment of data-quality 1

Table C1: Example of rating criteria for the semi-quantitative assessment of data quality required for key Life Cycle Inventory datasets. Process: dyeing 2 process 3

Quality level

Quality rating

Definition Time representativeness

Technological representativeness


Parameter uncertainty (relative standard deviation as a % if a Monte Carlo simulation is used, otherwise qualitative expert judgement)

Very good

1 Meets the criterion to a very high degree, without need for improvement.

2009-2012 Discontinuous with airflow dyeing machines

Central Europe mix Very low uncertainty ( 10%)

Good 2 Meets the criterion to a high degree, with little significant need for improvement.

2006-2008 e.g. "Consumption mix in EU: 30% Semi-continuous, 50% exhaust dyeing and 20% Continuous dyeing"

EU 27 mix; UK, DE; IT; FR

Low uncertainty (10% to 20%]

Fair 3 Meets the criterion to an acceptable degree, but merits improvement.

1999-2005 e.g. "Production mix in EU: 35% Semi-continuous, 40% exhaust dyeing and 25% Continuous dyeing"

Scandinavian Europe; other EU-27 countries

Fair uncertainty (20% to 30%]

Poor 4 Does not meet the criterion to a sufficient degree. Requires improvement.

1990-1999 e.g. "Exhaust dyeing" Middle east; US; JP High uncertainty (30% to

50%]

Very poor

5 Does not meet the criterion. Substantial improvement is necessary OR:

Very poor or unknown completeness ( 50%)

<1990; Unknown Continuous dyeing; other; unknown

Other; Unknown Very high uncertainty ( 50%)


246

Quality level

Quality rating

Definition Time representativeness

Technological representativeness


Parameter uncertainty (relative standard deviation as a % if a Monte Carlo simulation is used, otherwise qualitative expert judgement)

This criterion was not judged / reviewed or its quality could not be verified / is unknown.

1


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Annex D: Default loss rates per type of product 1

Table D1: Default loss rates per type of product during distribution and at consumer (including 2 restaurant, etc.) (assumptions if not specified otherwise). Out of simplification, the values for restaurant 3 are considered the same as for consumer at home. 4

Retail trade sector

Category Loss rate (incl. broken products but not products returned to manufacturer) during distribution (overall consolidated value for transportation, storage and retail place)

Loss rate at consumer (including restaurant, etc.)

Food Fruits and vegetables 10% (FAO 2011) 19% (FAO 2011)

Meat and meat alternatives

4% (FAO 2011) 11% (FAO 2011)

Dairy products 0.5% (FAO 2011) 7% (FAO 2011)

Grain products 2% (FAO 2011) 25% (FAO 2011)

Oils and fats 1% (FAO 2011) 4% (FAO 2011)

Prepared/processed meals (ambient)

10% 10%

Prepared/processed meals (chilled)

5% 5%

Prepared/processed meals (frozen)

0.6% (primary data based on Picard – oral communication from Arnaud Brulaire)

0.5% (primary data based on Picard – oral communication from Arnaud Brulaire)

Confectionery 5% 2%

Other foods 1% 2%

Beverages Coffee and tea 1% 5%


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Retail trade sector



Alcoholic beverages 1% 5%

Other beverages 1% 5%

Tobacco 0% 0%

Pet food 5% 5%

Live animals 0% 0%

Clothing and textile 10% 0%

Footwear and leather goods 0% 0%

Personal accessories

Personal accessories 0% 0%

Home and professional supplies

Home hardware supplies 1% 0%

Furniture, furnishings and decor

0% 0%

Electrical household appliances

1% 0%

Kitchen merchandise 0% 0%

Information and communication equipment

1% 0%

Office machinery and supplies

1% 0%


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Retail trade sector



Cultural and recreational goods

Books, newspapers and paper/paper supplies

1% 0%

Music and videos 1% 0%

Sporting equipment and gadgets

0% 0%

Other cultural and recreational goods

1% 0%

Healthcare 5% 5%

Cleaning/hygiene products, cosmetics and toiletries

5% 5%

Fuels, gases, lubricants and oils 1% 0%

Batteries and power 0% 0%

Plants and garden supplies

Flowers, plants and seeds 10% 0%

Other garden supplies 1% 0%

Other goods 0% 0%

Gas station Gas station products 1% 0%

1

Food losses at distribution center, during transport and at retail place, and at home: assumed to be 50% 2 trashed (i.e., incinerated and landfilled), 25% composting, 25% methanisation. 3

Product losses (excluding food losses) and packing/repacking/unpacking at distribution center, during 4 transport and at retail place: Assumed to be 100% recycled. 5


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Other waste generated at distribution center, during transport and at retailer (outside food and product 1 losses) such as repacking/unpacking are assumed to follow the same EoL treatment as for home waste. 2

Liquid food wastes (as for instance milk) at consumer (including restaurant, etc.) are assumed to be poured 3 in the sink and therefore treated in the wastewater treatment plant. 4

5

6


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Annex E: List of default values for A, R1, R2, R3 and Qs/Qp 1 The list of default values for A, R1 and R2 is available in the Excel file “CFF default parameters March 2018” 2 downloadable at the following link: 3

http://ec.europa.eu/environment/eussd/smgp/PEFCR_OEFSR_en.htm 4

5

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Annex F: Background information to calculate R2 for packaging materials 1 The table D.1 below presents per packaging sector (i) the data source to calculate R2, (ii) where in the 2 collection-recycling scheme these data are collected (see Figure 17 in section 3.5.8.17) and (iii) the applied 3 correction factor towards the output of the recycling process. 4

Table F1: Recycling rates for different packaging categories, including the source, the data collection 5 point and the recommended correction factor. Please note that the data sources used for the correction 6 factor are not always reviewed reports but may also be surveys or standards 7

Packaging sector

Data source Reference year

Data collection point (Figure 17)

Correction factor*

Source for correction factor

Liquid beverage cartonb

ACE 2014 8 Liquid packaging board: 92% Aluminium foil: 97% Plastic: 72%

No data source: The correction factors of paper and cardboard, aluminium cans, and generic plastics are recommended as proxy.

Aluminium cans

EA, + bottom ashesᶛ

2013 6† 97% Reviewed LCA: http://european-aluminium.eu/media/1329/environmental-profile-report-for-the-european-aluminium-industry.pdf (p58); Boin and Bertram 2005, Melting Standardized Aluminum Scrap: A Mass Balance Model for Europe.

PET bottle PETCORE 2014 2 73% Survey: Post-consumer PET recycling in Europe 2014 and prospects to 2019. Prepared on behalf of PETCORE Europe by PCI Ltd. 2015. http://www.pcipetpackaging.co.uk/

Container glass

FEVE 2013 8 90% Reviewed LCA: Life Cycle Assessment of Container Glass in Europe (Prepared on behalf of FEVE by RDC Environment), 2016. http://feve.org/new-life-cycle-assessment-proves-industry-success-reducing-environmental-footprint/

Steel for packaging

APEAL, + bottom ashesᶛ

2013 6† 98% Standard: Canadian standards’ Life cycle assessment of auto parts. http://shop.csa.ca/en/canada/life-cycle-assessment/spe-14040-14/invt/27036702014


PlasticsEurope 2014 8 73% LCA report: Increased EU Plastics Recycling Targets: Environmental, Economic and Social Impact Assessment. Prepared by Deloitte on behalf of Plastic Recyclers Europe. 2015 (See Table 7, value of 2012).


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Packaging sector

Data source Reference year

Data collection point (Figure 17)

Correction factor*

Source for correction factor

Paper and cardboard

CEPI 2014 8 92% Reviewed LCA: European Database for Corrugated Board Life Cycle Studies” (2015, FEFCO, CE Containerboard)

*Expressed as percentage of material (%) at the output of the recycling plant when considering a 100% input 1 at data collection point. The proposed correction factors are sector specific and to be used for correcting the 2 European average and country specific recycling rates. It is recognized that this is an over simplification as 3 the correction depends on the installations and market in place. However, the data available today asks for 4 this simplification. Some values are rounded. 5 ᶛThe recycling rates for aluminium cans and steel for packaging include bottom ash recovery. 6 †R2 provided by the national collection systems excludes impurities from the overall mass estimate of metal 7 packaging. Impurities are excluded from the correction factor. 8 b For liquid beverage carton three different material flows leave the recycling process at level Š. Therefore, 9 three correction factors are introduced, each to be used with the respective material flow. 10 11

12


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Annex G: Identifying Appropriate Nomenclature and Properties for Specific 1

Flows 2

The principal target audience for this Annex are experienced Environmental Footprint practitioners and 3 reviewers. This Annex is based on the “International Reference Life Cycle Data System (ILCD) Handbook - 4 Nomenclature and other conventions” (EC-JRC, 2010d). If further information and background is required on 5 nomenclature and naming conventions, please refer to the aforementioned document. 6

Different groups often use considerably different nomenclature and other conventions. As a consequence, 7 Resource Use and Emissions Profiles (for Life Cycle Assessment practitioners: Life Cycle Inventory (LCI) 8 datasets) are incompatible on different levels, thereby strongly limiting the combined use of Resource Use 9 and Emissions Profiles datasets from different sources or an efficient, electronic exchange of data among 10 practitioners. This situation also hampers a clear, unambiguous and efficient understanding and review of EF 11 and LCA study reports. 12

The purpose of this Annex is to support data collection, documentation and use for Resource Use and 13 Emissions Profiles and LCIs in EF and LCA studies by providing a common nomenclature and provisions on 14 related topics. The document also forms the basis for a common reference elementary flow list for use in 15 both EF and LCA activities. 16

This supports efficient EF, LCA and data exchange among different tools and databases. 17

The goal is to guide data collection, naming, and documentation in such a way that the data: 18

Are meaningful, precise and useful for further EF impact assessments, interpretation and 19 reporting; 20

Can be compiled and provided in a cost-efficient way; 21

Are comprehensive and do not overlap; 22

Can be efficiently exchanged among practitioners who have different databases and software 23 systems, thereby reducing the likelihood of errors. 24

This nomenclature and other conventions focus on elementary flows, flow properties and the related units, 25 and give suggestions for the naming of process datasets, product and waste flows, for better compatibility 26 among different database systems. Basic recommendations and requirements are also given on the 27 classification of source and contact datasets. Table G1 lists the ILCD Handbook rules that are required in LCA 28 studies. Table G2 specifies the rule-category and the relevant chapters of the ILCD Handbook. 29

Table G1: Required rules for each flow type 30

Items Required Rules from the ILCD - Nomenclature (see Table G2)

Raw material, Input 2, 4, 5

Emission, output 2, 4, 9

Product flow 10, 11, 13, 14, 15, 16, 17

31


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Table G2: Nomenclature Rules 1

Rule #

Rule Category Chapter section in ILCD Handbook - Nomenclature and other conventions

2 "Elementary flow categories" by issuing / receiving environmental compartment

Chapter section 2.1.1

4 Further differentiation of issuing/receiving environmental compartments

Chapter section 2.1.2

5 Additional, non-identifying classification of "Resources from ground" elementary flows

Chapter section 2.1.3.1

9 Recommended for both technical and non-technical target audience: additional, non-identifying classification of emissions

Chapter section 2.1.3.2

10 Top-level classification of Product flows, Waste flows, and Processes Chapter section 2.2

11 Second-level classifications of Product flows, Waste flows, and Processes (for preceding top-level classification)

Chapter section 2.2

13 “Base name” field Chapter section 3.2

14 “Treatment, standards, routes” name field Chapter section 3.2

15 “Mix type and location type” name field Chapter section 3.2

16 “Quantitative flow properties” name field Chapter section 3.2

17 Naming convention of flows and processes Chapter section 3.2

2

Example of Identifying Appropriate Nomenclature and Properties for Specific Flows 3

Raw material, Input: Crude oil (Rules 2, 4, 5) 4

(1) Specify "elementary flow category" by the issuing / receiving environmental compartment: 5

Example: Resources - Resources from ground 6

7

(2) Further differentiation of issuing / receiving environmental compartments 8

Example: Non-renewable energy resources from ground 9

10

11


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(3) Additional, non-identifying classification for "Resources from ground" elementary flows 1

Example: Non-renewable energy resources from ground (e.g. "Crude oil; 42.3 MJ/kg net calorific value") 2

3

Flow dataset: Crude oil: 42.3 MJ/kg net calorific value 4

5 6

Emission, output: Example: Carbon Dioxide (Rules 2, 4, 9) 7

(1) Specify "elementary flow categories" by issuing / receiving environmental compartment: 8

Example: Emissions – Emissions to air - Emissions to air, unspecified 9

10

(2) Further differentiation of issuing / receiving environmental compartments 11

Example: “Emission to air, DE” 12

13

(3) Additional, non-identifying classification of emissions 14

Example: Inorganic covalent compounds (e.g. "Carbon dioxide, fossil", "Carbon monoxide", "Sulphur 15 dioxide", "Ammonia", etc.) 16

17 18


257

Product flow: Example: T-shirt (Rules 10-17) 1

(1) Top-level classification for Product flows, Waste flows, and Processes: 2

Example: “System” 3

4

(2) Second-level classifications for Product flows, Waste flows, and Processes (for preceding top-level 5 classification): 6

Example: “Textiles, furniture and other interiors” 7

8

(3) “Base name” field: 9

Example: “Base Name: White polyester T-shirt” 10

11

(4) “Treatment, standards, routes” name field: 12

Example: “ ” 13

14

(5) “Mix type and location type” name field: 15

“Production mix, at point of sale” 16

17

(6) “Quantitative flow properties” name field: 18

Example: “160 grams polyester” 19

20

(7) Naming convention of flows and processes. 21

<“Base name”; “Treatment, standards, routes”; “Mix type and location type”; “Quantitative flow 22 properties”>. 23

Example: “White polyester T-shirt; product mix at point of sale; 160 grams polyester” 24

25

26


258

Annex H: EF-compliant datasets 1 A basic requirement of the PEF and OEF methods is that LCI data used shall be compliant with the entry level 2 (EL) requirements of the International Reference Life Cycle Data System (ILCD). Going beyond the ILCD EL 3 requirements, the EF requirements provide further specifications to ILCD EL and refer to provisions e.g. in 4 the Product Environmental Footprint (PEF) Guide (Rec 2013/179/EU - Annex II) or the Organisation 5 Environmental Footprint (OEF) Guide (Rec 2013/179/EU - Annex III). In those cases the more specific (and 6 sometimes more strict) EF requirements prevail over the ILCD EL requirements. Exceptions are allowed in 7 case EF-compliant datasets are not available (see section 7.19.5). 8

The requirements listed in this Annex will be used for any future call for secondary datasets launched starting 9 from 1st January 2018 and will be the basis for determining the EF-compliance of any LCI dataset starting 10 from 1st January 2021. 11

H.1 List of all technical requirements to be fulfilled by datasets to be recognised as EF 12 compliant 13

H.1.1 Documentation 14

ILCD format shall be used. The developer kit is available at: 15 http://eplca.jrc.ec.europa.eu/LCDN/developer.xhtml 16

Furthermore, the requirement available at: 17 http://eplca.jrc.ec.europa.eu/uploads/QMS_H08_ENSURE_ILCD_GuidanceDocumentationLCADataSets_Ver18 sion1-1Beta_2011_ISBN_clean.pdf shall be fulfilled. 19

The editor for datasets can be downloaded to: http://eplca.jrc.ec.europa.eu/LCDN/developer.xhtml 20

In the same page other tools and documents for the creation, editing and compliance validation of datasets 21 are also available. 22

H.1.2 Nomenclature 23

Nomenclature shall be compliant with “ILCD Handbook – Nomenclature and other conventions” (including 24 elementary flows see link for Elementary Flow lista available at: http://eplca.jrc.ec.europa.eu/repository/EF). 25

Details to fulfil this aspect are available at http://eplca.jrc.ec.europa.eu/uploads/MANPROJ-PR-ILCD-26 Handbook-Nomenclature-and-other-conventions-first-edition-ISBN-fin-v1.0-E.pdf 27

EF requirements allow some grouped flows (see the reference flow list available at 28 http://eplca.jrc.ec.europa.eu/repository/EF.php) . 29

As grouped flows like “AOX” or “heavy metals” are not preferable in the impact assessment phase, the EF 30 tries to avoid the use of such grouped flows and urges for further specification and the break-down of 31 grouped flows into their single components. 32

H.1.3 Review 33

The review report shall include at least: 34


259

File name and administrative information 1 o Data set name 2 o UUID (Universal Unique IDentifier) 3 o Data set provider 4 o Reviewer name(s) and affiliation(s), contact 5 o Review type applied (see Table) 6 o Date of review completion (DD/MM/YYYY) 7 o EF compliance 8

Review reporting items for the criterion “nomenclature” 9 Review reporting items for the criterion “documentation” 10 Review reporting items for the criterion “Methodological appropriateness and consistency. In 11

particular, the reviewer shall check and report in the review report the % of impact covered for each 12 impact category in order to fulfil the completeness criterion. This check shall be based on expert 13 judgement and could be performed by comparing the coverage of flows existing in equivalent datasets 14 available in other databases, or by referring to the elementary flows that contribute most to the JRC-15 provided normalisation data of the respective impact category. 16

Review reporting items for the criterion “Data quality”. The list of items checked and the procedure 17 used to check the data quality shall be included in the review report. 18

Review for the Data Quality score, including a check of the results of the contribution analysis to 19 determine the scoring of each parameter in the DQR formula. 20

21

Table H1: Typology of reviews 22

Typo

logy

and

num

ber o

f re

view

ers

Type 1 Panel of at least 3 independent reviewers, with at least one external

Type 2 Two independent reviewers, with at least one external reviewer

Type 3 Two independent internal reviewers

Type 4 One independent external reviewer

Type 5 One independent internal reviewer

23

H.1.4 Methodological requirements 24

In order to be considered EF-compliant a dataset shall fulfil all the modelling requirements described in 25 section 7 of this Guidance. 26 Moreover, the following additional requirements shall also be fulfilled: 27 Completeness: all 16 EF impact categories shall be covered in the dataset. The reviewer shall check that 28

for each impact category the most important elementary flows are included. 29 Water use: water use shall be modelled at country level using separate flows for water withdrawal, water 30

release and water evaporation. 31 Cut off: processes can be excluded up to 1.0%, based on material and energy flow and the level of 32

environmental significance, but it has to be clearly checked, documented (i.e. the processes subject to 33 cut-off have to be made explicit in the documentation) and confirmed by the reviewer, in particular with 34


260

reference to the environmental significance of the cut-off applied. A cut-off higher than 1.0% per process 1 and higher than 5% cumulative is not allowed and the dataset is considered as not-compliant with EF 2 requirements. 3

Direct land use change: Direct land use change shall be accounted for on the basis of a 20 year time 4 period (starting from when the land use happened) and implemented in the calculation of 1) Climate 5 Change according to the PAS2050-1:2012 method described at page 24 and 2) Land Use. 6

Carbon storage and delayed emissions: credits associated with temporary (carbon) storage or delayed 7 emissions up to 100 years shall not be considered. 8

Emissions off-setting: not to be included 9 Capital goods (including infrastructures) and their End of life: they shall be included unless they can be 10

excluded based on the 1.0% cut-off rule. The eventual exclusion has to be clearly documented. 11 System boundaries: system boundaries shall include all processes linked to the product supply chain (e.g. 12

maintenance), unless they can be excluded based on the cut-off rule. 13 Time period: emissions and removals shall be modelled as if released or removed at the beginning of the 14

assessment period (no time discounting is allowed). 15 The biogenic carbon content at factory gate (physical content and allocated content) shall be reported. 16

If derived from native forest, it shall report that the corresponding carbon emissions shall be modelled 17 with the elementary flow '(land use change)’. 18

The recycled content (R1) shall be reported. 19 The LCIA shall be reported, specifying which version of the EF method has been used for the assessment. 20 Calculation of Data Quality score. 21

H.2 Aggregation 22 An EF-compliant dataset should be available both as aggregated and disaggregated dataset (minimum at 23 level 1). The level 1 disaggregated dataset shall include, as a minimum, the following individual elements: 24

Sub-processes for energy input(s) (differentiated by energy carrier, including any potential energy 25 conversion of fuels and thus direct emissions, as “steam from [name of fuel]”, or “process heat from 26 [name of fuel]”). For each sub-process, the exact dataset (name and uuid) used in the aggregated version 27 of the dataset shall be indicated 28

Sub-processes in case system expansion is used as allocation: the datasets used for substitution. For 29 each sub-process, the exact dataset (name and uuid) used in the aggregated version of the dataset 30 shall always be indicated; 31

Sub-processes for each transport activity per input (material, ingredient, component, etc) entering 32 the gate of the process modelled92. For each sub-process, the exact dataset (name and uuid) used in 33 the aggregated version of the dataset shall always be indicated; 34

One aggregated sub-process for all the other processes that represent the background system 35 (blue box in Figure H1. The exact dataset (name and uuid) used in the aggregated version of the 36 dataset shall always be indicated). 37

The output product flow; 38

92 Some EF datasets tendered during the pilot phase might have one transport mode for all inputs summed together.


261

Elementary flows of direct emissions and resource outputs of the foreground system constituting 1 the final output product. 2

Elementary flows of direct resource inputs (e.g., land use, water use) of the foreground system 3 constituting the final output product. 4

5

6

Figure H1: Minimum level of disaggregation requested for a dataset aggregated at level 1. The yellow 7 box is optional when going beyond the minimum requirements 8

9

H.3 Data quality criteria and scores 10 The DQR of a dataset shall be calculated based on the equation J.193: 11

12

𝐷𝑄𝑅 = [Equation H1] 13

14 Where TeR is the Technical Representativeness, GR is the Geographical Representativeness, TiR is the Time 15 Representativeness and P is the Precision. The representativeness (technological, geographical and time-16 related) characterises to what degree the processes and products selected are depicting the system analysed, 17 while the precision indicates the way the data is derived and related level of uncertainty. 18

93 The EF datasets tendered during the pilot phase might apply a different approach, like expert judgement. The approach used is clarified in the respective dataset meta data information.


262

The DQR shall be calculated before any aggregation of sub-processes or elementary flows is performed. In 1 particular, the procedure shall be applied before the creation of the aggregated sub-process of the level-1 2 disaggregated dataset (the "blue box" in Figure H1). For secondary datasets (e.g., developed by database 3 providers) the following procedure applies94: 4

1) Select the most relevant sub-processes and direct (foreground) elementary flows that account for at least 5 80% of the total environmental impact of the secondary dataset, listing them from the most contributing to 6 the least contributing one; 7

2) Calculate the DQR criteria TeR, TiR, GR and P for each most relevant process and each most relevant direct 8 elementary flow. The values of each criterion shall be assigned based on Table H2. 9

2.a) Each most relevant elementary flow consists of the amount and elementary flow naming (e.g. 40 10 g carbon dioxide). For each most relevant elementary flow, evaluate the 4 DQR criteria named TeR-EF, 11 TiR-EF, GR-EF, PEF. For example, evaluate the timing of the flow measured, for which technology the flow 12 was measured and in which geographical area. 13

2.b) Each most relevant process is a combination of activity data and the secondary dataset used. For 14 each most relevant process, the 4 DQR criteria are calculated as follow: (i) TiR and P shall be evaluated 15 at the level of the activity data (named TiR-AD, PAD), while (ii) TeR, TiR and GR shall be evaluated at the 16 level of the secondary dataset used (named TeR-SD , TiR-AD and GR-SD). As TiR is evaluated twice, the 17 mathematical average of the activity data and secondary dataset represents the TiR of the most 18 relevant process. 19

3) Calculate the environmental contribution of each most-relevant process and elementary flow to the total 20 environmental impact of all most-relevant processes and elementary flows, in % (weighted using 13 EF 21 impact categories, with the exclusion of the 3 toxicity-related ones). For example, the newly developed 22 dataset has only two most relevant processes, contributing in total to 80% of the total environmental impact 23 of the dataset: 24

Process 1 carries 30% of the total dataset environmental impact. The contribution of this process to 25 the total of 80% is 37.5% (the latter is the weight to be used). 26

Process 1 carries 50% of the total dataset environmental impact. The contribution of this process to 27 the total of 80% is 62.5% (the latter is the weight to be used). 28

4) Calculate separately the TeR, TiR, GR and P for the secondary dataset as the weighted average of each 29 criteria of the most relevant sub-processes and most relevant direct elementary flows. The weight is the 30 relative contribution (in %) of each most relevant process and direct elementary flow calculated in step 3. 31

5) Calculate the total DQR of the secondary dataset using equation I.1, where 𝑇𝑒 , 𝐺 , 𝑇𝚤 , 𝑃 are the 32 weighted averages calculated as specified in point 4. In order to be EF-compliant, each single criteria in 33 cannot be higher than 3.0. 34

94 For datasets based on company-specific data the procedure described in section 7.19.4.3 applies.


263

Table H2: Quality rating for the data quality criteria 1

Quality rating

PEF and PAD TiR-EF and TiR-AD TiR-SD TeR-EF and TeR-SD GR-EF and GR-SD

1 Measured/calculated and verified The data (collection date) can be maximum 2 years old with respect to the "reference year" of the dataset.

The "reference year" of the tendered dataset falls within the time validity of the secondary dataset

Technology aspects have been modelled exactly as described in the title and metadata, without any significant need for improvement

The processes included in the dataset are fully representative for the geography stated in the “location” indicated in the metadata

2 Measured/calculated/literature and plausibility checked by reviewer

The data (collection date) can be maximum 4 years old with respect to the "reference year" of the dataset.

The "reference year" of the tendered dataset is maximum 2 years beyond the time validity of the secondary dataset

Technology aspects are very similar to what described in the title and metadata with need for limited improvements. For example: use of generic technologies’ data instead of modelling all the single plants.

The processes included in the dataset are well representative for the geography stated in the “location” indicated in the metadata

3 Measured/calculated/literature and plausibility not checked by reviewer OR Qualified estimate based on calculations plausibility checked by reviewer



Technology aspects are similar to what described in the title and metadata but merits improvements. Some of the relevant processes are not modelled with specific data but using proxies.

The processes included in the dataset are sufficiently representative for the geography stated in the ““location” indicated in the metadata. E.g. the represented country differs but has a very similar electricity grid mix profile,

4 Qualified estimate based on calculations, plausibility not checked by reviewer



Technology aspects are different from what described in the title and metadata. Requires major improvements.

The processes included in the dataset are only partly representative for the geography stated in the “location” indicated in the metadata. E.g. the represented country differs and has a substantially different electricity grid mix profile

5 Rough estimate with known deficits The data (collection date) is older than 8 years with respect to the "reference year" of the dataset.

The "reference year" of the tendered dataset is more than 4 years beyond the time validity of the secondary dataset

Technology aspects are completely different from what described in the title and metadata. Substantial improvement is necessary

The processes included in the dataset are not representative for the geography stated in the ““location” indicated in the metadata.

TiR-EF: time representativeness for the elementary flow 2 TiR-AD: time representativeness for the activity data 3 TiR-SD: time representativeness for the secondary dataset 4


264

1

How to report the DQR for the datasets: The dataset shall state as meta-data one numerical value for each 2 DQR criteria (namely 𝑇𝑒 ; 𝐺 ; 𝑇𝚤 ; 𝑃) and the total DQR numerical value, always referred to the dataset. 3

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265

ANNEX I: Definition of product types for marine litter accounting 1 2

The different characteristics and behavior (e.g., degradability) of plastic products was addressed in the 3 marine litter accounting by defining product typologies. The definition of the 13 categories under 4 consideration was performed by mapping the product typologies employed in the following sources (Table 5 I.1): 6

I) EU policy: COM(2018) 340 final Proposal for a DIRECTIVE OF THE EUROPEAN PARLIAMENT AND 7 OF THE COUNCIL on the reduction of the impact of certain plastic products on the environment 8 (European Commission, 2018) 9

II) JRC research: Marine Beach Litter in Europe – Top Items (Hanke, 2016) 10 III) WWF report: A plastic future. Plastics consumption and waste management in the UK (Elliott & 11

Elliott, 2018) 12 IV) Scientific paper on the topic: Benthic marine litter in four Gulfs in Greece, Eastern 13

Mediterranean; abundance, composition and source identification (Koutsodendris et al., 2008) 14 V) Scientific paper on the topic: Composition and potential origin of marine debris stranded in the 15

Western Indian Ocean on remote Alphonse Island, Seychelles (Duhec et al., 2015) 16 17

Table I1: Product typologies employed for the framework proposal regarding marine litter and mapping 18 with the product typologies employed in the reference documents 19

Product Typology

Reference document I II III IV V

1 Drink bottle, caps and lids

Beverage containers, caps, lids

Caps/lids Drink bottles Bottle caps

Drink bottles

Water bottles

Plastic beverage bottles Plastic caps

2 Cigarette buds Tobacco product filters

Cigarrette buts Cigarrette filters

3 Cotton buds stick Cotton bud sticks

Cotton bud sticks Cotton buds

4 Crisp packets /sweet wrappers

Packets & wrappers

Crisp/sweet packets and lolly sticks

Crisp packets Sweet wrappers

5

Sanitary application: sanitary towels / wet wipes

Sanitary items: wet wipes/ sanitary towels

Wet wipes Sanitary towels

6 Plastic bags Lightweight plastic carrier bags

Bags (shopping) Small plastic bags

Garbage bags

7 Cutlery, straws and drink stirrers

Cutlery, plates, stirrers, straws

Cuttlery/ trays/straws

Cutlery Straws Stirrers

8 Drink cups and cup lids

Cups for beverage Cups

Drink cups and lids

Water cups

9 Baloons baloon sticks

Sticks for balloons/ Balloons


266

10 Food container including fast food packaging

Food containers

Food incl. fast food containers

Food containers

11 Fishing gear Fishing gear

String and cord (⌀ <1cm) Rope (⌀> 1cm) Nets and pieces of net <50cm

Fishing items

12 Industrial packaging

Industrial packaging, plastic sheeting

Agricultural packaging

13 Other plastic

Plastic/PS pieces 2.5cm Plastic/PS pieces <2.5cm

Beach sandals Polystyrene Soft plastic (pieces) Hard plastic (macropieces) Polystyrene Small plastic fragments

Other materials Other

Other textiles Foam sponge Wood crates Other ceramic/ pottery Other paper items

Glass bottles Light bulbs Domestic items Foam sheets

1

References 2

Duhec, A. V., Jeanne, R. F., Maximenko, N., & Hafner, J. (2015). Composition and potential origin of 3 marine debris stranded in the Western Indian Ocean on remote Alphonse Island, Seychelles. 4 Marine pollution bulletin, 96(1-2), 76-86. 5

Elliott, T. and Elliott, L. (2018). A plastic future. Plastics consumption and waste management in the 6 UK. Final report. Eunomia. 7

European Commission (2018) COM(2018) 340 final Proposal for a DIRECTIVE OF THE EUROPEAN 8 PARLIAMENT AND OF THE COUNCIL on the reduction of the impact of certain plastic products on 9 the environment. 10

Hanke G (2016) Marine Beach Litter in Europe – Top Items. 11 Koutsodendris, A., Papatheodorou, G., Kougiourouki, O., & Georgiadis, M. (2008). Benthic marine 12

litter in four Gulfs in Greece, Eastern Mediterranean; abundance, composition and source 13 identification. Estuarine, Coastal and Shelf Science, 77(3), 501-512 14

GETTING IN TOUCH WITH THE EU

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