Exploration of Carbon Footprint of Electrical Products - NEMA · PDF fileEXPLORATION OF CARBON...

EXPLORATION OF CARBON FOOTPRINT

OF ELECTRICAL PRODUCTS

GUIDANCE DOCUMENT FOR PRODUCT

ATTRIBUTE TO IMPACT ALGORITHM

METHODOLOGY

June 2013

Elsa A. Olivetti, Huabo Duan, Randolph E. Kirchain

A publication of the

Materials Systems Laboratory

EXPLORATION OF CARBON FOOTPRINT OF ELECTRICAL PRODUCTS: GUIDANCE DOCUMENT | I

PREFACE

The following report represents the output of a research project conducted by the

Massachusetts Institute of Technology (MIT) Materials Systems Laboratory under contract

with the National Electrical Manufacturers Association (NEMA). It describes a novel

approach for assessing the environmental impact of electrical products, with a focus on

greenhouse gas emissions – i.e., the “carbon footprint.” The project was initiated in

response to increased global attention on anthropogenic carbon emissions, which have

more than tripled over the last 50 years. While public opinion on the rise in atmospheric

carbon levels may remain unsettled, many governmental bodies and leading retailers are

moving towards mandatory disclosure of the environmental impact of consumer products

as an attempt to curtail greenhouse gas emissions through the marketplace. This has led

producers to take a closer look at the impacts of their products, an effort that can be both

time consuming and costly for complex products with global supply chains.

This report describes, and provides high level guidance for applying, a credible, flexible, and

efficient tool for mapping the intrinsic attributes of an electrical product to energy use and

greenhouse gas emissions. Several applications of the methodology to NEMA products,

and the subsequent findings, are included as appendices.

Data and technical guidance on the specific applications of the methodology were obtained

from NEMA members in the relevant product sections. Ongoing oversight and expertise was

provided throughout the project by an Executive Working Group that included the following

NEMA members.

Bill Flanagan, PhD

GE Global Research

Philip Ling, PE

Powersmiths International

Jim Groome

The Okonite Company, Inc.

Callan Schoonenberg

Eaton Corporation

Chas Harris

ABB Low Voltage Products

Mitchell Sas

Miller Electric Mfg. Co.

Mark Kohorst, Senior Manager for Environment, Health, and Safety, served as NEMA’s

internal manager for the project.

EXPLORATION OF CARBON FOOTPRINT OF ELECTRICAL PRODUCTS: GUIDANCE DOCUMENT | II

TABLE OF CONTENTS

PREFACE .......................................................................................................................... I

Table of Contents ........................................................................................................... II

Abbreviation List ............................................................................................................ 4

Executive Summary ........................................................................................................ 5

1. Project Purpose and Objectives ................................................................................. 8

2. Life Cycle Assessment Methodology ........................................................................ 10

2.1 Scope .......................................................................................................................... 10

Figure 2: Schematic of proposed system boundaries for electro-industry products. Red

text signifies factors for which primary data are more readily available. ........................ 11

2.2 Overview of PAIA-based Model ................................................................................. 12

3. Inventory Data Organization and CF Modeling ........................................................ 14

3.1 Under-specification: Life Cycle Inventory .................................................................. 14

3.2 Other Life Stages ........................................................................................................ 18

3.3 High Level Assessment-CF Modeling ......................................................................... 20

3.4 Development of Potential Attributes for Products .................................................... 22

3.5 Reducing Attribute List Based on Impact ................................................................... 23

3.6 Regression for the Product Family ............................................................................. 23

3.7 Data Quality and Limitations ...................................................................................... 24

4. Conclusions .............................................................................................................. 26

References .................................................................................................................... 27

Equation Parameters .................................................................................................... 29

Appendix A: Motors ..................................................................................................... 30

A1 Summary ..................................................................................................................... 30

A2 Scope and Functional Unit .......................................................................................... 30

A3 Life Cycle Inventory ..................................................................................................... 31

A4 Data Limitations .......................................................................................................... 34

A5 Impact Assessment and Interpretation ...................................................................... 34

Appendix B: Energy efficient lamps ............................................................................. 39

B1 Summary ..................................................................................................................... 39

B2 Scope and Functional Unit .......................................................................................... 39

B3 Life Cycle Inventory ..................................................................................................... 40

EXPLORATION OF CARBON FOOTPRINT OF ELECTRICAL PRODUCTS: GUIDANCE DOCUMENT | III

B4 Data limitations ........................................................................................................... 42

B5 Impact Assessment and Interpretation: LED Lamps ................................................... 43

B6 Impact Assessment and Interpretation: CFLs ............................................................. 46

Appendix C: Electronic and magnetic ballasts ............................................................. 49

C1 Summary ..................................................................................................................... 49

C2 Scope and Functional Unit .......................................................................................... 49

C3 Life Cycle Inventory ..................................................................................................... 51

C4 Data limitations ........................................................................................................... 53

C5 Impact Assessment and Interpretation ...................................................................... 53

Appendix D: Electrical Connectors ............................................................................... 59

D1 Summary..................................................................................................................... 59

D2 Scope and Functional Unit .......................................................................................... 59

D3 Life Cycle Inventory ..................................................................................................... 61

D4 Data Limitations .......................................................................................................... 64

D5 Impact Assessment and Interpretation ...................................................................... 64

EXPLORATION OF CARBON FOOTPRINT OF ELECTRICAL PRODUCTS: GUIDANCE DOCUMENT | 4

ABBREVIATION LIST

Abbreviation Terms

BOA Bill of Activities

BOM Bill of Materials

CED Cumulative Energy Demand

CF Carbon Footprint

EDD Energy Demand Datasets

EI Energy Indicator (unit energy use in materials and manufacturing phases)

GHG Greenhouse Gas

ICE Inventory of Carbon & Energy

LCA Life Cycle Assessment

LCI Life Cycle Inventory

NEMA National Electrical Manufacturers Association

PAIA Product Attribute to Impact Algorithm

http://web.mit.edu/2.813/www/readings/ICE.pdf


EXECUTIVE SUMMARY

This project was conceived and supported by the National Electrical Manufacturers

Association (NEMA). Its main goal was to create a standardized, but streamlined method for

mapping product characteristics to potential energy use and greenhouse gas (GHG)

emissions. This method – termed the Product Attribute to Impact Algorithm (PAIA)-based

model - is a relatively quick, robust, and consistent approach for impact determination that can

be applied to a wide range of products in the electro-industry sector.

The results of the project are presented in two research reports. The first, completed in

September 2012, described the PAIA-based methodology and its application to specified

energy efficient lighting and motor products. The second phase activities, which are the

subject of this report, expanded the application to additional focal products and outlined a

generic PAIA-based methodology that can be used to assess a broader array of electrical

products and systems. The intention was to produce an analytic tool that offered widespread

utility across the NEMA membership.

The high level guidance presented in this report provides technical instruction and

recommendations for the procedures used to develop a PAIA-based model, including the

basic framework, data organization strategies, and necessary statistical techniques.

Originally developed to examine the carbon footprint (CF) for information technology

products, the PAIA-based model is a streamlined life cycle assessment (LCA) method that

maps the intrinsic attributes of products to environmental impact. There are three core steps to

the methodology, the first of which is fundamental to the field of LCA. The latter two steps are

more specialized for this streamlined approach. The three core steps are as follows.

1. High level triage assessment: Elements of this step include preliminary data

collection drawn from existing data sources, classification of data and identification of

probability distributions, Monte Carlo statistical simulations and sensitivity analysis,

followed by preliminary life cycle interpretation to target areas of greatest impact for

data refinement. In this step, a rough, yet comprehensive, life cycle inventory is


generated for each product under analysis. As a prerequisite to the analysis, the

individual database inventories that map activities (materials, transportation, etc.) to

environmental impact are structured into a hierarchy based on “under-specification”

of the relevant materials and processes. “Under-specification” is a term used to

indicate that information is limited or unavailable regarding the particular materials

that make up components in the product. The use of a hierarchy allows the analysis

to proceed in this limited knowledge context, noting for example that a product’s

housing consists simply of a non-ferrous metal, polymer or ceramic, etc. without

further detail on the composition (this concept will be explained further in the relevant

sections below).

2. Product attributes investigation and screening: This step involves evaluating and

selecting attributes that are considered critical to driving impact and most prototypal

across the range of products within a category. The list of attributes can then be

reduced to those that are related to the high-impact activities and data availability;

3. Model development: At this step, life cycle activities that constitute a significant

portion of the total product impact are mapped, primarily through statistical

regression, to related product attributes to form the algorithms that underlie the

PAIA-based model.

However, before beginning these three steps, the product to be evaluated (or “focal”

product) must be identified and its functional unit defined.

NEMA’s product scope is broad and complex. Thus in addition to expanding on the three

core steps, this report provides the strategies employed to select focal products within this

vast spectrum of electrical products and systems. As an LCA guidance document, the report

initially addresses the goals and scope of the analysis, such as functional unit and system

boundaries. Defining the goals and scope is the most fundamental part of an LCA because of

the influence these factors have on the resulting outcome.

Four products within the scope of NEMA companies were investigated under this project -

energy efficient light bulbs and ballasts, AC induction motors, and electrical connecters. In


general, these products were chosen because of their overall impact on global electricity

consumption, and also because they vary with regard to the impact of the use-phase on their

overall footprint.

Findings from the analyses of all four focal products are presented in Appendices A-D of

this report.

When applied to lighting and motor products, the methodology described in this report

showed that, because the products consume electricity and are either long-lived or run for

many hours over their lifetime, the carbon footprint they produce is dominated by the

use-phase. Manufacturing, materials usage, transportation, and end-of-life disposition are far

less significant to the overall impact. This will not necessarily be the case for other products

and systems, particularly those that do not consume electricity while performing their essential

function(s). The value of the methodology is in determining the relative contributions of these

life-cycle phases and highlighting the key factors within them.

The following sections outline the overall project goals, describe the NEMA product

classification used to identify focal products, and describe the methodology by way of the

three core steps listed above.


1. PROJECT PURPOSE AND OBJECTIVES

The design, operation and systems integration of electrical products can be an important

contributor to reducing global environmental impact, both in terms of energy the products

consume and energy savings they enable. NEMA undertook this project to improve its

members’ understanding of and ability to influence the environmental impact of their products.

The intent is to show how to evaluate these products systematically from a life-cycle

perspective [1], specifically focusing on greenhouse gas emissions, or “carbon footprint” (CF).

With fuller knowledge of their product’s footprint, firms are better equipped to pursue the most

cost effective carbon mitigation strategies.

Characterizing the carbon footprint of a product generally requires a comprehensive life

cycle assessment (LCA), proceeding from raw material extraction and transportation to

manufacturing (or service provision), distribution, consumer use, and end-of-life disposal. So

far, the available studies and methods for defining a product’s carbon footprint are far from

comprehensive. More detailed, robust analytic techniques are in high demand as companies

receive customer requests and regulatory pressure for this information. In addition, LCA

efforts by nature can be resource-intensive, complex, and fraught with uncertainty due to the

dynamics of supply chains, multitude of parts within a product, and lack of key data.

This project was initiated to develop a standardized approach to characterizing the

carbon footprint of electrical products. The method is streamlined and flexible and

incorporated into a spreadsheet-based LCA preliminary tool – the PAIA-based model, which

can be applicable to a wide variety of NEMA products and divisions.

The project has proceeded through two phases, resulting in two research reports and a

series of Excel-based calculator tools. The report for the initial phase 1 described the

approach and its application to the case of energy efficient lighting and motor products. 1 NEMA, Sept 2012, Exploration into the environmental assessment of electrical products

Phase I: Method Development for Carbon Footprint Assessment as Applied to Motor and Lighting Products,

available at:

http://www.nema.org/news/Pages/NEMA-First-Phase-Carbon-Footprint-Report-Now-Available-for-Member-Re

view.aspx


Second phase activities, which are the subject of this report, included evaluating additional

focal products and describing a generic PAIA-based model intended for application across

NEMA product categories. Specifically, the second phase included:

• Developing a concise and cogent database for metals, encompassing energy use

associated with the metal production process from cradle to gate, 2 ready for

assembly. The reason for focusing on metals in this stage is that many NEMA

products are steel and aluminum intensive (e.g., motors and connectors.)

Comprehensive CF information for these and other key metals thus provides useful

content for future NEMA analyses.

• Expanding the methodology to lamp ballasts and electric connectors

• Generalizing the PAIA-based model for application to a broader spectrum of NEMA

products and systems, and presenting the principles and guidelines for doing so.

2 Cradle-to-gate is an assessment of a partial product life cycle from resource extraction (the “cradle”) to the factory gate (i.e., before it is transported to the end-user).


2. LIFE CYCLE ASSESSMENT

METHODOLOGY

2.1 Scope

2.1.1 FOCAL PRODUCTS

NEMA member companies supply hundreds of thousands of diverse electrical products

and systems. Figure 1 presents a partial categorization scheme that illustrates the breadth of

the NEMA product scope.

To guide focal product selection, these products were first classified either as

components, products or systems. With the focus on products, the following criteria were

applied to narrow the universe and select appropriate subjects for analysis.

(1) The products are manufactured by many NEMA members, widely consumed within

the market, and commonly used by consumers or in industrial applications.

(2) There is a complex but describable supply chain for the selected products, allowing

for consideration of variance within the manufacturing and the distribution scenarios.

(3) The product is either a major contributor to energy consumption or composed of high

energy/carbon intensive materials (reflecting the priority given to the impact of energy

demand and carbon footprint within this study),

(4) The product’s basic characteristics or attributes can be gleaned from public

information (such as a company websites), so as to lower the cost of conducting the LCA

and reduce the burden on NEMA members of collecting data. In addition, data from

literature or commercial databases can help leverage existing company data to create

the best estimate for CF impact.


Figure 1: Scope of potential focal products and levels at which the functional unit can be

defined in terms of NEMA product categories

2.1.2 SYSTEM BOUNDARY

Figure 2 indicates the life cycle boundary used for each of the focal products within the

study. Each element of the life cycle contains quantitative data concerning amount of

materials or electricity, for example, as well as information about the GHG impact of those

materials and electrical processes.

Figure 2: Schematic of proposed system boundaries for electro-industry products. Red

text signifies factors for which primary data are more readily available.


2.2 Overview of PAIA-based Model

There are three core steps involved in the streamlined LCA. The first step is fundamental

to LCA and the latter two are particular to the methodology developed in this project.

2.2.1 HIGH LEVEL TRIAGE ASSESSMENT

The high-level triage assessment was performed using data from publicly available

literature and guidance provided by company representatives in the relevant NEMA product

sections. Information drawn from these sources was used to create a generic database that

reflected certain assumptions and generalizations [2].

Whether its goal is a benchmarking exercise, to answer a customer request, or provide a

recommendation for action, the reliability of an LCA depends on appropriate consideration of

uncertainty. In the case of streamlined assessment, explicitly capturing uncertainty enables

researchers or practitioners to identify priority areas for more refined data collection.

The high level triage assessment and refinement proceeds as follows.

• Data collection and preliminary evaluation, including bill of activities (BOA) and life

cycle inventory data. The life cycle inventory data captures the embodied impact of

materials, expressed as kg CO2 eq/kg (kilograms of carbon dioxide equivalent per

kilogram), manufacturing energy use factor expressed as MJ/kg (megajoule

equivalent per kilogram), or the combined impact factors for materials and

manufacturing phases, also in MJ/kg. The latter two measures can be converted to

kg CO2 through the application of the appropriate energy grid mix.

• Classification and identification of an appropriate probability distribution for these

data.

• Monte Carlo simulation and sensitivity analysis that provides an understanding of the

confidence assigned to the drivers of CF impact, digging down from life cycle phases

to particular components within the product class of interest.

• Data refinement for areas of significant contribution to total impact and contribution to

variation.


2.2.2 ATTRIBUTE INVESTIGATION

In this second step of the streamlined LCA approach, a laundry list of candidate attributes

is compiled and subsequently narrowed through discussions with experts in the industry and

reviews of publicly available data. The important attributes of the product under evaluation are

then screened for high impact activities identified in the previous step. Identifying these

attributes allows them to be assessed across representative products within a category rather

than individually for each product.

2.2.3 PAIA-BASED MODEL DEVELOPMENT

In this step, the activities that contribute a significant portion of the total impact are

mapped to related product attributes to form the algorithms that underlie the PAIA-based

model. This is done primarily through statistical regression that maps attributes to activities

and then CF impact. These regressions are based on data gathered from the literature,

provided by industry representatives, and obtained by the research team through disassembly

of products.


3. INVENTORY DATA ORGANIZATION AND

CF MODELING

The bill of activities is a list showing masses of materials, content of components, and

other product-related factors that have life cycle impacts (e.g., energy use, transportation).

The BOA can take many different forms, but in general consists of an accounting of

subcomponents, covering materials, components, and assemblies. Materials include

substances such as steel, aluminum, different types of plastics, and precious metals. This can

be a difficult task as information about the specific types of materials and processing

technologies is not always available. The case studies described in the appendix serve to

illustrate this challenge.

This section presents the data and assumptions used to conduct a life cycle inventory

analysis. The final carbon footprint is generated by combining bill of activities data for each

examined product with life cycle inventory data for related unit processes [3].

3.1 Under-specification: Life Cycle Inventory

3.1.1 UNIT PROCESS INVENTORY DATA: UNDER-SPECIFICATION

In previous work, the Materials Systems Laboratory identified and established a database

for classifying materials inventories for many materials and their underlying GHG potential

(expressed as kg CO2 eq/kg). The general approach to create the hierarchy of existing life

cycle inventory databases has been termed “structural under-specification,” which is the focus

of a recently published article by this research group.3 This section presents further detail

added for the purposes of this project.

Embodied Materials Impact

A number of life cycle inventory (LCI) databases regarding the embodied impact of a

material (E, expressed here as kg CO2-eq per kg) have been developed to support the use of

existing LCA methodologies and standards[4]. They include data drawn from observation,

3 Exploring the Viability of Probabilistic Under-Specification To Streamline Life Cycle Assessment Elsa

Olivetti, Siamrut Patanavanich, and Randolph Kirchain Environ. Sci. Technol., 2013, 47 (10), pp 5208–5216


obtained through quantitative research, and provided by manufacturers.

In models that employ under-specification, it is assumed that information regarding the

specific raw materials, sub-assemblies, intermediate assemblies, sub-components, and parts

needed to manufacture a product (i.e., the product’s “bill of materials” or BOM) is often

unavailable. Thus, a process for categorizing the information on materials was developed for

this project. This categorization means that a material may not be fully specified, but listed

generically - for example, as a metal, polymer or glass. The average impact and standard

deviation for this material is then derived from all similar materials (e.g., all other metals).

Using this method, LCA practitioners can understand the degree of uncertainty for

different types or classes of materials within a component or part. For motor or lighting

products evaluated in the first phase of this study, for example, a company may specify the

materials of a certain component as general use steel, where in actuality it is more specialized

electric steel. The LCA model assesses the impact of the component and product with the

uncertainty related to the level at which the material is specified. This allows the most

important materials to be identified and specified more completely. This is one way that the

overall LCA effort is reduced.

Figure 3 shows the hierarchy of materials classification emphasized within the

classification of iron and steel products.

Figure 3: Level of the data approaching for materials embodied impact (Ferrous metal

case)


Materials & Manufacturing Database Development

The manufacturing phase consists of the processing and finishing of the raw materials.

It does not account for extraction and pre-processing of the raw materials. However, a

database can be created combining and including the impact (i.e., energy use) of both the

materials and manufacturing phases, particularly for the metals products, as described below.

(1) Background

Because primary data on energy consumed during manufacturing and assembly of

products may be more difficult to obtain than information on materials, we used data from

literature and commercial databases to quantify energy consumption in manufacturing (M,

expressed as kilowatt hours per kg, kWh/kg) [5-12]. Energy demand emissions factors (G,

expressed as kg CO2-eq/kWh), classified by region and sources, were modeled based on

complex electricity/fuel mixes, taking into consideration the location of the finished product

suppliers.

The scope and boundaries of a unit process affect its ease of import into an LCA model.

One particular challenge is defining boundaries consistently between the materials and

manufacturing stages. Lack of consistency can cause impacts from the materials and

manufacturing stages to overlap. Because there are many processing technologies used to

fabricate a material for a particular use (e.g., steel), the data must reflect the materials used in

the focal products. If the necessary data are not available in LCA databases, the relevant

information must be compiled independently. In this study, an independent dataset- Energy

Demand Datasets (EDD) – has been built to summarize the energy use occurring within

materials to manufacturing phases for metals products.

Essentially, this approach broadens the scope of the database developed for the

Embodied Material Database outlined above. Which database to use would be dictated by the

level of information the practitioner has about the product of interest. For example, if primary

data exists on the manufacturing processes for particular components, the materials-only

focused database would be sufficient for those components.

(2) Approach to Building the Energy Demand Datasets

The objective of this dataset is to create an information base for metal-intensive products.


The steps for doing so are as follows:

• Create a dataset to collect energy use data (expressed as megajoules per kg, MJ/kg,

Energy Indicator-EI) for metals products which consist of detailed data at all process

levels, including raw materials mining and extraction, beneficiation (e.g., hydro- or

pyrometallurgy), shaping (e.g., casting, rolling and extrusion) and machining and

finishing (e.g., milling, drill and joining). Datasets for main metals such as iron/steel,

aluminum and copper were developed for this project.

• Differentiate the data source based on life cycle stage (i.e., primary product, semis,

and finished product) and processing technology, such as casting, rolling and

extrusion.

• Where possible, quantify uncertainty within the dataset. This may be based on

statistical representation across several relevant datasets or on quantitative

measures based on qualitative assessments of data quality

(3) Data Sources for the Energy Demand Datasets

Sources of the data for developing these datasets include the following:

• Metallurgy and metal manufacturing industries reports; energy demand

statistics/investigations/estimations produced by industry associations or the US

Department of Energy.

• Bath University ICE 2.0 database of embodied materials impacts, expressed in terms

of energy demand [13]; and ecoinvent and other commercial databases (e.g.,

cumulative energy demand (CED) exported from SimaPro LCA software).

• Published reports and journal papers related to the energy demands for metal

products materials and manufacturing [10, 14-37].

(4) Modeling

Individual ‘‘cradle-to-gate’’ spreadsheet datasets were developed for each metal

production process, with flow-sheets constructed at detail consistent with processing data

available in the literature or commercial database. Regrettably, some life stages such as

finishing and several process technologies are missing due to data limitations.

The inventory data used for targeted processing route were drawn from a variety of


sources. The potential processing route depends on the type, technology or grade of the

metal.

3.2 Other Life Stages

3.2.1 USE PHASE

The product’s use phase is evaluated on the basis of direct electricity use and does not

account for indirect impacts such as those on infrastructure and support systems. The use

stage is defined by the product’s active lifetime (h, # of hours), efficiency (η, %), and power (w)

required as a function of the duty cycle.

The grid emission factor is the amount of carbon dioxide emissions associated with each

unit of electricity produced by an electricity grid. It is common in carbon footprint estimation to

use standard emissions factors for grid electricity, expressed as kgCO2-eq/kWh. These

emissions factors will vary by region in accordance with the fuel sources used to produce the

electricity that supplies the grid. The PAIA-based model incorporates grid mix data from the

International Energy Agency [38, 39], World Resources Institute [40] and other sources such

as PE International, a life cycle analysis company and inventory database and ecoinvent, for

most countries in the world. Multiple electric grid regions exist in many countries, each with a

unique mix of power generation resources.

Calculation of the CF impact stemming from the use stage (CF impact, Cu) is expressed in

Equation 1:

Equation 1: C𝑢−𝐶𝑂2=

(ℎ×𝑤×𝐺𝑠−𝑈.𝑆.)

𝜂

[i.e., The total impact from use stage activities is a function of hours of usage, the power expended, the

electricity grid factors, and efficiency.]

3.2.2 TRANSPORT PHASE

The impact of transport throughout the life cycle is also addressed in the methodology.

This includes transport by lorry and cargo ship for onshore and offshore facilities, respectively.

Both global and domestic suppliers to the market of interest were taken into account to

calculate the emissions impact of transportation, which was limited to distribution of finished

product at province/state level, using shipment data. The CF impact factors (F, expressed as


kg CO2-eq/ton-kilometer, tkm) corresponding to transportation modes by truck, vessel and rail

(x) are drawn from LCI databases such as ecoinvent. The distances (k) were estimated using

information from the World Shipping Register (for maritime transport) and via Google (for truck

and rail transport, 50% to 50% split). Legs of transportation that were excluded from the

estimation include transport to retailers and consumer locations, and between consumer and

end-of-life management locations.

Calculation of the CF impact from transportation (Ct) is expressed by Equation 2.

Equation 2: C𝑡−𝐶𝑂2= 𝑞 × 𝐹𝑥 × 𝑘

[i.e., The total impact from transportation is a function of the mass that is transported, the impact of the transport

mode, and the distances involved.]

3.2.3 END OF LIFE PHASE

It is common for some electrical and electronics products to be refurbished and/or reused

at the end of the first customer use. This study does not account for these options, however,

but limits end-of-life options to either dismantling for recycling, ultimate disposal, or both (y). In

addition, the benefits of materials recovery and remanufacturing are not accounted for due to

lack of data. The energy demand factor of the end-of-life activities y (expressed as kWh/kg, V)

was obtained from the literature or commercial databases.

Calculation of the CF impact during the end-of-life stage (Cd) is expressed in Equation 3.

Equation 3: C𝑑−𝐶𝑂2= 𝑞 × 𝑉𝑦 × 𝐺𝑠−𝑡ℎ𝑒 𝑈.𝑆.

[i.e., The total impact of end-of-life activities is a function of the total mass of product, the energy use from

dismantling and disposal, and the grid factors]

The overall life impact is the sum of each life stage. As a reminder, total mass is defined

as 𝑞 (expressed as kg/product), which incorporates both the number of components (n) and

the mass of each component (q’) contained in a functional unit of a focal product. Thus ‘q’ is

highly determined based on the type of the product attributes.


3.3 High Level Assessment-CF Modeling

Database 1: Separating under-specification of materials and manufacturing impacts

By drawing on the embodied impact of the inventory of materials, the CF impacts

stemming from the materials and manufacturing phases can be expressed through Equation 4

and Equation 5, respectively.

Equation 4: C𝑚𝑡𝑙−𝐶𝑂2= ∑ (𝑞´𝑛

𝑛𝑖=1 × 𝐸𝑛)

[i.e., the total impact associated with materials in the product is the sum total of the masses of the

materials times each material’s embodied impact]

Equation 5: C𝑚𝑓𝑔−𝐶𝑂2= ∑ ∑ (𝑞´𝑛

𝑛𝑖=1 × 𝑀𝑛 × 𝐺𝑠)𝑠

𝑗=1

[i.e., the total impact caused by manufacturing the product is a function of the mass of material processed, the

energy consumed by each process stage, and the grid factors]

The total impact of the product – or carbon footprint - is expressed in Equation 6:

Equation 6: 𝐶𝑡𝑜𝑡𝑎𝑙 = 𝐶𝑚𝑓𝑔−𝐶𝑂2+𝐶𝑚𝑡𝑙−𝐶𝑂2

+𝐶𝑢𝑠𝑒−𝐶𝑂2+ 𝐶𝑡−𝐶𝑂2

+𝐶𝑑−𝐶𝑂2

[i.e., the total impact is the sum total of impacts in all phases, including materials, manufacturing, use,

transportation and end-of-life]

Database 2: Combining materials and manufacturing under-specification

The CF impacts of the materials and manufacturing phases (Cmtl+mfg) are expressed in

Equation 7, with the corresponding Energy Demand factor calculations EDn determined by

Equation 8.

Equation 7: C𝑚𝑓𝑙+𝑚𝑓𝑔−𝐶𝑂2= ∑ ∑ (𝑞´𝑛

𝑛𝑖=1 × 𝐸𝐷𝑛 × 𝐺𝑠)𝑠

𝑗=1

[i.e., the total impact associated with materials and manufacturing phases in the product is the sum total of the

masses of the materials times each material’s unit energy demand factor]

Equation 8: 𝐸𝐷𝑛 = ∑ 𝐸𝐼𝑃𝑝𝑧=1

[i.e., the material’s unit energy demand factor associated with materials in the product is the sum total of each

processing’s embodied energy impact]

The sum of the total impact is expressed in Equation 9:


Equation 9: 𝐶𝑡𝑜𝑡𝑎𝑙 = C𝑚𝑓𝑙+𝑚𝑓𝑔−𝐶𝑂2+𝐶𝑢𝑠𝑒−𝐶𝑂2

+ 𝐶𝑡−𝐶𝑂2+𝐶𝑑−𝐶𝑂2

[i.e., the total impact is the sum of impacts in all phases, including materials and manufacturing, use, transportation

and end-of-life]

As a reminder, database 2 is based on the combined emission factors in the materials and

manufacturing phases. The advantage for this approach is that the emission factor is detailed

in terms of the technology the materials (metal) within the same materials type (rolling steel or

casting steel). The first way of formatting the database calculates the impacts separately

based on the emission factor in materials and manufacturing phases, which depend on the

materials type (steel or aluminum). The emission factors for both approaches have been put

into the datasets with under-specification (see section 3.1.1). Again, the choice of using one

database over the other should be dictated by the form of the data a practitioner brings to a

particular study.

3.3.1 DATA REFINEMENT

Within both of these approaches to database construction the practitioner can refine the

analysis and through more detailed specification of the inventory:

(1) Using “hotspots” found in preliminary analysis as a guide, the practitioner can delve

into the bill of activities and request more information on the quantity of materials or

amount of manufacturing, where possible.

(2) Again using the hotspot analysis as a guide, the practitioner may seek more specific

information on material types or manufacturing processes used so that these items may

be specified more completely.

The second approach is illustrated below: Figure 4 shows the difference in CF impacts

between two levels of specificity for a 25 horsepower motor product during materials and

manufacturing phases. The more specific information on inventory used, the narrower the

error bars.


Figure 4: CF impact of Motor product (25 HP): Materials & Manufacturing phases: 5% and

95% percentage tail, within one standard deviation.

In “Level 1,” the unit process inventory data is based on unspecified processing

technology for metals parts (materials). For example, we did not differentiate the technology

for steel parts by casting, rolling or extrusion. In the Figure 4 box and whisker bar labeled

“specified level 2”, the inventory data is based the specific processing technology for each

metal part, such as cast iron for the frame, die-casting aluminum for the rotor.

As a reminder, there is still space to improve the levels (i.e., attain level 3) in terms of

information on the materials (e.g., chromium steel or low-alloyed steel) and/or more

completely specified processing technology.

3.4 Development of Potential Attributes for Products

Simultaneously with the previous steps, the product’s attributes must be investigated as

comprehensively as possible. These include:

• Functionality, related to the main function of the product

• Technical qualities, such as stability, durability, ease of maintenance

• Aesthetics, such as appearance and design

• Image of the product or the producer

• Specific environmental properties

• Additional services rendered during use and disposal

This study identified a comprehensive list of product and process attributes for the focal

products drawn from literature reviews, company interviews, and knowledge of the evolving

state of the technology. Attributes were selected on the basis of representativeness,

0

500

1,000

1,500

2,000

2,500

Unspecified_Level 1 Specified (based on metals processingtechnol)_Level 2

GH

G I

mp

act

kg

CO

2 -

eq (

Pro

du

ct

Cla

ss)


relationship to the BOA (e.g., materials mass) and data availability. The list of attributes was

then reduced to those related to the high-impact activities, discussed in the following section.

3.5 Reducing Attribute List Based on Impact

The previous section addressed the process for screening the attributes in the initial stage

of the life cycle analysis. This list must now be limited to those attributes that relate to

high-impact activities, to the extent that impact can be mapped from a particular attribute at

reasonable cost. After narrowing the list on this basis, we develop and test statistical models

for the remaining attributes of interest.

3.6 Regression for the Product Family

Generally, data collection is the most time-consuming part of an LCA. To help address this

challenge, the PAIA approach is designed to facilitate calculation of CF impacts for a set of

products within a category.

Correlation and regression are two relevant (and related) techniques for determining an

association between two variables: Correlation provides a unit-less measure of association,

whereas regression provides a means of predicting one variable from another [41]. We used

the technique of correlation to test the statistical significance of the association. We also used

regression analysis to describe the relationship between an independent variable and an

outcome by means of an equation that has predictive value. For example, weight is an

important characteristic in determining the materials and manufacturing impacts in this study.

Therefore, a weight prediction model has been developed with the aid of correlation and

regression, using the variable attributes as inputs.

As an example for the motor product, we developed a regression analysis across motors

ranging from 1HP to 100HP. We obtained their mass and power curve profiles, which provided

the minimum data to build the BOA lists using more detailed information on a limited number of

motor types provided by motor manufacturers, including 5HP, 25HP and100HP (general

purpose, polyphase, cast iron construction, NEMA premium, and TEFC).

The mass of each component within the motor is defined as 𝑞´, expressed in Equation 10,

where total mass is 𝑞 , f is the mass fraction (%) of the components, n is the type of


components/material, and q and f are determined by company-l, efficiency class-a, enclosure

type-b and poles-o, with w again representing a power factor.

Equation 10: 𝑞´𝑛 = 𝑞𝑙,𝑎,𝑏,𝑜,𝑤 × 𝑓𝑛

All of the targeted motors have the same components list, as well as the materials

breakdown by percentage. While the total mass (q) is increasing in association with the out

power (w, by HP), the mass fractions of the components (n) are slightly different, which may

be due to the variance of the manufacturers’ purpose in design. The Two-Way ANOVA

(unequal variance) method by using the Stata (Stata/IC 12.1) has been employed to test the

significance level of the mass fraction for each component among various horsepowers. While

there is significant difference (P-value=0.00, F=105.53) for the fractions of various

components, there is no significant difference for each component at various horsepowers

(P-value=1.00, F=10), and the interaction is non-significant (P-value=0.96, F=0.52). Therefore

we assume that the fractions of components for all the mid-size motors (1-100 HP) are

consistent with and equal to the normalized values for 5HP, 25HP and 100HP provided by the

manufacturers. A uniform distribution for the fraction of each component is assumed to

address the uncertainty in this regression analysis.

3.7 Data Quality and Limitations

3.7.1 SENSITIVITY ANALYSIS

Analysts must make numerous assumptions in the course of an LCA. A sensitivity

analysis is used to explore the extent to which variability in baseline scenario assumptions

affects the environmental impacts associated with the focal products. The analysis evaluates

a range of scenarios that deviate from the baseline. Examples include:

• Alternate materials for large components

• Lifetime usage and use intensity

• Efficiency performance

• Use phase electricity grid mix (e.g., national or regional average mix) – the

technology portfolio that supplies electric power for focal products.


3.7.2 LIMITATIONS

This PAIA model, because it is based on a life cycle approach, is subject to the same

limitations as LCA studies. The reliability of the results and the conclusions of an LCA depend

in large measure on the quality of the inventory data that is used.

Primary data on energy consumption during the manufacturing and assembly of the focal

products (i.e., facility data) is scarce, as is data on the impacts of end-of-life stage activities.

Detailed information is also lacking on transportation modes, although this is not expected to

contribute significantly to the product’s lifecycle emissions.

Moreover, focusing on CF impacts bears the risk of overlooking other relevant

environmental impacts [42]. Environmental management focused exclusively on carbon

emissions may inadvertently lead to problems in other areas when products are optimized to

become more “carbon friendly” [43].


4. CONCLUSIONS

The list below restates the objectives of this project and describes the ways in which they

were fulfilled:

• Develop a methodology to examine the CF impact for electrical products. The

model is a streamlined LCA method that maps the attributes of electrical products to

energy use and CF impact. This report contains high level guidance for applying the

methodology, designed for broader application within the electrical product sector.

• Demonstrate the methodology for four products screened from NEMA product

catalogue. The PAIA-based model was demonstrated for energy efficient lighting

products, ballasts, motors, and electrical connectors. A more complete sensitivity

analysis and detailed interpretation were produced for the motor product category.

Overall, the methods presented in this study succeeded in estimating ranges of

carbon emissions from the specified products.

• Provide a useful tool for use by NEMA members in assessing the opportunities

for carbon mitigation in the design and production of electrical products and

systems. The method will be evaluated as the potential basis for an international

standard that will serve to harmonize this type of analysis in the global

electro-product industry.


REFERENCES

[1] Hilty, L. M. CO2 reduction with ict: Prospects and barriers. Environmental Informatics and Systems

Research, EnviroInfo Warsaw 2007: 12-14.

[2] Pihkola, H.; Nors, M.; Kujanpää, M.; Helin, T.; Kariniemi, M.; Pajula, T.; Dahlbo, H.; Koskela, S.

Carbon footprint and environmental impacts of print products from cradle to grave; VTT; 2010.

[3] Montalbo, T.; Gregory, J.; Kirchain, R. Life cycle assessment of hand drying systems; Cambridge,

MA, USA: Materials Systems Laboratory, Massachusetts Institute of Technology; 2011.

[4] Okrasinski, T.; Malian, J.; Arnold, J. In Data assessment and collection for a simplified lca tool,

Carbon Management Technology Conference, 2012.

[5] Sutherland, J. W.; Adler, D. P.; Haapala, K. R.; Kumar, V. A comparison of manufacturing and

remanufacturing energy intensities with application to diesel engine production. CIRP Annals -

Manufacturing Technology 2008; 57, (1): 5-8.

[6] Boustead, I.; Hancock, G. F. Handbook of industrial energy analysis. E. Horwood: Chichester :

Horwood, 1979.

[7] Dalquist, S.; Gutowski, T. In Life cycle analysis of conventional manufacturing techniques: Die

casting, LMP Working Paper, Cambridge, 2004.

[8] Bettinghaus, J.; Kunes, T. P.; Nicol, J.; Presny, D.; Schepp, C.; Altfeather, N. Metal casting industry

energy best practice guidebook; Wisconsin: State of Wisconsin, Department of Administration, Division

of Energy; 2006.

[9] Schifo, J.; Radia, J. Theoretical/best practice energy use in metalcasting operations; Washington,

DC: Keramida Environmental, Inc; 2004.

[10] Green, J. A. S. Aluminum recycling and processing for energy conservation and sustainability.

ASM Intl: Materials Park, OH, 2007.

[11] IPAI. Life cycle inventory of the worldwide aluminum industry with regard to energy consumption

and emissions of greenhouse gases; International Primary Aluminum Institute (IPAI); 2000.

[12] Roberts, M. J. Modified life cycle inventory of aluminium die casting (Master Thesis),

Victoria:Deakin University; 2003.

[13] Hammond, G.; Jones, C. Inventory of carbon & energy: (ICE) version 2.0 (annex b: Methodologies

for recycling); UK: University of Bath; 2011.

[14] AGA. Hot-dip-galvanizing-for-sustainable-design; American Galvanizers Association; 2009.

[15] USDOE. Mining industry energy bandwidth study; Industrial Technologies Program (ITP) in the US

Department of Energy’s (DOE); 2007.

[16] Price, L.; Sinton, J.; Worrell, E.; Phylipsen, D.; Xiulian, H.; Ji, L. Energy use and carbon dioxide

emissions from steel production in china. Energy 2002; 27, (5): 429-446.

[17] IISI. Industry as a partner for sustainable development: Iron ad steel; United Kingdom:

International Iron and Steel Institute (IISI), United Nations Environment Programme; 2002.

[18] WSA. Steel and energy; World Steel Association; 2008.

[19] Peters, D. T.; Cowie, J.; Brush Jr, E.; Midson, S. In Use of high temperature die materials and hot

dies for high pressure die casting pure copper and copper alloys, 2002.

[20] Norgate, T.; Jahanshahi, S.; Rankin, W. Assessing the environmental impact of metal production

processes. Journal of Cleaner Production 2007; 15, (8): 838-848.

[21] FoE. Metal casting industry energy best practice guidebook; Focus-on-Energy (FoE); 2006.


[22] CSPA. The canadian steel industry; Ottawa, CA: Canadian Steel Producers Association for Natural

Resources Canada’s Office of Energy Efficiency; 2006.

[23] TAA. Aluminum: The element of sustainability; The Aluminum Association (TAA); 2011.

[24] Schifo, J. F.; Radia, J. T. Theoretical/best practice energy use in metalcasting operations;

Indianapolis, IN KERAMIDA Environmental, Inc. ; 2004.

[25] Norgate, T. E.; Jahanshahi, S.; Rankin, W. J. Assessing the environmental impact of metal

production processes. Journal of Cleaner Production 2007; 15, (8–9): 838-848.

[26] Nakano, A.; Rochman, N. T.; Sueyoshi, H. Lca of manufacturing lead-free copper alloys. Materials

transactions 2005; 46, (12): 2713.

[27] Haque, N.; Norgate, T. In Life cycle assessment of mining and metal production, High Temperature

Processing Symposium 2010, Melbourne, Australia, 2010.

[28] Eppich, R. E. Energy use in selected metalcasting facilities-2003; US Department of Energy; 2004.

[29] USDOE. Energy and environmental profile of the US mining industry; US Department of Energy

Office of Energy Efficiency and Renewable Energy; 2002.

[30] Alvarado, S.; Maldonado, P.; Jaques, I. Energy and environmental implications of copper

production. Energy 1999; 24, (4): 307-316.

[31] Patnayak, A.; Bansal, N. K.; Mathur, J. Energy analysis of manufacturing process of a motorcycle.

International Journal of Energy Technology and Policy 2004; 2, (4): 392-402.

[32] Alvarado, S.; Maldonado, P.; Barrios, A.; Jaques, I. Long term energy-related environmental issues

of copper production. Energy 2002; 27, (2): 183-196.

[33] Ashby, M. F. Materials and the environment: Eco-informed material choice.

Butterworth-Heinemann: Burlington, MA, 2009.

[34] IAI. Life cycle assessment of aluminium: Inventory data for the primary aluminium industry;

International Aluminium Institute; 2007.

[35] Choate, W.; Green, J. A. S. US energy requirements for aluminum production: Historical

perspectives, theoretical limits, and new opportunities; US Department of Energy; 2007. p 204.

[36] Grimes, S.; Donaldson, J.; Gomez, G. C. Report on the environmental benefits of recycling;

Bureau of International Recycling; 2008.

[37] Gutowski, T. G.; Branham, M. S.; Dahmus, J. B.; Jones, A. J.; Thiriez, A.; Sekulic, D. P.

Thermodynamic analysis of resources used in manufacturing processes. Environmental Science &

Technology 2009; 43, (5): 1584-1590.

[38] IEA. Energy balances of non-oecd countries 2009; International Energy Agency; 2009.

[39] IEA. Energy balances of non-oecd countries 2010; International Energy Agency; 2010.

[40] WRI/WBCSD. The greenhouse gas protocol & supply chain initiative (wri /wbcsd); Geneva: World

Business Council for Sustainable Development and World Resources Institute; 2007.

[41] Myers, R. Classical and modern regression with application. Duxbury: Virginia Polytechnic Institute

and State University, 1990.

[42] Quack, D.; Grießhammer, R.; Teufel, J. Requirements on consumer information about product

carbon footprint; Freiburg, Germany: Öko-Institut e.V.; 2010.

[43] Laurent, A.; Olsen, S. I.; Hauschild, M. Z. Limitations of carbon footprint as indicator of

environmental sustainability. Environmental Science & Technology 2012; 46, (7): 4100-4108.


EQUATION PARAMETERS

Abbreviation Definition Unit

E Embodied materials impact: unit emission

factor

kg CO2-eq per kg

M Energy use in manufacturing phase: unit

emission factor

kWh/kg

EI and p Energy Indicator (EI) and the number of

energy indicators (p): unit emission factor

kWh/kg

ED Aggregated energy demand in materials &

manufacturing phases; the sum of EI (p)

kWh/kg

G Grid mix emission factors kg CO2-eq per kWh

s and j Sources of grid mix scenarios based on

regions scope (s) and the number of the

sources (regions) (j)

-

F Freight (transportation): unit emission factor kg CO2-eq/tkm

x Transportation type (mode) -

k Distance of transportation (kilometer) km

V End-of-life stage: unit emission factor kg kWh/kg

y End-of-life stage options (recycling or ultimate

disposal)

-

Attribute related

h Hours, lifespan Hours

η Use stage efficiency %

w Default power of the product Watts

q Mass of the product (functional unit) kg

q’ and n Mass of each component contained in a

products, and the number of the components

kg

CF Impact

Cmtl-CO2 Materials (Mtl) Carbon footprint (C, CF

impact)

kg CO2-eq per unit of

product

Cmgf-CO2 Manufacturing (Mfg) Carbon footprint kg CO2-eq per unit of

product

Cu-CO2 Use stage (u) Carbon footprint kg CO2-eq per unit of

product

Cd-CO2 Disposal (End of Life) stage (d) Carbon

footprint

kg CO2-eq per unit of

product

Ct-CO2 Transportation stage (t) Carbon footprint kg CO2-eq per unit of

product


APPENDIX A: MOTORS

A1 Summary

Small and medium sized polyphase AC motors were selected for this analysis based on

their dominance of the motor market. This appendix describes the specific methods employed

to determine the GHG impact of the AC motor, based on the attributes of the motor as well as

the data available. It was found that the use phase of the motor makes up the vast majority

(99.8%) of its impact under “normal” conditions; however, motors operated under different

conditions may experience a different result. In terms of the materials and manufacturing

phases, the rotor, the stator, and frame contributed most of the motor’s impact, driven primarily

by their use of steel and cast iron.

A2 Scope and Functional Unit

A2.1 SYSTEM BOUNDARY AND GHG MODELING

The analysis will encompass raw materials extraction, transportation, conversion

processes, assembly and manufacturing, use and end-of-life/disposal activities. The use

phase for the motor will be given particular emphasis in this analysis, focusing on intensive

use scenarios.

A2.2 PRODUCT DESCRIPTION AND FUNCTIONAL UNIT

For this study, small/medium-sized polyphase AC motors, which constitute the most

prevalent motor types by market share, were chosen as focal products for analysis. The

materials within this product category include steel of various grades (including electrical

grade), cast iron, aluminum, copper, electronics, plastics, and packaging and insulation

material. The analysis also includes the wiring necessary to connect a motor to an electric

supply as well as the internal cabling. The functional unit is based on the output power and

speed of the motor.

A2.3 ATTRIBUTE CONSIDERATION

We have identified a comprehensive list of product and process attributes for motors

based on literature reviews, company interviews, and an understanding of the current and

evolving state of the technology. This list has been reduced based on the attributes that are

related to the high-impact activities, as well as whether impact can be mapped from a

particular attribute at “reasonable” cost. Using this pared down list of product and process

attributes, we develop and test statistical models for the remaining attributes of interest. Table


A1 summarizes our suggested list of attributes that effectively characterize motor products.

Table A1: Attributes considered for motor products

Number Attributes Priority

1 Purpose Definite

Explosion proof

General purpose

Severe duty

2 Efficiency class Premium (NEMA Table 12-12)

Energy efficient (NEMA Table 12-11,

EPACT Efficiency)

High efficient

3 Enclosure

Types

Drip proof (DP)

Enclosed fan cooled motors

Enclosed explosion proof

4 Horse power 5, 25, 100 HP

5 Poles 2,4,6,8 (related to the RPM

performance)

6 Foot/footless Foot

Footless

7 Construction Cast iron

Rolled steel

Cast aluminum

8 Voltage 230/460

575

9 Frame Size 143T, 184T…(Directly related to 4 and

5)

A3 Life Cycle Inventory

A3.1 BILL OF ACTIVITIES – MATERIALS

Basic information motor weight and attribute data was obtained from the NEMA member

company websites. The data for motor bills of materials (BOM) are from NEMA members, and

comparably analyzed through our own data collection activities. Table A2 lists the major

components/parts and the mass fraction of each.

For AC motors, the most important source material is steel, making up 50% of the mass

(electrical steel 40.1% and cold rolled steel 9.1%). Cast iron (used in frames and Endshields)


is next at 31%. Other materials — copper (stator winding), aluminum, and plastic — make up a

much smaller fraction of the mass of these motors, in the range of 4-10%. Components

making up less than 1% of the total mass were composed of plastic and electronic parts, the

quantities of which are unavailable from the NEMA member companies. However, we did

consider those items based on other data sources from related literature. In this case, the

mass fractions are based on the BOM information provided by the NEMA companies. What we

are presenting are the average values for a mid-size AC motor, polyphase, NEMA premium

efficiency, including cast iron construction.

Table A2 Components list for AC motor product (BOM)

Components & Materials Mass Fraction (%) Materials

Frame Frame 19.5 Cast Iron

Frame Endshields 11.6 Cast Iron

Bearings (Steel) Bearings 0.5 Steel

Terminal Box Terminal Box 1.5 Cast Iron

Terminal Cover 1.2 Cast Iron

Fan Cover 4.4 Cast Iron

Fan 0.2 Plastic

Stator Stator Winding 8.0 Copper

Stator Lamination Stack 24.9 Electrical Steel

Rotor Rotor Lamination Stack 15.2 Electrical Steel

Rotor Al Bars 4.9 Aluminum

Rotor Shaft 9.1 Cold-Rolled Steel

Other Plastic &Electronics <1%

A3.2 BILL OF ACTIVITIES – MANUFACTURING

As explained above the BOM was used as a starting point for inventory of components

and subassemblies. However, because almost no primary data existed for the energy

consumption during the manufacturing and assembly of products targeted for this study, we

used data from literature and commercial databases to quantify the energy consumption in

manufacturing. We also used secondary information as proxies for primary data to simplify

and streamline the evaluation process. Figures A1 illustrate the steps to quantify the energy

demand for motors, based on the PAIA-based methodology.

In general, energy demand variability (kWh/kg) for material machining was estimated

from a range of values including injection molding, machining, and finish machining (Gutowski

et al. 20094). Energy demand emissions factors (kg CO2-eq) were modeled based on complex

4 Gutowski, T. G.; Branham, M. S.; Dahmus, J. B.; Jones, A. J.; Thiriez, A.; Sekulic, D. P.

Thermodynamic analysis of resources used in manufacturing processes. Environ. Sci. Technol. 2009, 43(5), 1584-1590..


electricity/fuel mixes, considering the location of the finished product suppliers.

Figure

A1: Manufacturing phase energy consumption for AC motor products

The components and parts manufacturing processes were taken into account first,

followed by the assembly process. While primary data were unavailable, we included energy

consumption data based on the assembly process for undefined motor products. The data

sources are from existing literature.

A3.3 OTHER LIFE CYCLE PHASES

The use stage defines the product’s active lifetime and reflects the consumption of

electrical power. For AC motors, we assume that the running time is 5000 hours per year with

a uniformly distributed uncertainty range of 4000-6000 hours, with an average 20 years’

service lifetime (based on consultation with NEMA member experts). There is efficiency rating

for NEMA AC motors which either Epact or NEMA Premium. Given the same efficiency rating,

the actual efficiency (%) will vary by the horsepower, frame size, and enclosure type of the

product.

For transportation, both global and domestic suppliers to the U.S. market were taken into

account to calculate the emissions impact of transportation, using shipment level data for focal

products. The data are drawn from U.S. census, company reports, and literature. We assume

the inland transportation to be split evenly between truck and rail mode, and that ocean

shipment is by vessel. Air transportation is not included. Domestic distribution in the U.S. is

computed in proportion to population density, such that larger cities are assumed to receive a

higher percentage of shipments. The GHG impact factors (expressed as kg CO2-eq/tkm)

corresponding to transportation modes (by truck, vessel and rail) are drawn from commercially

available life cycle inventory databases, such as ecoinvent 2.2.

Deforming Process

Surface Treatment

Finish Machining

Other Processes

Sample Manufacturing Process

Mass evaluation (kg)

General materials/

components

identification

Literature

Commercial database

Electricity required

Product Component and PartsMajor Motor Parts Materials

Frame Cast Iron

Frame Endshields Cast Iron

Bearings Steel

Terminal Box Cast Iron

Cover Cast Iron

Fan Plastic

Stator Winding Wire Copper

Stator Lamination Stack Electrical Steel

Rotor Lamination Stack Electrical Steel

Rotor Al Bars Aluminum

Rotor Shaft Cold-rolled Steel

Plastic and Electronics... ....

Output:

Total energy

demand for

product components

and parts


For end-of-life phase, a basic recycling and disposal scenario is included based on the

data of the energy consumption and losses evaluation, which is from ABB Environmental

Product Declaration Report for AC motor5. Remanufacturing is an important phase for motor

products, but was beyond the scope of analysis for this work.

A4 Data Limitations

The reliability of the results and the conclusions of the LCA depend in large measure on

the quality of the inventory data that is used. Throughout the research process, NEMA and its

member companies collaborated to provide the BOM data; therefore the BOM viewed as

accurate. However, there are several limitations to the approach used for this study, as

described below:

(1) Primary data on energy consumption during the manufacturing and assembly of the

focal products is scarce.

(2) There is incomplete information on details concerning transportation (distribution

stage), although this is not expected to contribute significantly to the product’s lifecycle

emissions.

(3) Primary data on the impacts of end-of-life stage activities are missing. Thus the

scenarios related to disposal or recycling are estimated.

In addition, there are sources of uncertainty in the life cycle inventory data, such as the

assumed emissions factors and data obtained from the ecoinvent database. This uncertainty

may arise from measurement error, variation within processes, temporal discrepancies, and

geographical distributions. There is also substantial uncertainty within data drawn from the

literature due to system boundary definition. Secondary data can be incorporated as proxies

for primary data to simplify and streamline the evaluation process. However, many of these

data sources have been evaluated for uncertainty. The use of Monte Carlo simulations is

incorporated into the PAIA-based model to focus the effort used to understand parameter

uncertainty around the most sensitive aspects.

A5 Impact Assessment and Interpretation

A5.1 OVERALL LIFE CYCLE IMPACT

Evaluation of the GHG emissions throughout the overall life cycle of the AC motor (25HP,

general purpose, 6-pole, cast iron, premium efficiency, and total fan cooled enclosure) is

specified in Figures A2. The study shows that the use phase dominates other phases in terms

of energy consumption, comprising more than 99.8% of the impact. Materials and

5 ABB, Environmental Product Declaration: AC Motor.


manufacturing combined are responsible for less than 0.5% of total life cycle carbon

emissions. The manufacturing burden is slightly lower than the materials burden. However,

the assumptions adopted for the use phase can influence the overall results quite significantly.

Mtl Mfg Assembly Transport Use EoL0

600

1200

1800

2400

400000

800000

1200000

1600000

2000000G

HG

Imp

act

kg C

O2 e

q

Life stage

Note: Error bars represent one time standard deviation above and below the mean; the grid mix

emission factor is an average national value of approximately 0.69 kg CO2/kWh (US EPA eGRID

2007 database), with an assumption of 20% COV. The life time in use stage has a mean of 4000

hrs per year (20 years) with an assumption of 20% COV. The impact of assembly stage is around

3.5 kg CO2 eq (std: 0.6).

Figure A2: Overall life cycle impact for a 25 HP NEMA premium motor.

A5.2 MATERIALS AND MANUFACTURING PHASES IMPACT

When the use stage is excluded, the importance of certain parts becomes evident, as

shown in detail in Figure A3 and A4. A handful of parts dominate the impact caused by parts

production. These are, in order of importance: the rotor, the stator, and the frame. Together

these constitute almost 90% of the total product carbon footprint.


Figure A3: Impact of major components for a 25 HP NEMA premium motor- 5% and 95%

percentage tail, plus and minus one standard deviation.

Figure A4: Impact of all listed components for a 25 HP NEMA premium motor - 5% and 95%


We also looked at the impact of a specific motor product attributable to the materials

(Figure A5). Steel makes up more than half of the total GHG emissions (mean value), followed

by cast iron (31%). The main reason for the dominance of these two materials is that they

account for 85% of the total motor mass. The AC motor product uses a large amount of ferrous

metals.

0

400

800

1,200

1,600

2,000

Frame Bearings TerminalBox

Fan Stator Rotor

GH

G Im

pac

t

(kg

CO

2 -

eq/p

rod

uct

cla

ss)

0

400

800

1,200

1,600

GH

G Im

pac

t

(kg

CO

2 -

eq/p

rod

uct

cla

ss)


Figure A5: Impact evaluated by materials for 25 HP NEMA premium motor. - 5% and 95%


The findings of the contribution analysis are summarized in Tables A3. Four of the

components contribute 65% of the total impact; in order of significance, these are: stator

lamination stack, rotor lamination stack, cast iron frame, and steel shaft.

Table A3: Order of the components’ contribution analysis.

Components /parts Ordered by

contribution

Frame One (1) Cast Iron Frame 3

Two (2) Cast Iron Endshields 5

Bearings Steel

Terminal

Box

One (1) Cast Iron Box

One (1) Cast Iron Cover

Fan Cover [1] (Cast Iron)

One (1) Fan

Stator One (1) Copper Stator Winding

One (1) Electrical Steel Stator Lamination

Stack

1

Rotor One (1) Electrical Steel Rotor Lamination

Stack

2

All (Total) Aluminum Rotor Bars

One (1) Cold-Rolled Steel Shaft 4

A5.3 COMPARISON

Primary data were available for the bills of materials for three types of motors. They are 2

HP of 4-pole, 5 HP of 4-pole, 25 HP of 6-pole, 50 HP of 4-pole and 100 HP of 2-pole, with

0

500

1,000

1,500

2,000

2,500

Cast Iron Steel Alumnium Copper Plastic

GH

G Im

pac

t

kg C

O2 -

eq (

Pro

du

ct C

lass

)


characterizations/attributes as follows: general purpose, cast iron, premium efficiency, and

total fan cooled enclosure. Results comparing the different motor sizes are illustrated in Figure

A6. Basically, the larger AC motor generates more carbon emissions when compared with

smaller motors, showing a seemingly linear increase in emissions with greater horsepower.

However, the impact of 50 HP is slightly lower than 25 HP because the weight of 5HP of 4

poles is less heavy than 25 HP of 6 poles. The mass (materials consumption) does not

increase in a linear fashion with the horsepower.

Figure A6: Impact comparison (materials and manufacturing) for NEMA premium

motors of various HP- 5% and 95% percentage tail, plus and minus one standard

deviation.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2HP (4 poles) 5HP (4 poles) 25HP (6 poles) 50HP (4 poles) 100HP (2 poles)

GH

G Im

pac

t

(kg

CO

2 -

eq/p

rod

uct

cla

ss)

Out power (HP)


APPENDIX B: ENERGY EFFICIENT LAMPS

B1 Summary

Consuming more than 18% of all electric energy produced, lighting products have long been a focal

point for increasing energy efficiency. It is therefore worthwhile to assess and compare alternatives to

incandescent bulbs such as light emitting diode (LED) and compact fluorescent lamps (CFLs), not only to

inform consumer choice but also to improve product design. Findings from this comparative analysis

reveal the use stage is the largest contributor to GHG emissions for the lighting products examined,

accounting for 98% and 97% of the emissions for LED and CFLs, respectively. Within the materials and

manufacturing phases, divided by components, the ballast and lamp base (which includes an insulating

base and heat sink) are the major contributors to emissions for CFL and LED lamps. Widespread adoption

of LEDs could yield higher reductions in GHG emissions compared with CFLs because of the longer lifetime

of these products - however the electricity source is the dominant variable in determining the life cycle

carbon footprint of lighting products.

B2 Scope and Functional Unit

B2.1 SYSTEM BOUNDARY AND GHG MODELING

This analysis encompassed the complete, post-design life cycle of lighting products,

including materials, manufacturing, transportation, use, and end-of-life/disposal activities. It

was limited to use within the United States, which impacts transportation distances and use

phase grid mix. GHG emissions during the use stage are calculated with consideration of the

wattage and product lifetime.

B2.2 PRODUCT DESCRIPTION AND FUNCTIONAL UNIT

This analysis focused on commercial and residential applications of lighting products typically

used to replace incandescent lamps. Most modern CFLs and LED lamps screw into standard lamp

sockets and give off light that looks similar to the common incandescent. The most common styles of

internally ballasted lamps, 40 and 60 watts, were selected for investigation: (1) standard A-line and

flood PAR-LED lamps, and (2) spiral CFL lamps.

Materials within a typical CFL include a tin base, copper base pins, glass base insulation, tube

glass, PVC plastic base, printed wiring board and assembly, inert gas, and the mercury-containing

electrode assembly of the internal ballast. An LED lamp, by contrast, contains a semiconductor

component and does not include a filament. It is shock-resistant and has a longer life. Materials

inside an LED include chips, an aluminum heat sink, ballast (including a printed circuit board), a

plastic base with metal contacts, and a lens assembly with a thin film reflective aluminum coating

(Hendrickson et al., 2010). LEDs produce white light in two principal ways: 1) phosphor conversion of

blue or UV-LED light to white, and 2) color mixing LEDs to create white light.


B2.3 ATTRIBUTE CONSIDERATION

A comprehensive list of product and process attributes was identified for each focal product based

on literature reviews, discussion with manufacturer representatives, and recognition of the evolving state

of lighting technology. The list was reduced to highlight attributes that are related to high-impact

activities, as well as whether impact can be mapped from a particular attribute at “reasonable” cost.

Using this pared down list of product and process attributes, we developed and tested statistical models

for the remaining attributes of interest. The focal attributes that effectively characterize lighting products

are shown in Table B1.

Table B1 Attributes consideration for lighting products

Attributes LED CFL

Power (W) 7-12W 11-15W

Rated average life (h) 40000h (mean) 10000h (mean)

Color temperature (K) Cool/warm -

Type/styles (shape) A-line, Par Spiral, T-line

Chips-substrate Undefined -

Ballast type Electrical only Electrical only

B3 Life Cycle Inventory

B3.1 BILL OF ACTIVITIES - MATERIALS

Table B2 displays LED and CFL components and their respective mass fractions. Basic

information was obtained from the NEMA member company websites. The data for lamps was

supplemented by information drawn from dismantling and through targeted conversations with

NEMA members. The mass fraction for each lamp component was determined through disassembly

of ten sample bulbs. For several components and substances, such as the fill gases and e-coating

materials, measurement of mass was not possible. In those cases, their values have been assigned

based on data drawn from the literature and from interviews with NEMA industry experts.

Table B2 Components list and mass fractions for lighting products (BOM)

Components/parts LED Weight (g) CFL Weight (g)

Ballast PWBs PWBs 24.7 PWBs 13.1

Lamp Base

Insulating Base 15.8 Insulating Base 16.2

Potting Materials 28.5

-

Edison Screw 3.0 Edison Screw 3.1

Screw Glass 4.0 Screw Glass 4.1

Heat Sink (Aluminum) 76.8 -

- Fill Gas - Mercury 0.0025*


- Fill Gas - Argon 0.017*

Wires Wires 0.6

LED module

Chips

5.0

- -

Phosphor Powder - -

Lead Frame (PWBs) - -

Lens

Glass/Plastic 13.4 (plastic) Glass/Plastic 20.1

E-Coating materials Phosphor Coating 0.5*

Container Paper/Plastic 52.2(paper) Paper

board/Plastic

Note: *Values are assigned based on data drawn from literature and from interviews with NEMA member

experts. Note: PWB = Printed Wiring Board

B3.2 OTHER LIFE CYCLE PHASES

Manufacturing phase: The bill of materials (BOM) was used as a starting point for an inventory

of components and subassemblies. However, because almost no primary data exists for energy

consumption during the manufacturing and assembly of products targeted for this study, we relied

on industry literature and commercial databases to quantify the energy consumption in

manufacturing. Figure B1 provides the steps to quantify the energy demand for lamps. Energy

demand variability (kWh/kg) for material machining was estimated from a range of values including

injection molding, machining, and finish machining (Gutowski et al. 20096). Energy demand

emissions factors (kg CO2-eq) were modeled based on complex electricity/fuel mixes, considering the

location of the finished product suppliers.

Figure B1- Manufacturing phase energy consumption for lighting products

6 Gutowski, T.G.; Branham, M.S.; Dahmus, J.B.; Jones, A.J.; Thiriez, A.; Sekulic, D. P. Thermodynamic analysis of resources used in manufacturing processes. Environ Sci.& Technol. 2009, 43, 1584-1590.


Use phase: The use stage defines the product’s active lifetime and mainly reflects the

consumption of electrical power. Assuming that the lamps are used in the U.S., the study employs a

U.S.-based power mix for the carbon emission factor for the use phase.

We assume that a 60W incandescent is equivalent to a 13W CFL and a 12W LED in terms of

lumens, bearing in mind that equivalent wattage in LEDs largely depends on chip technology. The

lifetime for an LED lamp is set at 40,000 hours, which is four times that of a typical CFL. We assume

that performance does not diminish during a lamp’s lifetime, so the power consumption and light

emitted per watt are constant over the full analytical period.

Transportation: Both global and domestic suppliers to the U.S. market were taken into account

to calculate the emissions impact of transportation, using shipment level data for focal products. The

data are drawn from U.S. census, company reports, and the literature.

We assume the inland transportation to be split evenly between truck and rail mode, and that

ocean shipment is by vessel. Air transportation is not included. The inland supply distance from

overseas suppliers is based on the manufacturer’s distribution in a specific country. In the case of

CFLs, 90% of the plants that manufacture the lamps are located in several coastal cities in China.

From the production site to the shipping port, transport is presumed to be by truck over an average

estimated distance of 200-500 km, the estimated distance between manufacturers’ locations and

adjacent international maritime ports.

The distance between shipping and arrival ports is calculated based on the typical commercial

goods routes between export country and import country, such as the common maritime shipping

routes between China and the U.S. (e.g., Shanghai to Los Angeles). Both the route and distance are

available at the Sea-Rates7 website. The maritime distance may be obtained directly if the

international ports are known.

Domestic distribution in the U.S. is computed in proportion to population density, such that

larger cities are assumed to receive a higher percentage of shipments. The inland (for truck and rail

transport) distances were estimated using Google Maps. No data were obtained on handling and

stocking bulbs in distribution centers or retail stores, so potential impacts from these activities are

not included.

End-of-life phase: The end-of-life phase is addressed to capture its contribution to the total life

cycle impact, specifically focused on how recycling can mitigate the effects from the production

phase. In the end-of-life phase, detailed processes both for disposal and incineration as municipal

solid waste (in the U.S.) and for general physical recycling are taken into account for the lighting

products.

B4 Data limitations

The reliability of the results and conclusions from the LCA depend in large measure on the

quality of the inventory data that is used. Throughout the research process, NEMA and its member

7 Sea-Rates. Available at www.searates.com/reference/portdistance/.

http://www.searates.com/reference/portdistance/


companies collaborated to provide the most accurate data possible. Bill of materials information is

therefore viewed as reliable because it was supplied and vetted carefully by company

representatives. However, there are several limitations to the approach used for this study, as

described below.

Limitations on primary data include the following:

(1) When dismantling representative lamps to identify and measure the mass of components

not provided by manufacturers, we found some substances/components/parts to be challenging to

characterize, such as the fill gases and LED chips. We relied on supplemental information from the

literature for these components.

(2) Primary data on energy consumption during the manufacturing and assembly of the focal

products are scarce.

(3) Primary data on the impacts of end-of-life stage activities are missing. Thus the impacts of

disposal or recycling scenarios are estimates.

(4) There is also incomplete information on details of the transportation (distribution) stage,

although this is not expected to contribute significantly to the product’s lifecycle emissions.

The uncertainty associated with these data limitations has been included in the evaluation

where possible. The use of Monte Carlo simulations is incorporated into the PAIA-based model to

focus the effort used to prioritize better data collection for the most critical aspects.

B5 Impact Assessment and Interpretation: LED Lamps

B5.1 THE OVERALL LIFE CYCLE

The results of the GHG emissions throughout the overall life cycle of a typical LED lamp are

specified in Figure B2. The use phase dominates the GHG emissions of the life cycle, comprising 98%

of the total impact. There is a 90% confidence level that the use stage could exceed 98.5% of total

impact. Materials and manufacturing combined are responsible for less than 1% of total life cycle

carbon emissions. The manufacturing burden is almost twice that of the materials burden. Each

phase is examined separately in detail below.

Note: Baseline scenario: the lamp is used in the U.S. (supplied externally, primarily from East

Asia-China and Japan, and secondarily from the U.S.), with a rated lifespan of 40,000 hours.

Figure B2- High Level Screening Results for a typical A-Line LED lamp (12W): Overall life cycle

2.6 1.1 0.1

320.6

0.5 0

50

100

150

200

250

300

350

Mtl Mfg Trans Use EoL

GW

P Im

pac

t kg

CO

2 -

eq

/pro

du

ct c

lass

Mtl 0.80%

Mfg 0.33%

0.00%

Trans 0.02%

Use 98.71%

EoL 0.14%

Mean value


B5.2 MATERIALS AND MANUFACTURING IMPACT

Since the materials and manufacturing phases (here combined) represent a significant

contribution if the use stage is excluded, this phase was broken down further based on individual

part production. As shown in Figure B3, a handful of parts result in approximately 90% of the total

impact caused by part production: lamp base, ballast, and LED module. The lamp base, which

includes the aluminum heat sink, insulating base, and Edison screw, is the major contributor to GHG

emissions, followed by the ballast and LED module. An interesting observation is that while the

overall mass of the LED module is relatively low, the impact is third most significant.

Note: Packaging impact is the combination of container and finished product assembly. Error bars denote

combined uncertainties (5% percenile & 95% percentile) from Monte Carlo simulations.

Figure B3- GHG impact for the materials & manufacturing stages of a LED lamp (12W)

For comparison purposes, we examined the impact of different types of LED lamps. At

equivalent power, the visual shape and mass fraction of a Par30 lamp is bigger and heavier than an

A-line lamp. As a result, the materials and manufacturing GHG emissions of a Par30 LED are roughly

two times that of an A-line LED (Figure B4). For A-line lamps, a 12W LED produces greater impact

when compared to an 8W lamp, not only due to the additional materials used but also the need for

more chips to generate the increased lumens.

Figure B4- GHG impact for the materials & manufacturing stage for three types of LED lamps.

0.0

1.0

2.0

3.0

4.0

5.0

GW

P Im

pac

t

(kg

CO

2 -

eq/p

rod

uct

cla

ss)

0

2

4

6

8

8W_A-Line 12W_A-Line 12W_Par 30

GW

P Im

pac

t (k

g C

O2

-eq

/pro

du

ct c

lass

)

Ballast_

PWBs 35%

Lamp_ Base 43%

LED modul

e 15%

Lens 4%

Packaging 3%

Mean value


B5.3 SENSITIVITY ANALYSIS

The result of the contribution analysis is illustrated in Table B3. The findings reveal a confidence

level of 90% in the assertion that Ballast printed wiring boards (PWBs) contribute 55% of the impact;

four of the components [Ballast PWBs, heat sink (aluminum), potting materials (plastic), and LED

module] account for 85% of the total impact with a confidence of 95%.

Table B3- Order of the components’ contribution analysis

Components/parts Ordered by

contribution

Ballast_PWBs PWBs 1

Lamp_Base Insulating Base

Potting Materials 3

Edison Screw

Screw Glass

Heat Sink (Aluminum) 2

Wires

LED module Chips 4

Phosphor Powder

Lead Frame (PWBs)

Lens Glass/Plastic

E-Coating materials

Container Paper/Plastic

Figure B5 shows the sensitivity analysis results (contribution to variance, for materials and

manufacturing phases). The contribution to variance revealed that the embodied materials impact

(GWP) of the heat sink (part of a lamp base, typically aluminum) is the factor that contributes most

to the overall result, with a contribution to variance of nearly 30%, followed by the embodied

materials impact of polymers, which includes the insulating base and potting materials. By focusing

on reducing the uncertainty in these key items, the overall uncertainty in the materials and

manufacturing phase can be reduced most effectively.


Note: “Wt” refers to the weight/mass of the examined components/parts, “Mtl GWP” refers to the

embodied materials impact (GHGs, expressed as CO2 kg eq/kg), “Grid Mix GWP” means the involved

countries’ grid impact factor (GHGs, expressed as CO2 kg eq/kWh), which probably occurs in mfg, use or

EoL phases, and “Energy” means the energy impact factor (expressed as MJ/kg) for the manufacturing

process of the components/parts or their materials.

Figure B5 -Contribution to variance for a LED lamp (materals and manufacturing stage)

B6 Impact Assessment and Interpretation: CFLs

B6.1 THE OVERALL LIFE CYCLE

GHG emissions throughout the overall life cycle of the CFL are specified in Figure B6. As with

LEDs, the study shows that the use phase dominates other phases in terms of energy consumption,

comprising more than 97% of total emissions. There is a 90% confidence level that the use stage

exceeds 96% of total impact. Materials and manufacturing together are responsible for less than 2%

of total life cycle carbon emissions.

29.7%

19.9%

13.6%

7.4%

5.3%

5.2%

3.5%

3.1%

2.3%

1.6%

1.2%

7.2%

0% 10% 20% 30% 40%

Heat Sink (Aluminum) Embodied Mtl GWP

Polymers Embodied GWP

Ballast_PWBs Embodied GWP

Ballast_PWBs Wt

China Grid Mix GWP

Heat Sink_Aluminum Mfg Energy

Potting materials Wt

Heat Sink (Aluminum) Wt

LED_Chips Energy

LED_Chips (substrate) Wt

Insultating base Wt

Other


Scenario: The bulb is used in the U.S. (supplied from external markets, mainly from China), with a rated

lifespan of 10,000 hours.

Figure B6- High Level Screening Results- a CFL lamp (13W): overall life stage

B6.2 MATERIALS AND MANUFACTURING PHASES

As shown in Figure B7, the electronic ballast is a strong contributor to the environmental impact

of a CFL, exceeding 45% of total impact.

Figure B7- GHGs impact for the materials and manufacturing stage of a CFL lamp (13W)

C5.4 COMPARISON ANALYSIS

Between the 60W replacement equivalents for CFL and LED lamps, the LED (12W) emits more

carbon than a CFL during the materials and manufacturing stages (see Table B5 and Figure B8). To

provide a comparable service lifetime therefore, one LED lamp is assumed to be equal to four CFLs.

The LED uses less energy during its expected lifetime (40,000 hrs) than the CFLs (13W). Although the

materials and manufacturing burden is higher than with CFLs, LEDs are statistically superior to CFLs

1.49 0.58 0.04

88.96

0.13 0

20

40

60

80

100

120

140

Mtl Mfg Trans Use EoL

GW

P Im

pac

t kg

CO

2 -

eq

/pro

du

ct c

lass

Mtl

1.63% Mfg

0.64%

0.00%

Trans 0.04%

Use 97.54%

EoL 0.15%

Mean value

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Ballast_PWBs Lamp_Base Lens Packaging Total

GW

P Im

pac

t

(kg

CO

2 -

eq/p

rod

uct

cla

ss)


from a carbon emission standpoint due to their longer lifetimes. This has been determined through

statistical trials that show that 98% of the time (based on the assumptions described throughout),

the LED lifetime impact is lower than that of the CFL.

Table B5- GHGs emission for LEDs and CFLs s (product class)

Stage LED-12W* CFL-13W* 4CFL#

Kg CO2-eq (mean value)

Materials 2.60 1.49 5.95

Manufacturing 1.06 0.58 2.33

Transportation 0.07 0.04 0.16

Use 320.58 88.96 355.83

End of Life 0.48 0.13 0.53

In total 325 91.2 365

Note: *, Lumens efficiency: 60W replacement (Incandescent); #, equal lifespan: 1 LED (40,000h) equals 4

CFL (10,000h). No two lamps have the same lumens nor the same lifespans.

Figure B8- High Level Screening Results- LED vs CFL lamps (charts exclude the use stage)

0

4

8

12

16

LED CFL 4´CFL

GW

P I

mp

act

kg

CO

2 e

q o

f p

er b

ulb

(s)

0

2

4

6

8

10

LED CFL 4´CFL

End of Life

Transportation

Manufacturing

Materials

Mean value


APPENDIX C: ELECTRONIC AND MAGNETIC

BALLASTS

C1 Summary

Electronic ballasts for fluorescent lamps were selected as a focal product along with

high-intensity discharge lamp (HID) electronic and magnetic ballasts for comparison. This

appendix describes the application of the PAIA-based methodology to determine the

greenhouse gas (GHG) impact of ballasts based on product attributes, as well as the data

available for analysis. The study shows that the use phase dominates the ballast’s carbon

footprint based on resistance-related energy loss, comprising 70% to 80% of the impact for

fluorescent and HID electronic ballasts. For HID magnetic ballasts, the use phase is even

more significant, comprising 98% of the impact. The materials and manufacturing phases

combined are responsible for the majority of the remaining life cycle GHG emissions. A

handful of parts dominate the impact of parts production, where there is a significant difference

between the electronic and magnetic ballast. The dominating parts are, in order of importance:

diodes, inductors, capacitors, integrated circuits (ICs), and transistors for the electronic ballast;

and steel cores and shunts, aluminum or copper coils, plus the starters (electronic

components) for the magnetic ballast.

C2 Scope and Functional Unit

C2.1 SYSTEM BOUNDARY AND GHG MODELING

The analysis encompassed the overall life cycle of ballast products, including materials,

manufacturing, transportation, use, and end-of-life/disposal activities. The analysis described

here focuses on use in the United States, which impacts the transportation distances and use

phase grid mix. The materials and manufacturing phase analyses are combined, and the

assembly stage of the ballast product is out of the scope of this study due to lack of data. The

GHG impacts stemming from the materials and manufacturing phases, Cmtl+mfg, can be

expressed as follows:

Equation C11: C𝑚𝑡𝑙+𝑚𝑓𝑔−𝐶𝑂2= ∑ (𝑞𝑛


[i.e., the total impact caused by materials and manufacturing of the product is a function of the mass

of material processed, qn, and the embodied impact factor, En, for each component, n; where En

incorporates manufacturing burden as well as materials extraction]

The use stage impact is calculated considering the power loss to the lamp that is


determined by the power factor (pf) of each ballast type. The power factor is a measure of how

“efficiently” a lamp uses its power, which is usually expressed as a percentage from 0% to

100%.8 The GHG impact stemming from the use stage (GHG impact, Cu) is expressed in

Equation 1:

Equation C12: C𝑢−𝐶𝑂2= ℎ × 𝑤 × (1 − 𝑝𝑓) × 𝐺𝑠−𝑈.𝑆.

[i.e., the total impact from use stage is a function of hours of usage, h, the power expended, w,

power loss, (1 - pf) and the grid emissions factor.]

C2.2 PRODUCT DESCRIPTION AND FUNCTIONAL UNIT

Ballasts consume electricity while providing the necessary circuit conditions (voltage,

current, and wave form) to start and operate fluorescent lamps. Three types of ballasts are

sold for commercial applications in the U.S.: magnetic, hybrid, and electronic2. For this study,

fluorescent electronic, HID electronic, and HID magnetic ballasts were chosen as focal

products. These constitute the most common ballast types by market share.

• An electronic ballast is a device intended to limit the amount of current in an electric

circuit. A familiar and widely used example is the inductive ballast used in fluorescent

lamps.

• Magnetic ballasts are “core-and-coil” electromagnetic ballasts. They contain a

magnetic core of several laminated steel plates wrapped with copper windings.

Magnetic ballasts usually have twice the power loss compared to electronic ballasts.

A lamp-ballast system consisting of a magnetic ballast and two 32-W T8 lamps

requires approximately 70 W.9

• Hybrid ballasts use a magnetic core-and-coil transformer and an electronic switch for

the electrode-heating circuit.

Ballasts use one of three general methods to start fluorescent lamps, as defined by the

American National Standards Institute (ANSI): Preheat, instant-start, or rapid-start.

Programmed-start ballasts fall within the category of rapid-start ballasts, but are emerging as a

separate technology2. Thus the instant-start and programmed-start electronic ballasts for

fluorescent lamps have been selected to analyze and compare their GHG impact along with

the HID electronic and magnetic ballast.

The functional unit is the use of electronic or magnetic ballasts during continuous working

8 OSRAM-SYLVANIA. What is the difference between power factor and ballast factor? OSRAM SYLVANIA. 2008. http://assets.sylvania.com/assets/documents/FAQ0056-0605.8d13d344-4cd2-42f2-af91-100b2a1a8a4d.pdf. 9 NLPIP. The objective source of lighting product information: Electronic ballasts. Rensselaer Polytechnic Institute, The National Lighting Product Information Program (NLPIP). Troy, NY, 2000. http://www.lrc.rpi.edu/programs/NLPIP/PDF/VIEW/SREB2.pdf.


hours for half of its rated life time. The functional unit will be framed in terms of the out power

and bulb numbers. As a reminder, the use phase for the ballast was given particular emphasis

in the analysis, focusing on lamp power loss that is influenced by power factor of the ballast.

C2.3 ATTRIBUTE CONSIDERATION

As discussed in the guidance document, we identified a comprehensive list of product

and process attributes for each focal product based on literature reviews, company interviews,

and an understanding of the current and evolving state of the technology. This list was

reduced based on the attributes that are related to high-impact activities, as well as whether

impact can be mapped from a particular attribute at “reasonable” cost. Using this pared down

list of product and process attributes, we develop and test statistical models for the remaining

attributes of interest. This section summarizes our suggested list of attributes that effectively

characterize ballast products. The chosen focal attributes are shown in Table C1.

Table C1: Attributes consideration for ballast products


1 Number of lamps operated

2 Starting mode*

3 Lamp operating frequency

4 System efficacy

5 Ballast factor

6 Ballast efficacy factor

7 Total harmonic distortion (%)

8 Power factor

9 Lamp current crest factor

10 Lamp flicker index

11 Rated life (hours)

12 Sound rating

13 Dimming available

These items were selected for evaluation in this analysis.

C3 Life Cycle Inventory

C3.1 BILL OF ACTIVITIES - MATERIALS

For the ballast work, the mass fractions of materials within the product are based on the

bill of materials (BOM) information provided by NEMA companies. Figure C1 displays the


mass breakdowns for the three focal types of ballasts. In the fluorescent electronic ballast

(shown in Figure C1 (a)), the highest mass material is the enclosure (aluminum or copper

enclosure, plus plastic insulator), making up approximately half of the mass (from 40% to

65%). Transformers (inductors for HID electronic ballast) are the second largest portion of the

mass, at close to 15%. Other materials — inductors, capacitors, wires and transistors— make

up a much smaller fraction of the mass of these ballasts, in the range of 2-15%. There is a

similar mass fraction breakdown between the fluorescent electronic ballast and HID electronic

ballast. However, the components list for the HID magnetic ballast is very different. The most

important source material is the steel core and shunts, comprising more than 70% of the total

mass. The copper coil is next at approximately 10%, followed by the aluminum coil. There is

minor mass fraction attributed to the electronic components contained in the starter.

0

200

400

600

800

2 lampsInstant-start

4 lampsInstant-start

2 lampsProgrammed-start

4 lampsProgrammed-start

Mas

s (g

)

(a) Fluorescent Electronic Ballast OtherPWBEnclosureWireTransformerInductorICTransistorResistorDiodeCapacitor

0

200

400

600

800

39W 70W

Mas

s (g

)

(b) HID Electronic Ballast OtherPWBEnclosureWireTransformerInductorICTransistorResistorDiodeCapacitor

0

4,000

8,000

12,000

16,000

150W 400W 1000W

Mas

s fr

acti

on

(c) HID Magnetic Ballast

Other

Starter

Capacitor

Core & shunts

Coil-Copper

Coil-Aluminum


Figure C1: Major ballast components and their mass fractions (normalized, average value

across the companies) for the focal ballasts.

C3.2 OTHER LIFE CYCLE PHASES

Use phase: Ballasts do not consume energy directly. In this study, GHG impact in the use

phase was therefore measured through power loss to the lamp that is determined by the

power factor of ballast. For the product’s lifetime, we assumed that the running time was half of

the rated lifetime of the ballast with a coefficient of variation (COV) of 10%. The rated power

factors range from 90% to 91% for the electronic ballast and 70% to 90% for the magnetic

ballast, which varies by the rated out power, the ballast types, and the manufacturer.

Transportation and end-of-life stages: Assuming that the market share and transportation

of ballast is similar to that of bulbs, the GHG impact of transportation and end-of-life is the

same as for the lighting products.

C4 Data limitations

The data limitations are similar that of the motor and lighting products; however, there is

another data limitation for the ballast. In some cases, when the components list from

manufacturer A is slightly different to the same type of ballast from manufacturer B, there is a

significant difference in the mass for some components. This is due to design variations

between manufacturers. Therefore, the average mass values have been incorporated into the

modeling of GHG impact.

C5 Impact Assessment and Interpretation

C5.1 THE OVERALL LIFE CYCLE

The evaluation of GHG emissions throughout the overall life cycle of ballast products is

specified in Figures C2 and C3. The study shows that the use phase dominates other phases

in terms of energy loss, comprising 79%, 66% and 80% of the impact for 32W 2 lamps

instant-start, 4 lamps programmed-start fluorescent electronic, and HID electronic ballasts

respectively. The use phase comprises 98% of the impact for HID magnetic ballast. Materials

and manufacturing combined are responsible for the majority of the remaining life cycle

carbon emissions, with end-of-life and transport phases being inconsequential.


Figure C2: Overall life GHG impact (normalized, mean value) for ballasts

Note: (a) 2 lamps instant-start fluorescent electronic ballast; (b) 4 lamps programmed-start fluorescent

electronic ballast; (c) HID electronic ballast; (d) HID magnetic ballast.

Figure C3: Overall life GHG impact (fractions, mean value) for selected ballasts

C5.2 MATERIALS AND MANUFACTURING PHASES IMPACT

When the use stage is excluded, the importance of certain parts becomes evident, as

shown in detail in Figure C4 (fluorescent electronic ballast), Figure C5 (HID electronic ballast)

and Figure C6 (HID magnetic ballast). A handful of parts dominate the impact caused by parts

0

50

100

150

2-I

nst

ant

4-I

nst

ant

2-P

rogm

4-P

rogm

39

W

70

W

FluorescentElectronic

HIDElectronic

GH

Gs

Imp

act

kg C

O2 -

eq

0

1000

2000

3000

4000

150W 400W 1000W

HIDMagnetic

Eol

Transport

Use loss

Mtl & Mfg

Mtl & Mfg

20.7%

Use loss 78.9%

Transport 0.2%

Eol 0.2%

(a) 32W-2-Instant-FE

Mtl & Mfg

33.3%

Use loss 66.1%

Transport 0.3%

Eol 0.2%

(b) 32W-4-Programmed-FE

Mtl & Mfg

20.0%

Use loss 79.7%

Transport 0.1%

Eol 0.1%

(c) 70W-HIDE

Mtl & Mfg 1.6%

Use loss 98.3%

Transport 0.1%

Eol 0.1%

(d) 400W-HIDM


production and there is a significant difference between the HID electronic and HID magnetic

ballast. The dominating parts are, in order of importance: the diode, inductor, capacitor, ICs,

and the transistor for the electronic ballast; and steel core and shunts, aluminum or copper

coils, plus the starts (electronic components) for the magnetic ballast.

Figure C4: GHG impact of major components for a fluorescent ballast (32W 2 lamps,

instant start) - 5% and 95% percentage tail, plus and minus one standard deviation.

Figure C5: GHG impact of major components for a HID electronic ballast (70W) - 5% and 95%


0

2

4

6

8

10

12

GH

Gs I

mp

act

kg

CO

2 -

eq (

Pro

du

ct

Cla

ss)

0

2

4

6

8

10

12

GH

Gs I

mpact

kg C

O2 -

eq (

Pro

duct

Cla

ss)

0

5

10

15

20

25

GH

Gs Im

pact

kg C

O2 -

eq (

Pro

duct C

lass)


Figure C6: GHG impact of major components for a HID magnetic ballast (400W) - 5% and 95%


C5.3 SENSITIVITY ANALYSIS

Figures C7, C8 and C9 show the results of sensitivity analysis (contribution to variance,

materials and manufacturing stages) for different types of ballasts. In Figures C7 and C8, the

contribution to variance parameters revealed that the embodied impact factors of a number of

electronic components are the activities that most contribute to the overall uncertainty. In the

case of the fluorescent electronic ballasts, the embodied impact factor of the inductors

contributes to 37% of the total variance, followed by the diodes (23%) and insulators (defined

as polymers using underspecification, 9%). For the 70W HID electronic ballast, the embodied

impact factor of the inductors contributes to more than half of the total variance, followed by

the embodied impact factor of aluminum which is the material used in the enclosure.

Note: “Embodied Impact” means the embodied materials impact factor (GHG, expressed as CO2

kg eq per kg); Some components are listed with endings, such as SO, SOD and DO, because

there are corresponding emisson factors in the database for these specific components. The

emisson factors with underspecification are incorporated into impact modeling for many other

components that lack a detailed description.

Figure C7: Contribution to variance for a 32W, 8T, 2 lamps, Instant-start fluorescent

electronic ballasts

37% 23%

9%

1%

1%

1%

1%

28%

0% 10% 20% 30% 40%

Embodied Impact_Inductor

Embodied Impact_Diodes

Embodied Impact_Polymers

Embodied Impact_Diode signal SOD

Embodied Impact_Diode power DO

Embodied Impact_Capacitor Ceramic MLCC

Embodied Impact_IC SO

Other


Figure C8: Contribution to variance for a 70W HID electronic ballasts

Unlike the electronic ballast, the embodied impact factor of steel (core and shunts) is the

largest contributor to the variance of HID magnetic ballasts, comprising 67% of the total

variance.

Figure C9: Contribution to variance for a 150W HID magnetic ballasts

C5.4 COMPARISON ANALYSIS

Section 4.1 presented the comparison of overall lifetime impacts for various ballasts.. In

this section, differences concerning materials and manufacturing stages are evaluated. Figure

C10 shows that for the 32W T8 fluorescent electronic ballasts, both the number of lamps and

programmed-start will increase the impact. Unsurprisingly, the larger (out power) ballast

generates more carbon emissions when compared with smaller HID ballasts, showing a

seemingly linear increase in emissions with greater out power.

59%

7%

3%

3%

2%

1%

1%

1%

1%

22%

0% 20% 40% 60% 80%

Embodied Impact_Inductor

Embodied Impact_Aluminum

Embodied Impact_Diode power DO

Embodied Impact_Diode signal SOD


Embodied Impact_Thermoplastic

Embodied Impact_Tin

Embodied Impact_LED SMD high-efficiency

Embodied Impact_Electronics

Other

67%

4%

3%

2%

1%

1%

1%

1%

1%

22%

0% 20% 40% 60% 80%

Embodied Impact_Steel


Embodied Impact_Aluminum

Embodied Impact_Copper

Embodied Impact_Capacitor

Total mass

Embodied impact secondary_Aluminum

Embodied Impact_Resistor thick film flat chip

Embodied impct_PWBs

Other


Figure C10: GHGs impact comparison (materials & manufacturing) for ballasts

0

20

40

60

80

100

2-I

nsta

nt

4-I

nsta

nt

2-P

rog

ram

me

d

4-P

rog

ram

me

d

39

W-M

H E

lectr

on

ic

70

W-M

H E

lectr

on

ic

15

0W

-Ma

gn

etic

40

0W

- M

ag

ne

tic

10

00

W-

Ma

gn

etic

FE-32W HID

GH

Gs I

mp

act

kg

CO

2 -e

q (

Pro

du

ct

Cla

ss)


APPENDIX D: ELECTRICAL CONNECTORS

D1 Summary

This analysis focused on the GHG impact of several types of electrical connectors,

including pressure connectors (lugs), split bolts, and weld metal. Unlike the analyses

conducted on other focal products, primary data were available for the energy consumed

during the manufacturing and assembly of connector products, allowing us to model the GHG

impact for the material and manufacturing phases on that basis. The results show that the

impact of the manufacturing stage accounts for about 20% of the total impact for lugs. While

the aluminum lug is lighter than a similarly sized copper lug, the impact is roughly twice as

large. For the split bolt, the GHG impact of the materials stage is 10 times greater than for the

manufacturing stage. Similarly, while the total weight of the aluminum split bolt is around half

of the copper split bolt, the GHG impact is much higher. The exothermic welding materials and

graphite mold contributed most of the weld metal’s GHG impact. The impact in the

manufacturing stage for welded connections, which is similar to the impact in materials stage,

is mainly due to the graphite mold. However, one mold can be used across several

connections, so this manufacturing burden is much less by percentage after accounting for

this allocation.

D2 Scope and Functional Unit

D2.1 SYSTEM BOUNDARY AND GHG MODELING

The connector analysis included the materials, manufacturing, transportation and

end-of-life (EoL) stages. We excluded the use phase because the only burden stemming from

that phase would be attributed to power loss from the electrical connection, which was

deemed “out of scope” by the technical NEMA members advising the project. This analysis

focuses on use in the United States, which impacts the manufacturing grid mix. Taking

advantage of the primary data made available for the manufacturing phase, we modeled the

GHG impact related to the materials and manufacturing phases. When using the inventory of

materials embodied impact, the GHG impacts stemming from the materials, Cmtl, and

manufacturing phases, Cmfg, can be expressed in Equation 5 and D2, respectively.

Equation D13: C𝑚𝑡𝑙−𝐶𝑂2= ∑ (𝑞𝑛


[i.e., the total impact caused by materials using of the product is a function of the mass of material

processed, qn, and the embodied impact factor, En, (from raw materials extraction and beneficiation —

ready for manufacturing)]


Equation D2: C𝑚𝑓𝑔−𝐶𝑂2= ∑ (𝑞𝑛

𝑛𝑖=1 × 𝑀𝑛 × 𝐺𝑢𝑠)

[i.e., the total impact caused by manufacturing of the product is a function of the mass of material

processed, qn, and the unit energy consumption factor during machining and finishing processes, Mn.]

D2.2 PRODUCT DESCRIPTION AND FUNCTIONAL UNIT

According to the product definition and classification from the NEMA electrical connector

section10, there are six categories for connectors and related products stemming from different

functions and application:

Electric Power Connector

Pressure Connector

Overhead Lines Connector

Underground Distribution Type Cable Connectors and Accessories

Grounding Products

Installation Tooling

Within this broad classification, we selected pressure connectors (three sizes of lugs, with

further breakdown of copper and aluminum lugs), split bolts (one size, but with breakdown of

copper and aluminum connectors), and weld metal (only one size) for the analysis. Weld metal

is the product of an exothermic welding process, which is often used to join copper conductors.

The welding process joins two electrical conductors through the use of superheated copper

alloy. Different functional units will be based on the size and connector materials.

D2.3 ATTRIBUTE CONSIDERATION

As explained in the guidance document, we identified a comprehensive list of product and

process attributes for each focal product based on literature reviews, company interviews, and

an understanding of the current and evolving state of the technology. This list was reduced

based on the attributes that are related to high GHG impact activities, as well as whether

impact can be mapped from a particular attribute at “reasonable” cost. Using this pared down

list of product and process attributes, we develop and test statistical models for the remaining

attributes of interest. This section summarizes our suggested list of attributes that effectively

characterize connectors. The focal attributes are shown in Table D1.

Table D1: Attributes consideration for connector products


1 Product types (e.g., connectors, installation tools & associated

dies)

2 Installation methods (e.g., automatic, bolted, or compression…)

10 http://www.nema.org/Products/Documents/8-cc-scope-pictures.pdf.


3 Function or application (e.g., splices/taps/terminals…)

4 Conductor materials (e.g., copper/aluminum)

6 Dimensions (size)

7 Electrical conductivity (e.g., conductivity range, resistance…)

8 Insulated (yes or no)

9 Physical properties (e.g., mechanical strength..)

10 Environmental performance (e.g., corrosion resistance…)

11 Other features

These items have been evaluated.

D3 Life Cycle Inventory

D3.1 BILL OF ACTIVITIES - MATERIALS

In this case the mass fractions, or the percentage of mass attributed to a particular

component, are based on the bill of materials (BOM) information provided by the NEMA

companies.

For lugs (Figure D1 (a)), the most important component is the connector material

(tubes/collar, tang and screw), which makes up more than 80% of the connector’s mass for

different types of lugs. For larger lugs, such as the 10 kcmil AWG lugs, the connector’s

materials (copper or aluminum) are more likely to dominate the mass, which accounts for

above 99% of the total mass. Other connector materials— plating, inhibitor, plastic plugs, and

inner & outer box— make up a much smaller fraction of the mass, in the range of 2-15%.

There is minor mass difference between the small copper and aluminum lugs (e.g., 10 AGW);

however, for larger lugs, copper lugs are much heavier than aluminum lugs due to the

difference in density between the materials. The component list for split bolts, which includes

the bolt, nut and pressure bar, is shorter than for lugs (Figure D1 (b)). The mass fractions of

the bolts, nuts and pressure bars are 60%, 20% and 20% respectively, which is the same for

the copper and aluminum spit bolt. Similarly, the copper split bolt is about two times heavier

than the aluminum split bolt due to the density difference. For weld metal (Figure D1 (c)), the

graphic mold dominates the mass, accounting for 80% of the total mass, followed by welding

materials (the mixture of copper, copper oxides and aluminum).


Figure D1: Components list and mass breakdown (Normalized, average value across the

companies) for focal connectors and weld metal.

D3.2 BILL OF ACTIVITIES – MANUFACTURING

Primary data was available for the energy consumed during the manufacturing and

assembly of connector products targeted for this study. In general, energy demand variability

(kWh/unit) for manufacturing connector materials was determined for a range of processes

including machining and finishing (i.e., converting the aluminum and copper coil or wire into

final connectors products). Mining, extraction, metallurgy and primary processing are

elements of the materials stage of the life cycle. The embodied impact factors are taken from a

0

200

400

600

800

10AWG 4.0

AWG

1000

kcmil

AWG

10AWG 4.0

AWG

1000

kcmil

AWG

Copper Aluminum

Ma

ss (

g)

(a) Lugs

Inner & Out box

Plastic Plug

Plating

Screw

Tang

Collar/tube/connector

0

50

100

150

Copper

(6-8 Str/Sol.)

Aluminum

(6-10 Str/Sol.)

Ma

ss (

g)

(b) Split Bolt

Spacer

Pressure Bar

Nut

Bolt

18.6%

0.2%

79.8%

0.002% 1.4%

(c) Weld Metal (for cable size: 4/0): 850g

Exothermic Welding Material

Steel Disk

Graphite Mold

Mold TAG

Package


commercial database to model the GHG impact. Table D2 lists the possible processes for

energy data collection during connector manufacturing.

Table D2: Manufacturing processes ready for energy data collection for connector

products

Manufacturing stages Processes

Machining 1. Cut

2. Wash and tumble

3. Bevel cut

4. Swage

5. Anneal

6. Stamp

7. Other/combined

Finishing 1. Wash

2. Tumble

3. Plate/Coat

4. Other/combined

Mixing* 1. Mix

Assembly and packaging 1. Add screws, anti-oxidant

2. Box, label, instructions

* Only for weld metal

D3.3 OTHER LIFE CYCLE PHASES

Connectors themselves do not consume energy when in use, but the use stage impact

may be measured through the power loss that is caused by resistance. However, the

participating companies deemed this phase of the life cycle to be outside of the scope of the

analysis. The impact of the transportation stage is caused by the distribution of finished

connector products, which can therefore be modeled if we assume that the connectors are

manufactured in the US and distributed nationally in accordance with population density. For

the EoL phase, a basic recycling and disposal scenario is included based on flows of scrap

metals from construction and demolition waste. Since there are no statistical data at the

national level, estimates were obtained from a study conducted by the Northeast Waste

Management Officials' Association (NEWMOA)11, which shows a split of 52% for recycling and

48% for landfill disposal.

11 NEWMOA, Construction & Demolition Waste Management in the Northeast in 2006. June 30, 2009. http://www.newmoa.org/solidwaste/CDReport2006DataFinalJune302009.pdf

http://www.newmoa.org/solidwaste/CDReport2006DataFinalJune302009.pdf


D4 Data Limitations

The data limitations affecting the connectors analysis are similar to those encountered in

modeling the GHG impact for motor and lighting products (See companion appendices).

Connectors provide an additional complication, however: In the case of a specific type of

connector such as a lug, the components list from company X may be only slightly different

from that of a lug from company Y, but there is significant variation in the mass for some

components due to the design preference of the manufacturers. Therefore, the average mass

values have been incorporated into the modeling of GHG impact.

D5 Impact Assessment and Interpretation

D5.1 FULL LIFE CYCLE

The GHG impacts for the overall life cycle, which comprises the materials, manufacturing,

transport and EoL stages, are examined and shown in detail in Figure D2 (4.0 AWG lugs),

Figure D3 (split bolt), and Figure D4 (weld metal), respectively. As shown in Figure D2, the

materials stage dominates the impact in the overall lifespan, which accounts for 60~70% of

the impact, and followed by manufacturing and EoL phases. The impact of aluminum lugs is

much higher than the copper lugs. Similarly, GHG impacts for split bolts and weld metal are

dominated by materials, with aluminum having a slightly higher impact than copper.

Figure D2: GHG impact in whole lifespan for Lugs: Copper versus Aluminum- 5% and 95%

percentage tail, top and bottom of box are first and third quartiles


Figure D3: GHG impact in whole lifespan for Split Bolts: Copper versus Aluminum- 5% and

95% percentage tail, top and bottom of box are first and third quartiles

Figure D4: GHG impact in whole lifespan for Weld Metal (for cable size: 4/0) - 5% and 95%

percentage tail, top and bottom of box are first and third quartiles

D5.2 MATERIALS AND MANUFACTURING

The GHG impacts of materials and manufacturing phases are examined and shown in

detail in Figure D5 and D6 (lugs), Figure D7 (split bolt), and Figure D8 (weld metal). The

impact associated with the 1000 kcmil lug is much larger than the 4.0 AWG and 10 AWG lugs

due to difference in the size and mass. While the aluminum lug is less heavy than a copper lug,

the impact is roughly twice as large. This is because the embodied impact factor (expressed

as kg CO2 eq per kg) of aluminum is higher than that of copper. Except for the 10 AWG lug, the

impact of the manufacturing stage (machining and finishing processes) accounts for

0

0.2

0.4

0.6

0.8

1

Materials Mfg Transport EoL

GW

P Im

pa

ct/

pro

du

ct

(k

g C

O2

eq

)

Weld Metal


approximately 20% of the total impact for many types of lugs.

Figure D5: GHG Impact (materials & manufacturing, in total) for lugs - 5% and 95%

percentage tail, plus and minus one standard deviation

Figure D6: Comparison of GHG Impact between materials and manufacturing phases for

various lugs (normalized value)

For the split bolt, the GHG impact of the materials stage is greater than in the

manufacturing stage. While the total weight of aluminum split bolt is around half of the copper

split bolt, the GHG impact is much higher. The error bars reveal that embodied impact factors

are a significant cause of uncertainty.

0.0

2.0

4.0

6.0

8.0

10.0G

HG

Im

pa

ct

kg

CO

2 -

eq

(p

er

pie

ce

)

0%

20%

40%

60%

80%

100%Manufacturing Materials


Figure D7: Comparison of GHG Impact between copper and aluminum split bolts, error

bars represent one standard deviation above and below the mean

The GHG impact in the materials stage is much higher than in the manufacturing stage for

the welding material itself. The total weight of weld metal materials is around one-fourth of the

graphite mold, yet the GHG impact is less than half of the impact of the materials stage (Figure

D8 (a)).

The GHG impact of the graphite mold dominates the impact in the manufacturing stage

due to its complicated manufacturing process. However, each mold may service as many as

50 connections. If molds are allocated over multiple uses, therefore, the GHG impact in

materials stage dominates the impact, which accounts for above 90% of the total impact

(Figure D8 (b)). The error bars reveal the large uncertainty in the materials stage, again due to

uncertainty within the embodied impact factors. The error bars in the manufacturing stages

arose from the US grid mix emission factor.

0.0

0.2

0.4

0.6

Copper

(6-8 Str/Sol.)

Aluminum

(6-10 Str/Sol.)

GH

G im

pa

ct

(kg

CO

2 p

er

pie

ce

) Manufacturing

Materials

0.0

0.5

1.0

1.5

2.0

2.5

Materials Manufacturing

GH

G im

pa

ct

(kg

CO

2 p

er

pie

ce

)

(a) Without allocation

Package

Mold TAG

Graphite Mold

Electrotinned Steel



Figure D8: Comparison of GHG impact between various components of weld metal (for

cable size: 4/0), error bars represent one standard deviation above and below mean

0.0

0.5

1.0

1.5

2.0

2.5

Materials Manufacturing

GH

G im

pa

ct

(kg

CO

2 p

er

pie

ce

)

(b) With allocation (50 connections per graphite mold)

Package

Mold TAG

Graphite Mold

Electrotinned Steel


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