LCA Book - Chapter 5

Chapter 5: Data Acquisition and Management for Life Cycle Inventory Analysis

Life Cycle Assessment: Quantitative Approaches for Decisions That Matter – lcatextbook.com

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Chapter 5 : Data Acquisition and Management for Life Cycle Inventory Analysis

Now that the most important elements of the LCA Standard are better understood, we can begin to think about the work needed to get data for your study. In this chapter, we introduce the inventory analysis phase of the LCA Standard, as well acquiring and using data needed for the inventory phase of an LCA or an LCI study. As data collection, management, and modeling are typically the most time-consuming components of an LCA, understanding how to work with data is a critical skill. We build on concepts from Chapter 2 in terms of referencing and quantitative modeling. Improving your qualitative and quantitative skills for data management will enhance your ability to perform great LCAs. While sequentially this chapter is part of the content on process-based life cycle assessment, much of the discussion is relevant to LCA studies in general.

Learning Objectives for the Chapter At the end of this chapter, you should be able to:

1. Recognize how challenges in data collection may lead to changes in study design parameters (SDPs), and vice versa

2. Map information from LCI data modules into a unit process framework

3. Explain the difference between primary and secondary data, and when each might be appropriate in a study

4. Document the use of primary and secondary data in a study

5. Create and assess data quality requirements for a study

6. Extract data and metadata from LCI data modules and use them in support of a product system analysis

7. Generate an inventory result from LCI data sources

8. Perform an interpretation analysis on LCI results



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ISO Life Cycle Inventory Analysis After reviewing the ISO LCA Standard and its terminology in Chapter 4, you should be able to envision the level and type of effort needed to perform an inventory analysis of a chosen product system. Every study using the ISO Standard has an inventory analysis phase, but as discussed above, many studies end at this phase and are called LCI studies. Those that continue on to impact assessment are LCAs. That does not mean that LCI studies have better inventory analyses than LCAs, in fact LCAs may require more comprehensive inventory analyses to support the necessary impact assessment.

Figure 5-1, developed by the US EPA, highlights the types of high-level inputs and outputs that we might care to track in our inventory analysis. As originally mentioned in Chapter 1, we may be concerned with accounting for material, energy, or other resource inputs, and product, intermediate, co-product, or release outputs. Recall that based on how you define your goal, scope, and system boundary, you may be concerned with all or some of the inputs and outputs defined in Figure 5-1.

Figure 5-1: Overview of Life Cycle Assessment (Source: US EPA 1993)

Inventory analysis follows a straightforward and repeating workflow, which involves the following steps (as taken from ISO 14044:2006) done as needed until the inventory analysis matches the then-current goal and scope:

• Preparation for data collection based on goal and scope

• Data Collection



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• Data Validation (do this even if reusing someone else's data)

• Data Allocation (if needed)

• Translating Data to the Unit Process

• Translating Data to the Functional Unit

• Data Aggregation

As the inventory analysis process is iterated, the system boundary and/or goal and scope may be changed (recall the two-way arrows in Figure 4-1). The procedure is as simple as needed, and gets more complex as additional processes and flows are added. Each of the inventory analysis steps are discussed in more detail below, with brief examples for discussion. Several more detailed examples are shown later in the chapter.

Step 1 - Preparation for data collection based on goal and scope

The goal and scope definition guides which data need to be collected (noting that the goal and scope may change iteratively during the course of your study and thus may cause additional data collection effort or previously collected data to be discarded). A key consideration is the product system diagram and the chosen system boundary. The boundary shows which processes are in the study and which are not. For every unit process in the system boundary, you will need to describe the unit process and collect quantitative data representing its transformation of inputs to outputs. For the most fundamental unit processes that interface at the system boundary, you will need to ensure that the inputs and outputs are those elementary flows that pass through the system boundary. For other unit processes (which may not be connected to those elementary flow inputs and outputs) you will need to ensure they are connected to each other through non-elementary flows such as intermediate products or co-products.

When planning your data collection activities, keep in mind that you are trying to represent as many flows as possible in the unit process shown in Figure 5-2. Choosing which flows to place at the top, bottom, left, or right of such a diagram is not relevant. The only relevant part is ensuring inputs flow into and outputs flow out of the unit process box. You want to quantitatively represent all inputs, either from nature or from the technosphere (defined as the human altered environment, thus flows like products from other processes). By covering all natural and human-affected inputs, you have covered all possible inputs. You want to quantitatively represent outputs, either as products, wastes, emissions, or other releases. Inputs from nature include resources from the ground, from water, or air (e.g., carbon dioxide to be sequestered). Outputs to nature will be in the form of emissions or releases to 'compartments' in the ground, air, or water. Outputs may also be classified to 'direct human uptake' for food products, medicines, etc.



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Figure 5-2: Generalized Unit Process Diagram

As a tangible example, imagine a product system like the mobile phone example in Chapter 4 where we have decided that the study should track water use as an input. Any of the unit processes within the system boundary that directly uses water will need a unit process representation with a quantity of water as an input and some quantitative measure of output of the process. For mobile phones, such processes that use water as a direct input from nature may include plastic production, energy production, and semiconductor manufacturing. Other unit processes within the boundary may not directly consume water, but may tie to each other through flows of plastic parts or energy. They themselves will not have water inputs, but by connecting them all together, in the end, the water use of those relevant sectors will still be represented. The final overall accounting of inventory inputs and/or outputs across the life cycle within the system boundary is called a life cycle inventory result (or LCI result).

The unit process focus of LCA drives the need for data to quantitatively describe the processes. If data is not available or inaccessible, then the product system, system boundary, or goal may need to be modified. Data may be available but found not to fit the study. For example, an initial system boundary may include a waste management phase, but months of effort could fail to find relevant disposition data for a specific product of the process. In this case, the system boundary may need to be adjusted (made smaller) and other SDPs edited to represent this lack of data in the study. On the other hand, data that is assumed to not be available at first may later be found, which would allow an expansion of the system boundary. In general, system boundaries are made smaller not larger over the course of a study.

Step 2 - Data Collection

For each process within the system boundary, ISO requires you to "measure, calculate, or estimate" data to quantitatively represent the process in your product system model. In LCA, the "gold standard" is to collect your own data for the specific processes needed, called primary data collection. This means directly measuring inputs and outputs of the process



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on-site for the specific machinery use or transformation that occurs. For example, if you required primary data for energy use of a process in an automobile assembly line that fastens a component on to the vehicle with a screw, you might attach an electricity meter to the piece of machinery that attaches the screw. If you were trying to determine the quantity of fuel or material used in an injection molding process, you could measure those quantities as they enter the machine. If you were trying to determine the quantity of emissions you could place a sensor near the exhaust stack.

If you collect data with methods like this, intended to inventory per-unit use of inputs or outputs, you need to use statistical sampling and other methods to ensure you generate statistically sound results. That means not simply attaching the electricity meter one time, or measuring fuel use or emissions during one production cycle (one unit produced). You should repeat the same measurement multiple times, and perhaps on multiple pieces of identical equipment, to ensure that you have a reasonable representation of the process and to guard against the possibility that you happened to sample a production cycle that was overly efficient or inefficient with respect to the inputs and outputs. The ISO Standard gives no specific guidance or rules for how to conduct repeated samples or the number of samples to find, but general statistical principles can be used for these purposes. Your data collection summary should then report the mean, median, standard deviation, and other statistical properties of your measurements. In your inventory analysis you can then choose whether to use the mean, median, or a percentile range of values.

Note that many primary data collection activities cannot be completed as described above. It may not be possible to gain access to the input lines of a machine to measure input use on a per-item processed basis. You thus may need to collect data over the course of time and then use total production during that time to normalize the unit process inventory. For the examples in the previous paragraph, you might collect electricity use for a piece of machinery over a month and then divide by the total number of vehicles that were assembled. Or you may track the total amount of fuel and material used as input to the molding machine over the course of a year. In either case, you would end up with an averaged set of inputs and/or outputs as a function of the product(s) of the unit process. The same general principles discussed above apply here with respect to finding multiple samples. In this case you could find several monthly values or several yearly values to find an average, median, or range.

The ISO Standard (14044:2006, Annex A) gives examples of "data collection sheets" that can support your primary data collection activities. Note that these are only examples, and that your sheets may look different. The examples are provided to ensure, among other things, that you are recording quantities and units, dates and locations of record keeping, and descriptions of sampling done. The most likely scenario is that you will create electronic data collection sheets by recording all information in a spreadsheet. This is a fair choice because from our perspective, Microsoft Excel is the most popularly used software tool in support of LCA. Even practitioners using other advanced LCA software packages still typically use Microsoft Excel for data management, intermediate analysis, and graphing.



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Collecting primary data can be difficult or impossible if you do not own all the equipment or do not have direct access to it either due to geospatial or organizational barriers. This is often the case for an LCA consultant who may be tasked with performing a study for a client but who is given no special privileges or access to company facilities. Further, you may need to collect data from processes that are deemed proprietary or confidential by the owner. This is possible in the case of a comparative analysis with some long-established industry practice versus a new technology being proposed by your client or employer. In these cases, the underlying data collection sheets may be confidential. Your analysis may in these cases only "internally use" the average data points without publicly stating the quantities found in any subsequent reports. If the study is making comparative assertions, then it may be necessary to grant to third-party reviewers (who have signed non-disclosure agreements) access to the data collection sheets to appreciate the quality of the data and to assess the inventory analysis done while maintaining overall confidentiality.

Beyond issues of access, while primary data is considered the "gold standard" there are various reasons why the result may not be as good as expected in the context of an LCA study. First, the data is only as good as the measurement device (see accuracy and precision discussion in Chapter 2). Second, if you are not able to measure it yourself then you outsource the measurement, verification, and validation to someone else and trust them to do exactly as you require. Various problems may occur, including issues with translation (e.g., when measuring quantities for foreign-owned or contracted production) or not finding contacts with sufficient technical expertise to assist you. Third, you must collect data on every input and output of the process relevant to your study. If you are using only an electric meter to measure a process that also emits various volatile organic compounds, your collected data will be incomplete with respect to the full litany of inputs and outputs of the process. Your inventory for that process would undercount any other inputs or outputs. This is important because if other processes in your system boundary track volatile organics (or other inputs and outputs) your primary data will undercount the LCI results.

The alternative to primary data collection is to use secondary data (the "calculating and estimating" referenced above). Broadly defined, secondary data comes from life cycle databases, literature sources (e.g., from searches of results in published papers), and other past work. It is possible you will find data closely, but not exactly, matching the required unit process. Typical tradeoffs to accessibility are that the secondary data identified is for a different country, a slightly different process, or averaged across similar machinery. That does not mean you cannot use it – you just need to carefully document the differences between the process data you are using and the specific process needed in your study. While deemed inferior given the use of the word secondary, in some cases secondary data may be of comparable or higher quality than primary data. Secondary data is typically discoverable because it has been published by the original author who generated it as primary data for their own study (and thus is typically of good quality). In short, one analyst's primary data may be another's secondary data. Again, the "secondary" designation is simply recognition



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that it is being "reused" from a previously existing source and not collected new in your own study. Many credible and peer reviewed studies are constructed mostly or entirely of secondary data. More detail on identifying and using secondary data sources like LCI databases is below.

For secondary data, you should give details about the secondary source (including a full reference), the timestamp of the data record, and when you accessed it. In both cases you must quantitatively maintain the correct units for the inputs and outputs of the unit process. While not required, it is convenient to make tables that neatly summarize all of this information.

Regardless of whether your data for a particular process comes from a primary or secondary source, the ISO Standard requires you to document the data collection process, give details on when data have been collected, and other information about data quality. Data quality requirements (DQRs) are required scope items that we did not discuss in Chapter 4 as part of the SDP, but characterize the fundamental expectations of data that you will use in your study. As specified by ISO 14044:2006, these include statements about your intentions with respect to age of data, geospatial reach, completeness, sources, etc. Data quality indicators are summary metrics used to assess the data quality requirements.

For example, you may have a data quality requirement that says that all data will be primary, or at least secondary but from peer-reviewed sources. For each unit process, you can have a data quality indicator noting whether it is primary or secondary, and whether it has been peer-reviewed. Likewise, you may have a DQR that says all data will be from the same geospatial region (e.g., a particular country like the US or a whole region like North America). It is convenient to summarize the DQRs in a standardized tabular form. The first two columns of Figure 5-3 show a hypothetical DQR table partly based on text from the 2010 Christmas tree study mentioned previously. The final column represents how the requirements might be indicated as a summary in a completed study. The indicated values are generally aligned with the requirements (as they should be!).

Data Quality Category Requirement Data Quality Indicator

Temporal Data within 10 years of study Artificial trees: 2009 data Natural trees: 2002-2009 data

Geospatial Data matches local production Artificial trees: China Natural trees: US

Technological Most common production process basis

All processes used in study are representative of most common practices

Figure 5-3: Sample Data Quality Requirements (DQR) Table



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Beyond using primary or secondary data, you might need to estimate the parameters for some or all of the input and outputs of a unit process using methods as introduced in Chapter 2. Your estimates may be based on data for similar unit processes (but which you deem to be too dissimilar to use directly), simple transformations based on rules of thumb, or triangulated averages of several unit processes. From a third-party perspective, estimated data is perceived as lower quality than primary or secondary sources. However when those sources cannot be found, estimating may be the only viable alternative.

You could add consideration of non-electricity use of energy (e.g., for heating or cooling) with a similar method. Note that such ancillary support services like design, research and development, etc., generally have been found to have negligible impacts, and thus many studies exclude these services from their system boundaries.

Step 3 - Data Validation

Chapter 2 provided some general guidance on validating research results. With respect to validating LCI data, you generally need to consider the quantitative methods used and ensure that the resulting inventories meet your stated DQRs. Data validation should be done after data is collected but before you move on to the actual inventory modeling activities of your LCA.

Example 5-1: Estimating energy use for a service

Question: Consider that you are trying to generate a unit process associated with an internal corporate design function as part of the life cycle "overhead" of a particular product and given the scope of your study need to create an input use of electricity. Your company is all located in one building. There is no obvious output unit for such a process, so you could define it to be per 1 product designed, per 1 square foot of design space, etc., as convenient for your study.

Answer: You could estimate the input electricity use for a design office over the course of a year and then try to normalize the output. If you only had annual electricity use for the entire building (10,000 kWh), and no special knowledge about the energy intensity of any particular part of the building as subdivided into different functions, you could find the ratio of the total design space in square feet (2,000 sf) as compared to the total square feet of the building (50,000 sf), and use that ratio (2/50) to scale down the total consumption to an amount used for design over the course of a year (400 kWh). If your output was per product, you could then further normalize the electricity used for the design space by the unique number of products designed by the staff in that space during the year.



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As an example of validation, it may be useful to validate energy or mass balances of your processes. Using the injection molding process example from Step 2, one would expect that the total input mass of material to be greater than (but approximately equal to) the output mass of molded plastic. You can ensure that the total mass input of plastic resin, fuels, etc., is roughly comparable to the mass of molded plastic (subject to reasonable losses). If the balances are deemed uneven, you can assess whether the measured process is merely inefficient or whether there is a problem in your data collection, and thus resample.

You can use available secondary data to validate primary data collection. If you have chosen to collect your own data for a process that is similar to processes for which there is already secondary data available, you can quantitatively compare your measured results with the published data. Again, if there are significant differences then you will need to determine the source of the discrepancy. You can validate secondary data that you have chosen to use against other sources in similar ways.

The results of validation efforts can be included in the main text of your report or in an included Appendix, depending on the level of detail and explanation needed. If you collected primary data and compared it to similar data from the same industry, the following text might be included to show this:

"Collected data from the year 2012 on the technology-specific process used in this study was compared to secondary data on the similar injection molding process from 2005 (Reference). The mean of collected data was about 10% lower than the secondary data. This difference is not significant, and so the collected data is used as the basis for the process in the study."

If validation suggests the differences are more substantial, that does not automatically mean that the data is invalid. It is possible that there are no good similar data sources to compare against, or that the technology has changed substantially. That too could be noted in the study, such as:

"Collected data from the year 2012 on the technology-specific process used in this study was compared to secondary data on the similar injection molding process from 2005 (Reference). The mean of collected data was about 50% lower than the secondary data. This difference is large and significant, but is attributed to the significant improvements in the industry since 2005, and so the collected data is still chosen as the basis for the process in the study."

As noted above, the validation step is where you re-assess whether the quantitatively sound data you want to use also is within the scope of your DQRs. Many studies state DQRs to use all primary data at the outset, but subsequently realize it is not possible. Likewise studies may not be able to find sufficient geospatially focused data. In both cases, the DQRs would need to be iteratively adjusted as the study continues. This constant refining of the initial goal and



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scope may sound like "cheating", but the purpose of the DQRs is as a single and convenient summary of the study goals for data quality. It allows a reader to quickly get a sense of how relevant the study results are given the final DQRs. While not required, you can state initial goal DQRs alongside final DQRs upon completion of the study.

Step 4 - Data Allocation (if needed)

Allocation will be discussed more in Chapter 6, but in short, allocation is the quantitative process done by the study analyst to assign specific quantities of inputs and outputs to the various products of a process based on some mathematical relation between the products. For example, you may have a process that produces multiple outputs, such as a petroleum refinery process that produces gasoline, diesel, and other fuels and oils. Refineries use a significant amount of energy. Allocation is needed to quantitatively connect the energy input to each of the refined products. Without specified allocation procedures, the connections between those inputs and the various products could be done haphazardly. The ISO Standard suggests that the method you use to perform the allocation should be based on underlying physical relationships (such as the share of mass or energy in the products) when possible. For example, if your product of interest is gasoline, you will need to determine how much of the total refinery energy was used to make the gasoline. For a mass allocation, you could calculate it by using the ratio of the mass of the gasoline produced to the total mass of all of the products. You may have to further research the energetics of the process to determine what allocation method is most appropriate.

If physical relationships are not possible, then methods such as economic allocation—such as by eventual sale price— could be used. ISO also says that you should consistently choose allocation methods as much as possible across your product system, meaning that you should try not to use a mass-based allocation most of the time and an energy-based allocation some of the time. This is because mixing allocation methods could be viewed by your audience or reviewers as a way of artificially biasing the results by picking allocations that would provide low or high results. Allocation is conceptually similar to the design space electricity Example 5-1. Most allocations are just linear transformations of effects.

When performing allocation, the most important considerations are to fully document the allocation method chosen (including underlying allocation factors) and to ensure that total inputs and outputs are equal to the sum of the allocated inputs and outputs. It is possible that none of your unit processes have multiple products, and thus you do not need to perform allocation. You might also be able to avoid allocation entirely, as we will see later.

Step 5 - Translating Data to the Unit Process

In this step you convert the various collected data into a representation of the output of the unit process. Regardless of how you have defined the study overall, this step requires you to collect all of the inputs and outputs as needed for 1 unit output from that process. From



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Example 5-1, you would ensure that the electricity input matched the unit basis of your product flow (e.g., per 1 product designed). This result also needs to be validated.

Step 6 - Translating Data to the Functional Unit

The reason why this step is included in the ISO LCA Standard is to remind you that you are doing an overall study on the basis of 1 functional unit of product output. Either during the data collection phase, or in subsequent analysis, you will need to do a conversion so that the relative amount of product or intermediate output of the unit process is related to the amount needed per functional unit. Eventually, all of your unit process flows will need to be converted to a per-functional unit basis. If all unit processes have been so modified, then finding the total LCI results per functional unit is a trivial procedure. From Example 5-1, the design may be used to eventually produce 1 million of the widgets. The electricity use for one product design must be distributed to the 1 million widgets so that you will then have the electricity use for a single widget in the design phase (a very small amount). This result also needs to be validated.

Step 7 - Data Aggregation

In this step, all unit process data in the product system diagram are combined into a single result for the modeled life cycle of the system. What this typically means is summing all quantities of all inputs and outputs into a single total result on a functional unit basis.

Aggregation occurs at multiple levels. Figure 4-4 showed the various life cycle stages within the view of the product system diagram. A first level of aggregation may add all inputs and outputs under each of the categories of raw material acquisition, use, etc. A second level of aggregation may occur across all of these stages into a final total life cycle estimate of inputs and outputs per functional unit. Aggregated results are often reported in a table showing total inputs and outputs on per-process, or per stage, values, and then a sum for the entire product system. Example 5-2 shows aggregated results for a published study on wool from sheep in New Zealand. The purpose of such tables is to emphasize category level results, such as that half of the life cycle energy use occurs on farm. Results could also be graphed.

Example 5-2: Aggregation Table for Published LCA on Energy to Make Wool (Source: The AgriBusiness Group, 2006)

Life Cycle Stage Energy Use (GJ per tonne wool)

On Farm 22.6

Processing 21.7

Transportation 1.5

Total 45.7



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Beyond such tables, product system diagrams may be annotated with values for different levels of aggregation by adding quantities per functional unit. Example 5-3 shows a diagram for a published study on life cycle effects of bottled water and other beverage systems performed for Nestle Waters. Such values can then be aggregated into summary results.

We have above implied that aggregation of results occurs over a relatively small number of subcomponents. However, a product system diagram may be decomposed into multiple sets of tens or hundreds of constituent pieces that need to be aggregated. If all values for these subcomponents are on a functional unit basis, the summation is not difficult, but the bookkeeping of quantities per subcomponent remains an issue. If the underlying subcomponent values are not consistently on a per functional unit basis, units of analysis should be double checked to ensure they can be reliably aggregated.

Example 5-3: Aggregation Diagram for Bottled Water (Source: Quantis, 2010)



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Life Cycle Interpretation Because some studies only include an inventory (LCI), we discuss Interpretation, the final step for all LCAs and LCIs, now. For those studies (LCAs) that also include an impact assessment, the procedures for the assessment will be discussed in Chapter 10. There is little detail provided in the ISO Standard on what must be done in this phase, but interpretation is similar to the last step of the "three step" method introduced in Chapter 2. The interpretation phase refers to studying the results of the goal and scope, inventory analysis, and impact assessment, in order to make conclusions and recommendations that can be reported. As shown in Figure 4-1, interpretation is iterative with the three other phases. As this chapter is focused on inventory analysis, much of the discussion and examples provided relate to interpreting inventory results, but the same types of interpretation can be done with impact assessment results (to be discussed in Chapter 10).

A typical first task in interpretation is to study your results to determine whether conclusions can be made based on the inventory results that are consistent with the goal and scope. One of the most common and important interpretation tasks involves discussing which life cycle stage leads to the largest share of LCI results. A high-level summary helps to set the stage for subsequent analyses. For example, an LCA of a vehicle will likely show that the use phase (driving the car) is the largest energy user, as compared to manufacturing and recycling. An interpretation task could involve creating a tabular or graphical summary showing the energy use contributions for each of the stages. The interpretation of results from the study summarized in Example 5-2 could note that energy use on farms is about equal to that in the processing stage, and that transportation energy use appears negligible.

Part of your goal statement may have been to do a comparison between two types of products and assess whether the life cycle energy use of one is significantly less than the other. If your inventory results for the two products are nearly identical (say only 1% different) then it may be difficult to scientifically conclude that one is better than the other given the various uncertainties involved. Such an interpretation result could cause you to directly state that no appreciable difference exists, or it may cause you to change the system boundary in a way that ends up making them significantly different.

A key part of interpretation is performing relevant sensitivity analyses on your results. The ISO Standard does not require specific sensitivity analysis scenarios as part of interpretation, but some consideration of how alternative parameters for inputs, outputs, and methods used (e.g., allocation) would affect the final results is necessary. As discussed in Chapter 2, a main purpose of sensitivity analysis is to help assess whether a qualitative conclusion is affected by quantitative changes in the parameters of the study. For example, if your general qualitative conclusion is that product A uses significantly less energy than product B, the sensitivity analysis may test whether different quantitative assumptions related to A or B lead to results where energy use of A is roughly equal to B, or where A is greater than B. Any of the latter two outcomes is qualitatively different than the initial conclusion, and it would be important



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for the sensitivity results to be stated so that it was clear that there is a variable that if credibly changed by a specified amount, has the potential to alter the study conclusions.

While on the subject of assessing comparative differences, it is becoming common for practitioners in LCA to use a "25% rule" when testing for significant differences. The 25% rule means that the difference between two LCI results, such as for two competing products, must be more than 25% different for the results to be deemed significantly different, and thus for one to be declared as lower than the other. While there is not a large quantitative framework behind the choice of 25% specifically, this heuristic is common because it roughly expresses the fact that all data used in such studies is inherently uncertain, and by forcing 25% differences, then relatively small differences would be deemed too small to be noted in study conclusions. We will talk more about modeling and assessing uncertainties in Chapter 11 on uncertainty.

Interpretation can also serve as an additional check on the goal and scope parameters. This is where you could assess whether a system boundary is appropriate. As an example, while the ISO Standard encourages full life cycle stage coverage within system boundaries, it does not require that every LCA encompass all stages. One could try to defend the validity of a life cycle study of an automobile that focused only on manufacturing, or only on the use stage. The results of the interpretation phase could then internally weigh in on whether such a decision was appropriate given the study goal. If a (qualified) conclusion can be drawn, the study could be left as-is, if not, a broader system boundary could be chosen, with or without preliminary LCI results.

Regardless, the real purpose of interpretation is to improve the quality of your study, especially the quality of the written conclusions and recommendations that arise from your quantitative work. As with other quantitative analysis methods, you will need to also improve your qualitative skills, including documentation, to ensure that your interpretation efforts are worthwhile.

Identifying and Using Life Cycle Data Sources In support of modeling the inputs and outputs associated with unit processes, you will need a substantial amount of data. Even studies of simple product systems may require data on 10 different unit processes. While this may sound like a small amount of effort, as you will see below, the task of finding, documenting, manipulating, validating and using life cycle data is time consuming. The text above gave a fair amount of additional detail related to developing your own primary data via collection and sampling efforts. This section is related to the identification and use of secondary data.



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One prominent source of secondary data is the thousands of peer-reviewed journal papers done over time by the scientific community, also known as literature sources. Some of these papers have been explicitly written to be a source of secondary data, while authors of other papers developed useful data in the course of research (potentially on another topic) and made the process-level details available as part of the paper or in its supporting information. Sometimes the study authors are not just teams of researchers, but industry associations or trade groups (e.g., those trying to disseminate the environmental benefits of their products). Around the world, industry groups like Plastics Europe, the American Chemistry Council, and the Portland Cement Association have sponsored projects to make process-based data available via publications. It is common to see study authors citing literature sources, and doing so requires you to simply use a standard referencing format like you would for any source. Unfortunately, data from such sources is typically not available in electronic form, and thus there are potentials for data entry or transcription errors as you try to make use of the published data. It is due to issues like these that literature sources constitute a relatively small share of secondary data used in LCA studies.

There is a substantial amount of secondary data available to support LCAs in various life cycle databases. These databases are the main source of convenient and easy to access secondary data. Some of the data represented in these databases are from the literature sources mentioned above. Since the first studies mentioned in Chapter 1, various databases comprised of life cycle inventory data have been developed. The original databases were sold by Ecobilan and others in the mid-1990s. Nowadays the most popular and rigorously constructed database is from ecoinvent, developed by teams of researchers in Switzerland and available either by paying directly for access to their data website or by an add-on fee to popular LCA system tools such as SimaPro and GaBi (which in turn have their own databases). None of these databases are free, and a license must be obtained to use them. On the other hand, there are a variety of globally available and publicly accessible (free) life cycle databases. In the US, LCI data from the National Renewable Energy Laboratory (NREL)'s LCI database and the USDA's LCA Digital Commons are popular and free3. Figure 5-4 summarizes the major free and paid life cycle databases (of secondary data) in the world that provide data at the unit process level for use in life cycle studies. Beyond the individual databases, there is also an "LCA Data Search Engine," managed by the United Nations Environmental Programme (UNEP), that can assist in finding available free and commercial unit process data (LCA-DATA 2013). All of the databases have their own user's guides that you should familiarize yourself with before searching or using the data in your own studies.

3 Data from the NREL US LCI Database has been transferred over to the USDA LCA Digital Commons as of 2012. Both datasets can now be accessed from that single web database.



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Database Approximate Cost

Number of processes

Notes

ecoinvent 2,500 Euros ($3,000 USD)

4,000+ Has data from around the world, but majority is from Europe. Available directly, or embedded within LCA software.

US NREL LCI Database

Free (companies, agencies pay to publish data)

600+ US focused. Now hosted by USDA LCA Digital Commons.

USDA LCA Digital Commons

Free (manufacturers and agencies pay to publish data)

300+ Focused on agricultural products and processes. Geospatially specific unit processes for specific US states.

ELCD Free 300+ Relatively few processes, spread across various sectors. Additional data being added rapidly.

BEES Free Focused on building and construction materials.

GaBi $3,000 USD 5,000+ Database made by PE International. Global, but heavily focused on European data.

Figure 5-4: Summary of Data Availability for Free and Licensed LCA Databases (Sources provided at end of chapter)

These databases can be very comprehensive, with each containing data on hundreds to thousands of unique processes, with each process comprised of details for potentially hundreds of input or output flows. Collecting the various details of inputs and outputs for a particular unit process (which we refer to as an LCI data module but which are referred to as "datasets" or "processes" by various sources) requires a substantial amount of time and effort. This embedded level of effort for unit process data is important because even though it represents a secondary data source, to create a superior set of primary data for a study, you might need to collect data for 100 or more input and output flows for the process. Of course your study may have a significantly smaller scope that includes only 5 flows, and thus your data collection activities would only need to measure those. The databases do highlight an ongoing conundrum in the LCA community – the naïve stated preference for primary data when substantial high-quality secondary data is pervasive. Another benefit of these databases is that subsets of the data modules are created and maintained consistently, thus a common set of assumptions or methods would be associated with hundreds of processes. This is yet another difference to primary data which could have a set of ad-hoc assumptions used in its creation.



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Now that the availability of vast secondary data sources has been introduced, we discuss the data structures typical of these LCI data modules. As with many facets of LCA, there is a global standard for storing information in LCI data modules, known as EcoSpold. The EcoSpold format is a structured way of storing and exchanging LCI data, where details such as flows and allocation methods are classified for each process. There is no requirement that LCA tools use the EcoSpold format, but given its popularity and the trend that all of the database sources in Figure 5-4 use this format, it is worth knowing. Instead of giving details on the format (which is fairly technical and generally only useful for personnel involved in creating LCA software) we instead will demonstrate the way in which LCI data modules are typically represented in the database and allow you to think about the necessary data structures separately.

In the rest of this chapter we consider an LCI of the CO2 emitted to generate 1 kWh of coal-fired electricity in the United States. Our system boundary for this example (as in Figure 5-5) has only three unit processes: mining coal, transporting it by rail, and burning it at a power plant. The refinery process that produces diesel fuel, an input for rail, is outside of our boundary, but the effects of using diesel as a fuel are included. We can assume, beyond the fact that this is an academic example, that such a tight boundary is realistic because these are known to be significant parts of the supply chain of making coal-fired power. We will discuss the use of screening methods to help us set such boundaries in Chapter 8.

To achieve our goal of the CO2 emitted per kWh, we will need to find process-level data for coal mining, rail transportation, and electricity generation. In the end, we will combine the

Figure 5-5: Product System Diagram for Coal-Fired Electricity LCI Example



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results from these three unit processes into a single estimate of total CO2 per kWh. This way of performing process-level LCA is called the process flow diagram approach.

We will focus on the US NREL LCI database (2013) in support of this relatively simple example. This database has a built-in search feature such that typing in a process name or browsing amongst categories will show a list of available LCI data modules (see the Advanced Material at the end of this chapter for brief tutorials on using the LCA Digital Commons website, that hosts the US LCI data, as well as other databases and tools). Searching for "electricity" yields a list of hundreds of processes, including these LCI data modules:

• Electricity, diesel, at power plant

• Electricity, lignite coal, at power plant

• Electricity, natural gas, at power plant

• Electricity, anthracite coal, at power plant

• Electricity, bituminous coal, at power plant

The nomenclature used may be confusing, but is somewhat consistent across databases. The constituents of the module name can be deciphered as representing (1) the product, (2) the primary input, and (3) the boundary of the analysis. In each of the cases above, the unit process is for making electricity. The inputs are various types of fuels. Finally, the boundary is such that it represents electricity leaving the power plant (as opposed to at the grid, or at a point of use like a building). Once you know this nomenclature, it is easier to browse the databases to find what you are looking for specifically.

Given the above choices, we want to use one of the three coal-fueled electricity generation unit processes in our example. Lignite and anthracite represent small shares of the generation mix, so we choose bituminous coal as the most likely representative process and use the last data module in the list above (alternatively, we could develop a weighted-average process across the three types that would be useful). Using similar brief search methods in the US NREL website we would find the following unit processes as relevant for the other two pieces of our system:

• Bituminous coal, at mine

• Transport, train, diesel powered

These two processes represent mining of bituminous coal and the transportation of generic product by diesel-powered train.



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Figure 5-6 shows an abridged excerpt of the US NREL LCI data module for Electricity, bituminous coal, at power plant. The entire data module is available publicly4. Within the US NREL LCI database website, such data is found by browsing or searching for the process name and then viewing the "Exchanges". These data modules give valuable information about the specific process chosen as well as other processes they are linked to. While here we discuss viewing the data on the website, it can also be downloaded to a Microsoft Excel spreadsheet or as XML.

It is noted that this is an abridged view of the LCI data module. The complete LCI data module consists of quantitative data for 7 inputs and about 60 outputs. For the sake of the example in this section, we assume the abridged inventory and ignore the rest of the details. Most of the data modules in databases have far more inputs and outputs than in this abridged module; it is not uncommon to find data modules with hundreds of outputs (e.g., for emissions of combustion processes). If you have a narrow scope that focuses on a few air emission outputs, many of the other outputs can be ignored in your analysis. However if you plan to do life cycle impact assessment, the data in the hundreds of inputs and/or outputs may be useful in the impact assessment. If your study seeks to do a broad impact assessment, collecting your own primary data can be problematic as your impact assessment will all but require you to broadly consider the potential flows of your process. If you focus instead on just a few flows you deem to be important, then the eventual impact assessment could underestimate the impacts of your process. This is yet another danger of primary data collection (undercounting flows).

4 Data from the NREL US LCI database in this chapter are as of July 20, 2014. Values may change in revisions to the database that cannot be expressed here.



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Flow Category Type Unit Amount Comment

Inputs

bituminous coal, at mine root/flows ProductFlow kg 4.42e-01

transport, train, diesel powered

root/flows ProductFlow t*km 4.61e-01 Transport from mine to power plant

Outputs

electricity, bituminous coal, at power plant

root/flows ProductFlow kWh 1.00

carbon dixoide, fossil air/unspecified ElementaryFlow kg 9.94e-01

Figure 5-6: Abridged LCI data module from US NREL LCI Database for bituminous coal-fired electricity generation. Output for functional unit italicized. (Source: US LCI Database 2012)

Figure 5-6 is organized into sections of data for inputs and outputs. At the top, we see the abridged input flows into the process for generating electric power via bituminous coal. Recalling the discussion of direct and indirect effects from Chapter 4, the direct inputs listed are bituminous coal and train transport. The direct outputs listed are fossil CO2 emissions (which is what results when you burn a fossil fuel) and electricity. Before discussing all of the inputs and outputs, we briefly focus on the output section to identify a critical component of the data module – the electricity output is listed as a product flow, with units of 1 kWh. Every LCI process will have one or more outputs, and potentially have one or more product flows as outputs, but this module has only one. That means that the functional unit basis for this unit process is per (1) kWh of electricity. All other inputs and outputs in Figure 5-6, representing the US NREL LCI data module for Electricity, bituminous coal, at power plant are presented as normalized per 1 kWh. You could think of this module as providing energy intensities or emissions factors per kWh. Thinking back to the discussion above on data collection, its unlikely that the study done to generate this LCI data module actually measured the inputs and outputs needed to make just 1 kWh of electricity at a power plant – it is too small a value. In reality, it is likely that the inputs and outputs were measured over the course of a month or year, and then normalized by the total electricity generation in kWh to find these normalized values. It is the same process you would do if you were making the LCI data module yourself. We will discuss how to see the assumptions and boundaries for the data modules later in this chapter.

We now consider the abridged data module in more detail. In Figure 5-6, each of the input flows are a product flow from another process (namely, the product of bituminous coal mining and the product of train transportation). The unit basis assumption for those inputs is also given – kg for the coal and ton-kilometers (t*km) for the transportation. A ton-



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kilometer is a compound unit (like a kilowatt-hour) that expresses the movement of 1 ton of material over the distance of 1 kilometer. Both are common SI units. Finally the amount of input required is presented in scientific notation and can be translated into 0.442 kg of coal and 0.46 ton-km of train transport. Likewise, the output CO2 emissions to air are estimated at 0.994 kg. All of these quantities are normalized on a per-kWh generated basis. The comment column in Figure 5-6 (and which appears in many data modules) gives brief but important notes about specific inputs and outputs. For example, the input of train transportation is specified as being a potential transportation route from mine to power plant, which reminds us that the unit process for generating electricity from coal is already linked to a requirement of a train from the mine.5

Now that we have seen our first example of a secondary source LCI data module, Figure 5-7 presents a graphical representation of the abridged unit process similar to the generic diagram of Figure 5-2. The direct inputs, which are product flows from other man made processes, are on the left side as inputs from the technosphere. The abridged unit process has no direct inputs from nature. The direct CO2 emissions are at the top. The output product, and functional unit basis of the process, of electricity is shown on the right. All quantitative values are representative of the functional unit basis of the unit process.

Figure 5-7: Unit Process Diagram for abridged electricity generation unit process

Returning to our example LCA problem, we now have our first needed data point, that the direct CO2 emissions are 0.994 kg / kWh generated. Given that we have only three unit processes in our simple product system, we can work backwards from this initial point to get estimated CO2 emissions values from mining and train transport. Again using the NREL LCI database, Figure 5-8 shows abridged data for the data module bituminous coal, at mine. The 5 The unabridged version of the module has several other averaged transport inputs in ton-km, such as truck, barge, etc. Overall, the module gives a "weighted average" transport input to get the coal from the mine to the power plant. Since we are only using the abridged (and unedited) version, we will otherwise undercount the upstream CO2 emissions from delivering coal since we are skipping the weighted effects from those other modes.



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output and functional unit is 1 kg of bituminous coal as it leaves the mine. Two important inputs are diesel fuel needed to run equipment, and coal. It may seem odd to see coal listed as an input into a coal mining process, but note it is listed as a resource and as an elementary flow. As discussed in Chapter 4, elementary flows are flows that have not been transformed by humans. Coal trapped in the earth for millions of years certainly qualifies as an elementary flow by that definition! Further, it reminds us that there is an elementary flow input within our system boundary, not just many product flows. This particular resource is also specified as being of a certain quality, i.e., with energy content of about 25 MJ per kg. Finally, we can see from a mass balance perspective that there is some amount of loss in the process, i.e., that every 1.24 kg of coal in the ground leads to only 1 kg of coal leaving the mine.


Inputs

Coal, bituminous, 24.8 MJ per kg resource/ground ElementaryFlow kg 1.24

Diesel, combusted in industrial boiler root/flows ProductFlow l 8.8e-03

Outputs

Bituminous coal, at mine root/flows ProductFlow kg 1.00 Figure 5-8: Abridged LCI data module from US NREL LCI Database for bituminous coal mining.

Output for functional unit italicized. (Source: US LCI Database 2012)

Figure 5-9 shows the abridged NREL LCI data module for rail transport (transport, train, diesel powered). The output / functional unit of the process is 1 ton-km of rail transportation service provided. Providing that service requires 0.00648 liters of diesel fuel and emits .0189 kg of CO2, both per ton-km.



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Inputs

Diesel, at refinery root/flows ProductFlow l 6.48e-03

Outputs

Carbon dixoide, fossil air/unspecified ElementaryFlow kg 1.89e-02

transport, train, diesel powered

root/flows ProductFlow t*km 1

Figure 5-9: Abridged LCI data module from US NREL LCI Database for rail transportation. Output for functional unit italicized. (Source: US LCI Database 2012)

To then find the total CO2 emissions across these three processes, we can work backwards from the initial process. We already know there are 0.994 kg/kWh of CO2 emissions at the power plant. But we also need to mine the coal and deliver it by train for each final kWh of electricity. The emissions for those activities are easy to associate, since Figure 5-6 provides us with the needed connecting units to estimate the emissions per kWh. Namely, that 0.442 kg of coal needs to be mined and 0.461 ton-km of rail transport needs to be used per kWh of electricity generated. We can then just use those unit bases to estimate the CO2 emissions from those previous processes. Figure 5-8 does not list direct CO2 emissions from coal mining, although it does list an input of diesel used in a boiler6. If we want to assume that we are only considering direct emissions from each process, we can assume the CO2 emissions from coal mining to be zero7, or we could expand our boundary and acquire the LCI data module for the diesel, combusted in industrial boiler process. Our discussion below follows the assumption that direct emissions are zero.

Figure 5-9 notes that there are 0.0189 kg of CO2 emissions per ton-km of rail transported. Equation 5-1 summarizes how to calculate CO2 emissions per kWh for our simplistic product system. Other than managing the compound units, it is a simple solution: about 1 kg CO2 per kWh. If we were interpreting this result, we would note that the combustion of coal at the power plant is about 99% of the total emissions.

0.994 kg CO2 /kWh + 0.442 kg * 0 + (0.461 ton-km / kWh)*(0.0189 kg CO2 / ton-km) =

0.994 kg CO2 / kWh + 0.0087 kg CO2 / kWh = 1.003 kg CO2 / kWh (5-1)

The estimated CO2 emissions for coal-fired electricity of 1 kg / kWh was obtained relatively easily, requiring only three steps and queries to a single database (US NREL LCI). As always 6 This particular input of "diesel, combusted in industrial boiler" may not be what you would expect to find in an LCI data module, since it is a description of how an input of diesel is used. Such flows are fairly common though. 7 Also, the unabridged LCI data modules list emissions of methane to air, which could have been converted to equivalent CO2 emissions. Doing so would only change the result above by about 10%.



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one of our first questions should be "is it right?" We can attempt to validate this value by looking at external references. Whitaker et al (2012) reviewed 100 LCA studies of coal-fired electricity generation and found the median value to be 1 kg of CO2 per kWh, thus we should have reasonable faith that the simple model we built leads to a useful result. Of course we can add other processes to our system boundary (such as other potential transportation modes) but we would not appreciably change our simple result of 1 kg/kWh. Note that anecdotally experts often refer to the emissions from coal-fired power plants to be 2 pounds per kWh, which is a one significant digit equivalent to our 1 kg/kWh result.

Process-based life cycle models are constructed in this way. For each unit process within the system boundary, data (primary or secondary) is gathered and flows between unit processes are modeled. Since you must find data for each process, such methods are often referred to as "bottom up" studies because you are building them up from nothing, as you might construct a building on empty land.

Beyond validating LCI results, you should also try to validate the values found in any unit process you decide to use, even if sourced from a well-known database. That is because errors can and do exist in these databases. It is easy to accidentally put a decimal in the wrong place when creating a digital database. As an example, the US NREL LCI database had an error in the CO2 emissions of its air transportation process, of 53 kg per 1000 ton-km (0.053 kg per ton-km) for several years before it was fixed. This error was brought to their attention because observant users noted that this value was less than the per-ton-km emissions for truck transportation, which went against common sense. Major releases of popular databases are also imperfect. It is common to have errors found and fixed, but this may happen months after licenses have been purchased, or worse, after studies have been completed. These are additional reasons why despite being of high quality, you need to validate your data sources.

Details for Other Databases The discussion above was focused on the US NREL LCI Database, which contains only process data for US-based production, yet there are other considerations both for data access and metadata for the other databases. As noted in Figure 5-4, the ecoinvent database is far more geospatially diverse. While generally focused on Europe, data can be found in ecoinvent for other regions of the world as well. This fact creates a new challenge in interpreting available process data modules, namely, determining the country of production basis assumption for the data. While examining the metadata can be useful, ecoinvent and other databases typically summarize the country used within the process naming convention. For example, a process you might find within ecoinvent might be called electricity, hard coal, at power plant, DE, where the first part is the process name formatted similar to the NREL database, and at the end is an abbreviated term for the country or region to which that



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process is representative. Figure 5-10 summarizes some of the popular abbreviations used for country basis within ecoinvent.

Country or Region Abbreviation Country or Region Abbreviation

Norway NO Japan JP

Australia AU Canada CA

India IN Global GLO

China CN Europe RER

Germany DE Africa RAF

United States US Asia RAS

Netherlands NL Russian Federation RU

Hong Kong HK Latin America and the Caribbean RLA

France FR North America RNA

United Kingdom GB Middle East RME Figure 5-10: Summary of abbreviations for countries and regions in ecoinvent

Ecoinvent has substantially more available metadata for its data modules, including primary sources, representative years, and names of individuals who audited the datasets. While ecoinvent data are not free, the metadata is freely accessible via the database website. Thus, you could do a substantial amount of background work verifying that ecoinvent has the data you want before deciding to purchase a license.

A particular feature of ecoinvent data is its availability at either the unit process or system process level. Viewing and using ecoinvent system processes is like using already rolled-up information (and computations would be faster), while using unit processes will be more computationally intensive. This will be discussed more in Chapter 9.

LCI Data Module Metadata Our example using actual LCI data modules from the US NREL LCI database jumped straight into extracting and looking at the quantitative data. However, all LCI data modules provide some level of metadata, which is information regarding how the data was collected, how the modules were constructed, etc. Metadata is also referred to as "data about data".

The metadata that we care about for our unit processes are elements such as the year the data was collected, where it was collected, whether the values are single measurements or averages, and whether it was peer reviewed. To understand metadata more, we can look at the metadata available for the processes we used above. The US NREL LCI Database has three different metadata categories as well as the Exchanges information shown above.



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Figure 5-11 shows metadata from the Activity metadata portion of the US NREL LCI database for the Electricity, bituminous coal, at power plant process used above. This metadata notes that the process falls into the Utilities subcategory (used for browsing on the website) and that it has not yet been fully validated. It applies to the US, and thus it is most appropriate for use in studies looking to estimate impacts of coal-fired electricity generation done within the United States. Note that this does not mean that you can only use it for that geographical region. A process like coal-fired generation is quite similar around the world; although factors such as pollution controls may differ greatly by region. However, since capture of carbon is basically non-existent, if we wanted to use this process to estimate CO2 emissions from coal-fired generation in other regions it might still be quite useful.

The metadata field for "infrastructure process" notes whether the process includes estimated infrastructure effects. For example, one could imagine two parallel unit processes for electricity generation, where one includes estimated flows from needing to build the power plant and one does not (such as the one referenced above). In general, infrastructure processes are fairly rare, and most LCA study scopes exclude consideration of infrastructure for simplicity.

Name Electricity, bituminous coal, at power plant

Category Utilities - Fossil Fuel Electric Power Generation

Description Important note: although most of the data in the US LCI database has undergone some sort of review, the database as a whole has not yet undergone a formal validation process. Please email comments to [email protected].

Location US

Geography Comment United States

Infrastructure Process False

Quantitative Reference Electricity, bituminous coal, at power plant Figure 5-11: Activity metadata for Electricity, bituminous coal, at power plant process

Figure 5-12 shows the Modeling metadata for the coal-fired generation unit process. There is no metadata provided for the first nine categories of this category, but there are ten references provided to show the source data used to make the unit process. While a specific "data year" is not dictated by the metadata, by looking at the underlying data sources, the source data came from the period 1998-2003. Thus, the unit process data would be most useful for analyses done with other data from that time period. If we wanted to use this process data for a more recent year, we would either have to look for an LCI data module



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that was newer, or verify that the technologies have not changed much since the 1998-2003 period.

LCI Method

Modelling constants

Data completeness

Data selection

Data treatment

Sampling procedure

Data collection period

Reviewer

Other evaluation

Sources U.S. EPA 1998 Emis. Factor AP-42 Section 1.1, Bituminus and Subbituminus Utility Combustion

U.S. Energy Information Administration 2000 Electric Power Annual 2000

Energy Information Administration 2000 Cost and Quality of Fuels for Electric Utility Plants 2000

Energy Information Administration 2000 Electric Power Annual 2000

U.S. EPA 1998 Study of Haz Air Pol Emis from Elec Utility Steam Gen Units V1 EPA-453/R-98-004a

U.S. EPA 1999 EPA 530-R-99-010

unspecified 2002 Code of Federal Regulations. Title 40, Part 423

Energy Information Administration 9999 Annual Steam-Electric Plant Operation and Design Data

Franklin Associates 2003 Data Details for Bituminous Utility Combustion Figure 5-12: Modeling metadata for Electricity, bituminous coal, at power plant process

Finally, Figure 5-13 shows the Administrative metadata for the Electricity, bituminous coal, at power plant process. There are no explicitly-defined intended applications (or suggested restrictions on such applications), suggesting that it is broadly useful in studies. The data are



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not copyrighted, are publicly available, and were generated by Franklin Associates, a subsidiary of ERG, one of the most respected life cycle consulting business in the US. The "Data Generator" is a significant piece of information. You may opt to use or not use a data source based on who created it. A reputable firm has a high level of credibility. A listed individual with no obvious affiliation or reputation might be less credible. Finally, the metadata notes that it was created and last updated in October 2011, meaning that perhaps it was last checked for errors on this date, not that the data is confirmed to still be valid for the technology as of this date.

Intended Applications "

Copyright false

Restrictions All information can be accessed by everybody.

Data Owner

Data Generator Franklin Associates

Data Documentor Franklin Associates

Project

Version

Created 2011-10-24

Last Update 2011-10-24 Figure 5-13: Administrative metadata for Electricity, bituminous coal, at power plant process

Our metadata examples have focused on the publicly available US NREL LCI Database, but other databases like ELCD and ecoinvent have similar metadata formats. These other databases typically have more substantive detail, in terms of additional fields and more consistent entries in these fields. Since these other data sources are not public, we have not used examples here.

You should browse through the available metadata for any of the databases that you have access to, so that you can better appreciate the records that may exist within various metadata records. Remember that the reason for better appreciating the value of the metadata is to help you with deciding which secondary data sources to use, and how compatible they are with your intended goal and scope.



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Referencing Secondary Data When you use secondary data as part of your study it must be appropriately referenced, as with any other source. Referencing data sources was first mentioned in Chapter 2, but here we discuss several important additions for referencing data from LCA databases. As an example, the US NREL LCI database explicitly suggests the following referencing style for use of its data modules:

When referencing the USLCI Database, please use the following format: U.S. Life Cycle Inventory Database. (2012). National Renewable Energy Laboratory, 2012. Accessed November 19, 2012: https://www.lcacommons.gov/nrel/search

However, this is the minimum referencing you should provide for process data. First of all, you can not simply reference the database. You need to ensure that the specific unit process from which you have used data is clear to the reader, for example if they would like to validate your work. That means you need to explicitly reference the name of the process (either obviously in the text or in the reference section). In the US NREL database and other sources, there may be hundreds of LCI data modules for electricity. Thus, the danger is that in the report you loosely reference data for coal-fired electricity generation as being from "the NREL database", but do not provide enough detail for the reader to know which electricity process was used. Unfortunately, this is a common occurrence in LCA reports. This situation can be avoided by explicitly noting the name of the process used in the reference, such as:

U.S. Life Cycle Inventory Database. Electricity, bituminous coal, at power plant unit process (2012). National Renewable Energy Laboratory, 2012. Accessed Nov. 19, 2012: https://www.lcacommons.gov/nrel/search

A generic reference to the database, as given at the top of this section, may be acceptable if the report separately lists all of the specific processes used in the study, such as in an inventory data source table listing all of the processes used.

You will likely use multiple unit processes from the same database. You can either create additional references like the one above for each process, or use a combined reference that lists all processes as part of the reference, such as:

U.S. Life Cycle Inventory Database. Electricity, bituminous coal, at power plant; bituminous coal, at mine; transport, train, diesel powered unit processes (2012). National Renewable Energy Laboratory, 2012. Accessed Nov. 19, 2012: https://www.lcacommons.gov/nrel/search

The greater the number of similar processes, the greater the need to specify which specific data module you used in your analysis. This becomes especially important if you are using LCI data modules from several databases.



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A final note about referencing is that the LCA databases are generally not primary sources, they are secondary sources. Ideally, sources would credit the original author, not the database owner who is just providing access. If the LCI data module is taken wholesale from another source (i.e., if a single source were listed in the metadata), it may make sense to also reference the primary source, or to add the primary source to the database reference. In this case the reference might look like one of the following:

RPPG Of The American Chemistry Council 2011. Life Cycle Inventory Of Plastic Fabrication Processes: Injection Molding And Thermoforming. http://plastics.americanchemistry.com/Education-Resources/Publications/LCI-of-Plastic-Fabrication-Processes-Injection-Molding-and-Thermoforming.pdf. via U.S. Life Cycle Inventory Database. Injection molding, rigid polypropylene part, at plant unit process (2012). National Renewable Energy Laboratory, 2012. Accessed November 19, 2012: https://www.lcacommons.gov/nrel/search

U.S. Life Cycle Inventory Database. Injection molding, rigid polypropylene part, at plant unit process (2012). National Renewable Energy Laboratory, 2012. Accessed November 19, 2012: https://www.lcacommons.gov/nrel/search (Primary source: RPPG Of The American Chemistry Council 2011. Life Cycle Inventory Of Plastic Fabrication Processes: Injection Molding And Thermoforming. http://plastics.americanchemistry.com/Education-Resources/Publications/LCI-of-Plastic-Fabrication-Processes-Injection-Molding-and-Thermoforming.pdf)

As noted in Chapter 2, ideally you would identify multiple data sources (i.e., multiple LCI data modules) for a given task. This is especially useful when using secondary data because you are not collecting data from your own controlled processes. Since the data is secondary, it is likely that there are slight differences in assumptions or boundaries than what you would have used if collecting primary data. By using multiple sources, and finding averages and/or standard deviations, you could build a more robust quantitative model of the LCI results. We will discuss such uncertainty analysis for inventories in Chapter 10.

Additional Considerations about Secondary Data and Metadata Given the types and classes of data we are likely to find in life cycle studies, we introduce in this subsection a few more considerations to ensure you are finding and using appropriate types of data to match the needs of your study. These considerations are in support of the data quality requirements.



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Temporal Issues

In creating temporal data quality requirements, you will set a target year (or years) for data used in your study. For example, you might have a DQR of "2005 data" or "data from 2005-2007" or "data within 5 years of today". After setting target year(s) you then must do your best to find and use data that most closely matches the target. It is likely that you will not be able to match all data with the target year(s). When setting and evaluating temporal DQRs, the following issues need to be understood.

You may need to do some additional work to guarantee you know the basis year of the data you find, but this is time well spent to ensure compatibility of the models you will build. You will need to distinguish between the year of data collection and year of publication. In our CBECS example in Chapter 2, the data were collected in the year 2003 but the study was not published by DOE until December 2006 (or, almost 2007). It is easy to accidentally consider the data as being for 2006 because the publication year is shown throughout the reports. But the data were representative of the year 2003. If your temporal DQR was set at "2005", you might still be able to justify using the 2003 CBECS data, but would need to assess whether the electricity intensity of buildings likely changed significantly between 2003 and 2005. The same types of issues arise when using sources such as US EPA's AP-42 data, which are compilations of (generally old) previously estimated emissions factors. Other aspects of your DQRs may further help decide the appropriateness of data newer or older than your target year.

The same is true of dates given in the metadata of LCI data modules. You don't care about when you accessed the database, or when it was published in the database. You care about the primary source's years of analysis. Figure 5-12 showed metadata on the coal-fired electricity generation process where the underlying data was from 1998-2003, and which was put in the US LCI database in 2011. An appropriate "timestamp" for this process would be 1998-2003.

While on the topic of temporal issues, we revisit the point about age of data in databases. The US LCI database project started in the mid-2000s. Looking at the search function in that database, you can find a distribution of the "basis year" of all of the posted data modules. This is a date that is not visible within the metadata, but is available for downloaded data modules and summarized in the web server. Figure 5-14 shows a graph of the distribution of the years. In short, there is a substantial amount of relatively old data, and a substantial amount of data where this basis year is not recorded (value given as '9999'). Half of the 200 data modules updated in 2010 are from an update to the freight transportation datasets. These could be key considerations when considering the suitability of data in a particular database.



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Figure 5-14: Frequency Distribution of Data Years in US NREL LCI Database (as of August 15, 2013)

Geospatial Issues

You must try to ensure that you are using data with the right geographical and spatial scope to fit your needs. If you are doing a study where you want to consider the emissions associated with producing an amount of electricity, then you will find many potential data sources to use. The EIA has data that can give you the average emissions factors for electricity generation across the US. E-GRID (a DOE-EPA partnership) can give you emissions factors at fairly local levels, reflecting the types of power generation used within a given region. The question is the context of your study. Are you doing a study that inevitably deals with national average electricity? Then the EIA data is likely suitable. Or are you doing a study that needs to know the impact of electricity from a particular factory's production? In that case you likely want a fairly local data source, e.g., from E-GRID. An alternative is to leverage the idea of ranges, presented in Chapter 2, to represent the whole realm of possible values for electricity generation, including various local or regional averages all the way up to the national average.



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Uncertainty and Variability

Sadly, in the field of LCA there are many practitioners who actively or passively ignore the effects of uncertainty or variability in their studies. They treat all model inputs as single values and generate only a single result. The prospect of uncertainty or variability is lost in their model, and typically then that means those effects are lost on the reader of the study. How can we support a big decision (e.g., paper vs. plastic?) if there is much uncertainty in the data but we have completely ignored it? We are likely to end up supporting poor decisions if we do so. We devote Chapter 11 to methods of overcoming and structuring uncertainty in LCA models.

Chapter Summary Typically, the most time consuming aspect of an LCA (or LCI) study relates to the data collection and management phase. While the LCA Standard encourages practitioners to collect primary data for the product systems being studied, typically secondary data is used from prior published studies and databases. Using secondary data requires being knowledgeable and cognizant of issues relating to the sources of data presented and also requires accurate referencing. Data quality requirements help to manage expectations of the study team as well as external audiences pertaining to the goals of your data management efforts. Utilization of effective LCI data management methods leads to excellent and well-received studies.

References for this Chapter BEES LCA Tool, website, http://ws680.nist.gov/Bees/Default.aspx, last accessed August 12, 2013.

ecoinvent website, www.ecoinvent.ch, last accessed August 12, 2013.

ELCD LCA Database, website, http://lca.jrc.ec.europa.eu/lcainfohub/, last accessed August 12, 2013.

Environmental Protection Agency. 1993. Life Cycle Assessment: Inventory Guidelines and Principles. EPA/600/R-92/245. Office of Research and Development. Cincinnati, Ohio, USA.

Gabi Software, website, http://www.gabi-software.com/, last accessed August 12, 2013.



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LCA-DATA, UNEP, website, http://lca-data.org:8080/lcasearch, last accessed August 12, 2013.

Quantis, "Environmental Life Cycle Assessment of Drinking Water Alternatives and Consumer Beverage Consumption in North America", LCA Study completed for Nestle Waters North America, 2010, http://www.beveragelcafootprint.com/wp-content/uploads/2010/PDF/Report_NWNA_Final_2010Feb04.pdf, last accessed September 9, 2013.

The Agribusiness Group, "Life Cycle Assessment: New Zealand Merino Industry Merino Wool Total Energy Use and Carbon Dioxide Emissions", 2006, http://www.agrilink.co.nz/Portals/Agrilink/Files/LCA_NZ_Merino_Wool.pdf, last accessed September 1, 2013.

US NREL LCI Database, website, http://www.nrel.gov/lci/, last accessed August 12, 2013.

U.S. Life Cycle Inventory Database. Electricity, bituminous coal, at power plant, bituminous coal, at mine, and transport, train, diesel powered unit processes (2012). National Renewable Energy Laboratory, 2012. Accessed August 15, 2013: https://www.lcacommons.gov/nrel/search

USDA LCA Digital Commons, website, http://www.lcacommons.gov, last accessed August 12, 2013.

Whitaker, Michael, Heath, Garvin A., O'Donoughue, Patrick, and Vorum, Martin, "Life Cycle Greenhouse Gas Emissions of Coal-Fired Electricity Generation: Systematic Review and Harmonization", Journal of Industrial Ecology, 2012. DOI: 10.1111/j.1530-9290.2012.00465.x

End of Chapter Questions

Objective 1. Recognize how challenges in data collection may lead to changes in study design parameters (SDPs), and vice versa

1. Using the US NREL LCI Database (from the USDA Digital Commons) or another LCI database, search or browse amongst the available categories. For each of the following broadly defined processes in the list below, discuss how many different LCI data modules are available and qualitatively discuss what different assumptions have been used to generate the data modules.

a. Refining of petroleum

b. Generating electricity from fossil fuel



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c. Truck transportation

Objective 2. Map information from LCI data modules into a unit process framework AND

Objective 7. Generate an inventory result from LCI data sources

2. Redo the Figure 5-5 example shown in Equation 5-1, but include the diesel, combusted in industrial boiler process (referenced as an input in the bituminous coal mining process) within the system boundary. What is your revised estimate of CO2 emissions per kWh? How different is your updated estimate?

3. Redo the Figure 5-5 example but include within the system boundary refining of the diesel used in the coal mining and rail transportation processes. Assume you have LCI data that there are 2.5 E-04 kg fossil CO2 emissions per liter of diesel fuel refined. How is your revised estimate of fossil CO2 emissions per kWh compared to Equation 5-1?

Objective 3. Explain the difference between primary and secondary data, and when each might be appropriate in a study

4. Explain the difference between primary and secondary data. Provide an example of when each would be appropriate for a study.

Objective 4. Document the use of primary and secondary data in a study

5. The data identified in part 1c above would be secondary data if you were to use it in a study. If you instead wanted primary data for a study on trucking, discuss what methods you might use in order to get the data.

Objective 5. Create and assess data quality requirements for a study

6. If you had data quality requirements stating that you wanted data that was national (US) in scope, and from within 5 years of today, how many of the LCI data modules from Question 1 would be available? Which others might still be relevant? Justify your answer.

Objective 6. Extract data and metadata from LCI data modules and use them in support of a product system analysis

7. Using an LCI database available to you, search for one LCI data module in each of the following broad categories - energy, agriculture, and transportation. For each of the three, do the following:

a. List the name of the process.



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b. Identify the functional unit.

c. Draw a unit process diagram.

d. Try to do a brief validation of the data reported.

e. Comment briefly on an example LCA study that this process might be appropriate for, and one where it would not be appropriate.

f. Show how to appropriately reference the LCI data module in a study.

Objective 8. Perform an interpretation analysis on LCI results

8. Write an interpretation analysis for the various results expressed in Figure 5-5, and your results from Questions 3 and 4 above.



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Advanced Material for Chapter 5 The advanced material in this chapter will demonstrate how to find and access LCI data modules from various popular databases and software tools, and how to use the data to build simple models like the main model presented in the chapter related to coal-fired electricity.

Not all databases and software tools are discussed; however, access methods are generally very similar across tools. For consistency, we will demonstrate how to find the same process data as used in the chapter so that you can learn about the different options and selections needed to find equivalent data and metadata across tools. Specifically, we will demonstrate how to find data from the US LCI database by using the LCA Digital Commons Website, SimaPro (a commercial LCA tool) and openLCA (a free LCA tool).

The databases and tools use different terminology, categories, etc., to organize LCI data, but can all lead to the same data. Seeing how each of the tools categorizes and refers to the data is an important concept to understand.

Section 1 - Accessing Data via the US LCA Digital Commons The LCA Digital Commons is a free, US government-sponsored and hosted web-based data resource. Given that all of its data are publicly available, it is a popular choice for practitioners. Thus, it is also a great resource for learning about what LCI data looks like, how to access it, and how to build models.

The main purpose of the Digital Commons is to act as a resource for US Department of Agriculture (USDA) agricultural data and, as a result, accessing the home page (at https://www.lcacommons.gov/discovery) will filter access to those datasets. However, the US LCI database previously hosted by NREL (at http://www.nrel.gov/lci/), and mentioned extensively in Chapter 5, is also hosted via the Digital Commons website (at https://www.lcacommons.gov/nrel/search). Given its comprehensiveness, most of the discussion in this book is related to use of the NREL data. The examples provided below are for accessing the NREL data source, which has slightly different metadata and contents than the USDA data but a similar method for searching and viewing.

The LCI data modules on the Digital Commons website can be accessed via searching or browsing. Brief overviews are provided for both options, followed by how to view and download selected modules. Before following the tutorial below, you should consider registering for an account on the Digital Commons website (you will need separate accounts for the USDA and NREL data). While an account is not required to view all of the data, it is required if you wish to download the data. You can copy and paste the data from a web browser instead of downloading but this sometimes leads to formatting errors.



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Browsing for LCI Data Modules on the Digital Commons (NREL)

Figure 5-15 shows the NREL Digital Commons home page, where the left hand side shows how the data modules are organized, including dataset type (elementary flows or unit processes), high-level categories (like transportation and utilities), and year of data8.

Figure 5-15: Excerpt of LCA Digital Commons Website Home Page

Clicking on the + icon next to the categories generally reveals one or more additional sub-categories. For example, under the Utilities category there are fossil-fired and other generation types. Clicking on any of the dataset type, category/subcategory or year checkboxes will filter the overall data available. The "order by" box will sort the resulting modules. Filtering by (checking) Unit processes and the Fossil fuel electric power generation category under Utilities, and ordering by description will display a subset of LCI data modules, as shown in Figure 5-16. A resulting process module can be selected (see below for how to do this and download the data).

Figure 5-16: Abridged View of LCA Digital Commons Browsing Example Results 8 The examples of the NREL US LCI Database in this section are as of July 2014, and may change in the future.



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Searching for an LCI data module via keyword

The homepage has a search feature, and entering a keyword such as electricity and pressing the Go button on the right hand side, as shown in Figure 5-17, will return a list of data modules within the NREL LCI database that have that word in the title or category, as shown in Figure 5-18.

Figure 5-17: Keyword search entry on homepage of NREL engine of LCA Digital Commons Website

Figure 5-18: Abridged Results of e l e c t r i c i t y keyword search

Figure 5-18 indicates that the search engine returns more than 100 LCI data modules (records) that may be relevant to "electricity". Some were returned because electricity is in the name of the process and others because they are in the Electric power distribution data category. When searching, you can order results by relevance, description, or year. Once a set



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of search results is obtained, results can be narrowed by filtering via the options on the left side of the screen. For example, you could choose a subset of years to be included in the search results, which can help ensure you use fairly recent instead of old data (as discussed along with Figure 5-14). You can also filter based on the LCI data categories available, in this case by clicking on the + icon next to the high-level category for Utilities, which brings up all of the subcategories under utilities. Figure 5-19 shows the result of a keyword search for 'electricity', ordered by relevance, and filtered by the Utilities subcategory of Fossil fuel electric power generation and by data for year 2003. The fifth search result listed is the same one mentioned in the chapter that forms the basis of the process flow diagram example.

Figure 5-19: Abridged Results of e l e c t r i c i t y keyword search, ordered and filtered

Selecting and viewing an LCI data module

When you have searched or browsed for a module and selected by clicking on it, the module detail summary is displayed, as in Figure 5-20.



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Figure 5-20: Details for Elec t r i c i t y , b i tuminous coa l process on LCA digital commons

The default result is a view of the Activity tab, which was shown in Figure 5-11. The information available under the Modeling and Administrative tabs was presented in Figure 5-12 and Figure 5-13. Finally, an abridged view of the information available on the Exchanges tab was also shown in Figure 5-6. Not previously mentioned is that the module can be downloaded by first clicking on the shopping cart icon in the top right (adjacent to the "Next item" tag). This adds it to your download cart. Once you have identified all of the data you are interested in, you can view your current cart (menu option shown in Figure 5-21) and request them all to be downloaded (Figure 5-22).

Figure 5-21: Selection of Current Cart Download Option on LCA Digital Commons



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Figure 5-22: Cart Download Screen on LCA Digital Commons

After clicking download, you will be sent a link via the e-mail in your account registration. As noted, the format will be an Ecospold XML file. For novices, viewing XML files can be cumbersome, especially if just trying to look at flow information. While less convenient, the download menu (All LCI datasets submenu) will allow you to receive a link to a ZIP file archive containing all of the NREL modules in Microsoft Excel spreadsheet format (or you can receive all of the modules as Ecospold XML files). You can also download a list of all of the flows and processes used across the entire set of about 600 modules.

A spreadsheet of all flows and unit processes in the US LCI database (and their categories) is on the www.lcatextbook.com website in the Chapter 5 folder.

When uncompressed the Electricity, bituminous coal, at power plant module file has four worksheets, providing the same information as seen in the tabs of the Digital Commons/NREL website above. The benefit of the spreadsheet file, though, is the ability to copy and paste that values into a model you may be building. We will discuss building spreadsheet models with such data in Section 4 of this advanced material.

Section 2 – Accessing LCI Data Modules in SimaPro As mentioned in the chapter, SimaPro is a popular commercial software program specifically aimed at building quantitative LCA models. Its value lies both in these model-building support activities as well as in being able to access various datasets from within the program. Commercial installations of SimaPro cost thousands of dollars, but users may choose commercial databases (e.g., ecoinvent) to include in the purchase price. Regardless of which databases are chosen, SimaPro has the ability to use various other free datasets (e.g., US NREL, ELCD, etc.). This tutorial assumes that such databases have already been installed and will demonstrate how to find the same US NREL-based LCI data as in Section 1.



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This tutorial also does not describe any of the initial steps needed to purchase a license for or install SimaPro on your Windows computer or server. It will only briefly mention the login and database selection steps, which are otherwise well covered in the SimaPro guides provided with the software.

Note that SimaPro refers to the overall modeling environment of data available as a "database" and individual LCI data sources (e.g., ecoinvent) as "libraries". After starting SimaPro, selecting the database (typically called "Professional"), and opening or creating a new project of your choice, you will be presented with the screen in Figure 5-23. On the left side of the screen are various options used in creating an LCA in the tool. By default the "processes" view is selected, showing the names and hierarchy of all processes in the currently selected libraries of the database. This list shows thousands of processes (and many of those will be from the ecoinvent database given its large size).

Figure 5-23: Default View of Processes in Libraries When Starting SimaPro

You can narrow the processes displayed by clicking on "Libraries" on the left hand side menu, which will display Figure 5-24. Here you can select a subset of the available libraries for use in browsing (or searching) for process data. You can choose "Deselect all" and then to follow along with this tutorial, click just the "US LCI" database library in order to access only the US NREL LCI data.



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Figure 5-24: List of Various Available Libraries in SimaPro

If you then click the "Processes" option on the left hand side, you return to the original screen but now SimaPro filters and shows only processes from the selected libraries, as in Figure 5-25. Many of the previously displayed processes are no longer displayed.

Figure 5-25: View of Processes and Data Hierarchy for US-LCI Library in SimaPro



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Now that you have prepared SimaPro to look for the processes in a specific database library, you can browse or search for data.

Browsing for LCI Data Modules in SimaPro

Looking more closely at Figure 5-25, the middle pane of the window shows the categorized hierarchy of data modules (similar to the expandable hierarchy list in the Digital Commons tool). However, these are not the same categories used on the NREL LCA Digital Commons website. Instead, they are the standard categories used in SimaPro for processes in any library. Clicking on the + icon next to any of the categories will expand it and show its subcategories. To find the Electricity, bituminous coal process, expand the Energy category then expand Electricity by fuel, then expand coal, resulting in a screen like Figure 5-26. Several of the other processes burning coal to make electricity and mentioned in the chapter would also be visible.

Figure 5-26: Processes Shown by Expanding Hierarchy of Coal-Sourced Electricity in SimaPro

The bottom pane shows some of the metadata detail for the selected process. By browsing throughout the categories (and collapsing or expanding as needed) and reading the metadata you can find a suitable process for your model. The tutorial will demonstrate how to view or download such data after briefly describing how to search for the same process.



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Searching for a process in SimaPro

Once libraries have been specified as noted above, clicking on the magnifying glass icon in the toolbar brings up the search interface as shown in Figure 5-27. You enter your search term in the top box, and then choose from several search options. If you are just looking for process data (as in this tutorial) then you would want to restrict your choice of where to look for the data to only libraries you have currently chosen (i.e., via the interface in Figure 5-24) rather than all libraries. This will also make your search return results more quickly. Note the default search only looks in the names of processes, not in the metadata (the "all fields" option changes this behavior).

Figure 5-27: Search Interface in SimaPro

Figure 5-28 shows the result of a narrowed search on the word "electricity" in the name of processes only in "Current project and libraries" and sorted by the results column "Name". Since we have already selected only the US LCI database in libraries, the results will not include those from ecoinvent, etc. One of the results is the same Electricity, bituminous coal, at power plant process previously discussed.



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Figure 5-28: Results of Modified Search for Electricity in SimaPro

By clicking "Go to" in the upper right corner of the search results box, SimaPro "goes to" the same place in the drill-down hierarchy as shown in Figure 5-26.

Viewing process data in SimaPro

To view process data, choose a process by clicking on it (e.g., as in Figure 5-26) and then click the View button on the right hand side. This returns the process data and metadata overview shown in Figure 5-29. Similar to the Digital Commons website, the default screen shows high-level summary information for the process. Full information is found in the documentation and system description tabs.

Figure 5-29: Process Data and Metadata Overview in SimaPro



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Clicking on the input-output tab displays the flow data in Figure 5-30, which for this process is now quite familiar. If you need to download this data, you can do so by choosing "Export" in the File menu, and choosing to export as a Microsoft Excel file.

Figure 5-30: View of Process Flow Data (Inputs and Outputs) in SimaPro

Section 3 – Accessing LCI Data Modules in openLCA openLCA is a free LCA modeling environment (available at http://www.openlca.org/) available for Windows, Mac, and Linux operating systems. While installation and configuration can be quite complicated (and is not detailed here), various datasets are available. The tutorial assumes you have access to a working openLCA installation with the US LCI database, and discusses how to find the same US NREL-based LCI data as in Section 1.

After launching openLCA and connecting to your data source you should see a list of all of your databases, as shown in Figure 5-31. If you do not see the search and navigation tabs, you may add them under the "Window menu -> Show views option" to add them. If you have installed the US LCI database, it should be one of the options available.



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Figure 5-31: List of Data Connections in openLCA

Browsing for process data in openLCA

Clicking on the triangle to the left of the folder allows you to open it and see the standard hierarchy of information for all data sources in openLCA, like in Figure 5-32. This is where you could see the process data, types of flows, and units.

Figure 5-32: Hierarchical Organization of Information for openLCA Databases



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If you double click on the "Processes" folder it will display the same sub-hierarchy of processes (not shown here) that we saw in the NREL/Digital Commons website in Section 1. All of the data for unit processes are contained under that folder. If you click on the "Utilities" subcategory folder, then the "Fossil Fuel Electric Power Generation" folder, you will see the Electricity, bituminous coal, at power plant seen above, as shown in Figure 5-33. Several of the other processes burning coal to make electricity and mentioned in the chapter would also be visible.

Figure 5-33: Expanded View of Electricity Processes in Fossil Fuel Generation Category

Searching for a process in openLCA

Instead of using the Navigation tab, a search for process data can be done using the Search tab. Clicking on the search tab brings up the search interface, as shown in Figure 5-34.



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Figure 5-34: Default Search Interface in openLCA

In the first search option, you may search in all databases or narrow the scope of your search to only a single database (e.g., to the US-LCI database). In the second option, you may search all object types, or narrow the scope of your search to just "Processes", etc. Finally, you can enter a search term, such as "electricity". If you choose to search for "electricity" only in your US LCI database (note you may have named it something different), and only in processes, and click search you will be presented with the results as in Figure 5-35. Note that these results have been manually scrolled down to show the same Electricity, bituminous coal, at power plant process previously identified.



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Figure 5-35: Search Results for Electricity in US-LCI Database in OpenLCA

Unlike the other tools, there is no quick and easy way to skim metadata to ensure which process you want to use.

Viewing process data in openLCA

To view process data, choose a process by double-clicking on it from either the browse or search interface. This opens a new pane of the openLCA environment and returns the process data and metadata overview, as shown in Figure 5-36. Similar to the Digital Commons website, the default screen shows high-level summary information for the process (not all of the information is shown in the Figure). Additional information is available in the Inputs/Outputs, Administrative information, other tabs at the bottom of this pane.



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Figure 5-36: Process Data and Metadata Overview in SimaPro

Clicking on the Inputs/Outputs tab displays the flow data in Figure 5-37, which for this process is now quite familiar.

Figure 5-37: View of Process Flow Data (Inputs and Outputs) in openLCA

If you need to download this data, you can do so by choosing "Export" in the File menu, but you cannot export it as a Microsoft Excel file.



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Section 4 – Spreadsheet-based Process Flow Diagram Models Now that process data has been identified, quantitative process flow diagram-based LCI models can be built. Amongst the many tools to build such models, Microsoft Excel is one of the most popular. Excel has many built-in features that are useful for organizing LCI data and calculating results, and is already familiar to most computer users.

To make these examples easy to follow, we repeat the core example from Chapter 5 (and shown in Figure 5-5) involving the production of coal-fired electricity via three unit processes in the US LCI database. The US LCI database is used since it is freely available and indicative of many other databases (e.g., ELCD). To replicate the structure of the core model from Chapter 5, we need to manage our process data in support of our process flow diagram. The following steps illustrate the quantitative structure behind a process-flow diagram based LCI model.

1) Find all required process data

In the first few sections of the advanced material for this chapter, we showed how to find the required process data from the US LCI database via several different tools. Using similar browse and search methods, you can find the LCI data for the other two processes so that you have found US LCI data for these three core processes:

• Electricity, bituminous coal, at power plant

• Bituminous coal, at mine

• Transport, train, diesel powered

Depending on which tool you used to find the US LCI process data, it may be easy to export the input and output flows for the functional unit of each process into Excel. If not, you may need to either copy/paste, or manually enter, the data. Recall that accessing the US LCI data directly from the LCA Digital Commons can yield Microsoft Excel spreadsheet files.

2) Organize the data into separate worksheets

A single Microsoft Excel spreadsheet file can contain many underlying worksheets, as shown in the tabs at the bottom of the spreadsheet window. For each of the downloaded or exported data modules, copy / paste the input/output flows into a separate Microsoft Excel worksheet. If you downloaded the US LCI process data directly from the lcacommons.gov website, the input/output flow information is on the "X-Exchange" worksheet of the downloaded file (the US LCI data in other sources would be formatted in a similar way). The Transport, train, diesel powered process has 1 input and 9 outputs (including the product output), as shown in Figure 5-38.



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Figure 5-38: Display of Extracted flows for Transpor t , t ra in , d i e s e l powered process from US LCI

3) Create a separate "Model" worksheet in the Microsoft Excel file

This Model worksheet will serve as the primary workspace to keep track of the relevant flows for the process flow diagram. This sheet uses cell formulas to reference the flows on the other worksheets that you created from the process LCI datasets.

Beyond just referencing the flows in the other worksheets, the Model worksheet must scale the functional unit-based results as needed based on the process flow diagram. For example, in Equation 5-1, results were combined for 1 kWh of electricity from bituminous coal, 0.46 ton-km of train transportation, and from 0.44 kg of coal mining. Since the process LCI data modules are generally normalized on a basis of a functional unit of 1, we need to multiply these LCI results by 1, 0.46, or 0.44.

Basic LCI Spreadsheet Example

In this example, a basic cell formula is created on the Model worksheet to add the output flows of CO2 from the three separate process worksheets. We first make a summary output result cell for each of the three processes where we multiply the CO2 emissions value from each worksheet (e.g., the rounded value 0.019 in cell G8 of Figure 5-38) by the functional unit scale factor listed above. Then we find the sum of CO2 emissions across the three processes by typing = into an empty cell and then successively clicking on the three scaled process emissions values.

The Chapter 5 folder has a "Simple and Complex LCI Models from US LCI" spreadsheet file following the example as shown in the Chapter (which only tracked

emissions of fossil CO2). Figure 5-39 shows an excerpt of the "Simple Model" worksheet in the file. The same result as shown in the chapter (not rounded off) is visible in cell E8, with the cell formula =B8+C8+D8.



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Figure 5-39: Simple Spreadsheet-Based Process LCI Model

This simple LCI model shows a minimal effort result, such that using a spreadsheet is perhaps overkill. Tracking only CO2 emissions means that we only have to add three scaled values, which could be accomplished by hand or on a calculator. However this spreadsheet motivates the possibility that a slightly more complex spreadsheet could be created that tracks all flows, not just emissions of CO2.

Complex LCI Spreadsheet Example

Beyond the assumptions made in the simple model above, in LCA we often are concerned with many (or all) potential flows through our product system. Using the same underlying worksheets from the simple spreadsheet example, we can track flows of all of the outputs listed in the various process LCI data modules (or across all potential environmental flows). This not only allows us a more complete representation of flows, but better prepares us for next steps such as impact assessment.

In this complex example, we use the same three underlying input/output flow worksheets, but our Model worksheet more comprehensively organizes and calculates all tracked flows from within a dataset. Instead of creating cell formulas to sums flows for each output (e.g., CO2) by clicking on individual cells in other worksheets, we can use some of Excel's other built-in functions to pull data from all listed flows of the unit processes into the summary Model worksheet. An example file is provided, but the remaining text in this section describes in a bit more detail how to use Excel's SUMPRODUCT function for this task.

The SUMPRODUCT function in Microsoft Excel, named as such because it finds the sum of a series of multiplied values, is typically used as a built-in way of finding a weighted average. Each component of the function is multiplied together. For example, instead of the method shown in the Simple LCI spreadsheet above, we could have copied the CO2



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emissions values from the three underlying worksheets into the row of cells B8 through D8, and then used the function =SUMPRODUCT(B4:D4*B8:D8) to generate the same result.

The "Simple and Complex LCI Models" file has a worksheet "Simple Model (with SUMPRODUCT)" showing this example in cell E8, yielding the same result as above.

However the SUMPRODUCT function can be more generally useful, because of how Excel manages TRUE and FALSE values and the fact that the "terms" of SUMPRODUCT are multiplied together. In Excel, TRUE is represented as 1 and FALSE is represented as 0 (they are Booleans). So if we have "terms" in the SUMPRODUCT that become 1 or 0, we can use SUMPRODUCT to only yield results when all expressions are TRUE, else return 0. This is like achieving the mathematical equivalent of if-then statements on a range of cells.

The magic of this SUMPRODUCT function for our LCI purposes is that if we have a master list of all possible flows, compartments, and sub-compartments, we can find whether flow values exist for any or all of them. On the US LCI Digital Commons website, a text file can be downloaded with all of the nearly 3,000 unique compartment flows present in the US LCI database. This master list of flows can be pasted into a Model worksheet and then used to "look up" whether numerical quantities exist for any of them.

A representative cell value in the complex Model worksheet, which has similar cell formulas in the 3,000 rows of unique flows, looks like this (where cells A9, B9, and C9 are the flow, compartment, and subcompartment values we are trying to match in the process data):

=E$4*SUMPRODUCT((Electricity_Bitum_Coal_Short!$A$14:$A$65=A9)*(Electricity_Bitum_Coal_Short!$C$14:$C$65=B9)*(Electricity_Bitum_Coal_Short!$D$14:$D$65=C9)*Electricity_Bitum_Coal_Short!$G$14:$G$65)

This cell formula multiplies the functional unit scale factor in cell E4 by the SUMPRODUCT value of:

• whether the flow name, compartment, and subcompartment in the unit flows for the coal-fired electricity process match every item in the master list of flows.

• and, if the flow/compartment/subcompartment values match, the inventory value for the matched flow.

Within the SUMPRODUCT, if the flow/compartment/subcompartment in the unit process data doesn't match the flow/compartment/subcompartment on the row of the Model worksheet, the Boolean values are all 0's and the result is 0. If they all match, the Boolean results are 1, and the final part of the SUMPRODUCT expression (the actual flow quantity) is returned.



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Figure 5-40: Complex Spreadsheet-Based Process LCI Model

The Chapter 5 folder on the textbook website has spreadsheets with all of the flows and processes in the US LCI database, as downloaded from the LCA Digital Commons website.

The 'Simple and Complex LCI Models' file has a worksheet 'Complex Model' that shows how to use the SUMPRODUCT function to track all 3,000 flows present in the

US LCI database (from the flow file above). Of course the results are generally zero for each flow due to data gaps, but this example model expresses how to broadly track all possible flows. You should be able to follow how this spreadsheet was made and, if needed, add additional processes to this spreadsheet model.

Homework Questions for this Section

1. Answer Question 2 from the end of Chapter 5 by using the 'Simple and Complex LCI Models' spreadsheet introduced in this section.

LCA Book - Chapter 5

Documents

Transcript of LCA Book - Chapter 5