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 geographical 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, geographical 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 geographical 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
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.