The definition of goal and scope is essential to identify the impact areas that LCA focuses on, including the necessary assumptions and omissions of the assessment. The goal and scope definition incorporates three areas: the context of the study, the functional unit chosen as the basis of the assessment, and the system boundaries of the assessment. In the following sections we will examine the selected LCA studies on the basis of this framework.
The context within which a study is conducted plays an important role in the decision-making process for assumptions and omissions made in the analysis (Wenzel 1998). Organization affiliations and the intended purpose of the studies are two important aspects of context. Organizational affiliation is represented by the practitioner type (discussed above) and the data sources, which refers to the organizations that supplied the primary author with the product-related data. In many cases this source was the manufacturer of the product analyzed in the study. If a class of products was identified, then alternative sources of data are identified instead (e.g., Stobbe 2007). The intended purpose categories, defined above, are repeated here: External Marketing, Design, Policy, and Calculators. The LCA type/goal is a high-level summary of the purpose of the study from the perspective of the original authors. These aspects of context are summarized for each study in table 1.
Functional Unit and Assumptions
The functional unit defines the output by which products will be compared, and to which all of the analysis parameters are therefore normalized (Reap et al. 2008a). ISO defines the functional unit as the “quantified performance of a product system for use as a reference unit” (ISO 2006b, 4), and as “necessary to ensure comparability of LCA results” (ISO 2006a, 12). Defining a functional unit for imaging equipment is complex due to the multitude of functions that a particular product can perform for a consumer. For example, multifunctional devices (MFDs) combine scanning, faxing, copying, and printing into one machine. Even in the case of a single-function device (printing), the range of uses for the device can vary widely based on the purpose of the printed output and the postprinting operations required; this variation will have an impact on the definition of the functional unit. In addition, factors that affect the purchase decision, such as aesthetics or size, must also be accounted for when defining the functional unit.
A short description of the functional unit defined in each study is presented in table 2. In the studies reviewed, the functional units took the following forms: pages printed in a fixed time period, a specified print job, a volume of material, and a unit of information. This last unit enables comparison with communication media other than print.
Table 2. Functional unit and print characteristic assumptions
| Study || Functional unit || Print characteristics assumptions |
| Print speed (ppm)(1) || Total print volume (pages)(2) || Monthly print volume (pages/month) || Average page coverage(3) || Time period(4) |
|||Pages/month over useful period||50||1,200,000||25,000||5%–6%||4 years|
|||Pages by coverage for time period||17||30,000||2,500||5%||1 year|
|||100 one-sided pages(5)||25||100||N/A||N/A||N/A|
|||One image printed||UI||UI||N/A||N/A||UI|
|||Unit of information(6)||UI||10,000,000||N/A||5%–6%||UI|
|||5 functional years||N/A||N/A||N/A||N/A||N/A|
|||Four photocopier life cycles(7)||100(8), 65(9)||12,000,000||100,000||N/A||10-year maximum|
|||1 metric ton of toner||135||22,000,000||611,111||6%||3+ years|
|||21.6 tons of printer waste(10)||N/A||N/A||N/A||N/A||N/A|
|||Average daily use pattern (pages/job)(11)||N/A||N/A||N/A||N/A||N/A|
|||Variable; pages/year and printer life(11)||UI||UI [10,000](12)||UI (12)||N/A||UI [5 years](12)|
In addition to considering the functional unit, it is also important to understand the useful life of the imaging device in order to allocate reference flows on the basis of the functional unit. However, there can be variability in what is assumed to be the useful life of a device. It depends not only on when the machine is expected to go into disrepair, but also when a machine is expected to be replaced due to advances in technology (instead of loss of functionality). Factors such as changes in the market expectations for the products, modularity, and serviceability all further complicate estimation of the product's useful life. Within the surveyed literature, the projected useful life of imaging equipment ranged from two to eight years. This range is somewhat consistent with Silva and colleagues, who state that “it is generally known that the maximum number of functional years for a printer is set at 5 years” (2006, 4). An alternative to defining the life expectancy in useful years is to define it in terms of the number of pages or images1 printed during the life of the device.
Table 2 also provides key printer and usage characteristics used in the studies, where available. Printer speed and the time period (a measure of life expectancy) are characteristics related to the imaging device. The monthly print volume (measured in pages) and average page coverage (a measure of the fraction of the page that is covered with marking materials) are characteristics determined by user. For the cases where usage assumptions are documented, it is clear that standard industry averages were used. The use of 5% to 6% average coverage is consistent with the standard assumption that is used to determine cartridge yield (ASTM 2011). Figure 1 shows the relationship between printer speed and the monthly print volume. When compared to the Energy Star Qualified Imaging Equipment Typical Electricity Consumption (TEC) test procedure (U.S. EPA 2007), it is evident that a standardized approach is being used across the studies to derive the monthly print volume usage assumptions. This standardized approach, however, does not capture the wide variation that occurs in actual use. In the case of print, where the impacts are dominated by paper and consumable use (Ebner et al. 2009), this variability will have a significant effect on the impact calculations (figure 1).
Figure 1. Required paper volumes of Energy Star Imaging Equipment Typical Electricity Consumption (TEC) procedure compared with study assumptions (12 to 100 pages per minute). TEC images/month has been calculated using the TEC procedure to determine the assumed pages per month for printers with a speed of 12 to 100 pages per minute.
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Life Cycle Inventory
Most of the studies examined, including the design decision tools, consider inputs from all stages of the product life cycle. This does not mean, however, that all inputs from each stage are accounted for. In addition, the depth to which the environmental impacts for these inputs are accounted for also varies. In general, two sources of preventable data quality issues typically occur: those due to data gaps and those due to the use of proxy or generic data (Reap et al. 2008b). When an LCA is performed, practitioners often note difficulty in obtaining accurate data. In fact, five studies specifically note that this difficulty impacted their use of LCAs (Berglind and Eriksson 2002; Ebner et al. 2009; Ord et al. 2009; Silva et al. 2006; Stobbe 2007). Many of the studies included sensitivity analyses that used different assumptions for uncertain parameters, such as recycling rates. Unless specifically stated, it is hard to discern why certain data have been omitted.
To get a better idea of data inclusiveness and data quality, we used a grading system of A to E, similar to that used in Boguski (2010), to evaluate the level of detail at which each life cycle stage was explored. An “A” meant that the primary data were measured on site during the phase in question and all relevant aspects seem to have been accounted for. A “B” was given when the data were taken from a database or referenced literature. A “B” may also be missing part of a process. “C” indicates incomplete data or estimates, but the data are still representative of some impacts in this stage. “D” indicates that the data were not included in study scope, and “E” means that the stage was excluded due to a lack of applicability to study goals. The results of this grading effort are shown in table 3.
Table 3. Ratings of data quality
| Study || Marketing(1) || Design Tools(2) || Policy(3) || Calculators(4) |
|  ||  ||  ||  ||  ||  ||  ||  ||  ||  ||  ||  |
| Stage |
| Raw materials||B||B||B||B||C||B||C||B||C||C||D||C|
| End of life||B||B||B||C||B||B||B||B||B||B||D||C|
Transportation and packaging were lacking high-quality data across the studies evaluated here and are ignored in many of them. Raw materials acquisition is missing in the greatest number of studies, likely because the practitioners faced difficulties in obtaining upstream data. When it was included, the typical approach to raw materials acquisition and component manufacture by suppliers is to retrieve database impact attributes based on masses from a bill of materials obtained by disassembling the product. These product component masses are not preferable, especially for electronics with semiconductors, as input materials can have a mass that is much greater than that of the final product (Williams et al. 2002). It is also difficult for practitioners to determine adequate upstream cutoffs, as many times there are unknown processes involved in the production of component materials. Surprisingly, considering the difficulty in its accurate estimation, the end-of-life stage was the most populated. This is partly due to the focus of design tools on reuse and recyclability. Given the difficulty in estimating actual end-of-life practices, none of the examined studies could be scored an “A.”
The raw materials acquisition and manufacturing are two life cycle stages that have relatively high impacts in atmospheric and waterborne emissions (U.S. EPA 1993). However, in the studies reviewed here, these two stages are sparsely populated. Many LCAs are criticized for uncertainties or inaccuracies surrounding impacts from component manufacture processes, as often the materials used can be identified but the exact processes used cannot. One of the studies specifically stated that the “greatest source of error is the lack of data on component manufacturing and assemblage of the cartridge” (Berglind and Eriksson 2002, 2). Again, this supports a need for greater dissemination of upstream data in the supply chain. The two studies that were missing this stage were either focused on end of life (Mayers et al. 2005) or simply omitted it because other aspects were thought to have greater impact (Silva et al. 2006).
In nearly all of the studies examined, the electricity and paper used during the use stage were said to have had the most significant impacts for the imaging device. Both of the impacts are greatly influenced by the actual behavior of the user. For example, the extent to which power-saving settings on imaging devices are used will affect energy consumption. Similarly, the use of double-sided printing and print preview functions will impact actual paper consumption rates. This highlights the importance of the use stage, and the need for accurate and representative user characteristics with respect to device settings and habits in order to properly assess the environmental impacts.
End of life is another stage of the life cycle where large discrepancies exist in LCA practices, and the print industry is no exception. A major contributor is that waste management differs by locality, and not all options can be taken into account, therefore the use of different assumptions of waste management types for a given product can lead to different assessment results (Shen and Patel 2008). Remanufacturing, recycling, and reuse of equipment and consumables are other areas of debate for the printing industry because the benefits of these practices can be greatly influenced by the underlying assumptions of the analyses. All of the design tools examined in this work have included remanufacturing in some form in their analyses.
There were some sources of data that were used across several studies. The Energy Star standard (U.S. EPA 2007) and database are used frequently in these studies for several reasons. Having a set procedure (e.g., TEC) is useful for a program like Energy Star to standardize usage assumptions for classes of products. Likewise, such a standard procedure is useful when measuring energy use for LCAs. This standard procedure is also appealing because it is specific to imaging equipment. Many products also seek certification, so products being studied by an LCA may have already had TEC measured, making it an efficient choice for manufacturers. If a study is conducting policy analysis, the Energy Star database is attractive because it contains data for a large group of manufacturers, and all had to follow the standardized TEC procedure. The Intergovernmental Panel on Climate Change (IPCC 2006) is also cited frequently for the standard treatment of 100-year global warming potential (GWP). All but two of the studies determined impacts for GWP, and this is one of the few impact categories with a clear set of guidelines.