Site-Dependent Life-Cycle Analysis by the SAME ... - ACS Publications

Aug 11, 2005 - Research Center for Material Cycles and Waste Management, and Endocrine Disruptors and Dioxin Research Project,. National Institute for...
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Environ. Sci. Technol. 2005, 39, 7318-7328

Site-Dependent Life-Cycle Analysis by the SAME Approach: Its Concept, Usefulness, and Application to the Calculation of Embodied Impact Intensity by Means of an Input-Output Analysis K E I S U K E N A N S A I , * ,† YUICHI MORIGUCHI,† AND NORIYUKI SUZUKI‡ Research Center for Material Cycles and Waste Management, and Endocrine Disruptors and Dioxin Research Project, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan

This paper describes a practical approach to sitedependent life-cycle analysis (SDLCA) that differentiates sitedependent environmental impacts from a system’s processes by considering the geographical conditions of each process. This approach converts an environmental output into its impacts by using site-dependent characterization factors (SDCFs). This approach defines an areasthe Spatial Area of iMpact Equivalency (SAME)swithin the boundaries of the geographical system during site-dependent life-cycle inventory (SDLCI) analysis and calculates an environmental output from a process for the SAMEs. Each SAME represents a collection of geographical areas with internally homogeneous environmental impacts and can be mapped using a geographic information system. Preparing a SDLCI and SDCFs based on SAMEs facilitates the implementation of SDLCA by permitting the use of fewer regions during SDLCI. To demonstrate application of the SAME approach, an embodied impact intensity was formulated; it quantifies the impact directly and indirectly on the basis of the unit activity of a sector by means of inputoutput analysis with SDCFs. The validity of using SAMEs for SDLCA is demonstrated through two case studies: one studying suspended particulate matter, and one studying benzene. In both cases, the impact intensities are calculated using the SAME approach and the results are compared with those of site-generic LCI.

1. Introduction Life-cycle thinking has been endorsed in environmental evaluations of goods and services and has become increasingly essential in sustainability studies, especially those related to consumption and production (1). The approach first began to be incorporated in such analyses in a comparison study of beverage containers in the late 1960s (2) and in the field of energy analysis in the 1970s (e.g., 3-5). Currently, the “cradle to grave” analysis (life-cycle analysis) * Corresponding author phone: +81 29-850-2889; fax: +81 29850-2917; e-mail: [email protected]. † Research Center for Material Cycles and Waste Management. ‡ Endocrine Disruptors and Dioxin Research Project. 7318

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that originated in these studies is widely used to measure environmental performance, not only of an industrial product but also of a service, technology, or regional activity. This analysis often employs the basic framework of life-cycle assessment (LCA)-an environmental assessment tool for comprehensive evaluation of the environmental impact of a product system throughout its life cycle. The LCA framework requires the practitioner to define functions and a system boundary for the product system, perform a life-cycle inventory (LCI) analysis, and perform a life-cycle impact assessment (LCIA). LCI accounts for the environmental emissions and consumption of materials throughout the life cycle of the system, whereas LCIA quantifies the environmental impacts caused by the emissions and consumptions inventoried during the LCI phase. 1.1. Need for Quality Improvement in Life-Cycle Analysis. The various applications of life-cycle analysis can work effectively in the verification of the environmental effects of substitutes for a product, technology, or system. Hence, lifecycle analysis will be helpful for quantifying the expected environmental impact of the introduction of a new technology, production system, or lifestyle. When the environmental effects of such substitutes are evaluated, it is important to define comprehensive and consistent system boundaries for the targets that will be compared (the target to be substituted and its substitute). The comprehensiveness of the system boundary depends on the choice of LCI methods. For instance, Suh and Huppes (6) summarized the characteristics of six LCI methods and described the properties of the system boundary in each method. Two of these LCI types are based on process analysis, one is based on input-output analysis, and three are hybrid LCIs that combine these two methodologies. Although the most suitable LCI method depends on the conditions of the analysis, Suh and Huppes provided some guidelines for choosing the best approach in different situations and concluded that the integrated hybrid analysis of the hybrid LCI is the best approach in terms of the quality of the LCI results. Furthermore, where there are strict constraints on the time and resources available for compilation of the LCI, they proposed a stepwise approach that begins with the tiered hybrid analysis (e.g. 7) and then gradually extends the tiered hybrid analysis to the integrated hybrid analysis, with identifying the main problems in the analysis. The stepwise approach could improve the accuracy of the LCI results cost-effectively. This improvement in LCI quality is important, especially where the best choice among the targets being compared can be decided on the basis of the quantity of environmental outputs from the targets, such as the amount of carbon dioxide emission. In quantifying the relative environmental merits of the available substitutes, it is essential to examine whether a substitute (a new product or technology) that reduces one environmental impact adversely increases other environmental impacts. For example, a recycling technology may reduce the final disposal of waste, but the water consumed to clean the used products may have a significant impact on the water environment. Taking into account the diverse environmental burdens in this process of comparing alternatives, it is noted that there are cases where a life-cycle analysis can weigh the relative merits of the alternatives on the basis of the environmental impacts of an output rather than solely on the quantity of the output. Sometimes, the analysis must also consider an output for which the magnitude of the impact is high for sites with a specific set of geographical factors. From this point of view, the manner in which a life-cycle analysis treats this site-dependency of 10.1021/es047951n CCC: $30.25

 2005 American Chemical Society Published on Web 08/11/2005

FIGURE 1. Schema of the calculation steps used in the best practices approach to SDLCA, in which environmental outputs are estimated for each process and site and the results are converted into impacts by means of environmental models and statistical information. (This example of LCI is based on a process analysis with matrix representation.) impacts is clearly important. The site-dependency of impacts particularly becomes an issue for air, water, and chemical pollutants. For such outputs, consider the examples of products A and B, where, XA and XB express the respective total emissions of these products obtained by means of LCI, and YA and YB show their respective environmental impacts. Even when XA > XB, it is not necessarily true that YA > YB. For an LCA to appropriately evaluate such substances and not produce a misleading recommendation for the best alternatives or incorrect decision-making based on the analysis, the quality of the LCA must be further improved in terms of defining both the system boundaries and how to include the geographical factors that implicitly exist within those boundaries in the LCI. This relates strongly to whether the LCIA can adequately reflect the geographical characteristics of a given product system in terms of the consequences identified by the LCIA. In other words, the LCA approach requires new methods that can appropriately account for the site-dependent environmental impacts of processes within the product system by accounting for the geographical conditions specific to each process. For example, even if motor transportation in a populous area and that in an almost empty area discharge equal quantities of particulates, the former situation engenders a greater impact on human life than the latter because of the differences in a crucial geographical factor: population density. Suitable new methods must be able to identify this difference. This paper calls this type of LCA “site-dependent life-cycle analysis” (SDLCA). Hereafter, for consistency with this terminology, the LCI applicable to SDLCA is described as a site-dependent LCI (SDLCI) and the LCIA based on SDLCI as a site-dependent LCIA (SDLCIA). 1.2. “Best Practices” and “Practical” Approaches to SDLCA. This paper illustrates two typical approaches to SDLCA: the “best practices” approach and the “practical” approach. An example of LCI based on process analysis with a matrix representation (e.g., 6) is used to discuss differences in the methodology and characteristics of the two approaches. 1.2.1. Best Practices Approach. Figure 1 illustrates a schema for a best practices approach that represents the most precise SDLCA method. In addition to performing a conventional LCI based on process analysis, the best practices approach starts by compiling a technological inventory matrix, A ˜ |a˜ij|, that expresses the technological input-output relationships between commodities and processes for the target. Element a˜ij shows the input or output of good or service i of process j per unit activity of that process, and the functional unit

vector of the product system, k˜ |k˜ i|, shows the functional unit of the target. The next step is the estimation of e*sj, which is the amount of direct environmental output, or pollutant or natural resource emitted or consumed per unit activity of process j at site s, where that process occurs, and then the generation of a quantity-based environmental intervention matrix, E*|e*sj| (Step 1). In general, A ˜ |a˜ij| represents the averaged technologies (processes) for industries or companies with identical technology, so that, in practice, one or more production sites would exist for a given process. If a practitioner is concerned about site-specific differences in the input-output structure for process j, it would be suitable to extend A ˜ |a˜ij| by breaking down process j among its related sites rather than compiling the environmental intervention matrix for all sites considered. Subsequently, environmental models and statistical information are used to convert e*sj into its impacts, h*sj, which are used to prepare an impactbased environmental inventory, H*|h*sj| (Step 2). The next step is to determine the total direct environmental impact of process j, h*j, by summing h*sj over j to obtain the row vector h|h*j| (Step 3). Finally, using the equation h** ) h*A ˜ -1k˜ , the total direct and indirect impact of the evaluation target, h**, is calculated (Step 4). The best practices approach makes it possible to calculate the impact of each process in a product system in a way that ultimately reflects the geographical characteristics of each process; the result is a high-quality SDLCA that adequately considers the site-dependency of the impacts. However, this approach obviously requires a large amount of time, effort, and money to perform the SDLCI and SDLCIA. Consider two ways to construct the environmental intervention matrix: a bottom-up method and a top-down (allocation) method. The former method estimates e*sj individually, so that it can obtain highly accurate SDLCI results. Environmental outputs from a company’s own processes could be estimated in this way, but the application of this approach to all processes is impossibly elaborate work. Of course, it would be possible to investigate e*sj outside a company’s own processes by means of a questionnaire distributed to related companies in the same sector, but this might be ineffective because such information is often considered confidential by companies. In contrast, the top-down method estimates e*sj by allocating the total environmental output of process j among sites by using a surrogate value for e*sj, such as the production output, energy consumption, or working hours for each site. This approach can be used when it is not possible to obtain e*sj directly, owing to limitations of available data, and when VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Schema of the calculation steps used in the practical approach to SDLCA, which uses site-dependent characterization factors (a sample of LCI based on process analysis with matrix representation). it is possible to obtain only data on the total environmental output of process j, the locations of sites where this process occurs, and some surrogate data that can be used in place of e*sj. Some total environmental outputs can be obtained from various available databases, but the preparation of suitable surrogate data for every process remains difficult. On the other hand, the best practices approach has difficulties in feasibility of SDLCIA, for instance an SDLCA practitioner must have a profound understanding of impact assessment methods, including how to use environmental models. Without sufficient environmental data for a process and userfriendly supporting software and other tools for impact assessment, the best practices approach may be too difficult for most SDLCA practitioners. 1.2.2. Practical Approach. Figure 2 illustrates the schema for a simplified alternative approach that converts the quantity of environmental outputs into the corresponding impacts. This practical approach uses a coefficient called a site-dependent characterization factor (SDCF) for the conversion. First, an SDLCA practitioner collects SDCFs, denoted by tr, which represent the environmental impact per unit output (or consumption) for region r (Step 1). Here, it is important that the regions defined for the collected SDCFs conceptually cover the actual geographical boundaries of the product system being analyzed (see Figure S-1 in the Supporting Information (SI)). For example, when the geographical boundary of the product system is an entire country, regions r ) 1 to L must cover that country. Second, as in the best practices approach, the practical approach compiles a technological inventory matrix A ˜ |a˜ij| and a functional unit vector k˜ |k˜ i| for the system and establishes a quantity-based environmental intervention matrix, E′|e′rj|, in which the environmental output from process j, e′rj, is estimated for region r characterized by each SDCF (Step 2). Next, multiplying e′rj by tr yields h′rj, the impact from process j in region r, and the h′rj values can be used to construct an impactbased environmental inventory, H′|h′rj| (Step 3). Summing up the h′rj values for each process produces vector h′|h′j|, in which h′j represents the direct total impact of process j as a function of the geographical characteristics of that process (Step 4). Finally, calculating h** ) h′A ˜ -1k˜ gives the total direct and indirect environmental impact (Step 5). The number of regions (r) in the process is smaller than the number of sites (s) in the best practices approach, therefore the number of elements of matrix E′ is naturally smaller than that of E*. That is, both the bottom-up and top-down methods can reduce the number of entries required to construct an environmental intervention matrix. For 7320

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example, even if the bottom-up method of investigating e′rj were conducted individually based on interviewing the companies that performed process j, it would not be necessary to obtain detailed location data (addresses) for the process; it would be necessary only to know the region (r) to which it belongs. Collection of this type of information would be far easier than if it were necessary to do so for each site where the process occurs. Similarly, in the top-down method, the practical approach would mitigate the burden of preparing the surrogate data for allocation, because the quantity of surrogate data to be prepared is less than that in the best practices approach. In addition, although selecting SDCFs and collecting the necessary data still requires a significant amount of time, it is very simple to convert outputs into impacts, and the practitioner does not require detailed knowledge about the impact calculation. However, with respect to the accuracy with which the practical approach reflects geographical conditions, this method is inferior to the best practices approach, and much of its feasibility depends on the availability of databases for each SDCF. If the practitioner itself must first calculate required SDCFs, even the practical approach may become prohibitively difficult. Of course, it will be possible to define a characterization factor for process j (CFj) and convert the total output (ej) of that process (j) into an impact by multiplying ej × CFj. Strictly speaking, even if the process has the same name, its geographical conditions may influence the nature of the target; therefore, compilation of a database for any CFj may be difficult. If representative geographical properties were assumed for process j, it would be possible to prepare a CFj database, but such CFj values are not necessarily closely associated with the geographical properties of process j for the given target. This inconsistency may lead to an inappropriate outcome of the SDLCA. 1.3. Aim of the Present Study: A Higher-Quality Practical Approach to SDLCA. In the practical approach, SDLCI calculates environmental outputs for j × r situations. This certainly depends on data availability, but, in general, it can be seen that as the number of regions (r) decreases, preparing SDCFs and implementing SDLCI will become easier. SDCFs developed for countries or administrative regions can cover the geographical system boundary of the study system, and some previous studies have calculated SDCFs for these geographical units (e.g., 8-12). However, consider the example of a SDLCA case study in which the geographical system boundary is the whole of Japan, using SDCFs developed for each administrative region; in this example,

it is still necessary to estimate environmental outputs for at least 47 prefectures (the number in Japan) for each process. Furthermore, it is controversial whether building an SDCF based on such administrative regions is the best approach to ensure that the resulting SDLCIA differentiates among the magnitudes of each process’s impact based on the geographical properties that govern the process; that is, it is unclear whether the administrative regions are sufficient geographical areas to account for variations of site-dependent impacts. In light of this background, the objective of the present study was to develop a new approach to SDLCI that would reduce the time and labor requirements of the practical approach to SDLCA and to improve the quality of the analysis by reflecting the geographical characteristics of each process and quantifying the site-dependent impact of each process. The proposed approach allows the user to evaluate the different site-dependent impacts of each process by means of SDLCIA using fewer SDCFs, whose implicit regions cover the entire geographical boundary of the studied system. As a result, the approach requires the user to identify only a few regions during compilation of the environmental intervention matrix for the SDLCI. First, the principles behind this approach (how to define region r during the SDLCI phase) are explained, and then the technical details for carrying out SDLCI using this approach and the usefulness of the approach are described. Second, the approach is applied to calculation of the embodied impact intensities based on an input-output analysis. Next, the validity of this approach to SDLCI is analyzed through case studies using the embodied impact intensities. To conclude, the prospects for additional development of the proposed approach are discussed.

2. Materials and Methods 2.1. The SAME Concept: A Novel Definition of Regions. The key factor in successfully applying the practical approach to SDLCA is adequate specification of the SDCFs. It is no exaggeration to say that the design of an SDLCI depends entirely on this step. This paper describes a new SDLCI methodology and also describes the specifications for SDCFs that should be compiled in databases in the future. In terms of the practical approach, our concerns are how to avoid generating an impractically large number of regions while still effectively reflecting regional differences in the impacts generated by each process during SDLCIA. As a result, this study focuses on how to define regions during SDLCI. By specifically considering what kinds of geographical factors can lead to different impacts, a novel concept for defining regions was defined: a “Spatial Area of iMpact Equivalency” (SAME). The SAME schema posits that the geographical area defined as a SAME demarcates a zone with homogeneous environmental impacts. A SAME is thus defined on the basis of the main geographical characteristics that determine the magnitude of the environmental impact, rather than solely on the physical (spatial) boundaries of a single physical region. 2.2. Examples of a Typical SAME Definition. In the definition of a SAME, it has been assumed that the geographical characteristics that an SDLCA practitioner must focus on depend on the objectives of the SDLCAsthat is, on the type of environmental impact that the practitioner is attempting to understand by means of SDLCA. Accordingly, it is impossible to list all possible SAME definitions in this paper. Instead, some simple examples have been chosen to elucidate the SAME concept. Krewitt et al. (10) provided a good clue for the choice of such characteristics in terms of the magnitude of the impact. Their study differentiated four major categories that accounted for the properties of pollutants and their dose-

effect relationships. They defined the sensitivities of these four categories toward three groups of site-dependent parameters (the background pollutant emission level; the distribution of receptors, such as population, that are sensitive to the emission source; and the relevant meteorological conditions). Similarly, our study worked to choose sitedependent parameters that focused on SDLCI from three perspectives related to emission and consumption sites: (1) geographical conditions that contribute to the behavior of pollutants; (2) conditions associated with the background level and availability of resources; and (3) conditions involving the existence of environmental impact receptors. It is significant for the definition of a SAME to consider these locational characteristics in addition to the relevant type of environmental impact. Here, for the sake of simplicity in the impact examples, only six types of impact categories related to the site-dependency of impacts are considered from among the baseline impact categories (13): depletion of abiotic resources, human toxicity (health), ecotoxicity, photooxidant formation, acidification, and eutrophication. Table 1 shows some simple examples of SAME definitions regarded as their typical combinations. Clearly, more types of impact category can be developed to support the unique characteristics of other LCIA exercises. Focusing on the geographical conditions related to pollutant behavior and the human toxicity (health) impact, for instance, it is clear that the environmental impacts of ground-level and nonground-level emissions of an air pollutant will have different magnitudes, and as a result, it would be possible to use two SAME definitions during SDLCI: one SAME would represent the geographical area in which the pollutant is emitted at ground level, and the other would represent the geographical area in which the pollutant is emitted above ground level. The behavior of photochemical pollutants as volatile organic compounds is changed by ambient conditions such as temperature, humidity, and the degree of illumination by ultraviolet light. Photochemical smog is a typical phenomenon that falls under the impact category of photooxidant formation. Accordingly, one SAME could be defined as a geographical area in which a pollutant is emitted at high ambient temperatures, whereas another could be defined as a geographical area in which the pollutant is emitted at low ambient temperatures. On the other hand, discharging water pollutants into closed waters would engender a greater impact on the water environment than a comparable emission into open waters, particularly in terms of eutrophication. Demarcation of two different SAMEs for these disparate impacts illustrates the effectiveness of these definitions of SAMEs. The magnitude of the impact of consuming natural resources such as water, land, and various materials differs in characteristics of locations where resources are consumed or mined. The difference arises in the impact evaluation for the depletion of abiotic resources. Consumption from a resource-rich area and a resource-poor area can differ quantitatively in their environmental impacts. Consequently, a typical combination of SAMEs for this situation might lead the practitioner to define geographical areas in which water is consumed (respectively) from basins with high precipitation and low precipitation. From the viewpoints of the background level and availability of resources, differences in the background level of pollutants will affect the impact quantification of a pollutant, particularly one that has a nonlinear dose-response relationship that indicates a threshold. In particular, when a human toxicity (health) impact is accounted for, a geographical area in which a pollutant is released at a high background level and one in which the pollutant is released at a low background level could be identified as discrete SAMEs. VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Examples of Typical Combinations of SAME Definitions perspective of geographical conditions pollutant behavior

site-dependent impact categorya 2 2 2 4 6

background level and availability of resources

1

2

2 2, 3

impact receptor

2 2, 3, 5, 6 3

a

typical environmental output air pollutants

definition of SAME1

definition of SAME2

area where the pollutant is emitted at ground level area where the pollutant is emitted indoors

area where the pollutant is emitted above ground level air pollutants area where the pollutant is emitted outdoors soil pollutants area where the pollutant is released area where the pollutant is released on arable land on nonarable land photochemical high-temperature area/high-humidity area/ low-temperature area/ pollutants high-UV area low-humidity area/low-UV area water pollutants area where the pollutant is discharged area where the pollutant is discharged into a river that connects to open waters into a river that connects to closed waters resource resource-rich area (high-precipitation basins) resource-poor area consumption (low-precipitation basins) (e.g., water) pollutants: area with a high background level threshold of the pollutant dose-response effect noise area with a high background level of noise pollutants for which environmental standards exist air pollutants (short-lived) air pollutants (long-lived)

area with a low background level of the pollutant

area close to the limit of the criteria

area with a low background level of noise area well below the limit of the criteria

area with high population density

area with low population density

area where most of the pollutant is transported to areas in which many impact receptors exist water pollutants area where the pollutant is released upstream of the habitat of a certain creature in a river

area where most of the pollutant is transported to areas in which few impact receptors exist area where the pollutant is released downstream of the habitat of a certain creature in a river

1, Depletion of abiotic resources; 2, human toxicity (health); 3, ecotoxicity; 4, photooxidant formation; 5, acidification; 6, eutrophication.

Finally, the condition of the impact receptor (be it human, animal, or a protective zone) should be reflected in the estimation of environmental impact. The number of impact receptors becomes a particularly important factor. Geographic areas with high and low population densities would thus be different SAMEs. These could relate to the human toxicity (health) impact of short-lived air pollutants. When the transport of a pollutant is relevant, it is desirable to define a SAME that accounts for the relationship between the emission area of the pollutant and the condition of the impact receptors at the deposition area. It may be difficult to identify this relationship for all areas, but in those cases, it is possible to assume protection zones in which there are many impact receptors to be protected, such as fragile forested areas, precious lakes, or cities with large populations. In these cases, meteorological or environmental fate models experts can calculate an average trajectory of pollutant transport to the protection zones and to empirically determine which emission sources are the main contributors to the impact in such zones. Areas where most of the pollutant emitted in an area is transported to the protection zones can be defined as one SAME, and other emission areas can be defined as another SAME. By focusing on the key conditions that cause the maximum and minimum impacts, it is possible also to demarcate SAMEs that have multiple geographical characteristics. For instance, the geographical boundary of the system could be divided into three SAMEs: SAME1 could be defined as an area where a pollutant is emitted at ground level in an area with high population density, whereas SAME2 is defined as an area where the pollutant is emitted above ground level in an area with low population density, and SAME3 is defined as any areas not already included in SAME1 or SAME2. This approach to SAME definition can minimize the number of SAMEs required to define the geographical system boundary 7322

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during SDLCI and can effectively account for geographical differences in the impacts. 2.3. Visualizing SAMEs by Means of GIS. An SDLCI estimates the environmental output from each process for the defined SAMEs; thus we must know the actual boundaries of each SAME. However, it is more difficult to imagine the SAME that represents an arbitrary area that possesses certain geographical characteristics than it is to imagine the boundaries of administrative regions. Being able to visualize the actual shapes of SAMEs in the form of a map greatly facilitates understanding of their significance. In the present study, a geographical information system (GIS) was used to display the extents of SAMEs in the form of a map. Most GIS software allows users to display an arbitrary area on a map using three geometries: polygonal, linear, and point. Using this function of the GIS enables identification of the actual shapes of SAMEs that demarcate collective areas with defined geographical properties. In this paper, the geometric shape of the SAME that is displayed by the GIS software is referred to as “SAME geometry.” An SDLCI practitioner can calculate emission, e′rj, from process j to SAME r, while understanding the meaning of the SAME based on its SAME geometry (see Figure S-2 in the SI). Moreover, other GIS functions remain available for use during SDLCI, such as the ability to zoom in on the SAME geometry and to search for a geometry that matches given parameters (e.g., the location of a user of a process). As mentioned above, available databases of SDCFs should be compiled to support the use of the practical approach to SDLCA. Furthermore, it is desirable to provide users with both SDCFs based on the SAMEs and the corresponding SAME geometries; this helps the users to understand the shape and position of each SAME. Recently, some GIS software (e.g. 14, 15) designed exclusively to display these geometries has been made freely available, and these

programs will provide sufficient power for users to identify the provided SAMEs on a map. 2.4. Usefulness of the SAME Approach in Construction of an Environmental Intervention Matrix during SDLCI. The most notable characteristics of the SAME approach are that it uses only a few regions to cover the geographical system boundary and that it reflects the locational characteristics of each process during quantification of impacts. When using two contrasting SAME definitions (Table 1), the SDLCI practitioner needs only to collect information that associates the environmental outputs of each process with two SAMEs (i.e., two regions). As mentioned in the Introduction, data collection based on the bottom-up method for construction of the environmental intervention matrix runs into a significant problem: some companies hesitate to provide detailed information on the locations of their facilities (and the related processes). However, in a process with several SAMEs, companies may be able to reply to brief questionnaires about which SAMEs (regions) they belong to because they are not required to provide confidential information specific to individual facilities. Similarly, during the allocation of data for each process in the top-down method, using only a few SAMEs makes it easier for companies to supply the needed data. If it is not possible to obtain such allocation data directly through interviews, it would still be possible to obtain it indirectly from available statistics as introduced later. So far, this paper has explained the SAME approach using examples of LCI based on process analysis (Figure 2). However, the SAME approach is also applicable to LCI based on the input-output method and to hybrid LCI (6). In particular, the geographical system boundary used in an input-output LCI usually represents an entire country, and there are many processes (sectors) in the input-output system. This complexity makes it very difficult to accurately estimate an environmental output from a process that occurs in many regions. However, using only a few regions in the SAME approach makes it possible to at least prepare allocation data using the top-down method and available statistics. The following sections demonstrate the application of the SAME approach to an LCI based on input-output and to the calculation of embodied impact intensities. 2.5. Application to Calculation of Embodied Impact Intensities. The environmental extension of an input-output analysis calculates an embodied intensity of an environmental output for a given sector using an input-output table. The embodied intensity represents the quantity of an environmental output that is directly and indirectly produced per unit activity of the sector (e.g., 16-21), and the definition of such embodied intensities can facilitate LCI based on the input-output method and the tiered hybrid analysis, which have the advantage of defining a clear and wide system boundary for the LCI (22, 23) by avoiding the difficulty of environmental data compilation during part of the inputoutput analysis. The present study applies the SAME approach to the compilation of SDLCI for the embodied intensity (in short, to the compilation of an environmental intervention matrix for the input-output analysis) and calculates the embodied impact intensities for sectors. Each embodied impact intensity represents the total environmental impact that is directly and indirectly induced for each unit production activity of the sector and reflects sectoral differences in the impact attributed to differences in the location of each sector. The Supporting Information accompanying this paper describes the formulas for calculating the embodied impact intensities in more detail. Ultimately, eq 1 yields sectoral embodied impact intensities that exclude the impacts related to imported goods:

h ) [tE′{C - (I - M h )B}-1]T

(1)

Because identification of locations of industries is generally easier than that for commodities, this formulation employs the input-output system based on the make-use framework that has been well-explained by previous researchers (22, 24). Here, the column vector h|hi| is the embodied impact intensity vector for element hi, which represents the embodied impact intensity of sector (commodity) i, and the row vector t|tr| represents SDCFs for each SAME r. Matrix E′|e′rj| represents the quantity-based environmental intervention matrix for which element e′rj is the environmental output for SAME r per unit activity of sector (industry) j; the matrixes C|cij| and B|bij| represent the output matrix and input matrix, respectively, and their elements cij and bij show the output of commodity i per unit activity of industry j and the input of commodity i per unit activity of industry j, respectively. The matrix M h |m j ij| expresses the import matrix whose element m j ij denotes the input of import commodity i per unit production of commodity j. Matrix I is the identity matrix, and superscript T shows the transposition of the vector.

3. Results and Discussion 3.1. Validity of Using Only a Few SAMEs. The main objective of the SAME approach is to reduce the number of regions that must be analyzed during SDLCI. Therefore, it is crucial to verify the validity of using a reduced number of SAMEs. In other words, it is important to analyze the differences in the results for a scenario that uses only a few SAMEs and one that uses many. In this section, first the calculation process is described for the specific example of the embodied impact intensities for suspended particulate matter (SPM). This analysis used three types of SAMEs and simple SDCFs. The intensities were calculated using eq 1 and data from the 1995 Japanese Input-Output Table (25). We considered 94 industrial sectors and 94 commodity sectors. Table S-3 in the SI shows the sector names and their corresponding sector numbers, which are common to both the industrial sector and the commodity sector. Later in this section, a similar calculation process is described for benzene emissions. 3.1.1. SAME Definition. From the viewpoint of the existing impact receptor and human health impact, this case study defined three types of SAME that covered the geographical system boundary defined by the Japanese Input-Output Tables (i.e., the whole of Japan) (25). SAME1 represented areas with high population density, SAME2 represented areas with low population density, and SAME3 represented the sea or upper atmosphere. For simplicity, it was assumed that areas with population densities in the 90th percentile or greater were high-density areas; all others were considered to be low-density areas. 3.1.2. Visualization of the SAMEs. To visualize the defined SAMEs and identify their actual areas, Cadcorp SIS GIS software (Cadcorp Ltd., U.K.) was used to display the shapes of the SAME geometries, as follows. First, population densities were calculated using ca. 5-km × 5-km grid cells covering the habitable area in Japan and the 1995 Japanese population census data (26), public statistics that include population data for ca. 1-km × 1-km grid cells across the country. Next, the GIS software was used to produce two geometrical patterns. One was a multi-polygonal geometry that consisted of the ca. 5-km × 5-km grid cells for high population density areas and represented the geometry of SAME1. The second was also multipolygonal, but it encompassed all 5-km × 5-km grid cells that were not assigned to SAME1 and represented the geometry of SAME2 (Figure 3). The geometry of SAME3, which represents sea areas and the upper atmosphere, was not produced, because it is easy to visualize the actual area. 3.1.3. Compilation of the Environmental Intervention Matrix. For the calculations using eq 1, element e′rj of the matrix E′ represents the quantity of direct SPM emitted in VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 3. SAME geometries for SAME1 (areas with high population density) and SAME2 (areas with low population density). “High density” represents cells with a population in the 90th percentile or greater (>19 852 people per 25 km2); “low density” represents all other cells within the habitable area of Japan. SAME r per unit production (monetary unit) by industrial sector j. Matrix E′ was constructed by using the top-down method. To begin with, the total emissions of each industrial sector were determined by converting the commodity-based emissions obtained in the authors’ previous study (20) into industry-based emissions using the relationship between an industrial sector and its output commodities. Then, estimated sectoral emissions in the SAMEs, e′rj, were estimated by multiplying the total emission obtained for industrial sector j by the ratio of the number of companies in industrial sector j in SAME r to the total number of companies in that sector. The GIS software was then used to search for geometries that matched the given conditions to count the number of companies in SAME r. These location-based ratios were estimated for each industrial sector using the 1995 manufacturer census data (27) by including the number of manufacturing companies for each industrial type in each ca. 1-km × 1-km grid cell. A similar calculation was performed for agricultural sectors using land-use grid data (28), including areas of each land-use type (e.g., paddy fields, farms, or forests). Road transportation values were calculated from digital road maps provided with information from the 1997 road traffic census (29), which show the sectional length of roads, the number of automobiles in each section, their average travel speeds, and other data. In terms of service industry sectors, the location ratios were determined from the 1996 Enterprise Census (30), which includes the number of service companies in each 1-km × 1-km grid cell. Emissions from aircraft and ships were allocated into SAME3. Finally, the value of e′rj (t per million yen) was obtained for SPM by dividing the estimated emissions (t) by industrial sector j in SAME r by the total supply (million yen) for that sector. Each sector’s share of the emissions in SAME1, SAME2, and SAME3 was then determined (see Figure S-3 in the SI). 3.1.4. Calculation of SDCFs for SAMEs. In terms of element tr of the SDCF for SAME r in eq 1, this case study used hypothetical values to simplify the comparison with the scenario based on many SAMEs, which is described later in this paper. Methodological consistency is essential in calculating SDCFs to permit such comparisons. Assuming that a given SPM emission near many receptors has a higher impact than emission near a few receptors, eq 2 was used to calculate a simplified value of SDCFr, tr[people]

∑φ γ

k k

tr )

k

∑φ

(2) k

k

where the subscript k indicates the grid cell number included 7324

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in the SAME geometry of SAME r, and φk and γk, respectively, represent the emissions [t/y] and population [people] values for grid cell k. This equation does not consider atmospheric transport of SPM and only estimates the number of potentially exposed people near the emission source per unit emission. Accordingly, the embodied impact intensities calculated with this simplified SDCF do not represent the exact magnitude of the impact. This simplified calculation is one of a few possible approaches to determining the values of SDCFs that provide methodological consistency between the scenarios with few and many SAMEs. Here, simplified values only have been used as surrogate values for the actual impact in this comparison. First, to estimate the values of φk, and in the estimation of the sectoral emissions per SAMEs during the compilation of the environmental intervention matrix, the sectoral allocation ratios that would let us distribute the total emissions for each industrial sector to each grid cell k were calculated using the same statistical data described above. The sectoral emissions per grid cell were then determined by multiplying the total emissions of each sector by the corresponding allocation ratio (i.e., the proportion of that sector’s total production in a given cell) and summing these emissions for grid k to calculate φk. The population for grid cell k, γk, was obtained from the 1995 population census (26). The size of each grid cell k was ca. 5-km × 5-km. The respective estimated tr values for SAME1 (high population density) and SAME2 (low population density) were 122 763 and 7084, and the t3 of SAME3 (sea or upper atmosphere area) was assumed to be 0. 3.1.5. Comparison of Embodied Impact Intensities with Different Numbers of SAMEs. To validate the embodied impact intensities estimated based on only a few SAMEs, these results were compared with the intensities estimated based on many SAMEs. The environmental intervention matrix E′ of eq 1 for the latter intensities used 94 industrial sectors and about 13 500 SAMEs differentiated on the basis of population density. These SAMEs physically represent the approximately 13 500 grid cells ca. 5-km × 5-km in area that cover the habitable area of Japan plus the sea or upper atmosphere. SDCFs for each SAME were also calculated according to eq 2. To examine variation in the embodied impact intensity with a reduction in the number of SAMEs, two additional types of embodied impact intensities were calculated by the same manner described above. One used five SAMEs: geographical areas with the highest 5% population density (95th percentile or above), 5-10% (90th-95th percentiles), 10-55% (90th-45th percentiles), 55-100% (45th percentile), and the sea or upper atmosphere. The other employed nine SAMEs: geographical areas with the highest 2.5% population

FIGURE 4. Relationship between the number of SAMEs used in the analysis and the relative errors in the embodied impact intensities for suspended particulate matter, SPM (all values calculated with respect to the intensities for 13 500 SAMEs and eq 3). density, and those with 2.5-5%, 5-7.5%, 7.5-10%, 10-32.5%, 32.5-55%, 55-75.5%, 75.5-100%, and the sea or upper atmosphere area. Table S-4 in the SI summarizes the SDCFs tr for each SAME in scenarios with one, three, five, and nine SAMEs. The scenario with only one SAME used a single SAME that represented the whole of Japan; hence this case is referred to as the site-generic LCI (SGLCI) scenario hereafter. The differences in the SPM embodied impact intensities for each sector with the SGLCI method and with the three SDLCI scenarios (three, five, and nine SAMEs; see Figure 4) using the intensities calculated based on 13 500 SAMEs as an index value. The horizontal axis represents the number of the commodity sector in the Japanese Input-Output Table (25), and the vertical axis represents the relative error, ω(L)i, calculated using eq 3

ω(L)i ) |

h(L)i - h˙ i | hi

(3)

where h(L)i represents the SPM embodied impact intensity for commodity sector i, L represents the number of SAMEs, and h˙ i represents the standard intensity with 13 500 SAMEs. Similarly, Figures S-4 and S-5 in the SI depict the relative errors of the embodied impact intensities for emissions of NOx (nitrogen oxides) (20) and SOx (sulfur oxides) (20), obtained in the same manner as for SPM emission. For all three pollutants, the introduction of SDLCI greatly improved the embodied impact intensities of the Fisheries and Fish Culture (sector 11) and the Ocean Transport and Coastal Transport (sector 74) sectors. Relative errors of most sectors decreased as the number of SAMEs increased. This analysis also revealed that, even if the SDLCI defines only three SAMEs, the relative error of the embodied impact intensity is less than that with one SAME (i.e., SGLCI). In addition, the average accuracy of the SDLCI was quantified for different numbers of SAMEs by calculating an average relative error ω j (L) of an embodied impact intensity for each SAME scenario using eq 4

ω j (L) )

x

N

∑(ω(L) )

2

i

i)1

N

(4)

TABLE 2. Average Relative Error of an Embodied Impact Intensity as a Function of the Number of SAMEs average relative error: ω j (L) [s] number of SAMEs (L) pollutant

SGLCIa

3

5

9

SPM NOx SOx

11 (0.94) 23 (1.00) 24 (0.66)

0.20 0.13 0.15

0.10 0.075 0.088

0.060 0.053 0.060

a Values in parentheses exclude sectors with large errors (Fisheries and Fish Culture sector no. 11; Ocean Transport and Coastal Transport sector no. 74).

where N () 94) represents the number of sectors in the inputoutput table, L denotes the number of SAMEs, and the corresponding ω j (L) values are presented in Table 2. The average relative errors for SGLCI were 11 for SPM, 23 for NOx, and 24 for SOx. After excluding the Fisheries and Fish Culture and the Ocean Transport and Coastal Transport sectors, which had large errors, the average relative errors decreased to 0.94 for SPM, 1.0 for NOx, and 0.66 for SOx. In contrast, when SDLCI was performed, the average relative errors for the three SAMEs ranged from 0.13 to 0.20. The use of five and nine SAMEs in the SDLCI reduced the respective average relative errors to 0.075-0.10 and 0.053-0.060. 3.2. Calculation of Embodied Impact Intensity for Benzene. 3.2.1 Calculation Process and Data Compilation. In the previous section, it was confirmed that even if only a few SAMEs were used, SDLCI could improve the quality of estimation of the embodied impact intensities. However, the SDCFs that were used were simple values that did not consider pollutant transport; as a result, the intensities obtained were unrealistic in terms of the actual magnitude of the impact. To provide a more realistic example in which the fate of a pollutant is considered, a second case study was performed to calculate the embodied impact intensities for benzene emissions using an input-output analysis and the results from the SDLCI SAME approach compared with those of the SGLCI approach. First, as in the SPM case study, it was assumed that benzene emissions near many receptors had a higher impact than emissions near few receptors, and three SAMEs were defined on the basis of population density: top 10% (SAME1), the remaining 90% (SAME2), and the sea or upper atmosphere (SAME3). The geometries of these three VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 3. Site-Dependent Characterization Factors of Benzene Emissions for Four SAMEs SAME1 SAME definition

tr of benzene [s]

SAME2

high population density 6.06 × 10-4

SAME3

reference SAME

low sea or upper all Japan population atmosphere (t0) density 2.25 × 10-4 0 3.84 × 10-4 (assumption)

SAMEs were mapped using GIS software and the population densities summarized for ca. 1-km × 1-km grids (26). The environmental intervention matrix E′ was then compiled for benzene emission and the SDCFs of vector t in eq 1 as follows. The total direct benzene emission was determined by each industrial sector using the Japanese 2001 Pollutant Release and Transfer Register (PRTR) data (31). From the total reported emission of benzene, the sectoral direct benzene emissions were estimated for the three SAMEs, which are elements of matrix E′, using the same methods and statistics as in the previous case study. As a result, each sector’s share of the benzene emissions in SAME1, SAME2, and SAME3 was the same value shown in Figure S-3. To define the SDCFs for each SAME, a coefficient known as the exposure per emission coefficient (EXPEC) (32) or the intake fraction (33, 34) was used. It expresses the ratio of the amount of pollutant intake to the amount of a pollutant or its precursor that is emitted. Initially, a benzene emission map was compiled for each SAME based on the 1-km × 1-km grid cell (Figure S-6 in the SI). The total amount of benzene estimated in each map is equivalent to the annual total benzene emission in Japan, except for emissions from airplanes and ships. Next, the G-CIEMS (35) multimedia environmental fate model was used to calculate the annual average benzene concentrations in the air for each of the 1-km × 1-km grid cells; values for river water were calculated per river segment (using linear polygons), and those for soil were calculated per catchment (using polygonal geometry) by using the emission maps as input data for the emission parameters in the model (see Figure S-7 in the SI). It was then assumed that the only benzene exposure path for humans was through the atmosphere, and eq 5 was used to calculate element tr [-](the EXPEC value for SAME r) for vector t

∑η γ π

k k k

tr )

k

φr

(5)

where subscript k represents the grid cell number included

in the SAME geometry of SAME r, ηk indicates average atmospheric pollution levels [g/m3] caused by emissions in SAME r (here, φr in g/y) using the 1-km × 1-km grids, γk is the population [people] in 1995 (26) for the grid cell, πk () 18.5 [m3/people × day] × 365 [day/y]) represents the annual respiratory volume per person [m3/people × y], and φr represents annual emissions for SAME r [g/y]. The values of tr are summarized (Table 3), with tr of the sea or upper atmosphere assumed to be 0. The embodied impact intensities of benzene were then calculated using eq 1 and the intensities based on SGLCI were determined by using EXPEC for all of Japan (Reference SAME) as the characterization factor (Table 3). 3.2.2. Comparison of Embodied Impact Intensities of Benzene between SDLCI and SGLCI. Table 3 shows that t1 for SAME1 and t2 for SAME2 were 6.06 × 10-4 and 2.25 × 10-4, respectively, implying that emissions in high-population areas had about 2.7 times the impact of emissions in lowpopulation areas. In contrast, t0 for all of Japan (Reference SAME) was estimated at 3.84 × 10-4. There was no difference in the quantity of direct and indirect benzene emissions for each commodity sector between the SDLCI and SGLCI approaches. However, the SAME approach ensured that each sector’s emission contribution to each SAME and the difference in tr were appropriately reflected in the embodied impact intensities. The focus was then placed on some sectors in which the embodied impact intensity was substantially altered in the SDLCI scenario compared with the value in the SGLCI scenario. The sectors that exhibited the largest relative change in intensities are listed in Table 4. The intensity of the Air Transport sector increased mainly as a result of an increment in services relating to the Transport sector, which is a principal intermediate input sector for the Air Transport sector. Activities of services relating to the Transport sector include airplane refueling, which is assumed to emit 82% of the benzene in SAME1 and 18% in SAME2 (cf. Figure S-3 in the SI); as a result, the impact of the sector was quantified with a higher value when using SDLCI than when using SGLCI. This engendered a large increase in the embodied impact intensities for the Air Transport sector and the services related to the Transport sector. On the other hand, the embodied impact intensities of chemical products sectors increased by ca. 30% using the SAME approach. It was estimated that 72% of direct emissions from the sectors were allocated to SAME1 and 28% were allocated to SAME2. Consequently, the high impact of SAME1 was appropriately reflected in the embodied impact intensities of those sectors. 3.3. Outlook for Future Development of the SAME Approach. As described earlier in this paper, it is essential to prepare sufficient available SDCFs for the practical

TABLE 4. Sectors with the Largest Relative Changes in the Embodied Impact Intensities for Benzene When the SDLCI and SGLCI Scenarios Are Compared embodied impact intensity of benzene (g/M yena) sector no.

sector name

hSDb

hSGc

relative change in intensities |(hSD - hSG)/hSG|

75 77 72 91 35 30 31 34

air transport services relating to transport railway transport hotels and other lodging places final chemical products chemical fertilizer industrial inorganic chemicals chemical fibers

0.057 0.32 0.011 0.015 0.11 0.12 0.11 0.14

0.038 0.23 0.0085 0.012 0.082 0.090 0.083 0.11

0.48 0.40 0.34 0.31 0.29 0.29 0.29 0.29

a M Yen: Million (×106) yen. b h : Embodied impact intensities in the SDLCI scenario using the SAME approach. c h : Embodied impact SD SG intensities in the SGLCI scenario.

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approach to be feasible in SDLCA. Developing databases of SDCF values will facilitate future environmental analyses based on SDLCA. The SAME approach has been proposed for the compilation of SDLCI information using GIS software, and it has been demonstrated through case studies based on an input-output analysis that a SAME is a useful concept for deriving valid embodied impact intensities. If SDCFs based on the SAMEs and the corresponding SAME geometries are also well prepared in available databases, the SAME approach can become a practical and useful method for performing SDLCA without increasing to impractical levels the number of regions required to cover the geographical system boundary for SDLCI. When it is difficult to collect SDCFs matching the geographical system boundary of the target system, it would be possible to use surrogate SAMEs, such as the SAMEs for other countries, if the geographical characteristics of the corresponding surrogate SAMEs are identical to those of the SAMEs defined during the SDLCI phase, even if the actual areas are not identical. On the other hand, the quality of SDLCA could be improved by researching more appropriate SAME definitions that could better account for site-dependent impacts through experiments with environmental models. The present paper provided only simple SAMEs (Table 1). Finding effective, high-quality SAME definitions for use in SDLCIA would become an important research challenge for SDLCA in the future. The data used to define SAMEs, such as population distributions, meteorological data, and vegetation data, could be obtained from both existing statistics and from new satellite data. Such research would significantly support the original purpose of SDLCA, which is to differentiate site-dependent impacts based on the geographical properties that affect a process. For compilation of SDLCI information based on the SAME approach, it is also necessary to obtain locational information on the process being studied to estimate the environmental output for the SAMEs. In light of available data on the locations of processes and related industries, available environmental data such as PRTR for European countries and Japan, TRI (Toxic Release Inventory) and NEI (National Emission Inventory) for the United States, and recent corporate environmental reports will all be useful. These collections of environmental information are sufficient to determine average discharge ratios for the total environmental output from an industry (process) to the various SAMEs defined for a system. These ratios can be used to support the allocation process in the top-down approach. Even if such environmental data are limited, the use of gridbased data on industry or land use developed for GIS software, such as the information used in these case studies, could be enough to let us allocate the output from a process into relatively few SAMEs. For instance, if preliminary locational information on processes or industries for a GIS grid are prepared in the future, this will further facilitate SDLCI based on the SAME approach by taking advantage of GIS tools such as those introduced in this study. Because of present limitations in the available data on SDCFs for Japanese pollutant emissions, we applied the SAME approach only to the impact evaluation of benzene emissions on human health. When useful and effective SDCFs become increasingly available, the SAME approach will transform SDLCA into a more feasible tool for users.

Acknowledgments The valuable comments by, and discussions with, Dr. Seiji Hashimoto, Dr. Minoru Fujii, and Dr. Shinsuke Murakami of the National Institute for Environmental Studies (NIES); Dr. Yuki Kudoh of the National Institute of Advanced Industrial Science and Technology (AIST); Dr. Shigemi Kagawa of Tohoku University; and Dr. Rokuta Inaba of

Hokkaido University, Japan, are acknowledged. Thanks are also due to Dr. Sangwon Suh at University of Minnesota and the four anonymous reviewers for their excellent and helpful comments on the manuscript.

Supporting Information Available Detailed formulations of the calculations of embodied impact intensity by means of input-output analysis, tables of data and the names of the commodity and industrial sectors discussed, values of the SDCFs used for validating the use of relatively few SAMEs, and several useful figures. This material is available free of charge via the Internet at http:// pubs.acs.org.

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Received for review December 24, 2004. Revised manuscript received July 5, 2005. Accepted July 13, 2005. ES047951N