ABSTRACT. Drawing from the substantial body of literature on life cycle assessment / analysis (LCA), the article summarizes the methodology's limitations and ...
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Proceedings of DETC’03 ASME 2003 Design Engineering Technical Conferences and Computers and Information in Engineering Conference Chicago, Illinois, USA, September 2-6, 2003
DETC2003/DFM-48140
IMPROVING LIFE CYCLE ASSESSMENT BY INCLUDING SPATIAL, DYNAMIC AND PLACEBASED MODELING John Reap and Bert Bras1 Systems Realization Laboratory The George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta, GA 30332-0405 USA ABSTRACT Drawing from the substantial body of literature on life cycle assessment / analysis (LCA), the article summarizes the methodology’s limitations and failings, discusses some proposed improvements and suggests an additional improvement. After describing the LCA methodology within the context of ISO guidelines, the article summaries the limitations and failings inherent in the method’s life cycle inventory and impact assessment phases. The article then discusses improvements meant to overcome problems related to lumped parameter, static, site-independent modeling. Finally, the article suggests a remedy for some of the problems with LCA. Linking industrial models with spatially explicit, dynamic and site-specific ecosystem models is suggested as a means of improving the impact assessment phase of LCA. Keywords: Life Cycle Assessment, Life Cycle Analysis, Life Cycle Impact Assessment, LCA, Environmental Impact Assessment, Ecological Modeling 1. Introduction Efforts by firms in the late 1960s and early 1970s to quantify the direct and indirect material and energy consumed during product manufacture precipitated the creation of life cycle assessment methodologies [1]. These early inventories would later grow to encompass the entire product life cycle from “cradle to grave.” The environmental impacts of products ranging from milk to petrol have been compared using current LCA methods [2, 3]. With the passage of time LCA became an “…important tool for environmental policy, and even for industry” [4]. 1
Patrick J. Newcomb and Carol Carmichael Environmentally Conscious Design and Manufacturing Program Manufacturing Research Center Georgia Institute of Technology Atlanta, GA 30332-0560 USA Despite extensive application and importance to both government and industry, LCA is a tool beset by problems in practice and theory. For example, Ayres finds fault with the data sources used and mass balances created by many LCA practitioners [4]. And, at international LCA workshops, issues that influence the LCA methodology such as spatial resolution, temporal resolution and site specificity are topics of discussion [5]. These last three topics are of special significance for this article. Current life cycle assessments use lumped parameter, static, site-independent models to estimate environmental impact. The previous decade’s advances in computing and developments in systems ecology provide an opportunity to address the issues of space, time and site specificity using modeling. Linking industrial models of the resource extraction and manufacturing life cycle phases to ecosystem models may reduce errors caused by the spatial, temporal and site assumptions in current life cycle assessments. 2. LCA Within the Context of ISO 14041 To understand the advantages of linked models, one must first understand the current state of the art in life cycle assessment. With the advent of ISO 14040, the LCA methodology has started to consolidate. The ISO 14040 through 14043 standards deal with the Life-Cycle Assessment (LCA) procedure. The LCA approach described in ISO 14040-14043 is essentially the same as the one promoted by the Society of Environmental Toxicology and Chemistry (SETAC). Consoli and coauthors present a SETAC framework similar to the ISO 14040 framework [6]. The two only differ in choices of impact categories and weighting schemes.
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A conventional LCA consists of the following steps, which are outlined in the ISO 14040 - ISO 14043 standards and in SETAC’s ‘Code of Practice’ [6, 7]: 1. Goal definition and scoping - ISO 14040 2. Inventory analysis - ISO 14041 3. Impact assessment - ISO 14042 4. Improvement Assessment (SETAC term)/Interpretation (ISO term) - ISO 14043. Although the actual ISO 14040-14043 standard documents are relatively short, interpreting them can prove problematic. Cost effectiveness can also be a problem; a study of 34 Fortune 500 companies by Gloria and coworkers shows that just implementing the LCA standards costs between $15,000 to $30,000 per product [8]. 3. Revealing and Confronting the Limitations of LCA Despite the growing popularity and usefulness of LCA, the method is not without its drawbacks. Many of the complaints about LCA focus on the intensive amount of time, data, money and effort required to perform a detailed LCA. Although Gloria and coauthors showed that implementing the ISO 14040-14043 LCA standards costs between $15,000 to $30,000 per product, Graedel estimates that it can cost from tens to hundreds of thousands of dollars to perform just one LCA [8, 9]. Additional complaints commonly lodged against LCA include [5, 9, 10]: • Cannot consider temporal and spatial issues • Focuses on only environmental considerations (not economic or societal) • Regards all processes as linear (such as dose-response curves) • Is steady state – not dynamic • Is laden with value judgments and subjectivity • Requires difficult or impossible to find data For these (and other) reasons, LCA results have often been met with skepticism. The good news is that many LCA methodology developers and practitioners have been working to overcome the limitations of LCA. For example, to tackle the problem that a detailed LCA is too costly and time-intensive, more abbreviated LCA methods have been developed. These are usually referred to as streamlined LCA or abridged LCA [9]. The streamlined LCA has the advantage of being more efficient and cost effective, and it can be used in the early stages of design where quantitative data is scarce but design freedom is greater. These advantages mean that the streamlined LCA is more likely to be carried out, as it is not as frustrating and cumbersome as the detailed LCA [9]. In addition to methods attempting to address the whole LCA methodology, many efforts are aimed at improving the individual stages of LCA. For example, the inventory phase is particularly data intensive and is usually carried out in a bottom-up analysis of the industrial processes. There is a “school of thought” that approaches the inventory from the topdown, using the macroeconomic perspective of input-output analysis. The EIO-LCA approach developed by Hendrickson and coworkers is one of the better known examples (see also www.eiolca.net) [11]. Input-output analysis is believed to be more elegant, more complete, easier to perform, and less data intensive than process analysis, but it is less specific and less accurate [12]. Therefore, some LCA researchers suggest using
a hybrid LCA. The hybrid LCA uses both process and inputoutput analysis to develop the life cycle inventory. The hybrid approach has the advantage of eliminating the need to define strict production system boundaries, an often controversial task [12]. An alternative approach is to build the inventory on financial principles. Emblemsvåg and Bras advocate building a LCA based upon known financial accounting principles [13]. In particular, they propose to build upon the Activity-Based Costing framework in which resources are consumed by activities, which are consumed by (cost) objects such as products and services [14]. One advantage is that it allows for multiple product-LCAs to be conducted simultaneously, analogous to activity-based product costing. Building upon existing financial frameworks and terminology should lower the learning curve for beginning LCA practitioners. However, even with a financial costing framework as the foundation, data gathering remains a bottleneck because many companies do not have a costing system that accounts for the inventory details needed in LCA. Of all the limitations of LCA, the methods for impact assessment have traditionally been the most debated [15]. Hofstetter counts life cycle impact assessment (LCIA) problems as one of the two biggest problems with LCA [16]. In addition to issues with subjectivity, “LCA does not have an acceptable way to model impacts” [16]. In 1998, a conference of LCA practitioners and methodologists convened in Brussels to discuss the issues with LCIA [5]. There was great concern about the appropriate level of sophistication used in LCIA. Sophistication is defined as, “…the ability of the model to accurately reflect the potential impact of stressors” [5]. The conference identified many simplifications that are made to the LCIA phase: • Reduction in spatial discrimination (or ignoring spatial discrimination) • Ignoring fate • Ignoring background levels of pollutants • Assuming liner dose-response curves • Eliminating an impact category altogether b/c the appropriate assessment methodologies do not exist. Of the problems with LCIA, two of the most decried are a lack of spatial and temporal considerations [5, 10, 16]. Hofstetter believes that the most common form of the LCA, the LCA sensu stricto, has no detailed time or location information (or actually assumed geographical and meteorological conditions typical of Western Europe), and he even goes so far as to call place and time “the neglected children in LCA” [16]. The LCA sensu stricto uses a “…very simple model to represent a dynamic anthroposphere and very complex ecosphere” [16]. Some researchers have attempted to compensate for LCIA’s shortcomings. Potting and coauthors used a spatially explicit atmospheric model to develop acidification impact factors for emissions emanating from specific locations in Europe [17]. Besides taking advantage of spatially explicit deposition and concentration data provided by the utilized atmospheric model, their approach also benefits from the fact that it requires little extra data than that already collected for a standard LCA. Unfortunately, the low resolution of the atmospheric model limits their approach. The focus on
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airborne emissions also limits the model to atmospherically related impacts. Furthermore, it is unclear how changing ecosystem dynamics affect the proposed emissions factors. Lindeijer summarizes a number of methods meant to compensate for the lack of spatial considerations – specifically land use [18]. These methods uniformly depend upon aggregated indicators, and many do not explicitly consider time. Spatial patterns are important when considering ecosystem dynamics [19]. Thus, indicators based upon land use aggregations may not provide sufficient information to evaluate spatial or location specific environmental impacts. Tolle uses normalization factors to capture regional and local sensitivities to particular impacts [20]. This approach partially compensates for life cycle assessment’s lack of spatial considerations. But, these aggregated normalization factors do not account for landscape patterns, and they do not consider changes in ecosystem function. Operating under a broad definition of industrial system dynamics, Spath and coauthors conduct a life cycle analysis for each year of a power plant’s creation and operation [21]. The approach accounts for the entire life of a facility and its supporting industries, but it does not include ecosystem dynamics, ecosystem patterns or sitespecific considerations. The EPA developed a LCA software called the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI) [22]. This impact assessment tool contains data for a number of specific locations in the United States. Though TRACI accounts for local variations in environmental impact data where possible, the tool does not account for the effects of ecosystem patterns or dynamics. Many issues arise when the spatial and temporal dimensions are missing. As a result of its lack of temporal considerations, LCA cannot be used for predictive modeling [15]. Contributions of a product or process to the concentration of pollutions in a specific area cannot be known since the actual background concentrations are unknown. Even if they were known, nothing can be said about the sensitivity of the areas exposed [16]. Lack of spatial information often leads to the focus of impacts on so-called mid-points instead of endpoints. There are advantages to making impact assessment models “…as close as possible to the final endpoints of the environmental mechanism of the impact categories (e.g., quantifying fish kills and trees lost as opposed to the acidification potential of the substances” [5]. Guinee suggests that some regionalization is possible, but “LCA does not provide the framework for a full-fledged local risk assessment study, identifying which impacts can be expected due to the functioning of a facility in a specific locality” [10]. This is precisely what we propose to do: model the interactions of an actual facility placed in an actual ecosystem. 4. Ecological-Industrial Modeling – A Way Forward As noted in Section 3, traditional life cycle assessment disregards spatial distribution, ignores time and treats every location the same. Proposed modifications and extensions fall short of fully compensating for these shortcomings. A conceptually direct, though technically challenging, solution to these problems involves spatial and temporal modeling of the ecosystems directly impacted by industrial activities. By linking these ecosystem models with industrial models, one
would create an eco-industrial model capable of simulating environmental impacts. Before contemplating the technical challenges associated with this solution, consider the benefits of such an approach. A spatially explicit ecosystem model allows the capture of spatially explicit impacts. Though simplification is often desirable, the potential importance of spatially explicit modeling for the fate of chemical emissions has been demonstrated [23]. A dynamic ecosystem model permits the modeling of feedback and changes in the system as it responds to environmental burdens. Finally, a place-based model gives one the ability to differentiate between vastly different environments. Given these valuable benefits, the technical challenges are worth considering. 4.1 Modeling Industries To conduct such modeling one would need industrial models, ecosystem models and a means of linking the two. A life cycle inventory (LCI) mass and energy balance could play the role of the industrial model. Life cycle inventories include flows beyond those typically associated with ‘industrial activities;’ so, one needs to clearly define the scope of the industrial modeling one will later link with the ecosystem models. In the context of this article, industrial activities include one or more of the following processes: resource extraction, materials manufacture and product fabrication (See Fig. 1). Resource Extraction
Material Manufacture and Product Fabrication Use
Reuse, Recycling and Disposal
Industrial Activities
Figure 1: Product Life Cycle. However, resorting to a standard LCI would create a static industrial model. As mentioned in Section 3, the ActivityBased Costing (ABC) models of Emblemsvåg and Bras, which include mass and energy flows, could also represent industries [14]. Basic ABC models are also static, but one may use dynamic process data to augment the model. For example, an ABC model for a carpet manufacturing facility uses mass and energy time series data recorded by the accounting department and gathered by sensors [24]. One may use this data to conduct historical analyses, or one may use it to calibrate the model to better predict the resources consumed by and emissions resulting from future production scenarios. Currently, resource consumption and environmental burden information reported by the model is displayed directly to facility managers using a graphical user interface called a “Dashboard” [25]. In the future, static and dynamic data contained in the “Dashboard” database could be passed as anthropogenic inputs to an ecosystem model.
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4.2 Modeling Ecosystems Modeling industry falls under the purview of engineers, but one cannot expect and should not allow an engineer to model an ecosystem. Thankfully, such models already exist. Voinov and coauthors created the Patuxent Landscape Model (PLM) “…to simulate fundamental ecological processes on the watershed scale” [26]. To achieve a spatially explicit representation of an ecosystem, a modeled landscape “…is partitioned into a gird of square unit cells” [26]. A general ecosystem model (GEM) simulates the ecosystem dynamics within each cell [27]. For the PLM, the cellular ecosystem models include a number of modules (See Table 1).
As one can see, the approach is analogous to a structural Finite Element Analysis where a structure is divided into (simplified) elements that can be analyzed and are connected using boundary conditions between the elements.
Table 1: Description of primary modules used in the Patuxent Landscape Model (Voinov, Costanza et al. 1999) Module Unit Cell Hydrology Nutrients Macrophytes Dead Organic Decomposition Spatial Surface Hydrology Spatial Subsurface Hydrology
Description Simulates the vertical flux of water within a unit cell Simulates the cycling of nitrogen and phosphorus compounds within a unit cell Simulates the growth of plants within a unit cell Simulates the decomposition of plant material within a unit cell Simulates the flow of surface water and nutrients (runoff, streams and rivers) among cells Simulates the flow of subsurface water and dissolved nutrients among cells
Software developed by Maxwell and Costanza link the modules in the GEM and the unit cells together [28]. One models different locations and ecosystems (i.e. forest, grassland, swamp, etc.) by changing parameters in the GEM and by inputting the appropriate data for the unit cell grid. Figure 2 provides a highly abstract and schematic view of the described ecosystem modeling. It emphasizes the use of non-spatial ecosystem process models to capture the dynamics, and it illustrates the use of the unit cell grid in spatial modeling. Figure 3 displays one of the stock and flow ecosystem process modules used in each unit cell. The module is implemented using STELLA modeling software.
Figure 3: Dead Organic Material Ecosystem Process Module (http://www.uvm.edu/giee/LHEM/) These models require significant amounts of data. Table 2 gives a truncated list of data needed for the described type of ecosystem modeling. Table 2: Truncated List of Required Ecosystem Data Loosely Grouped by Type [29] Basic Types Geographic
Meteorological Hydrological
Unit Cells
Land Utilization Ecosystem Processes
Figure 2: Abstract Ecosystem Model
• • • • • • • • • • • • •
Data Sets Elevation Watershed Boundary Data Shoreline Delineation Soil Type Ambient Temperature Precipitation Ground Water Elevation Bathymetry Data Stream Flow Surface and Ground Water Quality Land Cover Vegetation Index Growth Coefficients
For the PLM, government agencies, academic institutions, research programs and regional databases served as data sources [29]. Costanza and coauthors offer a more detailed list and discussion of the data sources and types needed for their ecosystem models [29]. 4.3 Linking the Models Given an industrial model and given the previously described ecosystem model, one may link the two models to create an eco-industrial model. The PLM is modular; it contains multiple physical and ecological modules that simulate the system’s behavior [26]. And, the software supporting the PLM is designed to both support and enforce the development
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of dynamic simulation modules [28]. One could conceivably create and insert an industrial module into the landscape using the software, procedures and tools developed for ecosystem modeling. Figure 4 simply and abstractly depicts an industrial module added to one of the grid locations where it is linked with appropriate ecosystem modules.
Ecosystem Processes
Industrial Processes
Figure 4: Abstract Eco-Industrial Model 5. Outlining the Research Effort An effort to link industrial and ecosystem models is currently underway in a collaborative effort between the Gund Institute at the University of Vermont and Georgia Tech. Though the effort is in its initial stages, three primary research questions present themselves. 1. Which industrial models are appropriate for combination with the described type of ecosystem model? 2. How can one connect the two types of models; what problems wait in the technical details? 3. Can one detect the effects of industrial changes on the modeled ecosystems? How can one quantify these effects? These questions are being answered using a case study format. Initially, a model of a single industry will link with the ecosystem model (See Fig. 4). The process of selecting the type of model to link will answer the first question, and the act of linking will address the second. The next step would involve the linking of multiple industries to the same ecosystem model. Linking multiple industrial models will further address questions concerning the utilized technology. To address the last question, the simulated ecosystem state with an industrial model will be compared to the simulated ecosystem state without an industrial model. Efforts to answer these three questions form the basic steps along the path to creating ecoindustrial models. 5.1 Step 1: Know Thy Industrial Models Selecting the appropriate industrial models to link with the discussed type of ecosystem model may, on the surface, appear trivial, but thoughtful consideration leads one to the opposite conclusion. To illustrate the point, consider one simple question. Should one link steady state or dynamic industrial models with dynamic ecosystem models? Building steady state models requires less time and effort. Moreover, the cycle time for some industrial dynamics may occur on time scales far smaller than the response of a
surrounding ecosystem. For example, knowing the exact time at which a CNC lathe creates waste may not be as important as possessing an estimate of the number of parts produced per day and of the waste per part. On the other hand, modeling the timing of a release may prove exceptionally important for determining ecosystem impacts. For example, releasing materials that cause environmental damage through photochemical reactions might cause less damage if released at night when they would have a chance to dilute by morning. Though these examples are simple, they reveal the importance of considering what types of models are appropriate for linking. The research involved with answering the first question centers on classification. Specifically, a systematic means of matching ecosystem industry interactions with an industrial model that captures the detrimental behavior is needed. 5.2 Step 2: Dealing With the Details of Combination Once one can systematically and appropriately match industrial models with the described ecosystem models, one must undertake the task of linking the two types of models. One can explore the details of linking models by starting with simple industrial models and simple links, and then, one can gradually increase the complexity of both. As a first step, one could represent industry as number of static or exogenously varying inflows to and / or outflows from ecosystem process modules such as the one depicted in Figure 3. This configuration would reveal how an industrial facility affects the environment, but it would not reveal how the environment affects the facility. If one needs to account for interactions, one could convert an industrial model into a STELLA process module, or one could provide C++ code that represents the interactions. Conceiving and implementing linking methods such as these form the core of the research needed to answer the second question. 5.3 Step 3: Assessing Impacts and Validity Efforts to match and link models prepare the way for the primary focus of this research – environmental impact assessment. As mentioned, the detection of impacts will be explored by comparing a simulated facility in a simulated ecosystem to the same simulated ecosystem without a facility. Components of the PLM will be used as the ecosystem simulation [26]. Additionally, the impacts of a real facility in a real ecosystem will be compared with a model of the same facility in a model of its surrounding ecosystem. These two activities will not only answer the question of detecting impacts in an ecosystem but will also address the validity of ecoindustrial modeling. To detect impacts, one must possess a means of qualifying and quantifying the consequences of environmental burdens. The ecosystem model described in Section 4.2 can potentially improve qualification and quantification in the standard LCA impact categories of land use, biotic resources, eutrophication, and eco-toxicity on the regional and local scales. Land use in the ecosystem model alters in response to changes in hydrology and nutrient flows [29]. One could evaluate the environmental impact in a rough, qualitative way by simply comparing vegetation maps with industrial activity to the same maps without industrial activity. A number of metrics for quantifying landscape composition and configuration are available [19]. One could quantify land use impacts by using
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these metrics. Spatial patterns influence ecosystem processes [19, 30]. So, quantifying these patterns should also provide information about impacts on biotic resources. Deriving ecologically significant measures, such as net primary productivity, from the numerical data would provide another quantitative measure of environmental impact on the local and / or regional biotic community. Nutrient data would support more traditional impact measures such as eutrophication potential. The model would indicate the location and time of detrimental concentrations of nutrients, and if an ecosystem process modules for an aquatic ecosystem was available, one could simulate the degradation of the system. Though not currently modeled, one may even be able to include toxic materials in a manner similar to the way in which nutrients are modeled. If linking proves to be feasible and valid, one could simulate the impact of industrial activity. Environmental impact comparisons of industries operating separately with industries operating as an industrial ecosystem will become possible. The simulation would output a series of snapshots of the landscape changing with time, as well as a large quantity of supporting numerical data. The previous discussion focuses on the resource extraction and manufacturing phases of a product’s life cycle. This focus comes from a need to bound the research currently underway – not from an identified limitation. With intellectual and technical effort, the proposed type of modeling may apply to the use and disposal phases as well, though computational limitations may present a problem.
modeling errors are all research worthy challenges that need to be overcome before the approach can be applied. Computational requirements may limit the use of the augmentation to locales and regions. And, the absence of modules for toxic materials mandates continued reliance on traditional LCA methods of evaluating eco-toxicity.
6. Challenges and Limitations Unfortunately, spatially explicit, dynamic, place-based modeling of a linked industrial and ecological system comes at a high cost, and it is not without limitations. Standard life cycle assessment is data intensive; including eco-industrial modeling will exacerbate the situation. Installing a suite of process sensors to obtain environmentally related dynamic data for a facility can prove costly and time consuming [31]. Topographic, climatic, ecological, biological and other types of data are necessary inputs to the discussed ecosystem models. Since a significant portion of this data is site specific, each new ecosystem model requires extensive data acquisition and calibration. Even when calibrated, substantial error in the ecosystem models often remains. For the PLM’s spatial hydrology model, Costanza and coauthors report percent errors ranging from less than 1% to 41%, and for some nutrient concentrations in the ecosystem process modules, errors range from 16-91% [29]. Computational demands also present a challenge. During calibration of the PLM for example, the model’s spatial resolution was reduced from 200 m to 1 km square unit cells [29]. So, modeling areas larger than river valleys may prove computationally prohibitive. Furthermore, the described type of ecosystem model does not include toxic substances. Only anthropogenic nutrient loading is captured, the addition of modules for toxic substances may mitigate this limitation. These costs, challenges and limitations prevent the proposed approach from being immediately practical, and they potentially limit the scale and scope of its application. Developing the link between industrial and ecosystem models, overcoming the data obstacles and reducing ecosystem
REFERENCES
7. Closure As noted and discussed, life cycle assessment is not without limitations. Significant among these are the use of lumped parameters, static models and site independent data. Linking spatially explicit, dynamic, place-based ecosystem models with industrial models holds the potential to compensate for the mentioned limitations. However, two main problems limit the effectiveness of this augmentation for LCA. The discussed ecosystem model requires significant amounts of data not collected in a standard LCA, and it does not model the fate and transport of toxic materials. Nevertheless, the spatial, temporal and location specific properties of this type of modeling coupled with its adaptable modular architecture make it worth developing. ACKNOLWEDGEMENTS We gratefully acknowledge the support from the National Science Foundation and Georgia Tech’s Manufacturing Research Center. This paper is a result from the work sponsored by the National Science Foundation under NSF grants DMI-0085253 and DMI-0225871. John Reap is supported by grant DMI-0085253.
1.
2. 3. 4. 5.
6.
7.
6
Vigon, B.W., D.A. Tolle, B. Cornaby, H.C. Latham, C.L. Harrison, T.L. Boguski, R.G. Hunt, and J.D. Sellers, LifeCycle Assessment: Inventory Guidelines and Principles. 1993, Risk Reduction Engineering Laboratory U.S. Environmental Protection Agency: Cincinnati. Boer, I.J.M.d., 2002 "Environmental impact assessment of conventional and organic milk production". Livestock Production Science. Furuholt, E., 1995 "Life cycle assessment of gaoline and diesel". Resources, Conservation and Recycling. 14: p. 251-263. Ayres, R.U., 1995 "Life cycle analysis: A critique". Resources, Conservation and Recycling. 14: p. 199-223. Bare, J.C., H.A. Udo de Haes, and D.W. Pennington, An International Workshop on Life Cycle Impact Assessment Sophistication. 2000, National Risk Management Research Laboratory U.S. Environmental Protection Agency: Cincinnati. Consoli, F., D. Allen, I. Boustead, and J. Fava. Guidelines for Life-Cycle Assessment: A 'Code of Practice'. in The SETAC Workshop. 1993. Sesimbra, Portugal, 31 March - 3 April: Society of Environmental Toxicology And Chemistry (SETAC). Jensen, A.A., J. Elkington, K. Christiansen, L. Hoffmann, B.T. Møller, A. Schmidt, and F. van Dijk, Life Cycle Assessment (LCA) - A guide to approaches, experiences and information sources. 1997, dk-TEKNIK Energy & Environment: Søborg, Denmark.
Copyright © 2003 by ASME
8.
9. 10. 11.
12. 13.
14.
15. 16.
17.
18. 19. 20. 21. 22. 23.
24.
Gloria, T., T. Saad, M. Breville, and M. O'Connell, 1995 "Life-Cycle Assessment: A Survey of Current Implementation". Total Quality Environmental Management. 4(No. 3): p. 33 - 50. Graedel, T.E., Streamlined Life-Cycle Assessment. 1998, Upper Saddle River: Prentice Hall. Guinee, J.B., Handbook on Life Cycle Assessment Operational Guide to the ISO Standards. 2002, Dordrecht, Netherlands: Kluwar Academic. Hendrickson, C., A.J. Horvath, S., and L.B. Lave, 1998 "Economic Input-Output Models for Environmental Life Cycle Analysis". Environmental Science & Technology,(April). Lenzen, M., 2002 "A guide for compiling inventories in life-cycle assessments: some Australian results". Journal of Cleaner Production. 10(6): p. 545-572. Emblemsvåg, J. and B. Bras. An Activity-Based Life-Cycle Assessment Method. in 1995 ASME Design for Manufacturing Symposium, Design Engineering Technical Conferences and Computers in Engineering Conference. 1997. Sacramento, California: ASME. Emblemsvåg, J. and B.A. Bras, Activity-Based Cost and Environmental Management - A Different Approach to the ISO 14000 Compliance. ISBN 0-7923-7247-6 ed. 2000, Boston/Dordrecht/London: Kluwer Academic Publishers. 344. Curran, M.A., Environmental Life-Cycle Assessment. 1996, New York: McGraw-Hill. Hofstetter, P., Perspectives in Life Cycle Impact Assessment - A Structured Approach to Combine Models of the Technosphere, Ecosphere and Valuesphere. 1998, Dordrecht, Netherlands: Kluwar Academic. Potting, J., W. Schopp, K. Blok, and M. Hauschild, 1998 "Comparison of the Acidifying Impact from Emissions with Different Regional Origin in Life Cycle Assessment". Journal of Hazardous Materials. 61: p. 155-162. Lindeijer, E., 2000 "Review of land use impact methodologies". Journal of Cleaner Production. 8: p. 273281. Turner, M.G., R.H. Gardner, and R.V. O'Neill, Landscape Ecology in Theory and Practice: Pattern and Process. 2001, New York: Springer-Verlag. Tolle, D.A., 1997 "Regional Scaling and Normalization in LCIA". International Journal of Life Cycle Assessment. 2(4): p. 197-208. Spath, P.L., M.K. Mann, and D.R. Kerr, Life Cycle Assessment of Coal-fired Power Production. 1999, National Renewable Energy Laboratory: Golden. Bare, J.C. Developing a Consistent Decision-Making Framework by Using the US EPA's TRACI. in American Institute of Chemical Engineers. 2002. Keppler, O. and H.A.d. Hollander, 1999 "A comparison of spatially explicit and box models for the fate of chemicals in water, air and soil in Europe". Ecological Modelling. 116(2-3): p. 183-202. Bras, B., S. Duncan, M. Franz, T. Graver, Y.-H. Han, L. McGinnis, S. Velasquez, B. Wilgenbusch, and C. Zhou. Real-Time Integrated Economic and Environmental Performance Monitoring of a Production Facility. in Special Publication SP-1579-Environmental Issues in
25.
26.
27.
28. 29.
30.
31.
7
Vehicle Design and Manufacturing. 2001. Detroit, Michigan: SAE National Congress, SAE International. Wilgenbusch, B., Developing an Information Management System for and Environmental and Economic Monitoring System, in Mechanical Engineering. 2001, Georgia Institute of Technology: Atlanta. p. 215. Voinov, A., R. Costanza, L. Wainger, R. Boumans, F. Villa, T. Maxwell, and H. Voinov, 1999 "Patuxent landscape model: integrating ecological economic modeling of a watershed". Environmental Modelling & Software. 14(5): p. 473-491. Fitz, H.C., E.B. DeBellevue, R. Costanza, R. Boumans, T. Maxwell, L. Wainger, and F.H. Sklar, 1996 "Development of a general ecosystem model for a range of scales and ecosystems". Ecological Modelling. 88: p. 263-295. Maxwell, T. and R. Costanza, 1997 "A language for modular spatio-temporal simulation". Ecological Modelling. 103: p. 105-113. Costanza, R., A. Voinov, R. Boumans, T. Maxwell, F. Villa, L. Wainger, and H. Voinov, 2002 "Integrated Ecological Economic Modeling of the Patuxent River Watershed, Maryland". Ecological Monographs. 72(2): p. 203-231. Dramstad, W.E., J.D. Olson, and R.T.T. Forman, Landscape Ecology Principles in Landscape Architecture and Land-Use Planning. 1996, Washington, D.C.: Island Press. Duncan, S., A Mass and Energy Data Collection System to Support Environmental and Economic Assessment of a Coating Line in Carpet Manufacturing, in Mechanical Engineering. 2003, Georgia Institute of Technology: Atltanta.
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