Uncertainty Quantification in Life Cycle Assessments: Interindividual Variability and Sensitivity Analysis in LCA of Air‐Conditioning Systems

The life cycle environmental profile of energy‐consuming products, such as air conditioning, is dominated by the products’ use phase. Different user behavior patterns can therefore yield large differences in the results of a cradle‐to‐grave assessment. Although this variation and uncertainty is increasingly recognized, it remains often poorly characterized in life cycle assessment (LCA) studies. Today, pervasive sensing presents the opportunity to collect rich data sets and improve profiling of use‐phase parameters, in turn facilitating quantification and reduction of this uncertainty in LCA. This study examined the case of energy use in building cooling systems, focusing on global warming potential (GWP) as the impact category. In Singapore, building cooling systems or air conditioning consumes up to 37% of national electricity demand. Lack of consideration of variation in use‐phase interaction leads to the oversized designs, wasted energy, and therefore reducible GWP. Using a high‐resolution data set derived from sensor observations, energy use and behavior patterns of single‐office occupants were characterized by probabilistic distributions. The interindividual variability and use‐phase variables were propagated in a stochastic model for the life cycle of air‐conditioning systems and simulated by way of Monte Carlo analysis. Analysis of the generated uncertainties identified plausible reductions in global warming impact through modifying user interaction. Designers concerned about the environmental profile of their products or systems need better representation of the underlying variability in use‐phase data to evaluate the impact. This study suggests that data can be reliably provided and incorporated into the life cycle by proliferation of pervasive sensing, which can only continue to benefit future LCA.     

Approaches for Addressing Life Cycle Assessment Data Gaps for Bio‐based Products

There is an increasing need for life cycle data for bio‐based products, which becomes particularly evident with the recent drive for greenhouse gas reporting and carbon footprinting studies. Meeting this need is challenging given that many bio‐products have not yet been studied by life cycle assessment (LCA), and those that have are specific and limited to certain geographic regions. In an attempt to bridge data gaps for bio‐based products, LCA practitioners can use either proxy data sets (e.g., use existing environmental data for apples to represent pears) or extrapolated data (e.g., derive new data for pears by modifying data for apples considering pear‐specific production characteristics). This article explores the challenges and consequences of using these two approaches. Several case studies are used to illustrate the trade‐offs between uncertainty and the ease of application, with carbon footprinting as an example. As shown, the use of proxy data sets is the quickest and easiest solution for bridging data gaps but also has the highest uncertainty. In contrast, data extrapolation methods may require extensive expert knowledge and are thus harder to use but give more robust results in bridging data gaps. They can also provide a sound basis for understanding variability in bio‐based product data. If resources (time, budget, and expertise) are limited, the use of averaged proxy data may be an acceptable compromise for initial or screening assessments. Overall, the article highlights the need for further research on the development and validation of different approaches to bridging data gaps for bio‐based products.     

A Stochastic Approach to Model Dynamic Systems in Life Cycle Assessment

This article presents a framework to evaluate emerging systems in life cycle assessment (LCA). Current LCA methods are effective for established systems; however, lack of data often inhibits robust analysis of future products or processes that may benefit the most from life cycle information. In many cases the life cycle inventory (LCI) of a system can change depending on its development pathway. Modeling emerging systems allows insights into probable trends and a greater understanding of the effect of future scenarios on LCA results. The proposed framework uses Bayesian probabilities to model technology adoption. The method presents a unique approach to modeling system evolution and can be used independently or within the context of an agent‐based model (ABM). LCA can be made more robust and dynamic by using this framework to couple scenario modeling with life cycle data, analyzing the effect of decision‐making patterns over time. Potential uses include examining the changing urban metabolism of growing cities, understanding the development of renewable energy technologies, identifying transformations in material flows over space and time, and forecasting industrial networks for developing products. A switchgrass‐to‐energy case demonstrates the approach.     

Uncertainty and Variability in Product Carbon Footprinting

Recent years have seen increasing interest in life cycle greenhouse gas emissions accounting, also known as carbon footprinting, due to drivers such as transportation fuels policy and climate‐related eco‐labels, sometimes called carbon labels. However, it remains unclear whether applications of greenhouse gas accounting, such as carbon labels, are supportable given the level of precision that is possible with current methodology and data. The goal of this work is to further the understanding of quantitative uncertainty assessment in carbon footprinting through a case study of a rackmount electronic server. Production phase uncertainty was found to be moderate (±15%), though with a high likelihood of being significantly underestimated given the limitations in available data for assessing uncertainty associated with temporal variability and technological specificity. Individual components or subassemblies showed varying levels of uncertainty due to differences in parameter uncertainty (i.e., agreement between data sets) and variability between production or use regions. The use phase displayed a considerably higher uncertainty (±50%) than production due to uncertainty in the useful lifetime of the server, variability in electricity mixes in different market regions, and use profile uncertainty. Overall model uncertainty was found to be ±35% for the whole life cycle, a substantial amount given that the method is already being used to set policy and make comparative environmental product declarations. Future work should continue to combine the increasing volume of available data to ensure consistency and maximize the credibility of the methods of life cycle assessment (LCA) and carbon footprinting. However, for some energy‐using products it may make more sense to increase focus on energy efficiency and use phase emissions reductions rather than attempting to quantify and reduce the uncertainty of the relatively small production phase.     

Hybrid Framework for Managing Uncertainty in Life Cycle Inventories

Life cycle assessment (LCA) is increasingly being used to inform decisions related to environmental technologies and polices, such as carbon footprinting and labeling, national emission inventories, and appliance standards. However, LCA studies of the same product or service often yield very different results, affecting the perception of LCA as a reliable decision tool. This does not imply that LCA is intrinsically unreliable; we argue instead that future development of LCA requires that much more attention be paid to assessing and managing uncertainties. In this article we review past efforts to manage uncertainty and propose a hybrid approach combining process and economic input–output (I‐O) approaches to uncertainty analysis of life cycle inventories (LCI). Different categories of uncertainty are sometimes not tractable to analysis within a given model framework but can be estimated from another perspective. For instance, cutoff or truncation error induced by some processes not being included in a bottom‐up process model can be estimated via a top‐down approach such as the economic I‐O model. A categorization of uncertainty types is presented (data, cutoff, aggregation, temporal, geographic) with a quantitative discussion of methods for evaluation, particularly for assessing temporal uncertainty. A long‐term vision for LCI is proposed in which hybrid methods are employed to quantitatively estimate different uncertainty types, which are then reduced through an iterative refinement of the hybrid LCI method.     

LiSET: A Framework for Early‐Stage Life Cycle Screening of Emerging Technologies

While life cycle assessment (LCA) is a tool often used to evaluate the environmental impacts of products and technologies, the amount of data required to perform such studies make the evaluation of emerging technologies using the conventional LCA approach challenging. The development paradox is such that the inputs from a comprehensive environmental assessment has the greatest effect early in the development phase, and yet the data required to perform such an assessment are generally lacking until it is too late. Previous attempts to formalize strategies for performing streamlined or screening LCAs were made in the late 1990s and early 2000s, mostly to rapidly compare the environmental performance of product design candidates. These strategies lack the transparency and consistency required for the environmental screening of large numbers of early‐development candidates, for which data are even sparser. We propose the Lifecycle Screening of Emerging Technologies method (LiSET). LiSET is an adaptable screening‐to‐LCA method that uses the available data to systematically and transparently evaluate the environmental performance of technologies at low readiness levels. Iterations follow technological development and allow a progression to a full LCA if desired. In early iterations, LiSET presents results in a matrix structure combined with a “traffic light” color grading system. This format inherently communicates the high uncertainty of analysis at this stage and presents numerous environmental aspects assessed. LiSET takes advantage of a decomposition analysis and data not traditionally used in LCAs to gain insight to the life cycle impacts and ensure that the most environmentally sustainable technologies are adopted.     

Input‐Output‐based Life Cycle Inventory

An input‐output‐based life cycle inventory (IO‐based LCI) is grounded on economic environmental input‐output analysis (IO analysis). It is a fast and low‐budget method for generating LCI data sets, and is used to close data gaps in life cycle assessment (LCA). Due to the fact that its methodological basis differs from that of process‐based inventory, its application in LCA is a matter of controversy. We developed a German IO‐based approach to derive IO‐based LCI data sets that is based on the German IO accounts and on the German environmental accounts, which provide data for the sector‐specific direct emissions of seven airborne compounds. The method to calculate German IO‐based LCI data sets for building products is explained in detail. The appropriateness of employing IO‐based LCI for German buildings is analyzed by using process‐based LCI data from the Swiss Ecoinvent database to validate the calculated IO‐based LCI data. The extent of the deviations between process‐based LCI and IO‐based LCI varies considerably for the airborne emissions we investigated. We carried out a systematic evaluation of the possible reasons for this deviation. This analysis shows that the sector‐specific effects (aggregation of sectors) and the quality of primary data for emissions from national inventory reporting (NIR) are the main reasons for the deviations. As a rule, IO‐based LCI data sets seem to underestimate specific emissions while overestimating sector‐specific aspects.     

Analytical Propagation of Uncertainty in Life Cycle Assessment Using Matrix Formulation

Inventory data and characterization factors in life cycle assessment (LCA) contain considerable uncertainty. The most common method of parameter uncertainty propagation to the impact scores is Monte Carlo simulation, which remains a resource‐intensive option—probably one of the reasons why uncertainty assessment is not a regular step in LCA. An analytical approach based on Taylor series expansion constitutes an effective means to overcome the drawbacks of the Monte Carlo method. This project aimed to test the approach on a real case study, and the resulting analytical uncertainty was compared with Monte Carlo results. The sensitivity and contribution of input parameters to output uncertainty were also analytically calculated. This article outlines an uncertainty analysis of the comparison between two case study scenarios. We conclude that the analytical method provides a good approximation of the output uncertainty. Moreover, the sensitivity analysis reveals that the uncertainty of the most sensitive input parameters was not initially considered in the case study. The uncertainty analysis of the comparison of two scenarios is a useful means of highlighting the effects of correlation on uncertainty calculation. This article shows the importance of the analytical method in uncertainty calculation, which could lead to a more complete uncertainty analysis in LCA practice.     

Integration of life cycle assessment in the environmental information system

Background, aim, and scope  As the sustainability improvement becomes an essential business task of industry, a number of companies are adopting IT-based environmental information systems (EIS). Life cycle assessment (LCA), a tool to improve environmental friendliness of a product, can also be systemized as a part of the EIS. This paper presents a case of an environmental information system which is integrated with online LCA tool to produce sets of hybrid life cycle inventory and examine its usefulness in the field application of the environmental management. Main features  Samsung SDI Ltd., the producer of display panels, has launched an EIS called Sustainability Management Initiative System (SMIS). The system comprised modules of functions such as environmental management system (EMS), green procurement (GP), customer relation (e-VOC), eco-design, and LCA. The LCA module adopted the hybrid LCA methodology in the sense that it combines process LCA for the site processes and input–output (IO) LCA for upstream processes to produce cradle-to-gate LCA results. LCA results from the module are compared with results of other LCA studies made by the application of different methodologies. The advantages and application of the LCA system are also discussed in light of the electronics industry. Results and discussion  LCA can play a vital role in sustainability management by finding environmental burden of products in their life cycle. It is especially true in the case of the electronics industry, since the electronic products have some critical public concerns in the use and end-of-life phase. SMIS shows a method for hybrid LCA through online data communication with EMS and GP module. The integration of IT-based hybrid LCA in environmental information system was set to begin in January 2006. The advantage of the comparing and regular monitoring of the LCA value is that it improves the system completeness and increases the reliability of LCA. By comparing the hybrid LCA and process LCA in the cradle-to-gate stage, the gap between both methods of the 42-in. standard definition plasma display panel (PDP) ranges from 1% (acidification impact category) to −282% (abiotic resource depletion impact category), with an average gap of 68.63%. The gaps of the impact categories of acidification (AP), eutrophication (EP), and global warming (GWP) are relatively low (less than 10%). In the result of the comparative analysis, the strength of correlation of three impact categories (AP, EP, GWP) shows that it is reliable to use the hybrid LCA when assessing the environmental impacts of the PDP module. Hybrid LCA has its own risk on data accuracy. However, the risk is affordable when it comes to the comparative LCA among different models of similar product line of a company. In the results of 2 years of monitoring of 42-in. Standard definition PDP, the hybrid LCA score has been decreased by 30%. The system also efficiently shortens man-days for LCA study per product. This fact can facilitate the eco-design of the products and can give quick response to the customer's inquiry on the product's eco-profile. Even though there is the necessity for improvement of process data currently available, the hybrid LCA provides insight into the assessments of the eco-efficiency of the manufacturing process and the environmental impacts of a product. Conclusions and recommendations  As the environmental concerns of the industries increase, the need for environmental data management also increases. LCA shall be a core part of the environmental information system by which the environmental performances of products can be controlled. Hybrid type of LCA is effective in controlling the usual eco-profile of the products in a company. For an industry, in particular electronics, which imports a broad band of raw material and parts, hybrid LCA is more practicable than the classic LCA. Continuous efforts are needed to align input data and keep conformity, which reduces data uncertainty of the system.     

An Approach to Integrating Occupational Safety and Health into Life Cycle Assessment: Development and Application of Work Environment Characterization Factors

Integrating occupational safety and health (OSH) into life cycle assessment (LCA) may provide decision makers with insights and opportunities to prevent burden shifting of human health impacts between the nonwork environment and the work environment. We propose an integration approach that uses industry‐level work environment characterization factors (WE‐CFs) to convert industry activity into damage to human health attributable to the work environment, assessed as disability‐adjusted life years (DALYs). WE‐CFs are ratios of work‐related fatal and nonfatal injuries and illnesses occurring in the U.S. worker population to the amount of physical output from U.S. industries; they represent workplace hazards and exposures and are compatible with the life cycle inventory (LCI) structure common to process‐based LCA. A proof of concept demonstrates application of the WE‐CFs in an LCA of municipal solid waste landfill and incineration systems. Results from the proof of concept indicate that estimates of DALYs attributable to the work environment are comparable in magnitude to DALYs attributable to environmental emissions. Construction and infrastructure‐related work processes contributed the most to the work environment DALYs. A sensitivity analysis revealed that uncertainty in the physical output from industries had the most effect on the WE‐CFs. The results encourage implementation of WE‐CFs in future LCA studies, additional refinement of LCI processes to accurately capture industry outputs, and inclusion of infrastructure‐related processes in LCAs that evaluate OSH impacts.     

A Methodical Approach for Systematic Life Cycle Assessment of Wood‐Based Furniture

Existing life cycle assessment (LCA) studies for furniture focus on single pieces of furniture and use a bottom‐up approach based on their bill of materials (BOM) to build up the data inventories. This approach does not ensure completeness regarding material and energy fluxes and representativeness regarding the product portfolio. Integrating material and energy fluxes collected at company level into product LCA (top‐down approach) over‐rides this drawback. This article presents a method for systematic LCA of industrially produced furniture that merges the top‐down approach and bottom‐up approach. The developed method assigns data collected at the company level to the different products while, at the same time, considering that wood‐based furniture is a complex product. Hence, several classifications to reduce the complexity to a manageable level have been developed. Simultaneously, a systematic calculation routine was established. The practical implementation of the developed method for systematic LCA is carried out in a case study within the German furniture industry. The system boundary was set in accord with the EN 15804 specification cradle‐to‐gate‐with‐options. The analysis therefore includes the manufacturing phase supplemented by an end‐of‐life scenario. The case study shows that the manufacturing of semifinished products (especially wood‐based panels and metal components) as well as the electric energy demand in furniture manufacturing account for a notable share of the environmental impacts. A sensitivity analysis indicates that up to roughly 10% of the greenhouse gas emissions are not recorded when conducting an LCA based on a BOM instead of applying the developed approach.     

Structured Under‐Specification of Life Cycle Impact Assessment Data for Building Assemblies

The existence of uncertainties and variations in data represents a remaining challenge for life cycle assessment (LCA). Moreover, a full analysis may be complex, time‐consuming, and implemented mainly when a product design is already defined. Structured under‐specification, a method developed to streamline LCA, is here proposed to support the residential building design process, by quantifying environmental impact when specific information on the system under analysis cannot be available. By means of structured classifications of materials and building assemblies, it is possible to use surrogate data during the life cycle inventory phase and thus to obtain environmental impact and associated uncertainty. The bill of materials of a building assembly can be specified using minimal detail during the design process. The low‐fidelity characterization of a building assembly and the uncertainty associated with these low levels of fidelity are systematically quantified through structured under‐specification using a structured classification of materials. The analyst is able to use this classification to quantify uncertainty in results at each level of specificity. Concerning building assemblies, an average decrease of uncertainty of 25% is observed at each additional level of specificity within the data structure. This approach was used to compare different exterior wall options during the early design process. Almost 50% of the comparisons can be statistically differentiated at even the lowest level of specificity. This data structure is the foundation of a streamlined approach that can be applied not only when a complete bill of materials is available, but also when fewer details are known.     

Designing hybrid life cycle assessment models based on uncertainty and complexity

Purpose

Despite the wide use of LCA for environmental profiling, the approach for determining the system boundary within LCA models continues to be subjective and lacking in mathematical rigor. As a result, life cycle models are often developed in an ad hoc manner, and are difficult to compare. Significant environmental impacts may be inadvertently left out. Overcoming this shortcoming can help elicit greater confidence in life cycle models and their use for decision making.

Methods

This paper describes a framework for hybrid life cycle model generation by selecting activities based on their importance, parametric uncertainty, and contribution to network complexity. The importance of activities is determined by structural path analysis—which then guides the construction of life cycle models based on uncertainty and complexity indicators. Information about uncertainty is from the available life cycle inventory; complexity is quantified by cost or granularity. The life cycle model is developed in a hierarchical manner by adding the most important activities until error requirements are satisfied or network complexity exceeds user-specified constraints.

Results and Discussion

The framework is applied to an illustrative example for building a hybrid LCA model. Since this is a constructed example, the results can be compared with the actual impact, to validate the approach. This application demonstrates how the algorithm sequentially develops a life cycle model of acceptable uncertainty and network complexity. Challenges in applying this framework to practical problems are discussed.

Conclusion

The presented algorithm designs system boundaries between scales of hybrid LCA models, includes or omits activities from the system based on path analysis of environmental impact contribution at upstream network nodes, and provides model quality indicators that permit comparison between different LCA models.

     

Life Cycle–based Assessment of Energy Use and Greenhouse Gas Emissions in Almond Production,Part II: Uncertainty Analysis through Sensitivity Analysis and Scenario Testing

This is the second part of a two‐article series examining California almond production. The part I article describes development of the analytical framework and life cycle–based model and presents typical energy use and greenhouse gas (GHG) emissions for California almonds. This part II article builds on this by exploring uncertainty in the life cycle model through sensitivity and scenario analysis, and by examining temporary carbon storage in the orchard. Sensitivity analysis shows life cycle GHG emissions are most affected by biomass fate and utilization, followed by nitrous oxide emissions rates from orchard soils. Model sensitivity for net energy consumption is highest for irrigation system parameters, followed by biomass fate and utilization. Scenario analysis shows utilization of orchard biomass for electricity production has the greatest potential effect, assuming displacement methods are used for co‐product allocation. Results of the scenario analysis show that 1 kilogram (kg) of almond kernel and associated co‐products are estimated to cause between ?3.12 to 2.67 kg carbon dioxide equivalent (CO₂‐eq) emissions and consume between 27.6 to 52.5 megajoules (MJ) of energy. Co‐product displacement credits lead to avoided emissions of between ?1.33 to 2.45 kg CO₂‐eq and between ?0.08 to 13.7 MJ of avoided energy use, leading to net results of ?1.39 to 3.99 kg CO₂‐eq and 15.3 to 52.6 MJ per kg kernel (net results are calculated by subtracting co‐product credits from the results for almonds and co‐products). Temporary carbon storage in orchard biomass and soils is accounted for by using alternative global warming characterization factors and leads to a 14% to 18% reduction in CO₂‐eq emissions. Future studies of orchards and other perennial cropping systems should likely consider temporary carbon storage.     

Generalized Make and Use Framework for Allocation in Life Cycle Assessment

Allocation in life cycle inventory (LCI) analysis is one of the long‐standing methodological issues in life cycle assessment (LCA). Discussion on allocation among LCA researchers has taken place almost in complete isolation from the series of closely related discussions from the 1960s in the field of input?output economics, regarding the supply and use framework. This article aims at developing a coherent mathematical framework for allocation in LCA by connecting the parallel developments of the LCA and the input?output communities. In doing so, the article shows that the partitioning method in LCA is equivalent to the industry‐technology model in input?output economics, and system expansion in LCA is equivalent to the by‐product‐technology model in input?output output economics. Furthermore, we argue that the commodity‐technology model and the by‐product‐technology model, which have been considered as two different models in input?output economics for more than 40 years, are essentially equivalent when it comes to practical applications. It is shown that the matrix‐based approach used for system expansion successfully solves the endless regression problem that has been raised in LCA literature. A numerical example is introduced to demonstrate the use of allocation models. The relationship of these approaches with consequential and attributional LCA models is also discussed.     

Dimensionality Reduction and Visualization of the Environmental Impacts of Domestic Appliances

There is an increasing worldwide concern about the problem of dealing with the waste electrical and electronic equipment (WEEE), given the high volume of appliances that are disposed of every day. In this article, an environmental evaluation of WEEE is performed that combines life cycle assessment (LCA) methodology and multivariate statistical techniques. Because LCA handles a large number of data in its different phases, when one is trying to uncover the structure of large multidimensional data sets, multivariate statistical techniques can provide useful information. In particular, principal‐component analysis and multidimensional scaling are two important dimension‐reducing tools that have been shown to be of help in understanding this type of complex multivariate data set. In this article, we use a variable selection method that reduces the number of categories for which the environmental impacts have to be computed; this step is especially useful when the number of impact categories or the number of products or processes to benchmark increases. We provide a detailed illustration showing how we have used the proposed approach to analyze and interpret the environmental impacts of different domestic appliances.     

Perceived uncertainties of characterization in LCA: a survey

Purpose

Uncertainty analyses in life cycle assessment (LCA) literature have focused primarily on the life cycle inventory (LCI) phase, but LCA experts generally agree that the life cycle impact assessment (LCIA) phase is likely to contribute even more to the overall uncertainty of an LCA result. The magnitude of perceived uncertainties in characterization relative to that in LCI, however, has not been examined in the literature. Here, we use the pedigree approach to gauge the perceived uncertainty in the characterization phase relative to the LCI phase. In addition, we evaluate the level of approval on the pedigree approach as a means to characterize uncertainty in LCA.

Methods

Applying the Numeral Unit Spread Assessment Pedigree (NUSAP) approach to environmental risk assessment literature, we extracted the criteria for evaluating the uncertainty in the characterization phase. We used expert elicitation to identify a pool of experts and conducted a survey, to which 47 LCA practitioners from 12 countries responded. In order to reduce personal biases in perceived geometric standard deviation (GSD) values, we used two reference questions on weight and life expectancy at birth for calibration.

Results

Nearly half (49%) of respondents expressed their approval to the pedigree matrix approach as a means of characterizing uncertainties in LCA, and responses were highly sensitive to the respondent’s familiarity with the pedigree matrix. For instance, respondents who are highly familiar with the pedigree matrix were more polarized, with 15% and 19% of them expressing either strong approval or strong disapproval, respectively. Respondents less familiar with the pedigree approach were generally more favorable to its use. Compared with LCI, variability in characterization factors was influenced more strongly by geographical correlation and reliability of the underlying model, which showed 11 to 16% larger average GSDs when compared with the comparable criteria for LCI. Conversely, temporal correlation criterion was a less significant factor in characterization than in LCI.

Conclusions and discussion

Overall, survey respondents viewed LCIA characterization as only marginally more uncertain than LCI, but with a wider variability in responses on characterization than LCI. This finding indicates the need for additional research to develop more thorough methods for characterizing uncertainties in life cycle impact assessment that are compatible with the uncertainty measures in LCI.

     

Emergy as a Life Cycle Impact Assessment Indicator

Founded in thermodynamics and systems ecology, emergy evaluation is a method to associate a product with its dependencies on all upstream environmental and resource flows using a common unit of energy. Emergy is thus proposed as an indicator of aggregate resource use for life cycle assessment (LCA). An LCA of gold mining, based on an original life cycle inventory of a large gold mine in Peru, is used to demonstrate how emergy can be incorporated as an impact indicator into a process‐based LCA model. The results demonstrate the usefulness of emergy in the LCA context. The adaptation of emergy evaluation, traditionally performed outside of the LCA framework, requires changes to the conventional accounting rules and the incorporation of uncertainty estimations of the emergy conversion factors, or unit emergy values. At the same time, traditional LCA boundaries are extended to incorporate the environmental processes that provide for raw resources, including ores. The total environmental contribution to the product, doré, is dominated by mining and metallurgical processes and not the geological processes forming the gold ore. The measure of environmental contribution to 1 gram (g) of doré is 6.8E + 12 solar‐equivalent Joules (sej) and can be considered accurate within a factor of 2. These results are useful in assessing a process in light of available resources, which is essential to measuring long‐term sustainability. Comparisons are made between emergy and other measures of resource use, and recommendations are made for future incorporation of emergy into LCA that will result in greater consistency with existing life cycle inventory (LCI) databases and other LCA indicators.     

Integrated Economic Equilibrium and Life Cycle Assessment Modeling for Policy‐based Consequential LCA

Consequential life cycle assessment (CLCA) has emerged as a tool for estimating environmental impacts of changes in product systems that go beyond physical relationships accounted for in attributional LCA (ALCA). This study builds on recent efforts to use more complex economic models for policy‐based CLCA. A partial market equilibrium (PME) model, called the U.S. Forest Products Module (USFPM), is combined with LCA to analyze an energy demand scenario in which wood use increases 400 million cubic meters in the United States for ethanol production. Several types of indirect economic and environmental impacts are identified and estimated using USFPM‐LCA. A key finding is that if wood use for biofuels increases to high levels and mill residue is used for biofuels and replaced by natural gas for heat and power in forest products mills, then the increased greenhouse gas emissions from natural gas could offset reductions obtained by substituting biofuels for gasoline. Such high levels of biofuel demand, however, appear to have relatively low environmental impacts across related forest product sectors.