Confronting Uncertainty in Life Cycle Assessment Used for Decision Support

The aim of this article is to help confront uncertainty in life cycle assessments (LCAs) used for decision support. LCAs offer a quantitative approach to assess environmental effects of products, technologies, and services and are conducted by an LCA practitioner or analyst (AN) to support the decision maker (DM) in making the best possible choice for the environment. At present, some DMs do not trust the LCA to be a reliable decision‐support tool—often because DMs consider the uncertainty of an LCA to be too large. The standard evaluation of uncertainty in LCAs is an ex‐post approach that can be described as a variance simulation based on individual data points used in an LCA. This article develops and proposes a taxonomy for LCAs based on extensive research in the LCA, management, and economic literature. This taxonomy can be used ex ante to support planning and communication between an AN and DM regarding which type of LCA study to employ for the decision context at hand. This taxonomy enables the derivation of an LCA classification matrix to clearly identify and communicate the type of a given LCA. By relating the LCA classification matrix to statistical principles, we can also rank the different types of LCA on an expected inherent uncertainty scale that can be used to confront and address potential uncertainty. However, this article does not attempt to offer a quantitative approach for assessing uncertainty in LCAs used for decision support.     

Critical Components of Uncertainty Communication in Life Cycle Assessments of Emerging Technologies

Because of their recognition as a comprehensive tool of environmental assessments and their increasing use by governments and industries, life cycle assessments (LCAs) are positioned to be prominent sources of mass media information on new products and technologies. The LCA studies underlying media reports are often viewed by nonexperts after the initial reporting. However, uncertainty is rife in early assessments of emerging technologies, and LCA's ability to inform environmental opinions and decisions is limited without the accompanying communication on uncertainty. Though approaches to the technical aspects of uncertainty analysis in LCA are available in the literature, those on communicating that uncertainty, in ways that are cognitively accessible to the nonexperts, are still lacking despite their highlighted importance across various disciplines. With the focus on communication, this article uses the existing literature to derive five criteria for making uncertainty communication accessible to a nonexpert audience. Then, LCAs on engineered nanomaterial (ENM) and ENM‐enabled products, as a case study of emerging technologies where uncertainties abound, are reviewed for whether they meet these five criteria. The study concludes with recommendations for communicating uncertainty in LCAs in order to enhance their role as decision‐ and public opinion–informing assessments.     

Life cycle assessment of emerging technologies: Evaluation techniques at different stages of market and technical maturity

Life cycle assessment (LCA) analysts are increasingly being asked to conduct life cycle‐based systems level analysis at the earliest stages of technology development. While early assessments provide the greatest opportunity to influence design and ultimately environmental performance, it is the stage with the least available data, greatest uncertainty, and a paucity of analytic tools for addressing these challenges. While the fundamental approach to conducting an LCA of emerging technologies is akin to that of LCA of existing technologies, emerging technologies pose additional challenges. In this paper, we present a broad set of market and technology characteristics that typically influence an LCA of emerging technologies and identify questions that researchers must address to account for the most important aspects of the systems they are studying. The paper presents: (a) guidance to identify the specific technology characteristics and dynamic market context that are most relevant and unique to a particular study, (b) an overview of the challenges faced by early stage assessments that are unique because of these conditions, (c) questions that researchers should ask themselves for such a study to be conducted, and (d) illustrative examples from the transportation sector to demonstrate the factors to consider when conducting LCAs of emerging technologies. The paper is intended to be used as an organizing platform to synthesize existing methods, procedures and insights and guide researchers, analysts and technology developer to better recognize key study design elements and to manage expectations of study outcomes.     

Life cycle assessment of emerging technologies: A review

In recent literature, prospective application of life cycle assessment (LCA) at low technology readiness levels (TRL) has gained immense interest for its potential to enable development of emerging technologies with improved environmental performances. However, limited data, uncertain functionality, scale up issues and uncertainties make it very challenging for the standard LCA guidelines to evaluate emerging technologies and requires methodological advances in the current LCA framework. In this paper, we review published literature to identify major methodological challenges and key research efforts to resolve these issues with a focus on recent developments in five major areas: cross‐study comparability, data availability and quality, scale‐up issues, uncertainty and uncertainty communication, and assessment time. We also provide a number of recommendations for future research to support the evaluation of emerging technologies at low technology readiness levels: (a) the development of a consistent framework and reporting methods for LCA of emerging technologies; (b) the integration of other tools with LCA, such as multicriteria decision analysis, risk analysis, technoeconomic analysis; and (c) the development of a data repository for emerging materials, processes, and technologies.     

How can LCA include prospective elements to assess emerging technologies and system transitions? The 76th LCA Discussion Forum on Life Cycle Assessment, 19 November 2020

This paper summarizes the 76th LCA Discussion Forum end its main findings. Main issues when addressing emerging technologies identified were: the lack of primary data, the need for (shared) future background scenarios and (guidlines for) a common methodology. The following recommendations have been derived by the organizers: 1) Specific foreground inventories are always tailor-made, but consistency can be improved through lists of mandatory considerations. 2) Continue sharing (future) technology data and proxy processes, that can be readily replicated to new studies and assist in developing inventories. 3) Streamline and unify the process of including scenarios for background systems. New approaches may provide first important solutions to efficiently include consistent future scenarios in prospective LCA.

     

Life cycle assessment standards

The newly emerging LCA standards provide an opportunity to review and improve upon the current LCA methodology. As more industrial practitioners enter the arena, the opportunity arises to not only demand environmental improvement from industrial service and product providers but also to fill LCA data gaps. A framework is suggested for improvement in the current LCA framework that focuses on the business relationships of the industrial practitioner. The framework seeks to promote environmental improvement from industrial sectors through the identification of state-of-the-art technologies used throughout a life cycle. Basing LCAs on the best performers in an industry will create a market for a high level of environmental performance, disperse the responsibility of inventory data gathering, and improve upon the advancements already anticipated through the widespread application of LCA.     

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.     

Life-Cycle Assessment: Constraints on Moving from Inventory to Impact Assessment

Life-cycle assessment (LCA) is a technique for systematically analyzing a product from cradle-to-grave, that is, from resource extraction through manufacture and use to disposal. LCA is a mixed or hybrid analytical system. An inventory phase analyzes system inputs of energy and materials along with outputs of emissions and wastes throughout life cycle, usually as quantitative mass loadings. An impact assessment phase then examines these loadings in light of potential environmental issues using a mixed spectrum of qualitative and quantitative methods. The constraints imposed by inventory's loss of spatial, temporal, dose-response, and threshold information raise concerns about the accuracy of impact assessment. The degree of constraint varies widely according to the environmental issue in question and models used to extrapolate the inventory data. LCA results may have limited value in two areas: (I) local and/ortransient biophysical processes and (2) issues involving biological parameters, such as biodiversity, habitat alteration, and toxicity. The end result is that impact assessment does not measure actual effects or impacts, nor does it calculate the likelihood of an effect or risk Rather, LCA impact assessment results are largely directional environmental indicaton. The accuracy and usefulness of indicators need to be assessed individually and in a circumstance-specific manner prior to decision making. This limits LCAs usefulness as the sole basis for comprehensive assessments and the comparisons of alternatives. In conclusion, LCA may identify potential issues from a systemwide perspective, but more-focused assessments using other analytical techniques are often necessary to resolve the issues.     

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.     

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.     

Life cycle analysis of biochemical cellulosic ethanol under multiple scenarios

Cellulosic ethanol is widely believed to offer substantial environmental advantages over petroleum fuels and grain‐based ethanol, particularly in reducing greenhouse gas emissions from transportation. The environmental impacts of biofuels are largely caused by precombustion activities, feedstock production and conversion facility operations. Life cycle analysis (LCA) is required to understand these impacts. This article describes a field‐to‐blending terminal LCA of cellulosic ethanol produced by biochemical conversion (hydrolysis and fermentation) using corn stover or switchgrass as feedstock. This LCA develops unique models for most elements of the biofuel production process and assigns environmental impact to different phases of production. More than 30 scenarios are evaluated, reflecting a range of feedstock, technology and scale options for near‐term and future facilities. Cellulosic ethanol, as modeled here, has the potential to significantly reduce greenhouse gas (GHG) emissions compared to petroleum‐based liquid transportation fuels, though substantial uncertainty exists. Most of the conservative scenarios estimate GHG emissions of approximately 45–60 g carbon dioxide equivalent per MJ of delivered fuel (g CO₂e MJ^?1) without credit for coproducts, and 20–30 g CO₂e MJ^?1 when coproducts are considered. Under most scenarios, feedstock production, grinding and transport dominate the total GHG footprint. The most optimistic scenarios include sequestration of carbon in soil and have GHG emissions below zero g CO₂e MJ^?1, while the most pessimistic have life‐cycle GHG emissions higher than petroleum gasoline. Soil carbon changes are the greatest source of uncertainty, dominating all other sources of GHG emissions at the upper bound of their uncertainty. Many LCAs of biofuels are narrowly constrained to GHG emissions and energy; however, these narrow assessments may miss important environmental impacts. To ensure a more holistic assessment of environmental performance, a complete life cycle inventory, with over 1100 tracked material and energy flows for each scenario is provided in the online supplementary material for this article.     

Are future recycling benefits misleading? Prospective life cycle assessment of lithium-ion batteries

Life cycle assessment (LCA) quantifies the whole-life environmental impacts of products and is essential for helping policymakers and manufacturers transition toward sustainable practices. However, typical LCA estimates future recycling benefits as if it happens today. For long-lived products such as lithium-ion batteries, this may be misleading since there is a considerable time gap between production and recycling. To explore this temporal mismatch problem, we apply future electricity scenarios from an integrated assessment model—IMAGE—using “premise” in Brightway2 to conduct a prospective LCA (pLCA) on the global warming potential of six battery chemistries and four recycling routes. We find that by 2050, electricity decarbonization under an RCP2.6 scenario mitigates production impacts by 57%, so to reach zero-carbon batteries it is important to decarbonize upstream heat, fuels, and direct emissions. For the best battery recycling case, data for 2020 gives a net recycling benefit of −22 kg CO₂e kWh⁻¹ which reduces the net impact of production and recycling from 71 to 49 kg CO₂e kWh⁻¹. However, for recycling in 2040 with decarbonized electricity, net recycling benefits would be nearly 75% lower (−6 kg CO₂e kWh⁻¹), giving a net impact of 65 kg CO₂e kWh⁻¹. This is because materials recycled in the future substitute lower-impact processes due to expected electricity decarbonization. Hence, more focus should be placed on mitigating production impacts today instead of relying on future recycling. These findings demonstrate the importance of pLCA in tackling problems such as temporal mismatch that are difficult to capture in typical LCA.     

Environmental assessment of future technologies: how to trim LCA to fit this goal?

Introduction

New and innovative technologies may claim substantial efficiency gains in the future. However, they are often assessed based on their current performance, measured in the laboratory or in pilot plants. The goal of discussion forum 38 was, on one hand, to shed light on the main drivers and principles that ensure a sensible and fair assessment of far future technologies. On the other hand, the most recent European developments in prospective technology assessment of emerging energy technologies and the related quantification of external costs were touched upon.

Discussion

The discussion forum started with three talks dedicated to external costs and multicriteria decisions presenting results of the New Energy Externality Developments for Sustainability project. After three presentations considering long-term LCI modeling aspects, lectures were held covering industry implementation and case studies. The following main conclusions were drawn at the end of discussion forum 38: (a) life cycle assessment (LCA) is considered a useful tool for environmental assessments of future energy technology, (b) consistency in LCA modeling of future situations is achieved by adapting data in the foreground (electricity-generating technology) and in the background (electricity supply mix, material manufacture, transport services, etc.), (c) external cost assessments and multicriteria decision analysis involve value judgments and thus do lead to a variety of different conclusions, (d) the present situation must be known properly to be able to model possible future situations, and (e) challenges are the data availability and definition of consistent scenarios of the future.     

Using monte carlo simulation in life cycle assessment for electric and internal combustion vehicles

1 Background

The U.S. Government has encouraged shifting from internal combustion engine vehicles (ICEVs) to alternatively fueled vehicles such as electric vehicles (EVs) for three primary reasons: reducing oil dependence, reducing greenhouse gas emissions, and reducing Clean Air Act criteria pollutant emissions. In comparing these vehicles, there is uncertainty and variability in emission factors and performance variables, which cause wide variation in reported outputs.

2 Objectives

A model was developed to demonstrate the use of Monte Carlo simulation to predict life cycle emissions and energy consumption differences between the ICEV versus the EV on a per kilometer (km) traveled basis. Three EV technologies are considered: lead-acid, nickel-cadmium, and nickel metal hydride batteries.

3 Methods

Variables were identified to build life cycle inventories between the EVs and ICEV. Distributions were selected for each of the variables and input to Monte Carlo Simulation soft-ware called Crystal Ball 2000®.

4 Results and Discussion

All three EV options reduce U.S. oil dependence by shifting to domestic coal. The life cycle energy consumption per kilometer (km) driven for the EVs is comparable to the ICEV; however, there is wide variation in predicted energy values. The model predicts that all three EV technologies will likely increase oxides of sulfur and nitrogen as well as particulate matter emissions on a per km driven basis. The model shows a high probability that volatile organic compounds and carbon monoxide emissions are reduced with the use of EVs. Lead emissions are also predicted to increase for lead-acid battery EVs. The EV will not reduce greenhouse gas emissions substantially and may even increase them based on the current U.S. reliance on coal for electricity generation. The EV may benefit public health by relocating air pollutants from urban centers, where traffic is concentrated, to rural areas where electricity generation and mining generally occur. The use of Monte Carlo simulation in life cycle analysis is demonstrated to be an effective tool to provide further insight on the likelihood of emission outputs and energy consumption.     

Approximate Life-Cycle Assessment of Product Concepts Using Learning Systems

Parametric life-cycle assessment (LCA) models have been integrated with traditional design tools and used to demonstrate the rapid elucidation of holistic, analytical trade-offs among detailed design variations. A different approach is needed, however, if analytical environmental assessment is to be incorporated in very early design stages. During early stages, there may be competing product concepts with dramatic differences. Detailed information is scarce, and decisions must be made quickly.
This article explores an approximate method for providing preliminary LCAs. In this method, learning algorithms trained using the known characteristics of existing products might allow environmental aspects of new product concepts to be approximated quickly during conceptual design without defining new models. Artificial neural networks are trained to generalize on product attributes, which are characteristics of product concepts, and environmental inventory data from pre-existing LCAs. The product design team then queries the trained artificial model with new high-level attributes to quickly obtain an impact assessment for a new product concept. Foundations for the learning system approach are established, and then an application within the distributed object-based modeling environment (DOME) is provided. Tests have shown that it is possible to predict life-cycle energy consumption, and that the method could be used to predict solid waste, greenhouse effect, ozone depletion, acidification, eutrophication, winter and summer smog.     

农业生命周期评价研究进展

作为评价产品系统全链条环境影响的有效工具,生命周期评价（LCA）方法已广泛用于工业领域。农业领域也面临着高强度的资源和环境压力,LCA在农业领域的应用应运而生。旨在综述已有农业LCA研究的基础上,鉴别农业LCA应用存在的问题,并为农业LCA未来的发展提出建议。目前农业LCA存在系统边界和功能单位界定不明晰、缺少区域清单数据库、生命周期环境影响评价模型（LCIA）不能准确反映农业系统环境影响、结果解释存在误区等方面的问题。为了科学准确地衡量农业系统的环境影响,促进农业系统的可持续发展,文章认为农业LCA应该从以下几个方面加强研究,即科学界定评价的参照系、系统边界的扩大及功能单位的合理选取、区域异质性数据库构建与LCIA模型开发、基于组织农业LCA的开发以及对于利益相关者行为的研究。     

Scenario Modelling in Prospective LCA of Transport Systems. Application of Formative Scenario Analysis (11 pp)

Background Tools and methods able to cope with uncertainties are essential for improving the credibility of Life Cycle Assessment (LCA) as a decision support tool. Previous approaches have focussed predominately upon data quality. Objective and Scope. An epistemological approach is presented conceptualising uncertainties in a comparative, prospective, attributional LCA. This is achieved by considering a set of cornerstone scenarios representing future developments of an entire Life Cycle Inventory (LCI) product system. We illustrate the method using a comparison of future transport systems. Method Scenario modelling is organized by means of Formative Scenario Analysis (FSA), which provides a set of possible and consistent scenarios of those unit processes of an LCI product system which are time dependent and of environmental importance. Scenarios are combinations of levels of socio-economic or technological impact variables. Two core elements of FSA are applied in LCI scenario modelling. So-called impact matrix analysis is applied to determine the relationship between unit process specific socio-economic variables and technology variables. Consistency Analysis is employed to integrate unit process scenarios, based on pair-wise ratings of the consistency of the levels of socio-economic impact variables of all unit processes. Two software applications are employed which are available from the authors. Results and Discussion The study reveals that each possible level or development of a technology variable is best conceived of as the impact of a specific socio-economic (sub-) scenario. This allows for linking possible future technology options within the socio-economic context of the future development of various background processes. In an illustrative case study, the climate change scores and nitrogen dioxide scores per seat kilometre for six technology options of regional rail transport are compared. Similar scores are calculated for a future bus alternative and an average Swiss car. The scenarios are deliberately chosen to maximise diversity. That is, they represent the entire range of future possible developments. Reference data and the unit process structure are taken from the Swiss LCA database 'ecoinvent 2000'. The results reveal that rail transport remains the best option for future regional transport in Switzerland. In all four assessed scenarios, four technology options of future rail transport perform considerably better than regional bus transport and car transport. Conclusions and Recommendations. The case study demonstrates the general feasibility of the developed approach for attributional prospective LCA. It allows for a focussed and in-depth analysis of the future development of each single unit process, while still accounting for the requirements of the final scenario integration. Due to its high transparency, the procedure supports the validation of LCI results. Furthermore, it is well-suited for incorporation into participatory methods so as to increase their credibility. Outlook and Future Work. Thus far, the proposed approach is only applied on a vehicle level not taking into account alterations in demand and use of different transport modes. Future projects will enhance the approach by tackling uncertainties in technology assessment of future transport systems. For instance, environmental interventions involving future maglev technology will be assessed so as to account for induced traffic generated by the introduction of a new transport system.     

The Importance of Normalization References in Interpreting Life Cycle Assessment Results

A normalization step is widely exercised in life cycle assessment (LCA) studies in order to better understand the relative significance of impact category results. In the normalization stage, normalization references (NRs) are the characterized results of a reference system, typically a national or regional economy. Normalization is widely practiced in LCA‐based decision support and policy analysis (e.g., LCA cases in municipal solid waste treatment technologies, renewable energy technologies, and environmentally preferable purchasing programs, etc.). The compilation of NRs demands significant effort and time as well as an intimate knowledge of data availability and quality. Consequently only one set of published NRs is available for the United States, and has been adopted by various studies. In this study, the completeness of the previous NRs was evaluated and significant data gaps were identified. One of the reasons for the significant data gaps was that the toxic release inventory (TRI) data significantly underestimate the potential impact of toxic releases for some sectors. Also the previous NRs did not consider the soil emissions and nitrogen (N) and phosphorus (P) runoffs to water and chemical emissions to soils. Filling in these data gaps increased the magnitude of NRs for “human health cancer,” “human health noncancer,” “ecotoxicity,” and “eutrophication” significantly. Such significant changes can alter or even reverse the outcome of an LCA study. We applied the previous and updated NRs to conventional gasoline and corn ethanol LCAs. The results demonstrate that NRs play a decisive role in the interpretation of LCA results that use a normalization step.     

The Role of Scale and Technology Maturity in Life Cycle Assessment of Emerging Technologies: A Case Study on Carbon Nanotubes

Life cycle assessment (LCA) has been applied for assessing emerging technologies, where large‐scale production data are generally lacking. This study introduces a standardized scheme for technology and manufacturing readiness levels to contextualize a technology's development stage. We applied the scheme to a carbon nanotube (CNT) LCA and found that, regardless of synthesis technique, CNT manufacturing will become less energy intensive with increased levels of readiness. We examined the influence of production volume on LCA results using primary data from a commercial CNT manufacturer with approximately 100 grams per day production volume and engineering design of a scaled‐up process with 1 tonne per day production capacity. The results show that scaling up could reduce 84% to 94% of its cradle‐to‐gate impacts, mainly as a result of the recycling of feedstock that becomes economically viable only beyond certain minimum production volume. This study shows that LCAs on emerging technologies based on immature data should be interpreted in conjunction with their technology and manufacturing readiness levels and reinforces the need of standardizing and communicating information on these readiness levels and scale of production in life cycle inventory practices.