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.     

Integration of Life Cycle Assessment Into Agent-Based Modeling

A method is presented that allows for a life cycle assessment (LCA) to provide environmental information on an energy infrastructure system while it evolves. Energy conversion facilities are represented in an agent-based model (ABM) as distinct instances of technologies with owners capable of making decisions based on economic and environmental information. This simulation setup allows us to explore the dynamics of assembly, disassembly, and use of these systems, which typically span decades, and to analyze the effect of using LCA information in decision making.
We were able to integrate a simplified LCA into an ABM by aligning and connecting the data structures that represent the energy infrastructure and the supply chains from source to sink. By using an appropriate database containing life cycle inventory (LCI) information and by solving the scaling factors for the technology matrix, we computed the contribution to global warming in terms of carbon dioxide (CO₂) equivalents in the form of a single impact indicator for each instance of technology at each discrete simulation step. These LCAs may then serve to show each agent the impact of its activities at a global level, as indicated by its contribution to climate change. Similar to economic indicators, the LCA indicators may be fed back to the simulated decision making in the ABM to emulate the use of environmental information while the system evolves. A proof of concept was developed that is illustrated for a simplified LCA and ABM used to generate and simulate the evolution of a bioelectricity infrastructure system.     

Time Horizon and Dominance in Dynamic Life Cycle Assessment

Temporal aspects have traditionally not been recognized adequately in life cycle assessment (LCA). The dynamic LCA model recently proposed offers a significant step forward in the dynamic assessment of global warming impacts. The results obtained with dynamic LCA are highly sensitive to the choice of a time horizon. Therefore, decision making between alternative systems can be critical because conclusions are dependent on the specific time horizon. In this article, we develop a decision‐making methodology based on the concept of time dominance. We introduce instantaneous and cumulative time dominance criteria to the dynamic LCA context and argue why the dominance of an alternative should also imply preference. Our approach allows for the rejection of certain alternatives without the determination of a specific time horizon. The number of decision‐relevant alternatives can thereby be reduced and the decision problem facilitated. We demonstrate our methodology by means of a case study of end‐of‐life alternatives for a wooden chair derived from the original authors of dynamic LCA and discuss the implications and limitations of the approach. The methodology based on time dominance criteria is supplementary to the dynamic LCA model, but does not substitute it. The overall value of this article stretches beyond LCA onto more general assessments of global warming, for example, in policy where the choice of a time horizon is equally significant.     

Hierarchical Activity Model for Risk‐Based Decision Making

For the practical implementation of the assessment of environmental impact, actual procedures and data requirements should be clarified so that industrial decision makers understand them. Researchers should consider local risks related to processes and environmental impact throughout the life cycle of products simultaneously to supervise these adverse effects appropriately. Life cycle assessment (LCA) is a useful tool for quantifying the potential impact associated with a product life cycle. Risk assessment (RA) is a widely used tool for identifying chemical risks in a specific situation. In this study, we integrate LCA and RA for risk‐based decision making by devising a hierarchical activity model using the type‐zero method of integrated definition language (IDEF0). The IDEF0 activity modeling language has been applied to connect activities with information flows. Process generation, evaluation, and decision making are logically defined and visualized in the activity model with the required information. The activities, information flows, and their acquisitions are revealed, with a focus on which data should be collected by on‐site engineers. A case study is conducted on designing a metal cleaning process reducing chemical risks due to the use of a cleansing agent. LCA and RA are executed and applied effectively on the basis of integrated objective settings and interpretation. The proposed activity model can be used as a foundation to incorporate such assessments into actual business models.     

Classification in LCA: Building of a coherent family of criteria

There is a very close analogy between Life Cycle Assessment (LCA) and decision making tools such as the multicritcria approach. LCA is a particular model of a multicriteria decision making tool which is applied to environmental data. The similarities between LCA and multicriteria decision making tools are highlighted. The strict precision of multicriteria decision making tools is used to improve classification. For this, six dimensions (or axes of significance) of environmental impacts can be distinguished. The aim is to build a coherent family of environmental data from these considerations. Rules for the building of this family are proposed.     

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.     

Uncertainty of global warming potential for milk production on a New Zealand farm and implications for decision making

Background, aim and scope  

As a food exporting nation, New Zealand recognises that the Global Warming Potential (GWP) impact of agriculture has become important to food customers. Food production policy and industry analysts make GWP decisions based on greenhouse gas inventory and life cycle assessment (LCA) results. For decision making, the level of confidence associated with information is important. However, treatment of uncertainty has been problematic in LCA, especially in agricultural systems. In this paper, the GWP of 1 kg of milk was used as a case study to test the feasibility of quantifying uncertainties by Monte Carlo simulation in an LCA applied to an agriculture product. The study also contributes to the development of good practice and has implications for the incorporation of uncertainties into decision making.     

Data quality

A methodology is presented that enables incorporating expert judgment regarding the variability of input data for environmental life cycle assessment (LCA) modeling. The quality of input data in the life-cycle inventory (LCI) phase is evaluated by LCA practitioners using data quality indicators developed for this application. These indicators are incorporated into the traditional LCA inventory models that produce non-varying point estimate results (i.e., deterministic models) to develop LCA inventory models that produce results in the form of random variables that can be characterized by probability distributions (i.e., stochastic models). The outputs of these probabilistic LCA models are analyzed using classical statistical methods for better decision and policy making information. This methodology is applied to real-world beverage delivery system LCA inventory models. The inventory study results for five beverage delivery system alternatives are compared using statistical methods that account for the variance in the model output values for each alternative. Sensitivity analyses are also performed that indicate model output value variance increases as input data uncertainty increases (i.e., input data quality degrades). Concluding remarks point out the strengths of this approach as an alternative to providing the traditional qualitative assessment of LCA inventory study input data with no efficient means of examining the combined effects on the model results. Data quality assessments can now be captured quantitatively within the LCA inventory model structure. The approach produces inventory study results that are variables reflecting the uncertainty associated with the input data. These results can be analyzed using statistical methods that make efficient quantitative comparisons of inventory study alternatives possible. Recommendations for future research are also provided that include the screening of LCA inventory model inputs for significance and the application of selection and ranking techniques to the model outputs.     

Bringing science and pragmatism together a tiered approach for modelling toxicological impacts in LCA

Goal, Scope and Background  The EU 5^th framework project OMNIITOX will develop models calculating characterisation factors for assessing the potential toxic impacts of chemicals within the framework of LCA. These models will become accessible through a web-based information system. The key objective of the OMNIITOX project is to increase the coverage of substances by such models. In order to reach this objective, simpler models which need less but available data, will have to be developed while maintaining scientific quality. Methods. Experience within the OMNIITOX project has taught that data availability and quality are crucial issues for calculating characterisation factors. Data availability determines whether calculating characterisation factors is possible at all, whereas data quality determines to what extent the resulting characterisation factors are reliable. Today, there is insufficient knowledge and/or resources to have high data availability as well as high data quality and high model quality at the same time. Results  The OMNIITOX project is developing two inter-related models in order to be able to provide LCA impact assessment characterisation factors for toxic releases for as broad a range of chemicals as possible: 1) A base model representing a state-of-the-art multimedia model and 2) a simple model derived from the base model using statistical tools. Discussion. A preliminary decision tree for using the OMNIITOX information system (IS) is presented. The decision tree aims to illustrate how the OMNIITOX IS can assist an LCA practitioner in finding or deriving characterisation factors for use in life cycle impact assessment of toxic releases. Conclusions and Outlook  Data availability and quality are crucial issues when calculating characterisation factors for the toxicity impact categories. The OMNIITOX project is developing a tiered model approach for this. It is foreseen that a first version of the base model will be ready in late summer of 2004, whereas a first version of the simple base model is expected a few months later.     

Fusion of conflicting information for improving representativeness of data used in LCAs

Purpose

Employing representative data is necessary for producing a credible LCA informing decision making process. When the data is available from multiple sources, and in incompatible formats such as point estimates, intervals, approximations, and may even be conflicting in nature, it is important to synthesize it with minimal loss of information to enhance the credibility of LCA. This article introduces a framework for information fusion that can serve this purpose within the current operational procedure of LCA.

Methods

The character of information gathered from multiple sources is inherently different than that exhibited by the information generated by a single random source. The framework of possibility theory can be used to merge such heterogeneous information as demonstrated by its application in the diverse fields such as engineering, finance, and social sciences. This article introduces this methodology for LCAs by first introducing the theory behind data modeling and data fusion with possibility theory. Then, this framework is applied to the disparate data from literature on the manufacturing energy requirements for semiconductor device fabrication, and also to a hypothetical example of linguistic inputs from experts in order to demonstrate the operationalization of the theory. A flowchart is provided to recap the framework and for easy navigation through the steps of merging procedure.

Results and discussion

The framework for fusion of information applied the numerical and linguistic heterogeneous data in the LCA context illustrates that this methodology can be implemented relatively easily to increase the data quality and credibility of LCA. This can be done without making any changes in the usual preferred way of conducting an LCA. Information fusion may be performed either after the sensitivity analysis identifies the most impactful categories that need further investigation, or it can be performed upfront to the select input categories of interest.

Conclusions

The article introduces a well-established framework of information fusion to the field of LCA where disparate data may need to be fused to perform the assessment under certain conditions. This framework can be easily implemented, and will enhance data quality and LCA credibility. We also hope that data entry software such as ecoEditor make provision for the data entry mechanism necessary to enter fused data.     

On the use of weighting in LCA: translating decision makers’ preferences into weights via linear programming

Purpose

The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal.

Methods

We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve).

Results and discussion

A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused.

Conclusions

LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics.     

LCA and decision making: when and how to use consequential LCA; 62nd LCA forum,Swiss Federal Institute of Technology,Zürich, 9 September 2016

The 62nd life cycle assessment (LCA) forum was held on 9 September 2016 to discuss the state of research and application with regard to consequential life cycle assessment. This conference report presents the highlights of the LCA forum. The state of the art of consequential LCA was presented from different viewpoints. It was pointed out that consequential LCA is more than marginal mixes and avoided burdens and involves causal modelling. It was also said that social responsibility calls for consequential LCA. Currently, different models are used to support decision making. It was suggested to make use of the variety of models to check the conclusiveness of their results and thus the reliability of the LCAs. Current and future implementations of consequential LCI models in background databases and linking algorithms were presented. Several speakers presented consequential LCA case studies covering the sectors energy, transport, housing and mining. Some of the LCA models used in the case studies are complemented with general and partial computable equilibrium models and agent-based models and use environmentally extended input-output data or process-based LCA data. Some of the presentations focused on elements such as constrained production, marginal market mixes and technologies or recycling and system expansion. In three parallel workshops, the needs, contents and methodology, and implementation of consequential LCA approaches were discussed. The participants seemed to generally agree on the basic goal that LCA should be able to reflect the consequences of decisions. The inquiry among the participants showed that the demand for consequential LCA studies is hardly existent. The appropriate implementation of consequential modelling in LCA databases and on the appropriate model to be used in consequential LCA case studies was debated. It revealed a need for further and extensive discussions to be able to reach (minimum) consensus.     

Institutionalization of Life-Cycle Thinking in the Everyday Discourse of Market Actors

The widespread popularity of life-cycle assessment (LCA) is difficult to understand from the point of view of instrumental decision making by economic agents. Ehrenfeld has argued, in a 1997 issue of this journal, that it is the world-shaping potential of LCA that is more important than its use as a decision-making tool. The present study attempts to explore the institutionalization of this "LCA world view" among ordinary market actors. This is important because environmental policy relies increasingly on market-based initiatives. Cognitive and normative assumptions in authoritative LCA documents are examined as empirical data and compared with data from focus group interviews concerning products and the environment with "ordinary" manufacturers, retailers, and consumers in Finland. These assumptions are (1) the "cradle-to-grave" approach, (2) the view that all products have an environmental impact and can be improved, (3) the relativity of environmental merit, and (4) the way responsibility for environmental burdens is attributed. Relevant affinities, but also differences, are identified. It is argued that life-cycle thinking is not primarily instrumental, but rather is gaining a degree of intrinsic value. The study attempts to establish a broader institutional context in which the popularity of LCA can be understood. From the point of view of this broader context, some future challenges for the development of LCA and life-cycle thinking are suggested.     

A Decision Support Framework for Sustainable Waste Management

This article describes a decision support framework for the evaluation of scenarios for the integrated management of municipal solid waste within a local government area (LGA).
The work is initially focused on local government (i.e., municipal councils) in the state of Queensland, Australia; however, it is broadly applicable to LGAs anywhere. The goal is to achieve sustainable waste management practices by balancing global and regional environmental impacts, social impacts at the local community level, and economic impacts. The framework integrates life-cycle assessment (LCA) with other environmental, social, and economic tools. For this study, social and economic impacts are assumed to be similar across developed countries of the world. LCA was streamlined at both the life-cycle inventory and life-cycle impact assessment stages.
For this process, spatial resolution is introduced into the LCA process to account for impacts occurring at the local and regional levels. This has been done by considering social impacts on the local community and by use of a regional procedure for LCA data for emissions to the environment that may have impacts at the regional level.
The integration follows the structured approach of the pressure-state-response (PSR) model suggested by the Organisation for Economic Cooperation and Development (OECD). This PSR model has been extended to encompass nonenvironmental issues and to guide the process of applying multiple tools.
The framework primarily focuses on decision analysis and interpretation processes. Multiattribute utility theory (MAUT) is used to assist with the integration of qualitative and quantitative information. MAUT provides a well-structured approach to information assessment and facilitates objective, transparent decisions. A commercially available decision analysis software package based on MAUT has been used as the platform for the framework developed in this study.     

Weighting in Practice:Implications for the Use of Life-Cycle Assessment in Decision Making

This article investigates how environmental trade-offs are handled in life-cycle assessment (LCA) studies in some Nordic companies. Through interviews, the use and understanding of weighting methods in decision making was studied. The analysis shows that the decision makers require methods with which to aggregate and help interpret the complex information from life-cycle inventories. They agreed that it was not their own values that should be reflected in such methods, but they were found to have different opinions concerning the value basis that should be used. The analysis also investigates the difficulties arising from using such methods. The decision makers seemed to give a broader meaning to the term weighting, and were more concerned with the comparison between environmental and other aspects than the weighting of different environmental impacts. A conclusion is that decision makers need to be more involved in modeling and interpretation. The role of the analyst should be to interpret the information needs of the decision maker, and help him or her make methodological choices that are consistent with these needs and relevant from his or her point of view. To achieve this, it is important that decision makers do not view LCA as a highly standardized calculation tool, but as a flexible process of collecting, organizing, and interpreting environmental information. Such an approach to LCA increases the chances that the results will be regarded as relevant and useful.     

Resolution of uncertainty in prefrontal cortex

Making optimal decisions in the face of uncertain or incomplete information arises as a common problem in everyday behavior, but the neural processes underlying this ability remain poorly understood. A typical case is navigation, in which a subject has to search for a known goal from an unknown location. Navigating under uncertain conditions requires making decisions on the basis of the current belief about location and updating that belief based on incoming information. Here, we use functional magnetic resonance imaging during a maze navigation task to study neural activity relating to the resolution of uncertainty as subjects make sequential decisions to reach a goal. We show that distinct regions of prefrontal cortex are engaged in specific computational functions that are well described by a Bayesian model of decision making. This permits efficient goal-oriented navigation and provides new insights into decision making by humans.     

PIQET: the design and development of an online ‘streamlined’ LCA tool for sustainable packaging design decision support

Background, aim and scope  

‘Streamlined’ life cycle assessment (LCA) tools hold out the possibility of providing LCA information quickly and easily in order to support a variety of decision-making environments and situations. The utility of such tools is closely related to the accuracy needs and possibilities, and the particular decisions to be supported. In order to facilitate the provision and application of LCA information in decision making during packaging design, development and utilisation, there is a prima facia case for a ‘streamlined’ LCA tool, provided it meets a set of requirements, including functionality, accuracy, validity, reliability and usability.     

An approach to LCSA: the case of concrete recycling

Purpose

The framework of life cycle sustainability analysis (LCSA) has been developed within the CALCAS project but the procedure on how an LCSA should be carried out is still far from standardized. The purpose of this article is to propose an approach to put the LCSA framework into practice. This approach is illustrated with an on-going case study on concrete recycling.

Methods

In the context of an EC-FP7 project on technology innovation for concrete recycling, five operational steps to implement the LCSA framework are proposed: (1) broad system definition, (2) making scenarios, (3) defining sub-questions for individual tools, (4) application of the tools and (5) interpreting the results in an LCSA framework. Focus has been put on the goal and scope definition (steps 1–3) to illustrate how to define a doable and meaningful LCSA. Steps 4–5 are not complete in the case study and are elaborated theoretically in this paper.

Results and discussion

The experience from the case study shows that the operational steps are especially useful at the stage of defining the goal and scope. Breaking down the sustainability questions into different scales and different aspects gives the possibility to define the sub-questions suitable to be assessed by the individual analytical tools (e.g., LCA, LCC, SLCA, MFA, etc.). The C2CA-LCSA shows a practical approach to model the life cycle impacts of the broad system is to start by modelling the technological system at the micro level and then scale it up with the realistic scenario settings that are generated with the knowledge gained from the MFA studies at the meso-level and from the policy/economic studies at the macro level. The combined application of LCA, LCC and SLCA at the project level shows not all the cost items and only one social impact indicator can be modelled in the process-based LCA structure. Thus it is important to address the left out information at the interpretation step.

Conclusions

Defining sub-questions on three different levels seems most useful to frame an LCSA study at the early stage of goal and scope definition. Although this study provides some useful steps for the operationlisation of the LCSA concept, it is clear that additional case studies are needed to move LCSA into a practical framework for the analysis of complex sustainability problems.     

Application of the impact pathway analysis in the context of LCA

Current LCA practice is mass oriented, i.e. is focused on the amount of chemicals released, disregarding place and time of release. Life cycle impact assessment aims at evaluating potential impacts, and a variety of weighting schemes is discussed to he used for ranking and aggregation of impacts. To establish a closer link between the quantity of a burden released and a decision making context, we propose to follow a detailed impact pathway analysis to estimate actual impacts for some priority impact categories, and use measured individuals’ preferences for impact valuation. Results from a case study illustrate the relevance of site specific impact assessment in the context of LCA.