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Industrial Ecology (IE) is the core science of data- and model-driven sustainability research. IE quantifies the environmental and social consequences of human activities by linking environmental, social and economic data into a consistent accounting and modeling framework. Topics of IE research range from analysing global material cycles, to identifying the impacts of products and technologies, to the estimation of the environmental footprints of countries and regions. Driven by the growing complexity of economic systems as well as advances in our understanding of environmental and social interactions, IE research becomes increasingly computational and data intensive. For example, typical Multi-Regional Input-Output databases for the calculation of environmental footprints now contain more than 10 000 country-sector pairs combined with up to 1000 specific social and environmental pressures, resulting in systems with up to 1 billion variables. These databases, however, currently form monolithic data-silos; a common, open e-infrastructure for analysing and sharing IE results is missing. Besides the issues common to most data heavy scientific disciplines (large data versioning, provenance tracking, common ontologies, ...) IE research faces two specific challenges: IE research relies on data published by non-scientific agencies (e.g. businesses, national statistical agencies, international economic databases). These data rarely come with unique identifiers or version control and might be updated/replaced/deleted without notification. Currently, the only way to incorporate these data into a reproducible workflow is to document the access date and, if possible, store a local snapshot of the data. Many of the data sources used by IE contain confidential data (e.g. sales data, tax data). This hinders the implementation of a fully open workflow, demanding access control to raw data. To what extend results derived from such data can follow Open Access and Open Data standards still needs to be determined. Recently, a Data Transparency Task Force (DTTF) was formed by the International Society of Industrial Ecology and has established mandatory minimum requirements for data transparency and accessibility for the Journal of Industrial Ecology publications. Now, the DTTF evolved into the Industrial Ecology Open Science (IEOS) project, a bottom-up initiative for facilitating FAIR IE data as well as procedural transparency (Open Source/Workflow). First activities of the IEOS include: outlining steps towards better reuse of sustainability research software establishing a central IE repository for gathering scattered data and software setup of a Zenodo community for preprints and data/software sharing developing common databases for gathering research outcomes in a consistent way building an ontology for data classification developing software frameworks which unifies access to sustainability data in different formats None of these activities are currently part of the European Open Science Cloud (EOSC) or the European Data Infrastructure (EDI); it can be argued that IE belongs to the long tail of scientific fields as defined by the EOSCpilot. The purpose of this presentation is to discuss opportunities and challenges for moving the bottom-up driven activities into the EOSC and find synergies between current efforts and the existing EDI. &nbsp

With the growth of the field of industrial ecology (IE), research and results have increased significantly leading to a desire for better utilization of the accumulated data in more sophisticated analyses. This implies the need for greater transparency, accessibility, and reusability of IE data, paralleling the considerable momentum throughout the sciences. The Data Transparency Task Force (DTTF) was convened by the governing council of the International Society for Industrial Ecology in late 2016 to propose best-practice guidelines and incentives for sharing data. In this article, the members of the DTTF present an overview of developments toward transparent and accessible data within the IE community and more broadly. We argue that increased transparency, accessibility, and reusability of IE data will enhance IE research by enabling more detailed and reproducible research, and also facilitate meta-analyses. These benefits will make the results of IE work more timely. They will enable independent verification of results, thus increasing their credibility and quality. They will also make the uptake of IE research results easier within IE and in other fields as well as by decision makers and sustainability practitioners, thus increasing the overall relevance and impact of the field. Here, we present two initial actions intended to advance these goals: (1) a minimum publication requirement for IE research to be adopted by the Journal of Industrial Ecology; and (2) a system of optional data openness badges rewarding journal articles that contain transparent and accessible data. These actions will help the IE community to move toward data transparency and accessibility. We close with a discussion of potential future initiatives that could build on the minimum requirements and the data openness badge system.

Journal of Industrial Ecology, 22 (1), ISSN:1088-1980, ISSN:1530-9290

Many countries see biofuels as a replacement to fossil fuels to mitigate climate change. Nevertheless, some concerns remain about the overall benefits of biofuels policies. More comprehensive tools seem required to evaluate indirect effects of biofuel policies. This article proposes a method to evaluate large‐scale biofuel policies that is based on life cycle assessment (LCA), environmental extensions of input‐output (I‐O) tables, and a general equilibrium model. The method enables the assessment of indirect environmental effects of biofuels policies, including land‐use changes (LUCs), in the context of economic and demographic growth. The method is illustrated with a case study involving two scenarios. The first one describes the evolution of the world economy from 2006 to 2020 under business as usual (BAU) conditions (including demographic and dietary preferences changes), and the second integrates biofuel policies in the United States and the European Union (EU). Results show that the biofuel scenario, originally designed to mitigate climate change, results in more greenhouse gas emissions when compared to the BAU scenario. This is mainly due to emissions associated with global LUCs. The case study shows that the method enables a broader consideration for environmental effects of biofuel policies than usual LCA: Global economic variations calculated by a general equilibrium economic model and LUC emissions can be evaluated. More work is needed, however, to include new biofuel production technologies and reduce the uncertainty of the method. This is the peer reviewed version of an article, which has been published in final form at [https://doi.org/10.1111/jiec.12640]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

Conservation of mass and energy are essential to physical accounting, just as price and market balances are essential to economic accounting. These principles guide data collection and inventory compilation in industrial ecology. The resulting balanced surveys, however, can rarely be used directly for life cycle assessment (LCA) or environmentally extended input‐output (EEIO) analysis; some modeling is necessary to recast coproductions by multifunctional activities as monofunctional unit processes (a.k.a. Leontief production functions or technical “recipes”). This modeling is done with allocations in LCA and constructs in input‐output. In this article, we ask how these models respect or perturb the balances of the original inventory. Which allocations or constructs, applied to what type of data set, have the potential to simultaneously respect its multiple physical, financial, and market balances? Our analysis builds upon the recent harmonization of allocations and constructs and the ongoing development of multilayered supply and use inventory tables. We derive the necessary and sufficient conditions for balanced models, investigate the role of data aggregation, and clarify these models' relation to system expansion. We find that none of the modeling families in LCA and EEIO are balanced in general, but special data characteristics can allow for the respect of multiple balances. An analysis of these special cases allows for clear guidance for data compilation and methods integration. This is the peer reviewed version of an article, which has been published in final form at [https://doi.org/10.1111/jiec.12273]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving

Electric vehicles have no tailpipe emissions, but the production of their batteries leads to environmental burdens. In order to avoid problem-shifting, a life cycle perspective should be applied in the environmental assessment of traction batteries. The goal of this study is to provide a transparent inventory for a lithium-ion nickel-cobalt-manganese traction battery based on primary data and to report its cradle-to-gate impacts. The study was carried out as a process-based attributional life cycle assessment. The environmental impacts were analyzed using midpoint indicators. The global warming potential of the 26.6 kilowatt-hour (kWh), 253 kg battery pack was found to be 4.6 tonnes carbon dioxide equivalents. Regardless of impact category, the production impacts of the battery are caused mainly by the production chains of battery cell manufacture, the positive electrode paste, and the negative current collector. The robustness of the study was tested through sensitivity analysis, and results were compared with preceding studies. Sensitivity analysis indicates that the most effective approach to reduce climate change emissions would be to produce the battery cells with electricity from a cleaner energy mix. On a per-kWh basis, cradle-to-gate greenhouse gas emissions of the battery are within the range of those reported in preceding studies. Contribution and structural path analysis allowed for identification of the most impact-intensive processes and value chains. This article provides an inventory based mainly on primary data, which can easily be adapted to subsequent EV studies, and offers improved understanding of environmental burdens pertaining to lithium ion traction batteries.

Electric vehicles have no tailpipe emissions, but the production of their batteries leads to environmental burdens. In order to avoid problem-shifting, a life cycle perspective should be applied in the environmental assessment of traction batteries. The goal of this study is to provide a transparent inventory for a lithium-ion nickel-cobalt-manganese traction battery based on primary data and to report its cradle-to-gate impacts. The study was carried out as a process-based attributional life cycle assessment. The environmental impacts were analyzed using midpoint indicators. The global warming potential of the 26.6 kilowatt-hour (kWh), 253 kg battery pack was found to be 4.6 tonnes carbon dioxide equivalents. Regardless of impact category, the production impacts of the battery are caused mainly by the production chains of battery cell manufacture, the positive electrode paste, and the negative current collector. The robustness of the study was tested through sensitivity analysis, and results were compared with preceding studies. Sensitivity analysis indicates that the most effective approach to reduce climate change emissions would be to produce the battery cells with electricity from a cleaner energy mix. On a per-kWh basis, cradle-to-gate greenhouse gas emissions of the battery are within the range of those reported in preceding studies. Contribution and structural path analysis allowed for identification of the most impact-intensive processes and value chains. This article provides an inventory based mainly on primary data, which can easily be adapted to subsequent EV studies, and offers improved understanding of environmental burdens pertaining to lithium ion traction batteries. © 2013 by Yale University. This is the authors' accepted and refereed manuscript to the article.