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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

© 2017 The Authors. Journal of Industrial Ecology, published by Wiley Periodicals, Inc., on behalf of Yale University. Additive manufacturing (AM) proposes a novel paradigm for engineering design and manufacturing, which has profound economic, environmental, and security implications. The design freedom offered by this category of manufacturing processes and its ability to locally print almost each designable object will have important repercussions across society. While AM applications are progressing from rapid prototyping to the production of end-use products, the environmental dimensions and related impacts of these evolving manufacturing processes have yet to be extensively examined. Only limited quantitative data are available on how AM manufactured products compare to conventionally manufactured ones in terms of energy and material consumption, transportation costs, pollution and waste, health and safety issues, as well as other environmental impacts over their full lifetime. Reported research indicates that the specific energy of current AM systems is 1 to 2 orders of magnitude higher compared to that of conventional manufacturing processes. However, only part of the AM process taxonomy is yet documented in terms of its environmental performance, and most life cycle inventory (LCI) efforts mainly focus on energy consumption. From an environmental perspective, AM manufactured parts can be beneficial for very small batches, or in cases where AM-based redesigns offer substantial functional advantages during the product use phase (e.g., lightweight part designs and part remanufacturing). Important pending research questions include the LCI of AM feedstock production, supply-chain consequences, and health and safety issues relating to AM. Keywords: additive manufacturing; energy efficiency; industrial ecology; resource efficiency; sustainability; 3D printing

This paper analyzes Extended Producer Responsibility (EPR) today, twenty years since the emergence of the concept. The paper concentrates on the question of how the (improper) allocation of responsibilities amongst the producers may impact the core aspect of EPR: the creation of design incentives. The analysis takes the prominent area of electrical and electronic equipment (EEE) as a case study. The narrative in the paper proceeds on four fronts. First, it attempts to clarify certain key concepts relating to EPR, because they have evolved and sometimes become blurred during the practical implementation of EPR. Second, the paper will discuss issues, which today appear to constitute the most prominent challenges in creating product design incentives through EPR from the perspective of division of responsibilities. These challenges should be at the top of the policy debate and agenda. Third, the paper makes use of more than ten years of practical experience that has accumulated in EPR policies in the European Union (EU), the U.S. and Japan by highlighting practical examples of approaches and solutions to creating design incentives through a proper division of responsibilities . Fourth, the analysis uses multi-level governance to develop a systemic framework of analysis for EPR. More generally speaking the paper serves as a case for portraying the challenges that face second generation environmental law and policy tools in greening the global economy.

This report documents the interim findings of the INSEAD IPR Network in relation to investigating practical solutions enabling implementation of Individual Producer Responsibility (IPR) for the WEEE Directive.