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Deterministic experimental and modelling approaches to understanding cement and concrete properties have greatly advanced over many years. Presently, the physicochemical properties of high Portland cement (PC) materials are generally well known, as they also are for an increasing number of low(er) PC materials involving supplementary cementitious materials like fly ash. However, low(er) PC materials with increasingly diverse chemistry and performance continue to be developed to reduce CO2 emissions, which is increasing the number of viable cement related materials (CRMs) available for use. In turn, a growing number of physicochemical processes need to be better understood in order to analyse their performance, making applications of deterministic approaches more challenging. Similarly, it is becoming more challenging to assess their environmental burdens. Here, we describe version one (v.0.1) of the Panoramix algorithm and its application in exploring relationships among key chemical and physical characteristics, and also impacts of CRMs. Panoramix couples properties of binders, raw materials, and their reactivities with thermodynamic modelling and a life cycle assessment model. We validate the algorithm by applying it to CEM I binders. The results are discussed with a view to identify CRMs that approach optimal technical and environmental performance: we find that hydrated PC binders have lower intrinsic porosity, i.e., potentially lower diffusivity and higher compressive strength, at higher bulk SO3 and Al2O3 content, and lower CaO content, at constant greenhouse gas emissions. We thus show that Panoramix can be used to guide the development, optimisation, and use of higher technical performance and lower impact CRMs.

In Switzerland, the advantages of timber buildings for the climate are broadly discussed. In the following paper, a comparative sustainability assessment of four building alternatives is presented. Especially the contribution of implementing Swiss timber versus the implementation of imported timber is highlighted. Additionally, the timber-hybrid building structures are compared to a pure reinforced concrete structure. The timber-hybrid structure, with Swiss timber, has clear ecological advantages with only half the greenhouse gas emissions and half the non-renewable energy consumption compared to the reinforced concrete alternative. Comparing the Swiss timber alternative to the imported timber alternative, there are clear ecological advantages, as well. In terms of economic and social sustainability assessment criteria, the reinforced concrete alternative has the lowest production costs and the lowest labor intensity (measured in terms of full-time equivalents). Additionally, the paper includes an analysis of biogenic CO2 emissions and CO2 storage within the timber building alternatives. Finally, an up-scaling to the national level is attempted, showcasing the ecological and economic advantages of promoting the use of locally produced timber., Sustainability, 13 (3), ISSN:2071-1050

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

The Group of Seven (G7) member countries requested that the International Resource Panel conduct an assessment of the potential contribution of Resource Efficiency to Climate Change mitigation (RECC). The Yale Center for Industrial Ecology is currently leading a team of researchers in order toidentify policy scenarios and to develop a modelling framework that enables an assessment of the global socio-economic system. In previous work the International Resource Panel investigated the overall potentials and implications of resource efficiency (RE) and found that it has the potential to reducenatural resource use globally by 28% and greenhouse gas emissions by up to 20% in 2050, compared to a reference scenario.

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