Growth in resource use is a key driver of climate change and environmental degradation. In recognition of this, a few governments have set targets to reduce their countries' per capita resource use while striving to achieve the goals set out in the Paris Agreement. In this context, the concept of the circular economy (CE) has attracted mounting interest as a means of mitigating climate change, reducing resource use and waste generation, while advancing economic performance. This paper uses a mass-balanced biophysical model (CeAT) linked to a macroeconomic model (DYNK) of the Austrian economy to analyze different stock-flow scenarios, combining a decarbonization scheme with CE strategies at varying levels of ambition. The analysis examines Austria's buildings, transport, and electricity sectors, evaluating economic impacts by employment, GDP, and disposable income results. The framework incorporates two stylized indirect rebounds effects arising from CE strategies of narrowing, i.e. reduced growth in infrastructure, buildings or car fleets. These rebound effects, driven by the reallocation of financial resources, manifest through two distinct consumption pathways: service-oriented and goods-oriented expenditure patterns. Findings indicate, the strong CE scenario can achieve substantial dematerialization. At the same time, it shows potentially the highest average growth rates in disposable income when combined with a service-oriented rebound. First order CE strategies (refuse, rethink, reduce) are therefore of key importance for the triple agenda of climate mitigation, resource use reductions and economic growth. Particularly, demand-side reductions can enhance economic performance if the rebound effects are constrained by reallocating the freed-up expenditure to low-material-intensity services.

The production of iron and steel poses numerous sustainability challenges, including the often-overlooked environmental impact of iron ore mining through its land use. Although the global area affected is small, iron ore mining involves intensive land exploitation, with sites typically located far from steel users, creating a disconnect between impacts and beneficiaries. This study overcomes this disconnect by applying a consumption-based footprint perspective to quantify land-related pressures and impacts of mining, providing quantitative evidence for debates about responsibility and impact mitigation. A multi-regional physical input–output model of global iron-steel supply chains is extended with geospatial data on biomes, mining-related land use, and associated impacts (human appropriation of net primary production). The results show that the majority of mining-related land impacts are attributable to steel use in China and Europe (48 http://jie.click/badges . http://jie.click/badges

The widely heralded decarbonization of economies is a significant intervention in countries' societal metabolism, which eliminates the use of fossil fuels but also requires renewing societal stocks such as buildings, vehicles, and power plants, which in turn requires materials and energy. The circular economy (CE) shifts a country's metabolism toward less material demand, waste, and emissions, moving away from a linear resource flow pattern to one that narrows and slows flows and closes loops, in order to support climate protection. This article uses the example of Austria to examine how decarbonization and CE interact in the buildings, transport, and electricity sectors. We use scenarios to analyze the contribution of decarbonization and CE strategies to achieve targets set by Austrian policy: (1) carbon neutrality by 2040, (2) ambitious reductions in material consumption, and (3) limiting annual land take. A scenario focusing on "decarbonization" alone reduces processed materials by 7% compared to the reference scenario, but is associated with high risks: it requires large supplies of green electricity, technology-critical elements, and smooth permitting procedures. A "weak CE" scenario shows little mitigating effects on these risks. CE and land take targets are missed in the two scenarios. Avoiding further expansion of buildings and roads on unbuilt land as part of a "strong CE" scenario is identified as key to narrow the processed materials of respective sectors from 102 to 26 Mt/a consistent with all three policy targets. It reduces inter alia demand for green electricity facilitating decarbonization and additionally generating co-benefits for health.

Accurate assessments of global primary material extraction, trade of primary materials and products, material use, waste, and emissions support the development of policies that facilitate the decoupling of economic activity, natural resource use, and related environmental impacts. Here, we quantify all crucial aspects of global and country-by-country material requirements needed to fuel economic activities, covering both territorial- and demand-based indicators. These data have been assembled by a consortium of research partners that compile the global material flow and resource productivity online database for the International Resource Panel, which contributes to the global dataset for the System of Environmental–Economic Accounting (SEEA) framework and is employed to monitor progress for the Sustainable Development Goal (SDG) indicators 8.4 and 12.2. We present the main findings of the 2024 update, including methodological improvements and result differences, and discuss the main findings and limitations. Since the last update, we have identified a slowing of global materials extraction since about 2014, a continuation of solid growth in direct trade of materials and products, persistent inequality in resource use between high- and low-income countries, and a prolonged improvement in global material productivity. The full dataset used herein can be downloaded from the Global Material Flow Database hosted by the United Nations Environment Program International Resource Panel.

Extracting raw materials and processing them into products used in industry constitute a substantial source of CO2 emissions, which are currently lacking process detail in many integrated assessment models (IAMs). To broaden the space of climate change mitigation options to include material-oriented strategies such as the circular-economy and material efficiency measures in IAM scenario analysis, we develop the MESSAGEix-Materials module, representing material flows and stocks within the MESSAGEix-GLOBIOM IAM framework. We provide a fully open-source model that can assess different industry decarbonization options under various climate targets for the most energy- and emissions-intensive industries: aluminum, iron and steel, cement, and petrochemicals. We illustrate the model's operation with a baseline and mitigation 2-degrees (2 °C) scenario setup and validate base year results for 2020 against historical datasets. We also discuss the industry decarbonization pathways and material stocks of the electricity generation technologies resulting from the new model features. The next steps are to extend the model to other sectors, end uses and materials, as well as the combined modeling of various supply- and demand-side measures.

EXIOBASE 3 provides a time series of environmentally extended multi-regional input‐output (EE MRIO) tables ranging from 1995 to a recent year for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply‐use tables (SUT) in a 163 industry by 200 products classification as the main building blocks. The tables are provided in current, basic prices (Million EUR). For any questions regarding access, support or licence clarification please email: exiobase-support@googlegroups.com . The database is provided free of charge to users under a CC-BY-SA license. There is a discussion about different licence options, please reach out for information. For help in use of EXIOBASE data for spend-based emission factors, email exiobase-support@googlegroups.com EXIOBASE 3 is the culmination of work in the FP7 DESIRE project and builds upon earlier work on EXIOBASE 2 in the FP7 CREEA project and EXIOBASE 1 of the FP6 EXIOPOL project. These databases are available at the official EXIOBASE website. A special issue of Journal of Industrial Ecology (Volume 22, Issue 3) describes the build process and some use cases of EXIOBASE 3. This includes the article by Stadler et. al 2018 describing the compilation of EXIOBASE 3. Further informations (data quality, updates, ...) can be found in the blog post describing a previous release at the Environmental Footprints webpage. Various concordance tables for the database are available here. For more (background) information see the Readme file. For any questions regarding access, support or licence options please email: exiobase-support@googlegroups.com Previous EXIOBASE 3 Versions Some previous versions (3.7, 3.8) are also available on Zenodo. The even earlier public releases of the data (EXIOBASE v3.3 and v3.4) are available upon request. We recommend, however, to use the latest version due to significant updates of the economic data as well as major differences in water and land use accounts. End year The original EXIOBASE 3 data series ends 2011. In addition, we also have estimates based on a range of auxiliary data, but mainly trade and macro-economic data which go up to 2022 when including IMF expectations. A lot of care must be taken in use of this data. It is only partially suitable for analyzing trends over time! New data incorporating a full update for all SUTs to 2020 is soon available on request, reach out to exiobase-support@googlegroups.com The basic description of the process employed is in the relevant deliverable (link to pdf download).. As of v3.8 (doi: 10.5281/zenodo.4277368), the end years of real data points used are: 2015 energy, 2019 all GHG (non fuel, non-CO2 are nowcasted from 2018), 2013 material, 2011 for most others, land, water. More details are available in the readme file. The EXIOBASE country disaggregated dataset EXIOBASE3rx provides land updates to 2015. Some work is going on to update the extensions, but other collaborative efforts are more than welcome. Bulk Download To allow the download of specific years we uploaded the data as zip archives per year and mrio type (industry by industry: ixi, and product by product: pxp). If you need all data, we recommend the excellent zenodo_get python utility for the download. After installing the tool, you can download the latest version with: zenodo_get 10.5281/zenodo.3583070 Previous versions are available by replacing the latest DOI with previous record numbers. Alternatively, you can contact us at exiobase-support@googlegroups.com for alternative access options. IOT download and Pymrio integration If your are only interested in the IO tables, Pymrio (version >= 0.4.5) includes an automatic EXIOBASE 3 download function which works with the EXIOBASE upload on zenodo. The EXIOBASE 3 files can then be parsed and analysed directly. Nomenclature Archives: IOT_YYYY_ixi.zip - MRIO archive for Year YYYY in industry by industry format IOT_YYYY_ixi.zip - MRIO archive for Year YYYY in product by product format MRSUT_YYYY.zip - Multi-regional Supply-Use table for year YYYY SUT.zip - Domestic Supply Use for each country and year Content of IOT*.zip: (the archive can be read directly by pymrio without unpacking). The economic core is stored in the root of the archive, containing among others: Z.txt - flow/transactions matrix A.txt - matrix/inter-industry coefficients, (direct requirements matrix) Y.txt - final demand x.txt - gross/total output unit.txt - Units of the flow data We provide two set of extension data (stored in the sub-folders with the same name): satellite - uncharacterized stressors data - e.g. CO2 emissions, land use per category, etc. impacts - characterized stressors (=> impacts) - e.g. total GWP100, total land use, etc The total list of stressors and impacts are in the index of all files, most conveniently in the 'unit.txt'. Both extension subfolder contain: F.txt - Factors of productions/stressors/impacts F_Y.txt - Stressors/impacts of the final demand, S.txt - Direct stressor/impact coefficients S_Y.txt - Stressor/impact coefficients of the final demand M.txt - MRIO extension multipliers (total requirement factors of consumption) D_cba.txt - Consumption based accounts per sector D_pba.txt - Production based accounts per sector D_cba_reg.txt - Consumption based accounts per region D_pba_reg.txt - Production based accounts per region D_imp_reg.txt - Import accounts per region D_exp_reg.txt - Export accounts per region unit.txt - Absolute units of the stressor and impacts The unit of the coefficient data M and S are given be the unit of the satellite account per unit of the economic core (e.g. kg CO2eq/Million Euro) Announcements We use the EXIOBASE google group for announcing new versions of the database. For any questions regarding access, support or licence options please email: exiobase-support@googlegroups.com

Informed environmental-economic policy decisions require a solid understanding of the economy's biophysical basis. Global physical input–output tables (gPIOTs) collate a vast array of information on the world economy's physical structure and its interdependence with the environment, which can help to monitor progress toward a sustainable circular economy. However, building gPIOTs requires dealing with mismatched and incomplete primary data with high uncertainties, which makes it a time-consuming and labor-intensive endeavor. We address this challenge by introducing the PIOLab: A virtual laboratory for building gPIOTs. This represents the newest branch of the industrial ecology virtual laboratory (IELab) concept, a cloud-computing platform and collaborative research environment through which participants can pool resources to assemble individual input–output tables that target specific research questions. To overcome the lack of primary data, the PIOLab builds extensively upon secondary data derived from a variety of models commonly used in industrial ecology. We use the case of global iron-steel supply chains to describe the architecture of the PIOLab and highlight its analytical capabilities. A major strength of the gPIOT is its ability to provide mass-balanced indicators on both apparent/direct and embodied/indirect flows, for regions and disaggregated economic sectors. We present the first gPIOTs for 10 years (2008–2017), covering 32 regions, 30 processes, and 39 types of iron/steel flows. Diagnostic tests of the data reconciliation show a good level of adherence between raw data and the values realized in the gPIOT. We conclude with elaborating on how the PIOLab will be extended to cover other materials and energy flows. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.

The sustainable development goals (SDGs) were adopted in 2015, succeeding the Millennium Development Goals (MDGs). While the MDGs focused on improving well-being in the developing world, the 17 SDGs address all countries and aim at reconciling economic and social with ecological goals. We adopt a social ecology perspective and critically reflect on the SDGs’ potential for monitoring, supporting, and bringing about a transformation towards sustainability. Starting from a literature review on the SDGs, we link empirical findings from social ecology with analyses of SDG targets and indicators. First, we find that the SDGs fail to monitor absolute trends in resource use and thus prioritize economic growth over ecological integrity. Second, we discuss the contradictions between economic growth and sustainable resource use in early and late stages of industrialization processes and show that they are responsible for important trade-offs among SDG targets. Third, we analyze the transformative potential of the SDGs with a focus on the actors and institutions addressed to bring about transformative change. We find that the SDGs rely mainly on those institutions responsible for unsustainable resource use, and partly propose measures that even reinforce current trends towards less sustainability. Despite ascertaining limited transformative potential to the SDGs from an analytical perspective, we conclude by stressing the strategic relevance of the SDGs for visions, research, and practices of statt towards transformative change towards sustainability.

To keep global heating and other negative consequences of socioeconomic activities within manageable boundaries, industrialized countries must undergo substantial decarbonization, requiring the exploitation of synergies with other environmental endeavors. Improving resource efficiency—that is, reducing the resources required to generate a unit of economic output—is a prominent goal pursued across levels of scale. How does resource efficiency relate to decarbonization? Do economies decrease their emissions as they become more efficient? We examine this relationship for Austria from 2000 to 2015 by conducting an index decomposition analysis at the sectoral level by using consumption‐based indicators from the multi‐regional input–output model Exiobase. Our analysis shows that for Austria, the currently popular pursuit of material efficiency appears to run the risk of coinciding with higher emissions, suggesting that the opportunities to achieve both decarbonization and dematerialization are limited. The Austrian service sectors could contribute to a reduction of the CO 2 footprint via material efficiency improvements, but strong economic growth foils this possibility coming to fruition. The Austrian economy would do well to either curb demand for goods and services driving global CO 2 emissions or to produce imported goods and services domestically in an environmentally more benign manner.

Biodiversity and ecosystem service losses driven by land-use change are expected to intensify as a growing and more affluent global population requires more agricultural and forestry products, and teleconnections in the global economy lead to increasing remote environmental responsibility. By combining global biophysical and economic models, we show that, between the years 2000 and 2011, overall population and economic growth resulted in increasing total impacts on bird diversity and carbon sequestration globally, despite a reduction of land-use impacts per unit of gross domestic product (GDP). The exceptions were North America and Western Europe, where there was a reduction of forestry and agriculture impacts on nature accentuated by the 2007-2008 financial crisis. Biodiversity losses occurred predominantly in Central and Southern America, Africa and Asia with international trade an important and growing driver. In 2011, 33% of Central and Southern America and 26% of Africa's biodiversity impacts were driven by consumption in other world regions. Overall, cattle farming is the major driver of biodiversity loss, but oil seed production showed the largest increases in biodiversity impacts. Forestry activities exerted the highest impact on carbon sequestration, and also showed the largest increase in the 2000-2011 period. Our results suggest that to address the biodiversity crisis, governments should take an equitable approach recognizing remote responsibility, and promote a shift of economic development towards activities with low biodiversity impacts.

In various international policy processes such as the UN Sustainable Development Goals, an urgent demand for robust consumption-based indicators of material flows, or material footprints (MFs), has emerged over the past years. Yet, MFs for national economies diverge when calculated with different Global Multiregional Input-Output (GMRIO) databases, constituting a significant barrier to a broad policy uptake of these indicators. The objective of this paper is to quantify the impact of data deviations between GMRIO databases on the resulting MF. We use two methods, structural decomposition analysis and structural production layer decomposition, and apply them for a pairwise assessment of three GMRIO databases, EXIOBASE, Eora, and the OECD Inter-Country Input-Output (ICIO) database, using an identical set of material extensions. Although all three GMRIO databases accord for the directionality of footprint results, that is, whether a countries' final demand depends on net imports of raw materials from abroad or is a net exporter, they sometimes show significant differences in level and composition of material flows. Decomposing the effects from the Leontief matrices (economic structures), we observe that a few sectors at the very first stages of the supply chain, that is, raw material extraction and basic processing, explain 60% of the total deviations stemming from the technology matrices. We conclude that further development of methods to align results from GMRIOs, in particular for material-intensive sectors and supply chains, should be an important research priority. This will be vital to strengthen the uptake of demand-based material flow indicators in the resource policy context.

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web.Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge.Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine.The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud.In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases.We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community.Finally, we provide a few technical solutions and insights that can address these challenges.Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.

1Institute for Ecological Economics, Vienna University of Economics andBusiness, Vienna, Austria 2Institute for Social Ecology, University ofNatural Resources and Life Sciences, Vienna, Austria 3Commonwealth Scientific and Industrial ResearchOrganisation, Canberra, Australia 4Fenner School of Environment and Society, AustralianNational University, Canberra, Australia 5School of Earth and Environment, University of Leeds, Leeds, UK Correspondence StefanGiljum, Institute forEcological Economics, ViennaUniversity ofEconomics andBusiness, Welthandelsplatz1/D5, 1020Vienna,Austria. Email: stefan.giljum@wu.ac.at Funding Information Thisworkwas supportedby funding fromthe Organisation forEconomicCo-operationand Development (OECD)under the contractsNo. 500050077andNo. 500061944, aswell as by theEuropeanCommissionunder theERC ConsolidatorGrant “FINEPRINT” (GrantNo. 725525).AnneOwen's timewas fundedbya UKEngineering andPhysical SciencesResearch Council FellowshipGrant (EP/R005052/1). EditorManagingReview:RichardWood Abstract In various international policy processes such as the UN Sustainable Development Goals, an urgent demand for robust consumption-based indicators of material flows, or material footprints (MFs), has emerged over the past years. Yet, MFs for national economies diverge when calculated with different Global Multiregional Input–Output (GMRIO) databases, constituting a significant barrier to a broad policy uptake of these indicators. The objective of this paper is to quantify the impact of data deviations between GMRIO databases on the resulting MF. We use two methods, structural decomposition analysis and structural production layer decomposition, and apply them for a pairwise assessment of threeGMRIOdatabases, EXIOBASE, Eora, and theOECD InterCountry Input–Output (ICIO) database, using an identical set of material extensions. Although all three GMRIO databases accord for the directionality of footprint results, that is, whether a countries’ final demand depends on net imports of rawmaterials from abroad or is a net exporter, they sometimes show significant differences in level and composition of material flows. Decomposing the effects from the Leontief matrices (economic structures), we observe that a few sectors at the very first stages of the supply chain, that is, raw material extraction and basic processing, explain 60% of the total deviations stemming from the technology matrices. We conclude that further development of methods to align results from GMRIOs, in particular for material-intensive sectors and supply chains, should be an important research priority. This will be vital to strengthen the uptake of demand-basedmaterial flow indicators in the resource policy context.

Funding information H2020EuropeanResearchCouncil, Grant/AwardNumber: 725525; SpanishMinistry ofEconomyandCompetitiveness,Grant/Award Number:MDM-2015-0552 Abstract Input–output analysis is one of the central methodological pillars of industrial ecology. However, the literature that discusses different structures of environmental extensions (EEs), that is, the scopeof physical flowsand their attribution to sectors in themonetary input–output table (MIOT), remains fragmented. This article investigates the conceptual and empirical implications of applying two different but frequently used designs of EEs, using the case of energy accounting, where one represents energy supply while the other energy use in the economy. We derive both extensions from an official energy supply–use dataset and apply them to the same single-region input– output (SRIO) model of Austria, thereby isolating the effect that stems from the decision for the extension design. We also crosscheck the SRIO results with energy footprints from the global multi-regional input–output (GMRIO) dataset EXIOBASE. Our results show that the ranking of footprints of final demand categories (e.g., household and export) is sensitive to the extension design and that product-level results can vary by several orders of magnitude. The GMRIO-based comparison further reveals that for a few countries the supply-extension result can be twice the size of the use-extension footprint (e.g., Australia and Norway). We propose a graph approach to provide a generalized framework to disclosing the design of EEs. We discuss the conceptual differences between the two extension designs by applying analogies to hybrid life-cycle assessment and conclude that our findings are relevant formonitoring of energy efficiency and emission reduction targets and corporate footprint accounting.

Input–output analysis is one of the central methodological pillars of industrial ecology. However, the literature that discusses different structures of environmental extensions (EEs), that is, the scope of physical flows and their attribution to sectors in the monetary input–output table (MIOT), remains fragmented. This article investigates the conceptual and empirical implications of applying two different but frequently used designs of EEs, using the case of energy accounting, where one represents energy supply while the other energy use in the economy. We derive both extensions from an official energy supply–use dataset and apply them to the same single‐region input–output (SRIO) model of Austria, thereby isolating the effect that stems from the decision for the extension design. We also crosscheck the SRIO results with energy footprints from the global multi‐regional input–output (GMRIO) dataset EXIOBASE. Our results show that the ranking of footprints of final demand categories (e.g., household and export) is sensitive to the extension design and that product‐level results can vary by several orders of magnitude. The GMRIO‐based comparison further reveals that for a few countries the supply‐extension result can be twice the size of the use‐extension footprint (e.g., Australia and Norway). We propose a graph approach to provide a generalized framework to disclosing the design of EEs. We discuss the conceptual differences between the two extension designs by applying analogies to hybrid life‐cycle assessment and conclude that our findings are relevant for monitoring of energy efficiency and emission reduction targets and corporate footprint accounting.

Stadler, Konstantin; Wood, Richard; Bulavskaya, Tatyana; Sodersten, Carl-Johan ; Simas, Moana; Schmidt, Sarah ; Usubiaga, Arkaitz; Acosta-Fernández, José; Kuenen, Jeroen; Bruckner, Martin ; Giljum, Stefan ; Lutter, Stephan ; Merciai, Stefano; Schmidt, Jannick Højrup; Theurl, Michaela C. ; Plutzar, Christoph ; Kastner, Thomas ; Eisenmenger, Nina ; Erb, Karl-Heinz ; Koning, Arjan de ; Tukker, Arnold

Journal of Industrial EcologyVolume 22, Issue 4 p. 943-966 FOREIGN LANGUAGE ABSTRACTSFree Access Chinese Abstracts Journal of Industrial Ecology Volume 22, Number 4 First published: 03 August 2018 https://doi.org/10.1111/jiec.12681AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume22, Issue4August 2018Pages 943-966 Translations 《产业生态学报》中文摘要 (JIE Chinese Abstracts) Resúmenes en Español de la Revista de Ecología Industrial (JIE Spanish Abstracts) RelatedInformation

Globalization led to an immense increase of international trade and the emergence of complex global value chains. At the same time, global resource use and pressures on the environment are increasing steadily. With these two processes in parallel, the question arises whether trade contributes positively to resource efficiency, or to the contrary is further driving resource use? In this article, the socioeconomic driving forces of increasing global raw material consumption (RMC) are investigated to assess the role of changing trade relations, extended supply chains and increasing consumption. We apply a structural decomposition analysis of changes in RMC from 1990 to 2010, utilizing the Eora multi-regional input-output (MRIO) model. We find that changes in international trade patterns significantly contributed to an increase of global RMC. Wealthy developed countries play a major role in driving global RMC growth through changes in their trade structures, as they shifted production processes increasingly to less material-efficient input suppliers. Even the dramatic increase in material consumption in the emerging economies has not diminished the role of industrialized countries as drivers of global RMC growth.

Environmentally extended multiregional input‐output (EE MRIO) tables have emerged as a key framework to provide a comprehensive description of the global economy and analyze its effects on the environment. Of the available EE MRIO databases, EXIOBASE stands out as a database compatible with the System of Environmental‐Economic Accounting (SEEA) with a high sectorial detail matched with multiple social and environmental satellite accounts. In this paper, we present the latest developments realized with EXIOBASE 3—a time series of EE MRIO tables ranging from 1995 to 2011 for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply‐use tables (SUTs) in a 163 industry by 200 products classification as the main building blocks. In order to capture structural changes, economic developments, as reported by national statistical agencies, were imposed on the available, disaggregated SUTs from EXIOBASE 2. These initial estimates were further refined by incorporating detailed data on energy, agricultural production, resource extraction, and bilateral trade. EXIOBASE 3 inherits the high level of environmental stressor detail from its precursor, with further improvement in the level of detail for resource extraction. To account for the expansion of the European Union (EU), EXIOBASE 3 was developed with the full EU28 country set (including the new member state Croatia). EXIOBASE 3 provides a unique tool for analyzing the dynamics of environmental pressures of economic activities over time.