• Energy Research

SummaryEnvironmentally 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.

Supplementary Data File for: Vita, G., Rao, N. D., Usubiaga‐Liaño, A., Min, J. & Wood, R. Durable goods drive two-thirds of global households’ final energy footprints. Environ. Sci. Technol. (2021).

Energy system optimization models (ESOMs) such as MARKAL/TIMES are used to support energy policy analysis worldwide. ESOMs cover the full life-cycle of fuels from extraction to end-use, including the associated direct emissions. Nevertheless, the life-cycle emissions of energy equipment and infrastructure are not modelled explicitly. This prevents analysis of questions relating to the relative importance of emissions associated with the build-up of infrastructure and other equipment required for decarbonization. We have soft-linked an environmentally-extended input-output (EEIO) model to a European TIMES Model (ETM-UCL) with the aim of addressing the following questions: - In what ways does the inclusion of indirect emissions change the optimal technology pathway for decarbonizing the European energy system? - How much does the present value of key low-carbon technologies change when indirect emissions are accounted for in a decarbonization scenario for Europe? We show that, although indirect emissions are a relatively small portion of overall power sector emissions (<10% in 2050), including them in the model leads to changes in the optimal power sector portfolio. Renewable energy technologies become relatively less attractive once indirect emissions are included within the optimization framework, and we quantify this effect, showing that it is not large. Changes to the relative attractiveness of specific renewable energy technologies are more pronounced than the reduction in attractiveness of renewable energy as a whole: in our main scenarios wind energy saw increased relative deployment in 2050 when indirect emissions are accounted for, since it displaced other technologies with higher life-cycle emissions (notably solar PV). Optimal cumulative installed capacity of PV in the EU 2050 is at least 7% lower when indirect emissions are included. We conclude that policy advice derived from ESOMs that focuses on the roles of specific technologies should ensure that it is robust to the possible effects of indirect emissions.

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). 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. 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 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 analysing trends over time! The basic description of the process employed is in the relevant deliverable. 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 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 Richard Wood or Konstantin Stadler for access to the Box data repository. 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 The satellite accounts and characterized impacts are stored in the subfolder "satellite" and "impacts", both containing: 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. v3.8.1 is a minor update to v3.8 due to the existing of a few bugs (negative values) in the v3.8 release. As a result, the balancing has been redone in v3.8.1 with a different constraint set on maximum/minimum allowable values. In the process, the objective function was switched from a QP to Cross Entropy objective. In addition, IOT tables now include flow (F, Z) and coefficient matrices (A, S) as well as consumption based accounts (footprints, D_cba) and multipliers (M). IOT tables now also include characterized impacts (in the extension "impacts"). More information can be found in the included README file. {"references": ["Stadler et al. 2018 (10.1111/jiec.12715)"]}

Abstract China is increasingly known for its ambitions towards an ‘ecological civilisation’ and a circular economy. Our article assesses the implications of an accelerated shift towards steel recycling in China. Given the relevance of steel for development worldwide as well as its environmental intensity, any such shift is likely to have implications for competitiveness in China and beyond. Recent findings suggest that China could take advantage of an increasing availability of obsolete steel scrap in the coming decades, moving towards more circular, and potentially greener, steel production. We assess such industrial restructuring from an economic perspective and address the competitiveness of China relative to other developing and industrialised regions. The analysis uses a novel global economy-wide modelling framework (ENGAGE-materials) to assess the aggregate and sector-level impacts of different scrap use options in China in the 2019–2030 time frame. The results show moderate GDP gains for China of cumulated USD 589 billion in GDP gains by 2030 despite a replacement of primary steel capacity. A more comprehensive industrial policy mix aimed at improved recycling practices and more adaptive downstream sectors could increase gains to USD 819 billion. The international implications are mixed, with losses for iron ore producers (Australia, Brazil and India) and gains for most developing countries benefiting from lower steel prices. Another result is an increasing demand for coal in electricity production if such a shift wouldn’t be aligned with an accelerated energy transition towards low carbon pathways. We discuss policy implications of such alignment, potential co-benefits, and a need for green international partnerships.

Replacing traditional technologies by renewables can lead to an increase of emissions during early diffusion stages if the emissions avoided during the use phase are exceeded by those associated with the deployment of new units. Based on historical developments and on counterfactual scenarios in which we assume that selected renewable technologies did not diffuse, we conclude that onshore and offshore wind energy have had a positive contribution to climate change mitigation since the beginning of their diffusion in EU27. In contrast, photovoltaic panels did not pay off from an environmental standpoint until very recently, since the benefits expected at the individual plant level were offset until 2013 by the CO2 emissions related to the construction and deployment of the next generation of panels. Considering the varied energy mixes and penetration rates of renewable energies in different areas, several countries can experience similar time gaps between the installation of the first renewable power plants and the moment in which the emissions from their infrastructure are offset. The analysis demonstrates that the time-profile of renewable energy emissions can be relevant for target-setting and detailed policy design, particularly when renewable energy strategies are pursued in concert with carbon pricing through cap-and-trade systems.