Description: This data set provides spatially explicit estimates of the area directly used for surface mining on a global scale. It contains more than 21,000 polygons of activities related to mining, mainly of coal and metal ores. Several data sources were compiled to identify the approximate location of mines active at any time between the years 2000 to 2017. This data set does not cover all existing mining locations across the globe. The polygons were delineated by experts using Sentinel-2 cloudless (https://s2maps.eu by EOX IT Services GmbH (contains modified Copernicus Sentinel data 2017 & 2018)) and very high-resolution satellite images available from Google Satellite and Bing Imagery. The derived polygons cover the direct land used by mining activities, including open cuts, tailing dams, waste rock dumps, water ponds, and processing infrastructure. The main data set consists of a GeoPackage (GPKG) file, including the following variables: ISO3_CODE〈string〉, COUNTRY_NAME〈string〉, AREA〈double〉 in squared kilometres, FID〈integer〉 with the feature ID, and geom〈polygon〉 in geographical coordinates WGS84. The summary of the mining area per country is available in comma-separated values (CSV) file, including the following variables: ISO3_CODE〈string〉, COUNTRY_NAME〈string〉, AREA〈double〉 in squared kilometers, and N_FEATURES〈integer〉 number of mapped features. Grid data sets with the mining area per cell were derived from the polygons. The grid data is available at 30 arc-second resolution (approximately 1x1 km at the equator), 5 arc-minute (approximately 10x10 km at the equator), and 30 arc-minute resolution (approximately 55x55 km at the equator). We performed an independent validation of the mining data set using control points. For that, we draw a 1,000 random samples stratified between two classes: mine and no-mine. The control points are also available as a GPKG file, including the variables: MAPPED〈string〉, REFERENCE〈string〉, FID〈integer〉 with the feature ID, and geom〈point〉 in geographical coordinates WGS84. The overall accuracy calculated from the control points was 88.4%, other accuracy metrics are shown below. Confusion Matrix and Statistics Reference Prediction Mine No-mine Mine 394 106 No-mine 10 490 Accuracy : 0.884 95% CI : (0.8625, 0.9032) No Information Rate : 0.596 P-Value [Acc 〉 NIR] : 〈 2.2e-16 Kappa : 0.768 Mcnemar's Test P-Value : 〈 2.2e-16 Sensitivity : 0.9752 Specificity : 0.8221 Pos Pred Value : 0.7880 Neg Pred Value : 0.9800 Precision : 0.7880 Recall : 0.9752 F1 : 0.8717 Prevalence : 0.4040 Detection Rate : 0.3940 Detection Prevalence : 0.5000 Balanced Accuracy : 0.8987 This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme grant number 725525 FINEPRINT project (https://www.fineprint.global/).

Description: This dataset updates the global-scale mining polygons (Version 1) available from https://doi.org/10.1594/PANGAEA.910894. It contains 44,929 polygon features, covering 101,583 km² of land used by the global mining industry, including large-scale and artisanal and small-scale mining. The polygons cover all ground features related to mining, .e.g open cuts, tailing dams, waste rock dumps, water ponds, processing infrastructure, and other land cover types related to the mining activities. The data was derived using a similar methodology as the first version by visual interpretation of satellite images. The study area was limited to a 10 km buffer around the 34,820 mining coordinates reported in the S&P metals and mining database. We digitalized the mining areas using the 2019 Sentinel-2 cloudless mosaic with 10 m spatial resolution (https://s2maps.eu by EOX IT Services GmbH - Contains modified Copernicus Sentinel data 2019). We also consulted Google Satellite and Microsoft Bing Imagery, but only as additional information to help identify land cover types linked to the mining activities. The main data set consists of a GeoPackage (GPKG) file, including the following variables: ISO3_CODE〈string〉, COUNTRY_NAME〈string〉, AREA〈double〉 in squared kilometres, FID〈integer〉 with the feature ID, and geom〈polygon〉 in geographical coordinates WGS84. The summary of the mining area per country is available in comma-separated values (CSV) file, including the following variables: ISO3_CODE〈string〉, COUNTRY_NAME〈string〉, AREA〈double〉 in squared kilometres, and N_FEATURES〈integer〉 number of mapped features. Grid data sets with the mining area per cell were derived from the polygons. The grid data is available at 30 arc-second resolution (approximately 1x1 km at the equator), 5 arc-minute (approximately 10x10 km at the equator), and 30 arc-minute resolution (approximately 55x55 km at the equator). We performed an independent validation of the mining data set using control points. For that, we draw 1,000 random samples stratified between two classes: mine and no-mine. The control points are also available as a GPKG file, including the variables: MAPPED〈string〉, REFERENCE〈string〉, FID〈integer〉 with the feature ID, and geom〈point〉 in geographical coordinates WGS84. The overall accuracy calculated from the control points was 88.3%, Kappa 0.77, F1 score 0.87, producer's accuracy of class mine 78.9 % and user's accuracy of class mine 97.2 %.

Description: This repository provides a set of essential variables to support research on forest loss driven by mining. All variables have been resampled to 30 arcsec spatial resolution (approximately 1 by 1 km at the equator) and are encoded in Geographic Tagged Image File Format (GeoTIFF). The grid extends from the longitude −180 to 180 degrees and from the latitude −90 to 90 degrees in the geographical reference system WGS84. Cells over water have no-data values. Below we describe the list of variables, sources, and processing steps.area_of_mines_circa_2018.tif: mining area in square metres. This layer was derived from a global-scale data set of mining polygons [Maus et al., 202a,b0] available from [doi:10.1594/PANGAEA.910894] under CC BY-SA 4.0 license. The mining area for each 30 arcsec grid was calculated intersecting cells and mining polygons.distance_to_mine_circa_2018.tif: distance to the nearest mine in metres. This layer was derived by calculating the Euclidean distance between each grid cell's centroid to the centroid of the closest grid cell with mine presence, i.e. cells where area_of_mines_circa_2018.tif 〉 0.area_of_forest_cover_circa_2000.tif: area of forest cover in square metres. This layer was derived from the Global Forest Change (GFC) dataset [Hansen et al., 2013] version 1.7 available from [https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html] under CC BY 4.0 license. We aggregated the GFC data from 1 arcsec to our 30 arcsec grid cells by summing the area of forest cover pixels weighted by their surface intersection with the 30 arcsec cells.area_of_forest_cover_within_mines_circa_2000.tif: area of forest cover in square metres. This layer was derived using the same methods as area_of_forest_cover_circa_2000.tif; however, it only includes forest area intersecting mining polygons, i.e. the on-site forest cover circa 2000.area_of_forest_cover_loss_yearly_from_2001_to_2019.tif: area of forest cover loss in square metres. This GeoTIFF file has 19 layers (one layer per year) starting from 2000. We aggregated the GFC data from 1 arcsec to our 30 arcsec grid cells by summing the area of forest loss pixels weighted by their surface intersection with the 30 arcsec cells.ecoregions2017_code.tif: an integer with the ecoregions code (ECO_ID) rasterized from the Ecoregion 2017 polygons [Dinerstein et al., 2017; Resolve, 2017], which is available from [https://ecoregions2017.appspot.com/] under CC BY 4.0 license. The polygons were rasterized to a 30 arcsec grid by the major class present. The ecoregion class names corresponding to the GeoTIFF file values are available in the auxiliary file ecoregions_2017_concordance_tbl.csv, which contains the following variables ECO_ID, ECO_NAME, BIOME_NUM, BIOME_NAME, where ECO_ID is a unique identifier. The layers available from this repo can be stacked together with other variables essential for land-use modelling. Some of these variables are openly available at the same spatial extent and resolution, for example, grided population [NASA, 2018], elevation and slope [Amatulli et al., 2018a,b].

Description: This contribution presents the state of the art of economy-wide material flow accounting. Starting from a brief recollection of the intellectual and policy history of this approach, we outline system definition, key methodological assumptions, and derived indicators. The next section makes an effort to establish data reliability and uncertainty for a number of existing multinational (European and global) material flow accounting (MFA) data compilations and discusses sources of inconsistencies and variations for some indicators and trends. The results show that the methodology has reached a certain maturity: Coefficients of variation between databases lie in the range of 10% to 20%, and correlations between databases across countries amount to an average R2 of 0.95. After discussing some of the research frontiers for further methodological development, we conclude that the material flow accounting framework and the data generated have reached a maturity that warrants material flow indicators to complement traditional economic and demographic information in providing a sound basis for discussing national and international policies for sustainable resource use.

Description: This paper presents the strategy for a large EU-funded Integrated Project: EXIOPOL ("A New Environmental Accounting Framework Using Externality Data and Input-Output Tools for Policy Analysis"), with special attention for its part in environmentally extended (EE) input-output (IO) analysis. The project has three principal objectives: (a) to synthesize and further develop estimates of the external costs of key environmental impacts for Europe; (b) to develop an EE IO framework for the EU-27 in aglobal context, including as many of these estimates as possible, to allow for the estimation of environmental impacts (expressed as LCA themes, material requirement indicators, ecological footprints or external costs) of the activities of different economic sectors, final consumption activities and resource consumption; (c) to apply the results of the work to external costs and EE I-O for illustrative policy questions.

Description: Technological breakthroughs and policy measures targeting energy efficiency and clean energy alone will not suffice to deliver Paris Agreement-compliant greenhouse gas emissions trajectories in the next decades. Strong cases have recently been made for acknowledging the decarbonisation potential lying in transforming linear economic models into closed-loop industrial ecosystems and in shifting lifestyle patterns towards this direction. This perspective highlights the research capacity needed to inform on the role and potential of the circular economy for climate change mitigation and to enhance the scientific capabilities to quantitatively explore their synergies and trade-offs. This begins with establishing conceptual and methodological bridges amongst the relevant and currently fragmented research communities, thereby allowing an interdisciplinary integration and assessment of circularity, decarbonisation, and sustainable development. Following similar calls for science in support of climate action, a transdisciplinary scientific agenda is needed to co-create the goals and scientific processes underpinning the transition pathways towards a circular, net-zero economy with representatives from policy, industry, and civil society. Here, it is argued that such integration of disciplines, methods, and communities can then lead to new and/or structurally enhanced quantitative systems models that better represent critical industrial value chains, consumption patterns, and mitigation technologies. This will be a crucial advancement towards assessing the material implications of, and the contribution of enhanced circularity performance to, mitigation pathways that are compatible with the temperature goals of the Paris Agreement and the transition to a circular economy.