Do mining activities foster regional development? Evidence from Latin America in a spatial econometric framework

Against the backdrop of steadily increasing global raw material demand, the socio-economic implications of metal ore extraction in developing countries are of major interest in academic and policy debates. This work investigates whether mining activities relate to the economic performance of mining regions and their surrounding areas. Usually, subnational impact assessments of mining activities are conducted in the form of qualitative in-field case studies and focus on a smaller sample of mining properties and regions. In contrast, we exploit a panel of 32 Mexican, 24 Peruvian and 16 Chilean regions over the period 2008 - 2015 and, in doing so, relate mine-specific data on extraction intensity to regional economic impacts. The study employs a Spatial Durbin Model (SDM) with heteroskedastic errors to provide a flexible econometric framework to measure the impact of natural resource extraction. The results suggest that mining intensity does not significantly affect regional economic growth in both short-run and medium-run growth models. Popular arguments of the mining industry that the extractive sector would trigger positive impulses for regional economic development cannot be verified. Rather, the findings support narratives that mining regions do not benefit from their wealth in natural resources due to low labour intensity, loose links to local suppliers and profit outflows.Series: Ecological Economic Paper

A global-scale data set of mining areas

The area used for mineral extraction is a key indicator for understanding and mitigating the environmental impacts caused by the extractive sector. To date, worldwide data products on mineral extraction do not report the area used by mining activities. In this paper, we contribute to filling this gap by presenting a new data set of mining extents derived by visual interpretation of satellite images. We delineated mining areas within a 10 km buffer from the approximate geographical coordinates of more than six thousand active mining sites across the globe. The result is a global-scale data set consisting of 21,060 polygons that add up to 57,277 km². The polygons cover all mining above-ground features that could be identified from the satellite images, including open cuts, tailings dams, waste rock dumps, water ponds, and processing infrastructure. The data set is available for download from https://doi.org/10.1594/PANGAEA.910894 and visualization at www.fineprint.global/viewer

A set of essential variables for modelling environmental impacts of global mining land use

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]

Ecosystem services costs of metal mining and pressures on biomes

Metal mining has significant impacts on the land it uses. With increasing demand for metals, these impacts will continue to intensify. One way to look at land use and related environmental impacts is the concept of ecosystem services (ES), defined as the benefits people derive from services provided by ecosystems. This paper estimates the costs of the reduction of ES due to metal mining`s global land use by analysing four key metal ores – bauxite (aluminium), copper, gold and iron, and by doing so, provides also novel information from which biomes those metals are extracted. The overall ES cost caused by metal mining is estimated at about USD 5.4 billion/year (2016), with about two thirds in forested areas. If added to prices, it would lead to increases of between 0.8 % and 7.9 % for the four commodities studied. The authors do not understand ES valuation as a market-based, stand-alone tool to lower the land impact of metal mining. Other policy tools would have to play a leading role, such as zoning regulations, environmental minimum standards or closure legislation. However, it would be a useful support for such policy tools in all stages of mining where land use aspects play a role.Fil: Tost, Michael. Montanuniversitaet Leoben; AustriaFil: Murguia, Diego Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; ArgentinaFil: Hitch, Michael. Tallinn University of Technology; EstoniaFil: Lutter, Stephan. Institute For Ecological Economics; AustriaFil: Luckeneder, Sebastian. Institute For Ecological Economics; AustriaFil: Feiel, Susanne. Montanuniversitaet Leoben; AustriaFil: Moser, Peter. Montanuniversitaet Leoben; Austri

Global-scale mining polygons (Version 2)

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, COUNTRY_NAME, AREA in squared kilometres, FID with the feature ID, and geom 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, COUNTRY_NAME, AREA in squared kilometres, and N_FEATURES 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, REFERENCE, FID with the feature ID, and geom 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 %

Global-scale mining polygons (Version 1)

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, COUNTRY_NAME, AREA in squared kilometres, FID with the feature ID, and geom 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, COUNTRY_NAME, AREA in squared kilometers, and N_FEATURES 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, REFERENCE, FID with the feature ID, and geom 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/)