GoKawiilData
To map and analyse the spatial extent of direct mining-induced deforestation of dense forest across sub-Saharan Africa, we used previously published data23 that map post-deforestation land use across sub-Saharan Africa between 2001 and 2020 at a resolution of 30 m. The dataset first used the global forest change data24 to identify areas of forest loss between 2001 and 2020, before combining an active learning framework with high-resolution (5 m) Planet–Norway’s International Climate and Forests Initiative data to train a deep-learning model that predicts post-deforestation land use. Post-deforestation land use is assigned to one of 15 different classes by the model, one of which is mining. Mining is defined as land used for extractive subsurface and surface mining activities (such as underground and strip mines, quarries and gravel pits), including all associated surface infrastructure as described previously23. Mining as a post-deforestation land use is mapped with high accuracy, with a 98% user’s accuracy and an 82% producer’s accuracy (see ref. 23 for original accuracy metrics). We used all instances of mining mapped previously23 to represent areas of mining activity in this analysis.
The mining data are presented at a resolution of 30 m pixels, with pixels representing either direct mining-induced deforestation or not. It was thus important to group proximate mining pixels together to create distinct ‘clusters’ of mining activity for use in the analysis. We therefore used distance-based density clustering to group together all nearby mining pixels into one cohesive mining cluster. Clustering was performed to group together all pixels within 1 km of another mining pixel, with a minimum of 5 pixels required to form a cluster. Notably, this clustering method does not require any predefined shape or size of clusters, allowing clusters to be created that can accurately reflect the staggered growth of mining activities, which can often spread across long distances and follow particular directions (for example, the growth of mining activities along a riverbank). After performing the clustering process, 67,586 distinct mining clusters remained across sub-Saharan Africa. However, because we were interested in mining-induced deforestation, we then filtered these mining clusters to retain only clusters that were located in densely forested regions, which we defined as having more than one-third dense forest cover (defined as pixels with ≥50% tree cover) in a 5 km buffer from the mine cluster at the start of the analysis period in 2000. We did not consider areas to be forest if they were classified as plantations by the latest version (v.2) of the Spatial Database of Planted Trees (SDPT)54. This final filtering step left 16,627 mining clusters in forested areas for analysis.
Deforestation measures around mines
We define three different forms of deforestation associated with mining activity in and around our mining clusters.
First, direct deforestation defined as annual deforestation caused directly by the mine in the mining cluster footprint (such as, pits and tailing ponds). This includes all pixels with ≥50% tree cover in 2000 that became deforested between 2001 and 2020 with the end use classified as mining 23.
Second, offsite deforestation defined as annual deforestation through any other processes (such as, road construction, and agricultural and/or urban expansion), outside the mining footprint that may be triggered by mine establishment. This represented all pixels that with ≥50% tree cover in 2000 that became deforested between 2001 and 2020 as described previously24 (v1.11) and that were not classified as mining23 and were not identified as plantations in the SDPT v.2 data54.
Third, total deforestation defined as the annual sum of both the direct and offsite deforestation.
DID framework
To estimate the additional total deforestation triggered by mine establishment, we used recent advances in heterogeneity-robust DID models. To assess mining-induced deforestation across spatial scales, we created four concentric ring buffers of increasing size (0–1 km, 1–5 km, 5–10 km and 10–20 km) around the centre of each mine (Extended Data Fig. 1). We defined our response variable as the total (sum of offsite and direct) deforestation around clusters in each buffer per year between 2001 and 2020. We calculated the total annual deforestation in each buffer as a proportion of the total forested area (≥50% tree cover) present in 2000.
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