Systematic Review of GIS and Remote Sensing Applications for Assessing the Socioeconomic Impacts of Mining

As the mining industry expands, a comprehensive understanding of its socioeconomic risks and benefits is urgently needed. This paper systematically reviews 71 studies (1996–2021) that utilized spatially integrated approaches to evaluate socioeconomic mining impacts. The number of studies that utiliz...

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Bibliographic Details
Published in:The journal of environment & development 2023-09, Vol.32 (3), p.243-273
Main Authors: Ang, Michelle Li Ern, Owen, John R., Gibbins, Christopher N., Lèbre, Éléonore, Kemp, Deanna, Saputra, Muhamad Risqi U., Everingham, Jo-Anne, Lechner, Alex M.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Coal
Coal industry
Coal mining
Culture
Data mining
Geographic information systems
Housing
Information processing
Information systems
Infrastructure
Landsat
Life cycle analysis
Minerals
Mining industry
Remote sensing
Socioeconomic factors
Socioeconomics
Sustainability
Systematic review
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Summary:As the mining industry expands, a comprehensive understanding of its socioeconomic risks and benefits is urgently needed. This paper systematically reviews 71 studies (1996–2021) that utilized spatially integrated approaches to evaluate socioeconomic mining impacts. The number of studies that utilize geographic information systems and remote sensing to study mining impacts increased from 2014 onwards. A framework was used to classify the mining impacts studied in the literature and all eight framework categories – Environment, Land, People, Community, Culture, Livelihoods, Infrastructure and Housing – were captured by the literature though Culture was least studied. Coal mining, active mining phase, Landsat data and classic remote sensing algorithms were most highlighted. Future research should focus on advancing geospatial technology like artificial intelligence (AI) to better capture intangible socioeconomic impacts, under-researched minerals and long-term mine lifecycle components. Spatially referenced social data can improve stakeholder involvement and support spatially explicit planning to ensure sustainability.
ISSN:1070-4965
1552-5465
DOI:10.1177/10704965231190126