A review of data challenges and opportunities for large-scale assessments of mining’s social and environmental impacts
Year: 2026
Type: article
Abstract: The global shift towards decarbonization has intensified demand for critical minerals, placing the mining sector under greater scrutiny for its socio-environmental impacts. While corporate sustainability disclosures remain the primary source of public information, they fall short in scale, granularity, embedded contextual relevance, and comparability. This review examines four alternative data approaches - crowdsourcing, data scraping, remote sensing, and environmental, social and governance indicators - to assess their potential in supporting large-scale mining impact assessments and overcoming the data challenges. These approaches are complements to corporate reporting, enabling independent, spatially explicit, and cross-comparable analyses. However, distinct limitations exist, including data quality, interoperability, and fitness-for-use. To address these challenges, we propose the exploration of hybrid methodologies, guided by principles of fitness-for-use assessments, data integration, and recognition of data limitations. We also highlight the critical role of open-access platforms with built-in geospatial tools for harmonizing heterogeneous datasets and facilitating dynamic, cross-scalar monitoring of impacts. We recognize that while these emerging technologies provide useful opportunities for assessing and communicating mining impacts to stakeholders, they cannot address the ongoing needs for site-specific baseline data and diagnosis. Ultimately, greater emphasis is necessary on data fitness evaluation, judicious integration, and improved accessibility to support effective, transparent, and contextually grounded assessments of the sector’s socio-environmental performance.
Authors: Michelle L.E. Ang, Eleonore Lebre, Neil McIntyre
Institution: The University of Queensland
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,470
OpenAlex ID: W7128471660