A new global risk-mapping study shows how environmental pressure, governance gaps, and social vulnerabilities could disrupt the minerals needed for batteries, solar power, and the low-carbon transition.
Study: Revealing environmental, social, and governance-driven conflict risk in global energy transition minerals supply. Image Credit: Pla2na / Shutterstock
The global transition toward a low-carbon economy relies heavily on reliable access to critical engineering materials. A recent study published in the journal Communications Earth & Environment introduced an advanced machine learning (ML) framework to evaluate localized ESG-driven conflict risks across global energy transition mineral mining projects, focusing on lithium, cobalt, platinum, antimony, and tungsten.
Using data from 112,766 historical natural resource conflict events, researchers examined how geopolitical instability, community opposition, and environmental vulnerabilities affect mineral extraction. The findings showed significant geographic variation: many advanced economies exhibited relatively stable conditions, whereas several high-risk mineral-producing areas remained more exposed to supply and mining-project disruptions.
Importance of Materials for Renewable Energy
The global transition from fossil-fuel-based energy systems to renewable energy architectures is driving demand for critical minerals and advanced materials. Technologies such as battery electric vehicles (EVs), solar photovoltaics (PVs), and wind turbines rely heavily on rare elements/materials with unique chemical and physical properties.
Lithium, cobalt, and nickel are essential for modern battery technologies, while indium and gallium are widely used in high-efficiency photovoltaic devices. Other materials, including antimony and tungsten, play important roles in advanced solar technologies and specialized industrial applications. Because the global distribution of many of these resources is highly concentrated, supply chains remain vulnerable to disruptions, making effective risk assessment and resource planning increasingly important.
Framework and Advanced Computational Modeling
To model complex ESG-driven conflict risks, researchers combined data from geological records, satellite observations, and socioeconomic indicators. The datasets were standardized through spatial resampling and normalization to ensure consistency across all variables.
The study compared four ensemble ML approaches: Random Forest, Adaptive Boosting, eXtreme Gradient Boosting, and Gradient Boosting Decision Trees (GBDT). Model performance was evaluated using accuracy and area under the receiver operating characteristic curve within a five-fold cross-validation framework. Hyperparameter tuning was performed with an initial randomized search followed by a grid search.
Among the evaluated models, GBDT delivered the strongest overall performance and was selected as the primary analytical framework because of its ability to capture complex nonlinear relationships while limiting overfitting. Conflict-related target data were obtained from the Polecat Global Event Dataset. To examine geographical changes, a regional risk difference index was applied across seven global regions. The relative importance of 16 indicators spanning environmental, social, and governance dimensions was also evaluated to determine their contribution to localized ESG-driven conflict risk.
Global Material Vulnerabilities and Risk Profiles
The model identified environmental factors as the largest contributor to modeled ESG-driven conflict risk, accounting for more than half of the overall risk score. Within this category, terrain ruggedness, seismic hazards, and water stress were the most influential variables.
Governance factors accounted for 29.6% of total risk, with voice and accountability emerging as the leading governance determinant. Social factors accounted for the remaining share, with regional adaptive capacity leading the way.
Among the minerals analyzed, tungsten exhibited the highest overall risk profile due to elevated social and governance vulnerabilities. Antimony ranked second, reflecting widespread governance-related risks across producing regions.
Lithium showed particularly high environmental risk due to water-stress concerns in major South American extraction areas. In contrast, platinum recorded the lowest overall risk level, with a large proportion of projects located in relatively stable regions that may benefit from stronger mining regulations and more transparent supply chains.
Materials Management and Sourcing Diversification
These findings have practical value for materials engineers, procurement managers, and policymakers seeking to strengthen supply chain resilience. The risk maps and mining-intensity analyses developed in the study can help organizations identify vulnerable sourcing regions and diversify their supply networks before disruptions occur.
For example, companies that depend on lithium for battery production could use these assessments to balance sourcing strategies between water-stressed South American brine deposits and lower-risk hard rock mining operations in regions such as Western Australia. Similarly, industries reliant on tungsten and antimony can use the risk framework to prioritize supplier oversight and strengthen compliance with international environmental and governance standards. According to researchers, integrating localized ESG-driven conflict risk assessments into procurement and policy planning could help reduce exposure to geopolitical, environmental, and social disruptions while supporting more sustainable and resilient mineral supply chains.
Conclusion and Future Directions
In summary, the study demonstrates that combining advanced ML frameworks with localized socioeconomic and environmental data can improve risk assessment across critical mineral supply chains. Effective resource governance requires targeted strategies tailored to each mineral's specific risk profile, rather than relying solely on broad sustainability frameworks.
However, the authors cautioned that the model predicts ESG-driven conflict risk, rather than broader ESG vulnerabilities that may exist without triggering recorded conflict events. They also noted that the Polecat conflict-event data may not capture all ways in which ESG pressures constrain critical mineral mining projects.
Researchers also highlighted the potential for future dynamic monitoring systems that could integrate real-time satellite observations and community sentiment data to identify emerging risks more rapidly. Strengthening the resilience of the energy transition will require a shift from reactive crisis response to proactive, data-driven management of critical material resources.
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Source:
- Zhu, Y., Zhu, J., Lin, X. et al. (2026). Revealing environmental, social, and governance-driven conflict risk in global energy transition minerals supply. Communications Earth & Environment. DOI: 10.1038/s43247-026-03689-4, https://www.nature.com/articles/s43247-026-03689-4