Ethical Implications of Algorithmic Environmental Risk Mapping for Prioritizing Pollution Monitoring and Regulatory Enforcement Resources

Author

Li Jang

Abstract

 Environmental governance increasingly relies on data-driven technologies to identify ecological hazards, distribute regulatory attention, and address unequal patterns of pollution exposure. Algorithmic environmental risk mapping has emerged as a decision-support approach that integrates emissions inventories, geospatial data, environmental sensors, demographic characteristics, and enforcement histories to identify locations requiring regulatory intervention. However, translating environmental conditions into algorithmic risk classifications raises significant ethical concerns about whose exposure is measured, how environmental harm is quantified, and which communities receive institutional attention. This study examines the ethical implications of using algorithmic risk mapping to prioritize pollution monitoring and regulatory enforcement resources. It focuses specifically on spatial data inequality, historical monitoring gaps, model opacity, distributive injustice, community exclusion, and accountability for algorithm-informed decisions. Particular concern is given to underserved communities whose limited historical monitoring data may systematically underestimate cumulative environmental burdens. The study proposes a justice-centred approach combining algorithmic evidence with community knowledge, transparent prioritization criteria, participatory validation, independent auditing, and accessible mechanisms for challenging risk classifications and regulatory resource-allocation decisions.

Keywords

Algorithmic Environmental Risk Mapping; Environmental Justice; Pollution Monitoring; Regulatory Enforcement; Spatial Data Inequality; Distributive Justice.

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References

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