American Journal of Advanced Multidisciplinary Research and Innovation

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Volume 8 Issue 5
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AI-Supported Environmental Justice: Identifying Unequal Exposure to Climate and Pollution Risks

Author(s) Darrell Schulze
Country United States
Abstract Environmental risks are rarely distributed equally across populations. Differences in income, geography, infrastructure, land use, housing quality and access to environmental resources can result in certain communities experiencing disproportionately high exposure to air pollution, extreme heat, flooding, hazardous industrial activities and other climate-related risks. Recent advances in artificial intelligence (AI), remote sensing, geospatial analytics and environmental data integration provide new opportunities to identify and quantify these inequalities. This paper examines the role of AI-supported environmental justice approaches in detecting unequal exposure to climate and pollution risks. A conceptual framework is developed that integrates satellite observations, environmental sensors, meteorological information, demographic datasets, land-use records and socioeconomic indicators with machine-learning and spatial-analysis techniques. The framework enables the identification of environmental-risk hotspots, exposure disparities and potentially underserved communities. The paper further explores how predictive modelling can support climate-risk forecasting, pollution monitoring and evidence-based environmental policy. However, AI-based environmental justice systems also face challenges related to data gaps, spatial bias, algorithmic discrimination, privacy, interpretability and unequal access to digital technologies. Particular attention is given to the importance of community participation and transparent governance, since environmental justice cannot be determined exclusively through computational models. The paper argues that AI should function as an evidence-generation and decision-support mechanism that strengthens, rather than replaces, community knowledge and institutional accountability. A human-centred framework is proposed for integrating AI-driven risk mapping, vulnerability assessment, participatory validation and equitable policy intervention. The study concludes that responsible AI can contribute substantially to identifying environmental inequalities when technological capabilities are combined with high-quality data, social context, community engagement and justice-oriented governance.
Keywords Environmental Justice, Artificial Intelligence, Climate Risk, Pollution Exposure, Environmental Inequality, Machine Learning, Remote Sensing, Geospatial Analytics, Climate Vulnerability, Sustainable Development.
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-29
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