Publication Details
Abstract
This study evaluates the economic, environmental, and social implications of deploying artificial intelligence (AI)-enabled environmental monitoring stations at JSC “Uzbekcoal”, Uzbekistan’s principal coal producer. The case study integrates company environmental and production records with a structured before–after assessment, an environmental performance index, a corporate social responsibility (CSR) linkage model, and a cost–benefit framework. The proposed architecture combines Internet of Things sensors, edge validation, a centralized data platform, and AI-supported anomaly detection and forecasting. Company records associated with the monitoring initiative indicate a 44.5% reduction in environmental penalties, a 20–25% reduction in harmful emissions, and an estimated reduction of up to 20% in adverse environmental exposure affecting nearby communities. These values are interpreted as case-specific operational outcomes rather than universal causal effects because the available dataset does not support randomized attribution. The analysis shows how continuous measurement can convert environmental management from periodic compliance reporting into a preventive decision system: exceedances are detected earlier, mitigation resources are targeted more precisely, and auditable data improve stakeholder transparency. Economically, avoided penalties, lower incident-response expenditure, resource savings, and reduced downtime constitute the principal benefit channels. Socially, improved exposure control, disclosure, and grievance responsiveness strengthen the environmental and social dimensions of CSR. The paper proposes a phased implementation roadmap that emphasizes sensor co-location and calibration, data governance, human oversight, cybersecurity, and alignment with ISO 26000 and ESG reporting. The study contributes an applied evaluation framework for AI-enabled environmental governance in coal-mining enterprises in emerging economies.