Artificial Intelligence and the Institutional Efficiency of Environmental Policy: A Transaction Cost Perspective

Abstract

Environmental policy effectiveness is often constrained not by instrument design itself, but by implementation frictions, information asymmetry, high monitoring and enforcement costs, and slow policy adjustment under rapidly changing climate and market conditions. This conceptual article develops a transaction cost perspective on how artificial intelligence (AI) can function as a governance infrastructure that strengthens environmental regulation. It highlights three channels: (1) intelligent monitoring that upgrades Monitoring, Reporting, and Verification (MRV) from periodic reporting to continuous data-driven oversight; (2) predictive modeling that supports adaptive recalibration of policy parameters through a policy feedback loop; and (3) improved integrity of carbon pricing and emissions trading systems (ETS) via anomaly detection and cross-validation of emissions claims. The analysis outlines expected economic effects, such as lower administrative and compliance costs, more reliable price signals, and stronger incentives for green investment, while emphasizing that benefits depend on institutional safeguards. A governance risk framework is proposed to address algorithmic bias, digital inequality, AI energy use, and technological dependency through measurable KPIs. Overall, AI complements rather than replaces environmental policy by expanding institutional capacity for effective regulation.