Quantitative Modeling of AI Data Center Emissions Under Alternative Energy Policies

This paper develops a quantitative framework for evaluating the environmental impact of AI-related data center electricity demand under alternative energy policy regimes. Using projected AI infrastructure electricity consumption, carbon intensity estimates, and Monte Carlo simulation methods, the analysis compares carbon taxes, renewable energy subsidies, dynamic energy pricing, and clean energy mandates. Results suggest that dynamic energy pricing combined with clean energy mandates achieves substantial emissions reductions while minimizing economic distortion and public cost. The paper contributes to emerging research on AI infrastructure economics, carbon-aware compute allocation, and energy systems optimization.