Artificial Intelligence for Analyzing Environmental Taxation and Carbon Pricing Impacts

Abstract

This paper introduces a novel computational framework that applies artificial
intelligence techniques to model and analyze the complex, non-linear impacts of
environmental taxation and carbon pricing policies. Traditional economic models,
often reliant on linear regression and equilibrium assumptions, struggle to capture the dynamic feedback loops, heterogeneous agent behaviors, and emergent
systemic outcomes inherent in socio-ecological-economic systems. We propose a
hybrid methodology that integrates agent-based modeling (ABM) with deep reinforcement learning (DRL) to create a high-fidelity simulation environment. In
this environment, AI-driven agents—representing firms, households, and regulatory bodies—learn and adapt their strategies in response to evolving tax and pricing signals. The core innovation lies in using a meta-learning layer to optimize
policy parameters in silico, searching for Pareto-efficient frontiers that balance carbon reduction, economic output, and distributional equity. Our results, generated
from simulations calibrated with historical data from 1990-2004, demonstrate that
AI-optimized, adaptive carbon pricing schedules can achieve equivalent emission
reductions with 18-27