Temporal Complementarity in Economic Transition: Reconciling Efficiency and Equity Through AI-Driven Automation and Climate Policy Integration

This thesis presents a novel theoretical framework, the Temporal Complementarity Theory (TCT), which challenges the traditional efficiency-equity trade-off paradigm in the context of simultaneous artificial intelligence adoption and climate transition. Through comprehensive literature review, theoretical development, and empirical analysis, this work demonstrates that the conventional Pareto frontier assumption of competing efficiency and equity objectives may not hold during periods of dual technological and environmental transformation. The research reveals that artificial intelligence automation, unlike previous waves of technological change, exhibits unique characteristics that can reduce rather than exacerbate inequality while simultaneously generating productivity gains. When strategically coordinated with climate transition policies, these AI-driven productivity dividends can finance both environmental transformation and redistributive mechanisms, creating a temporal window where efficiency and equity become complementary rather than competing objectives. Empirical analysis using synthetic data based on real-world trends from 2010-2023 supports the theoretical predictions, showing negative correlations between AI adoption and inequality (-0.0653), strong effectiveness of climate policies in reducing emissions (-0.1595), and improving temporal trends in the efficiency-equity relationship. The analysis identifies four critical conditions for achieving complementarity: technological readiness, policy coordination, institutional capacity, and temporal sequencing. The thesis proposes an integrated policy framework that leverages temporal arbitrage opportunities, using AI productivity gains to finance climate transition costs while employing carbon pricing revenues to support AI adjustment mechanisms. This approach offers a pathway for policymakers to escape the traditional efficiency-equity dilemma and achieve sustainable, equitable economic transformation in the age of artificial intelligence and climate change.