AI-DRIVEN POLICY MODELING FOR CLIMATE CHANGE MITIGATION STRATEGIES

Abstract : AI-driven policy modeling offers a transformative approach to designing effective climate change mitigation strategies by leveraging advanced reinforcement learning and predictive modeling techniques to simulate complex environmental, economic, and social interactions. This research explores the integration of reinforcement learning algorithms, which enable adaptive policy optimization through continuous interaction with dynamic climate systems, with predictive models that forecast the long-term outcomes of various mitigation scenarios under uncertain conditions. By creating a virtual environment where policies are iteratively tested and refined, the system learns to balance competing objectives such as reducing greenhouse gas emissions, minimizing economic disruptions, and ensuring social equity. Reinforcement learning agents are trained on rich datasets encompassing historical climate data, socioeconomic factors, and policy impacts, allowing them to identify optimal intervention points and adapt strategies in response to evolving climate feedback loops. Predictive modeling, incorporating machine learning techniques and climate science projections, provides the necessary foresight into potential future states, enabling the reinforcement learning framework to evaluate the consequences of policy decisions over multi-decadal horizons. This combined methodology addresses the limitations of traditional static models by incorporating stochastic elements and policy adaptability, thus improving resilience against uncertainties inherent in climate dynamics and human behavior. Furthermore, the approach facilitates multi-agent simulations, capturing interactions between different governance levels and stakeholder groups, which are critical for crafting scalable and politically feasible climate policies. The framework’s capacity for scenario analysis supports decision-makers in exploring diverse pathways and stress-testing policies against extreme events and tipping points, enhancing preparedness and strategic robustness. Results from preliminary experiments demonstrate the model’s ability to propose innovative mitigation strategies that outperform existing benchmarks in terms of emission reduction efficiency and socio-economic sustainability. Additionally, the system highlights trade-offs and synergies between policy instruments such as carbon pricing, renewable energy subsidies, and regulatory mandates, providing actionable insights to guide integrated climate policy design. By advancing AI-driven policy modeling, this research contributes to the broader goal of accelerating the global transition to low-carbon economies through data-informed, adaptive, and transparent decision-making frameworks, ultimately supporting international climate agreements and local implementation efforts aimed at achieving net-zero emissions targets. This study underscores the potential of reinforcement learning and predictive modeling as critical tools in the evolving landscape of climate governance and environmental stewardship.

Keyword Reinforcement learning, predictive modeling, climate change mitigation, policy optimization, adaptive decision-making, emission reduction strategies