Optimizing Carbon Reduction Strategies with ACost Benefit Approach to Climate Economics
DOI:
https://doi.org/10.71202/paper44Abstract
This research introduces an AI-driven, multi-objective optimization framework designed to balance carbon emission reductions with economic costs from a global policy perspective. By applying a modified NSGA-II algorithm to real-world data, the approach systematically explores trade-offs between minimizing CO₂ emissions and minimizing economic burdens. The framework leverages non-dominated sorting and crowding distance measures to maintain solution quality and diversity, while iterative genetic operations—including crossover and mutation—drive convergence toward optimal strategies. The result is a suite of Pareto-optimal solutions that illuminate potential pathways for carbon reduction without disproportionately constraining economic growth. This methodology, supported by comprehensive data pre-processing and validation, equips policy-makers and stakeholders with a transparent, evidence-based tool for shaping effective climate and economic strategies.
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