China's provincial long-term carbon emissions reduction planning considering fairness, efficiency and gradualness: A multi-objective programming approach.

Abstract

This study investigates the imperative challenge of achieving carbon neutrality in China by implementing effective strategies for carbon emissions. While existing studies on carbon emissions reduction often focus on efficiency or fairness in isolation, few provide a comprehensive framework that integrates efficiency, fairness, and gradualness-key principles for achieving carbon neutrality in a diverse and dynamic context like China. To address this gap, the study presents an innovative multi-objective optimization model meticulously designed for long-term provincial carbon emissions reduction planning. Prioritizing these three principles, this model serves as a powerful tool in guiding policy formulation. The paper introduces the MNSGA-III, a novel multi-objective genetic algorithm with distinctive features like initial solution generation, dedicated crossover, and mutation operations, offering a refined method to navigate this complex landscape. The study further provides cost estimates for achieving carbon neutrality under optimistic, neutral, and pessimistic scenarios, illuminating the financial implications. Additionally, it underscores the urgent need for ambitious mitigation strategies aligned with the IPCC's guidelines to limit global warming to 1.5 °C with 50 % certainty. These findings provide policymakers scientifically robust insights for effective carbon emissions reduction planning.