Yongxin ZhouThis email address is being protected from spambots. You need JavaScript enabled to view it.
Zhengzhou University of Science and Technology, Zhengzhou, HeNan, China 450064
Received: April 3, 2025 Accepted: May 14, 2025 Publication Date: May 30, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Carbon neutrality, as a fundamental goal of global sustainability, is constrained by the carbon lock-in effect, which limits the progress of low-carbon transitions. Ecological compensation mechanisms offer a promising solution by optimizing land use and enhancing carbon sequestration capacity. However, existing approaches often oversimplify ecological processes, restrict decision-making to discrete actions, and lack robustness against environmental uncertainties. To address these limitations, this paper proposes a reinforcement learning framework based on the deep deterministic policy gradient algorithm (RL-MEC-CL), enabling a more precise representation of the dynamic interactions among carbon emissions, carbon sequestration, and land use. Specifically, RL-MEC-CL, in a continuous action space, leverages an actor-critic architecture with experience replay and target networks to optimize compensation strategies adaptively, balancing carbon reduction benefits, sequestration enhancement, and policy costs. Experiment results demonstrate that RL-MEC-CL not only improves the efficiency of ecological compensation strategies but also exhibits strong robustness and adaptability, offering valuable insights for optimizing ecological governance pathways.
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