| 中国森林碳汇效率测度、总量预测与资源配置 |
| 摘要点击 528 全文点击 31 投稿时间：2025-01-10 修订日期：2025-04-02 |
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| 中文关键词 森林碳汇 三阶段DEA 逆DEA GAN-KOA-CNN-BiLSTM模型 资源配置 |
| 英文关键词 forest carbon sink three-stage DEA inverse DEA GAN-KOA-CNN-BiLSTM model resource allocation |
| DOI 10.13227/j.hjkx.20260213 |
| 作者 | 单位 | E-mail | | 周建力 | 新疆大学经济与管理学院, 乌鲁木齐 830046
西北能源碳中和教育部工程研究中心, 乌鲁木齐 830046
新疆能源碳中和战略与决策研究中心, 乌鲁木齐 830046 | 15652112686@163.com | | 王雅琪 | 新疆大学经济与管理学院, 乌鲁木齐 830046
新疆能源碳中和战略与决策研究中心, 乌鲁木齐 830046 | | | 徐子瀚 | 新疆大学经济与管理学院, 乌鲁木齐 830046
新疆能源碳中和战略与决策研究中心, 乌鲁木齐 830046 | | | 刘丹丹 | 新疆大学经济与管理学院, 乌鲁木齐 830046
新疆能源碳中和战略与决策研究中心, 乌鲁木齐 830046 | | | 杨诚 | 新疆大学经济与管理学院, 乌鲁木齐 830046
新疆能源碳中和战略与决策研究中心, 乌鲁木齐 830046 | |
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| 中文摘要 |
| 在气候变化挑战与践行“双碳”目标的大背景下，准确评估各地区森林碳汇效率，识别效率差异背后的原因，并据此提出有效的资源配置路径，对于促进区域协调发展、提高资源利用率和实现固碳减排意义重大. 基于中国2004~2021年的省级数据，构建三阶段DEA模型测度30个省市的森林碳汇效率，并进行区域差异分析；同时建立基于GAN和KOA-CNN-BiLSTM-Attention的预测模型，预测2030年各地区的碳汇总量目标，并设置情景提高模型精度；进一步基于三阶段理论改进逆DEA模型，讨论各地区要达到预测碳汇目标的资源配置路径规划方案，特别对各地区当前投入冗余与不足情况进行反馈. 结果表明：①中国森林碳汇效率水平虽然呈现增长态势，但整体处于中等偏低水平，且区域差异显著. 效率水平较高地区主要分布在西南及东北地区；华北地区水平较低，需要予以重视；其他地区介于中间水平，仍然有待发展. ②自然资源禀赋（涉及森林资源与自然条件）与林业关切程度（涉及政策支持与资源倾斜）是地区间森林碳汇效率存在差异的核心原因，科学的经营管理对地区森林碳汇能力起到加持作用. ③东北地区与西南地区在森林碳汇总量方面贡献度显著，国家范围来看森林碳汇总量到2030年将实现7%~30%的增幅，森林碳汇在固碳减排方面将始终发挥重要作用. ④各地区至少有一项投入需要做出增量改进；土地投入将成为未来实现碳汇目标的主要挑战，同时也是当前森林碳汇效率水平的关键制约因素；从长远来看，黑龙江与内蒙古森林碳汇发展前景最好. 研究可为政府及相关行业践行“双碳”目标提供决策参考、助力提升碳汇效率与资源配置效率. |
| 英文摘要 |
| In the context of the challenge posed by climate change and the pursuit of the “dual carbon” goals， accurately assessing the efficiency of forest carbon sinks in various regions， identifying the reasons behind efficiency differences， and accordingly proposing effective resource allocation pathways are of great significance for promoting coordinated regional development， improving resource utilization efficiency， and achieving carbon sequestration and emission reduction. Based on provincial data from China spanning from 2004 to 2021， a three-stage DEA model was constructed to measure the forest carbon sink efficiency of 30 provinces and municipalities， and a regional difference analysis was conducted. Simultaneously， a prediction model based on GAN and KOA-CNN-BiLSTM-Attention was established to forecast the total carbon sink targets that each region intends to achieve by 2030， and scenarios were set up to enhance the accuracy of the model. Based on the three-stage theory， further improvements were made to the inverse DEA model to discuss the resource allocation path planning schemes for various regions to achieve their predicted carbon sink targets. In particular， feedback was provided on the current input redundancies and deficiencies in each region. The results showed that： ① Although China's forest carbon sink efficiency showed a trend of growth， it was still at a moderately low level overall. Moreover， there were significant regional differences. Regions with higher efficiency levels were mainly distributed in the southwest and northeast， while those in North China had lower levels and require attention. The remaining regions fell in between and still had room for development. ② The core reasons for the differences in forest carbon sink efficiency among regions were natural resource endowments （including forest resources and natural conditions） and the degree of concern for forestry （including policy support and resource allocation preferences）. Scientific management and operation played a reinforcing role in enhancing the forest carbon sink capacity of the regions. ③ The northeast and southwest regions have made significant contributions in terms of total forest carbon sink. Nationally， the total forest carbon sink is expected to increase by 7% to 30% by 2030， and the forest carbon sink will continue to play a crucial role in carbon emission reduction and sequestration. ④ Each region needs to make incremental improvements in at least one input area. Land input will be the main challenge in achieving future carbon sink targets and is also a key limiting factor for the current level of forest carbon sink efficiency. In the long run， Heilongjiang and Inner Mongolia have the best prospects for forest carbon sink development. The research can provide decision-making references for governments and related industries in pursuing the “dual carbon” goals and help enhance carbon sequestration efficiency and resource allocation efficiency. |
