中国农业科学 ›› 2024, Vol. 57 ›› Issue (22): 4507-4521.doi: 10.3864/j.issn.0578-1752.2024.22.010

• 土壤肥料·节水灌溉·农业生态环境 • 上一篇    下一篇

中国农业净碳汇再测算:现状特征、时空格局及其影响因素

田云1,2(), 王骁睿1, 尹忞昊1, 张蕙杰3()   

  1. 1 中南财经政法大学工商管理学院,武汉 430073
    2 中南财经政法大学WTO与湖北发展研究中心,武汉 430073
    3 中国农业科学院农业信息研究所,北京 100081
  • 收稿日期:2023-10-06 接受日期:2024-07-23 出版日期:2024-11-16 发布日期:2024-11-22
  • 通信作者:
    张蕙杰,E-mail:
  • 联系方式: 田云,E-mail:tianyun1986@163.com。
  • 基金资助:
    国家自然科学基金(71903197); 国家现代农业产业技术体系专项资金(CARS-08); 中南财经政法大学中央高校基本科研业务费专项资金资助项目(2722022AL003); 2023年度河南省高校哲学社会科学创新人才支持计划(2023-CXRC-21)

Re-Evaluation of China’s Agricultural Net Carbon Sink: Current Situation, Spatial-Temporal Pattern and Influencing Factors

TIAN Yun1,2(), WANG XiaoRui1, YIN MinHao1, ZHANG HuiJie3()   

  1. 1 School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073
    2 WTO and Hubei Development Research Center, Zhongnan University of Economics and Law, Wuhan 430073
    3 Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081
  • Received:2023-10-06 Accepted:2024-07-23 Published:2024-11-16 Online:2024-11-22

摘要:

【目的】基于当前“双碳”战略目标,厘清农业净碳汇现状特征、时空格局及其影响因素,为加快推进农业增汇减排提供重要支撑。【方法】在科学重构指标体系的基础上,利用碳汇/碳排放因子法对中国农业净碳汇进行测算并分析其现状特征;而后运用空间自相关模型探讨其空间依赖性与空间异质性;最后利用最小二乘法剖析影响农业净碳汇强度变化的主要因素。【结果】2005—2022年中国农业净碳汇总量虽存在一定年际波动,但整体上升趋势明显,结合其演变特征可大致划分为“持续上升”“波动下降”“快速上升”与“缓慢上升”4个阶段;农业净碳汇强度同样处于上升态势仅演变轨迹略有区别,结合其增速差异可大致划分为“持续较快增长”“缓慢增长”“波动起伏”与“缓慢增长”4个阶段。2022年农业净碳汇量省际差异较大,且以内蒙古居首,上海最末,相比2005年所有省份均显著增加;2022年农业净碳汇强度以河南居首,青海最末,相比2005年所有省份均有不同程度提升。中国省域农业净碳汇强度整体表现出明显的空间依赖性,同时也存在局部空间聚类现象,超过7成省份呈现出明显的空间集聚特征,且位于高-高集聚和低-低集聚的省份数量正趋于接近。耕地利用结构、城镇化水平、农村居民收入水平和农业内部产业结构均对农业净碳汇强度产生显著影响。具体表现为粮食作物播种面积占比越高、或城镇化率越高、或农村居民收入水平越高、或种植业与畜牧业占比越大,农业净碳汇强度越高。【结论】中国农业净碳汇总量与强度均处于波动上升态势,且省际间差异明显;中国农业净碳汇强度表现出明显的空间依赖性与空间异质性;农业净碳汇强度受耕地利用结构、城镇化水平、农村居民收入水平和农业内部产业结构等因素的影响。应通过建立健全低碳农业发展政策支持体系、强化省际交流与合作、加大财政支农资金投入力度等措施推进增汇减排,促进农业净碳汇量的提升。

关键词: 农业碳排放, 农业净碳汇, 时空格局, 影响因素, 中国

Abstract:

【Objective】Based on the current “dual carbon” strategic goal, this study aimed to clarify the current characteristics, spatio-temporal pattern and influencing factors of agricultural net carbon sink, so as to provide the important support for accelerating agricultural sink increase and emission reduction.【Method】Based on the scientific reconstruction of the index system, the carbon sink/carbon emission factor method was used to measure and analyze the current situation of China’s agricultural net carbon sink. Then the spatial autocorrelation model was used to discuss the spatial dependence and spatial heterogeneity. Finally, the least-squares method was used to analyze the main factors affecting the change of its intensity. 【Result】From 2005 to 2022, the total amount of agricultural net carbon sink in China was in an obvious upward trend, although there were some interannual fluctuations, and its evolutionary characteristics could be roughly divided into four stages, namely, “continuous rise”, “fluctuating decline”, “rapid rise”, and “slow rise”; the intensity of agricultural net carbon sink was also in an obvious upward trend, with only a slight difference in the trajectory of the evolution, and the difference in its growth rate could be roughly categorized into four stages: “continuous rapid growth”, “slow growth”, “fluctuating ups and downs”, and “slow growth”. 2022, the amount of agricultural net carbon sink had a large interprovincial difference, with Inner Mongolia being the first and Shanghai being the last, and compared with the year of 2005, all the provinces had a significant increase. In 2022, the net carbon sink intensity of agriculture would be the highest in Henan and the lowest in Qinghai, with all provinces showing different degrees of increase compared with 2005. China’s provincial agricultural net carbon sink intensity as a whole showed obvious spatial dependence, but there was also a local spatial clustering phenomenon, more than 70% of the provinces showed obvious spatial clustering characteristics, and the number of provinces located in the high-high clustering and the low-low clustering was approaching. The structure of arable land use, urbanization level, rural residents' income level and the internal industrial structure of agriculture all had a significant impact on the intensity of agricultural net carbon sink; specifically, the higher the ratio of sown area of grain crops, or the higher the urbanization rate, or the higher the income level of rural residents, or the larger the ratio of plantation industry to animal husbandry, the higher the intensity of net carbon sink in agriculture.【Conclusion】The total amount and intensity of China’s agricultural net carbon sink were in a fluctuating upward trend and there were obvious inter-provincial differences. The intensity of China’s agricultural net carbon sink showed obvious spatial dependence and spatial heterogeneity. The intensity of the agricultural net carbon sink was affected by the structure of arable land use, the level of urbanization, the level of rural residents' income, and the structure of the internal industries of agriculture. The measures should be taken to promote the enhancement of sink and emission reductions and to promote the enhancement of agricultural net carbon sink in agriculture, such as establishing a sound policy support system for the development of low-carbon agriculture, strengthening inter-provincial exchanges and cooperation, and increasing financial support for agriculture.

Key words: agricultural carbon emissions, agricultural net carbon sink, spatio-temporal pattern, influencing factors, China