石志恒, 王瑞霞. TOE框架下我国省域农业碳排放影响因素组态分析−NCA与fsQCA方法相结合[J]. 中国生态农业学报 (中英文), 2024, 32(9): 1−12. DOI: 10.12357/cjea.20240087
引用本文: 石志恒, 王瑞霞. TOE框架下我国省域农业碳排放影响因素组态分析−NCA与fsQCA方法相结合[J]. 中国生态农业学报 (中英文), 2024, 32(9): 1−12. DOI: 10.12357/cjea.20240087
SHI Z H, WANG R X. Configuration analysis of factors influencing carbon emissions from provincial agriculture in China under the TOE framework: Combining NCA and fsQCA methods[J]. Chinese Journal of Eco-Agriculture, 2024, 32(9): 1−12. DOI: 10.12357/cjea.20240087
Citation: SHI Z H, WANG R X. Configuration analysis of factors influencing carbon emissions from provincial agriculture in China under the TOE framework: Combining NCA and fsQCA methods[J]. Chinese Journal of Eco-Agriculture, 2024, 32(9): 1−12. DOI: 10.12357/cjea.20240087

TOE框架下我国省域农业碳排放影响因素组态分析NCA与fsQCA方法相结合

Configuration analysis of factors influencing carbon emissions from provincial agriculture in China under the TOE framework:Combining NCA and fsQCA methods

  • 摘要: 发展低碳农业是实现农业可持续发展的必然要求, 而探索农业碳排放影响因素的联动效应是发展低碳农业的必经之路。为厘清我国省域农业碳排放影响因素的复杂因果关系, 本研究基于“技术—组织—环境” (TOE)框架构建了我国省域农业碳排放影响因素的理论框架, 采用投入产出模型核算2017年我国30个省(自治区、直辖市, 不包括中国香港、澳门、台湾和西藏)农业碳排放总量, 并将必要条件分析(NCA)与模糊集定性比较分析(fsQCA)相结合对2017年我国各省农业碳排放的影响因素展开组态分析。结果表明: 1)单个因素并不是实现农业低碳排放的必要条件, 只有技术、组织和环境三方因素相互促进才能实现农业低碳排放; 2)驱动农业低碳排放的影响路径可总结为3条: 机械主导型、机械-结构协同型与结构-环境协同型, 由此可见, 各省农业低碳发展呈多元化驱动路径; 3)导致农业非低碳排放的影响路径可总结为两条: 机械-结构缺失型和机械-环境缺失型。综合农业低碳排放和非低碳排放的影响路径来看, 农业机械化、农业产业结构合理化和环境规制是影响农业碳排放的关键因素, 若对其充分利用或持续优化, 一定会对发展低碳农业起到至关重要的作用。本研究扩充了TOE框架的使用领域, 研究结论为我国各省发展低碳农业提供了多元化的驱动路径。

     

    Abstract: Developing low-carbon agriculture is an inevitable requirement for achieving sustainable agricultural development, and assessing the linkage effect of factors influencing agricultural carbon emissions is a necessary approach for developing low-carbon agriculture. Herein, on the basis of the “Technology–Organization–Environment” framework, we constructed a theoretical framework of factors influencing carbon emissions from Chinese provincial agriculture, with the aim of clarifying the complex causal relationships among these factors. Having calculated the total agricultural carbon emissions of Chinese provinces (autonomous regions and municipalities, excluding Hong Kong, Macao, Taiwan and Xizang) in 2017 using an input–output method, and subsequently calculating the intensity of agricultural carbon emissions in these provinces, we analyzed the necessity of single factors for agricultural low-carbon emissions or non-low-carbon emissions based on Necessary Condition Analysis, and analyzed the adequacy of multi-factors for agricultural low-carbon emissions or non-low-carbon emissions by performing fuzzy set Qualitative Comparative Analysis (fsQCA), which enabled us to assess the complex configuration relationships between influencing factors and agricultural carbon emissions. By combining Necessary Condition Analysis (NCA) with fsQCA, we accordingly established that agricultural mechanization, agricultural financial support, agricultural technician, environmental regulation, and rationalization of the agricultural industrial structure are non-essential conditions for agricultural low-carbon emissions or non-low-carbon emissions. On the basis of fsQCA, we identified three influencing pathways for generating agricultural low-carbon emissions, which are summarized as machinery leading, machinery and structure collaborating, and structure and environment collaborating types, as determined by summing up core factors. The solution coverage of the three influencing pathways was found to be 0.611, which indicates that more than half of the provinces with agricultural low-carbon emissions can be explained. In addition, we identified two influencing pathways contributing to agricultural non-low-carbon emissions, which are summarized as machinery and structure absenting, and machinery and environment absenting types, as detemined by summing up core factors. The solution coverage of the two influencing pathways was 0.427, which indicates that almost half of the provinces with agricultural non-low-carbon emissions can be explained. Finally, on the basis of a comprehensive assessment of the pathways influencing agricultural low-carbon emissions and non-low-carbon emissions, we established that agricultural mechanization, rationalization of the agricultural industrial structure, and environmental regulations are key factors influencing agricultural carbon emissions. The three influencing pathways for generating agricultural low-carbon emissions include at least one of the three key influencing factors, and the two influencing pathways contributing to non-low-carbon emissions lack two of the three key influencing factors. Consequently, our findings indicate that the development of low-carbon agriculture can only be achieved by ensuring the mutual promotion of technology, organization, and environment. These findings provide a new perspective for examining carbon emissions, and expand the scope of the “Technology-Organization-Environment” framework. Moreover, our findings highlight the diverse approaches that can be adopted to develop low-carbon agriculture in the Chinese provinces.

     

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