戴小文, 何艳秋, 钟秋波. 中国农业能源消耗碳排放变化驱动因素及其贡献研究*——基于Kaya恒等扩展与LMDI指数分解方法[J]. 中国生态农业学报(中英文), 2015, 23(11): 1445-1454. DOI: 10.13930/j.cnki.cjea.150500
引用本文: 戴小文, 何艳秋, 钟秋波. 中国农业能源消耗碳排放变化驱动因素及其贡献研究*——基于Kaya恒等扩展与LMDI指数分解方法[J]. 中国生态农业学报(中英文), 2015, 23(11): 1445-1454. DOI: 10.13930/j.cnki.cjea.150500
DAI Xiaowen, HE Yanqiu, ZHONG Qiubo. Driving factors and their contributions to agricultural CO2 emission due to energy consumption in China: Based on an expended Kaya identity and LMDI decomposition method[J]. Chinese Journal of Eco-Agriculture, 2015, 23(11): 1445-1454. DOI: 10.13930/j.cnki.cjea.150500
Citation: DAI Xiaowen, HE Yanqiu, ZHONG Qiubo. Driving factors and their contributions to agricultural CO2 emission due to energy consumption in China: Based on an expended Kaya identity and LMDI decomposition method[J]. Chinese Journal of Eco-Agriculture, 2015, 23(11): 1445-1454. DOI: 10.13930/j.cnki.cjea.150500

中国农业能源消耗碳排放变化驱动因素及其贡献研究*——基于Kaya恒等扩展与LMDI指数分解方法

Driving factors and their contributions to agricultural CO2 emission due to energy consumption in China: Based on an expended Kaya identity and LMDI decomposition method

  • 摘要: 农业低碳化发展方式是农业现代化背景下农业可持续发展的有效实现途径。判断农业碳排放影响因素的驱动力、驱动方向等对有的放矢地制定低成本、高效率低碳农业发展策略与措施意义重大。在前人研究的基础上, 为探讨常规因素之外还有哪些其他因素对农业碳排放有所影响, 本研究以Kaya恒等式为基础, 利用Kaya恒等式的数学性质将中国农业碳排放的影响因素分解为一般技术因素、农业低碳技术因素、农村生活水平因素、间接城镇化因素以及人口规模因素等5个因素, 并利用LMDI指数分解方法对这些因素进行了驱动强度与贡献率的分析。研究发现: 农村生活水平提高是促成农业碳排放的最主要因素; 一般技术因素与农业低碳技术因素都负向地驱动农业碳排放, 相比较而言, 农业低碳技术变动比一般技术变动的碳排放驱动力更为强劲; 总人口变动因素对农业碳排放呈现出正向驱动力, 但无论从整个长跨度区间还是细分区间来看, 其正向驱动力都不强; 由扩展的Kaya恒等式得出的间接城镇化指标与一般城镇化指标之间关于50%的水平在坐标系中对称, 经转换与修正发现城镇化水平对农业碳排放表现出温和的正向驱动力。1990—2013年中国总的农业碳排放中一般技术因素贡献为25.85%, 农业低碳技术因素贡献率为166.55%, 农村生活水平因素贡献率为220.65%, 城镇化水平贡献率为57.63%, 人口规模因素的贡献率则为14.12%。文章建议在发展农业现代化过程中, 通过发展通用和低碳农业技术, 合理有序推进城镇化进程以及营造低碳发展的社会氛围等方面创造更适宜的环境, 以达到农业低碳化发展与可持续发展目标。

     

    Abstract: Low carbon development pattern is an effective way to achieve sustainable development of agricultural modernization. Revealing driving forces, driving directions and contributions of the factors which affect agricultural CO2 emission could help us to develop low-cost and high-efficiency modern agricultural development strategies, and to generate accurate measurements. By using the expanded Kaya identity mathematical properties, we decomposed the driving factors of agricultural CO2 emission in China into general technological factor, agricultural low-carbon technology factor, rural affluence factor, indirect urbanization factor and total population factor. Based on the factorization, we then used the LMDI exponential decomposition method to analyze the driving strength and contribution rate of the five factors of China agriculture CO2 emission. The data used in the study was from Yearbook of China and Agricultural Yearbook of China from 1990 to 2013, and Compilation Statistics of 60 Years of New China. The rise of living standards in rural areas was the main factor driving CO2 emission due to energy consumption of agriculture. General technology and low-carbon agricultural technology were two important factors negatively driving CO2 emission of agriculture. The driving force of change of low-carbon agricultural technology for CO2 emission of agriculture was more powerful than that of development of general agricultural technology. Also change in total population appeared to positively influence CO2 emission in the agriculture, though the driving force of the total population change was weak irrespective of the calculated method used — data for whole observation period or data for specific segments of the observation period. According to the extended Kaya identity, indirect urbanization ratio and normal urbanization rate were symmetric at 50% in terms of coordinate system. Through conversion and revising, change in urbanization ratio was a moderately positive driving factor of CO2 emission in the agriculture. From 1990 to 2013, general technology contributed –25.85%, agricultural low-carbon technology contributed –166.55%, rising living standard contributed 220.65%, urbanization ratio contributed 57.63% and total population contributed 14.12% to total agricultural CO2 emission. It was suggested that more attention should be paid to relevant factors such as general technology and low-carbon agricultural technology development, promotion of reasonable and orderly urbanization, and to the development of low-carbon social atmosphere in order to achieve low-carbon and sustainable development in agriculture.

     

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