论文系统测度了中国大陆31省区2000—2014年间的人均灰水足迹,首次将生产要素中最关键的资本和劳动力要素引入到人均灰水足迹的驱动效应研究中,同时耦合了传统的环境效率与技术效率因素。应用扩展的Kaya恒等式和LMDI模型,综合分析了上述因素对人均灰水足迹的驱动效应。结果表明：1）从全国范围来看,技术效率效应的减量作用最大,资本产出效应的减量作用近年有所提高,资本深化效应的增量作用最大。2）技术效率效应、资本产出效应和资本深化效应都呈现西北高、东南低的分布格局;河北、北京、天津和山东的环境效率效应和技术效率效应对人均灰水足迹的减量作用较大,其他省区技术效率效应和资本产出效应更有利于人均灰水足迹的降低;资本深化效应在各地都会造成人均灰水足迹的显著提升。
Abstract
This paper systematically measures the per capita grey water footprint in 31 provinces in the mainland of China during 2000-2014. Capital and labor factors which are most critical in production factors are first introduced into the research about driving effect of per capita grey water footprint, besides, traditional environmental efficiency factors and technical efficiency factors are coupled. Subsequently, the driving effect of the above factors on per capita grey water footprint is analyzed synthetically by using the extended Kaya identity and LMDI model. The results show that: 1) Nationwide, the biggest reduction of per capita grey water footprint comes from technical efficiency effect, the reduction effect of capital output has been improved in recent years, and the most increment effect is from capital deepening (the annual average value exceeds 52.29 m3 per capita). 2) The spatial distributions of the technical efficiency effect, the capital output effect and the capital deepening effect are all high in the northwest and low in the southeast; environmental efficiency and technical efficiency have greater decrement effect on the per capita grey water footprint in Hebei, Beijing, Tianjin and Shandong, and technological efficiency and capital output are more favorable for the reduction of per capita grey water footprint in other provinces; in addition, capital deepening can lead to significant increase in per capita grey water footprint in all provinces, and it causes the increment over 400 m3 per capita in Tibet.
关键词
LMDI模型 /
驱动效应 /
人均灰水足迹 /
要素与效率
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Key words
driving effect /
factor and efficiency /
LMDI model /
per capita grey water footprint
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脚注
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基金
国家社会科学重点基金项目（16AJY009）
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