区域水-能源-粮食绿色效率、地区差异及影响因素

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Abstract In view of the green development concept, a multi-dimensional input-output index system was established by constructing Meta-SSBM and Meta-Malmquist-Luenberger models. The static and dynamic green efficiencies of water-energy-food across the mainland of China during 2004~2018 were evaluated. The characteristics of temporal and spatial differences at the green development level in terms of the east, central and western areas were correspondingly decomposed by the Theil index. The results show that: (1) there was a great difference in the efficiency under the meta- and group-frontier caused by the inefficiency of the technology gap, being the potential optimized technical level in the eastern area and being a risk of overestimation in both the central and western areas; (2) the inefficient implementation of both management and technology was responsible for the inefficiency in the eastern area, the central area and the western area; (3) the highest growth rate of water-energy-food green TFP in eastern area was due mainly to technological progress, and the decline in green TFP in the central and western areas due to the slow down in technological progress; (4) the Theil index of green TFP growth rate in the western area was the highest and the difference within the group was much greater than that among the groups, especially for the western area; (5) the proportion of the education and the science and technology expenditure in the fiscal expenditure, per capita meat production, and per capita vegetable production, along with the water-energy-grain green TFP, changed in the same direction, but in the opposite direction for urbanization rate, population density, and per capita GDP.

Key words： water-energy-food green efficiency Meta-SSBM-ML model meta-frontier model regional heterogeneity

Received: 09 June 2021

PACS:	X32
	F205

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	ZHANG Jing-jing
	WANG Wen-sheng
	LI Yang

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ZHANG Jing-jing,WANG Wen-sheng,LI Yang. Regional water-energy-food green efficiency and heterogeneity and their influencing factors[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(1): 483-496.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I1/483

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