| With China's economy entering the "new normal" phase,the traditional extensive economy is no longer sustainable.The report of the 19 th National Congress of the Communist Party of China has clearly put forward to promote the green development,and set the sustainable green development goal of "not only Jinshan Yinshan but also green water and green mountains".And the key to achieving this goal is to ensure economic growth while taking into account the reduction of energy consumption and pollutant emissions.How to allocate the target value of energy conservation and emission reduction to each administrative region,and what measures to improve regional energy efficiency are important to promote green development.Based on it,the paper studies the measurement and influencing factors of green total factor energy efficiency.The main contents and basic conclusions of the paper are as follows:Firstly,considering the heterogeneity of technologies between regions,the paper chooses the meta-frontier method to measure the green total factor energy efficiency of each province.As we kown,the rational classification of regions is the basis of the meta-frontier approach.Therefore,this paper constructs an index system based on the main factors affecting the green development efficiency of each region,then uses factor analysis to reduce the dimensions,and finally performs cluster analysis on 31 provinces,autonomous regions,and municipalities in China(except Hong Kong,Macao and Taiwan)based on factor scores,and analysis the rationalization of the group.The conclusions are as follows:(1)the clustering results of 31 provincial administrative regions in China in the three periods from the "Eleventh Five-Year" period to the "Thirteenth Five-Year" start year are different,and each period inner clustering results are relatively stable.The four groups formed by the cluster are named green development better group,green development general group,preference environment group and preference development group.(2)The clustering results of the three periods do not fully reflect the regional characteristics.The traditional regional division by geographical location is not entirely suitable for the study of green development efficiency.Secondly,in order to measure the green total factor energy efficiency and energy saving potential of different regions,the paper constructs and decomposes the green total factor energy performance index,and then uses the non-radial directional distance function on the meta-frontier method to measure the green total factor energy efficiency of 30 provincial administrative regions in China except Hong Kong,Macao,Taiwan and Tibet from 2006 to 2016.Finally,it studies the differences of different regions' s energy efficiency,technology gaps,and each province's potential energy savings.The conclusions are as follows:(1)the green total factor energy efficiency of China's provincial administrative regions is generally low,and there is much room for improvement.(2)The green total factor energy efficiency of the provinces,autonomous regions and municipalities under the group-frontier will often overestimate the actual level of energy efficiency.The green total factor energy efficiency under the meta-frontier reflects the potential improvement of energy efficiency.(3)There is a significant difference in the green total factor energy efficiency of the provinces,regions,and cities included in the four groups.The green total factor energy efficiency level of group 1(the better green development group)far exceeds the other three groups,which represents the highest level of energy efficiency in China.(4)During the inspection period,Beijing,Tianjin,and Shanghai were always on the meta-frontier,and they were the regions with the highest green total factor energy efficiency in China.Thirdly,the paper explores the changes in green total factor energy efficiency from a dynamic perspective.The meta-frontier Malmquist-Luenberger index based on the non-radial directional distance function is used to measure the green total factor energy efficiency of China's provincial administrative regions and group under the contemporaneous benchmark technology,the intertemporal benchmark technology and the global benchmark technology.The efficiency growth rate is decomposed is used to analyze the dynamic development trend of its composition,and then the technology "innovator" provinces in the group and the countryare identified.The research found that:(1)the green total factor energy efficiency of China's provincial administrative regions basically accords with the rule of the lowest the global benchmark technology,followed by the inter-temporal benchmark technology,and the highest the contemporaneous benchmark technology.According to the choice of different benchmarktechnology,there is a large difference in the green total factor energy efficiency,which indicates that there is a huge technological gap between green energy-saving technologies in various provinces,autonomous regions,and municipalities in China.At the same time,it confirms that the green total factor energy efficiency must be compared at the same level of technology to be meaningful.(2)China's overall green total factor energy efficiency has remained basically unchanged.The growth rate of the green total factor energy efficiency of the four groups shows a trend of group 4 being greater than group 1 greater than group 3 greater than group 2.The growth is mainly due to the contribution of technological progress,and the contribution of improved technological efficiency is small.(3)During the inspection period,some provinces,autonomous regions and municipalities became regional “technical innovators” in a certain year,but no province became a “technical innovator” nationwide.Fourthly,the paper studies the influencing factors of regional green total factor energy efficiency from a spatial perspective.The Moran Index was used to test the spatial correlation of green total factor energy efficiency,and static and dynamic spatial panel regression econometric models were constructed to analyze the influencing factors of green total factor energy efficiency.The study found that:(1)there is a significant spatial dependence of green total factor energy efficiency.And there is a significant spatial diffusion effect of the green total factor energy efficiency between regions.When the green total factor energy efficiency in adjacent regions is high,the factor energy efficiency of this region will be higher.(2)When considering the inertia of regional green total factor energy efficiency,the spatial effect of green total factor energy efficiency is obviously weakened.(3)Energy consumption structure and government influence factors have a significant negative impact on green total factor energy efficiency,foreign direct investment has a significant positive impact on green total factor energy efficiency,and resource endowment does not affect green total factor energy efficiency.Moreover,economic development level and industrial structure factors have a positive impact on green total factor energy efficiency,but the saliency of estimated coefficients between dynamic and static space model are opposite.Property ownership,technological progress and capital-labor ratio have a negative impact on green total factor energy efficiency,but the significance of estimated coefficients of dynamic and static space models is opposite.The negative impact of technological progress on green total factor energy efficiency is inconsistent with conventional cognition,possibly because of the rebound effect.Finally,the paper explores the impact of technological progress on green total factor energy efficiency.First of all,the paper theoretically analyzes the impact mechanism of technological progress on green total factor energy efficiency,and thebuilds an energy rebound effect model with the endogenous growth model.Then,applying the panel data of 25 provincial administrative regions with relatively complete data from 2006 to 2017,the long-term and short-term energy rebound effects at the national and provincial levels are measured based on the assumption that capital is variable.The study finds that:(1)the output elasticity coefficient of capital stock is 0.353,the output elasticity coefficient of labor force is 0.333,and the elasticity coefficient of energy consumption is 0.494,which is the largest of the three factors,indicating that the rapid growth of China's economy depends more on energy consumption.(2)The average short-term energy rebound effect of China's macroeconomics is 3.46.Although the short-term energy rebound effect of various regions occasionally appears as "super energy saving",the overall performance is a reverse effect.It indicates that the improvement of energy efficiency not only does not cause the reduction of energy consumption,but also promotes a new round of energy consumption demand.Namely,the energy savings brought by the improvement of energy efficiency have not been shown at all.(3)The average long-term energy rebound effect of China's macro-economy is-1.36.The long-term energy rebound effect of variousregions is generally shown as "super energy-saving".Some provinces,autonomous regions and municipalities have experienced "partial rebound" and adverse reactions in some years.The effect indicates that energy policy is conducive to energy conservation in the long run. |
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