Statistical Measurement And Spatial Characteristics Of China’s Regional Environmental Governance Performance

Since the implementation of the reform and opening-up in 1978,China’s economy has witnessed sustained and rapid development and achieved fruitful results.China’s GDP maintained an annual growth rate of about 10%over the past 30 years of reform and opening up,and surpassed Japan to become the world’s second largest economy for the first time in 2010,marking China’s entry into the era of a large economy.However,China is still in the stage of development,the economic development mode is relatively rough,rapid economic development has brought a series of serious environmental pollution problems.Environmental pollution is an important factor hindering the sustainable development of China’s economy.To carry out environmental protection work is not only a strategic choice for China’s long-term development,but also an inevitable requirement for solving resource and environmental problems and realizing the comprehensive harvest of "gold and silver mountains" and "green water and green mountains".Against the background that the party,the government and the public attach great importance to environmental pollution control,research on environmental governance performance has become a hot issue in the academic circle.However,due to the limitations of data and methods,there are still some deficiencies in the existing studies on China’s environmental governance performance.There are few studies on regional differences in environmental governance performance and laws of development and change,which cannot provide good data support for the formulation of differentiated and long-term environmental protection policies.The research on the influencing factors of environmental governance performance lacks consideration of spatial correlation,and the research results are biased.The research objects are relatively macro,mostly at the provincial level,and the lack of research at the municipal level is not conducive to the detailed analysis of China’s environmental governance performance.At the present stage,the contradiction between economic development and environmental pollution is increasing day by day.Administrative levels in China based on as the research object,with the new economic growth theory,environmental economics theory and productivity theory,spatial economics theory as the theoretical basis,such as environmental efficiency and the green of China’s 285 cities as statistical measure of total factor productivity analysis the development trend of the regional difference,and the spatial econometric analysis method,on the basis of fully considering the effect of regional space,analyze the green main influence factors of total factor productivity level.The research content of this paper is divided into seven parts:the first chapter introduces the research background and topic basis,research content and significance,as well as the main innovation and shortcomings;The second chapter of environmental governance performance review and review related literature at home and abroad,according to statistical measure of environmental governance,the differences between the environmental governance characteristics analysis and influence factors of convergence,to summarize the advantages and shortcomings of existing research results,in order to understand and detailed discussion of China’s environmental governance performance related issues,innovation research method and research perspectives;The third chapter,first of all,on the environment and green efficiency measurement of total factor productivity are briefly introduced,points out that the existing literature,the commonly used ones are the defects of traditional data envelopment analysis(dea)and directional distance function,which leads to contain the expected output of ultra-efficient common frontier(Mate-Fronitier)model and combining the Malmquist-Luenberger(ML)index,to China’s 285 cities as environmental governance performance and to evaluate total factor productivity and statistical measure,and the total factor productivity index analysis,The driving factors of environmental governance performance and total factor productivity change are analyzed from the perspective of efficiency change and technology progress.The fourth chapter mainly studies the convergence of environmental governance efficiency and green total factor productivity.According to the convergence theory,the convergence and convergence models of environmental governance efficiency and green total factor productivity are constructed to analyze the steady growth path and the final convergence state of regional environmental governance efficiency and green total factor productivity.The fifth chapter according to the measurement of environmental governance efficiency and total factor productivity,through measuring method to measure China’s 285 cities as income gap environmental governance efficiency and green regional difference of total factor productivity,considering different diversity index of sensitivity to the data,comprehensive comparing the GINI coefficient,the generalized entropy index(logarithmic average deviation and Theil index)analysis,and respectively analyzed the eastern,central,western and northeast China the internal differences;Chapter 6 for China environment space of the total factor productivity effect analysis,the exploratory spatial data analysis of green space correlation of total factor productivity test,verify the existence of spatial correlation,on the basis of the introduction of spatial weight matrix to construct green space of the total factor productivity measurement model,a systematic analysis of China’s 285 cities as green are the main factors causing the total factor productivity and its mechanism of action,direction,path and size,and the green the spatial effect in the decomposition of total factor productivity,to measure the area between green total factor productivity spillover effect;Chapter 7 comprehensively summarizes the main research conclusions of this paper,and combined with the actual situation of each region,based on the results of empirical analysis of the corresponding policy recommendations.Based on the research in this paper,the opinions and conclusions obtained mainly include the following four aspects:First of all,technical efficiency and technological change are the main driving forces for the change of China’s green total factor productivity index.At present,the potential of China’s green technology changes has not been fully utilized,resulting in low-level development of green total factor productivity.Although the development of green technology in China has tended to change green technology in recent years,it has neglected the improvement of the efficiency level of green technology,which is not conducive to the long-term development of China’s green total factor productivity.Secondly,due to the vast territory of China,the natural resources and economic development of different cities and regions are quite different.The environmental efficiency and green total factor productivity of China as a whole and in the eastern,central,western and northeastern regions are not obvious.Convergence,but under the constraints of per capita GDP,industrial structure,foreign direct investment,population density and R&D expenditure,there are absolute convergence,conditional convergence and club convergence,showing a convergence trend.Thirdly,in terms of regional green total factor productivity,according to the GINI coefficient,the logarithmic dispersion mean and the overall change of Theil index growth rate,different difference indicators generally show similar trends,but the changes are different..From the absolute values of GINI coefficient,logarithmic dispersion mean and Theil index growth rate,Theil index has the largest change,followed by the logarithmic mean deviation,and the GINI coefficient has the smallest change;during this period,the green Prefecture-level cities with factor productivity at the highest level have the largest changes,followed by lower-level prefecture-level cities with lower levels,and middle-level cities with the lowest level of change.Finally,according to the test results of Moran index of 285 prefecture-level cities in China,the MML,MEC and MTC indicators all show strong spatial correlation,that is,the existence of green total factor productivity and its decomposition between prefecture-level cities in China.Strong spatial correlation.From 2005 to 2007,the correlation degree between green total factor productivity of China’s prefecture-level cities showed a fluctuating state.Since 2011,the spatial correlation between variables began to increase,with the regional economic level and the level of green total factor productivity.Ascending,the focus of the development of green total factor productivity has changed in various places,and its spatial correlation has also shown great fluctuations.The main innovations of this paper include:(1)Innovative research objects:Most of the previous studies have analyzed environmental efficiency(green total factor productivity)at the national or provincial level.A few studies have studied in a single city,with less The study measured the environmental efficiency(green total factor productivity)of 285 prefecture-level cities across the country.(2)Innovation of research methods:When measuring environmental efficiency(green total factor productivity),this paper combines ML index and adopts super-efficient SBM-DEA model with undesired output,which not only considers undesired output,but also It effectively solves the situation that the effective decision-making units cannot be compared and sorted,and compensates for the defects of the traditional DEA model or SBM model to a certain extent,and the calculation result is more real and reliable.(3)Innovative research perspective:This paper is innovative in the analysis of the factors affecting green total factor productivity.It incorporates spatial effects into the analytical framework and uses exploratory spatial data analysis to analyze the green total factor productivity of 285 prefecture-level cities in China.Spatial dependence and spatial heterogeneity,using the spatial Dubin model to explore the main influencing factors of green total factor productivity,the results are more in line with the actual situation.The shortcomings of this paper are mainly to study the complexity of China’s economic development and environmental governance,as well as the limitations of data availability.As a result,the analysis of the driving factors of green total factor productivity needs to be further improved,and the policy recommendations presented in this paper still lack integrity.And more macroscopic.