Study On Energy Efficiency Measurement Of China Industrial Sector And Its Influencing Factors

Today,China's energy consumption ranks first in the world in terms of volume and speed,and huge energy consumption also has a very bad impact on the environment.In 2014,the Chinese economy announced that it had entered the "new normal",and the development model would shift from the past "input expansion" to achieve quantitative accumulation to "output optimization" to achieve a qualitative improvement.As a source of power for economic and social development,the contradiction between energy consumption and environmental development has always existed and intensified,and people have been constantly searching for the best solution to the problem of sustainable development.The industrial sector is the main sector of energy consumption.In the context of coping with global climate change and the transformation of economic development methods,the research on the energy efficiency of the Chinese industrial sector will be the top priority.This paper first uses the non-parametric frontier analysis method to measure the total factor energy efficiency of industrial sectors in China and provinces and cities.At the same time,it uses the global total factor productivity index to measure and decompose the total factor energy productivity of industrial provinces and cities into four Part: Changes in pure technical efficiency,changes in technological progress,changes in scale efficiency,and changes in technical scale efficiency,to explore the source of changes in total factor energy efficiency in the industrial sector at a deeper level.Then use the panel data quantile regression model to perform regression analysis on the factors that affect energy efficiency,obtain the impact mechanism of each influencing factor on energy efficiency,and finally put forward relevant policy recommendations in combination with the two parts of the analysis conclusions.The main conclusions are as follows: First,from an inter-provincial perspective,the overall level of energy efficiency in each province is relatively low,and there is still much room for improvement,and the energy efficiency of each province and city is unevenly distributed,and some provinces and cities have large efficiency values Fluctuations.Second,by region,the energy efficiency in the eastern region is the highest,mainly driven by technological progress,followed by the northeastern region,mainly driven by technological efficiency,and the energy efficiency in the central and western regions is lower,mainly driven by scale efficiency and technological progress.Third,it is different from the ordinary conditional least squares regression analysis.The results of the panel data quantile regression model show that the impact degree and direction of each influencing factor are different under different energy efficiency levels,and the impact of each factor on energy efficiency is clarified Mechanism is the key to formulating energy policy.Fourth,the panel data quantile regression of the commonly used reference indicators of common energy policies and the commonly used measurement indicators of energy efficiency research can be found that the same influencing factors such as energy prices and capital deepening levels may show completely opposite results,indicating that If the energy efficiency policy relies too much on single-factor energy efficiency,it may run counter to the goal and make it difficult to achieve the desired results.