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Study On Regional Differences Of Influencing Factors Of Energy Consumption In China’s High Energy-consuming Industries

Posted on:2019-02-02Degree:MasterType:Thesis
Country:ChinaCandidate:Y X LiuFull Text:PDF
GTID:2359330542958800Subject:Public Management
Abstract/Summary:PDF Full Text Request
Energy is an essential material basis for the development of industry and national economy.The problem of high energy consumption caused by the rapid growth of energy in China has always been the concern of scholars at home and abroad.Excessive energy consumption poses a great threat to China’s energy security.On the other hand,energy consumption also restricts the development of Chinese industry.Facing the increasingly severe environmental problems and economic development.Reducing the intensity of energy consumption has a very important role in optimizing resource allocation,improving energy use efficiency,and industrial productivity.The intensity of energy consumption is a very important indicator of measuring energy efficiency.The higher the intensity of energy consumption,the lower the efficiency of energy use.Most of the analysis of energy intensity and its changing factors is based on changes in the overall energy consumption intensity.However,the research on energy consumption intensity of subregion is insufficient.But with the rapid economic development.The problem of unbalanced regional economic development has also become increasingly prominent.Most of our energy is concentrated in the central and western regions.The level of economic development in the eastern region is far better than in the western region.Therefore,we need to consider the factors that influence the intensity of energy consumption in different regions.Because of the two-digit industry,high-energy-consuming industries account for the vast majority of the total.Therefore,according to the intensity of energy consumption,this article divides the two-digit industry into high,medium and low energy consumption industry groups.It mainly analyzes the regional distribution of high energy-consuming industrial clusters.Based on the panel data of 23 provinces,municipalities and autonomous regions in China from 2005 to 2013,this paper empirically analyzes the distribution of energy consumption in high-energy-consuming industries in China and the influencing factors of unit energy consumption intensity in high-energy-consuming industries in the subregion.First,the system GMM model was used to conduct a regression analysis on the relationship between energy consumption intensity across the country and variables such as environmental regulation,transportation,marketization,R&D intensity,energy abundance,energy structure,and industrial concentration.To formulate policy recommendations for the subregion.The static panel regression analysis was used to analyze the relationships between variables such as environmental regulation,transportation,marketization,R&D intensity,energy abundance,energy structure,and industrial concentration in the eastern,central and western regions.Based on the results of the regression analysis.Targeted policy guidance recommendations.The results show that the energy intensity in various regions of China has a very significant spatial correlation.And it shows the phenomenon of the eastern,central and western regions.Environmental regulations,transportation,marketization,R&D intensity,energy abundance,energy structure,and industrial agglomeration all have more or less impact on the energy intensity of a unit.The degree of industrial clustering in the east is positively correlated with the intensity of energy consumption per unit.Environmental regulations in the central region have a negative correlation with energy consumption per unit.The energy structure in the western region has a large correlation with the intensity of energy consumption per unit.Different energy,economic and industrial policies are formulated for different regions.It will play a positive role in reducing the intensity of energy consumption in various regions.
Keywords/Search Tags:Energy intensity, Regional distribution, LISA clustering, System GMM estimation method, Static panel analysis
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