Research On The Spatial And Temporal Evolution Of Coupling Degree And Optimization Of Decoupling Economic Growth From Energy Consumption In China

During 40 years of reform and opening up,energy consumption is one of the important supports for China's rapid economic development.Extensive economic growth mode has made energy consumption show the characteristics of large amount,low efficiency and serious energy waste,and has formed unreasonable industrial and energy structure.At the same time,the unreasonable exploitation and inefficient utilization of energy have brought serious environmental pollution,which not only hinders economic growth,but also threatens the health of residents.The decoupling of economic growth from energy consumption is an important way to achieve sustainable development in China.However,most of the existing research on decoupling focuses on the relationship between carbon emission and economic growth in China.There is less research on decoupling economic growth from energy consumption.This dissertation focuses on the evolution and optimization of decoupling state between energy consumption and economic growth in China.The concept of decoupling refers to the process whereby aggregate economic activity gives rise to reduced environmental impact and the coupling degree between these two is continuously reduced or even eliminated.The environmental impact can be material flows,energy consumption,discharge emissions to the air,etc.Hence,decoupling research contains two aspects: dematerialization and depollution.Our research,decoupling of economic growth from energy consumption belongs to the first aspect.In quantitative research of decoupling,decoupling state which reflects the coupling degree between energy consumption and economic growth,needs to be measured.The lower the coupling degree is,the better the decoupling state is.Firstly,based on the literature review as well as the theory of sustainable development,3E system and decoupling,this dissertation builds a theoretical framework of decoupling economic growth from energy consumption.Secondly,according to the theoretical framework,a multi-level index system is established to analyze the driving factors of decoupling,which can clarify the influence direction and degree of each factor.Thirdly,this dissertation proposes a dynamic adaptive time sampling model for decoupling state measurement.Using this model,we analyze the evolution of coupling degree between energy consumption and economic growth from national and regional level 1996-2016,so as to discover the decoupling state in different periods.Finally,according to the theoretical framework and driving factors of decoupling,an artificial neural network model is established to predict energy consumption,economic growth and decoupling state of these two.Appling the improved differential evolution algorithm to the network model,taking the relevant energy and economy planning objectives as constraints,we optimize the three prediction indexes.With comparation of results and input parameters between optimization and prediction,the corresponding policy suggestions are put forward to provide theoretical and policy support for the sustainable development in China.The main research conclusions of this dissertation are follows:1.Economic scale effect and energy intensity effect are the main driving factors for the decoupling economic growth from energy consumption.Economic scale effect drives the growth of energy consumption,while energy intensity effect promotes the decline of energy consumption,and the change of the consumption ultimately influences the overall decoupling state.The eastern region is the most affected region by energy intensity,economic scale,population and urbanization effect.The effect of industrial structure and residents' living standard on the western and northeast region respectively is the most significant.The influence direction and degree of energy structure effect in all regions are unstable.2.China's energy consumption and economic growth were basically in weak decoupling state from 1996 to 2016.This indicates that the coupling degree between these two decreased;meanwhile energy efficiency has improved with certain stand of limitation.Energy consumption still increased with the growth of economy.During the study period,the decoupling state in the central and western regions deteriorated which reflects the increase of coupling degree between energy consumption and economic growth.The decoupling state in the eastern and northeastern regions was relatively stable.The decoupling evolution level is a comprehensive indicator reflecting quality of decoupling state and stability of evolution.During 1996-2016,the decoupling evolution level only increased in the northeast region,and it decreased in the other three regions.3.Comparing the results and the corresponding input parameters of decoupling optimization and two scenario prediction,policy suggestion to promote the decoupling state of energy consumption and economic growth in the future is proposed: further promote the urbanization construction and improve the population quality;promote the development of cleaner energy structure and advanced industrial structure;improve innovation-driven capacity.The optimized indicators of energy consumption and economic growth all exceed the relevant planning objectives in 2020 and 2030,while the results of two scenario prediction only achieved the energy targets.The optimized decoupling state is better than the predicted decoupling state.Based on the existing research,this dissertation studies the decoupling of energy consumption and economic growth.Its innovations points are from the following aspects.First of all,a multi-level index system for decoupling factor decomposition is constructed to improve the systematization of analysis.Secondly,a dynamic adaptive time sampling method for decoupling state measurement is proposed,which can reduce the subjectivity of time interval division in the measurement and can improve the rationality of results.Thirdly,regional differences are introduced into the decoupling state prediction model of national level,which effectively improves the accuracy of prediction results.Finally,for the first time,differential evolution algorithm is applied to decoupling optimization research,and the existing algorithm is improved,which not only improves the probability of searching global optimization solution but also improve the quality of results.