The Collaborative Optimization Of The Industrial Structure And The Energy Structure In China Under The Carbon Emission Reduction Targets Of The Paris Agreement

The Paris Agreement ratified through the United Nations climate change conference in 2015 has made the arrangement for the global action on climate change after the year of 2020.Meanwhile,the independent contribution commitment of China and its achievement of CO₂ reductions in the future become the focus of international attention.China is the world’s second largest economy,and the rapid development of China’s economy always needs the support of energy.However,China’s energy consumption structure is absolutely strongly based on fossil fuels and CO₂ emissions stem mainly from the burning of fossil fuels,which brings many tough challenges to China in terms of dealing with climate change.Meanwhile,clean energy,such as wind and hydro electricity,are difficult to be widely used in various departments and industries in the short term.So how to make adjustment of the industrial structure to control CO₂ emissions also deserves further research.The relationship between industrial structure and energy structure is close and complex.Therefore,the purpose of this study is to explore how China can balance the implementation of the Paris Agreement and promote the collaborative optimization of the dual structure.Based on this,the study carries out a series of research work.First,this paper makes a prediction of the reduction potential of China’s CO₂ emissions in 2030 based on back propagation（BP） neural network model,scenario assumptions,and trend extrapolation.That is,the rationality of the carbon emission target and the possibility of its realization should be judged in some degree.Second,the model and index of China’s industrial structure and energy structure co-development are constructed,and based on the principal component and regression analysis,the evaluation of the level of the collaborative development of China’s dual structure is achieved.Third,GM（1,N） model of the dual-structure collaborative optimization under the emission reduction target is established and solved at the national level.Fourth,this paper makes empirical analysis on the collaborative optimization of the industrial structure and the energy structure at the provincial level based on FGLS regression model.This study draws several conclusions based on the above in-depth research,which are as followed.First,when the reduction potential of China’s CO₂ emissions is studied,in the medium and short term development trend,the goal of letting CO₂ emissions per unit of GDP in China decrease 60% to 65% below that of 2005 can be achieved.But it’s still a daunting work for the proportion of non-fossil energy to reach 20% in China.Second,there is a certain consistency between the order degrees of industrial structure and the energy consumption structure subsystems.The collaborative degree of the dual structure shows a fluctuating trend and it is not high.Third,according to the optiminization plan designed in this study,namely the collaborative optimization plan,is more reasonable for it takes the common goals of economic growth,energy consumption and carbon dioxide emissions into account.The carbon dioxide emissions in 2030 will be closed to the Paris Agreement target,economy target will be sacrificed a little,and there is still some optimization space of the structure adjustment.From a provincial perspective,fossil energy consumption index,energy consumption and the proportion of the secondary industry have significantly positive correlation relationship with carbon dioxide emissions in different regions,the increase of the collaborative degree of the dual structure has inhibitory effect on carbon dioxide emissions,and the impact of GDP and population growth on carbon dioxide emissions varies from region to region.Finally,this paper puts forwards the policy suggestions for the collaborative optimization of industrial structure and energy structure of China under the carbon emission reduction targets of the Paris Agreement.