Study On Synergistic Emission Reduction Of Industrial Air Pullutants And Carbon Dioxide In China

Industrial waste gas is not only one of the main sources of air pollutant emissions,but also a major source of carbon emissions.As a crucial sector to implement carbon emission mitigation commitments,industry is facing the pressure of reducing air pollutant emissions and carbon emissions.Hence,how to coordinate the relationship between air pollutant emissions and carbon emissions is an urgent issue needs to be solved.This paper aims at measuring the Synergistic Emission Reduction(SER)of China's industrial air pollutants and carbon dioxide and identifying the main factors that influence SER,and then predicts the different scenarios of SER.Some measures to achieve SER of industrial air pollutants and carbon dioxide are proposed,which is of great importance in theory and practice.This paper mainly studies the SER of industrial air pollutants and carbon dioxide.First,the influencing factors and internal relations of SER are identified by defining the meaning of air pollutants and greenhouse emission.Then the degree of SER in different regions is calculated,Last,the strategies to achieve SER of industrial air pollutants and carbon dioxide are suggested.The main research contents of are as follows:The meaning of SER and the research boundary of air pollutants and carbon dioxide are defined theoretically.With the application of Dynamic Stochastic General Equilibrium(DSGE),this paper describes The energy-environment-economy system for SER are described by the method of Dynamic Stochastic General Equilibrium(DSGE).Meanwhile,a simulation analysis on this system is conducted.Furthermore,the effect of energy intensity,energy structure,industry structure,technological learning and other factors on SER of air pollutant intensity and carbon dioxide emission are analyzed.The measurement indicators of SER are constructed based on its concept.And The degree of SER is calculated by means of these indicators,and the trend of SER of industrial air pollutants and carbon dioxide in different provinces and regions are analyzed.An empirical model is established to analyze the influencing factors of SER based on the mediation effect model and the above theoretical analysis.In this model,environmental regulation,industry structure,technological learning and energy structure are adopted to compare the key influence factors of SER in different regions.In the strategy study of synergistic emission reduction of industrial air pollutants emission and carbon dioxide,this paper s Last,the scenario of SER is simulated based on the reduction degree and empirical analysis of influencing factors.Thus,some strategies of SER of industrial air pollutants and carbon dioxide are proposed on this basis.This paper contributes to the existing literature in three aspects:First,a DSGE model that focuses on the synergistic emission reduction of air pollutants and carbon dioxide is constructed.Compared to the conventional models that only focuse on industry air pollutants and carbon dioxide reduction,this model can depict an equation which includes each subject in this system and describe their changes.DSGE is a typical energy-environment-economy model,which can simulate the different reduction of air pollutants and carbon dioxide under the impact of technology shock and environmental regulation shock.Second,the measurement index of SER is established.Based on the existing SER index,the SER index for industry air pollutants and carbon dioxide are built,and the divergences of SER degree in different province are compared.Conventional analysismodel mainly lay emphasis on the amount of emission reduction,while this study analyzes the synergy degree of SER in relative quantity change.In the end,the major influencing factors of SER are identified.Conventional analysis classifies the structure change,technology and policy as the co-influencing factors of SER,while it fails to distinguish the relationship among these variables.In this paper,we conduct an analysis on the relationship among them and identify the key mediator variables through mediation model.