Spatial-temporal Evolution And Scenario Simulation Of Carbon Emission In China’s Energy Mining Industry

Clarifying the spatial and temporal characteristics,evolutionary mechanisms,system processes,scenario simulation and carbon emission reduction responsibility allocation of the energy extraction industry,it is of great significance for China’s carbon peak in 2030,and carbon neutral goal by 2060 in China’s energy extraction industry and the high-quality development of green production in the energy industry.The energy extraction industry is one of the six highest energy-intensive industries in China.The energy extraction industry,mainly represented by coal,oil and natural gas,consumes a large amount of energy in the process of mineral extraction,transportation and refining,and the resulting carbon emission problem exacerbates the risk of atmospheric pollution.In this paper,multiple fractal,general convergence,spatial convergence,impulse response,spatial measures,GDIM exponential decomposition,STIRPAT model,SD system dynamics model,SVR machine learning,BCC efficiency decomposition,entropy TOPSIS multi-criteria decision making and other research methods are used to analyze the multiple fractal characteristics,convergence characteristics,co-integration and impulse response characteristics,carbon emissions of China’s energy extraction industry,spatial effect,spillover effect,SD system dynamics process,carbon emission redundancy measure,and multi-criteria carbon emission reduction responsibility allocation decision based on equity and efficiency.The study has important theoretical and practical significances for analyzing the mechanism of spatial and temporal evolution of carbon emissions in energy extraction industry,assessing the carbon peaking potential in energy resource-rich areas,and formulating carbon reduction strategies.This paper is divided into seven chapters.The theoretical part focuses on the interpretation of the intrinsic mechanism and evolutionary law of carbon emissions in energy extraction industry from the perspectives of endogenous growth theory,environmental economic theory,system dynamics theory,and renewable resource economic theory.The empirical department mainly includes three core parts.First,based on the theories and methods of statistics,econometrics,geography and other disciplines,we analyze the spatial and temporal characteristics and convergence of carbon emissions of the energy extraction industry,and analyze the multiple fractal characteristics,convergence characteristics,spatial correlation and spillover effects exhibited by the carbon emission system of the energy extraction industry in China.Second,based on factor decomposition,exponential decomposition and spatial panel model,we analyze the drivers,contribution of factors,impact effects and spatial effects of carbon emissions from energy exploitation,and analyze the spatial and temporal evolution process,driving mechanism and spatial effects of carbon emissions from energy exploitation,in order to understand the nonlinear characteristics,key drivers,coercion and response process of energy exploitation system in China.In addition,based on complex nonlinear systems,machine learning and other theories and methods,we analyze the nonlinear interaction,dynamic effects,scenario simulation and trend prediction of the energy extraction system.With the results of carbon emission reduction key impact factors,we measure and predict the future carbon emission reduction potential of energy extraction and clarify the carbon emission reduction responsibility of energy-rich regions.The research results show that(1)the energy extraction carbon emission system is a nonlinear,complex and open giant system,and the energy production,energy consumption,carbon intensity and carbon emission all show obvious multiple fractal characteristics;(2)the energy production,carbon intensity and energy consumption of the energy extraction industry are important factors affecting the carbon emission of the energy extraction industry,and the national,eastern,central and western energy extraction carbon emissions show conditional convergence and spatial convergence characteristics;(3)the GDIM index decomposition and spatial model results show that carbon intensity,energy consumption intensity,investment effect and technology effect have the largest contribution,and energy extraction carbon emissions show spatial heterogeneity characteristics during the sample period;(4)under the green growth scenario,energy extraction carbon emissions show a short rise in 2020 and reach a peak at 546 million tons in 2021,then show a continuous downward trend;energy demand from the energy extraction industry shows a stable growth trend based on 2019,peaking at 117.6 million tons of standard coal in 2027;(5)Each major energy extraction province shares a common but different carbon emission reduction responsibility,and the provinces with the highest carbon emission reduction responsibility in 2030 relative to 2019 are Hubei,Hunan,Inner Mongolia,and Liaoning,all with emission reduction responsibility shares exceeding 10 million tons of CO2,showing significant geographical differences.The innovation of this study is reflected in(1)the integration of a multidisciplinary research method for carbon peak driving mechanism and scenario prediction in energy extraction industry.It integrates fractal theory,factor decomposition,spatial econometrics,machine learning,and system dynamics analysis to solve non-linear and complex real-world problems,and then examines the multiple fractal characteristics,spatio-temporal evolution process,convergence characteristics,spatial spillover effects,system dynamic interaction characteristics,and carbon efficiency characteristics of carbon emissions in the energy extraction system to provide decision support for the exploration of carbon peak attainment paths and countermeasure optimization in China’s energy extraction industry.(2)The analysis method for multi-criteria decision making of carbon emission reduction in the energy extraction industry is proposed in.Based on the identification of the spatial and temporal characteristics and influencing factors of carbon emissions in the energy extraction industry,a green production and sustainable development multi-scenario is constructed,and a study on the governance system and governance capacity of carbon emission reduction in China’s energy extraction industry is carried out from the perspective of energy governance system and governance capacity innovation,and a multi-dimensional path based on "system-potential-responsibility" is proposed.The study proposes a multi-dimensional path based on "system-potential-responsibility" to achieve the goal of carbon emission reduction and carbon peaking in energy extraction,so as to guide the energy extraction industry to build a common but different carbon emission reduction allocation scheme among regions based on energy technical efficiency,resource endowment and social factors,and to promote the adaptation of energy extraction and factor allocation in energy-rich regions.(3)Perspectives on the spatiotemporal differentiation and correlation effects of carbon emissions in China’s energy mining industry.By constructing energy mining carbon emission cointegration model,impulse response model,threshold effect model,geographically weighted spatial model,system dynamic model,grey correlation model and other models,the time series characteristics and spatial pattern evolution law of carbon emissions from China’s energy mining industry were identified.The carbon emission process and its correlation effects with the economic system,environmental system,and energy system are discussed,and it provides experience and decision support for the carbon emission reduction scenario simulation and trend prediction of the energy mining industry under the constraints of the carbon peaking target in 2030.