Abstract

The main purpose of this article is to link the environment, economy, electricity, and society and put forward a new point of view. The current research mainly explores the relationship between the environment, economy, and society and lacks a discussion on electricity. Using a new research framework, this article examines the relationship between energy intensity, energy consumption structure, population density, urbanization rate, and carbon intensity based on relevant data from 2000 to 2017 in China. In the empirical research, according to the cluster analysis, China's 30 provinces are divided into three regions according to the electrification rate standard. The cross-sectional dependence test method is used to verify the cross-sectional dependence of the data, and the second-generation panel unit root test method is used. Exploring the relationship between the variables, this article finally uses the convergence analysis method to explore the degree of influence of each variable on the carbon intensity. The empirical results show that there are both short-term effects and long-term relationships in various regions, and the influencing factors of each region are different. It further shows that the carbon intensity of the four panels shows convergence, β absolute convergence, and β conditional convergence, but the main influencing factors in different regions are different. Finally, based on the results of empirical research, policy recommendations for reducing carbon intensity in different regions are put forward.

Highlights

  • China's economy has entered a new normal, developing to a higher level

  • It should be noted that different levels and methods of economic development in different regions of China make it difficult to implement a unified carbon reduction policy

  • The acceleration of China's urbanization process has an impact on carbon intensity, and this effect is different in various regions

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Summary

Introduction

China's economy has entered a new normal, developing to a higher level. The division of labor is clearer, and the growth rate has changed from high-speed growth to medium-high growth. Based on perfect indicators and dynamic spatial panel model, Zhang F et al established a comprehensive framework to quantify the impact of industrial structure and technological progress on CI, and conducted empirical research on 281 prefecture-level cities in China from 2006 to 2016(Zhang F et al.2020). Based on the statistical data of 30 provinces, municipalities and autonomous regions in China from 2004 to 2016, Feng , Zhang and Wang used Kernel density estimation method and LMDI factor decomposition method to study and analyze the dynamic evolution characteristics and main influencing factors of China's direct living energy consumption carbon intensity(Feng S et al.2018). Heterogeneous panel technology is used to study the relationship between energy intensity, carbon intensity, energy consumption structure, urbanization rate and population density in different regions , which helps to formulate corresponding carbon emission reduction policies according to the characteristics of regional development.

Proposed model and data
Descriptive statistics of variables
Cross-sectional dependence test
Panel unit root test
Convergence analysis method
Convergence analysis results
Conclusions and policy implications
Ethical Approval
Availability of data and materials
Full Text
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