Spatial Dependence Pattern of Energy-Related Carbon Emissions and Spatial Heterogeneity of Influencing Factors in China: Based on ESDA-GTWR Model

Kai Su,Chienming Lee

doi:10.46488/nept.2021.v20i01.003

Abstract

Spatial Dependence Pattern of Energy-Related Carbon Emissions and Spatial Heterogeneity of Influencing Factors in China: Based on ESDA-GTWR Model

Highlights

Global warming largely caused by carbon emissions is a serious problem threatening ecosystems and human development globally (Villoria-Sáez et al 2016)
The existing literature rarely studies the spatial heterogeneity of the factors affecting carbon emissions based on this method. To address this gap, taking 30 provinces in China as the research object, this study used exploratory spatial data analysis (ESDA) and geographically and temporally weighted regression (GTWR) model to analeyzmeitshseiospnatriaeldhuectetrioognenpeoitlyicoifefsacitnorsCahffiencatinagnedn-provide ergy-replaatretdiccuarlbaorlnyeamidsesivoenlsoipniCnhgincao. uTnhteryre,stuoltsccaarnrybeout relev used as data support for the formulation and implementation of energy-related carbon emission reduction policies in China and proMvidAeTa EreRferIeAncLeSfoAr oNthDer cMouEntTriHes/OreDgiSons, a developing country, to carry out relevant research at the proCvianclciaul lsacatileo.n of Carbon Emissions
Considering the deficiency of past literature study on spatial heterogeneity of factors affecting carbon emissions, this study introduced geographically and temporally weighted regression (GTWR) into the spatial analysis of carbon emissions, which provides a new approach to spatial heterogeneity testing

Summary

INTRODUCTION

Global warming largely caused by carbon emissions is a serious problem threatening ecosystems and human development globally (Villoria-Sáez et al 2016). Energy-related carbon emissions are influenced by factors such as the industrial structure, economic development level, urbanization and population size of the region, and the potential correlation between carbon emissions in the surrounding areas. The existing literature rarely studies the spatial heterogeneity of the factors affecting carbon emissions based on this method To address this gap, taking 30 provinces in China as the research object, this study used exploratory spatial data analysis (ESDA) and GTWR model to analeyzmeitshseiospnatriaeldhuectetrioognenpeoitlyicoifefsacitnorsCahffiencatinagnedn-provide ergy-replaatretdiccuarlbaorlnyeamidsesivoenlsoipniCnhgincao. UTnhteryre,stuoltsccaarnrybeout relev used as data support for the formulation and implementation of energy-related carbon emission reduction policies in China and proMvidAeTa EreRferIeAncLeSfoAr oNthDer cMouEntTriHes/OreDgiSons, a developing country, to carry out relevant research at the proCvianclciaul lsacatileo.n of Carbon Emissions

MATERIALS AND METHODS

Findings

DISCUSSION AND CONCLUSIONS