Research on the Correlation Pattern and Trend of Land Use Carbon Emissions and Its Influencing Factors in Counties of Hubei Province from the Perspective of Flow Space
The purposes of this study are to analyze the spatial correlation network structure of land use carbon emissions(LUCE)in counties of Hubei Province from the perspective of flow space and to explore its influencing factors,to promote the synergistic carbon emission reduction among regions.The research methods include nighttime lighting data simulation and energy consumption model,land use carbon emission measurement model,modified gravity model,social network analysis and QAP regression model.The results show that:1)during the study period,the spatial pattern of LUCE in Hubei Province presents a high-east and low-west distribution.The spatial correlation of LUCE among counties is obvious.The overall network is a multi-cluster structure with the Wuhan area as the major core and the municipal districts as the minor cores.The network robustness is strong,and the hierarchical structure has weakened but the network tightness needs to be improved.2)Urban areas,such as Wuhan,Huangshi,Jingmen,Xiangyang,Yichang,Jingzhou,and Shiyan,are at the core position of the network and act as net beneficiaries.3)Population flow,information flow and economic flow all significantly promote the formation of carbon emission correlation networks among counties.Their influencing roles have non-linear and interactive characteristics.Geographic proximity and the differences in economy,industrial structure,informatization and innovation have differentiated impacts on the network formation.In conclusion,it is necessary to regulate the correlation strength of LUCE in counties and establish regional differentiated emission reduction strategies based on the roles of each county in the correlation network to form a synergistic carbon emission reduction situation.
correlation network of LUCEfactors flowinfluencing factorscounties of Hubei Provincesocial network analysisQAP analysis
万方数据
维普
