Spatial network structure and influencing factors of carbon emission intensity in Guangdong-Hong Kong-Macao greater bay area

Abstract

Introduction: In response to China’s ambitious dual-carbon goals, this study investigates the spatial correlation and influencing factors of carbon emission intensity within the Guangdong-Hong Kong-Macao Great Bay Area (GBA), a region pivotal for the nation’s energy conservation and emission reduction efforts. Through a comprehensive analysis encompassing the period from 2000 to 2020, this research aims to delineate the spatial dynamics of carbon emissions and identify actionable insights for regional low-carbon development.Methods: Utilizing an integrated methodology comprising spatial autocorrelation analysis, Social Network Analysis (SNA), and the Quadratic Assignment Procedure (QAP), the study analyzes carbon emission data alongside socio-economic variables. These methodologies allow for a nuanced exploration of the spatial correlation structure and the determination of factors influencing carbon emission intensity across the GBA.Results: Findings reveal a cyclical fluctuation in the spatial network of carbon emissions from 2000 to 2020, characterized by varying degrees of cohesion among cities, indicating significant opportunities for spatial optimization. A “core-periphery” pattern emerges, with economically robust cities such as Hong Kong and Macao at the core, and less developed cities like Huizhou and Jiangmen on the periphery. Cities like Guangzhou and Shenzhen play crucial mediator roles. The QAP analysis further identifies six major influencing factors: geographic spatial proximity, economic development level, urbanization rate, industrial configuration, level of technological innovation, and environmental protection efforts, with the latter four having a markedly positive impact on spatial relevance.Discussion: The study’s insights underscore the importance of understanding the spatial dynamics of carbon emissions and the role of socioeconomic factors in shaping these patterns. For policymakers and stakeholders in the GBA, these findings highlight the necessity of targeted intervention strategies that consider both the unique position of cities within the spatial network and the broader socio-economic context. This approach can significantly contribute to achieving China’s dual-carbon objectives, promoting energy conservation, and facilitating the transition to a low-carbon economy.