Carbon Lock-In Dilemma: Tracking Path Dependence and Divergence in Urban Agglomeration Emissions under SDGs

Abstract

Urban agglomeration (UA) is a spatial unit with a relatively clustered concentration of carbon emissions (CEs), a vanguard for achieving the dual carbon goal, and an important part of exploring regional sustainable development. With the current acceleration of urbanization, the increase in CEs is a serious impediment to the achievement of the Sustainable Development Goals (SDGs). Currently, reducing UA CEs and achieving sustainable urban development in cities constitute an urgent problem. In this study, the Dagum Gini coefficient, spatial autocorrelation analysis, kernel density estimation, and spatial Markov chain were used to explore the regional differences and evolutionary dynamics of CEs from 2005 to 2020 in the Central Plains Urban Agglomeration (CPUA) and to predict long-term evolutionary trends. The results show that CPUA CEs follow an overall upward trend fluctuating from 5.0323 t/person in 2005 to 7.9930 t/person in 2020. Currently, there are clear and expanding regional differences within CEs. UA CEs show significant spatial correlation and gradual geographical polarization. Additionally, the club convergence phenomenon and Matthew effect of CEs in UAs are obvious. The predictive results demonstrate future pessimistic trends of CEs in the CPUA, and the distribution of CEs shows a trend of concentrations at high values. This study reveals the regional differences and dynamic evolution trends of CEs in the development of UAs and provides SDG-oriented research ideas for the governance of CEs in UAs.