Exploring the low-carbon development path of resource-based cities based on scenario simulation

Resource-based cities (RBCs) have historically been constrained by their inherent characteristics, impeding rapid shifts in energy consumption patterns and exerting substantial pressure on regional decarbonization efforts. Herein, 18 RBCs in southwestern China were taken as the research object. Firstly, a resilience index system was constructed for the resource ecosystem and socio-economic system of RBCs, and the optimization mutation level algorithm was used to measure the resilience level of each city. Secondly, an interval prediction model was established for carbon emissions in RBCs based on the GA-DBN-KDE algorithm. Finally, by setting 16 scenarios, the carbon emission range and "carbon peak" time range of RBCs in Southwest China from 2023 to 2040 were predicted, and the scientific path of low-carbon development of RBCs was explored under differentiated scenarios. The research results indicated that: (1) The carbon emissions and urban resilience levels of RBCs in southwestern China were both on the rise; (2) The interval prediction model based on GA-DBN-KDE demonstrated excellent prediction performance; (3) The simulation results of 16 scenarios revealed varying specific paths for 18 cities to achieve carbon peak, underscoring the necessity for city-specific policy formulation. Overall, this paper provides a new analytical method for the low-carbon transformation and development of RBCs, further forging a basis for decision-makers to formulate carbon reduction measures.