Scenario simulation of urban energy-related CO₂ emissions by coupling the socioeconomic factors and spatial structures

As cities constitute the main sources of CO₂ emissions, accurate simulation and prediction of urban CO₂ emissions are becoming increasingly necessary for understanding environmental impacts and supporting the policy-making toward a low-carbon development. However, most previous studies on estimating CO₂ emissions have only considered the effects of socioeconomic driving factors while disregarding the contributions of urban spatial structures (with the exception of those of urban expansion) to carbon abatement. Therefore, this study presented a model that integrates system dynamics, cellular automata and support vector regression to evaluate the impacts of different socioeconomic developments and urban spatial structures on the CO₂ emissions of Guangzhou city. In the integrated model, system dynamics was used to model the developments of socioeconomic variables including urban land-use demand. An artificial neural network cellular automata model based on patch simulation strategy was then built to simulate the urban spatial structures, which were further quantified by landscape metrics. Using both socioeconomic variables and landscape metrics, a support vector regression with polynomial kernel function was finally employed to predict CO₂ emissions. Through comparisons drawn between the simulated results and actual data, the integrated model coupling socioeconomic factors and urban spatial structures was demonstrated to be an effective tool for accurately simulating CO₂ emissions. Furthermore, scenario simulations derived from the integrated model showed that the scenario of executing moderate population and economic growth, more technological investment, and the compact development of urban spatial pattern constitutes the best development mode for Guangzhou to balance economic growth and CO₂ emissions reduction. From these findings, it is suggested that the government should not only develop a series of socioeconomic policies on carbon mitigation but also construct an ideal urban structure of compact and multiple-nuclei development through urban planning and spatial optimization for building a low-carbon city.