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

• Published in: Applied energy Vol. 238; pp. 1163 - 1178
• Main Authors: Liu, Xiaoping, Ou, Jinpei, Chen, Yimin, Wang, Shaojian, Li, Xia, Jiao, Limin, Liu, Yaolin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2019
• Subjects: carbon; carbon dioxide; Cellular automata; cities; CO2 emissions; environmental impact; greenhouse gas emissions; issues and policy; land use; landscapes; neural networks; prediction; Scenario simulation; Socioeconomic development; socioeconomic factors; urban planning; Urban spatial structure; urbanization; Cellular automata; Scenario simulation; Urban spatial structure; CO2 emissions; Socioeconomic development
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2019.01.173

•An integrated model was proposed to forecast CO2 emissions in cities.•The model was developed by coupling socioeconomic factors and spatial structures.•The city's CO2 emissions were simulated under different development strategies.•CO2 abatement can be achieved through urban planning and spatial optimization. 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.