In addition to socioeconomic factors, urban planning and transportation organization are beginning to play an increasingly important role in the reduction of CO2 emissions. However, little attention has been paid to the ways in which this emerging role can be framed. Therefore, this study aims to examine the combined impacts of socioeconomic and spatial planning factors on CO2 emissions in cities that have experienced rapid urbanization, using an econometric model and a comprehensive panel dataset incorporating socioeconomic, urban form, and transportation factors for four Chinese megacities Beijing, Tianjin, Shanghai and Guangzhou, in the period 1990-2010. Making use of remote sensing land-use data, the digitization of transportation maps, and a set of socioeconomic data, we developed an extended STRIPAT model in order to empirically estimate the impacts of the selected variables on CO2 emission levels in these cities. The results indicate that the socioeconomic factors of economic growth, urbanization, and industrialization will lead to increased CO2 emissions, while the service level and technology level can contribute to the reduction of CO2 emissions. The results also suggest that the expansion of urban land use and increases in urban population density should be controlled through urban planning measures in order to reduce CO2 emissions. In addition, pursuing compact urban development patterns would also help to reduce CO2 emissions. Transportation factors including urban road density and the traffic coupling factor were both found to have exerted significant negative effects on CO2 emission levels, indicating that increases in the coupling degree between urban spatial structure and traffic organization can also contribute to reducing such emissions. Our results cast a new light on the importance of practices of urban planning and spatial optimization measures in achieving CO2 emission reductions. The findings obtained in this study are seen as providing important decision support in building low carbon cities in China. (C) 2016 Elsevier Ltd. All rights reserved.