The role of urban carbon dioxide (CO2) emissions has attracted city authorities' attention. Several entities face challenges when developing inventory method for local communities, due to limited data. This study proposes a top-down method to estimate CO2 emissions at an urban scale, using nighttime light imagery and statistical energy data. We find that nighttime light imagery is appropriate in CO2 estimations at an urban scale. The proposed method is particularly significant for the developing countries, of which CO2 emissions increase rapidly but lack in energy data at an urban scale. It also contains some limitations due to the inherent shortcomings of the data sources and methodological errors. It has very limited value when applying in urban areas with rare population. A case study is implemented in urban China. The results show that the share of urban emissions increases over the period of 1995-2010. Meanwhile, per capita CO2 emissions in China continuously grow, the values of which are much higher than the national averages. In a spatiotemporal perspective, per capita CO2 emissions in eastern coastal China are lower than that in inland China. These results have significant implications for local authorities to guide their policies in carbon reduction and climate change.