Estimating built-up area carbon emissions through addressing regional development disparities with population and nighttime light data

Carbon emission inventories in urban built-up areas are critical for formulating effective emission reduction policies. Nighttime light data, reflecting socioeconomic activities, is routinely used as a proxy for estimating emissions. However, the reliability of this proxy is limited in areas with minimal or absent lighting and demonstrates variability according to the urban development. To address these limitations, a novel approach was developed. Firstly, it enhances the representation of human activities under different light values through integrating light values and population distributions into multidimensional proxy features. Secondly, cities with different socio-economic development were clustered to minimize the development disparities within the same cluster. Four clusters were detected including ‘Agriculture-dependent & Low-density’, ‘Agriculture-dependent & High-density’, ‘Industry-oriented’, and ‘Tertiary-oriented’. Then A multilayer perceptron regressor, refined with a moving average method, was employed to model the nonlinear relationships between emissions and these proxy features. The results show a significant increase in modeling accuracy when regional development disparities are incorporated through this clustering strategy. Additionally, the Shapley Additive Explanatory method was utilized to analyze the influence of regional development heterogeneity on the estimation model. It demonstrated that areas with high light intensity and population significantly influence emission estimates in all clusters, except for the ‘Industry-oriented cluster’. This finding underscores the varying effectiveness of proxy features across different regions, emphasizing the importance of considering regional development disparities. Finally, based on the estimated inventory obtained, spatio-temporal hot spot detection pinpointed eastern and central China as significant emission hot spots. This research not only enhances the data resources for managing carbon emissions but also underscores the need for inter-city cooperation strategies to achieve China's carbon reduction targets.