Optimized estimation and spatialization of carbon emissions integrating multi-source datasets

Abstract Accurately estimating and spatializing county-level carbon emissions is essential for ‘carbon peaking and neutrality’ strategies in China. In this study, we proposed an improved framework for estimating and spatializing carbon emissions with multi-source datasets. First, Luojia-1 nighttime remote sensing imagery was employed to prove the correlation of carbon emissions and nighttime light data. Second, major function-oriented zones (MFOZs) were introduced to the regression and estimation to consider development diversities. Finally, the spatialization results of the total and intensity of carbon emissions were generated in a 100 × 100 m grid and, spatial autocorrelation analyses were conducted. The results revealed (1) Combining Luojia-1 nighttime remote sensing imagery and MFOZs can obtained more accurate estimation results than previous studies and this method performed better for counties with developed tertiary industries. (2) The spatialization results in this study represented higher quality; therefore, it reflects the spatial distribution features of carbon emissions more clearly than mainstream datasets. (3) The carbon emission intensity of the core area of the city was lower than that of the outside region, indicating that urbanization facilitated carbon emission efficiency improvement. Moreover, the spatial autocorrelation features of the total and intensity of carbon emission exhibited significant differences. This study proposes a precise and easily implementable method for carbon emission estimation and spatialization and provides guidance on the implementation of China’s ‘carbon peak and neutrality’ strategy.