Spatiotemporal Analysis of Carbon Emissions Based on Night-time Light Data in Western Provinces of China

This study focuses on the fusion and calibration of DMSP–OLS and NPP–VIIRS night-time light data, sourced from the National Geophysical Data Centre in the United States. A long-time series night-time light dataset, spanning from 1999 to 2019, for the western provinces of China is created. In conjunction with carbon emission statistics from these 11 western provinces, an estimation model is constructed to analyse the changes in the spatiotemporal pattern of carbon emissions within the region, revealing the characteristics of carbon emissions caused by human night-time economic activities in western China, and provides theoretical reference for the formulation of energy conservation and emission reduction policies. Results show that: – A strong correlation coefficient of 0.9067 exists between carbon emissions and the total digital number (DN) value of night-time light in the western provinces, with a negligible average relative error, and this result indicates the effectiveness of the estimation model; – The study reveals an increasing trend in carbon emissions across all 11 provinces from 1999 to 2019, and this growth forms a radial expansion pattern centred around the provincial capitals of Sichuan, Shaanxi, and Inner Mongolia; – By integrating night-time light images and calculated carbon emissions through the estimation model, a significantly positive spatial correlation of carbon emissions is discernible. This outcome presents as a high carbon agglomeration area in the Inner Mongolia Autonomous Region and a low carbon agglomeration area in Qinghai Province. On the basis of these findings, the study proposes transformation measures to promote low carbon emissions in China’s western provinces. These practical suggestions provide a point of reference for China as it aims to meet its “carbon neutrality” and “peak carbon emissions” targets.