Spatio-temporal pattern evolution of energy consumption carbon emissions at the city and county levels based on the population-kNDVI correction of nighttime light from 2000 to 2020

作者：Liang Zhang, Jingchun Zhou, Xi Wang, Jinliang Wang · 发表于：Sustainable Cities and Society · 年份：2025 · DOI：10.1016/j.scs.2025.106655 · 被引用次数：4 · 研究领域：Impact of Light on Environment and Health、Urban Transport and Accessibility

Energy consumption increases carbon emissions,exacerbating climate change. The study of the spatio-temporal evolution of carbon emissions contributes to the formulation of carbon reduction policies.The characteristics of the spatio-temporal evolution of carbon emissions are important for the precise formulation of carbon reduction strategies. However,carbon emissions estimates focus primarily on provincial level. Due to insufficient data and deviations in energy statistics, accurate carbon emissions estimates at these scales remain challenging at city or county level.Human activities can be recorded through nighttime light, which is useful for estimating carbon emissions. Therefore, how to process nighttime light to enhance the accuracy of carbon emissions estimates is a critical issue.This study combines nighttime light with population density and kernel NDVI to propose PKANTL for calibrating nighttime light,Bayesian optimization of long short-term memory networks is used to construct long-term sequence nighttime light.Using a geographically weighted regression model to link carbon emissions with PKANTL to estimate carbon emissions, generating the spatial distribution of multi-scale energy consumption carbon emissions from 2000 to 2020.The research results are as follows: the R²of BO-LSTM fitting DMSP-OLS and NPP-VIIRS is 0.9422, demonstrating high accuracy. PKANTL can effectively process nighttime light, highlighting human activities.The average R 2 of carbon emissions simu...