Carbon Peak Trends and Stepped Emission Reduction Paths in Yangtze River Economic Belt Cities

Abstract Cities not only contribute significantly to carbon emissions but also serve as key drivers for promoting carbon emission reduction. They play a pivotal role in demonstrating the achievement of 'carbon peak' and 'carbon neutrality' objectives. This paper utilizes DMSP/OLS stable nighttime lighting data from 2000 to 2013 and NPP/VIIRS nighttime lighting data from 2013 to 2019 to simulate the carbon emissions of cities within the Yangtze River Economic Belt. A BP neural network model is constructed and combined with scenario analysis and a geographically weighted regression model to systematically analyze the carbon emission characteristics, carbon peak trends, and graded carbon emission reduction paths of cities within the Yangtze River Economic Belt. This analysis considers various factors such as time series, geography, and per capita metrics. Subsequently, tailored graded carbon emission reduction paths and countermeasures are proposed for different types of cities experiencing various carbon peak scenarios, aiming to accelerate carbon emission reduction efforts within the Yangtze River Economic Belt.