Dynamic simulation of street-level carbon emissions in megacities: A case study of Wuhan City, China (2015–2030)

Abstract

Dynamic simulation of carbon emissions (CE) in megacities is crucial for regional carbon reduction management, however, limited simulation accuracy hinders its application in carbon reduction policies. An integrated modeling framework was developed based on high-resolution multi-source data to analyze the street-level carbon emissions in Wuhan from 2015 to 2030. First, we conducted principal components analysis on the 5 driving factors of carbon emissions (including point of interests, electricity consumption, gross domestic product, population, and nighttime light) to delineate single carbon emission character region (CECR). Then, a machine learning method was used to simulating CE and explaining the contributions of different CECRs. Multi-scenarios CE accountings also were conducted with CECR simulations and an improved cellular automata model. Results shows that: (1) CE in Wuhan showed strong aggregation during the historical period. The five CECRs formed a near-concentric circle. The changes of CECR reflected the enhancement of human activity. (2) CE gradually shifted from the central urban areas to the surrounding regions during the scenario period, showing significant spatial spillover effects. Administrative districts with lower CE density exhibited greater scenario variation in total carbon emissions, indicating a higher potential for carbon reductions. (3) The total CE of Wuhan (148.11 Mt) in 2030 is projected to increase by 42.6 % compared to 2015 in the baseline scenario, representing 105 % of the low scenario and 91 % of the high scenario. The growth rate of total CE in Wuhan significantly slows down (<1 %) under all scenarios. The high-resolution dynamic simulation of CE will provide an important scientific basis for low-carbon city management in China.