The Relationship Between Three-Dimensional Spatial Structure and CO2 Emission of Urban Agglomerations Based on CNN-RF Modeling: A Case Study in East China

Abstract

Good urban design helps mitigate carbon dioxide emissions and is important for achieving global low-carbon goals. Previous studies have mostly focused on the two-dimensional level of urban socio-economic activities, urban land use changes, and urban morphology, neglecting the importance of the three-dimensional spatial structure of cities. This study takes 30 cities in East China as an example. By using urban building data and carbon emission datasets, four machine learning algorithms, BP, RF, CNN, and CNN-RF, are established to build a CO2 emission prediction model based on three-dimensional spatial structure, and the main influencing factors are further studied. The results show that the CNN-RF model performed optimally in both the testing and validation phases, with the coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) of 0.85, 0.82; 10.60, 22.32; and 2.53, 1.92, respectively. Meanwhile, in the study unit, S, V, NHB, AN, BCR, SCD, and FAR have a greater impact on CO2 emissions. This indicates a strong correlation between urban three-dimensional spatial structure and carbon emissions. The CNN-RF model can effectively evaluate the relationship between them, providing strategic support for spatial optimization of low-carbon cities.