The Relationship Between Three-Dimensional Spatial Structure and CO 2 Emission of Urban Agglomerations Based on CNN-RF Modeling: A Case Study in East China

Banglong Pan (), Doudou Dong, Zhuo Diao, Qi Wang, Jiayi Li, Shaoru Feng, Juan Du, Jiulin Li and Gen Wu
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Banglong Pan: School of Architecture and Urban Planning, Anhui Jianzhu University, Hefei 230009, China
Doudou Dong: School of Architecture and Urban Planning, Anhui Jianzhu University, Hefei 230009, China
Zhuo Diao: College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China
Qi Wang: School of Architecture and Urban Planning, Anhui Jianzhu University, Hefei 230009, China
Jiayi Li: School of Architecture and Urban Planning, Anhui Jianzhu University, Hefei 230009, China
Shaoru Feng: School of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 130012, China
Juan Du: School of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 130012, China
Jiulin Li: School of Architecture and Urban Planning, Anhui Jianzhu University, Hefei 230009, China
Gen Wu: Anhui Urban Construction Design Institute Co., Ltd., Hefei 230091, China

Sustainability, 2024, vol. 16, issue 17, 1-16

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 CO 2 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 (R 2 ), 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 CO 2 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.

Keywords: three-dimensional spatial structure; CO 2 emissions; machine learning; urban design; low-carbon cities (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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