The importance of the functional mixed entropy for the explanation of residential and transport CO2 emissions in the urban center of China. (20th December 2022)

• Record Type: Journal Article
• Authors: Zuo, Shudi; Dai, Shaoqing; Ju, Jiaheng; Meng, Fanxin; Ren, Yin; Tian, Yunfeng; Wang, Kaide
• Is Part Of: Journal of cleaner production. Volume 380:Part 1(2022)
• Publication Date: 2022-12-20
• Subjects: CO2 mapping -- Downscaling interpolation -- Functional mixed entropy -- Urban form -- Spatial analysis; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.134947
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The influence of urban spatial form on the environment is complex and lengthy. The spatial analysis for the urban form and residential-related CO2 emissions at the city scale is challenging due to the lack of extensive urban form data and fine-grain CO2 emission maps. This research uses remote sensing data and downscaling interpolation to generate residential and transport (RTCE) maps in 130 m spatial resolution of urban center regions from 31 major cities in China, then investigates the relationship between 3 types of urban form indicators (Internal characteristics, external morphology, and development intensity) and RTCE through Geographical Weighted Regression method. The results reveal that urban form indicators could explain about 45.9% of RTCE. The 2D building shape indicator has the second greatest positive impact as the external morphology indicator, which complicates the influence. The internal characteristics indicators have relatively strong influences than the development intensity indicators. For instance, the influence of functional mixed entropy (FME) is the greatest positive influence and decreases exponentially with FME increases. Therefore, cities with the FME lower than the threshold (0.28) should increase it appropriately, while cities with the FME around and larger than 0.28 should maintain and probably reduce it. Highlights: Luojia-01 data were used to downscale 130m CO2 emissions maps of 31 Chinese cities. GWR reveals that mean R 2 of eight urban form indicators of 31 cities was 45.9%. The rank of the influences of eight indicators was FME > LSI > BS > RI > BF > RL > GS > BA. FME impact decreased with its value increase based on the exponential function. The influence of FME decreases to be gentle at the tangent point of 0.28.