Urban Form and Function Optimization for Reducing Carbon Emissions Based on Crowd-Sourced Spatio-Temporal Data

Fangjie Cao, Yun Qiu, Qianxin Wang () and Yan Zou
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Fangjie Cao: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Yun Qiu: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Qianxin Wang: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Yan Zou: School of Humanity and Law, Beijing University of Civil Engineering and Architecture, Beijing 102616, China

IJERPH, 2022, vol. 19, issue 17, 1-17

Abstract: The low-carbon city has become an important global urban development-oriented goal. One important aspect of urban space is low-carbon urban planning, which has a vital role in urban carbon emissions. Which types of urban form and function allocations are conducive to reducing carbon emissions is therefore a key issue. In this study, the Futian and Luohu Districts of Shenzhen, Guangdong Province, China, are taken as an example to investigate this issue. Firstly, a “head/tail” breaks method based on the third fractal theory is adopted to obtain the minimum evaluation parcel of urban space. Then, the Landscape Shape Index (LSI), Fragmentation Index (C), Shannon’s Diversity Index (SHDI), and Density of Public Facilities (Den) are used to evaluate the form and function allocation of each parcel. In addition, the CO 2 concentration distribution in this study area is acquired from remote sensing satellite data. Finally, the relationships between urban form, function allocation, and CO 2 concentration are obtained. The results show that the lower the urban form index or the higher the urban function index, the less the CO 2 concentration. To verify this conclusion, three experiments are designed and carried out. In experiment A, the CO 2 concentration of the tested area is reduced by 14.31% by decreasing the LSI and C by 6.1% and 9.4%, respectively. In experiment B, the CO 2 concentration is reduced by 15.15% by increasing the SHDI and Den by 16.3% and 12.1%, respectively. In experiment C, the CO 2 concentration is reduced by 27.72% when the urban form and function are adjusted in the same was as in experiments A and B.

Keywords: low-carbon urban planning; Landscape Shape Index; POI; OSM; CO 2 concentration (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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