Research on the Driving Factors and Prediction Model of Urban Underground Space Demand in China

Yansheng Deng (), Jun Chen, Baoping Zou (), Qizhi Chen, Jingyuan Ma and Chenjie Shen
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Yansheng Deng: School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, China
Jun Chen: School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, China
Baoping Zou: School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, China
Qizhi Chen: School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, China
Jingyuan Ma: School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, China
Chenjie Shen: School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, China

Sustainability, 2024, vol. 16, issue 9, 1-14

Abstract: The development and utilization of urban underground space (UUS) have emerged as critical strategies to address the challenges posed by urban population growth and land resource depletion. Accurate prediction of UUS demand serves as the cornerstone for scientifically planning underground space and promoting sustainable urban development. In this study, statistical analysis methods were used to investigate the relationship between potential driving factors and UUS demand based on collected data from 16 cities in China. The identification of primary driving factors involves correlation, path, and determination coefficient analyses. Subsequently, univariate regression, multiple linear regression, and LASSO regression methods are employed to construct prediction models for UUS demand. Additionally, the link between historical data and UUS demand in each city was studied separately. The findings reveal that GDP per km 2 and GDP per capita comprehensively capture the influence of urban population, economy, and transportation on UUS demand. Notably, GDP per km 2 makes the most significant contribution to the proposed regression models, followed by GDP per capita. The application of LASSO regression proves effective in selecting potential factors while maximizing data utilization, presenting itself as a valuable auxiliary tool for UUS planning.

Keywords: UUS; driving factor; UUS demand; prediction model (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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