Abstract
Evaluation of intensive land use (ILU) over long time series is essential for the rational use of land and urban development. We propose a novel framework for analyzing ILU in the Pearl River Delta (PRD) region of China. First, we used Google Earth Engine (GEE) to obtain cities’ built-up land information. Second, we calculated the ILU degree and constructed an evaluation index system based on the Pressure–State–Response (PSR) theoretical framework. Third, we employed Geodetector to determine the dominant influencing factors on ILU. The findings are as follows: (1) It is accurate and effective to extract land use data using GEE. From 2000 to 2020, all cities’ built-up areas increased, but the increases differed by city. (2) While the ILU level in all cities has increased over the past 20 years, the ILU level in each city varies. Specifically, Shenzhen had the highest ILU degree in 2020, followed by core cities such as Guangzhou, Dongguan, and Zhuhai, while cities on the PRD region’s periphery, such as Zhaoqing and Jiangmen, had relatively low ILU levels. (3) In terms of time, the dominant factors influencing ILU in the PRD region have shifted over the past two decades. During this period, however, two factors (economic density and disposable income per capita) have always played a dominant role. This suggests that improving economic output efficiency and the city’s economic strength is a feasible way to raise the ILU level at this time.
Funder
Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference49 articles.
1. Land Financialization, Uncoordinated Development of Population Urbanization and Land Urbanization, and Economic Growth: Evidence from China
2. Urban economic structure, technological externalities, and intensive land use in China
3. The Evaluation of Urban Land Use Intensity and its Time-Spatial Differences in Shandong Province;Wang;China Popul. Resour. Environ.,2012
4. Operating Efficiency-Based Data Mining on Intensive Land Use in Smart City
5. Evaluation of intensive urban land use and analysis of obstacle factors in northern slope of Tianshan mountains;Zhao;Trans. Chin. Soc. Agric. Eng.,2018
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献