Spatiotemporal Evolution and Driving Forces of Sustainable Development of Urban Human Settlements in China for SDGs

• Main Authors: Xueping Cong, Xueming Li, Yilu Gong
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Land
• Subjects: urban human settlements; sustainable development; temporal and spatial differentiation; driving force; prefecture-level cities in China
• Online Access: https://www.mdpi.com/2073-445X/10/9/993

As the world’s largest developing country, China has actively implemented the UN Sustainable Development Goals (SDGs). Sustainable development of urban human settlements is the result of localization and the deepening of sustainable development theory in China. This study combines SDGs to construct an evaluation index system for the sustainable development of urban human settlements in China, using optimization methods, such as natural breaks (Jenks), exploratory spatial data analysis, and GeoDetector, to conduct systematic research on the spatiotemporal evolution of the current sustainable development level and analyze the core driving forces of urban human settlements in 285 prefecture-level cities in China from 2000 to 2019. Our study revealed that: (1) The overall sustainable development level of urban human settlements and their subsystems in China has improved steadily, but the levels of subsystems are quite different; (2) the sustainable development level of the urban human settlements in China can be expressed as a spatial pattern of “high in the east and low in the west, high in the south and low in the north” and has relatively significant spatial correlation characteristics; notably, the development level of each subsystem has different spatial characteristics; (3) the sustainable development level of urban human settlements is mainly based on medium sustainability, and the main development model is to progress from a medium-low development level to a medium-high development level; (4) the sustainable development level of urban human settlements is mainly driven by the per capita gross domestic product (GDP), housing price-to-income ratio, investment in education and scientific research, Internet penetration, and PM_2.5.