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Huang M, Liu F, Gong D, Lin H, Chen Y, Hu B, Ge Y, Xiao C. Spatiotemporal evolution of land use efficiency in 357 cities across mainland China from 2000 to 2020 based on SDG 11.3.1. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 954:176157. [PMID: 39260504 DOI: 10.1016/j.scitotenv.2024.176157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 08/11/2024] [Accepted: 09/07/2024] [Indexed: 09/13/2024]
Abstract
High-efficiency land use facilitates the maximization of land utilization, lowers urban construction costs, and optimizes urban functional patterns. The Sustainable Development Goal 11.3.1 (SDG 11.3.1) can be used to assess land use efficiency (LUE), understand the current state of land use, and identify the potential for optimization. This study combines SDG 11.3.1 with other supplementary indicators to establish a land use efficiency evaluation system. This system provides a more precise understanding of internal city changes and enables a scientific assessment of urban LUE in Mainland China. The results showed that: (1) A significant number of cities were growing cities, particularly in the eastern region, with the population of built-up areas increased by 2.92 times from 2000 to 2020; (2) From 2000 to 2020, cities in China underwent rapid urban expansion, with the most significant urban expansion index in 2015-2020; (3) The coordination between population growth rate (PGR) and land consumption rate (LCR) worsened in the western region, while the central and eastern regions showed better coordination. (4) As the urban expansion index increased, the compactness index of the cities in the above three regions decreased and were at lower levels. This study establishes an evaluation system to assess the LUE and reveals the spatial and temporal characteristics of urban and population change. It holds paramount significance in enhancing LUE and encouraging sustainable development in Mainland China and serves as a valuable reference for global urban management.
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Affiliation(s)
- Min Huang
- School of Geography and Environment/Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), Jiangxi Normal University, Nanchang 330022, China; Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province, Jiangxi Normal University, Nanchang 330022, China
| | - Fen Liu
- School of Geography and Environment/Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), Jiangxi Normal University, Nanchang 330022, China
| | - Daohong Gong
- School of Geography and Environment/Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), Jiangxi Normal University, Nanchang 330022, China
| | - Hui Lin
- School of Geography and Environment/Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), Jiangxi Normal University, Nanchang 330022, China; Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province, Jiangxi Normal University, Nanchang 330022, China; Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
| | - Yong Chen
- Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province, Jiangxi Normal University, Nanchang 330022, China
| | - Bisong Hu
- School of Geography and Environment/Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), Jiangxi Normal University, Nanchang 330022, China
| | - Yong Ge
- School of Geography and Environment/Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), Jiangxi Normal University, Nanchang 330022, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Changjiang Xiao
- College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China.
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Monitoring Land-Use Efficiency in China’s Yangtze River Economic Belt from 2000 to 2018. LAND 2022. [DOI: 10.3390/land11071009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Monitoring of the indicator Sustainable Development Goal (SDG) 11.3.1 is important for understanding the coordination between land consumption rate (LCR) and population growth rate (PGR). However, the spatiotemporal indicator SDG 11.3.1 changes at the urban agglomeration (UA) level, and the relationship between LCR and PGR in the prefecture-level cities from different UAs remains unclear. In this study, we monitored the spatiotemporal indicator SDG 11.3.1 in the Yangtze River Economic Belt (YREB) and its three major UAs (i.e., Chengdu–Chongqing (CC), the Middle Reaches of the Yangtze River (MRYR), and the Yangtze River Delta (YRD)) for the periods 2000–2010, 2010–2015, and 2015–2018, using the space–time interaction (STI) method and Pearson’s method. Our major findings were as follows: (1) Compared with the world average of 1.28 for LCRPGR (i.e., ratio of LCR to PGR), except for the LCRPGR of the YRD (2000–2018) and CC (2000–2010), the LCRPGR of CC, the MRYR, and the YREB was lower than 1.28 during 2000–2018. (2) The gaps in both population and built-up area between the YREB and the three UAs did not narrow, but widened. (3) Compared with the LCRPGR in China, except for the LCRPGR of the YRD (2000–2018) and CC (2000–2010), the LCRPGR of the YREB increased from 1.21 to 1.23 between 2000–2010 and 2010–2015, and then decreased to 1.16 in 2015–2018, indicating that the relationship between LCR and PGR in the YREB is relatively stable. (4) A significant positive relationship (p < 0.001) was found between LCR and PGR in CC, the MRYR, the YRD, and the YREB. We conclude that the indicator SDG 11.3.1 is a helpful tool for evaluating land-use efficiency caused by the LCR and PGR at the UA level. Our results provide information support for promoting sustainable and coordinative development between LCR and PGR.
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