Temporal-Spatial Characteristics and Driving Factors of Urban Land Use Performance: Evidence from Guangdong
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School of Economics, Shanghai University, Baoshan District 200444, China
Weiyong Zou   

School of Economics, Shanghai University, China
Submission date: 2021-11-13
Final revision date: 2022-01-31
Acceptance date: 2022-02-07
Online publication date: 2022-04-26
Publication date: 2022-06-20
Pol. J. Environ. Stud. 2022;31(4):3477–3490
Based on the panel data of 21 cities in Guangdong Province from 2000 to 2019, this paper investigates the temporal-spatial characteristics and driving factors of urban land use performance (ULUP) in Guangdong Province by using entropy weight TOPSIS method, Dagum Gini coefficient, natural discontinuity method, standard deviation ellipse, gravity center model and geographic detector. The main conclusions are as follows: (1) There are great differences in the distribution of land use performance among cities in Guangdong Province, which decreases from the Pearl River Delta to the surrounding cities. From the time dimension, the land use performance of Guangdong Province generally shows an upward trend. (2) The cities in the Pearl River Delta are basically located in the first and second echelons, showing an agglomeration development trend and radiation driving role in space, which greatly improves the land use performance of the surrounding cities. Regional differences constitute the main source of internal differences in land use performance in Guangdong Province, followed by intraregional differences, and then over variable density. (3) During the study period, the directionality of the spatial distribution of ULUP in Guangdong Province has weakened, showing a spatial distribution pattern from northeast to southwest, and the evolution force of spatial distribution comes from the growth in northeast to southwest. The focus of land use performance is located in Dongguan and gradually moved to the border with Shenzhen. (4) From the perspective of division dimension, the driving factors from large to small are industrial economy, infrastructure, humanistic factors and government support. From the perspective of sub factors, five indicators such as economic growth, scientific and technological innovation talents, public transport, industrial upgrading and technological innovation have become the biggest driving factors.