Lin, Q.; Bai, S.; Qi, R. Cultivated Land Green Use Efficiency and Its Influencing Factors: A Case Study of 39 Cities in the Yangtze River Basin of China. Sustainability2024, 16, 29.
Lin, Q.; Bai, S.; Qi, R. Cultivated Land Green Use Efficiency and Its Influencing Factors: A Case Study of 39 Cities in the Yangtze River Basin of China. Sustainability 2024, 16, 29.
Lin, Q.; Bai, S.; Qi, R. Cultivated Land Green Use Efficiency and Its Influencing Factors: A Case Study of 39 Cities in the Yangtze River Basin of China. Sustainability2024, 16, 29.
Lin, Q.; Bai, S.; Qi, R. Cultivated Land Green Use Efficiency and Its Influencing Factors: A Case Study of 39 Cities in the Yangtze River Basin of China. Sustainability 2024, 16, 29.
Abstract
In recent years, the Chinese government pays more and more attention to agricultural development and ecological protection. While improving the cultivated land green use efficiency(CLGUE) is the key to promote the sustainable development of agriculture. This study aims to study the current situation and influencing factors of agricultural production from the perspective of green utilization efficiency of cultivated land. It takes 39 cities in the upper, middle and lower reaches of the Yangtze River basin in China as an example. The CLGUE values in those 39 cities from 2011 to 2020 were specifically measured, using the Super-SBM model, kernel density estimation and geographic detector method. Their temporal and spatial heterogeneity was described, and the influencing factors were detected at both single and interactive levels. The results showed that: （1）From 2011 to 2020, the green utilization efficiency value of cultivated land in the Yangtze River Basin showed an upward trend on the whole; （2）There is a clear spatial heterogeneity the CLGUE values in the Yangtze River Basin cities, as shown by: downstream region > midstream region > upstream region; (3) Cultivated land resource endowment, socioeconomic development, and agricultural production technology are important factors affecting the variability of CLGUE values. However, there are some differences in the degree and direction of influence of different influencing factors on different sample subgroups.
Keywords
CLGUE; kernel density estimation; geographic detector method; Yangtze River Basin; regional differences
Subject
Environmental and Earth Sciences, Geography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
