Analysis and comprehensive evaluation of sustainable land use in China: Based on sustainable development goals framework

Based on UN 2030 Sustainable Development Goals (SDGs), this study selects 43 indicators from three dimensions to construct an evaluation index system for sustainable land use (SLU). Thirty provincial administrative regions in China from 2002 to 2018 are chosen to comprise the study area. Real-coded accelerating genetic algorithm-projection pursuit techniques and an exploratory spatial data analysis model are used to study the spatiotemporal differences in the SLU levels of the provinces in China. Results show the following: (1) During the study period, the SLU level in China demonstrated an overall upward trend of fluctuations, along with obvious regional differences. (2) The spatial pattern of the SLU level in China was high in the eastern region and low in the western region in the east-west direction; for the north-south direction, an inverted U-shaped distribution was noted. (3) The main types of local spatial autocorrelation are H-H and L-L types, and both have evident spatial clustering characteristics. (4) The realization of Goal A is the main driving force for the improvement of the SLU level, whereas the failure to realize Goals B and C effectively is the main bottleneck in the improvement of the SLU level.

Automated summary

Based on the UN's SDGs, this study constructed an evaluation index system for sustainable land use (SLU) using 43 indicators from three dimensions. Using advanced techniques such as real-coded genetic algorithms and spatial data analysis, the study found that China's SLU levels have shown an overall upward trend with regional variations. Spatially, the SLU level in China is higher in the east and lower in the west, with an inverted U-shaped distribution in the north-south direction. The main types of local spatial autocorrelation were identified as H-H and L-L types, showing clear spatial clustering characteristics. Failure to achieve Goals B and C effectively was identified as the main bottleneck in improving SLU levels.