Assessment of urban land use efficiency in China: A perspective of scaling law

In the context of urbanization and sustainable development, efficient urban land use is essential, especially in China, the world's most populous country. Within this context, the law of urban scaling reveals the nonlinear scale relationship between urban indicators and urban population, which can be applied to adjust the bias of the raw or the per capita indices used in the measurement of local urban performance at different scales. However, the manner in which the urban scaling law applies to China and how it can be used to assess urban land use efficiency (ULUE) is still unclear. In this study, we employ scale-adjusted metropolitan indicators (SAMIs) to assess ULUE in Chinese cities. We first considered the urban population to calculate the land input performance (LIP) and land output performance (LOP), then we quantify ULUE and identify four related patterns. We further investigate the temporal and spatial variations of ULUE and explore the characteristics and policy implications of ULUE values. Results from our study indicate that ULUE assessments from the perspective of the urban scaling law can effectively correct the bias caused by the urban size, thereby allowing for an objective understanding of performance of cities in different sizes. From 2012 to 2016, ULUE of Chinese cities showed a steadily rising trend. The ULUE patterns of most cities remained unchanged and showed significant “path dependence.” However, the disparity in ULUE between regions is widening. Specifically, the cities in the south are better than those in the north, and the cities in the northeast have significantly deteriorated ULUEs. Some cities showed a high ULUE, especially Shenzhen, Hangzhou, and Wuhan. Spatial autocorrelation analysis suggests that geographically neighboring cities are likely to perform similarly regarding ULUE. In terms of policy implications, our work can provide a clear direction for development of cities in urban size and urban efficiency.