Evaluation of Chinese Urban Land-Use Efficiency (Sdg11.3.1) Based on High-Precision Urban Built-up Area Data

Quantifying SDG11.3.1, Ratio of Land Consumption Rate to Population Growth Rate (LCRPGR), is crucial to guide decision makers in urban spatial development planning, protect the ecological environment, social and economic resources sustainability. High-precision urban built-up area data is the prerequisite for accurate calculation of land consumption rate. However, high-precision data is lacking. This study has developed an algorithm and generated accurate urban built-up area data products using China's geographical condition monitoring data with a resolution of 2m from 2015 to 2020, and assessed the impact of different levels of precision in urban built-up areas on the outcomes of the SDG11.3.1 indicator. Results showed that: (1) The accuracy of our urban built-up area data is significantly improved. Additionally, the evaluation results of SDG11.3.1 demonstrate a higher level of accuracy in reflecting the spatio-temporal variation law. The indicator demonstrates a positive correlation with the level of accuracy in the data pertaining to the built-up area; (2) From 2015 to 2020, Chinese cities at the prefecture-level and above have experienced a more rapid urbanization in terms of land expansion compared to population growth. This has consequently led to a suboptimal utilization of land resources on a broader scale. Only the population urbanization in extra-large cities exhibits a relatively balanced pattern. However, in other regions and cities of varying scales, population urbanization lags behind land urbanization. Notably, Northeast China and small to medium-sized cities face significant pressure in terms of population urbanization. The methodology and methods employed in this study can be utilized as a comprehensive framework for evaluating SDG 11.3.1 across different nations and regions, providing a valuable guide for assessment purposes. The study's data product on urban areas and urban built-up area provides essential data for calculating a minimum of five indicators related to SDG11.