Assessment of Chinese urban land-use efficiency (SDG11.3.1) utilizing high-precision urban built-up area data

城市化 地理 城市规划 中国 人口 可持续发展 建成区 土地利用 人口增长 资源(消歧) 市区 环境资源管理 环境规划 遥感 环境科学 计算机科学 土木工程 经济增长 工程类 经济 计算机网络 人口学 经济 考古 社会学 政治学 法学
作者
Hao Wang,Yafei Liu,Lianze Sun,Xiaogang Ning,Guangzhe Li
出处
期刊:Geography and sustainability [Elsevier BV]
被引量:5
标识
DOI:10.1016/j.geosus.2024.06.007
摘要

Assessment of SDG11.3.1 indicator of the United Nations Sustainable Development Goals (SDGs) is a valuable tool for policymakers in urban planning. This study aims to enhance the accuracy of the SDG11.3.1 evaluation and explore the impact of varying precision levels in urban built-up area on the indicator's assessment outcomes. We developed an algorithm to generate accurate urban built-up area data products based on China's Geographical Condition Monitoring data with a 2 m resolution. The study evaluates urban land-use efficiency in China from 2015 to 2020 across different geographical units using both the research product and data derived from other studies utilizing medium and low-resolution imagery. The results indicate: (1) A significant improvement in the accuracy of our urban built-up area data, with the SDG11.3.1 evaluation results demonstrating a more precise reflection of spatiotemporal characteristics. The indicator shows a positive correlation with the accuracy level of the built-up area data; (2) From 2015 to 2020, Chinese prefecture-level cities have undergone faster urbanization in terms of land expansion relative to population growth, leading to less optimal land resource utilization. Only in extra-large cities does urban population growth show a relatively balanced pattern. However, urban population growth in other regions and cities of various sizes lags behind land urbanization. Notably, Northeast China and small to medium cities encounter significant challenges in urban population growth. Our comprehensive framework for evaluating SDG11.3.1 with high-precision urban built-up area data can be adapted to different national regions, yielding more accurate SDG11.3.1 outcomes. Our urban area and built-up area data products provide crucial inputs for calculating at least four indicators related to SDG11.

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