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 ...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 popula tion 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.The comprehensive framework developed 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.展开更多
基金funded by the National Key Research and De-velopment Program of China(Grant No.2023YFC3804001)the Natural Resources Planning and Management Project(Grant No.A2417,A2418)the Fundamental Scientific Research Funds for Central Public Wel-fare Research Institutes(Grant No.AR2409).
文摘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 popula tion 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.The comprehensive framework developed 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.
