首页|Identifying influencing factors and characterizing key issues in urban sustainable development capacity through machine learning
Identifying influencing factors and characterizing key issues in urban sustainable development capacity through machine learning
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
In response to the United Nations Sustainable Development Goals and China's"Dual Carbon"Goals(DCGs means the goals of"Carbon Peak and carbon neutrality"),this paper from the perspective of the construction of China's Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach com-bining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Cou-pling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future di-rections of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influ-encing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and char-acterize key issues based on actual urban development needs.
Urban sustainable development capacitySDGsDual Carbon GoalsFactor identificationIssue characterizationMachine learning
Houbo Zhou、Lijie Gao、Longyu Shi、Qiuli Lv
展开 >
Key Laboratory of Urban Environment and Health,Institute of Urban Environment,Chinese Academy of Sciences,Xiamen 361021,China
University of Chinese Academy of Sciences,Beijing 100049,China
万方数据
维普
