{"title":"基于深度学习和多准则决策方法的城市绿色弹性评价——以广东省为例","authors":"Minglong Han , Yupeng Liu","doi":"10.1016/j.scs.2025.106755","DOIUrl":null,"url":null,"abstract":"<div><div>Extreme climate conditions place considerable environmental pressure on urban systems. Therefore, determining how to evaluate and enhance urban green resilience (UGR) is crucial to achieving sustainable development. This paper introduces a methodological framework that combines deep learning and multicriterion decision-making (MCDM) approaches to evaluate UGR and examine its spatiotemporal characteristics. We defined the concept of UGR and developed a corresponding evaluation index system. Additionally, we used a modified assessment model optimized by graph convolutional network and gated recurrent unit architectures to evaluate the green resilience of Guangdong Province’s cities. We conducted spatial heterogeneity and city network analyses to explore the spatial distribution of green resilience. Our results indicate the following. First, ecology has a major effect on green resilience, acting as a source-driven factor. Second, new energy penetration, water pollution pressure, urban greening, and low-carbon infrastructure considerably influence UGR. Third, the green resilience of cities in Guangdong Province is improving, with northern mountainous areas experiencing considerably high growth. Fourth, Pearl River Delta cities excel in terms of infrastructure and social engagement, whereas northern mountainous regions excel in terms of governance. This paper also outlines optimization paths for three representative cities, providing a theoretical basis and practical guidance for future studies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"131 ","pages":"Article 106755"},"PeriodicalIF":12.0000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluating urban green resilience through deep learning and multicriterion decision-making approaches: A spatiotemporal analysis of Guangdong Province\",\"authors\":\"Minglong Han , Yupeng Liu\",\"doi\":\"10.1016/j.scs.2025.106755\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Extreme climate conditions place considerable environmental pressure on urban systems. Therefore, determining how to evaluate and enhance urban green resilience (UGR) is crucial to achieving sustainable development. This paper introduces a methodological framework that combines deep learning and multicriterion decision-making (MCDM) approaches to evaluate UGR and examine its spatiotemporal characteristics. We defined the concept of UGR and developed a corresponding evaluation index system. Additionally, we used a modified assessment model optimized by graph convolutional network and gated recurrent unit architectures to evaluate the green resilience of Guangdong Province’s cities. We conducted spatial heterogeneity and city network analyses to explore the spatial distribution of green resilience. Our results indicate the following. First, ecology has a major effect on green resilience, acting as a source-driven factor. Second, new energy penetration, water pollution pressure, urban greening, and low-carbon infrastructure considerably influence UGR. Third, the green resilience of cities in Guangdong Province is improving, with northern mountainous areas experiencing considerably high growth. Fourth, Pearl River Delta cities excel in terms of infrastructure and social engagement, whereas northern mountainous regions excel in terms of governance. This paper also outlines optimization paths for three representative cities, providing a theoretical basis and practical guidance for future studies.</div></div>\",\"PeriodicalId\":48659,\"journal\":{\"name\":\"Sustainable Cities and Society\",\"volume\":\"131 \",\"pages\":\"Article 106755\"},\"PeriodicalIF\":12.0000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Cities and Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210670725006298\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670725006298","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Evaluating urban green resilience through deep learning and multicriterion decision-making approaches: A spatiotemporal analysis of Guangdong Province
Extreme climate conditions place considerable environmental pressure on urban systems. Therefore, determining how to evaluate and enhance urban green resilience (UGR) is crucial to achieving sustainable development. This paper introduces a methodological framework that combines deep learning and multicriterion decision-making (MCDM) approaches to evaluate UGR and examine its spatiotemporal characteristics. We defined the concept of UGR and developed a corresponding evaluation index system. Additionally, we used a modified assessment model optimized by graph convolutional network and gated recurrent unit architectures to evaluate the green resilience of Guangdong Province’s cities. We conducted spatial heterogeneity and city network analyses to explore the spatial distribution of green resilience. Our results indicate the following. First, ecology has a major effect on green resilience, acting as a source-driven factor. Second, new energy penetration, water pollution pressure, urban greening, and low-carbon infrastructure considerably influence UGR. Third, the green resilience of cities in Guangdong Province is improving, with northern mountainous areas experiencing considerably high growth. Fourth, Pearl River Delta cities excel in terms of infrastructure and social engagement, whereas northern mountainous regions excel in terms of governance. This paper also outlines optimization paths for three representative cities, providing a theoretical basis and practical guidance for future studies.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;