AI big model and text mining-driven framework for urban greening policy analysis
Abstract Policy analysis is essential to improving the rationality and adaptability of policies. Traditional policy analysis easily generates biased results due to different individual perspectives and personal experiences. Text mining emerges as an efficient way, but is not widely used in urban gre...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-05842-z |
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| author | Li Li Xuesong Yang Sijia Liu Feiyang Deng |
| author_facet | Li Li Xuesong Yang Sijia Liu Feiyang Deng |
| author_sort | Li Li |
| collection | DOAJ |
| description | Abstract Policy analysis is essential to improving the rationality and adaptability of policies. Traditional policy analysis easily generates biased results due to different individual perspectives and personal experiences. Text mining emerges as an efficient way, but is not widely used in urban greening policy analysis. Moreover, existing policy studies are mostly limited to topic categorization, and there is a lack of systematic policy text analysis and real-time policy tracking. Here, we constructed a multidimensional dynamic policy analysis framework for systematic evaluation of urban greening policies by introducing AI big models and text mining. With Wuhan as an example, the framework was used to analyze the evolution of policy topics, distribution of annual topics, and spatial and temporal changes in greening indicators. Moreover, the framework supports real-time tracking and in-depth interpretation of policies, and the results can be presented through visualization scenarios. Analysis with the framework revealed variations of greening policies in Wuhan over the past 15 years, such as transformation from basic greening to ecological remediation and policy focus shift from flower planning to wetland protection. This methodology marks a new paradigm for intelligent policy evaluation, and significantly improves the efficiency and accuracy of policy formulation and implementation in smart cities. |
| format | Article |
| id | doaj-art-9db781b7fa3c4c00ba428e2eaa311ff7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9db781b7fa3c4c00ba428e2eaa311ff72025-08-20T04:02:45ZengNature PortfolioScientific Reports2045-23222025-08-0115111710.1038/s41598-025-05842-zAI big model and text mining-driven framework for urban greening policy analysisLi Li0Xuesong Yang1Sijia Liu2Feiyang Deng3School of Urban Design, Wuhan UniversitySchool of Urban Design, Wuhan UniversityCollege of Horticulture and Forestry, Huazhong Agricultural UniversityQuality Assurance Department, Beijing Glory PKPM Technology Company of China Academy of Building ResearchAbstract Policy analysis is essential to improving the rationality and adaptability of policies. Traditional policy analysis easily generates biased results due to different individual perspectives and personal experiences. Text mining emerges as an efficient way, but is not widely used in urban greening policy analysis. Moreover, existing policy studies are mostly limited to topic categorization, and there is a lack of systematic policy text analysis and real-time policy tracking. Here, we constructed a multidimensional dynamic policy analysis framework for systematic evaluation of urban greening policies by introducing AI big models and text mining. With Wuhan as an example, the framework was used to analyze the evolution of policy topics, distribution of annual topics, and spatial and temporal changes in greening indicators. Moreover, the framework supports real-time tracking and in-depth interpretation of policies, and the results can be presented through visualization scenarios. Analysis with the framework revealed variations of greening policies in Wuhan over the past 15 years, such as transformation from basic greening to ecological remediation and policy focus shift from flower planning to wetland protection. This methodology marks a new paradigm for intelligent policy evaluation, and significantly improves the efficiency and accuracy of policy formulation and implementation in smart cities.https://doi.org/10.1038/s41598-025-05842-zPolicy analysisGreeningMethodological frameworkAI big modelText mining |
| spellingShingle | Li Li Xuesong Yang Sijia Liu Feiyang Deng AI big model and text mining-driven framework for urban greening policy analysis Scientific Reports Policy analysis Greening Methodological framework AI big model Text mining |
| title | AI big model and text mining-driven framework for urban greening policy analysis |
| title_full | AI big model and text mining-driven framework for urban greening policy analysis |
| title_fullStr | AI big model and text mining-driven framework for urban greening policy analysis |
| title_full_unstemmed | AI big model and text mining-driven framework for urban greening policy analysis |
| title_short | AI big model and text mining-driven framework for urban greening policy analysis |
| title_sort | ai big model and text mining driven framework for urban greening policy analysis |
| topic | Policy analysis Greening Methodological framework AI big model Text mining |
| url | https://doi.org/10.1038/s41598-025-05842-z |
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