[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127263-en":3,"doc-seo-127263-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127263,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Pilot climate resilient cities and ecological resilience - evidence from double machine learning method","Climate change increasingly endangers urban ecological systems, making ecological resilience a priority for climate governance. This study uses China’s Pilot Climate Resilient Cities (PCRC) initiative as a quasi-natural experiment and applies the Double Machine Learning approach to estimate its causal effect on ecological resilience. Results show PCRC significantly improves ecological resilience. Mechanisms include reduced resource dependence and increased green innovation, with stronger impacts in resource-based cities, ecologically fragile regions, and central-western China.","TYPE Original Research PUBLISHED 17 June 2025  \nDOI 10.3389/fenvs.2025.1530104  \nOPEN ACCESS  \nEDITED BY  \nCarmine Massarelli,  \nNational Research Council of ItalyConstruction Technologies Institute, Italy  \nREVIEWED BY  \nRuopu Li,  \nSouthern Illinois University Carbondale, United States  \nJiansong Zheng,  \nMacao Polytechnic University, China  \n*CORRESPONDENCE  \nChao Wang,  \n [18790108839@163.com](18790108839@163.com)  \nRECEIVED 18 November 2024  \nACCEPTED 12 February 2025  \nPUBLISHED 17 June 2025  \nCITATION  \nZhang T, Wang C and Duan D (2025) Pilot climate resilient cities and ecological resilience:  \nevidence from double machine learning method.  \nFront. Environ. Sci. 13:1530104 .  \ndoi: 10.3389/fenvs.2025.1530104  \nCOPYRIGHT  \n© 2025 Zhang, Wang and Duan. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPilot climate resilient cities and ecological resilience: evidence from double machine learning method  \nTian Zhang 1,2, Chao Wang 1,2* and Dingkang Duan 1,2  \n1Economics and Management College, China University of Geosciences, Wuhan, China, 2Economics and Environment Research Center, China University of Geosciences, Wuhan, China  \nAs climate change poses an escalating threat to the global ecological environment, strengthening urban ecological resilience has become a pressing priority. Our research leverages China’s “Pilot Climate Resilient Cities”(PCRC) initiative as a quasi-natural experiment, employing the Double Machine Learning approach to assess its impact on ecological resilience. The findings reveal that the PCRC significantly enhances pilot cities’ ecological resilience. Mechanism analysis indicates that reducing resource dependence and fostering green innovation are the two primary channels through which the PCRC improves ecological resilience. Heterogeneity analysis indicates that the PCRC’s effects are particularly pronounced in resource-based cities, as well as in ecologically fragile regions and central and western areas of China. This study not only provides empirical support for the formulation and optimization of climate adaptation policies, but also offers crucial theoretical insights for designing differentiated policies across various types of cities.  \nKEYWORDS  \nclimate governance, ecological resilience, double machine learning method, resource dependence, green innovation  \n1 Introduction  \nClimate change has emerged as a significant challenge in global environmental governance, and the frequency of extreme weather events and increased pressure on ecosystems seriously threaten the sustainable development of cities (IPCC, 2023; Yuan et al., 2024) . The “2023 State of the Global Climate” report highlights that the earth has set new records in average temperature, glacier melt, sea level rise, and ocean heat content.1 As oneof the world’s most prominent monsoon climate regions, China is particularly susceptible to frequent meteorological disasters and complex climate risks (Cai et al., 2025; Tang et al., 2024), positioning it as highly sensitive to climate change (Hong et al., 2019; Jiang and Jiang, 2024; Zhou et al., 2023) . China’s climate challenges extend beyond domestic ecological and social concerns, significantly influencing global climate governance (Huang et al., 2023) . China bears a substantial responsibility in international climate negotiations, and its climate governance actions are critical to the global effort to mitigate climate change (Liao, 2024; Qi and Dauvergne, 2022; Xu and Zhang, 2022) . Climate change is no longer merely an  \n1 Source: [https://wmo.int/publication-series/state-of-global-clima","cbCaihChwbqcrOfA","https://ap.wps.com/l/cbCaihChwbqcrOfA","pdf",2782530,1,16,"English","en",105,"# Introduction\n## Climate change and urban ecological risk\n## Urban resilience and ecological resilience concept\n## Policy relevance and study objectives","[{\"question\":\"How does the study evaluate the impact of the Pilot Climate Resilient Cities (PCRC) program?\",\"answer\":\"It treats PCRC as a quasi-natural experiment and applies the Double Machine Learning approach to assess its effect on ecological resilience.\"},{\"question\":\"What mechanisms explain how PCRC improves ecological resilience?\",\"answer\":\"The analysis indicates two main channels: reducing resource dependence and fostering green innovation.\"},{\"question\":\"Which cities or regions benefit most from PCRC according to the heterogeneity analysis?\",\"answer\":\"The effects are strongest in resource-based cities, ecologically fragile regions, and central and western areas of China.\"}]","Pilot climate resilient cities and ecological resilience - evidence from double machine learning method | PDF",1785937835,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"pilot-climate-resilient-cities-and-ecological-resilience-evidence-from-double-machine-learning-method","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/pilot-climate-resilient-cities-and-ecological-resilience-evidence-from-double-machine-learning-method/127263/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the study evaluate the impact of the Pilot Climate Resilient Cities (PCRC) program?","Question",{"text":76,"@type":77},"It treats PCRC as a quasi-natural experiment and applies the Double Machine Learning approach to assess its effect on ecological resilience.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What mechanisms explain how PCRC improves ecological resilience?",{"text":81,"@type":77},"The analysis indicates two main channels: reducing resource dependence and fostering green innovation.",{"name":83,"@type":74,"acceptedAnswer":84},"Which cities or regions benefit most from PCRC according to the heterogeneity analysis?",{"text":85,"@type":77},"The effects are strongest in resource-based cities, ecologically fragile regions, and central and western areas of China.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":29,"slug":119},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]