{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:31:27Z","timestamp":1785407487899,"version":"3.56.0"},"reference-count":52,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T00:00:00Z","timestamp":1782000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72271135"],"award-info":[{"award-number":["72271135"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["72271135"],"award-info":[{"award-number":["72271135"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>To systematically examine the impact of climate risks on China\u2019s financial system, this study employs the EGARCH-SGED model to precisely fit financial market volatility based on China\u2019s Climate Change News Index. It then combines the LASSO-CoVaR method to measure tail risk spillover effects within China\u2019s financial system under climate risk shocks, constructs a risk contagion network, and innovatively utilizes the RF-AdaBoost model to establish the risk early warning system. Findings reveal that climate risk is a key driver of dynamic correlation evolution within the financial system, with heterogeneous impacts across different markets. Physical climate risk events intensify short-term risk contagion while generating long-term effects; transition risks undergo a dynamic process, initially amplifying uncertainty before enhancing systemic stability over the long term. The RF-AdaBoost model outperforms traditional machine learning models in risk warning, demonstrating outstanding predictive accuracy and generalization capabilities, thereby providing effective intellectual support for climate risk prevention and financial stability management.<\/jats:p>","DOI":"10.3390\/e28060711","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T08:14:12Z","timestamp":1782375252000},"page":"711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks\u2014From the Perspective of Complex Networks and Machine Learning"],"prefix":"10.3390","volume":"28","author":[{"given":"Xiao-Li","family":"Gong","sequence":"first","affiliation":[{"name":"School of Economics, Qingdao University, Qingdao 266061, China"},{"name":"Laboratory of Complex Economic Systems and Digital Governance, Qingdao University, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Han","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Economics, Qingdao University, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergey Aleksandrovich","family":"Philin","sequence":"additional","affiliation":[{"name":"Higher School of Management, Plekhanov Russian University of Economics, 109992 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101438","DOI":"10.1016\/j.intfin.2021.101438","article-title":"Do climate risks matter for green investment?","volume":"75","author":"Marshall","year":"2021","journal-title":"J. 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