{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T01:11:05Z","timestamp":1776820265562,"version":"3.51.2"},"reference-count":73,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T00:00:00Z","timestamp":1752451200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China\u2019s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders managing supply chain sustainability risks. This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009\u20132021), integrating financial and non-financial determinants traditionally examined in isolation. Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. Key non-financial determinants include patent portfolios, CSR training initiatives, pollutant emissions, and charitable donations, while financial factors such as current assets and gearing ratios prove influential. Sectoral analysis reveals that manufacturing firms are evaluated through pollutant emissions and technical capabilities, whereas non-manufacturing firms are assessed on business taxes and intangible assets. These insights provide essential tools for multinational corporations to anticipate supply chain sustainability conditions.<\/jats:p>","DOI":"10.3390\/systems13070578","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T09:45:52Z","timestamp":1752572752000},"page":"578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Unraveling the Drivers of ESG Performance in Chinese Firms: An Explainable Machine-Learning Approach"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4767-4892","authenticated-orcid":false,"given":"Hyojin","family":"Kim","sequence":"first","affiliation":[{"name":"School of Business, Konkuk University, Seoul 05029, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8398-9165","authenticated-orcid":false,"given":"Myounggu","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Business, Konkuk University, Seoul 05029, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,14]]},"reference":[{"key":"ref_1","unstructured":"Heinzer, I., and Mezzanzanica, A. 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