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There is an urgent need to improve the resilience of the industrial chain and supply chain. This paper identifies 21 resilience-influencing factors from 4 perspectives: absorption capacity, adaptability, recovery capacity, and self-learning capacity; it then constructs an evaluation indicator system. The Interval Type 2 Fuzzy-Decision-Making Trial and Evaluation Laboratory-Analytic Network Process (IT2F-DEMATEL-ANP) method is adopted to determine the weights of the indicator system, and a resilience evaluation is performed based on the Interval Type 2 Fuzzy-Prospect Theory-Technique for Order Preference by Similarity to an Ideal Solution (IT2F-PT-TOPSIS) method. Furthermore, in the case of the CTL industrial chain and supply chain of China Shenhua Energy Group Ningxia Coal Industry Co., Ltd. (CENC) (Ningxia, China), this study ranks the resilience level from 2018 to 2022 to identify the factors that have contributed to a reduction in resilience and to implement measures to enhance the resilience of the CTL industrial chain and supply chain. The results show that the level of the CTL industrial chain and supply chain resilience was lowest in 2020, while it was highest in 2021. Factors such as the degree of domestication of key technologies, the rationality of the CTL industry layout, and the stability of supply and demand chains are identified as significant determinants of resilience levels. This points the way to enhancing the resilience of the CTL industry and supply chain.<\/jats:p>","DOI":"10.3390\/systems12100395","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T06:55:52Z","timestamp":1727333752000},"page":"395","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluation Research on Resilience of Coal-to-Liquids Industrial Chain and Supply Chain"],"prefix":"10.3390","volume":"12","author":[{"given":"Anbo","family":"Wu","sequence":"first","affiliation":[{"name":"College of Management, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Energy Economic Research Center, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Pingfan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Management, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Energy Economic Research Center, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Linhui","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Management, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Energy Economic Research Center, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Chang","family":"Su","sequence":"additional","affiliation":[{"name":"College of Safety Science and Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Xinping","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Management, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Energy Economic Research Center, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113923","DOI":"10.1016\/j.rser.2023.113923","article-title":"Spatiotemporal variation and convergence analysis of China\u2019s regional energy security","volume":"189","author":"Wang","year":"2024","journal-title":"Renew. 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