{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T19:37:18Z","timestamp":1793129838539,"version":"4.5.0"},"reference-count":38,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>\n                    This study intends to optimize the carbon footprint management model of power enterprises through artificial intelligence (AI) technology to help the scientific formulation of carbon emission reduction strategies. Firstly, a carbon footprint calculation model based on big data and AI is established, and then machine learning algorithm is used to deeply mine the carbon emission data of power enterprises to identify the main influencing factors and emission reduction opportunities. Finally, the driver-state-response (DSR) model is used to evaluate the carbon audit of the power industry and comprehensively analyze the effect of carbon emission reduction. Taking China Electric Power Resources and Datang International Electric Power Company as examples, this study uses the comprehensive evaluation method of entropy weight- technique for order preference by similarity to ideal solution (TOPSIS). China Electric Power Resources Company has outstanding performance in promoting renewable energy, with its comprehensive evaluation index rising from 0.5458 in 2020 to 0.627 in 2022, while the evaluation index of Datang International Electric Power Company fluctuated and dropped to 0.421 in 2021. The research conclusion reveals the actual achievements and existing problems of power enterprises in energy saving and emission reduction, and provides reliable carbon information for the government, enterprises, and the public. The main innovation of this study lies in: using artificial intelligence technology to build a carbon footprint calculation model, combining with the data of International Energy Agency Carbon Dioxide (IEA CO\n                    <jats:sub>2<\/jats:sub>\n                    ) emission database, and using machine learning algorithm to deeply mine the important factors in carbon emission data, thus putting forward a carbon audit evaluation system of power enterprises based on DSR model. This study not only fills the blank of carbon emission management methods in the power industry, but also provides a new perspective and basis for the government and enterprises to formulate carbon emission reduction strategies.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0316537","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T13:45:22Z","timestamp":1735911922000},"page":"e0316537","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["Optimization of carbon footprint management model of electric power enterprises based on artificial intelligence"],"prefix":"10.1371","volume":"20","author":[{"given":"Liangzheng","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1152-412X","authenticated-orcid":true,"given":"Kaiman","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengdong","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieren","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"pone.0316537.ref001","doi-asserted-by":"crossref","first-page":"4435","DOI":"10.1016\/j.jclepro.2017.02.147","article-title":"Towards a universal carbon footprint standard: A case study of carbon management at universities","volume":"172","author":"OJ Robinson","year":"2018","journal-title":"Journal of Cleaner Production"},{"key":"pone.0316537.ref002","doi-asserted-by":"crossref","first-page":"118104","DOI":"10.1016\/j.jclepro.2019.118104","article-title":"Carbon emission quota allocating on coal and electric power enterprises under carbon trading pilot in China: Mathematical formulation and solution technique","volume":"239","author":"B Wang","year":"2019","journal-title":"Journal of Cleaner Production"},{"key":"pone.0316537.ref003","doi-asserted-by":"crossref","first-page":"1225326","DOI":"10.3389\/fenrg.2023.1225326","article-title":"Assessment of carbon footprint performance by electric power enterprises in China","volume":"11","author":"P Yuan","year":"2023","journal-title":"Frontiers in Energy Research"},{"issue":"10","key":"pone.0316537.ref004","doi-asserted-by":"crossref","first-page":"2602","DOI":"10.3390\/en13102602","article-title":"Possibilities and Challenges for the Inclusion of the Electric Vehicle (EV) to Reduce the Carbon Footprint in the Transport Sector: A Review","volume":"13","author":"A Ghosh","year":"2020","journal-title":"Energies"},{"key":"pone.0316537.ref005","doi-asserted-by":"crossref","first-page":"125949","DOI":"10.1016\/j.jclepro.2021.125949","article-title":"Can low-carbon technological innovation truly improve enterprise performance? 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