{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T17:16:58Z","timestamp":1772471818217,"version":"3.50.1"},"reference-count":24,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"The science and technology project of the State Grid Corporation of China","award":["1400-202312335A-1-1-ZN"],"award-info":[{"award-number":["1400-202312335A-1-1-ZN"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>Human activities that generate greenhouse gas emissions pose a significant threat to urban green and sustainable development. Production activities in key industrial sectors are a primary contributor to high urban carbon emissions. Therefore, effectively reducing carbon emissions in these sectors is crucial for achieving urban carbon peak and neutrality goals. Carbon emission monitoring is a critical approach that aids governmental bodies in understanding changes in industrial carbon emissions, thereby supporting decision-making and carbon reduction efforts. However, current industry-oriented carbon monitoring methods suffer from issues such as low frequency, poor accuracy, and inadequate privacy security. To address these challenges, this article proposes a novel privacy-protected \u201celectricity-carbon\u2019\u2019 nexus model, long short-term memory with the vertical federated framework (VF-LSTM), to monitor carbon emissions in key urban industries. The vertical federated framework ensures \u201cusable but invisible\u201d privacy protection for multisource data from various participants. The embedded long short-term memory model accurately captures industry-specific carbon emissions. Using data from key industries (steel, petrochemical, chemical, and nonferrous industries), this article constructs and validates the performance of the proposed industry-level carbon emission monitoring model. The results demonstrate that the model has high accuracy and robustness, effectively monitoring industry carbon emissions while protecting data privacy.<\/jats:p>","DOI":"10.1177\/2167647x251392796","type":"journal-article","created":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T08:43:58Z","timestamp":1763801038000},"page":"441-452","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Monitoring Carbon Emission from Key Industries Based on VF-LSTM Model"],"prefix":"10.1177","volume":"13","author":[{"given":"Yang","family":"Wang","sequence":"first","affiliation":[{"name":"China State Grid Tianjin Electric Power Company Tianjin Hebei District, Tianjin, China."}]},{"given":"Tianchun","family":"Xiang","sequence":"additional","affiliation":[{"name":"China State Grid Tianjin Electric Power Company Tianjin Hebei District, Tianjin, China."}]},{"given":"Shuai","family":"Luo","sequence":"additional","affiliation":[{"name":"Economic and Technological Research Institute of State Grid Tianjin Electric Power Company, Tianjin, China."}]},{"given":"Yi","family":"Gao","sequence":"additional","affiliation":[{"name":"Economic and Technological Research Institute of State Grid Tianjin Electric Power Company, Tianjin, China."}]},{"given":"Xiangyu","family":"Kong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Smart Grid, Ministry of Education, Tianjin University, Tianjin, China."}]}],"member":"179","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"issue":"03","key":"e_1_3_2_2_2","first-page":"821","article-title":"Key scientific problems and research framework for carbon perspective research of new power systems","volume":"46","author":"Chongqing K","year":"2022","unstructured":"Chongqing K, , Ershun D, , Yaowang L. 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