{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T14:55:08Z","timestamp":1789052108958,"version":"build-2803163510"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Environ Sci Pollut Res"],"DOI":"10.1007\/s11356-026-37429-0","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T18:38:20Z","timestamp":1769107100000},"page":"1528-1544","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Forecasting CO2 emissions to achieve net-zero emission targets for North American cement industry"],"prefix":"10.1007","volume":"33","author":[{"given":"\u00c1ngel Francisco Galaviz","family":"Rom\u00e1n","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9591-0629","authenticated-orcid":false,"given":"Golam","family":"Kabir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyedmehdi","family":"Mirmohammadsadeghi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"37429_CR1","volume":"17","author":"M Amran","year":"2022","unstructured":"Amran M, Makul N, Fediuk R, Lee YH, Vatin NI, Lee YY, Mohammed K (2022) Global carbon recoverability experiences from the cement industry. Case Stud Constr Mater 17:e01439","journal-title":"Case Stud Constr Mater"},{"key":"37429_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2020.111642","volume":"144","author":"J Belbute","year":"2020","unstructured":"Belbute J, Pereira A (2020) Reference forecasts for CO2 emissions from fossil-fuel combustion and cement production in Portugal. Energy Policy 144:111642. https:\/\/doi.org\/10.1016\/j.enpol.2020.111642","journal-title":"Energy Policy"},{"key":"37429_CR3","unstructured":"Global Cement and Concrete Association. (2021). Concrete future, https:\/\/gccassociation.org\/concretefuture\/wp-content\/uploads\/2021\/10\/GCCA-Concrete-Future-Roadmap-Document-AW.pdf"},{"key":"37429_CR4","unstructured":"Cement Association of Canada. (2023). Canada\u2019s cement and concrete industry action plan to net-zero, Concrete Zero, https:\/\/cement.ca\/app\/uploads\/2023\/05\/ConcreteZero-Report-FINAL-reduced.pdf"},{"key":"37429_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.123302","volume":"276","author":"F Costa","year":"2020","unstructured":"Costa F, Ribeiro D (2020) Reduction in CO2 emissions during production of cement, with partial replacement of traditional raw materials by civil construction waste (CCW). J Clean Prod 276:123302. https:\/\/doi.org\/10.1016\/j.jclepro.2020.123302","journal-title":"J Clean Prod"},{"key":"37429_CR6","doi-asserted-by":"publisher","unstructured":"Eid M, Kutscher T. Statistical models for analyzing stability and change in happiness. InStability of happiness 2014 Jan 1 (pp. 261-297). Academic Press. https:\/\/doi.org\/10.1016\/B978-0-12-411478-4.00013-8","DOI":"10.1016\/B978-0-12-411478-4.00013-8"},{"key":"37429_CR7","doi-asserted-by":"publisher","first-page":"8046","DOI":"10.1016\/j.egyr.2022.06.025","volume":"8","author":"MO Faruque","year":"2022","unstructured":"Faruque MO, Rabby MAJ, Hossain MA, Islam MR, Rashid MMU, Muyeen SM (2022) A comparative analysis to forecast carbon dioxide emissions. Energy Rep 8:8046\u20138060","journal-title":"Energy Rep"},{"key":"37429_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2021.128408","volume":"317","author":"R Feng","year":"2021","unstructured":"Feng R, Dinghong L (2021) Carbon emission forecasting and scenario analysis in Guangdong Province based on optimized fast learning network. J Clean Prod 317:128408. https:\/\/doi.org\/10.1016\/j.jclepro.2021.128408","journal-title":"J Clean Prod"},{"key":"37429_CR9","doi-asserted-by":"publisher","unstructured":"F\u0131\u015fk\u0131n CS, Cerit AG. Forecasting domestic shipping demand of cement: comparison of sarimax, ann and hybrid sarimax-ann. In2019 4th International conference on computer science and engineering (UBMK) 2019 Sep 11 (pp. 68-72). IEEE. https:\/\/doi.org\/10.1109\/UBMK.2019.8907210.","DOI":"10.1109\/UBMK.2019.8907210"},{"issue":"7","key":"37429_CR10","first-page":"373","volume":"5","author":"E Fradinata","year":"2015","unstructured":"Fradinata E, Suthummanon S, Suntiamorntut W (2015) Forecasting determinant of cement demand in Indonesia with artificial neural network. J Asian Sci Res 5(7):373","journal-title":"J Asian Sci Res"},{"key":"37429_CR11","doi-asserted-by":"crossref","unstructured":"Fradinata E, Suthummanon S, Sirivongpaisal N, Suntiamorntuthq W. ANN, ARIMA and MA timeseries model for forecasting in cement manufacturing industry: Case study at lafarge cement Indonesia\u2014Aceh. In2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA) 2014 Aug 20 (pp. 39-44). IEEE.","DOI":"10.1109\/ICAICTA.2014.7005912"},{"key":"37429_CR12","unstructured":"Ge M, Friedrich J, & Vigna L (2020). 4 Charts explain greenhouse gas emissions by countries and sectors. World Resources Institute. https:\/\/www.wri.org\/insights\/4-charts-explain-greenhouse-gas-emissions-countries-and-sectors"},{"key":"37429_CR13","doi-asserted-by":"publisher","unstructured":"Gilli M, Maringer D, Schumann E. (2019). A gentle introduction to financial simulation, Numerical Methods and Optimization in Finance, Pages 153\u2013188, ISBN 9780128150658, https:\/\/doi.org\/10.1016\/B978-0-12-815065-8.00019-4.","DOI":"10.1016\/B978-0-12-815065-8.00019-4"},{"key":"37429_CR14","unstructured":"Hanle L, Jayaraman K, & Smith J (2004). CO2 emissions profile of the US cement industry. Washington DC: Environmental Protection Agency, https:\/\/citeseerx.ist.psu.edu\/document?repid=rep1&type=pdf&doi=4b5a08fbecf43996c52337a8d7cc52f59180ac93"},{"key":"37429_CR15","unstructured":"IEA (2023), CO2 Emissions in 2022, IEA, License: CC BY 4.0, Paris https:\/\/www.iea.org\/reports\/co2-emissions-in-2022"},{"key":"37429_CR16","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/B978-0-12-814761-0.00012-5","volume-title":"Data science","author":"V Kotu","year":"2019","unstructured":"Kotu V, Deshpande B (2019) Time series forecasting. Data science. Elsevier, Amsterdam, The Netherlands, pp 395\u2013445. https:\/\/doi.org\/10.1016\/B978-0-12-814761-0.00012-5"},{"issue":"165","key":"37429_CR17","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.energy.2018.09.152","volume":"15","author":"W Li","year":"2018","unstructured":"Li W, Gao S (2018) Prospective on energy related carbon emissions peak integrating optimized intelligent algorithm with dry process technique application for China\u2019s cement industry. Energy 15(165):33\u201354","journal-title":"Energy"},{"issue":"11","key":"37429_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/atmos13111871","volume":"13","author":"Y Meng","year":"2022","unstructured":"Meng Y, Noman H (2022) Predicting CO2 emission footprint using AI through machine learning. Atmosphere 13(11):1871","journal-title":"Atmosphere"},{"key":"37429_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2020.109945","volume":"130","author":"J Ofosu-Adarkwa","year":"2020","unstructured":"Ofosu-Adarkwa J, Xie N, Javed SA (2020) Forecasting CO2 emissions of China\u2019s cement industry using a hybrid Verhulst-GM(1, n) model and emissions\u2019 technical conversion. Renew Sustain Energy Rev 130:109945. https:\/\/doi.org\/10.1016\/j.rser.2020.109945","journal-title":"Renew Sustain Energy Rev"},{"key":"37429_CR20","doi-asserted-by":"publisher","unstructured":"Pisner DA, Schnyer DM. Support vector machine. InMachine learning 2020 Jan 1 (pp. 101-121). Academic Press. https:\/\/doi.org\/10.1016\/B978-0-12-815739-8.00006-7.","DOI":"10.1016\/B978-0-12-815739-8.00006-7"},{"key":"37429_CR21","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1016\/j.rser.2015.05.031","volume":"50","author":"W Shen","year":"2015","unstructured":"Shen W, Cao L, Li Q, Zhang W, Wang G, Li C (2015) Quantifying CO2 emissions from China\u2019s cement industry. Renew Sustain Energy Rev 50:1004\u20131012","journal-title":"Renew Sustain Energy Rev"},{"key":"37429_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2021.127277","volume":"306","author":"V Sousa","year":"2021","unstructured":"Sousa V, Bogas JA (2021) Comparison of energy consumption and carbon emissions from clinker and recycled cement production. J Clean Prod 306:127277","journal-title":"J Clean Prod"},{"key":"37429_CR23","unstructured":"Tiseo I (2023). Greenhouse gas emissions from cement production in the United States from 1990 to 2021, Statista, https:\/\/www.statista.com\/statistics\/451804\/green-house-gasemissions-in-united-states-from-cementproduction\/#:~:text=Cement%20produced%20in%20the%20United,percent%20compared%20with%202005%20levels."},{"key":"37429_CR24","unstructured":"Uzzaman I, Rahman M, Alam MS, Alam S (2016) Simulation of cement manufacturing process and demand forecasting of cement industry. Global Journal of Researches in Engineering: G Indust Eng 16(2)."},{"key":"37429_CR25","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.resconrec.2016.04.016","volume":"112","author":"BJ Van Ruijven","year":"2016","unstructured":"Van Ruijven BJ, Van Vuuren DP, Boskaljon W, Neelis ML, Saygin D, Patel MK (2016) Long-term model-based projections of energy use and CO2 emissions from the global steel and cement industries. Resour Conserv Recycling 112:15\u201336","journal-title":"Resour Conserv Recycling"},{"key":"37429_CR26","doi-asserted-by":"crossref","unstructured":"Vollmer NI, Gernaey KV, Sina G. Sensitivity analysis and risk assessment for the in-silico design and use of optimized cell factories in a xylitol biorefinery. InComputer aided chemical engineering 2022 Jan 1 (Vol. 51, pp. 1627-1632). Elsevier.","DOI":"10.1016\/B978-0-323-95879-0.50272-1"},{"key":"37429_CR27","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.jclepro.2016.02.053","volume":"122","author":"S Wei","year":"2016","unstructured":"Wei S, Mohan L (2016) Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China. J Clean Prod 122:144\u2013153. https:\/\/doi.org\/10.1016\/j.jclepro.2016.02.053","journal-title":"J Clean Prod"},{"issue":"2","key":"37429_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/atmos14020267","volume":"14","author":"C Xu","year":"2023","unstructured":"Xu C, Gong Y, Yan G (2023) Research on cement demand forecast and low carbon development strategy in Shandong province. Atmosphere 14(2):267","journal-title":"Atmosphere"},{"issue":"3","key":"37429_CR29","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s11053-014-9241-0","volume":"24","author":"Y Zagayevskiy","year":"2015","unstructured":"Zagayevskiy Y, Deutsch CV (2015) A methodology for sensitivity analysis based on regression: applications to handle uncertainty in natural resources characterization. Nat Resour Res 24(3):239\u2013274","journal-title":"Nat Resour Res"},{"issue":"11","key":"37429_CR30","doi-asserted-by":"publisher","first-page":"4453","DOI":"10.3390\/en16114453","volume":"16","author":"C Zhou","year":"2023","unstructured":"Zhou C, Xuan D, Miao Y, Luo X, Liu W, Zhang Y (2023) Accounting CO2 emissions of the cement industry: based on an electricity\u2013carbon coupling analysis. Energies 16(11):4453","journal-title":"Energies"},{"key":"37429_CR31","doi-asserted-by":"crossref","unstructured":"Zhu L, Li W, Ma L, Wang L, Cai Y, Zhu L, Hu Z, Chen W. A Carbon Emission Model in Cement Industry Based on IWOA-BP Neural Network. In2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) 2023 Jan 14 (pp. 1-6). IEEE.","DOI":"10.1109\/ICCSIE55183.2023.10175299"}],"container-title":["Environmental Science and Pollution Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11356-026-37429-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11356-026-37429-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11356-026-37429-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T06:45:17Z","timestamp":1770878717000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11356-026-37429-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,22]]},"references-count":31,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["37429"],"URL":"https:\/\/doi.org\/10.1007\/s11356-026-37429-0","relation":{},"ISSN":["1614-7499"],"issn-type":[{"value":"1614-7499","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,22]]},"assertion":[{"value":"29 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}