{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T20:14:21Z","timestamp":1790626461900,"version":"4.1.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"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\/s11356-026-37394-8","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T10:22:38Z","timestamp":1768818158000},"page":"1250-1263","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Forecasting CO$$_2$$ emissions in Iraq using ARIMAX and artificial neural networks: a comparative modeling approach"],"prefix":"10.1007","volume":"33","author":[{"given":"Sham Azad","family":"Rahim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5465-8989","authenticated-orcid":false,"given":"Delshad","family":"Shaker Ismael Botani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"37394_CR1","doi-asserted-by":"publisher","unstructured":"Ahmed SM, Sheab GI, Hasan AH, Hanon MM (2025) Using artificial neural networks to forecasting carbon dioxide emissions in Iraq. JOIV Int J Inform Vis 9(1). https:\/\/doi.org\/10.62527\/joiv.9.1.2456","DOI":"10.62527\/joiv.9.1.2456"},{"issue":"10","key":"37394_CR2","doi-asserted-by":"publisher","first-page":"3169","DOI":"10.1007\/s13762-020-03079-z","volume":"18","author":"S Altikat","year":"2021","unstructured":"Altikat S (2021) Prediction of CO2 emission from greenhouse to atmosphere with artificial neural networks and deep learning neural networks. Int J Environ Sci Technol 18(10):3169\u20133178. https:\/\/doi.org\/10.1007\/s13762-020-03079-z","journal-title":"Int J Environ Sci Technol"},{"key":"37394_CR3","doi-asserted-by":"publisher","unstructured":"Ayodele OF, Ayodele BV, Mustapa SI, Fernando Y (2021) Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters. Energy Conv Manag: X 12:100111. https:\/\/doi.org\/10.1016\/j.ecmx.2021.100111","DOI":"10.1016\/j.ecmx.2021.100111"},{"key":"37394_CR4","unstructured":"Bala H, Aliyu Mustapha U, Ya\u2019u A, Khatoon G, Nik Mohd Rashid N (2023) Evaluating the impact of carbon emissions on Iraq\u2019s economic growth. Rev Econ Financ 21(1):1647\u20131653. https:\/\/refpress.org\/wp-content\/uploads\/2023\/11\/Paper-14_REF.pdf"},{"issue":"1","key":"37394_CR5","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s40807-023-00092-x","volume":"10","author":"A Hamdan","year":"2023","unstructured":"Hamdan A, Al-Salaymeh A, AlHamad IM, Ikemba S, Ewim DRE (2023) Predicting future global temperature and greenhouse gas emissions via LSTM model. Sustain Energy Res 10(1):21. https:\/\/doi.org\/10.1186\/s40807-023-00092-x","journal-title":"Sustain Energy Res"},{"key":"37394_CR6","doi-asserted-by":"crossref","unstructured":"Jamieson D (2014) Reason in a dark time: why the struggle against climate change failed \u2014 and what it means for our future. OUP USA. https:\/\/academic.oup.com\/book\/12765","DOI":"10.1093\/acprof:oso\/9780199337668.001.0001"},{"issue":"2","key":"37394_CR7","doi-asserted-by":"publisher","first-page":"462","DOI":"10.3390\/forecast6020026","volume":"6","author":"T Janhuaton","year":"2024","unstructured":"Janhuaton T, Ratanavaraha V, Jomnonkwao S (2024) Forecasting Thailand\u2019s transportation CO2 emissions: a comparison among artificial intelligent models. Forecasting 6(2):462\u2013484. https:\/\/doi.org\/10.3390\/forecast6020026","journal-title":"Forecasting"},{"key":"37394_CR8","doi-asserted-by":"publisher","first-page":"130053","DOI":"10.1016\/j.jclepro.2021.130053","volume":"332","author":"SA Khadem","year":"2022","unstructured":"Khadem SA, Bensebaa F, Pelletier N (2022) Optimized feed-forward neural networks to address CO2-equivalent emissions data gaps-application to emissions prediction for unit processes of fuel life cycle inventories for Canadian provinces. J Clean Prod 332:130053. https:\/\/doi.org\/10.1016\/j.jclepro.2021.130053","journal-title":"J Clean Prod"},{"key":"37394_CR9","doi-asserted-by":"publisher","unstructured":"Khosravi H, Raihan AS, Islam F, Nimbarte A, Ahmed I (2025) A comprehensive approach to CO2 emissions analysis in high-human-development-index countries using statistical and time series approaches. Sustainability (2071-1050) 17(2). https:\/\/doi.org\/10.3390\/su17020603","DOI":"10.3390\/su17020603"},{"key":"37394_CR10","unstructured":"Kubursi AA (1988) Oil and the Iraqi economy. Arab Stud Q 10(3):283\u2013298. https:\/\/www.jstor.org\/stable\/41854163"},{"issue":"55","key":"37394_CR11","doi-asserted-by":"publisher","first-page":"116601","DOI":"10.1007\/s11356-022-21723-8","volume":"30","author":"S Kumari","year":"2023","unstructured":"Kumari S, Singh SK (2023) Machine learning-based time series models for effective CO2 emission prediction in India. Environ Sci Pollut Res 30(55):116601\u2013116616. https:\/\/doi.org\/10.1007\/s11356-022-21723-8","journal-title":"Environ Sci Pollut Res"},{"key":"37394_CR12","unstructured":"Lee MH, Suhartono, Hamzah N (2010) Calendar variation model based on ARIMAX for forecasting sales data with Ramadhan effect. In: Proceedings of the regional conference on statistical sciences, vol\u00a010, pp 349\u2013361. https:\/\/www.researchgate.net\/publication\/267786379_Calendar_variation_model_based_on_ARIMAX"},{"issue":"55","key":"37394_CR13","doi-asserted-by":"publisher","first-page":"117485","DOI":"10.1007\/s11356-022-23541-4","volume":"30","author":"X Li","year":"2023","unstructured":"Li X, Zhang X (2023) A comparative study of statistical and machine learning models on carbon dioxide emissions prediction of China. Environ Sci Pollut Res 30(55):117485\u2013117502. https:\/\/doi.org\/10.1007\/s11356-022-23541-4","journal-title":"Environ Sci Pollut Res"},{"key":"37394_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.catena.2024.108400","volume":"246","author":"C Li","year":"2024","unstructured":"Li C, Wang W, Wang K, Wang Y, Zhang M (2024) Responses of greenhouse gas emissions to increased precipitation events in different ecosystems: a meta-analysis. CATENA 246:108400. https:\/\/doi.org\/10.1016\/j.catena.2024.108400","journal-title":"CATENA"},{"issue":"19","key":"37394_CR15","doi-asserted-by":"publisher","first-page":"28469","DOI":"10.1007\/s11356-021-18205-8","volume":"29","author":"M Mahmudimanesh","year":"2022","unstructured":"Mahmudimanesh M, Mirzaee M, Dehghan A, Bahrampour A (2022) Forecasts of cardiac and respiratory mortality in Tehran, Iran, using ARIMAX and CNN-LSTM models. Environ Sci Pollut Res 29(19):28469\u201328479. https:\/\/doi.org\/10.1007\/s11356-021-18205-8","journal-title":"Environ Sci Pollut Res"},{"issue":"8","key":"37394_CR16","doi-asserted-by":"publisher","first-page":"20704","DOI":"10.1007\/s11356-022-23541-4","volume":"30","author":"W Nie","year":"2023","unstructured":"Nie W, Ao O, Duan H (2023) A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application. Environ Sci Pollut Res 30(8):20704\u201320720. https:\/\/doi.org\/10.1007\/s11356-022-23541-4","journal-title":"Environ Sci Pollut Res"},{"issue":"6","key":"37394_CR17","doi-asserted-by":"publisher","first-page":"24","DOI":"10.55559\/sjahss.v3i6.362","volume":"3","author":"Z Rahmani","year":"2024","unstructured":"Rahmani Z, Ahmadi J (2024) The impact of human activities on climate change. Sprin J Arts Hum Soc Sci 3(6):24\u201327. https:\/\/doi.org\/10.55559\/sjahss.v3i6.362","journal-title":"Sprin J Arts Hum Soc Sci"},{"issue":"6","key":"37394_CR18","doi-asserted-by":"publisher","first-page":"426","DOI":"10.3390\/forecast6020026","volume":"14","author":"E Sudarmaji","year":"2024","unstructured":"Sudarmaji E, Yatim MR, Harsono H, Masrio H, Azizah W (2024) Quantifying drivers of GHG emissions in ASEAN: modeling CO2 emissions using LMDI and ARIMAX approaches. Int J Energy Econ Policy 14(6):426\u2013435. https:\/\/doi.org\/10.3390\/forecast6020026","journal-title":"Int J Energy Econ Policy"},{"key":"37394_CR19","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1016\/j.jclepro.2015.04.097","volume":"112","author":"W Sun","year":"2016","unstructured":"Sun W, Xu Y (2016) Using a back propagation neural network based on improved particle swarm optimization to study the influential factors of carbon dioxide emissions in Hebei Province, China. J Clean Prod 112:1282\u20131291. https:\/\/doi.org\/10.1016\/j.jclepro.2015.04.097","journal-title":"J Clean Prod"},{"issue":"1\u20132","key":"37394_CR20","doi-asserted-by":"publisher","first-page":"123","DOI":"10.3354\/cr00953","volume":"47","author":"KE Trenberth","year":"2011","unstructured":"Trenberth KE (2011) Changes in precipitation with climate change. Clim Res 47(1\u20132):123\u2013138. https:\/\/doi.org\/10.3354\/cr00953","journal-title":"Clim Res"},{"issue":"5","key":"37394_CR21","doi-asserted-by":"publisher","first-page":"216","DOI":"10.11648\/j.ajtas.20211005.12","volume":"10","author":"CI Ugoh","year":"2021","unstructured":"Ugoh CI, Uzuke CA, Ugoh DO (2021) Application of ARIMAX model on forecasting Nigeria\u2019s GDP. Am J Theor Appl Stat 10(5):216. https:\/\/doi.org\/10.11648\/j.ajtas.20211005.12","journal-title":"Am J Theor Appl Stat"},{"key":"37394_CR22","doi-asserted-by":"publisher","unstructured":"Verma A, Routhu LC, Chigrupaatii R, Choubey A, Misra R, Singh T (2024) Big data-driven long term accurate CO2 emission forecasting using advanced recurrent neural networks. In: 2024 IEEE International Conference on Big Data (BigData), pp 4438\u20134447. IEEE. https:\/\/doi.org\/10.1109\/BigData62323.2024.10825045","DOI":"10.1109\/BigData62323.2024.10825045"},{"key":"37394_CR23","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.apenergy.2012.05.038","volume":"100","author":"Z Wang","year":"2012","unstructured":"Wang Z, Yin F, Zhang Y, Zhang X (2012) An empirical research on the influencing factors of regional CO2 emissions: evidence from Beijing City, China. Appl Energy 100:277\u2013284. https:\/\/doi.org\/10.1016\/j.apenergy.2012.05.038","journal-title":"Appl Energy"},{"issue":"20","key":"37394_CR24","doi-asserted-by":"publisher","first-page":"8753","DOI":"10.3390\/su16208753","volume":"16","author":"DS Yamacli","year":"2024","unstructured":"Yamacli DS, Tuncsiper C (2024) Modeling the CO2 emissions of Turkey dependent on various parameters employing ARIMAX and deep learning methods. Sustainability 16(20):8753. https:\/\/doi.org\/10.3390\/su16208753","journal-title":"Sustainability"},{"key":"37394_CR25","doi-asserted-by":"publisher","unstructured":"Yucesan M, Pekel E, Celik E, Gul M, Serin F (2025) Forecasting daily natural gas consumption with regression, time series and machine learning based methods. Energy Sources Part A Recover Utilization Environ Eff 47(1):4605\u20134620. https:\/\/doi.org\/10.1080\/15567036.2021.1875082","DOI":"10.1080\/15567036.2021.1875082"},{"issue":"14","key":"37394_CR26","doi-asserted-by":"publisher","first-page":"7464","DOI":"10.1002\/2017GL074024","volume":"44","author":"W Zhang","year":"2017","unstructured":"Zhang W, Villarini G, Scoccimarro E, Vecchi GA (2017) Stronger influences of increased CO2 on subdaily precipitation extremes than at the daily scale. Geophys Res Lett 44(14):7464\u20137471. https:\/\/doi.org\/10.1002\/2017GL074024","journal-title":"Geophys Res Lett"}],"container-title":["Environmental Science and Pollution Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11356-026-37394-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11356-026-37394-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11356-026-37394-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T05:40:32Z","timestamp":1770874832000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11356-026-37394-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,19]]},"references-count":26,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["37394"],"URL":"https:\/\/doi.org\/10.1007\/s11356-026-37394-8","relation":{},"ISSN":["1614-7499"],"issn-type":[{"value":"1614-7499","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,19]]},"assertion":[{"value":"23 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 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":"This is not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors have consent to publish the above manuscript.","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":"Conflict of interest"}}]}}