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While most research focuses on predicting annual CO\n                    <jats:sub>2<\/jats:sub>\n                    emissions, which are crucial for setting long-term emission mitigation targets, the precise prediction of daily CO\n                    <jats:sub>2<\/jats:sub>\n                    emissions is equally vital for setting short-term targets. This study examines the performance of 14 models in predicting daily CO\n                    <jats:sub>2<\/jats:sub>\n                    emissions data from 1\/1\/2022 to 30\/9\/2023 across the top four polluting regions (China, India, the USA, and the EU27&amp;UK). The 14 models used in the study include four statistical models (ARMA, ARIMA, SARMA, and SARIMA), three machine learning models (support vector machine (SVM), random forest (RF), and gradient boosting (GB)), and seven deep learning models (artificial neural network (ANN), recurrent neural network variations such as gated recurrent unit (GRU), long short-term memory (LSTM), bidirectional-LSTM (BILSTM), and three hybrid combinations of CNN-RNN). Performance evaluation employs four metrics (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    , MAE, RMSE, and MAPE). The results show that the machine learning (ML) and deep learning (DL) models, with higher\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    (0.714\u20130.932) and lower RMSE (0.480\u20130.247) values, respectively, outperformed the statistical model, which had\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    (\u2212\u20090.060\u20130.719) and RMSE (1.695\u20130.537) values, in predicting daily CO\n                    <jats:sub>2<\/jats:sub>\n                    emissions across all four regions. The performance of the ML and DL models was further enhanced by differencing, a technique that improves accuracy by ensuring stationarity and creating additional features and patterns from which the model can learn. Additionally, applying ensemble techniques such as bagging and voting improved the performance of the ML models by approximately 9.6%, whereas hybrid combinations of CNN-RNN enhanced the performance of the RNN models. In summary, the performance of both the ML and DL models was relatively similar. However, due to the high computational requirements associated with DL models, the recommended models for daily CO\n                    <jats:sub>2<\/jats:sub>\n                    emission prediction are ML models using the ensemble technique of voting and bagging. This model can assist in accurately forecasting daily emissions, aiding authorities in setting targets for CO\n                    <jats:sub>2<\/jats:sub>\n                    emission reduction.\n                  <\/jats:p>","DOI":"10.1007\/s11356-024-35764-8","type":"journal-article","created":{"date-parts":[[2025,1,12]],"date-time":"2025-01-12T20:13:26Z","timestamp":1736712806000},"page":"2510-2535","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6228-5430","authenticated-orcid":false,"given":"Adewole Adetoro","family":"Ajala","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oluwatosin Lawrence","family":"Adeoye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olawale Moshood","family":"Salami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayoola Yusuf","family":"Jimoh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"35764_CR1","doi-asserted-by":"publisher","unstructured":"Adegboye OR, Feda, AK, Agyekum EB, Mbasso WF, Kamel S (2024) Towards greener futures: SVR-based CO2 prediction model boosted by SCMSSA algorithm. 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