An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models

Abstract Human-induced global warming, primarily attributed to the rise in atmospheric CO 2 , poses a substantial risk to the survival of humanity. While most research focuses on predicting annual CO 2 emissions, which are crucial for setting long-term emission mitigation targets, the precise prediction of daily CO 2 emissions is equally vital for setting short-term targets. This study examines the performance of 14 models in predicting daily CO 2 emissions data from 1/1/2022 to 30/9/2023 across the top four polluting regions (China, India, the USA, and the EU27&UK). The 14 models used in the study include four statistical models (ARMA, ARIMA, SARMA, and SARIMA), three machine learning models (support…

• UO
  University of Hull