Forecasting carbon dioxide emission for Malaysia using fractional order multivariable grey model

• Main Authors: Sulaiman, Assif Shamim Mustaffa, Shabri, Ani, Marie, Rashiq Rafiq
• Format: Article
• Published: Springer Science and Business Media Deutschland GmbH 2022
• Subjects: QA Mathematics
• Online Access: http://eprints.utm.my/id/eprint/99683/; http://dx.doi.org/10.1007/978-3-030-98741-1_14

Forecasting the amount of carbon dioxide (CO2 ) emissions has been crucially important to the civilisation of society to ensure that we can inhabit this planet in years to come. Hence, the study that focuses on the prediction on the amount of CO2 releases into the environment has always been the focal point in any international level climate change conferences to ensure the target set would be considerably reached in the future. As the conventional multivariable grey model or GM (1,N) model has widely been used in the study to forecast short-term sample size data, this model possessed issues when dealing with prioritization of information as the weightage was evenly spread across all data points, causes an ineffective forecasting result. This study will use the fractional order multivariable grey model, or FAGM (1,N) model to predict the amount of CO2 emissions for Malaysia within the 10 years timeframe data set. As the FAGM (1,N) model focuses on the prioritization of newer information, the proposed model will be able to forecast the CO2 emissions better compared to the GM (1,N) model even with a small sample size data.