Forecasting CO2 emissions in Malaysia using Group Method of Data Handling / Basri Badyalina ... [et al.]

Critical environmental issues, such as climate change, pollution, and resource depletion, urgently require data-driven decision-making and accurate forecasting to guide sustainable policies and interventions. However, forecasting is a complex task that necessitates rigorous research to ensure precise predictions essential for addressing these environmental challenges effectively. To meet these forecasting challenges, this study utilized Group Method of Data Handling (GMDH) method, focusing on CO2 emission in Malaysia. The analysis of the GMDH forecasting model for CO2 emission provides distinct patterns in the behaviors of three input variables X1, defined as (yt−2,yt−3,yt−5), X2 defined as (yt−1,yt−5,yt−6,yt−7) and X3 defined as (yt−2,yt−5,yt−6,yt−8,yt−10). Notably, X2 consistently exhibits strong performance, whereas X1 and X3 face difficulties, particularly in the forecasting phase. The GMDH model demonstrates proficiency in adaptive self-organization, automatic feature extraction, managing non-linear relationships, and interpretability, enhancing its effectiveness in capturing complex patterns in CO2 emission data. The observed decrease in performance for specific inputs during the forecasting process highlights the necessity for improving and adjusting the model and developing a detailed grasp of the dynamics of the variables involved.