Bayesian Structural Time Series Models for Predicting the \({\textrm{CO}}_2\) Emissions in Afghanistan

Abstract

There are numerous forecasting methods, and these approaches take only data, analyse it, produce a prediction by analysing, ignore the prior information side, and do not take into account the variations that occur over time. The Bayesian structural time series (BSTS) models are the best way to forecast \({\textrm{CO}}_2\) emissions and is updated. Because \({\textrm{CO}}_2\) emissions play an essential part in climate change, forecasting future \({\textrm{CO}}_2\) emissions is critical for all countries where global warming is a hazard to the planet. This study models and forecasts \({\textrm{CO}}_2\) emissions in Afghanistan from 1990 to 2019 using the BSTS models, bsts function from the bsts R package statistical tool. We did a diagnostics test of the normality of the residuals out of the bsts R package. According to the findings for 12 years ahead, \({\textrm{CO}}_2\) emissions will rise by 2031 in all models findings. The study’s findings indicate that \({\textrm{CO}}_2\) emissions in Afghanistan are projected to rise, exposing the country to climate-related concerns.