Forecasting of CO2 Emissions in Algeria Using Discrete Wavelet Transform –Based Autoregressive Integrated Moving Average Models

Abstract The increasing impact of climate change and rising temperatures has made the reduction of carbon dioxide emissions a top priority globally. Accurately forecasting these emissions is a crucial aspect of transitioning towards a clean energy economy. This paper introduces a new method for estimating CO2 emissions by combining the wavelet technique with both an autoregressive integrated moving average (DWT-ARIMA) and ARIMA model, applied to annual carbon dioxide emissions data in Algeria from 1970 to 2022. The study provides decision makers with crucial information to help find effective environmental protection solutions in Algeria. The results suggest that the wavelet-ARIMA model is more effective compared to the traditional ARIMA model.