From Static to Dynamic: Improving Scope 2 Emission Factor Predictions with ARIMA Data-Driven Models

This study examines the efficacy of Autoregressive Integrated Moving Average (ARIMA) models in forecasting Scope 2 emission factors, focusing on the temporal dynamics of Consumption-based Hourly Emissions Factors (CHEFs). The research method involves selecting ARIMA models based on the lowest Akaike Information Criterion (AIC) values. Once identified, the best-performing model for each timestep underwent further evaluation for predictive …