Forecasting Global Annual Average CO2 Concentrations

Abstract

This study aims to enhance the accuracy of CO₂ level forecasts, compare the effi cacy of diff erent predicti ve models, and provide insights for policy development. Employing ti me series and regression analysis techniques, the study uses historical data from global monitoring stati ons (1979- 2022) to model the annual mean concentrati on of atmospheric CO2 The results reveal that the ARIMA (1,1,1) model outperforms the simple linear regression model in predicti ve accuracy. Nevertheless, the regression model came across a technical problem as residuals are signifi cantly autocorrelated. The Augmented Dickey-Fuller test was applied to ensure stati onarity of the fi rst diff erence of the original series. The model was trained using data from 1979 to 2022 and validated for 2023. The errors of the ARIMA(1,1,1) was found to be white noise. The ARIMA model projected CO₂ concentrati ons of 419.5, 421.8 and 424.2 for the years 2023, 2024, and 2025 respecti vely, with a percentage error of just 0.048% for the 2023. In contrast, the corresponding percentage of error for the simple linear regression model was -1.236%. These fi ndings underscore the ARIMA model’s superior performance in forecasti ng future CO₂ levels and its suitability for environmental monitoring and climate change miti gati on strategies. This research provides a valuable methodological framework for future atmospheric science studies and informs policy decisions aimed at addressing rising CO₂ concentrations.