Predicting CO₂ Emission Footprint Using AI through Machine Learning

Adequate CO₂ is essential for vegetation, but industrial chimneys and land, space and oceanic vehicles exert tons of excessive CO₂ and are mostly responsible for the greenhouse effect, global warming and climate change. Due to COVID-19, CO₂ emission was in 2020 at its lowest level compared to prior decades. However, it is unknown how long it will take to reduce CO₂ emission to a tolerable point. Furthermore, it is also unknown to what extent it can increase or change in the future. Accurate forecasting of CO₂ emissions has real significance for choosing the optimum ways of reducing CO₂ emissions. Although some existing models have noticeable CO₂ emission forecasting accuracy, the models implemented in this work have more efficacy in prediction due to incorporating COVID-19’s effect on CO₂ emission. This paper implements four prediction models using SARIMA (SARIMAX) based on ARIMA. The four models are based on the time period of the surge of the COVID-19 pandemic. The main objective of this work is to compare these four models to suggest an effective model to predict the total CO₂ emissions for the future. The study forecasts global total CO₂ emission from 2022 to 2027 for near future prediction, 2022 to 2054 for future prediction and 2022 to 2072 for far future prediction. Among the various error measures, mean absolute percentage error (MAPE) is chosen for accuracy comparison. The calculation yields different accuracy for the four SARIMAX models. The MAPEs for the four methods are: pre-COV (MAPE: 0.32), start-COV (MAPE: 0.28), trans-COV (MAPE: 0.19), post-COV (MAPE: 0.09). The MAPE value is relatively low for post-COV (MAPE: 0.09). Hence, it can be inferred that post-COV are suitable models to forecast the global total CO₂ emission for the future. The post-COV predictions for the global total CO₂ emission for the years 2022 to 2027 are: 36,218.59, 36,733.69, 37,238.29, 37,260.88, 37,674.01 and 37,921.47 million tons (MT). This study successfully predicts CO₂ emission either for the COVID-19 period or the post-COVID-19 normal periods. The Machine Learning (ML) method used in this study has shown good agreement with the IPCC model in predicting the past emissions, the current emissions due to COVID-19 and the emissions of the upcoming future. These prediction results can be an asset for the decision support system to develop a suitable policy for global CO₂ emission reduction. For future research, a number of other external influence variables responsible for CO₂ emission can be added for finer forecasts. This research is an original work in predicting COVID-19-affected CO₂ emission using AI through the ML methodology.