Artificial Intelligence Based Modelling for Predicting CO2 Emission for Climate Change Mitigation in Saudi Arabia
Sultan Alamri, Shahnawaz Khan
Abstract
Climate change (such as global warming) causes the barrier in the attaining sustainable development goals. Emission of greenhouse gases (primarily carbon dioxide CO2 emission) are the root cause of global warming. This research analyses and investigates the emission of CO2 and attempts to develop an optimal model to forecast the CO2 emission. Several machine learning and statistical modeling techniques have been implemented and evaluated to explore the patterns and trends of CO2 emissions to develop an optimal model for forecasting future CO2 emissions. The implemented methods include such as Exponential Smoothing, Transformers, Temporal Convolutional Network (TCN), and neural basis expansion analysis for interpretable time series. The data for training these models have been collected and synthesized from various sources using a web crawler. The performance of these models has been evaluated using various performance measurement metrices such as RMSE, R2 score, MAE, MAPE and OPE. The N-BEATS model demonstrated an overall better performance for forecasting CO2 emission in Saudi Arabia in comparison to the other models. In addition, this paper also provides recommendations and strategies for mitigating the climate change (by reducing CO2 emission).