A machine learning process for examining the linkage among disaggregated energy consumption, economic growth, and environmental degradation

Improving environmental quality is at the heart of the Saudi Vision 2030. Within this context, this study seeks to extend previous environmental economics literature by examining the relationship between disaggregated energy use, economic growth, and environmental quality in Saudi Arabia using machine learning (ML) techniques. Using data from 1980 to 2020, we found that reducing CO2 emissions cannot be done in Saudi Arabia without a complete transition from fossil to renewable resources and a more viable road to sustainability. ML-based regression and prediction shows that CO2 emissions will continue to grow until 2024. Beginning in 2025 and beyond, the emissions decrease (i.e., reducing CO2 emissions) must be accompanied by an increment use of renewable energies to guarantee stable economic growth.

Automated summary

This study examines the relationship between disaggregated energy use, economic growth, and environmental quality in Saudi Arabia using machine learning techniques. It finds that reducing CO2 emissions requires a complete transition from fossil to renewable resources and a more viable road to sustainability. The study also predicts that CO2 emissions will continue to grow until 2024, and afterwards, a decrease in emissions must be accompanied by an increase in renewable energy use to ensure stable economic growth.