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The potential growth, privileged geographic location, emerging economies, and large market led to much potential research in the Middle East and North Africa (MENA) region. However, the role of Artificial intelligence (AI), along with other variables for energy efficiency (EE), to deliver economic and environmental benefits are yet underexplored in this region and brings a research lacuna. Thus, this study aims to present scientific evidence for the EE through the interaction effects of AI and energy use (ENEU) along with other variables in the MENA region from 2000–2021 using the dual approaches of data envelope analysis (DEA) and stochastic frontier analysis (SFA). The results confirm that countries such as Algeria, Bahrain, Egypt, Iran, Iraq, Israel, Malta, Oman, Saudi Arabia, Tunisia, UEA, and the West Bank are the most energy efficient, deriving an efficiency value of one from their optimal solution of DEA. The panel’s SFA analysis shows that AI, patents, renewable energy, and gross domestic product per capita (GDPPC) are meaningful and directly contribute to the study countries’ EE gains. The interaction term of AI and ENEU is significant, which implies the relevance of AI and ENEU in achieving EE. Furthermore, EE is negatively related to carbon intensity between 17.4% and 64.5% or worsens EE by 49.3% depending on the country, allowing the region to meet its climate targets and achieve the environmental benefits of deploying AI. In addition, it means that the MENA region can integrate AI and ENEU to curb the increasing decentralization of the national grid, which leads to energy efficiency gains in this region. This calls for integrating Al and energy in an effective way to enhance the human consumption of energy, taking the needs of the environment into consideration in order to attain efficiency and intelligent energy use. The study attempts to shed light on energy use while integrating AI to achieve economic and environmental efficiencies in the MENA region.
Hossin et al. (Tue,) studied this question.