Artificial Intelligence Adoption and Role of Energy Structure, Infrastructure, Financial Inclusions, and Carbon Emissions: Quantile Analysis of E-7 Nations

The E-7 nations face significant challenges in harmonizing artificial intelligence (AI) adoption with sustainable economic and environmental goals. While AI holds transformative potential to revolutionize energy structures, modernize infrastructure, broaden financial inclusion, and reduce carbon emissions, its effective integration is frequently hindered by policy inertia, economic limitations, and long-standing institutional barriers. Using the multi-level perspective (MLP), this study employs the method of moments quantile regression (MMQREG) on panel data from 2004 to 2024 to investigate the determinants of artificial intelligence (AI) adoption, focusing on the roles of energy structure (ES), infrastructure (INFRA), financial inclusion (FI), economic growth (GDP), patent activity (Tpatent), population (TP), and carbon emissions (CE) across E-7 nations. The study findings reveal that economic growth and energy structure play a significant role in driving AI adoption, while inadequacies in infrastructure and limited financial inclusion significantly hinder AI progress. Additionally, the analysis reveals a positive relationship between AI adoption and CO2 emissions, where early stages of technology uptake lead to increased emissions, but sustained integration eventually results in efficiency gains that help to reduce them. These findings underscore the need for E-7 nations to adopt targeted policies that modernize digital and physical infrastructure, broaden financial access, and expedite the transition to sustainable energy systems. This study offers actionable insights for policymakers to align digital innovation with sustainable development goals.