A Comprehensive Framework for Energy Decision‐Making Toward Achieving Net Zero by 2050 Using Machine Learning

Achieving net‐zero emissions by mid‐century is essential to limit global warming within internationally endorsed thresholds and avert the most acute consequences of climate change. This study presents a policy‐anchored, machine learning (ML)–based forecasting framework to project the long‐term evolution of a national electricity mix under decarbonization commitments. Using the United Arab Emirates (UAE) as a representative case of an energy‐intensive economy pursuing diversification, we integrate historical data (1985–2021) with supervised ML models to forecast shifts in generation sources, CO 2 emissions, and temperature trends through 2050. Results reveal a structural transformation: fossil fuels decline from 94% of electricity generation in 2021 to 49% by 2049, while renewables and nuclear rise to 39% and 6.1%, respectively. These transitions drive a projected 92% reduction in energy‐related CO 2 emissions and a 32% slowdown in the rate of surface temperature increase—from ~0.0415°C/year to ~0.0283°C/year. However, comparison with an idealized net‐zero pathway highlights a persistent gap: emissions levels by 2050 remain nearly 30 million tonnes above the target benchmark. This divergence underscores that while current efforts yield significant climate benefits, they are insufficient for full decarbonization. The findings emphasize the need for deeper systemic change, including accelerated clean technology deployment, grid modernization, and enhanced demand‐side efficiency. By embedding national policy signals into a quantitative and reproducible framework, the study offers actionable insights for strategic energy planning and long‐term climate mitigation.