Navigating the Path to Sustainability: Modeling Romania’s Energy Transition with AI and Econometrics

This paper focuses on Romania and its transition to a sustainable energy model within the framework of global efforts to address climate change and foster economic modernisation. Although Romania has a broad energy mix, which includes hydro, wind, and solar resources with considerable potential for application for renewable energy, there are still challenges, particularly through reliance on fossil fuels and climate vulnerabilities. The research uses Fractional Multinomial Logistic Models (FMNL) and Recurrent Neural Networks (RNNs) to analyse relationships between energy production, GDP per capita, and input coefficients between different economic sectors. The research investigates the impact of energy sources on economic structures, the predictive accuracy of advanced models, and policy strategies for optimising Romania’s energy transition. The findings reveal a projected shift toward low-carbon, technology-driven industries by 2031, facilitated by renewable energy integration. The results highlight the importance of investments in green infrastructure, energy network modernisation, and support for emerging industries. This research provides actionable insights for policymakers and stakeholders, emphasising Romania’s potential to lead regional sustainability efforts. The research contributes to broader discussions on energy sustainability and economic resilience in the context of global environmental challenges by emphasising the significance of advanced modelling techniques.