Artificial intelligence (AI) is a machine-based system that can make predictions, recommendations or decisions influencing real or virtual environments. It has a potential to play a pivotal role in addressing the complex challenges of climate change and facilitating the transition to sustainable energy systems. Indeed, AI could unlock innovative solutions that optimize energy production, distribution, and consumption while mitigating environmental impacts. It also could help to enable the integration of renewable energy sources like solar and wind into existing grids, to enhance energy efficiency in various sectors, and to facilitate predictive analytics for climate modeling, emissions reduction disaster management, and environmental conservation.

As AI technologies play an increasingly prominent role in shaping sustainable solutions to mitigate climate change and to speed up energy transition, AI sustainability, i.e. job displacement across several industries, exacerbation of resource and energy use, increase of the circulation of misinformation and bias, as well as ethical considerations must guide its development and implementation. This involves ensuring transparency, accountability, and fairness throughout the AI lifecycle, from data collection to decision-making processes. Ultimately, handling these challenges can enhance trust, foster inclusivity, and maximize the positive impact of AI in addressing climate and energy transition issues.

This special issue welcomes all original research manuscripts related to the role of AI in mitigating climate change and facilitating energy transition, as well as the need for considering the ethics of using AI when addressing such crucial issues. We especially invite multidisciplinary contributions about (but not limited to):
- AI and economics of energy demand,
- AI and evaluation of climate change and energy transition risks,
- AI and energy market pricing and trading,
- AI and carbon Trading and Emissions Management
- AI and climate modelling,
- AI and natural disaster prediction and response,
- AI and carbon capture and storage management,
- AI and emission reduction,
- AI and renewable energy diffusion,
- AI and smart grid management,
- AI and energy storage optimization,
- AI and forecasting (renewable) energy production and demand response,
- AI and climate and energy public policy making,
- AI and the development of climate and energy public awareness and education,
- Ethics of AI.

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Frontiers in Environmental Economics

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