  Parallel to the rising debates over sustainable energy and artificial
intelligence solutions, the world is currently discussing the ethics of
artificial intelligence and its possible negative effects on society and the
environment. In these arguments, sustainable AI is proposed, which aims at
advancing the pathway toward sustainability, such as sustainable energy. In
this paper, we offered a novel contextual topic modeling combining LDA, BERT,
and Clustering. We then combined these computational analyses with content
analysis of related scientific publications to identify the main scholarly
topics, sub-themes, and cross-topic themes within scientific research on
sustainable AI in energy. Our research identified eight dominant topics
including sustainable buildings, AI-based DSSs for urban water management,
climate artificial intelligence, Agriculture 4, the convergence of AI with IoT,
AI-based evaluation of renewable technologies, smart campus and engineering
education, and AI-based optimization. We then recommended 14 potential future
research strands based on the observed theoretical gaps. Theoretically, this
analysis contributes to the existing literature on sustainable AI and
sustainable energy, and practically, it intends to act as a general guide for
energy engineers and scientists, AI scientists, and social scientists to widen
their knowledge of sustainability in AI and energy convergence research.
