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<p>The intersection between artificial intelligence (AI) and climate change mitigation has received increased attention as a result of pressing global environmental concerns and technological advancements. This paper will cover key scientific literature about the development and implementation of AI in tackling climate change challenge. Methodologically, bibliometric and scientometric analyses, including cooccurrence and co-citation analysis, were employed to systematically examine research trends and patterns. The major research question focuses on tracing the evolution of AI research in relation to climate change, investigating important drivers of interest and identifying influential works and research groups. The data find a considerable increase in AI and climate change papers after 2015, demonstrating a growing appreciation for AI’s potential to provide novel solutions. Implications of this trend include the use of AI technologies for environmental monitoring, prediction, and policy makers. Moreover, interdisciplinary collaborations on AI and climate change between experts from various fields shown the importance of global climate issues. This paper provides a new idea for the study on AI in climate change, highlighting opportunities for further exploration and collaboration.</p>
</abstract>

The intersection between artificial intelligence (AI) and climate change mitigation has received increased attention as a result of pressing global environmental concerns and technological advancements. This paper will cover key scientific literature about the development and implementation of AI in tackling climate change challenge. Methodologically, bibliometric and scientometric analyses, including cooccurrence and co-citation analysis, were employed to systematically examine research trends and patterns. The major research question focuses on tracing the evolution of AI research in relation to climate change, investigating important drivers of interest and identifying influential works and research groups. The data find a considerable increase in AI and climate change papers after 2015, demonstrating a growing appreciation for AI’s potential to provide novel solutions. Implications of this trend include the use of AI technologies for environmental monitoring, prediction, and policy makers. Moreover, interdisciplinary collaborations on AI and climate change between experts from various fields shown the importance of global climate issues. This paper provides a new idea for the study on AI in climate change, highlighting opportunities for further exploration and collaboration.

Exploring the Climate Change-AI Nexus: A Bibliometric and Scientometric Study<sup>1</sup>

Proceedings of the International Conference on Business Excellence

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the Climate Change-AI Nexus: A Bibliometric and Scientometric Study

The intersection between artificial intelligence (AI) and climate change mitigation has received increased attention as a result of pressing global...

Exploring the Climate Change-AI Nexus: A Bibliometric and Scientometric Study¹

Published Online: Jul 03, 2024

Page range: 1658 - 1670

DOI: https://doi.org/10.2478/picbe-2024-0138

Keywords
artificial intelligence, climate change, renewable energy, machine learning, bibliometric analysis

© 2024 Florentina Chițu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the Climate Change-AI Nexus: A Bibliometric and Scientometric Study1

Published Online: Jul 03, 2024

Page range: 1658 - 1670

DOI: https://doi.org/10.2478/picbe-2024-0138

Keywordsartificial intelligence, climate change, renewable energy, machine learning, bibliometric analysis

© 2024 Florentina Chițu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the Climate Change-AI Nexus: A Bibliometric and Scientometric Study¹

Keywords
artificial intelligence, climate change, renewable energy, machine learning, bibliometric analysis