Climate change and artificial intelligence: assessing the global research landscape

被引：0

作者：

Lewis, Joanna I. ^{[1
]}

Toney, Autumn ^{[2
,3
]}

Shi, Xinglan ^{[4
]}

机构：

[1] Science, Technology and International Affairs Program, Edmund A. Walsh School of Foreign Service, Georgetown University, Washington, DC

[2] Center for Security and Emerging Technology, Edmund A. Walsh School of Foreign Service, Georgetown University, Washington, DC

[3] Department of Computer Science, Georgetown University, Washington, DC

[4] Communications, Culture and Technology Program, Georgetown University, Washington, DC

来源：

Discover Artificial Intelligence | 2024年 / 4卷 / 01期

关键词：

AI; AI tasks and methods; China; Climate change; Publication analysis;

D O I：

10.1007/s44163-024-00170-z

中图分类号：

学科分类号：

摘要：

Artificial Intelligence (AI) could revolutionize our ability to understand and address climate change. Studies to date have focused on specific AI applications to climate science, technologies, and policy. Yet despite the vast demonstrated potential for AI to change the way in which climate research is conducted, no study has presented a systematic and comprehensive understanding of the way in which AI is intersecting with climate research around the world. Using a novel merged corpus of scholarly literature which contains millions of unique scholarly documents in multiple languages, we review the community of knowledge at the intersection of climate change and AI to understand how AI methods are being applied to climate-related research and which countries are leading in this area. We find that Chinese research institutions lead the world in publishing and funding research at the intersection of climate and AI, followed by the United States. In mapping the specific AI tasks or methods being applied to specific climate research fields, we highlight gaps and identify opportunities to expand the use of AI in climate research. This paper can therefore greatly improve our understanding of both the current use and the potential use of AI for climate research. © The Author(s) 2024.

引用

共 63 条

[41]

Min C., Zhao Y., Yi B., Ding Y., Wagner C.S., Has China caught up to the US in AI research? An exploration of mimetic isomorphism as a model for late industrializers, arXiv, (2023)

[42]

Mishra M., Panigrahi R.R., Rout P.K., A combined mathematical morphology and extreme learning machine techniques based approach to micro-grid protection, Ain Shams Eng J, 10, 2, pp. 307-318, (2019)

[43]

Mosavi A., Ozturk P., Chau K.-W., Flood prediction using machine learning models: literature review, Water, 10, 11, (2018)

[44]

Nichol J.J., Peterson M.G., Peterson K.J., Matthew Fricke G., Moses M.E., Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change, J Comput Appl Math, 395, (2021)

[45]

Ojo M., The Future of UK Carbon Pricing: Artificial Intelligence and the Emissions Trading System. MPRA Paper, (2019)

[46]

Park S.J., Lee D.K., Prediction of coastal flooding risk under climate change impacts in South Korea using machine learning algorithms, Enviro Res Lett, 15, 9, (2020)

[47]

Ploszaj-Mazurek M., Rynska E., Grochulska-Salak M., Methods to optimize carbon footprint of buildings in regenerative architectural design with the use of machine learning, convolutional neural network, and parametric design, Energies, 13, 20, (2020)

[48]

Rolnick D., Donti P.L., Kaack L.H., Kochanski K., Lacoste A., Sankaran K., Ross A.S., Et al., Tackling climate change with machine learning, (2019)

[49]

Shaamala A., Yigitcanlar T., Nili A., Nyandega D., Algorithmic green infrastructure optimization: review of artificial intelligence driven approaches for tackling climate change, Sustain Cities Soc, 101, February, (2024)

[50]

Shin J.-Y., Kim K.R., Ha J.-C., Seasonal forecasting of daily mean air temperatures using a coupled global climate model and machine learning algorithm for field-scale agricultural management, Agric For Meteorol, 281, (2020)

← 1 2 3 4 5 6 7 →