The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
RETRACTED ARTICLE: Artificial intelligence-based solutions for climate change: a review
Open accessAbstract
Climate change is a major threat already causing system damage to urban and natural systems, and inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because artificial intelligence integrates internet resources to make prompt suggestions based on accurate climate change predictions. Here we review recent research and applications of artificial intelligence in mitigating the adverse effects of climate change, with a focus on energy efficiency, carbon sequestration and storage, weather and renewable energy forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, and resilient cities. We found that enhancing energy efficiency can significantly contribute to reducing the impact of climate change. Smart manufacturing can reduce energy consumption, waste, and carbon emissions by 30–50% and, in particular, can reduce energy consumption in buildings by 30–50%. About 70% of the global natural gas industry utilizes artificial intelligence technologies to enhance the accuracy and reliability of weather forecasts. Combining smart grids with artificial intelligence can optimize the efficiency of power systems, thereby reducing electricity bills by 10–20%. Intelligent transportation systems can reduce carbon dioxide emissions by approximately 60%. Moreover, the management of natural resources and the design of resilient cities through the application of artificial intelligence can further promote sustainability.
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Citations
Cites
1 retracted
Tyagi, A. K., & Aswathy, S. U. (2021) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2021.07.002 · Cited by 79 · cited 2×
“A Developing self‑governing vehicles presents the potential to significantly reduce emissions by refining fuel efficiency and decreasing traffic congestion (Tyagi and Aswathy 2021).”
RetractedDwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · cited 1×
“E Dwivedi et al. (2021) reported that arti‑ ficial intelligence improves efficiency by combining manage‑ ment methods.”
Strategies to achieve a carbon neutral society: a review
Chen, L., Msigwa, G., Yang, M., Osman, A. I., Fawzy, S., Rooney, D. W., & Yap, P.-S. (2022) · Environmental Chemistry Letters · doi:10.1007/s10311-022-01435-8 · Cited by 1,326
Self-citationArtificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125834 · Cited by 1,258
Estimating economic damage from climate change in the United States
Hsiang, S., Kopp, R., Jina, A., Rising, J., Delgado, M., Mohan, S., Rasmussen, D. J., Muir-Wood, R., Wilson, P., Oppenheimer, M., et al. (2017) · Science · doi:10.1126/science.aal4369 · Cited by 1,239
Sarker, I. H. (2022) · SN Computer Science · doi:10.1007/s42979-022-01043-x · Cited by 1,208
On big data, artificial intelligence and smart cities
Allam, Z., & Dhunny, Z. A. (2019) · Cities · doi:10.1016/j.cities.2019.01.032 · Cited by 1,178
Cost, environmental impact, and resilience of renewable energy under a changing climate: a review
Osman, A. I., Chen, L., Yang, M., Msigwa, G., Farghali, M., Fawzy, S., Rooney, D. W., & Yap, P.-S. (2023) · Environmental Chemistry Letters · doi:10.1007/s10311-022-01532-8 · Cited by 1,048
Self-citationArtificial Intelligence and Business Value: a Literature Review
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022) · Information Systems Frontiers · doi:10.1007/s10796-021-10186-w · Cited by 1,046 · cited 1×
“Some scholars contend that the exorbitant cost of artificial intelligence technology is a major obstacle to its application in energy efficiency (Enholm et al. 2022; Yang 2022; Zhao et al. 2022).”
BackgroundBaduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104440 · Cited by 1,007 · cited 2×
“Some scholars contend that the exorbitant cost of artificial intelligence technology is a major obstacle to its application in energy efficiency (Enholm et al. 2022; Yang 2022; Zhao et al. 2022).”
Cited by
Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2023.100330 · Cited by 536
Green building practices to integrate renewable energy in the construction sector: a review
Chen, L., Hu, Y., Wang, R., Li, X., Chen, Z., Hua, J., Osman, A. I., Farghali, M., Huang, L., Li, J., et al. (2024) · Environmental Chemistry Letters · doi:10.1007/s10311-023-01675-2 · Cited by 280
Self-citationLife cycle assessment and techno-economic analysis of sustainable bioenergy production: a review
Osman, A. I., Fang, B., Zhang, Y., Liu, Y., Yu, J., Farghali, M., Rashwan, A. K., Chen, Z., Chen, L., Ihara, I., et al. (2024) · Environmental Chemistry Letters · doi:10.1007/s10311-023-01694-z · Cited by 236
Self-citationPandey, D. K., Hunjra, A. I., Bhaskar, R., & Al-Faryan, M. A. S. (2023) · Resources Policy · doi:10.1016/j.resourpol.2023.104250 · Cited by 68
Silva, D. B., Pachla, E. C., Bolina, F. L., Graeff, Â. G., Lorenzi, L. S., & da Silva Filho, L. C. P. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.108716 · Cited by 15
Metadata
- Authors
- Chen, L., Chen, Z., Zhang, Y., Liu, Y., Osman, A. I., Farghali, M., Hua, J., Al-Fatesh, A., Ihara, I., Rooney, D. W., & Yap, P.-S.
- Venue
- Environmental Chemistry Letters
- Published
- 2023-06-13
- Volume
- 21
- Issue
- 5
- Pages
- 2525-2557
- ISSN
- 1610-3653 (ISSN-L)
- 1610-3661
- OpenAlex
- W4380628716
- Cited by
- 385
- References
- 19
- FWCI
- 33.82
- Type
- journal article
- Language
- en
- Status
- Retracted
- Open access
- hybrid
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- Chen, L., Chen, Z., Zhang, Y., Liu, Y., Osman, A. I., Farghali, M., Hua, J., Al-Fatesh, A., Ihara, I., Rooney, D. W., & Yap, P.-S. (2023). RETRACTED ARTICLE: Artificial intelligence-based solutions for climate change: a review. Environmental Chemistry Letters. https://doi.org/10.1007/s10311-023-01617-y
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
