AI Technology Is Revolutionizing Climate Change Mitigation: An Overview
Main Article Content
Abstract
Climate change is a global problem that has a significant impact on human health and economic well-being. Artificial intelligence (AI) has been shown to have great potential in reducing the effects of climate change. This article tries to provide a basic overview of the relationship between AI and mitigating climate change, highlighting AI's revolutionary potential in combating this pressing global issue. In particular, this article looks at how big data is essential to the success of climate action programs and how AI technologies may use these enormous databases to help develop more efficient mitigation measures for climate change and adapt to them. We have investigated novel methods for comprehending climate dynamics, maximizing renewable energy systems, enhancing climate resilience, and improving environmental justice via the use of AI technology.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Bakirtzis, A., Angelidakis, A., Bakirtzis, A. G., & Hatziargyriou, N. D. (2021). Artificial intelligence techniques for energy management in power systems with renewable energy sources. IEEE Transactions on Sustainable Energy, 12(1), 520-530. https://doi.org/10.1109/TSTE.2020.2984851
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., & Zemp, M. (2021). The World Climate Research Programme: Serving society with climate science. Bulletin of the American Meteorological Society, 102(1), E1-E22. https://doi.org/10.1175/BAMS-D-20-0034.1
Borkowski, R., Gryllias, K., Papadopoulos, A., & Mutale, J. (2020). Artificial intelligence in renewable energy systems: A review. Energies, 13(4), 926. https://doi.org/10.3390/en13040926
Bullard, R. D., Mohai, P., Saha, R., & Wright, B. (2020). Toxic waste and race at twenty 1987–2007: Grassroots struggles to dismantle environmental racism in the United States. Environmental Justice, 13(6), 165-173. https://doi.org/10.1089/env.2020.0026
Chakraborty, J., & Maantay, J. (2020). A spatiotemporal perspective on the application of artificial intelligence in environmental health research and practice. International Journal of Environmental Research and Public Health, 17(11), 3824. https://doi.org/10.3390/ijerph17113824
Eckstein, L., Uhl, J. H., Hahmann, S., & Wachter, T. (2021). Towards environmental justice? On the visibility of disadvantaged communities in AI-based air quality analyses. Environmental Research Letters, 16(2), 024007. https://doi.org/10.1088/1748-9326/abc58b
Feng, X., Ma, K., Liu, Y., Yu, X., & Li, S. (2021). Advances in artificial intelligence for ecological adaptation to climate change. Global Ecology and Conservation, 26, e01458.
https://doi.org/10.1016/j.gecco.2021.e01458
Garcia, K., Löwe, P., Huth, A., & Hermwille, L. (2020). AI for climate: Bringing the environmental crisis and the digital revolution together. Digital Policy, Regulation and Governance, 22(6), 570-576. https://doi.org/10.1108/DPRG-07-2020-0047
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2020). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 222, 1–4. https://doi.org/10.1016/j.rse.2019.02.019
Gupta, A., Kothari, D. P., & Tiwari, A. K. (2020). Application of artificial intelligence in renewable energy systems: A review. Renewable and Sustainable Energy Reviews, 130, 109955.
https://doi.org/10.1016/j.rser.2020.109955
Haklay, M., Ather, A., & Harvey, F. (2021). Artificial intelligence for environmental science: Premises, promises, and challenges. Patterns, 2(2), 100221. https://doi.org/10.1016/j.patter.2021.100221
Hao, Z., Huang, J., Li, H., He, G., & Xie, L. (2020). A review on the application of artificial intelligence in hydroelectric power generation. Renewable and Sustainable Energy Reviews, 124, 109773. https://doi.org/10.1016/j.rser.2020.109773
Horne, J., Shirzaei, M., Yue, Y., & Yu, X. (2021). Air quality assessment and community-led air monitoring using machine learning in Richmond, California. Environmental Monitoring and Assessment, 193(4), 200. https://doi.org/10.1007/s10661-021-08918-5
IEA. (2021). Renewables 2021. International Energy Agency. Retrieved from
https://www.iea.org/reports/renewables-2021
Intergovernmental Panel on Climate Change. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.
Intergovernmental Panel on Climate Change. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
Kleinberg, B., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2020). Human decisions and machine predictions. The Quarterly Journal of Economics, 135(1), 237-293.
https://doi.org/10.1093/qje/qjz032
Lai, Y., Luo, J., Wu, L., Tang, H., & Yu, W. (2021). Spatial and temporal patterns of urban heat islands and their relationships with socioeconomic factors in the Chinese cities. Science of The Total Environment, 761, 143258. https://doi.org/10.1016/j.scitotenv.2020.143258
Li, Y., Li, Y., Yang, Q., & Li, C. (2021). A review of the application of artificial intelligence in the operation and maintenance of power system equipment. IOP Conference Series: Earth and Environmental Science, 690(1), 012022. https://doi.org/10.1088/1755-1315/690/1/012022
Mohai, P., Pellow, D., & Roberts, J. T. (2020). Environmental justice. Annual Review of Environment and Resources, 45(1), 331-362. https://doi.org/10.1146/annurev-environ-012420-021406
Murray, G., Ruegg, J., Kurz, W. A., & Banerjee, A. (2020). Learning from big data: The importance of starting small. Environmental Research Letters, 15(12), 120201. https://doi.org/10.1088/1748-9326/abbf6d
Nguyen, T. T. H., Kim, Y., Ho, M. H., & Lee, Y. S. (2020). A review on the application of artificial intelligence in infrastructure management. Sustainability, 12(9), 3744. https://doi.org/10.3390/su12093744
Padgham, M., Friggens, S., Wright, D. J., & Goodwin, J. (2021). A collaborative approach to conservation planning: Engaging local knowledge in environmental management decisions. Conservation Science and Practice, 3(6), e403. https://doi.org/10.1111/csp2.403
Rathore, M. M., Ahmad, A., Paul, A., Wan, J., & Rho, S. (2020). Environmental Sustainability through the Lens of AI. Sustainability, 12(2), 470. https://doi.org/10.3390/su12020470
Rosenzweig, C., Solecki, W. D., Romero-Lankao, P., Mehrotra, S., Dhakal, S., Ali Ibrahim, S., & Cao, S. (2020). Climate change and cities: Second assessment report of the urban climate change research network. Cambridge University Press.
Schwalm, C. R., Anderegg, W. R. L., Michalak, A. M., Fisher, J. B., Biondi, F., Koch, G., Litvak, M., Ogle, K., Shaw, J. D., Wolf, A., Huntzinger, D. N., Schaefer, K., Cook, R., Wei, Y., Fang, Y., Hayes, D., Huang, M., Jain, A., Jin, C., … Huntzinger, D. N. (2021). Global patterns of drought recovery. Nature, 596(7873), 270–274. https://doi.org/10.1038/s41586-021-03657-8
Tromble, R., Dafoe, A., & Wilson, C. (2020). The online public sphere and the 2020 US election: A tale of two democracies. Social media + Society, 6(3), 2056305120962802.
https://doi.org/10.1177/2056305120962802
Wang, S., Huang, Q., Jiang, X., & Liu, Q. (2020). A review of artificial intelligence applications for predicting heatwaves and droughts. Earth-Science Reviews, 203, 103109.
https://doi.org/10.1016/j.earscirev.2020.103109
Wang, X., Chen, J., Hua, Z., & Zou, S. (2021). Climate adaptation policies in China: A systematic review based on text mining and topic modeling. Environmental Science & Policy, 123, 28–39.
https://doi.org/10.1016/j.envsci.2021.06.006
Wilson, A., Chaudhuri, S., & Mandal, S. (2021). Air quality prediction using machine learning algorithms: A review. Environmental Science and Pollution Research, 28(36), 49703-49729.
https://doi.org/10.1007/s11356-021-15512-w
Yang, Y., Huang, Y., Li, X., & Qiao, W. (2021). A review on applications of artificial intelligence in wind energy. Renewable and Sustainable Energy Reviews, 137, 110418.
https://doi.org/10.1016/j.rser.2020.110418
Zhang, J., Liu, X., Liu, C., & Xie, Y. (2020). Understanding global spatiotemporal patterns of urban carbon emissions and the driving forces. Science of The Total Environment, 713, 136719.
https://doi.org/10.1016/j.scitotenv.2020.136719
Zhang, W., Jiang, C., Deng, J., Chen, Y., & Jiang, H. (2020). Research progress on ecological and environmental effects of artificial intelligence. Journal of Cleaner Production, 258, 120949. https://doi.org/10.1016/j.jclepro.2020.120949
Zhang, Y., Zheng, Y., & Cai, W. (2021). Artificial intelligence for enhancing climate resilience in agriculture: A review. Agricultural Systems, 191, 103130. https://doi.org/10.1016/j.agsy.2021.103130.
Peters, D. P., Havstad, K. M., Cushing, J., Tweedie, C., Fuentes, O., & Villanueva-Rosales, N. (2014). Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology. Ecosphere, 5(6), 1-15.
Chen, C., Hu, Y., Karuppiah, M., & Kumar, P. M. (2021). Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies. Sustainable Energy Technologies and Assessments, 47, 101358