Abstract
Climate change is a reality that can be felt. There are more and more symptoms: droughts, floods, global temperature change… This is causing public opinion to react and worry. The scientific community is no stranger to this feeling and is looking to science and technology, specifically artificial intelligence, for the means and mechanisms to help reduce this impact. This study demonstrates that the scientific community’s interest in artificial intelligence and climate change is a constant and growing reality. To achieve this objective, a bibliometric study is used with the following methodology: first, scientific papers related to artificial intelligence and climate change are obtained from the Scopus database, then they are processed through VOSviewer and analyzed by the dimensions of time, topics, and countries, and finally a network map is visualized where it can be seen how climate change is surrounded by areas related to artificial intelligence.
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References
Sivonen, J.: Attitudes toward global and national climate policies in Finland—the significance of climate change risk perception and urban/rural-domicile. GeoJournal 88(2), 2247–2262 (2023)
Van Baal, K., Stiel, S., Schulte, P.: Public perceptions of climate change and Health—a cross-sectional survey study. Int. J. Environ. Res. Public Health 20(2) (2023)
Alexandridis, N., Feit, B., Kihara, J., Luttermoser, T. et al.: Climate change and ecological intensification of agriculture in sub-saharan Africa—a systems approach to predict maize yield under push-pull technology. Agric. Ecosyst. Environ., 352 (2023)
Kim, G.: Development of groundwater utilization technology to solve drought problems in the era of climate change. J. Geol. Soc. Korea 59(1), 1–2 (2023)
Zagrebelnaya, N.S., Sheveleva, A.V.: Applying digital technology to combat climate change in Russia and the EU. In: Current Problems of the Global Environmental Economy Under the Conditions of Climate Change and the Perspectives of Sustainable Development, pp. 143–154. Springer, Cham (2023)
Ahmed, M., Hayat, R., Ahmad, M., et al.: Impact of climate change on dryland agricultural systems: a review of current status, potentials, and further work need. Int. J. Plant Prod. 16(3), 341–363 (2022)
Aminifar, F., Abedini, M., Amraee, T., Jafarian, P., et al.: A review of power system protection and asset management with machine learning techniques. Energy Syst. 13(4), 855–892 (2022)
Arriola, I.C.M., Santana-Cárdenas, S., Uriarte, P.J.L., Magaña-González, C.R.: Food insecurity and food vulnerability in communities: a systematic review. Revista Espanola de Nutrición Comunitaria 28(1), 133–143 (2022)
Badejo, O., Skaldina, O., Gilev, A., Sorvari, J.: Benefits of insect colours: a review from social insect studies. Oecologia 194(1–2), 27–40 (2020)
Balogun, A., Tella, A., Baloo, L., Adebisi, N.: A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science. Urban Climate, 40 (2021)
Bertoglio, R., Corbo, C., Renga, F.M., Matteucci, M.: The digital agricultural revolution: a bibliometric analysis literature review. IEEE Access 9, 134762–134782 (2021)
Bhaga, T.D., Dube, T., Shekede, M.D., Shoko, C.: Impacts of climate variability and drought on surface water resources in sub-saharan Africa using remote sensing: a review. Remote Sensing 12(24), 1–34 (2020)
Bikomeye, J.C., Balza, J.S., Kwarteng, J.L., Beyer, A.M., Beyer, K.M.M.: The impact of greenspace or nature-based interventions on cardiovascular health or cancer-related outcomes: a systematic review of experimental studies. PLoS ONE, 17 (2022)
Chatterjee, J., Dethlefs, N.: Scientometric review of artificial intelligence for operations & maintenance of wind turbines: the past, present and future. Renew. Sustain. Energy Rev., 144 (2021)
Chiloane, C., Dube, T., Shoko, C.: Impacts of groundwater and climate variability on terrestrial groundwater dependent ecosystems: a review of geospatial assessment approaches and challenges and possible future research directions. Geocarto Int. 37(23), 6755–6779 (2022)
Dayioğlu, M.A., Türker, U.: Digital transformation for sustainable future-agriculture 4.0: a review. Tarim Bilimleri Dergisi 27(4), 373–399 (2021)
Debrah, C., Chan, A.P.C., Darko, A.: Green finance gap in green buildings: A scoping review and future research needs. Build. Environ., 207 (2022)
Dwivedi, K.A., Huang, S., Wang, C.: Integration of various technology-based approaches for enhancing the performance of microbial fuel cell technology: a review. Chemosphere, 287 (2022)
Hamitouche, M., Molina, J.: A review of AI methods for the prediction of high-flow extremal hydrology. Water Resour. Manage 36(10), 3859–3876 (2022)
Ibrahim, K.S.M.H., Huang, Y.F., Ahmed, A.N., Koo, C.H., El-Shafie, A.: A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting. Alex. Eng. J. 61(1), 279–303 (2022)
Jain, P., Coogan, S.C.P., Subramanian, S.G., Crowley, M., Taylor, S., Flannigan, M.D.: A review of machine learning applications in wildfire science and management. Environ. Rev. 28(4), 478–505 (2020)
Kaginalkar, A., Kumar, S., Gargava, P., Niyogi, D.: Review of urban computing in air quality management as smart city service: an integrated IoT, AI, and cloud technology perspective. Urban Climate, 39 (2021)
Karyono, K., Abdullah, B.M., Cotgrave, A.J., Bras, A.: The adaptive thermal comfort review from the 1920s, the present, and the future. Develop. Built Environ., 4 (2020)
Kong, L., Wang, L., Li, F., Guo, J.: Toward product green design of modeling, assessment, optimization, and tools: a comprehensive review. Int. J. Adv. Manuf. Technol. 122(5–6), 2217–2234 (2022)
Masoudi Soltani, S., Lahiri, A., Bahzad, H., Clough, P., Gorbounov, M., Yan, Y.: Sorption-enhanced steam methane reforming for combined CO2 capture and hydrogen production: a state-of-the-art review. Carbon Capture Sci. Technol., 1 (2021)
Phy, S.R., Sok, T., Try, S., Chan, R., Uk, S., Hen, C., Oeurng, C.: Flood hazard and management in Xambodia: A review of activities, knowledge gaps, and research direction. Climate 10(11) (2022)
Polymeni, S., Athanasakis, E., Spanos, G., Votis, K. & Tzovaras, D.: IoT-based prediction models in the environmental context: a systematic literature review. Internet of Things, 20 (2022)
Roslim, M.H.M., Juraimi, A.S., Che’ya, N.N., Sulaiman, N., Manaf, M.N.H.A., Ramli, Z. & Motmainna, M.: Using remote sensing and an unmanned aerial system for weed management in agricultural crops: a review. Agronomy 11(9) (2021)
Sapienza, M., Nurchis, M.C., Riccardi, M.T., Bouland, C., Jevtić, M., Damiani, G.: The adoption of digital technologies and artificial intelligence in urban health: a scoping review. Sustainability 14(12) (2022)
Shah, A., Shah, K., Shah, C., Shah, M.: State of charge, remaining useful life and knee point estimation based on artificial intelligence and machine learning in lithium-ion EV batteries: a comprehensive review. Renew. Energy Focus 42, 146–164 (2022)
Sharif, M.Z., Di, N., Liu, F.: Monitoring honeybees (apis spp.) (hymenoptera: Apidae) in climate-smart agriculture: a review. Appl. Entomol. Zoology 57(4), 289–303 (2022)
Subramaniam, S., Raju, N., Ganesan, A. et al.: Artificial intelligence technologies for forecasting air pollution and human health: a narrative review. Sustainability 14(16) (2022)
Tardaguila, J., Stoll, M., Gutiérrez, S., Proffitt, T., Diago, M.P.: Smart applications and digital technologies in viticulture: a review. Smart Agric. Technol., 1 (2021)
Vidas, L., Castro, R.: Recent developments on hydrogen production technologies: State-of-the-art review with a focus on green-electrolysis. Appl. Sci. 11(23) (2021)
Waltersmann, L., Kiemel, S., Stuhlsatz, J., Sauer, A., Miehe, R.: Artificial intelligence applications for increasing resource efficiency in manufacturing companies—a comprehensive review. Sustainability 13(12) (2021)
Wang, D., Cao, W., Zhang, F., Li, Z., Xu, S., Wu, X.: A review of deep learning in multiscale agricultural sensing. Remote Sens. 14(3) (2022)
Wong, W.Y., Al-Ani, A.K.I., Hasikin, K., et al.: Water, soil and air pollutants’ interaction on mangrove ecosystem and corresponding artificial intelligence techniques used in decision support systems—a review. IEEE Access 9, 105532–105563 (2021)
Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H. & Lippitt, C. D.: Google earth engine and artificial intelligence (AI): a comprehensive review. Remote Sens. 14(14) (2022)
Yang, L., Driscol, J., Sarigai, S., Wu, Q., Lippitt, C.D., Morgan, M.: Towards synoptic water monitoring systems: a review of AI methods for automating water body detection and water quality monitoring using remote sensing. Sensors 22(6) (2022)
Zalnezhad, A., Rahman, A., Nasiri, N. et al.: Artificial intelligence-based regional flood frequency analysis methods: a scoping review. Water 14(17) (2022)
Zhang, H., Xu, Y., Kanyerere, T.: A review of the managed aquifer recharge: Historical development, current situation and perspectives. Phys. Chem. Earth, 118–119 (2020)
Zhao, L., Nazir, M.S., Nazir, H.M.J., Abdalla, A.N.: A review on proliferation of artificial intelligence in wind energy forecasting and instrumentation management. Environ. Sci. Pollut. Res. 29(29), 43690–43709 (2022)
Zhao, X., Kim, J., Warns, K. et al.: Prognostics and health management in nuclear power plants: an updated method-centric review with special focus on data-driven methods. Frontiers Energy Res., 9 (2021)
Galán, J.J., Carrasco, R.A., LaTorre, A.: Military applications of machine learning: a bibliometric perspective. Mathematics 10, 1397 (2022)
Kashi, A., Shah, M.E.: Bibliometric review on sustainable finance. Sustainability 15(9) (2023)
Owolabi, T.A., Sajjad, M.: A global outlook on multi-hazard risk analysis: a systematic and scientometric review. Int. J. Disaster Risk Reduct. 92 (2023)
Vo, T.P.T., Ngo, H.H., Guo, W. et al.: Influence of the COVID-19 pandemic on climate change summit negotiations from the climate governance perspective. Sci. Total Environ. 878 (2023)
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Galán Hernández, J.J., Carrasco González, R.A., Marín Díaz, G. (2024). The Growing Scientific Interest in Artificial Intelligence for Addressing Climate Change: A Bibliometric Analysis. In: Ibáñez, D.B., Castro, L.M., Espinosa, A., Puentes-Rivera, I., López-López, P.C. (eds) Communication and Applied Technologies. ICOMTA 2023. Smart Innovation, Systems and Technologies, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-99-7210-4_13
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DOI: https://doi.org/10.1007/978-981-99-7210-4_13
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