Abdel-GawadS. (2008). Actualizing the right to water: An Egyptian perspective for an action plan. Water as a Human Right for the Middle East and North Africa, 133–146. International Development Research Centre.
2.
Abou AmerA., MohamadD., & RoosliR. (2023). The impact of green energy & water practices on the development of sustainable tourism: A case study of 5-star hotels in Hurghada and Mecca. Planning Malaysia, 21.
3.
AdgerW. N., HuqS., BrownK., ConwayD., HulmeM., et al. (2003). Adaptation to climate change in the developing world. Progress in Development Studies, 3(3), 179–195.
4.
AgrawalaS., MoehnerA., El RaeyM., ConwayD., Van AalstM., et al. (2004). Development and climate change in Egypt: Focus on Coastal Resources and the Nile. Organisation for Economic Co-operation and Development, 1, 1–68.
5.
AntonopoulosI., RobuV., CouraudB., KirliD., NorbuS., et al. (2020). Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renewable and Sustainable Energy Reviews, 130, 109899.
BeharaR. K., & SahaA. K. (2022). Artificial intelligence methodologies in smart grid-integrated doubly fed induction generator design optimization and reliability assessment: A review. Energies, 15(19), 7164.
8.
Boston Consulting Group (BCG). (2022). New report from AI for the planet alliance, BCG, and BCG Gamma reveals a strong appetite for using AI to tackle climate change, but organizations face obstacles to achieving impact at scale. Boston Consulting Group. Available from: https://www.bcg.com/press/7july2022-ai-is-critical-in-fight-against-climate-change
9.
CasperJ. K. (2010). Greenhouse Gases: Worldwide Impacts. Infobase Publishing.
10.
Central Intelligence Agency (CIA). (2011). The World Factbook, 2011, edition. Central Intelligence Agency.
11.
CheongS. M., SankaranK., & BastaniH. (2022). Artificial intelligence for climate change adaptation. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(5), e1459.
12.
CowlsJ., TsamadosA., TaddeoM., & FloridiL. (2023). The AI gambit: Leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI & Society, 38(1), 283–307.
13.
DarwishK. H., SafaaM., MomouA., & SalehS. A. (2013). Egypt: Land degradation issues with special reference to the impact of climate change. In Combating desertification in Asia, Africa, and the Middle East: Proven practices, 113–136.
Egyptian Environmental Affairs Agency (EEAA). (2010). Egypt’s second national communication under the United Nations Framework Convention on Climate Change. Available from: https://unfccc.int/resource/docs/natc/egync2.pdf
16.
FoudaT. (2020). Impact of the fourth industrial revolution on the development of scientific research in the field of agricultural engineering in Egypt and Arab world. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development, 20, 253–258.
17.
HassaniH., SilvaE. S., UngerS., TajMazinaniM., & Mac FeelyS. (2020). Artificial intelligence (AI) or intelligence augmentation (IA): What is the future? AI, 1(2), 143–155.
18.
HuntingfordC., JeffersE. S., BonsallM. B., ChristensenH. M., LeesT., et al. (2019). Machine learning and artificial intelligence to aid climate change research and preparedness. Environmental Research Letters, 14(12), 124007.
19.
JobinA., IencaM., & VayenaE. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
KaackL., DontiP., StrubellE., & RolnickD. (2020). Artificial intelligence and climate change: Opportunities, considerations, and policy levers to align AI with climate change goals. Hertie School. Available from: https://opus4.kobv.de/opus4-hsog/frontdoor/index/index/docId/4129
22.
KhouzamR. F. (2002). Economic aspects of wastewater reuse: The Egyptian case. Economic Research Forum for the Arab Countries, Iran & Turkey.
23.
KirciP., OzturkE., & CelikY. (2022). A novel approach for monitoring of smart greenhouse and flowerpot parameters and detection of plant growth with sensors. Agriculture, 12(10), 1705.
24.
KosovicB., HauptS. E., AdriaansenD., AlessandriniS., WienerG., et al. (2020). A comprehensive wind power forecasting system integrating artificial intelligence and numerical weather prediction. Energies, 13(6), 1372.
25.
LaneB. A. (2019). Alternative light-and heavy-duty vehicle fuel pathway and powertrain optimization. University of California, Irvine.
26.
Leal FilhoW., WallT., MucovaS. A. R., NagyG. J., BalogunA. L., et al. (2022). Deploying artificial intelligence for climate change adaptation. Technological Forecasting and Social Change, 180, 121662.
27.
McCarthyD., KoelingR., WeedsJ., & CarrollJ. A. (2004). Finding predominant word senses in untagged text. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), 279–286.
28.
MilfontTaciano L., WilsonJessie., DinizPollyane.Time perspective and environmental engagement: A meta‐analysis. International Journal of Psychology. 2012. 47(5) 325–334. Oct 2012. 10.1080/00207594.2011.647029
RutenbergI., GwagwaA., & OminoM. (2021). Use and impact of artificial intelligence on climate change adaptation in Africa. In African handbook of climate change adaptation. Springer International Publishing: Cham. 1107–1126.
33.
TalaviyaT., ShahD., PatelN., YagnikH., & ShahM. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58–73.
34.
WalshK. A., SpillaneS., ComberL., CardwellK., HarringtonP., et al. (2020). The duration of infectiousness of individuals infected with SARS-CoV-2. The Journal of Infection, 81(6), 847–856.
35.
ZhaoX., MaX., ChenB., ShangY., & SongM. (2022). Challenges toward carbon neutrality in China: Strategies and countermeasures. Resources, Conservation and Recycling, 176, 105959.