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Artificial Intelligence in Climate Change Mitigation and Adaptation

Dr. Sivaranjani B., Priya Dharshini M., Dinesh Kumar A.

Abstract


Addressing the pressing global issue of climate change demands rapid and creative approaches. Artificial Intelligence (AI) has surfaced as a potent instrument for addressing the complex issues linked to climate change. It provides inventive answers for diminishing greenhouse gas emissions and getting ready for the impacts of a shifting climate. This article conducts a comprehensive examination of AI's pivotal role in addressing climate change. It explores various aspects, including AI-driven climate modeling, the optimization of renewable energy sources, strategies for capturing and securely storing carbon emissions, initiatives aimed at enhancing climate resilience, and the analysis of environmental data. Furthermore, it delves into ethical considerations, hurdles, and the wide-ranging potential applications of AI in the ongoing battle against climate change. Notable areas of focus encompass greenhouse gas sequestration, adaptation to climate variations, and strategies for promoting climate resilience.


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References


Abdella GM, Kucukvar M, Onat NC, Al-Yafay HM, Bulak ME. Sustainability assessment and modeling based on supervised machine learning techniques: the case for food consumption. J Clean Prod. 2020;251(April):119661. doi: 10.1016/j.jclepro.2019.119661. [CrossRef] [Google Scholar]

Abrell J, Kosch M, Rausch S (2019) How effective was the UK carbon tax?—A machine learning approach to policy evaluation. SSRN Scholarly Paper ID 3372388. Social Science Research Network, Rochester. 10.2139/ssrn.3372388

ACM(2020)Artifact review and badging—current. https://www.acm.org/publications/policies/artifact-review-and-badging-current

Aftab M, Chen C, Chau C-K, Rahwan T. Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system. Energy Build. 2017;154:141–156. doi: 10.1016/j.enbuild.2017.07.077. [CrossRef] [Google Scholar]

Ahmed N, Wahed M (2020) The de-democratization of AI: deep learning and the compute divide in artificial intelligence research. http://arxiv.org/abs/2010.15581 [Cs]

Al-Jarrah OY, Yoo PD, Muhaidat S, Karagiannidis GK, Taha K. Efficient machine learning for big data: a review. Big Data Res. 2015;2(3):87–93. doi: 10.1016/j.bdr.2015.04.001. [CrossRef] [Google Scholar]

Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Van Esesn BC, Awwal AAS, Asari VK (2018) The history began from AlexNet: a comprehensive survey on deep learning approaches. http://arxiv.org/abs/1803.01164 [Cs]

Amodei D, Hernandez D(2018)AI and compute.OpenAI. https://openai.com/blog/ai-and-compute/

Andrae A, Edler T. On global electricity usage of communication technology: trends to 2030. Challenges. 2015;6(1):117–157. doi: 10.3390/challe6010117. [CrossRef] [Google Scholar]

Anthony LFW, Kanding B, Selvan R (2020) Carbontracker: tracking and predicting the carbon footprint of training deep learning models. http://arxiv.org/abs/2007.03051 [Cs, Eess, Stat]

Avgerinou M, Bertoldi P, Castellazzi L. Trends in data centre energy consumption under the European code of conduct for data centre energy efficiency. Energies. 2017;10(10):1470. doi: 10.3390/en10101470. [CrossRef] [Google Scholar]

Barnes EA, Hurrell JW, Ebert-Uphoff I, Anderson C, Anderson D. Viewing forced climate patterns through an AI lens. Geophys Res Lett. 2019;46(22):13389–13398. doi: 10.1029/2019GL084944. [CrossRef] [Google Scholar]

Belkhir L, Elmeligi A. Assessing ICT global emissions footprint: trends to 2040 & recommendations. J Clean Prod. 2018;177(March):448–463. doi: 10.1016/j.jclepro.2017.12.239. [CrossRef] [Google Scholar]

Bender EM, Gebru T, McMillan-Major A (2021) On the dangers of stochastic parrots: can language models be too big. In: Proceedings of FAccT

Berner C, Chan B, Cheung V, Dębiak P, Dennison C, Farhi D, Fischer Q et al (2019) Dota 2 with large scale deep reinforcement learning. http://arxiv.org/abs/1912.06680 [Cs, Stat]


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