AI Breakthroughs in Carbon Emission Reduction

Uyên Nguyễn Cao Thục, Francesca Virgilio () and Subhankar Das ()
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Uyên Nguyễn Cao Thục: Duy Tan University
Francesca Virgilio: University of Molise
Subhankar Das: Duy Tan University

A chapter in Generative AI for a Net-Zero Economy, 2025, pp 57-73 from Springer

Abstract: Abstract The advancing climate crisis requires a new approach to fast-tracking decarbonization. This chapter reviews how transformative artificial intelligence (AI) applications could reduce carbon emissions in three key sectors—carbon capture and storage (CCS), renewable energy optimization, and smart grid management. Based on academic research and specialist knowledge from recognized authorities, the study illustrates how AI can lower costs of carbon capture and storage (CCS) via faster and more adaptive solvent design, geological risk minimization, smooth integration of renewable energy using predictive analytics, and resilient smart grids empowered by dynamic demand–response systems. The proposed framework is practical, incorporating cross-sectoral data infrastructure, domain-specific AI solutions, ethical governance, and equitable deployment. However, challenges, including energy-intensive training of AI and data privacy solutions, will require effective policy alignment and inclusion in strategies to harness the power of AI. In this area, the potential benefits are enormous, such as reducing the costs of CCS capture to be 50% cheaper by 2030. The findings highlight the potential for AI to close the divide between climate ambitions and action solutions, but doing so will require cross-disciplinary collaboration and sustainable practices.

Keywords: Artificial intelligence; Carbon emission reduction; Carbon capture and storage; Renewable energy optimization; Smart grid management; Ethical governance (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-8015-3_4

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DOI: 10.1007/978-981-96-8015-3_4

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