The Carbon Code: Decoding AI’s Role in Climate Mitigation

Vasiliki G. Vrana () and Subhra R. Mondal ()
Additional contact information
Vasiliki G. Vrana: School of Economics and Administration, The Campus of Serres, International Hellenic University
Subhra R. Mondal: Duy Tan University

A chapter in Generative AI for a Net-Zero Economy, 2025, pp 1-18 from Springer

Abstract: Abstract The climate crisis calls for new, scalable solutions to address environmental threats and our way of life. In summary, this study explores how artificial intelligence can transform climate action, including decoding complex climate systems, optimizing decarbonization strategies, and democratizing data-driven decisions. Drawing upon an integrated theoretical approach, practical frameworks, and interdisciplinary research, we examine the applications of artificial intelligence across three key domains—namely, monitoring (through the use of machine learning algorithms and satellite data to track emissions in real time and preserve ecosystems), prediction (through the use of neural networks to predict extreme weather events and climate risks with previously unattainable accuracy), and optimization (using reinforcement learning to optimize the efficiency of renewables and carbon capture technologies). Though artificial intelligence enables revolutionary discoveries like hybrid physics-informed models that merge data science with climate theory, its practice faces significant challenges, including data inequity, algorithmic bias, and the carbon footprint of mega-scale computations. Here, the authors advocate for a novel framework that embraces participatory design, explainable artificial intelligence, and decentralized data governance to inform technological innovation that aligns with climate justice values. Federated learning and open-source platforms reduce Global South data gaps, and green artificial intelligence practices limit emissions of compute power. However, success will depend on the ability to work across disciplines, govern ethically, and integrate policies to avoid techno-solutionist traps. This chapter first introduces examples of how socially artificial intelligence must prioritize equity, with community co-design and work-centric just transitions. The study finds that artificial intelligence’s potential in climate mitigation is as upstream as it gets: it is not just technical, but socio-political, making holistic approaches that weigh the trade-offs of efficiency and equity, innovation and accountability, and planetary boundaries and human needs imperative. By managing these tensions, artificial intelligence can help facilitate a resilient, equitable pathway to a low-carbon future—but only if the technology is treated like a complement, not a substitute, for comprehensive socio-ecological change.

Keywords: Artificial intelligence; Climate mitigation; Machine learning; Ethical governance; Sustainability; Climate justice (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-8015-3_1

Ordering information: This item can be ordered from
http://www.springer.com/9789819680153

DOI: 10.1007/978-981-96-8015-3_1

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().