Can AI Accelerate Net Zero Transition and a Climate Resilient Future?

Initial concerns about AI’s impact on climate change center on its vast energy requirements, which dwarf those of traditional data centers [1]. It is well-documented [2] that AI leaders such as OpenAI, Meta, and xAI are racing to build gigantic, energy-intensive supercomputers to train their AI models, and these will be primarily powered by natural gas due to factors like speed of deployment, cost, and grid reliability. Recent reports highlight that unchecked AI growth could increase tech industry energy use by up to 25 times by 2040, potentially derailing net zero efforts if not managed properly.

However, despite AI’s large and expanding energy footprint [4], it has the potential to play a significant role in accelerating the energy transition across several key areas, particularly if milestones in quantum computing are achieved and the two technologies can be deployed synergistically [5]. New studies from 2025 estimate AI could reduce global emissions by 3.2–5.4 billion tonnes of CO₂ equivalent annually by 2035—more than the EU's entire yearly output—primarily in energy, transport, and food sectors.

Here are some areas where AI could have a postive impact on Net Zero transition.

Energy Optimization and Decarbonization

• Renewable Energy Integration: AI can enhance the efficiency of renewable energy sources like solar and wind by forecasting energy production and demand, optimizing grid operations, and reducing reliance on fossil fuels [6]. For example, AI-driven predictive maintenance minimizes downtime for wind turbines [7].

• Energy Efficiency: AI algorithms optimize energy use in buildings, transportation, and industries, reducing waste. Smart grids leverage AI to balance supply and demand dynamically, cutting emissions [8].

• Carbon Capture and Storage: Though controversial due to its mixed climate impact [9], AI could improve the design and monitoring of carbon capture technologies, potentially making them more cost-effective and scalable [10].

  
  

Chemical and Materials Design

• Chemical Reactions: AI-aided design is transforming energy-intensive chemical processes and predicting new chemical reactions, reducing energy consumption [11].

• Metallurgy and Solid-State Materials: Optimizing the production of key raw materials like steel or designing new alloys, glasses, and nanomaterials will profoundly impact technology and materials innovation. These advancements can reduce energy consumption and create more efficient, longer-lasting products and infrastructure [12].

• Battery and Electrolyte Design: AI-driven design of electrodes [13] and electrolytes [14] could improve energy density and reduce production costs, enhancing battery performance for energy storage and electric vehicles.

  
  

Climate Modeling and Prediction

• Improved Forecasting: AI refines climate models by processing vast datasets, enabling more accurate predictions of climate impacts such as extreme weather events, sea-level rise, and ecosystem shifts [15].

• Disaster Preparedness: AI-powered early warning systems for floods, hurricanes, or wildfires improve response times and support resilience [16].

  
  

Sustainable Agriculture and Land Use

• Precision Agriculture: AI optimizes farming practices, reducing emissions from fertilizers and improving crop yields to ensure food security in a changing climate [17].

• Reforestation and Conservation: AI monitors deforestation, identifies optimal reforestation sites, and tracks biodiversity, aiding ecosystem restoration [18].

  
  

Transportation and Supply Chains

• Electrification and Logistics: AI optimizes electric vehicle charging networks and logistics routes, lowering transportation emissions [19].

• Circular Economy: AI supports recycling and waste management, reducing methane emissions from landfills [20].

  
  

Adaptation and Resilience

• Infrastructure Resilience: AI models help design climate-resilient infrastructure, such as flood-resistant urban planning or heat-tolerant materials [21].

• Water Management: AI predicts water scarcity and optimizes distribution, critical for drought-prone regions [22].

  
  

Challenges and Considerations

• Energy Footprint: AI systems, especially large-scale models, consume significant energy. Transitioning AI infrastructure to renewable energy is crucial to avoid offsetting climate benefits [23]. Recent examples include Google's emissions rising 51% due to AI demands, underscoring the risk of "magical thinking" in relying on AI for net zero without addressing its own footprint.theguardian.comscientificamerican.com

• Equity: AI solutions must prioritize vulnerable communities to ensure a just transition.

• Governance: Ethical AI deployment and international collaboration are needed to maximize impact and avoid unintended consequences [24].

  
  

To illustrate AI's dual-edged impact, here's a comparison table:

AI's Role in Net Zero	Potential Benefits	Key Challenges	Mitigation Strategies
Energy Optimization	5–10% global emissions cut by 2030 via smart grids and renewables [8]	High energy use (e.g., AI training equals transatlantic flight CO₂) [23]	Shift to renewable-powered data centers; initiatives like Microsoft's AI sustainability roadmapnationalcentreforai.jiscinvolve.org
Materials Design	Faster battery/chemical innovations reducing industrial emissions [11–14]	Resource-intensive computing	Synergize with quantum computing for efficiency [5]
Climate Adaptation	Accurate forecasting saving lives in disasters [15–16]	Equity gaps in access for vulnerable regions	Prioritize global south deployment; UN initiatives [16]
Overall Impact	Up to 5.4B tons CO₂ reduction by 2035nam.orgtrellis.net	Risk of derailing net zero if energy use unchecked	Ethical governance and clean energy mandates [24]

Conclusion

As a recent Financial Times article highlights, “the transport, power, and food sectors account for roughly half of global emissions, so anything that shrinks their emissions matters” [25].

AI could be a powerful tool for achieving Net Zero and building climate resilience, particularly if utilized alongside likely advances in quantum computing. However, its deployment must be strategic, equitable, and ultimately powered by clean energy to maximize climate impact. Policymakers must align AI with clean energy to realize its full potential, avoiding greenwashing and ensuring real-world deployment.

References

1. International Energy Agency (2024). Energy Demand from AI. https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai

2. McKinsey & Company (2024). The Cost of Compute: A 7-Trillion-Dollar Race to Scale Data Centers. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-cost-of-compute-a-7-trillion-dollar-race-to-scale-data-centers

3. Interesting Engineering (2024). xAI to Use Tesla Megapack Batteries to Power World’s Largest AI Supercomputer. https://interestingengineering.com/energy/xai-to-use-tesla-megapack-batteries-to-power-worlds-largest-ai-supercomputer

4. Nature (2022). The Growing Footprint of Digital Technology. https://www.nature.com/articles/d41586-022-01983-7

5. McKinsey & Company (2024). Enabling the Next Frontier of Quantum Computing. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-forward/enabling-the-next-frontier-of-quantum-computing

6. World Economic Forum (2024). Harness Power: Generative AI in the Energy Transition. https://www.weforum.org/stories/2024/06/harness-power-generative-ai-energy-transition/

7. Forbes (2024). Practical Applications of AI-Powered Predictive Maintenance for Renewable Energy Infrastructure. https://www.forbes.com/councils/forbestechcouncil/2024/06/13/practical-applications-of-ai-powered-predictive-maintenance-for-renewable-energy-infrastructure/

8. BCG (2024). CEOs Achieving AI and Climate Goals. https://www.bcg.com/publications/2024/ceos-achieving-ai-and-climate-goals

9. IPCC (2022). Sixth Assessment Report: Mitigation of Climate Change. https://www.ipcc.ch/report/ar6/wg3/

10. Climate Adaptation Platform (2023). AI Accelerates Discovery of Next-Gen Carbon Capture Materials. https://climateadaptationplatform.com/ai-accelerates-discovery-of-next-gen-carbon-capture-materials/

11. ChemCoPilot (2024). How AI Optimizes Formulations in the Chemical Industry. https://www.chemcopilot.com/blog/how-ai-optimizes-formulations-in-the-chemical-industry

12. Composite Structures (2025). Artificial Intelligence in Materials Science and Engineering. https://www.sciencedirect.com/science/article/abs/pii/S0263822325005847

13. Journal of Polymer Science (2025). AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation Batteries. https://onlinelibrary.wiley.com/doi/10.1002/pol.20250198

14. EV Design & Manufacturing (2024). Artificial Intelligence to Accelerate New Battery Material Development. https://www.evdesignandmanufacturing.com/news/artificial-intelligence-accelerate-new-battery-material-development/

15. Nature Communications (2025). An Artificial Intelligence (AI)-Based Approach to Climate Action and the Global Stocktake. https://www.nature.com/articles/s41467-024-53956-1

16. International Telecommunication Union (2024). New UN Initiative to Reduce Disaster Risk with AI. https://www.itu.int/hub/2024/08/new-un-initiative-to-reduce-disaster-risk-with-ai/

17. Food and Agriculture Organization (2024). AI Can Be a Game-Changing Solution for Farmers, FAO Innovation Chief. https://www.fao.org/newsroom/detail/ai-can-be-a-game-changing-solution-for-farmers--fao-innovation-chief/en

18. Meta Sustainability (2024). Using Artificial Intelligence to Map the Earth’s Forests. https://sustainability.atmeta.com/blog/2024/04/22/using-artificial-intelligence-to-map-the-earths-forests/

19. Transportation Research Interdisciplinary Perspectives (2024). Artificial Intelligence Applications for Sustainable Transportation Systems. https://www.sciencedirect.com/science/article/pii/S2667010024000209

20. Ellen MacArthur Foundation (2023). Life-Friendly Chemistry, Biomaterials, and AI: The Future of the Circular Economy. https://www.ellenmacarthurfoundation.org/podcasts/life-friendly-chemistry-biomaterials-and-ai-the-future-of-the-circular

21. Reuters (2024). How AI Is Arming Cities’ Battle for Climate Resilience. https://www.reuters.com/sustainability/climate-energy/how-ai-is-arming-cities-battle-climate-resilience-2024-05-23/

22. UNESCO (2023). Applications of Artificial Intelligence in Water Management. https://www.unesco.org/en/articles/applications-artificial-intelligence-water-management

23. MIT Technology Review (2025). AI’s Energy Usage and Climate Footprint. https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/

24. OECD (2024). OECD Principles on Artificial Intelligence. https://www.oecd.org/en/topics/sub-issues/ai-principles.html

25. Financial Times (2025). How Hopeful Can We Be About AI Climate Tech?. https://www.ft.com/content/bd835b8f-e39a-4e5f-84d0-2fb019b47b80