The use of artificial intelligence (AI) has risen dramatically over the past two years, with tools like ChatGPT becoming highly capable. This rapid growth has sparked concerns about the direct and indirect greenhouse gas emissions associated with AI, which requires large quantities of electricity to power the data centers on which it runs. While there is notable concern about AI’s climate impact, AI is also emerging as a real force for reducing greenhouse gas emissions across many sectors while driving innovative climate adaptation strategies.
What can AI do for climate mitigation?
AI helps mitigate climate change by leveraging data, predictive analytics, and optimization to accelerate emissions reductions, boost resource efficiency, and pave the way for breakthroughs. AI achieves this by advancing existing processes and developing innovative solutions to reduce greenhouse gas emissions across multiple sectors.
Key sectors for AI climate potential
Electric power
AI improves clean energy deployment by informing decisions about where to site new solar and wind power plants and maximizing clean electricity output for existing plants. AI also enhances grid operations through predictive vegetation management and dynamic line ratings, which help maintain grid reliability and increase transmission capacity. Low-cost batteries are important for expanding grid-scale energy storage for renewables, and AI is dramatically accelerating the search for novel battery materials. Additionally, AI optimizes distributed energy resources, helping to enable virtual power plants that allow households and businesses to contribute to grid stability.
Manufacturing
In the manufacturing sector, AI improves energy efficiency, reduces waste through optimized manufacturing schedules, and minimizes logistics overhead. While this is particularly important in heavy-emitting sectors like steel and cement, AI plays a vital role in advancing climate-relevant practices across various manufacturing areas, including integrating complex feedstocks like biomass and recycled plastics.
Aviation
In aviation, AI can help accelerate the development and testing of new types of sustainable aviation fuel (SAF), improve the design process for fuel-efficient aircraft engines and airframes, and optimize airport operations to reduce unnecessary fuel burn. AI can also help predict and avoid aviation-induced contrails and their considerable climate warming effect.
Carbon management
AI can improve the cost and performance of carbon capture technology. AI-enabled materials design and selection aids in identifying new, high-performance carbon dioxide separation materials while accounting for their life-cycle greenhouse gas emissions. AI can also help assess carbon dioxide geological storage site performance and identify optimal storage locations. Additionally, AI advances carbon dioxide utilization strategies by optimizing technologies and processes to convert captured carbon dioxide into valuable products.
Cross-cutting capabilities
Beyond sector-specific applications, AI provides essential cross-cutting capabilities in climate mitigation. For instance, it enables precise and accurate emissions monitoring of carbon dioxide and methane across the globe. This makes it possible to attribute emissions to individual facilities and locations, helping to assess the overall impact of climate policies and preventing fugitive emissions.
While many applications are incremental and technical, AI has the potential to deliver dramatic and far-reaching greenhouse gas reductions.
The ability of AI to simulate the performance of new materials can lead to transformational breakthroughs (e.g., replacements for structural steel).
AI could also greatly accelerate energy permitting by applying large language models to draft or review permit documents and by rapidly building power flow models for renewable power transmission interconnects.
Finally, AI can safeguard frontline communities and sensitive ecosystems by optimizing for multiple outcomes, including cost, equity, and environmental impact.
Leveraging AI for climate adaptation
As we suffer the consequences of climate change through extreme weather, droughts, and other damages, helping communities adapt is essential. A key barrier to effective climate adaptation is the lack of access to high-quality advanced weather forecasting in many parts of the world. This challenge has been recognized by the United Nations through its Early Warnings For All initiative.
Conventional weather forecasting requires costly supercomputers but AI-based weather models are revolutionizing the field. These models out-perform conventional methods while using a tiny fraction of the computing power and cost, enabling countries with limited resources to access high-quality weather forecasts and prepare communities for extreme weather events.
AI can also optimize fire management by predicting where wildfires will occur and providing rapid detection of new wildfire ignitions. Flooding predictions can also be improved and extended globally using AI, and AI-based models are enhancing the accuracy of predicting deadly heatwaves. Although these applications are making rapid progress and already provide many benefits for community protection, it will be important to carefully assess and address potential biases and inequalities that undermine the usefulness of these tools for disadvantaged communities.
Strategies for adopting AI for climate action
So what’s needed to improve and accelerate these uses of AI for climate mitigation and adaptation? Some of the main recommendations from the ICEF Artificial Intelligence for Climate Mitigation Roadmap spell out which stakeholders should take action and what activities are most urgently needed to realize AI’s potential for climate. Some of these include:
Every organization working on climate change mitigation should consider opportunities for AI to contribute to its work, including prioritizing AI skills-development and capacity-building.
Governments, businesses, and philanthropies should fund collaborative forums where AI experts and climate change experts can jointly explore climate change solutions.
Companies with climate-relevant datasets should consider sharing portions of these datasets publicly.
Governments, philanthropies and information technology companies should fund the development of large-scale open-source foundation models tailored to address climate challenges.
The opportunity for using AI to combat climate change is large and growing. Many different types of stakeholders will need to work together to make it a reality, and it will be particularly important to bring together expertise on modern AI tools and methods with expertise on climate change mitigation needs and opportunities.
Read the full 2024 report, ICEF Artificial Intelligence for Climate Change Mitigation Roadmap.
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Carbon Reduction
Carbon Removal
