AI’s growth is reshaping both the energy industry and environmental science. In the energy sector, machine learning is being used to optimize everything from renewable power forecasts to grid reliability.
At the same time, powering AI itself demands significant electricity. For example, data centres (which run AI services) already consumed about 415 TWh in 2024 – roughly 1.5 % of global electricity – and this is projected to more than double by 2030.
Meeting this demand will require diverse sources: the IEA finds about half of new data-centre electricity will come from renewables (with natural gas, nuclear and others making up the rest). This dual nature – AI needing energy even as it helps manage energy – means energy and tech are on a joint journey.
Applications of AI in the Energy Sector
AI is already transforming how we produce, distribute and consume power. Key applications include:
- Renewable Forecasting and Integration: Machine learning can dramatically improve short- and medium-term forecasts of wind and solar output. By analyzing vast meteorological and grid data, AI makes it easier to integrate variable renewables without wasting excess energy.
For instance, a 2019 IRENA report notes that AI-driven weather and generation forecasts could reduce curtailments of solar and wind. The IEA similarly emphasizes that AI-based forecasting helps balance grids with more distributed generation, “reducing curtailment and emissions” of renewables.
More accurate predictions let operators bid better in energy markets and dispatch generation more efficiently. - Grid Optimization and Resilience: Modern power grids are complex and often strained by peak demands. AI helps by automatically detecting faults and managing flow.
For example, AI-based systems can pinpoint equipment failures faster, shortening outages by 30–50 %. Smart sensors and control algorithms can also increase the effective capacity of transmission lines.
The IEA projects that AI tools could unlock up to 175 GW of extra transmission capacity without building new lines. In a digitalized “smart grid”, AI continually learns load patterns to shave peaks and balance supply. - Industrial and Building Efficiency: AI is widely used to streamline energy use in factories, refineries, offices and homes. In industry, AI accelerates design and optimizes processes.
The IEA reports that applying existing AI to industrial energy use could save more energy than the entire annual consumption of Mexico. In buildings, AI manages heating/cooling and lighting.
Existing AI-based HVAC control systems, if scaled globally, could cut electricity demand by roughly 300 TWh per year (comparable to the combined annual generation of Australia and New Zealand). In transport and mobility, AI optimizes traffic flow and logistics: one estimate suggests AI-driven route planning could save as much energy as 120 million cars use in a year, though rebound effects (like more driving) must be managed. - Energy Storage and Market Operations: AI is crucial for energy storage and electricity market design. In battery systems, AI learns price and demand patterns to buy/store power when cheap and sell when valuable.
For example, Tesla’s Hornsdale battery project in Australia uses an AI “autobidder” that quintuples revenue compared to human bidding. In real-time markets, AI algorithms can trade power in milliseconds to keep grids balanced.
IRENA notes that such “advanced AI” models are ideal for managing intraday markets and flexible demand. - Maintenance and Forecasting: Beyond energy flows, AI aids predictive maintenance. Sensors on turbines, transformers, and boilers feed AI models that predict failures before they happen.
This reduces downtime and extends equipment life. In oil and gas, AI is already spotting leaks and predicting pipeline health. In renewables, AI can estimate when a wind turbine needs service, ensuring higher uptime with less energy waste.
Together, these applications help reduce costs, raise reliability and cut emissions. The IEA notes that using AI across the power system can directly shrink operational emissions – for example by improving plant efficiency or optimizing fuel mix – even as AI-driven energy demand grows.
Applications of AI in Environmental Conservation
Outside energy, AI is a powerful tool for the environment and climate science. It excels at finding patterns and anomalies in large datasets, making it useful for monitoring, modeling and management:
- Climate and Weather Modeling: Major science agencies are now using AI to make weather and climate models more accurate. For instance, NASA and IBM released the open-source Prithvi weather-climate AI model, trained on decades of historical data.
This model can enhance the spatial resolution of climate simulations (down to regional scales) and improve short-term forecasts. Such AI models enable better predictions of extreme weather and climate trends, directly informing adaptation planning. - Deforestation and Land Monitoring: Satellites generate petabytes of Earth imagery. AI analyzes these images to monitor forests and land use.
For example, AI-driven platforms have been used in 30+ countries to map millions of hectares of deforestation and estimate carbon stored in forests. By automating image analysis, AI gives conservationists near real-time maps of habitat loss and helps target reforestation.
Similar techniques track urban expansion, glacier melt, and other land-cover changes that affect carbon and biodiversity. - Ocean and Pollution Cleanup: AI also helps map pollution and guide cleanup. Organizations like The Ocean Cleanup use machine vision to detect and map floating plastics in remote ocean regions.
By training AI on satellite and drone images, they create detailed pollution maps so clean-up vessels can target high-density areas efficiently. AI is likewise used on landfills and recycling plants: one startup’s AI system scanned billions of waste items and identified tens of thousands of tons of recyclable material that was being thrown away.
In both cases, AI dramatically speeds up processes that were once done manually or not at all. - Water and Agriculture: In water management, AI models forecasts of drought and floods by integrating weather, soil and usage data. Farmers use “precision agriculture” tools (often powered by AI) to optimize irrigation and fertilizer, boosting yields while cutting runoff.
Global experts note that AI can accelerate adoption of sustainable farming, reducing waste and conserving resources. (For example, AI-driven irrigation systems have demonstrated savings of up to 40 % in water and energy use.) - Disaster Response and Biodiversity: Emergency services use AI to predict wildfire spread, optimize evacuation routes, and even coordinate relief logistics.
AI models are being trained to read satellite imagery for signs of drought or pest outbreaks (early warning for farmers). Wildlife conservation uses AI to identify animals in motion-camera footage or audio recordings, helping protect endangered species.
An AI system in Africa, for instance, learned to predict regional weather patterns to warn villages in Burundi, Chad and Sudan about upcoming floods or droughts.
These applications show AI’s broad value: processing complex environmental data in real time, providing insights (e.g. on emissions, resource use, or ecosystem changes) that humans alone cannot handle.
As UNESCO’s AI for the Planet initiative stresses, combining AI with global data can empower better decisions – for example creating early-warning systems for severe weather and sea-level rise to protect over three billion vulnerable people.
Challenges and Ethical Considerations
Despite its promise, AI also raises important challenges for energy use and the environment:
- Energy and Carbon Footprint: Training and running AI models – especially large language models (LLMs) – consumes a lot of electricity. The IEA warns that data centres are among the fastest-growing electricity consumers.
Generative AI already draws a power load comparable to that of a small country. According to UNESCO, serving one AI prompt can use ~0.34 Wh (meaning over 300 GWh per year globally, as many as 3 million people’s annual consumption).
If unchecked, AI’s share of global emissions could rise from ~0.5 % today toward 1–1.5 % by 2035. (By comparison, end-use AI applications could cut energy-sector CO₂ by up to 5 % by 2035 – a benefit far larger than the AI footprint – but unlocking that requires overcoming many barriers.) - Resource Consumption: Building and cooling data centres requires raw materials and water. Producing a single computer for AI can demand hundreds of kilograms of minerals and metals, and the specialized chips use rare elements like gallium (over 99 % of gallium refining is in China).
These add to electronic waste and mining impacts. Data centres also consume enormous water volumes for cooling – one estimate suggests AI-related cooling could use over six times Denmark’s national water use.
Such impacts mean we must carefully manage AI’s growth. - Rebound and Equity Effects: Efficiency gains from AI can be offset if users increase consumption (e.g. cheaper travel or energy use). The IEA cautions that without careful policy, AI’s net climate benefit may be undermined by rebound effects.
Furthermore, AI adoption is uneven: only a few countries and companies currently have the infrastructure and data to leverage AI fully. The IEA notes the energy sector lacks AI expertise relative to tech industries, and many regions (especially in the Global South) have limited data centres.
This could worsen digital divides unless addressed. - Ethical and Governance Issues: Beyond carbon, AI carries social risks. Automated decision-making in energy and environment must be fair and transparent.
Privacy (e.g. in smart meters), bias in algorithms, and cybersecurity in critical infrastructure are serious concerns. Experts emphasize the need for standards and policies: UNESCO and UN initiatives encourage countries to adopt AI ethics and sustainability guidelines.
For example, UNESCO’s AI ethics recommendation (2021) includes a chapter on environmental impact. Collaborative frameworks and regulations will be essential to ensure AI tools truly serve sustainability goals without unintended harm.
Global Initiatives and Future Outlook
Governments and international bodies are recognizing AI’s role. The U.S. Department of Energy, for instance, has launched programs to modernize the grid with AI.
A DOE report (2024) highlights AI in grid planning, permitting and resilience, and even envisions LLMs helping federal reviews. Similarly, the IEA has published its own global analysis (“Energy and AI”, 2025) to guide policymakers.
On the UN side, UNESCO’s AI for the Planet Alliance (with UNDP, tech partners and NGOs) seeks to prioritize and scale AI solutions for climate change. Its goals include identifying top AI use-cases (e.g. tracking emissions) and connecting innovations with funding and stakeholders.
Looking ahead, AI’s influence will only grow. Advances like smaller, more efficient models can cut AI’s footprint dramatically.
At the same time, AI-driven energy solutions (like smart renewables grids and adaptive climate forecasting) offer tools to tackle the climate crisis. Realizing the benefits will require continued R&D, open data sharing, and responsible policies.
As the World Economic Forum notes, AI is no magic bullet – but with collaborative effort, it can be a powerful accelerator for sustainable energy and environmental stewardship.
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AI is revolutionizing energy systems and environmental science, offering improved efficiency and new insights iea.org science.nasa.gov. However, its rapid growth also consumes energy and resources, raising sustainability concerns unesco.org unep.org.
The net impact will depend on managing both AI’s demands and its potential: deploying AI to cut emissions and protect ecosystems, while minimizing AI’s own environmental footprint.
International initiatives (IEA, UNESCO, DOE, etc.) underscore that policy, innovation and global cooperation are essential to ensure AI becomes an ally – not an adversary – in the fight against climate change and in the transition to clean energy iea.org unesco.org.
