When most people think of AI, they think of the massive data centres consuming energy and exacerbating climate change. But, what they might not know is that machine learning accounts for only a fraction of 1% of global emissions and that insights from AI could help reduce global emissions by 5-10% by 2030.
AI insights can both help mitigate emissions and help alleviate impacts through adaptation:
Extreme weather forecasting:
Machine learning models can analyze satellite, radar, and sensor data to provide hyper-local predictions of floods, hurricanes, and wildfires, giving communities and businesses more time to prepare.
Agricultural resilience:
Growing Degree Days (GDD) trackers, yield forecasts, and pest/disease early-warning systems help farmers adapt planting and harvesting to changing conditions.
Water management:
AI that predicts droughts and optimizes irrigation scheduling to conserve increasingly scarce water resources.
Supply chain adaptation:
Tools that combine climate projections with trade and logistics data to help companies reroute goods, change suppliers, or adapt the timing of shipments to reduce disruption from climate-related events and trends.
Mitigation
AI is also being deployed to directly reduce greenhouse gas emissions and accelerate the transition to a low-carbon economy. Examples include:
Energy efficiency:
Smart building and grid systems that use AI to cut energy waste, balance renewable inputs, and lower demand.
Industrial decarbonization:
AI-driven optimization of cement, steel, and chemical production processes to reduce fuel use and emissions intensity.
Transportation optimization:
Route planning and fleet management tools that minimize fuel consumption and increase EV charging efficiency.
Carbon accounting & finance:
AI platforms that automate measurements of Scope 1, 2, and 3 emissions across value chains and help companies or investors target high-impact decarbonization efforts.