SUSTAINABILITY

AI for sustainability

At Google, we're seeking to make AI helpful for everyone – including the planet – to improve the lives of as many people as possible. Some of the most exciting innovations are happening in places you might never notice – in the clouds formed behind airplanes, the timing of a traffic light at a busy city intersection, the route of your local commute, or realizing the solar potential of a building’s rooftop.

AI is already helping address emissions in key sectors like transportation and energy. In 2024 alone, just five of Google’s products – such as fuel-efficient routing in Google Maps and Green Light – enabled others to collectively reduce an estimated 26 million metric tons of greenhouse gas emissions (tCO2e).1 For context, Google’s total emissions in 2024 were 11.5 million tCO2e.2
SUSTAINABILITY

AI for sustainability

At Google, we're seeking to make AI helpful for everyone – including the planet – to improve the lives of as many people as possible. Some of the most exciting innovations are happening in places you might never notice – in the clouds formed behind airplanes, the timing of a traffic light at a busy city intersection, the route of your local commute, or realizing the solar potential of a building’s rooftop.

AI is already helping address emissions in key sectors like transportation and energy. In 2024 alone, just five of Google’s products – such as fuel-efficient routing in Google Maps and Green Light – enabled others to collectively reduce an estimated 26 million metric tons of greenhouse gas emissions (tCO2e).1 For context, Google’s total emissions in 2024 were 11.5 million tCO2e.2

Project green light

Reducing stop-and-go traffic in cities

Green Light uses AI to optimize traffic lights to reduce vehicle emissions in cities, mitigating climate change and improving urban mobility

Project Green Light
Road traffic is a major source of greenhouse gas emissions, especially at city intersections where pollution can be 29x higher than on open roads. Half of intersection emissions come from stop-and-go-traffic, which could be prevented with optimized traffic light timing. However, current optimization methods are costly and provide limited information.

Green Light uses AI and Google Maps driving trends to model traffic patterns and build intelligent recommendations for city engineers to improve traffic flow. Early numbers indicate the potential to reduce stops at intersections by up to 30% and reduce emissions at intersections by an average of over 10%.3 By optimizing each intersection, we can help reduce stop-and-go traffic in cities.
Project Green Light
Helping people save money and fuel

Fuel-Efficient routing

Helping people save money and fuel

Fuel-efficient routing leverages AI in Google Maps, so people can get to their destinations as quickly as possible while minimizing fuel or battery consumption. In 2024, we estimate that fuel-efficient routing enabled over 2.7 million metric tons of greenhouse gas emissions reductions4 – equivalent to taking approximately 630,000 gasoline-powered cars off the road for a year.5
Learn more

PROJECT contrails

How AI can help mitigate the warming effects of aviation

We’re using AI and satellite imagery to reduce climate-warming contrails

Project Contrails
Clouds created by contrails (short for condensation trails) account for roughly 35% of aviation’s global warming impact. Google Research teamed up with American Airlines and Breakthrough Energy to bring together huge amounts of data – like satellite imagery, weather, and flight path data – and used AI to develop contrail forecast maps to test if pilots can choose routes that avoid creating contrails. After these test flights, we analyzed satellite imagery and found that the pilots were able to reduce contrails by 54%.6
Project Contrails
Estimating potential solar savings for homeowners

solar savings estimator

Estimating potential solar savings for homeowners

The solar savings estimator in Google Search uses AI to generate a 3D rendering of any home’s roof, then assesses factors like roof space, weather, tree shade, estimated installation costs, local utility rates, and available incentives to help homeowners understand potential return on investment. If installing solar panels isn’t right for a home, users can also check the estimated savings from joining nearby community solar projects.

Solar Savings Estimator

flood forecasting

Making critical flood forecasting information universally accessible

Making critical flood forecasting information universally accessible
Since 2018 we’ve made progress applying AI to forecast riverine floods. By building a breakthrough global hydrological AI model and combining it with publicly available data sources, we are able to predict floods up to seven days in advance. We are providing forecasts on our Flood Hub platform in more than 100 countries on five continents covering sites where more than 700 million people live.7
Flood Forecasting
Making critical flood forecasting information universally accessible

firesat

Providing wildfire information to affected communities

We’re using AI to create breakthroughs in wildfire detection

FireSat

Google Research has been partnering with the US Forest Service to expand our existing fire simulation work. In partnership with Earth Fire Alliance and Muon Space, we're also developing FireSat, a constellation of satellites dedicated entirely to detecting and tracking wildfires. FireSat provides global high resolution imagery that is updated every 20 minutes, enabling the detection of wildfires that are roughly the size of a garage.

FireSat
Tackling extreme heat in cities using AI

heat resilience

Tackling extreme heat in cities using AI

To lower city temperatures and keep communities healthy, Google Research is continuing its efforts to use AI to build tools that help address extreme heat. Our new Heat Resilience tool applies AI to satellite and aerial imagery, helping cities to quantify how to reduce surface temperatures with cooling interventions, like planting trees and installing highly reflective surfaces like cool roofs.

Environmental Insights Explorer

Responsibly managing the environmental impact of AI

Model optimization
Efficient infrastructure
Emissions reductions

Optimizing models to be faster and more efficient

Learn more

We’ve long been at the forefront of AI and machine learning, evolving years of deep learning research to develop techniques like quantization, which has boosted LLM training efficiency by 39% on Cloud TPU v5e8 – enabling models that are higher quality, faster, and less compute-intensive to serve. We also help developers reduce their digital footprint with tools like the Go Green Software guide.

Building the world’s most energy-efficient computing infrastructure

Learn more

Our data centers are among the most efficient in the world, and we regularly work to optimize their use of electricity, water, and materials. In fact, our data centers now deliver six times more computing power per unit of electricity than they did just five years ago.9 Ironwood, our seventh-generation TPU, is nearly 30 times more power efficient than our first Cloud TPU from 2018.10

Our 24/7 carbon-free energy moonshot

Learn more

We’ve set a climate moonshot to run on carbon-free energy, 24 hours a day, 7 days a week, 365 days a year – by 2030. In 2024, we achieved 66% carbon-free energy on average across all of our data centers, and we signed contracts for over 8 GW of clean energy. We’ll continue to procure clean energy to address the overall impact and resource demands of AI.

Model optimization

Optimizing models to be faster and more efficient

We’ve long been at the forefront of AI and machine learning, evolving years of deep learning research to develop techniques like quantization, which has boosted LLM training efficiency by 39% on Cloud TPU v5e8 – enabling models that are higher quality, faster, and less compute-intensive to serve. We also help developers reduce their digital footprint with tools like the Go Green Software guide.

Learn more

Efficient infrastructure

Building the world’s most energy-efficient computing infrastructure

Our data centers are among the most efficient in the world, and we regularly work to optimize their use of electricity, water, and materials. In fact, our data centers now deliver six times more computing power per unit of electricity than they did just five years ago.9 Ironwood, our seventh-generation TPU, is nearly 30 times more power efficient than our first Cloud TPU from 2018.10

Learn more

Emissions reductions

Our 24/7 carbon-free energy moonshot

We’ve set a climate moonshot to run on carbon-free energy, 24 hours a day, 7 days a week, 365 days a year – by 2030. In 2024, we achieved 66% carbon-free energy on average across all of our data centers, and we signed contracts for over 8 GW of clean energy. We’ll continue to procure clean energy to address the overall impact and resource demands of AI.

Learn more

1 To estimate aggregate enabled emissions reductions, we first estimated annual reductions for five products individually (Google Earth Pro, Solar API, Nest thermostats, fuel-efficient routing, and Green Light) and then combined the totals. For details about the individual calculation methodologies, refer to endnotes 89, 16, 91, 17, and 86, respectively.

2 This figure reflects our "ambition-based" emissions boundary, which represents the subset of emissions from our total carbon footprint that are within the boundaries we’ve set for our climate ambitions.

3 Reductions in stops estimates are based on early data points from Google’s analysis of traffic patterns before and after recommended adjustments to traffic signals that were implemented during tests conducted in 2022 and 2023. Emissions reductions estimates are modeled using a Department of Energy emissions model. A single fuel-based vehicle type is used as an approximation for all traffic, and it is not yet adjusted for local fleet mix. These data points are averaged from coordinated intersections, and are subject to variation based on existing scenarios. We expect these estimates to evolve over time and look forward to sharing continued results as we perform additional analysis.

4 Google uses an AI prediction model to estimate the expected fuel or energy consumption for each route option when users request driving directions. We identify the route that we predict will consume the least amount of fuel or energy. If this route is not already the fastest one and it offers meaningful energy and fuel savings with only a small increase in driving time, we recommend it to the user. To calculate enabled emissions reductions, we tally the fuel usage from the chosen fuel-efficient routes and subtract it from the predicted fuel consumption that would have occurred on the fastest route without fuel-efficient routing and apply adjustments for factors such as: CO2e factors, fleet mix factors, well-to-wheels factors, and powertrain mismatch factors. This figure covers estimated enabled emissions reductions for the calendar year, from January through December. Enabled emissions reductions estimates include inherent uncertainty due to factors that include the lack of primary data and precise information about real-world actions and their effects. These factors contribute to a range of possible outcomes, within which we report a central value. The data and claims have not been verified by an independent third-party.

5 “Greenhouse Gas Equivalencies Calculator,” U.S. Environmental Protection Agency, November 2024, accessed June 2025.

6 Using satellite imagery, large-scale weather data, and flight data, we trained a contrails prediction model. For this trial, we partnered with American Airlines to integrate contrail likely zone predictions into the tablets that their pilots used in flight so they could make real time adjustments in altitude to avoid creating contrails. We evaluated the model’s performance using satellite imagery, comparing the number of contrails produced in flights where pilots used predictions to avoid contrails, to the number of contrails created in flights where pilots didn’t use contrail predictions.

7 The estimated population covered is based on the forecasted flood risk area, using the WorldPop Global Project Population dataset.

8 This estimate is based on our internal analysis comparing the BFLOAT16 / INT8 model step time ratio measured on the MLPerf 3.1 GPT-3 175B model. The results (11,798ms / 8,431ms = 139%) can be interpreted as a 39% speed improvement and, in turn, training efficiency.

9 According to Google’s platform-neutral measurement analyzed over a five-year period from 2019–2024.

10 These calculations are based on internal data, as of March 2025. Google’s TPU power efficiency relative to the earliest generation Cloud TPU v2 is measured by peak FP8 flops delivered per watt of thermal design power per chip package.