Google reveals how AI and machine learning are shaping its sustainability strategy

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Google’s latest annual environmental report reveals details about how the technology giant’s ‘artificial intelligence-first’ policy is shaping its ongoing efforts to minimise the environmental footprint of its customers’ operations

Caroline Donnelly,
Senior Editor, UK

Published: 25 Jul 2023 13:42

Google has lifted the lid on how artificial intelligence (AI) and machine learning (ML) are assisting it with helping consumers and businesses shrink the environmental footprint of their activities by allowing them to make real-time adjustments that can curb their greenhouse gas (GHG) emissions.

Details of its work in this area can be found in the tech giant’s most recent annual Environmental report. Covering the 12 months to 31 December 2022, the document provides updates on how the tech giant’s efforts to run its datacentres and offices on carbon-free energy (CFE) round-the-clock are progressing and how its bid to reduce the water consumed by its operations is going.

“We achieved approximately 64% round-the-clock CFE across all of our datacentres and offices, [and] this year, we expanded our CFE reporting to include offices and third-party datacentres, in addition to Google-owned and operated datacentres,” said the company.

“At the end of 2022, our contracted watershed projects have replenished 271 million gallons of water – equivalent to more than 400 Olympic-sized swimming pools – to support our target to replenish 120% of the freshwater we used.”

The report also documents how, seven years after declaring itself as being an “AI-first company”, this technology is underpinning the company’s own climate change mitigation efforts.

To this point, the company said it was using AI to accelerate the development of climate change-fighting tools that can provide “better information to individuals, operational optimisation for organisations, and improved predicting and forecasting”.

As an example, the company pointed to the way Google Maps uses AI to help users plan journeys in a more eco-friendly way by minimising the amount of fuel and battery power they use to get from A to B.

“Eco-friendly routing has helped prevent 1.2 metric tonnes of estimated carbon emissions since launch – equivalent to taking approximately 250,000 fuel-based cars off the road for a year,” it reported.

The technology is also proving useful in the company’s work to reduce the environmental footprint of its AI models by helping the datacentres in which they are hosted run in a more energy-efficient way.

“We’ve made significant investments in cleaner cloud computing by making our datacentres some of the most efficient in the world and sourcing more carbon-free energy,” it said in the report. “We’re helping our customers make real-time decisions to reduce emissions and mitigate climate risks with data and AI.”

To reinforce this point, the company cited the roll-out of its Active Assist feature to Google Cloud customers, which uses machine learning to identify unused and potentially wasteful workloads so they can be stopped to save money and cut the organisation’s carbon emissions at the same time.

On the flipside, though, the report went on to acknowledge that ramping up the use of AI in this way also increases the amount of work its datacentres are doing, which is giving rise to concerns about the environmental impact and energy consumption habits of its AI workloads.

“With AI at an inflection point, predicting the future growth of energy use and emissions from AI compute in our datacentres is challenging,” the report continued.

“Historically, research has shown that as AI/ML compute demand has gone up, the energy needed to power this technology has increased at a much slower rate than many forecasts predicted. We have used tested practices to reduce the carbon footprint of workloads by large margins; together, these principles have reduced the energy of training a model by up to 100x and emissions by up to 1,000x.”

The report added: “We plan to continue applying these tested practices and to keep developing new ways to make AI computing more efficient.”

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