Keep AI From Doing More Climate Harm Than Good

By Kasey Panetta | 5-minute read | August 28, 2023

Artificial intelligence (AI) holds the key to all kinds of economic, social and environmental benefits, but it also poses a threat to natural resources. AI models are trained using power-hungry servers in data centers already notorious for their hefty carbon footprint and are now under attack for guzzling water. The trade-offs lie in combining “AI for sustainability” with the “sustainability of AI.”

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Big Picture

How to leverage AI while controlling its appetite and footprint

AI models and techniques can help drive a range of environmental goals. They can:

  • Monitor and predict climate and weather-change trends such as global warming

  • Manage waste and optimize recycling processes and operations

  • Make transportation, mobility and routes more efficient to enhance fuel efficiencies and reduce carbon footprints

But data centers where AI is trained already account for about 2% of all U.S. electricity use, consuming 10 to 50 times more energy per floor space of a typical commercial office building. One recent study argues that ChatGPT needs to “drink” a 500ml bottle of water for every simple 20-50 questions and answers (and GPT-4 is even thirstier). 

Making AI itself more environmentally friendly is a key component of any sustainable technology program. Here are five ways to develop more sustainable AI.

No. 1: Make AI as efficient as the human brain

  • Consider adopting so-called composite AI, which uses network structures to organize and learn similarly to the efficient human brain.

  • Composite AI uses knowledge graphs, causal networks and other “symbolic” representations to solve a wider range of business problems in a more effective manner.

No. 2: Put your AI on a health regimen

  • Monitor energy consumption during machine learning, and stop training AI as soon as improvements flatten out and no longer justify the costs of continuing. 

  • Keep data for model training local, but share improvements at a central level. This type of “federated machine learning” reduces electricity consumption and bolsters data privacy. 

  • Reuse models that have already been trained, and contextualize them, if necessary. 

  • Use more energy-efficient hardware and networking equipment. 

“The trade-offs lie in combining ‘AI for sustainability’ with the ‘sustainability of AI.”

No. 3: Run AI in the right place and at the right time

  • Manage when and where the AI workload happens. The carbon intensity of local energy supplies varies by country, generating authority, time of day, weather conditions, transfer agreements, fuel supply and other factors. 

  • Balance follow-the-sun data center workloads, which are better for clean energy production, with unfollow-the-sun measures, which are better for water efficiency.

  • Use energy-aware job scheduling, along with carbon tracking and forecasting services to reduce related emissions.

No. 4: Buy new clean power where you plan to consume it

  • Procure power purchase agreements (PPAs) when possible, or source renewable energy certificates (RECs) that reduce or offset greenhouse gas emissions and add new renewable energy to the grid where your organization will consume electricity. 

  • Prepare for future protocols. PPAs and RECs aren’t perfect or always available, so start building a detailed plan of clean power by location, time of day or both. This type of analysis can help you build a clean-power strategy, which regulators may require going forward.

No. 5: Make environmental impact a key factor in considering AI use cases

  • Model environmental impacts, as well as business benefits, as you build AI strategy, and move forward with use cases that create more value than they destroy.

  • Reduce the risk and energy of existing AI initiatives before proceeding. Improve their energy efficiency and lessen intellectual property and proprietary data risk.

  • Do not invest in AI use cases that could damage business value or the environment.

The story behind the research

From the desk of Kristin Moyer, Distinguished VP Analyst, Gartner

“Understanding the impact AI technologies have on human life and our planet is becoming increasingly critical. Taking a sustainability-aware approach to AI adoption is key to ensure AI technology does no significant harm and can contribute to the achievement of sustainable goals in terms of environmental, social and human-centric impacts.”

3 Things to Tell Your Peers


Trading off the use of AI to drive sustainability and the sustainability of AI itself will challenge business leaders as demand rises for AI pilots, including those that incorporate generative AI.


Curbing AI’s use of natural resources and its climate impact requires deliberate action to develop and use it as efficiently as possible.


Consider environmental impacts from the start in any AI strategy.

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Kristin Moyer is a Distinguished VP, Analyst in Gartner's CEO and Digital Business Leader practice.

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