Considering the Environmental Impacts of Generative AI to Spark Responsible Development

Use of generative artificial intelligence (Gen-AI) is expanding rapidly, and as with many large-scale technology-induced shifts, its benefits and high demand are taking center stage. While the perceived advantages are vast, neglecting to consider the negative effects alongside these potential benefits can lead to uncontrolled growth with lasting consequences on the environment. A new open access paper by an interdisciplinary team of MIT researchers explores some of the key drivers of this growth and why sustainability considerations for Gen-AI are lacking – ultimately calling for more responsible development of Gen-AI using a comparative benefit-cost evaluation framework. Published as a preprint by MIT Press, “The Climate and Sustainability Implications of Generative AI” highlights how the excitement around Gen-AI is leading to an incomplete consideration of value that ignores the potential costs. The resulting social and environmental impacts require detailed analysis, coordination, innovation, and adoption across diverse stakeholders to steer the direction toward responsible growth.

Gen-AI’s perceived benefits are powerful. The reality of the environmental impacts, which include expanding demand for computing power, larger carbon footprints, shifts in patterns of electricity demand, and an accelerated depletion of natural resources, is largely overshadowed by the exciting potentials, such as enhanced productivity and boundless creativity, and a narrow focus on energy efficiency. While stakeholders across industries have invested in Gen-AI’s promising capabilities and transformative impact, potential does not always equate to practical outcomes.

And while there have been efficiency improvements in the AI space, these gains have not reduced Gen-AI’s overall energy consumption because of voracious demand and opportunity. Continuing to strive for efficiency gains is an important pre-requisite, but such gains will not solve the problem alone.

The paper’s authors – Noman Bashir, Priya Donti, James Cuff, Sydney Sroka, Marija Ilic, Vivienne Sze, Christina Delimitrou, and Elsa Olivetti – argue that responsible development of Gen-AI requires a focus on sustainability beyond only efficiency improvements, and that a narrow focus on energy efficiency can make Gen-AI’s challenges even more pronounced.

“The growth of Gen-AI is driving increased electricity demand, which runs counter to necessary efficiency gains to achieve net-zero greenhouse gas emissions,” said Noman Bashir, who leads the MCSC Data & Computing pathway. “Our current capacity to build sustainably also cannot keep pace with the datacenter construction necessary to support Gen-AI. Furthermore, this explosive growth exacerbates supply chain challenges, affecting essential goods and services that rely on the tech industry and potentially creating macroeconomic impacts.”

This is where the team’s proposal of a benefit–cost evaluation framework comes in.

Their framework has three elements:

1. defining the scope and boundaries by aligning with life cycle assessment (LCA) methodology, but also leveraging methods beyond LCA;
2. developing baseline and scenarios from which comparisons can be made to quantify the benefit as well as the burden; and
3. building data inventory for accounting for the cost of operating and manufacturing computing devices and infrastructure, the benefit and cost of the Gen-AI application itself (such as a search query), as well as the benefit and cost at a societal level.

Implementing a robust life cycle assessment framework will prove complex and fraught with uncertainty, argue the researchers, but it is possible. Even taking steps towards quantifying the benefits and costs of Gen-AI would make a positive difference by providing the transparency needed to begin instilling accountability for more sustainable practices.

Developing the specifics of this framework would engage a wide range of stakeholders (e.g. technical and sociotechnical experts, corporate entities, policymakers, and civil society) who would each provide necessary considerations for social and environmental impacts into technological advancement assessments. In the paper, the authors dive into a range of stakeholders and identify action items catered to their specific roles, to build their proposed benefit–cost evaluation capability.

“The capability we describe would promote Gen-AI to develop in ways that can support social and environmental sustainability goals alongside economic opportunity, which is the type of responsible growth we are calling for,” added Bashir.

Researchers say the framework would be responsive to legislation designed to investigate the impacts of AI, such as the new bill put forth by Senator Edward J. Markey, Senator Martin Heinrich, Representative Anna Eshoo, and Representative Don Beyer. This newly introduced Artificial Intelligence Environmental Impacts Act of 2024 would investigate and measure the environmental impacts of AI – and the framework put forth in the MIT team’s paper could accelerate implementation.

The challenges surrounding Gen-AI’s rapid growth and its vast implications on energy use and emissions are multifaceted and complex, and addressing them requires deep, technical collaborations across disciplines and areas of expertise. In writing “The Climate and Sustainability Implications of Generative AI,” the authors drew on their different backgrounds and experiences to explore these challenges as they pertain to the information and communications technology sector – from computing algorithms and hardware to data centers all the way to networks and power systems. They take an integrated systems perspective to make progress toward reduced computational requirements for existing machine learning approaches. This perspective helps account for how innovations in computing algorithms might impact chip fabrication or how data center design and architecture enhance or hinder network or electrical grid optimization.

The process of writing “The Climate and Sustainability Implications of Generative AI” included pulling from a wide range of sources and drawing links across them, including two of the other selected MIT papers on Gen-AI, underscoring the MCSC’s collaborative approach to its work. In exploring the framework’s initial applications, the authors drew from some of the key concepts outlined in “Closing the Execution Gap in Generative AI for Chemicals and Materials: Freeways or Safeguards” and “Learning from Nature to Achieve Material Sustainability: Generative AI for Rigorous Bio-Inspired Materials Design.” The first provided important background on incorporating the ways in which Gen-AI accelerates the determination of process pathways, reaction yields, synthesis byproducts, device performance, and manufacturing process into scenario and baseline development. The second was valuable in thinking about the applications of Gen-AI on discovery related to chemicals, such as innovations to advance chemicals for catalysis, more recyclable or biodegradable materials.

Within the MCSC, the theme of Data & Computing has been an ongoing emphasis through workshops and programming that engage member companies and the MIT community. In 2022, the MCSC hosted a two-day workshop on the Climate Implications of Computing & Communications, in collaboration with MIT-IBM Watson AI Lab and Schwarzman College of Computing, attracting hundreds of participants from academia and industry. The workshop presentations explored a host of energy-efficiency options, including specialized chip design, data center architecture, better algorithms, hardware modifications, and changes in consumer behavior. Later that year, the climate implications of computing was a central theme of the MCSC’s Annual Symposium.

The opportunity for furthering responsible use of Gen-AI is woven into all of the impact pathways, as increasingly, Gen-AI and foundation models are finding applications in such domains. The benefit-cost framework described in the paper can be leveraged to account for the implications of using machine learning and Gen-AI in sustainability applications such as monitoring soil carbon accrual, assessing the effectiveness of carbon offsets, tracking materials flows to drive beneficial circularity, or to build more resilient supply chains. As we apply Gen-AI methods in a broad set of sustainability cases, we must ensure the benefits outweigh the costs.

Last fall, MIT President Sally Kornbluth and Provost Cynthia Barnhart provided more than two dozen research teams from across MIT with seed funds to develop impact papers that would articulate effective roadmaps, policy recommendations, and calls for action across the broad domain of generative AI and its effects on society. “The Climate and Sustainability Implications of Generative AI” was one of 27 across the Institute selected in the first round of funding. A second round of seed grants were awarded to 16 additional finalists this spring. The open access papers have been published as preprints online under the auspices of the MIT Open Publishing Services program from the MIT Press.