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If you’re working to address your company’s carbon emissions — as a CFO, chief sustainability officer or other senior leader — the impact of generative AI (GenAI) should be on your radar. In PwC’s 2023 Emerging Tech Survey, just 22% of business leaders cited sustainability impact as a top issue in GenAI deployment.
GenAI’s corporate use is rapidly accelerating and understanding how to balance its positive and negative impacts is crucial and should be part of your GenAI strategy and business case discussions.
At PwC, we’re studying this sustainability issue closely as we undertake our own rapid rollout and scaling of GenAI and help clients do the same. We’ve considered both ongoing use in enterprise applications as well as the impact of designing, building and training the foundation models that we and many businesses license. Our analysis also builds upon PwC’s prior work on blockchain's sustainability impact and its potential to accelerate decarbonization.
Here are some of our initial findings.
To meet existing and proposed regulations, make progress on net-zero goals and provide greater stakeholder transparency, your company may be working to disclose direct (Scope 1), indirect (Scope 2) and value chain (Scope 3) emissions. Here are some GenAI dimensions to consider.
In conducting our analysis on PwC’s initial use of GenAI, we estimated labor hours, number of processors used, power per processor and an emissions factor for metric tons of carbon dioxide equivalent.
We found that the greatest impact on emissions came from model inferencing, not our share of model training, or customization. We also found that even for heavy corporate GenAI users (such as ourselves), this impact may be limited.
Our analysis was a first step. Now we’re studying how GenAI is likely to further impact emissions going forward. As GenAI makes workflows and business processes more efficient — reducing both manual activities and non-GenAI compute workloads — the drop in emissions could be significant. This is true across enterprise operations, including those activities directly related to carbon emissions. For example, we can use GenAI and analytics to help clients in prioritizing decarbonization efforts. The solution can run through millions of permutations of potential decarbonization levers, helping business stakeholders better understand those options to drive decision-making.
GenAI and related technologies are also advancing in other ways that could affect emissions in a wide range of ways.
GenAI’s relationship to emissions is complex — with both positive and negative direct and indirect impacts — and it’s evolving quickly. To better manage GenAI’s impact, we recommend these important actions.
Measure it: Based on our study, we believe that it’s possible — with the help of a life-cycle assessment process — to estimate the emissions impact of potential GenAI solutions and then categorize these across your emissions budget.
Be thorough: A rigorous analysis should consider the emissions impact of the pre-trained models, of your customization of these models and of everyday, application-level use.
Start early: If you conduct this assessment early — when evaluating potential GenAI models and their business case and use cases — you can make choices that align with your sustainability goals. This includes considering whether GenAI is the right solution for your business application.
Consider convergence: Look at how GenAI is converging with other technologies driving transformation and decarbonization to better understand its potential and collective impact.
Generative AI is already transforming business. Contact us to learn more about this rapidly evolving technology – and how you can begin putting it to work in a responsible and sustainable way.