The Generative AI Revolution: Key Impacts to the Environmental & Energy Sectors

Generative artificial intelligence (AI) is transforming the way we live and work. At its core, AI is the ability of machines to think and learn without encoded commands, mimicking our own cognition. Within two months of its initial release to the public, ChatGPT reached 100 million monthly active users, making it the fastest-growing consumer application in history. Since then, other popular generative AI tools have proliferated with limited human involvement.

AI also has significantly impacted the environmental and energy sectors. US Environmental Protection Agency (EPA) Administrator Michael Regan recently stated that the Biden Administration will use advanced technologies like AI to decarbonize the economy in an effort to reach its target reduction in greenhouse gas emissions of net zero by 2050.  This alert identifies several ways in which AI has already influenced the environment and the energy industry. While AI-powered tools are energy-intensive, they can help businesses improve their environmental compliance, optimize energy consumption, reduce waste, develop and implement sustainable practices, enhance the use of renewable energy, and modernize the electricity grid. AI also can be used by litigants to identify potential greenwashing and other claims, transforming how we approach environmental litigation.

1. Energy Usage

We have previously written about the potentially energy-intensive nature of the Metaverse and cryptocurrency mining. The same can be true for AI tools. Training and operating an AI model requires substantial computational resources which demand considerable amounts of energy. As AI becomes more complex, the models will use even more data and require even more energy. In turn, the focus has increasingly shifted to how all that energy is generated. One study from the University of Massachusetts Amherst estimated that the carbon footprint of training a single AI natural language processing model is equal to about 300,000 kg of carbon dioxide emissions, equivalent to 125 round-trip flights between New York and Beijing.  

Strategies to mitigate the carbon impact of AI include using renewable energy sources to power AI neural networks, producing more efficient graphics processing units, or buying renewable energy credits to offset the carbon produced by AI training and operation.

2. ESG & Greenwashing

We have also blogged extensively about the rise in environmental, social, and governance (ESG) considerations, including the SEC’s proposal to require US-registered companies to disclose certain climate-related information, such as energy and water usage and waste production, in their registration statements and annual reports. Concurrently with this focus on ESG has been the rise in greenwashing cases, or litigation targeting corporate statements on environmental impacts or sustainability. 

AI is playing a dual role in this space. On the one hand, AI tools are empowering prospective plaintiffs to develop new greenwashing claims. For example, ClimateBert, an AI neural language model, is being used to analyze and fact check corporate reporting and environmental disclosures. According to ClimateBert, it has been pretrained on more than 2 million paragraphs of climate-related texts from various sources, including climate reporting of companies, research articles, and newspapers. Litigants will likely seek to use this and other AI tools to double down on challenging a company’s product claims or environmental disclosures.

On the other hand, AI can be used by businesses to optimize energy consumption, reduce waste, and improve sustainability. Based on a survey of 800 industry executives and 300 AI and climate experts, the Copenhagen Centre on Energy Efficiency published a report in 2020 stating that AI has enabled businesses to decrease their greenhouse gas emissions by 13% and improve power efficiency by 11%. For example, Maximpact is an AI tool that can help monitor, control, evaluate, and manage energy consumption in buildings and factories. It can automate energy usage, identify any problems, and detect equipment failures before they occur. This and other AI tools can analyze large sets of data to monitor and interpret information to optimize energy consumption in real time. AI can also be used to optimize production processes, identify areas of waste, identify potential ways to decrease emissions, and integrate sustainable practices into various industries. For another example, AI-powered farming strategies, like precision agriculture, can automate operations, thereby improving agricultural production sustainability and decreasing the reliance on pesticides.

3.  Renewable Energy & Energy Forecasting

Utility companies and the energy sector are likewise turning to AI. Among other things, utilities are leveraging AI to optimize the use of renewable energy sources within their portfolios because AI can improve the reliability of solar and wind power by analyzing massive amounts of meteorological data to help predict when to gather, store, and distribute energy from those sources. Companies like Nvidia also offer AI-powered solutions to help utilities forecast energy demand, identify real-time outage risks, predict maintenance of system infrastructure, and manage energy supply. Other energy companies are using AI-powered technologies to provide day-ahead and real-time energy price forecasting for power markets, facilitating strategic decisions related to their power generation assets and to help further modernize the grid and improve reliability of the system.

If you have questions about any of these issues or would like to explore how you may be able to better leverage AI, contact one of the authors or a member of our Environmental, Energy & CleanTech, and AI, Metaverse & Blockchain teams. And click here to watch our latest Fox Forum as we talk with Mike Pell, the visionary innovation leader at Microsoft, which is a principal investor in OpenAI and the trailblazing company behind the creation of ChatGPT.


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