Reducing the Environmental Impact of AI in Academia

Artificial intelligence is reshaping research and learning. But as adoption accelerates, so does the need to understand its environmental impact, from the energy used to run large models to the infrastructure required to support them.

At Clarivate, we share the academic community’s focus on sustainability and are committed to using AI responsibly. We cannot claim perfection, but we can commit to transparency about the steps we’re taking to reduce the environmental footprint of our AI-powered solutions.

How we reduce our AI footprint

The Clarivate Academic AI Platform serves as the backbone for our AI solutions and is designed to carefully balance performance, cost, and environmental impact. Our approach:

We use pre-trained models. Pre–trained LLM models avoid the large energy consumption and carbon emissions required to train new models from scratch.
We choose the right model for the task. We prioritize lightweight models (e.g., GPT-4.0-mini or O4-mini) over large, general-purpose models to reduce energy consumption while maintaining high-quality output.
We optimize prompts and outputs. By shortening prompts, setting clear length limits, and minimizing unnecessary tokens, we reduce the computational resources required to generate a response.
We cache responses for repeated use. Caching LLM outputs avoids repeated calls for identical requests. For example, if one user asks for a document’s key concepts, that response is stored and served to subsequent users without triggering a new LLM call.
We use text compression. Compressing documents before sending them to an LLM reduces the volume of processed data, lowering energy use and carbon emissions.
We build on optimized cloud infrastructure. Our solutions are deployed on infrastructure from leading cloud providers, who are committed to ambitious sustainability targets and increasingly powered by renewable energy. Additionally, using cloud infrastructure can help reduce energy use and emissions compared to maintaining traditional on-premises systems.
We enable flexibility and choice. We acknowledge that institutions and individuals may wish to make their own decisions about when and how to use AI. Where possible, we provide flexibility, allowing AI features to be enabled or disabled in line with institutional policies and user preferences.
We have a centralized AI governance. A cross-functional team of AI experts provides oversight to ensure consistent, accountable, ethical and environmentally sustainable management of our AI systems.

Our broader sustainability commitment

AI is just one layer of academia’s digital infrastructure. We continually evaluate and improve our data storage, network management, and system designs with the goal of enhancing efficiency and reducing environmental impact.

We report our greenhouse gas (GHG) emissions annually through the Carbon Disclosure Project (CDP), in line with the GHG Protocol to measure and manage Scopes 1 and 2. We are developing our journey towards reporting on relevant Scope 3 emissions. We have established internal goals and are tracking progress against our 2040 commitment to achieve net zero emissions, ensuring transparency and alignment with global standards.

You can learn more in our Sustainability Report and Environmental Management Statement.

Sustainability in AI is rapidly evolving, and we’re committed to transparency, collaboration and adoption of best practices. We welcome feedback and ideas from the academic community to help shape a future of responsible and sustainable AI.

Learn more about Clarivate Academic AI solutions.