This post is the second in a series exploring the intersection of AI and sustainability. In the first post, the growing resource demands of AI and potential solutions were discussed. Now, let's explore a few ways higher education institutions can navigate AI for sustainable development.

Like the industrial revolution, AI technologies have the potential to reshape every facet of our economy and potentially offer unprecedented opportunities to improve social, environmental, and economic objectives.

Recent White Paper – Higher Ed, AI, and SDGs

The "White Paper for Universities on Navigating Artificial Intelligence Innovation Ecosystems in AI for Sustainable Development" highlights the critical role of higher education institutions (HEIs) in leveraging AI to achieve Sustainable Development Goals (SDGs). With only 17% of SDG targets on track for 2030, the paper emphasizes AI's transformative potential across economic, environmental, and social pillars.

The paper outlines five bold moves for universities to advance their AI-SDG capabilities:

  1. Revolutionize Ranking Metrics: Align incentives with SDG impact measurements.
  2. Stop Competing, Start Collaborating: Foster global partnerships for AI-SDG innovation.
  3. Build Future-Ready Curricula: Integrate comprehensive AI-SDG education.
  4. Champion Open Data Access: Promote open AI-SDG repositories.
  5. Move Beyond Tenure and Embrace Lifelong Learning: Encourage continuous faculty development.

By implementing these five strategies, universities can position themselves as leaders in AI-enabled sustainable development, driving noteworthy progress towards global sustainability goals. The white paper also calls for immediate and sustained action by students, faculty, staff, and administrators to harness AI's potential for transformative change.

The Association for the Advancement of Sustainability in Higher Education (AASHE)

The Association for the Advancement of Sustainability in Higher Education (AASHE) is a nonprofit that supports colleges and universities to help ensure our world’s future leaders are motivated and equipped to solve sustainability challenges. It offers resources, professional development, and a collaborative platform to integrate sustainability into academics, operations, and community engagement. AASHE’s Sustainability Tracking, Assessment & Rating System (STARS) program helps institutions assess and improve their sustainability performance.

Recently, there’s been community discussion around using AI tools to analyze STARS data more effectively, suggesting a budding interest in potentially leveraging AI for sustainability reporting and visualization.

The Coalition for Sustainable AI: Pioneering a Greener Future

Another organization, the Coalition for Sustainable AI, was recently launched at the AI Action Summit in February 2025.  The Coalition for Sustainable AI argues that a holistic approach to AI development – spanning both hardware and software - is necessary to prevent AI from exacerbating environmental harm and to harness its transformative power to accelerate the decarbonization of economies, reduce pollution, and protect our biodiversity, ecosystems, and oceans.

The coalition’s Initiatives Hub is mapping efforts to minimize AI’s ecological footprint or harness its potential for environmental and social benefit. Current use cases that highlight the practical applications of sustainable AI technologies include the following:

  • Agriculture & Ecosystem Monitoring: AI enhances sustainable agriculture through precision techniques that improve yields and reduce resource use. It also supports ecosystem preservation by using sensors and drones to track environmental health and detect issues like deforestation and pollution.
  • Energy Efficiency: AI manages power use in data centers and smart grids, reducing energy consumption and optimizing renewable integration. These applications contribute significantly to lowering carbon emissions from energy systems.
  • Climate Modeling & Waste Management: Advanced AI models improve climate forecasting and help develop mitigation strategies. In waste management, AI boosts efficiency in recycling and collection, cutting environmental impact.
  • Governance & Standards Initiatives: International efforts aim to standardize how AI’s environmental footprint is measured and managed, encouraging best practices and stakeholder collaboration. The AI Energy Score  initiative offers an evaluation framework for model energy efficiency across tasks and platforms. Another initiative is working to standardize the assessment of AI’s environmental impact, by developing a clear and coherent roadmap.

Finally, the site hosts an inventory of reports on Sustainable AI, which brings together publications essential to understanding advances, challenges and best practices in the design, use, and environmental impact of artificial intelligence.

Conclusion

Peter Drucker, a key contributor to modern business management, is often quoted as saying, “You can't manage what you can't measure.”  One of the challenges with Sustainable AI is that standardized, clear metrics are still under development. However, several organizations, including those mentioned in this blog, are working across sectors to empower various stakeholders with actionable recommendations and frameworks as this field emerges.

Reflection Questions

  1. Considering AI’s environmental footprint, how can individuals—such as students and educators—make more mindful decisions about when and how they use AI tools in their daily lives?
  2. How can the principles of sustainable AI be integrated into your current curriculum or research projects?
  3. How can students and educators collaborate to drive the adoption of environmentally responsible AI practices on campus?