AI for sustainability spans several applications: sustainability strategy, ESG data tracking, scenario planning, supply chain decarbonization, circular economy and eco-design, and social impact work in education, accessibility, healthcare information, nonprofits, and workplace inclusion. Gen AI for Sustainability covers each of these in a single module, with case studies, interview calls on GitHub's role in climate tech and on Decisions Lab, and a 60-minute applied project.

It depends on the project, and the course teaches you to examine that question directly. AI can support environmental goals, and it also carries an environmental cost from training and use. The course addresses both sides, with lessons on the environmental cost of AI training and usage and on strategies for greener AI models, a reading on generative AI's environmental impact, and a peer-reviewed activity on digital carbon footprint. Together these give you a structured way to weigh a project's benefits against the environmental impact of AI.

Sustainability decisions often have to be made with incomplete information. AI for sustainability can support these decisions by tracking ESG data, modelling scenarios, and structuring a transition strategy, which is how AI can enhance strategic decision-making for sustainability. Gen AI for Sustainability covers these tasks in two lessons: one on ESG data tracking, modelling, and scenario planning, and one on transition strategization. A third lesson addresses the limitations of AI, and the course's final learning outcome is to evaluate the feasibility and robustness of AI for sustainability projects.