The University of Toronto students investigated whether (and how) machine learning techniques could be used to extract meaningful climate change actions from existing content. While the project work was intended to be an exploration/feasibility study, the results are extremely promising. During their research they explored many potential NLP, NLU, and other techniques and libraries. The high quality of their project deliverable testifies to the success of their research, teamwork, and problem solving approach. Importantly for such a complicated domain, they were able to articulate the complex math and ML techniques and approaches in a relatively non-technical way. This allowed me to gain an understanding of what they were doing, and why. They asked for feedback to adjust and improve their algorithm and align it with project goals and objectives. They proposed some very practical approaches to identifying and extracting imperative actions. They also indicated many areas for future improvement or investigation. Overall the students' result was of great insight and met my expectations. I recommend them and their work.