Harnessing Artificial Intelligence for a Sustainable Future

Harvard Extension School

ENVR E-217

Section 1

CRN 26843

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As the world grapples with a triple planetary crisis of climate change, biodiversity loss, and pollution, the need for scalable sustainable technologies has never been greater. Artificial intelligence (AI) is uniquely positioned to tackle complex climate challenges with a focus on climate mitigation, adaptation, and resilience. AI-infused solutions are already making strides in the field of sustainability. Recent research suggests that Al environmental applications could reduce greenhouse gas emissions by 2.4 gross tonnage (Gt) of carbon dioxide equivalent by 2030, while boosting the global economy by $5.2 trillion. This course examines the intersection of AI and sustainability, shedding light on the opportunities and challenges presented by this emerging technology. Students gain a deep understanding of a wide array of Al applications spanning sectors such as energy, information technology, transportation, and agriculture. The course also delves into how the new AI wave is accelerating the progress on sustainable development goals (SDGs). It further investigates the potential detrimental consequences of AI in relation to ethical and environmental dimensions. The course draws on case studies and contributions from industry practitioners. Throughout the course, students take part in an immersive learning experience with peer learning opportunities in addition to individual activities.

Instructor Info

Ahmad Antar, PhD

Founder and Executive Director, Digital Emissions


Meeting Info

Sa 11:00am - 1:00pm (1/27 - 5/17)

Participation Option: Online Asynchronous or Online Synchronous

In online asynchronous courses, you are not required to attend class at a particular time. Instead you can complete the course work on your own schedule each week.

Deadlines

Last day to register: January 23, 2025

Notes

This course meets via web conference. Students may attend at the scheduled meeting time or watch recorded sessions asynchronously. Recorded sessions are typically available within a few hours of the end of class and no later than the following business day.

Syllabus

All Sections of this Course

CRN Section # Participation Option(s) Instructor Section Status Meets Term Dates
26843 1 Online Asynchronous, Online Synchronous Ahmad Antar Open Sa 11:00am - 1:00pm
Jan 27 to May 17