ΑΙhub.org
 

AI and climate change – a virtual briefing with Climate Change AI researchers


by
03 February 2021



share this:
earth

In December, Heinrich-Böll-Stiftung hosted a virtual briefing featuring researchers from Climate Change AI (CCAI). They talked about the role machine learning can play in facilitating climate change mitigation and adaptation strategies, AI applications that increase emissions, and energy use in AI itself.

AIhub focus issue on climate action

On the topic of facilitating climate change mitigation and adaptation strategies, a number of examples were given where AI could help. These include: gathering information, forecasting, improving operational efficiencies, predictive maintenance, accelerating scientific experimentation, and approximating time-intensive simulations.

The researchers then talked about AI applications that increase emissions. There are two aspects to this. Firstly, the use of AI in applications that directly increase emissions, such as use in the oil, gas and mining industries. Secondly, AI applications with uncertain impact. For example, AI is a key component in creating new technologies, like autonomous vehicles. In technologies such as these there is no clear understanding of whether their implementation would have a positive or negative effect on the climate.

The team discussed the energy consumption of AI systems. They touched on the need to understand the power consumption for applications during both the training and usage phases.

Finally, we heard about the role of policy implementation with regards to aligning the use of AI with climate change strategies.

You can watch the briefing in full here:

Taking part in the session were:

  • Priya L. Donti, Carnegie Mellon University, and Co-founder and Chair, Climate Change AI
  • Lynn H. Kaack, ETH Zürich, and Co-founder and Chair, Climate Change AI
  • David Rolnick, McGill University and Mila, and Co-founder and Chair, Climate Change AI

CCAI is a group of volunteers from academia and industry who believe that tackling climate change requires concerted societal action, in which machine learning can play an impactful role.

Read the paper Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals by Lynn H. Kaack, Priya Donti, Emma Strubell and David Rolnick.



tags: ,


Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

Interview with Minji Park: humans in the loop to keep language models up to date

  06 Nov 2026
Human-AI co-inference through a shared editable state.

AI plus chemistry can expand battery electrolyte design

  05 Nov 2026
Researchers are using artificial intelligence to speed the search for better battery materials.
coffee corner

AIhub coffee corner: Are humanoids the future of embodied AI?

  04 Nov 2026
The AIhub coffee corner captures the musings of AI experts over a short conversation.

“With AI, we need to reconsider what it means to learn”

  03 Nov 2026
Artificial intelligence is forcing universities to reinvent how they teach and assess students’ knowledge.

Forthcoming machine learning and AI seminars: November 2026 edition

  02 Nov 2026
A list of free-to-attend AI-related seminars that are scheduled to take place in the next couple of months.
monthly digest

AIhub monthly digest: October 2026 – neurosymbolic planning, time-series forecasting, and robots for the home

  30 Oct 2026
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
AI pioneers

From teaching machines to teaching people: an interview with Michael Littman

  29 Oct 2026
AI Pioneer Michael Littman reflects on the evolution of reinforcement learning, the power of science communication, and how generative AI is changing the way we teach and learn.

From lab to life: Why translating AI advances to the real world is a major challenge

  28 Oct 2026
Systems that perform well in controlled lab settings often fall short when deployed in homes, in hospitals or on city streets.


↑


AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence