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.

            AIhub is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

The Good Robot podcast: what makes a drone “good”? with Beryl Pong

  20 Feb 2026
In this episode, Eleanor and Kerry talk to Beryl Pong about what it means to think about drones as “good” or “ethical” technologies.

Relational neurosymbolic Markov models

and   19 Feb 2026
Relational neurosymbolic Markov models make deep sequential models logically consistent, intervenable and generalisable

AI enables a Who’s Who of brown bears in Alaska

  18 Feb 2026
A team of scientists from EPFL and Alaska Pacific University has developed an AI program that can recognize individual bears in the wild, despite the substantial changes that occur in their appearance over the summer season.

Learning to see the physical world: an interview with Jiajun Wu

and   17 Feb 2026
Winner of the 2019 AAAI / ACM SIGAI dissertation award tells us about his current research.

3 Questions: Using AI to help Olympic skaters land a quint

  16 Feb 2026
Researchers are applying AI technologies to help figure skaters improve. They also have thoughts on whether five-rotation jumps are humanly possible.

AAAI presidential panel – AI and sustainability

  13 Feb 2026
Watch the next discussion based on sustainability, one of the topics covered in the AAAI Future of AI Research report.

How can robots acquire skills through interactions with the physical world? An interview with Jiaheng Hu

  12 Feb 2026
Find out more about work published at the Conference on Robot Learning (CoRL).

From Visual Question Answering to multimodal learning: an interview with Aishwarya Agrawal

and   11 Feb 2026
We hear from Aishwarya about research that received a 2019 AAAI / ACM SIGAI Doctoral Dissertation Award honourable mention.



AIhub is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence