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:



Related posts :



AAAI presidential panel – AI reasoning

  09 Jan 2026
Watch the third panel discussion in this series from AAAI.

The Machine Ethics podcast: Companion AI with Giulia Trojano

Ben chats to Giulia Trojano about AI as an economic narrative, companion chatbots, deskilling of digital literacy, chatbot parental controls, differences between social AI and general AI services and more.

What are small language models and how do they differ from large ones?

  06 Jan 2026
Let’s explore what makes SLMs and LLMs different – and how to choose the right one for your situation.

Forthcoming machine learning and AI seminars: January 2026 edition

  05 Jan 2026
A list of free-to-attend AI-related seminars that are scheduled to take place between 5 January and 28 February 2026.

AAAI presidential panel – AI perception versus reality video discussion

  02 Jan 2026
Watch the second panel discussion in this series from AAAI.

More than half of new articles on the internet are being written by AI

  31 Dec 2025
The line between human and machine authorship is blurring, particularly as it’s become increasingly difficult to tell whether something was written by a person or AI.
monthly digest

2025 digest of digests

  30 Dec 2025
We look back through the archives of our monthly digests to pick out some highlights from the year.



 

AIhub is supported by:






 












©2025.05 - Association for the Understanding of Artificial Intelligence