Modelling action on climate change for natural prosperity
13 January 2022, 1:00 pm–2:00 pm
![Natural prosperity](/bartlett/igp/sites/bartlett_igp/files/styles/large_image/public/events/original_size_black_and_white_entertainment_news_twitter_post_1_1.jpg?itok=4INNmcp2)
The IGP welcomes Andreas Gieges (Climate Analytics) for a Soundbite
This event is free.
Event Information
Open to
- All
Availability
- Yes
Cost
- Free
Organiser
-
Institute for Global Prosperity
The presentation will review the Climate Action Tracker's efforts in assessing future global prosperity as a direct consequence of the current action and targets in climate mitigation. It will highlight the challenges of transferring a complex prediction problem into information or benchmarks that are easy to understand and most useful for a wide spread of stakeholders. The latest Conference of Parties (COP) in Glasgow will serve as an excellent example for mutual interactions between climate modelling and political landscape. Thus, the latest findings of the Climate Action Tracker will be presented showing the how critical future livelihoods are dependent on drastic actions in the next decade.
The Speaker:Â
Dr Andreas Geiges is a member of the Climate Policy Team at and is involved in multiple projects, including the Climate Action Tracker and IMPACT.
Andreas has a broad background in stochastic modelling, Bayesian statistical analysis, uncertainty quantification, as well as in information theory. While working on a wide range of model types (including fluid-flow, economic and agent-based models), he has developed a high level of expertise in data analytics and management, conceptual model development, and trans-disciplinary work.
Andreas also contributed to the development of a publication examining strategies for better interaction between policy and science in the stakeholder engagement process. This interactive process was aimed at incorporating stakeholder feedback into the development of socio-technical systems models using interactive visualisation of intermediate results and model assumptions.