CEMRACS 2026 : Modeling and AI for Environmental Transition / Centre d'Eté Mathématique de Recherche Avancée en Calcul Scientifique

Collection CEMRACS 2026 : Modeling and AI for Environmental Transition / Centre d'Eté Mathématique de Recherche Avancée en Calcul Scientifique

Organizer(s) Fischer, Aurélie ; Gruet, Pierre ; Kobeissi, Ziad ; Lopez, Olivier ; Tankov, Peter ; Vermandel, Gauthier ; Zerbib, Olivier David

Date(s) 13/07/2026 - 17/07/2026

linked URL https://conferences.cirm-math.fr/3643.html

00:00:00 / 00:00:00

10 19

Computational and AI methods in climate economics - Lecture 3

By Simon Scheidegger

This mini-course introduces computational and AI methods for climate economics to the participants of the CEMRACS 2026 summer school. Over three lectures it builds a modern deep-learning toolkit for solving, estimating, and performing uncertainty quantification on high-dimensional dynamic economic models with a running application to climateeconomy integrated assessment models (IAMs) and optimal carbon taxation.

Information about the video

Date of recording 15/07/2026
Date of publication 03/08/2026
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers, Graduate Students
Director(s) Luca Recanzone
Format MP4

Citation data

DOI 10.24350/CIRM.V.20519003
Cite this video Scheidegger, Simon (15/07/2026). Computational and AI methods in climate economics - Lecture 3. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.20519003
URL https://dx.doi.org/10.24350/CIRM.V.20519003

Domain(s)

Economics

Bibliography

AZINOVIC, Marlon, GAEGAUF, Luca, et SCHEIDEGGER, Simon. Deep equilibrium nets. International Economic Review, 2022, vol. 63, no 4, p. 1471-1525. - https://doi.org/10.1111/iere.12575
FOLINI, Doris, FRIEDL, Aleksandra, KÜBLER, Felix, et al. The climate in climate economics. Review of Economic Studies, 2025, vol. 92, no 1, p. 299-338. - https://doi.org/10.1093/restud/rdae011
CHEN, Hui, DIDISHEIM, Antoine, et SCHEIDEGGER, Simon. Deep surrogates for finance: With an application to option pricing. Journal of Financial Economics, 2026, vol. 177, p. 104222. - https://doi.org/10.1016/j.jfineco.2025.104222
FRIEDL, Aleksandra, KÜBLER, Felix, SCHEIDEGGER, Simon, et al. Deep uncertainty quantification: With an application to integrated assessment models. Work. Pap., Univ. Lausanne, Lausanne, Switz, 2023. - https://www.aeaweb.org/conference/2024/program/paper/32yGkdAi
SCHEIDEGGER, Simon, SURBEK, Oliver, et al. Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules. arXiv. org, 2025. - https://doi.org/10.48550/arXiv.2507.01704
SCHEIDEGGER, Simon et BILIONIS, Ilias. Machine learning for high-dimensional dynamic stochastic economies. Journal of Computational Science, 2019, vol. 33, p. 68-82. - https://doi.org/10.1016/j.jocs.2019.03.004

MSC codes

91B76 Environmental economics

Document(s)

https://github.com/sischei/cemracs2026_DEQN

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