Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules

Felix Kubler, Simon Scheidegger and Oliver Surbek
Additional contact information
Felix Kubler: University of Zurich
Simon Scheidegger: University of Lausanne - School of Economics and Business Administration (HEC-Lausanne); London School of Economics, Grantham Research Institute on Climate Change and the Environment
Oliver Surbek: University of Lausanne - Department of Economics (DEEP)

No 25-82, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We develop a computational framework for deriving Pareto-improving and constrained optimal carbon tax rules in a stochastic overlapping generations (OLG) model with climate change. By integrating Deep Equilibrium Networks for fast policy evaluation and Gaussian process surrogate modeling with Bayesian active learning, the framework systematically locates optimal carbon tax schedules for heterogeneous agents exposed to climate risk. We apply our method to a 12-period OLG model in which exogenous shocks affect the carbon intensity of energy production, as well as the damage function. Constrained optimal carbon taxes consist of tax rates that are simple functions of observables and revenue-sharing rules that guarantee that the introduction of the taxes is Pareto improving. This reveals that a straightforward policy is highly effective: a Pareto-improving linear tax on cumulative emissions alone yields a 0.42% aggregate welfare gain in consumption-equivalent terms while adding further complexity to the tax provides only a marginal increase to 0.45%. The application demonstrates that the proposed approach produces scalable tools for macro-policy design in complex stochastic settings. Beyond climate economics, the framework offers a template for systematically analyzing welfare-improving policies in various heterogeneous-agent problems.

Keywords: Climate Policy; Optimal Policy; Ramsey Taxation; Pareto Frontier; Deep Learning; Gaussian Processes (search for similar items in EconPapers)
JEL-codes: C61 C63 D58 H23 Q54 Q58 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2025-07
New Economics Papers: this item is included in nep-dge
References: Add references at CitEc
Citations:

Downloads: (external link)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5335457 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2582

Access Statistics for this paper

More papers in Swiss Finance Institute Research Paper Series from Swiss Finance Institute Contact information at EDIRC.
Bibliographic data for series maintained by Ridima Mittal ().