Optimal Pricing of Climate Risk

Thomas F. Coleman (), Nicole S. Dumont (), Wanqi Li (), Wenbin Liu () and Alexey Rubtsov ()
Additional contact information
Thomas F. Coleman: University of Waterloo
Nicole S. Dumont: University of Waterloo
Wanqi Li: University of Waterloo
Alexey Rubtsov: Ryerson University

Computational Economics, 2022, vol. 60, issue 3, No 12, 1134 pages

Abstract: Abstract The climate change model of Daniel et al. (Proc Natl Acad Sci USA 116(42):20886-20891, 2019. https://doi.org/10.1073/pnas.1817444116) is an important contribution to the carbon pricing literature. However, the computational methodology proposed in this paper is costly, thus limiting the flexibility and scalability of this basic approach. In this paper we introduce several modern computational techniques, often used in optimization applications, such as vectorization and automatic differentiation for gradient computation, to dramatically improve computational performance: this allows for increased scalability and sensitivity analysis (including the analysis of suboptimal policies). Such studies are reported in this paper yielding a number of new experimental insights. For example, our new code illustrates the cost of postponing climate change mitigation is significant. Specifically, postponing climate change mitigation until 2030 is equivalent to giving up $5.4 trillion (in 2015 US dollars).

Keywords: Automatic differentiation; Vectorization; Large-scale optimization; Climate change; Social cost of carbon (search for similar items in EconPapers)
JEL-codes: D81 Q5 Q54 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-021-10179-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:kap:compec:v:60:y:2022:i:3:d:10.1007_s10614-021-10179-6

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-021-10179-6

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().