# Optimal climate policy: Uncertainty versus Monte Carlo

*Benjamin Crost* and
*Christian Traeger*

*Economics Letters*, 2013, vol. 120, issue 3, pages 552-558

**Abstract:**
The integrated assessment literature frequently replicates uncertainty by averaging Monte Carlo runs of deterministic models. This Monte Carlo analysis is, in essence, an averaged sensitivity analyses. The approach resolves all uncertainty before the first time period, drawing parameters from a distribution before initiating a given model run. This paper analyzes how closely a Monte Carlo based derivation of optimal policies is to the truly optimal policy, in which the decision maker acknowledges the full set of possible future trajectories in every period. Our analysis uses a stochastic dynamic programming version of the widespread integrated assessment model DICE, and focuses on damage uncertainty. We show that the optimizing Monte Carlo approach is not only off in magnitude, but can even lead to a wrong sign of the uncertainty effect. Moreover, it can lead to contradictory policy advice, suggesting a more stringent climate policy in terms of the abatement rate and a less stringent one in terms of the expenditure on abatement.

**Keywords:** Climate change; Uncertainty; Integrated assessment; Monte Carlo; Risk aversion; DICE (search for similar items in EconPapers)

**JEL-codes:** Q54 Q00 D61 D90 C61 C63 (search for similar items in EconPapers)

**Date:** 2013

**References:** View references in EconPapers View complete reference list from CitEc

**Citations** View citations in EconPapers (12) Track citations by RSS feed

**Downloads:** (external link)

http://www.sciencedirect.com/science/article/pii/S0165176513002565

Full text for ScienceDirect subscribers only

**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:** http://EconPapers.repec.org/RePEc:eee:ecolet:v:120:y:2013:i:3:p:552-558

Access Statistics for this article

Economics Letters is currently edited by *Economics Letters Editorial Office*

More articles in Economics Letters from Elsevier

Series data maintained by Shamier, Wendy ().