Pollution Control with Time-Varying Model Mistrust of the Stock Dynamics

Fidel Gonzalez

Computational Economics, 2018, vol. 51, issue 3, No 9, 569 pages

Abstract: Abstract I develop a framework to analyze the robust emissions tax policy for a stock pollutant when the environmental authority is not fully confident about its estimated model of pollution dynamics and, in contrast to previous research, the degree of model mistrust may change over time. I characterize the effect of time-varying model mistrust on emission taxes, pollution stocks and welfare. The general results of this paper show that introducing the possibility of a time-varying degree of model mistrust produces different emission taxes, abatement and welfare compared to the traditional assumption of a time-fixed model mistrust. This result holds even if the probability that the model mistrust may change in the future is small. If the environmental authority expects that the model uncertainty may decrease in the future then current emissions taxes should also decrease. Conversely, if the model mistrust may increase in the future then an active approach compatible with the Precautionary Principle is optimal and current emissions taxes should also increase.

Keywords: Robust control; Markov chain; Knightian uncertainty; Stock dynamics; Pollution control (search for similar items in EconPapers)
JEL-codes: C61 Q58 (search for similar items in EconPapers)
Date: 2018
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/s10614-016-9622-z 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:51:y:2018:i:3:d:10.1007_s10614-016-9622-z

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

DOI: 10.1007/s10614-016-9622-z

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 ().