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Repeated Experimentation to Learn About a Flow-Pollutant Threshold

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  • Rolf Groeneveld

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  • Michael Springborn
  • Christopher Costello

Abstract

We examine in discrete time the management of a flow pollutant that causes damage when it crosses a fixed but unknown threshold. The manager sequentially chooses a pollution level that allows learning about the threshold, thereby improving future decisions. If crossed, damage can be reversed at some cost. We analyze the conditions under which experimentation is optimal, and explore how experimentation depends on restoration costs, information about the threshold, and the discount rate. Our results suggest that the level of experimentation, defined as the difference between the optimal activity with and without learning, is non-monotonic in costs and decreasing in the discount rate. We identify two stopping boundaries for the experiment, depending on cost levels compared to the lower bound of the threshold’s interval. We show that when costs are high the stopping boundary under an infinite number of decisions is the same as when there are only two decision moments. A computational extension to more than two decisions suggests that an optimal sequence of experiments can cross the same threshold several times before experimentation ceases. These results shed light on a large class of environmental decision problems that has not been examined in the literature. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Rolf Groeneveld & Michael Springborn & Christopher Costello, 2014. "Repeated Experimentation to Learn About a Flow-Pollutant Threshold," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 58(4), pages 627-647, August.
  • Handle: RePEc:kap:enreec:v:58:y:2014:i:4:p:627-647
    DOI: 10.1007/s10640-013-9713-4
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    References listed on IDEAS

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    1. Naevdal, Eric & Oppenheimer, Michael, 2007. "The economics of the thermohaline circulation--A problem with multiple thresholds of unknown locations," Resource and Energy Economics, Elsevier, vol. 29(4), pages 262-283, November.
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    7. Costello, Christopher & Karp, Larry, 2004. "Dynamic taxes and quotas with learning," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1661-1680, June.
    8. Craig A. Bond & John B. Loomis, 2009. "Using Numerical Dynamic Programming to Compare Passive and Active Learning in the Adaptive Management of Nutrients in Shallow Lakes," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 555-573, December.
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    Cited by:

    1. Nkuiya, Bruno, 2015. "Transboundary pollution game with potential shift in damages," Journal of Environmental Economics and Management, Elsevier, vol. 72(C), pages 1-14.
    2. Charles Sims & David Finnoff, 2016. "Opposing Irreversibilities and Tipping Point Uncertainty," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(4), pages 985-1022.
    3. Springborn, Michael R., 2014. "Risk aversion and adaptive management: Insights from a multi-armed bandit model of invasive species risk," Journal of Environmental Economics and Management, Elsevier, vol. 68(2), pages 226-242.
    4. Diekert, Florian K., 2017. "Threatening thresholds? The effect of disastrous regime shifts on the non-cooperative use of environmental goods and services," Journal of Public Economics, Elsevier, vol. 147(C), pages 30-49.
    5. repec:oup:renvpo:v:12:y:2018:i:1:p:92-112. is not listed on IDEAS

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