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Precaution under mixed uncertainty: Implications for environmental management

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  • Zemel, Amos

Abstract

A model of pollution control subject to two types of uncertainty is presented. First, the natural decay of the pollution stock follows stochastic dynamics that drives a diffusion pollution process (“stochastic uncertainty”). Moreover, the damage coefficient which determines the amount of damage corresponding to each pollution stock can undergo an abrupt increase at some uncertain future time (“event uncertainty”). The model admits an explicit and simple dynamic characterization of the optimal emission rule and the ensuing pollution process. When only one type of uncertainty is permitted (by setting either the variance of the stochastic process or the hazard rate for the damage increase to zero) it acts to promote the intuitive response of precaution. However, allowing the two types to interact gives rise to a non-monotonic behavior, whereby increasing the stochastic variance first enhances, then diminishes the response to the hazard. The results confirm and expand recent findings based on discrete-time formulations.

Suggested Citation

  • Zemel, Amos, 2012. "Precaution under mixed uncertainty: Implications for environmental management," Resource and Energy Economics, Elsevier, vol. 34(2), pages 188-197.
  • Handle: RePEc:eee:resene:v:34:y:2012:i:2:p:188-197 DOI: 10.1016/j.reseneeco.2011.11.002
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    References listed on IDEAS

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    Cited by:

    1. de Frutos Cachorro, Julia & Erdlenbruch, Katrin & Tidball, Mabel, 2014. "Optimal adaptation strategies to face shocks on groundwater resources," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 134-153.
    2. repec:kap:enreec:v:68:y:2017:i:3:d:10.1007_s10640-016-0026-2 is not listed on IDEAS
    3. Frederick van der Ploeg, 2012. "Resource Wars and Confiscation Risk," OxCarre Working Papers 097, Oxford Centre for the Analysis of Resource Rich Economies, University of Oxford.
    4. Frederick van der Ploeg, 2017. "Rapacious Oil Exploration in face of Regime Switches: Breakthrough Renewable Energy and Dynamic Resource Wars," Development Working Papers 415, Centro Studi Luca d'Agliano, University of Milano, revised 21 Feb 2017.
    5. Tsur, Yacov & Zemel, Amos, 2012. "Dynamic and stochastic analysis of environmental and natural resources," Discussion Papers 120017, Hebrew University of Jerusalem, Department of Agricultural Economics and Management.
    6. 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.
    7. Michele Baggio, 2012. "The Optimal Management of a Natural Resource with Switching Dynamics," IED Working paper 12-19, IED Institute for Environmental Decisions, ETH Zurich.
    8. Thomas S. Lontzek & Daiju Narita & Ole Wilms, 2016. "Stochastic Integrated Assessment of Ecosystem Tipping Risk," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(3), pages 573-598, November.

    More about this item

    Keywords

    Stochastic control; Environmental pollution; Uncertainty; Optimal management; Regime shifts;

    JEL classification:

    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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