Pollution Control: When, and How, to be Precautious
AbstractThe precautionary principle (PP) applied to environmental policy stipulates that, in the presence of physical uncertainty, society must take robust preventive action to guard against worst-case outcomes. It follows that the higher the degree of uncertainty, the more aggressive this preventive action should be. This normative maxim is explored in the case of a stylized dynamic model of pollution control under Knightian uncertainty. At time 0 a decision-maker makes a one-time investment in damage-control technology and subsequently decides on a desirable dynamic emissions policy. Adopting the robust control framework of Hansen and Sargent , we investigate optimal damage-control and mitigation policies. We show that optimal investment in damage control is always increasing in the degree of uncertainty, thus confirming the conventional PP wisdom. Optimal mitigation decisions, however, need not always comport with the PP and we provide analytical conditions that sway the relationship one way or the other. This result is interesting when contrasted to a model with fixed damage-control technology, in which it can be easily shown that a PP vis-a-vis mitigation unambiguously holds. We conduct a set of numerical experiments to determine the sensitivity of our results to specific functional forms of damage-control cost. We find that when the cost of damage-control technology is low enough, damage-control investment and mitigation may act as substitutes and a PP with respect to the latter can be unambiguously irrational.
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Bibliographic InfoPaper provided by Fondazione Eni Enrico Mattei in its series Working Papers with number 2011.18.
Date of creation: Feb 2011
Date of revision:
Risk; Ambiguity; Robust Control; Precautionary Principle; Pollution Control;
Other versions of this item:
- Stergios Athanassoglou & Anastasios Xepapadeas, . "Pollution control: when, and how, to be precautious," DEOS Working Papers 1112, Athens University of Economics and Business.
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
This paper has been announced in the following NEP Reports:
- NEP-AGR-2011-05-14 (Agricultural Economics)
- NEP-ALL-2011-05-14 (All new papers)
- NEP-ENV-2011-05-14 (Environmental Economics)
- NEP-RES-2011-05-14 (Resource Economics)
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