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Spatially Structured Deep Uncertainty, Robust Control, and Climate Change Policies

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  • Anastasios Xepapadeas
  • Athanasios Yannacopoulos

Abstract

In view of the ambiguities and the deep uncertainty associated with climate change, we study the features of climate change policies that account for spatially structured ambiguity. Ambiguity related to the evolution of the damages from climate change is introduced into a coupled economy-climate model with explicit spatial structure due to heat transport across the globe. We seek to answer questions about how spatial robust regulation regarding climate policies can be formulated; what the potential links of this regulation to the weak and strong version of the precautionary principle (PP) are; and how insights about whether it is costly to follow a PP can be obtained. We also study the emergence of hot spots, which are locations where local deep uncertainty may cause robust regulation to break down for the whole spatial domain.

Suggested Citation

  • Anastasios Xepapadeas & Athanasios Yannacopoulos, 2018. "Spatially Structured Deep Uncertainty, Robust Control, and Climate Change Policies," DEOS Working Papers 1807, Athens University of Economics and Business.
    • Handle: RePEc:aue:wpaper:1807
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    More about this item

    Keywords

    Climate change; ambiguity; robust control; spatial regulation;
    All these keywords.

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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