Poster's note: one paper in a seemingly-increasing trend to apply formal
economic analysis to a variety of hypothetical geoengineering situations. I
personally find this emerging sub-discipline a very helpful contribution to
the debate.

https://www.sciencedirect.com/science/article/pii/S0095069617300232

Journal of Environmental Economics and Management
<https://www.sciencedirect.com/science/journal/00950696>
Available online 13 September 2018
In Press, Accepted Manuscript
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Optimal geoengineering experiments☆
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Author links open overlay panelLassiAhlvika
<https://www.sciencedirect.com/science/article/pii/S0095069617300232#!>Antti
Ihob <https://www.sciencedirect.com/science/article/pii/S0095069617300232#!>
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Highlights

•

We study geoengineering measures with uncertain effects that can be learned
by experimentation.
•

The prospect of learning may increase or decrease the optimal level of
investment.
•

We apply the analysis for artificial oxygenation of the Baltic Sea deeps.
•

A small field test is profitable despite the negative expected welfare
effects.
Abstract

We characterize optimal investment in pollution control measures with
uncertain effects that can be learned by experimentation. The anticipation
of learning through experimentation introduces two effects. The Inquisitive
Effect appears because the planner wants to invest in geoengineering to
gather socially valuable information on its effects. This effect encourages
investments in geoengineering and may justify field tests even where the
expected benefits fall short of the costs. The Flexibility Effect stems
from the planner optimally preparing for the post-learning stage, where the
field test is either ramped up or scaled down, depending on the outcome of
the experiment. This effect can encourage or discourage investments in
geoengineering. We demonstrate this set-up through an economic analysis of
an artificial oxygenation scheme designed to mitigate eutrophication in the
Baltic Sea and find that while the expected marginal benefit falls short of
costs, a field test representing some 10 percent of full deployment would
be optimal.
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