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Abstract

This paper analyzes the optimal policy choice for the conservation of privately owned open space when future land cover types are uncertain. Policymakers must use land use policies to make conservation decisions under uncertainty over the social benefits of future vegetation, due to the uncertain effects of climate change on suitable habitat ranges. If policymakers fail to account for future information gains when designing land use policies, expected social welfare may not be maximized. To examine this situation, I consider three policy instruments: urban growth boundaries (UGB), location-independent development fees (LIF), and location-dependent development fees (LDF). I analyze them in a spatial-dynamic model in which climate change is treated as a land use externality with an uncertain future value. I derive the privately and socially optimal land allocations under open-loop and closed-loop control. By comparing the privately and socially optimal land allocations for each control problem, I identify the optimal trajectory of each instrument over time. Results depend on whether or not there is a cumulative externality from urban development. When no cumulative externality exists, welfare-maximizing UGB and LIF depend on the control problem. In contrast, LDF are identical in expectation across the two control problems. As a consequence, LDF are the first best policy when landowners do and policymakers do not anticipate (or cannot respond to) the future availability of climatic information. When a cumulative externality exists, none of the policies are robust to the type of control problem, including LDF, and only UGB are time consistent. Therefore, UGB are the first best policy when policymakers anticipate future climatic information and there is a cumulative externality. This work implies that conservation programs should amend current methods for ranking conservation choices to account for future ecosystem movement, and return lands to other uses if climate change causes conservation goals to not be achieved.

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