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Tradable Set-Aside Requirements (TSARs): Conserving Spatially Dependent Environmental Amenities

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Abstract

Land conversion patterns can conflict with endangered species protection by fragmenting the landscape. Incentive mechanisms can help mitigate the threat of habitat fragmentation by aggregating landowner conservation decisions across the landscape. The optimal conservation strategy for endangered species can target the most connected habitat cluster as an initial starting point, and then expand the conservation patch to maximize connectivity. Herein we present an incentive mechanism, the tradable set-aside requirements (TSARs), designed to target the low cost contiguous conservation landscape and share the burden of conservation among landowners. In the lab, we examine the performance of two land use conservation policies: TSARs, and the TSARs combined with an agglomeration bonus. Evaluated by economic and biological measures of efficiency, we find that TSARs, relative to a command and control policy, increases patch size and habitat connectivity within the landscape. Additionally, combining TSARS with the agglomeration bonus increases biological efficiency (habitat connectivity and patch size within the landscape) but at a price—higher opportunity cost. TSARs with the agglomeration bonus can be more cost-effective than a TSARs only policy for species sensitive to large core habitat requirements and landscape connectivity.

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Notes

  1. See for example the work of Latacz-Lohmann and Van der Hamsvoort (1997), Bean (1998), Shogren et al. (1999), Ferraro and Kiss (2002), Smith and Shogren (2002), Parkhurst and Shogren (2003), Stoneham et al. (2003), Langpap (2004, 2006), Lewis and Plantinga (2007), Feng (2007), Adler (2008), Ferraro (2008), Lewis et al. (2009, 2011), and Hanley et al. (2012).

  2. See for example Nelson et al. (2008), Drechsler et al. (2010), Hennessy and Lapan (2010), and Werling et al. (2014); promote the use of spatially explicit incentive mechanisms in agricultural landscapes. Warziniack et al. (2007) apply the agglomeration bonus to a forest landscape. Polasky et al. (2011) discuss implications of spatially explicit incentives in a landscape that provides numerous and competing environmental amenities.

  3. Parkhurst et al. (2002) introduced the idea of the agglomeration bonus to facilitate the coordination of land retirement decisions across landowners or landscape attributes. Albers et al. (2008) find the agglomeration bonus can attenuate the “crowding out” effect. As one might expect based on transaction cost theory, Banerjee et al. (2012) find agglomeration bonus induced coordination occurs less frequently in larger networks. Reeson et al. (2011) show a multi-round auction with information feedback can improve coordination within the landscape. Wissel and Wätzold (2010) proposed a tradable scheme that adds a “neighborhood bonus” to the value of the permit through the alteration of the trade ratio. The neighborhood bonus is similar to the agglomeration bonus idea, but differs in two ways: (1) it is internalized in the value of the biodiversity credit—more connected, more biodiversity value; and (2) it is based on the Moore (the eight cells surrounding a central cell) rather than the von Neumann Neighborhood (the four cells orthogonally surrounding a central cell).

  4. The agglomeration bonus mechanism we use in this paper coordinates land within a landowners land holdings, but not across landowners. The agglomeration bonus is a menu of subsidies that can meet numerous conservation objectives including coordinating across landowners, coordinating within an individual’s own landholdings, coordinating along an environmental amenity such as a river or protected wilderness area, and coordinating to create large or small reserves and corridors (see Parkhurst and Shogren 2007, 2008).

  5. Goldman et al. (2007) find an agglomeration bonus (cooperation bonus) to be more straightforward and more flexible than a conservation bank like mechanism (entrepreneur incentive). Adding the bonus to TSARs has the potential to improve on the design of the conservation landscape.

  6. By “artificial isolation” Russell points the reader toward the experimental method and mindset, in which the researcher gains insight into a complex mechanism by using a sterile environment to control for noise and confounding factors (see for example Conant 1951; Smith 2008).

  7. See Martín-López et al. (2008) for an overview of the difficulties in calculating economic value of biodiversity for endangered species.

  8. Each session was constrained to eight subjects because the experimental lab had a maximum capacity of ten subjects and the experimental design required 4 subjects in each group. See the Appendix for the exact instructions, which is available on request from the authors.

  9. See Parkhurst and Shogren (2007) for an example of similar calculations of the potential strategy set when considering the agglomeration bonus incentive scheme.

  10. Communication, Information, and History. Communication. Participants were also provided the opportunity to communicate one message per round. Communication was non-binding, unstructured with no restrictions on timing or content, and in which a common language was implemented by allowing subjects to send messages in their natural language (Crawford 1998). Information. After all four participant’s choices were submitted the resulting grid was presented to the group. They had common knowledge regarding payoffs and strategies. History. The entire \(10 \times 10\) grid showing the configuration of brown cells and the payoffs for each subject within the group then appeared in the history box. Participants were also provided with record sheets to further help them keep track of their own and the other group members’ choices of strategies and associated payoffs in previous rounds.

  11. The range of market prices is determined as the average price per TSAR for the seller on the lower bound and the average price per TSAR for the buyer on the upper bound.

  12. We use Fig. 4 to clarify the BE gradient. In round 1, 28 of a maximum 31 borders are shared between conserved parcels, implying BE = 90.3 %. In round 3, BE = 71.0 % (22 of 31 borders shared). In round 16, BE = 100 % (31 of 31 borders shared).

  13. Maximum earnings depend on the institutional structure of the incentive mechanism, which differs across treatments. RE is an indicator of the ability of groups to earn the maximum available rents.

  14. Recall the predicted quantity traded in the TO treatment is 14. Acquisition of TSARs was to the landowner 2. Landowners 1 and 3 should have sold 5 brown out cell requirements (TSARs) and the landowner 4 should have sold 4 TSARs. Predicted market price is all whole integers in the interval of \(-\$28.00\) to \(-\$40.00 \{-\$28, -29,{\ldots }, -39, -40\}\). For the TAB treatment, predicted quantity was 15, with the Landowner 2 acquiring 5 TSARs from each of the other participants. Predicted market price is \(\{ \$10, 11,{\ldots }, 59, 60\}\).

  15. OC does not address other additional costs such as administrative costs, monitoring and enforcing agreements, creating the infrastructure to facilitate trades, opportunity costs of habitat destruction, and other costs associated with rent seeking behavior. These costs can vary significantly across incentive mechanisms (See Parkhurst and Shogren 2003).

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Authors and Affiliations

  1. Department of Economics, Weber State University, Ogden, UT, 84408, USA

    Gregory M. Parkhurst

  2. Department of Economics and Finance, University of Wyoming, Laramie, WY, 82071, USA

    Jason F. Shogren & Thomas Crocker

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  1. Gregory M. Parkhurst
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  2. Jason F. Shogren
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  3. Thomas Crocker
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Correspondence to Jason F. Shogren.

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Thanks to participants at workshop on Mechanism Design and the Environment at the Royal Society of Edinburgh for their helpful comments and funding. We also thank Brandon Koford, Travis Warziniack, and the reviewers for their insightful comments. Thanks to the US Department of Agriculture, and the University of Wyoming Stroock and Bugas funds for partial financial support. We thank the Norwegian University of Life Sciences for their support while working on this project.

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Parkhurst, G.M., Shogren, J.F. & Crocker, T. Tradable Set-Aside Requirements (TSARs): Conserving Spatially Dependent Environmental Amenities. Environ Resource Econ 63, 719–744 (2016). https://doi.org/10.1007/s10640-014-9826-4

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