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Environmental Management

Erschienen in:

01.03.2014

Optimal Advanced Credit Releases in Ecosystem Service Markets

verfasst von: Todd K. BenDor, Tianshu Guo, Andrew J. Yates

Erschienen in: Environmental Management | Ausgabe 3/2014

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Abstract

Ecosystem service markets are popular policy tools for ecosystem protection. Advanced credit releases are an important factor affecting the supply side of ecosystem markets. Under an advanced credit release policy, regulators give ecosystem suppliers a fraction of the total ecosystem credits generated by a restoration project before it is verified that the project actually achieves the required ecological thresholds. In spite of their prominent role in ecosystem markets, there is virtually no regulatory or research literature on the proper design of advanced credit release policies. Using U.S. aquatic ecosystem markets as an example, we develop a principal–agent model of the behavior of regulators and wetland/stream mitigation bankers to determine and explore the optimal degree of advance credit release. The model highlights the tension between regulators’ desire to induce market participation, while at the same time ensuring that bankers successfully complete ecological restoration. Our findings suggest several simple guidelines for strengthening advanced credit release policy.

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The 2008 federal mitigation regulation explicitly states that preservation is the lowest preference of regulators and should rarely be accepted as compensatory mitigation.

In actual practice, the regulator may issue advance credit releases at several points over time. Using a nine-step model of the credit release process, BenDor et al. (2011) aggregated all Corps district credit release schedules into the simple (before and after certification) classification shown in Fig. 1.

Prinicipal–agent models have a long history in the economic literature. See Luenberger (1995) for an introductory textbook treatment and Myerson (1982) for a more technical analysis. Although the actual contracts between bankers and regulators are often much more complex than we represent here, our principal–agent model allows us to determine insights into the optimal structure of these contracts. Future work will need to offer empirical data to further test these findings.

Although many other factors completely determine success, including the lack of a disastrous event like a hurricane, we will simplify our analysis to focus primarily on the factors that a banker can control.

This assumption could be relaxed if well-defined systems were in place to use independent, third-party verifiers to perform sample-based monitoring as part of mitigation banking contractual agreements. In this case, one could consider additional contracts that would be conditioned on effort.

We discuss the implications of this assumption in the "Conclusions" section.

In theory, a banker may try to price discriminate by charging an initial high price for the advance credit release, and then subsequently a lower price for the remaining credits if the project is successful. In practice, there is no evidence that bankers charge different prices at different times (Robertson 2006). So for simplicity we assume that all credits are sold at the end of the project, whether successful or not. We also assume that the decisions to enter and choose the effort level are relatively contemporaneous so that we do not need to consider discounting any of the components of profit.

Calculated by the authors from data described in BenDor et al. 2011.

Also see the discussion of trade-offs between ecological protection and administrative efficiency in Mann and Goldman-Carter (2008).

This rent is defined as \(R(x) = \int_0^x V(\tilde{x}) d\tilde{x} - V(x)x\).

We assume the loss function is separable across projects, so we need not be concerned with losses that may occur from credits purchased by the developer for other projects.

We assume the mitigation ratio (the amount of aquatic resource restored or replaced to that destroyed) is 1:1. This is a reasonable assumption given previous Corps data studies (BenDor and Brozovic 2007).

We assume that this function is continuous.

See Luenberger (1995) for a textbook discussion of the revelation principle. It holds quite generally, but it is easy to see why it holds in our model provided Condition A is satisfied. For any contract function, the banker will report a value of \(\varepsilon\) that gives the greatest value for θ. The same outcome can be obtained by offering a simple contract with a single value of θ for all values of \(\varepsilon. \) This latter contract trivially induces truthful reporting.

Note that Condition A is satisfied when m > 2n.

We have \(E^\prime = \frac{\alpha (m-2 n \theta)}{3} (2 + K) (1 - K)^2 + \frac{1}{3} \sqrt{\frac{1}{\alpha}} \left(\frac{\varepsilon_\ell}{m-n-m \theta + n \theta^2}\right)^\frac{3}{2} (m-2 n \theta)\), where \(K=\sqrt{\frac{F-m \theta + n \theta^2}{m-n-m \theta + n \theta^2}}\).

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Titel: Optimal Advanced Credit Releases in Ecosystem Service Markets
verfasst von: Todd K. BenDor
Tianshu Guo
Andrew J. Yates
Publikationsdatum: 01.03.2014
Verlag: Springer US
Erschienen in: Environmental Management / Ausgabe 3/2014
Print ISSN: 0364-152X
Elektronische ISSN: 1432-1009
DOI: https://doi.org/10.1007/s00267-013-0219-1

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