## Abstract

The scientific information needed to precisely link land use changes to changes in ecosystem service provision is often unavailable, particularly for hydrological ecosystem services. Potential buyers of ecosystem services must weigh the risk of paying to induce conservation that fails to deliver valuable ecosystem services against the risk that land is developed before its value is known. This paper uses a two period model to assess when expected future improvements in scientific information should substantially impact payments for conservation today. Optimal current payments increase, often substantially, as the quality of expected future information increases, but the gain from increasing payments to account for the expected degree of improved information is small in many cases. Larger values occur when the buyer believes that the land whose private development value is the highest also provides the highest ecosystem service value, when the buyer faces relatively more uncertainty about ecosystem service provision than about the cost of inducing conservation, and when the buyer believes land is highly likely to be developed absent incentive payments.

This is a preview of subscription content, access via your institution.

## Notes

For example, Quintero et al. (2009) use a hydrological modeling tool called SWAT to provide suggestions for targeting future PES payments in the same programs where such information was identified as lacking in Wunder and Albán (2008). Methods to improve understanding of hydrological ecosystem service provisions are discussed by numerous authors including Keeler et al. (2012), Guswa et al. (2014), Grizzetti et al. (2016), and Trisurat et al. (2016).

Throughout the model, I assume that indifference is resolved in favor of conservation.

Engel et al. (2008) note that in many government-financed PES programs, the buyers of an ecosystem service “have no first-hand information on its value, and generally cannot observe directly whether it is being provided” (p. 666).

Since the model is designed to capture long time frames, there is no a priori assumption that the two time periods have equal length. As a result, I require \(\delta >0\), but do not impose \(\delta <1\). If the time until information is received is relatively short and the parties expect decisions in the second period to persist for a long time frame, \(\delta >1\) is reasonable. The simulations place equal weight on the two periods.

Given the multivariate normal assumption, there is a probability that the buyer might want to set \(\phi <0\) to induce a landowner to develop land the landowner would prefer to conserve. Such payments are outside the typical scope of payment for ecosystem service plans and are not considered in this analysis.

I assume that the buyer views payments as costs rather than redistributive transfers. This is consistent with a buyer representing direct beneficiaries of ecosystem services (e.g. a downstream water utility making payments to upstream landowners). To the extent that the buyer represents a government who views the payments as transfers, the cost of payments in (2) and (7) could be multiplied by a factor \(\nu \) representing the opportunity cost of funds to pay for the transfers.

For the derivation of this condition, see “Appendix C”.

Increasing or decreasing the value of \(\delta \) has the expected impact of increasing or decreasing the impact of improved information on today’s outcomes. Also note that as long as \(B\left( p\right) =0\) has a unique solution, the value of \(\delta \) does not influence the landowner’s decision.

These categories are reminiscent of Engel et al. ’s (2008) sources of inefficiency from PES programs but do not correspond exactly except in the case of category II—payments for non-additional conservation, which correspond to Engel et al.’s case D. Category IV here includes both situations where the value of conservation is less than the cost (\(p>e\)) so the outcome is efficient and situations where \(e>p\) but the buyer’s best estimates lead to an offer that was too small to induce conservation (what Engel et al. term Case B). Moreover, because of the uncertainty, category III may include parcels where the value of conservation ends up being less than the cost so the buyer would have been better off not inducing conservation (what Engel et al. term Case C).

Assuming all parcels remain undeveloped at time

*T*is equivalent to assuming that category V is empty.The probability the parcel is developed immediately (category V) is the space between the top of the bar and 1.

See “Appendix E” for a figure illustrating the difference between the lines shown in Fig. 6.

See “Appendix E” for a similar figure that replicates the left hand panels of Fig. 5 with correlation.

Figures illustrating the impact of correlation on the change in the other variables graphed in Fig. 6 due to improved information are provided in “Appendix E”.

Muñoz-Piña et al. (2008) report that landowners are prohibited from receiving payments for more than 5 years through the program.

CompEcon is a Matlab toolbox developed to complement Miranda and Fackler (2004). The most recent version of the toolbox is freely available by contacting Mario Miranda.

## References

Arguedas C, van Soest DP (2011) Optimal conservation programs, asymmetric information and the role of fixed costs. Environ Resour Econ 50(2):305

Arnold MA, Duke JM, Messer KD (2013) Adverse selection in reverse auctions for ecosystem services. Land Econ 89(3):387–412

Asquith NM, Vargas MT, Wunder S (2008) Selling two environmental services: in-kind payments for bird habitat and watershed protection in los negros, bolivia. Ecol Econ 65(4):675–684

Bardsley P, Burfurd I (2013) Auctioning contracts for environmental services. Aust J Agric Resour Econ 57(2):253–272

Brauman KA, Daily GC, Duarte TK, Mooney HA (2007) The nature and value of ecosystem services: an overview highlighting hydrologic services. Annu Rev Environ Resour 32:67–98

Cason TN, Gangadharan L (2004) Auction design for voluntary conservation programs. Am J Agric Econ 86(5):1211–1217

Costello C, Polasky S (2004) Dynamic reserve site selection. Resour Energy Econ 26(2):157–174

DePiper GS (2015) To bid or not to bid: The role of participation rates in conservation auction outcomes. Am J Agric Econ 97(4):1157–1174

Derissen S, Quaas MF (2013) Combining performance-based and action-based payments to provide environmental goods under uncertainty. Ecol Econ 85:77–84

Dissanayake ST, Önal H (2011) Amenity driven price effects and conservation reserve site selection: a dynamic linear integer programming approach. Ecol Econ 70(12):2225–2235

Engel S, Pagiola S, Wunder S (2008) Designing payments for environmental services in theory and practice: an overview of the issues. Ecol Econ 65(4):663–674

Ezzine-de Blas D, Wunder S, Ruiz-Pérez M, del Pilar Moreno-Sanchez R (2016) Global patterns in the implementation of payments for environmental services. PLoS ONE 11(3):e0149847

Ferraro PJ (2008) Asymmetric information and contract design for payments for environmental services. Ecol Econ 65(4):810–821

Ferraro PJ, Simpson RD (2002) The cost-effectiveness of conservation payments. Land Econ 78(3):339–353

Fooks JR, Higgins N, Messer KD, Duke JM, Hellerstein D, Lynch L (2016) Conserving spatially explicit benefits in ecosystem service markets: experimental tests of network bonuses and spatial targeting. Am J Agric Econ 98(2):468–488

Grizzetti B, Lanzanova D, Liquete C, Reynaud A, Cardoso A (2016) Assessing water ecosystem services for water resource management. Environ Sci Policy 61:194–203

Guswa AJ, Brauman KA, Brown C, Hamel P, Keeler BL, Sayre SS (2014) Ecosystem services: challenges and opportunities for hydrologic modeling to support decision making. Water Resour Res 50(5):4535–4544

Johnson KA, Polasky S, Nelson E, Pennington D (2012) Uncertainty in ecosystem services valuation and implications for assessing land use tradeoffs: an agricultural case study in the minnesota river basin. Ecol Econ 79:71–79

Keeler BL, Polasky S, Brauman KA, Johnson KA, Finlay JC, O’Neill A, Kovacs K, Dalzell B (2012) Linking water quality and well-being for improved assessment and valuation of ecosystem services. Proc Nat Acad Sci 109(45):18619–18624

Kinzig AP, Perrings C, Chapin FS, Polasky S, Smith VK, Tilman D, Turner B (2011) Paying for ecosystem services—promise and peril. Science 334(6056):603–604

Miranda MJ, Fackler PL (2004) Applied computational economics and finance. MIT Press, Cambridge

Muñoz-Piña C, Guevara A, Torres JM, Braña J (2008) Paying for the hydrological services of mexico’s forests: analysis, negotiations and results. Ecol Econ 65(4):725–736

Newburn D, Reed S, Berck P, Merenlender A (2005) Economics and land-use change in prioritizing private land conservation. Conserv Biol 19(5):1411–1420

Newburn DA, Berck P, Merenlender AM (2006) Habitat and open space at risk of land-use conversion: targeting strategies for land conservation. Am J Agric Econ 88(1):28–42

Parkhurst GM, Shogren JF, Bastian C, Kivi P, Donner J, Smith RB (2002) Agglomeration bonus: an incentive mechanism to reunite fragmented habitat for biodiversity conservation. Ecol Econ 41(2):305–328

Pattanayak SK, Wunder S, Ferraro PJ (2010) Show me the money: do payments supply environmental services in developing countries? Rev Environ Econ Policy 4(2):254–274

Polasky S, Lewis DJ, Plantinga AJ, Nelson E (2014) Implementing the optimal provision of ecosystem services. Proc Nat Acad Sci 111(17):6248–6253

Porras IT, Grieg-Gran M, Neves N (2008) All that glitters: a review of payments for watershed services in developing countries. Number 11. London: International Institute for Environment and Development

Quintero M, Wunder S, Estrada RD (2009) For services rendered? Modeling hydrology and livelihoods in andean payments for environmental services schemes. For Ecol Manag 258(9):1871–1880

Rabotyagov SS (2010) Ecosystem services under benefit and cost uncertainty: an application to soil carbon sequestration. Land Econ 86(4):668–686

Reeson AF (2011) Adapting auctions for the provision of ecosystem services at the landscape scale. Ecol Econ 70(9):1621–1627

Shah P, Ando AW (2016) Permanent and temporary policy incentives for conservation under stochastic returns from competing land uses. Am J Agric Econ 98(4):1074–1094

Sheriff G (2009) Implementing second-best environmental policy under adverse selection. J Environ Econ Manag 57(3):253–268

Springborn M, Yeo B-L, Lee J, Six J (2013) Crediting uncertain ecosystem services in a market. J Environ Econ Manag 66(3):554–572

Trisurat Y, Eawpanich P, Kalliola R (2016) Integrating land use and climate change scenarios and models into assessment of forested watershed services in southern thailand. Environ Res 147:611–620

Vedel SE, Jacobsen JB, Thorsen BJ (2015) Forest owners’ willingness to accept contracts for ecosystem service provision is sensitive to additionality. Ecol Econ 113:15–24

Wunder S, Albán M (2008) Decentralized payments for environmental services: the cases of pimampiro and profafor in ecuador. Ecol Econ 65(4):685–698

Wunder S, Engel S, Pagiola S (2008) Taking stock: a comparative analysis of payments for environmental services programs in developed and developing countries. Ecol Econ 65(4):834–852

## Acknowledgements

This work was inspired in part by conversations with Drew Guswa and Casey Brown. I would like to thank Vis Taraz for helpful discussions and comments on earlier drafts of this work. I also appreciate the comments and suggestions of participants at the 2016 NAREA Annual Meeting and two anonymous referees.

## Author information

### Authors and Affiliations

### Corresponding author

## Electronic supplementary material

Below is the link to the electronic supplementary material.

## Appendices

### Appendix A

### 1.1 Simplification of Buyer’s Payoff

The buyer’s single period payoff as a function of the payment offered is given by

The multivariate normal assumption allows us to simplify this expression considerably. First, note that

so the inner integral can be rewritten as

To simplify derivations, let \(\alpha =\mu _{e}+\frac{\sigma _{e}\left( \rho _{es}-\rho _{ep}\rho _{sp}\right) }{\left( 1-\rho _{es}^{2}\rho _{ep}^{2}\right) }s-\frac{\sigma _{e}\left( \rho _{ep}-\rho _{ep}\rho _{sp}\right) }{\sigma _{p}\left( 1-\rho _{es}^{2}\rho _{ep}^{2}\right) }\mu _{p}-\phi \) and let \(\beta =\frac{\sigma _{e}\left( \rho _{ep}\left( 1-\rho _{es}^{2}\right) \right) }{\sigma _{p}\left( 1-\rho _{es}^{2}\rho _{ep}^{2}\right) }\). The full first integral is thus

or

The latter integral can also be simplified under the normality assumption to

Combining elements, we have the full first integral as

or

We also know that

and

Finally, note that

so

Putting all these pieces together, we have

### Appendix B

### 1.1 Proof of Proposition 1

*Case1*\(\bar{p}\) is finite.

If \(\bar{p}\) is finite, then the interval \(\left[ 0,\bar{p}\right] \) is closed and bounded and since \(W\left( \phi ,s\right) \) is continuous, it attains a maximum on this interval.

*Case2*
\(\bar{p}=\infty \)

In this case, the buyer believes the landowner will choose to conserve in period 1 regardless of the value of \(p\). The \(\lim _{\phi \rightarrow \infty }W\left( \phi ,s\right) =-\infty \) so there exists a value \(\hat{\phi }\) such that \(W\left( \phi ,s\right) <W\left( \hat{\phi },s\right) \) for any \(\phi >\hat{\phi }\). Again, continuity of \(W\left( \phi ,s\right) \) implies that it attains a maximum on the closed and bounded interval \(\left[ 0,\hat{\phi }\right] \), with the maximized value greater than or equal to \(W\left( \hat{\phi },s\right) \). Thus, this value also maximized \(W\left( \phi ,s\right) \) on \(\phi \ge 0\).

### Appendix C

### 1.1 Proof of Proposition 2

A local interior maximum requires \(\frac{\partial W}{\partial \phi }>0\) and \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\le 0\).

Since \(p|s\sim N\left( \mu _{p}+\sigma _{p}\rho _{es}\rho _{ep}s,\sigma _{p}^{2}\left( 1-\rho _{es}^{2}\rho _{ep}^{2}\right) \right) \), we know that

giving

where \(\gamma =\frac{1-\rho _{es}^{2}}{1-\rho _{es}^{2}\rho _{ep}^{2}}\). The second derivative is

Using the same substitution as above, we get

Letting \(a=\left( \gamma \frac{\sigma _{e}\rho _{ep}}{\sigma _{p}}-1\right) \), \(b=\frac{1}{\sigma _{p}^{2}\left( 1-\rho _{es}^{2}\rho _{ep}^{2}\right) }\), \(c=\frac{\mu _{p}+\sigma _{p}\rho _{es}\rho _{ep}s}{\sigma _{p}^{2}\left( 1-\rho _{es}^{2}\rho _{ep}^{2}\right) }\), and \(d=\sigma _{e}\rho _{es}\left( 1-\gamma \rho _{ep}^{2}\right) s+\mu _{e}-\frac{\sigma _{e}\rho _{ep}}{\sigma _{p}}\gamma \mu _{p}\), we have

or

or

Since \(f_{p|s}\left( \phi |s\right) >0\) for all \(\phi \), \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) =0\) only when the term in brackets equals 0. This expression is quadratic in \(\phi \), implying that \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) \) has at most two zeros. If there are multiple interior local maxima of \(W\left( \phi ,s\right) \), they must both have \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) \le 0\) and be separated by an interior minimum where \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) \ge 0\). This implies that there must be two zeros of \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) \) between the local maxima. Since \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) \) has at most two zeros, this requires that the sign of \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) \) is the same for any value of \(\phi \) above the largest value of \(\phi \) that results in a local maximum. Since we must have\(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) <0\) and \(\frac{\partial W}{\partial \phi }\left( \phi ,s\right) =0\) at the largest local maximum, this requires that \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) <0\) and \(\frac{\partial W}{\partial \phi }\left( \phi ,s\right) =0\) for any value of \(\phi \) above the largest value of \(\phi \) that results in a local maximum. Moreover, it requires that \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) <0\) and \(\frac{\partial W}{\partial \phi }\left( \phi ,s\right) >0\) for any value of \(\phi \) below the smallest value of \(\phi \) that results in a local maximum. Note that the \(\lim _{\phi \rightarrow \infty }-\,ba\phi ^{2}+\left( ca-bd\right) \phi +a+dc-1=-\,ba\left( \infty \right) \) and \(b>0\), suggesting that the sign of \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) \) at very high values of \(\phi \) is the opposite of the sign of *a*. Since we must have \(\frac{\partial ^{2}W}{\partial \phi ^{2}}\left( \phi ,s\right) <0\) at high values of \(\phi \), we can only have two local maxima of \(W\left( \phi ,s\right) \) if \(a>0\). The \(\lim _{\phi \rightarrow -\infty }\frac{\partial W}{\partial \phi }\left( \phi ,s\right) =\left( 1-\gamma \frac{\sigma _{e}\rho _{ep}}{\sigma _{p}}\right) \infty =-a\infty \). Thus, at very low values of \(\phi \), the sign of \(\frac{\partial W}{\partial \phi }\left( \phi ,s\right) \) is opposite the sign of *a*. So if \(a>0\), then \(\frac{\partial W}{\partial \phi }\left( \phi ,s\right) <0\) at very low values of \(\phi \), which contradicts the requirement that \(\frac{\partial W}{\partial \phi }\left( \phi ,s\right) >0\) for values of \(\phi \) below the smallest value of \(\phi \) that results in a local maximum. Since \(W\left( \phi ,s\right) \) has at most one interior local maximum on \(\left( -\infty ,\infty \right) \), it has at most one interior local maximum on \(\left[ 0,\bar{p}\right] \).

### Appendix D

### 1.1 Numerical Simulation Details

As described in Sect. 3, neither the first nor the second period buyer optimization problems have closed form solutions. To explore the implications of the model, I use numerical simulations programmed in Matlab. The second period problem is a fairly straightforward maximization problem. There is a closed form expression for both the buyer’s objective function and the first order conditions for an interior maximum and we know that there is at most one interior local maximum. In contrast, the first period problem is fairly complex because the buyer’s objective function involves taking expectations over possible future signals, recognizing that the second period offer will be optimized in response to the observed signal. I use Matlab’s built-in fmincon solver to solve the first period maximization problem. Within the buyer’s objective function, I use a vectorized global adapative quadrature algorithm from the Mathworks File Exchange (quadvgk) to calculate the integral in Eq. 2. The quadvgk algorithm works by picking sets of values for the variable of integration (the observed signal) and computing the integrand simultaneously for each of these possible values to economize on computations.

Since the value of the integrand is the optimized second period payoff conditional on a particular signal times the probability of observing that signal in period 2, the function called by quadvgk must compute the solution to the second period optimization problem for many observed signal values. To solve this maximization problem efficiently for multiple observed signal values simultaneously I use the nonlinear complementarity problem solver ncpsolve from the CompEcon 2016 toolbox, which looks for solutions to the Kuhn-Tucker conditions of the individual maximization problems simultaneously.^{Footnote 16}

For most parameter sets, the second period problem is well behaved and solves quickly and easily, but three issues can arise. First, as described in Sect. 2.1, there is a single local interior maximum to the buyer’s problem, but the true maximum may occur at one of the endpoints and there may be local minima within the interval. This can lead the ncpsolve algorithm to get stuck in a region away from the solution. I use a coarse grid search on possible values to identify good starting points for the solver algorithm to avoid this problem. Second, for some parameter sets, the buyer’s objective function is extremely flat. These examples represent cases with a very high expected private value relative to the expected conservation value. The buyer will receive a payoff of 0 by making no offer but can increase the payoff a very small amount by making an offer arbitrarily close to the conditional expected benefit of conservation. The offer is virtually certain to be rejected and hence yields the buyer an expected payoff of almost but not quite zero. When this gain gets too small, the algorithm may fail to identify this true maximum. I supplement the ncpsolve algorithm with checks to identify these cases and manually set the optimal offer in these cases. Finally, for some parameter cases, the ncpsolve algorithm is unable to solve the period 2 problem simultaneously for all of the different signal values due to large scaling differences between the cases. When the algorithm fails in these cases, I revert to solving the remaining individual problems case by case using Matlab’s built-in nonlinear constrained optimization algorithm (fmincon). The simulation codes are present in the electronic supplementary material of this article.

### Appendix E

### 1.1 Supplemental Graphs

#### 1.1.1 E.1 Benefit of Improved Information

In Fig. 6, the impact of information on optimal values is shown by the different lines. It can be difficult to see how the size of these differences varies from this figure. Figure 9 emphasizes these difference by subtracting the value for \(\rho _{es}=0\) (e.g. the lightest line in Fig. 6) from the lines for each remaining value of \(\rho _{es}\) and plotting the result.

#### 1.1.2 E.2 Impact of Correlation

Figure 10 replicates the left-hand columns of Fig. 5 with negative and positive correlation. Two effects are apparent from this figure. First, in the left hand column, we see that with negative correlation, the optimal offer first increases and then decreases as the observed signal of conservation value rises. At low signal value values, the increase in the expected conservation value dominates and leads to higher offers. When a high signal is observed, this causes the buyer to believe the development is likely to be low, suggesting that the parcel can be induced to conserve with a lower offer. Second, we see that when there is correlation, the signal reveals information about the expected development value, so the probability the parcel is in each location in the figure changes as the signal increases. With negative correlation, the probability of observing high *p* and low *s* or low *s* and high *p* increases, while with negative correlation, the probability of observing a high *p* and a high *s* or a low *p* and a low *s* increases.

Figures 11, 12, and 13 illustrate the impact of correlation on optimal offers, the probability of first period conservation, and the optimized expected buyer payoff.

## Rights and permissions

## About this article

### Cite this article

Sayre, S.S. Pay for the Option to Pay? The Impact of Improved Scientific Information on Payments for Ecosystem Services.
*Environ Resource Econ* **73**, 591–625 (2019). https://doi.org/10.1007/s10640-018-0275-3

Accepted:

Published:

Issue Date:

DOI: https://doi.org/10.1007/s10640-018-0275-3