Simple and Approximately Optimal Contracts for Payment for Ecosystem Services

Wanyi Dai Li (), Itai Ashlagi () and Irene Lo ()
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Wanyi Dai Li: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Itai Ashlagi: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Irene Lo: Department of Management Science and Engineering, Stanford University, Stanford, California 94305

Management Science, 2023, vol. 69, issue 12, 7821-7837

Abstract: Many countries have adopted payment for ecosystem services (PES) programs to reduce deforestation. Empirical evaluations find such programs, which pay forest owners to conserve forest, can lead to anywhere from no impact to a 50% reduction in deforestation level. To better understand the potential effectiveness of PES contracts, we use a principal–agent model, in which the agent has an observable amount of initial forest land and a privately known baseline conservation level. Commonly used conditional contracts perform well when the environmental value of forest is sufficiently high or sufficiently low, but can do arbitrarily poorly compared with the optimal contract for intermediate values. We identify a linear contract with a distribution-free per-unit price that guarantees at least half of the optimal contract payoff. A numerical study using U.S. land use data supports our findings and illustrates when linear or conditional contracts are likely to be more effective.

Keywords: contract design; payment for ecosystem services; information asymmetry; additionality; conditionality (search for similar items in EconPapers)
Date: 2023
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