Simple and Approximately Optimal Contracts for Payment for Ecosystem Services

34 Pages Posted: 18 Feb 2021 Last revised: 19 May 2021

See all articles by Wanyi Li

Wanyi Li

Stanford University Management Science & Engineering

Itai Ashlagi

Stanford University - Department of Management Science & Engineering

Irene Lo

Stanford

Date Written: May 18, 2021

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 United States land use data supports our findings and illustrate when linear or conditional contracts are likely to be more effective.

Keywords: contract design, payment for ecosystem services

Suggested Citation

Li, Wanyi and Ashlagi, Itai and Lo, Irene, Simple and Approximately Optimal Contracts for Payment for Ecosystem Services (May 18, 2021). Available at SSRN: https://ssrn.com/abstract=3754041 or http://dx.doi.org/10.2139/ssrn.3754041

Wanyi Li (Contact Author)

Stanford University Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

Itai Ashlagi

Stanford University - Department of Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

Irene Lo

Stanford ( email )

United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
323
Abstract Views
1,053
Rank
141,906
PlumX Metrics