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Designing payments for ecosystem services: Lessons from previous experience with incentive-based mechanisms

Open access
Jack, B. Kelsey, Kousky, Carolyn, Sims, Katharine R. E.·2008·Proceedings of the National Academy of Sciences

Abstract

Payments for ecosystem services (PES) policies compensate individuals or communities for undertaking actions that increase the provision of ecosystem services such as water purification, flood mitigation, or carbon sequestration. PES schemes rely on incentives to induce behavioral change and can thus be considered part of the broader class of incentive- or market-based mechanisms for environmental policy. By recognizing that PES programs are incentive-based, policymakers can draw on insights from the substantial body of accumulated knowledge about this class of instruments. In particular, this article offers a set of lessons about how the environmental, socioeconomic, political, and dynamic context of a PES policy is likely to interact with policy design to produce policy outcomes, including environmental effectiveness, cost-effectiveness, and poverty alleviation.

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Metadata

Authors
Jack, B. K., Kousky, C., & Sims, K. R. E.
Venue
Proceedings of the National Academy of Sciences
Published
2008-07-15
Volume
105
Issue
28
Pages
9465-9470
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2071040227
Cited by
807
References
0
FWCI
29.97
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Designing payments for ecosystem services: Lessons from previous experience with incentive-based mechanisms. https://app.cochat.ai/papers/10.1073/pnas.0705503104
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Jack, B. K., Kousky, C., & Sims, K. R. E. (2008). Designing payments for ecosystem services: Lessons from previous experience with incentive-based mechanisms. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0705503104

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.