A review of spatial targeting methods of payment for ecosystem services

Abstract

Payments for Ecosystem Services (PES) have been studied extensively over the past decade as an important policy tool for coordinating ecological protection and regional socioeconomic development. One of the greatest challenges of PES implementation is to understand where to pay, i.e., spatial targeting, which can directly impact PES effectiveness and efficiency. In this study, we conducted a systematic review of spatial targeting methods based on literature analysis using Citespace. Firstly, peer-reviewed articles related to spatial targeting of PES were selected from the Web of Science database based on keywords. Cases applying PES spatial targeting methods were then chosen and analyzed after all articles were read. In total, 70% of the chosen cases focused on improving the compensation efficiency of biodiversity or another single environmental objective, whereas the remaining cases focused on coordinating trade-offs between equity and efficiency or multiple environmental objectives. The main PES spatial targeting approaches included cost-benefit analysis, multi-objective optimization, data envelope analysis and other methods aimed at specific issues. Of these, cost-benefit analysis has been most widely applied at different scales, including county, regional and watershed scales. Significant differences among the different PES spatial targeting methods were found, including in PES spatial targeting dimensions, efficiency optimization approaches and method application conditions. The practice of PES spatial targeting requires the selection of appropriate methods based on contextual biophysical and socioeconomic conditions as well as relevant environmental issues. The combined application of PES spatial targeting methods, compensation willingness of stakeholders and dynamic implementation of PES spatial targeting should be considered in future research.

Keywords

Payment for ecosystem services / Spatial targeting / Efficiency / Cost-benefit analysis / Multi-objective optimization / Data envelopment analysis

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This study was supported by the Second Tibetan Plateau Scientific Expedition and Research (STEP)program (Grant No. 2019QZKK0307), the China National Social Science Funding of Major Projects (Grant No. 18VSJ100) and National Natural Science Foundation of China (Grant No. 41925005).

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