Pay-for-practice or Pay-for-performance? A coupled agent-based evaluation tool for assessing sediment management incentive policies

Chung Yi Lin, Y. C.Ethan Yang, Anil Kumar Chaudhary

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Cost-shared programs have been applied to incentivize the adoption of agricultural best management practices (BMPs) to address the long-standing water quality issue in the Chesapeake Bay watershed, US. However, the business-as-usual (BAU) incentive program (i.e., pay-for-practice, paying cost share for implementing BMPs) is likely to miss the Total Maximum Daily Load target to reduce 20% of the total suspended sediment (TSS) in 2010 by 2025. Some field experiments indicate that pay-by-performance (PFP; paying lower cost share but with additional bonus payment per unit sediment reduction) can better motivate community involvement leading to greater water quality control outcomes. However, the effectiveness of different incentive policies is still unclear at a basin scale. We propose a coupled agent-based modeling tool to quantify the performance of different incentive policies. The tool considers farmers’ (i.e., agents’) BMP adoption dynamics affected by the social norm and the potential bonus payment. Specifically, we compare individual-based PFP (PFPi) and group-based PFP (PFPg) with BAU. Results of our proposed model applied to the selected study area, the Susquehanna River Basin, Chesapeake Bay's largest tributary watershed, suggest that PFP can achieve higher TSS reduction with less cost. PFPg shows the best basin-wide TSS reduction associated with the least uncertainty among all tested policies. Also, the performance of PFPg is less impacted by the change in the bonus payment compared to PFPi attributed to farmers’ collaboration efforts. Potentially, the proposed policy evaluation tool can better inform an achievable target with policy suggestions in assistance with social studies (e.g., surveys and behavioral experiments).

Original languageEnglish (US)
Article number129959
JournalJournal of Hydrology
Volume624
DOIs
StatePublished - Sep 2023

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

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