Meta-analysis of landowner participation in voluntary incentive programs for provision of forest ecosystem services

Abstract

Many voluntary incentive programs for provision of forest ecosystem services (ES) have low participation rates, insufficient enrollment areas, and inefficient ecological outcomes. Understanding participation behavior in such programs has therefore become a crucial part of policy success. We synthesized a large body of literature on the behavior of nonindustrial private forest owners based on surveys of stated (intended) participation or data on actual participation in existing or hypothetical programs. Using metaregression analysis, we examined how methodological, program-characteristic, and economic-incentive variables affected participation rate estimates. Participation rates tended to be overestimated when landowner participation was elicited in hypothetical choice situations (compared with real situations) and when dichotomous choice surveys (compared with census data) were used. The marginal effect sizes were quite large, for example, a 31% increase with use of stated choices in hypothetical scenarios, and practitioners should therefore be aware of them. However, use of choice experiment surveys in a modified scenario based on existing programs had no effect on participation when all other determinants were controlled for. Participation rates decreased significantly as length of the contract increased and when there was no withdrawal option. These results suggest that perpetual contracts have a lower negative impact on participation than time-limited contracts with a duration of over 50 years. We confirmed that as compensation amounts increased, participation increased. One-time up-front payments were more effective in increasing initial participation than annual payments for contracts of over 5 years. We identified the robust determinants and the effect sizes of those determinants on landowner participation rate estimates, thereby contributing to a better understanding of forest owner behavior and offering useful insights to enable researchers and resource managers to improve the design and efficiency of new and existing forest ES programs.