Nonlinearities in biodiversity incentive schemes: A study using an integrated agent-based and metacommunity model

Abstract

We report results from over 20,000 runs of a coupled agent-based model of land use change and species metacommunity model. We explored the effect of increasing government incentive to improve biodiversity, in the context of other influences on land manager decision-making: aspirations, input costs, and price variability. The experiments test the four kinds of policy varying along two dimensions: activity-versus-outcome-based incentive, and individual-versus-collective incentive. The results from the experiments using boundedly rational agents, and comparison with profit-maximisation reveal thresholds in incentive schemes, where a sharp increase in environmental benefit occurs for a small increase in incentive. Further, the context affects the level of incentive at which turning points occur, and the degree of effect. Variability in outcome can also change with incentive and context, and some evidence suggests that environmental benefits are not always monotone increasing functions of incentives. Intuitively, if the incentive signal is large enough, land managers will farm the subsidy; and if the subsidy does not exactly match desired landscape outcomes, deterioration in environmental benefits may occur for higher incentives. Our results, whilst they suggest that outcome-based incentives may be more robust than activity-based, also highlight the importance of context in determining the success of agri-environmental incentive schemes. As such, they lend theoretical support to schemes, such as the Scottish Rural Development Programme, that include a localised component.