Which pro-environmental farming behaviors should be priorities for funding? An approach based on matching ecosystem services (ESs) demand and supply.

Eco-compensation (EC) for farmland conservation being implemented in China is a type of payments for ecosystem services (PES) scheme that seeks to balance ecosystem services (ESs) consumption and supply. Despite the growing body of literature on the integration of the demand for, and supply of ESs or the identification of mismatches between them, still many challenges remain to structurally integrate them in policy making facing fund-saving. Therefore, integrating ESs demand and supply at the micro-scale to provide novel insights into using compensation funds efficiently and optimizing the design of PES schemes deserves increasing attention. This study investigated an approach for integrating the demand for, and supply of farmland ESs, i.e., the public's preferences and farmers' pro-environmental behaviors related to farmland ESs, into the design of differentiated EC policies to obtain more ESs with lower input of EC funds. It proposed a theoretical framework and research approach to identify the pro-environmental farming behaviors that should be funded as priorities by matching micro farmland ES demand and supply in multiple scenarios. Then, using survey data collected from four cities in Hubei Province, China, and expert consultation data, empirical research was conducted. By matching the 'key ESs' on the demand side and 'specific ESs' on the supply side in scenarios I, II and III, eight most critical pro-environmental farming behaviors that should be funded as priorities for each scenario were identified. We maintain that the efficient and targeted use of EC funds can be better determined by matching ESs demand and supply from a micro-perspective since this approach provides insight and a framework to identify priorities for action. This approach could be extended to the conservation of different types of ecosystems as well as the design of corresponding PES schemes.