& Key message More and more environmental and resource economists are taking a particular interest in research on forest ecosystem services (FES), especially in a context of climate change. Spatial and temporal issues are crucial to economic analyses and for the design of conservation policies. Interdisciplinary research involving ecological and economic disciplines is a prerequisite for the more effective management of forest ecosystems. & Context Economists define non-market ecosystem services (ES) as public or common goods due to their characteristics of non-rivalry in terms of consumption and/or non-excludability. Just because they do not have a price does not mean that ES have no economic value because their social benefits are undoubtedly considerable. These features, associated with the market demand for timber and a poor climate risk assessment, may lead to the under-provision of non-market forest ES and the over-harvesting of timber. & Aims In this article, we review research questions that are central to the enhancement of FES provision. Beyond the economic modelling of the joint provision of FES, we focus on issues related to the design of public policies to guide forest management. The objective is to provide crucial insights concerning the importance of a spatial and sustainable provision of FES. & Results First, we provide an economic interpretation of the FES concept and a review of economic models of forest management. Second, we explain how spatial and temporal dimensions of FES can have major implications on their supply and demand. Both dimensions explain why FESs have to be taken into account in production decisions and public policies (including the design of payment for environmental services (PESs)). & Conclusion A better understanding of FES provision and public policies to be enhanced is not possible without accounting for spatial and temporal dimensions. This helps to analyse the impact of intervention on FES and the cost-effectiveness of economic instruments, implying a coordinated effort to bring together ecological and economic data and models.