The contribution of bio-economic assessments to better informed land-use decision making: An overview
Luz María Castro, Filippo Lechthaler
There is an increasing call for tools that provide insight into the complex field of land-use systems, especially considering the broad range of sustainability issues related to policy intervention, technological innovation and continuous changes in environmental conditions. Stakeholders ranging from farmers, community leaders to national policy decision makers often fail in managing land due to various reasons, such as imperfect information, lacking awareness of ecosystem service provision and
... externalities, risk aversion, management quality and skills. To make better informed land-use decisions, they demand information about socio-economic and environmental properties of land-use systems and how they respond to changes in external stimuli and shocks such as climate change. Bioeconomic modelling has been positioned as an integrative approach able to deliver advice to sustainability related problems. This review describes bioeconomic models applied to land-use decisions, with a particular focus on the provision of ecosystem services. For this review, we have considered the main two brands of modelling, empirical (or econometric) and mechanistic, to analyse how mutually exclusive applications as well as newer combinations of both approaches have been implemented in the field to guide decision making towards sustainable land-use management. With the development of new computer-based techniques, bioeconomic modelling has become more complex, accurate and comprehensive in the range of alternatives they are able to evaluate. From previous models limited to a reduced number of decision factors, we see a tremendous evolution towards integrated modelling based on multiple-goal and spatial applications. This step forward in bioeconomic modelling enables it to incorporate multiple time and spatial scales. Despite the undeniable advance achieved by the improved performance of bioeconomic models, we found that their transfer to stakeholders and their expected evolution towards policies and other decision instruments appear to be underperformed, evidence suggests that the gap between science and decision making is still wide To reduce the gap, scientists need to integrate stakeholders during the modelling process, and improve communication platforms.