Modeling desirable futures at local scale by combining the nature futures framework and multi-objective optimization

Abstract

Abstract Envisioning positive scenarios that recognize the multiple values of nature is fundamental for designing transformative changes in local socio-ecological systems. This study developed a protocol with three specifications for operationalizing the Nature Futures Framework (NFF) in a landscape scenario analysis using a multi-objective optimization framework composed of: (1) exploring nature-positive futures, (2) seeking alternative pathways for targets satisfying visions of plural values, and (3) screening key direct drivers to achieve the targets. This research conducted a case study of a rural landscape in northeastern Japan. First, 110 strategies of landscape management options were simulated from 2015 to 2100 using a forest landscape model, LANDIS-II. The simulation developed a data frame of four integrated indicators of the NFF values for each year and strategy. Second, nature-positive strategies were screened using the common values. Pareto optimal strategies were then identified to obtain equally good solutions. Finally, the key response options to achieve good nature-positive futures were identified using decision tree analysis. Our protocol identified (1) multiple, but few nature-positive and Pareto optimal strategies that satisfied NFF visions, (2) nature-positive, but not Pareto optimal strategies, and (3) non-nature-positive strategies. In most Pareto optimal strategies, the maximized value perspectives changed over time. Our protocol also identified key response options to achieve three different NFF value perspectives in the case study area: (1) clear or selective cutting in forestry and (2) solar PV installation on abandoned pastureland in agriculture and energy sectors. We discussed the implication for local landscape management, localizing NFF narratives to develop future scenarios and modeling practice of NFF. The protocol does not depend on a specific model and indicator. Thus, our scalable protocol can be applied to scenarios and model practices in any region to support envisioning plausible, feasible, and positive futures, and designing future stakeholder collaboration.