Iterative scenarios for social-ecological systems

Abstract

Managing social-ecological systems toward desirable regimes requires learning about the system being managed while preparing for many possible futures. Adaptive management (AM) and scenario planning (SP) are two systems management approaches that separately use learning to reduce uncertainties and employ planning to manage irreducible uncertainties, respectively. However, each of these approaches have limitations that confound management of social-ecological systems. Here, we introduce iterative scenarios (IS), a systems management approach that is a hybrid of the scopes and relationships to uncertainty and controllability of AM and SP that combines the "iterativeness" of AM and futures planning of SP. Iterative scenarios is appropriate for situations with high uncertainty about whether a management action will lead to intended outcomes, the desired benefits are numerous and crossscale, and it is difficult to account for the social implications around the natural resource management options. The value of iterative scenarios is demonstrated by applying the approach to green infrastructure futures for a neighborhood in the city of Cleveland, Ohio, U.S., that had experienced long-term, systemic disinvestment. The Cleveland green infrastructure project was particularly well suited to the IS approach given that learning about environmental factors was necessary and achievable, but what would be socially desirable and possible was unknown. However, iterative scenarios is appropriate for many social-ecological systems where uncertainty is high as IS accommodates real-world complexity faced by management.

Towards a global sustainable development agenda built on social–ecological resilience

“…Yet, monitoring alone is insufficient for sound environmental governance if the information does not lead to social learning. When dealing with dynamic SESs, a structured, iterative process for governance that incorporates monitoring information at decision points is essential for learning and sound governance ( Herrmann et al, 2021 ). The SDG indicators used for monitoring and evaluation have already come under heavy scrutiny for their failure to capture indispensable system variables, important differences between contexts, and feedbacks between the environment and society ( Reyers & Selig, 2020 ; Reyers et al, 2017 ; Szabo et al, 2016 ; Zeng et al, 2020 ), as well as for their highly contested and political nature ( Fisher & Fukuda-Parr, 2019 ; Fukuda-Parr & McNeill, 2019 ).…”

“…Targets are by definition end points to be achieved. By contrast, governance consistent with our understanding of social-ecological resilience should be dynamic and focused upon processesthat is, governance able to define and re-define the targets themselves in a structured, iterative framework in response to changing conditions (Garmestani & Benson, 2013;Herrmann et al, 2021). More importantly, the SDGs and targets tempt governments to think solely within their own borders rather than considering a dynamic and interactive system of systems that operates across several scales, none of which neatly match national borders (Gunderson & Holling, 2002;Scown, 2020).…”

“…The current SDGs fail to incorporate several key factors critical to the productive governance of SESs at multiple scales that are accounted for by social–ecological resilience. The next global sustainable development agenda should be a structured, iterative process that enhances learning about the dynamic process of sustainability through time, and nested within a resilience-based governance framework ( Garmestani & Benson, 2013 ; Herrmann et al, 2021 ). Environmental governance at all scales – global, national, regional, and local – must embrace the best available understanding of SES dynamism if humanity is to navigate and thrive in the Anthropocene ( Biermann et al, 2012 ; Garmestani et al, 2019 ; Sterner et al, 2019 ).…”

Games for experiential learning: triggering collective changes in commons management

“…The scaling challenge is therefore to develop effective and efficient intervention packages, potentially containing games as one element. Experiential learning tools such as the ones discussed, can also be incorporated into long-term system change processes in an iterative and adaptive manner (Herrmann et al 2021). This will intensify the learning experience, help relate it to specific real-life decisions, and support feedback into real-life context.…”

Adaptation, Transformation and Resilience in Healthcare Comment on "Government Actions and Their Relation to Resilience in Healthcare During the COVID-19 Pandemic in New South Wales, Australia and Ontario, Canada"

“…Also, scenarios should not be seen as static endpoints and therefore need to be refined iteratively, 20 given potential social-ecological realities that may have made scenarios obsolete (eg, future climate scenarios likely changing from less ideal to worst) or others emerged from the unknown unknowns through interdisciplinary collaborations (eg, new technological solutions). Scenario planning may be ultimately facilitated by big data and computer science, including artificial intelligence and machine learning approaches, which become more powerful with technological development and the accumulation of social, technological, environmental and economic data.…”