System dynamic modelling to assess economic viability and risk trade-offs for ecological restoration in South Africa

Abstract

Can markets assist by providing support for ecological restoration, and if so, under what conditions? The first step in addressing this question is to develop a consistent methodology for economic evaluation of ecological restoration projects. A risk analysis process was followed in which a system dynamics model was constructed for eight diverse case study sites where ecological restoration is currently being pursued. Restoration costs vary across each of these sites, as do the benefits associated with restored ecosystem functioning. The system dynamics model simulates the ecological, hydrological and economic benefits of ecological restoration and informs a portfolio mapping exercise where payoffs are matched against the likelihood of success of a project, as well as a number of other factors (such as project costs and risk measures). This is the first known application that couples ecological restoration with system dynamics and portfolio mapping. The results suggest an approach that is able to move beyond traditional indicators of project success, since the effect of discounting is virtually eliminated. We conclude that systems dynamic modelling with portfolio mapping can guide decisions on when markets for restoration activities may be feasible.

Interdisciplinary and multi-institutional higher learning: reflecting on a South African case study investigating complex and dynamic environmental challenges

“…The supervisors and the PhD candidate worked closely with the masters students to develop a system dynamics model, which acted as a "boundary object" [53] in this program. Towards the end of the program the model, which captured biophysical variability as well a measure of risk in restoration investment decisions [28], allowed the program team to co-develop a shared understanding of how to achieve the overall program goal.…”

“…The aim was to "determine the economic risk/return parameters for developing a market for ecosystem goods and services following the restoration of natural capital [by utilising] a system dynamics approach" [59] (Table 1). Designed by a core leadership team with prior cross-interdisciplinary collaborative work experience (Table 2), the program focused on a meta-analysis of the hydrological, ecological and socio-economic impacts of eight different ecological restoration projects across South Africa [28]. The sites were selected to represent large scale restoration trials dealing with real-life degradation issues and involving various stakeholder communities (e.g.…”

“…Concurrent to the masters case studies at each of the eight sites, a PhD candidate (with input from the core, interdisciplinary research team), was tasked to develop a system dynamics model for economic evaluation of each case study which allowed for integration across all sites [28]. The system dynamics model provided a platform for shared data collection protocols and a vehicle to integrate different data types and sources, and can be viewed as a "boundary object" in this work.…”

“…We therefore carefully designed our educational research program to actively connect a team of postgraduate students and their supervisors across the disciplinary fields of ecology, hydrology and economics. Interactive learning experiences, which included stakeholders from industry and civil society, facilitated primary data collection and systems understanding at a range of sites; these data were then synthesized [28], as discussed below. We document this process and reflect on the method of interactive learning used as a contribution to the design of multi-institutional and interdisciplinary studies worldwide.…”

Ecological modelling of a wetland for phytoremediating Cu, Zn and Mn in a gold–copper mine site using Typha domingensis (Poales: Typhaceae) near Orange, NSW, Australia

“…Furthermore, heavy metals bioaccumulate through food chains causing irreversible negative impact for all life forms even at low levels in the environment (Pejman et al, 2015). Computational modelling tools have gained relevance in recent years with specific reference to ecological applications in agriculture (Altaweel & Watanabe, 2012), forestry (Bastos et al, 2012), fisheries (Johnson et al, 2013), meteorology (Krajewski et al, 2000) and hydrology (Michot et al, 2011), amongst several others (Svenning et al, 2011;Crookes et al, 2013;Gonzalez et al, 2013;Santos et al, 2013;Yue et al, 2013).…”