Only a limited amount of quantitative and qualitative modelling work has been carried out that is pertinent to the normative transition proposals set out on this site, but hopefully that will improve as sustainable transition becomes a larger and wider research agenda. Where appropriate, existing work has been cited in relevant section of the site.
According to Kohler et al. (2018), who have extensively reviewed models associated with certain kinds of socio-technical transitions, transition models should be able to:
- represent non-linear behaviour (most transition pathways do not unfold in a straight line)
- represent qualitatively different system states (elements of transition are not all at the same stage of evolution)
- represent changes in social values and norms
- represent diversity and heterogeneity
- represent dynamics at and across different scales (from the local to the national)
- incorporate open processes and uncertainties or contingencies (things can happen suddenly or unpredictably)
From their review, no modelling approaches are able to optimize all these variables, and can not capture all the dynamics of transition. So different approaches have to be tailored to different themes and scenarios. And transition thinkers must always remember the model is not the transition path, it just informs the thinking about the path.
Scenario analysis is another tool that is often more qualitative than quantitative, not necessarily requiring sophisticated computer modelling. Originally used by the military, it can be applied to complex and uncertain environments to identify transition paths. It can look forward from the present (a form of forecasting) or it can imagine a future point in time and then look backward from that (backcasting), identifying pathways that might produce that specific future endpoint. Urban growth management plans can be developed from both approaches, for example. A municipality might set a target desired population for the year 2050, and then identify scenarios working backward from that date to identify things that need to be done to help make it a reality. Forecasting and backcasting have also been used in somewhat limited fashion for Demand - supply Co-ordination analysis (see Goal 2, Demand - Supply Co-ordination, Pertinent Studies and other citations for the entire change area).
Some other examples of potentially helpful processes can be found in Research Projects, MacRae et al. (2009, 2013), ERL et al. (2015), Galzki et al. (2017) and Miller and Mann (2020).
