Measuring progress – Early opportunity indicators in other sectors

We have discussed how one might apply EOIs to a socio-technical transition, using the EV transition as a case study. This approach seems to show some success and requires consideration of how we may apply it to other positive tipping points.

We propose that further work is required to investigate these indicators for other PTPs, in order to add value to existing work on determining when tipping points may happen. Some of these system changes may have a social element, such as consumer demand and preferences, and as such social data (where it exists) would be useful here; one such example could involve discourse around plant-based diets and meat alternatives. As well as exogenous drivers, some social tipping points may be strongly driven by network effects and social contagion, such as the agroforestry project TIST discussed in Chapter 4.3 (Box 4.3.9). Network-based statistics can aid in predicting tipping points (Lu et al., 2021; see Chapter 1.6 for more details) and therefore investigating these networks’ structures may explain if and why a tipping point is being approached or where contagion can be facilitated.

These indicators may be observed in datasets which measure different elements of the transition – in this case, ICEV sales and EV advert views. They can give some measure of the effect of external intervention and show how ‘resilient’ the undesirable status quo is, and therefore how easy or hard it may be to tip out of (in our case study, this is the incumbent ICEV regime). From the EV advert views, we can see that changes in the system response to external perturbations, such as policy announcements and economic factors, offer a way to detect the social response to these. One approach to utilising this is to measure the resilience of the existing (undesirable) regime and to monitor how it responds to interventions, with a system approaching a tipping point showing the largest effect from an intervention. They can therefore be conceived of as both a measure of ‘progress’ towards a goal, and also as an indicator of when a system is losing resilience and can therefore experience greater return on targeted efforts to push it towards a tipping point. 

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