the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A global social activation model of enabling conditions for positive social tipping – the role of sea-level rise anticipation and climate change concern
Marc Wiedermann
Jonathan F. Donges
Jobst Heitzig
Ricarda Winkelmann
Abstract. Effective climate change mitigation necessitates swift societal transformations. Social tipping processes, where small triggers initiate qualitative systemic shifts, are potential key mechanisms instigating societal change. A necessary foundation for societal tipping processes is the creation of enabling conditions. Here we assess future sea-level rise estimates and social survey data within the framework of a social activation model to exemplify the enabling conditions for tipping processes. We find that in many countries, climate change concern is sufficient, the enabling conditions and opportunities for social activation already exist. Further, drawing upon the interrelation between climate change concern and anticipation of future sea level rise, we report three qualitative classes of tipping potential that are regionally clustered, with greatest potential for tipping in Western Pacific rim and East Asian countries. These findings propose a transformative pathway where climate change concern increases the social tipping potential, while extended anticipation time horizons can trigger the system towards an alternative trajectory of larger social activation for climate change mitigation.
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E. Keith Smith et al.
Status: open (until 18 Oct 2023)
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RC1: 'Comment on egusphere-2023-1622', Anonymous Referee #1, 28 Aug 2023
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General Comments
The manuscript is well-written and addresses the very important topic of better understanding enabling conditions for social tipping towards climate action. Quantitative approaches for socio-ecological systems are urgently needed to complement existing research for societal transformation towards climate resilience in its many facets. As this transformation is urgent and requires broad societal agreement, understanding the pathways for shift perception into an aware and ‘climate active’ state is timely and this approach provides a valuable contribution in this regard.
While the framing and approach are valuable, there are a number of points that would require additional work and analysis for the paper to achieve its full potential.
One of the key issues I see with the paper is that it focusses on one very important and also detrimental climate impact, but one that is very localised in that it applies to coastal regions only. While these are home to large cities and a large share of the global population, including in higher density settings, nonetheless it is for many regions not one of the most pressing impact and not as globally applicable (as the authors also point out in their manuscript). The anticipation horizon, as also discussed and assessed in the paper, is another key issue with SLR that, in my view, does not make it the ideal impact to focus on.
Another concern relates to the idea of ‘active population’ and how these active groups then lead to the transformative shift in climate action. I think more granularity and context is needed here. Concern does not necessarily translate into action and active populations don’t necessarily trigger the needed policy-shift.
Some of the more specific comments below provide further granularity to these to concerns, which hopefully help in revising the manuscript.
Specific comments
There are a number of globally relevant impacts that are already detrimental today and affect people globally. The most detrimental effects of SLR are very long-term and thus often operate on timescales beyond human comprehension (see Figure 1, for example), as the authors also point out in the para p2-3 l30-35. Those impacts of SLR felt today are often very localised to smaller coastal communities and even in highly affected areas today, awareness of cc let alone activism is not present. It is not sufficiently clear to me why SLR would be chosen as the relevant impact variable, when other impacts might be much more central in the current perception. As the authors point out on p3 l50-55, “Experienced climate impacts such as floods and heat waves have the potential to shift attitudes and behaviours towards climate change and instigate social tipping processes”. Why not focus on one of these widely experienced impacts for the model?
In the end, which impact actually triggers the enabling conditions for mitigation doesn’t matter as the result is the same across impacts. I would therefore disagree with the statement on p3 l73/74. In addition, the cited reference from 2012 seems both outdated in light of currently experienced impacts and the paper also does not seem to substantiate the claim made in the sentence.
It is not quite clear what is defined as ‘active population’ and how that relates to mitigation action. Of course, awareness of the population is an essential ingredient, but awareness is far from activism, let alone government follow-through, which would be what is needed to actually mitigate in line with the Paris Agreement. An EIB study found that for Germany, for example, 63% of the population in 2021 would favour stricter climate measures. According to my reading of “active” within the study, this would then be the majority of the German population. However, we do not see a tipping in mitigation action.
While I see the importance of the model developed in this study, I think it needs more nuancing in terms of what is can actually show and how this activation of tipping then leads to large-scale global action. Maybe this would be better placed within the specific environment of policy-makers and how tipping can be induced with them?
I would also like to get more context for the consideration of ‘certainly active’ population as defined on p.6 line 129. While I see the point of assuming these should be ‘certainly active’ in terms of climate awareness, this is not necessarily the case for various reasons (e.g. US in Florida; West Africa). There are certainly no large activist movements there, despite the obvious current impacts and future exposure.
Maybe it would be worth further specifying (and calibrating) the model around a specific type of climate engagement within a specific group to first understand the context under which these groups have been activated to then be able to extrapolate to that specific type of action. The extension by which the activated population would then be able to force policy-action would then need to be an additional (albeit essential) extension of the analysis.
As mentioned above, I think it is critical to further define what is meant by ‘active’. Large parts of the population are convinced that climate action is needed, but are not openly active about it. How would these be placed within the model? In this context, adding different degrees of ‘active’ – along the lines of ‘aware’ to ‘willing to engage in existing activism’ to ‘active’ might be useful to then understand how activism may trickle down. Simply having an ‘aware’ population (as seems to be the definition of active in this paper) does not lead to the action that would be need to address the climate challenge. The authors provide quite a nuanced discussion of this in Sec. 1.1, but I’m not sure I see this reflected in the final assessment. Linking back to my comment above, this could also be linked to the reflection around which climate impacts drive awareness and action and create the enabling conditions for change.
Finally, I think the discussion of enabling conditions (line 45 and following) might require the consideration of some additional literature that provides quite a lot of a additional granularity (see e.g. IPCC WG2 Ch17 on adaptation and risk or IPCC WG3 Ch17 on Transitions). Clearly for a modelling approach, some reduction of complexity is needed, but I feel that inclusion of some of this complexity, including interactions between different aspects of enabling conditions, would be needed. As per some of my comments below, I feel results to some extent appear obvious out of the model set-up and if this complexity is not accounted for, I’m not convinced a model is actually needed for the results we get.
Results
- 12 lines 287-289 isn’t this conclusion rather obvious from the model set-up?
- 14 lines 320/325 These are super interesting results and also point towards what I mentioned in the General Comments above: there are different types of impacts that would most likely be a good predictor of climate action. In the Pacific with above average SLR rates, SLR may well be the perceived most pressing impact (though tropical storms may well contribute to the awareness as well). It may be interesting to choose regionally specific impact drivers that better reflect regionally specific risks , as this would likely provide a much closer to reality situation.
Discussion
The discussion raises a number of important points and also highlights the importance of better understanding social tipping processes to target interventions where they are most likely to yield results. Exactly due to the importance of such work, I would strongly encourage a further sharpening of the analysis to be more directly relevant for understanding the complexities.
In summary, as outlined above, on the one hand, I think the approach would strongly benefit from considering further key impacts that drive awareness globally. Unfortunately, the recent years have given plenty of examples of what these may be (wildfires, droughts, heatwaves, flooding…). On the other hand, more nuancing of what ‘active’ means and how this translates into the needed policy action would be important to include.
Citation: https://doi.org/10.5194/egusphere-2023-1622-RC1
E. Keith Smith et al.
E. Keith Smith et al.
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