We thank the reviewer for the constructive feedback. We are glad that the paper was enjoyable to read and was perceived as helpful. 

The reviewer suggested revising the abstract slightly, including the specific negative social tipping dynamics we are exploring, and we agree, we will do so in the revised paper.

The reviewer had some questions with respect to the logistic map approach, referenced in the section on Anomie. The reference to the logistic map paper may indeed seem out of place, but the method of logistic map is much more general and has been applied to study various dynamic systems, including the ENSO. We believe that it can be used to study some social phenomena too, such as social disintegration (i.e. anomie). When thinking about the choices that present to our social systems in the warming climate, it helps to frame some of the deliberation in the context of phase transitions and more generally chaos theory. The main purpose of using Chaos theory is to help identify the type of dynamical system properties that a given system can exhibit. It is a framing that can explain how a system can alter and change as it moves through non-linear transitions. Part of the thinking that emerges from these systems is that they can exhibit scale invariant and universal system features. A chaotic map – such as the Logistic map is especially important for the context of tipping cascades as it represents the dynamics of period doubling and increased complexity as a system tumbles towards chaos. Due to the universal features of the logistic map, it can be applied to a range of systems and allows us to know what to expect due to its period doubling properties. In the area of social sciences the use of chaos theory is less developed, but has been discussed for some time (May, 1976; Faber and Koppelaar, 1994; Gordon, 1992; Boeing, 2016). So, from the point of view of social systems when considering the coupling of two systems, and for tipping cascades (period doubling) the logistic map can be viewed as a very natural type of chaotic system map to further develop in the context of negative social tipping points. In this paper, we develop the ideas around the consequences of tipping cascades for social dynamics in the climate futures we are envisaging. We will make this more explicit in the revised version of the manuscript and also include additional references.

Many thanks as well for pointing out that it was not clear what kind of deliberate polarisation we are describing in line 145. What we meant here was that creating polarisation can be deliberate to make sure no climate action is taken. This can be both in terms of supporting divergent views on the seriousness of climate change and fostering support for delaying climate policy responses. We will revise this sentence for greater clarity, explaining these two options.

The reviewer is also correct that we have insufficiently explained how the complex lens and HECS/SFL framework are helpful in understanding tipping mechanisms with respect to conflict. HECS refers to the human–environmental–climate security (HECS) nexus framework developed by Daoudy (2021). SFL refers to the social feedback loop framework by Kolmes (2008). We believe bringing these two frameworks together can advance our understanding of conflicts as a social tipping phenomenon. Specifically, self-reinforcing feedbacks emerge in social-ecological systems as a result of complex interactions among socio-economic, environmental and political events and variables, such as institutional capacity for solving social-ecological problems. These complex interactions result in the amplification of social-ecological shocks potentially disrupting the system in concern. These disruptions can result in a conflict, i.e. a phase transition takes place from cooperation to conflict, with the affected society becoming entrapped in the conflict state until sufficient incentives can move it out. We will include a more explicit explanation in a revised version of the paper.

With respect to Financial Destabilisation section, it is true that we have not explored further the economics of crossing climate tipping points. This is mainly because the paper does not focus on discussing all possible impacts and economic costs that crossing climate tipping points may have, as valuable as that might be. We only look at impacts that could result in negative social tipping points and here we also explicitly state that we do not claim to have covered all potential negative social tipping points. In the paper, we mention some potential negative social tipping points that were omitted, including supply chain breakdown, which is linked to economics of crossing climate tipping points. However, we acknowledge the value in sign-posting other research that has explored the economic costs of crossing climate tipping points and will include a sentence with the reference to the Dietz (2021) paper (and other papers) at the start of the Financial Destabilisation chapter as a response to this comment.

We thank the reviewer for insightful comments on the food security section of our paper. In our revised manuscript we will incorporate, as suggested, the analysis by Ritchie et al. (2020) which alludes to a change in precipitation patterns, even in an optimistic mitigation scenario of levelling temperatures at 1.5 degrees C and then reducing those temperatures. We will also include a reference to a recent paper by Kornhuber et al. (2023) on the dangers of simultaneous harvest failures across major crop-producing regions are a threat to global food security. However, we decided to remove the analysis of ENSO in the revised version of the paper as this merits a broader discussion which is beyond the scope of the paper - while a positive ENSO cycle did contribute to a reduction in rainfall, which ultimately led to crop failure and livestock death, there were other factors that contributed to high levels of food insecurity such as lack of preparedness, uncertainty in forecasting weather conditions, etc. This is an interesting analysis on its own but is not the purpose of the paper and has been discussed elsewhere. As such, we have also opted not to include dates in Figure 3. The figure is meant as a generic example of how tipping points in food security might be defined and conceptualised, rather than a specific case study for further analysis in this paper.

With respect to Figure 1, our original intention when including these spatial and temporal scales was to highlight how the various negative social tipping points can play out at various spatial and temporal scales. But we understand that this may be confusing and maybe removing the scales would be sensible. Instead, we could add a sentence to state explicitly how the tipping points can unfold at different temporal and spatial scales. We will discuss this suggestion further and make a respective decision for the revision of the manuscript. With respect to Figure 2, we agree that including time frames in the description boxes where they were missing, would make the figure more impactful. We will update the figure and caption accordingly in the revised version of the paper. We provided an answer with respect to Figure 3 above but would also like to add that this is a published figure already.

We also thank the reviewer for a couple of editorial corrections. These will be included in the revised paper.

Overall, many thanks again for the valuable suggestions, which will help us enormously to revise the manuscript.

References:

Boeing, G. (2016) Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction. Systems, 4, 37. https://doi.org/10.3390/systems404003

Daoudy, M. (2021). Rethinking the Climate–Conflict Nexus: A Human–Environmental–Climate Security Approach. Global Environmental Politics, 21 (3), 4–25. https://doi.org/10.1162/glep_a_00609 

Dietz, S., Rising, J., Stoerk, T. and Wagner, G. (2021). Economic impacts of tipping points in the climate system. PNAS, 118(34). e2103081118. https://doi.org/10.1073/pnas.2103081118

Faber, J., Koppelaar, H. (1994) Chaos theory and social science: A methodological analysis. Qual Quant 28, 421–433. https://doi.org/10.1007/BF01097019

Gordon, T.J. (1992), Chaos in social systems, Technological Forecasting and Social Change, Volume 42, Issue 1, Pages 1-15, ISSN 0040-1625, https://doi.org/10.1016/0040-1625(92)90069-6

Kolmes, S.A. (2008). The Social Feedback Loop. Environment: Science and Policy for Sustainable Development, 50(2), 57-58.https://doi.org/10.3200/ENVT.50.2.57-58

Kornhuber, K., Lesk, C., Schleussner, C.F. et al. (2023). Risks of synchronized low yields are underestimated in climate and crop model projections. Nature Communications, 14, 3528. https://doi.org/10.1038/s41467-023-38906-7

May, R. (1976). Simple mathematical models with very complicated dynamics. Nature, 261, 459–467. https://doi.org/10.1038/261459a0.

Ritchie, P.D.L., Smith, G.S., Davis, K.J. et al. (2020). Shifts in national land use and food production in Great Britain after a climate tipping point. Nature Food, 1, 76–83. https://doi.org/10.1038/s43016-019-0011-3

We thank the reviewer for the critical yet constructive feedback. We agree that the piece in its original form was abstract, this is why we are now working to include a few case studies or concrete examples in the revised version of the paper and throughout different sections (we noted that the reviewer found the concrete example of Ethiopia on food insecurity useful and convincing). But indeed, the main goal of this paper is inspiring new research, setting a research agenda, because there are a lot of gaps in our understanding of how various dynamics do/can play out. To facilitate this, we try to synthesise what we do know and then to draw out the pathways for further research. We will make this goal of the paper more explicit in the revised version. 

With respect to the premise of whether social tipping points are knowable, we believe that the literature and studies we are referencing amongst others suggest that tipping can occur in social systems and has been observed empirically at least at small scales (which are more feasible to study). In mathematical terms there are many, many examples of social systems or sectors undergoing non-linear change which starts slowly and then accelerates due to positive feedbacks and due the lack, loss, or inadequacy of buffering capacity / negative feedbacks. Many of these tipping points are 'knowable' (at least subsequently) and there are plenty of examples (e.g., impacts of technology on communication or manual labour). The reviewer might have had a more demanding definition of tipping points in mind than we do, particularly in terms of absolute irreversibility and far-reaching societal change or collapse. However, far-reaching change is more a feature of tipping of large systems, such as Earth systems. Generally speaking, systems exist at various scales and large systems often consist of many sub-systems. Mathematically speaking a phase-transition in a dynamical system “just” means the change from one state of a system to another. Most studies of social tipping points focus necessarily (for feasibility reasons) on specific subsystems and phase transitions in these subsystems may be far-reaching for that sub-system (e.g. one neighbourhood turning from not segregated to segregated) but may not translate into a complete overhaul of a society. But this does not mean that there was no social tipping in that sub-system. If several of the inter-linked sub-systems of a larger system undergo tipping processes, then we are indeed more likely to see far-reaching social change across the overall system that consists of these sub-systems and we touch upon such processes in the Cascading section of the paper. We will make our understanding of tipping points clearer in the revised paper.

But we agree with the reviewer that our current instruments are insufficient to know social tipping points in advance and we do state this in the paper and will try to make the point even stronger when revising the paper. It might be also more useful to talk about tipping processes in the social context rather than specific tipping points, as indeed identifying these tipping points, particularly in advance, is extremely difficult (and so far, has not been possible, even for geophysical systems). And while we are very unlikely to develop early warning systems that will tell us when we reach a tipping point and where exactly it is, early warning systems could potentially detect irregular behaviour of a system that suggests it is becoming increasingly unstable, as we have demonstrated for instance in the paper with the food security example. From that perspective, we believe it is important to study potential negative social tipping processes that may be triggered by ecological destabilisation. We also agree that human agency plays an important role and can seed tipping processes usually unintentionally, but ultimately tipping processes are studied on the aggregate, emergent level of social systems and on that macro level patterns nevertheless can emerge that can display mathematically formalisable and hence to some extent predictable dynamics, of course always within probabilistic rather than deterministic terms. This includes the probabilistic prediction of a breakdown of regular patterns, i.e. chaotic behaviour of social systems, which are particularly relevant in the context of negative social tipping points. How individual level agency can lead to tipping at macro level can be itself studied through agent-based models for instance. However, we do agree that at this stage we need to be cautious, particularly when extrapolating/forecasting given the many gaps we still have in our understanding of complex social processes. We will expand our discussion of knowability of negative social tipping points/processes in the paper to make these various points clearer and acknowledge the issues raised.

The reviewer was not sure about the “unit of analysis” of the discussed phenomena. The negative social tipping points that we discuss in the paper can occur indeed across various spatial and temporal scales, so indeed they can happen on communities’ level, national state level or even international level. We will make this point more explicit in the revised paper.

We also would like to point out we do not claim “social equilibrium” (please note “social equilibrium” is a very specific term and not at all what we refer to), we do briefly write about “newly reached equilibrium”, what we mean here is that the system is reaching a new stable state. Many of the studied systems (including systems in the biosphere) can be in a stable state but are from equilibrium in strict sense. We will remove the word “equilibrium” in the revised version of the paper to avoid confusion.   

The reviewer was also sceptical of the concept of anomie. We believe that social disintegration, i.e. anomie, is an important potential consequence of an escalating climate change and first studies that we reference, such as the one by Brown (2022), exist that highlight this. It might be useful to think of anomie as the societal or community-level equivalent of failed states. Furthermore, studies exist, and we reference these, that show clearly how breakdown of norms, trust, social ties etc. leads to breakdown of cooperation, which is crucial for responding to the climate crisis (across different societal levels) and hence could lead to further escalating climate crisis. But it is true that anomie is a complex, multifaceted phenomenon that is difficult to study, still proxies for it indicate that it could display tipping dynamics, and this is why we included the phenomenon, even if it is one of the least well understood phenomena we discuss. But given the paper is meant to inspire new research, we hope that this phenomenon will receive more attention in future research. We will include a clearer description of the phenomenon and the gaps we identified in understanding this phenomenon.

With respect to logistic maps, as we have explained in greater detail in our response to the first reviewer, we will expand the explanation of the logistic map methods to study social tipping phenomena such as anomie in the revised paper. We agree that the current explanation is not sufficient. We also agree that we should reference Table 1 when discussing it in the text. We will also include a descriptive example.

The reviewer asked moreover to avoid or better explain technical terms to make the paper more accessible to readers, who might be less familiar with various methodological approaches. We will do our best to do so and have noted the terms the reviewer explicitly mentioned (autocorrelation, heterogeneity, etc.). Though we also would like to state that we assume we are talking to a particular community with this paper, i.e. the community that would typically read papers published by the ESD journal and hence we can assume some familiarity with these concepts. On the other hand, we understand that this is a special issue and is meant to reach a wider audience, we will therefore try to make fewer assumptions when revising the paper.

We also would like to add that we are currently working on further updating the paper throughout and revising various sections, also in response to some critical reflection the manuscript received in a recent working paper by Kopp et al. (2024). The revised paper will hence respond also to the critical issues raised by other researchers, beyond the reviewers.

We would like to thank the reviewer again for their critical questions, which will help us to further improve the paper.

References

Brown, A.R. (2022). Environmental anomie and the disruption of physical norms during disaster, Current Sociology, https://doi.org/10.1177/00113921221129316.

Kopp (2024). Tipping points’ confuse and can distract from urgent climate action. https://doi.org/10.22541/essoar.170542965.59092060/v1