Social dynamics can delay or prevent climate tipping points by speeding the adoption of climate change mitigation

TL;DR

This paper develops a coupled socio-climate model that incorporates tipping points in both the climate and social systems. By embedding a sigmoid tipping term activated at a critical temperature into an Earth system model and linking it to a two-group behavioural dynamics model, the authors explore how mitigation adoption, learning rates, and social norms influence tipping likelihood, timing, and severity. Key findings show that faster social learning can delay or even prevent climate tipping, while stronger social norms can trigger social tipping once climate tipping begins; high-risk scenarios amplify tipping probabilities and the critical temperature plays a pivotal role. The work emphasizes the importance of accelerating social learning and mitigation adoption to mitigate tipping-induced impacts, while acknowledging simplifications such as homogeneous mixing and binary choices, and outlining avenues for more nuanced future models with broader bifurcation analyses.

Abstract

Social behaviour models are increasingly integrated into climate change studies, and the significance of climate tipping points for `runaway' climate change is well recognised. However, there has been insufficient focus on tipping points in social-climate dynamics. We developed a coupled social-climate model consisting of an Earth system model and a social behaviour model, both with tipping elements. The social model explores opinion formation by analysing social learning rates, the net cost of mitigation, and the strength of social norms. Our results indicate that the net cost of mitigation and social norms have minimal impact on tipping points when social norms are weak. As social norms strengthen, the climate tipping point can trigger a tipping element in the social model. However, faster social learning can delay or prevent the climate tipping point: sufficiently fast social learning means growing climate change mitigation can outpace the oncoming climate tipping point, despite social-climate feedback. By comparing high- and low-risk scenarios, we demonstrated high-risk scenarios increase the likelihood of tipping points. We also illustrate the role of a critical temperature anomaly in triggering tipping points. In conclusion, understanding social behaviour dynamics is vital for predicting climate tipping points and mitigating their impacts.

Figures (8)

• Figure 1: Possibility of avoiding climate tipping point through social actions. In this figure, The Baseline model is illustrated in blue and the modified model with an additional tipping term is illustrated in orange. The dynamic of $x$ (fraction of mitigators) versus time is also depicted in both plots in a smaller box. Two plots differ in the values of $\kappa$ and $R_{max}$ (GtC/year) that were used: (0.01, 5) for plot A, where it shows tipping behaviour, and (0.1, 1) for plot B, where it does not. Note the difference in vertical scale.
• Figure 2: The difference in AUC versus social behaviour variables. In this figure, the difference in AUC is depicted against social behaviour parameters and $R_{max}$ (GtC/year). The top plots represent the high-risk scenario ($T_c$ = 2), and the bottom ones correspond to the low-risk scenario ($T_c$ = 3). The plots illustrate the difference in AUC versus Social Learning Rate on the left, Net Cost of Mitigation in the middle, and Strength of Social Norm on the right.
• Figure 3: Time to the tipping point for different emission and mitigation scenarios. Time to the tipping point is plotted against $R_{max}$ (GtC/year) and $\kappa$ for six different cases. The top plots represent the results of the high-risk scenario ($T_c$ = 2), and the bottom ones show the results of the low-risk scenario ($T_c$ = 3). The plots on the left, middle, and right depict the results for different values of the threshold (d = 1.1, 1.25, and 1.5).
• Figure 4: Peak Temperature Anomaly for Different Emission and Mitigation Scenarios Peak temperature anomaly (Celsius) is depicted against $R_{max}$ (GtC/year) and $\kappa$ for the high-risk scenario (left) and the low-risk scenario (right). Isoclines show that as K increases, for the high-risk scenario, the decline in the peak of temperature is lower than low-risk scenario.
• Figure 5: The difference in AUC versus critical temperature and social learning rate The Difference in Area under the curve (AUC) is depicted versus critical temperature and $\kappa$ (left). Two examples representing two different choices of $T_c$ and $\kappa$ are chosen and illustrated on RHS. The plot at the top (orange circle) represents a case in which the modified model does not go through a tipping point, and the one at the bottom (blue star) represents a case of tipping point