Climate-denying rumor propagation in a coupled socio-climate model: Impact on average global temperature

Abstract

Individual attitudes vastly affect the transformations we are experiencing and are vital in mitigating or intensifying climate change. A socio-climate model by coupling a model of rumor dynamics in heterogeneous networks to a simple Earth System model is developed, in order to analyze how rumors about climate change impact individuals' opinions when they may choose to either believe or reject the rumors they come across over time. Our model assumes that when individuals experience an increase in the global temperature, they tend to not believe the rumors they come across. The rumor rejectors limit their CO2 emissions to reduce global temperature. Our numerical analysis indicates that, over time, the temperature anomaly becomes less affected by the variations in rumor propagation parameters, and having larger groups (having more members) is more efficient in reducing temperature (by efficiently propagating rumors) than having numerous small groups. It is observed that decreasing the number of individual connections does not reduce the size of the rejector population when there are large numbers of messages sent through groups. Mitigation strategies considered by the rejectors are highly influential. The absence of mitigative behavior in rejectors can cause an increase in the global average temperature by 0.5°C. Our model indicates that rumor propagation in groups has the upper hand in controlling temperature change, compared to individual climate-denying propagation.

Fig 1. Dynamics of the coupled model for the baseline parameters.

(a,d) Times series showing the rumor dynamics for baseline parameter values (b,e) Time series of emission scenario (GtCO₂yr^-1) and (c,f) temperature anomaly (°C) for baseline parameter values. (a,b,c) is the time series for the baseline parameter value and (d,e,f) is the time series plot generated using the accepted parameter values after applying ABC. Each of the multiple lines in (d,e,f) is a time series generated using each set of parameter values that were generated by ABC.

Fig 2. Emission scenarios for limiting global temperature change.

(a) Emissions levels at Ψ = 0.1 (d) Temperature anomaly < 2.5°C at Ψ = 0.1 (b) Emission levels at Ψ = 1 (e) Temperature anomaly at 1.5°C (c)Emission levels when Ψ = 11 (f) Temperature anomaly at < 1.5°C with all other parameters at the baseline values.

Fig 3. Temperature sensitivity is more with variation in emission limiting constant than group believing probabilities.

Parameter planes of CO₂ emissions (GtCO₂yr^-1) at (a) 2021, (b) 2070, and (c) 2200 and temperature anomaly (°C) at (d) 2021, (e) 2070 and (f) 2200 by varying group believing probability (ω) and emission limiting constant (Ψ).

Fig 4. Global temperature anomaly is more sensitive to the emission-liming constant than group rejecting probabilities.

(a-c) Parameter planes of CO₂ emissions (GtCO₂yr^-1) at (a) 2021, (b) 2070, and (c) 2200 and (d-f) temperature anomaly (°C) at (d) 2021, (e) 2070 and (f) 2200 by varying group rejecting probability (ζ) and emission limiting constant (Ψ).

Fig 5. Global temperature anomaly is unaffected by low network degree at a high number of group messages.

Parameter plane showing the changes in (a) rejector population and (b) temperature anomaly due to the variation in the average number of messages transmitted per group ($\overline{d}$) and the average degree of the network (k). The dashed lines show equal $\overline{d}*k$ values and the gray lines show equal size of rejector population and equal temperature anomaly.