Reporting summary

Our primary outcome variable measures the concern about the consequences of climate change and is thus climate change specific. However, the SOEP also elicits concerns about environmental protection, a broader concept encompassing various environmental issues, including climate change and biodiversity loss 1,28 . We re-run our analysis using environmental protection concerns as an alternative outcome. We find a statistically significant albeit slightly smaller effect of climate protests on concerns about environmental protection (Supplementary Table 9). In our preferred specification (Column (6)), climate protests significantly increase the probability that a respondent is concerned about environmental protection on average by 0.9 percentage points (p = 0.044) in the 14 days after a protest. Given the protest movement’s dominant focus on climate change embedded in a broader environmental discourse, this finding aligns with our expectations. It also highlights the strong but not perfect correlation between attitudes towards climate change and the environment 29 . These findings provide further evidence that concerns about climate change and the environment increase after climate protests. Further, we disaggregate the analysis and estimate the effects of each protest. Supplementary Fig. 6 displays the effect of individual climate protests on climate change concerns. While most protests yield positive coefficients, the statistical significance is sometimes reduced, not reaching the five percent significance level. This is likely due to the reduced number of observations at the protest level. Given this limitation regarding the sample sizes, protest-specific effects should be interpreted cautiously. As a general observation, we tend to observe larger positive coefficients for the first and last protests, potentially due to comparatively lower levels of concern before these protests took place (Fig. 1) in line with the aggregate finding concerning pre-protest concern levels (see below).

While investigating the effects of recent forms of climate protest tactics (e.g., protestors gluing themselves to streets or throwing liquids at paintings) on, for instance, public support for mitigation policies or support for the protest movement 23 would be highly relevant, we cannot speak to those outcomes with our data. Furthermore, future studies should investigate the effect of climate protests on other climate-related outcomes.

Method

where i denotes the individual living in state s, p the respective protest, d the date of the SOEP interview and t the year. Post represents the treatment effect of protests. The dummy equals one if the individual is interviewed after the protest and zero otherwise. X_i,t is a vector of several (socioeconomic) individual-level characteristics in year t that have been shown to be associated with beliefs and concerns about climate change 33,34 . We include the respondent’s age, self-reported sex (dummy), number of years in education, employment status (dummy), the 2-digit industry code of the respondent’s work (categorical), household size, the number of children aged 14 to 18 in the household, household labor income and the respondent’s interest in politics as well as their political orientation. Political orientation is elicited on a Likert scale ranging from 0 (far left) to 10 (far right). We include a categorical variable indicating “left- leaning” (values 0–4), “right-leaning” (values 6-10), and “center” (value 5 which is the largest category). Similarly, interest in politics is elicited on a 1-4 scale, where we include “(very) strong” (values 1-2) and “weak or none” (3-4). Both variables are pre-treatment values. I_i,t is a vector of interviewer characteristics. To avoid the loss of observations due to missing values in these variables, we include dummies indicating missing information Д°lk buluЕџmadan sonra ne yazД±lmalД± in a variable in X_i,t and I_i,t.

The study was granted ethics approval by Hertie School’s Research Ethics Officer under the application ID 20230220-27. To perform the empirical analysis, we have used Stata MP 16 64-bit (packages: reghdfe version 5.7.3, ebalance version 1.5.4, estout version 3.17, coefplot version 1.8.5, mlogit version 11.4.2, and gologit2 version 3.2.5) and R 4.3.1 (packages:.ggplot2 version 3.4.3, lubridate version 1.9.2, readtext version 0.90, dplyr version 1.1.2, gridExtra version 2.3, haven version 2.5.3, and patchwork version 1.1.3). For further details please refer to the replication package of this study 57 .