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Rich kids of Europe? Social basis and strategic choices in the climate activism of Fridays for Future

Published online by Cambridge University Press:  28 December 2021

Donatella della Porta*
Affiliation:
Faculty of Political and Social Sciences, Scuola Normale Superiore, Florence, Italy
Martín Portos
Affiliation:
Faculty of Political and Social Sciences, Scuola Normale Superiore, Florence, Italy Department of Social Sciences, Universidad Carlos III de Madrid, Getafe, Spain
*
*Corresponding author. Email: donatella.dellaporta@sns.it

Abstract

In 2018, Swedish teenager Greta Thunberg began a school strike that quickly spread across the globe. After a ritual strike every Friday by school pupils to call for urgent action against climate change had gone on for several months, what had become Fridays for Future (FFF) called for various global days of action throughout 2019, bringing millions of people out onto the streets in the largest climate protests in world history. Drawing on unique protest survey data on FFF events across European cities in 2019, this article explores the structural bases of organized collective mobilization for climate justice. Nuancing narratives that focus on either the privileged background of climate justice protesters or the environmentalism of the poor, our results show the heterogeneity of the social composition of the protests, suggesting the need for cross-class alliances for mass mobilizations. Moreover, our analysis reveals that the social background of protesters shaped their attitudes regarding what institutions and approaches can be relied upon to tackle climate and environmental challenges. This suggests an important and under-studied connection between social background and the strategic choices of environmental movements.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Società Italiana di Scienza Politica
Figure 0

Figure 1. Distribution of sampled FFF participants per country and event date (March and September 2019). Source: Zamponi et al. (2021).

Figure 1

Figure 2. Participants in 2019 FFF events by subjective class and job status.

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Figure 3. Average levels of trust in science, governments, companies and the market, and lifestyle actions to solve environmental problems/stop climate change among 2019 FFF protesters.

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Figure 4. Strategic focus on actors/institutions to solve environmental problems/stop climate change by subjective class and job status among 2019 FFF protesters.

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Figure 5. Impact of subjective class on trusted strategies to solve environmental problems, multivariate linear regression with robust standard errors (models 5–8, Appendix Table A4 and models 1–4, Appendix Table A5).Note: City dummies are included in the regressions but are not plotted here. 95% confidence intervals. Coefficients are log odds.

Figure 5

Figure A1. Participants in 2019 FFF events disaggregated by supervising the work of others and university education of parents.

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Figure A2. Participants in 2019 FFF events disaggregated by subjective class and job status per country.

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Figure A3. Participants in 2019 FFF events disaggregated by supervising the work of others and university education of parents per country.

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Figure A4. Histogram of trust in science to solve environmental problems (1 = strongly disagree; 5 = strongly agree) by country.

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Figure A5. Histogram of trust in governments to solve environmental problems (1 = strongly disagree; 5 = strongly agree) by country.

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Figure A6. Histogram of trust in companies to solve environmental problems (1 = strongly disagree; 5 = strongly agree) by country.

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Figure A7. Histogram of voluntary lifestyle actions to stop climate change (1 = strongly disagree; 5 = strongly agree) by country.

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Figure A8. Trust among 2019 FFF protesters in actors/institutions to solve environmental problems disaggregated by supervising the work of others (left) and university education of parents (right).

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Figure A9. Impact of subjective class on trust in actors/institutions to solve environmental problems/stop climate change, OLS regression with robust standard errors – models 1–4, Table A2.Note: City dummies are included in the regressions but are not plotted here. 95% CI coefficients are log odds.

Figure 14

Figure A10. Impact of job status, supervising the work of others and parents’ university education on trust in actors/institutions to solve environmental problems/stop climate change, OLS regression with robust standard errors – models 1–4, Table A4.Note: City dummies are included in the regressions but are not plotted here. 95% CI coefficients are log odds.

Figure 15

Figure A11. Marginal effects of subjective class on trust in science (upper left), governments (upper right), companies and the market (lower left), and lifestyle changes (lower right) to solve environmental problems/stop climate change, OLS regression with robust standard errors – models 5–8, Table A4.

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Figure A12. Marginal effects of job status on trust in science (upper left), government (upper right), companies and the market (lower left), and lifestyle actions (lower right) to solve environmental problems/stop climate change, OLS regression with robust standard errors – models 1–4, Table A5.

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Table A1. Descriptive statistics

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Table A2. Determinants of trust in different ways of solving environmental problems, OLS regressions with robust standard errors

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Table A3. Determinants of strategies to solve environmental problems/stop climate change, OLS regressions with robust standard errors

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Table A4. Determinants of strategies to solve environmental problems/stop climate change, OLS regressions with robust standard errors

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Table A5. Determinants of strategies to solve environmental problems/stop climate change, OLS regressions with robust standard errors