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Collective Action and Citizen Responses to Global Warming

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Abstract

This paper expands the relevance of the collective interest model of mass political action to explain collective-action behavior in the context of global warming and climate change. The analysis is an attempt to answer Ostrom’s call for a behavioral model of collective action that can be generalized beyond political protest to other collective-action problems. We elaborate, specify, and empirically test a collective interest model approach to citizen policy support, environmental political participation, and environmental behavior related to the issue of global warming. Key elements of the collective interest model—perceived risk, personal efficacy, and environmental values—are found to be directly, and positively, related to support of government policies and personal behaviors that affect global warming. We also discuss the links between the collective interest model and other important approaches to political behavior.

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Notes

  1. The original version of the collective interest model emphasizes dissatisfaction with government policies as the most important motivator to political protest. We found no support for this hypothesis in a previous analysis in the context of air pollution activism. For environmental issues, risk reduction is the more relevant collective benefit.

  2. Another potential component of political context is the existence of local or state policies that might influence various components of the CI model. To test this hypothesis, we estimated versions of the models below that included a dummy variable for whether a citizen lived in a city that was a member of the Cities for Climate Protection campaign. The CCP began in 1993 and has the goal of reducing carbon dioxide emissions by 20% of 1990 levels, which is more ambitious than the Kyoto Protocol targets. We found no evidence that city policy context influenced citizen behavior. While it is possible state policies could influence behavior, we did not gather data to test the influence of state policies. We also believe local policies are a better measure of policy context because they more narrowly target local citizen behaviors, and cities have become involved in climate change policies earlier on average than states.

  3. The effect of race on environmental activism is subject to the most disagreement among the demographic variables. While most theories suggest that minorities face barriers to participation, some theories of environmental justice suggest that minorities may be more motivated to participate in environmental activism in the face of disproportionate risk exposure. However, this is more likely to be the case of immediate and visible problems like air pollution rather than future and diffuse problems like global warming. Providing a theoretical framework to understand the distinctions between different types of environmental issues remains a major task.

  4. The majority of survey participants are female (55.6% vs. 44.4% male), and the average age is 47.31 (SD = 16.40). 36.9% of respondents hold a college or post-graduate degree, and 2.5% have no high school diploma. The ethnicity of the respondents was white non-Hispanic (84.1%), followed by African American (8.1%), Hispanic (2.9%), Native American (1.2%), and Asian American (.2%). On self-reported political ideology, 42.0% of respondents regard themselves as conservative, compared to 32.7% leaning liberal. Compared to the national US Census figures, our sample is older in terms of average age (45.43 vs. 32.3) and better educated (1/5 of Americans are without a high school diploma), and undercounts males (44.4% vs. 49.1%), African Americans (8.1% vs. 12.3%), Hispanics (2.9% vs. 12.5%), and Asian Americans (.2% vs. 3.6%).

  5. The civic engagement survey data is collected bi-annually by Mediamark Research (http://www.mediamark.com/). Adults are selected randomly from a national population list of 90 +  million households on an area probability basis. Each wave consists of 12,000 +  field interviews, totaling 25,000 per year since 1979. Researchers at Applied Geographic Solutions Inc. (www.appliedgeographic.com) use Geographic Information Systems technology to map the MRI household records to the county level.

  6. We also tried an interaction term that multiplied the social discussion variable with a survey question asking about the overall level of concern about global warming within the respondents overall social network (Likert scale, 0 = not concerned, 10 = concerned). The interaction effect was significant and positive in some models, and suggests that “concerned” social networks have a stronger positive effect on global warming activism. However, “unconcerned” networks do not have a negative effect, and the magnitude of the interaction effect is always very small. The most likely explanation for this is a significant and positive correlation (r = .27, p < .05) between contact frequency and network concern—discussion about global warming tends to be among people who are worried about the problem.

  7. Because the policy support and environmental behavior scales are averaged responses over multiple questions, they are no longer categorical variables. For example, the policy support scale has 80 unique values and the environmental behavior scale has 43 unique values. The number of unique values for the scales justifies using OLS instead of an alternative model such as ordered probit. Further justification is provided by the predicted values, which do not fall outside of the range of the dependent variables except a very small percentage (2.8%) below zero for the political participation model. We also analyzed the political participation model with Poisson regression, and found no differences in the direction or significance of the independent variables. Thus, in the interest of methodological consistency and simplicity, we report regression results for all three variables. Results of the Poisson regression are available upon request from the corresponding author.

  8. The relative influence of the collective interest variables could also be assessed by computing the marginal effects of each variable on the probability of observing a certain response category for each individual behavior. The probability analysis provides the exact same ordering except that “walk/ride bike” moves between “turn off electricity” and “buy low emission vehicles”.

  9. The count of GOOGLE hits comes from using the following search terms on August 14, 2006: insulate home, adjust thermostat, citizen tree planting, walk ride bike, car pool, turn off electricity, low emission vehicles, recycle. Each of these terms was then paired with “global warming” and “climate change”, and the results summed for each environmental behavior. Obviously different search terms for each behavior will elicit greater or fewer hits, so this should only be considered illustrative data. However, we do think the GOOGLE data reflects an increasing amount of public discourse along the gradient shown in Fig. 1.

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Authors and Affiliations

  1. Department of Environmental Science and Policy, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA

    Mark Lubell

  2. Department of Sociology, Colorado State University, B258 Clark Building, Fort Collins, CO, 80523-1784, USA

    Sammy Zahran

  3. Institute for Science, Technology and Public Policy, Texas A&M University, 4350 TAMU, College Station, TX, 77843-4350, USA

    Arnold Vedlitz

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  1. Mark Lubell
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Correspondence to Mark Lubell.

Additional information

This material is based upon research conducted by the Institute for Science, Technology and Public Policy at Texas A&M University and supported under Award No. NA03OAR4310164 by the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration or the Department of Commerce.

Appendix A: Survey Question Wording

Appendix A: Survey Question Wording

   
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Table A1 Descriptive statistics for collective interest model variables
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Table A2 Ordered probit models for individual environmental behaviors
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Lubell, M., Zahran, S. & Vedlitz, A. Collective Action and Citizen Responses to Global Warming. Polit Behav 29, 391–413 (2007). https://doi.org/10.1007/s11109-006-9025-2

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