Abstract
Though a global phenomenon, climate change will impact different countries to varying degrees. Different countries and industries also vary in how cost effectively they can mitigate climate change. These heterogeneities—one in marginal benefits derived from greenhouse mitigation (“benefit heterogeneity”), the other in marginal productivity in organizing collective action toward greenhouse mitigation (“production heterogeneity”)—have not been sufficiently studied, nor have they been directly compared. The paper tests for the effects of these two heterogeneities in a linear public goods setting, allowing the identification of different drivers of cooperative behavior. We find that heterogeneous assemblies are less able to collectively provide a public good such as greenhouse gas mitigation. Crucially, the type of heterogeneity matters. When there are less-productive mitigators, or when mitigation benefits other actors more than oneself—scenarios that mirror the incentives facing many developed nations—collective action is least effective. Results suggest that emphasizing reciprocity may improve collective action toward mitigation, but this depends on whose behavior is reciprocated. In addition to these empirical findings, the paper advances a methodological innovation. Whereas previous studies manually sorted individuals into contribution groups, which is impractical in larger data sets and yields difficult-to-replicate classifications, this paper uses machine learning to classify players according to their conditional contribution behavior.
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