Carbon taxes are widely regarded as a potentially effective and economically efficient policy instrument for decarbonizing the global energy supply and thus limiting global warming. The main obstacle is political feasibility because of opposition from citizens and industry. Earmarking revenues from carbon taxation for spending that benefits citizens (i.e., revenue recycling) might help policy makers escape this political impasse. On the basis of choice experiments with representative samples of citizens in Germany and the United States, we examine whether revenue recycling could mitigate two key obstacles to achieving sufficient public support for carbon taxes: (i) declines in support as taxation levels increase and (ii) concerns over the international economic level playing field. For both countries, we find that revenue recycling could help achieve majority support for carbon tax levels of up to $50 to $70 per metric ton of carbon, but only if industrialized countries join forces and adopt similar carbon taxes.

Abstract Models testing interactive and quadratic hypotheses are common in Political Science but control strategies for these models have received little attention. Common practice is to simply include additive control variables, without relevant product terms, into models with interaction or quadratic terms. In this paper, we show in Monte Carlos that interaction terms can absorb the effects of other un-modeled interaction and non-linear effects and analogously, that included quadratic terms can reflect omitted interactions and non-linearities. This problem even occurs when included and omitted product terms do not share any constitutive terms. We show with Monte Carlo experiments that regularized estimators, the adaptive Lasso, Kernel Regularized Least Squares (KRLS), and Bayesian Additive Regression Trees (BART) can prevent the misattribution of interactive/quadratic effects, minimize the problems of efficiency loss and overfitting, and have low false-positive rates. We illustrate how inferences drawn can change when relevant product terms are used in the control strategy using a recent paper. Implementing the recommendations of this paper would increase the reliability of conditional and non-linear relationships estimated in many papers in the literature.

In this paper, we seek to identify robust predictors of individuals' attitudes towards climate change and environmental degradation. While much of the extant literature has been devoted to the individual explanatory potential of individuals' characteristics, we focus on the extent to which these characteristics provide robust predictions of climate and environmental attitudes. Thereby, we adjudicate the relative predictive power of psychological and sociodemographic characteristics, as well as the predictive power of combinations of these attributes. To do so, we use a popular machine learning technique, Random Forests, on three surveys fielded in China, Switzerland, and the USA, using a variety of outcome variables. We find that a psychological construct, the consideration of future consequences (CFC) scale, performs well in predicting attitudes, across all contexts and better than traditional explanations of climate attitudes, such as income and education. Given recent advances suggesting potential psychological barriers of behavioural change Public (Weaver, Adm Rev 75:806-816, 2015) and the use of psychological constructs to target persuasive messages

Success of the 2015 Paris Agreement, which is founded on Nationally Determined Contributions (NDCs), hinges on whether domestic support for international environmental agreements would be undermined if countries that are crucial to the global effort fail to reduce their emissions. Here we find that citizens in China (n = 3000) and the United States (n = 3007) have strong preferences over the design of international climate agreements, and contributions of other countries to the global effort. However, contrary to what standard accounts of international politics would predict, a survey-embedded experiment in which respondents were randomly exposed to different information on other countries’ behavior showed that information on other countries failing to reduce their emissions does not undermine support for how international agreements are designed. While other factors still make large emission cuts challenging, these results suggest that the Paris approach per se is not posing a problem.