Search

People support inefficient spending on preventing disasters, and these preferences are translated into inefficient policies as elected officials try to appeal to their constituents. Here, we find preferences for prevention spending are biased by the "cost conflation" mechanism, where people assume expensive problems have expensive solutions. In this article, we present a formal model of collective action, and illustrate how cost conflation causes people to deviate from the equilibria. We test for these hypothesized deviations using an incentivized experiment. The experimental subjects engage in cost conflation-they believe the costs of disaster prevention are positively related to the costs of disaster damages, even when explicitly told otherwise. As a result, they fail to prevent smaller disasters and pay too much to prevent large disasters when cheap solutions exist. Furthermore, we provide evidence that overemphasizing disaster damages undermines successful disaster prevention because people view these disasters as too big to solve.

Some technologies, such as solar or wind power, create certain but relatively small reductions in greenhouse gas emissions. Others, such as carbon sequestration devices, have larger potential upsides, but a greater possibility of failure. Here we show using economic games that people will invest in high-risk high-reward technologies when more certain options will not be sufficient. Groups of players had to contribute enough to avoid a simulated climate change disaster. Players could defect, make a certain contribution or make a risky contribution with a high potential gain. Across four studies using both laboratory (n = 296 and n = 297) and online (n = 501 and n = 499) samples, we found that more players made riskier contributions when necessary targets could not be met otherwise, regardless of the magnitude of potential losses. These results suggest that individuals are willing to invest in risky technology when it is necessary to mitigate climate change. In economic games, players shift to riskier contributions when targets that prevent catastrophic losses cannot be met otherwise, suggesting people are willing to invest in riskier technology when more certain options will not be sufficient to mitigate climate change.

Feeling affected by climate change related natural disasters is an important predictor of engaging in climate change mitigation behavior. We therefore collected data to identify who felt affected by Hurricane Florence, which made landfall in the United States on September 14th, 2018. In the months before Hurricane Florence, we collected survey responses from a nationally representative sample of United States citizens. We measured their attitudes towards climate change, emotional predispositions, and demographic information. Then, in the days after the hurricane, we re-contacted respondents to identify whether or not they felt personally affected by Hurricane Florence. These data can be used first to identify variables associated with climate change attitudes, and second to identify the traits that predispose individuals to feel affected by climate change related disasters.