How much does the far future matter? A hierarchical Bayesian analysis of the public's willingness to mitigate ecological impacts of climate change

How much does the far future matter? This question lies at the heart of many important environmental policy issues, such as global climate change, biodiversity loss, and the disposal of radioactive waste. Although philosophers, experts, and others offer their viewpoints on this deep question, the solution to many environmental problems lies in the willingness of the public to bear significant costs now to make the far future a better place. Short of national plebiscites, the only way to assess the public's willingness to mitigate impacts in the far future is to ask them. Using a unique set of survey data in which respondents were provided with sets of scenarios describing different amounts of forest loss due to climate change, along with associated mitigation methods and costs, we can infer the respondents' willingness to bear additional costs to mitigate future ecological impacts of climate change. The survey also varied the timing of the impacts, which allows us to assess how the willingness to mitigate depends on the timing of the impacts. The responses to the survey questions are a consequence of latent utilities with complex ordinal structures that result in nonrectangular probabilities. Whereas the nonrectangular probabilities complicate standard maximum likelihood-based approaches, we show how the nonrectangular probabilities fit neatly into a hierarchical Bayesian model. We show how to fit these models using the Gibbs sampler, overcoming problems in parameter identification to improve mixing of the induced Markov chain. The results indicate that the public's willingness to incur additional costs to mitigate ecological impacts of climate change is an increasing nonlinear function of the magnitude of the impact, and that they discount future impacts at somewhat less than 1% per year.