Valuing Climate Change Mitigation

Valuing Climate Change Mitigation

Applying Stated Preferences in the Presence of Uncertainty

Sonia Akter and Jeff Bennett

Valuing Climate Change Mitigation discusses the role of uncertainty in valuing the benefits of climate change mitigation policies using contingent valuation and choice experiment techniques. It treats climate change using three dimensions of uncertainty: scenario, policy and preference. Conceptual frameworks are advanced to account simultaneously for these various dimensions of uncertainty. The authors then explore the impact of introducing these uncertainties into benefit estimates for the Australian Carbon Pollution Reduction Scheme.

Chapter 8: Climate Change Uncertainty and Choice Experiment Welfare Estimates

Sonia Akter and Jeff Bennett

Subjects: economics and finance, environmental economics, valuation, environment, climate change, environmental economics, valuation


8.1 INTRODUCTION This chapter investigates whether prior beliefs regarding climate change policy uncertainty influence support for a mitigation measure when objective uncertainties are given. A three-option CE survey was conducted to obtain data for this research question. To keep the analysis straightforward the future scenario (the scale of temperature change) was presented as certain. Individuals’ self-stated best, high and low guesses of likelihood of success of the CPRS were used as measures of subjective policy uncertainty. Objective policy uncertainty was used as an attribute in the CE. An analytical model is developed first in this chapter to draw testable hypotheses. The model helps to assess individual support for the CPRS by simultaneously accounting for subjective and objective climate change policy uncertainty. Two hypotheses are constructed from the model. The first is that respondents attach a non-zero weight to their prior beliefs regarding probability. The second hypothesis is that weighting behaviour follows a Bayesian updating rule; that is, the value of the estimated weight lies between zero and one. The primary data collected using the threeoption CE subsample were used to test these hypotheses. The next section describes the development of the analytical model. The key explanatory variables and the utility functions to be estimated are outlined in Section 8.3. The empirical results obtained from analysing the data are presented in Section 8.4. Section 8.5 presents the CE welfare estimates. A discussion of the findings and concluding remarks are included in Section 8.6. 8.2 BAYESIAN UPDATING OF POLICY UNCERTAINTY IN A...

You are not authenticated to view the full text of this chapter or article.

Elgaronline requires a subscription or purchase to access the full text of books or journals. Please login through your library system or with your personal username and password on the homepage.

Non-subscribers can freely search the site, view abstracts/ extracts and download selected front matter and introductory chapters for personal use.

Your library may not have purchased all subject areas. If you are authenticated and think you should have access to this title, please contact your librarian.

Further information