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Previous literature on the distribution of willingness to pay has focused on its heterogeneity distribution without addressing exact interval estimation. In this paper we derive and analyze Bayesian confidence sets for quantifying uncertainty in the determination of willingness to pay for carbon dioxide abatement. We use two empirical case studies: household decisions of energy-efficient heating versus insulation, and purchase decisions of ultralow-emission vehicles. We first show that deriving credible sets using the posterior distribution of the willingness to pay is straightforward in the case of deterministic consumer heterogeneity. However, when using individual estimates, which is the case for the random parameters of the mixed logit model, it is complex to define the distribution of interest for the interval estimation problem. This latter problem is actually more involved than determining the moments of the heterogeneity distribution of the willingness to pay using frequentist econometrics. A solution that we propose is to derive and then summarize the distribution of point estimates of the individual willingness to pay under different loss functions.

In this paper we use Bayes estimates of a multinomial probit model with fully flexible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the probit Gibbs sampler, we use stated-preference data on vehicle choice from a Germany-wide survey of potential light-duty-vehicle buyers using computer-assisted personal interviewing. We show that Bayesian estimation of a multinomial probit model with a full covariance matrix is feasible for this medium-scale problem. Using the posterior distribution of the parameters of the vehicle choice model as well as the GHK simulator we derive the choice probabilities of the different alternatives. We first show that the Bayes point estimates of the market shares reproduce the observed values. Then, we define a base scenario of vehicle attributes that aims at representing an average of the current vehicle choice situation in Germany. Consumer response to qualitative changes in the base scenario is subsequently studied. In particular, we analyze the effect of increasing the network of service stations for charging electric vehicles as well as for refueling hydrogen. The result is the posterior distribution of the choice probabilities that represent adoption of the energy-effcient technologies.

BACKGROUND: Using choice microdata (N=2723) across the USA, this paper analyzes elicited acceptance of hypothetical COVID-19 vaccines. METHODS: The hypothetical vaccines in a choice experiment were described in terms of effectiveness, days for antibodies to develop, duration of protection, risk of both mild and severe side effects, which health agency mainly supports the vaccine, country of origin, and when the vaccine was developed. Out-of-pocket cost was also considered as characteristic of the vaccines to derive welfare measures. RESULTS: All vaccine attributes had expected signs with significant estimates. Vaccines developed in the USA and the UK were preferred to a hypothetical German vaccine, whereas a Chinese origin was very negatively perceived. Since the choice scenarios also gave the option to opt out from taking the vaccine, odds ratios were derived to characterize the segments that are more and less likely to accept vaccination. More likely to opt out were found to be those who stated to be against vaccination in general, African Americans, individuals without health insurance, and older people. Males, democrats, those who took the flu vaccine appear as more willing to accept vaccination. CONCLUSIONS: Estimates of the fitted choice models in this study are informative for current and future immunization programs.

In this article we evaluate the impacts of travel demand management strategies to reduce the share of automobiles as a commuting mode by using stated preference data. The analysis is carried out on university members in Quebec City. We measure the impact of travel time and cost as well as attitudes toward automobiles, transit, and the environment. We find elasticities with respect to time and cost that are low, implying that large changes in the characteristics of commuting modes are required to have a noticeable impact. We find, however, that combining several policy interventions is more effective.

Featuring in-depth coverage of passenger and freight transportation, this comprehensive resource discusses contemporary transportation systems and options for improving their sustainability. The book addresses vehicle and infrastructure design, economics, environmental concerns, energy security, and alternative energy sources and platforms. Worked-out examples, case studies, illustrations, equations, and end-of-chapter problems are also included in this practical guide

AbstractAlthough evidence suggests that photographs can enhance persuasive messaging by offering "proof," less research considers their utility relative to other visual forms that ostensibly convey more information but more abstractly. Drawing on communication and information processing theory, this study examines the influence of visual features and personal experience variables in a domain with urgent need to better understand their role: hurricane messaging. In a between subjects experiment, residents of New York, New Jersey, and Connecticut (N = 1052) were exposed to a hypothetical hurricane forecast accompanied by a photograph of storm surge inundating a house (indexical image), a map of projected storm surge (iconic image), or no image (control), depending on condition. Results revealed that participants in the indexical condition perceived the greatest risk overall and were more likely to mention evacuation as a behavioral intention than did those in the iconic and control conditions, controlling for individual differences (gender, state of residence, etc.). Moreover, risk perception was greatest among residents in the indexical condition reporting fewer personal impacts of hurricanes, suggesting a moderating effect of hurricane experience on risk judgment but not on behavioral intention. Consistent with a dual-process model perspective, when exposed to an image of an identifiable "victim," participants with less direct experience may have employed an affect heuristic, resulting in heightened risk perceptions. Practically speaking, using evocative photographs as proof may be preferable to a map or text-only approach when warning public audiences of a given hazard, but ethical issues and empirical questions remain.

AbstractResearch suggests that hurricane‐related risk perception is a critical predictor of behavioral response, such as evacuation. Less is known, however, about the precursors of these subjective risk judgments, especially when time has elapsed from a focal event. Drawing broadly from the risk communication, social psychology, and natural hazards literature, and specifically from concepts adapted from the risk information seeking and processing model and the protective action decision model, we examine how individuals' distant recollections, including attribution of responsibility for the effects of a storm, attitude toward relevant information, and past hurricane experience, relate to risk judgment for a future, similar event. The present study reports on a survey involving U.S. residents in Connecticut, New Jersey, and New York (n = 619) impacted by Hurricane Sandy. While some results confirm past findings, such as that hurricane experience increases risk judgment, others suggest additional complexity, such as how various types of experience (e.g., having evacuated vs. having experienced losses) may heighten or attenuate individual‐level judgments of responsibility. We suggest avenues for future research, as well as implications for federal agencies involved in severe weather/natural hazard forecasting and communication with public audiences.

Travel demand forecasting models play an important role in guiding policy, planning, and design of transportation systems. There is no shortage of literature critiquing the accuracy of model forecasts (see, for example, Pickrell, 1989; Wachs, 1990; Pickrell, 1992; Flyvbjerg, Skamris Holm, and Buhl 2005; Richmond, 2005; Flyvbjerg, 2007; Bain, 2009; Parthasarathi and Levinson, 2010; Welde and Odeck, 2011; Hartgen, 2013; Nicolaisen and Driscoll, 2014; Schmitt, 2016; Odeck and Welde, 2017, and Voulgaris, 2019), not to mention several high-profile lawsuits (Saulwick 2014, Stacey 2015, Rubin 2018). Many researchers and practitioners feel more can be done to advance rigorous travel analysis methods for the public good (see, e.g., zephyrtransport.org). Motivated by these critiques, a two-day, NSF-funded workshop was held at UC Berkeley in the Spring of 2017 to engage in a fundamental review of the state of the art in travel demand modeling, to discuss the future of the field, and to propose new directions and processes for advancing the science.Travel demand forecasting is an inherently practical enterprise. While academics drive the fundamental research, the users of travel demand models and forecasts are typically government agencies and transport operators that use the models to inform long-range investment, funding, and planning decisions. Private firms play a key role in assisting the agencies in both development and application of the models, and, more recently, high-tech firms have entered the development fray. While all of these actors have important roles in advancing the science of the field, in this report we focus our attention primarily on the academic side of the enterprise, consistent with the orientation of the funding agency (NSF), and in order to make the task manageable. That said, other sectors are represented in various parts of this report as they interface with academics or play particularly central roles in our proposals for advancing the science.