Hurricane Sandy revealed the higher-risk vulnerability to natural hazards of civil infrastructure systems in coastal megacities such as New York. In particular, critical deficiencies in the NYC metropolitan area’s transportation system emerged after Sandy. Unfortunately, experts predict that future sea level rise and storms will exacerbate the problems caused by these deficiencies. There are thus several challenges to improving strength and resilience of transportation systems. In particular, preparedness, survival, and recovery require the identification of adequate funding sources to collect revenue for public investments to improve resilience of the systems under threat.

Traditional sources of funding for both recovering from disasters and preventing future damages are not only limited, but also do not account for benefit transfers of the externalities induced by the provision of resilient infrastructure. In principle, property owners should be willing to pay an amount equal to the perceived benefit, if this positive externality is internalized by them following some pricing mechanism. Monetizing these benefit transfers can be used as a tool not only to leverage scarce public resources, but also to achieve a socially optimal resource allocation. A key element is then the estimation of the willingness to pay for risk reductions, because this measure can be exploited to determine the cost share the community is willing to cover to secure infrastructure systems as well as to receive the benefits from minimizing potential damage. There is an open research question on how to properly express risks of extreme events, and how respondents of discrete choice experiments process information that involves infrequent but extremely damaging events.

This proposal requests funds to determine the community’s willingness to pay for improvements in the resiliency to extreme events of the transportation system in New York City. This objective seeks to provide better tools for better informing planning investments to improve both resilience and security of transportation infrastructure and services. A structural choice model will be derived aiming at a more general representation of decision-making under risk and uncertainty, using non-compensatory decision rules to determine the community’s willingness to pay for improvements in the resiliency to extreme events of the transportation system in New York City. Choice microdata will be collected in coastal communities in the NYC area, while aiming at advancing the state-of-the-art in choice modeling for addressing different attitudes toward risk.