Abstract
In recent years, the increase in the frequency and intensity of hurricanes has posed a significant threat to coastal infrastructures, particularly the electricity supply system. In response to these challenges, several policies have been proposed to improve the resilience of electricity systems, specifically focusing on expediting the restoration of disrupted utilities. However, implementing these resilience plans comes with considerable costs, which must be balanced against the potential benefits experienced by households. This study examines the willingness of Florida residents to financially support the improvement of the electricity infrastructure resilience in response to hurricanes in Florida. We conduct a Discrete Choice Experiment involving 1138 Floridians to assess their willingness to pay for different scenarios aimed at improving electricity system resilience. Three panel mixed logit models are estimated, accounting for preference heterogeneity. Results indicate that the annual welfare estimates per individual range from $525.51 to $604.70 across the restoration scenarios. The findings offer compelling evidence, indicating strong support for minimizing hurricane-induced power disruptions by implementing the proposed resilience programs in Florida.
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Data Availability
Data will be provided upon reasonable request, contingent on adherence to Institutional Review Board (IRB) guidelines.
Notes
For example, cumulative percentages of 40% in 1–3 days, 50% in 4–6 days, 70% in 1 week, and 80% in 2 weeks are coded as 40% in 1–3 days, an additional 10% in 4–6 days, an addition 20% by 1 week, and an additional 10% by 2 weeks.
It is important to note that to mitigate potential hypothetical bias, respondents were presented with a realistic payment mechanism and a reminder of their budget constraints.
We compared models that simultaneously estimated separate parameters for choice task 1 and choice task 2. We then tested for differences in estimated parameters and WTP values across tasks. Wald tests showed no significant differences between parameters (i.e., SQASC, cost attribute, time-to-restoration attributes) or WTP values. These results suggest that respondent’s hypothetical behavior does not meaningfully differ between the two choice tasks.
In the survey, we highlighted the area impacted by Hurricane Irma, specifically the whole state of Florida, and noted that the majority of residents in Florida were affected by the hurricane.
For the PML model, we limited the analysis to respondents with complete socioeconomic information and respondents living in single-family homes, condos, duplexes, and townhomes. Respondents living in apartments were excluded from the analysis.
U.S. Census Bureau, American Community Survey (ACS). For more information, see: (https://www.census.gov/quickfacts/fact/table/floralcitycdpflorida,FL,US/PST045221)
In addition to the PML model, we analyzed the choices of our DCE using a latent class model (LCM). In the LCM, populations are assumed to contain a finite number of preference classes; thus, unobserved heterogeneity is modeled with class-specific rather than respondent-specific preferences. The results from a LCM with two classes are reported in the appendix.
It is unclear why female respondents are more likely than males to select the status quo option. One possible explanation is that females, particularly in lower income households, have less agency over household resources and are thus less likely to support a program that increases monthly utility payments. We find some support for this hypothesis in an alternate set of models that included three-way interactions between the SQASC, female respondent indicator, and household income indicators. Results suggest that the difference in female and male choices is substantially more pronounced among low-income households.
We also estimated alternative specifications of Model 2 and Model 3 that incorporated the region of residence via interactions between the SQASC and regional indicators (i.e., Panhandle, North, Central, and South). Model results showed that the likelihood of selecting the status quo option did not significantly differ across regions.
Although the survey was administered 2–3 years after a major hurricane, the estimated WTP values may be higher than they would be if more time had passed since the last hurricane.
Our SQ scenario aligns with reality in highly affected areas by Hurricane Irma. For instance, the paper published by Mitsova et al. (2018) shows that the power restored in Collier County (i.e., one of the highly affected counties by Hurricane Irma) within the first 3 days are less than 10%. We designed our SQ scenario and improvement scenarios accordingly.
The assessment of compensating surplus for various improvement scenarios involves examining the changes in their attribute levels relative to the status quo scenario.
According to the Bureau of Economic and Business Research at the University of Florida, there are 8,676,264 households in Florida (by April 1, 2021). For more information, see: https://www.bebr.ufl.edu/wp-content/uploads/2022/02/households_2021.pdf
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Acknowledgments
The authors acknowledge the support received from the National Science Foundation [Award #1832693: CRISP 2.0 Type 2: Collaborative Research: Organizing Decentralized Resilience in Critical Interdependent-infrastructure Systems and Processes (ORDER-CRISP)].
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Appendix
Appendix
Enhancing Resiliency of Power Infrastructure in Florida
Hurricane Irma was one of the most destructive and the costliest hurricanes in U.S. history. It struck Florida on September 10, 2017 as Category 4 hurricane and led to millions of residents experiencing electricity outage and water supply disruption in the state during and after the hurricane. In this regard, suppose that the State of Florida is proposing to establish ‘Florida Power Resiliency Fund’ (FL-PRF), which will mobilize resources statewide to improve resiliency of electricity infrastructures to hurricanes and other natural hazards in Florida. Using ‘FL-PRF’, the State Government in collaboration with local utility providers will be able to modify and upgrade the power and electricity infrastructure to minimize power outages and disruptions and reduce the recovery time for the residents. Considering this, would you be willing to support ‘FL-PRF’ by contributing a specific amount of money which will be added to your annual electricity bill for next 10 years (which can be flexibly distributed across billing cycles each year).
You have several options to select the type of the ‘Florida Power Resiliency Fund’ (FL-PRF) with varying contribution levels. Option A will ensure a moderate level of investment in power infrastructure to provide a faster restoration of electricity supply compared to the ‘Current Situation’. Option B will provide even faster restoration than Option A. Remember If you choose neither Option A nor Option B, it means that you are supporting the ‘Current Situation’ (Opt Out Option).
Please select the option that you prefer most for each of the two sets of scenarios presented (see Fig. 1).
see Table 7
see Table 8
see Table 9
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Asadi, M., Price, J.I., Kessels, R. et al. Hurricane-Induced Power Disruptions: Household Preferences for Improving Infrastructure Resilience. EconDisCliCha (2024). https://doi.org/10.1007/s41885-024-00145-5
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DOI: https://doi.org/10.1007/s41885-024-00145-5