What Drives Hazard Mitigation Policy Adoption?

FEMA’s Property Buyout Program in Virginia Counties

Qiong Wang
Virginia Tech

Yang Zhang
Virginia Tech

Kristin Owen
Henrico County, Virginia

Publication Date: 2022

Abstract

Following the Great Flood of 1993 in the Midwest, FEMA introduced the property buyout program as a hazard mitigation strategy to reduce the flood risk. There is a growing body of research on the property buyout policy. The existing literature mainly focused on resident decisions to participate in the buyout program, their experiences with the program, the financial and social impacts of relocation on residents and land, and justice issues. Some previous studies of buyouts also discussed characteristics of the property buyout policy and funding problems. There is less research on property buyout policy from a government perspective, however. This research examined factors that influence the adoption of the FEMAs property buyout program in Virginia using a new theoretical framework. It investigated factors such as flooding, social vulnerability, institutional capacity (including individual professional capacity, organizational management capacity, and system capacity), policy diffusion, and upper-level policy environment. Results of an online survey indicated that the individual capacity of local floodplain managers (e.g., hazard mitigation experience, awareness of flood risk and buyout benefits, and the ability to innovate) influences local government adoption of buyout policies. Findings suggest that local governments with experienced floodplain managers who are enthusiastic about the benefits of buyouts are more likely to influence community buy-in and political will.

Introduction and Literature Review

Flooding was among the top three most common natural hazards in the United States from 1980 to 2020 (Smith, 20211). The First Street Foundation released a report in 2020 showing that 14.6 million properties have substantial flooding risks across the country (First Street Foundation, 20202). As of January 2021, the National Flood Insurance Program (NFIP) owed the U.S. Treasury more than $20.5 billion, leaving it only $9.9 billion from a $30.43 billion limit allowed by law (Insurance Information Institute3). Furthermore, in October 2021, the NFIP had more than five million flood insurance policies totaling more than $1.3 trillion in coverage (Congressional Research Service, 20214). Consequently, governments should undertake innovative hazard mitigation efforts to reduce flood risk and better prepare for the future.

Following the Great 1993 Midwest Flood, the Federal Emergency Management Agency (FEMA) introduced the property buyout program as a hazard mitigation strategy to reduce the flood risk and repetitive flood loss (Federal Emergency Management Agency [FEMA], 20205). Through FEMA buyout funds, residents in high flood risk areas have an opportunity to voluntarily sell their properties to local governments (National Archives, n.d.6). Currently, most buyout programs are funded through federal programs such as the FEMA Hazard Mitigation Grant Program (HMGP) and the Department of Housing and Urban Development Community Development Block Grant Disaster Recovery funds (Peterson, et al., 20207). From 1993 to 2020, the FEMA HMGP approved and implemented 59,468 properties for buyouts across the country, which only represents 0.04% of properties with substantial flooding risk (FEMA, n.d.8).

Although there is a growing body of research on property buyout policy, the existing literature mainly focused on resident decisions to participate in the buyout program (Binder et al., 20159; Bukvic & Owen, 201710, their experiences during the process of buyout implementation (Binder & Greer, 201611; de Vries & Fraser, 201212; Perry & Lindell, 199713), the financial and social impacts of relocation on residents and land (Binder et al., 201814; Blaze & Shwalb, 200915; McGhee, 201716; Zavar & Hagelman, 201617), and justice issues (Siders, 201818). Some previous studies of buyouts also discussed characteristics of the property buyout policy and funding issues (Moore & Weber, 201919; Peterson et al., 20207). There is less research examining the property buyout policy from a government perspective, however. Existing studies of buyout in terms of government issues are related to the land use changes of buyout sites, as well as buyout investment and management. Very limited research studied the buyout policy adoption (Mach et al., 201920). Flood hazard mitigation is a policy issue with great significance for all levels of government, however, local governments bear the primary functional responsibility for hazard mitigation (Binder et al., 2018). Meanwhile, the public policy process is dynamic and complex. Therefore, it is necessary to explore what factors influence the buyout program adoption at the local governmental level.

Current Study

Based on a synthesis of literature about policy innovation and natural hazard policy adoption, this research developed a theoretical framework for understanding the dynamics that drive the uptake of buyout policy at the local level. There are several existing theoretical frameworks in the public policy field, such as the Multiple Streams (Kingdon, 201421) and diffusion models (Berry & Berry, 200722). However, each has limitations. For example, the Multiple Streams (MS) framework requires that the “problem-, policy-, and politic-streams” be considered separately, which can be difficult. Furthermore, since policymakers can collect information or data from their preferred sources, and information is easily be manipulated, the MS framework is flawed. Traditional diffusion models are highly sequential and driven by the needs of potential users, making them overly instrumental (Wainwright & Waring, 200723). Although some recent research has studied the factors of hazard mitigation or adaptation policy adoption by using quantitative and qualitative methods (Bolson & Broad, 201324; Krause et al., 201925; Massey et al., 201426; Miao, 201927; Shi et al., 201528), a broad scanning approach cannot effectively extract the complex linkages between the uptake of specific policy, governmental context, political environment, and other factors (Linkous et al., 201929). In addition, an increasing amount of research looks into why homeowners may accept or reject the buyout program (Binder et al., 2015; Bukvic & Owen, 2016). However, limited studies have focused on the property buyout policy from a government perspective.

Guided by theories of policy innovation and adoption in an incentive-based, non-coercive environment, and literature gaps, we proposed a theoretical framework to examine the major parameters for the hazard mitigation policy adoption, and the property buyout policy is a specific example (Figure 1). The theoretical model supports the identification of key internal and external factors that motivate counties to have a sustained interest in pursuing the buyout policy. Internal factors are conditions inherent to a locality and the external factors are precedents from outside a locality that may impact its willingness and ability to take up the policy. Internal factors include problem identification, social vulnerability, and institutional capacity. External factors are policy diffusion and policy environment.

Figure 1. Hazard Mitigation Policy Adoption Conceptual Framework


This study focused on the buyouts in Virginia counties. In Virginia, recurrent flooding and sea-level rise are considered the highest probable hazards (Mitchell et al., 201330; Moore & Acker, 201831). Sea levels in Virginia are rising more quickly than the average global levels due to the sinking of land (Atkinson et al., 201332). Scientists predict the water level will rise by one and a half feet in the next 20 to 50 years and by five feet or more by 2100 (Recurrent Flooding Sub-Panel, 201433). From 1996 to 2020, only 303 buyout projects have been implemented across flood-prone communities (FEMA, n.d.). The diverse characteristics of these adopters and the non-adopters (i.e., communities with flood hazards, but without buyout projects) present an ideal setting to study the proposed research question.

Hypotheses

This research uses this new theoretical framework to examine buyout, with the following hypotheses in mind:

H1: Flooding property damage will positively affect buyout program adoption. The severity of past events is related to the average of per capita flood-related property damage and repetitive property damage in localities (Mach et al., 2019; Robinson et al., 201834).

H2: A locality with higher social vulnerability will tend to adopt the property buyout policy. A social vulnerability index is a collection of data or variables that can indicate the level of vulnerability in a community. Highly vulnerable communities face more flooding-related problems (Cutter et al., 200335).

Hypotheses 3 through 5 are related to capacities. Institutional capacity includes individual professional capacity, organizational management capacity, and system capacity, which can have an impact on local government decision-making.

H3: Individual professional capacity will positively affect buyout program adoption. Individual professional capacity is the capacity of individuals in institutions, such as motivation and ability (Willems & Baumert, 200336).

H4: Organizational management capacity will positively affect buyout program adoption. Organizational management capacity refers to an organization’s ability to utilize its expertise and resources to influence decision-making and meet stated goals (Babu & Blom, 201437).

H5: System capacity will positively affect buyout program adoption. System capacity is the enabling environment, for example, networking between organizations (Willems & Baumert, 2003).

Furthermore, the external factors which are outside of a locality, reflect the influence of policy diffusion and environment on the policy adoption. Hypotheses 6 through 7 address this.

H6: Policy diffusion will positively affect buyout program adoption.

H7: Federal and state policy support will positively affect buyout program adoption.

Methods

Sources of Data

Primary data was collected from the local floodplain managers in Virginia using an online survey. The author used Qualtrics to develop and send out the survey. Secondary data included the FEMA property buyout program from 1996 to 2020 (FEMA, n.d.), social vulnerability index from Centers for Disease Control and Prevention (2021)38, flooding repetitive loss from the Virginia Department of Conservation and Recreation (2019)39, and flooding-related property loss from the Spatial Hazard Events and Losses Database for the United States (SHELDUS) (Arizona State University, 202040).

Analysis

The survey instrument was pre-tested in June 2020 by a group of invited experts who were not part of the final sample. Based on their comments on the survey, the author revised the survey questions. Quantitative analysis was used to examine the correlation and causation between the internal and external factors of the buyout program adoption. Stata/SE 16.0 was used as a statistical technique for testing and estimating causal relations.

Participants and Sampling

The participants in this research are mainly floodplain managers across the commonwealth of Virginia. In localities with no designated floodplain managers, local officials/urban planners who assume the role of floodplain managers were identified. Virginia Department of Conservation and Recreation compiled a list of 275 floodplain managers with full contact information for Virginia municipalities. A total of 98 local governments in Virginia completed at least part of the survey. The final analysis excluded respondents that did not respond fully to the survey questions in each section of the survey. Therefore, the sample size for multiple regression analysis was reduced to 59 localities. Figure 2 shows that the survey participants are from inland and coastal areas with riverine and coastal flooding risks.

Figure 2. Map of Virginia Localities Represented in the Survey


Human Subject Research Approval

The author submitted the survey and interview instruments, consent forms, and email recruitments to the Institutional Review Board (IRB) of Virginia Tech on July 4, 2020. The IRB approved the application on September 09, 2020 (#20-569).

Recruiting

Survey recruiting and consent emails along with the survey link were first sent to the list of Virginia floodplain managers. Three email reminders were sent to non-responding participants at a two-week interval. A reminder was also posted on the Virginia Floodplain Management Association website. In the recruiting materials, we explained that the purpose of this study was to collect information about the property acquisition/buyout policy adoption in Virginia floodplains. The survey process concluded five months after the initial email.

Measures

We developed the survey instrument based on thorough review of the FEMA property buyout program, including policy innovation, adoption, and local hazard mitigation. The survey questions covered the uptake of the property buyout program, the institutional capacity of local governments, policy diffusion, and upper-level policy environment. Structured questions were used to capture the full range of factors. Owing to the complex nature of institutional capacity, policy diffusion, and environment, which are difficult to be represented by one indicator, we included several indicators for each variable. The buyout policy adoption is the dependent variable. The expected independent variables included hazard problem (e.g., repetitive loss from flooding), social vulnerability, institutional capacity, policy diffusion, and policy environment. This research measured the factors of buyout adoption using ordinal scales. Respondents were asked five categories of questions on the individual, organizational, and system institutional capacity, policy diffusion, and policy environment on a scale from 0 to 10. For example, a question about individual capacity stated, “How beneficial do you think the acquisition/buyout program would be for flood mitigation in your jurisdiction? An overall estimate of each factor was measured by summing and averaging the observed scores for all indicators in one category.

Results

Data Analysis

The regression model involved two steps. First, we tested the association between the dependent variable and each potential predictor in the survey by using Pearson’s product-moment correlation coefficients. Only predictors identified as significant in the correlations were included in the multivariate models. Then, the aggregate average of responses for each category of factors was calculated. Second, we conducted logistic multiple regression analyses (see Appendix).

For the survey data, individual professional capacity was defined operationally as hazard mitigation working experience, awareness of flood risk and buyout benefits, and innovative ability (the ability for a person to be innovate within the confines of their organization). Organizational management capacity was defined as flood mitigation goals and commitment (e.g., flood mitigation as a core value and mission priority for local governments), innovative culture, financial resources, and buyout-related training. System capacity included communication and cooperation with other agencies and organizations, support for public involvement in flood mitigation, and transparent information of flood mitigation. Policy diffusion is comprised of policy learning indicators. The transmission of policy innovation from one government to the next is known as policy diffusion (Shipan & Volden, 200841). Learning refers to policymaker’s ability to learn from the success of other governments, which focuses on action (Gray, 197342; Shipan & Volden, 2008). Policy environment incorporates the state’s regulatory framework, network capacity of organizations, and awareness of responsibility in the buyout for governments at different levels. Network capacity refers to a collection of interconnected organizations and the links that connect them (Fischer & Jasny, 201743). Stata/SE 16.0 was used as a statistical technique for testing and estimating causal relations (see Appendix).

Preliminary Findings

Table 1 presented results based on the logistic regression models. The estimated coefficients of independent variables were calculated as odds ratios for ease of interpretation. In Model 1, we started the analysis with a model that only includes internal factors. Model 2 was the traditional integrated diffusion model. In Model 3, we took in the policy environment that reflected the impact of local government and upper-level government interactions on the uptake of the property buyout program. Model 3 presented a version of the integrated model with all the internal and external parameters.

Table 1. Odds Ratio of Factors Influencing Property Buyout Policy Adoption

Variable Model 1 Model 2 Model 3
Internal Variables
Problem (Repetitive loss) 2.629* 3.038* 3.096*
Problem (Property damage) 1.001 1.000 0.998
Social Vulnerability 1.040 1.069 1.086
Individual Capacity 2.028** 2.210** 2.310**
Organizational Capacity 1.508 1.527 1.622
System capacity 1.316 1.343 1.362
External Variable
Policy Diffusion 0.848 0.916
Policy Enviornment 0.826
AIC 51.544 53.160 54.973
BIC 66.086 69.780 73.671

Note. N = 59. AIC = Akaike information criterion; BIC = Bayesian information criterion. *significant at 10% level, **significant at 5% level, ***significant at 1% level.

In terms of problem identification factor, the results showed a positive relationship between repetitive loss from flooding and buyout adoption at the local level. However, the p value of per capita flood-related property damage in a city/county was larger than 10%. These findings were partially consistent with Hypothesis 1. With respect to the social vulnerability variable, the results, inconsistent with our research hypothesis, indicated that localities with higher social vulnerability scores are not necessarily more active in the buyout program. As for institutional capacity, the models highlighted the significant predictive power of the capacity of individuals in institutions. This supported Hypothesis 3. However, the results of our analyses did not provide evidence for Hypotheses 4 (organizational capacity) and 5 (system capacity). Finally, regarding the external variables, the findings did not support Hypothesis 6 (policy diffusion). The policy diffusion variable was nonsignificant for the adoption of buyout at the local level. In addition, our results did not indicate the presence of an upper-level policy environment to be a significant predictor of buyout uptake in our sample. Hypothesis 7 was not supported.

Discussion

Problem Identification

The results showed that repetitive loss from flooding has a significant relationship with buyout adoption at the local level. Its p value was .081, which meant it was moderately significant. Based on our estimates, a $10,000 increase in the average payment of repetitive loss in a county or city increased the odds that it would adopt the property buyout program by 209.6% in Model 3. Therefore, repetitive loss from flooding was an important factor motivating counties to reduce financial loss. Although we expected that counties or cities with more per capita flood-related property damage would have a stronger need for the adoption, the p value of the property damage variable was larger than 10%, which gave evidence for nonsignificant relationship in the models.

Social Vulnerability

The results showed that localities with higher social vulnerability scores were not necessarily more active in the buyout program. One possible reason could be that the overall ranking of the 15 social factors represented the social vulnerability index; however, not all the factors were related to flooding issues and weaken the key social factor effects.

Institutional Capacity

The capacity of individuals within institutions was significant in influencing local governments to adopt the property buyout policy. They also predicted that the odds of a county or city engaging in the property buyout program with each one-unit increase in individual institutional capacity. Specifically, a locality with local public officials who had one unit increase in the probability of the working experience and creativity of flood mitigation policy as well as awareness of flood risk and buyout beneficial will be more likely to take up the property buyout program by 131% (Model 3). The importance of individual capacity persisted when adding external variables, indicating that the strong individual capacity of floodplain managers at the local level can facilitate governments to engage in the property buyout program. Floodplain managers who had more flood mitigation experience and innovation as well as those who saw buyouts as beneficial could influence community buy-in and political will. They, as local advocates, played the role of messenger to help spread an idea for the buyouts since the property buyout program was voluntary and local governments carried the major functional responsibility for hazard mitigation.

Organizational capacity was a nonsignificant predictor of buyout policy adoption for our sample. In past studies, researchers suggested that the facets of organizational capacity, such as flood mitigation commitment (Shi et al., 2015), innovative culture (Laurian et al., 201744), as well as financial and training support (Krause, 201145) can contribute to initiating hazard mitigation policies. However, the results of our analyses showed that organizational capacity was not a significant predictor of buyout policy adoption for our sample. This could be explained by the structures of buyouts. Since FEMA buyout projects are cost-reimbursement programs, they require a larger amount of funds to complete compared to the property elevation program. To have sufficient upfront funding to support buyout projects, communities needed to seek various funding resources and could not only rely on the local government itself. Moreover, system capacity, which had the indicators of communication and cooperation with other agencies and organizations, support for public involvement in flood mitigation, and transparent information of flood mitigation, did not influence local governments to adopt the property buyout policy based on the results. Since local governments as a subgrantee of FEMA-funded buyouts applied for, received, and managed federal funds, they were in charge of the buyout policy adoption process. Therefore, it was easy to understand that elements beyond local governments themselves could not impact a city or county government’s decision on the buyouts.

Policy Diffusion and Environment

The study did not provide evidence to support Hypothesis 6 (policy diffusion). Although policy diffusion was an important predictor for take-up of other policies, it did not work for the buyout because the FEMA buyout program is a voluntary policy. Each locality had its own characteristics and situation, which were internal factors, that decided whether it should adopt the buyout policy and could not be influenced by other localities. In addition, our results did not indicate the presence of an upper-level policy environment to be a significant predictor of buyout uptake in our sample.

Hypothesis 7 (policy environment) was also not supported. There was nonsignificant evidence that federal and state guidance, technique support, and cooperation for local governments were unimportant, but rather that the local government played a key role in the property buyout program application and implementation, and federal or state support did not relate their suggestions to localities. Usually, non-structural techniques need to involve a higher level of collaboration across different stakeholders. However, this establishment of networks among staff members from different organizations within a jurisdiction was particularly important in the policy implementation process, instead of the adoption process (Brody et al., 200946). Furthermore, a lack of transparency regarding the approval criteria and processes of FEMA buyout projects and subjective cost-benefit logic at the federal level (Siders, 2018) might be the reason. This uncertainty did not consistently influence local government decisions on buyouts.

In summary, the analysis supported two of the seven hypotheses we put forward. Repetitive flood loss might trigger decision makers to accept innovative flooding mitigation strategies. Furthermore, the individual capacity of local government was a strong predictor of adoption outcomes. Local floodplain managers with more flood mitigation experience and creativity, as well as awareness of flood risk and buyout benefits, would be more likely to add the property buyout program to the agenda. However, social vulnerability, organizational and system capacity, policy diffusion, and environment did not influence the local government decisions on the property buyout adoption.

Limitations and Future Directions

This study comes with several limitations. First, with a cross sectional study (one time point) and regression models, the direction of effects is never clear. Second, this study used a quantitative approach to collecting data about and analyzing different categories of internal and external factors for the uptake of buyouts. Additional qualitative interviews could be used to explore more detailed reasons about why and how local governments adopt the buyout program. Finally, since our sample size was limited, we had to assume that counties and towns had similar internal dynamics. However, this may not be true in reality. In the future, more research is necessary to see if dynamics differ depending on the sort of municipal administration.

Another limitation is the FEMA-funded buyout program itself. FEMA buyout programs have several drawbacks and are unable to incentivize community/property owners’ participation. The approval and implementation processes are usually lengthy, complicated, and time-consuming. One typical buyout project funded by FEMA can take over five years to complete (Moore & Weber, 2019). Moreover, FEMA buyouts may also create social inequity because low-cost housing with lower property values is more likely to fail the Benefit-Cost Analysis requirements (Moore & Weber, 2019; Siders, 2018). Since FEMA’s buyout is voluntary for residents, most acquired lands are not clustered in space. This can make it difficult to convert the buyout properties to low-impact land use (e.g., parklands or other recreational open space) (University of North Carolina Institute for the Environment, 201647). Therefore, it is necessary to develop a market-based buyout and help the city achieve its resilience and managed retreat goal in high flood risk areas.

The results of this study will be included in a PhD dissertation and future publications. This study can improve the understanding of local flood hazard mitigation policies and offer recommendations for localities to improve their flood mitigation practices.

Conclusions

Based on the proposed theoretical framework, this research investigates internal and external factors of hazard mitigation policy adoption at the local level and takes the property buyout policy as a case study. Stakeholders have suggested that policy adoption and implementation research does not require more variables, but rather more structure (Hupe, 201448). The theoretical model we provided is the foundation for powerful analytical structures and simply complex policy adoption dynamics. The findings contribute to our understanding of local decision-making dynamics about hazard mitigation, an issue with national prominence and federal financial incentives while its adoption and continuing actions are completely controlled at the local level.

This study provides evidence that local governments that have floodplain managers with more flood mitigation experience and policy innovation, as well as awareness of flood risk and buyout benefits, tend to adopt the property buyout policy to mitigate flooding in a locality. Different from other policies, organizational and system capacity (e.g., commitment, financial issue, network, etc.) do not influence local governments to take up the buyout. Therefore, policymakers need to consider the individual capacity to conduct hazard mitigation strategies innovatively and efficiently.

Specifically, one recommendation for FEMA would be to conduct more property buyout policy-related trainings for local floodplain managers, including both technical training on flood risk analysis and program training on the buyout policy. Another recommendation would be to modify the buyout program to simplify the application process of the program and reduce its length, triggering more localities to consider buyouts.

This report will be disseminated through the state floodplain manager associations to facilitate local governments in initiating the property buyout program. This research work may be repeated elsewhere, and the survey instrument used in the analysis is available upon request to any interested scholars.

References


  1. Smith, A. (2021). 2020 U.S. Billion-dollar Weather and Climate Disasters in Historical Context. Climate.gov. https://www.climate.gov/disasters2020 

  2. First Street Foundation. (2020). First Street Foundation Releases New Data Disclosing Flood Risk of Every U.S. Home. First Street Foundation. https://firststreet.org/press/2020-first-street-foundation-flood-model-launch/ 

  3. Insurance Information Institute. (n.d.). Facts + Statistics: Flood insurance. https://www.iii.org/fact-statistic/facts-statistics-flood-insurance 

  4. Congressional Research Service. (2021). Introduction to the National Flood Insurance Program (NFIP). https://sgp.fas.org/crs/homesec/R44593.pdf 

  5. FEMA. (2020). Public assistance program and policy guide version 4 - FEMA. https://www.fema.gov/sites/default/files/documents/fema_pappg-v4-updated-links_policy_6-1-2020.pdf  

  6. National Archives. (n.d.). Guidance on Property Acquisition and Relocation for the Purpose of Open Space 44 CFR Part 80. https://www.ecfr.gov/current/title-44/chapter-I/subchapter-B/part-80 

  7. Peterson, K., Apadula, E., Salvesen, D., Hino, M., Kihslinger, R., & BenDor, T. K. (2020). A review of funding mechanisms for US floodplain buyouts. Sustainability, 12(23), 10112. https://doi.org/10.3390/su122310112 

  8. Federal Emergency Management Agency (FEMA). (n.d.). Hazard mitigation assistance mitigated properties. Hazard Mitigation Assistance Mitigated Properties | FEMA.gov.https://www.fema.gov/openfema-data-page/hazard-mitigation-assistance-mitigated-properties 

  9. Binder, S. B., Baker, C. K., & Barile, J. P. (2015). Rebuild or relocate? resilience and postdisaster decision-making after Hurricane Sandy. American Journal of Community Psychology, 56(1-2), 180–196. https://doi.org/10.1007/s10464-015-9727-x 

  10. Bukvic, A., & Owen, G. (2017). Attitudes towards relocation following Hurricane Sandy: should we stay, or should we go? Disasters, 41(1), 101-123. https://doi.org/10.1111/disa.12186 

  11. Binder, S. B., & Greer, A. (2016). The devil is in the details: Linking home buyout policy, practice, and experience after Hurricane Sandy. Politics and Governance, 4(4), 97–106. https://doi.org/10.17645/pag.v4i4.738 

  12. de Vries, D. H., & Fraser, J. C. (2012). Citizenship rights and voluntary decision making in post-disaster U.S. floodplain buyout mitigation programs. International Journal of Mass Emergencies and Disasters, 30(1), 1-33. http://www.ijmed.org/articles/589/ 

  13. Perry, R. W., & Lindell, M. K. (1997). Principles for managing community relocation as a hazard mitigation measure. Journal of Contingencies and Crisis Management, 5(1), 49–59. https://doi.org/10.1111/1468-5973.00036 

  14. Binder, S. B., Greer, A., & Baker, C. (2018). Home buyout programs: recommendations for policy & practice. https://www.unisdr.org/preventionweb/files/58500_binderbakergreer2018buyoutpolicy.pdf 

  15. Blaze, J. T., & Shwalb, D. W. (2009). Resource loss and relocation: A follow-up study of adolescents two years after Hurricane Katrina. Psychological Trauma: Theory, Research, Practice, and Policy, 1(4), 312–322. https://doi.org/10.1037/a0017834 

  16. McGhee, D. (2017). Were the post-sandy Staten Island buyouts successful in reducing national vulnerability? DukeSpace. https://dukespace.lib.duke.edu/dspace/handle/10161/14168 

  17. Zavar, E., & Hagelman III, R. R. (2016). Land use change on U.S. floodplain buyout sites, 1990-2000. Disaster Prevention and Management, 25(3), 360–374. https://doi.org/10.1108/dpm-01-2016-0021 

  18. Siders, A. R. (2018). Social Justice Implications of US managed retreat buyout programs. Climatic Change, 152(2), 239–257. https://doi.org/10.1007/s10584-018-2272-5 

  19. Moore, A. W. & Weber, A. (2019). (rep.). Going under: Long wait times for post-flood buyouts leave homeowners underwater. Natural Resources Defense Council (NRDC). https://www.nrdc.org/sites/default/files/going-under-post-flood-buyouts-report.pdf 

  20. Mach, K. J., Kraan, C. M., Hino, M., Siders, A. R., Johnston, E. M., & Field, C. B. (2019). Managed retreat through voluntary buyouts of flood-prone properties. Science Advances, 5(10). https://doi.org/10.1126/sciadv.aax8995 

  21. Kingdon, J. W. (2014). Agendas, alternatives, and public policies. Pearson Education Limited. 

  22. Berry, F. S., & Berry, W. D. (2007). Innovation and diffusion models in policy research. In Sabatier, P. A. & Weible, C. M. (Eds.), Theories of the policy process (pp. 223-260). Westview Press. 

  23. Wainwright, D. W., & Waring, T. S. (2007). The application and adaptation of a diffusion of Innovation Framework for Information Systems Research in NHS General Medical Practice. Journal of Information Technology, 22(1), 44–58. https://doi.org/10.1057/palgrave.jit.2000093 

  24. Bolson, J., & Broad, K. (2013). Early adoption of climate information: Lessons learned from South Florida Water Resource Management. Weather, Climate, and Society, 5(3), 266–281. https://doi.org/10.1175/wcas-d-12-00002.1 

  25. Krause, R. M., Hawkins, C. V., Park, A. Y., & Feiock, R. C. (2019). Drivers of Policy Instrument Selection for environmental management by local governments. Public Administration Review, 79(4), 477–487. https://doi.org/10.1111/puar.13025 

  26. Massey, E., Biesbroek, R., Huitema, D., & Jordan, A. (2014). Climate policy innovation: The adoption and diffusion of adaptation policies across Europe. Global Environmental Change, 29, 434–443. https://doi.org/10.1016/j.gloenvcha.2014.09.002 

  27. Miao, Q. (2019). What affects government planning for climate change adaptation: Evidence from the U.S. states. Environmental Policy and Governance, 29(5), 376–394. https://doi.org/10.1002/eet.1866 

  28. Shi, L., Chu, E., & Debats, J. (2015). Explaining progress in climate adaptation planning across 156 U.S. municipalities. Journal of the American Planning Association, 81(3), 191–202. https://doi.org/10.1080/01944363.2015.1074526 

  29. Linkous, E., Laurian, L., & Neely, S. (2019). Why do counties adopt transfer of development rights programs? Journal of Environmental Planning and Management, 62(13), 2352–2374. https://doi.org/10.1080/09640568.2018.1559044 

  30. Mitchell, M., Hershner, C., Herman, J., Schatt, D. E., & Eggington, E. (2013). (rep.). Recurrent flooding study for Tidewater Virginia. Virginia Institute of Marine Science. http://ccrm.vims.edu/recurrent_flooding/Recurrent_Flooding_Study_web.pdf 

  31. Moore, J., & Acker, L. (2018). (rep.). Recurrent flooding, sea level rise, and the relocation of at-risk communities: Case studies from the commonwealth of Virginia. College of William & Mary Law School. https://scholarship.law.wm.edu/cgi/viewcontent.cgi?article=1021&context=vcpclinic 

  32. Atkinson, L. P., Ezer, T., & Smith, E. (2013). Sea Level Rise and Flooding Risk in Virginia. Sea Grant Law & Policy Journal, 5(2), 3-14. https://heinonline.org/HOL/P?h=hein.journals/sglum5&i=217

  33. Recurrent Flooding Sub-panel. (2014). (rep.). Recommendations to the Secure Commonwealth Panel on the Issue of Sea Level Rise and Recurrent Flooding in Coastal Virginia. Commonwealth of Virginia. http://ccrm.vims.edu/SCPRecommendationsReport_Sept2014.pdf 

  34. Robinson, C. S., Davidson, R. A., Trainor, J. E., Kruse, J. L., & Nozick, L. K. (2018). Homeowner acceptance of voluntary property acquisition offers. International Journal of Disaster Risk Reduction, 31, 234–242. 

  35. Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242-261. https://doi.org/10.1111/1540-6237.8402002 

  36. Willems, S. and Baumert, K. (2003). Institutional capacity and climate actions. Organization for Economic Co-operation and Development (OECD). https://www.oecd.org/env/cc/21018790.pdf 

  37. Babu, S. and Blom, S. (2014). Resilience for food and nutrition security. (Eds). In F., Shenggen, PL., Rajul, & Y., Sivan. Building capacity for resilient food systems (Chapter 14. Pp. 119-126). Washington, D.C.: International Food Policy Research Institute (IFPRI). 

  38. Centers for Disease Control and Prevention. (2021, August 27). CDC/ATSDR SVI data and documentation download. Centers for Disease Control and Prevention. https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html 

  39. Virginia Department of Conservation and Recreation. (2019). Repetitive Loss from Flooding Data. Unpublish. 

  40. Arizona State University. (2020). Spatial Hazard Events and Losses Database for the United States. [Data set]. Center for Emergency Management and Homeland Security. https://cemhs.asu.edu/sheldus 

  41. Shipan, C. R., & Volden, C. (2008). The mechanisms of policy diffusion. American Journal of Political Science, 52(4), 840–857. https://doi.org/10.1111/j.1540-5907.2008.00346.x 

  42. Gray, V. (1973). Innovation in the states: A diffusion study. American Political Science Review, 67(4), 1174–1185. https://doi.org/10.2307/1956539 

  43. Fischer, A. P., & Jasny, L. (2017). Capacity to adapt to environmental change: Evidence from a network of organizations concerned with increasing wildfire risk. Ecology and Society, 22(1). https://doi.org/10.5751/es-08867-220123 

  44. Laurian, L., Walker, M., & Crawford, J. (2016). Implementing Environmental Sustainability in local government: The impacts of framing, agency culture, and structure in US cities and counties. International Journal of Public Administration, 40(3), 270–283. https://doi.org/10.1080/01900692.2015.1107738 

  45. Krause, R. M. (2011). Policy Innovation, intergovernmental relations, and the adoption of climate protection initiatives by U.S. cities. Journal of Urban Affairs, 33(1), 45–60. https://doi.org/10.1111/j.1467-9906.2010.00510.x 

  46. Brody, S. D., Zahran, S., Highfield, W. E., Bernhardt, S. P., & Vedlitz, A. (2009). Policy learning for flood mitigation: A longitudinal assessment of the community rating system in Florida. Risk Analysis, 29(6), 912–929. https://doi.org/10.1111/j.1539-6924.2009.01210.x 

  47. University of North Carolina Institute for the Environment. (2016). (rep.). Case Study: Rocky Mount, North Carolina. https://www.eli.org/research-report/floodplain-buyout-case-study-kinston-nc 

  48. Hupe, P. (2014). What happens on the ground: Persistent issues in implementation research. Public Policy and Administration, 29(2), 164–182. https://doi.org/10.1177/0952076713518339 

Suggested Citation:

Wang, Q., Zhang, Y., & Owen, K. (2022). What Drives Hazard Mitigation Policy Adoption? FEMA’s Property Buyout Program in Virginia Counties Natural Hazards Center Mitigation Matters Grant Report Series, 10. Boulder, CO: Natural Hazards Center, University of Colorado Boulder. Available at: https://hazards.colorado.edu/mitigation-matters-report/what-drives-hazard-mitigation-policy-adoption

Wang, Q., Zhang, Y., & Owen, K. (2022). What Drives Hazard Mitigation Policy Adoption? FEMA’s Property Buyout Program in Virginia Counties Natural Hazards Center Mitigation Matters Grant Report Series, 10. Boulder, CO: Natural Hazards Center, University of Colorado Boulder. Available at: https://hazards.colorado.edu/mitigation-matters-report/what-drives-hazard-mitigation-policy-adoption