Local-level managers’ attitudes towards natural hazards resilience: the case of Texas

ABSTRACT

Prior research on natural hazards resilience demonstrate that citizens favor immediate and direct solutions to disasters, and are less supportive of future-oriented prevention strategies. These perceptions create incentives for politicians to adopt similar views. In this study, we explore the views of an intermediate group of stakeholders – public and professional managers who play a role in the process of designing disaster relief and prevention policies. Using a survey of professional stakeholders in Texas, we map their views on the causes and policy solutions for the damages following Hurricane Harvey (Summer 2017). Overall, we find that professional stakeholders hold fairly similar views to the public. Yet, they differ from citizens as they are less supportive of distributing direct payments, instead preferring to allocate funds towards long-term prevention solutions, and creating a regulatory environment to reduce the risks. Also, our results indicate that having personal experience of hazard events further enhance those views, and stakeholders in government positions prefer even less direct governmental involvement in disaster resilience. Our findings expand existing knowledge regarding individual perceptions and policy preferences facing natural hazards by discussing the views of professionals who play important roles in designing solutions to mitigate the growing risks from climate change.

KEYWORDS:

• Natural hazards
• infrastructure resilience
• stakeholders
• risk perceptions

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.houstonpublicmedia.org/articles/shows/houston-matters/2020/02/13/360473/houston-sets-once-in-a-generation-goals-in-disaster-resilience-plan/

2 The long-term approach was adopted elsewhere in the US. In 2012, the state of Louisiana introduced a Comprehensive Master Plan for Sustainable Coast with the objectives of preventive flood risks and restore the damages to coastal communities in the ‘coming decades’ (Groves & Sharon, 2013).

3 Studies also explore questions related to the measurement of resilience (across various dimensions) using different indicators, see for example Cutter (2016), Cutter and Derakhshan (2020).

4 For a more specific application in infrastructure development see Thacker et al. (2019). Also, the IPCC 2022 Climate change report (Link)

5 Natural hazards are one of several outcomes of climate change (Trenberth, 2018)

6 Organizations included in this study were broadly selected from local, regional, state and national governmental units, and more locally centered businesses, health providers, advocacy groups and nonprofit organizations within the 11 counties that make up the Coastal Bend Council of Governments.

7 The survey instrument and the sample methodology were designed by the Institute for Science, Technology and Public Policy (ISTPP) at Texas A&M University. The survey was administered by Public Policy Research Institute (PPRI) at Texas A&M University. The survey was approved by the Texas A&M university IRB (IRB ID: IRB2018-1679M).

8 Stakeholders sample recruitment procedures: we contacted via email an initial list of 2000 potential respondents. Email reminders were sent to those who did not complete the survey. 460 phone calls were initiated for nonresponses of the initial email invite. Additional reminders were sent to respondents who did not complete the entire questionnaire. When the survey closed on March 10th, 448 surveys were opened. We removed all respondents who did not complete any portion of the survey, click through the entire study, or did not complete the majority of the survey. The final sample size is 217, and completion rate is 10.85%.

9 We recode this measure and offer a clearer scale for the responses, see details in the results section.

10 Complete survey instrument is available from the authors upon request.

11 Studies in political behavior suggest that ‘Don’t know’ answers (similar to our ‘Not sure’) might represent guessing and may skew the distribution of knowledge (Laurison, 2015). Graham (2021) offers a more complex model to explore these issues. His approach is too demanding of our data and beyond the scope of this research.

12 In addition, the differences between the first group (larger than mean) and second (smaller than mean) are significant as the error-bars do not overlap.

13 Proportions are based on the original survey items (measured on a 1–5 scale). We collapse the responses to three categories (oppose, neutral, and support) by combining the proportion of strongly oppose and oppose into one, and combining strongly support and support into another one.

14 https://www.chron.com/news/houston-texas/article/Limited-building-in-floodways-wins-council-1667371.php

15 https://gov.texas.gov/news/post/governor-abbott-announces-new-flood-prevention-efforts-in-kingwood-texas

16 https://www.congress.gov/bill/109th-congress/senate-bill/3721

17 An example is the survey of elite decision-makers conducted by the Chicago council of Foreign affairs, latest survey is from 2018, see https://www.thechicagocouncil.org/research/public-opinion-survey/coming-together-or-coming-apart

18 UN report: https://www.ipcc.ch/report/ar6/wg1/#SPM

Additional information

Funding

This work was supported by Texas Commission on Environmental Quality [grant number RESILIENCE Program award RCEGR480001-01-00].