Do planners matter? Examining factors driving incorporation of land use approaches into hazard mitigation plans

Abstract

Local hazard mitigation plans help communities organise a comprehensive set of policies and actions to reduce long-term risks from natural hazards. Land use policies hold the greatest long-term risk reduction potential, but are under-utilised. Using multivariate regression models, we assessed the influence of involvement of local planners on hazard mitigation planning committees on the inclusion of land use policies into three principles of plan quality, controlling for state and local factors. Results indicate a need for greater involvement of local planners and stronger emphasis by federal and state officials on integrating land use planning approaches into mitigation plans. Findings may be relevant to other areas of public policy with important land use dimensions for which non-planning agencies and professions have lead responsibility or historical dominance, such as transportation and climate change.

Keywords:

• Plan evaluation
• plan quality
• hazard mitigation
• land use
• participation

Acknowledgements

This material is based upon work supported by a Royster Fellowship from the Graduate School at the University of North Carolina at Chapel Hill and PERISHIP Fellowship supported by the National Science Foundation, the University of Colorado Natural Hazards Center, Swiss Re and the Public Entity Risk Institute. The material also is based upon work supported by the US Department of Homeland Security, Center for Natural Disasters, Coastal Infrastructure and Emergency Management under Award Number: 00313690. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the US Department of Homeland Security. The authors are grateful to the graduate research assistants on the coding team: Casey Dillon, Frank Glover, Ted Mansfield, Rachel Meyerson, Sara Reynolds and Casey Weissman-Vermeulen.

Notes

1. Only 25 jurisdictions were included in the sample for Georgia because that is the total number of coastal jurisdictions in the state that met the minimum population threshold.

2. We also assessed the reliability of our data using Krippendorff's alpha, as recommended by Berke and Godschalk (2009). Krippendorff's alpha is a more general agreement measure than percent agreement and is considered preferable for measuring reliability (Krippendorff 2004, Chapter 11). Alpha scores can range from 1.00 to -1.00, with lower scores indicating lower reliability. Individual item Krippendorff alpha scores in our dataset ranged from 0.80 to -0.06. We have elected to keep all of the plan coding data for two main reasons. First, Krippendorff recommends precaution in accepting or rejecting measured reliabilities under certain conditions, including when there is insufficient variation in the sample; that is when a potential score is rarely present in the dataset (Krippendorff 2004, Chapter 11). Many of the land use policies (i.e. policies such as density transfer provisions that are very useful for risk reduction are infrequently used by communities to do so) were rarely observed in our dataset, meaning our dataset meets this precautionary condition. Second, as noted in the text, our reliability scores using the traditional method were within the range of scores in the plan quality literature. Of more than 40 peer reviewed plan quality studies published to date, none have reported Krippendorff's alpha. Thus, there are no planning-specific benchmarks against which to compare our Krippendorff's alpha scores.

3. These data were generated through the same content analysis process described in the Research Design and Methods section. For more information on the items used for these indexes, see www.ie.unc.edu/cscd/projects/dma.cfm

4. A potential concern is that the relationship detected for the planner variable is in fact a spurious relationship because jurisdictions with more resources or larger populations may have a greater ability to have their own planners engaged in hazard mitigation planning. Inclusion of the median house value, as done by Burby and Dalton (1994), Berke et al. (1996) and Berke et al. (1999), addresses this concern somewhat by accounting for the resources available for a larger planning staff from the tax base. As an additional test, all three models were run including each jurisdiction's population prior to plan adoption as an independent variable. The population variable (logged) was not found to be statistically significant in any model in the fact base or policies models, but was in the implementation model (coefficient -0.107; p-value 0.032). Inclusion of the population variable did not affect the sign, magnitude or significance of any variables in the fact base, policies or implementation principles models in a way that would impact the interpretations presented in this paper.

5. The negative relationship of population density to land use policies fits with previous findings that as density increases the amount land available for development decreases, raising pressure to develop in hazardous locations (Burby and Dalton 1994). As density increases, communities may opt instead to pursue structural approaches (e.g. levees and seawalls) to try and protect vulnerable areas. We did not detect a relationship for community wealth (i.e. median house value), as has been found previously (Burby and Dalton 1994; Berke et al. 1996; Berke et al. 1999).