Incorporation of Equity into Infrastructure Decision-Making: Development of an Equity Metric for Infrastructure Retrofitting

Abstract

When infrastructure service outages occur, especially after natural hazards, the impacts are not felt uniformly across a community but are disproportionately clustered in vulnerable groups (e.g., low-income, minority, etc.). Furthermore, areas of greatest vulnerability are often serviced by more fragile infrastructure that is more prone to outages, circuitously increasing the likelihood of these disproportionate impacts (Highfield et al., 2014; Van Zandt, 2019). To equitably serve infrastructure service to a community, unequitable impacts should be accounted for in infrastructure decision-making. While priority is often placed on improving the overall system performance, limited work has incorporated equity considerations into retrofit prioritizations by which disproportionate impacts on the most vulnerable could be alleviated and ultimately increase overall community resilience. An equity-based infrastructure performance metric can allow for equity to be optimized in tandem with other cost, temporal, and overall system performance metrics commonly considered. Therefore, this work develops a metric to evaluate infrastructure component retrofit prioritization schemesﾒ equity towards groups of differing vulnerabilities. The equity metric targets distribution pole retrofits aiming to ensure a more equitable spread of service performance to household structures and resultant adverse impacts.

The equity metric is built upon Theilﾒs T (Theil, 1967), which is an entropy-based measure of the inequality present in the distribution of a scarce resource, and is decomposable allowing for the evaluation of equity differences between specific vulnerable and less-vulnerable populations. In this study, infrastructure service performance and its impact are used for the metric development. The service performance is measured by household infrastructure resilience scores which are based on a householdﾒs impact likelihood. Varying nodal vulnerability and the networked interactions of all nodeﾒs performance quality are modeled by considering vulnerability correlation. The developed metric and its equity quantification will be demonstrated in a case study in Galveston, TX for electric distribution poles.

Highfield, W. E., Peacock, W. G., & Van Zandt, S. (2014). Mitigation planning: Why hazard exposure, structural vulnerability, and social vulnerability matter. Journal of Planning Education and Research, 34(3), 287-300. https://doi.org/10.1177/0739456X14531828.

Theil, H. (1967). Economics and information theory. North-Holland Pub. Co.

Van Zandt, S. (2019). Impacts on socially vulnerable populations. In The Routledge Handbook of Urban Disaster Resilience (pp. 33-51). Routledge.