Technical Papers
Jul 26, 2024

Quantitatively Incorporating Social Equity in Water Network Maintenance and Rehabilitation Decision-Making

Publication: Journal of Water Resources Planning and Management
Volume 150, Issue 10

Abstract

Water infrastructures are critical assets that ensure communities served by them have access to clean and reliable water. Because the cost of creating a water infrastructure is relatively high and the service life of infrastructures ranges from a few decades to more than a century, the process of systematic maintenance and rehabilitation is essential for preserving them at an optimal functioning capacity. From an asset management perspective, water infrastructure maintenance and rehabilitation (M&R) decision-making primarily involves assessing the condition and performance of the infrastructure, accounting for aspects such as complying with regulatory requirements, allocating resources based on system constraints, and mitigating network failure risks. The existing frameworks for water infrastructure maintenance and rehabilitation rely on models that allocate resources for M&R operations based on network condition and criticality under budgeting constraints. However, existing M&R frameworks have not included social equity (e.g., fair and just distribution of water resources) as a variable that directs or influences the M&R decisions. To improve the equitability of water infrastructures, a framework that incorporates social equity into the M&R decision-making is needed to attain equitable resource allocations for M&R projects. Hence, in this study, we developed a framework that optimizes resource allocations for water infrastructure M&R decisions by directly incorporating social equity objectives. The developed social equity objectives were derived from four social equity theories, utilitarianism, Rawlsianism, egalitarianism, and socialism, and integrated into three different policies A, B, and C, to (1) maximize performance, (2) maximize performance and improve access to disadvantaged groups, and (3) minimize the gap in performance between the disadvantaged group and the rest of network, respectively. The developed framework was applied to a numerical case study of a water distribution pipeline network in the City of Portland, Oregon, and the results of the modeling were discussed. The case study findings indicate that Policies B and C, which are equity inclined, without causing considerable economic setbacks result in a smaller performance disparity between the condition scores of assets in privileged (advantaged) versus underprivileged (disadvantaged) neighborhoods. The study results provide evidence that quantitatively incorporating social equity into the decision-making of water infrastructure M&R can improve asset allocations to disadvantaged communities without significantly compromising the broader network’s performance.

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Data Availability Statement

All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

We thank the editor and anonymous reviewers for reviewing the manuscript and providing valuable feedback.

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Journal of Water Resources Planning and Management
Volume 150Issue 10October 2024

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Received: Sep 30, 2023
Accepted: Apr 5, 2024
Published online: Jul 26, 2024
Published in print: Oct 1, 2024
Discussion open until: Dec 26, 2024

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Aadhityaa Mohanavelu, S.M.ASCE [email protected]
Ph.D. Student, Dept. of Civil and Environmental Engineering, Stanford Univ., Stanford, CA 94305. Email: [email protected]
Assistant Professor, Dept. of Civil and Environmental Engineering, Stanford Univ., Stanford, CA 94305 (corresponding author). ORCID: https://orcid.org/0000-0002-9804-8322. Email: [email protected]
Assistant Professor, Dept. of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India. ORCID: https://orcid.org/0000-0002-5824-6527. Email: [email protected]
Jojo France-Mensah, Ph.D., M.ASCE [email protected]
Director, Customer Engagement Strategy and Operations, ServiceNow, 2225 Lawson Lane, Santa Clara, CA 95054. Email: [email protected]

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