Measuring Welfare Losses from Urban Water Supply Disruptions

Buck, Steven; Auffhammer, Maximilian; Hamilton, Stephen F.; Sunding, David L.

doi:10.1086/687761

Cited by 19 publications

(30 citation statements)

References 25 publications

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“…The dollar value is equal to the weighted sum of flows, based on the flow volume between two points in the network and the specified unit cost. The economic value of losses associated with each water supplier is assessed using a demand function procedure with estimated water prices and elasticities of demand derived from existing sources (Jenkins et al 2003, Buck et al 2016, Porse et al 2018b. The model formulation includes constraints that emphasize minimum deliveries of water to districts for health and safety (in-home residential) and commercial and industrial needs.…”

Section: Water Management Modelingmentioning

confidence: 99%

Energy use for urban water management by utilities and households in Los Angeles

Porse

Mika

Escriva-Bou

et al. 2020

Environ. Res. Commun.

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Reducing energy consumption for urban water management may yield economic and environmental benefits. Few studies provide comprehensive assessments of energy needs for urban water sectors that include both utility operations and household use. Here, we evaluate the energy needs for urban water management in metropolitan Los Angeles (LA) County. Using planning scenarios that include both water conservation and alternative supply options, we estimate energy requirements of water imports, groundwater pumping, distribution in pipes, water and wastewater treatment, and residential water heating across more than one hundred regional water agencies covering over 9 million people. Results show that combining water conservation with alternative local supplies such as stormwater capture and water reuse (nonpotable or indirect potable) can reduce the energy consumption and intensity of water management in LA. Further advanced water treatment for direct potable reuse could increase energy needs. In aggregate, water heating represents a major source of regional energy consumption. The heating factor associated with grid-supplied electricity drives the relative contribution of energyfor-water by utilities and households. For most scenarios of grid operations, energy for household water heating significantly outweighs utility energy consumption. The study demonstrates how publicly available and detailed data for energy and water use supports sustainability planning. The method is applicable to cities everywhere.

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Section: Water Management Modelingmentioning

confidence: 99%

Energy use for urban water management by utilities and households in Los Angeles

Porse

Mika

Escriva-Bou

et al. 2020

Environ. Res. Commun.

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“…This derivation and assumptions are based on an analysis by Buck et al . [].

C_{a d \min} = \sum_{i j} (|, s_{i j} * c_{i j}) + \sum_{i} (|, {p_{b u y} * w c c}_{i, b o u g h t}) - \sum_{i} (|, p_{i, s e l l} * {w c c}_{i, s o l d}) + f i n e,

C_{s o c i a l} = (|, \frac{E_{i}}{1 + E_{i}} * \frac{P_{i} ([]|, 1 - {(1 - r_{i})}^{(1 + {scriptE}_{i}) / {scriptE}_{i}})}{r_{i}} - {m c}_{i}) Q_{i} * r_{i},

c o s t s a v i n g s = ([]|, C_{a d \min, m} + C_{s o c i a l, m}) - true[C_{a d \min, r e f} +

…”

Section: Methodsmentioning

confidence: 99%

“…For this study, we follow baseline elasticity and price values as estimated by Buck et al . [] for each of the BAWSCA service areas. These estimates were computed based on observed long‐term trends in BAWSCA.…”

Section: Methodsmentioning

confidence: 99%

Coordinating water conservation efforts through tradable credits: A proof of concept for drought response in the San Francisco Bay area

Gonzales

Ajami

Sun

2017

Water Resources Research

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Water utilities are increasingly relying on water efficiency and conservation to extend the availability of supplies. Despite spatial and institutional interdependency of many utilities, these demand‐side management initiatives have traditionally been tackled by individual utilities operating in isolation. In this study, we introduce a policy framework for water conservation credits that enables collaboration at the regional scale. Under the proposed approach, utilities have the flexibility to invest in water conservation measures that are appropriate for their specific service area. When utilities have insufficient capacity for local cost‐effective measures, they may opt to purchase credits, contributing to fund subsidies for utilities that do have that capacity and can provide the credits, while the region as a whole benefits from more reliable water supplies. This work aims to provide insights on the potential impacts of a water conservation credit policy framework when utilities are given the option to collaborate in their efforts. We model utility decisions as rational cost‐minimizing actors subject to different decision‐making dynamics and water demand scenarios, and demonstrate the institutional characteristics needed for the proposed policy to be effective. We apply this model to a counterfactual case study of water utility members of the Bay Area Water Supply and Conservation Agency in California during the drought period of June 2015 to May 2016. Our scenario analysis indicates that when the institutional structure and incentives are appropriately defined, water agencies can achieve economic benefits from collaborating in their conservation efforts, especially if they coordinate more closely in their decision‐making.

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“…For example, in power systems, the penalty cost of lost load can be calculated using the commonly used value of lost load [40], [41], which is defined as the estimated amount that power customers receiving electricity with firm contracts would be willing to pay to avoid a disruption in their electricity service [40]. Similar definitions can be applied when other CI systems are considered, e.g., the cost of disruption of gas supply [42] and the cost of welfare losses from urban water supply disruptions [43]. Accordingly, the total system costs under nominal operation condition can be represented as…”

Section: A Problem Statementmentioning

confidence: 99%

Resilient Critical Infrastructure Planning Under Disruptions Considering Recovery Scheduling

Fang

Zio

et al. 2021

IEEE Trans. Eng. Manage.

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Reliable and safe critical infrastructures are crucial for the sustainability of modern societies. To cope with increasing disruptive events such as man-made and natural disasters attacking infrastructures, resilience should be considered as an integrated perspective into the system planning process. This paper presents a p-robust optimization model for infrastructure network planning against spatially localized disruptions. The optimization aims at minimizing the investment costs for system hardening and expansion and the total system costs under nominal operating conditions, while incorporating resilience requirements by the p-robustness constraints. Importantly, instead of only mitigating system vulnerability, the proposed model integrates the arranging of the repair sequence of damaged components under limited repair resources into the preevent system planning. The complexity of the proposed model is analyzed, and a hybrid algorithm that combines scenariobased decomposition and variable neighborhood search is developed for its efficient solution. The effectiveness of the approach is illustrated through an application to a real power transmission system. Quantitative analysis can assist managers in decision making regarding investing in different system protection actions and making a tradeoff between desired resilience and budget constraints.

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Measuring Welfare Losses from Urban Water Supply Disruptions

Cited by 19 publications

References 25 publications

Energy use for urban water management by utilities and households in Los Angeles

Energy use for urban water management by utilities and households in Los Angeles

Coordinating water conservation efforts through tradable credits: A proof of concept for drought response in the San Francisco Bay area

Resilient Critical Infrastructure Planning Under Disruptions Considering Recovery Scheduling

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