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Multi-Dimensional Modeling for Environmental Impact Assessment at Intersections of the Food-Energy-Water Nexus.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multi-Dimensional Modeling for Environmental Impact Assessment at Intersections of the Food-Energy-Water Nexus./
Author:	Daignault, Jessica Kathryn.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	138 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
Contained By:	Dissertations Abstracts International83-08B.
Subject:	Civil engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28866186
ISBN:	9798780624523

Multi-Dimensional Modeling for Environmental Impact Assessment at Intersections of the Food-Energy-Water Nexus.
Daignault, Jessica Kathryn.

Multi-Dimensional Modeling for Environmental Impact Assessment at Intersections of the Food-Energy-Water Nexus. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 138 p.

Source: Dissertations Abstracts International, Volume: 83-08, Section: B.

Thesis (Ph.D.)--Michigan Technological University, 2021.

This item is not available from ProQuest Dissertations & Theses.

This dissertation uses multi-dimensional modeling for environmental impact assessment at intersections of the Food-Energy-Water (FEW) Nexus, including life cycle assessment (LCA) modeling for quantification of environmental impacts due to household FEW consumption, a linear regression framework for quantification of water-use impacts of marginal electricity generation, and a multi-objective optimization model to assess monetization of water withdrawals for electricity generation and impacts to water stress due to electricity dispatch schemes. Chapter 2 of this dissertation summarizes the development of an LCA model that quantifies the direct and indirect environmental impacts of household FEW consumption. The model is executed through a novel household consumption tracker called the HomeTracker. The result of this work is an open-source software application that has been used to support experimental research taking place in suburban households in the midwestern United States for identification of effective interventions to inform household consumption behavior change. Chapter 3 addresses the need to quantify the water-use impacts of marginal electricity generation. A linear regression methodology is used to quantify water withdrawal and consumption impacts due to marginal generation, and a case study is presented to demonstrate how the framework can be applied to generate marginal water factors (MWFs) at multiple temporal resolutions. Results illustrate that MWFs vary in space and time and are lower when renewables are deployed on the margin. Chapter 4 investigates the effect of implementing a dispatch cost per unit water withdrawals for electricity generation on water stress at the watershed scale. Impacts to water stress are assessed using a freshwater withdrawal to availability ratio, which quantifies water stress at the watershed level. Adding a dispatch cost per unit water withdrawal decreases water withdrawals up to 92% with a 45% increase in generation cost. The key contribution of this work is an advancement of knowledge of FEW Nexus systems at multiple spatial and temporal scales through life cycle assessment modeling, statistical modeling, and optimization modeling. Future work will include spatial and temporal improvements to models including expansion of geographic coverage and increased temporal resolution as data becomes available.

ISBN: 9798780624523Subjects--Topical Terms:

860360
Civil engineering.
Subjects--Index Terms:

FEW nexus

Multi-Dimensional Modeling for Environmental Impact Assessment at Intersections of the Food-Energy-Water Nexus.
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506 $a This item is not available from ProQuest Dissertations & Theses.
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520 $a This dissertation uses multi-dimensional modeling for environmental impact assessment at intersections of the Food-Energy-Water (FEW) Nexus, including life cycle assessment (LCA) modeling for quantification of environmental impacts due to household FEW consumption, a linear regression framework for quantification of water-use impacts of marginal electricity generation, and a multi-objective optimization model to assess monetization of water withdrawals for electricity generation and impacts to water stress due to electricity dispatch schemes. Chapter 2 of this dissertation summarizes the development of an LCA model that quantifies the direct and indirect environmental impacts of household FEW consumption. The model is executed through a novel household consumption tracker called the HomeTracker. The result of this work is an open-source software application that has been used to support experimental research taking place in suburban households in the midwestern United States for identification of effective interventions to inform household consumption behavior change. Chapter 3 addresses the need to quantify the water-use impacts of marginal electricity generation. A linear regression methodology is used to quantify water withdrawal and consumption impacts due to marginal generation, and a case study is presented to demonstrate how the framework can be applied to generate marginal water factors (MWFs) at multiple temporal resolutions. Results illustrate that MWFs vary in space and time and are lower when renewables are deployed on the margin. Chapter 4 investigates the effect of implementing a dispatch cost per unit water withdrawals for electricity generation on water stress at the watershed scale. Impacts to water stress are assessed using a freshwater withdrawal to availability ratio, which quantifies water stress at the watershed level. Adding a dispatch cost per unit water withdrawal decreases water withdrawals up to 92% with a 45% increase in generation cost. The key contribution of this work is an advancement of knowledge of FEW Nexus systems at multiple spatial and temporal scales through life cycle assessment modeling, statistical modeling, and optimization modeling. Future work will include spatial and temporal improvements to models including expansion of geographic coverage and increased temporal resolution as data becomes available.
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