Impacts of Combined Cooling, Heating and Power Systems, and

Nov 11, 2017 - Brook Byers Institute for Sustainable Systems, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta,...
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Atlanta

Business-as-usual development

development Pagegrowth Environmental 1 of 27 More compact Science & Technology

Air Emissions

CCHP in new and existing buildings w/net metering

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NOX (103 tonnes)

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-50% 20 -90%

106 gallons per day 400 Water-for-energy

2005-2030

CO2 (106 tonnes)

300

-93%

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Impacts of Combined Cooling, Heating and Power Systems and

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Rainwater Harvesting on Water Demand, Carbon Dioxide and NOx

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Emissions for Atlanta

4 Authors: Jean-Ann James†*, Sangwoo Sung±, Hyunju Jeongͼ, Osvaldo A. Broesicke†, Steve P. French‡, Duo Li§, John C. Crittenden†§

5 6 7



8

Georgia Institute of Technology, Atlanta, Georgia 30332, United States

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±

Brook Byers Institute for Sustainable Systems, School of Civil and Environmental Engineering,

The Department of Geography, Planning and Environment, East Carolina University,

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Greenville, North Carolina, 27858, United States

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ͼ College

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30332, United States

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§

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Chaoyang District, Beijing 100102, China

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*Corresponding author ([email protected], T: 404-894-7895, Address: 828 West Peachtree Street, Suite 320,

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Atlanta, Georgia 30332, USA)

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Abstract

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The purpose of this study is to explore the potential water, CO2 and NOx emission, and cost

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savings that the deployment of decentralized water and energy technologies within two urban

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growth scenarios can achieve. We assess the effectiveness of urban growth, technological, and

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political strategies to reduce these burdens in the 13-county Atlanta metropolitan region. The

of Engineering, Arkansas State University, Jonesboro Arkansas 72467, United States

School of City and Regional Planning, Georgia Institute of Technology, Atlanta, Georgia

Crittenden and Associates, C-305, Building E, Wangjing High-Tech Park, Lizezhong Er Road,

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urban growth between 2005 and 2030 was modeled for a business as usual (BAU) scenario and a

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more compact growth (MCG) scenario. We considered combined cooling, heating and power

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(CCHP) systems using microturbines for our decentralized energy technology and rooftop

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rainwater harvesting and low flow fixtures for the decentralized water technologies.

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Decentralized water and energy technologies had more of an impact in reducing the CO2 and

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NOx emissions and water withdrawal and consumption than an MCG growth scenario (which

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does not consider energy for transit). Decentralized energy can reduce the CO2 and NOx

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emissions by 8% and 63%, respectively. Decentralized energy and water technologies can reduce

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the water withdrawal and consumption in the MCG scenario by 49% and 50% respectively.

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Installing CCHP systems on both the existing and new building stocks with a net metering policy

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could reduce the CO2, NOx, and water consumption by 50%, 90%, and 75% respectively.

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Introduction

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By 2030 cities are expected to house 90% of the United States’ population.1 To accommodate

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this growth requires 427 billion square feet of building infrastructure2 and heavy investment into

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water and energy infrastructure. Both water and energy, the two main growth-limiting resources

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of an urban region, are highly interdependent.3 Water collection and treatment requires energy

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and most energy-generation facilities require water for system operation and raw fuel processing.

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Subsequently, both the amount of water needed for energy generation and the energy needed to

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treat water will increase with population growth. Half of the total water withdrawal of the US in

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2005 was used for energy generation, while 4% of the electricity generated nationally was used

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to treat and distribute water.3,4 To meet the additional water demand, the energy required by

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municipal plants to treat surface water will increase by an estimated 5-10%.3 Therefore, it is

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important to consider the interactions between water and energy when determining how to meet

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the future demand of these resources.

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As population increases, regions are prone to urban sprawl, which leads to water and

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energy losses that are amplified by inefficiencies in the treatment, generation, and distribution

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systems..5 In 2005 leaks in the water distribution system led to daily potable water losses of

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approximately 16% (7 billion gallons) of total potable water supply.4,6 The aging and

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deteriorating electrical infrastructure results in energy losses within the system.6 In 2011

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approximately 40% of the primary energy use in the US was for electricity generation.7 From the

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total US electricity generated, major losses manifested as heat (67%) as well as transmission and

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distribution inefficiencies (6.5%).8 Losses in the electrical distribution system result in

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approximately 0.13 gallons of water loss per kWh from the average U.S. power production

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plant.9

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Compact growth strategies may help mitigate many of the negative effects of urban

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sprawl since they promote the development of walkable, transit-oriented, and mixed-used

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neighborhoods.10,11 The decreased urban and automobile footprint improves air and water

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quality, reduces emissions, and conserves open space,11 thereby reducing linked health risks12,13

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and improving the quality of life. Compact growth also enables decentralized water and energy

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technology deployment, such as low-impact development (LID) and combined cooling, heating

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and power (CCHP), to meet user needs and reduce system inefficiencies. Additionally,

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decentralizing these infrastructures may help cities curtail water consumption and emissions

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(CO2 and NOx).

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In this study we examine the potential that decentralized water and energy technologies

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have in reducing water consumption, emissions – specifically carbon dioxide (CO2) and nitrous

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oxides (NOx) – and costs under two urban growth scenarios: (1) business as usual (BAU) and (2)

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more compact growth (MCG). Accordingly, we also compare the effectiveness of two policy

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strategies for increasing the sustainability of growing cities. The study scope is the 13-county

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Atlanta metropolitan region, one of the fastest growing urban regions in the US.14 The impacts of

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transportation changes were not included in this study. By coupling models for energy

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generation, water management, and land-use projections, this study also serves as a proof-of-

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concept for model integration to examine the dynamics and complexity of urban systems.

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Methods

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We modeled the water and energy consumption for BAU15 and MCG for the 13-county Atlanta

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metropolitan region.16 We then considered changes to the regional water and energy

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consumption if decentralized water and energy systems and a net metering energy policy were

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implemented. A spatial growth model was used to predict the potential residential and

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commercial growth between 2005 and 2030 under the two urban growth scenarios, BAU and

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MCG. The energy demand for five prototype buildings (3-commercial, 2-residential) in the

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Atlanta metropolitan region was obtained from the Open Energy Information (OpenEI)

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database.17 Based on the heating, cooling and hot water energy demands of a building we sized

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CCHP systems to meet these energy demands.

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The maps in this paper were created using ArcGIS® software by Esri. ArcGIS® and

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ArcMapTM are the intellectual property of Esri and are used herein under license. Copyright

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©Esri. All rights reserved. For more information about Esri® software, please visit

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www.esri.com.18

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Spatial growth model

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“What If?”, a community land use planning support system (PSS), was used to predict the future

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land-use patterns of the 13-county Atlanta Metropolitan region.19 “What If?” is a rule-based20

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and projections-for-urban-planning (PUP) model21 that has been used widely as a PSS to

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compare urban growth and land use planning scenarios.19,22–27 “What If?” was selected as the

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most suitable urban growth testing platform because of its transparency, flexibility, user-friendly

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interface and deterministic procedures in the spatial growth modeling procedure. The procedures

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involve three steps: (1) suitability analysis, (2) land demand analysis, and (3) allocation analysis.

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The suitability analysis generates suitability maps using spatial datasets by adopting overlay

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techniques28,29 and weighted linear combination (WLC) multiple criteria analysis (MCA)

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techniques.30 The land demand analysis projects future land requirement using exogenous socio-

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economic data of population and employment growth estimates. The allocation analysis assigns

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population and employment to the future land based on the suitability maps and the land demand

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analysis projection result. For more details, see Klosterman (1999)19 and Pettit and Pullar

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(2004)22. Limitations and uncertainties are discussed in Klosterman et al. (2005)20 and

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Klosterman (2012).31

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Our model integrated GIS datasets for population, employment, and housing projections

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published by the Atlanta Regional Commission (ARC) quinquennially.32 The ARC projection

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was generated prior to 2010 and was not modified for the 2005-2017 timeframe. Accordingly,

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we expect a discrepancy between the 2005-2017 projection results and the actual population and

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employment of Atlanta. The focus in this land-use simulation was to develop scenarios to

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visualize different spatial growth maps related to population-employment densities.

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To generate the suitability maps, our suitability analysis combined thirteen factor layers:

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(1) distance from major roads, (2) distance from floodplains, (3) distance to parks, (4) distance to

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highway ramps, (5) distance to rail, (6) distance to town centers, (7) distance from lakes and

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rivers, (8) distance to “not-in-my-back-yard” facilities(e.g., wastewater treatment plants and

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landfill sites), (9) distance to existing industry, (10) city boundaries, (11) public land and

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national parks, and (13) slope of the land. The factor layers were given weights and rankings to

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create the residential and employment land-suitability scores from 10 (least suitable) to 90 (most

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suitable), following the WLC and MCA technique. The assigned weights and rankings of each

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suitability factor were initially determined to reflect the relationship between urban development

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potential and land location or price, which is normally a reversed function of distance to existing

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urban services and physical landscape. The rationality of the weights and rankings was checked

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through the consultation with planning students and faculty group. This “focus group approach”

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to determine the relative importance of multi-criteria is one of multiple methods.33,34 Allen and

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Lu (2003)34 suggested that proper use of a focus group such as local experts, planners, and

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interest groups improve the reliability of growth prediction. Details of the GIS attributes, weights

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and rankings can be found in Tables S 1a-c of the supporting information (S.I.).

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Our land demand analysis used the 2005 residential and employment density values

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provided by the ARC GIS dataset.32 The county-by-county land demand and land-allocation

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projection require the different application of conditions and parameters – associated with spatial

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growth patterns, land requirements, housing profiles, and density values – for both the BAU and

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MCG scenarios. The BAU and MCG scenarios had four major differences in the input of spatial

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and non-spatial parameters. In contrast to the BAU scenario, the MCG scenario (1) stimulated

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urban growth near preexisting public transit-subway and bus-stop corridors; (2) increased the

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residential and employment density (50-100%); (3) increased the allocation of multi-family

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housing units for future residential use by 17%, and; (4) assumed higher infill rates in the land

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demand analysis. Details of the inputs used in the “What If?” model for each scenario can be

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found in Table S 2 of the S.I.

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Finally, in the land allocation analysis, the locations of new developments were

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prioritized based on the suitability scores for each scenario. We combined GIS layers that were

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generated in the previous steps (i.e., ARC LandPro 2005 land use data, population and

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employment projections, US Census block group data and suitability maps) to create uniform

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analysis zones (UAZs). The UAZs are homogenous-minimum-permissible-size land units (1

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acre) that “What If?” uses to allocate future land use. The allocation analysis is first performed

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within each separate county prior to combining allocation the 2005 to 2030 results or each

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projection year. Figure S 1 in the SI summarizes the urban growth modeling procedure.

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Water demand and consumption

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The total residential water demand was calculated using the population growth estimates and the

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gallons per capita per day (gpcd) water-use coefficients for each county from the North Georgia

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Metropolitan Planning District (MNGWD).35 The non-residential water demand was calculated

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using employment estimates and the gallons used per employee per day (GED) coefficients from

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literature (S.I. Table S 3).36 The MCG scenario assumed that all new residential buildings would

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be built with EPA WaterSense low-flow fixtures.37 Consequently, all 13 counties decreased

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residential indoor water use by 20.7% in the MCG scenario. The single-family and multifamily

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residential outdoor water uses for each county were modified for the MCG scenario using the

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BAU-to-MCG residential density ratio. The ratios were multiplied by the current outdoor gpcd to

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get a modified gpcd value for the MCG scenario. The BAU and MCG residential indoor and

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outdoor water-use coefficients for each county are presented in Table S 4 of the S.I. Previous

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studies indicate a 21-50% potential efficiency improvements of water devices in non-residential

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sectors.36–40 In this study, we assumed an efficiency improvement of 20% as a reasonable GED

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reduction for local and regional water demand for the MCG scenario. We used modified water-

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use coefficients (See S.I. Table S5) to calculate the total water use for each employment sector.

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The water consumption was calculated for each scenario using the weighted average of the

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outdoor water use to the total use for each residential building type.35

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Low Impact Development (LID)

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The LID technology considered in this study was rooftop rainwater harvesting. LID technologies

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can control stormwater runoff and harvest rainwater for non-potable water use, which reduces

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the amount of water needed from the centralized water treatment systems for non-potable

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purposes.41 Various regression models were tested to estimate the future residential and

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commercial roofing areas. The model was used to estimate the future roofing area in future.

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Further detail on the regression model can be found in the S.I.

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The estimated roofing area and the 30-year average annual rainfall (49.7 inches in

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Atlanta) was used to calculate the average daily rainwater harvesting potential from new

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development using Equation 1. We assumed a collection efficiency of 0.5. We assumed no

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difference of rainwater harvesting from buildings constructed before 2005 between either BAU

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and MCG with rainwater harvesting (RWH) scenario. The estimated water demand in the MCG

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scenario was calculated by subtracting the volume of water saved by implementing rainwater

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harvesting from the estimated regional water demand.

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Average daily water saving potential (gal) = [49.7 inches of annual rainfall predicted area of roof surface * 0.5 collection efficiency ÷365 days. (1)

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Building energy simulation

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We used the energy demand for 5 prototype buildings (Table 1) and assumed that all future

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residential and commercial growth will be met by these five buildings. The energy consumption

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of the buildings was obtained from OpenEI, which used Energyplus and the Department of

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Energy’s commercial reference building models.17 These prototypes were chosen to provide an

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estimation of how typical residential and commercial energy demand would unfold over the

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projection lifetime, similar to a previous study.42

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Table 1. Residential and commercial building characteristics. Adapted from James et al (2016).43

Building Types Small Office

Medium Office

Large Office

Multifamily Residential

Single Family Residential

Square footage (ft2)

5,500

53,628

500,000

33,740

2,546

Number of floors

1

3

12

4

1

Building electrical demand (kWh) [cooling+ plug load]

68,171

728,547

6,963,487

258,790

2,548

Building heating demand(kWh)

7,447

18,019

419,346

107,795

2,068

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Combined cooling, heating and power (CCHP)

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The CCHP system is composed of a Capstone air-cooled microturbine as the primary generation

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unit (PGU), a heat recovery unit and an absorption chiller. The CCHP system was designed to

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follow the hourly thermal load (FTL). The operational parameters, thermal outputs, and electrical

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outputs of the microturbine were compiled from the Capstone technical reference assuming

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natural gas as its primary energy source.44 Capstone commercially manufactures three turbines

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sizes (30kW, 65kW, and 200kW) but a larger system size can be determined by operating

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multiple turbines.

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The thermal load was considered to be the energy required for heating, cooling, and hot

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water.43 During the winter, the outputs of the CCHP system are electricity, hot water, and space

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heating, whereas in summer the outputs are electricity, hot water, and space cooling. Space

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cooling is provided when the microturbine’s waste heat is converted to cooling by the absorption

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chiller.

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Estimating energy demand, water-for-energy withdrawal and consumption,

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emissions (CO2 and NOx), and cost for the growth scenarios

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The SMARTRAQ (Strategies for the Metro Atlanta Region’s Transportation and Air Quality)

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project, initiated in 1998, was a multidisciplinary and multi-institutional research effort to

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examine the impacts of land use on transportation choice, vehicular emissions, and physical

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activity.45,46 The SMARTRAQ database generated by contains the land use data and attributes of

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an area (see the Health & Community Design Lab at the University of British Columbia).47 We

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used the SMARTRAQ46 database estimates to quantify the total commercial building square

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footage in the small (less than 10,000 ft2), medium (10,000 ft2 to 100,000 ft2) and large (100,000

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ft2) office building categories for 2005 (Table S 6). The calculated percentages were applied to

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the 5-year “What If?” growth increment for each county to determine the square footage of each

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building type. We used data from our previous study to determine the change in emissions, and

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water–for-energy consumption within all office buildings with and without a CCHP system

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between 2005 and 2030 (Table S7).43 We also considered the impact a net metering policy,

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which is a mechanism that allows the excess CCHP-generated electricity to be used by grid-

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connected buildings. This policy can reduce overall emissions since centralized power plants can

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redirect this excess electricity to meet demand rather than ramp up centralized electricity

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production.

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In the case of the multifamily residential building, the number of housing units estimated

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was translated to total square feet by multiplying the number of housing units by the average size

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of a unit in the South.48 Using the total square footage growth in each scenario along with the

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per square foot estimates for the emissions and water consumption (Table S7), we were able to

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estimate the change in emissions and water consumption with and without a CCHP system.

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The sizing of the residential CCHP systems was determined using the 5-year growth

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outputs of the “What If?” model and the number of new housing units that would be needed for

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each census tract. We assumed that all new buildings for each census tract within a growth

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period would be a new community that would have a CCHP system sized to meet the demand of

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the community. A MATLAB model was designed to determine the maximum CCHP system size

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that would be needed for a new community based on the maximum hourly thermal load of the

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community.43 Since the CCHP system is composed of multiple microturbine units, a large

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community can have multiple smaller units distributed throughout the community without the

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need for a centralized CCHP unit. . Upon discussion with the management of the St. Paul, MN

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district energy system, we considered thermal losses in the system negligible. We did not

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account for the cost of the thermal grid because this would require unique designs for each

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community. The emissions produced and water consumption for energy generation were

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determined for each community using the equations and emissions factors from our previous

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study.43

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The total emissions and water-for-energy generation for the growth in the residential and

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commercial buildings in the Atlanta Metropolitan region under the two growth scenarios (BAU

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and MCG) and three energy scenarios (No CCHP, CCHP, CCHP with net metering) were

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determined. We calculated the water loss for electricity generation using Georgia Power’s

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estimates of the 10% consumptive water loss of all water withdrawn.49 Using the total square

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footage for all building types and the per square foot average annual cost estimates of grid

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energy and the CCHP system we were able to estimate the total annual cost of energy under the

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two growth scenario and three energy. We also estimated the range of CCHP system costs based

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on the maximum and minimum costs of the individual components in our previous study.43

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Estimating the impact of CCHPs on old buildings

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We determined a best-case scenario in which we estimated the impact of installing CCHP

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systems in all new and existing commercial and residential buildings. The scenario assumed that

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all buildings prior to 2005 would fit the five building prototypes previously discussed.

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Accordingly, we assume that new and existing buildings have the equivalent efficiency. The total

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emissions and water consumption for the old buildings with CCHP systems was determined

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using the total estimated square footage of all the office buildings and multifamily buildings in

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2005 along with the emissions and water consumption estimates (See Table S7). The emissions

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and water consumption for existing single-family buildings was determined using the results

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from the 2005 to 2030 projections. Based on the projected community size, we determined how

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the per-building emissions and water consumption changes with community growth. We used

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the maximum emissions and water consumption values to be conservative, along with the

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number of buildings in the base year to determine the impact of installing CCHPs on the existing

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single family building stock.43

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Results and Discussion

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In our results, we examine how changing the regional growth pattern and selected decentralized

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technologies would affect the water-for-energy, municipal water demand, and emissions (CO2

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and NOx) of a region. The results indicate that decentralized energy alternatives can be more

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impactful in reducing the water (consumption and withdrawal) and emissions impacts than

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changing the growth pattern of a region. It is important to note that these results do not account

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for the water and emissions impacts of transportation. Our study framework incorporates urban

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growth models, water use, and energy use to provide useful insights on the impact of various

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policy decisions on the urban region.

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Spatial growth implications

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Figure 1 shows the land-use differences between the BAU and MCG scenarios (enlarged in

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Figures S2-S4). The results indicate that the largest portion of the new land demand is from

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single-family residential homes. The BAU scenario requires 663.7 thousand acres of new land to

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meet future (2030) development needs for residential and employment uses. In comparison, the

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MCG scenario requires 299.1 thousand acres to meet future development and employment needs.

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The percentage of multifamily households in the MCG scenario increases to 31% of the total

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number of residential units in 2030 as compared to 26.7% in the BAU scenario (Table 2 and S8).

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The increase in the number of multifamily units in the MCG scenario was most likely not as

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significant as expected because of the “What If?” land use parameters and constraints. The

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validation of the “What If?” model to actual land-use allocation is presented and discussed in the

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S.I. (Figures S6 – S7 and Table S9) for 2010.

283 284 285 286 287 288 289

Figure 1. Land use/land cover changes from the base year 2005 (center) to 2030 for both BAU (left) and MCG (right) scenarios. The BAU scenario is dominated by the sprawl of single family residential units, which displaces much of the forest cover. Because multifamily residential units displace single family residential units in the MCG scenario, much of the forest cover is conserved. Accordingly, much of the development is constrained to major roads and highways. Larger land use/cover figures are available in the SI for the base year (Figure S2), BAU (Figure S3), and MCG (Figure S4). 50 Notes: W.S = Whole Sale; Pub. = Public; Res = Residential. Adapted from James (2015) .

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Table 2. Estimated square footage (office buildings) or number of buildings (residential) in the base year and the estimated growth between 2030 and the base year (Table S8 have county-by-county estimated square footage and residential building results).

2005

2005-2030 BAU

Large Office Multifamily Residential Single Family Residential

Units

Medium Office

106 ft2

Small Office

MCG

106

343

343

249

279

279

204

445

445

458,207 192,459 305,707 1,170,283 614,617 504,205

293 294

Water Demand and CO2 and NOx Emissions

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Municipal water demand is responsible for 29%, 17%, and 14% of the total water withdrawal

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and 46%, 30%, and 19% of the total water consumed for the base year, BAU and MCG

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scenarios, respectively. The population and employment allocation resulting from the BAU

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scenario leads to approximately 57% increase in the projected water withdrawal from 2005

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(506.9 MGD) to 2030 (800.3 MGD) (Figure 2a). The municipal water demand in the MCG

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scenario, which includes low flow fixtures and LID, increases by 33% to 667 MGD in 2030.

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Therefore, there is approximately a 16% (133 MGD) reduction in the municipal water

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withdrawal between BAU and MCG scenarios in 2030. Implementing low flow fixtures and LID

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systems in the MCG scenario resulted in a 34% (55.5MGD) reduction in the municipal water

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consumed when compared to the BAU scenario (Figure 2b).

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Figure 2. Estimated municipal and energy generation water demand for the base year (2005) and projected new commercial and residential growth (between 2005 and 2030) for business-as-usual (BAU) and more compact growth (MCG) with low-impact development scenarios assuming grid energy, a CCHP system, and a net metering policy: a) Water-for-energy withdrawal and domestic use; b) Water-for-energy consumption and domestic use.

310 311

Most of the water withdrawal and evaporative losses result from energy generation. We

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ran two simulations when considering electricity from the grid. The first simulation assumed that

313

grid electricity comes from the current and projected grid mix in Atlanta – see Choi and

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Thomas51 – and the second assumes the electricity from the grid is provided only by combined

315

cycle natural gas plant (CCNG). Adding a CCHP system, in the grid mix simulation, can reduce

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the water-for-energy (withdrawal and consumption) by 86% in the BAU case and 85% in the

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MCG in comparison to their respective “No CCHP” scenario (Figure 2). Incorporating a net

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metering policy may further reduce the water-for-energy by 95% and 94% in the BAU and MCG

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scenarios, respectively. If the grid were composed solely of CCNG plants, then the water-for-

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energy would be 12% of the demand with the grid mix for both the BAU and MCG scenarios

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with and without CCHP. The reductions in a generation scenario with CCNG and CCHP are

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similar to that of the grid mix scenario (S.I. Figure S 8 and Table S10). Overall, combining

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MCG, LID, CCHP systems and net metering can reduce the water withdrawal and consumption.

324

While an MCG scenario can reduce the water withdrawal and consumption of a region,

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modifying the energy generation scheme is more effective in reducing regional water

326

consumption.

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As shown in Figure 3, CO2 emissions in the BAU grid mix scenario decrease by 9.5%

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and 24.8% without and with net metering, respectively. In the MCG scenario, the CO2 emissions

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decrease by 7.2% and 25.4% without and with net metering, respectively. When we consider the

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electricity from the grid coming from CCNG plants, the CO2 emissions without and with net

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metering decrease by 9.4% and 36.4%, respectively, in the BAU scenario and 6.9% and 34.7% in

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the MCG scenario, respectively (Figure 3). Other studies have found similar CO2 emissions

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reduction within these ranges.42,52,53 Howard et al. (2013) simulated an aggregate CO2 emissions

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reduction of 4% and 9% in New York City for individual building systems and microgrid

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systems, respectively.42 Similarly, Lee et al. (2013) estimate a 4-5% reduction in CO2 emissions

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in Boston over 20 years.53 Duquette et al. (2013) estimated approximately 24% and 32% CO2

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emissions reduction in Ontario using two different CHPs; however, this study also analyzed a

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widespread district energy system.52 In comparing our results to these studies a few caveats

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should be considered: (1) these three studies take place in colder climates, which increases CHP

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systems’ efficiency; (2) the generation and dispatch strategies in these studies differ in that they

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follow the electrical load, optimize for pricing, or make differing assumptions on energy needs,

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and; (3) the CHP system design differs from the design considered in this study.

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The BAU NOx emissions decrease by approximately 61% without net metering and 73%

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with net metering under the grid mix (Figure 3). In the MCG scenario, the NOx emissions

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decrease by 63% without net metering and 82% with net metering. If grid electricity comes

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solely from CCNG plants, the NOx emissions can be further reduced 24-40%, depending on the

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growth scenario and if there is net metering (Figure 3). This can have interesting policy

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implications for the benefits of converting coal-fired power plants to natural gas. Switching to

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CCHP systems would result in a greater decrease in the emissions and water consumption than

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solely switching to CCNG power plants.

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352 353 354 355

Figure 3: Projected annual 2030 CO2 and NOX emissions from energy consumption commercial and residential buildings for two growth scenarios. These values only represent emissions from new construction between 2005-2030 assuming electricity is provided by either the grid or a CCNG in conjunction with a CCHP.

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The average annual cost of energy in 2030 for the commercial and residential buildings

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built between 2005 and 2030 will be slightly lower, 1% (~$51M) in the BAU scenario and 3%

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($150M) in the MCG, when a CCHP system is implemented (Figure 4a). Net metering further

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reduces the cost of energy associated with CCHP systems to 7% (BAU) and 8% (MCG). As

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shown by the upper bound cost bar, the costs would be higher than the cost from the

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conventional grid if the maximum CCHP systems cost is used. Lower system costs would make

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implementing CCHP systems more economically feasible (Figure 4a). Increased system

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efficiencies would also improve the financial viability of CCHP systems as more electricity

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would be produced reducing the electricity required from the grid. Lower fuel costs could also

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significantly increase the economic feasibility of the system as shown in our previous work.43

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Figure 4: The approximate annual energy-generation cost in 2030 (includes the cost of energy required for the grid and cost of the HVAC system) versus the average cost of the CCHP systems, for the BAU and MCG scenarios. A) Energy costs to accommodate the 2005 to 2030 population and employment growth. B) Energy costs in 2030 assuming CCHP installation of existing (2005) and new building stocks.

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Best case scenario: CCHP in existing and new buildings

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Compared to their respective “No CCHP” scenario with the grid mix, installing CCHPs in all

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residential and commercial buildings decreases the CO2 emissions by 9% (without net metering)

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and 49% (with net metering) in the BAU scenario and by 8% (without net metering) and 50%

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(with net metering) in the MCG scenario (Figure 5). Similarly, NOx emissions decrease by 61%

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(without net metering) and 86% (with net metering) in the BAU scenario and 63% (without net

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metering) and 90% (with net metering) in the MCG scenario (Figure 5). Furthermore, when

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compared to the “No CCHP” scenario, the average cost with a CCHP system would increase by

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1% and 0.14% in the BAU and MCG “CCHP” scenarios, respectively. If net metering is

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considered, the cost would be lower than a “No CCHP” scenario in both growth cases by

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approximately 7% (Figure 4b). As shown by the lower bound of the cost bar, if minimum CCHP

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system cost is assumed, implementing a CCHP system will always be less expensive than a “No

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CCHP” scenario. The emissions (CO2 and NOx) (Figure 5) and water consumption reductions

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(Figure 6) differ from the projections when we consider CCHPs in new and existing buildings

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because the ratio of the different types of buildings has changed. The estimates of the single-

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family residential emissions also influence the reductions. Similarly, if the maximum CCHP

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system cost is assumed, it will always be more expensive to implement these systems (Figure

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4b).

390 391 392 393

Figure 5: Total annual CO2 and NOx emissions of the 13-county Atlanta Metropolitan region, from energy consumption commercial and residential buildings for two growth scenarios. The dashed line represents the CO2 and NOX emission levels in the base year (2005). The solid bars represent the total emissions for existing and new buildings when only new

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buildings have CCHP systems. The crosshatched pattern indicates the CO2 and NOX emission levels if all buildings, existing and new, have CCHP systems.

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397 398 399 400 401 402 403

Figure 6: Water-for-energy withdrawal and consumption in 2030 for the 13-county Atlanta metropolitan region. The dashed line represents the water-for-energy withdrawals and consumption of the base year (dashed line). The solid bars represent the total withdrawals and consumption for existing and new buildings when only new buildings have CCHP systems. The crosshatched pattern indicates the total withdrawals and consumption levels if all buildings, existing and new, have CCHP systems.

Overall, this study has demonstrated three factors that could impact the water-for-energy

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and emissions (CO2 and NOx) of the Atlanta region as it grows. These are: 1) Moving to an

405

MCG scenario does not significantly reduce the water demand nor emissions. However, these

406

results do not account for how an MCG scenario would affect energy for transit. 2)

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Decentralized water and energy systems reduce, water-for-energy, water from a centralized

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plant, cost and emissions (CO2 and NOx) of a region more so than increasing the density of

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residential communities. 3) Net metering policy for distributed energy generation systems can

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significantly aid municipalities in reducing their water impact by incentivizing the adoption of

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decentralized generation. However, while this study integrates various models to assess the co-

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benefits of infrastructure investments it has three main limitations: 1) the assumption that the

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energy demand of all prototype buildings match that of the actual demand of in-situ buildings; 2)

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transportation changes due to growth scenario differences can significantly affect the emissions;

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and 3) Consumer preference and metrics, such as quality of life, can significantly influence urban

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growth scenarios and technology options. Further discussion of the limitations is included in the

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S.I.

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The results demonstrated by our study are intended to inform policy on potential trends

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and outcomes when considering technology, development, or policy schemes. Since this study

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was performed, the City of Atlanta has pledged to meet all building energy demand with

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renewable energy generation by the year 2035.54,55 Accordingly, newer studies should focus on

422

identifying barriers and vulnerabilities of a fully renewable grid in Atlanta. The also suggest that

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compact growth is ineffective at reducing environmental impacts; however, this study did not

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incorporate transit choice and the subsequent emissions nor the improvements to human health

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and the quality of life that compact growth provides. Moreover, the results presented did not

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assess the co-benefits of reduced land development. This paper weighted the direct cost, water,

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and emissions impacts more heavily than potential co-benefits from land use. Finally, the

428

methodology employed herein does not incorporate consumer choice, adoption, and fits an

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energy model to a projected land use model. Accordingly, the results showcase the potential

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improvements that LID, compact growth, and CCHP systems can provide, but do not incorporate

431

all of the interdependencies and interactions that exist between the water-energy-transportation

432

(WET) nexus. Nevertheless, this paper provides the framework to integrate infrastructure

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systems and aid municipalities with a holistic approach to urban development.

434

Acknowledgments

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The Brook Byers Institute sponsored this research for Sustainable Systems, Hightower Chair, and the Georgia Research Alliance at the Georgia Institute of Technology. This work was also supported by a grant for “Resilient Interdependent Infrastructure Processes and Systems (RIPS) Type 2: Participatory Modeling of

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Complex Urban Infrastructure Systems (Model Urban SysTems),” (#1441208) and “Resilient and Sustainable Infrastructure: Sustainable Infrastructures for Energy and Water Supply (SINEWS),” (#0836046) from National Science Foundation, Division of Emerging Frontiers in Research and Innovations (EFRI). The views and ideas expressed herein are solely of the authors and do not represent the ideas of the funding agencies in any form.

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Supporting Information

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Further information regarding the land use, water, and energy modeling, as well as expanded maps and results associated with this paper is available free of charge via the ACS Publications website at http://pubs.acs.org.

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Nomenclature

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ARC – Atlanta regional commission

566

BAU - Business as usual

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CCHP - Combined cooling, heating and power

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CCNG - Combined cycle natural gas

569

FTL – Follow thermal load

570

Gpcd - Gallons per capita per day

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GED - gallons per employee per day

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LID - Low impact development

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PGU – Primary generating unit

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MCG - More compact growth

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RWH - Rainwater harvesting

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UAZs – Uniform analysis zones

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TOC abstract

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