Quantifying the Urban Food-Energy-Water (FEW) Nexus: The Case of

Subscriber access provided by YORK UNIV

Environmental Modeling

Quantifying the Urban Food-Energy-Water (FEW) Nexus: The Case of the Detroit Metropolitan Area Sai Liang, Shen Qu, Qiaoting Zhao, Xilin Zhang, Glen Daigger, Joshua Newell, Shelie A. Miller, Jeremiah X. Johnson, Nancy G. Love, Lixiao Zhang, Zhifeng Yang, and Ming Xu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06240 • Publication Date (Web): 12 Dec 2018 Downloaded from http://pubs.acs.org on December 16, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 23

Environmental Science & Technology

1 2

Quantifying the Urban Food-Energy-Water (FEW) Nexus: The Case of the Detroit Metropolitan Area

3 4 5

Sai Liang 1, Shen Qu 2, Qiaoting Zhao 2, Xilin Zhang 2, Glen T. Daigger 3, Joshua P. Newell 2, Shelie A. Miller 2, Jeremiah X. Johnson 4, Nancy G. Love 3, Lixiao Zhang 1, Zhifeng Yang 1, Ming Xu 2,3,*

6 7

1

8 9

2

10 11

3

12 13

4

State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, People’s Republic of China School for Environment and Sustainability, University of Michigan, Ann Arbor, Michigan 48109-1041, United States Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109-2125, United States Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695-7908, United States

14 15 16

* Corresponding author.

17

E-mail: [email protected] (Ming Xu).

18

Phone: +1-734-763-8644; fax: +1-734-936-2195.

19 20

1

ACS Paragon Plus Environment


21

ABSTRACT

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

The efficient provision of food, energy, and water (FEW) resources to cities is challenging around the world. Because of the complex interdependence of urban FEW systems, changing components of one system may lead to ripple effects on other systems. However, the inputs, intersectoral flows, stocks, and outputs of these FEW resources from the perspective of an integrated urban FEW system have not been synthetically characterized. Therefore, a standardized and specific accounting method to describe this system is needed to sustainably manage these FEW resources. Using the Detroit Metropolitan Area (DMA) as a case, this study developed such an accounting method by using material and energy flow analysis to quantify this urban FEW nexus. Our results help identify key processes for improving FEW resource efficiencies of the DMA. These include: 1) optimizing the dietary habits of households to improve phosphorus use efficiency; 2) improving effluent-disposal standards for nitrogen removal to reduce nitrogen emission levels; 3) promoting adequate fertilization, and 4) enhancing the maintenance of wastewater collection pipelines. With respect to water use, better efficiency of thermoelectric power plants can help reduce water withdrawals. The method used in this study lays the ground for future urban FEW analyses and modeling.

37

TOC

38 39

2


Page 2 of 23

Page 3 of 23


40

INTRODUCTION

41 42 43 44 45 46 47 48 49 50 51 52

Food, energy, and water are three essential resources for meeting basic human needs. The efficient provision of food, energy, and water is important, but challenging due to the complex interdependence of these three systems1, 2. For example, water is required for food and energy production; energy is needed for food and water production as well as wastewater treatment; and industrial agriculture and food production lead to the oversupply of nitrogen and phosphorus in the water system from fertilizer uses. Such complex interdependence makes the food-energywater (FEW) nexus a complex system of sub-systems. Changing components of one system may lead to ripple effects (desired or undesired) on other systems. Therefore, policy and technology solutions addressing challenges in individual FEW systems need to be evaluated through the lens of the FEW nexus to identify co-benefits and avoid unintended consequences. Ultimately, instead of examining FEW sub-systems individually, we need to examine them simultaneously – as an integrated whole – when developing policy and technology solutions.

53 54 55 56 57 58 59 60 61 62

As of 2017, 55% of the world’s population lives in urban areas 3, a proportion projected to increase to 66% by 2050, with the expected addition of 2.5 billion people to cities 4. Cities have become the primary users of FEW resources. Managing FEW resources wisely in cities can contribute significantly to the sustainable management of FEW resources at the global scale. To help cities better manage its FEW resources, the first and foremost step is to understand how FEW resources flow across and within a city. Accounting for FEW resource flows and stocks of a city provides a quantitative wiring diagram of the city’s FEW nexus, allowing the city to measure the efficiency of its utilization of FEW resources, to identify critical processes that are key for the demand of FEW resources, and to develop policy and technology solutions accordingly.

63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84

Previous studies at the urban scale either analyze FEW systems individually (e.g., nutrient flows energy flows 18-25, and water flows 26-28), or examine two of the three FEW systems (e.g., water-energy nexus 29-41 and food-water nexus 42-44). Only a few studies consider all three FEW systems, but only to evaluate the impact of one system on the other two, such as how innovations in the urban water sector change food and energy flows 45-47. Studies have also examined environmental footprints of an urban system using life cycle-based approaches 48, 49. However, the inputs, intersectoral flows, stocks, and outputs of FEW resources within the integrated urban FEW system have not been synthetically characterized. This synthesis can lay a solid ground for subsequent studies on the investigation of structural characteristics of the urban FEW nexus and to evaluate the consequences of specific policies or technology solutions. Despite the importance of measuring urban FEW flows and stocks, we lack case studies and, more importantly, a standardized specific accounting method to describe the integrated FEW system of cities.

5-17,

This study addresses these gaps by developing a method to quantify these urban FEW flows and stocks and their interactions. We develop this method based on material and energy flow analysis (MEFA) which quantifies flows and stocks of a substance, a group of substances, bulk materials, or energy within a system during a given period of time 50-52. Applying this method enables a mathematic representation of the urban FEW nexus, which can be the foundation of further analyses and modeling of these systems. We demonstrate this method using the Detroit Metropolitan Area (DMA) in 2012 as a case study. The DMA grew rapidly in the 1950s and then declined significantly. Now it is experiencing a rebirth, with redevelopment plans and projects that are reshaping flows of food, energy, and water. Thus, the DMA typifies a city-region that has changed significantly over the past decades and offers a dynamic case to demonstrate the 3



85 86 87

method’s flexibility. We also developed an Excel-based template to streamline the data compilation process largely using publically available sources for easy adoption to other cities, particularly cities in the U.S.

88 89

METHOD AND MATERIALS

90 91 92 93 94 95 96

MEFA quantifies the amounts of materials and energy flowing in to and out from an economic system as well as the flows and stocks of materials and energy within the economic system. In other words, MEFA traces the flows and stocks of materials and energy of an economic system 50-52 based on mass and energy balance principles. In this study, we use MEFA to characterize the urban FEW nexus by tracking and quantifying the flows and stocks of food (represented by the amounts of nitrogen and phosphorus embedded in food), water, and energy in an urban area during a certain period of time.

97 98 99 100 101 102 103

In this study, we focus on the interconnections among processes of FEW sub-systems rather than just interconnections among different flows. For example, the energy-food nexus refers to energy flows from processes of the energy sub-system to processes of the food sub-system (e.g., energy use by food production) and food flows from processes of the food sub-system to processes of the energy sub-system (e.g., electricity generation from food wastes). As such, FEW sub-system processes are interconnected through FEW flows, and interventions in a process of a sub-system can have ripple effects through other internal and external processes.

104

Urban FEW Flows

105 106 107 108 109 110 111 112

Figures 1a to 1c conceptualize the flows and stocks of food, energy, and water systems, respectively. The food system consists of eight processes including fertilizer production, plantation, feed production, husbandry, food processing, food retail, domestic consumption, and solid waste management (Figure 1a). Given the variety of food crops and products, we use nitrogen (N) and phosphorus (P) flows as the currency to represent the flows of materials in the food system. Plantation and husbandry processes fix N and P from the environment, and discharge N and P to the environment through N/P loss, N/P embedded in wastewater, and N/P embedded in landfilled solid wastes.

113 114 115

The energy system (Figure 1b) extracts fossil fuels or utilizes renewable resources (e.g., hydrological power, solar power, and wind power) to produce electricity. After production, waste heat, air pollutants, and solid wastes are discharged. Solid wastes are either reused or landfilled.

116 117 118 119 120 121 122

The water system primarily consists of water treatment and supply, wastewater collection and treatment, and residual processing (Figure 1c). Water treatment processes withdraw surface and ground water, and supply treated water to businesses and households for use. Wastewater is collected from businesses and households, treated, and discharged to natural water bodies. There are water and wastewater losses in water distribution and wastewater collection processes. Sludge from water and wastewater treatment facilities is treated using chemical-facilitated residuals processing before discharge.

123 124 125 126 127

Figure 1d shows the flows and stocks of the integrated FEW system. The food system uses energy produced by the energy system; the energy system uses solid wastes and landfill gas from the food system to produce electricity. The food system also uses water from the water system and generates wastewater collected and treated by the water system. The energy system provides energy for water system to use and generates wastewater collected and treated by the water 4


Page 4 of 23

Page 5 of 23


128 129

system. The urban FEW systems are also connected with the outside regions through imports and exports of products.

130 131 132 133 134 135 136

In this study, we first quantify the flows and stocks of individual FEW systems for major processes (Figure 1) across the urban system. Second, we identify and measure the flows connecting the individual FEW systems. Finally, we characterize each individual FEW system as a network of processes connected by resource and energy flows. We integrate the three individual FEW flow networks as a network of three networks. Subsequently, we get an integrated FEW flow network which provides a topological representation of the urban FEW nexus.

137 138 139 140 141 142

This method does not consider material and energy flows embodied in imports and exports, because there are already established methods 53 and abundant case studies 54, 55. Such methods can be easily integrated with our method in the future. Moreover, stakeholders in an urban area have limited control of material and energy flows in other regions. Thus, examining material and energy flows closely associated with an urban system is more likely to provide information to guide actionable policies for cities.

143

Data for Detroit Metropolitan Area FEW nexus

144 145 146 147 148 149 150

We use the city-region concept to define the system boundary for the urban FEW nexus. A cityregion denotes a metropolitan area and hinterland, typically an urban area with multiple administrative districts but sharing resources like water and energy infrastructures 56. The system boundary of the city-region in this study—the Detroit Metropolitan Area (DMA)—is the Metropolitan Statistical Area (MSA) of Detroit-Warren-Dearborn and comprises six counties: Lapeer, Livingston, Macomb, Oakland, St. Clair, and Wayne. We characterize the urban FEW nexus for the DMA in 2012, the year for which most recent data are available.

151 152 153 154 155 156

In 2012, the DMA was ranked 14th in population (4.3 million people), 13th in gross domestic products (GDP, $218.5 Billion), 109th in per capita income ($42,168/capita), and 95th in per capita GDP ($50,893/capita) among all MSAs in the United States (US) 57. Data used to construct the DMA FEW nexus network are primarily from publically available sources. Estimations were made when data were unavailable. Table S1 summarizes data sources and estimation methods for constructing the DMA FEW nexus network in 2012.

157 158 159 160 161 162 163 164

Data for food flows of the DMA FEW nexus network are mostly from the US Department of Agriculture (USDA) 58, 59. Given the variety of food crops and products, we use the amounts of nitrogen (N) and phosphorus (P) contained in these crops and products as the currency to represent the food flows. Flows of the crops and products are converted into N and P flows by multiplying their weight with N/P content parameters 60-62. In particular, food imports and exports of DMA are from the Commodity Flow Survey 63. Data for crop straws, fertilizers, and biological fixation of N/P are estimated based on agricultural activity levels 64-69. Stock changes of N and P related to animals and human body weight changes are estimated by mass balance.

165 166 167 168 169 170

Data for water withdrawal and supply are from the US Geological Survey (USGS) 70. In particular, USGS water data are updated every five years, and the latest data are for 2010. We used the 2010 USGS data for DMA as a proxy for 2012. We assume that water used by each process mostly became wastewater. The amounts of treated wastewater and generated sludge were obtained from the Detroit Water and Sewerage Department (DWSD) 71. Water losses from water distribution and wastewater collection are estimated by water loss rates 72.

5



171 172 173 174 175 176 177 178 179 180

Data for fossil fuel extraction are from the USDA Economics Research Service 73. Data for electricity generation and fuel uses are from the US Energy Information Administration (EIA) 74, 75. Data for oil refinery in the DMA come from Marathon Petroleum 76. The USDA Census of Agriculture 58 only has aggregated energy use data for agricultural activities. We estimated energy use of each agricultural process by multiplying agricultural activity data with corresponding energy intensities 77, 78. DWSD provides data for gasoline used to support water and wastewater treatment 71. Data for electricity uses for water treatment, wastewater treatment, and residual processing were estimated by activity data and electricity use intensities 79. Data for electricity use by households is from the EIA 80. Data for energy imports and exports are from the Commodity Flow Survey 63.

181 182 183 184 185 186 187

This study uses the DMA as a case to illustrate the processes for data collection and estimation, but the method and data used are not unique to the DMA. For example, most of the data sources used in this study (e.g., USDA, USGS, Commodity Flow Survey, and EIA) contain not only the data for the DMA but also the data for each Metropolitan Statistical Area of the U.S. Moreover, there may be differences in the statistics of different countries. However, by properly adjusting certain data estimation methods and parameters, the proposed methods and templates can also be used in FEW nexus analyses of other countries.

6


Page 6 of 23

Page 7 of 23


188 189 190 191 192 193

Figure 1. System boundaries and processes of food system (a), energy system (b), water system (c), and FEW nexus (d) of a simplified urban system. These graphs illustrate the simplified interactions of urban FEW systems. Graphs (a), (b), and (c) describe inputs, inter-process flows, and outputs of food system, energy system, and water system, respectively. Graph (d) describes the integrated urban FEW system. Dashed arrows indicate inter-system flows, indicating flows from processes of one system to processes of another system. 7



194

RESULTS

195

FEW Flows in Detroit Metropolitan Area

196 197 198 199 200 201 202

Table 1 shows the inputs and outputs of FEW resources in the DMA in 2012. Household consumption and related services are the main drivers within the city-region. Upstream processes including extraction, plantation, processing, and manufacturing are primarily located outside the city-region for food and energy. In particular, total inputs of N and P were 119 thousand tonnes (Kt) (83% imported) and 16 Kt (94% imported), respectively. Energy inputs were 1,270 petajoule (PJ), in which 99% were imported. In contrast, water inputs (5 billion tonnes) were totally withdrawn from local water bodies.

203 204 205 206 207 208 209

Total N output of DMA was 114 Kt (73 Kt discharged to local environment and 41 Kt exported to other regions), and N stock in DMA increased by 5 Kt. Total P output of DMA was 16 Kt (12 Kt to local and 5 Kt exported), and P stock in DMA increased by 0.1 Kt. Used water was discharged into natural water bodies in the form of treated wastewater (4,342 million tonnes (Mt)), water loss from distribution (140 Mt), and wastewater loss from collection (193 Mt). Energy outputs included heat loss during energy use (916 PJ) and energy product exports to other regions (354.7 PJ).

210 211 212 213 214 215 216 217

Figure 2 compares per capita N and P intakes of the DMA with those of other cities reported in the literature. Per capita N intake of the DMA (7.3 kg/capita) is at the medium level among studied cities, lower than Beijing, Paris, and Vienna, and higher than Linkoping and Toronto. Per capita P intake of DMA (0.9 kg/capita) is higher than most of the studied cities (e.g., Harare, Chaohu Watershed in 1995 and 2012, Busia, Linkoping, and Thachin Basin). However, it is lower than the Chaohu Watershed in 1978 (1.1 kg/capita) and Phoenix during 2005–2010 (1.0 kg/capita). These comparisons indicate that there are still potentials to reduce the pressures of N/P demands in the DMA.

218 219 220 221 222 223 224 225 226

Figure 3 compares per capita N and P emissions to water bodies in DMA with those in other cities reported in the literature. Per capita N emissions to water bodies in DMA were 4.0 kg/capita, the highest among investigated cities (e.g., Beijing, Paris, and Linkoping). Per capita P emissions to water bodies in DMA were 0.4 kg/capita, at the medium level of investigated cities. It was lower than the Chaohu Watershed, Beijing in 1998 and 2008, and Tianjin, but higher than Beijing in 1978 and 1988. P emission level of the DMA was much better than other cities reported in the literature. However, N emission level of the DMA was much worse than other investigated cities. Thus, reducing per capita N emissions should be particularly concerned in the DMA.

227 228 229 230 231 232

Figure 4 shows N, P, energy, and water flows of the integrated FEW system in the DMA. N and P flows are mainly attributable to five processes: plantation, food processing, food retail, domestic consumption, and solid waste management. The largest N and P flows are those embedded in fly ash from solid waste incineration, 53 Kt and 9 Kt, respectively. The second largest N flow is embedded in imports of processed foods from other regions (42 Kt), while the largest P flow is embedded in imports of agricultural products from other regions (8 Kt).

233 234 235

Energy flows are mainly related to liquid fuel/coke production. Top three largest energy flows are imports of liquid fuel/coke (513 PJ), liquid fuel/coke use by other sectors (452 PJ), and heat release from other sectors to the environment (769 PJ).

8


Page 8 of 23

Page 9 of 23


236 237 238 239 240 241 242

Water flows are mainly related to electricity generation, water treatment and supply, and wastewater collection and treatment processes. Electricity generation withdrew 3,256 Mt water from local water bodies for cooling purposes. The used water was then returned to surface water bodies or evaporated to the atmosphere. Other major water flows are water withdrawal and supply (872 Mt), wastewater collection (1,209 Mt), and wastewater treatment (1,016 Mt). In particular, water flows related to the food system are relatively small in the DMA, because agricultural activities mainly occur outside of it.

243

FEW Nexus in DMA

244 245 246 247 248 249

Figure 4 also shows the nexus among individual FEW systems. In particular, the food system used 425 Mt water, and discharged 424 Mt wastewater, 4 Kt N, and 1 Kt P to the water system through wastewater flows. Food wastes, sludge, and landfill gas were in part used to generate 8 PJ electricity. On the other hand, the food and water systems used 63 PJ and 4 PJ energy, respectively. Moreover, the energy system withdrew 3,262 Mt water from the environment and returned the same amount of used water to the environment.

250 251 252 253

Compared with other states in the US, water withdrawal for generating unitary thermoelectric power in DMA (34.8 gallon/kWh) was at a relatively higher level (Figure 5). In contrast, water withdrawals for unitary N and P in DMA crops (0.03 gallon/gram and 0.21 gallon/gram, respectively) were lower than those in most states (Figure 5).

9



254

Page 10 of 23

Table 1. FEW resource inputs and outputs of the DMA, 2012. INPUTS Items

OUTPUTS Quantities

Units

Domestic N from biological fixation N in water withdrawals P from biological fixation P in water withdrawals Water withdrawals Energy extraction/capture from nature

18.3 1.9 0.6 0.4 4,675 14.5

Kt Kt Kt Kt Mt PJ

Imports N P Water Energy

98.5 15.4 0 1,256

Kt Kt Mt PJ

Items Domestic N in wastewater to nature N in fertilizer loss N in wasted crop straws N in water loss N in wastewater loss N in landfilled solid wastes N in landfilled fly ash P in wastewater to nature P in fertilizer loss P in wasted crop straws P in water loss P in wastewater loss P in landfilled solid wastes P in landfilled fly ash Wastewater to nature Water/wastewater loss Heat loss/release Exports N P Water Energy

255

10


Quantities

Units

10.6 4.4 2.3 0.1 2.0 0.3 53.0 0.5 0.6 0.2 0.04 0.5 0.6 9.2 4,342 333 916

Kt Kt Kt Kt Kt Kt Kt Kt Kt Kt Kt Kt Kt Kt Mt Mt PJ

41.0 4.6 0 355

Kt Kt Mt PJ

Page 11 of 23


256 257

(A) Per capita N intake

258 259 260 261 262

(B) Per capita P intake Figure 2 Comparisons of per capita N and P intakes of DMA with those of other cities. Detailed data sources for N and P are listed in Tables S2 and S3, respectively.

11



263 264

(A) Per capita N emissions to water bodies

265 266 267 268

(B) Per capita P emissions to water bodies Figure 3. Comparison of per capita N and P emissions to water bodies in the DMA with those of other cities. Detailed data sources are listed in Tables S2 and S3, respectively.

12


Page 12 of 23

Page 13 of 23


269 270

Figure 4. FEW Nexus of the DMA, 2012.

271

13



272 273

(A) Water withdrawal for unitary thermoelectric power

274 275

(B) Water withdrawals for unitary N in crops

276 277 278 279 280 281 282

(C) Water withdrawals for unitary P in crops Figure 5. Comparison of FEW nexus of the DMA with those of other states in the US. Data for water withdrawals and thermoelectric power are from the US Geological Survey (USGS). N and P in crops are calculated by crop yields multiplied by N and P content parameters. Data for crop yields are from the Census of Agriculture of the USDA 66. N and P content parameters come from literature 60.

14


Page 14 of 23

Page 15 of 23


283

DISCUSSION

284 285 286 287 288

This study developed a standardized and specific accounting method to synthetically characterize the inputs, intersectoral flows, stocks, and outputs of FEW resources from the perspective of an integrated urban FEW system. This method can help identify key processes for improving FEW resource efficiencies of urban FEW systems. It also lays the ground for future urban FEW analyses and modeling.

289

Policy Implications for the DMA

290 291 292

We quantified the FEW nexus of the DMA and compared its FEW resource efficiencies at certain processes with those in other cities reported in literature. Our results help identify key processes for improving FEW resource efficiencies of the DMA.

293 294 295 296

The DMA has a relatively high per capita P intake, indicating that domestic consumption is a key process for improving P use efficiency. Thus, it is necessary to optimize the dietary habits of households in the DMA. For instance, using a certification scheme (e.g., green labeling of goods and services) to educate households about P footprints of their purchased products.

297 298 299 300 301

Moreover, the DMA has relatively high per capita N emissions to water bodies. Wastewater treatment, plantation, and wastewater collection are three major sources of N emissions in the DMA. Potential solutions to reduce N emission levels can be improving effluent-disposal standards for N removal, promoting adequate fertilization, and enhancing the maintenance of wastewater collection pipelines.

302 303 304

Electricity generation is the largest water user in the DMA. The DMA has a relatively high level of water withdrawal for unitary thermoelectric power. Thus, improving water use efficiency of thermoelectric power plants in DMA can help reduce water withdrawals.

305 306 307 308 309 310

Since food, energy, and water systems are interconnected with one another, changing components of one system may lead to ripple effects (desired or undesired) on other systems 81. For example, using air-cooled units to substitute water-cooled units in power plants can reduce water use but would increase the demand of energy materials and subsequent emissions of carbon dioxide 82. Thus, assessing the effectiveness of above potential solutions must be in the context of the urban FEW nexus.

311 312 313 314 315

Currently, government departments of most countries make policy decisions individually. The nexus of FEW subsystems is not fully taken into account. The findings in this study inform governments to coordinate FEW-related policies of different departments to maximize cobenefits and avoid unintended consequences. The method developed in this study can serve as a basic tool for FEW resource management of urban governments.

316

Data Availability, Applicability for Other Regions, and Uncertainties

317 318 319 320 321 322

We proposed a method to quantify urban FEW nexus based on MEFA. This method uses publically available data including government statistics and peer reviewed estimation methods. This makes it easy to adopt this method to quantify urban FEW nexus across multiple years and multiple cities, although this study only used the DMA in 2012 as a demonstration. In particular, this method is more adoptable to cities in the US, because data sources and parameters for estimations are more relevant to the US context.

323 324

Some data required for quantifying FEW nexus are not readily available for urban areas. We need to estimate those data using peer reviewed estimation methods, energy and mass balances, 15



325 326

or proxies of other cities or national averages. These estimations can bring in uncertainties to the results. Improving urban statistical systems in future can help reduce these uncertainties.

327

Templates for Urban FEW Nexus Accounting

328 329 330 331 332 333 334 335 336

To encourage the adoption of the proposed method for urban FEW nexus accounting, we developed a set of Excel-based templates to streamline the data compilation process (Supporting Information). These templates consist of three Excel files for N and P flows, energy flows, and water flows, respectively, and one Excel file for final balanced tables and summarized results. Each of the former three Excel files includes several sheets for data inputs, pre-defined parameters, intermediate calculations, and initial result outputs. The latter Excel file mainly balances initial results based on mass and energy balances and then summarizes the final results. Users can input data for a particular city or region, adjust parameters using region-specific data if available, and then get the final results quantifying the FEW nexus.

337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354

We present the final results in the format of physical input-output tables (PIOTs) 53 to show the mass and energy flows for N, P, energy, and water. There are four main matrixes in the PIOTs. The core matrix shows material and energy flows among various processes within the integrated urban FEW system, including various processes related to FEW and an “other sectors” processes. The final demand matrix shows FEW stock changes within the system and material and energy flows between the local FEW system and the outside (i.e., FEW imports and exports). The From-Nature and To-Nature matrixes describe FEW flows between the local FEW system and the environment. Particularly, the From-Nature matrix shows the biological fixation of N/P, N/P embedded in water withdrawal, water withdrawal, and energy extraction/capture from the environment. The To-Nature matrix shows N/P emissions, wastewater discharge, water loss, and energy loss to the environment. There are row and column balances for each process, respectively. Total outputs represent the sum of each row, indicating the total FEW outflow of each process. Total inputs indicate the sum of each column, representing the total inflow of each process. Total output of each process equals to its total input, indicating mass or energy balance of each process. The PIOT layout clearly shows transactions and stock changes within the urban FEW system, its interactions with the outside through imports/exports, and its interactions with the environment. The summary sheet shows general inputs and outputs of individual FEW systems and the whole integrated FEW system.

355 356 357 358

These templates are developed based on the US statistical systems for metropolitan statistical areas. Thus, application to other US urban systems is relatively simple. In addition, the templates can also be adapted for urban areas in other countries with necessary but minimal modifications to accommodate specific data sources.

359

Future Research

360 361

The proposed method quantifies the urban FEW nexus, which provides the basis for a variety of further investigations of this nexus.

362 363 364 365 366 367

First, we can evaluate the holistic features of the quantified urban FEW nexus. The quantified urban FEW nexus can be treated as a network, with processes as nodes and mass/energy flows among processes as links. We can then apply network analysis tools to evaluate the efficiency and resilience of the urban FEW network 83. We can also detect the community structure 84, 85 of this network, which show the clustering characteristics of the urban FEW network. The community structure can help policy makers to identify processes that will be strongly

16


Page 16 of 23

Page 17 of 23


368 369

influenced by interventions at a specific process, because processes falling into the same cluster are more closely interconnected with one another than with processes outside this cluster 84.

370 371 372 373 374 375 376

Second, we can identify critical processes and supply chain paths within the urban FEW nexus. Applying input-output analysis techniques 53 to the quantified urban FEW nexus, we can identify critical processes from multiple viewpoints (e.g., primary suppliers 86-88, transmission centers 84, 89, final producers 54, 90, and final consumers 91-94) and critical supply chain paths 95, 96 that strongly influence FEW demands and wastes/emissions of the integrated FEW system. These results identify “hotspots” for different types of policy decisions (e.g., supply-side measures 86, 87, demand-side measures 90, 92, 93, and production efficiency improvement measures 54, 89, 90).

377 378 379 380 381 382 383 384 385

Third, we can evaluate the consequences of specific policy or technology solutions within the FEW system based on the quantified urban FEW nexus. Since processes are interconnected with one another, interventions at a specific process can lead to co-benefits or unintended consequences at other processes 81. The urban FEW nexus accounting quantifies these interconnections among processes and describes the equilibrium state of the integrated FEW system using mass and energy balances. When there is an intervention at certain process, the whole FEW system will change and reach a new equilibrium state. We can quantify the consequences of specific policy or technology solutions by combining the urban FEW nexus accounting with methods like system dynamics modelling.

386 387 388 389 390 391

Finally, current statistics on cities are not detailed enough to characterize the FEW nexus for specific products. For example, the USGS has only aggregated water use data for irrigation at the county level, instead of water use data for each type of agricultural product. 70 Thus, the method used in this study can reveal the FEW nexus at the macro- (i.e., regional) and meso- (i.e., sectoral) levels, but it cannot yet reveal micro-level processes (i.e., product-level). Applying this method at the micro-level will require more detailed statistical data.

392 393

ACKNOWLEDGEMENTS

394 395 396 397

We thank the financial support of the National Natural Science Foundation of China (51661125010, 51721093, and 71874014), the U.S. National Science Foundation (1605202), the Fundamental Research Funds for the Central Universities, and Interdiscipline Research Funds of Beijing Normal University.

398 399

SUPPORTING INFORMATION

400 401

The Supporting Information provides additional tables and a set of excel templates for quantifying FEW nexus of the US urban areas.

402 403

REFERENCES

404 405 406 407 408

1. Bazilian, M.; Rogner, H.; Howells, M.; Hermann, S.; Arent, D.; Gielen, D.; Steduto, P.; Mueller, A.; Komor, P.; Tol, R. S. J.; Yumkella, K. K., Considering the energy, water and food nexus: towards an integrated modelling approach. Energy Policy 2011, 39, (12), 7896-7906. 2. Logan, B., Urgency at the nexus of food, energy, and water systems. Environmental Science & Technology Letters 2015, 2, (6), 149-150.

17



409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453

3. WorldBank, World development indicators (http://data.worldbank.org/datacatalog/world-development-indicators). In The World Bank Group: Washington, DC, USA, 2017. 4. UN 2014 revision of world urbanization prospects; United Nations: New York, NY, USA, 2014. 5. Barles, S., Feeding the city: Food consumption and flow of nitrogen, Paris, 1801-1914. Sci. Total Environ. 2007, 375, (1-3), 48-58. 6. Billen, G.; Barles, S.; Garnier, J.; Rouillard, J.; Benoit, P., The food-print of Paris: longterm reconstruction of the nitrogen flows imported into the city from its rural hinterland. Regional Environmental Change 2009, 9, (1), 13-24. 7. Forkes, J., Nitrogen balance for the urban food metabolism of Toronto, Canada. Resour. Conserv. Recycl. 2007, 52, (1), 74-94. 8. Ma, L.; Guo, J. H.; Velthof, G. L.; Li, Y. M.; Chen, Q.; Ma, W. Q.; Enema, O.; Zhang, F. S., Impacts of urban expansion on nitrogen and phosphorus flows in the food system of Beijing from 1978 to 2008. Glob. Environ. Change-Human Policy Dimens. 2014, 28, 192-204. 9. Ma, L.; Velthof, G. L.; Wang, F. H.; Qin, W.; Zhang, W. F.; Liu, Z.; Zhang, Y.; Wei, J.; Lesschen, J. P.; Ma, W. Q.; Oenema, O.; Zhang, F. S., Nitrogen and phosphorus use efficiencies and losses in the food chain in China at regional scales in 1980 and 2005. Sci. Total Environ. 2012, 434, 51-61. 10. Metson, G. S.; Bennett, E. M., Phosphorus cycling in Montreal's food and urban agriculture systems. PLoS One 2015, 10, (3), 18. 11. Neset, T. S. S.; Bader, H. P.; Scheidegger, R., Food consumption and nutrient flows nitrogen in Sweden since the 1870s. J. Ind. Ecol. 2006, 10, (4), 61-75. 12. Neset, T. S. S.; Bader, H. P.; Scheidegger, R.; Lohm, U., The flow of phosphorus in food production and consumption - Linkoping, Sweden, 1870-2000. Sci. Total Environ. 2008, 396, (23), 111-120. 13. Qiao, M.; Zheng, Y. M.; Zhu, Y. G., Material flow analysis of phosphorus through food consumption in two megacities in northern China. Chemosphere 2011, 84, (6), 773-778. 14. Wu, H.; Zhang, Y.; Yuan, Z.; Gao, L., Phosphorus flow management of cropping system in Huainan, China, 1990–2012. Journal of Cleaner Production 2016, 112, 39-48. 15. Yuan, Z. W.; Shi, J. K.; Wu, H. J.; Zhang, L.; Bi, J., Understanding the anthropogenic phosphorus pathway with substance flow analysis at the city level. J. Environ. Manage. 2011, 92, (8), 2021-2028. 16. Gierlinger, S., Food and feed supply and waste disposal in the industrialising city of Vienna (1830-1913): a special focus on urban nitrogen flows. Regional Environmental Change 2015, 15, (2), 317-327. 17. Zhang, Y.; Lu, H.; Fath, B. D.; Zheng, H.; Sun, X.; Li, Y., A network flow analysis of the nitrogen metabolism in Beijing, China. Environmental Science & Technology 2016, 50, (16), 8558-8567. 18. Chen, S.; Chen, B., Network environ perspective for urban metabolism and carbon emissions: a case study of Vienna, Austria. Environmental Science & Technology 2012, 46, (8), 4498-4506. 19. Chen, S.; Chen, B.; Su, M., Nonzero-sum relationships in mitigating urban carbon emissions: a dynamic network simulation. Environmental Science & Technology 2015, 49, (19), 11594-11603.

18


Page 18 of 23

Page 19 of 23

454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499


20. Zhang, L.; Hu, Q.; Zhang, F., Input-output modeling for urban energy consumption in Beijing: dynamics and comparison. PLoS One 2014, 9, (3), e89850. 21. Zhang, Y.; Li, S.; Fath, B. D.; Yang, Z.; Yang, N., Analysis of an urban energy metabolic system: comparison of simple and complex model results. Ecol. Model. 2011, 223, (1), 14-19. 22. Zhang, Y.; Yang, Z.; Fath, B. D.; Li, S., Ecological network analysis of an urban energy metabolic system: Model development, and a case study of four Chinese cities. Ecol. Model. 2010, 221, (16), 1865-1879. 23. Liang, S.; Wang, C.; Zhang, T., An improved input–output model for energy analysis: a case study of Suzhou. Ecological Economics 2010, 69, (9), 1805-1813. 24. Liang, S.; Zhang, T., Managing urban energy system: a case of Suzhou in China. Energy Policy 2011, 39, (5), 2910-2918. 25. Chen, S.; Chen, B., Coupling of carbon and energy flows in cities: A meta-analysis and nexus modelling. Applied Energy 2017, 194, 774-783. 26. Zhang, Y.; Yang, Z.; Fath, B. D., Ecological network analysis of an urban water metabolic system: model development, and a case study for Beijing. Sci. Total Environ. 2010, 408, (20), 4702-4711. 27. Bai, H.; Zeng, S. Y.; Dong, X.; Chen, J. N., Substance flow analysis for an urban drainage system of a representative hypothetical city in China. Front. Env. Sci. Eng. 2013, 7, (5), 746-755. 28. Chevre, N.; Guignard, C.; Rossi, L.; Pfeifer, H. R.; Bader, H. P.; Scheidegger, R., Substance flow analysis as a tool for urban water management. Water Sci. Technol. 2011, 63, (7), 1341-1348. 29. Ahjum, F.; Stewart, T. J., A systems approach to urban water services in the context of integrated energy and water planning: a City of Cape Town case study. J. Energy South. Afr. 2014, 25, (4), 59-70. 30. Carmin, J.; Carrington, T.; Ebinger, J.; Evans, B.; Gerner, F.; Hamso, B.; Lim, A.; Nakat, Z.; Plecas, A.; Webster, M.; Zhang, Y. F., The built environment: cities, water systems, energy, and transport. Adapting to Climate Change in Eastern Europe and Central Asia 2010, 121-137. 31. Cohen, E.; Ramaswami, A., The water withdrawal footprint of energy supply to cities conceptual development and application to Denver, Colorado, USA. J. Ind. Ecol. 2014, 18, (1), 26-39. 32. Kenway, S.; McMahon, J.; Elmer, V.; Conrad, S.; Rosenblum, J., Managing water-related energy in future cities - a research and policy roadmap. J. Water Clim. Chang. 2013, 4, (3), 161175. 33. Kenway, S. J.; Lant, P.; Priestley, T., Quantifying water-energy links and related carbon emissions in cities. J. Water Clim. Chang. 2011, 2, (4), 247-259. 34. Kenway, S. J.; Lant, P. A.; Priestley, A.; Daniels, P., The connection between water and energy in cities: a review. Water Sci. Technol. 2011, 63, (9), 1983-1990. 35. Miller, L. A.; Ramaswami, A.; Ranjan, R., Contribution of water and wastewater infrastructures to urban energy metabolism and greenhouse gas emissions in cities in India. J. Environ. Eng.-ASCE 2013, 139, (5), 738-745. 36. Nair, S.; George, B.; Malano, H. M.; Arora, M.; Nawarathna, B., Water–energy– greenhouse gas nexus of urban water systems: review of concepts, state-of-art and methods. Resour. Conserv. Recycl. 2014, 89, 1-10. 37. Novotny, V., Water and energy link in the cities of the future - achieving net zero carbon and pollution emissions footprint. Water Sci. Technol. 2011, 63, (1), 184-190.

19



500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544

38. Ruddell, D. M.; Dixon, P. G., The energy-water nexus: are there tradeoffs between residential energy and water consumption in arid cities? Int. J. Biometeorol. 2014, 58, (7), 14211431. 39. Salazar, R.; Rojano, A.; Lopez, I., Energy and environmental costs related to water supply in Mexico City. Water Sci. Technol.-Water Supply 2012, 12, (6), 768-772. 40. Venkatesh, G.; Chan, A.; Bratteho, H., Understanding the water-energy-carbon nexus in urban water utilities: comparison of four city case studies and the relevant influencing factors. Energy 2014, 75, 153-166. 41. Chen, S.; Chen, B., Urban energy–water nexus: A network perspective. Applied Energy 2016, 184, 905-914. 42. Barthel, S.; Isendahl, C., Urban gardens, agriculture, and water management: sources of resilience for long-term food security in cities. Ecological Economics 2013, 86, 224-234. 43. Siegfried, T.; Sobolowski, S.; Raj, P.; Fishman, R.; Vasquez, V.; Narula, K.; Lall, U.; Modi, V., Modeling irrigated area to increase water, energy, and food security in semiarid India. Weather, Climate, and Society 2010, 2, (4), 255-270. 44. Swaney, D. P.; Santoro, R. L.; Howarth, R. W.; Hong, B.; Donaghy, K. P., Historical changes in the food and water supply systems of the New York City Metropolitan Area. Regional Environmental Change 2012, 12, (2), 363-380. 45. Villarroel Walker, R.; Beck, M.; Hall, J., Water — and nutrient and energy — systems in urbanizing watersheds. Front. Env. Sci. Eng. 2012, 6, (5), 596-611. 46. Villarroel Walker, R.; Beck, M. B.; Hall, J. W.; Dawson, R. J.; Heidrich, O., The energywater-food nexus: Strategic analysis of technologies for transforming the urban metabolism. J. Environ. Manage. 2014, 141, 104-115. 47. WEF, Water security: the water-food-energy-climate nexus. World Economic Forum: Washington, D.C., USA, 2011. 48. Das, T.; Cabezas, H., Tools and concepts for environmental sustainability in the foodenergy-water nexus: Chemical engineering perspective. Environmental Progress & Sustainable Energy 2018, 37, (1), 73-81. 49. Sherwood, J.; Clabeaux, R.; Carbajales-Dale, M., An extended environmental input– output lifecycle assessment model to study the urban food–energy–water nexus. Environmental Research Letters 2017, 12, (10), 105003. 50. Brunner, P. H., Substance flow analysis: a key tool for effective resource management. J. Ind. Ecol. 2012, 16, (3), 293-295. 51. Brunner, P. H.; Ma, H.-W., Substance flow analysis: an indispensable tool for goaloriented waste management. J. Ind. Ecol. 2009, 13, (1), 11-14. 52. Voet, E. v. d., Substance flow analysis methodology. In A Handbook of Industrial Ecology, Ayres, R. U.; Ayres, L. W., Eds. Edward Elgar: Cheltenham, UK & Northampton MA, USA, 2002. 53. Miller, R. E.; Blair, P. D., Input-output analysis: Foundations and extensions. Cambridge University Press: New York, 2009. 54. Liang, S.; Wang, Y.; Zhang, C.; Xu, M.; Yang, Z.; Liu, W.; Liu, H.; Chiu, A. S. F., Final production-based emissions of regions in China. Economic Systems Research 2018, 30, (1), 1836. 55. Liang, S.; Zhang, C.; Wang, Y.; Xu, M.; Liu, W., Virtual atmospheric mercury emission network in China. Environmental Science & Technology 2014, 48, (5), 2807-2815.

20


Page 20 of 23

Page 21 of 23

545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588


56. Demographia Definition of urban terms (http://demographia.com/db-define.pdf). (10/15/2015), 57. BEA, Interactive Data of Bureau of Economic Analysis (http://www.bea.gov/itable). In Bureau of Economic Analysis, United States Department of Commerce: 2012. 58. USDA, 2012 Census of Agriculture, Michigan State and County Data. In Department of Agriculture, Ed. National Agricultural Statistics Service: 2014; Vol. 1. 59. ERS, Loss-Adjusted Food Availability. In Economics Research Service, U.S. Department of Agriculture: 2015. 60. Warncke, D.; Dahl, J.; Jacobs, L. Nutrient Recommendations for Field Crops in Michigan; Department of Crop and Soil Sciences, Michigan State University Extension: 2009; p 8. 61. Analytical_Methods_Comm_AMCTB_63, Meat and poultry nitrogen factors Analytical Methods Committee AMCTB No 63. Anal Methods-Uk 2014, 6, (13), 4493-4495. 62. USDA Food Composition Databases In United States Department of Agriculture, Agricultural Research Service: 2015. 63. Geographic Area Series: Shipment Characteristics by Origin Geography by Commodity: 2012 and 2007 Commodity Flow Survey. In Dec 9 ed.; United States Census Bureau: 2014. 64. Lal, R., World crop residues production and implications of its use as a biofuel. Environment International 2005, 31, (4), 575-584. 65. US Fertilizer Consumption and Use - By Year. In 1960-2011, 7/12/2013 ed.; United States Department of Agriculture, Economics Research Service: 2013. 66. USDA, 2012 Census of Agriculture, United States Summary and State Data. In Department of Agriculture, Ed. National Agricultural Statistics Service: 2014; Vol. 1, p 50. 67. Kucharik, C. J.; Brye, K. R., Integrated BIosphere Simulator (IBIS) yield and nitrate loss predictions for Wisconsin maize receiving varied amounts of nitrogen fertilizer. J Environ Qual 2003, 32, (1), 247-68. 68. Tabbara, H., Phosphorus loss to runoff water twenty-four hours after application of liquid swine manure or fertilizer. J Environ Qual 2003, 32, (3), 1044-52. 69. Salvagiotti, F.; Cassman, K. G.; Specht, J. E.; Walters, D. T.; Weiss, A.; Dobermann, A., Nitrogen uptake, fixation and response to fertilizer N in soybeans: A review. Field Crops Research 2008, 108, (1), 1-13. 70. USGS, Water Use Data for Michigan. In U.S. Department of the Interior, U.S. Geological Survey: 2010. 71. DWSD, Summary of Operating Statistics, fiscal year 2011-2012. In Detroit Water and Sewerage Department, Ed. 2013; p 77. 72. Water audits and water loss control for public water systems. In United States Environmental Protection Agency, Ed. Office of Water: 2013. 73. County-level Oil and Gas Production in the U.S. In USDA, Ed. Economics Research Service: 2000-2011. 74. Electric generator capacity data. In U.S. Energy Information Administration: 1990-2015. 75. Electric power data In U.S. Energy Information Administration: 2011-2016. 76. Marathon Detroit Refinery Fact Sheet (https://www.marathonpetroleum.com/Operations/Refining_and_Marketing/Refining/Detroit_Re finery/); Marathon Petroleum Corporation: 2016.

21



589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633

77. Camargo, G. G. T.; Ryan, M. R.; Richard, T. L., Energy Use and Greenhouse Gas Emissions from Crop Production Using the Farm Energy Analysis Tool. BioScience 2013, 63, (4), 263-273. 78. Karimi, M.; Moghaddam, H., On-farm energy flow in grape orchards. Journal of the Saudi Society of Agricultural Sciences 2016. 79. Pabi, S.; Amarnath, A.; Goldstein, R.; Reekie, L. Electricity Use and Management in the Municipal Water Supply and Wastewater Industries; Water Research Foundation, Electric Power Research Institute: 2013. 80. EIA, Residential Energy Consumption Survey (RECS) (https://www.eia.gov/consumption/residential). In U.S. Energy Information Administration: 2010. 81. Liang, S.; Xu, M.; Suh, S.; Tan, R. R., Unintended environmental consequences and cobenefits of economic restructuring. Environmental Science & Technology 2013, 47, (22), 1289412902. 82. Zhang, C.; Anadon, L. D.; Mo, H.; Zhao, Z.; Liu, Z., Water−carbon trade-off in China’s coal power industry. Environmental Science & Technology 2014, 48, (19), 11082-11089. 83. Goerner, S. J.; Lietaer, B.; Ulanowicz, R. E., Quantifying economic sustainability: Implications for free-enterprise theory, policy and practice. Ecological Economics 2009, 69, 7681. 84. Liang, S.; Feng, Y.; Xu, M., Structure of the global virtual carbon network: Revealing important sectors and communities for emission reduction. J. Ind. Ecol. 2015, 19, (2), 307-320. 85. Newman, M. E. J., Modularity and community structure in networks. Proc. Natl. Acad. Sci. U. S. A. 2006, 103, (23), 8577-8582. 86. Liang, S.; Qu, S.; Zhu, Z.; Guan, D.; Xu, M., Income-based greenhouse gas emissions of nations. Environmental Science & Technology 2017, 51, (1), 346-355. 87. Liang, S.; Wang, H.; Qu, S.; Feng, T.; Guan, D.; Fang, H.; Xu, M., Socioeconomic drivers of greenhouse gas emissions in the United States. Environmental Science & Technology 2016, 50, (14), 7535-7545. 88. Marques, A.; Rodrigues, J.; Lenzen, M.; Domingos, T., Income-based environmental responsibility. Ecological Economics 2012, 84, 57-65. 89. Liang, S.; Qu, S.; Xu, M., Betweenness-based method to identify critical transmission sectors for supply chain environmental pressure mitigation. Environmental science & technology 2016, 50, (3), 1330-1337. 90. Liang, S.; Wang, Y.; Cinnirella, S.; Pirrone, N., Atmospheric mercury footprints of nations. Environmental science & technology 2015, 49, (6), 3566-3574. 91. Hui, M.; Wu, Q.; Wang, S.; Liang, S.; Zhang, L.; Wang, F.; Lenzen, M.; Wang, Y.; Xu, L.; Lin, Z.; Yang, H.; Lin, Y.; Larssen, T.; Xu, M.; Hao, J., Mercury flows in China and global drivers. Environmental Science & Technology 2017, 51, (1), 222-231. 92. Liang, S.; Guo, S.; Newell, J. P.; Qu, S.; Feng, Y.; Chiu, A. S. F.; Xu, M., Global drivers of Russian timber harvest. J. Ind. Ecol. 2016, 20, (3), 515-525. 93. Liang, S.; Stylianou, K.; Jolliet, O.; Supekar, S.; Qu, S.; Skerlos, S. J.; Xu, M., Consumption-based human health impacts of primary PM2.5: The hidden burden of international trade. Journal of Cleaner Production 2017, 167, 133-139. 94. Chen, L.; Meng, J.; Liang, S.; Zhang, H.; Zhang, W.; Liu, M.; Tong, Y.; Wang, H.; Wang, W.; Wang, X.; Shu, J., Trade-Induced Atmospheric Mercury Deposition over China and

22


Page 22 of 23

Page 23 of 23

634 635 636 637 638 639 640


Implications for Demand-Side Controls. Environmental Science & Technology 2018, 52, (4), 2036-2045. 95. Lenzen, M., Structural path analysis of ecosystem networks. Ecol. Model. 2007, 200, (3– 4), 334-342. 96. Liang, S.; Wang, Y.; Zhang, T.; Yang, Z., Structural analysis of material flows in China based on physical and monetary input-output models. Journal of Cleaner Production 2017, 158, 209-217.

641

23


Quantifying the Urban Food-Energy-Water (FEW) Nexus: The Case of

Recommend Documents