From water use to water scarcity footprinting in environmentally

Subscriber access provided by University of Winnipeg Library

Policy Analysis

From water use to water scarcity footprinting in environmentally extended input-output analysis Bradley George Ridoutt, Michalis Hadjikakou, Martin Nolan, and Brett A Bryan Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b00416 • Publication Date (Web): 18 May 2018 Downloaded from http://pubs.acs.org on May 18, 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 35

Environmental Science & Technology

1

From water use to water scarcity footprinting in

2

environmentally extended input-output analysis

3

Bradley G. Ridoutt,*,†,‡ Michalis Hadjikakou,§ Martin Nolan,⊥ and Brett A. Bryan§

4

†

5

Food, Clayton South, Victoria 3169, Australia

6

‡

7

Africa

8

§

9

Australia

Commonwealth Scientific and Industrial Research Organisation (CSIRO), Agriculture and

University of the Free State, Department of Agricultural Economics, Bloemfontein 9300, South

Deakin University, School of Life and Environmental Sciences, Burwood, Victoria 3125,

10

⊥CSIRO

11

ABSTRACT: Environmentally extended input-output analysis (EEIOA) supports environmental

12

policy by quantifying how demand for goods and services leads to resource use and emissions

13

across the economy. However, some types of resource use and emissions require spatially-

14

explicit impact assessment for meaningful interpretation, which is not possible in conventional

15

EEIOA. For example, water use in locations of scarcity and abundance is not environmentally

16

equivalent. Opportunities for spatially-explicit impact assessment in conventional EEIOA are

17

limited because official input-output tables tend to be produced at the scale of political units

Land and Water, Urrbrae, South Australia 5064 Australia

ACS Paragon Plus Environment

1


Page 2 of 35

18

which are not usually well aligned with environmentally relevant spatial units. In this study,

19

spatially-explicit water scarcity factors and a spatially disaggregated Australian water use

20

account were used to develop water scarcity extensions that were coupled with a multi-regional

21

input-output model (MRIO). The results link demand for agricultural commodities to the

22

problem of water scarcity in Australia and globally. Important differences were observed

23

between the water use and water scarcity footprint results, as well as the relative importance of

24

direct and indirect water use, with significant implications for sustainable production and

25

consumption-related policies. The approach presented here is suggested as a feasible general

26

approach for incorporating spatially-explicit impact assessment in EEIOA.

27

TOC/Abstract Art

28 29 30

INTRODUCTION

31

As the global community searches for solutions to the many complex and interrelated

32

environmental sustainability challenges,1 environmentally extended input-output analysis

33

(EEIOA) has developed into an important assessment tool.2,3 Input-output tables reflect the sale

34

and purchase relationships between different economic sectors and regions. They reveal the

35

interconnected nature of modern economies. When these monetary tables are supplemented with


2

Page 3 of 35


36

data describing resource use and emissions, the resulting models can be used to assess the

37

economy-wide, or full-supply-chain, environmental implications of demand for goods and

38

services, and serve as a basis for mitigation policy. The most widely used application of EEIOA

39

is in greenhouse gas (GHG) emissions accounting, also referred to as carbon footprinting.4-7 Of

40

importance, EEIOA has shown that although many developed countries have been successful in

41

reducing territorial GHG emissions over time, national consumption-based emissions have

42

actually risen, indicating a displacement of emissions via international trade.8,9 This leakage of

43

GHG emissions has undermined efforts to reduce total global emissions. EEIOA has also been

44

used to study so-called rebound effects which can compromise GHG mitigation efforts.10-12

45

Apart from GHG emissions, EEIOA has been used to investigate the production, consumption

46

and trade relationships linked to a variety of other environmental concerns, including material

47

use,13 biodiversity loss,14,15 water and land resource use16-19 mercury emissions,20,21 sulphur oxide

48

emissions,22 ozone precursor emissions23 and various other air pollutants.24 At this point, a

49

distinction must be made between global environmental impact categories and those that are

50

regional or local in nature. Emission types that become well dispersed in the atmosphere and

51

contribute equally to a global environmental phenomenon can be aggregated and compared

52

regardless of where the emission occurred. The well-mixed GHGs are an example. However,

53

other environmental impact categories require spatially-explicit assessment to be

54

environmentally meaningful. For example, water use in a region of water abundance does not

55

pose the same risks to human health and ecosystem quality as water use in a region of water

56

scarcity.25-27 If water use from different regions is aggregated, the result becomes uninterpretable

57

from the perspective of environmental sustainability.28 The same problems arise in relation to


3


Page 4 of 35

58

different types of land use, different types of material use that are not substitutable, and some air

59

pollutants.

60

While the intent to broaden the application of EEIOA across multiple environmental impact

61

categories is commendable, the reality is that EEIOA is presently limited in its ability to

62

characterise environmental impacts that require spatially-explicit impact assessment. This is

63

because EEIOA is based on input-output tables that are produced at the scale of political units

64

(countries, states, provinces, etc.) and are therefore not usually aligned with environmentally

65

relevant spatial units (e.g. river basin in the case of surface water use). The need to develop

66

EEIOA models incorporating spatially-differentiated impact assessment has recently been

67

emphasised in reference to the US economy.29

68

Agriculture is an important sector of the world economy, both from the perspective of food

69

security as well as employment and contribution to rural livelihoods. Agriculture is also a major

70

source of environmental burden from land and water use as well as GHG, nutrient and other

71

types of emissions.30-32 Concern about the environmental impacts of agriculture is compounded

72

by the increasing world population and the shifts to resource-intensive patterns of food

73

consumption that often accompany economic development (e.g. some diets high in animal-based

74

foods). This has led to the emergence of sustainable diets as a focal point for food-system

75

analysis and food policy,33 and numerous attempts to characterise lower environmental impact

76

dietary patterns, many involving the application of environmental footprinting techniques such as

77

life cycle assessment (LCA) and EEIOA.34 However, much of the evidence-base concerning the

78

environmental impacts of food systems and diets lacks policy insight because resource use that

79

requires spatially-explicit impact assessment is aggregated as though it were contributing to a

80

global impact category.34


4

Page 5 of 35

81


Globally, and in many individual countries, agriculture is the most important sector of the

82

economy in terms of water use35, and is therefore critical to addressing problems related to water

83

scarcity. Water scarcity is also a major international concern, identified in the United Nation’s

84

Sustainable Development Goal 6,36 although the incidence of water scarcity can vary greatly

85

both within and between countries, complicating the application of EEIOA. In this research,

86

water scarcity extensions incorporating high-resolution impact assessment were developed for a

87

broad range of agricultural and industrial sub-sectors for use in a customised EEIOA and

88

compared to conventional water use estimates. To our knowledge, the resulting national water

89

scarcity footprint based on EEIOA is the first of its kind. The example we demonstrate, for the

90

large and diverse Australian agricultural sector, is viewed as a paradigm for extending the use of

91

EEIOA where environmental impact categories require spatially-explicit impact assessment.

92

METHODS

93

General approach. Conventional EEIOA involves the supplementation of an input-output

94

table with satellite data-sets describing natural resource use or emissions to the environment that

95

are directly associated with the operations of each industry sector or sub-sector defined in the

96

table. The approach taken in this study was to spatially differentiate each industry sector and

97

evaluate resource-use by applying spatially-explicit impact assessment models from the field of

98

process LCA.37 In this way, the input-output table was able to be supplemented with satellite

99

data sets containing LCA impact category indicator results for each industry sector, rather than

100

inventory-level results for natural resource use. The sector-level impact category indicator results

101

were subsequently propagated through the input-output model to produce economy-wide impact

102

category indicator results relating to demand for goods and services (Figure 1). This approach is

103

generalizable to any environmental concern where spatially-explicit impact assessment is needed


5


104

to inform reliably about environmental impact.38 The example demonstrated in this study is a

105

water scarcity footprint.39 A description of the individual model components and data sources

106

follows.

107 108

Page 6 of 35

[Figure 1] Agricultural water use. The production of 23 irrigated and non-irrigated agricultural

109

commodities was mapped on high spatial resolution (0.01 degree, about 1.1 km) for the year

110

2006 by Marinoni et al.40 This mapping exercise included modelled estimates at the grid cell

111

level for production, revenues, costs and profit at full equity, based on the integration of data

112

from a wide range of census, multi-temporal satellite, farm handbook and other sources

113

following methods detailed in Bryan et al.41 As a part of this process, national agricultural water

114

use statistics42 were spatially disaggregated to land use and commodity type. Here, agricultural

115

water use refers to the volume of water applied for irrigation that has been sourced from surface

116

or groundwater bodies. This is sometimes referred to as blue water, as distinct from green water,

117

the soil moisture derived from natural rainfall over agricultural lands, which was not considered.

118

The use of surface and groundwater was not differentiated. In summary, this data source

119

provided an account of irrigation water use and water use intensity (in L per $ of total sector

120

output) by each agricultural commodity. For water use in each grid cell, the spatially-relevant

121

water scarcity characterization factor was subsequently applied, as explained below (Water

122

scarcity satellite data sets, Equation 1).

123

Industrial sector water use. National water use statistics for 2013/2014 for each industry sub-

124

sector43 and describing net water use (withdrawals minus return flows; also termed “water

125

consumption”), were spatially disaggregated on the basis of number of businesses in each region,


6

Page 7 of 35


126

using data describing counts of Australian businesses,44 reported at the level of Statistical Area

127

Level 4 (SA4) by the Australian Bureau of Statistics (ABS). At this spatial scale, the ABS

128

divides the country into 106 regions with population in the range 100,000 to 500,000. This

129

approach did not recognise differences in the size of individual businesses within an industry

130

sub-sector. Similarly, it did not recognise possible differences in water use efficiency between

131

businesses within the same industry sub-sector. In total, spatially disaggregated data for 75

132

industrial sectors were produced, including utilities such as water, gas and electricity suppliers.

133

Landless agricultural sectors, poultry farming for eggs, poultry farming for meat, and pig

134

farming, were treated separately since they were not included in the agricultural water use data

135

mentioned above. Total direct water use in each of these 3 agricultural sectors was obtained by

136

multiplying water use per unit of production45-47 by total production,48-50 which was then spatial

137

disaggregated to the same SA4 level on the basis of business counts, as described above.

138

Water scarcity satellite data sets. General requirements for the calculation of a water scarcity

139

footprint are described in ISO14046:2014.39 No particular set of water scarcity characterisation

140

factors is prescribed. As such, three different indicators, with distinctly different conceptual basis

141

and model structure, were used to develop the water scarcity extensions. Firstly, a modified form

142

of the Water Stress Index of Pfister et al.51 was applied. Individual characterisation factors were

143

divided by the global average value52 such that water scarcity footprint results are expressed

144

relative to water use at the global average level of water stress. This method (denoted WSIWORLD-

145

EQ),

146

wide range of studies in different parts of the world and is also the default method recommended

147

by the Australian Life Cycle Assessment Society.53 Secondly, the AWARE54 method was

148

applied, with the AWARE agricultural factors used in relation to agricultural water use and the

based on a modification of the water withdrawal to availability ratio, has been applied in a


7


Page 8 of 35

149

AWARE non-agricultural factors used in other cases. The AWARE method was chosen because

150

it is the product of an important international collaboration known as the Life Cycle Initiative

151

(see www.lifecycleinitiative.org). The AWARE indicator is based on an assessment of the

152

relative level of water remaining in an area once demand by humans and aquatic ecosystems has

153

been met. The third method applied (denoted WSIHH,EQ) was a recently developed water scarcity

154

index that integrates results from models that separately assess the impacts of water consumption

155

on human health and ecosystem quality.55 Detailed information about the data, equations and

156

assumptions underpinning each model is available in the associated references.51-55 For the water

157

scarcity indicators, vector files were rasterized to the same spatial resolution as the irrigation

158

water use (0.01 degree).

159

For each agricultural and industry sector a national water scarcity footprint extension was

160

calculated for each indicator i. For each agricultural and industry sector (j), direct water use

161

(WU) in each spatial unit k was multiplied by the relevant local water scarcity characterization

162

factor (CF), and the individual results were summed as follows:

163

DWSF, = ∑ , ∗ CF,

(1)

164

where DWSFi,j is the direct water scarcity footprint extension using indicator i for agricultural or

165

industry sector j.

166

Input-output analysis. A tailored national Supply Use Table (SUT) in Australian dollars

167

(basic prices) for the year 2013 with 101 sectors (including 23 land-based and three landless

168

agricultural sectors) was generated using the Australian Industrial Ecology Laboratory56,57 engine

169

on the basis of official national-scale input-output tables and Australian National Accounts from

170

the ABS.58 The chosen IO classification was tailored to the available DWSF data for 23 land-


8

Page 9 of 35


171

based agricultural sectors (23 irrigated and non-irrigated agricultural commodities) and 78

172

industrial and service sectors (including the landless agricultural sectors producing eggs,

173

chickens and pigs). This follows the ABS Australian and New Zealand Standard Industrial

174

Classification (ANZSIC). Water use and water scarcity footprint intensities (in L/$ of output in

175

2006 AUD) were adjusted for inflation on the basis of producer price indices for each

176

agricultural subsector59 to ensure compatibility with the 2013 IO table (see SM1 for all input

177

datasets and price adjustments).

178

The national single-region SUT was then augmented with a 26-sector rest of the world (RoW)

179

region and associated AUS-RoW trade flows. This adjustment was carried out using 2013 data

180

from the simplified Eora26 multi-regional input-output (MRIO) database,2,3 by aggregating all

181

nations other than Australia to create a two-region MRIO matrix with full sets of economic

182

transactions and trade flows between 101 AUS commodities, 101 AUS industries and 26 RoW

183

industries (n = 228) (see Figure S1). This level of RoW sector resolution was deemed appropriate

184

since, for the Australian agricultural sectors, inputs from outside Australia are few and of minor

185

importance from a water footprint perspective (e.g. only 3.7 ± 3.4% of sectoral water use on

186

average occurs outside Australia, see SM3). Water scarcity extensions were calculated for the

187

RoW region by converting the Eora 2013 water use extensions using published WSIWORLD-EQ,

188

WSIHH-EQ and AWARE factors for agriculture and other economic sectors51-55 to ensure

189

consistency with national scarcity-adjusted water use per sector estimated in Equation 1. Full

190

supply chain water scarcity footprints were subsequently calculated as Leontief Type I

191

multipliers using the standard Leontief quantity model,60,61 as per the following equation:

192

TWSF = F I − A

(2)


9


Page 10 of 35

193

where TWSF is a matrix containing the resulting total water scarcity footprints (TSWFs) per $1

194

of economic output of each sector of the economy, F is a matrix of direct intensity factors

195

derived by concatenating DWSF from equation 1 with the RoW extensions and then dividing by

196

each sector’s total economic output x, I is an n-by-n identity matrix, and A is the n-by-n

197

technology matrix. I − A is the standard Leontief Inverse (L).

198

TWSFs for agricultural sectors were converted to water use/scarcity per kg of product for each

199

agricultural sector on the basis of monetary to physical transformation factors (total output in

200

relation to total production for each sector). This was based on data from Marinoni et al.40 and

201

Bryan et al.41 reflecting inter-annual variability in commodity yields and prices over a period of

202

ten years (see SM1 for conversion factors). TWSFs were finally decomposed by post-

203

multiplying a diagonal matrix of each DSWF indicator with L to reveal the upstream

204

contribution from all other sectors to each sector’s water scarcity footprint.61 The decomposition

205

analysis was used to highlight the percentage of upstream water impacts from four key parts of

206

the supply chain: direct impact from the sector itself, upstream contribution from other

207

agricultural sectors, upstream contribution from other AUS sectors (non-agricultural), and water

208

embedded in imports from the RoW. All direct and total water scarcity footprints in addition to

209

decomposition results for all indicators are available in SM2 and SM3. The SI also contains links

210

providing free access to the IO tables and source code used in the analysis.

211

Uncertainty analysis. To account for the significant inter-annual variability in rainfall, crop

212

yields, sector output and resulting water use intensities across all economic sectors, a historical

213

timeseries (2005-2015) of water use intensity data across agricultural, industrial and service

214

sectors was calculated on the basis of the data available from the ABS on gross sectoral values62,

215

sectoral water use42,43 and producer price indices59 to enable conversion of all intensity values to


10

Page 11 of 35


216

2013 prices42,43,62 (see SM1). The coefficient of variation for each of the water use extensions

217

(DWSFi) was calculated as the standard deviation over the mean of the historical water use

218

intensity across agricultural commodities, industry sectors and service sectors. This varied from

219

as low as 14% for fruit and nuts to as high as 47-49% for broadacre crops such as cereals,

220

oilseeds and legumes (see SM 1).

221

To assess the combined effect of uncertainty in historical water intensities on derived total

222

water footprints, a uniformly distributed random sample (chosen as a conservative approximation

223

of the distribution due to a scarcity of sufficient data points) covering the minimum and

224

maximum values over the 2005-2015 period (see SM 1), was used to perform a Monte Carlo

225

uncertainty analysis with 1000 simulation runs. The input to each simulation was a random

226

combination of water use intensities within each sector’s historical range (see SM1 for sector

227

uncertainty ranges). Each random sample was used to create a new F matrix as an input into

228

Equation 2. The resulting median, 5th percentile and 95th percentile values were taken as the

229

average, lower and upper uncertainty boundaries respectively. Full results for all agricultural

230

sectors are available in SM2. Other sources of uncertainty include uncertainties in the underlying

231

input-output tables and trade flows with the RoW, and in the factors used to pass from monetary

232

to physical units. These have not been quantified but it has been shown that these errors tend to

233

be random and result in a reduced final error once they have been aggregated.63,64

234

RESULTS AND DISCUSSION

235

Water footprint of agricultural commodities. For some Australian agricultural commodities,

236

water scarcity footprint results (incorporating impact assessment) differed markedly from water

237

use results, highlighting the important insights gained through spatially-explicit impact


11


Page 12 of 35

238

assessment modelling (Figure 2). For example, total water use for sugarcane cultivation,

239

including supply chain water use, was 38.4 L kg-1, compared to a water scarcity footprint of 4.7

240

L-eq kg-1 (WSIWORLD-EQ model, Figure 2, SM2). Similarly, tropical stone fruit (e.g. mango,

241

avocado) and plantation fruit (e.g. banana) had water scarcity footprint results that were more

242

than 80% below the results for total water use. This is explained by these agricultural industries

243

being predominantly located in regions of low water scarcity. In other cases, water scarcity

244

footprint results were similar to or exceeded total water use results, reflecting water use in

245

regions with water scarcity similar to or exceeding the global average (e.g. rice, water use 1138

246

L kg-1, water scarcity footprint 1382 L-eq kg-1, WSIWORLD-EQ model, Figure 2). These results

247

underscore the importance of evaluating water use in the context of local water scarcity. Some

248

crops requiring irrigation are grown in water scarce regions and others are not.65,66

249

[Figure 2]

250

The water scarcity footprint results differed greatly depending on the particular water scarcity

251

impact model used (Figure 2), with the UNEP-SETAC-recommended AWARE model delivering

252

results much larger than the other two models by a factor of more than 100. Nevertheless, the

253

different indicator results were highly correlated (Spearman’s rank correlation 0.95-0.98). For

254

example, the most water-use intensive crops, cotton (3101 L kg-1), tree nuts (1748 L kg-1) and

255

rice (1138 L kg-1), were always among the most water-scarcity-footprint intensive crops,

256

regardless of the particular water scarcity impact model used. Cotton had the highest water-

257

scarcity-footprint intensity with the WSIHH_EQ and AWARE models (969 and 2.82E+05 L-eq kg-1

258

respectively) and the third highest water-scarcity-footprint intensity with the WSIWORLD-EQ model

259

(607 L-eq kg-1). Winter cereals and winter legumes were consistently among the lowest water-

260

scarcity-footprint intensive crops. The relative consistency of results obtained using the different


12

Page 13 of 35


261

indicators means that all three indicators are considered reliable for informing an organisation’s

262

internal decision-making aimed at reducing water scarcity impacts. However, the differences in

263

absolute values obtained using the different indicators mean that results obtained with different

264

indicators are not directly comparable. Overall, crops with higher water scarcity footprints were

265

clearly distinguishable from crops with lower water scarcity footprints, taking the uncertainty in

266

water scarcity quantification into account (Figure 2). Once an organization becomes aware that a

267

particular purchased commodity makes a large contribution to the organization’s water scarcity

268

footprint, further investigations would be needed to evaluate response options.

269

Critically, the contribution of supply chain water use to the total cradle-to-farm-gate water

270

scarcity footprint also varied substantially between sectors (SM3). For some sectors, especially

271

those relying heavily on irrigation, the indirect water use contributed less than 10% of the water

272

scarcity footprint (e.g. rice; Figure 3). In other cases, supply chain water use contributed over

273

90% of the water scarcity footprint. An example was egg production where direct water use for

274

livestock servicing was relatively minor and more than 95% of the water scarcity footprint was

275

in the supply chain, largely related to feed production. Plantation fruit was an interesting

276

example where 37% of water use was in the supply chain, but 70 to 85% of the water scarcity

277

footprint was in the supply chain, depending on the particular water scarcity indicator used

278

(Figure 3, SM3), reflecting higher water scarcity associated with the production of inputs. These

279

results highlight the importance of considering how an industry sector relies on inputs coming

280

from higher water scarcity locations. They also have significant implications with respect to each

281

sector’s on-site versus upstream responsibility, which, if based on water use as opposed to water

282

scarcity indicators, may lead to an undesirable policy outcome (such as increased water use in

283

regions with high water scarcity).


13


284 285

Page 14 of 35

[Figure 3] The complete accounting of supply chain water use (i.e. all economic sectors) is an important

286

feature of EEIOA and one that distinguishes the approach from process-based LCA where a

287

system boundary is established as one of the modelling choices. As such, the water scarcity

288

footprints reported in this study exceed those reported using process LCA. For example, milk

289

production in south-eastern Australia, which represents approximately two thirds of national

290

production, was reported as 87.7 L-eq L-1 using process LCA and the WSIWORLD-EQ model,67

291

which is about 60% of the value reported in this study using EEIOA. A difficulty in making

292

direct comparisons is that process LCA results typically apply to a selected supply chain and not

293

a complete national industry. Supply chain water use is an important feature of livestock

294

production and when fully accounted, the water scarcity footprints of Australian poultry and pig

295

production (45.3 and 44.4 L-eq kg-1 live weight respectively, WSIWORLD-EQ model, Figure 2)

296

exceeded the water scarcity footprint of lamb production, but not beef cattle production (19.4 and

297

53.5 L-eq kg-1 live weight respectively).

298

Implications for sustainable consumption and production patterns. The United Nations

299

Sustainable Development Goal 12 highlights the need to pursue responsible consumption and

300

production in order to address global environmental challenges such as water scarcity (Target

301

6.4).36 At the global level, 69% of water withdrawals are to the agricultural sector, and in the

302

continents of Asia and Africa the proportions are even higher, exceeding 80%.35 As such, the

303

agricultural sector and the food system are focal points to address water scarcity. Water use

304

efficiency is a longstanding goal in agriculture with gains achievable through means such as

305

water use efficient crop varieties, improved irrigation technologies and farming systems.68


14

Page 15 of 35


306

Attention has also been drawn to the need for responsible consumption of food and changes to

307

dietary patterns as a way of mitigating water scarcity.33

308

The assessment of the water scarcity impacts of diets is a challenging endeavour due to the

309

very large number of individual foods being consumed. In the Australian national dietary survey

310

more than 4,500 individual foods were recorded.69 In addition, agricultural production systems

311

for the same commodity can differ greatly, as can the local environmental contexts where

312

production occurs. Due to this complexity, sustainable diet studies almost always report water

313

use rather than contribution to water scarcity, which is the actual environmental concern.34 A

314

common conclusion is that water savings can be achieved by reducing the fraction of animal

315

products in the diet or by adopting vegetarian diets.70-73 Mekonnen and Hoekstra74 argued that

316

the water footprint of any animal product is larger than the water footprint of crop products with

317

equivalent nutritional value. Figure 2 reports water footprint results for agricultural commodities,

318

and even though the data does not include post farm-gate processing, it is evident that the

319

abovementioned generalisations about plant and animal foods cannot be supported when impacts

320

on water scarcity are taken into consideration, at least not in the Australian context. Poultry was

321

found to have a farm-gate water footprint of 45.3 L-eq kg-1 live weight (WSIWORLD-EQ model,

322

Figure 2). With a carcase yield of 70%, the water footprint of retail poultry cuts rises to around

323

65 L-eq kg-1. This is still well below the farm-gate water footprint of Australian rice, summer

324

legumes, summer oilseeds, citrus, stone fruit (e.g. peach, nectarine), tropical stone fruit, nuts and

325

grapes. Considering lamb, with a carcase yield of 47% and a recovery of prime cuts of 86% of

326

the carcase,75 the water footprint of retail cuts is then around 48 L-eq kg-1, lower than poultry and

327

even lower than the farm-gate water footprint of Australian vegetables.


15


Page 16 of 35

328

However, the main purpose of this article was not to compare the water footprints of retail-

329

ready foods. Our contribution is primarily to highlight the importance of using environmentally-

330

relevant metrics to inform sustainable consumption and production policies. By coupling input-

331

output analysis with water scarcity footprint extensions incorporating spatially-explicit impact

332

assessment, it becomes possible to assess the economy-wide implications of policies and

333

interventions in relation to the environmental concern of water scarcity, rather than water use. In

334

the same way that this study has called into question some common assumptions about

335

sustainable foods, the application of EEIOA with water scarcity footprint extensions could

336

provide important insights into other consumption domains, including materials and energy

337

sources. It is also important to note that any interventions aimed at reducing water scarcity

338

impacts need to be also evaluated in terms of impacts on other environmental impact categories

339

(such as GHG emissions), as well as wider social and economic concerns.

340

Implications for water footprint accounting. Organisations can contribute to water scarcity

341

directly through the water used in their operations. They can also contribute indirectly to water

342

scarcity through the consumption of purchased goods and services that required the use of water

343

during production (upstream water scarcity impacts). A further indirect contribution to water

344

scarcity can come from the demand for water associated with the use and disposal of an

345

organisation’s products (downstream water scarcity impacts). These indirect aspects are

346

important because for many organisations the major contributions to water scarcity are in the

347

value chain. If organisations focus exclusively on direct water use, they can be neglecting

348

significant opportunities for improvement. Furthermore, when organisations report to

349

stakeholders only on direct water use, they may be failing to disclose the most significant

350

contributions to the water scarcity problem. This situation is analogous to GHG emissions


16

Page 17 of 35


351

accounting where emissions occurring in the value chain are known as Scope 3 emissions.

352

EEIOA is an effective methodology that is widely used to quantify Scope 3 emissions, resolving

353

the practical difficulties of quantifying the relationship between numerous purchased goods and

354

services and GHG emissions in the wider economy. The coupling of input-output analysis with

355

water scarcity footprint extensions, as demonstrated in this project, makes possible

356

organisational water scarcity footprint accounting. This would be a major advancement on

357

current practice which usually considers water use rather than water scarcity and excludes the

358

value chain dimension.

359

Implications for the development of EEIOA. Impact assessment is a longstanding practice in

360

the field of LCA and the number of available impact assessment models that are spatially

361

differentiated continues to increase.37 For some environmental aspects, the use of spatially-

362

explicit impact assessment is considered critical for meaningful environmental assessment. This

363

is particularly the case with water use.39 From the perspective of environmental impact, water

364

use in a region of water abundance cannot be directly compared to water use in a region of

365

scarcity. Generally, LCA studies involve a particular product system and it is feasible for

366

information to be compiled about specific locations where major production processes take

367

place, enabling application of spatially-explicit impact assessment models. High spatial

368

resolution modelling of emissions and resource use is a recent development in EEIOA.17,76-77

369

However, the transition to spatially-explicit impact assessment, using best practice impact

370

assessment models from the field of LCA, has yet to fully take place.

371

A novelty of the approach developed in this study was the application of impact assessment

372

models prior to input-output modelling. As such, the input-output table was augmented with life

373

cycle impact category indicator results for each sector. Conventionally, input-output tables are


17


Page 18 of 35

374

augmented with resource use or emissions data for industry sectors.2,3 This approach precludes

375

the application of spatially-explicit impact assessment models; once input-output modelling is

376

undertaken, spatial relationships are lost. The conventional approach is useful, but only for

377

global impact categories where spatially-explicit impact assessment is unnecessary. One solution

378

could be to spatially disaggregate the input-output table following the examples of sectoral

379

disaggregation – to increase the number of industry sub-sectors – or temporal disaggregation – to

380

estimate intra-year input-output matrices.78 However, apart from introducing additional bias, the

381

practical extent of spatial disaggregation would appear limited. While economic models, such as

382

gravity models, can be used to understand patterns of trade, in most cases there is no reliable

383

basis for estimating the intra-regional spatial origin of an array of specific commodities. Sub-

384

national multi-regional input output (MRIO) models usually operate at the scale of sub-national

385

administrative units or major regions, not at scales which are necessarily environmentally

386

relevant for impact assessment modelling.56,79 It is not presently conceivable that a MRIO model

387

would be constructed with individual regions on a 1.1 km2 grid – the spatial resolution employed

388

in this study. For some impact categories, especially those relating to land use impacts, high

389

spatial resolution of this kind is relevant. Therefore, the approach demonstrated in this article for

390

water scarcity, appears to be a feasible general approach for incorporating spatially-explicit

391

impact assessment into EEIOA.

392

Limitations of the study. Care was taken to ensure the highest quality input data possible at

393

every stage of the analysis. However, the study was limited by the quality of the underlying ABS

394

water use accounts, which are reported to have a relative standard error typically between 2 and

395

5% at the national level. In addition, the high spatial resolution map of agricultural water use

396

came from one specific time period. However, subsequent analysis showed that water use


18

Page 19 of 35


397

intensity in this period was close to the 12 year average and uncertainty analysis using Monte

398

Carlo techniques (see Methods) was carried out to establish combined error margins for each

399

individual agricultural commodity (SM2). The EEIOA methodology applied economic allocation

400

to partition flows in cases of co-production (e.g. milk and animals for slaughter in the dairy

401

sector). Alternative allocation procedures, as sometimes applied in LCA, can alter results. The

402

outputs of EEIOA are also indicative of average production and not marginal production and

403

need to be interpreted accordingly.

404

The water scarcity models used were all spatially-explicit global models that may not reflect

405

changes in water scarcity that can occur briefly in specific localities.80 Modelling was based on

406

annual water use, not monthly water use. As such, it was not possible to apply monthly water

407

scarcity indicators. That said, monthly indicators are probably not relevant in the case of

408

groundwater use and in the case of surface water use from rivers where flows are managed by

409

reservoirs and other infrastructure. For the agricultural sector in Australia, inputs from outside

410

the country are few and their origin was summarized into one rest-of-world region. This was also

411

a necessity because high spatial resolution of water use is not a feature of current global MRIO

412

models. The application of high spatial resolution impact assessment models is reliant upon

413

having similarly high spatial resolution information about resource use. This is an important area

414

for future MRIO model development with several sub-national databases opening up this

415

possibility.79 Finally, the results presented in this study are cradle-to-farm-gate water scarcity

416

footprints only and do not include downstream food processing and distribution, final demand or

417

exports. Such modelling is the subject of ongoing research as the method introduced in this study

418

has significant implications for consumption-based footprints.

419

ASSOCIATED CONTENT


19


420

Page 20 of 35

Supporting Information.

421

The following files are available free of charge.

422

SI - MRIO table structure and links to input-output tables and source code used in the analysis

423

(PDF)

424

SM1 - Input data – direct intensities and historical variation (XLXS)

425

SM2 - All footprint results with uncertainty margins (XLXS)

426

SM3 - Agricultural commodity decomposition results (XLXS)

427

AUTHOR INFORMATION

428

Corresponding Author

429

*Email: [email protected]

430

ORCID

431

Bradley G. Ridoutt: 0000-0001-7352-0427

432

Michalis Hadjikakou: 0000-0002-3667-3982

433

Brett A. Bryan: 0000-0003-4834-5641

434

Notes

435

Author contributions: B.G.R., M.H. and B.A.B. designed research; B.G.R., M.H. and M.N.

436

performed research; B.G.R. and M.H. analyzed data; B.G.R., M.H. and B.A.B interpreted

437

results; B.G.R. and M.H. wrote the paper, and all authors contributed substantially to revisions.

438

The authors declare no competing financial interest.


20

Page 21 of 35


439

ACKNOWLEDGMENT

440

We acknowledge the financial support of CSIRO and Deakin University. We thank Javier

441

Navarro-Garcia and Oswald Marinoni for their work on the agricultural profit map from which

442

the production quantities and water requirements were taken. The input-output tables

443

underpinning this research were sourced from the Industrial Ecology Virtual Laboratory (IELab,

444

www.ielab.info), which is supported by the Australian Research Council (grant number

445

LE160100066). IELab data feeds and routines written by Manfred Lenzen, Arne Geschke and

446

Arunima Malik were used.

447

REFERENCES

448

(1) Steffen, W.; Richardson, K.; Rockstrom, J.; Cornell, S. E.; Fetzer, I.; Bennett, E. M.; Biggs,

449

R.; Carpenter, S. R.; de Vries, W.; de Wit, C. A.; et al., et al. Planetary boundaries: Guiding

450

human development on a changing planet. Science 2015, 347, 1259855.

451

(2) Lenzen, M.; Moran, D.; Kanemoto, K.; Geschke, A. Building EORA: A global multi-

452

region input-output database at high country and sector resolution. Econ. Syst. Res. 2013, 25, 20-

453

49.

454 455 456 457 458 459

(3) Lenzen, M.; Kanemoto, K.; Moran, D.; Geschke, A. Mapping the structure of the world economy. Environ. Sci. Technol. 2012, 46, 8374-8381. (4) Hertwich, E. G.; Peters, G. P. Carbon footprint of nations: A global, trade-linked analysis. Environ. Sci. Technol. 2009, 43, 6414-6420. (5) Davis, S. J.; Peters, G. P.; Caldeira, K. The supply chain of CO2 emissions. Proc. Natl. Acad. Sci. USA 2011, 108, 18554-18559.


21


Page 22 of 35

460

(6) Barrett, J.; Peters, G.; Wiedmann, T.; Scott, K.; Lenzen, M.; Roelich, K.; Le Quere, C.

461

Consumption-based GHG emission accounting: A UK case study. Clim. Policy 2013, 13, 451-

462

470.

463

(7) Wieland, H.; Giljum, S. Carbon footprint decomposition in MRIO models: identifying EU-

464

supply chain hot-spots and their structural changes over time, Working Paper Series Nr. 13/Year

465

2/2016; WU Vienna, Institute for Ecological Economics: Welthendelsplatz, 2016.

466 467

(8) Peters, G. P.; Minx, J. C.; Weber, C. L.; Edenhofer, O. Growth in emission transfers via international trade from 1990 to 2008. Proc. Natl. Acad. Sci. USA 2011, 108, 8903-8908.

468

(9) Kanemoto, K.; Moran, D.; Lenzen, M.; Geschke, A. International trade undermines

469

national emission reduction targets: New evidence from air pollution. Global Environ. Chang.

470

2013, 24, 52-59.

471 472 473 474

(10) Thomas, B. A.; Azevedo, I. L. Estimating direct and indirect rebound effects for U.S. households with input-output analysis. Part 2: Simulation. Ecol. Econ. 2013, 86, 188-198. (11) Druckman, A.; Chitnis, M.; Sorrell, S.; Jackson, T. Missing carbon reductions? Exploring rebound and backfire effects in UK households. Energ. Policy 2011, 39, 3572-3581.

475

(12) Chitnis, M.; Sorrell, S.; Druckman, A.; Firth, S. K.; Jackson, T. Turning lights into flights:

476

Estimating direct and indirect rebound effects for UK households. Energ. Policy 2013, 55, 234-

477

250.

478 479

(13) Wiedmann, T. O.; Schandl, H.; Lenzen, M.; Moran, D.; Suh, S.; West, J.; Kanemoto, K. The material footprint of nations. Proc. Natl. Acad. Sci. USA 2015, 112, 6271-6276.


22

Page 23 of 35

480 481


(14) Lenzen, M.; Moran, D.; Kanemoto, K.; Foran, B.; Lobefaro, L.; Geschke, A. International trade drives biodiversity threats in developing nations. Nature 2012, 486, 109-112.

482

(15) Wilting, H. C.; Schipper, A. M.; Bakkenes, M.; Meijer, J. R.; Huijbregts, M. A. J.

483

Quantifying biodiversity losses due to human consumption: A global-scale footprint analysis.

484

Environ. Sci. Technol. 2017, 51, 3298-3306.

485

(16) Shao, L.; Guan, D.; Wu, Z.; Wang, P.; Chen, G. Q. Multi-scale input-output analysis of

486

consumption-based water resources: Method and application. J. Clean. Prod. 2017, 164, 338-

487

346.

488

(17) Lutter, S.; Pfister, S.; Giljum, S.; Wieland, H.; Mutel, C. Spatially explicit assessment of

489

water embodied in European trade: A product-level multi-regional input-output analysis. Global

490

Environ. Chang. 2016, 38, 171-182.

491 492

(18) Yu, Y.; Feng, K.; Hubacek, K. Tele-connecting local consumption to global land use. Global Environ. Chang. 2013, 23, 1178-1186.

493

(19) Tukker, A.; Bulayskaya, T.; Giljum, S.; de Koning, A.; Lutter, S.; Simas, M.; Stadler, K.;

494

Wood, R. Environmental and resource footprints in a global context: Europe’s structural deficit

495

in resource endowments. Global Environ. Chang. 2016, 40, 171-181.

496

(20) Hui, M. L.; Wu, Q. R.; Wang, S. X.; Liang, S.; Zhang, L.; Wang, F. Y.; Lenzen, M.;

497

Wang, Y. F.; Xu, L. X.; Lin, Z. T.; et al. Mercury flows in China and global drivers. Environ.

498

Sci. Technol. 2017, 51, 222-231.


23


Page 24 of 35

499

(21) Li, J.S.; Chen, B.; Chen, G. Q.; Wei, W. D.; Wang, X. B.; Ge, J. P.; Dong, K. Q.; Xia, H.

500

H.; Xia, X. H. Tracking mercury emission flows in the global supply chains: A multi-regional

501

input-output analysis. J. Clean. Prod. 2017, 140, 1470-1492.

502

(22) Acquaye, A.; Feng, K. S.; Oppon, E.; Salhi, S.; Ibn-Mohammed, T.; Genovese, A.;

503

Hubacek, K. Measuring the environmental sustainability performance of global supply chains: A

504

multi-regional input-output analysis for carbon, sulphur oxide and water footprints. J. Environ.

505

Manage. 2017, 187, 571-585.

506

(23) Román, R.; Cansino, J. M.; Rueda-Cantuche, J. M. A multi-regional input-output analysis

507

of ozone precursor emissions embodied in Spanish international trade. J. Clean. Prod. 2016, 137,

508

1382-1392.

509

(24) Lin, J. T.; Pan, D.; Davis, S. J.; Zhang, Q.; He, K. B.; Wang, C.; Streets, D. G.; Wuebbles,

510

D. J.; Guan, D. B. China’s international trade and air pollution in the United States. Proc. Natl.

511

Acad. Sci. USA 2014, 111, 1736-1741.

512 513 514 515 516 517

(25) Ridoutt, B. G.; Huang, J. Environmental relevance – The key to understanding water footprints. Proc. Natl. Acad. Sci. USA 2012, 109, E1424. (26) Wichelns, D. Volumetric water footprints, applied in a global context, do not provide insight regarding water scarcity or water quality degradation. Ecol. Econ. 2017, 74, 420-426. (27) Verones, F.; Moran, D.; Stadler, K.; Kanemoto, K.; Wood, R. Resource footprints and their ecosystem consequences. Sci. Rep.-UK 2017, 7, 40743.


24

Page 25 of 35


518

(28) Chenoweth, J.; Hadjikakou, M.; Zoumides, C. Quantifying the human impact on water

519

resources: a critical review of the water footprint concept. Hydrol. Earth Syst. Sc. 2014, 18,

520

2325-2342.

521

(29) Yang, Y.; Ingwersen, W.W.; Meyer, D. E. Exploring the relevance of spatial scale to life

522

cycle inventory results using environmentally-extended input-output models of the United States.

523

Environ. Modell. Softw. 2018, 99, 52-57.

524 525

(30) Assessing the Environmental Impacts of Consumption and Production: Priority Products and Materials; United Nations Environment Programme: Paris, 2010.

526

(31) Tukker, A.; Huppes, G.; Guinée, J.; Heijungs, R.; de Koning, A.; van Oers, L.; Suh, S.;

527

Geerken, T.; Van Holderbeke, M.; Jansen, B.; et al. Environmental Impact of Products (EIPRO),

528

Technical Report EUR22284EN; EC Joint Research Centre—IPTS: Seville, 2006.

529

(32) Hadjikakou, M.; Wiedmann, T. Shortcomings of a growth-driven food system. In

530

Handbook on Growth and Sustainability; Victor, P. A., Dolter, B., Eds.; Edward Elgar

531

Publishing: Cheltenham, UK 2017; pp 256-276.

532

(33) Garnett, T. Plating up solutions. Science 2016, 353, 1202-1204.

533

(34) Ridoutt, B. G.; Hendrie, G. A.; Noakes, M. Dietary strategies to reduce environmental

534

impact: A critical review of the evidence base. Adv. Nutr. 2017, 8, 933-946.

535

(35) AQUASTAT – FAO Information System on Water and Agriculture;

536

www.fao.org/nr/water/aquastat/water_use/index.stm (Accessed Nov 8, 2017).


25


537 538 539 540

Page 26 of 35

(36) Transforming our World: The 2030 Agenda for Sustainable Development; United Nations Division for Sustainable Development: New York, 2015. (37) Hellweg, S.; Milà i Canals, L. Emerging approaches, challenges and opportunities in life cycle assessment. Science 2014, 344, 1109-1113.

541

(38) Ridoutt, B.; Fantke, P.; Pfister, S.; Bare, J.; Boulay, A. M.; Cherubini, F.; Frischknecht,

542

R.; Hauschild, M.; Hellweg, S.; Henderson, A.; et al. Making sense of the minefield of footprint

543

indicators. Environ. Sci. Technol. 2015, 49, 2601-2603.

544 545

(39) ISO14046 Environmental Management—Water Footprint—Principles, Requirements and Guidelines; International Organization for Standardization: Geneva, 2014.

546

(40) Marinoni, O.; Garcia, J. N.; Marvanek, S.; Prestwidge, D.; Clifford, D.; Laredo, L. A.

547

Development of a system to produce maps of agricultural profit on a continental scale: An

548

example of Australia. Agr. Syst. 2012, 105, 33-45.

549

(41) Bryan, B. A.; Barry, S.; Marvanek, S. Agricultural commodity mapping for land use

550

change assessment and environmental management: An application in the Murray-Darling Basin,

551

Australia. J. Land Use Sci. 2009, 4, 131-155.

552 553

(42) 4618 Water Use on Australian Farms, 2005-06; Australian Bureau of Statistics: Canberra, 2008.

554

(43) 4610 Water Account Australia, 2013-14; Australian Bureau of Statistics: Canberra, 2015.

555

(44) 8165 Counts of Australian Businesses, June 2015; Australian Bureau of Statistics:

556

Canberra, 2016.


26

Page 27 of 35


557

(45) Wiedemann, S. G.; McGahan, E. J. Environmental Assessment of an Egg Production

558

Supply Chain using Life Cycle Assessment; Australian Egg Corporation Limited: North Sydney,

559

2011.

560

(46) Wiedemann, S.; McGahan, E.; Poad, G. Using Life Cycle Assessment to Quantify the

561

Environmental Impact of Chicken Meat Production; Rural Industries Research and Development

562

Corporation: Canberra, 2012.

563

(47) Wiedemann, S.; McGahan, E.; Grist, S.; Grant, T. Environmental Assessment of Two Pork

564

Supply Chains Using Life Cycle Assessment; Rural Industries Research and Development

565

Corporation: Canberra, 2010.

566

(48) 2010 Annual Report; Australian Egg Corporation Limited: North Sydney, 2010.

567

(49) 7215.0 Livestock Products, Australia; Australian Bureau of Statistics: Canberra, 2016.

568

(50) 7218.0.55.001 Livestock and Meat, Australia; Australian Bureau of Statistics: Canberra,

569 570 571

2016. (51) Pfister, S.; Koehler, A.; Hellweg, S. Assessing the environmental impacts of freshwater consumption in LCA. Environ. Sci. Technol. 2009, 43, 4098-4104.

572

(52) Ridoutt, B. G.; Pfister, S. A new water footprint calculation method integrating

573

consumptive and degradative water use into a single stand-alone weighted indicator. Int. J. Life

574

Cycle Assess. 2013, 18, 204-207.

575

(53) Renouf, M. A.; Grant, T.; Sevenster, M.; Logie, J.; Ridoutt, B.; Ximenes, F.; Bengtsson,

576

J.; Cowie, A.; Lane, J. Best Practice Guide for Mid-point Life Cycle Impact Assessment in

577

Australia; Australian Life Cycle Assessment Society, 2016; http://www.alcas.asn.au/.


27


Page 28 of 35

578

(54) Boulay, A.M.; Bare, J.; Benini, L.; Berger, M.; Lathuillière, M.J.; Manzardo, A.; Margni,

579

M.; Motoshita, M.; Núñez, M.; Pastor, A.V.; et al. The WULCA consensus characterization

580

model for water scarcity footprints: Assessing impacts of water consumption based on available

581

water remaining (AWARE). Int. J. Life Cycle Assess. 2018, 23, 368-378.

582

(55) Ridoutt, B. G.; Pfister, S. Water footprinting using a water stress index (WSI) that

583

integrates stress on humans and ecosystems. In Proc. 3rd LCA AgriFood Asia Conference;

584

Bangkok, 22–24 May, 2014.

585

(56) Lenzen, M.; Geschke, A.; Wiedmann, T.; Lane, J.; Anderson, N.; Baynes, T.; Boland, J.;

586

Daniels, P.; Dey, C.; Fry, J.; et al. Compiling and using input–output frameworks through

587

collaborative virtual laboratories. Sci. Total Environ. 2014, 485–486, 241-251.

588 589 590 591 592 593 594 595 596 597

(57) Wiedmann, T. An input–output virtual laboratory in practice – survey of uptake, usage and applications of the first operational IELab. Econ. Syst. Res. 2017, 29, 296-312. (58) 5209.0.55.001 Australian National Accounts: Input-Output Tables, 2013-14; Australian Bureau of Statistics: Canberra, 2016. (59) 6427.0 Producer Price Indexes, Australia Dec 2017; Australian Bureau of Statistics: Canberra, 2018. (60) Miller, R. E.; Blair, P. D. Input-output Analysis: Foundations and Extensions; Cambridge University Press: Cambridge, 2009. (61) Wiedmann, T. On the decomposition of total impact multipliers in a supply and use framework. J. Econ. Structures 2017, 6, 11.


28

Page 29 of 35

598 599 600 601 602 603


(62) 7503.0 Value of Agricultural Commodities Produced, Australia, 2013-14; Australian Bureau of Statistics: Canberra, 2015. (63) Lenzen, M.; Wood, R.; Wiedmann, T. Uncertainty analysis for multi-region input–output models–a case study of the UK's carbon footprint. Econ. Syst. Res. 2010, 22, 43-63. (64) Lenzen, M. Errors in conventional and input‐output—based life-cycle inventories. J. Ind. Ecol. 2000, 4, 127-148.

604

(65) Ridoutt, B.G.; Pfister, S. A revised approach to water footprinting to make transparent the

605

impacts of consumption and production on global freshwater scarcity. Global Environ. Chang.

606

2010, 20, 113-120.

607

(66) Pfister, S.; Bayer, P.; Koehler, A.; Hellweg, S. Environmental impacts of water use in

608

global crop production: hotspots and trade-offs with land use. Environ. Sci. Technol. 2011, 45,

609

5761-5768.

610

(67) Ridoutt, B.; Hodges, D. From ISO14046 to water footprint labeling: A case study of

611

indicators applied to milk production in south-eastern Australia. Sci. Total Environ. 2017, 599-

612

600, 14-19.

613 614

(68) Wallace, J. S. Increasing agricultural water use efficiency to meet future food production. Agr. Ecosyst. Environ. 2000, 82, 105-119.

615

(69) Ridoutt, B.; Baird, D.; Bastiaans, K.; Darnell, R.; Hendrie, G.; Riley, M.; Sanguansri, P.;

616

Syrette, J.; Noakes, M.; Keating, B. Australia’s nutritional food balance: situation, outlook and

617

policy implications. Food Secur. 2017, 9, 211-226.


29


618 619 620 621

Page 30 of 35

(70) Hoekstra, A.Y. Water for animal products: a blind spot in water policy. Environ. Res. Lett. 2014, 9, 091003. (71) Jalava, M.; Kummu, M.; Porkka, M.; Siebert, S.; Varis, O. Diet change – a solution to reduce water use? Environ. Res. Lett. 2014, 9, 074016.

622

(72) Marlow, H. J.; Harwatt, H.; Soret, S.; Sabaté, J. Comparing the water, energy, pesticide

623

and fertilizer usage for the production of foods consumed by different dietary types in California.

624

Public Health Nutr. 2014, 18, 2425-2432.

625 626 627 628 629 630 631 632 633 634 635 636

(73) Vanham, D.; Hoekstra, A. Y.; Bidoglio, G. Potential water saving through changes in European diets. Environ. Int. 2013, 61, 45-56. (74) Mekonnen, M. M.; Hoekstra, A. Y. A global assessment of the water footprint of farm animal products. Ecosystems 2012, 15, 401-415. (75) Ridoutt, B. G.; Sanguansri, P.; Nolan, M.; Marks, N. Meat consumption and water scarcity: beware of generalizations. J. Clean. Prod. 2012, 28, 127-133. (76) Moran, D.; Kanemoto, K. Tracing global supply chains to air pollution hotspots. Environ. Res. Lett. 2016, 11, 094017. (77) Moran, D.; Kanemoto, K. Identifying species threat hotspots from global supply chains. Nat. Ecol. Evolution 2017, 1, 0023. (78) Fernandes, A.; Avelino, T. Disaggregating input-output tables in time: the temporal inputoutput framework. Econ. Syst. Res. 2017, 29, 313-334.


30

Page 31 of 35

637 638


(79) Geschke, A.; Hadjikakou, M. Virtual laboratories and MRIO analysis – an introduction. Econ. Syst. Res. 2017, 29, 143-157.

639

(80) Jaeger, W. K.; Amos, A.; Bigelow, D. P.; Chang, H.; Conklin, D. R.; Haggerty, R.;

640

Langpap, C.; Moore, K.; Mote, P. W.; Nolin, A. W.; et al. Finding water scarcity amid

641

abundance using human-natural system models. Proc. Natl. Acad. Sci. USA 2017, 114, 11884-

642

11889.

643 644

(81) Land use of Australia version 4, 2005-06 dataset; Australian Bureau of Agricultural and Resource Economics – Bureau of Rural Sciences: Canberra, 2010.

645


31


Page 32 of 35

Figure 1. General approach to EEIOA including spatially-explicit impact assessment, and examples of (A) winter cereal and (B) sugarcane. Data source for land use map: Bureau of Rural Sciences, Australia.81


32

Page 33 of 35



33


Page 34 of 35

Figure 2. Life cycle (cradle-to-farm gate) water use and water scarcity footprint of Australian agricultural commodities. Water use refers to water from a surface or groundwater resource. Water scarcity footprint was calculated using 3 spatially-explicit impact assessment models: WSIWORLD-EQ, WSIHH,EQ and AWARE. See text for modelling details. Error bars for the national average value denote the 5th and 95th percentile of values generated following a Monte Carlo uncertainty analysis, sampling across the entire range of historical inter-annual water use intensity across all sectors of the economy. Results are expressed per kg of crop product and per kg live weight in the case of animals.


34

Page 35 of 35


Figure 3. Disaggregated water scarcity footprints for selected Australian agricultural commodities: apples, plantation fruit, eggs and rice. Water scarcity footprints were calculated using the WSIWORLD-EQ spatially-explicit impact assessment model. The direct component relates to direct water use in the producing industry sector. The indirect component relates to water use in the supply chain. For the purposes of presentation, the indirect component is grouped according to other agricultural sectors in Australia, non-agricultural sectors in Australia, and industry sectors from the rest of the world (RoW).


35

From water use to water scarcity footprinting in environmentally

Recommend Documents