Water Flows in the Spanish Economy: Agri-Food Sectors

Article pubs.acs.org/est

Water Flows in the Spanish Economy: Agri-Food Sectors, Trade and Households Diets in an Input-Output Framework Ignacio Cazcarro,* Rosa Duarte, and Julio Sánchez-Chóliz Department of Economic Analysis, Faculty of Economics and Business, University of Zaragoza, Gran Vía, 2, 50005, Zaragoza, Spain S Supporting Information *

ABSTRACT: Seeking to advance our knowledge of water flows and footprints and the factors underlying them, we apply, on the basis of an extended 2004 Social Accounting Matrix for Spain, an open Leontief model in which households and foreign trade are the exogenous accounts. The model shows the water embodied in products bought by consumers (which we identify with the Water Footprint) and in trade (identified with virtual water trade). Activities with relevant water inflows and outflows such as the agrarian sector, textiles, and the agrifood industry are examined in detail using breakdowns of the relevant accounts. The data reflect only physical consumption, differentiating between green and blue water. The results reveal that Spain is a net importer of water. Flows are then related to key trading partners to show the large quantities involved. The focus on embodied (or virtual) water by activity is helpful to distinguish indirect from direct consumption as embodied water can be more than 300 times direct consumption in some food industry activities. Finally, a sensitivity analysis applied to changes in diets shows the possibility of reducing water uses by modifying households’ behavior to encourage healthier eating.

1. INTRODUCTION The Spanish economy has undergone profound structural changes over the last 20 years, growing increasingly open to trade since European Union (EU) integration in 1986. This has affected domestic production processes, the technologies used, and consumption patterns, which in turn influence water demand and impacts. These developments have affected not only direct but also indirect demand for water (via inputs). Moreover, structural change has occurred in parallel with an increase in trade flows with other EU member States and the rest of the world (RW), which also affect water demand and pressure on Spanish and foreign water resources. This occurs because water consumption is driven by different factors including production structure, trade composition, technology and consumption habits as determinants of the water flows within the economy. This study seeks to advance our knowledge of water flows and the factors underlying them. Specifically, this paper has a 2-fold objective: first, to evaluate water flows in the different sectors in the Spanish economy and interactions with the rest of the world; and second to use these results for evaluating the pressure on water in different spending scenarios for changes in Spanish diets. The key concept in our analysis is the embodied water in a product, that is to say, the water that directly or indirectly has been necessary to produce it, but the concept of virtual water1 (VW) is also used in parallel to show better the importance of the © 2012 American Chemical Society

relationship between production, trade and water; so from now on, we will use as equivalent the concepts of VW content of a product and embodied water in the product. Moreover, the model used shows the water embodied in products bought by domestic consumers, which we identify with the water footprint2 (WF), and in trade, identified with VW trade. One of the approaches to estimate the water embodied in sectors/activities/products is input-output (IO) analysis. The method applied is an open Leontief model based on the data we have obtained for an extended social accounting matrix (SAM) with water accounts, which allows us to estimate the embodied water required to produce the goods and services assigned to the exogenous accounts, in this study comprising “households” and “foreign trade”. A number of papers illustrate the possibility of using the VW concept in an IO framework to analyze the water pressure derived from economic activities.3−5 In many of them, boundary input is given by water withdrawal of the sector (here we will consider physical consumption) and output, given by the water footprint (the sum of total domestic water and the net virtual water import of the region, see more on this in refs 6,7). Received: Revised: Accepted: Published: 6530

March 1, 2011 May 7, 2012 May 21, 2012 May 21, 2012 dx.doi.org/10.1021/es203772v | Environ. Sci. Technol. 2012, 46, 6530−6538

Environmental Science & Technology

Article

on both domestic water use and water uses associated with trade, a Symmetric IO table including water accounts was built for 2004 (at basic prices) using the industry technology assumption.13 The SAM extends the information contained in the IO tables to represent the circular flow of income. In this sense, although the underlying models in IO and SAM are formally similar, all the accounts involved to obtain a unit of income are considered in the SAM models. The SAM used for the analysis is also squared and allows the definition of linear models which are formally analogous to IO linear models. So although we use the information from a SAM, since we leave as exogenous accounts the household consumption and foreign trade; it is not a traditional SAM analysis, but more an extended input-output analysis. Having obtained the SAM table and extended it with water accounts, we break down the agri-food system and the accounts where water flows are expected to be most important, obtaining a disaggregated extended SAM with water accounts for 2004 and 68 productive activities (2004 DSW), which is available in the Supporting Information (SI). The water information was also broken down for these accounts. For these purposes, our main references are 14−21. Finally, a KRAS algorithm22 is applied at different stages of the process in order to balance the matrix, while preserving the values of some cells and groups of cells, and to detect and correct conflicts in the data. Modeling and Foreign Trade. The linear model used is based on the DSW, taking households and foreign trade (comprising the accounts of “European Union”, and “Rest of the World”) as the exogenous. A matrix A of accounting multipliers (which are formally similar to the technical coefficients of I−O models) is obtained by dividing the columns of endogenous accounts by the account total. The following linear model, described in five blocks of accounts, calculates the levels of output (resources), where x is the vector of outputs for the endogenous accounts and y the exogenous demand or final demand of the model:

This methodology has been used at the national level for Spain and for some regions,8,9 but not based on a disaggregated table embracing the whole Spanish agri-food complex (defined as the set of agricultural and livestock activities, the food industry, and trade in all those products), given that the country’s farm (i.e., agrarian) sector and food industry represent an important share of national output, employment and income generation. The use of a disaggregated table allows precise estimates of the embodied water per euro in a product of domestic final demand, or embodied in any product imported or exported. Disaggregation of IO tables in activities related with the agri-food complex is described in various papers in the literature,10,11 and others deal with disaggregation for the purposes of environmental analysis.12 This paper follows the IO methodology, offering a step forward in the analysis of water uses, flows, trade and consumption patterns in line with some of the new concepts developed in the field of water management. We will focus on physical consumption (PC, also called consumptive water use), considering both the blue water collected from groundwater or surface sources (the water usually studied in relation to the political and economic management of the resource) and green water (i.e., the water evaporated from rainwater stored in the ground as soil moisture). Section 2 describes the linear model and the items obtained from the SAM. Section 3 contains the results and discussion of the embodied water in the products of the Spanish economy, which are completed with the valuations of embodied water in imports and exports. Finally, we provide an analysis of how uses might vary given shifts in domestic final demand, especially in food consumption patterns.

2. DATA AND METHODOLOGY Social Accounting Matrix for 2004. Our starting points are the supply and use tables provided by the Spanish Statistics Institute (NSI) and the Water Satellite Accounts (WSA) for 2004. Given our objectives and the availability of information ⎡ xP ⎤ ⎢x ⎥ ⎢ F⎥ ⎢ x C ⎥= ⎢x ⎥ ⎢ G⎥ ⎣⎢ xS ⎦⎥

⎡ APP 0 ⎢ ⎢ AFP 0 ⎢ ⎢ 0 A CF ⎢A A GF ⎢ GP ⎢⎣ 0 0

APC APG APS ⎤⎡ xP ⎤ ⎡ yPH ⎤ ⎥ ⎢ ⎥ 0 0 0 ⎥⎢ xF ⎥ ⎢ yFH ⎥ ⎢ ⎥ ⎥ A CC A CG 0 ⎥⎢ x C ⎥ + ⎢ yCH ⎥ ⇔ x = Ax + y ⇔ x = (I − A)−1y ⎥ ⎢ ⎥ ⎢ A GC A GG A GS ⎥⎢ x G ⎥ ⎢ yGH ⎥ ⎥⎢ x ⎥ ⎢ ⎥ A SC A SG 0 ⎥⎦⎣ S ⎦ ⎣ ySH ⎦

The accounts (block accounts) are P, productive sectors; F, factors; C, companies; G, government; S, savings-investments; and H, households and exports. With this choice of exogenous accounts I−O, we place the focus on the pressure on the water resources due to the final demand of goods outside the country, and due to the final demand of the households (and easily per citizen), although obtaining embodied coefficients with more dispersion than traditional only sectoral I−O, and gathering longer chains (a few interesting ones, such as capturing embodied water not only due to the demand of goods and services, but government expenditure or investments, which lead to further demand of goods and services, etc.). Blue direct PC of industrial and services activities is obtained from the WSA provided by the NSI, and the agrarian direct (blue and green) water from,23 which follows the methodology

(1)

proposed, for example, in ref 17, but making a clear distinction between dryland and irrigated land requirements. Aggregating all types of water (we do not consider here gray water) we get blue and green PC. If we define a vector w of direct unit coefficients for water, where {wi}i=1,...,n is the PC of each activity i per € value of total output x of activity i, we can obtain a vector of water values by category of final demand: Λ′ = (λi)′ = w′(I − A)−1

(2)

These values capture all the water incorporated, directly or indirectly, by unit of exogenous demand, so that λi is the embodied water in each monetary unit of the Households and Foreign trade accounts. The same formulation applies to any other water vector w. 6531

dx.doi.org/10.1021/es203772v | Environ. Sci. Technol. 2012, 46, 6530−6538


Article

Table 1. Values of the Main Water Accounts Obtained from the 2004 DSW (hm3) direct consumption + imports

embodied water consumption in final demand

total GCa 1 2 3 4−5 6−9 10−11

12−17 18 19−22 23 24 25 26−33 34−38 39−40 41−45

classifications−sectors cereals and leguminous plants vegetables and fruits industrial crops and woody olive tree and grape wine livestock other agrarian, fishing and forestry activity total agricultural, forestry and fishing (1−11) extraction minerals and energy and water supply chemicals metallurgy, machinery and equip. meat industry diaries industrial oils and greases other food industries beverages and tobacco total food industry (23−38) textiles, leather and footwear all other industries

66−68 69−71 72−73 74 75 76

total industry (12−45) wholesale and retail agrarian and food nonfood trade restaurants, cafe, bars and similar hotels, lodging and catering services transport and private and domestic services public education, health and services factors NPISH and societies public administration savings/investment households

1−76 77 78

total national (1−76) european union rest of the world

77−78 1−78

total imports (77−78) total (sum, 1−78)

46−53 54 55−56 57 −58 59−65

total

households

blue

blue +green

blue

blue +green

blue

blue +green

6172 4329 3510 1634 234 812

12 410 6956 4774 4386 234* 1301

876 3782 43 186 248 105

3048 7864 144 506 1006 398

613 1558 0 176 163 74

2137 3252 0 482 666 302

263 2224 43 10 85 31

911 4612 144 24 340 96

16 699

30 070

5240

12 967

2584

6839

2656

6128

672

672

276

1446

211

849

67

597

5 93 18 8 8 36 18 90 25 170

5 93 18 8 8 36 18 90 25 170

68 186 1675 619 1178 2973 456 6901 338 236

1158 4131 7461 3064 4958 12 354 1875 29 712 3815 1535

15 39 1388 549 607 2134 283 4960 204 108

252 957 6188 2717 2554 8858 1365 21 682 2312 694

53 148 287 70 571 838 173 1941 133 126

906 3174 1273 347 2404 3496 510 8030 1503 841

1054 2

1054 2

8006 600

41 801 2553

5538 550

26 745 2342

2468 51

15 056 211

7 41 9 31

7 41 9 31

191 1963 179 656

1120 7848 745 3734

163 1692 65 492

929 6718 287 2763

35 271 114 164

191 1130 458 971

45 0 0 0 0 201

45 0 0 0 0 201

4 0 143 539 367 201

21 0 882 3065 2381 810i

4 0 92 522 367 201

21 0 565 2962 2381 810b

0 0 50 17 0 0

0 0 317 103 0 0

18 090

31 461 14 623 31 840

18 090

46 463 77 924

blue +green

exports

0

blue

12 266

77 924

0

5824

53 364

0

24 560

GC: group code. bThe embodied imported water due to Spanish tourists’ consumption abroad is recognized in these accounts. *No direct green (pastures...) water consumption considered. a

∑ (widom)′(I − A)−1yr = ∑ wdom′(I − A)−1yr

To calculate the total PC of water associated with economic activity, we must deal with three components: The water consumed domestically to produce the goods and services demanded by households and foreign trade, direct consumption of water in exogenous activities, and embodied water in imports (which we associate to VW import). Beginning with the first component, if wdom=(wdom i ) is the vector of domestic PC coefficients (defined accordingly with the total of the matrix), the total water consumed in Spain to meet exogenous demand yr (r = households, foreign sector) will be given by:

r

r

(3)

The second component is associated with the direct consumption of water in the activities or accounts that we considered exogenous. If dEx r is the vector of water PC per (monetary) unit of exogenous account r, this will be Σr dEx r ′yr. To address the embodied water in imports, the third component, we look at the technological conditions of the main source countries for the (global and agrarian) products entering the country as imports. Thus we have used IO tables24 for the 6532



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embodied consumption is shown in the second and fourth columns. The break down between households and exports for both types of water can be seen in columns 5 to 8. The pressure of the Spanish households (citizens) on the global water resources (which we associate with our boundary output, the blue and green WF), represented in the sixth column in Table 1, comes from the domestic water consumptive use (WC), plus the embodied water in imports (VWimp), see second column, minus embodied water in exports (VWexp): WF = WC + VWimp − VWexp The total PC of water in production plus embodied water in imports and direct uses, as expressed in eq 5 (which we relate to WF budget), is 77 924 hm3/year, while the embodied water in imports (third addend of eq 5) is 46 463 hm3/year, the 60% of the total PC. From the total PC only 18 090 hm3/year is domestic blue water (less than one-fourth), being consumed most of them, 16 699 hm3/year, in the agrarian activities and the rest in the industrial production and in services. Also to agriculture corresponds the highest domestic green water consumption, 13 371 hm3/year. In Table 1 we may see that more than 60% of the imported water comes from non-EU countries, and as we will see, mainly from primary and transformed farm products. Similar results were obtained in ref 17 As shown in the last column of Table 1 (eq 6), meanwhile, the total embodied water in exports is 24 560 hm3/year, about a third of the total PC. Hence, Spain is a net importer of VW, in line with previous process analysis type of studies (as defined in ref 32), focused on agrarian and food trade, as seen in refs 17,23 As may be seen in Table 1, the embodied water volumes in the final demand from Food industry (29 712 hm3) are more than 300 times the direct consumption of water of the sector, demonstrating the relevance of water flows from other activities, especially Agriculture and Livestock. A similar result is observable for the total of Industries (41 801 hm3) which is more than 39 times its direct consumption of water, due mainly to differences in the Food industry, and in industrial accounts, as textiles, clothing, leather, and footwear. Flow transfers in the food industry can be better understood by focusing on the different sectors involved. The meat industry directly consumes only 18 hm3 of water, but the water needed to satisfy final demands (households or exports) is 7,461 hm3; other subindustries such as dairies, and industrial oils and greases directly consume about 8 hm3, but their embodied consumption is 3064 hm3 and 4958 hm3 respectively. Analogously, the restaurants, cafe, bars and similar directly consume 41 hm3, but the water needed to satisfy final demands is high at around 7848 hm3. 3.2. Water Consumption and Spanish Trade. According to the results presented above, Spain is a net importer of water. As much of Spain is arid or semiarid, it will be very interesting to examine the VW exported and imported in greater depth. Taking only domestic PC into account, we may observe that 12 266 hm3 of the total 18 090 hm3 of domestic blue water consumed every year remain in the country, while the remaining 5823 hm3 of water, almost 1/3 of the total, is exported. We find a similar picture for total consumption (blue and green water,): 53 364 hm3 of the total 77 924 hm3 (68.5%) are embodied in household consumption, whereas the remaining 24 560 hm3 (31.5%) is exported. The most significant sectors in terms of blue VW exports are vegetables and fruits; pork products; chemicals; transport equipment; industry of vegetables, of milling, of wines and ciders, rubber,

countries from which Spain bought most imports in 2004 (tables harmonized in Euros), estimating more accurately the embodied water of imports, based on the transformation processes of the countries of origin, as improvement from the single region approach. Table SI2 in the SI shows results for the 10 countries from which Spain imported most in 2004, which were also the main sources of imports over the whole of the decade, eight of them also being the main destinations of exports. The countries concerned are France, Germany, Portugal, Italy, UK, Netherlands, U.S., Belgium, China, and Japan. However, other countries such as Argentina, Brazil, Canada, South Africa, and Indonesia are also significant and their IO tables are considered. Trade data were obtained from the Spanish Revenue Agency. This information was combined with water data for the countries concerned to calculate vectors of water values for each individual country and for groups of countries, c, as follows:: Λcimp′ = (λciimp) = wc′(I − A c)−1

(4)

where wc is the vector of water consumption per unit of basic output in c (and hence for every unit of imported output from c to Spain), and Ac is the matrix of technical coefficients of c. Once Λimp and the vector of imports Mc=(Mci) are known, we c can obtain the unit coefficients for each country c as wimp = c imp (wimp ci ) = (λci Mci/xi) where xi is the total Spanish output of sector i. Thus, the embodied water in Spanish production is

∑ w dom′(I − A)−1yr + ∑ drExyr r

r

+

∑∑ r

w cimp′(I

− A)−1yr

(5)

c

If we focus only on exports, assuming y for households to be equal to 0, and that yr is export demand, the above expression tells us that the embodied water in exports is W exp = w dom′(I − A)−1yr +

∑ wcimp′(I − A)−1yr c

(6)

which comprises the domestic water consumed to obtain exports, plus the embodied water in inputs imported to produce them. Uncertainty in the Model. Finally, we consider the underlying uncertainties following this long process of combining tables, matching and balancing data sources, comprising those inherent in the primary input-output, trade and water data, the detailed data used for the disaggregation, price indexes, and model uncertainties.25 A summary of the sensitivity analysis performed in this respect is shown in the results. The approach used is not new in the field (for example, see refs 26 and 27) but relatively little attention has been paid to this issue, becoming recently a matter of concern in multiregional IO models, environmental IO models,28−30 and with respect to the WF.31

3. RESULTS AND DISCUSSION 3.1. Blue and Green Water Account for the 2004 DSW. Estimates for blue and green water associated with both production and consumption, appear in Table 1. The first and third columns of numerical results respectively show direct and embodied blue consumption, given the cost of the blue resource and its importance for many social and economic activities. The total (blue and green water) direct and 6533



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Scheme 1. Schematic Main Net Water Flows (Blue -Big Flows- and Black Arrows) Across Spanish Sectors (Orange Ovals) and with Other Countries (Red Arrows)a,b

a

BE: Belgium, GE: Germany, FR: France, GB: Great Britain, IT: Italy, NL: The Netherlands, PT: Portugal, Other EU: Other European Union countries, JP: Japan, US: United States, CH: China, BR: Brazil, AR: Argentina, RUS: Russia, CAN: Canada, IN: India, INDO: Indonesia, TUR: Turkey, SA: South Africa, SK: South Korea, RW: Rest of the World. bBlue arrows show the main water flows across sectors of the Spanish economy (ovals), being the black ones less important in terms of water. The size of the selected main net flows is given by the size of the spheres represented, while the importance of the agrarian sector in the flow is given by the color, being the yellow ones those with the highest water content from the Agrarian Sector.

for changes in economic behavior and appraisal of their effects on economic and environmental flows. This section is a first approximation to the changes in the water consumed in the face of variations in the food products purchased by households, since we have identified that expenditure as one of the key determining the WF. Moreover, according to various references,33,34 consumption of meat and meat products in the Spanish diet is higher than recommended, but less cereals and cereal products, vegetables, fruit, and legumes are eaten than would be desirable. In this context, in Table 2 we present two scenarios based on a comparison of actual and recommended diets. In Scenario I, changes in spending are associated with a recommended diet by36 for Spain. Changes of this nature would reduce spending on food by 11 611 Euros, 1.51% of household expenditure. In Scenario II, the changes in agriculture, livestock and food spending are accompanied by the return of the savings generated to the economic circuit, in the form of higher spending on other activities. This approximates with a simple assumption a possible rebound effect from the extra money generated with Scenario I. The necessary data on food supply quantities (in kg per capita per year, by crop and by specific animal classification) is found in ref 35 for 2004. The percentage change in spending is estimated from the ratio of changes between the recommended diet and the current diet, assuming no changes in prices.

plastics, and other manufactures; hotel, pensions and similar; and transport and communications. Supplementing the above information, the map in Scheme 1 (and Table SI2 in the SI) present the main net water flows across sectors and embodied in net exports. Spain is a net importer of embodied water flows, especially from non EU countries, but generally is net exporter to EU partners, 18 986 hm3 versus 14 623 hm3, although clearly not with France, from where much water comes from agrarian and forestry goods. Spanish VW exports to the United Kingdom, Italy and Portugal are also fairly significant, especially in the case of agri-food products (standing out Vegetables and Fruits, especially citrus). Meanwhile, embodied water in imports from the U.S. (2467 hm3), China (3115 hm3), and the RW (25,749 hm3), see SI Table SI2, may avoid considerable domestic consumption, as the water balance with these countries (with importance of the agrarian sector in the VW flows, as given by the yellow spheres in each country) is significantly negative. Notably, around 50% of the embodied water in imports from China comes from textile products, leather, and footwear. As discussed in the SI in the subsection of uncertainty in the scenario analysis, scenario analysis about trade would benefit from a model where trade is endogenous. 3.3. Scenarios Referring to Changes in Diet and Household Water Consumption. One of the advantages of working in an IO framework, with disaggregated sector information, is that it allows simulation of different scenarios 6534


6535

total industry (12−45) wholesale agrarian & food 4.48% nonfood trade restaurants, cafe, bars & similar hotels, lodging & catering services transport & private & domestic serv. public education, health & services factors NPISH & companies public administration savings/investment households european union rest of world total all accounts (1−78)

39−40 41−45

12−45 46−53 54 55−56 57−58 59−65 2084 0 31 623 213 694 61 663 21 235 6944 5013 769 943

110 153 26 826 49 748 69 231 5351 157 241

13 183 14 251

4589 21 525 11 785 6429 2008 15 798 5688 41 709

14 897

708 5042 0 449 613 2321 9134

household expendit. (million €)

22 0 566 2962 2381 201c 0 0 52 756

26 747 2342 929 6718 287 2763

2312 694

252 958 6188 2717 2554 8857 1366 21 682

849

2137 3252 0 482 666 302 6839

embodied consumption of water

0.04% 0.% 1.07% 5.61% 4.51% 0.38% 0% 0% 100%

50.70% 4.44% 1.76% 12.73% 0.54% 5.24%

4.38% 1.32%

0.48% 1.82% 11.73% 5.15% 4.84% 16.79% 2.59% 41.10%

1.61%

4.05% 6.16% 0% 0.91% 1.26% 0.57% 12.96%

% of embodied consumpt.

2084 0 31 623 213 694 61 663 21 235 6944 5013 758 332

99 907 25 686 49 748 69 231 5351 157 241

−1% −100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% −1.51%

13 183 14 251

4589 21 525 2149 6345 2008 15 272 5688 31 463

14 897

764 6119 0 449 112 1465 8909


0% 0%

0% 0% −82% −1% 0% −10% 0% −25%

0%

8% 21% 0% 0% −82% −39% −2%

variation in expendit.

22 0 566 2962 2381 201 i 0 0 47 850

21 788 2154 929 6718 287 2763

2312 694

252 958 1128 2681 2554 8993 1366 16 721

850

2306 3946 0 482 121 225 7080

embodied consumpt. of water

scenario i

0.05% 0% 1.18% 6.19% 4.98% 0.42% 0% 0% 100%

45.53% 4.50% 1.94% 14.04% 0.60% 5.77%

4.83% 1.45%

0.53% 2.00% 2.36% 5.60% 5.34% 18.79% 2.85% 34.94%

1.78%

4.82% 8.25% 0% 1.01% 0.25% 0.47% 14.80%


1.86% 0% 1.86% 1.86% 1.86% 1.86% 1.86% 1.86% 0%

1.86% 0% 1.86% 0% 1.86% 1.86%

1.86% 1.86%

1.86% 1.86% −82% −1% 0% 0% 0% −25%

0%

8% 21% 0% 0% −82% −39% 1.86%

variation in expendit.

2123 0 32 213 217 677 62 812 21 630 7074 5107 769 943

101 184 25 686 50 675 69 231 5451 160 171

13 429 14 518

4674 21 927 2149 6345 2008 15 272 5688 31 463

15 174

764 6119 0 449 112 1465 8909


22 0 576 3018 2426 201 i 0 0 48 129

21 882 2155 946 6718 293 2814

2355 707

257 975 1128 2681 2554 8993 1366 16 721

866

2306 3946 0 482 121 225 7080

embodied consumpt. of water

scenario ii

0.05% 0% 1.20% 6.27% 5.04% 0.42% 0% 0% 100%

1.97% 13.96% 0.61% 5.85%

45.47%

4.89% 1.47%

0.53% 2.03% 2.34% 5.57% 5.31% 18.69% 2.84% 34.74%

1.80%

4.79% 8.20% 0% 1.00% 0.25% 0.47% 14.71%


GC: group code. bserv.: services; NPISH is non profit institutions serving households. cThe embodied imported water due to Spanish tourists’ consumption abroad is not recognized here (hence resulting in a different total than in Table 1).

a

textiles, leather & footwear all other industries

18 19−22 23 24 25 26−33 34−38 23−38

66−68 69−71 72−73 74 75 76 77 78 1−78

extraction minerals, energy & water supply chemicals metallurgy, machinery & equip. meat industry dairies industrial oils & fats other food industries beverages & tobacco total food industry (23−38)

12−17

classifications−sectors

cereals & leguminous plants vegetables & fruits industrial crops olive trees & vineyards livestock other agrarian, fishing & forestry total agrarian s., forestry & fish.

b

1 2 3 4−5 6−9 10−11 1−11

GC

a

current situation

Table 2. Variations in Embodied Water (hm3) in the Main Accounts from Dietary Changes

Environmental Science & Technology Article



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distinguish between hydrological basins), a first approximation reveals that a 10% cut in domestic water consumption (assumed to be equal for the Northwest, Northeast, and Central, and South) would shift the ratio of water consumption to hydrological availability from approximately 20%, 40%, and 80% for each of the three regions to 15%, 30%, and 70%. 3.4. Uncertainty and Sensitivity of the Results to Data Sources and Modeling. As discussed more extensively in the SI, we have used various different methods to check our data and results. With respect to virtual (embodied) water content, Figure 1 presents Monte Carlo simulations (1000 perturbations

Scenario I results in a 21% increase in both fresh and prepared vegetables and fruit, an 8% increase in cereals and legumes, an 82% decrease in livestock and meat products and, finally, a 39% decrease in both fresh and canned fish. The water saving associated with the recommended diet is relevant, 4906 hm3, the 9.30% of total, mainly because of the large increase in the consumption of cereals and legumes, and vegetables and fruit, and the decrease in meat consumption. The significant change in meat consumption outweighs the switch to cereals, legumes and fruit and vegetables even when the water content of meat per euro of expenditure (VW intensity) is lower (as it is also a more expensive product). While maintaining the changes in the food accounts described above, Scenario II examines the change in the total WF when the extra money (11 611 Euros) obtained from lower expenditure on food products is spent on other activities not directly related with food consumption. Current expenditure on these activities is 623 043 million Euros, so this would represent an increase of around 1.86% with respect to current spending on these goods. The resulting reduction in the WF is smaller than in Scenario I, although it is still high, 8.77%. Looking at the participation of the different sectors under the baseline situation and scenarios, columns third, seventh and 11th in Table 2, we may observe that water reductions are mainly driven in both Scenario I and II by reductions in embodied water consumed from livestock and meat, which is one of the most representative in water consumption. As it can be seen, scenarios affect not only the water embodied in the different sectors but also the fraction represented by each of them (% with respect to total consumption). Thus, embodied water becomes higher in the simulated scenarios than in the current situation for sectors such as industrial oils and fats, wholesale agrarian and food, restaurants, cafe, bars and similar or diaries and other food industries (even when from this two last ones it is reduced the absolute embodied water consumption). In any case, the results indicate that a move toward healthier diets would lead to a lower WF. A change in diets also implies altering the proportion of domestic to imported water that it is finally consumed by the Spanish households. In the base scenario, 40% of water PC necessary to satisfy Spanish households’ demands is domestic water, being 60% the imported water. With the change in diets, assuming the current structure of production (see subsection Exogenous bilateral and total trade f lows in the SI for the analysis of further changes in the structure of production), it should be expected a reduction on both domestic and VW import, being bigger the reduction in VW import. That reduction would be especially marked in those from Italy, Poland, Denmark, Sweden, etc. (high relative imports from livestock); on the contrary, due to increase in beans and leguminous consumption, VW imports from U.S., UK, Canada, Mexico, and France, would not vary that much. Finally regarding the water stress, some tools and date used can also be applied to obtain country-based calculations and additional interpretations of the water footprints estimated (see ref 36). To begin with differential characteristics, we may note the significant share of agricultural water use in Spain and the high ratio of total annual freshwater withdrawals and consumption to availability (leading to high “stress indexes”) compared to European and other countries at a similar stage of development. Also, consumptive uses exact a high toll in terms of adverse impacts on ecosystem quality. Given the limits and differences in approach (our focus was global and did not

Figure 1. Frequency distribution of deviations of perturbed water multipliers M. As variation from their unperturbed value. The distribution is obtained from 1000 perturbations of F* and A*, resulting in more than 100 000 perturbed multipliers.

of F* and A*) of standard errors in VW multipliers (or embodied intensities), showing that they are much smaller (almost all below 10% and mostly below 5%, with a peak around 0%) than in the coefficients matrix A* (ranging up to 60% as defined by the authors based on ref 32) and direct water intensities F* (up to 150%). Finally, with respect to the current food consumption, its information is highly detailed and the main uncertainties are probably due to consideration of scenarios that are too general in terms of the recommended changes. Our main conclusion, based on these sensitivity scenarios, is that although we should look at issues such as possible changes in the proportion of blue and green, in trade (especially with the scenarios of changes in diets) or in consumption of food related industries, the most important variations in the estimations would arise from agri-food system data aggregation.

4. IMPLICATIONS AND FURTHER RESEARCH WITH THE 2004 DSW This paper offers a detailed study of the water flows associated with economic activity in Spain. To this end, a SAM with a high level of disaggregation in the agri-food system was built and extended to include water accounts, unlike the usual IO models, containing few agri-food accounts. This represents a step forward with respect to usual IO model with few agri-food accounts, and to other analyses with precise agri-food information, but narrow system boundaries selection,37 by trying to profit best the advantages of both approaches. The paper thus implicitly highlights the importance of breaking down the agri-food system in order to obtain relevant water intensities. Also, since the imported and exported water volumes are so high, although we did not construct a 6536



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of Science and Innovation for a FPU grant (AP2007-03816). Any remaining errors are our sole responsibility.

multiregional model, import coefficients were carefully considered using IO tables, process analysis type of studies as,17 water data, and data on agricultural imports from source countries. The main characteristics of blue and green water accounting in the Spanish economy were examined on the basis of this information, which could easily be extended to further studies of technological changes (e.g., in irrigation) and impacts on the environment using the 2004 DSW. The results indicate that Spain is a net importer of VW, being more than 60% of VW imports sourced from countries outside the EU (though France stands out as major source), and from primary and transformed agrarian products. The analysis of sensitivity to changes in diet suggests the possibility of obtaining water use reductions through changes in household behavior. The diet variation scenarios presented here are relevant, given the high water intensity of food and because making policy recommendations to households is likely to be more effective than addressing agents (e.g., consumers) abroad. Also, although an accurate description of the consequences on water demand of different dietary scenarios would require considering issues as elasticities, prices and substitution effects,, the results highlight that there is room for water reductions moving toward a healthier diet. The results confirm a 9% reduction in the WF by following scientifically supported recommendations (see refs 34 and 35) to eat a healthier diet including less meat (which is avidly consumed in Spain). We have revealed three important aspects regarding both Spanish households’ consumption and water use in Spain. First, the estimation of the WF has shown that the households are responsible of an important part of total water consumption in Spain, being this consumption highly linked to the consumption patterns and in particularly to a diet with high meat content. Thus, changes in consumption patterns may contribute to alleviate water pressures in Spain. Second, we have seen that a significant part of water consumption in Spain, is to some extent, avoided through imports of products, being Spain a net VW importer. The results show that trade policy and the international context have an important effect on the future of the Spanish water resources. Tools like the ones presented serve us to identify unsustainable paths, and simulate scenarios and policies in order to reverse those processes. We believe that this type of disaggregated information in a SAM framework enables interesting applications linking fiscal policies and natural resources.

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(1) Allan, J. A. Policy responses to the closure of water resources: regional and global issues. In Water Policy: Allocation and Management in Practice; Howsam, P.; Carter, R. C., Eds.; E&FN Spon: London, 1996; pp 3−12. (2) Hoekstra, A. Y.; Hung, P. Q. Virtual water trade: A quantification of virtual water flows between nations in relation to international crop trade. Virtual Water Trade: A Quantification of Virtual Water Flows Between Nations in Relation to International Crop Trade 2002. (3) Lenzen, M. Victorian Water Trust. Report on the Virtual Water Cycle of Victoria; GHD: Melbourne. Victoria, 2008. (4) Yu, Y.; Hubacek, K.; Feng, K. S.; Guan, D. B. Assessing regional and global water footprints for the UK. Ecol. Econ. 2010, 69 (5), 1140−1147. (5) Zhao, X.; Yang, H.; Yang, Z.; Chen, B.; Qin, Y. Applying the input-output method to account for water footprint and virtual water trade in the haihe river basin in China. Environ. Sci. Technol. 2010, 44 (23), 9150−9156. (6) Yang, Z. F.; Mao, X. F.; Zhao, X.; Chen, B. Ecological network analysis on global virtual water trade. Environ. Sci. Technol. 2012, 46 (3), 1796−803. (7) Mao, X. F.; Yang, Z. F. Functional assessment of associated aquatic ecosystems of associated aquatic ecosystems in the Baiyangdian BasinAn ecological-network-analysis based approach. Ecol. Model. 2011, 222 (23−24), 811−3820. (8) Velázquez, E. Water trade in Andalusia. Virtual water: An alternative way to manage water use. Ecol. Econ. 2007, 63 (1), 201− 208. (9) Duarte, R.; Sánchez-Chóliz, J.; Bielsa, J. Water use in the Spanish economy: An input-output approach. Ecol. Econ. 2002, 43 (1), 71−85. (10) Peterson, E. Disaggregating Agricultural and Food Sector (Chapter 15), GTAP 4 Data Base Documentation. Center for Global Trade Analysis; Purdue University, 1999. (11) Müller, M.; Pérez Domínguez, I; Gay, S. H. Construction of social accounting matrices for the EU-27 with a disaggregated agricultural sector; Institute for Prospective Technological Studies. JRC European Comission, 2010. (12) Lenzen, M. Aggregation versus disaggregation in input-output analysis of the environment. Econ. Syst. Res. 2011, 23 (1), 73−89. (13) Rueda-Cantuche, J. M.; Beutel, J.; Neuwahl, F.; Mongelli, I.; Loeschel, A. A symmetric input-output table for EU27: Latest progress. Econ. Syst. Res. 2009, 21 (1), 59−79. (14) Titos Moreno, A.; de Haro, T.; Gómez Muñoz, A. C.; Ramos Real, F. El Sistema Agroalimentario Español: Tabla Input-Output y Análisis de las Relaciones Intersectoriales; Ministry of Agriculture, Fisheries and Food in Spanish, General Technical Secretary: Madrid, 1995. (15) FAO, Technical Conversion Factors for Agricultural Commodities, Food and Agriculture Organization of the United Nations, Rome, Italy, 2003. (16) Chapagain, A. K.; Hoekstra, A. Y. Virtual Water Flows Between Nations in Relation to Trade in Livestock and Livestock Products, Value of Water Research Report Series 13; UNESCO-IHE, Delft, the Netherlands, 2003. (17) Chapagain, A. K.; Hoekstra, A. Y. Water Footprints of Nations, Value of Water Research Report Series 16; UNESCO-IHE: Delft, the Netherlands, 2004. (18) MAPA. Agrarian Accountancy Net (RECAN); Ministry of Agriculture, Fisheries and Food: Madrid, Spain, 2004 (in Spanish). (19) National Statistics Institute (NSI). Accounting Series (Spanish National Accounts, Industrial Products Survey, Annual Survey of Services, Annual Survey of Trade, Continuous Household Budget Survey, Wage Structure Survey, Labour Force Survey, Health, Tourism Satellite Account, Satellite Accounts of water, Survey of Water Supply and Treatment, Surveys on Water Use in Agriculture and in Industry); National Accounts of Spain: Madrid.

ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*Phone +34 976-761826 and +34 646019737; fax: +34-976761996; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank the anonymous reviewers for their helpful comments in previous versions of the paper which improved the work considerably. Ignacio Cazcarro also thanks the Spanish Ministry 6537



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(20) FAO. Aquastat. Food and Agriculture Organization of the United Nations: Rome, Italy, 2007. (21) Eurostat, Environmentl-Water- Annual water abstraction by source and by sector, 2010. (22) Lenzen, M.; Gallego, B.; Wood, R. Matrix balancing under conflicting information. Econ. Sys. Res. 2009, 21 (1), 23−44. (23) Garrido, A.; Llamas, M. R.; Varela-Ortega, C.; Novo, P.; Rodríguez-Casado, R.; Aldaya, M. M. Water Footprint and Virtual Water Trade in Spain: Policy Implications; Springer - Fundación ́ 2010. Marcelino Botin, (24) Yamano, N.; Ahmad, N. The OECD Input Output Database: 2006 ed.. 2006/8; Organization for Economic Cooperation and Development: Paris, 2009. (25) Weber, C. L. Uncertainties in constructing environmental multiregional input-output models. Paper presented at the International input-output meeting on managing the environment, 2008, Seville, Spain. (26) Bullard, C.; Sebald, A. Monte Carlo sensitivity analysis of inputoutput models. Rev. Ec. Stat. 1988, 70, 708−712. (27) Lenzen, M.; Pade, L.; Munksgaard, J. CO2 multipliers in multiregion input-output models. Econ. Syst. Res. 2004, 16 (4), 391−412. (28) Wiedmann, T.; Lenzen, M.; Wood, R. Uncertainty Analysis of the UK-MRIO Model − Results from a Monte-Carlo Analysis of the UK Multi-Region Input-Output Model (Embedded Carbon Dioxide Emissions Indicator), Report to the UK Department for Environment, Food and Rural Affairs by Stockholm Environment Institute at the University of York and Centre for Integrated Sustainability Analysis at the University of Sydney; Defra: London, UK, 2008. (29) Weber, C. L.; Matthews, H. S. Embodied emissions in U.S. international trade: 1997−2004. Environ. Sci. Technol. 2007, 41 (14), 4875−4881. (30) Yamakawa, A.; Peters, G. P. Using time-series to measure uncertainty in environmental input-output analysis. Econ. Sys. Res. 2009, 21 (4), 337−362. (31) Hoekstra, A. Y.; Chapagain, A. K.; Aldaya, M. M.; Mekonnen, M. M. The Water Footprint Assessment Manual: Setting the Global Standard, Earthscan: London, UK, 2011. (32) Lenzen, M. A generalized input-output multiplier calculus for Australia. Econ. Syst. Res. 2001, 13 (1), 65−92. (33) SENC. Guiá de la alimentación saludable. Sociedad Española de Nutrición Comunitaria, 2004, 2007. (34) Varela Moreiras, G.; del Pozo, S.; Á vila, J. M.; Cuadrado, C.; Ruiz, E.; Moreiras, O. Valoración de la dieta Española De Acuerdo al Panel de Consumo Alimentario; Ministerio de Medio Ambiente, y Medio Rural y Marino/Fundación Española de la Nutrición, 2008. (35) FAO. Food Balance Sheets/ Commodity Balances: Crops Primary Equivalents and Livestock and Fish Primary Equivalents. Agriculture and Food Trade: Crops and Livestock, Primary and Processed; Food and Agriculture Organization of the United Nations: Rome, Italy, 2010. (36) Pfister, S.; Koehler, A.; Hellweg, S. Assessing the environmental impacts of freshwater consumption in LCA. Environ. Sci. Technol. 2009, 43 (11), 4098−4104. (37) Suh, S.; Lenzen, M.; Treloar, G. J.; Hondo, H.; Horvath, A.; Huppes, G.; Jolliet, O.; Klann, U.; Krewitt, W.; Moriguchi, Y.; Munksgaard, J.; Norris, G. System boundary selection in life-cycle inventories using hybrid approaches. Environ. Sci. Technol. 2004, 38 (3), 657−664.

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Water Flows in the Spanish Economy: Agri-Food Sectors

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