Ecological Network Analysis on Global Virtual Water Trade

Article pubs.acs.org/est

Ecological Network Analysis on Global Virtual Water Trade Zhifeng Yang,* Xufeng Mao, Xu Zhao, and Bin Chen State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China S Supporting Information *

ABSTRACT: Global water interdependencies are likely to increase with growing virtual water trade. To address the issues of the indirect effects of water trade through the global economic circulation, we use ecological network analysis (ENA) to shed insight into the complicated system interactions. A global model of virtual water flow among agriculture and livestock production trade in 1995−1999 is also built as the basis for network analysis. Control analysis is used to identify the quantitative control or dependency relations. The utility analysis provides more indicators for describing the mutual relationship between two regions/countries by imitating the interactions in the ecosystem and distinguishes the beneficiary and the contributor of virtual water trade system. Results show control and utility relations can well depict the mutual relation in trade system, and direct observable relations differ from integral ones with indirect interactions considered. This paper offers a new way to depict the interrelations between trade components and can serve as a meaningful start as we continue to use ENA in providing more valuable implications for freshwater study on a global scale.

1. INTRODUCTION There has been an increasing water demand in the world due to the rapid economic growth, population explosion, and need for better quality of life. Globally, water for food accounts for a large share of the total water use. Therefore, water scarce regions are suggested to alleviate their water crisis by importing food, which corresponds to importing water. The water embodied in food crops that are traded internationally is called virtual water.1 Although there are several factors that drive the virtual water trade among nations, and most of the times political and economical facts are the crucial ones determining the virtual water fluxes, there has been a dramatic increase in the virtual water literature with an attempt to quantify its potential to alleviate regional water scarcity and save water globally.2−7 The results of these studies showed that water is globally saved due to the virtual water trade of food products. However, not all the countries are beneficiaries of the virtual water trade. The complexity of trade leads to the paradox that some countries are extremely scarce of water and export their food products.6 In terms of virtual water trade, these countries are contributors as opposed to the beneficiaries. One of the explanations is that virtual water flows are independent of water resource endowments.8,9 The driving forces behind international trade of food products can be any other production factors such as labor, land, and capital.10 So if the virtual water contributors will also benefit from the virtual water trade, the first step is to show the mutual relationships with regard to the virtual water trade among countries in the world. Then norms, principles, rules, and decision-making systems can be designed and take effect for the fair distribution of global water resources.11 Making clear the mutual relationships between © 2012 American Chemical Society

trade participators is not an easy task because increasing international virtual water trade has organized the world into a huge web, in which nations that trade large volumes of water are more likely to link to and cluster with other nations.12,13 In such a web-like trade system, trade components impact each other directly and indirectly. For example, consumption is a driving force behind the trade and resource depletion. The increase of one country’s consumption of a product may lead to the corresponding rise of domestic water withdrawal for supporting the production of the product and the virtual water import. In addition, consumption brings about a chain of activity including direct and indirect trade flows. The increase of the virtual water import derived by the domestic consumption will lead to the growth of other countries’ virtual water export, and thus make them increase the domestic water withdrawal and virtual water import accordingly. The indirect effect happens when the newly increased virtual water export further leads to a new round of water withdrawal and virtual water flows. The indirect virtual water flow will last until the water withdrawal of related countries fulfill all the direct and indirect consumption needs of these countries. Indirect effect is important because it implies that conclusions based on direct observations may give a false impression of the relation between objects, which might mislead management decisions.14 However, the existing methods in most virtual water trade studies fail to assess the Received: Revised: Accepted: Published: 1796

October 23, 2011 December 19, 2011 January 3, 2012 January 3, 2012 dx.doi.org/10.1021/es203657t | Environ. Sci. Technol. 2012, 46, 1796−1803

Environmental Science & Technology

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indirect effect due to the water resources or economic circuits. Input−output analysis is an acknowledged method to analyze the economic direct and indirect interdependency on sector resources driven by consumption, which has been applied in studies of the virtual water trade and water footprint.15−17 For example, an input−output analysis based on the water footprint accounting framework was built to account for water footprint and virtual water trade of final consumptive products, which avoided overestimating the saving by including the re-exported virtual water.18 It is regretful that the above studies are all at the national or subnational level within different domestic sectors. A computable general equilibrium model was used to analyze the changes of the global virtual water trade due to reduced local water.19 More recently, the global virtual water trade was analyzed and quantified using complex network theory.12,13 That work significantly improved the understanding of the interactions between water resource and international trade on food production, and evoked a fundamental question: What are the interdependencies among multiple regions through food production trade? This question is extremely important because decision-makers should evaluate first the international water dependence before making decisions. Derived from the economic input−output analysis, ecological network analysis (ENA) is further developed to holistically assess the complex interactions within an ecosystem.20 ENA starts with the assumption that a system can be represented as a network of nodes (compartments, components, etc.) and connections among them (arcs, links, flows, etc.), and delves into system interactions for a given ecosystem structure (connection pattern), function (flow regime), boundary input and output. It is not only successfully served in specific ecological systems,21−23 but also extended into hydrological systems24−26 and water metabolic system.27 The successful application has proved that ENA is a potential approach for the study of complex global virtual water trade system, with the above important question needing to be further investigated. To give a holistic assessment of the indirect effects of the global virtual water trade driven by consumption, and comprehensively depict the mutual interrelationships in terms of virtual water, ENA is introduced in the current study. The rest of the paper is organized as follows: Section 2 presents the model and methodology employed to measure the ecological relations of virtual water trade system; Section 3 reports and interprets the results of network analysis and compares them with empirical-based results; and Section 4 discusses the insights evoked from an ecological network perspective.

Figure 1. Network flow model for three regions in the trade network (z boundary input, y boundary output, f interflows).

products implies international flows of virtual water) from component j to component i. zk and yk are boundary input and boundary output of component k, which represent water withdrawal (m3 yr−1) and water footprint (m3 yr−1) (the sum of total domestic water withdrawal and the net virtual water import of a country/region) of the kth compartment, respectively. According to the mass balance theory, the total amounts of virtual water flowing into a component should be equal to the virtual water flowing out of the component (see eq 1) and water footprint yi can be calculated by eq 2. n

Ti(in) =

zi

∑ fij

+

water withdrawal

j=1 total import

= Ti(out) n

yi

=

+

water consumption

⎛ n yi = ⎜ ∑ fij − ⎜ ⎝ j=1

n

∑ f ji j=1 total export

⎞

∑ f ji ⎟⎟ + Zi j=1

(1)

⎠

(i = 1...n) (2)

Data on water withdrawal and global virtual water flows related to 38 primary agriculture trade products and 8 livestock and livestock trade products in the period 1995−1999 were used to quantify the current model.2,28 The matrices of interflows (F = f13×13), boundary inputs (Z = z13×1), and boundary outputs(Y = y1×13) of the model are presented in Supporting Information (Table S1). A MATLAB function was used to facilitate the network calculations.29 2.2. Ecological Network Analysis. 2.2.1. Network Control Analysis. Network control analysis is a mathematical method to quantify the static flow dependencies among different biological species (components) in an ecosystem.30 The interdependence between components can be achieved by looking at the contribution (e.g., material or energy) of each component to the other component’s input and output, respectively.31,32 The measure, expressed in a matrix CN = (cnij), is based on the ratio of integral flow (nij/nji′) from compartment j to i (nij) to the integral flow from i to j (n′ji). The integral flow is the summation of the boundary, direct, and indirect flows in a system. The calculation for integral flow is provided in the Supporting Information. The control relationship is further modified so that when nij/nji′ < 1, cnij = 1 − nij/

2. METHODS AND DATA 2.1. Construction of Trade Network Model. We developed a steady state model to illustrate how the indirect flow and mutual relationships could be depicted by ENA. The world was classified into thirteen regions, and each region was represented by a numbered component from 1 to 13: 1 Central Africa, 2 Central America, 3 East and South Asia, 4 Western Europe, 5 Union of Socialist Soviet Republics (USSR), 6 Middle East, 7 North Africa, 8 North America, 9 Oceania, 10 South America, 11 Southeast Asia, 12 Southern Africa, and 13 Eastern Europe. All virtual water trade flows among regions can be represented by directional lines that connect components (nodes) in the network, resulting in a global virtual water trade network model (Figure 1). In the above network model, f ij represents the virtual water trade flow (m3 yr−1) (international trade in food and other 1797

dx.doi.org/10.1021/es203657t | Environ. Sci. Technol. 2012, 46, 1796−1803


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nji′; otherwise cnij = 0.32 Compartment j is said to dominate i if its output effect on i is larger than input effect on j (cnij = nij/nji′ > 1). Contrarily, if the output of j has less influence on i than i receives as input from j, then j is said to be dependent on or controlled by i. However, pairwise control analysis seems inadequate to understand a component’s total influence on the entire system. Inspired by Patten’s31 and Patten and Auble’s32 original environ control measure, we proposed an index named integral control intensity (ICI) to explore how much a component influences the whole trade system through virtual water import or export. The input-oriented ICIi in and output-oriented ICIiout are depicted in eqs 3 and 4, respectively. n

n

ICIiin =

n

∑ (N − I )ij ∑ ∑ (N − I )ij i=1 j=1

j=1 n

ICIout j

relationships, and Dm (m ≥ 2) reflects the indirect interactions between compartments. The integral matrix U indicates the intensity and pattern of integrated utilities between two compartments in the network. Based on the pairing signs of the elements in matrix (D) and matrix (U), the pattern of interaction between components in a network can be determined.35 For example, (sd21, sd12) = (+, −) indicates the conceptual relationship that compartment 2 exploits compartment 1, whereas if (sd21, sd12) = (−, +), then compartment 2 is exploited by compartment 1. (sd21, sd12) = (0, 0) represents neutralism, indicating the absence of transactions or the equality of opposing flows. (su21, su12) = (−, −) represents competition, which indicates compartment 1 competes with compartment 2 (leading to negative impacts for both compartments), whereas if (su21, su12) = (+, +), then the relationship between the two compartments is a mutualism (both compartments benefit from their interaction). Besides, a mutualism index (M) can be established to reflect the proportions of positive and negative signs in the sign matrices, which is expressed as M = J(·) = S+(·)/S−(·). If we take the integral utility matrix (U) as an example, the mutualism index of system can be expressed as M = S+(U)/ S−(U). Here S+(U) = ∑ ijmax (sign (uij), 0) is the number of all positive signs in matrix U and S−(U) = ∑ ij(− min (sign (uij), 0)) is the number of all negative signs in matrix U.38,39 If the matrices have more positive signs than negative signs, the system exhibits mostly positive relationships between compartments and thus represents network mutualism.14 Conversely, the system exhibits mostly negative relationships between compartments and many problematic relationships must be solved or mitigated.27 Through the mutualism index, the global properties of virtual water trade can be explored.

=

n

(3)

n

∑ (N ′ − I )ij ∑ ∑ (N ′ − I )ij i=1

j=1 i=1

(4)

where ∑j =n 1 (N − I)ij (i, j = 1...n) is the integration of ith row elements in matrix (N−I) and ∑i =n 1 (N′ − I)ij (i, j = 1...n) is the integration of jth column elements in matrix (N′−I). (N−I) and (N′−I) are, respectively, the input-oriented and outputoriented integral flow matrices that combine both direct and indirect flows. Index ICIiin reflects how much an individual component influences the overall system through importing virtual water and index ICIiout reflects how much an individual component influences the overall system through exporting virtual water (both direct and indirect flow are considered). To show the influence from indirect flow, we compared the ICI with direct control intensity (DCI) (only the direct flow is considered). The input-oriented DCIiin and output-oriented DCIiout are depicted in eqs 5 and 6, respectively. n

DCIiin =

n

∑ Fij ∑ ∑ Fij j=1 n

DCIout j

n

3. RESULTS 3.1. Network Control Relationships. The pairwise control relationship is shown in matrix CN in Figure 2. Each

=

i=1 j=1 n

(5)

n

∑ Fij ∑ ∑ Fij i=1

∑j =n 1 Fij

j=1 i=1

(6)

∑i =n 1 Fij

where (i, j = 1...n) and (i, j = 1...n) are the integration of ith row elements and jth column elements in interflow matrix F (Table S1), respectively. 2.2.2. Network Utility Analysis. Network utility analysis is an ecological network approach that was first introduced by Patten33,34 to express the relative benefit to cost relationships in networks. In ecosystems, a gain of resources provides positive utility and a loss of resources provides negative utility.35 Through the analysis of gains and losses of utility between two components, the method identifies both direct and indirect qualitative and quantitative ecological relationships in a network, therefore revealing the integration and complexity of ecosystem behaviors.14,36,37 In utility analysis, intercompartmental flow utilities are given by net interflow from compartment j to compartment i, and can be expressed as dij= ( f ij − f ji)/Ti. From the direct utility matrix D = (dij), a dimensionless integral utility intensity matrix U = (uij) could be computed: U = D0 + D1 + D2 + D3 + ··· +Dm = (I − D)−1. Matrix D0 reflects the initial input flows through each compartment, the matrix D1 represents the direct utility

Figure 2. Pair-wise control relationship between virtual water trade components.

region exhibits some degree of control (influence) on other regions through virtual water export. The elements in control matrix range from 0.01 to 0.99, indicating some regions dominate the control. For example, cn81 = 0.98 means the dependence of Central Africa (component 1) on North America (component 8) was 98%. Central Africa receives a large amount of crop production (equivalent to an average of 578 × 106 m3 yr−1 virtual water) each year from North America, which accounts for 0.2% of the total exports of North America. Contrarily, Central Africa’s agricultural food exports to North America are very low (equivalent to an average of 10 × 106 m3 1798



1799

18.7 22.3 7.7 11.4 (12) (8) (12) (12) (5) (6) (4) (6) 7.5 6.3 10.1 8.0 (8) (11) (2) (2) 3.4 2.3 12.1 11.7 (11) (12) (3) (5) 1.6 1.1 10.4 8.1 5.2 8.2 38.4 36.1 (3) (5) (11) (11) (4) (4) (10) (10) (9) (9) (9) (7)

Numbers in the brackets are the components’ rank. a

DCI (%) ICIiin (%) DCIout (%) ICIiout (%)

0.5 (13) 0.5 (13) 0.1 (13) 0.19 (13)

5.9 3.4 6.1 2.8

(6) (7) (6) (8)

34.4 32.5 5.8 11.5

(1) (1) (7) (3)

2.8 2.5 3.0 2.4

(10) (10) (8) (9)

2.9 3.0 2.9 3.1

5 4 3

in

2 1 component

Table 1. Direct CI and Integral CI in Input and Output Directionsa

7.6 7.0 1.7 2.0

6

8.1 6.5 1.1 1.1

7

8

(7) (3) (1) (1)

9

10

11

1.4 3.1 0.6 1.0

12

13

yr−1 virtual water). Therein, Central Africa can be said to be mostly influenced (controlled) by North America during their trade interactions. Africa remains the world’s poorest and most underdeveloped region and the lack of funding has hindered their agricultural development. They have to import a large amount of crop production to maintain domestic food supply. The lack of self-sufficiency will make this region very vulnerable. If for whatever reason food supplies from North America ceasebe it due to war, political confrontation, or a natural disaster in export regionsthis region will suffer severely. System-level ICI helps to reflect how much a region influences the global trade network by exporting or importing virtual water (Table 1). Unlike DCI, ICI reflects the control intensity in a more holistic way that combines both direct and indirect impacts. As we can see from Table 1, ICI differs from DCI to varying degrees in both directionsimport and export. For example, the export DCI of East and South Asia increased from DCI3out = 5.8% to ICI3out = 11.5%. These changes indicate that this region’s export influence is actually far stronger than what we have expected intuitively, which can be attributed to the impact from the indirect interactions that are important but easy to be ignored. We can explain the indirect interactions via a simple flow from A→B→C. The flows from A→B and B→C are direct, whereas the flow from A to C is indirect and mediated by B. Their inert influence will be apparent only when A, B, and C are analyzed as one single system. Thus, there is a systematic change when the indirect flows are taken into account. Considering its upward position (third) in export control, more emphasis should be placed on the region during the analysis of international virtual water interdependencies. 3.2. Network Utility Relationships. The direct utility intensity matrix (D) and the integral utility intensity matrix (U) enable us to distinguish if two regions are competitors or cooperators and the type of their ecological relationship (e.g., exploitation, neutralism), which are presented in Figure 3a and b. The sign (−) in matrix (U) means the element value is smaller than −0.0001. The principle diagonal elements of matrix U are always positive because it is assumed that all compartments have a positive effect on themselves.12 Direct ecological relationships can be elicited from direct utility intensity matrix (D). There are two types of ecological relationships including exploitation and neutralism. Exploitation relationship accounts for 97% of the total relationships. An example of exploitation can be given as (D32, D23) = (0.0137, −0.1683), which indicates East and South Asia got a net positive utility of 0.0108 from Central America while Central America got a net negative utility of −0.1683 from East and South Asia. Obviously, their mutual virtual water trade produced more impacts on Central America than on East and South Asia. Central America and East and South Asia are, respectively, the contributors and the beneficiaries during the trade interactions. The other type of ecological relationship, neutralism, appears between Central Africa and USSR (sd51, sd15) = (0, 0) and North Africa and Southern Africa (sd12,7, sd7,12) = (0, 0). It is not because there is no transaction between two regions but because their opposing transactions are coincidentally identical. The integral utility intensity matrix (U) captures both direct and indirect utility and thus provides a more holistic picture of ecological relationships. Two types of ecological relationships including exploitation and competition can be drawn from matrix (U), which account for 82% and 18% of all ecological

(2) (2) (5) (4)

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Figure 3. Direct utility intensity matrix (D) and integral utility intensity matrix (U).

mutualism index will always equal 1.0 in direct utility matrix) to M = J (U) = 75/94 = 0.79, indicating the current trade network would likely exhibit a relationship of competition between compartments. In a competitive environment, we should be cautioned about the potential risk from global virtual water trade at the same time of enjoying the benefits of it. Different water resources strategies should be taken by different regions with respect to the current analysis results.

relationships, respectively. For example, the integral ecological relationship between Central America and East and South Asia is (U32, U23) = (0.0137, −0.1937). Central America contributes about 0.193 positive utility to East and South Asia through virtual water exports, which accounts for about 22% of the total positive utility that East and South Asia has acquired. As a large contributor, Central America should be cautioned about the potential negative impacts produced by a large amount of virtual water export like overexploitation of water resources. A total of 14 pairs of competition relationships have emerged in integral utility matrix. An example of competition can be given as the ecological relationship (su12,7, su7,12) = (−, −) between North Africa and Southern Africa, indicating they are competitors in global virtual water trade market. If we compared the sgn (D) and sgn (U), the variations between pairwise direct and integral ecological relationships can be detected. Two kinds of changes emerged in the current study. The first one is the change from neutralism (0, 0) to competition (−, −). For example, the aforementioned ecological relationship between North Africa and Southern Africa changed from neutralism (0, 0) to competition (−, −), indicating North Africa and Southern Africa are potential competitors for freshwater although there are equivalent transactions between these two regions. The second one is the change from exploitation ((+, −) or (−, +)) to competition (−, −). For example, the ecological relationship between Southeast Asia and Central America changed from exploitation ((sd11,2, sd2,11) = (−, +)) to competition ((su11,2, su2,11) = (−, −)), indicating that even if the virtual water exporting regions/ countries may be potential competitors for freshwater, while it is unobservable from direct empirical data. The above results indicate network competition exists in the current network, which can be further verified by the mutualism index (M). The index dropped from M = J (D) = 76/76 = 1(remember that the

4. DISCUSSION Within the global water system, a number of biophysical, socioeconomic, and institutional teleconnections exist, which exposes water resources to exogenous pressures originating in other regions and/or sectors.40 These teleconnections link causes and effects on water resources over long distances and may result in severe disturbance or even breakdown of waterdependent social−ecological systems. Virtual water trade is such a key teleconnection in the global water system. Virtual water is useful as it globalizes perspectives on water scarcity, ecological sustainability, food production, and consumption.41 Considering the complexity of the global trade network, however, this kind of perspective should be built upon clear understandings of dependencies and relationships among trade regions. The current study highlights the importance of network connections between regions in the global water system driven by virtual water trade, and it outlines an integrated and adaptive network method for future studies in water interdependent relationships. Results indicate that there are close interdependences between different regions and a small group of regions play a key role in the control of global water resources. Exploitation and competition appear mostly in the ecological relationships between different regions. The application of 1800



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relationship between Southeast Asia and Central America ((su11,2, su2,11) = (−, −)). Recurrent water shortages, recent emergence of food crisis, and food exports limitation have partly demonstrated the correctness of above results. Different regions/countries should take well-targeted strategies with respect to their own water resources conditions as well as social and economic situations. It is also important to recognize that there are some limitations with this paper. First, international virtual water trade itself is not just the result of relative water abundance or shortages.50 The driving force behind international trade in water-intensive products can be water scarcity in the importing countries, but more often other factors, such as political, economic, and social factors, play a decisive role.47,51 It is important but difficult to include these factors into the network analysis to get a more comprehensive assessment for the global virtual water trade. Fortunately, two recent papers have analyzed and quantified the global virtual water trade from complex network theory, one of which extended the network analysis by analyzing two important factors (GDP and precipitation) driving virtual water.12,13 Results indicate nations that trade large volumes of water are more likely to link to and cluster with other nations in a global hierarchy.12 A small group of nations play a key role in the connectivity of the network and in the global redistribution of virtual water, and there exist preferential virtual water routes that are mainly driven by geographical, political, and economical factors.13 Inspired by their meaningful work, we propose to incorporate some other important factors (e.g., GDP and precipitation) into the network analysis so as to provide a more comprehensive and accurate assessment of global virtual water trade. For example, we may divide the CI by the normalized GDP to find the interrelationship between CI and GDP. Second, several computations with ENA require an assumption of steady state condition for ecosystems. One must construct the network stocks and fluxes in such a way that inputs and outputs for each component and the network overall are balanced. Unlike the living systems, virtual water trade network is a rapidly changing system. Although the steady state trade network can be achieved through mass balance between total input and output water, it is different from the self-sustaining and self-regenerating steady state of natural ecosystems. In the former, the interdependency is a static snapshot of a certain period and the dynamic characteristics of network cannot be reflected. One option would be to develop a longitudinal study to solve this limitation. The third limitation is that the data are imperfect. Water used by agricultural products and livestock and livestock products is uncertain, variable, and estimated with a rough methodology.52 Most recently updated data indicate that the largest share (76%) of the virtual water flows between countries is related to international trade in crops and derived crop products in the period 1996−2005.53 In the agricultural sector, about 19% of the global water footprint was not for domestic consumption but for export. Considering this substantial percentage and the upward trend (about 13% of the water used for crop production in the current study was not used for domestic consumption but for export), the interdependence and interplay between trade regions will be more intense. The potential risks we have mentioned above should be paid more attention in the future. A network-based analysis to the global virtual trade seems to be more important and necessary in the study of international virtual water trade.

virtual water trade as a policy tool may also bring potential risks to both high import and export control regions. As we have stated above, the water “saved” from importing food may actually have negative effects on the region in utilizing their own water resources and in improving the food security. For example, Central Africa’s average dependence on other regions is as high as 72.63%. The high water import dependence will inevitably hinder its own agricultural development. Some research has raised the awareness of the negative impacts of the cheap and often subsidized food from the major exporting countries on the local food prices and food production in the importing countries, especially in poor countries or regions.6 The lack of self-sufficiency will make some regions very vulnerable to potential risks, such as war, natural disasters, and so on.42 As for a region with large export control, the largest risk is that the indirect effects of outsider consumption are externalized to this region.43 Results of both control and utility analysis indicate the North America is one of the largest contributors with strong export control in the virtual water trade network. Virtual water consumption in other regions may indirectly affect the hydrological and environmental system in this region even apart from long geographical distance.44−47 For example, many rivers and aquifers have been overexploited causing serious regional water resources depletion and environmental degradation in the central and western United States.48 In addition, the high water productivity in some major exporting regions is partly due to the high inputs of chemical fertilizers and pesticides, whose environmental impacts may be high.6 However, water management and governance have not yet adapted to these cross-scale interdependencies on a global scale with a network perspective. Most countries have no explicit strategies for virtual water trade.45 The current results may provide some useful information for future national and regional water policies. An important but often neglected factor is the indirect effect. The underlying strength of ENA is the incorporation of direct and indirect effects which construct the whole regime of the interacted network. The system wholeness is arguably more critical in determining the system’s behavior than the direct effects alone.49 There are some interesting findings when both direct and indirect flows are considered by the ENA method. For example, the export DCI of East and South Asia increased from DCI3out = 5.8% to ICI3out = 11.5%, resulting in a sharp rise from the seventh position to the third position in its export control. These changes suggest this region’s export influence is actually far stronger than we have expected, which can be attributed to the influence from the indirect interactions that are important but easy to be ignored. We can explain the indirect interactions via a simple flow from A→B→C. The flows from A→B and B→C are direct, whereas the flow from A to C is indirect and mediated by B. Their inert-influence will be apparent only when A, B, and C are analyzed as one single system. Thus, there is a systematic change when the indirect flows are taken into account. Considering its upward position (third) in export control, more emphasis should be placed on the region during the analysis of international virtual water interdependencies. Besides, aforementioned potential ecological and environmental risks should not be neglected in this region. Another interesting finding is that global food trade market was staying in a competitive environment, which can be detected by the decreased mutualism index M. In a competitive environment, even the virtual water exporting regions may be potential competitors for water resources, such as the ecological 1801



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These limitations should be solved to provide a more precise network analysis on the real trade system. The primary goal of the paper is to provide new insight into interdependence between system components related to global virtual water trade. We hope this paper may help decisionmakers to better evaluate the international water dependence and call for a greater emphasis on global-level virtual water study toward more practical applications. The paper should be seen as an initial step.

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ASSOCIATED CONTENT

S Supporting Information *

More information regarding the ENA approach and virtual water calculation. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +86-10-58807596; fax: 86-10-58800397; e-mail: zfyang@ bnu.edu.cn.

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ACKNOWLEDGMENTS This work was supported by the Key Program of National Natural Science Foundation (Grant 50939001) and National Basic Research Program of China (973 Program, Grant 2006CB403303).

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REFERENCES

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Ecological Network Analysis on Global Virtual Water Trade

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