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, Applying the Input-Output Method to Account for Water Footprint and Virtual Water Trade in the Haihe River Basin in China XU ZHAO, HONG YANG, ZHIFENG YANG,, *
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Environ. Sci. Technol. 2010, 44, Applying the Input-Output Method to Account for Water Footprint and Virtual Water Trade in the Haihe River Basin in China XU ZHAO, HONG YANG, ZHIFENG YANG,, * BIN CHEN, AND YAN QIN State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing , China, and Swiss Federal Institute of Aquatic Science and Technology (Eawag), Uberlandstrasse 133, P.O. Box 611, CH8600, Duebendorf, Switzerland Received April 4, Revised manuscript received September 19, Accepted September 28, The virtual water strategy which advocates importing water intensive products and exporting products with low water intensity is gradually accepted as one of the options for solving water crisis in severely water scarce regions. However, if we count the virtual water embodied in imported products as the water saved for a region, we might overestimate the saving by including the virtual water that is later re-exported in association with the proceeded products made from the originally imported products. This problem can be avoided by accounting for the saved water through calculating water footprint (WF) in domestic final consumptive products. In this paper, an input-output analysis (IOA) based on the water footprint accounting framework is built to account for WF and virtual water trade of final consumptive products in the water stressed Haihe River basin in China for the year 1997, 2000, and The input-output transaction tables of the three years are constructed. The results show WF of 46.57, 44.52, and billion m 3 for the three years, respectively. These volumes are higher than the water used directly in the corresponding years in the basin. A WF intensity (WFI) indicator is then used to assess if the economic activities in the basin are consistent with the virtual water strategy. The temporal change of the WFI is also decomposed by the index number analysis method. The results showed that the basin was silently importing virtual water through the trade of raw and processed food commodities under the background of the whole economic circulation. 1. Introduction The water policy relevant to the solutions to water scarcity is often regional or river basin issues (1). Two approaches that could be used for dealing with water crisis at the river basin level are to transfer real water into the basin and to import virtual water. The term virtual water was first put forward by Allan (2) and was defined as water embodied in the traded products. Over the years, both the terminology * Corresponding author phone/fax: 86 (10) ; Beijing Normal University. Swiss Federal Institute of Aquatic Science and Technology (Eawag). and the scope of virtual water have been extended beyond the original purpose (3). Currently, virtual water is commonly used to refer to the water that is required for the production of commodities (1, 4). By importing food which uses a large amount of water to produce and exporting products of low water requirement, water scarce regions and countries can alleviate their water stress from the net inflow of virtual water. In value term, this trade pattern involves importing products of low water use value (product value per unit of water use) and exporting products of high water use value (5). This trade pattern has been referred to as the virtual water strategy, which is advocated by several groups for being an alternative of high cost large water transferring projects (6, 7). Although announced by few regions or countries, large amounts of virtual water have been flowing into the water scarce regions in Middle East, North Africa and North China, which has silently reduced the demand for their local water resources (8-10). But the food is not always imported for direct consumption by local households. The products imported to a region can be for intermediate use or for final consumption. While the latter brings real water saving due to the virtual water imported to the region, the former does not necessarily save water because the final products produced with imported intermediate products in a region could be re-exported, resulting in water export. For example, if region B imports milk from region A as raw material to produce cheese, and exports all the cheese to region C, then region C actually saves all the water embodied in the consumed cheese that region B used for processing the milk into the cheese and region A used for raising the dairy cattle. In this case, region B is not a virtual water importer in terms of final consumption, but a virtual water exporter by selling cheese. The impact of people s final consumption on water resources can be indicated by water footprint (WF). Introduced by Hoekstra and Hung (11), WF is defined as the total volume of freshwater that is used to produce the goods and services consumed by the inhabitants of a certain area. Chapagain and Hoekstra (12) calculated WF of individual nations. In addition, they also made a series of WF accounting for single product with consideration of different processing stages of the commodities (e.g., refs 13 and 14). These studies generally used the so-called apparent consumption method to account for WF by adding imports to production and subtracting exports (15). For a chosen final product, for example, the ones mentioned above, this method works well. But for accounting for WF and virtual water trade for all the products in a regional/national economy, the method is cumbersome, time-consuming, and subject to many approximations. Particularly, data limitations impede the method to effectively distinguish the intermediate and the final products in the accounting procedure. To overcome this problem, Zhao et al. (16) used the input-output (IO) method to calculate WF of all the economic sectors (divided into 23 sectors, and each sector is considered as a product ) in China. It shows that the IO-based WF accounting can well calculate the virtual water import or export from the final demand of each sector without worrying about the problem of double accounting as illustrated by the milk-cheese case earlier. The method can systematically evaluate WF and virtual water trade of final demand in the context of the whole economic circulation instead of only crops or animal products as seen in most of the previous studies. In recent years, life cycle assessment (LCA) has been used for WF accounting in some studies (e.g., refs 17 and 18). These studies emphasize the environmental impact of water ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 23, /es100886r 2010 American Chemical Society Published on Web 10/14/2010 consumption through incorporating certain impact factors (such as water scarcity index) to the actual water consumption in different regions. The results of which can be considered as a weighted WF. It should be pointed out that quantification of WF and virtual water trade derived by final demand is still incapable of illustrating if the import or the export of different sectors is consistent with the virtual water strategy. For a water poor region, the value of water use matters. To assess a region s compliance with the virtual water strategy, an indicator called WF intensity (WFI) derived from the WF accounting framework of IO model is introduced in this study (16). WFI is calculated as the volume of water use in the per unit currency final product (m (3)/$) (The detail is given in Section 2.3). Accordingly, the virtual water strategy involves a trade pattern in which water scarce regions/countries import products of high WFI and export products of low WFI. This paper applies the WF accounting framework to investigate virtual water trade for the Haihe River Basin (HRB) in China. An IO framework to calculate WF at the river basin scale is established by constructing an IO table of the HRB in years 1997, 2000, and The HRB is a densely populated, highly developed and the most water scarce region in China. The region has been transferring real water from outside through various water diversion projects. The well-known South-North Water Transfer Project to be completed in 2010 (the first phase) is expected to transfer nearly 7 billion m 3 of water from the Yangtze River to the HRB. Even with this large amount of water transfer, it is projected that there will still be a shortage of about 6.7 billion m 3 of water for agriculture, industry and domestic uses and for environmental use in the basin (19). In this context, the virtual water strategy provides an additional option for the basin to cope with water scarcity in the near future. Combined with the results of the virtual water trade of final demand, the WFI of the HRB are accounted and utilized to evaluate if the trade of sectors with high water use intensity is in accord with the virtual water strategy. 2. Methodology and Data 2.1. The WF Model. An IO based WF accounting framework is employed in this paper to calculate the WF of a given product and a production sector. The IO method and WF modeling approach are described in Supporting Information. In order to account for WF as well as virtual water trade, it is necessary to determine virtual water content (VWC) of different products, which is defined as the water needed for producing one unit (usually a ton) of a single product in a region. The WF can be calculated by multiplying the VWC with the volume of the product consumed by the people of a region. Since agricultural trade in most countries/regions accounts for the largest proportion of virtual water, VWC of crops has so far the main interests of the quantification (5, 20, 21). For adapting VWC to the monetary IO framework, VWC in this study is defined as the total amount of water embodied in one monetary unit of final demand for a product (16). The formula for the IO based VWC is available in the Supporting Information. Considering the water need for final demand, this definition also changes the VWC concept from the production domain to the consumption domain; The VWC of imported products is set up to be the same as the domestic VWC. This is in accord with the demand driven vision of VWC advanced by Renault (22), who pointed out that the value of virtual water imported by a region is not the real water consumed on the production site, but the volume that the region would have consumed if it had to produce the product itself. Under this assumption, whether the imported crops are from irrigated or rainfed conditions are not relevant to the WF accounting for the final demand of the importing region. This is because the virtual water (or external WF) embedded in imported crops is measured on the basis of domestic virtual water contents. The VWC of a sector is composed of direct water input (DWI) and indirect water input (IWI). DWI represents the direct use of water by the sector itself to generate one unit of output, and IWI refers to the water used indirectly from the supply of other sectors to generate one unit of output. Distinguishing the direct and the indirect water input is a big advantage of the IO method. The WF accounting also consists of internal and external water footprints (12). The internal WF (IWF) is defined as the use of domestic water resources to produce goods and services consumed by the inhabitants of the region, which is the domestic production water use minus the water used for export. The external WF (EWF) is defined as the annual volume of water resources used in other regions to produce the goods and services consumed by the inhabitants of the concerned region, which is equal to the virtual water import into the region minus the volume of virtual water exported to other regions as a result of re-export of imported products Net Virtual Water Import of Final Demand (NV- WIFD). The IO framework of WF accounting can calculate the net virtual water import of final demand (NVWIFD) of all sectors in a regional/national economy. The virtual water import (VWI) can be derived directly from the results of EWF calculated above (16). The virtual water export (VWE) to fulfill the oversea consumers final demand can be written as VWE ) [u j ]u j ) δ j e j (1) where u j is the virtual water export for the final demand of oversea sector j, δ j is the VWC in sector j, e j is the exports from sector j. Thus NVWIFD can be acquired as NVWIFD ) EWF - VWE (2) 2.3. The WF Intensity. If a product sold to consumers uses more water in terms of per unit currency product than other products, the water productivity of the production of this product is lower. The relative volumes of water use in the per unit currency final products between sectors can be expressed as the WF intensity (WFI) (16). In analogy with the energy intensity (energy/gdp), WFI in sector j can be written as WFI j ) WF j f j (3) where WFI j is the WFI in sector j, WF j is the WF in sector j, and f j is the domestic final demand in sector j. High WFI j indicates a low water productivity in the final consumption of sector j. In order to show the relative WFI j of individual sectors, we standardize it by dividing it with the aggregate WFI which is the ratio of total WF ( WF j )tothe total domestic final demand ( f j ). The aggregate WFI can show the average situation of all the WFI j in a year. Then the newly created indicator can be called index of WFI (IWFI) and written in sectoral form: IWFI j ) WF WF j j / f j f j In formula 4, if the WFI in sector j is higher than the average situation, IWFI j 1, then we can say that sector j has high WFI; when IWFI j 1 sector j has low WFI, and IWFI j ) 1 means that water use in sector j is the same as the average situation. (4) VOL. 44, NO. 23, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY FIGURE 1. VWC at the sector level of the HRB in years 1997, 2000, and But the above method has the problem when it is applied to time series analysis, because the aggregate WFI differs from each year. Here we use the index number analysis to show the change in WFI with time sequence. For showing the change of aggregate WFI, an aggregate changing index can be written as D T ) ( WF j / f j) T ( WF j / f j) 0 (5) Where ( WF j / f j ) 0 and ( WF j / f j ) T are the aggregate WFI in year 0 and year T, as the base year s D T, D 0 equals 1. If the aggregate WFI has increased from year 0 to year T, D T will be greater than 1, whereas D T will be less than 1 for the decrease of WFI from year 0 to year T. Then the IWFI can be adjusted with time sequence by multiplying the corresponding years D T, which can be shown as IWFI T ) IWFI D T (6) where IWFI T is the aggregated IWFI adjusted with time sequence. Moreover, index number analysis considers that the change of aggregate intensity is decided by structural effects (D str T ), that is, changes in the relative sizes of different economic sectors, and intensity effects (D int T ), that is, the changes caused by altered intensities within individual sectors (23). In this study we use the Divisia index method (24) to decompose D T into D str T and D int T. The meaning of the results derived by such method can be shown as D int T greater than 1 means the sectoral WF intensities have increased, whereas D int T less than 1 means more efficient sectoral WF intensities. D str T greater than 1 indicates that economy shifts toward more WF intensive sectors, whereas D str T less than 1 indicates a shift away from WF intensive sectors Implementation of the IO-Based WF Model in the Haihe River Basin. A regional IO transaction table is the first need for the use of IO method to calculate WF on a river basin scale. But an IO table of the catchment area is always unavailable because official statistics are only provided based on administrative unit. In most cases survey-based approach would be expensive and time-consuming. Typically, therefore one has to resort to an adaptation of national input-output coefficients. In this paper, the transaction table is derived using the GRIT framework, which was developed by Jensen et al. (25). This method consists of a series of mechanical steps to modify the national IO coefficients to regional equivalents, while providing opportunities for the insertion of superior data Data. In this study, water use is the water input for production in the 14 sectors. Water use here only includes blue water. It does not include green and gray water. The industrial water use is counted in terms of water intake in the production processes, excluding internal water reuse. The agricultural water use refers to the gross quantity of irrigation water distributed to agricultural users, including water losses in conveyance. Here we assume that the water loss is largely evaporated and cannot be reused. The reason for using the water use instead of water consumption in this study is because in the Chinese statistics, only water use data in different sectors are available. It is worth noting that the term water use in this study is different from the consumptive water use commonly used in the literature of virtual water and water footprint accounting. Concerning agricultural production, the latter refers to evapotranspiration (ET) in crop production. It does not include water losses during the conveyance and in the field. In the IO table, the values of individual sectors are measured in producers prices. All the product values are in current prices. The detail of the structure of the input-output table and the sources of data are provided in Supporting Information. 3. Results 3.1. Virtual Water Content. VWC at the sector level of the HRB in years 1997, 2000, and 2002 is provided in Figure 1, and the corresponding sector names in the figure can be found in Table 1. Figure 1 shows that agriculture sector and electricity, gas, and water production and supply sector are the top two sectors with DWI. These two sectors are wellknown water intensive sectors mainly because they use a lot of water directly. The IWI of these sectors is relatively small and has often been ignored in water resources management (2) ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 23, 2010 TABLE 1. WF of the HRB in 1997, 2000, 2002 Billion m agriculture internal agricultural WF external agricultural WF mining food and tobacco processing textile, leather, and other fiber products lumbering and paper products petroleum processing and coking chemicals nonmetal mineral products metal products machinery and equipment electricity, gas, and water production and supply construction wholesale and retail trade and passenger transport other services WF IWF EWF water use for production The results of IWI show that indirect water use in the HRB accounts for a large proportion for the products of final demand. The sum of IWI is about 60% of the sum of DWI in three years average. IWI of food and tobacco processing and construction is high. VWC of food and tobacco processing ranks second among all the sectors and its IWI is about 10 times of its DWI. The technical coefficient matrix A shows that the input of food and tobacco processing is mainly from agriculture, which is the reason for their high IWI. VWC of the HRB declines from 1997 to 2002 in almost every sector, but the relative ratio of VWC in each sector changes little. The largest decrease of DWI happened in agriculture (sector 1) from 1997 to 2000, which dropped about 56 m 3 /thousand CNY (about 38% in percentage). This may be related to the increase in crop water productivity and decrease in irrigation water losses. Some climate-related factors, such as hydrological wet year, may also contribute to a decrease in irrigation water input. The sector of electricity, gas, and water production and supply (sector 11), the second largest sector of DWI, also drops by 13 m 3 /thousand CNY from 1997 to 2000 (about 37% in percentage). The decline was caused by decrease in water use and increase in final demand from 1997 to 2000 in the sectors of agriculture and electricity, gas, and water production and supply
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