A Water-Withdrawal InputâOutput Model of the Indian Economy

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Article

A Water-Withdrawal Input-Output Model of the Indian Economy SHELLY BOGRA, Bhavik R Bakshi, and Ritu Mathur Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b03492 • Publication Date (Web): 06 Jan 2016 Downloaded from http://pubs.acs.org on January 13, 2016

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Environmental Science & Technology

A Water-Withdrawal Input-Output Model of the Indian Economy Shelly Bogra1 , Bhavik R. Bakshi2 and Ritu Mathur3 1 Department of Energy and Environment,TERI University, New Delhi, India 2 William G. Lowrie Department of Chemical and Biomolecular Engineering The Ohio State University, Columbus, OH 43210, USA 3 TERI, New Delhi-110003, India

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Abstract

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Managing freshwater allocation for a highly populated and growing economy like India can benefit from knowledge about the effect of economic activities. This study transforms the 2003-04 economic Input-Output(IO) table of India into a water withdrawal input-output model to quantify direct and indirect flows. This unique model is based on a comprehensive database compiled from diverse public sources, and estimates direct and indirect water withdrawal of all economic sectors. It distinguishes between green (rainfall), blue (surface and ground), and scarce groundwater. Results indicate that the total direct water withdrawal is nearly 3052 Billion Cubic Meter (BCM) and 96% of this is used in agriculture sectors with the contribution of direct green water being about 1145 BCM, excluding forestry. Apart from 727 BCM direct blue water withdrawal for agricultural, other significant users include ‘Electricity’ with 64 BCM, ‘Water supply’ with 44 BCM and other industrial sectors with nearly 14 BCM. ‘Construction’, ‘Miscellaneous food products’, ‘Hotels and restaurants’, ‘Paper, paper products and newsprint’ are other significant indirect withdrawers. The net virtual water import is found to be insignificant compared to direct water used in agriculture nationally, while scarce ground water contribution associated with crops is a large contribution in northern states.

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Introduction

With per capita water availability of 1000-1700 m3 per year, India is categorized among water stressed regions [1]. India’s annual nonrenewable groundwater withdrawal of 68 Billion Cubic Meters (BCM) is largest in the world [2]. Inadequate water infrastructure [3], possible biases in water-allocation policies [4, 5], water use inefficiencies and aggravating water scarcities [6], are causing socio-economic changes [7, 8] and regional water allocation disputes [9]. With climate change, precipitation is predicted to increase while the number of rainy days are predicted to decrease, thus increasing the likelihood of extreme events [10, 11]. These factors, combined with a population of 1.21 billion [12] and increasing consumerism [13] make it imperative for India to have deep understanding of its water requirements, and use this insight to inform government policies, corporate decisions and consumer behavior. Agriculture is the largest direct withdrawer of water in India at 90%, followed by domestic withdrawal of 7%, and 2% withdrawal by industry [14, 15, 16]. Other studies have emphasized sustainable use of ground water focusing on agriculture and food security in a dynamic environment of climate change [17], virtual water trade [18] and water footprint of agricultural food crops [19]. To meet requirements of the Global Reporting Initiative (GRI), many businesses are reporting their water use through corporate sustainability reports (CSR). But, lack of environmental reporting standards makes such reports of limited use for assessing the environmental impact of water withdrawal and release [20, 21]. Though such information can help in determining the major users of water, it does not link the impact of economic activities with water use. Previous studies report that existing pattern of inter-state virtual water trade is exacerbating scarcities in water scarce states and virtual water flows are influenced by factors different than water endowments of states [22]. Since withdrawal of water is triggered by demand for various economic goods and services, understanding the demand side or the extent to which specific economic goods and services cause water withdrawal can help in making consumer and corporate decisions, and government policies that are waterefficient.

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A large number of studies have resulted in models for determining the direct and indirect role of water in satisfying economic activities. These studies introduced concepts of virtual water, life cycle water withdrawal (LCWW) and footprint. Virtual Water (VW) is the volume of water required directly to produce a commodity or the water that is virtually embedded in the commodity [23]. LCWW includes direct and indirect flows, and measures VW content of a life cycle network. Water footprint (WF) [24] is the total (direct and indirect) water consumed to produce any commodity and distinguishes the sources of total water as blue, green and gray. Green water is water from rain and in the soil, while blue water includes ground and surface water. Gray water is the water needed to dilute a pollutant to an acceptable level. This study accounts for water ‘withdrawal’ and not ‘consumption’ of green and blue water. Withdrawal means the resource is used but may not be fully consumed in the operation. Additional details are in Section S.2.3. of the Supporting Information (SI). Environmentally-extended input-output (EEIO) modeling [25] has been popular for quantifying direct and indirect flow of resources and emissions due to economic activities at regional, national and global scales. Recent studies calculate ecological footprint [26], exergy consumption [27, 28], carbon dioxide emissions [29], and nitrogen flows [30] among others. EEIO modeling has been used to account water flows at regional [31, 32, 33] and national [34, 35] scales. Applications include estimating inter-sectoral water relationships [36], water consumption of industrial sectors [37], water footprint of energy systems [38], farm lands and water use [39], trade and VW flows [40, 41] and many more. Such models have been employed to quantify VW transfers to forecast water crises for states exporting huge quantities of VW [42], assessing environmental implications of urbanization and lifestyle change [43] and quantification of potential contribution of production systems towards water scarcity [44]. Though models combining IO and water data have not been developed for India, IO has been used for calculation of direct and indirect emissions from foreign trade [45], energy use in Indian households [46] and CO2 emissions [47]. Usually IO studies employ monetary information to capture inter-sectoral transfers, final demand and total throughput of sectors, however hybrid as well as physical accounting are considered more appropriate to account for flows between economic sectors and the environment. Most studies have tended to highlight the importance of prominent water users, such as, agriculture, electricity and industries, and few even aggregate sectors in broad categories like manufacturing and services. Some studies use either demand function or have inter-water sectoral coefficients [36] and others already have water data in the form of a database of each sector’s water withdrawal or consumption [48]. Hence, unlike the present study, effort for determining water flows to each sector is rarely required. This paper describes a water-withdrawal IO model for India by combining data about direct water withdrawal obtained from diverse sources using the 2003-04 economic IO table. The main reason for selecting this earlier period is that data for many quantities is readily available from diverse sources, which enables comparison of results. The 2003-04 Input-Output table for India consists of 130 sectors, and is developed under Industry-Technology assumption at factor cost, with first twenty sectors representing agriculture [49]. More details are in Section S.2.2. In this work, direct water withdrawal is quantified from data about water use per unit of product, and quantity of products produced by each sector. The latter data is collected by government and non-governmental organizations, but the former data is rarely measured for any sector at national scale. Even environmental audits mostly focus on energy, and not water. In addition, data about scarce groundwater use for agricultural sectors is also obtained for each state. The resulting inventory of water withdrawal data for Indian economic sectors is the first database of its type, making it a significant contribution along with the Water-Withdrawal Input-Output model. This model is used to gain insight into the direct and indirect, green and blue water withdrawal for all the sectors of the Indian economy and the role of scarce groundwater. Normalized results per unit of physical production and monetary value are also calculated. This model can be used for tasks such as calculating LCWW and determining the vulnerability of sectors to events such as inadequate rain, and disruption in the irrigation infrastructure. The rest of this paper is organized as follows. The next section describes the methodology along with details about the water data compiled in this work. The subsequent section uses the model to gain insight into the direct and indirect, green and blue water withdrawal in the Indian economy. This analysis also sheds light into the potential impact of water scarcities due to events such as failure of the monsoon or groundwater deterioration. Opportunities for future work are discussed in the last section.


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Approach Methodology

This work has collected original data about water withdrawal by economic sectors in India for developing a water-withdrawal input-output model. Direct water withdrawal by each sector, Vph is calculated from ∗ information about the total quantity produced by each sector, q and water required per unit produced, Vph ∗ ∗ as, Vph = Vˆph q. Here, Vˆph is a diagonal matrix of direct water intensities, and the production in each sector, q is typically in diverse units such as mass for grains, area for textiles, number of animals for animal services, and number of cars for automobile manufacturing. This physical information about direct water withdrawal by each sector is combined with the monetary IO table to calculate indirect and total water withdrawal for each sector. Using input-output algebra, the relationship between value added, V and throughput in each sector, X is written as, X = (I − GT )−1 V (1) and is known as the Ghosh supply-constraint model. The term, (I − GT )−1 is known as the Ghosh or output inverse [50]. These equations can be evaluated using either monetary or physical numbers or both. However, monetary values are most common since that is the form in which most economic input-output models are available. The water intensity of each sector may be calculated as [28], ˆ −1 (I − GT )−1 Vph R=X

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The same intensity values (Table S.6) may also be obtained by using the Leontief inverse and other variations, as shown in Table S.11 [51]. Equation 2 may be interpreted as distributing the direct water input, Vph to each sector among sectors in proportion to the monetary flow between them. Furthermore, the LCWW by each sector, Xph may be calculated as, Xph = (I − GT )−1 Vph

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(2)

(3)

Calculation of direct water for each sector is specific to production, technology and other resource use/consumption factors. The direct water input to each sector is classified as blue or green water depending on the water source. For example, for agricultural sectors like paddy and wheat, water withdrawal is determined from data about total quantity produced (q), total area under selected crop, annual rainfall, area irrigated and the average water requirements of the crop. The fraction of land area under irrigation along with total water requirement are used to estimate the contribution of blue water and the rest is taken as green water. Given the spatial distribution of crops, variability in their direct water requirement is captured by using data from multiple sources. Numbers about crop water requirements are obtained from sources such as the Food and Agriculture Organization (FAO) and from agricultural institutes at the state level. Thus, national data for production (cropped land under selected crop) with estimated median of crop water requirements for selected crop are used for calculating total direct water. The direct water requirement is combined with total quantity produced (q) to arrive at per unit requirement numbers. Though total area and fraction irrigated are not reported in the SI, sources where this information is available are mentioned. In India, many large corporations are doing environmental accounting. Thus for mining, industrial and manufacturing categories, the reported water numbers are based on CSRs of large companies or from environmental audits done by government and non-government institutions. Production data are from the Annual Survey of Industries [52]. Details are in Section S.2.5. Numerous sources of data for each sector provide different estimates, which in turn determine the range for direct operational needs. For example, in electricity generation across the nation, since different technologies and infrastructure were implemented at different scales and times, the plant loading ratios and thus direct water for operational needs differ. This provides the range of values associated with electricity generation. A similar procedure is adopted for evaluation of service sectors such as, railways and airports: the associated numbers are estimated on the basis of related technical reports of operational requirements, with details in Table S.3. Section 3 provides the results of direct and total water withdrawals for each sector normalized by quantities such as total physical production of corresponding sectors or monetary throughput to obtain different



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types of water intensity factors. The results are analyzed for individual sectors and are aggregated under broader categories such as Agriculture; Mining; Processed Foods & Textiles; Industrial Products and Manufacturing & Services. Details are in Section S.5. Double counting is avoided in this water satellite accounting by ensuring that a utility sector such as ‘Water supply’ accounts only for domestic water withdrawal estimated using population statistics and daily water requirements as mentioned in the literature. Thus, this sector does not consider the direct withdrawal of water that may then be used by other economic sectors. Though available government accounts states that ‘Water supply’ sector supplies water to industrial sectors [49], such flows of water to non-peripheral sectors are considered as direct inputs to these sectors. This avoids double counting and may enable greater accuracy. Additional details are in Section S.2.4. Water scarcity is considered for groundwater and is determined based on data about ground water use in agricultural sectors for 13 major crops in each state combined with appropriate water stress indicators and withdrawal to availability ratios. Additional details are provided in Section S.3.2.

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Sources of Data

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Obtaining data about quantity produced by each sector and direct water withdrawal per unit of production for all sectors of the Indian economy presented a formidable challenge in this work. Such data is not available from any centralized source, and not all of it is on-line. Thus, the data collected in this work along with incorporation of its variability due to spatial or temporal factors constitute unique aspects of this study and are included in Tables S.2 and S.3 for all sectors. Data sources include various agricultural and related organizations, meteorological department, ground water monitoring agencies, industrial reports, and private and government owned mining industries. For sectors using multiple technologies, information about the fraction of each technology is also obtained and used to determine the total water withdrawal. The complete list of data sources is reported in Tables S.2 and S.3, whereas Table S.13 summarizes the sources under broad categories. Very few industries report data about their water withdrawal directly and in physical units while some report in monetary units. Converting this into physical units is challenging since determining the cost of water is not easy, and water is usually highly subsidized. Further, some industries report aggregated water withdrawal for all their products. For developing this model, such data were allocated to individual products based on technical reports of production processes. It is assumed that water use is equivalent to water withdrawn by industry, as storing and using blue water also incurs cost, and is rarely done. Leakage and loss of water due to operational handling are not considered. Incorporation of physical data assists not only in estimation of actual water withdrawn per unit of quantity produced and used in various sectors of the economy but the reported quantity can also enable assessment of vulnerability associated with various indirect contributors in a supply network of a sector. Further, it assists in extending the reported monetary IO table to incorporate natural wealth as suggested in indicators like green GDP [53, 54, 55].

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Limitations

Due to lack of water data for some sectors, withdrawal per unit could not be determined as shown in detail in Table S.3. In some cases, information about water withdrawal per unit of production was available, but the total quantity produced could not be found. For certain sectors like ‘Forestry and logging’ and ‘Other crops’, the maximum and minimum values were not calculated because these numbers were already obtained from annual reported rainfall in any area. There is no range given for reported area and rainfall, therefore range for direct green water requirement is not calculated. For some sectors only a single estimate is available ∗ ∗ hence no variation is provided. Table S.5 provides water coefficients Vph (vph,i ). Due to unavailability and inconsistency in data for some commodities (especially industrial products) considered in the economy, along with the huge differences in geographical availability and use of water; the model in this work, although approximate, is the best that can be determined based on currently available public domain data about water withdrawal in the Indian economy. Also, this work does not account for gray water.


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Among non-agricultural activities, ‘Electricity’ sector withdraws nearly 64 BCM of blue water directly with a reported range of 3 to 180 m3 per MWHr generated from hydro to nuclear power generation. The median value used for estimation is 115 m3 /MWHr. This number represents thermal power generation technology since it contributes most to electricity generation. Numbers for other sectors are in Table S.3. The estimated total direct blue contribution to non-agricultural and non-mining categories after accounting for ‘Electricity’ and ‘Water Supply’ sectors is a minuscule 6 BCM only, compared to 727 BCM of direct blue water associated with agricultural (including Fishing and Poultry) sector. Details are in Section S.5.7.

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Total withdrawal

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Total withdrawal consists of direct withdrawal and indirect contribution. It represents the total embodied water of the production chain of a sector. Figure 2 depicts largest total water withdrawal distinguished in terms of direct withdrawal and indirect dependence as well as blue and green water. The lighter shades represent indirect flows and error bars report total variation that arises on account of reported range in direct water. ‘Forestry and Logging’ sector is the top total water withdrawer, however, majority of water entering this sector is direct contribution from rainfall. ‘Paddy’ sector follows next with the total water dependence of nearly 1050 BCM. The indirect blue water contribution for Paddy sector stands at 162 BCM and indirect green is 164 BCM. Indirect blue water contribution to ‘Other crops’ sector is 34 BCM and green water contributes 68 BCM indirectly. Indirect contribution of blue water to ‘Wheat’ sector is 82 BCM and green contributes 28 BCM. These four sectors are also the largest in terms of direct withdrawal. Non-crop sector of ‘Construction’ follows next with its significant indirect dependence, requiring nearly 189 BCM of green and 30 BCM of blue water. Though negligible direct water withdrawal is reported for this sector (less than 1 BCM), it may be an under-estimate, as this number does not take into account the infrastructure being constructed and considers only the direct water used in mixing cement. However, the high total withdrawal makes this sector the largest withdrawer among non-agricultural sectors. Next, ‘Miscellaneous food products’ sector withdraws 188 BCM in total with the indirect contribution being nearly 100% and ratio of green to blue being 58 to 41, again highlighting the significance of indirect dependence on green water. For ‘Paper, paper products and newsprint’, total withdrawal is 115 BCM with direct use being only 0.8 BCM and indirect green to blue ratio being 97 to 2. ‘Electricity’ sector uses nearly 56% of its total water (115 BCM) as blue water for its direct operational needs and though its direct green water use is negligible its indirect dependence on green water is nearly 71%. Thus, blue water contributes about 85% to its total water dependence. In ‘Hotels and restaurants’ sector, with the total water use of 115 BCM and direct withdrawal of about 7 BCM only, the indirect green contributes 62%. The information conveyed in figure 2 is reported in Table S.4.

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Total water withdrawal per Rupee

Water withdrawal per rupee is calculated as the ratio of the LCWW for a sector, Xph to the total monetary throughput for the sector, X, and is shown in Figure 3. Graphs showing green and blue water withdrawals per Rupee are included in the SI as figures S.3 and S.4, respectively. As illustrated in figure 3, ‘Forestry and logging’ is still the leader though this may be an overestimate as only wood products are accounted and all the rainfall that falls in forests is taken into account. Sectors such as Jowar (Sorghum); Jute; and Bajra (Pearl Millet) dominate total withdrawal per rupee, that is for per rupee of economic throughput, Jowar uses 1.645 m3 of water, Jute 1.116 m3 and 1.088 m3 goes to Bajra sector. Figure S.3 shows that prominent green water using sectors belong to agriculture, highlighting the role of green water in crop production. In India, this category of water is mainly provided by the monsoon rains, and these results highlight its importance. Figure S.4 shows ‘Water supply’ sector as the largest withdrawer of blue water. This is mainly for domestic use. Several agricultural sectors are also dependent on blue water, including Paddy, Sugarcane and Wheat, thereby highlighting the importance of irrigation. The results also indicate ‘Electricity’ sector along with some mining sectors in the industrial category have relatively high dependence on blue water. Complete results are reported in Table S.6.


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Table 1: Comparison of current study with Kampman (2007) [19] Current Study

Crop

Direct

Paddy Rice Wheat Pulses Sugarcane Gram(Chick Pea) Pigeon Peas Dry beans Other oilseeds Jowar (Sorghum) Bajra (Pearl Millet) Groundnut Cotton Maize

724 n.a. 146 76 79 56 n.a. n.a. 57 61 58 48 46 48

a

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a

D.K Study

Direct 8178 5398 2027 5114 336 9860 n.a. n.a. 2280 9079 4819 5894 19751 3185

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TVW

Ratio Current to DK

b c

n.a. 4073 1412 n.a. 234 2071 5922 7923 n.a. 3589 4222 4372 10633 2399

Billion Cubic Meter(BCM); b Total Virtual Water;

n.a. 1.3253 1.4355 n.a. 1.4363 4.7609 n.a. n.a. n.a. 2.5296 1.1413 1.3482 1.8575 1.3276 c

m3 per unit

withdrawal numbers using information about actual water use. For the production of selected primary crops, Kampman [19] reveals that the total volume of water used is equivalent to 792 BCM per year,with contribution of blue water being 219 BCM (27.6%), green water use is 479 BCM (60.5%) and total gray water use is equivalent to 95 BCM per year. The current study reveals that after excluding the ‘Forestry’ sector, contribution of green water is nearly 60.6% of total direct water going in agriculture and the rest is contributed by blue water. Current study does not estimate gray water. Using reported product fraction of rice (milled) [19], that is, the ratio of rice to paddy, equal to 0.66, the current study estimates direct water use to be 5398 m3 per tonne compared to 4073 m3 per tonne in [19] and 4113 m3 per tonne in [57]. Kampman [19] reports a range of 2914 (Punjab) to 8142 (Madhya Pradesh) m3 per tonne, the wide range arising owing to yield differences, which are highly correlated with irrigated area. Additional details are in Section S.5.1.2. In general, the current study has comparable but larger water flows than Kampman, probably because Kampan’s theoretical estimates tend to underestimate actual values. The current study’s estimates are also compared with those reported by Multi-Regional Input-Output (MRIO) databases such as World Input-Output Database (WIOD) and EXIOBASE 2.0. Details are given in S.5.2. Due to lack of water inventory, both EXIOBASE and WIOD use WFN’s estimates [58, 59, 60, 61, 62, 63] but report different numbers and thus data from both are found to be quite different from those in the current study. No values are available for India in MRIO-EORA [64]. Compared to other MRIOs, WIOD [65] was chosen for comparison, as shown in Table S.8 since it offers estimates for more categories/sectors. However, WIOD categories are much more aggregated than in the current study. For the agriculture category WIOD reports significantly smaller green and blue water consumption [60, 61] compared to aggregated withdrawal numbers from nearly 25 sectors of current study. In general, results from WIOD and this work are quite different, most likely due to differences between withdrawal versus consumption and different and often inconsistent sector definitions. Comparison with the World Bank’s (WB) [16] withdrawal numbers is in table S.9 and Section S.5.3. These results show that the WB numbers lie within the range of numbers in this study. For example, the maximum variation with current study is reported for agricultural sectors wherein WB’s reported value for 2002 is approximately 558 BCM, while current study reports a range of 454-1056 BCM. This study estimates ground-water withdrawal for crops equivalent to 433 BCM and scarce ground-water equal to 227 BCM while FAO-Aquastat [66] estimated fresh ground water withdrawals in 2004 stands at 230.4 BCM. Lenzen et al 9 ACS Paragon Plus Environment


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[67] indicate that nearly 346 BCM of scarce water is being used in India.

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Water embodied in agricultural exports & trade in virtual water

Many studies demonstrate the large resource use and emissions embodied in trade [68, 69]. The quantity of water embodied in agricultural exports from India is determined based on FAO statistics [70] and shown in Table S.10. This analysis indicates that nearly 33 BCM of water is embodied in top twenty exported commodities. As the total direct water associated with agriculture, including livestock and meat products and excluding the ‘Forestry and logging’ sector, is about 1872 BCM, the fraction embodied in food exports is about 1.8% only. Thus, agricultural exports embody only a small amount of water withdrawn in Indian economy. Assuming equal production intensities for both exports and imports and based on their monetary values, this study estimates that net water (difference between exports and imports) imported by India in 2003-04 was 6 million cubic meter. This is insignificant compared to direct water associated with agriculture reported earlier in this section. Details are given in Table S.12.

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Discussion

This paper indicates that in 2003-04 nearly 3052 BCM of total freshwater was directly withdrawn in India’s economy and 96% of this direct water was used in agriculture sectors including ‘Forestry and Logging’. Total direct green water contribution was 2202 BCM, of which nearly 1057 BCM is contributed by ‘Forestry and Logging’ sector. This study estimates significant indirect contribution of water towards food related sectors such as ‘Paddy’, ‘Wheat’ and ‘Sugarcane’. ‘Paddy’ withdraws 162 BCM of indirect blue water and 164 BCM of indirect green water. For ‘Wheat’, these numbers are 82 BCM as indirect blue and 28 BCM for green water. The indirect contribution of green water towards ‘Sugarcane’ is 3 BCM and about 14 BCM of blue water is required indirectly. Sectors in non-agriculture categories withdraw less water than agriculture, but indirect dependence can be quite high. For ‘Electricity’ the indirect water use is 44% and for ‘Water supply’ it is 15%. Sectors like ‘Construction’; ‘Miscellaneous food products’; ‘Hotels and restaurants’; ‘Paper, paper products and newsprint’ among others are found to be primarily indirect withdrawers. Previous water footprint studies for India [2, 16, 19, 57, 65, 58] are not as comprehensive as this work, but for sectors common to multiple studies such as agricultural activities, the results are comparable. A significant contribution of this study is the data repository for direct water withdrawal, particularly for non-agricultural activities. The reported sources highlight the effort required to build such coherent databases to estimate resource requirements of building an EEIO table. The model accounts for water at the point of its direct use to avoid double counting of water that just passes through some sectors such as ‘Water Supply’. To gain credibility, the numbers estimated within this study, are compared with other studies such as estimates from the World Bank and World Input-Output database. This paper estimates only water withdrawal and no estimation is done for water pollution or its dilution. Therefore, water that is withdrawn but not used, as may happen when open pit mining activities drain aquifers, or when discarded waste water pollutes more fresh water, are not included. Hence the actual fresh water that is used or withdrawn may be underestimated for certain sectors such as mining or leather tanneries. Blue and green water flows are determined in this work. Estimated blue water contribution to agriculture is about 727 BCM and represents nearly one-fourth of total direct withdrawal by agriculture. For food security of India, current study presents a comparative assessment for a primary grain crop, Paddy. This comparison is important for northwest Indian states having large areas sowing grain crops that are facing major water issues in the form of decreasing ground water tables [2] along with deteriorating water quality. Furthermore, the same crops are large users of green water as well. Thus, erratic rainfall or failed monsoon means even more dependence on blue water for these grain crops along with increasing demand for blue water for energy generation. Additionally, a failed monsoon also means less water in surface-water bodies, which is critical to drive power plants. Therefore, agricultural sectors that are dependent on large quantities of blue water are also likely to be large users of energy resources making them vulnerable to both water and energy infrastructure of their supply network. Additionally, as both irrigated and power driven systems are highly vulnerable to changes in rainfall patterns or monsoon failure, thereby both pose a high risk to the Indian economy. While the vulnerability to such changes for 10 ACS Paragon Plus Environment

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sectors that have direct dependence on green water may be easy to see, this work shows the high indirect dependence of most non-agricultural sectors on green water. As climate change is likely to increase variability of rainfall in the Indian subcontinent, policy makers face a difficult challenge of devising policies on water allocation for food, water and energy security, along with satisfying ecological needs. Using the current model, the LCWW of each sector can be tracked in the Indian economy which can help in designing infrastructure and policies to manage this vital element not only at the level of the production chain but at the economy scale as well. Results of this research may be used to identify major water hot-spots in supply chains of infrastructure, lifestyle, and food related sectors.

Associated Content Supporting Information. Details about the data and methods used in this work. This material is available free of charge via the Internet at http://pubs.acs.org. Author Information. Corresponding Author: Phone: 1-614-292-4904. Fax: 1-614-292-3769. E-mail: [email protected]. Notes. The authors declare no competing financial interest.

Acknowledgment Partial financial support to the first author from Robert S. McNamara Fellowship Program, The World Bank and CSIR-HRDG, Government of India are gratefully acknowledged. The authors would like to thank the three anonymous reviewers for their insightful comments.

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