Water Global Practice Discussion Paper Water, Poverty, and the Economy Simulating the Macroeconomic Impact of Future Water Scarcity Roberto Roson About the Water Global Practice Launched in 2014, the Word Bank Group's Water Global Practice brings together financing, knowledge, and implementation in one platform. By combining the Bank's global knowledge with country investments, this model generates more firepower for transformational solutions to help countries grow sustainably. Please visit us at www.worldbank.org/water or follow us on Twitter at @WorldBankWater. 3 Introducing Commercial Finance into the Water Sector in Developing Countries Simulating the Macroeconomic Impact of Future Water Scarcity Roberto Roson © 2017 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington, DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Please cite the work as follows: Roson, Roberto. 2017. “Simulating the Macroeconomic Impact of Future Water Scarcity.” Discussion Paper. World Bank, Washington, DC. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights​@­worldbank.org. Cover design: Jean Franz, Franz & Company, Inc. Abstract T his paper considers some of the economic implications of climate change scenarios as described in the  Shared Socioeconomic Pathways (SSPs). By comparing potential water demand with estimates of sustainable) water availability in different regions, it identifies regions whose future economic growth (­ potential is likely to be constrained by the scarcity of water resources. The paper assesses the macroeconomic impact of water scarcity under alternative allocation rules, finding that constrained regions can effectively ­ related climate risks and adapt to a changing water environment by assigning more water to neutralize water-­ sectors in which it has a higher value, shifting production to less water-intensive sectors, and importing more  water-intensive goods. However, this adaptation effort is likely to imply some radical changes in water management policies. Introduction improvements in water efficiency (defined as fresh water needed per unit of economic activity). This paper assesses the macroeconomic implications of possible future water scarcity. In order to do so, the Because demand for water is mostly an indirect sustainability of a number of economic growth scenar- demand, depending on the level of economic activity ios in terms of water resources are considered. The and income, a global general equilibrium model is analysis is based on a comparison between potential used to conduct simulation experiments aimed at demand for water and estimated water availability. assessing changes in economic structure and trade flows, from which the demand for water is obtained. Water supply is calculated using the Global Change Assessment Model (GCAM).1 Three different climatic The economic model considers 14 macro-regions: Global Circulation Models (GCMs) were used as inputs— 1. North America 8. Central Africa CCSM, FIO, and GISS—to feed the complex hydrologic model.2 The main output of this model is an estimate of 2. Central America 9. Southern Africa runoffs and water inflows for many regions in the world. 3. South America 10. Central Asia In this study, sustainable (renewable) water supply is 4. Western Europe 11. Eastern Asia defined as the total yearly runoff (where necessary, 5. Eastern Europe 12. South Asia increased by water inflow) within a given region, and sce- narios are considered in which this is the only available 6. Middle East and 13. Southeast Asia source of water. Therefore, the possible exploitation of North Africa 14. Australasia3 nonrenewable water resources (such as so-called “fossil 7. Sahel water”) is implicitly ruled out, whereas the adoption of  unconventional water supply means (desalination, In each region, the model considers the household sec- ­ recycling, harvesting) is indirectly accounted for as tor, as well as the following 20 industries: 1. Rice 4. Vegetables and fruits This discussion paper was authored by Roberto Roson, Ca’ Foscari 2. Wheat 5. Oil seeds University, Venice, and IEFE (Center for Research on Energy and Environmental Economics and Policy), Bocconi University, Milan, Italy. 3. Cereals 6. Sugar Simulating the Macroeconomic Impact of Future Water Scarcity 1 7. Fibers 15. Electricity Effects of Water Demand and Water Supply on Economic Growth 8. Other crops 16. Gas The levels of income per capita (real GDP) in each 9. Meat 17. Water services of  the 14 macro-regions considered are depicted in 10. Extraction 18. Construction ­ figure 1, in the base year at which parameters of the model are calibrated (2004) and in the four scenarios 11. Processed food 19. Transport and (SSP1 and SSP3, 2050 and 2100). The figure helps high- communication 12. Textiles light the salient features of the four cases. SSP1/2050 20. Other Services (s1u2050) is characterized by dramatic income growth 13. Light manufacturing in East Asia, but also Australasia, where income levels 14. Heavy rise to those similar to in North America and Europe. In manufacturing SSP1/2100 (s1u2100), growth rates are very high all over the world. South Africa is the fastest growing region, This exercise is conducted whereas income per capita declines in East Asia with The analysis shows that although for two future reference years, respect to 2050. SSP3/2050 (s3u2050) is characterized economic growth occurs in all 2050 and 2100, but policy by a dual world, where developed regions (North regions, there is significant ­ analysis focuses only on 2050. America and Western Europe) experience limited divergence in future income per Two Shared Socioeconomic growth, but developing regions (most notably East capita between scenarios where Pathways4 (SSP; Kriegler et al., Asia) grow fast. In SSP3/2100 (s3u2100), income distri- regions cooperate to mitigate 2012) were chosen to represent the effects of climate change on bution is more balanced. North America and Western distinct two plausible, but ­ water versus scenarios where Europe slow down further after 2050 and East Asia future economic reference they take a short-term outlook. stops growing altogether, whereas Africa and the pathways: SSP1, termed Middle East accelerate. Sustainability, and SSP3, termed Regional Rivalry. SSP1 is characterized by the Water demand projections are based on water-­ intensity following narrative:  “Sustainable development pro- coefficients: that is, water per unit of output. These are ceeds at a reasonably high pace, inequalities are less- obtained as ratios between sectoral water usage and out- ened, technological change is rapid and directed put in the base calibration year. In turn, sectoral con- toward environmentally friendly processes, includ- sumption has been estimated by elaborating information ing lower carbon energy sources and high productiv- from various sources: the WIOD project (Dietzenbacher ity of land.”5 By contrast, SSP3 is characterized by et al. 2013; Mekonnen and Hoekstra 2011), the European the following narrative: “Unmitigated emissions are research project WASSERMed (Mielke, Diaz Anadon, high due to moderate economic growth, a rapidly and Narayanamurti 2010; Roson and Sartori 2015), and growing population, and slow technological change the U.S. Energy Information Administration (2015).6 in the energy sector, making mitigation difficult. Investments in human capital are low, inequality is Water-intensity coefficients can be used in principle, high, a regionalized world leads to reduced trade to  translate the results of any simulation with the flows, and institutional development is unfavorable, numerical economic model (for example, industrial out- leaving large numbers of people vulnerable to cli- put volumes) in terms of water demand. However, it is mate change and many parts of the world with low necessary to take into consideration that water usage per adaptive capacity.” unit of production (or consumption) does vary over time. 2 Simulating the Macroeconomic Impact of Future Water Scarcity Figure 1. Per Capita Income in the 14 Regions under Four Scenarios, 2050 and 2100 (2005 US$) 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0 sia e ia a a sia sia a pe a ia a l ric d ic ic ic op ric ic he As As Af an ro la lA tA er fr er er a Af Sa r ra lA Eu Eu rn rn m Am Am rt ast ra as st rn lA e he ra nt rn he rn st No le E Au he h h nt ut ra Ce te Ea e h ut rt ut ut st Ce nt So No d es So So Ea id So Ce W M Base S1u2050 S1u2100 S3u2050 S3u2100 Note: s1u = Sustainability Shared Socioeconomic Pathway; s3u = Regional Rivalry Shared Socioeconomic Pathway. In this study, it is assumed that efficiency gains are availability, as will be water ­ endogenous and dependent on production growth. better explained in the  next By 2100, excess water demand Specifically, it is assumed that only a fraction d of the section. To guard against exag- will exist in nearly every increase in industrial production volumes in a country, gerating impacts, the assump- region of the world—with the from q’ to q”, translates into higher water consumption tions about technology change exceptions of North and South w”, as specified in the following equation: err on the side of optimism. America and Europe—implying that growth expectations for Table 1 shows the crude first w” = i [q’ + d (q” − q’)] = i [(1 − d) q’+ dq”], the 21st century will likely not be stage results obtained met if the current water regime where i is the relevant baseline  water-intensity for  potential water demand persists. coefficient (water per unit of production), and the ­ (consumption of water value of 0.5, or 50 percent, is assumed for the d param- resources), which simply mirror the economic growth eter. Further improvements in water efficiency are scenario, and are not affected by any water supply posited whenever potential water demand exceeds constraint. Simulating the Macroeconomic Impact of Future Water Scarcity 3 Table 1. Projections of Sectoral Water Demand 4 Water demand/usage (millions of m3 ) and percent 6 Middle 9 13 1 North 2 Central 3 South 4 Western 5 Eastern 8 Central 10 Central 11 Eastern 12 South 14   East and 7 Sahel Southern Southeast America America America Europe Europe Africa Asia Asia Asia Australasia North Africa Africa Asia Baseline 2004 Agriculture 1,320,159 462,666 956,679 360,114 838,905 533,776 345,160 496,424 276,015 192,685 1,341,460 1,684,088 1,042,806 182,646 Industrial 509,594 123,345 172,642 172,151 363,591 508,932 6,400 51,398 57,925 48,604 301,802 111,472 111,377 17,777 Municipal 38,677 25,540 17,794 16,250 28,695 29,255 2,788 3,263 6,098 5,228 80,122 63,757 24,215 1,605 Total 1,868,430 611,551 1,147,115 548,516 1,231,191 1,071,963 354,348 551,084 340,038 246,517 1,723,384 1,859,318 1,178,398 202,028 2050 SSP1 Agriculture 1,955,926 990,699 2,198,107 468,565 1,828,737 1,280,867 2,737,204 3,083,502 1,051,455 798,891 8,549,132 8,030,985 5,367,159 421,624   48.16% 114.13% 129.76% 30.12% 117.99% 139.96% 693.02% 521.14% 280.94% 314.61% 537.30% 376.87% 414.68% 130.84% Industrial 700,836 288,666 497,493 238,685 947,801 1,659,859 86,803 604,495 397,279 344,408 2,443,783 751,185 730,931 38,737   37.53% 134.03% 188.16% 38.65% 160.68% 226.15% 1256.32% 1076.12% 585.85% 608.60% 709.73% 573.87% 556.27% 117.91% Municipal 65,660 59,006 43,494 25,683 57,253 82,789 21,782 24,977 32,240 23,383 395,768 285,798 105,966 3,831   69.77% 131.03% 144.43% 58.04% 99.53% 182.99% 681.26% 665.38% 428.69% 347.24% 393.96% 348.26% 337.60% 138.67% Total 2,722,422 1,338,371 2,739,094 732,932 2,833,791 3,023,515 2,845,790 3,712,974 1,480,974 1,166,681 11,388,684 9,067,968 6,204,056 464,193   45.71% 118.85% 138.78% 33.62% 130.17% 182.05% 703.11% 573.76% 335.53% 373.27% 560.83% 387.70% 426.48% 129.77% Var. GDP 142.88% 399.98% 456.41% 157.58% 379.45% 484.67% 2160.78% 2085.80% 1341.60% 1204.73% 1426.42% 1175.79% 1151.44% 300.67% 2100 SSP1 Agriculture 2,576,822 1,347,124 2,941,365 606,135 2,097,823 1,779,373 13,481,650 10,712,068 3,529,485 1,014,491 6,732,773 14,165,877 9,119,300 620,023   95.19% 191.17% 207.46% 68.32% 150.07% 233.36% 3805.91% 2057.85% 1178.73% 426.50% 401.90% 741.16% 774.50% 239.47% Industrial 970,751 426,260 730,056 329,211 1,174,845 2,642,777 602,869 3,161,105 2,038,592 532,039 2,017,938 1,685,731 1,459,962 56,300   90.49% 245.58% 322.87% 91.23% 223.12% 419.28% 9319.96% 6050.29% 3419.34% 994.65% 568.63% 1412.24% 1210.83% 216.71% Municipal 85,075 80,685 54,438 31,884 63,922 111,587 103,995 100,349 149,064 30,498 301,933 521,091 174,747 5,049   119.97% 215.91% 205.94% 96.21% 122.77% 281.42% 3629.95% 2975.05% 2344.44% 483.34% 276.84% 717.31% 621.64% 214.52% Total 3,632,648 1,854,068 3,725,858 967,231 3,336,589 4,533,736 14,188,515 13,973,522 5,717,141 1,577,028 9,052,644 16,372,699 10,754,008 681,373   94.42% 203.17% 224.80% 76.34% 171.01% 322.94% 3904.12% 2435.64% 1581.32% 539.72% 425.28% 780.58% 812.60% 237.27% Var. GDP 334.80% 897.57% 869.69% 360.11% 603.08% 1033.52% 14511.25% 11754.79% 9392.58% 2030.24% 1268.25% 2954.64% 2585.61% 624.45% 6 Middle 9 13 1 North 2 Central 3 South 4 Western 5 Eastern 8 Central 10 Central 11 Eastern 12 South 14   East and 7 Sahel Southern Southeast America America America Europe Europe Africa Asia Asia Asia Australasia North Africa; Africa Asia 2050 SSP3 Agriculture 1,675,704 950,289 1,951,556 366,545 1,587,041 1,169,091 1,472,979 2,038,368 720,910 749,425 6,372,489 5,644,760 3,763,355 287,768   26.93% 105.39% 103.99% 1.79% 89.18% 119.02% 326.75% 310.61% 161.19% 288.94% 375.04% 235.18% 260.89% 57.56% Industrial 594,637 263,415 416,307 178,118 785,532 1,351,739 34,718 298,378 197,637 301,477 1,732,473 467,757 473,415 25,737   16.69% 113.56% 141.14% 3.47% 116.05% 165.60% 442.47% 480.53% 241.19% 520.28% 474.04% 319.62% 325.06% 44.78% Municipal 50,095 60,480 41,939 17,899 48,770 76,964 13,269 16,463 21,253 21,887 292,409 202,855 77,095 2,620   29.52% 136.80% 135.69% 10.15% 69.96% 163.08% 375.93% 404.50% 248.51% 318.62% 264.96% 218.17% 218.38% 63.22% Total 2,320,436 1,274,184 2,409,802 562,563 2,421,343 2,597,794 1,520,967 2,353,210 939,799 1,072,788 8,397,372 6,315,372 4,313,866 316,126   24.19% 108.35% 110.08% 2.56% 96.67% 142.34% 329.23% 327.01% 176.38% 335.18% 387.26% 239.66% 266.08% 56.48% Var. GDP 73.44% 308.59% 331.47% 49.09% 267.02% 347.84% 830.60% 955.50% 568.12% 1020.51% 953.98% 644.31% 669.92% 133.21% 2100 SSP3 Agriculture 1,579,208 1,583,227 3,064,792 330,349 2,087,559 1,946,611 4,700,938 6,177,767 1,896,506 1,129,303 5,884,684 8,798,887 5,769,197 281,219   19.62% 242.20% 220.36% −8.27% 148.84% 264.69% 1261.96% 1144.46% 587.10% 486.09% 338.68% 422.47% 453.24% 53.97% Industrial 541,553 444,730 707,642 164,499 1,088,114 2,402,876 136,372 1,186,300 641,149 522,309 1,615,993 822,991 789,906 22,389   6.27% 260.56% 309.89% −4.45% 199.27% 372.14% 2030.84% 2208.08% 1006.85% 974.63% 435.45% 638.29% 609.22% 25.94% Municipal 43,144 96,541 62,444 14,809 62,063 120,288 35,161 46,656 56,783 32,374 250,046 294,545 108,890 2,263   11.55% 278.00% 250.93% −8.87% 116.29% 311.17% 1161.10% 1329.70% 831.16% 519.22% 212.08% 361.98% 349.68% 40.94% Total 2,163,905 2,124,498 3,834,878 509,657 3,237,736 4,469,776 4,872,471 7,410,723 2,594,439 1,683,986 7,750,723 9,916,423 6,667,992 305,870   15.81% 247.40% 234.31% −7.08% 162.98% 316.97% 1275.05% 1244.75% 662.98% 583.11% 349.74% 433.34% 465.85% 51.40% Var. GDP 82.57% 793.82% 748.21% 63.51% 494.36% 847.50% 3632.63% 4317.64% 2726.50% 1944.13% 937.64% 1293.47% 1292.11% 146.53% Note: Water use/demand is measured in millions of m3. Data in percent refers to changes w.r.t. baseline. SSP = Shared Socioeconomic Pathways; SSP1 = sustainability scenario; SSP3 = regional rivalry scenario; var. GDP = variation in gross domestic product. 5 To estimate the regional “sustainable water supply,” However, in 2050 and 2100, water resources become results from the GCAM hydrologic model have insufficient in several other regions, all located in been  used. Water supply in each macro-region is Africa and Asia. This implies that for those regions, the expressed as the sum of yearly runoffs of all countries strong economic development scenarios are incompat- belonging to the region, averaged for three GCMs ible with the estimated availability of water resources. climate scenarios. Results are summarized in table 2. ­ Equivalently, the analysis highlights that water (or water scarcity) has been neglected in the definition Observe that regional water availability is not expected of the Shared Socioeconomic Pathways, suggesting a to change dramatically during the 21st ­century, whereas potential inconsistency. (potential) water demand would necessarily follow the  underlying assumptions of baseline GDP and Policy Scenarios ­population. The emerging regional gap between poten- tial demand and actual “sustainable” water supply is How can the emerging water demand gap be accom- highlighted in tables 3 (SSP1) and 4 (SSP3). modated in the water-constrained regions? Three complementary ways are envisaged: Water consumption in the Middle East—and, to a lesser extent, in South Asia (India and neighboring coun- • If water is a nonsubstitutable production factor, tries)—already exceeds “sustainable” water consump- production should fall in all water-consuming ­ tion in these scenarios. This suggests that in these industries by the same percentage of the excess regions, nonrenewable water resources would need demand gap. Tables 3 and 4 indicate that this gap is to  be exploited, which might include unsustainable generally large, which would imply dramatic and abstraction of groundwater. unrealistic drops in production levels.  Water  is a Table 2. Water Supply Data billions of m3 Average total runoff Standard deviationa Region 2005 2050 2100 2005 2050 2100 1 North America 5,455 5,252 5,304 210 159 206 2 Central America 2,022 1,971 1,544 111 127 354 3 South America 8,101 8,186 8,519 472 325 1,199 4 Western Europe 1,434 1,456 1,463 19 56 18 5 Eastern Europe 5,797 5,088 5,059 39 123 190 6 Middle East 499 393 362 36 36 28 7 Sahel 1,129 947 953 71 57 79 8 Central Africa 2,642 2,336 2,544 170 69 40 9 Southern Africa 1,275 1,396 1,345 101 210 205 10 Central Asia 532 437 414 76 31 38 11 Eastern Asia 2,539 2,320 2,282 83 115 34 12 S_Asia 1,698 1,711 1,792 240 86 188 13 Southeast Asia 4,822 5,367 5,373 345 225 423 14 Australasia 1,027 1,067 1,085 198 114 20 a. Standard deviation refers to variability among the three reference GSM climate scenarios used for the estimates. 6 Simulating the Macroeconomic Impact of Future Water Scarcity substitutable production factor (in  limited ways), • In addition to efficiency-­ so this represents the worst case that is unlikely to improving reallocations within Under business-as-usual prevail. However, at least some part of the demand industries, water would be scenarios, future global water gap (in this exercise, one-quarter is assumed) trans- reallocated between indus- supply is insufficient to keep lates into production cuts—or, in economics jargon, tries. This either requires up with future global water into reductions of multifactor productivity. establishing water markets or demand. Nevertheless, smart specific policies at the policies, coupled with increases • As water becomes a scarcer resource, its explicit national or regional level. in water use efficiency, can market price or its shadow cost would rise, reduc- The inverse of the water-in- prevent production shortfalls and avoid reductions of growth ing the relative competitiveness of water-intensive tensity coefficient is the in most regions. activities. Within each industry in the large mac- value of production per unit ro-regions, activities would then be reallocated of water: that is, the water in time and space (by specific policies or by mar- industrial productivity. Recognizing that perfect ket forces), and more efficient water techniques reallocations are improbable and unrealistic, policy ­ would be adopted. These mechanisms end up scenarios are explored in which the cut in water reducing the industrial water-intensity coefficients ­consumption levels is not applied uniformly across all by increasing overall water efficiency. It is assumed industries, but smaller reductions are applied where here that this effect can cover three-quarters of the water is relatively more valuable (and vice versa). demand gap. (Other parameter values have also discussed here: (1) no water realloca- Three cases are ­ been used to test robustness, but  for brevity are tion between industries [NO-WR]; (2) mild [MILD]; not discussed here.) and (3) strong [STRONG] water reallocation. Table 3. Water Demand Projections for the Sustainability Scenario (SSP1) and Percentage Excess Demand (billions of m3) SSP1 Gap (percent) Region 2005 2050 2100 2005 2050 2100 1 North America 1,868 2,722 3,633 0.0 0.0 0.0 2 Central America 612 1,338 1,854 0.0 0.0 −16.7 3 South America 1,147 2,739 3,726 0.0 0.0 0.0 4 Western Europe 549 733 967 0.0 0.0 0.0 5 Eastern Europe 1,231 2,834 3,337 0.0 0.0 0.0 6 Middle East 1,072 3,024 4,534 −53.5 −87.0 −92.0 7 Sahel 354 2,846 14,189 0.0 −66.7 −93.3 8 Central Africa 551 3,713 13,974 0.0 −37.1 −81.8 9 Southern Africa 340 1,481 5,717 0.0 −5.8 −76.5 10 Central Asia 247 1,167 1,577 0.0 −62.5 −73.8 11 Eastern Asia 1,723 11,389 9,053 0.0 −79.6 −74.8 12 S_Asia 1,859 9,068 16,373 −8.7 −81.1 −89.1 13 Southeast Asia 1,178 6,204 10,754 0.0 −13.5 −50.0 14 Australasia 202 464 681 0.0 0.0 0.0 Note: SSP1 = sustainability scenario. Gap = the emerging regional gap between potential demand and actual “sustainable” water supply. Simulating the Macroeconomic Impact of Future Water Scarcity 7 Table 4. Water Demand Projections for the Regional Rivalry Scenario (SSP3) and Percentage Excess Demand billions of m3 SSP3 Gap (percent) Region 2005 2050 2100 2005 2050 2100 1 North America 1,868 2,320 2,164 0.0 0.0 0.0 2 Central America 612 1,274 2,124 0.0 0.0 −27.3 3 South America 1,147 2,410 3,835 0.0 0.0 0.0 4 Western Europe 549 563 510 0.0 0.0 0.0 5 Eastern Europe 1,231 2,421 3,238 0.0 0.0 0.0 6 Middle East 1,072 2,598 4,470 −53.5 −84.9 −91.9 7 Sahel 354 1,521 4,872 0.0 −37.7 −80.4 8 Central Africa 551 2,353 7,411 0.0 −0.8 −65.7 9 Southern Africa 340 940 2,594 0.0 0.0 −48.2 10 Central Asia 247 1,073 1,684 0.0 −59.2 −75.4 11 Eastern Asia 1,723 8,397 7,751 0.0 −72.4 −70.6 12 S_Asia 1,859 6,315 9,916 −8.7 −72.9 −81.9 13 Southeast Asia 1,178 4,314 6,668 0.0 0.0 −19.4 14 Australasia 202 316 306 0.0 0.0 0.0 Note: SSP3 = regional rivalry scenario. Gap = the emerging regional gap between potential demand and actual “sustainable” water supply. Table 5 (SSP1) and Table 6 (SSP3) present estimates of Table 5. Percentage Variation in Real GDP variations in real GDP, for all macro-regions and for the (SSP1/2050) world as a whole, under the three policy scenarios Region NO-WR MILD STRONG NO-WR, MILD, and STRONG, relative to the 2050 base- 1 North America −0.02 −0.02 0 line of unconstrained economic growth. 2 Central America 0.07 0.08 0.14 3 South America −0.04 −0.02 0.01 Without reallocation of water resources among sectors, 4 Western Europe −0.02 −0.02 −0.01 water scarcity imposes a reduction to the world real GDP 5 Eastern Europe 0.1 0.08 0.05 of −0.37 percent in the SSP1 and −0.49 percent in the 6 Middle East −14 −8.93 −6.02 SSP3. However, there are large disparities across regions, 7 Sahel −11.7 −10.67 −0.82 with a large drop in income for some regions, but small 8 Central Africa −7.08 −5.52 −3.09 gains in some other regions (such as Central America) 9 Southern Africa −0.75 −0.42 0.17 due to improved terms of trade and relative competitive- 10 Central Asia −10.72 −7.47 11.5 ness. In monetary terms, the global welfare impact of water scarcity (equivalent variation) amounts to $762 bil- 11 Eastern Asia −7.05 −3.75 3.32 lion for SSP1 and $712 billion for SSP3, with most of the 12 S_Asia −10.1 −7 1.44 burden concentrated in East Asia (around 62 percent of 13 Southeast Asia −1.98 −1.12 1.46 the total) and the Middle East (23 percent). 14 Australasia −0.05 −0.02 0.04 WORLD −0.37 −0.21 0.08 A complete different picture emerges when some redis- Note: SSP1 = sustainability scenario; NO-WR = no interindustrial water tribution of water resources across sectors is allowed. reallocation. 8 Simulating the Macroeconomic Impact of Future Water Scarcity Table 6. Percentage Variation in Real GDP Figure 2. Range of Variation in Global GDP in 2050 under SSP1 (SSP3/2050) and SSP3, and at Three Different Policy Levels Region NO-WR MILD STRONG 15 1 North America −0.02 −0.01 0 11.5 10 Range of variation in GDP (%) 2 Central America 0.08 0.09 0.15 5 3.32 3 South America −0.02 −0.01 0.02 1.46 0 –0.01 0.38 4 Western Europe −0.01 −0.01 −0.01 0 –0.02 –0.02 –0.82 –1.98 5 Eastern Europe 0.07 0.05 0.03 –5 –6.02 –7.08 –7.05 6 Middle East −13.96 −8.95 −6.21 –10 –11.7 –10.72 7 Sahel −7.21 −6.7 −0.98 –15 –14 North Western Middle Sahel Central Central East Asia Southeast 8 Central Africa 0.18 0.21 0.38 America Europe East Africa Asia Asia 9 Southern Africa −0.07 −0.01 0.09 10 Central Asia −10.3 −7.19 10.98 productivity. In monetary terms, the welfare equiva- 11 Eastern Asia −6.44 −3.43 2.95 lent cost of water scarcity becomes a gain, of $214 12 S_Asia −9.33 −6.51 1.03 billion for SSP1 and $165 billion for SSP3. 13 Southeast Asia −0.06 −0.04 0.03 This “reversal effect” shown most clearly in figure 2, 14 Australasia −0.03 −0.01 0.04 which displays the range of the effect of water scarcity WORLD −0.49 −0.28 0.09 on global growth, for all four scenarios. The lower Note: SSP3 = regional rivalry scenario; NO-WR = no interindustrial water bounds in this figure come from the SSP1, no water reallocation. reallocation [NO-WR] scenario for all regions. The upper bound is from SSP1, strong water reallocation for all regions except for Central Africa, where SSP3, Industrial water reallocations are guided by an equa- strong water reallocation leads to better growth. tion where an elasticity parameter (with values set at 0, However, regardless of which SSP is chosen, the dif- 0.1, 0.25 for the three policy scenarios) determines the ference between the two policy scenarios can be dra- sensitivity to the relative water productivity. With a matic in some regions, most notably in Central Asia limited reallocation of water (MILD), the reduction of (which experiences a net increase of GDP of around ­ cenarios, global GDP is reduced by 42 percent in both s +22.2 percent from moving from no water reallocation whereas regional reductions range from −22 percent to strong water reallocation). This is due to a combina- to −67 percent. tion of factors. First, a region may be characterized by Furthermore, when the water reallocation is more large differences in the industrial water productivity, pronounced (STRONG), it turns out that global real so that when the allocation scheme becomes more GDP increases. The same applies to regional GDP in sensitive to productivity differentials, significant vari- many water-constrained regions, although GDP ations in water endowments and, consequently, on losses are still observed where the water demand the overall factor productivity will follow (see table 7). gap is very large (as in the Middle East). This is Second, the net aggregate effect also depends on how because, with a sufficiently high value for the elas- significant the “winning industries” are in the regional ticity parameter, some industries (where water is economic structure. For example, in Central Asia more valuable) get cuts in water endowments that when Extraction, Light Manufacturing, Transport, are more than compensated by improvements and Communication are allowed to use more in  water efficiency, ultimately increasing total water  (despite  reductions in total regional water Simulating the Macroeconomic Impact of Future Water Scarcity 9 consumption), this vastly improves overall industrial amounts (depending on relative water returns), but all productivity. Furthermore, these sectors are already industries improve in terms of water efficiency. When relatively large in the structure of the Central Asian improvements in water efficiency more than compen- economy, making their impact on regional GDP sate for the cuts in water availability, industrial pro- substantial. ductivity rises. This generally implies a shift in the economic structure away from agricultural produc- tion, to the benefit of manufacturing and food Projected Water Allocations processing. among Industries To examine water alloca- Shifting Patterns of Imported and Exported Simulations show that with tions  from one industry to Water strong water reallocation, another, simulations with Another interesting way to look at the changes in water scarcity will lead to a the  Computable General the  economic structure is by analyzing the varia- large reduction in agricultural Equilibrium (CGE) model tions  in virtual water trade flows. Virtual water production in water scarce entail shocking industrial pro- trade  refers to  the implicit content of water in regions, where production ductivity parameters, in a way import  and export flows. The water-intensity coeffi- will shift to the less intensive that is consistent with the cients can be employed to estimate the amount of water manufacturing sector. underlying hypotheses of that was used to produce goods that have been subse- water availability and water quently transferred abroad, which can be interpreted as intensity in each sector. The model computes a coun- a virtual export of water. Table 8 presents the changes terfactual equilibrium for the world economy and pro- in virtual water flows (in billions m3) among the 14 mac- vides a rich set of output in terms of: production and ro-regions, again for the scenario SSP1/2050/STRONG. consumption volumes, investments, relative prices, trade flows, and many other economic variables. See The reduction in agricultural production and other box 1 for a more thorough description of the CGE water-consuming activities in water-constrained model. regions implies a substitution of domestic water-​ ­consuming goods with imports: that is, an increase in It is not possible to illustrate in detail all the findings virtual water imports. The difference between row of the different simulation exercises in this brief paper. and column totals gives the changes in the “virtual Rather, to show how the economic structure is typi- water trade balance” for each region. These differ- cally affected, some results for the SSP1/2050 scenario ences are summed and presented in figure 3. As a with STRONG water reallocation between industries consequence of market mechanisms affecting are described next. regional economic structures, the most water-­ Table 7 shows how the multi- constrained region, the Middle East, increases its net By shifting production to less factor productivity changes in imports of virtual water by about 478 billion m3. water-intensive sectors, and the water-consuming indus- Other water-constrained regions also increase net importing more water-intensive tries of the various regions. imports of virtual water: Sahel by 210 billion m3; goods, water scarce regions Industries in regions that are Central Asia by 164 billion m3; and Central Africa by can adapt to a changing water not water constrained are 98 billion m3. The global virtual water trade balance environment unaffected. In the other cases, must equal zero, implying that regions that are there can be both increases and decreases in produc- not  water constrained will expand their exports of tivity. This is because water is reduced, by different virtual water. 10 Simulating the Macroeconomic Impact of Future Water Scarcity Box 1. A Brief Description of the GTAP Model The Global Trade Analysis Project (GTAP) is an international network that builds, updates, and distributes a comprehensive and detailed database of trade transactions among different industries and regions in the world, framed as a Social Accounting Matrix (SAM). The SAM is typically used to calibrate parameters for a Computable General Equilibrium (CGE) model. The GTAP database is accompanied by a relatively standard CGE model and its software. The model structure is quite complex and is fully described in Hertel and Tsigas (1997). For brevity, summaries of the meaning of the main equations of the model are presented, and a graphical representation of income flows in the model is shown in figure B1.1. Figure B1.1. Income Flows in the GTAP Model Regional household TAXES TAXES PRIVEXP GOVEXP SAVE Private household Government Global savings TAXES VOA (endow) NETINV XTAX MTAX VDPA VDGA VIPA VIGA Producer VDFA VIFA VXMD Rest of world Source: Brockmeier 2001. Note: GTAP = Global Trade Analysis Project. box continues next page Simulating the Macroeconomic Impact of Future Water Scarcity 11 Box 1. continued Equation and identities in the model include the following conditions: Production of industry i in region r equals intermediate domestic consumption, final demand (private consumption, public consumption, demand for investment goods), and exports to all other regions. • Endowments of primary factors (such as labor and capital) matches demand from domestic industries. • Unit prices for goods and services equals average production costs, including taxes. • Representative firms in each regional industry allocate factors on the basis of cost minimization. • Available national income equals returns on primary factors owned by domestic agents. • National income is allocated to private consumption, public consumption, and savings. • Savings are virtually pooled by a world bank and redistributed as regional investments, on the basis of expected future returns on capital; • The structure of private consumption is set on the basis of utility maximization under the budget constraint. • Intermediate and final demand are split according to the source of production: first between domestic production and imports, and then the imports among the various trading partners. Allocation is based on relative market prices, including transportation, distribution, and tax margins. Goods in the same industry but produced in different places are regarded as imperfect substitutes. • There is perfect domestic mobility for labor and capital (single regional price), but no international mobility. • There is imperfect domestic mobility for land (industry-specific price), but no international mobility. Land allocation is driven by relative returns. From a mathematical point of view, the model is a very large nonlinear system of equations. Structural parameters are set so that the model replicates observational data in a base year. Simulations entail changing some exogenous variables or parameters, bringing about the determination of a counterfactual equilibrium. The partition between endogenous and exogenous variables, as well as the regional and industrial disaggregation level, is not fixed but depends on the scope of the simulation exercise. 12 Simulating the Macroeconomic Impact of Future Water Scarcity Table 7. Changes in Multifactor Productivity (SSP1/2050/STRONG) (percent) North Central South Western Eastern Middle Central Southern Central Eastern Southeast Sahel S_Asia Australasia America America America Europe Europe East Africa Africa Asia Asia Asia Rice 0.00 0.00 0.00 0.00 0.00 −42.30 −15.92 −17.10 −4.01 −42.47 −37.12 −40.21 −6.00 0.00 Wheat 0.00 0.00 0.00 0.00 0.00 −40.47 −20.23 −0.98 −1.32 −25.34 −44.20 −28.34 −4.19 0.00 Cereals 0.00 0.00 0.00 0.00 0.00 −41.89 −31.20 −10.70 −2.72 −18.23 −48.74 −34.41 −3.08 0.00 VegFruit 0.00 0.00 0.00 0.00 0.00 −28.74 −4.92 −9.96 −1.95 2.57 −15.86 −14.37 −4.43 0.00 Oilseeds 0.00 0.00 0.00 0.00 0.00 −37.66 −24.66 −6.87 −2.38 −84.58 −31.88 −21.49 1.43 0.00 Sugar 0.00 0.00 0.00 0.00 0.00 −33.51 −22.90 −3.78 −1.78 −10.71 −35.44 −20.61 −4.14 0.00 Other crops 0.00 0.00 0.00 0.00 0.00 −21.01 −7.76 −9.88 −1.10 11.42 −0.57 6.91 −1.52 0.00 Other Agr. 0.00 0.00 0.00 0.00 0.00 −21.41 −14.66 −11.35 −0.78 6.53 −8.26 −9.25 −2.15 0.00 Extr 0.00 0.00 0.00 0.00 0.00 −15.64 15.74 0.71 −0.21 17.46 4.72 9.86 2.16 0.00 P.Food 0.00 0.00 0.00 0.00 0.00 6.81 29.57 7.12 1.11 30.82 18.57 22.97 4.61 0.00 Textiles 0.00 0.00 0.00 0.00 0.00 7.45 28.02 6.25 0.98 29.37 18.68 24.65 4.43 0.00 L. Man. 0.00 0.00 0.00 0.00 0.00 7.59 30.63 7.72 1.21 31.82 18.27 19.89 4.83 0.00 H. Man. 0.00 0.00 0.00 0.00 0.00 4.60 28.63 6.60 1.03 29.94 16.42 19.13 4.35 0.00 Utilities 0.00 0.00 0.00 0.00 0.00 −12.96 15.68 −0.66 −0.09 17.78 3.10 8.79 1.43 0.00 Note: L. Man = light manufacutring; H. Man = heavy manufacturing; SSP1 = sustainability scenario; STONG = strong water reallocation. 13 14 Table 8. Changes in Virtual Water Trade Flows (SSP1/2050/STRONG) (billions m3) North Central South Western Eastern Middle Central Southern Central Eastern Southeast From\to Sahel S_Asia Australasia Total America America America Europe Europe East Africa Africa Asia Asia Asia North America 0 −2,280 −288 −581 1 2,867 −63 −66 −60 2 4,271 83 82 12 3,982 Central America 68 0 6 231 23 248 1 8 1 0 430 −21 7 4 1,005 South America 35 19 0 626 227 4,404 32 161 −29 19 2,014 37 140 14 7,699 Western Europe −42 −4 −2 0 −17 559 12 32 −12 0 −3 −77 −11 0 435 Eastern Europe 91 −27 −2 −2,035 0 9,263 −107 10 −29 13 487 90 206 6 7,966 Middle East −29,405 −1,157 −3,579 −74,484 −6,055 0 −576 −2,170 −12,783 −416 −373,879 −50,614 −78,477 −2,021 −635,615 Sahel −8,976 −1,783 −1,712 −43,556 −2,211 −26,877 0 −25,440 −3,719 −24 −78,762 −8,492 −14,663 −456 −216,669 Central Africa −24,641 −1,094 −5,374 −68,558 −3,746 −4,582 −2,038 0 −4,930 −243 −62,724 −24,279 −6,567 −396 −209,170 Southern Africa −417 −76 −80 −3,971 −220 −46 −52 −385 0 −19 −4,871 −130 −653 −30 −10,947 Central Asia −2,660 −2,724 −586 −22,522 −86,955 −21,663 −21 −60 −283 0 −25,800 −1,858 −646 −100 −165,879 Eastern Asia −45,054 −6,324 −3,242 −52,678 −9,300 −10,402 −1,049 −2,907 −3,871 −827 0 −3,219 −46,907 −3,327 −189,104 S_Asia −53,602 −8,264 −2,393 −99,100 −9,193 −115,817 −1,990 −75,700 −23,714 −736 −108,192 0 −128,967 −2,882 −630,550 Southeast Asia −11,803 −1,504 −965 −24,063 −1,847 2,591 −1,035 −4,737 −2,324 −32 −43,591 −2,306 0 −2,691 −94,306 Australasia 13 −21 −12 −255 0 1,515 −33 −54 −213 0 27 121 −49 0 1,040 Total −176,393 −25,239 −18,227 −390,945 −119,293 −157,940 −6,919 −111,306 −51,966 −2,261 −690,591 −90,665 −276,505 −11,866 Figure 3. Virtual Water Trade Balance 500 400 300 200 Virtual water trade balance (billion m3) 100 0 –100 –200 –300 –400 –500 –600 Middle Sahel Central Central North Southeast Western East Asia Africa America Asia Europe Conclusion context of a changing climate. It also forcefully illus- trates that prudent management of water resources is This paper presents findings of some numerical simu- likely to be sufficient to neutralize some of the unde- lation exercises aimed at assessing the macroeconomic sirable impacts. consequences of a possible future scarcity of water. It is important to emphasize that models are not designed The analysis introduces several assumptions, which to forecast the future. As with all modeling exercises, are all more or less questionable. Nevertheless, the the analysis is based upon a litany of assumptions and main results are robust to alternative conjectures, and cannot be interpreted as predictions of future changes three main messages emerge from the analysis. in GDP. Instead, the exercise serves to improve under- First, scenarios of economic development that have standing of the magnitude and direction of changes been recently proposed to support the scientific analy- and how alternative policies can either accentuate or ses of climate change have ignored water availability. mitigate the adverse impacts. The underlying assumptions of sustained economic The results demonstrate that water remains a signifi- growth, especially for developing countries, would cant obstacle to growth and development in the imply an excessive consumption of water, even when Simulating the Macroeconomic Impact of Future Water Scarcity 15 substantial improvements in water efficiency are is an Earth System Model (ESM) developed by the First Institute of Oceanography (FIO) in China. envisaged. 3. Australasia consists of Australia, New Zealand, and Pacific small island Second, and related to the previous point, the emerg- states. ing water scarcity will mainly affect developing 4. SSPs are reference pathways describing plausible alternative as trends ­ countries in Africa and Asia, hampering their pros- in the evolution of society and ecosystems over a century timescale, in pects of economic growth. This means that water the absence of climate change or climate policies (O’Neill et al., 2014). scarcity will increase economic inequality around 5. O’Neill et al. (2014). the world. 6. WIOD = World Input-Output Database; WASSERMed = Water Availability and Security in Southern Europe. Third, an intelligent reallocation of scarce water resources toward sectors where the economic return per unit of water is higher can be a very effective pol- References icy response to the emerging water scarcity and its Brockmeier. 2001. A graphical exposition of the GTAP model, GTAP Technical Paper No. 8, Global Trade Analysis Project, http://www.gtap.org. consequences. The analysis reveals that with a STRONG reallocation of water (implying aggressive Dietzenbacher, E., B. Los, R. Stehrer, M. Timmer, and G. de Vries. 2013. “The Construction of World Input-Output Tables in the WIOD Project.” policies in many countries), it would be possible to Economic Systems Research 25: 71–98. mitigate the macroeconomic impacts (measured by Hertel and Tsigas. 1997. “Structure of GTAP”, in: T.W. Hertel (ed.), Global GDP) due to water resources scarcity. Of course, the Trade Analysis: Modeling and Applications, Cambridge University Press. model says nothing about how this reallocation could Kriegler, E., B. C. O’Neill, S. Hallegatte, T. Kram, R. J. Lempert, R. H. Moss, be implemented in practice. The introduction of water and T. Wilbanks. 2012. “The Need for and Use of Socioeconomic markets (through efficient water pricing) or a more Scenarios for Climate Change Analysis: A New Approach Based on Shared Socioeconomic Pathways.” Global Environmental Change 22: 807–22. market-oriented planning of water infrastructure could be part of the solution. These are issues that Mekonnen, M., and A. Hoekstra. 2011. National Water Footprint Accounts: The Green, Blue and Grey Water Footprint of Production and Consumption, have been widely discussed in the water management Value of Water Research Report50, Volumes I and II. Delft, Netherlands: literature and are beyond the scope of this modeling UNESCO-IHE Institute for Water Education. exercise. Mielke E., L. Diaz Anadon and V. Narayanamurti. 2010. “Water Consumption of Energy Resource Extraction, Processing, and Conversion.” Energy Technology Innovation Policy Discussion Paper 2010-15, Harvard Notes Kennedy School, Cambridge, MA. 1. The Global Change Assessment Model (GCAM) is a dynamic-recursive O’Neill, B.C., E. Kriegler, K. Riahi, K. L. Ebi, S. Hallegatte, T. R. Carter, model with technology-rich representations of the economy, energy R. Mathur, and D. P. van Vuuren. 2014. “A New Scenario Framework sector, land use and water linked to a climate model, developed at for Climate Change Research: The Concept of Shared Socioeconomic the Joint Global Change Research Institute of the University of Pathways.” Climatic Change 122: 387–400. Maryland. For more information, visit http://www.globalchange​ .umd.edu​/­models/gcam. Roson, R., and M. Sartori. 2015. “System-wide Implications of Changing Water Availability and Agricultural Productivity in the Mediterranean 2. CCSM (the Community Climate System Model) is a Global Circulation Economies.” Water Economics and Policy 1 (1): 1450001.1–30. doi: 10.1142​ Models (GCM) developed by the University Corporation for /S2382624X14500015. Atmospheric Research. GISS (the Goddard Institute for Space Studies) model is primarily aimed at the development of coupled atmo- U.S. Energy Information Administration. 2015. Monthly Energy Review, sphere-ocean models for simulating Earth’s climate system. FIO-ESM June 2015. Washington, DC. 16 Simulating the Macroeconomic Impact of Future Water Scarcity SKU W16006