According to results from the ordinary least-squares (OLS) regression analyses, the governing factors and their effects for the anthropogenic water use variables were similar regardless of whether water withdrawal or consumption was considered, both on a per country and per capita basis. As such, the following analyses focus on water consumption. The results for 16 anthropogenic water consumption accounts including the blue and total water consumption associated with, andof primary and manufactured goods and services, on aandbasis are presented in Tables 2 and 3 and Table S3

Previously, a nation’s agricultural activity was postulated as a more critical determinant for national blue water use patterns and magnitudes than affluence level; (6) however, international virtual total water flows (e.g., blue and green water) were found to be more strongly correlated to the availability of arable land than to that of renewable freshwater. (48) Results from this study ( Table 2 ) revealed that compared to population the various country-level water consumption metrics were much less sensitive to natural resource availability (e.g., the areas of land, agricultural land, or renewable freshwater resources). While the national consumption of total water associated with, andwere sensitive to arable land availability (in hectares per person), they only increased by 53%, 21%, and 63%, respectively, for each doubling of arable land availability. Further, natural resource availability appeared to have minimal effects when only blue water was considered. On the other hand, factors that were underrepresented in previous studies did have significant effects on the blue water use metrics. The sectoral structure of a country’s territorial blue water withdrawals (i.e., the percentages of agricultural, industrial, and domestic (or municipal) water withdrawal of a country’s total freshwater withdrawal), independent of its size, affected its blue water consumption associated with, andof primary and manufactured goods and services. The effects were further analyzed absent of the population effects in the following section.

For a nation’s virtual water import, bothandplay a significant role ( Table 2 ). The volume of national blue and total virtual water imports increased by ∼80% with each doubling of total population or average income. For the rest of the water use metrics (i.e.,, and), the role ofwas more complex. Further, theeffects appeared to be less or not significant when the total water was accounted (see Table 2 ), supporting the hypothesis that green water is less economically valued than the already undervalued and underpriced blue water. (47) The relationships betweenand the per capita water consumption accounts, eliminating theeffects, are further discussed in the next section.

Figure 1. Scatter plots of the observed ( y -axis) versus predicted ( x -axis) values of blue ( top panels ) and total (i.e., blue and green; bottom panels ) water consumption associated with Producing , Consuming , Importing , and Exporting of primary and manufactured goods. The predicted values are based on the regression models where all significant factor(s) were included (see Table 1 , i.e., “ All (Affluence + Population + Technology) ”. For both the observed and predicted values, the unit is initially Mm 3 /year and they were analyzed and presented in the natural logarithmic form.

The national blue and total water consumption variables were well explained (= 0.8–0.9) by a few predictor variables: total population, affluence level (represented by per capita GDP, $2007 PPP), the structure of territorial (domestic) water usage, and the availability of arable land (see Figure 1 and Table 2 ).was the most significant driver of national water consumption associated with, andof primary and manufactured goods and services. In the most parsimonious models (i.e., population being the only explanatory variable), ∼60% and 70–90% of the cross-national variances for blue and total water were explained, respectively. The regression results further indicate that the blue water use accounts associated withandmay increase more rapidly than population (i.e., theelasticities > 1). This phenomena was previously observed for direct COemissions at both the national and global scales, (36, 46) highlighting the importance of taking into account the disproportionate impact of population change in climate change policy discussions. While supporting the concerns that population increase will lead to continued degradation of freshwater resources, this study suggests that theeffect on human water use may be more significant than the commonly assumed unitaryelasticity. Given human water appropriations generate immediate impacts locally and the locations of production and consumption are becoming further separated, the elastic population-water relationships further suggest that the water resource impacts incountries, where population growths are anticipated, may be outsize.

Drivers of Per Capita Water Appropriations

For all four blue water consumption accounts measured on a per capita basis, the OLS tested a variety of variables including (1) affluence, (2) both affluence and its quadratic form, and (3) 29 independent variables (i.e., all except population). Results are presented in Table 3 , indicating that most independent variables dropped out of the stepwise analysis. With the population effects neglected, per capita water consumption indicates a country’s water use intensity. Across the 110 countries analyzed, ∼ 50%–70% and ∼20%–65% of the variations of the per capita water consumption accounts can be explained by these variables, respectively, considering blue and total water ( Table 3 and Table S3 ).

Affluence was a significant driver for all of the per capita blue water consumption accounts as well as the total water consumption accounts associated with importing. The affluence effects, however, varied among the per capita water use categories and within the range of the observations. For per capita blue water consumption associated with producing and exporting, the significant and positive coefficients of Affluence and the significant and negative coefficients of the quadratic form of Affluence suggest inverted “U-shaped” relationships, or potential Environmental Kuznets curves (producing countries. At the higher income levels, these regression relationships are consistent with observed declining trends of per capita blue water use for the U.S. and several other developed countries despite continued economic growth.Affluence changed from positive to negative. However, a further examination of the observed income range and the distribution of the countries on the right side of the turning points in virtual water export are arid, oil-rich countries (i.e., Qatar, Kuwait, and UAE), which do not well represent the overall development patterns for the rest of the world. was a significant driver for all of the per capita blue water consumption accounts as well as the total water consumption accounts associated with. The affluence effects, however, varied among the per capita water use categories and within the range of the observations. For per capita blue water consumption associated withand, the significant and positive coefficients ofand the significant and negative coefficients of the quadratic form ofsuggest inverted “U-shaped” relationships, or potential Environmental Kuznets curves ( Figure 2 B). At the lower income levels, the inverted “U-shaped” relationships suggest economic development being a stronger driving force, causing a (disproportionally) larger increase of blue water consumption and subsequently more environmental impacts incountries. At the higher income levels, these regression relationships are consistent with observed declining trends of per capita blue water use for the U.S. and several other developed countries despite continued economic growth. (6, 11, 13) The OLS results further revealed a turning point at the income level of ∼$76,300/person/year and ∼$49,400/person/year ($2007 PPP) for per capita production-based blue water consumption and blue virtual water export, respectively, where the effects ofchanged from positive to negative. However, a further examination of the observed income range and the distribution of the countries on the right side of the turning points in Figure 2 B weakened the support for the decoupling arguments. As shown in Figure 2 B, only Qatar, Luxembourg, and Kuwait surpassed the $76,300/person/year limit. Half of the countries on or below the declining curve for per capitaare arid, oil-rich countries (i.e., Qatar, Kuwait, and UAE), which do not well represent the overall development patterns for the rest of the world.

Figure 2 Figure 2. Regression plots of per capita blue water consumption associated with Producing, Consuming, Exporting, and Importing of primary and manufactured goods. A. Scatter plots of the observed (y-axis) versus predicted (x-axis) per capita blue water consumption values. The predicted values are a function of (1) Affluence, i.e., GDP per capita and/or (GDP per capita)2 and (2) Affluence and factors representing the structure of territorial sectoral water withdrawal (as shown in Table 3); B. Observed per capita blue water consumption variables (y-axis) against per capita GDP (x-axis). The solid lines represent the least-squares regression lines based on all significant factors, plotted against GDP per capita, and assuming mean values for the rest of significant factors. The dashed lines and $ represent the turning point, i.e., the income level at which the Affluence effects on the water use accounts change from positive to negative.

The per capita blue water consumption related to the consuming activities increased linearly with income, indicating that more affluent lifestyles in high-income countries were still associated with greater blue water consumption. With each doubling of income, blue water embedded in the goods and services a nation consumed on a per capita basis increased by 82% across the 110 countries analyzed in 2007. This fails to support the Environmental Kuznets Curve hypothesis. Further, the OLS results showed Affluence was the most critical driver of the per capita blue and total water consumption related to importing activities. With each doubling of income, the blue and total water embedded in the goods and services a nation imported on a per capita basis increased by more than 80%. The proposed “decoupling” of domestic water use and economic growth in high-income countries could be, at least in part, attributed to importing virtual water through imported goods and services from abroad. Despite the potentially higher need in water-scarce regions, which initially led to the proposal of the virtual water concept, this study suggests virtual water is mainly flowing toward affluence. Water scarcity represented by a country’s total and per capita renewable water availability was not a significant determinant of the varying virtual water imports observed in 2007.

Affluence, per capita water consumption was much less sensitive to food consumption patterns. The food consumption variables, that is, per capita calorie supplies from various plants and animal sources or their percentages, did not explain much of the residuals of the per capita water consumption models reported in Meat, Wheat, and Vegetables, were highly correlated with affluence. Given that the complex affluence-water relationships were significant in driving water use measured on the per capita basis (i.e., a country’s water use intensity), the varying elastic/inelastic and positive/negative effects across the Affluence range were plotted in Affluence at different economic development stages. Depending on the water use account of interest, for example, blue or total, production-based or consumption based, the affluence effects may be different or even opposite. Despite the existing perception of the strong water-diet implications, the results here indicate, in comparison to, per capita water consumption was much less sensitive to food consumption patterns. The food consumption variables, that is, per capita calorie supplies from various plants and animal sources or their percentages, did not explain much of the residuals of the per capita water consumption models reported in Table 3 and Table S3 . Rather, the few significant food consumption factors that did explain some of the residuals, for example, the calorie supplies from, andwere highly correlated with affluence. Given that the complex affluence-water relationships were significant in driving water use measured on the per capita basis (i.e., a country’s water use intensity), the varying elastic/inelastic and positive/negative effects across therange were plotted in Figure 3 . Distinct from the homogeneous affluence-water relationships conventionally assumed, the results highlight the potentially varying water use implications associated withat different economic development stages. Depending on the water use account of interest, for example, blue or total, production-based or consumption based, the affluence effects may be different or even opposite.

Figure 3 Figure 3. Affluence elasticities, that is, the effects of affluence on per capita water uses associated with Producing, Consuming, Importing, and Exporting of primary and manufactured goods at varying affluence levels. The income levels, after adjusting for inflations, were classified based on the new classifications defined by the World Bank.(49)