**3. Results**

#### *3.1. Virtual Water Transfers Embodied in Electricity Transmission*

The divergence between power generation provinces and power consumption provinces are increasing, which can be represented directly by the rapidly increasing of VWT's magnitude. The total volume of VWT increased from 450 Mm<sup>3</sup> in 2005 to 5010 Mm<sup>3</sup> in 2014, a factor of 11.3. As for VSWT, it increased from 204 Mm<sup>3</sup> in 2005 to 899 Mm<sup>3</sup> in 2014, a factor of 4.4. The different increasing speed between VWT and VSWT can be ascribed to the imbalance distribution of water resources in China. Additionally, both volumes of VWT and VSWT increased significantly than electricity transmission in the decade, which can be attributed to the expanding of hydropower that owned intensive water consumption.

Similar to the direction of electricity transmission flow, the embodied VWT mainly follows a west-to-east pattern. However, the difference between VWT and VSWT is also obvious because of the different water stress levels and power generation mix between northwestern provinces and southwestern provinces. We use the Circos tool [41] to represent the VWT network in this paper. Figure 2 shows the structure of pair-wise inter-provincial VWT in 2005 and 2014. The five largest VW flows in 2005 are Shanxi-to-Hebei (35 Mm3), Hubei-to-Jiangsu (34 Mm3), Hubei-to-Guangdong (30 Mm3), Jilin-to-Liaoning (30 Mm3), and Shanxi-to-Beijing (27 Mm3), while those flow are Yunnan-to-Guangdong

(916 Mm3), Sichuan-to-Jiangsu (473 Mm3), Sichuan-to-Shanghai (418 Mm3), Guizhou-to-Guangdong (384 Mm3), and Sichuan-to-Zhejiang (336 Mm3), respectively in 2014. Those top five pair-wise flows' summation account for 51% of all volumetric VWT. In comparison with the VWT between 2005 and 2014, southwestern provinces are playing a more and more important role in the VWT network, especially in Sichuan and Yunnan. However, the structure of VWT in the two provinces is different. In 2014, Sichuan mainly exports VW to the Yangtze River Delta, while Yunnan exports VW to Guangdong. It is ascribed to the distribution of transmission lines constructed between east to the west.

**Figure 2.** Pair-wise virtual water flows between provinces in 2005 (**a**), and 2014 (**b**). The top five flows are labeled. The color of the ribbon represents the different subnational grid. The ribbon touches inner circle refers to the export, while out of touch means an import. AH—Anhui, BJ—Beijing, CQ—Chongqing, FJ—Fujian, GD—Guangdong, GS—Gansu, GX—Guangxi, GZ—Guizhou, HEB—Hebei, HEN—Henan, HLJ—Heilongjiang, HUB—Hubei, HUN—Hunan, IM—Inner Mongolia, JL—Jilin, JS—Jiangsu, JX—Jiangxi, LN—Liaoning, NX—Ningxia, QH—Qinghai, SAX—Shaanxi, SC—Sichuan, SD—Shandong, SH—Shanghai, SX—Shanxi, TJ—Tianjin, XJ—Xinjiang, YN—Yunnan, ZJ—Zhejiang.

In the view of VSWT, its structure and evolution differ from the counterpart. As is shown in Figure 3, Northern provinces dominated the VWT network, while southwestern provinces are weakened. The top five VSWT flows in 2005 are Shanxi-to-Hebei (36 Mm3), Shanxi-to-Beijing (27 Mm3), Shanxi-to-Jiangsu (19 Mm3), Jiangsu-to-Zhejiang (19 Mm3), and Jiangsu-to-Shanghai (18 Mm3), while those are Inner Mongolia-to-Hebei (88 Mm3), Shanxi-to-Hebei (74 Mm3), Gansu-to-Qinghai (73 Mm3), Ningxia-to-Shandong (49 Mm3), and Sichuan-to-Jiangsu (45 Mm3), respectively, in 2014. The summation of top flows accounts for over 30% of the total VWST flows in the network. The comparison of VWT and VSWT could bring us more details. Both northwestern and southeastern provinces are main electricity exporters, but the environmental impacts posed by exporting electricity to local water resources are disproportionate because of different water stress and power generation mix in those provinces. Northeastern provinces are dominated by coal power plants, and the water scarcity in those provinces is much serious, leading to a large amount of VW and VSW run away. In contrast, southwestern provinces are water-abundant and most of the electricity is generated by hydropower plants. Thus, even the water coefficient of hydropower is large compared to thermal power, when VW adjusted to VSW, the volumes of VSW exported from southwestern provinces are very small.

**Figure 3.** Pair-wise virtual scarce water flows between provinces in 2005 (**a**), and 2014 (**b**). The top five flows are labeled. The color of the ribbon represents the different subnational grid. The ribbon touches inner circle refers to the export, while out of touch means an import. AH—Anhui, BJ—Beijing, CQ—Chongqing, FJ—Fujian, GD—Guangdong, GS—Gansu, GX—Guangxi, GZ—Guizhou, HEB—Hebei, HEN—Henan, HLJ—Heilongjiang, HUB—Hubei, HUN—Hunan, IM—Inner Mongolia, JL—Jilin, JS—Jiangsu, JX—Jiangxi, LN—Liaoning, NX—Ningxia, QH—Qinghai, SAX—Shaanxi, SC—Sichuan, SD—Shandong, SH—Shanghai, SX—Shanxi, TJ—Tianjin, XJ—Xinjiang, YN—Yunnan, ZJ—Zhejiang.

#### *3.2. Driving Forces of Overall Virtual Water Transfers*

Di fferent driving factors (i.e., power generation mix, power transmission structure, power generation structure, power demand structure, and power demand) behind the evolution of the VWT network are determined by using a modified SDA model. Decomposition results at the national level for VWT and VSWT are illustrated in Figures 4 and 5 respectively. Overall, changes in the power generation mix, power demand, and power transmission are the main drivers for the increase of VWT in 2005 to 2014. The contribution of the above-mentioned main drivers' change di ffers between di fferent periods (see Figure 4). For example, the changes in power generation mix contribute to the increase of VWT in 2005 to 2009, and then to the decrease of VWT in 2010 to 2011 and 2012 to 2013. Similar to the power generation mix, the contribution of power transmission changes is di fferent in the decade. Excepted for the period between 2010 and 2011, the change of power transmission contributes to the increase of VWT. The change of power demand always contributes to the increase of VWT because of soaring power consumption in the decade. Furthermore, the contributions from power generation structure and power demand structure were very small compared to the other factors. This phenomenon can be ascribed to two reasons. First, although the electricity transmission increased by 3.1 times from 2005 to 2014, the power generation structure and power demand structure changed a little. In this case, the e ffects of power generation structure and power demand structure are limited.

**Figure 4.** The contribution of driving factors to the changes of virtual water transmission.

In comparison with VWT, the contribution of each factor to the evolution of VSWT is di fferent to some extent. Changes in power demand contribute most to the increase of VSWT while changes in the power generation mix and power transmission also played a vital role in leading the evolution of VSWT. In addition, the extent of e ffects for factors di ffers from di fferent periods. For instance, the changes in the power generation mix are the main driver for the decrease of VSWT in 2011 to 2010 (−54%), and 2013 to 2014 (−19%), respectively. The contribution of changes in power transmission is very small (less than 1%) in 2012 to 2013, and it increased to a large share (57%) in 2013–2014. Additionally, changes of power demand structure and power generation structure contribute little to the evolution of VSWT, which is similar to VWT.

To clearly show di fferent policies implemented in di fferent periods, we divide the overall time span (2005–2014) into two periods i.e., 2005 to 2010 and 2010–2014 (see Figure 6). In the first period, the changes in power generation mix contribute most to the increase of VWT (60%) and VSWT (40%), which can be ascribed to the increasing amount of hydropower plants in this period. To reduce the air pollution of the power system, China encourages to improve the share of renewable energies, especially hydropower, as the cost of solar and wind power is expensive at that period. In the second period, the changes of power transmission (51%) are the main driver to the increase of VWT while it is power demand (61%) in VSWT. Additionally, the change of power generation mix contributes to the increase of VWT, but to the decrease of VSWT. It can be attributed to the different WSI of provinces.

**Figure 5.** The contribution of driving factors to the changes of virtual scarce water transmission.

**Figure 6.** The contribution of different factors to the change of (**a**) virtual water transmission; (**b**) virtual scarce water transmission.

#### *3.3. Driving Forces Analysis at the Provincial Level*

The analysis at the national level reveals the overall contribution of factors to the evolution of the virtual water network but covers the details about the contribution of factors to the evolution of the virtual water network in each province. Thus, we investigated the effects of factors on the evolution of VWT and VSWT in the top ten VW and VSW exporters, respectively. The top ten VW exporters in 2014 are Sichuan, Yunnan, Hubei, Guizhou, Inner Mongolia, Shanxi, Gansu, Anhui, Guangxi, and Hunan, in which the VW exporting accounts for 91% of the total VWT. Additionally, at the provincial level, power generation structure and power demand structure should be considered as external power generation structure and external power demand structure based on modified SDA [32].

The factor decomposition results for the top ten VW exporters from 2005 to 2014 are shown in Figure 7. The contributions of factors differ from various provinces and periods. Generally, power demand, power transmission and power generation mix jointly determined the evolution of the VWT network. Sichuan is the largest VW exporter in 2014. In Sichuan, the changes in power generation mix (114%, 104%) dominated the increase of VWT in 2005–2006 and 2006–2007, while the changes of power transmission contribute mostly to the increase of VWT between 2011–2012 (89%) and 2013–2014 (85%). Excepted Sichuan, the same condition occurs in other southwestern exporters such as Hubei, Yunnan, and Guizhou. In those provinces, the power transmission is the main driver of the increase of VWT (see Figure 7) in some periods, but it can be the main driver of the decrease of VWT in other periods. For example, in the 2009–2010 and 2010–2011 periods, the power transmission contributes mostly to the decrease of VWT in Hubei. Compared to southwestern exporters, northwestern exporters show some di fferent characters. Power transmission dominated the change of VWT, which is similar to the southwestern provinces, but the change of power demand replaced the power generation mix and contributes to the second-largest increase of VWT. Especially, between 2013 to 2014, the change of external power generation structure (36%) contributes to the most increase in Inner Mongolia while the change of power transmission is the main driver (25.9%) of the VWT's decrease.

**Figure 7.** (**<sup>a</sup>**–**j**) The contribution of driving factors to the change of virtual water transmission in the top ten virtual water exporters in 2014.

The top ten VSW exporters are Inner Mongolia, Shanxi, Sichuan, Gansu, Ningxia, Xinjiang, Shaanxi, Yunnan, Liaoning, and Hubei, in which the accumulation of VSW accounts for 88.9% of the total VSWT. Figure 8 shows the decomposition results of the VSWT network. The contribution of each factor di ffers from di fferent periods and provinces. Inner Mongolia is the largest VSW exporter in 2014, and the power transmission contributes mostly to the increase of the VSWT between 2005 to 2009, but it is replaced by power demand in other periods. Similar to the VWT evolution, power generation mix, and power transmission play di fferent roles in di fferent periods. As for the change of external power generation structure and external power demand structure, it generally plays a small role in driving the change of VSWT. However, the change of external power generation structure contributes

largely to the change of VSWT in a specific period. For example, it contributes mostly to the increase of VSWT in Inner Mongolia between 2013 and 2014, but to the decrease of VSWT in Ningxia between 2013 and 2014.

**Figure 8.** (**<sup>a</sup>**–**j**) The contribution of driving factors to the evolution of virtual scarce water transmission in the top ten exporters in 2014.

To further investigate the impacts posed by different policies on the evolution of VSWT, we further divided the decade into two periods, i.e., 2005 to 2010 and 2010 to 2014. Figure 9 shows the contributions of factors for the top ten VW and VSW exporters in the two periods. In the first period (here is 2005 to 2010), the power generation mix and power transmission are the main drivers to the change of VWT. In the second period (here is 2010–2014), power demand and power transmission contribute mostly to the change of VWT. However, the change of power transmission in one province (i.e., Shanxi) plays a negative role in the change of VWT in the first period, while it increased to five provinces in the second province. This phenomenon can be ascribed to the evolution of the transmission structure. In comparison with the first period, the power generation structure and the power demand structure play a more important role in the second period, especially in Inner Mongolia and Anhui.

Figure 9 also shows the decomposition results for the top ten VSW exporters in the two periods. In the first decade, Inner Mongolia is the largest VSW exporter, and the change of power transmission (64%) contributes mostly to the increase of VSWT. In the second decade, the power demand replaced the power transmission and plays the largest positive role (75%) in the evolution of VSWT in Inner Mongolia. The transform between the two periods can be ascribed to the different policies implemented in different periods. Especially, in the first period, the change of power transmission contributes mostly to the decrease of VWT and VSWT in Shanxi. In the second period, the power generation mix is the main driver of the VSWT's decrease in Hubei.

**Figure 9.** The evolution of virtual water transmission (**<sup>a</sup>**,**b**), and virtual scarce water transmission (**<sup>c</sup>**,**d**) of top ten provinces in two periods.
