*2.4. Data Sources*

This paper analyzed VWT based on electricity transmission among thirty provincial grids in mainland China. The power consumption and generation of Tibet are excluded because Tibet is isolated from other grids. In reality, the Inner Mongolia power grid is divided into the eastern and western parts, which are operated by two companies. Here, to directly investigate the overall transmission of Inner Mongolia, we assemble the eastern and western parts as one provincial grid, i.e., the Inner Mongolia power grid.

In our study, two groups of data are used i.e., electricity data and water consumption of power generation data. We put the data in the Supplementary Materials. As for electricity data, electricity generation data by different power generation technologies at the provincial level are collected from the China Electricity Yearbook, in which electricity consumption data by provinces are also reported. Similar to the previous study [37], pair-wise electricity transmission data are collected from the Annual Complication of Statistics of Power Industry in China [38].

Water consumption for different power generation technologies has been fully investigated at different scales by using different models. Additionally, the same power generation technologies may have different water inventory because of different cooling units. For example, the water consumption of coal-fired power plants differs from different cooling systems, while the water consumption of hydropower is impacted by various factors such as evaporation and season's change. In this study, we collected water inventory of thermal power generation, hydropower generation, and nuclear power generation from [20,22], which fully considered the spatial distribution of different power plants' types. As for wind and solar, we collected the water inventories from previous studies [17,39]. As administrative provinces are the objectives of this study, we estimated the water inventory of provincial electricity generation by:

$$\text{PWC} = \frac{\sum\_{k=1}^{k=5} EG^k \cdot w\_k}{EG} \tag{21}$$

where *EG<sup>k</sup>* is the power generation from *kth* technology, and *wk* is the water coefficient for technologies.

Power generation provinces extracted large amounts of water to satisfy local power plants and virtual water is exported to load hubs when power generation provinces exported its electricity. As the spatial imbalance distribution between energy resources and water resources exists, it is vital to distinguish virtual water delivered from water-abundant provinces and water-scarce provinces. The water stress index (WSI) proposed by [40] is thus used to adjust VW into virtual scarce water (VSW). The WSI indicator could represent the water pressure that a region faced. It is calculated by adjusting the withdrawal-to-availability (WTA) ratio into a constant ranged from 0 to 1, which is shown as follows:

$$\text{WSI} = \frac{1}{1 + \mathbf{e}^{-6.4 \cdot \text{WTA}} \left(\frac{1}{0.01} - 1\right)}\tag{22}$$

The WSI and electricity generation mix of each province is presented in Figure 1, which clearly shows the spatial mismatch between energy resources and water resources. According to the previous study [21], we divide provinces with different WSI into four levels, i.e., no water stress (WSI under 0.2), moderate water stress (WSI 0.2–0.6), serious water stress (WSI 0.6–0.8), and extreme water stress (WIS 0.8–1.0). The spatial distribution of water resources is extremely unbalanced (see Figure 1), with most southern provinces being classed as humid and northern provinces classed as arid. In addition, the spatial distribution of different primary energy resources determines the power generation mix in each province. Even the speed of decarbonization in China's power sector is increasing, the power generated by thermal is still dominated generation mix in most provinces. Most of hydropower plants located in the southern provinces, while other renewable energies located in northwestern and northeastern provinces.

**Figure 1.** The water stress index (WSI) and electricity generation mix of each province in 2014.
