Evolution of Virtual Water Transfers in China’s Provincial Grids and Its Driving Analysis

Abstract

In China, electricity transmission has increased rapidly over the past decades, and a large amount of virtual water is delivered from power generation provinces to load hubs. Understanding the evolution of the virtual water network embodied in electricity transmission is vital for mitigating water scarcity. However, previous studies mainly calculated the virtual water transferred in short periods in low-spatial resolution and failed to reveal driving forces of the evolution of virtual water. To solve this problem, we investigated the historical evolution of the virtual water network and virtual scarce water network embodied in interprovincial electricity transmission between 2005 and 2014. The driving forces of the evolution of virtual (scarce) water networks were analyzed at both national level and provincial level. The results show that the overall virtual water transmission and virtual scarce water transmission increased by five times, and the direction was mainly from southwest and northwest provinces to eastern provinces. Sichuan, Yunnan, and Guizhou played an increasingly important role in virtual water exporting, and northwestern provinces had dominated the virtual scarce water exporting in the decade. At the national level, the increase of virtual water is mainly driven by the change of power generation mix and power transmission. At the provincial level, the increase of virtual water transmission in the largest virtual water exporter (Sichuan) is driven by the power generation mix and the power transmission, between 2005 and 2010, and 2010 and 2014, respectively. Considering the expanding of electricity transmission, the development of hydropower in the southwestern provinces and other renewable energies (solar and wind) in the northeastern provinces would overall mitigate the water scarcity in China.

Water Footprints and Virtual Water Flows Embodied in the Power Supply Chain

“…The Node-Flow model takes the indirect imports and exports of power into account and is hence more suitable than the direct trade-adjustment approach for quantifying virtual water transfers of power trade. Comparing these two methods, the virtual water transfers in China calculated by the Node-Flow method [64] are twice as important as those calculated with the direct trade-adjustment method [69] in the same year. The large difference of these two methods can be explained as follows: with virtual water estimation, the consumed power may be generated by multiple regional grids, where power is generated under different energy sources, technologies, and different geological and climatic conditions.…”

“…For other energy sources, such as wind and solar power, although they impose a small impact due to their negligible water footprints, recent work has begun to pay more attention to renewable power in power trade. The virtual water transfers via China's electric grids (including renewable power) [64] are nearly four-fold as important as those (excluding renewable power) [65] in 2014. However, renewable power cannot be missing in the power trade.…”

“…The global virtual water flow of power trade in 2017 (the power trade data are extracted from the United Nations database [29], and the virtual water flows are estimated using the water footprints provided by [3]); virtual water transfers of regional power trade during 2010-2016. Virtual water transfers in the United States [63] and Europe [62] are calculated by the direct trade-adjustment method, and the Node-Flow method is used for virtual water transfers in China [64]. Callouts: trends of the virtual water flows with time (a) the United States, (b) Europe, (c) China.…”

Electricity-Related Water Network Analysis in China Based on Multi-Regional Input–Output Analysis and Complex Network Analysis

“…At the electricity grid level, Zhu et al [14] estimated the flows of virtual water and scarce water in the power system based on the water intensity method, but the spatial resolution of the study was not high. At the provincial level, Zhang et al [15] investigated the evolution of the inter-provincial virtual water network from 2005 to 2014 based on an electricity transmission network.…”

Exploring Virtual Water Network Dynamics of China’s Electricity Trade: Insights into the Energy–Water Nexus

“…However, research on the spatial formation mechanisms of virtual water networks for electricity trade is currently inadequate. The logarithmic Mean Divisia Index (LMDI) [38] and Structural Decomposition Analysis (SDA) [39] are the main methods used in the existing literature to investigate the drivers of the evolution of electricity-related virtual water flows over time. These two methods are limited when analyzing socio-environmental factors, as the drivers they examine depend on the virtual water accounting formula.…”