Local and non-local drivers of consumption-based water use in China during 2007–2015: Perspective of metacoupling
Graphical abstract
Introduction
As one of the critical determinants of achieving the 2030 Agenda, water portrays a crucial role in many of the United Nations Sustainable Development Goals (UN SDGs) (Taka et al., 2021). Global initiatives including International Decade for Action ‘Water for Life’ 2005–2015 underline the importance of ensuring the availability and sustainable management of water and sanitation for all by 2030 (FAO, 2020; Hoekstra et al., 2017). Water stress, exacerbated by climate change, population growth, economic development, food production, and diet shifts, has been recognized as one of the top five global risks threatening economy and ecosystems (World Economic Forum, 2020). According to UN-Water (2021), two-thirds of the world's population are estimated to be living in water-stressed areas by 2025 if current consumption patterns continue, with many developing countries facing the most severe situation (WBCSD, 2014).
Water demand, or the total amount of freshwater required for services or producing goods and providing services, is usually depicted by consumption-based water use (WU) and its associated indicator virtual water (VW, defined as part of the water consumed in one area supported by other areas through virtual water trade) (D'Odorico et al., 2019; Gohari et al., 2013). WU and VW reveal the geographical mismatch between water suppliers and water demanders. Water-intensive sectors such as agriculture are critical in reducing WU and better managing water resources (Tuninetti et al., 2020; Zhao et al., 2019).
Over the past few decades, rapid urbanization and growing population have exacerbated water-related problems worldwide including water shortages, degraded water quality, floods, and energy shortage. As one of the world's most water-stressed and most rapidly urbanizing countries, China has developed diverse water conservancy measures to avoid the long-term water crisis since the 1980s (Giordano, 2007; Zhou et al., 2020). These policy measures have contributed to China's transition towards a more resource-efficient and circular economy in achieving social development and technological progress. In this context, the high dynamism and heterogeneity of China's consumption-based water use (WU) links widely and deeply to, multiple socioeconomic, political, and environmental factors (e.g., economic development, industrialization, and urbanization) that operate concurrently and that vary spatially (Bryan et al., 2018; Liu, 2016, Liu, 2017, Liu et al., 2018, Liu et al., 2019, Liu et al., 2020, 2017).
Previous literature reports extensive research on water consumption and drivers of its evolution in China, using correlation analysis, IPAT framework, STIRPAT model, decomposition analysis methods, among others (Fan et al., 2019; Zhao et al., 2014; Zhi et al., 2015; Zhou et al., 2020). Analytic dimensions derive usually from size, structure, intensity, or the dimension of population, affluence, and technology, or their combinations. Zhao et al. (2014) explored the influences of population, affluence, urbanization level, and diet structure on agriculture products-related water footprint change by applying an extended STIRPAT model. Zhi et al. (2015) developed the IO-IPAT-SDA approach to decompose WU drivers and evaluate their impacts on WU changes by different final users. Zhou et al. (2020) identified the deceleration of China's WU and quantified the relative contributions of 14 socioeconomic drivers using a log-mean Divisia index (LMDI) model. Fan et al. (2019) improved the traditional SDA model to identify the driving factors of different types of final demand. Most studies have focused on the whole of China or a single region within China and have rarely considered inter-regional interactions when identifying the factors influencing dynamic evolution in WU.
A small body of literature has targeted WU change from the perspective of regional relevance. For instance, given that global population growth and the associated water demand increase is partly sustained by virtual water trade (D'Odorico et al., 2019), Liu et al. (2019) examined the effects of production fragmentation on virtual water trade and concluded that the value chain-related trade increased national water use most. Liu (2016) reckoned that for an area with a high degree of external dependence like Beijing, economic and technical conditions of the importing region would have a great influence on its water footprint together with the product import volume. Hence, Liu (2016) disassembled the water consumption of an area into two parts (internal and external water footprint), analyzed their driving factors respectively, and defined them as internal and external driving factors. Gao et al. (2020) considered the spatial heterogeneity of the final demand when analyzing China's production WU, inferred the regional coordination situation based on result of the WU distribution between regions and sectors, and then put forth policy suggestions. However, these studies failed to quantify independent variables that explicitly characterized potential impacts from other regions or establish a direct mapping relationship between the independent variables and the total water consumption by regions. From this perspective, little is known about how the WU change would be affected by meta-coupled processes including human-nature interactions within and across adjacent and distant places. Local WU drivers have been intensively analyzed involving consumption level, population scale, WU efficiency, and local final demand (Fan et al., 2019; Zhao et al., 2014; Zhi et al., 2015; Zhou et al., 2020), yet local and non-local factors have almost never been explicitly considered at the same time.
To fill this research gap, we seek to answer the following question: how strongly do different drivers influence WUs both at local level and beyond regional boundaries? Our working hypotheses is that local and non-local factors simultaneously modulate such dynamics, acting differently across provinces. Our indicators for evaluating WU heterogeneity involve total WU, per capital WU, and WU per GDP. Our accounting method for WU volume is environmentally-extended input-output analysis. Local and non-local factors are selected based on previous studies and the metacoupling framework (Liu, 2017), and their relative contributions to WU change are quantified at both provincial and sectoral levels using index decomposition analysis. The findings of this study are conducive to identifying hotspots and priority steps for water saving in China, serving also as a reference for other countries in achieving sustainable water resource management.
Section snippets
Environmentally-extended multiregional input-output analysis
Many recent studies have focused on WU at multiple scales with the help of recent advanced methods such as the environmentally-extended MRIO analysis (Zhao et al., 2015), life cycle assessment (Forin et al., 2020; Jefferies et al., 2012), food balance method (Dalin et al., 2014), ecological network analysis (Suweis et al., 2013; Yang et al., 2012), and GIS-based Environmental Policy Integrated Climate modeling (Liu et al., 2020). These methods can be classified into bottom-up (or process-based)
Heterogeneity in consumption-based water use
It is obvious that the largest WUs were found in provinces with a large GDP per capita or population: Guangdong (43.9 billion m³), Jiangsu (30.0 billion m³), Hubei (27.9 billion m³), Shandong (20.1 billion m³), and Hunan (19.9 billion m³) (Fig. 1). WWs were generally smaller than WUs in coastal provinces that depend more heavily on water inflow from other provinces, while the contrary was the case of inland provinces (WW represents water withdrawal for domestic production) (Fig. 2a).
The largest
Comparison with the literature
This research revealed that total national WU generated a possible peak around 2012 and has thereafter decreased under water conservancy policies. This is in line with observations by Zhang et al. (2020) and Liu et al. (2019). Capital investment and rapid urbanization are likely contributing factors for WU changes in provinces with urbanization levels below the national average (Fig. S8). The relationship between the growth rates of urbanization and WU reached a turning point when the annual
Conclusion
We analyzed China's final consumption-based water use during 2007–2015 and investigated the relative contributions of socioeconomic factors through the lens of metacoupling. The results show that the total national WU decreased under water conservancy policies and generated a possible peak around 2012, with WU trends varying by province and sector. The surge in capital investment and rapid urbanization process fundamentally shaped the WU increase pattern in low developed inland provinces which
Author contribution
Yueyue Du: Conceptualization, Data curation, Formal analysis, Visualization, Writing- original draft, Revision; Dandan Zhao: Methodology, Software, Writing – review & editing; Meng Jiang: Conceptualization, Writing – review & editing; Yan Bo: Data curation, Software, Investigation; Changxian Wu: Investigation, Visualization; Olli Varis: Revision & Language polish; Jian Peng: Revision & Edit; Feng Zhou: Conceptualization, Supervision, Review & Editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The authors are grateful for financial support from the National Natural Science Foundation of China (No. 41977082, No. 41625001). Yueyue Du is additionally supported by PKU-IIASA International Postdoctoral Fellowship Program. Dandan Zhao is additionally supported by Aalto University.
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