Grey water footprints of U.S. thermoelectric power plants from 2010–2016

Chini, Christopher M.; Logan, Lauren H.; Stillwell, Ashlynn S.

doi:10.1016/j.advwatres.2020.103733

Cited by 22 publications

(13 citation statements)

References 40 publications

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“…One proposal for water categorization includes 17 separate categories (Boulay et al, 2011): high resolution is desirable but can be evasive in practice given water data quality constraints (Grubert et al, 2020; Perrone et al, 2015). Similarly, choices to include rain water (in water footprinting parlance, green water [Hoekstra et al, 2011]) or polluted water volumes (gray water [Hoekstra et al, 2011]) vary by study (Chini et al, 2020; Marston et al, 2018), though green water is mainly relevant for systems including agriculture and/or silviculture (Grubert et al, 2020). When the water use of electricity in a specific area is the quantity of interest, challenges also arise in determining the relevant geographic attribution of water used for electricity (Siddik et al, 2020).…”

Section: Water‐for‐energy Inventory and Impact Assessmentmentioning

confidence: 99%

Water for energy: Characterizing co‐evolving energy and water systems under twin climate and energy system nonstationarities

Grubert

Marshall

2022

WIREs Water

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The energy system is a major water user, but understanding how much water is consumed and withdrawn for energy is a challenging empirical task: nonevaporative volumetric water use is not easily calculated from first principles. Water use also has impacts that differ in important ways depending on where water is abstracted and used, timing of use, and socioenvironmental context. Moreover, different decarbonization pathways and policy environments have very different water use implications. We currently face a crisis of twin nonstationarities in hydrology and energy systems that make understanding water use for energy critical for decision support as hydroclimate and energy systems change. Currently, water‐for‐energy data are highly uncertain and not centrally collected, which means researchers spend substantial effort collecting inventory data. Recent advances in impact assessment methods for water volumes focus largely on spatially resolved water scarcity evaluations, but robust conclusions can be elusive due to uncertain and low‐metadata inventory information. As water‐for‐energy quantification efforts progress, research should emphasize decision support for energy system design, incorporating crucial hydrologic dynamics. Beyond the location of water use, relative scarcity, and potential competing uses, these include sub‐daily to interannual temporal dynamics, the impacts of climate change on these dimensions, potential feedbacks between energy and water systems, and the impacts of hydrologic variability or change on policy‐based incentive structures. This article reviews prior US‐focused efforts to quantify water use for energy, highlights why these nonstationarities are analytically relevant with a brief policy case study, and highlights research needs for decision support under twin nonstationarities. This article is categorized under: Engineering Water > Sustainable Engineering of Water Engineering Water > Planning Water Engineering Water > Methods

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Section: Water‐for‐energy Inventory and Impact Assessmentmentioning

confidence: 99%

Water for energy: Characterizing co‐evolving energy and water systems under twin climate and energy system nonstationarities

Grubert

Marshall

2022

WIREs Water

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“…For instance, recent studies of the electricity grid in Europe investigated the virtual trade of water footprints between countries at a monthly scale (Chini & Stillwell, 2020) and at a finer resolution to identify impacts of COVID‐19 lockdowns (Roidt et al., 2020). Additionally, monthly gray water footprints (that account for thermal pollution) were analyzed for thermoelectric power generation in the United States (Chini et al., 2020). However, even within these intraannual studies, static water footprint factors of electricity are assumed.…”

Section: Water Footprints and Seasonalitymentioning

confidence: 99%

Opportunities for Robustness of Water Footprints in Electricity Generation

Chini

Delorit

2021

Earth's Future

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“…Corredor et al [ 32 ] applied GWF analysis in an artisanal mining region in Colombia, and found that higher water pollution pressure was associated with dumping of suspended solids containing mercury. Chini et al [ 33 ] found significant seasonal variation in water pollution based on estimating the GWF from thermoelectric power plants in the USA. Chen et al [ 34 ] estimated the water quality situation of the irrigated region of Yinchuan (China) using the GWF model and revealed its potential driving factors.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Regional Coordination Relationship between Water Utilization and Urbanization Based on Decoupling Analysis: A Case Study of the Beijing–Tianjin–Hebei Region

Shen

Yao

2022

IJERPH

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Understanding the potential association between the urbanization process and regional water shortage/pollution is conducive to promoting the intensive utilization of local water resources. In this study, the water footprint model was used to estimate water utilization status in terms of both water quantity (virtual water footprint (VWF)) and water quality (grey water footprint (GWF)) in the Beijing–Tianjin–Hebei region (China) during 2004–2017. Their potential coordination relationship with the local urbanization process represented by the gross domestic product (GDP), population (POP), and built-up area (BA) was examined using the Tapio decoupling model. The results showed that from 2004 to 2017, (1) VWF in Beijing and Tianjin showed non-significant decreasing trends, with reductions of 1.08 × 109 and 1.56 × 109 m3, respectively, while that in Hebei showed a significant increasing trend, with an increase of 5.74 × 109 m3. This indicated a gradually increasing water demand in Hebei and decreasing demand in Beijing and Tianjin. In all three regions, the agricultural sector accounted for a relatively high proportion of VWF compared to other sectors. (2) GWF in Beijing, Tianjin, and Hebei all showed declining trends, with reductions of 2.19 × 1010, 2.32 × 1010, and 1.66 × 1011 m3, respectively, indicating considerable local water quality improvement. The domestic sector contributed as the main component of GWF in Beijing, while agriculture was the main contributor in Hebei. The major contributor in Tianjin transitioned from the domestic (before 2015) to the agricultural sector. (3) We found good coordination between VWF and GDP in all three regions, as their local economic development was no longer overly dependent on water consumption. However, the expansion of urban built-up area or population would bring about accelerated depletion of water resources. (4) GWF in the three provinces showed good coordination with GDP, POP, and BA in most years, implying that the development of urbanization no longer strongly caused the pollution of water resources. In sum, policymakers should focus on improving agricultural irrigation efficiency and residents’ awareness of water conservation, so as to gradually achieve sustainable water resource management in the BTH region.

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Grey water footprints of U.S. thermoelectric power plants from 2010–2016

Cited by 22 publications

References 40 publications

Water for energy: Characterizing co‐evolving energy and water systems under twin climate and energy system nonstationarities

Water for energy: Characterizing co‐evolving energy and water systems under twin climate and energy system nonstationarities

Opportunities for Robustness of Water Footprints in Electricity Generation

Exploring the Regional Coordination Relationship between Water Utilization and Urbanization Based on Decoupling Analysis: A Case Study of the Beijing–Tianjin–Hebei Region

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