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Producing energy resources requires significant quantities of fresh water. As an energy sector changes or expands, the mix of technologies deployed to produce fuels and electricity determines the associated burden on regional water resources. Many reports have identified the water consumption of various energy production technologies. This paper synthesizes and expands upon this previous work by exploring the geographic distribution of water use by national energy portfolios. By defining and calculating an indicator to compare the water consumption of energy production for over 150 countries, we estimate that approximately 52 billion cubic meters of fresh water is consumed annually for global energy production. Further, in consolidating the data, it became clear that both the quality of the data and global reporting standards should be improved to track this important variable at the global scale. By introducing a consistent indicator to empirically assess coupled water?energy systems, it is hoped that this research will provide greater visibility into the magnitude of water use for energy production at the national and global scales.
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The water consumption of energy
production: an international comparison
E S Spang
, W R Moomaw
, K S Gallagher
, P H Kirshen
and D H Marks
Center for Water-Energy Efciency, University of California, Davis, CA, USA
The Fletcher School, Tufts University, Medford, MA, USA
University of New Hampshire, Durham, NH, USA
Massachusetts Institute of Technology, Cambridge, MA, USA
Received 18 August 2013, revised 20 June 2014
Accepted for publication 29 August 2014
Published 8 October 2014
Producing energy resources requires signicant quantities of fresh water. As an energy sector
changes or expands, the mix of technologies deployed to produce fuels and electricity determines
the associated burden on regional water resources. Many reports have identied the water
consumption of various energy production technologies. This paper synthesizes and expands
upon this previous work by exploring the geographic distribution of water use by national energy
portfolios. By dening and calculating an indicator to compare the water consumption of energy
production for over 150 countries, we estimate that approximately 52 billion cubic meters of
fresh water is consumed annually for global energy production. Further, in consolidating the
data, it became clear that both the quality of the data and global reporting standards should be
improved to track this important variable at the global scale. By introducing a consistent
indicator to empirically assess coupled waterenergy systems, it is hoped that this research will
provide greater visibility into the magnitude of water use for energy production at the national
and global scales.
SOnline supplementary data available from
Keywords: water-energy nexus, energy portfolio, water footprint
1. Introduction
Producing energy resources often requires signicant quan-
tities of freshwater (Gleick 1994). Water is required for nearly
all production and conversion processes in the energy sector,
including fuel extraction and processing (fossil and nuclear
fuels as well as biofuels) and electricity generation (thermo-
electric, hydropower, and renewable technologies). As an
energy sector changes or expands, the mix of technologies
deployed to produce fuels and electricity determines the
associated burden on regional water resources.
To guide the assessment of the water use impact of
energy production on water resources, it is useful to apply the
well-developed concept known as the water footprint. A
water footprint is the volume of water needed for the pro-
duction of goods and services consumed by the inhabitants of
the country(Hoekstra and Chapagain 2007, 35). The water
footprint is further specied by type of water use, with blue
waterrepresenting consumption of surface and groundwater,
green waterrepresenting consumption of water via soil
strata (e.g. rain-fed agriculture), and gray wateras the
amount of water required to dilute pollutant ows into the
environment (Mekonnen and Hoekstra 2010).
A number of previous studies have applied the concept of
the water footprint to the energy sector (either directly or
indirectly) by consolidating estimates of water use coef-
cients for a range of energy technologies, with emphasis on
fuel production (Wu et al 2009, Mittal 2010, Mekonnen and
Hoekstra 2010), electricity generation (Barker 2007, Mack-
nick et al 2011), or both (Gleick 1994, DOE 2006, Fthenakis
and Kim 2010, Mulder et al 2010, Mielke et al 2010,
Environmental Research Letters
Environ. Res. Lett. 9(2014) 105002 (14pp) doi:10.1088/1748-9326/9/10/105002
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distribution of this work must maintain attribution to the author(s) and the
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1748-9326/14/105002+14$33.00 © 2014 IOP Publishing Ltd1
Meldrum et al 2013). The results of these studies collectively
demonstrated that the quantity and quality of water demanded
varies signicantly by energy process and technology, from
rather negligible quantities of water used for wind and solar
electricity generation to vast, agricultural-scale water use for
the cultivation of biofuel feedstock crops. Hence, the selec-
tion of technologies deployed for energy production within a
given location has important implications on regional
water use.
Additional studies have explored this geographic
approach to water use for energy systems, including country-
level or regional analyses of water consumption across entire
energy portfolios (DOE 2006, Elcock 2010) or global ana-
lyses of water consumption by a single energy type (Vassolo
and Döll 2005). This study builds on this previous work by
providing the rst international, country-level comparison of
water consumption for both fuels and electricity production.
For consistency and brevity, the metric for estimating
water consumption for energy productionwill be referred to
as water consumption of energy production (WCEP). The
WCEP indicator is conceptually similar to the water footprint,
but is more specically dened as a detailed estimate of
regional blue waterconsumed by the processes and tech-
nologies specically for producing energy, including both
fuels and electricity. Further, in addition to the categories of
freshwater and groundwater, we also include highly treated
water from impaired sources, such as desalinated seawater,
within the category of blue waterto indicate that the post-
treatment quality of the water sufciently merits its reclassi-
cation as a more competitive resource. Finally, while energy
production does have both green and gray water footprints as
well, these categories of water use fall outside the scope of
this paper.
In further dening water use, it is important to distinguish
between water withdrawals and water consumption. As
dened by the US Geological Survey (USGS), water with-
drawals are dened as the amount of water removed from the
ground or diverted from a water source for use(USGS 2009,
49). Water consumption is a subset of the withdrawals cate-
gory and refers to the amount of water withdrawn that is
evaporated, transpired, incorporated into products or crops, or
otherwise removed from the immediate water environment
(USGS 2009, 47).
The WCEP indicator focuses on water consumption,
rather than withdrawals, as the key water use variable in this
study. While both consumption and withdrawals are impor-
tant variables to consider within the broader regional man-
agement of water, water consumption is especially useful in
understanding the impact of energy sector operations on the
water sector. Consumption represents an exclusionary use of
water where use by one user directly prevents other users
from accessing that quantity of the resource, providing a
direct measurable impact on water security and sustainability.
In contrast, water withdrawals may be returned to the water
source (albeit at a potentially lower quality) to be used again
by other consumers or by the natural environment, and hence
represent a more equivocal metric for assessing regional water
Finally, this paper does not include water consumption
estimates for hydropower. While some studies allocate eva-
porative reservoir losses as a consumptive use of water by
hydropower, this association is ambiguous. Reservoirs serve a
multitude of other critical societal purposes, most notably
water supply storage and regional ood control (WCD 2000,
Fthenakis and Kim 2010). Therefore, assigning reservoir
water evaporation to hydropower can be misleading. For this
reason, and in following the precedent of other substantive
studies that have excluded hydropower from water for energy
studies (Elcock 2010, Macknick et al 2012), calculations of
water consumption for hydropower are not included in the
overall WCEP assessment.
2. Methodology
This research compares the total water consumption of
national energy portfolios by energy type. Because data on
actual water consumption for energy systems do not exist at
the international scale, estimating these values required con-
solidating country-level energy production data and applying
water consumption factors by energy production technology
or process to approximate a water volume.
Existing estimates of water consumption factors for
energy production vary substantially in the literature. Hence,
establishing the consistency of the WCEP metric required
dening clear parameters for the selected estimates. For this
paper, only operationalwater consumption (i.e. consump-
tion that is limited to processes directly related to energy
production) is considered (Macknick et al 2011). It does not
include the embedded water in equipment and materials (e.g.,
in fabricating photovoltaic panels) or related to power plant
construction. Similarly, water used at energy production
facilities for auxiliary purposes (e.g., bathrooms at a power
plant) is not included. In sum, water consumption factors
were consolidated from the literature and selected specically
from the most recent reports that were able to clearly and
consistently identify the consumptive use of freshwater for
operational energy production.
2.1. Fuel production
Fuel production data were gathered from the US Energy
Information Administration International Energy Statistics
Database (EIA 2011). Energy production data for each energy
category were consolidated and converted to a common unit
(gigajoule; GJ) to ease aggregation and water consumption
calculations. For fossil, nuclear, and biomass-based fuels, it
was necessary to nd data on the fuel production quantities
for each major process in the fuel cycle. Because fuel
extraction and processing do not necessarily take place in the
same country, we collected as much readily available data as
possible to trace water consumption geographically according
to each phase of the fuel cycle.
For fossil fuels, the EIA database does not provide detail
on the production of conventional vs. unconventional fossil
fuels, so additional data were acquired from the EIA for oil
Environ. Res. Lett. 9(2014) 105002 E S Spang et al
sands (EIA 2010), and from the World Energy Council
(WEC) for heavy oil, shale oil, and shale gas
(WEC 2010a,2010b). Water use associated with fossil fuel
transformations (such as coal gasication, coal liquefaction,
and hydrogen production) was not included in this study, but
could be included in future assessments as these processes
become more widespread.
Water consumption estimates for fuel production are
mostly related to direct extraction and processing
(Gleick 1994). Given the limited data on specic fossil fuel
extraction and processing technologies utilized within each
country studied, it made sense to apply the water consumption
estimate that reected the most frequently applied technolo-
gies or processes. For example, for oil production we used an
averaged water consumption factor for primary and secondary
extraction (Wu et al 2009), since we did not have data on the
deployment of more advanced technologies, such as
Enhanced Oil Recovery (EOR).
Table 1provides a summary of the categories of fuel
production included in this study, the sources for international
estimates of fuel production on a country-by-country basis,
and the estimates and sources for the water consumption
factors applied to each fuel production category.
For nuclear fuel extraction and processing, data were col-
lected from the International Atomic Energy Agencys(IAEA)
online database of nuclear fuel cycle processing capacity, the
Nuclear Fuel Cycle Information System (NCFIS, IAEA 2011).
The NCFIS database contained uranium production data dis-
aggregated by country and by process stage in the nuclear fuel
cycle. Since the data are given in terms of production capacity
and not actual production, it was assumed that all plants were
operating at full capacity to provide annual production esti-
mates. Finally, while the NCFIS database provided uranium
production in terms of mass, all the water consumption factors
were linked to units of nuclear fuel energy (Meldrum
et al 2013). Nuclear fuel mass units were converted to energy
units using conversion factors from the World Information
Service on Energy (WISE 2009) Uranium Project (2009). See
table SI-1 in the supplementary information (SI), available at, for more details.
Unlike the data for fossil fuels, where extraction quan-
tities were clearly disaggregated from renery production,
the biofuel data source (EIA 2011) does not specify between
biofuel cultivation and processing. As such, all biofuels
produced in a country were assumed to have been
derived from feedstock crops grown in that country. Further,
Table 1. Fuel production categories with water consumption factors and data sources.
Water consumption factor (m
Energy category Sub-category Energy production source
Min Max Source
Fossil fuel Coal
[1] 0.043 0.006 0.242 [7]
Conventional oil
[1] 0.081 0.036 0.140 [8]
Oil sands
[2], [3] 0.114 0.072 0.132 [8]
Oil rening [1] 0.040 0.026 0.048 [8]
Conventional gas [1] 0.004 0.001 0.027 [7]
Shale gas [4] 0.017 0.003 0.221 [7]
Nuclear fuel Uranium mining
[5] 0.033 0.000 0.252 [7]
Milling [5] 0.012 0.003 0.030 [7]
Conversion [5] 0.011 0.004 0.014 [7]
Diffusion (enrichment) [5] 0.037 0.034 0.039 [7]
Centrifuge (enrichment) [5] 0.004 0.003 0.006 [7]
Fuel fabrication [5] 0.001 0.001 0.003 [7]
Fuel reprocessing [5] 0.007 0.007 0.007 [7]
Biofuel processing Ethanol [1] 0.145 0.092 0.290 [9]
Biodiesel [1] 0.031 0.031 0.031 [9]
Biofuel cultivation Sugarcane (ethanol) [3], [6] 24.550 0.000 156.000 [10]
Maize (ethanol) [3], [6] 8.090 0.000 554.000 [10]
Sugarbeet (ethanol) [3], [6] 9.790 0.000 157.000 [10]
Rapeseed (biodiesel) [3], [6] 19.740 0.000 270.000 [10]
Soybean (biodiesel) [3], [6] 11.260 0.000 844.000 [10]
Palm oil (biodiesel) [3], [6] 0.000 0.000 0.850 [10]
Sources for global energy production estimates (all data for 2008 unless otherwise specied) [1]: EIA (2011), Oil rening data for 2006 [2];
EIA (2010) for oil sands data [3]; WEC (2010a) [4]; WEC (2010b) [5]; IAEA (2011) [6]; Cushion et al (2009).
All water consumption factor estimates are for the median values except for the biofuel feedstock cultivation estimates, which represents the
average value. This is consistent with the estimates in the literature.
Sources for water consumption factor estimates [7]: Meldrum et al (2013) [8]; Wu et al (2009) [9]; Mittal (2010) [10]; Mekonnen and
Hoekstra (2010).
The source paper for the water consumption factor (Meldrum et al 2013) differentiates the coal fuel cycle and uranium mining by underground
and surface mining. However, since the EIA and IAEA energy production data does not specify mining type for coal or uranium production, the
average of the underground and surface water consumption factors was calculated within each of the energy categories.
Assumed all oil production was through primary/secondary recovery since enhanced oil recovery (EOR) processes were not specied in data.
EOR methods can be 3× more water-intensive than primary/secondary recovery (Mielke et al 2010).
Includes estimation of oil shale and heavy oil as well.
Environ. Res. Lett. 9(2014) 105002 E S Spang et al
given the lack of a comprehensive international database of
biofuel production by crop feedstock, we needed to derive
estimates for biofuel feedstock production for each country
based on secondary reports (WEC 2010a, Cushion
et al 2009).
Similarly, water consumption factors for biofuels had to
be consolidated for both cultivating the feedstock crops and
processing the feedstock into biofuels. The biofuel crops
studied were rst-generation biofuels in current production,
not second-generation biofuels derived from algal or cellu-
losic feedstocks. Water consumption calculations were based
on applying water consumption coefcients to the specic
biofuel feedstock cultivation of the following fuel crops:
rapeseed, soybean, and palm oil for biodiesel production, and
sugarcane, maize, and sugarbeet for ethanol production.
Irrigation requirements vary widely based on the regional
climate, irrigation technology deployed, local farming prac-
tices, and regional land use change (Mekonnen and Hoek-
stra 2010). To accommodate this variation we consolidated
country-specic water consumption factors for irrigation by
crop type from a detailed analysis by Mekonnen and Hoekstra
(2010). Water consumption factors for biofuel processing
were selected from Mittal (2010).
Figure 1consolidates the estimates of water consumption
factors for fuel production (median and range) as listed in
table 1. The median estimates for water consumption factors
for oil production tend to be higher than those for any other
fuel category (aside from ethanol processing). Further,
unconventional fossil fuel sources (oil sands and shale gas)
tend to consume more water than conventional sources.
Finally, it is worth noting the wide range of water con-
sumption estimates for coal, shale gas, uranium mining and
ethanol processing, suggesting that these estimates might be
more variable from site to site based on local conditions or
type of technologies deployed (Mittal 2010, Meldrum
et al 2013).
A separate gure (gure 2) shows water consumption
factors (mean and range), for biofuel feedstock cultivation
since these factors are one to two orders of magnitude greater
than all other fuel production processes. Further the vast range
of water consumption within each biofuel feedstock category
is signicant, with estimates of zero to represent rain-fed
feedstock crop cultivation and maximum values that extend
6x to 80x beyond the value of the median values for each
category. Palm oil remains an outlier in this category, where
estimated water consumption is assumed to be negligible,
since palm oil production does not frequently require direct
2.2. Electricity generation
Calculating the WCEP for electricity generation required a
similarly high level of data resolution as for fuel production.
Water consumption varies signicantly by generation tech-
nology, fuel type, and cooling type at the scale of the indi-
vidual power plant. While water is used for a variety of
processes in the production of electricity (e.g., ue gas
desulfurization, washing solar panels), the majority of water
use is for cooling in thermoelectric power plants. Because of
its high specic heat, water is an ideal heat transfer medium
for cooling steam after it exits the generator turbine. Some
power plants use seawater for cooling or even dry cooling
technologies (using air rather than water for heat transfer), but
the vast majority of power plants consume freshwater for
cooling (Platts 2010).
To consider this level of technological detail in power
plants at the international scale required extracting and pro-
cessing data from the Platts World Electric Power
Plants (WEPP) Database (2010). While the WEPP database
Figure 1. Consolidated estimates of water consumption factors for primary fuel extraction and processing. Note: ER = enrichment.
Environ. Res. Lett. 9(2014) 105002 E S Spang et al
is relatively comprehensive for generator technology and
fuel type, it only contains cooling technology information
for roughly 37% of relevant power plants in the database.
For power plants with no cooling type specied, the
cooling portfolio mix by generator and fuel type exhibited
in the rest of the country (or region
, as necessary) was
As with nuclear fuel cycle data, the WEPP database
provides information on power plant capacity, but not annual
production. To convert the installed capacity of the power
plants to an estimate of annual electricity production, each
technology was assumed to have operated at its capacity
factor, as estimated by the National Renewable Energy
Laboratory (NREL 2010), also shown in table 2. Converting
installed capacity to estimated annual electricity generation is
calculated using (2):
=Estimated Generation(MWh) Installed Capacity
*Capacity Factor*365 days/year *24 h/day. (1)
To cross-validate the power generation amounts for
each power plant technology calculated from the WEPP
database capacity data, the total energy generation portfolio
was normalized to national electricity production data from
the EIA for 2008 (EIA 2011). In this way, we could use
the higher resolution data from the WEPP database to
determine the relative signicance of each sub-technology
within each national-level power plant portfolio, while
improving the accuracy of comparisons between countries
by ensuring total generation quanitities were in line with
international data sources. Equation 2summarizes this
normalization where the annual electricity generation for
each technology type (i) is equal to the WEPP generation
calculation for that technology multiplied by the ratio of
EIA total generation over WEPP total generation.
Estimated Generation WEPP Generation
* EIA Generation /WEPP Generation . (2)
total total
Figure 3consolidates the water consumption factors for
electricity-generating technologies by fuel source, generation
technology, and cooling type (where applicable). For ther-
moelectric production systems, evaporative cooling towers
(CTs) show signicantly higher consumption than once-
through cooling (OTC) systems and cooling ponds (CP). As
an aside, even though OTC systems consume less water, they
withdraw between 20 to 50 times more water than CT sys-
tems (Meldrum et al 2013). While the bulk of this water is
returned to the original waterway (albeit with an associated
thermal pollution load), this high withdrawal demand leaves
the power plant considerably vulnerable in times of regional
water shortages (NETL 2009).
While dry cooling systems (Dry) look like a great tech-
nology option in terms of reducing water consumption, they
carry an efciency penalty of about 2% (DOE 2006) for the
power plant, thereby reducing the electricity output per unit
fuel input. In other words, dry cooling leads to both an eco-
nomic penalty (higher capital costs and higher operating costs
from reduced production per unit fuel), as well as increased
carbon emissions per unit energy produced for fossil fuel-
based plants (DOE 2006).
In contrast to other electricity technologies, solar photo-
voltaic (PV) and wind power production consume only
marginal quantities of water, mostly associated with the
occasional requirement to wash PV panels and wind turbine
blades (Meldrum et al 2013).
Figure 2. Consolidated estimates of water consumption factors for biofuel feedstock cultivation.
Regional aggregation in the WEPP database as follows: Africa; Australia,
New Zealand, & Oceania; Asia; Commonwealth of Independent States (CIS);
Europe; Latin America; Middle East, and North America.
Environ. Res. Lett. 9(2014) 105002 E S Spang et al
2.3. WCEP calculation
Once the data for energy production by each energy category
(i) was consolidated on a country-by-country basis, the water
consumption factors could be used to calculate the WCEP
values by using equation (3).
WCEP m Energy Production (GJ)
*Water Consumption Factor m /GJ . (3)
WCEP estimates for each energy category were then
summed to get an estimate of total water consumption for
each country in the studys entire energy production portfolio.
While the nal calculation of WCEP is straightforward,
limitations in the source data affect the accuracy of WCEP
estimates. While some of the data limitations were discussed
in the previous sections, some key challenges merit review,
among them: overly aggregated and incomplete energy data,
difculties in tracking international energy processing cycles,
Table 2. Electricity generation categories with capacity factors, water consumption factors and data sources.
Electricity generation category
Water consumption factor (m
Fuel Technology
Capacity factor
Min Max Source
Coal ST CT 0.85 0.722 0.505 1.157 [1]
OTF 0.85 0.263 0.105 0.333 [1]
CP 0.85 0.573 0.315 0.736 [1]
AIR 0.85 0.027 0.027 0.027 [1]
Nuclear ST CT 0.90 0.757 0.610 0.936 [2]
OTF 0.90 0.421 0.105 0.421 [2]
CP 0.90 0.641 0.421 0.757 [2]
Gas/oil ST CT 0.85 0.768 0.589 1.157 [2]
OTF 0.85 0.305 0.200 0.431 [2]
CP 0.85 0.284 0.284 0.284 [2]
AIR 0.85 0.027 0.027 0.027 [1]
CC CT 0.85 0.221 0.049 0.315 [2]
OTF 0.85 0.105 0.021 0.242 [2]
CP 0.85 0.252 0.252 0.252 [2]
AIR 0.85 0.004 0.004 0.126 [2]
GT NA 0.85 0.053 0.053 0.358 [2]
Biomass ST CT 0.68 0.581 0.505 1.015 [1]
OTF 0.68 0.315 0.315 0.315 [1]
AIR 0.68 0.027 0.027 0.027 [1]
Waste heat ST CT 0.68
0.581 0.505 1.015 [1]
OTF 0.68
0.315 0.315 0.315 [1]
CP 0.68
0.641 0.421 0.757 [2]
AIR 0.68
0.027 0.027 0.027 [1]
Geothermal ST CT 0.84 0.736 0.736 0.736 [2]
OTF 0.84 0.315 0.315 0.315 [1]
CP 0.84 0.410 0.315 0.505 [1]
AIR 0.84 0.305 0.284 0.662 [2]
Solar ST CT 0.32 0.852 0.778 0.904 [2]
AIR 0.32 0.027 0.027 0.027 [2]
PV NA 0.20 0.006 0.001 0.027 [2]
Wind NA NA 0.39 0.000 0.000 0.001 [2]
All data for global electricity production comes from two sources: Platts (2010) and EIA (2011).
Electricity generation technology types: ST = steam turbine; CC = combined cycle; GT = gas turbine; PV = photovoltaic;
NA = not applicable.
Thermoelectric cooling technologies: CT = cooling tower; OTF = once-through freshwater; CP = cooling pond; AIR = dry
NREL (2010).
All water consumption factor estimates are for the median values, which is consistent with estimates in the literature.
Sources for water consumption factor estimates [1]: Macknick et al (2011) [2]; Meldrum et al (2013).
Inferred from the Macknick et al (2011) estimate of Solar ST-AIR because it was the only steam turbine-linked estimate of
dry cooling water consumption.
The NREL (2010) study did not provide a capacity factor estimate for waste-heat-based steam turbine generators, so the
relatively conservative estimate for biofuel-based power plants was applied.
Inferred from the Macknick et al (2011) estimate of Biomass ST-CT and ST-OTF because it was assumed that waste heat
and geothermal were both lower grade fuel sources, like biomass relative to coal, gas and nuclear.
Inferred from the Meldrum et al (2013) estimate of Nuclear ST-CP as the least water-efcient comparable ST-CP
Environ. Res. Lett. 9(2014) 105002 E S Spang et al
inconsistency in denitions of water use and energy pro-
cesses, and unavailability of regionally appropriate water
consumption estimates.
Current international energy data do not provide suf-
cient resolution for highly accurate water consumption cal-
culations. For example, the EIA provides detailed information
on the total oil produced in the country and the number of
rened petroleum products produced, but includes no infor-
mation on the oils extraction in terms of primary production,
secondary recovery, or enhanced oil recovery (EIA 2011).
Additional gaps exist between data sets that provide actual
energy production quantities and others that list overall pro-
duction capacity (e.g. providing nominal capacity of power
plants rather than actual annual electricity production).
Raw materials are commonly extracted, processed, and
consumed across national borders. Calculating a national
WCEP requires knowing where these processing steps are
taking place for each resource. While this information is
readily available for nuclear fuel processing, it is more dif-
cult to segregate processing steps for current global biofuel
Consistently differentiating water useestimates in terms
of water consumption rather than water withdrawals is
essential. However, these different denitions of water use are
unevenly delineated in the literature. Also, modes of water
consumption associated directly with energy production (e.g.,
CTs) as opposed to consumption for auxiliary purposes at the
facilities should be clearly dened.
The global scope of this study necessitated WCEP at the
national level, but developing estimates of WCEP into more
granular regional estimates would provide additional insights
into the consumption of water for energy, especially for the
very large countries. Results at a ner geographical resolution
would allow for an improved assessment of water impacts at
relevant sub-national geographic scales, such as regional
watersheds. This approach would require improved data on
the specic location of energy production operations (mining,
biofuel crop cultivation, fuel processing, and electricity gen-
eration), which are not yet broadly available.
Further renement in determining the regional water
impact of energy processes would involve improved data on
source water quality. Using high-quality surface water has
different implications than using impaired or brackish water.
For example, water ooding is a water-intensive technique
used to enhance production of older oil reservoirs, but most of
the time, produced water from the oil eld itself (often a low-
quality water) is used for this purpose rather than freshwater
from a more competitive source (Wu et al 2009). Under-
standing the deployment (location and scale) of similar water
reuse and recycling opportunities for energy systems would
enhance our ability to classify the broader watershed impacts
of WCEP as well as highlight potential opportunities for the
transfer of innovative technologies from one region to
Similarly, applying universal water consumption factors
obscures regional variation in WCEP. Water consumption per
unit energy depends not just on the technologies employed,
but also on local conditions. For example, quality of local
source water, specic attributes of process equipment (e.g.
age, efciency), and regional climate conditions can impact
the amount of water consumed to cool thermoelectric power
plants (Yang and Dziegielewski 2007). Currently, most
coefcients applied in the literature are highly US-centric, so
developing a more robust portfolio of water consumption
estimates derived from direct regional measurements would
contribute signicantly to this eld of study.
Figure 3. Consolidated estimates of water consumption factors for electricity generation. Notes: electricity generation technology types:
ST = steam turbine; CC = combined cycle; GT = gas turbine; PV = photovoltaic; NA = not applicable. Thermoelectric cooling technologies:
CT = cooling tower; OTF = once-through freshwater; CP = cooling pond; AIR = dry cooling.
Environ. Res. Lett. 9(2014) 105002 E S Spang et al
Despite these data limitations, we believe there is great
value in synthesizing the best available water consumption
estimates with widely available energy production data to
provide this preliminary view of WCEP at the global scale. It
is hoped that these results will further engage the research
community and engender ongoing efforts to collect and share
better data, and ultimately, produce more rened and higher
resolution estimation of international WCEP values over time.
3. Results and discussion
This paper provides a global perspective of WCEP at the
national level for 158 countries. As discussed in the metho-
dology section, international energy data were consolidated
from multiple sources to dene the composition and scale of
energy production portfolios.
The graphical representations for consolidating the
results in this section are ranked bar charts for the top 25
countries with the highest values for the related indicator.
Since nearly all the rankings drop precipitously in value at
some point within the top 25 countries, it was considered
sufcient to focus on the highest-ranked countries in terms of
the various water consumption metrics. However, results for
all individual countries are available in map form as well as
listed in table format in the SI section. As a nal layer of
analysis, the WCEP results are compared to existing energy-
based water use studies.
3.1. Water consumption: total energy production
The global WCEP was estimated at approximately 52 billion
cubic meters of fresh water. Of this global WCEP volume, oil
and gas production has the highest proportional WCEP (40%)
relative to the additional energy categories of coal, nuclear
fuel, biodiesel, ethanol, coal-based electricity (steam turbine,
ST), nuclear ST electricity, other non-renewable electricity
(oil ST, gas ST, combined cycle, and gas turbine), and
renewable electricity (biomass ST, waste heat ST, geothermal
ST, solar ST, solar PV, and wind), as shown in gure 4
below. As described above, this estimate does not assign any
water consumption to the production of hydropower.
Oil and gas WCEP demonstrates the greatest share of
global WCEP, representing more than all (non-hydro) elec-
tricity generation combined. It is also worth noting that the
amount of water consumed at the global scale for ethanol
production is roughly equivalent to global water consumption
for coal-red power plants, even though global ethanol pro-
duction represents approximately 1/100th the energy content
of global coal-red electricity production. Finally, in terms of
renewable energy, the total WCEP for all renewable elec-
tricity production is roughly 1/10th the total WCEP for bio-
fuel production. Hence, while renewable electricity may
represent opportunities for reducing both water consumption
and carbon emissions, the water impact of biofuels requires
important consideration as the worlds regions seek to tran-
sition to lower-carbon energy portfolios.
In terms of country-by-country WCEP estimates, map 1
provides a global overview. Total energy WCEP is dominated
by the United States and the BRIC (Brazil, Russia, India, and
China) countries, which reects the inuence of the physical
and economic scale of these large countries. Saudi Arabia,
Canada, Germany, France, and Iran round out the top ten
WCEP countries.
Disaggregating WCEP by energy subcategory (gure 5)
shows fossil fuels consuming signicant proportions of water
in most countries (less so for India, Brazil, Germany, and
France within the top ten.) Nuclear fuel production plays a
minimal role overall, with the United States and Canada
having the highest nuclear fuel WCEP values. Biofuel WCEP
is signicant in the United States, India, and Brazil.
Figure 4. Total global WCEP by major energy category, 2008.
Environ. Res. Lett. 9(2014) 105002 E S Spang et al
Meanwhile, the United States and China consume by far the
most water for electric power generation.
3.2. Fossil fuel WCEP
Global fossil fuel WCEP was estimated at 26 727 million m
National level estimates of fossil fuel WCEP by sub-category
are provided in gure 6(for the top 25 countries). The results
show that total consumption of water for fossil fuel produc-
tion is dominated by countries that are large in physical size
and population (BRIC countries: Russia, China, Brazil, and
India), economically productive (Organisation for Economic
Co-operation and Development [OECD] countries: United
States, Canada, Mexico, Norway, and the United Kingdom,
among others) and major petroleum producers (Organization
of the Petroleum Exporting Countries [OPEC] countries:
Saudi Arabia, Iran, Venezuela, the United Arab Emirates,
Iraq, among others).
The production and rening of crude oil dominates the
portfolio of every country in the ranking, except for China,
India, and Indonesia, and Australia, where coal production
consumes the most water. Several countries with no sig-
nicant indigenous oil resources, such as Japan, Germany,
South Korea, and Italy, nonetheless have reneries, with
attendant water impacts. Natural gas barely contributes to
the overall fossil fuel WCEP within any country, though it
shows up in the greatest magnitude in the United States and
Map 1. Total water consumption for energy production (WCEP) 2008.
Figure 5. Total WCEP by energy category, 2008.
Environ. Res. Lett. 9(2014) 105002 E S Spang et al
While the analysis does include unconventional fossil
fuel production (oil sands, heavy oil, shale oil, and shale gas),
the commercial production of these fuels was only taking
place in a few countries in 2008. Hence, the overall scale of
unconventional fossil fuel WCEP is not signicant at the
global scale. WCEP for shale oil contributes noticeably to the
fossil portfolio in Canada, while heavy oil contributes to the
WCEP in Venezuela. Further, while WCEP for shale gas
production visibly contributes to the overall portfolio within
this 2008 data set, it is likely this technology is contributing to
a much greater portion of the United States WCEP given the
recent years of growth in the use of this technology
(EIA 2014).
3.3. Nuclear fuel WCEP
The scale of nuclear fuel production at the global level is
signicantly more limited than fossil fuel production in terms
of both available uranium deposits as well as nuclear fuel
production. Consequently, the total consumption of water for
nuclear fuel production worldwide (2117 million m
) is a full
order of magnitude less than that for fossil fuels (26 727
million m
). Water consumption coefcients were applied to
each stage of the nuclear fuel cycle, including uranium ore
mining and processing, milling, conversion, enrichment,
fabrication, and reprocessing to produce the results shown in
gure 7.
Figure 6. WCEP for fossil fuel extraction and processing, 2008.
Figure 7. WCEP for nuclear fuel extraction and processing, 2008.
Environ. Res. Lett. 9(2014) 105002 E S Spang et al
Many top nuclear fuel producers process the fuel at
multiple stages of the nuclear fuel cycle (Canada, United
States, Russia, France, and the UK), but the operations of
some countries (Kazakhstan, Australia, South Africa, Niger,
Uzbekistan, and Kyrgyzstan) are more limited to ore mining
and processing. The United States uses the most water for
nuclear fuel WCEP, specically for the relatively more water
intensive process of diffusion enrichment (IAEA 2011).
Meanwhile, Canada, with the second largest nuclear fuel
WCEP, uses signicantly more water for uranium milling
than any other country, yet hardly uses any water for
enrichment. This highlights the role of trade in balancing the
cycle of uranium production across multiple countries and,
therefore, the differentiated water consumption impacts across
these participating countries.
3.4. Biofuel WCEP
Global biofuel WCEP was estimated as approximately 10 119
million m
(with roughly 25% of biofuel WCEP for biodiesel
and 75% for ethanol). Figure 8shows that the United States,
India, Brazil, and China have the highest aggregate levels of
water consumption for biofuel cultivation and processing.
The United States leads all other countries in water con-
sumption for biofuels, consuming vast amounts of irrigation
water to produce maize-based ethanol. Signicantly, the top
ve water-consuming countries for biofuels have quite dif-
ferent biofuel feedstock portfolios. India mostly uses rapeseed
to produce biodiesel; Brazil relies heavily on sugarcane to
produce ethanol; China, like the United States, produces
mostly maize-based ethanol; and Spain consumes water
mostly for sugarbeet ethanol and rapeseed biodiesel. The
remaining countries are mostly warmer climate countries
producing limited quantities of sugarcane ethanol, or EU
countries experimenting with rapeseed biodiesel or sugarbeet
As discussed in the methodology section, the interna-
tional data were limited, and could not provide a detailed
composition of feedstock crops for biofuels. Hence, an
improved database of biofuel production by feedstock would
be highly valuable to future waterenergy research, especially
since the cultivation of biofuel feedstocks is by far the most
water-intensive energy production pathway. Further,
advancements in second-generation biofuels that rely on crop
residues and/or cellulosic feedstocks have the potential to
change the biofuel WCEP equation signicantly and should
be incorporated in future research as they become more pre-
valent. In sum, while biofuels remain a potentially important
low-carbon alternative to fossil fuels, better data should be
made available to track the water impacts of these resources,
and further development of biofuels should be managed
carefully within the context of regional water management.
3.5. Electricity WCEP
WCEP for electricity generation at the global scale represents
about 12 895 million m
of water. Water consumption for
electricity includes the most diverse portfolio of technology
options (31 combinations of fuel, generator type, and cooling
type) for producing energy (see table 2). To aid the visuali-
zation of electricity WCEP rankings (gure 9), these multiple
sub-categories were aggregated into eight major categories,
including: coal-based steam turbine (ST), gas- and oil-pow-
ered ST, nuclear ST, biomass and waste heat ST, geothermal
ST, solar ST, combined cycle, and gas turbine. Wind and
solar PV were not included in the graphic because their
WCEP are so low relative to the other technologies that they
do not appear at this scale of presentation, but the country-by-
Figure 8. WCEP for biofuel cultivation and processing, 2008.
Environ. Res. Lett. 9(2014) 105002 E S Spang et al
country WCEP values for these technologies are provided in
table SI-2.
The United States and China are the largest water con-
sumers in this energy category, with these two countries
accounting for approximately 56% of total global water
consumption for electricity production. Both countries depend
mostly on coal-based power plants, and as a result, water
consumption for coal power plants represents 59% of total
electricity WCEP in the United States and 98% in China.
France and Germany follow next with high levels of water
consumption, with signicant consumption for both countries
coming from nuclear electricity (87% and 36%, respectively).
Across the remaining countries, the composition of
electricity generation technologies varies signicantly based
on the different electricity portfolios. Coal is a consistent
contributor to electricity WCEP across the top 15 countries,
except where signicantly displaced by nuclear power
(France, Germany, Russia, Canada, and Spain). Gas- and oil-
based steam turbines play a more prominent role in the lower
ranked countries (Romania, Netherlands, Iran, and Egypt).
Geothermal provides a signicant contribution only within
the United States and Mexico, while other renewable
resources play only a minimal role in scale (biomass, waste
heat, and solar thermal) of water impact.
3.6. Comparing WCEP results to existing studies
Given the scale of the global WCEP analysis and the het-
erogeneity of the data sets that informed the analysis, com-
paring the estimates to related studies is useful for
benchmarking the WCEP results. Unfortunately, the vast
majority of these estimates are for the United States
(Gleick 1994, DOE 2006, USGS 2009, Elcock 2010), so it is
difcult to compare the numbers from this study to interna-
tional gures. Nevertheless, gure 10 compares the WCEP
estimates from this study (red values) to a number of other
estimates from related literature (blue values.) All estimates
relate to the United States across a range of years as identied
in the x-axis values.
The results provided in gure 10 show that the estimates
from this study correlate well to the estimates from the lit-
erature, and if anything, tend to be lightly lower than esti-
mates from other studies. The conservative trend in the
WCEP values of this research in comparison to the other
papers is likely a result of using more recent water con-
sumption factors in this study (Wu et al 2009, Mielke
et al 2010, Mekonnen and Hoekstra 2010, Macknick
et al 2011, Meldrum et al 2013) as compared to the other
studies. The latest estimates of water consumption by tech-
nology tend to be lower than the earlier estimates provided by
Gleick (1994) that are applied in many other studies
(DOE 2006, Elcock 2010). Many of the energy technologies
assessed by Gleick have become more water-efcient, so
newer numbers would suggest less water consumption per
unit energy.
The one exception is coal, where the WCEP estimate is
signicantly higher (roughly four times higher than the
average estimate by DOE (2006). However, as we saw in
gure 1, estimates of water consumption for coal mining and
processing fall across a wide range and the values selected for
the DOE study (0.0040.024 m
) fall far below the
median value selected for this study (Meldrum 2013). Further,
the DOE estimate was for EIA data for 2003, and the data for
this study were for 2008 (over which time coal production
increased by 9.3%, EIA 2011).
In sum, while there is a lack of detailed estimates from
around the world for testing the methodology and results of
this global WCEP assessment, the estimates match well to
existing studies of water consumption across multiple energy
categories in the United States.
Figure 9. WCEP for electricity generation (non-hydro), 2008.
Environ. Res. Lett. 9(2014) 105002 E S Spang et al
4. Conclusion
The purpose of this research was to estimate water con-
sumption by national-level energy production portfolios from
a global perspective. By synthesizing existing estimates of
water consumption for specic energy technologies with
detailed data on national energy technology portfolios, this
study provides a new global perspective on the water impacts
of energy systems. This empirical approach included calcu-
lating WCEP by individual energy technologies as well as for
complete national energy portfolios for 158 countries. At the
global scale, we determined that the processes and technol-
ogies that produce energy consume approximately 52 billion
cubic meters of water on an annual basis.
Since this study estimated absolute consumption of water
by energy portfolios, many of the largest countries (in terms of
both physical and economic size) consistently ranked highly in
the WCEP results, as would be expected. However, some
smaller countries that are biased toward particular energy
categories were highlighted as consuming large amounts of
water as well, e.g., many of the Middle Eastern nations in
relation to fossil fuel production and processing. The results
from this study allow for endless permutations of comparisons
across technologies, countries and regions, and to encourage
these efforts by other researchers the full WCEP results for
each energy category are provided in the SI section.
One clear opportunity for advancing this work is the
collection and dissemination of higher-quality data. The
currently available data for assessing the global water con-
sumption of energy systems vary in both quality and acces-
sibility. Higher-resolution data on energy technologies and
the local context of operation will lead to more accurate
results. Improving the quality of the metrics would be highly
relevant for regional policy making as well as for designing
more comprehensive assessments, including grid-based spa-
tial mapping of WCEP values, time series trends of WCEP
estimates, inclusion of source water-quality data, and esti-
mating the potential for water reuse technologies, among
other potential projects.
While improving the data is certainly an important sug-
gested follow-up to this work, it represents a longer-term goal
in the water-energy eld. In the meantime, this study makes a
foundational contribution by establishing a consistent indi-
cator and an initial global baseline estimate of WCEP that can
continue to be rened with improved data applied to future
studies. The existing results from this investigation provide a
high-level view of the consumption of water for energy at the
macro-scale, and it is hoped that these results will serve as a
reference for decision-makers and future researchers inter-
ested in understanding and expanding the eld of water
consumption by energy systems at the global scale.
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Water and energy are the facets of the same coin. The two are inseparable, ensuring the social and economic development of mankind. The numerical growth of the population as well as the development of the society has led to the increase of water and energy consumption. Both are needed to increase agricultural and industrial production and daily comfort. The analysis of the duality of water and energy is necessary to understand the implications on sustainable development, their tariffs and also the implications on the environment. Both the aspects regarding the water consumption required for each energy branch and the energy aspect for the treatment and purification of the water destined for consumption were analyzed. All this for understanding the relationship between water and energy and making the best choices related to the integrated management of water resources by decision makers.KeywordsWaterEnergyUsage
The MENA region (the Middle East and North Africa) extends across the eastern Mediterranean, the Middle East, and North Africa and is home to some 500 million inhabitants. Observations attest for the fact that climate change in the Mediterranean Basin, in general, and in the MENA countries, in particular, exceeds global mean values significantly. Future projections of climate change, based on numerical model results indicate that this trend will continue in the near future. Climate change will result in significantly increased demands for water and energy, particularly in urban settings and major cities in the region. The general economic development, rapid population growth and increasing urbanization, changes in lifestyle, and shifting consumption patterns are likely to exacerbate these demands. There are strong linkages between the provision of water and energy. Maintaining water and energy security in the MENA region, therefore, needs holistic considerations of these issues in the framework of the water‒energy nexus (WEN). The nexus approach focusses on the interdependencies and interrelationships between water and energy provision and consumption. It allows the specification of suitable mitigation and adaptation strategies and measures aimed to minimize the impacts of climate change and enable a sustainable future in the MENA region.
Energy and water have been fundamental to powering the global economy and building modern society. This cross-disciplinary book provides an integrated assessment of the different scientific and policy tools around the energy-water nexus. It focuses on how water use, and wastewater and waste solids produced from fossil fuel energy production affect water quality and quantity. Summarizing cutting edge research, it describes the scientific methods for detecting contamination sources in the context of policy and regulations. The authors highlight the growing evidence that fossil fuel production, from both conventional and unconventional sources, leads to water quality degradation, while regulations for the water and energy sector remain fractured and highly variable across and within countries. This volume will be a key reference for scholars, industry professionals, environmental consultants and policy makers seeking information on the risks associated with the energy cycle and its impact on the environment, particularly water resources.
Water is a precious resource essential for all forms of life. It can be thought of as the blood of the earth. Although there is plenty of water to meet the demand for the present population and even for a projected population of about 9 billion, there is significant spatial and temporal variation in the global distribution of this precious resource. As a result, there are water rich countries and water poor countries with the latter facing water stress and water scarcity which in extreme situations can lead to water-related conflicts and even ‘water-wars’. The World Health Organization (WHO) has identified unsafe drinking water as a major killer in the world. The motivation for writing this book Water for Life: Drinking Water, Health, Food, Energy Nexus is primarily to throw light on the multi-faceted uses and importance of water in life, in particular to highlight the water, health, food, energy nexus. It is hoped that the contents would help students in civil engineering, geography, and earth and social sciences to perceive the big picture of water management for all human and biotic populations without causing negative effects on the environment.
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This report provides estimates of operational water withdrawal and water consumption factors for electricity generating technologies in the United States. Estimates of water factors were collected from published primary literature and were not modified except for unit conversions. The water factors presented may be useful in modeling and policy analyses where reliable power plant level data are not available. Major findings of the report include: water withdrawal and consumption factors vary greatly across and within fuel technologies, and water factors show greater agreement when organized according to cooling technologies as opposed to fuel technologies; a transition to a less carbon-intensive electricity sector could result in either an increase or a decrease in water use, depending on the choice of technologies and cooling systems employed; concentrating solar power technologies and coal facilities with carbon capture and sequestration capabilities have the highest water consumption values when using a recirculating cooling system; and non-thermal renewables, such as photovoltaics and wind, have the lowest water consumption factors. Improved power plant data and further studies into the water requirements of energy technologies in different climatic regions would facilitate greater resolution in analyses of water impacts of future energy and economic scenarios. This report provides the foundation for conducting water use impact assessments of the power sector while also identifying gaps in data that could guide future research.
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This study quantifies the green, blue and grey water footprint of global crop production in a spatially-explicit way for the period 1996–2005. The assessment is global and improves upon earlier research by taking a high-resolution approach, estimating the water footprint of 126 crops at a 5 by 5 arc min grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid cell. In addition, the water pollution associated with the use of nitrogen fertilizer in crop production is estimated for each grid cell. The crop evapotranspiration of additional 20 minor crops is calculated with the CROPWAT model. In addition, we have calculated the water footprint of more than two hundred derived crop products, including various flours, beverages, fibres and biofuels. We have used the water footprint assessment framework as in the guideline of the water footprint network. Considering the water footprints of primary crops, we see that global average water footprint per ton of crop increases from sugar crops (roughly 200 m<sup>3</sup> ton<sup>−1</sup>), vegetables (300 m<sup>3</sup> ton<sup>−1</sup>), roots and tubers (400 m<sup>3</sup> ton<sup>−1</sup>), fruits (1000 m<sup>3</sup> ton<sup>−1</sup>), cereals} (1600 m<sup>3</sup> ton<sup>−1</sup>), oil crops (2400 m<sup>3</sup> ton<sup>−1</sup>) to pulses (4000 m<sup>3</sup> ton<sup>−1</sup>). The water footprint varies, however, across different crops per crop category and per production region as well. Besides, if one considers the water footprint per kcal, the picture changes as well. When considered per ton of product, commodities with relatively large water footprints are: coffee, tea, cocoa, tobacco, spices, nuts, rubber and fibres. The analysis of water footprints of different biofuels shows that bio-ethanol has a lower water footprint (in m<sup>3</sup> GJ<sup>−1</sup>) than biodiesel, which supports earlier analyses. The crop used matters significantly as well: the global average water footprint of bio-ethanol based on sugar beet amounts to 51 m<sup>3</sup> GJ<sup>−1</sup>, while this is 121 m<sup>3</sup> GJ<sup>−1</sup> for maize. The global water footprint related to crop production in the period 1996–2005 was 7404 billion cubic meters per year (78% green, 12% blue, 10% grey). A large total water footprint was calculated for wheat (1087 Gm<sup>3</sup> yr<sup>−1</sup>), rice (992 Gm<sup>3</sup> yr<sup>−1</sup>) and maize (770 Gm<sup>3</sup> yr<sup>−1</sup>). Wheat and rice have the largest blue water footprints, together accounting for 45% of the global blue water footprint. At country level, the total water footprint was largest for India (1047 Gm<sup>3</sup> yr<sup>−1</sup>), China (967 Gm<sup>3</sup> yr<sup>−1</sup>) and the USA (826 Gm<sup>3</sup> yr<sup>−1</sup>). A relatively large total blue water footprint as a result of crop production is observed in the Indus River Basin (117 Gm<sup>3</sup> yr<sup>−1</sup>) and the Ganges River Basin (108 Gm<sup>3</sup> yr<sup>−1</sup>). The two basins together account for 25% of the blue water footprint related to global crop production. Globally, rain-fed agriculture has a water footprint of 5173 Gm<sup>3</sup> yr<sup>−1</sup> (91% green, 9% grey); irrigated agriculture has a water footprint of 2230 Gm<sup>3</sup> yr<sup>−1</sup> (48% green, 40% blue, 12% grey).
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This article provides consolidated estimates of water withdrawal and water consumption for the full life cycle of selected electricity generating technologies, which includes component manufacturing, fuel acquisition, processing, and transport, and power plant operation and decommissioning. Estimates were gathered through a broad search of publicly available sources, screened for quality and relevance, and harmonized for methodological differences. Published estimates vary substantially, due in part to differences in production pathways, in defined boundaries, and in performance parameters. Despite limitations to available data, we find that: water used for cooling of thermoelectric power plants dominates the life cycle water use in most cases; the coal, natural gas, and nuclear fuel cycles require substantial water per megawatt-hour in most cases; and, a substantial proportion of life cycle water use per megawatt-hour is required for the manufacturing and construction of concentrating solar, geothermal, photovoltaic, and wind power facilities. On the basis of the best available evidence for the evaluated technologies, total life cycle water use appears lowest for electricity generated by photovoltaics and wind, and highest for thermoelectric generation technologies. This report provides the foundation for conducting water use impact assessments of the power sector while also identifying gaps in data that could guide future research.
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The power sector withdraws more freshwater annually than any other sector in the US. The current portfolio of electricity generating technologies in the US has highly regionalized and technology-specific requirements for water. Water availability differs widely throughout the nation. As a result, assessments of water impacts from the power sector must have a high geographic resolution and consider regional, basin-level differences. The US electricity portfolio is expected to evolve in coming years, shaped by various policy and economic drivers on the international, national and regional level; that evolution will impact power sector water demands. Analysis of future electricity scenarios that incorporate technology options and constraints can provide useful insights about water impacts related to changes to the technology mix. Utilizing outputs from the regional energy deployment system (ReEDS) model, a national electricity sector capacity expansion model with high geographical resolution, we explore potential changes in water use by the US electric sector over the next four decades under various low carbon energy scenarios, nationally and regionally.
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Various studies have attempted to consolidate published estimates of water use impacts of electricity generating technologies, resulting in a wide range of technologies and values based on different primary sources of literature. The goal of this work is to consolidate the various primary literature estimates of water use during the generation of electricity by conventional and renewable electricity generating technologies in the United States to more completely convey the variability and uncertainty associated with water use in electricity generating technologies.
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The production of energy feedstocks and fuels requires substantial water input. Not only do biofuel feedstocks like corn, switchgrass, and agricultural residues need water for growth and conversion to ethanol, but petroleum feedstocks like crude oil and oil sands also require large volumes of water for drilling, extraction, and conversion into petroleum products. Moreover, in many cases, crude oil production is increasingly water dependent. Competing uses strain available water resources and raise the specter of resource depletion and environmental degradation. Water management has become a key feature of existing projects and a potential issue in new ones. This report examines the growing issue of water use in energy production by characterizing current consumptive water use in liquid fuel production. As used throughout this report, 'consumptive water use' is the sum total of water input less water output that is recycled and reused for the process. The estimate applies to surface and groundwater sources for irrigation but does not include precipitation. Water requirements are evaluated for five fuel pathways: bioethanol from corn, ethanol from cellulosic feedstocks, gasoline from Canadian oil sands, Saudi Arabian crude, and U.S. conventional crude from onshore wells. Regional variations and historic trends are noted, as are opportunities to reduce water use.
Utility companies, which use substantial amounts of water for plant cooling and other needs are doing their part by pursuing water-conserving technologies, innovative recycling schemes, and alternative sources of water to deal with the squeeze on freshwater availability. One key element of the strategy is to reduce the hot-weather loss of cooling efficiency for air-cooled condensers. A significant portion of this work was cofunded by DOE, the California Energy Commission, and EPRI's Technology Innovation Program. EPRI has also worked closely with the national energy laboratories on the Energy-Water Nexus Report to Congress, the Energy-Water Nexus Research Roadmap, and the ZeroNet Research Initiative and has collaborated with Electricité de France on creating and testing risk management tools to address the impacts of climate change on water availability for electric power generation.
New global-scale gridded estimates of industrial water use around 1995 are presented which, for the first time, distinguish between water use for cooling of thermal power stations and for manufacturing. Estimates of annual values of both water withdrawal and consumption are provided with a spatial resolution of 0.5° by 0.5°. Thermoelectric power water use is based on the geographical location of 63,590 thermal power stations. Manufacturing water use is computed by first estimating country-specific water withdrawal values, which are then distributed as a function of city nighttime lights. A comparison to industrial water use in the 50 states of the United States and 89 regions in Russia shows that the developed data set represents thermoelectric power water use satisfactorily, while manufacturing water use remains highly uncertain.
In the future, competition for water will require electricity generators in the United States to address conservation of fresh water. There are a number of avenues to consider. One is to use dry-cooling and dry-scrubbing technologies. Another is to find innovative ways to recycle water within the power plant itself. A third is to find and use alternative sources of water, including wastewater supplies from municipalities, agricultural runoff, blackish groundwater, or seawater. Dry technologies are usually more capital intensive and typically exact a penalty in terms of plant performance, which in turn raises the cost of power generation. On the other hand, if the cost of water increases in response to greater demand, the cost differences between dry and wet technologies will be reduced. EPRI has a substantial R & D programme evaluating new water-conserving power plant technologies, improving dry and hybrid cooling technologies, reducing water losses in cooling towers, using degraded water sources and developing resource assessment and management decision support tools. 5 refs., 10 figs.