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The internet and associated Information and Communications Technologies (ICT) are diffusing at an astounding pace. As data centers (DCs) proliferate to accommodate this rising demand, their environmental impacts grow too. While the energy efficiency of DCs has been researched extensively, their water footprint (WF) has so far received little to no attention. This article conducts a preliminary WF accounting for cooling and energy consumption in DCs. The WF of DCs is estimated to be between 1047 and 151,061 m3/TJ. Outbound DC data traffic generates a WF of 1-205 liters per gigabyte (roughly equal to the WF of 1 kg of tomatos at the higher end). It is found that, typically, energy consumption constitues by far the greatest share of DC WF, but the level of uncertainty associated with the WF of different energy sources used by DCs makes a comprehensive assessment of DCs' water use efficiency very challenging. Much better understanding of DC WF is urgently needed if a meaningful evaluation of this rapidly spreading service technology is to be gleaned and response measures are to be put into effect.
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Sustainability 2015, 7, 11260-11284; doi:10.3390/su70811260
ISSN 2071-1050
The Water Footprint of Data Centers
Bora Ristic, Kaveh Madani * and Zen Makuch
Center for Environmental Policy, Imperial College London, London SW7 1NA, UK;
E-Mails: (B.R.); (Z.M.)
* Author to whom correspondence should be addressed; E-Mail:;
Tel.: +44-20-7594-9346.
Academic Editor: Arjen Y. Hoekstra
Received: 16 June 2015 / Accepted: 12 August 2015 / Published: 18 August 2015
Abstract: The internet and associated Information and Communications Technologies (ICT)
are diffusing at an astounding pace. As data centers (DCs) proliferate to accommodate this
rising demand, their environmental impacts grow too. While the energy efficiency of DCs
has been researched extensively, their water footprint (WF) has so far received little to no
attention. This article conducts a preliminary WF accounting for cooling and energy
consumption in DCs. The WF of DCs is estimated to be between 1047 and 151,061 m3/TJ.
Outbound DC data traffic generates a WF of 1–205 liters per gigabyte (roughly equal to the
WF of 1 kg of tomatos at the higher end). It is found that, typically, energy consumption
constitues by far the greatest share of DC WF, but the level of uncertainty associated with
the WF of different energy sources used by DCs makes a comprehensive assessment of DCs’
water use efficiency very challenging. Much better understanding of DC WF is urgently
needed if a meaningful evaluation of this rapidly spreading service technology is to be
gleaned and response measures are to be put into effect.
Keywords: water footprint; data center; water; water-energy nexus; water use
1. Introduction
The world is experiencing an era of dramatic socio-technical change. The internet and associated
information and communications technologies (ICT) have been diffusing at an astounding pace.
In 2001, 8% of humanity was online, but by 2013, 38% were [1]. Not only is the Internet being adopted
quickly, but global data traffic is rising at an exponential rate: 100 gigabytes/second (GB/s) in 2002,
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2000 GB/s in 2007, and 28,875 GB/s in 2013 [2]. This change has been facilitated by a rapid deployment
of data centers (DCs) to store, retrieve, and transmit information.
DCs have increased in number and sophistication, enabling the rapid adoption of Internet use and,
more recently, the use of cloud-based services and mobile Internet access. DCs are amongst the most
rapidly-growing electricity consumers. The installed base of servers worldwide has grown from just
under 5 million in 1996, to 30 million in 2007 [3]. In the US, between 2013 and 2020 DC energy use is
predicted to rise by 53% to 139 billion kilowatt-hours (kWh) [4].
Simultaneous to this information revolution, the world is also experiencing rapidly-rising
environmental stress. Alongside the implications of climate change, many freshwater and other natural
systems are losing the ability to maintain their ecological functions while simultaneously being
compelled to service humanity’s growing needs. Such stresses are imposing new constraints on both
public and private sector decision-making. These systems and the human systems for controling energy
and water are highly inter-linked and exhibit reinforcing feedbacks. Using more energy increases the
use of water, which in turn increases energy use even further [5]. Global energy use is set to rise by
35%–40% [6,7] and electricity by 70% by 2035 [8]. As a result, global water withdrawals for thermal
electricity are set to rise by 40% [8] and water consumption by more than 100% by 2050 [9]. Globally,
the energy sector will likely see an increase in the annual water footprint (WF) between 37% and 66%
by 2035 with WF per unit energy produced rising between 5% and 10% [10]. In the United States and
Europe, 50% and 43% of freshwater withdrawals respectively are used for cooling purposes in electricity
generation [8]. Such rising demand means that energy and water consumers are in tight competition for
resources. The spread of DCs and their growing energy use compete alongside the established players.
Worldwide, DCs accounted for 1.1%–1.5% of all electricity use in 2010 [11]. ICT end-user-equipment
and infrastructure accounted for 7.7% of the total EU-27 electricity use in 2011 [12]. DCs and
telecommunication networks themselves accounted for 1.9% of electricity consumption, but this is set
to rise 35% between 2011 and 2020, with the the ICT sector serving as the greatest driver for electricity
consumption growth [12]. This rise in DC electricity consumption, coupled with concerns about energy
systems’ greenhouse gas emissions, has promoted interest in their energy efficiency. DCs can reduce
energy use in the broader economy enough to offset their own energy use [13]. The application of
ICT-based efficiency applications in other sectors, as well as the dematerialization of economic activity
through the application of ICT, will deliver reductions in the EU’s net energy consumption even under
business-as-usual scenarios without a push for ICT-based efficiency [14]. Without such a push, however,
ICT may not have a positive effect on net CO2 emissions [14].
Reviewing the literature on DC energy efficiency, Koomey [11] finds the rapid diffusion of volume
servers to be the main driver of DC electricity use. He argues that while large DCs can reap the greatest
efficiencies, measures applied to the servers themselves have the greatest efficiency improvement
potential because most DCs are still small and medium sized. Additionally, the growth rate of DC
electricity use had slowed to 56% in the 2005–2010 period, relative to the 2000–2005 period which saw
a doubling [11]. Koomey finds that this drop was mainly due to lower-than-expected installed server
base as opposed to energy efficiency improvements. These points highlight the role of server
manufacturers and other suppliers in reducing DC environmental footprints on top of innovations by DC
operators themselves. Studies of particular interest in this area are the lifecycle assessments of data
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centres, which broadly indicate that electricity production forms the major part of DC impacts [15,16].
A series of studies also consider the cooling technologies applied and their impacts [17–19].
In the context of reducing DC carbon emissions, Masanet et al. [20] developed a formal model for
determining DC electricity demand. They identified characteristics of low-carbon DCs, providing insight
into the factors driving DC carbon performance which revolve mainly around energy efficiency. This is
one among several publications dealing with the carbon emissions or renewables in relation to DCs. In
particular, the literature is rich with new algorithms for optimizing workload distribution among data
centres [21–29] and server electricity demand optimization [30,31]. Some publications have also highlighted
the role of geographic location or siting in carbon emissions and energy use optimization [32–34].
Carbon emissions are only one form of environmental impact. Ignoring the impacts of DCs on water
resources could lead to the partial displacement of impacts from the climate to water. Such a dynamic
can take place when shifting from non-renewable to renewable energy sources [35]. Evaluating different
energy sources along multiple criteria of environmental footprint, Hadian and Madani [35] find that even
if an energy technology has a low carbon footprint, it cannot be considered “green” or “sustainable”
unless its other footprints (i.e., water and land use footprints), together with cost, compare favourably to
other energy technologies. Assessing the sustainability of DCs should take a similar approach. Namely,
evaluating multiple criteria, in order to give a better understanding of the trade-offs involved with siting
and cooling technology. Without a comprehensive assessment of the water impacts of energy soruces,
our understanding of the resource use efficiency of DCs remains incomplete.
Even in terms of energy efficiency, much work remains to be done. The EU Commission’s Joint
Research Center reported that within the EU, DCs are subject to only six “Green ICT” measures (one
mandatory) which they see as inadequate [12]. 31% of data-center-related companies report on their
water usage while 21% report on CO2 emissions [36]. Global Reporting Initiative data indicate that in
the computer, as well as in the telecommunications, sector 64% report on water withdrawals by
source [37]. Very few however report on total water discharge, recycling rates, waters affected by
discharge, runoff or withdrawal, and associated biodiversity impacts [37]. Reporting will have to
increase and be comminucated more widely for a better understanding of not just DC energy efficiency,
but their overall environmental footprint and sustainability.
In terms of the water efficiency of DCs, very little has been published. Even the extremely detailed
handbooks on cooling technologies published by the American Society of Heating, Refrigeration, and
Air-Conditioning Engineers (ASHRAE), give very little guidance on water efficiency in DC cooling
systems. To the authors’ knowledge, the Hewlett-Packard’s (HP) Sustainable IT Ecosystem Laboratory
was the first to publish work dealing directly with DCs’ impacts on water [38]. They consider the
infrastructure, in particular the cooling systems’ water use, and opportunities for water savings through
building design and different cooling solutions. They find that a 1 megawatt (MW) DC can consume
18,000 gallons (68 m3) of water a day for cooling and provide guidance on managing water-energy
trade-offs. They also consider opportunities for electricity use reductions, focusing on the energy use of
direct and indirect water usage [39]. In Bash et al. [40], as part of the ongoing work at HP, this method
was incorporated into a broader “cloud sustainability dashboard”. Frachtenberg [41], building on
previous work in thermal design and high-efficiency DCs, published on the engineering principles for
reducing environmental footprint deployed in the Facebook’s Open Compute Project. Ren [42], building
on the vast body of literature on load distribution algorithms, developed a “software approach” which
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highlighted non-structural (soft) engineering solutions for water use optimization. This came in the form
of an algorithm distributing workload between DCs that minimizes electricity costs, maximizes renewables
use, and maximizes water efficiency across different times and locations. The team at HP have also
developed methods to combine supply- and demand-side optimization to address energy use, carbon
emissions, and water consumption [43]. The Green Grid has published on the topic of water use in DCs
and, of greatest import, has developed the Water Use Effectiveness (WUEsource) metric which takes the
indirect form of water use arising from electricity use into account [44]. This will be explored in more
detail alongside water footprint below.
To be able to understand the water impacts of DCs, consistent water metrics should be applied,
otherwise comparisons are invalid [5]. While water metrics such as withdrawal, consumption, and
footprint are commonly used for evaluating the impacts of different processes on water resources, the
literature has failed to use them consistently, making the resulting values incomparable in many
cases [5]. Water use is a generic term referring to the use of water resources in human processes.
It can be quantified by the following metrics. “Water withdrawal” measures the total freshwater input
into a process. “Water consumption” measures the volume of total water input that has become
unavailable for reuse due to evaporative losses, incorporation into a product, or transfer to another
catchment. Water withdrawals can affect the environment through reduced water availability but this is
not necessarily the case, particularly when good quality water is returned to the environment quickly.
Likewise, water consumption implies reduced water availability but does not capture the environmental
impact of polluted (but not consumed) water being returned to the environment. To overcome these
issues Water Footprint (WF) [45] can be used as a comprehensive metric [5,10].
WF measures the quantity of freshwater consumed and polluted and is divided into blue, green, and
grey water footprint [41]. Blue footprint covers the volume of freshwater (surface or groundwater)
consumed. Green footprint covers the volume of rainwater consumed—rainwater being water that does
not become run-off at the field level. The grey footprint is an indicator of water pollution and is defined
“as the volume of freshwater that is required to assimilate the load of pollutants given natural background
concentrations and existing ambient water quality standards” [46]. Ideally, WF also covers the full
lifecycle of a process capturing both the direct (on-site) and indirect form of WF [46]. As such, WF
offers the most comprehensive scoping of the measurement of impacts on water resources out of the
metrics considered, as well as avoiding the problems associated with only considering water withdrawals
or consumption. However, WF has not yet been used for measuring the water impacts of DCs. Thus, the
objective of this article is to address this gap in the literature.
WF has a well-systematized measurement methodology which includes water used for energy
use on top of on-site water use, and is broadly accepted as the standard for water use accounting.
This makes it a valuable metric for assessing trade-offs in the water use of different cooling systems,
their performance in different climatic conditions, and the indirect WF of the energy sources which are
here taken together to define the WF of DCs. Equipped with this understanding further research can
begin compiling comparable data and benchmarks applicable to efforts at minimizing the environmental
footprint of our information-heavy societies.
Below, the application of WF in the water use component of the WUE will be developed and the
question of the units of DC WF will be discussed. This will be followed by a review of DC structure and
the likely sources of WF for each component. Subsequently, generic issues in WF of electricity and WF
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of cooling technologies are considered, taking particular notice of WF uncertainties and associated
trade-offs. Next, using reported energy source values, the WF range for Apple’s DCs is calculated. Then,
the uncertainties in the WF of energy will be highlighted. These uncertainties make decisions on the
cooling technology difficult in the context of a dataset on different cooling technologies for a DC in
Phoenix, Arizona. As a penultimate step, taking the above-mentioned considerations into account, a
rough calculation for the global average WUE of DCs as well as a WF per outbound GB is performed.
Finally, caveats, trade-off management principles, and potential corporate and governmental policy
responses to deliver DC WF sustainability innovations are discussed. Although sources of blue, green,
and grey WF throughout the lifecycle and supply chain of a DC will be identified, this “first-step” study
into the WF of DCs will largely be limited to direct consumptive blue WF and indirect consumptive blue
WF because of data and comparability constraints.
2. Water Footprint for Measuring Water Use Efficiency
Emerging as the industry standard in measuring DC water use is Water Use Effectiveness (WUE), as
developed by the Green Grid [44]. It is defined as the total facility water use divided by the energy going
solely to the IT equipment. A higher WUE indicates a more water-intensive DC (IT equipment energy
is a proxy measure for DCs’ product. More detail on why this is the case is given later in this section).
 =    
   (1)
While WUE differs somewhat from the WF, WF can be used to inform the WUEs Total Facility
Water Use. Using WF as a metric for water use is helpful, particularly for avoiding the limitations of
“withdrawals” or “consumption” as metrics. The Green Grid guidance on measuring total facility water
use in WUE calls for the inclusion of consumptive water use in humidification, cooling, and in the
production of energy. WF can account for all of these while opening the possibility for a broader WF
account which can include green and grey WF along the full life-cycle and supply chain. Unlike WF,
WUE, explicitly, does not take into account the full lifecycle [44] but does come in a second form, which
captures indirect water use:
 =   +    
   (2)
The reason for truncating the supply chain in the WUE to just energy is two-fold: firstly, servers have
extremely complex supply chains with producers rarely, if ever, measuring and disclosing water use.
This makes it prohibitiveley difficult to find accurate and not-double-counted values for server
components. Secondly, the supply chain usually encompasses the greatest part of the WF of any
process [46]. Subsequently, indirect WF is important to consider since often simple adjustments in the
supply chain can dramatically reduce WF. However, including the full supply chain, and not just the WF
of source energy, would grow DC’s WF enormously and subsequently the differences in the design and
operation of the DC would be, proportionally, much smaller as opposed to differences in supply [44].
Full life-cycle and supply chain WF helpfully indicate how to supply DC water conscientiously.
In respect of the operation of DCs, a narrowed scope can however be more helpful. The point here,
as with the units of measurement discussed below, is that different measures provide different
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information useful in different contexts. Confusion between these measures should be avoided by clear
scoping. This study will focus on direct WF in cooling technology and indirect WF from energy use, in
order to be applicable to WUE while indicating how WF could expand the remit of WUE.
WF is always expressed as a volume of water over some unit, such as number of products, quantity
of energy, or time [46]. The question of which units will be used to express the WF of DCs is not
immediately answerable since the product of DCs is debatable. Arguably, DCs offer information and
communication services, as evidenced by Service Level Agreements or Quality of Service agreements
(contracts many data centres sign with customers) [47–50].
This topic has been helpfully addressed by an international taskforce dealing with this question in the
context of energy and greenhouse gas efficiency [51]. In defining DC energy productivity it recommends
following the example set by the Green Grid: “useful work that a DC produces based on the amount of
energy it consumes. ‘Useful work’ is a sum of tasks that are completed in a specified period of time,
where each task is weighted based on a specified relative value” [51]. While the taskforce recommends
that DCs self-assess what counts as useful work and what the relative weightings of different tasks are,
it also provides guidance on proxy measures. Three proxy measures identified by the taskforce as
particularly promising are:
Network traffic (bits) per kilowatt-hour: Outbound bits/DC energy use
Weighted CPU utilization: (SPECint_rate × CPU utilization)/DC energy use
IT equipment energy efficiency (ITEE) × IT equipment utilization (ITEU): (Benchmark
performance/rated power) × (IT energy/IT rated energy) [52]
where network traffic per kilowatt-hour measures how many bits (binary digits) are transmitted out of
the data centre per kilowatt-hour of energy used by the whole DC.
Weighted CPU utilization gives another measure of the effectiveness of the IT equipment, but is
based on a test of certain parts of the IT equipment as maintained by the Standard Performance
Evaluation Corporation (SPEC). A set of programs are run on the servers and the speed at which they
are run is measured. This value measures the amount of useful work that can be done by the IT
equipment. Multiplied by the ratio of IT equipment actually used, this gives a sense of how much useful
work is being done.
The ITEE × ITEU measures the IT equipment’s energy efficiency multiplied by the utilization of the
IT equipment. Rated power and rated energy simply mean the capacity of the equipment as given by the
producer on the equipment specification.
The metrics from the international taskforce mentioned above, could be converted into use for a WF.
In such a case WF could be expressed as:
 =  
  (3)
_ =  
rate × CPUutilization (4)
The ITEE × ITEU could not be converted in the same way because IT equipment is not rated for
water use as the IT equipment itself does not directly use water. While future studies could use these
units, they do entail serious challenges as values for bits and SPECint_rate are rarely readily available.
Sustainability 2015, 7 11266
Therefore, a simpler, less data-limited, method for approximating the useful work done in a DC is
power use effectiveness (PUE), as developed by the Green Grid [44]. In this metric, work done is
represented simply by the electricity going to the IT equipment. This metric is much more readily
available and more useful if one does not want to exhaustively determine how efficient the IT equipment
and software itself is but rather how efficient the surrounding DC infrastructure is; including heating,
ventilation, and air conditioning (HVAC). Here DC operators have more control and can deliver
environmental innovation more readily.
Clearly, the question of the denominator of the WF is a challenging one. As with WF scoping above,
it is wise to deploy different measures for different research objectives. Overall, however, it can be seen that
WF can be used easily in WUE as a good measure of total facility water use. Clearly, WF is also compatible
with IT equipment energy use as a proxy for useful work done. Later, values for DC WF will be expressed
in the above formulae (i.e., WF and WUE) and units (i.e., per IT energy used and per outbound bits).
3. Data Center Structure and Water Footprint of Its Components
Figure 1 depicts a schematic of the components of a typical DC. It highlights some of the key drivers
of DC WF which are elaborated below: IT equipment; heating, ventilation, and air conditioning (HVAC)
systems; climatic conditions (psychrometrics); uninterruptible power supply equipment; and the energy
source portfolio. After a brief overview of each component’s function and typical mechanism for
generating water footprint, a more detailed review of uncertainties involved in the WF of energy use and
HVAC systems follows.
Figure 1. DC components and DC WF drivers: HVAC, Climate, and Energy Sources.
3.1. IT Equipment and Uniterruptible Power Supply
IT equipment covers a DC’s servers and communications equipment. Servers store and process data
while the communications equipment sends and receives it. This component is “mission-critical”,
meaning that proper functioning must be absolutely guaranteed. A serious failure of the IT equipment
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could entirely halt businesses reliant on data traffic (e.g., a bank that could no longer process financial
transactions). Such a failure would most likely mean that the data centre has violated its contracts with
its customers and, hence, would be obliged to pay substantial penalties. DC operators seeking to deliver
environmental-friendly innovations are then looking for ways to reduce the environmental footprint
without placing continued service provision at risk.
IT equipment does not directly consume water. It does, however, directly consume electricity.
In order to guarantee the continued supply of electricity (even in case of grid supply failure), DCs are
typically equipped with uninterruptible power supply equipment. This includes a switch which
immediately turns on backup generators. Uninterruptible power supply is also responsible for transforming
grid electricity to a form applicable in the DC. Importantly, power conversion efficiency improvements
can play an important part of reducing electricity use and, hence, indirect WF.
During operation, IT equipment releases a lot of waste heat which, if not removed, will accumulate
and cause the IT equipment to overheat and break down. The high cooling load for a DC can be between
five and 10 times that of a office or a meeting room [52]. Greenberg et al.’s study of energy use in 22
DCs shows great variation in DC energy use going to HVAC, servers, and other functions, with IT
equipment typically accounting for the largest share of energy use. HVAC is in second place, followed
by conversion efficiency in power supply [53]. No definitive values exist due to the lack of measurement
and reporting but a typical ratio of total facility energy use over IT equipment energy use is around
2 [54]. This means that IT equipment typically uses half of the energy going to the facility as a whole;
the other half going to HVAC, power conversion losses, lighting, and other uses. As mentioned above,
larger facilities often have better ratios.
3.2. Direct WF—HVAC
HVAC includes all the technologies monitoring and maintaining temperature and humidity within
the DC at levels deemed appropriate to the functioning of the IT equipment. Temperature affects the
efficiency of IT equipment components and can cause overheating. Low humidity can cause static
buildup and sparks, while high humidity can cause dew to collect on and disrupt circuits. ASHRAE gives
the following guidance on appropriate temperature and humidity (psychrometric conditions) for IT
equipment: “a temperature range of 18–27 °C (64–81 °F), a dew point range of 5–15 °C (41–59 °F), and
a maximum relative humidity of 60% for DC environments” [55]. ASHRAE also provides looser,
“allowable” ranges and is encouraging DC operators to consider operating in less stringent conditions
to save energy and reduce environmental footprints. This is one of the more convenient innovations to
implement for DC operators seeking to reduce environmental footprints.
In order to achieve these conditions HVAC systems use many varied components but typically they
employ a vapor-compression cycle and have three key components: the chiller, coolant loop (cycling
water or a refrigerant), and airflow control (how cool air is distributed to the IT equipment) [44,55,56].
The vapor-compression cycle uses the thermal dynamics of coolant vaporization to extract heat from
one place and compression to release it in another. When pressurizing the coolant, it releases heat and
when vaporizing, it absorbs it.
The chiller includes the main components used to facilitate this process. Compressors pressurise the
coolant. Condensers remove the heat the coolant releases using water and/or ambient air. Flow control
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mechanisms ensure the correct pressures in different parts of the cycle. In evaporators the coolant
vaporization absorbs heat from air which is then distributed to the IT equipment by the airflow control
(the fans, ducts, false-floor, convection currents in the room with the IT equipment, or otherwise).
Thereby, the IT equipment is kept from overheating [56].
As mentioned, condensers can be cooled by air or water. This distinction is important to consider in
the context of WF. Broadly speaking, water-cooled condensers are less energy intensive as water can
cool the condensers more effectively than air [57,58]. For example, a Pacific Gas and Electric (PG&E) Co.
study found that in a typical 100,000 m2 building in California, air-cooled condensers used 440,000 kWh
while water-cooled condensers used only 190,000 kWh per year [59]. The question of whether
air- or water-cooled condensers have a lower water use is addressed in more detail in [59] and in the
example from Phoenix towards the end of this paper.
In dry conditions, HVAC systems can also employ evaporative cooling, where raising the humidity
of air lowers the temperature. Although, in general, there are evaporative losses whenever water is used
(e.g., water-cooled condensers evaporate some of the coolant in cooling towers to improve cooling),
evaporative cooling is a key driver of blue WF. HVAC systems also generate grey WF through water
pollution via expelling hot water to the environment if they are once-through cooling systems without a
cooling pond and through the discharges of dirty water for cleaning and replacing water in the coolant
loop. For more detail on the chemical use in HVAC systems refer to [57] whose values could be used to
determine grey WF. As mentioned in the introduction, grey WF is not addressed here beyond indicating
the need to study it further. Finally, but very importantly, HVAC systems also generate indirect WF
through the electricity they consume for compressors, sensors, and control systems.
3.3. Psychrometrics: Temperature and Humidity
Psychrometrics examines the physical and thermal characteristics of mixtures of gas and vapor.
In the context of DCs it is useful in managing the relationships between air temperature and humidity.
A greater deviation in the DC’s environment from the standards necessary for the IT equipment means
that the HVAC has more temperature and humidity control to do. That greater workload generates a
greater WF through the evaporation of water and/or the consumption of electricity (indirect WF).
ASHRAE [56] offers detailed understandings of the relation between climate and cooling and the Green
Grid provides geographic maps of where free cooling and air-side economization can serve as an
effective cooling option [57]. In air-side economization, the returning, warm coolant is first passed
through a condenser exposed to outside air before being cooled further. In free cooling, outside air is
filtered and directly used to cool the IT equipment. This is already prevalent in approximately 50% of
DCs in the United States [60]. While the Green Grid’s maps are a useful tool, they should also be subject
to the uncertainties arising from climate change. This is important particularly when siting and designing
DCs which are expected to be operational for many decades.
Metzger et al. [61] conduct a bin analysis of four cooling technologies (individually and in combination
with others) by their climate appropriateness and energy use reduction as compared to just a typical
direct exapansion (DX) air-conditioning. While that study focused on energy reductions as indicators of
efficiency, varying WF between these technological options should also be considered. The uncertainties
involved in this are discussed in an example later in this paper.
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The technology options in the Metzger et al. study are: (1) direct expansion (DX): typically an
off-the-shelf Computer Room Air Conditioning (CRAC) unit; (2) air-side economizer: outside air is
used for cooling after treatment for air quality and humidity; (3) direct evaporative cooling: evaporation
into air lowers its temperature; and (4) multistage indirect evaporative cooling: two cooling systems are
connected to deliver cooling and humidity control. Seven combinations of these technologies are
identified, defining seven cooling strategies. In Figure 2, the zones identifying under which temperature
and humidity each cooling strategy is effective are mapped onto the psychrometric chart. In the middle
of the chart is the ASHRAE recommended environment for IT equipment.
Figure 2. Psychrometric chart displaying bin zones for the recommended envelope and
alternative cooling strategies (after [61]). The blue zone represents the ASHRAE
“recommended” temperature and humidity for IT equipment. The psychrometric chart is taken
from [62].
For environments with >80% relative humidity and >15 °C dewpoint, DX is still the best option.
With >15 °C dewpoint and <80% relative humidity, adding multistage indirect evaporation can reduce
energy consumption. In environments with the ASHRAE recommended absolute humidity but below
recommended temperature, air-side economizers can deliver the required cooling by themselves with
great reductions in energy use. At a lower humidity ratio, an evaporative cooler should be used in
combination with air economizers. For 5.5–15 °C wet-bulb temperature at lower than recommended
relative humidity the evaporative cooler can deliver recommended air conditioning alone by the cooling
effect of directly evaporating water into the air. Finally for >15 °C wet-bulb temperature and <15 °C
Sustainability 2015, 7 11270
dew point the multistage indirect evaporation alone will reduce energy use because it can reduce
temperature without changing the humidity.
3.4. Indirect WF—Energy Source
Energy consumed by DCs during operation, is a key driver of DC WF. Determining the WF of the
energy consumed by DCs (WFsource) can be done by determining the WF of each energy source in the
electricity generation portfolio and then calculating a portfolio-share weighted average to get a a WF per
unit of electricity supplied. The WFsource is then multiplied by the amount of energy used to get the WF
of energy used.
 =(
 ×
) (5)
where n is the number of energy sources in the energy supply portfiolio, WFEi is WF per unit of energy
produced by energy technology i, and TSi is the percentage share of energy technology i in the total
electricity supplied.
The energy source portfolio therefore determines the WF for the electricity used in the DC. This means
that to reduce WF, DC can look for energy sources with a low WF as well as improving energy use
efficiency, which reduces the amount of energy used in a DC and thereby also the indirect WF.
The most common electrical components of a DC and their relation to energy efficiency are reviewed
in depth in Report to Congress on Server and DC Energy Efficiency [13] among many other publications
dealing with this [13,44,53,63]. According to Miller [64], typical measures for reducing electricity are
to: reduce power conversion efficiency losses, optimize airflow, economize on outside air, relax
temperature and humidity standards, and centralize controls and sensors. Further energy efficiency
measures for DCs are given by the Green Grid [57], US Department of Energy [65], and Environmental
Protection Agency [66].
Regarding green WF, DCs typically do not interact with rainwater (green WF). That being said, it is
worth briefly noting that large facilities may have substantial outdoor areas on campus which may
generate green WF. Apple reports on changes in landscaping at one of their campuses which reduces
green WF [67]. While the method for calculating the water footprint of DC energy source is relatively
simple, large uncertainties remain in practice which are addressed in the section below.
4. Uncertainty Ranges in Determining the Water Footprint of Source Energy
Figure 3 depicts WF per unit of electricity output as sorted by the range minimum [68]. The values
reflect consumptive blue WF for fuel supply, construction, and operation.
Wind is clearly the least water intensive, and firewood and hydropower the most intensive. However,
large ranges (Figure 3) in most other technologies indicate large uncertainty and variability. Therefore,
understanding the particularities of each case of a technology is required for accurate WFE accounting.
For example, all technologies except wind, photovoltaic, and firewood include 1000 m3·TJe1 as a value
in the range. It is likely that method of operation, its efficiency, location, regulatory and psychrometric
setting, or other factors could at times overtake the energy technology itself as dominant drivers of WFE.
The overlap between nuclear and many fossil fuels could be explained by the common reliance on steam
Sustainability 2015, 7 11271
turbines and associated uses of water. It is then possible that the steam turbine system’s WF is a greater
driver of the energy generation technologies WF than the feedstock (e.g., coal or nuclear). Some further
division and granular analysis of the dominant technologies employed per electricity type and their
accompanying WFs would be helpful. It is also important to note that these values are the most up to
date, but do not reflect the full WF account, which would also include the green and grey WF. Slightly
older values for the full water footprint of energy technologies can be found in [10].
Figure 3. The consumptive WF per unit of electricity output for different energy sources for
the total production chain of electricity (after [68]).
That being said, WF must be assigned to each generation technology, but it is important that as much
detail as possible is given for the particular energy supply facility. Without such specific information,
uncertainty will propagate through the WF assessment making it more difficult to decide on optimal
technology choices. To highlight this, the above ranges will be carried into the subsequent analyses,
showing how these uncertainties make decision between WF reduction strategies difficult.
Other than for backup, most DC operators will not invest in their own electricity generation
technology and will, therefore, simply purchase electricity from the grid. Figure 4 shows the estimated
WF of electricity in a few sample regions around the world. The ranges have been calculated based on
the technological mix in each country or area [6,69–71] with respect to the recent energy WF estimations
of Mekonnen et al. [68] using the the WFsource equation. The figure therefore shows the WF of an average
unit of electricity output to the grids in those areas. The WF of electricity varies by the mix of more
and less water intensive electricity generation technologies and their associated uncertainty ranges. In
particular, low-share, high-WF sources like biofuels and hydroenergy can drive WFsource disproportionately.
Natural Gas
Shale Gas
Conventional Oil
Concentrated Solar Power
Unconventional Oil (shale)
Unconventional Oil (sand)
WF per unit of net energy output
(m3 /TJ) Logarithmic Scale
Sustainability 2015, 7 11272
Figure 4. Consumptive WF per unit of energy in different locations.
This relationship comes through particularly with the difference between the WFsource of France and
the US. France relies much more on nuclear than the US. In the US, fossil fuels account for the majority
of energy production. As seen above, nuclear’s range minimum is clearly below that of the fossil fuels.
This allows France to enjoy a lower WFsource range minimum. However, France’s range maximum is
well above that of the US. This could not be explained by the large share of nuclear, since nuclear’s
range maximum is similar to that of the fossil fuels. France has a substantially larger share of energy
coming from hydro than the US does. This drives France’s WFsource maximum well above that of the US.
Apple Inc., in operating its large DCs, provides an interesting case of DC operators adopting
environmental innovations because it claims to be ambitiously pursuing environmental sustainability,
including a zero carbon emissions policy, clean water programme, and others [69]. Apple has been
building its own electricity generation capacities and reports a successful decarbonization of its
DCs [69]. It also reports that the introduction of a new water reusing cooling system at the Maiden,
North Carolina DC has reduced water consumption by 20%. Simultaneously, however, Apple reports it
has seen a rise in water consumption in its DCs as a result of growing server room cooling load.
Figure 5 shows the calculated WFsource associated with Apple’s DCs based on their energy mixes as
shown in Table 1. Apple facilities in Newark, Prineville, and Reno have a relatively low WF. The Newark
facility still purchases all of its electricity from the grid. This is done through California’s Direct Access
Program where consumers can buy renewable energy directly from generators. Although this means
Apple can buy electricity to meet its zero carbon emissions policy, the exact electricity supply portfolio
is not clear and so neither is the range of the WF. As a proxy, California’s renewables mix was used here
to give a rough indication of what the values could be in this case [72]. The USA’s WFsource is replicated
here to give a benchmark.
Figure 5. WFsource for Apple’s DCs. Values are per unit of electricity as opposed to the total
WF of electricity consumed annually.
Arizona France China USA World
WFsource (m3/TJ)
Logarithmic Scale
Reno, NE USA
WFsource (m3/TJ)
Logarithmic scale
Sustainability 2015, 7 11273
Table 1. Apple Data Centers’ energy sources. Values are based on Apple’s 2014 Environmental
Footprint Report [67].
Location Electricity
Solar Grid
Electricity Wind Geothermal
Maiden, North Carolina 576 47% * 53%
Newark, California 443 100% **
Prineville, Oregon 65 100%
Reno, Nevada 11 100%
* WF of Biogas from landfill: 833.3–4166.6 m3/TJ [73], ** Apple reports that it purchases all electricity from renewable
energy generators under California’s Direct Access Program.
In pursuing its zero carbon strategy and investing in its own generating capacity, Apple has reaped
the co-benefit of reduced WF. This however, is not the case in Newark, if its electricity supply portfolio
is the same as California’s renewables mix which has a substantial bioenergy share (11.5%). Therefore,
the WF associated with that electricity comes out higher than the that for the US weighted average.
While the range for the Newark facility is already large, it does not include the uncertainties associated
with the share of renewables in the energy mix. Retrospectively it is possible to fix the portfolio shares but
these are not definite indicators of the future mix as it is subject to variablity in solar and wind energy
supply, as well as the regulatory and policy uncertainty surrounding renewable energy.
To ascertain the total WF of electricity for each facility, the above WF of energy values would have
to be multiplied by the amount of electricity each facility uses. This would still not yield the total WF of
DC as the direct WF of each facility (particularly the WF of HVAC) would still have to be added.
Uncertainties in that stage of the WF account are considered below for a DC in Phoenix, Arizona as
an example.
4.1. Uncertain WFs of HVAC Systems
As discussed throughout, in order to understand the WF of DCs both direct and indirect WF should
be taken into account. Thus, in addition to the indirect WF of DCs through energy use, one needs to
quanitify the WF of different HVAC systems. This can be done by reviewing different HVAC systems’
energy use and WF in a given climate.
Figure 6 shows four different options for a DC cooling unit with a capacity of 36,000 cubic feet per
minute (61,164 m3/min) operating on a DC in Phoenix, Arizona as compared by their direct and indirect
WF. All options use outside air economization. They vary by two factors: (1) air- or water-cooled
condersers and (2) direct evaporation or no evaporation. Direct evaporation is equivalent to the direct
evaporative cooling option in Metzger et al. [61]. The third option, “no evaporation or air-cooled
condenser” is essentially equivalent to the direct expansion option in the Metzger study. As expected
from the Metzger et al. findings, the HVAC systems employing evaporative cooling use less electricity
in the dry Arizona climate. Also, as expected, the water-cooled systems use less electricity.
Sustainability 2015, 7 11274
Figure 6. Annual water consumption of different cooling technologies in a DC cooling
technology with a capacity of 61,164 m3/min, based on inlet supply temperature to servers
of 27 °C in Phoenix, Arizona. (After Vokoun [74] who reports United Metals Products
values. Vokoun uses National Renewable Energy Laboratory (NREL) figures for the water
consumption for electricity production (8254 m3/TJ) [75]. Note that NREL is using only
evaporative water consumption not WF).
When considering the values in Figure 6, some cooling systems appear to be less water intensive in
terms of direct water consumption, but because of greater electricity demand, end up with greater total
water consumption. In particular, while the “air cooled condensers/no evaporation” option has no direct
WF, it requires significantly more electricity to achieve the same cooling. The WF of generating this
additional electricity more than neutralizes the gains of not having a direct footprint. Thus, the ‘direct
evaporation/air cooled condenser’ option has the lowest water consumption.
Figure 7 shows the annual WF of HVAC including direct and indirect WF as calculated with the
WFsource range determined for Arizona above multiplied by the electricity consumption for the different
HVAC options. At the low end of Arizona’s WFsource range (70 m3/TJ), the ‘no evaporation/air-cooled
condenser’ comes out with the lowest footprint. This is despite the less efficient air-cooled condenser,
and the lack of evaporative cooling (most appropriate to a dry climate) generating the greatest electricity
use of all four options. At the high end (540,544 m3/TJ), “direct evaporation/water-cooled chiller” comes
out with the lowest footprint. This runs counter to the result of the water consumption figures from
NREL (8254 m3/TJ), where “direct evaporation/air-cooled condenser” came out best. These differences
are indicative of the proportions in play. As the WF of electricity rises, the relative impact of electricity
consumption over direct water consumption on WF rises. Subsequently, it becomes apparent that the
uncertainty range involved in determining the WFsource hinders a definitive recommendation on which
HVAC technology has the lowest total WF, hence which technologies can provide environmental
innovations most readily.
192 915 0
7,748 7,500
Direct Evaporation or
Direct Evaporation or
No Evaporation or Air-
No Evaporation or
Water Consumption (m3)
Water Consumption in Cooling Technology Water Consumption of Energy
Sustainability 2015, 7 11275
Figure 7. Annual WF of different HVAC systems. The direct water consumption values are
taken from Vokoun [74] and added to Arizona WF of energy ranges from Figure 4.
The dataset from Vokoun used in these calculations has at least two important limitations. Firstly, the
data comes from one firm (United Metal Products) [74]. The data are therefore dependant on that firm’s
designs and are not necessarily representative of evaporative, air and water cooled chiller technologies
in general. There is substantial variability between designs which is another source of uncertainty that
must be acknowledged. Secondly, the above data do not include the grey water footprint that is
associated with water cooling. This could again skew the HVAC system choice recommendation.
4.2. Water Footprint of Data Centres and Data Centre Outbound Traffic
This section provides a rough calculation of the WF of DCs. This is done by summing the WF of the
energy consumed in DCs with the direct WF from HVAC systems.
To get the WF of total energy used by DCs, Koomey’s values for worldwide DC energy use in 2010
were applied (732,240–978,480 TJ/year) [11]. The range was multiplied by the WFsource range for the
global average energy portfolio (1047–150,317 m3/TJ) as calculated for Figure 4. This gives the range
767–147,082 million cubic meters (mcm) for the annual WF of energy consumed by DCs. As a point of
referance, the top end of the range is comparable with Italy’s WF of 130,000 mcm/year, and the bottom
end of the range is comparable with the 650 mcm/year WF of the Bahamas [76].
Using Vokoun’s data on the water consumption in Arizona’s HVAC systems we can roughly
calculate an uncertainty range for the WF of HVAC systems. This is done by dividing the HVAC water
consumption by the HVAC electricity consumption. Given the electricity consumed by HVAC we can,
thereby, establish a rough ratio between that and the direct WF of HVAC. This gives us a range of
0–1554 m3/TJ. Although, Sharma et al.’s [39] reported linear relationship of around 0.5 m3/TJ, for a
water-cooled chiller it is compatible with this range, but it is at the very low end. This could imply that
the distribution of values within the range are skewed towards the lower end.
To get the annual WF of DC HVAC systems, the above range is multiplied by the share of energy
going to DC infrastructure annually in 2010 (as reported by Koomey: 331,200–468,720 TJ) [11]. This
is an impefect metric as it includes lighting and other electricity uses in DCs but is used here as a rough
proxy measure. This gives the range 0–728 (mcm) per annum for the direct WF of HVAC. This is a
much smaller value than for the WF of DC energy use.
50,931 50,028 62,207 60,500
Direct Evaporation or
Direct Evaporation or
No Evaporation or
No Evaporation or
Annual WF of HVAC (m3)
Logarithmic scale
Sustainability 2015, 7 11276
Added together the WF of HVAC and the WF of energy used in DCs gives us the range
767–147,810 mcm per annum for all the world’s DCs. Dividing this value by the useful work done by
DCs will give us values comparable between DCs. Firstly, we can again divide by the total energy used
in DCs thereby resulting in the WUEsource ranging from 1047 to 151,061 m3/TJ. Importantly, this value
is much higher than the 787 m3/TJ reported by the Sharma et al. [38], and differs because of the use of
WF accounting.
As discussed with regards to units of measurement, the range for annual WF of worldwide DC is
divided by the outbound data traffic for which Cisco Systems, Inc. values were used [2]. Only 23.3% of
all DC data traffic (3.1 zettabytes per year) actually goes to other DCs or to users giving us the value
722 exabytes (EB) outbound traffic per year in 2013. Dividing the above calculated values for annual
DC WF gives us the range: 1–205 mcm/EB or liters per gigabyte of data sent out of DCs. The high end
of the range is comparable to the WF of a kilogramme of tomatoes which is 214 litres [77], reflecting
the high but overlooked water effects of data use and communcation.
5. Caveats for Policy and Innovation Recommendations
The three key uncertainties involved in determining the WF of DCs and recommending innovations
towards reducing environmental footprint are: WF of electricity, psychrometrics, and WF of HVAC.
Due to the very high energy use in DCs, and broad tendency for IT equipment to use half or more of
the total energy in a DC, indirect WF (WF of electricity) is typically much larger than direct WF.
Therefore, the first focus for reducing DC WF should be on air-side economization and energy
efficiency. Helpfully, this yields the important co-benefit of reduced operating costs going to electricity
and reduced greenhouse gas emissions. If at least these three criteria are considered (i.e., cost, water, and
carbon footprint), then DC innovation should remain strongly focused on energy efficiency. Also, given
the possibility, investing in less water-intensive electricity generation technologies can dramatically
reduce DC WF as seen in the case for Apple’s DCs.
Secondly, attention should be given to the choice of HVAC technology. Under high WFsource, HVACs
with direct WF as opposed to those without any, can reduce total WF as seen in the case of the DC in
Phoenix. On the other hand, under a very low WFsource, switching away from HVAC systems with direct
WF can reduce DC WF. This technology decision, however, would have to be made with a more detailed
understanding of the relative performances of different designs in different climates.
With regards to the WF values calculated for worldwide DCs above, the share of energy going to
HVAC and the WF of HVAC remain very uncertain as specific values were not available. The proxy
measure used was based on one simple dataset from one location. As such it should be interpreted very
cautiously but does provide reaffirmation that the direct WF is relatively small compared to the WFsource.
Consequently, the focus for WF reduction broadly remains on energy efficiency.
DC location choice can also greatly impact its WF. With regards to reducing WF, DC siting is subject
to considerations of WFsource (if purchasing from grid) and psychrometric conditions in different possible
locations. In this optimization, it is important to note that if a given amount of electricity is being bought
from the grid, the WF of that electricity has a linear relationship with the total DC WF, assuming that
the energy supply portfolio does not change by the amount of energy supplied. As in the case of Apple’s
DCs, the total amount of energy used does not necessarily affect WFsource. However, this linearity will
Sustainability 2015, 7 11277
not necessarily hold when directly purchasing from generators. Relative prices or supply constraints
from sources with a lower WF may make DCs buy a share of electricity from higher WF sources. The
amount of energy used can affect the technology shares and, hence, WFsource. Thus, higher electricity
consumption increases total WF which may be non-linearily greater because of the need to buy from
energy sources with a higher WF.
Additionally, in considering siting, deviation from psychrometric conditions amenable to IT
equipment is likely to display a non-linear relationship with the total WF. Such deviations imply not
only changes in the amount of work needed to be done by HVAC (i.e., HVAC WF and energy
consumption), but also the kinds of systems that can or must be deployed. This would be particuarly
pronounced when crossing psychrometric envelopes in which free cooling with outside air is effective.
Large DC operators such as Facebook, Google, and others therefore are building their new DCs in cold
climates where outdoor air is most easily exploited [78–80]. As provided in the Green Grid and other
sources, this psychrometric deviation is quantified by the average number of hours in a year where
outside air has the psychrometric values appropriate to IT equipment cooling. Different locations have
different frequency distributions for that number of hours and hence imply the possibility of non-linearity
in the relationship between deviation of average psychrometric conditions and amount of energy and
water used on HVAC. Possible siting trade-offs then include circumstances where an increase in WFsource
at an alternative location may much more than offset the total WF increase by a much more
air-side-economization-friendly climate. These difficulties in defining the relationships between the key
uncertainties and DC WF make definitive decision-support difficult without the specific values which
are generally challenging to come by.
For a given DC, the uncertainties are reducible when exploring specific options for the siting, design,
and build stages of a DC. Once the operational phase has begun, reporting on a facility’s WF (and related
carbon footprint and other environmental performance criteria) becomes possible with a reasonable
degree of accuracy and can be validated by a third party should the desire to do so arise. Noting that
site-specific uncertainty can be reduced, a number of corporate and regulatory/policy responses could
reduce uncertainty for generic assessments, and boost innovation and best practice.
It is fundamental that firms collaborate in the development of leading industry standards such as the
WUE. As a parallel activity, standardization, monitoring, and reporting templates for DC WF should be
created. Currently, corporate sustainability frameworks, such as the Global Reporting Initiative (GRI)
are not fit for purpose in this regard [37]. Standardized WF and WUE could be, summarily, introduced
into the corresponding GRI methodology with a corresponding reporting template. This would improve
knowledge of the water use in average DCs. It would also allow firms that rely upon DC service
provision to select DC service suppliers by environmental performance, as well as cost and other metrics.
Additionally, this would allow for the creation of a performance league table rewarding first mover
advantage while calling attention to laggards, and enticing firms to improve performance.
More ambitiously, WF reduction methodologies and implementation solutions (including HVAC
technologies, design solutions, and siting) could be built into the licensing or permitting frameworks for
DCs. Implementing industry best practice in the form of “best available techniques (BAT)” is a standard
feature of industrial installations throughout Europe in respect of how they are regulated under the
Industrial Emissions Directive [81]. Though it is not suggested that DCs be regulated under this
Directive, modest versions of BAT reference documents (BREFs) specifically designed for DCs would
Sustainability 2015, 7 11278
appear to be sensible given their growing contribution to energy and water resource use. Much of the
content of BREFs would come from industry in any event as an exemplar of sectoral best practice.
6. Conclusions
Given the rising use of DCs coupled with rising environmental stress, substantially more attention
should be paid to the WF of DCs. To do this, all three factors of WFsource, psychrometrics, and HVAC
must be considered. In particular, more research and disclosure is needed to understand the relative
performance of different DCs, the energy and water intensity of different HVAC technologies in
different climates, and the water intensity of electricity generation. Large uncertainties remain in the
quantification of the values necessary for addressing trade-offs involved in DC design, siting, and
operation. These uncertainties also mean that the values determined in this paper should be considered
with caution and are more indicative of the uncertainties involved than a definite quantitification of WF
values. Building on the work done here, further research for determining WF of data traffic could
develop interesting insights into the WF of the Internet and the associated virtual water flows.
In order for such values and understandings to develop, consistent metrics and methodologies need
to be applied. WF offers a systematic approach for understanding the water impacts of DCs and can be
applied in WUE and WUEsource. Different metrics yield different recommendations for reducing water
footprint. When considering direct WF, the focus is on HVAC: evaporation for humidification and
cooling, and the gray WF from heat pollution and chemical discharge. When we include the indirect
WF, electricity use takes over as the biggest driver of DC WF. The focus then shifts to less
water-intensive energy sources and boosting energy efficiency of both IT equipment as well as the
facility more broadly. As seen in the comparison of different HVAC systems at a DC in Phoenix, systems
which came out unfavourably when only considering direct WF can deliver a lower total WF when
WFsource is included. Determining which HVAC system is the best requires a consideration of the
psychrometry of the data center given its cooling needs, its (changing) climatic environment, and the
energy and water performance of the HVAC systems.
Future studies in this area should seek to reduce the uncertainties surrounding the WF of electricity
and provide a more systematic understanding of HVAC systems’ direct and indirect WF. Although,
having considered other sources, this study has been limited to one simple dataset on different HVAC
systems operating in Phoenix, because very little data can be found for the WF of DCs, or even HVAC
systems, generally. More data for the water and energy performance of different HVAC systems in
different climates would provide a better basis on which to develop trade-off management principles.
Future studies should also consider expanding the scope of analysis to include a quantification of other
parts of the DC supply chain and also to consider the grey and green WF in much more depth than the
mention these factors recieved here. Apple, for example, reports on changes in landscaping which have
led to reductions in green WF. While Apple and other large firms have environmental reporting, most
DCs are small and have little to no reporting on environmental footprints. Although academia, industry,
and the public sector have started making such efforts, much work remains to be done.
Sustainability 2015, 7 11279
We would like to thank Cristobal Irazoqui, Policy Officer—ICT Environmental Sustainability, DG
CONNECT, European Commission, for his consultation on this article.
Acknowledgement is also due to the Natural Environment Research Council (NERC) for the financial
support provided to the first author.
Author Contributions
All authors have contributed equally to the realization of this research, albeit in different ways. The
research question was concieved by Kaveh Madani who lead the study team. Bora Ristic was responsible
for data collection and analysis, interpretation of results, and writing. Kaveh Madani and Zen Makuch
both contributed substantially to improving the analysis and with suggestions for improvement in the
manuscript. All authors have read and approved the final manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
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... Despite the steadily increasing energy consumption in data centers, to the best of the authors' knowledge there is a limited number studies in the literature that are concerned with the analysis of environmental impacts caused by the operation of data centers. Ristic et al. [5] calculated the water footprint (WF) of data centers by considering solely the cooling and electricity consumption. They found that the WF values vary between 1047 and 151,061 m 3 /TJ, with electricity consumption being the hotspot. ...
... This particular functional unit was chosen because a data center does not have a specific output which could have otherwise been used as a functional unit. Some studies use the amount of electricity consumed as the functional unit [5,6], but we believe that such an approach would not be compatible with the LCA approach used in this study. ...
... Material input has an average share of 1.5 % whereas end-of-life treatment has an average share of merely 0.1 %. These results are not surprising and highly consistent with the literature in the sense that earlier studies also reported that that the vast majority of the environmental impacts of data centers is due to the electricity consumption, while the contributions of other stages such as material supply, transportation, or end-of-lifetreatment are negligible [5,6]. When the electricity consumed in the data center is analyzed more closely, it can be seen that exactly 50 % of the electricity is consumed for data processing, followed by cooling with a share of 29.8 %. ...
... In the particular sites of datacenters, where data is collected, processed, and stored, the energy costs associated with cooling the multitude of computing devices has brought about demand for natural resources. Ristic and colleagues [71] estimate that each gigabyte of data output at a datacenter has the potential water footprint equivalent to a kilogram of tomatoes -up to 205 litres of water. Where Lally and colleagues [49] demonstrate a 'parasitic', and thereby non-deterministic, relationship between BitCoin miners and low electricity costs associated with hydropower in central Washington, other analysis shows a more directed set of policies and practices for the exploitation of land and water and the displacement or destruction of arable lands and agricultural practice. ...
... The global carbon footprint of Internet use ranges from 28 to 63 g (g) CO 2 equivalent per Gigabyte (GB), whereas the water and land footprints are 0.1-35 L (L)/GB and 0.7-20 cm 2 /GB, respectively (Obringer et al., 2021). In 2015, a water footprint of data centers up to 205 L/GB was reported by (Ristic et al., 2015). This includes footprints associated with both transmission and data storage globally in a data center. ...
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Internet data centers have received significant scientific, public, and media attention due to the challenges associated with their greenhouse gas, water, and land footprint. This resource greedy data services sector continues to rapidly grow driven by data storage, data mining, and file sharing activities by a wide range of end-users. A fundamentally important question then arises; what impact does data storage have on the environment and is it sustainable? Water is used extensively in data centers, both directly for liquid cooling and indirectly to generate electricity. Data centers house a huge number of servers, which consume a vast amount of energy to respond to information requests and store files and large amounts of resulting data. Here we examine the environmental footprint of global data storage utilizing extensive datasets from the latest global electricity generation mix to throw light on this data sustainability issue. The analysis also provides a broad perspective of carbon, water, and land footprints due to worldwide data storage to through some light on the real impact of data centers globally. The findings indicate that if not properly handled, the annual global carbon, water and land footprints resulting from storing dark data might approach 5.26 million tons, 41.65 Gigaliters, and 59.45 square kilometers, respectively.
... Data centers are predicted to need between 1047 and 151,061 m 3 /TJ of water. Data center outbound data traffic has a water factor of 1-205 l/ gigabyte [36]. According to the Organization for Economic Cooperation and Development (OECD), worldwide water consumption for industrial industries would rise 400% between 2000 and 2050. ...
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India's GHG emissions pathway will be critical to keeping temperatures below 20C. It is still unclear how to reconcile the impact of rising household consumption with climate change goals. Here, we examine the role of household consumption by calculating the carbon emissions of 12 expenditure categories using 33 products and services reported in the 2011-12 expenditure statistics for urban and rural communities in 35 Indian states and union territories (UTs). Results show that per person impacts are higher in urban populations (2.7 tCO2eq) than in rural areas (2.2 tCO2eq); however, due to a larger population share, rural households contributed two-thirds of total emissions, equivalent to 2.6 GtCO2eq. For commodities, fuel and lighting, transportation, milk and dairy products, meat and eggs, and rice contribute 1020, 280, 610, 430, and 130 MtCO2eq, respectively. A higher emission load is observed for transportation in urban areas, while for animal food, the load is higher in rural areas than in urban areas. When examining the climate stabilization target of 1.50C, the scenarios show that switching to solar energy (supply-side transition), moving away from OECD-type animal diets, and avoiding status-based mobility behavior (demand reduction) are able to achieve emission savings of 52 GtCO2(20% of business-as-usual emissions) by 2050, assuming that environmental feedbacks are excluded.
The climate impacts of the information and communications technology sector—and Big Data especially—is a topic of growing public and industry concern, though attempts to quantify its carbon footprint have produced contradictory results. Some studies argue that information and communications technology's global carbon footprint is set to rise dramatically in the coming years, requiring urgent regulation and sectoral degrowth. Others argue that information and communications technology's growth is largely decoupled from its carbon emissions, and so provides valuable climate solutions and a model for other industries. This article assesses these debates, arguing that, due to data frictions and incommensurate study designs, the question is likely to remain irresolvable at the global scale. We present six methodological factors that drive this impasse: fraught access to industry data, bottom-up vs. top-down assessments, system boundaries, geographic averaging, functional units, and energy efficiencies. In response, we propose an alternative approach that reframes the question in spatial and situated terms: A relational footprinting that demarcates particular relationships between elements—geographic, technical, and social—within broader information and communications technology infrastructures. Illustrating this model with one of the global Internet's most overlooked components—subsea telecommunication cables—we propose that information and communications technology futures would be best charted not only in terms of quantified total energy use, but in specifying the geographical and technical parts of the network that are the least carbon-intensive, and which can therefore provide opportunities for both carbon reductions and a renewed infrastructural politics. In parallel to the politics of (de)growth, we must also consider different network forms.
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The continuing growth of digital technology has been accompanied by an explosion in digital data generation. This data is processed, stored, managed and exchanged in data centres, which have become the driving hub of the economy and in some ways of society. This article outlines the origins and characteristics of data centres and offers an exploratory discussion of their locational dynamics and their changing geography within the UK. The findings reveal that while data centres were initially concentrated in London, a range of new urban, suburban and remote rural locations are becoming increasingly important. That said, while the geography of data centres may be changing the need to design ever more sustainable centres, which increase energy efficiency and reduce carbon emissions, seems likely to remain constant.
Conference Paper
Data centres are powerful and power-hungry facilities which aim at hosting ICT services. The current trend is to, on the one hand, try to reduce the overall consumption of a data centre, and on the other hand to prioritize the utilization of renewable energies over brown energies. Renewable energies tend to be very variable in time (e.g. solar energy), and thus renewable energy aware algorithms tries to schedule the applications running in the data centres accordingly. However, one of the main problems is that most of the time very little information is known about the applications running in data centres. More specifically, we need to have more information about the current and planned workload of an application, and the tolerance of that application to have its workload rescheduled. In this paper, we present a work in progress on Plug4Green, a flexible VM manager able to reduce energy consumption in data centres. We extend Plug4Green with the second goal of increasing the usage of renewable energy in data centres. This includes the development of specific application profiles, and a new optimization technique.
The growth in demand for Information Technology (IT) systems and the requirements to better control carbon emissions is a large challenge for data centre design. Air-cooled data centres are becoming more efficient by layout and the adoption of compressor free cooling, but for higher densities, further efficiencies can be achieved with liquid (water) cooled systems, where liquid is either brought to the cabinet or is fed directly into the IT systems. This paper makes a comparison of the full energy consumption between hybrid air-cooled and direct liquid-cooled systems based on real operational systems using comparable IT components. The results based on real data demonstrate a significant level of energy reduction for a high density data centre solution that uses enclosed, immersed, direct liquid-cooled servers.
Data centre services hold promise for reducing societal carbon emissions, but an imperfect and evolving portfolio of performance metrics obscures which data centre characteristics correspond to low-carbon operations. Meanwhile, policymakers face a pressing question: can we identify and promote tangible characteristics that reliably represent low-carbon data centres today while the world awaits better metrics? Fortunately, data centre energy models can provide actionable guidance. Here, we present results that identify such characteristics and illuminate the factors that govern a data centre's actual carbon performance. These results can help public and private sector policymakers accelerate the transition to a low-carbon Internet by aligning data centre incentives with factors that truly matter.