Uppsala University

Uppsala University
Uppsala University
This is an accepted version of a paper published in The International Journal of Life
Cycle Assessment. This paper has been peer-reviewed but does not include the final
publisher proof-corrections or journal pagination.
Citation for the published paper:
Davidsson, S., Höök, M., Wall, G. (2012)
"A review of life cycle assessments on wind energy systems"
The International Journal of Life Cycle Assessment
URL: http://dx.doi.org/10.1007/s11367-012-0397-8
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Permanent link to this version:
Published in International Journal of Life Cycle Assessment
Article in press
A review of life cycle assessments
on wind energy systems
Simon Davidsson*,**, Mikael Höök*,**, Göran Wall+
Corresponding author: [email protected], phone: +46 18-471 7643, fax: +46
* Uppsala University, Global Energy Systems, Department of physics and astronomy, Box 535,
Lägerhyddsvägen 1, SE-751 21, Sweden, http://www.fysast.uu.se/ges
** Uppsala University, Global Energy Systems, Department of Earth Sciences, Villavägen 16, SE-752 36,
+ Independent researcher, Solhemsgatan 46, 43144 Mölndal, Sweden, http://www.exergy.se
Several life cycle assessments (LCA) of wind energy published in recent years are reviewed to identify
methodological differences and underlying assumptions.
A full comparative analysis of 12 studies were undertaken (10 peer-reviewed papers, 1 conference paper, 1
industry report) regarding six fundamental factors (methods used, energy use accounting, quantification of
energy production, energy performance and primary energy, natural resources, and recycling). Each factor
is discussed in detail to highlight strengths and shortcomings of various approaches.
Several potential issues are found concerning the way LCA methods are used for assessing energy
performance and environmental impact of wind energy, as well as dealing with natural resource use and
depletion. The potential to evaluate natural resource use and depletion impacts from wind energy appears to
be poorly exploited or elaborated on in the reviewed studies. Estimations of energy performance and
environmental impacts are critically analyzed and found to differ significantly.
Conclusions and recommendations
A continued discussion and development of LCA methodology for wind energy and other energy resources
are encouraged. Efforts should be made to standardize methods and calculations. Inconsistent use of
terminology and concepts among the analyzed studies are found and should be remedied. Different
methods are generally used and the results are presented in diverse ways, making it difficult to compare
studies with each other, but also with other renewable energy sources.
Keywords: life cycle assessment, wind energy, wind power, natural resource use, primary energy
conversion, energy accounting
1. Introduction
At the end of 2009, wind energy generated about 1.8% of the global demand for
electricity with almost 160 GW of installed capacity (IPCC, 2011). This equals about
0.2% of the total global primary energy demand, or 273 TWh (IPCC, 2011). Wind energy
capacity is currently growing at a high rate, with global installed capacity roughly
doubling every three years, and growth rates seems to be accelerating (WWEA, 2010).
This makes wind energy an important renewable energy option for the future. The IPCC
even estimates that wind energy could potentially provide over 20% of the global
electricity demand by 2050 (IPCC, 2011), while other more moderate estimations foresee
a smaller contribution (Höök et al., 2012).
Energy systems based on wind, as well as other renewable energy sources, are
often automatically assumed to be sustainable and environmental-friendly sources of
energy in much of the mainstream debate. However, all systems for converting energy
into usable forms have various environmental impacts, not to mention a requirement of
natural resources. It is essential to have consistent evaluation methods for analyzing all
aspects of a given energy source. Without such methods, it is difficult to compare them
and make the right decisions when planning and investing in energy systems for the
Good decisions on how to construct future energy systems relies on having access
to suitable feasibility indicators for different options so one can pinpoint the most optimal
pathway. Academics, practitioners, and policy makers continue to debate the benefits and
costs of alternative energy sources, primarily the renewable ones. The economics of wind
energy have been thoroughly covered by previous studies such as Blanco (2009) and
Valenzuela and Wang (2011). However, Welch and Venkateswaran (2009) found that
decisions that incorporate both environment concern and investors’ desire for shareholder
value maximization are more likely to be truly sustainable.
Future growth of any new energy systems, in this case wind power, will require
energy, as well as other resources during the expansion phase, and these implications
need to be considered when planning future developments. A need for meticulous
environmental impact assessments and energy performance evaluations can be seen here.
Environmental concerns are perhaps more difficult to quantify than economic issues and
many attempts have been made based on different approaches.
1.1 Scope of this study
A popular way of measuring the environmental impact and energy performance of wind
energy is life cycle assessments (LCA). Many analysts have tried to handle these
problems for wind energy based on various approaches. This study focuses on how LCA
methodologies are used for evaluating environmental impact energy performance of wind
energy systems by reviewing existing publications, discussing differences in
methodology and results.
Several recently published LCA studies for wind energy are reviewed (Table 1).
Some has been published as scientific articles (Schleisner, 2000; White, 2006; Ardente et
al., 2008; Lee and Tzeng, 2008; Crawford, 2009; Martinez et al., 2009a; 2009b; Tremeac
and Menuier, 2009; Weinzettel et al., 2009; Guezuraga 2012), while others are
conference papers (Lee et al., 2006) or reports released by wind turbine producers
(Vestas, 2011). Not all assessments claim to follow the ISO-standards, and some are
more of energy and CO2 assessments than full LCAs. Vestas (2011) has been reviewed
by an external reviewer that states that the study is carried in compliance with the
currently best LCA practices and in accordance with ISO-standards. The authors would
like to highlight several potential issues and call for a methodological discussion
concerning how LCA is used for these purposes.
Table 1. Rated power and general turbine types in the reviewed studies.
Type of turbine assessed
Ardente et al. (2008)
Crawford (2009)
Guezuraga et al. (2012)
Lee et al. (2006), Lee and Tzeng (2008)
Martinez et al. (2009a, 2009b)
Schleisner (2000)
Tremeac and Menuier (2009)
Vestas (2011)
Weinzettel et al. (2009)
White (2006)
Wind farm, 0.66 MW turbines
0.85 MW turbine and 2 MW turbine
1.8 MW gearless turbine, 2.0 MW geared turbine
Three wind farms in Taiwan with 0.66 MW, 0.6 MW and 1.75
MW turbines
2 MW turbine
Wind farms onshore and offshore with 0.5 MW turbines
4.5 MW turbine and 0.25 MW turbine
3 MW turbines
Floating turbine, 5 MW
Three wind farms in the US with 0.345 MW, 0.75 MW and 0.60
MW turbines
2. Methodological differences
The idea of measuring the energy performance of products evolved in the 1970s. The
energy analysis method was stated at a conference held by the International Federation of
Institutes for Advanced Studies in 1974 (Mortimer, 1991). Life cycle assessment (LCA)
has many similarities to energy analysis, but is not restricted to just energy. LCA started
to evolve in the mid-1980s and became increasingly common during the 1990s when
scientific publications started to reach wider audiences. As LCA methodology evolved,
many different methods and guidelines emerged. Recent development is well described
by Finnveden et al. (2009) or Guinée et al. (2011).
An LCA can be defined as “the compilation and evaluation of the inputs, outputs
and potential environmental impact of a product system throughout the life cycle”
(Guinée, 2001). Life cycle assessments generally follow the same four basic steps: goals
and scope, life cycle inventory, impact assessment and interpretation.
The goals and scope phase determines the method, the functional unit, and system
boundaries as well as studied environmental impacts and level of detail. Choices made
here about methodology, system boundaries, cut-off limits or functional units can have
large impact on the final results (Baumann and Tillmann, 2004). This is seen clearly in
the reviewed studies.
In the life cycle inventory (LCI), inputs and outputs throughout the entire life
cycle are estimated, according to the chosen system boundaries and methods. There are
several different ways to do this and choices in methodology can have a large impact on
final results. Ekvall and Weidema (2004) describe a way to categorize two main methods,
namely attributional and consequential LCI. An attributional LCI describes the physical
flows relevant to environmental impact in and out of the life cycle system boundaries,
while consequential LCI attempts to generate information about consequences of actions
made by describing how the physical flows relevant to environmental impact will change
with certain changes in the life cycle. It should be mentioned that there is not always a
clear practical distinction between attributional and consequential LCI.
The next step is the life cycle impact assessment (LCIA), where results from the
inventory are converted into environmentally relevant information (Baumann and
Tillmann, 2004). Martinez et al. (2010) compares no less than seven different methods to
perform a LCIA. Sometimes an attempt is made to express the impact on a common scale
through weighting or further evaluate the results LCIA. This can never be based solely on
purely objective factors, as subjective values always must be introduced (Baumann and
Tillmann, 2004). As a consequence, LCA is not necessarily a method that meets the
standards of strict natural science. This should hopefully be handled in a good way in the
interpretation of the results.
2.1. Discrepancies in the LCI
While none of the studies can regarded as using consequential LCI methodology, there
are two other main approaches that can be used for the LCI: process chain analysis
(PCA) and input-output (I/O) analysis. I/O analysis uses economic data to estimate the
resource use in different parts of the economy, while PCA estimates the actual physical
flows of mass and energy. In I/O-based LCA, inter-industrial relationships are quantified
in an I/O matrix, representing interactions between different sectors of the economy. All
of the reviewed studies primarily use attributional approaches based on PCA, but two
studies also partly use I/O analysis.
Hendrickson et al. (1997) compared PCA and I/O methods and finds no major
differences in results between them. However, Crawford (2009) states that a PCA is
generally seen as more accurate and relevant than an I/O analysis, but that it is also
accused of missing a great deal of important information that can be accounted for with
I/O analysis. Crawford (2009) quantifies this by claiming that the system boundaries for a
PCA can be up to 87% incomplete. Both Hendrickson (1997) and Crawford (2009)
propose the use of hybrid methods, capable of fusing the advantages of both techniques.
However, only two of the reviewed studies (White, 2006; Crawford, 2009) relied on
hybridized methods. It appears to be more far common to use PCA.
White (2006) uses I/O analysis for some parts of the lifecycle otherwise lacking
data, while Crawford (2009) uses a hybrid method of PCA and I/O. For one of the three
wind farms assessed in White (2006), I/O is used for assessing energy use of the
operations and maintenance phase and concludes that this phase of the life cycle
contributes with about 34% of the total energy use of that wind farm. For the other two
farms assessed in White (2006), the operations and maintenance phase contributes with
16% and 11% of the energy use. So, it appears that when using I/O instead of PCA for
the operations and maintenance phase, the energy use of that part of the LCA becomes
many times greater, even within the same study. Other studies, which do not use I/O
methods, appear to present much lower relative numbers for operations and maintenance.
For example, Ardente et al. (2008) states that energy usage for operations and
maintenance is only 6.5% of the total energy use.
Crawford (2009) uses a hybrid method of PCA and I/O where only 22% of the
embodied energy comes from process data, while 78% comes from I/O data. He draws
the conclusion that other “embodied energy analysis methods” of wind energy can be up
to 78% incomplete. This tendency to primarily rely on PCA might very well lead to
underestimated figures for energy use, but it is also possible that I/O analysis exaggerates
energy use. Further studies on this issue are both required and should be encouraged.
2.2 Impact assessment method dependency
All reviewed studies estimate energy performance, although in slightly different ways.
For other environmental impact, some studies present only greenhouse gas emissions
(Lee et. al., 2006; Lee and Tzeng, 2008; White, 2006; Crawford, 2009; Guezuraga,
2012). Others use different impact assessments to express environmental impacts, in
vastly different ways. Ardente et al. (2008) describe emissions of seven different air
emissions, seven water emissions and three kinds of wastes. Schleisner (2000) presents
the environmental impact as seven different emissions. Weinzettel et al. (2009) uses the
CML 2 baseline 2000 V2.03-method and presents the environmental impact in 8 different
impact categories. Vestas (2011) claims to use CML 2009 update and presents
environmental impact in 15 different impact categories. Tremeac and Menuier (2009)
uses a method called Impact 2002+ that links 14 midpoint categories from the LCA to
four damage categories: climate change, resources, ecosystem quality and human health.
As an example of methodology dependence, Martinez et al. (2009a) and Martinez
et al. (2009b) are done by the same authors, on the same turbine model, at the same wind
farm with the same assumed production, but using different impact methodologies giving
significantly different results. Martinez et al. (2009a) uses the method Eco-Indicator 99
using 11 different impact factors. Martinez et al. (2009b) uses CML-methodology,
presenting environmental impact in 10 different impact categories presented as
equivalents of different emissions, as well as cumulative energy demand.
The environmental impact results of these two studies of are difficult to compare,
especially since they are presented in different units. However, the most interesting thing
is perhaps that the resulting energy performances differ significantly. Martinez et al.
(2009a) presents an energy payback ratio (EPBT) of 0.4 years, while Martinez et al.
(2009b) presents an EPBT of 0.58 years. Basically, it appears that the same people
assessing the same thing but using a different methodology gives a 45 % longer EPBT.
It could be added that LCA methodology during the course of these studies has
been, and are still evolving. At the same time vastly different methodology are being used
today. Clearly, using different methodologies can cause widely different results as
illustrated by the Martinez studies. The different methodologies used make it difficult to
compare assessments to each other and creates questions if the results using different
methodology should be compared at all.
3. Energy-relates issues
In the reviewed studies, there are several issues related to energy. Some include energy
use accounting, while others relate to quantification of energy production and the actual
presentation of the energy performance.
3.2. Energy use accounting
Some energy carriers, especially electricity, can be valued differently. Electricity may be
seen as being produced by other energy carriers, according to an electricity generation
mix, and converted into primary energy using thermal equivalents. How and if the
electricity should be converted into primary energy is often not obvious, and depending
on the electricity generation mix and the heating values used for the calculations, the
results can be significantly different.
In most of the reviewed studies, it is difficult to see which different energy
resources are used, especially for electricity. In fact, none of the reviewed studies, except
Weinzettel et al. (2009), says anything about how much electrical energy compared to
direct thermal energy have been used during the life cycle. The electricity is converted
into primary energy and the total primary energy use is presented either as one fixed
number or divided into various primary energy carriers.
Energy use is frequently only described as one single number in primary energy
terms, which summarize all different energy carriers involved, while other studies
express the energy use somewhat divided into different fuels (Table 2). Some specify the
electricity generation mix that is used for the conversions (Table 2), but what the
electricity generation mixes look like and how the energy use is divided into energy
carriers is often explained poorly, or not at all. The five studies that specify energy
carriers present totally different ones, making comparisons quite difficult.
Table 2. Electricity generation mixes used for energy inputs in the reviewed studies as
well as presented energy carriers.
Ardente et al. (2008)
Crawford (2009)
Guezuraga et al. (2012)
Electricity generation mix
Average European ? *
Not specified
National (Germany, Denmark,
Lee et al. (2006), Lee and Tzeng
Martinez et al. (2009a, 2009b)
Not specified
Schleisner (2000)
National (Denmark for all
Tremeac and Menuier (2009)
Weinzettel et al. (2009)
Not specified (4.5 MW )
National (Finland) (250 W)
National (country specific for
manufacturing countries)
Not specified
White (2006)
Not specified
Vestas (2011)
Not specified (a)
National (Spain) (b)
Presented energy carriers
Not specified
Not specified
Nuclear, brown coal, natural gas,
crude oil, black coal, residual
biomass, hydro, wind, waste
Not specified
Not specified (a)
Fossil, nuclear, hydro, biomass and
others (b)
Coke, coal, oil, natural gas
Not specified
Crude Oil, hard coal, lignite, natural
gas, renewable fuels, wood
Electricity, oil, diesel and electricity
from oil
Not specified
* The data for energy and environmental impacts from manufacturing of turbines and
towers is claimed to refer to “average European data” without further specifications.
How the electricity should be converted into primary energy is not obvious and
results can be significantly different depending on the electricity generation mix used.
Guezuraga et al. (2012) presents different cases with German, Danish and Chinese
electrical generation mixes, with the resulting EPBT ranging from 1.15 with German
electricity to 2.36 with Chinese electricity, indicating that the choice of electrical
generation mix could have a large impact on the resulting energy performance.
The studies that mention electricity mix all use national generation mixes, except
for Ardente et al. (2008) that claims to use average European data for energy use for
manufacturing of turbines and towers since none of the components are manufactured in
Italy. Schleisner (2000) assumes the energy supply system to follow Danish conditions
despite the origin of inputs, however, it is pointed out that the aluminum is in fact
produced in Norway where the electricity is mainly produced by hydropower. How the
energy is divided into fuels, how much of the energy use have been electricity or what
“Danish conditions” look like are not explained. Vestas (2011) on the other hand claims
that “country-specific energy mixes” are used for processes in different countries. So,
appears like Ardente et al. (2008) use average European generation mix for processes in
other countries, Schleisner (2000) use Danish conditions for all countries and Vestas
(2011) uses country specific generation mixes processes in different countries.
The European electrical grid is largely interconnected and the use of national
electricity generation mixes is not necessarily the most accurate way to convert the
electricity into primary energy, as electrons pay little care to national borders. Two
potentially plausible alternatives are to use an average European mix or rely on marginal
electricity, often originating from coal. Perhaps national electricity generation mixes are
in fact preferable, but in any case, a more transparent and consistent treatment of
electrical energy and other used energy carriers would greatly reduce the opacity in LCA
methodology. Currently, the energy use and the corresponding LCA results can be
significantly affected by the analyst’s personal choices for electricity mix and energy
conversions, unfortunately often under a lack of transparency. Standardization and
improved transparency would likely reduce the spread in results and make LCA studies
appear less inconsistent to planners and policy makers.
3.3. Quantification of energy production
LCA results are generally presented as an environmental impact expressed per kWh of
produced electricity. Energy performance is also highly dependent on the energy
production. This makes the estimated energy production have a huge impact on final
results, since everything is expressed in relation to the produced electricity. Basically, a
doubling of the production of electrical energy cuts an apparent emission in half and
makes the energy performance twice as good.
The amount of electricity that will be produced depends on many factors such as
wind class, wind speed, rotor size, turbine power rating and distance to grid. Naturally the
actual wind resource at a specific site is a very important factor. Different types of sites
can be divided into different wind classes based on average annual wind speed, extreme
wind gusts and turbulence (Vestas, 2011). Modern wind turbines are in fact designed for
certain wind classes, and Vestas (2011) suggests that comparisons should only be made
between wind turbines within a specific wind class.
In this paper, turbines of different sizes and types that are evaluated in different
ways are compared to each other. What we wish to do is not to compare different turbines
to each other, but rather compare different methods to assess environmental impact and
energy performance of wind turbines. The methods used to estimate the annual
production of electricity of a wind farm varies significantly between the different studies.
To be able to compare estimated production, the conceptual capacity factor is used,
which is the ratio between the energy actually produced and the energy possible to
produce if running at rated power all the time. Consequently, comparisons are far from
trivial and the simple analysis done here does not go into any detail but rather attempts to
pinpoint some underlying differences among the reviewed studies.
Lenzen and Munksgaard (2002) reviewed many energy and CO2 assessments
performed since the 1970s, and found capacity factors ranging from 0.08 to 0.50. Modern
wind turbines typically lie between 0.20–0.35. In the reviewed assessments, the capacity
factors ranges between 0.19 and 0.53 (Table 3). Not all the reviewed studies express a
capacity factor, so for some of them a capacity factor have been calculated from given
production or assumed full load hours.
Some of the reviewed studies use measured data from actual wind farms while
others use more theoretical quantifications of the electricity production (Table 3). For
example, Garrett (2011) states that Vestas (2011) use sophisticated real-time diagnostic
tools and sensors, operating on around 20 % of global installed wind capacity, which
measure individual turbine performance, power output and health status (such as fatigue
loading and turbine condition), as a base for quantification of production of electricity for
the turbine operating in a certain wind class. In contrast, other assessments have more or
less well-motivated assumptions for electricity production.
Table 3. Capacity factors used in the reviewed studies and method for quantification of
Ardente et al. (2008)
Capacity factor
0.19 – 0.30
Crawford (2009)
0.34 (850 kW)
0.33 (3.0 MW)
0.34 (geared)
0.21 (gearless)
Guezuraga et al. (2012)
Lee et al. (2006), Lee and
Tzeng (2008)
Martinez et al. (2009a, 2009b)
Schleisner (2000)
Tremeac and Menuier (2009)
Vestas (2011)
Weinzettel et al. (2009)
White (2006)
18.9, 30.9, 42.6 for three
different wind farms
0.23 *
0.29 (offshore) **
0.25 (onshore) **
0.30, 0,20***
0.43 **
0.26, 0.29, 0.20
Method for quantification of production
Low estimate: measured operation data of
actual wind farm
High estimate: design estimation
Estimated from wind data of typical site
and characteristic power curve of turbines
Assumed figure from manufacturer
Measured operation data (gearless)
Measured operation data of actual wind
Assumed as equivalent full-load hours
Derived from Vestas statistical database
Measured operation data of wind farms
* Calculated from given full load hours per year.
** Calculated from estimated production.
*** Based on a 50 W average power of 250 W installed.
White (2006) and Ardente et al (2008) assess actual existing wind farms and use
actual measured production data of the specific location and turbine type. In both cases,
actual production data is significantly lower than expected. White (2006) compares
projected capacity factors for three wind farms with measurements, which were
significantly lower. The projected capacity factors were 0.33, 0.35 and 0.31, while the
actual capacity factors were 0.26, 0.29 and 0.20 respectively. Ardente et al. (2008) finds
the actual capacity factor measured over a year to be 0.19 compared to the design
capacity factor of 0.30. Guezuraga et al. (2012) compares a 2 MW geared turbine with a
1.8 MW gearless turbine. For the geared turbine data provided from the manufacturer of a
typical good wind site location is used, giving a capacity factor of 0.34. For the gearless
turbine, actual operating data from a turbine measured at a site over 9 years is used,
resulting in a capacity factor of 0.21. It is possible that the two different turbines would
get somewhat different production if located at the same wind conditions, but comparing
two turbines of different types operating under totally different conditions becomes
somewhat problematic. This could indicate a tendency to exaggerate expected production
rates of wind farms and highlight issues with using expected production numbers for
quantification of production.
Communicating emissions and energy use as a fixed value per kWh of produced
electricity can itself be problematic. This primarily stems from the vast differences seen
in the result depending on the production and the fact that it is not a fixed feature of a
certain type of turbine. If measured numbers of a specific wind farm are used, the result
can potentially be expressed as a fixed feature for that specific wind farm. However, this
does not mean that the specific turbine type will produce the same amount of electricity
in other locations. White (2006) states that no two wind farms with the same technology
can be expected to have the same energy payback ratio at different locations. Vestas
(2011) propose that comparisons should be made between turbines operating in the same
wind class. Production should perhaps be quantified not as a fixed production rate, but
rather as high and low estimates, giving different scenarios.
3.4. Energy performance and primary energy
The energy performance is usually expressed as energy return on investment (EROI),
defined as cumulative electricity generated divided by cumulative primary energy
required, or energy payback time (EPBT), defined as the amount of time it takes to “pay
back” the energy used over the life cycle. EROI and EPBT can be good indicators on
whether a wind turbine actually produces more energy than is consumed during its life
cycle. However, some studies also include a conversion of the produced electrical energy
to primary energy. Table 4 contains the energy performance results from the reviewed
A few examples are worth noticing. Weinzettel (2009) states that the EPBT is 13
months, but also converts the electrical energy with an assumed thermal efficiency of
0.40 and gets a primary energy payback time (PEPBT) of 5.2 months. Schleisner (2000)
also converts the produced electricical energy to primary energy with an efficiency of 0.4
but calls the resulting 4.7 months for an offshore wind farm and 3.1 months for an
onshore wind farm, energy payback time, instead of primary energy payback time. It
appears that the EPBT presented is in fact PEPBT, but this is not explained. In contrast, a
more holistic presentation of both an “ordinary” payback time and several PEPBTs for
different electricity generation mixes were presented by Vestas (2011).
When Schleisner (2000) converts electricity production to primary energy but still
confusingly calls this the time before the energy is “paid back” (i.e. EPBT), instead of
PEPBT, this causes great problems with comparing the energy performances from
different studies. Weinzettel et al. (2009) denotes payback time after conversion final
payback time, and compares it to a Vestas study where the EPBT is 6 to 7 months.
However, the Vestas reference used in Weinzettel et al. (2009), presents the electricity
produced as a direct equivalents, thus two completely different things are compared to
each other. Another example is Lee et al. (2006) and Lee and Tzeng (2008) who found
and EPBT of 1.3 months, which compared to a few other assessments with EPBTs
ranging from 3 to 8 months. It seems like Lee et al. (2006) use direct energy payback
time without any conversion, but still receives far better results than for instance
Schleisner (2000) that used a primary energy conversion through thermal equivalents,
comparing different concepts. This also makes the extremely short EPBT result in Lee et
al. (2006) even more odd, since the return ratio gets many times greater after conversion.
There is no consensus on how conversion to primary energy should be done, or
even if and why it should be done. Schleisner (2000) converts the produced electricity to
the primary energy that would be produced in a conventional power plant with an
efficiency of 40%. Tremeac and Meunier (2009) use the efficiency of the French national
electric network of 0.35.
Table 4. Energy return on investment (EROI) or primary energy return on investment
(PEROI) and energy payback time (EPBT) or primary energy payback time (PEPBT) of
the reviewed studies. When only payback time is given, return on investment is calculated
and vice versa. When payback time is given in years, it is converted to months for easy
Ardente et al. (2008)
Crawford (2009)
Guezuraga et al. (2012)
Lee et al. (2006), Lee and Tzeng (2008)
Martinez et al. (2009a, 2009b)
50 (a)
34.4 (b)
Schleisner (2000)
Tremeac and Menuier (2009)
Vestas (2011)
Weinzettel et al. (2009)
White (2006)
11.8 (4.5 MW)
3.1 (250 W)
11, 24, 28
21 (850 kW) **
23 (3.0 MW) **
1.3 months
4.8 months (a)
7.0 months (b)
51.3 * (onshore)
69.0 * (offshore)
34.5 (4.5 MW)
8.7 (250 W)
7.2–27.6 months
20.4 months (4.5 MW)
78 months (250 W)
8 months
13 months
21.8, 10.2, 8.6 months
6–12 months
11.4 months
10.4 months
4,7 months (offshore) *
3.5 months (onshore)*
7.0 months (4.5 MW)
27.5 months (250 W)
2.2–3.5 months
5.2 months
* Expressed as energy payback time, but based on the assumption that the electrical
energy produced replaces a conventional power plant with an efficiency of 0.40.
** Expressed as energy yield ratio (EYR), but the energy output is earlier described as
being converted to primary energy using the factor 0.34.
Tremeac and Meunier (2009) argue that the PEPBT-method should be used since
it is more consistent and claims that different energy forms are compared otherwise.
However, this view is not obvious. Wind turbines can only produce electricity and cannot
easily replace other energy forms. The primary energy used for producing the replaced
electricity can therefore be seen as irrelevant, making the entire focus on primary energy
unjustifiable. Also, the direct impact of the produced electricity is difficult to assess
(IPCC, 2011). In the short run, produced electricity will probably replace some kind of
fossil fuel power plant on the margin. In the long run, introduction of wind energy has
wider implications on the electricity grid and may even necessitate expansion of other
energy sources (IPCC, 2011). The impact of wind energy on the electrical energy system
is complicated and system specific, thus difficult to forecast with any precision (IPCC,
2011). It is also questionable if we now can say what the energy system will look like in
the future. Who can accurately say what the electrical energy generation mix will look
like in 20 years from now, if we compare electrical energy to the efficiency of today’s
electrical system?
LCA methods cannot evaluate which other production facility, if any, will reduce
production and environmental benefits such production reductions are also difficult to
approximate. LCA is simply not a methodology that contains the necessary tools for
estimating changes to the electric system layout as a result from increased electricity
production from wind power. This can be seen in the variation in factors used for
converting the electrical energy to primary energy. A better consensus on how to present
produced electricity and how to estimate energy performance of wind power should
definitively be aimed for.
If the energy payback time or energy return on investment is still presented in
primary energy terms, this must be clearly explained, which is only done in some of the
reviewed studies. Also, similar terminology should be used to better portray the
differences between primary energy and direct energy performances. The existing
confusion regarding terminology, seen in several of the reviewed papers, is actually
misleading or even harmful since it adds more confusion than applicability for outsiders
trying to use the LCA results for planning or investments. By introducing standardized
concepts, studies would be significantly easier to compare. We propose that both
“ordinary” EPBT or EROI and their primary-energy-based versions should be presented
and clearly defined in any LCA that aims to be holistic.
4. Natural resources
Building a wind turbine creates a demand for natural resources, both energy resources
and material resources. Stewart and Weidema (2005) state that natural resource use has
been included as an important impact category in most LCA impact methods. However,
methods to quantify resource depletion within LCA methodology have been under debate
and no consensus seems to exist on which one to use. Finnveden (2005) pinpoints the
lack of a generally accepted framework and highlights that resource use is important to
the overall results for many LCA weighting techniques.
4.1 Natural resource inputs
Most of the reviewed studies express the inputs of material resources as amount of
refined resources that are used for building the wind turbine. It could be interesting to
discuss what should actually be considered an input. For iron, is the input iron ore or
refined forms of iron (cast iron, steel bars, specialized iron-alloys, etc)? The only
assessment that mentions amounts of raw material used, instead of refined ones, is Vestas
(2011) where masses of unrefined material resources from the LCI are presented in a
table. This is an interesting attempt, but could be somewhat problematic since it the
quantities can be very different depending on, for instance, assumed ore grades. What is
even more problematic with the Vestas (2011) LCI table is that recycling credits is
included in the table, and some resources even have negative values of usage for different
phases of the life cycle. Although this is somewhat intuitively strange, this can partially
be explained with that a substitution approach is used instead of allocation of different
co-products (Gbegbaje-Das, 2011). When a metal is mined, the co-products are also
assumed to be used, thus replacing other production. For some resources, the end-of-life
phase is larger than the other phases, making the total resource use negative. As an
example, if the two different iron ore grades presented in Table 5 are combined to a total
amount of iron ore it adds up to a total of –2.61*10-4 kg iron ore per kWh of produced
electricity. This could be interpreted as if you would actually gain 0.26 grams of iron ore
for every kWh of electricity produced by the wind turbine, which from a physical
perspective on reality is clearly nonsensical. In personal correspondence with Vestas
about this issue, representatives claimed that there has been an accounting error related to
mass-balancing in Vestas (2011) and that it will be corrected in an updated version of the
2011-report (Garrett, 2011).
Table 5. Iron ore use in different stages of life cycle of Vestas V112 wind turbine
Adapted from Vestas (2011).
Material resources
kg / kWh produced
Iron ore (56.86%)
Iron ore (65%)
Wind plant
set up
End of
Vestas (2011) should get some credit for the effort of trying to present the amount
of natural resources used for building a turbine, but an issue with the numbers is that they
obviously do not represent the actual amounts of resources used without subtracting
substitution or recycling credits in the numbers. This makes it impossible to use the
numbers for assessing the amounts of resources used for building a turbine without using
these factors. Presenting negative resource use numbers as “LCI data” is highly
questionable and raises questions about what the numbers are actually used for in the
LCA. If negative numbers of resource use have been used for assessing environmental
impacts, the validity of the study could be questioned.
4.2 Resource depletion and input bottlenecks
Many of the studies have resource use or depletion included in the LCIA as different
impact factors. Tremeac and Menuier (2009) use an impact assessment method called
Impact 2002+ where one of the four damage categories is called natural resource
depletion. The resource depletion is expressed in GJ primary non-renewable energy and
does not seem to take account for non-energy resources. Martinez (2009b) and Vestas
(2011) has an impact category called “abiotic depletion” that expresses an abiotic
depletion factor for extraction of minerals and fossil fuels in antimony equivalents per kg
extraction, based on concentration of reserves and rate of de-accumulation. Vestas (2011)
also includes several different types of resource consumption in the impact categories:
Abiotic resource depletion (ADP elements), Abiotic depletion (ADP fossils) and water
consumption. There are also impact factors called recyclability, primary energy from
renewable raw materials (net calorific value) and primary energy from resources (net
calorific value). ADP fossils is claimed to describe the amount of non-energetic resources
that are directly withdrawn from the geosphere to reflect the scarcity of the materials,
expressed in antimony equivalents. In Vestas (2011), the use of one single ore, coppermolybdenum-gold-silver ore, accounts for about 75% of the total of this impact category,
which is a result worth noticing. An important thing to understand is that many different
resources is included and presented in many different units between different studies.
If the use of natural resources is an important aspect of building renewable energy
converters, like wind turbines, this should be reflected upon. Klejn and van der Voet
(2010) concludes that a growth of wind energy to contribute with 15% of the global
primary energy use by 2050 would necessitate substantial amounts of resources, requiring
global production of copper, iron ore and cement to increase dramatically. Jacobson and
Delucchi (2011) claims that such bulk materials are not likely to be of any immediate
concern for a fast expansion of wind energy, but rare earth elements (REEs) such as
neodymium (Nd) used for permanent magnets in certain generator designs are likely
more problematic materials for future development.
Although REEs are fairly commonly occurring globally, they could still become a
limiting factor for a fast growth of wind energy globally (Figure 1). The global REE
market is currently totally dominated by China with its 95% share (Chen, 2011), and this
dependence might be seen as questionable from an energy security perspective. That
standpoint got reinforced when the Chinese government chose to restrict exports, aimed
at protecting domestic supply (Tse, 2011). Recent studies have highlighted rare earth
elements as critical raw materials for clean energy and found its availability to be
associated with high risks in the foreseeable future (Haxel et al., 2002; Long et al., 2010,
British Geological Survey, 2010; US Department of Energy, 2010; Moss et al., 2011).
Kanawaza and Kamitani (2006) also highlighted how REEs nearly always are associated
with radioactive heavy metals, resulting in environmental problems. In fact, one of the
key reasons for dismantling of REE mining in the USA was associated radioactive
releases (Haxel et al., 2002; Castor, 2008). Recently, the portfolio managers of Morgan &
Spitz even decided to exclude neodymium-based direct drive turbines from their
environmental fund motivated by severe environmental impacts generated by mining
(Renewables International, 2011).
None of the reviewed assessments specifically mentions any use of neodymium,
but according to Biggs (2011), the Vestas V112 turbine that is assessed in Vestas (2011)
does contain neodymium, which is confirmed by Garrett (2011). No quantities of
neodymium are mentioned, but the LCI table of Vestas (2011) contains a post called
Rare-earth ore and there is materials listed in the refined materials inventory as special
metals and magnet, which could include REEs like neodymium. It should also be
mentioned that rare earth metals, like neodymium, in the V112–3.0 MW tower magnets
results in a saving of around 10 tons of steel per turbine (Garrett, 2011).
None of the other reviewed studies mentions neodymium or any other special
metals. For analysts with interest in such questions, few or none of the reviewed LCAs
are especially helpful. One reason for this could be that the use of non-energy resources
does not seem to be of any significant concern in the reviewed studies. Another reason
could be that many studies uses different kinds of cut off criterion and cut off limits,
possibly excluding important materials, making up small parts of the total mass. Martinez
et al. (2009a, b) claims to take account for elements that together make up more than 95%
of the foundations, 95% of the tower and 85% of the nacelle and rotors. Ardente et al.
(2008) states that any process of activity that contributes to less than 1% of the total
environmental impact of one impact category can be neglected. In general, steel and
concrete often make up a total of 95% of the weight of the wind farm. With cut-off limits
at this level or lower, certain critical material accounting for only a small part of the mass
could potentially be missed in an LCA. It is possible that the other turbines assessed do
not contain REEs, like neodymium, but for instance Guezuraga et al. (2012) assesses a
direct driven turbine, which almost certainly contains large amounts of REEs.
Figure 1. Short term (0-5 years) and medium term (5-15 years) outlook and risk for
neodymium and other elements for clean energy as identified by US Department of
Energy (2010).
Finnveden (2005) argues that LCA is not the proper methodology to handle
resource use, but simultaneously states that it could a useful tool for investigating
resource use aspects of various products. The demand for material resources for building
wind farms if often not of any great concern in an LCA, but if the installed capacity of
wind power is projected to grow extremely fast in coming years and the amounts of
certain materials that are going to be used for this development can be important for the
maximum possible growth rate, the cost of development as well as the apparent
feasibility of wind energy in a wider context. Material shortages, production bottlenecks,
price spikes of key components due to raw material depletion, and similar events can
very well pose looming challenges for future expansion so it may be wise to include
material use to a greater extent in LCA studies on wind energy.
4.3 Recycling
International standards for LCA allow for different ways of dealing with recycling, but
states that the approach of inflows and outflows of recycled materials should be
consistent (Ekvall and Weidema, 2004). Many assessments credit the beneficial
environmental impact of recycling the materials in the future to the overall environmental
impact of the life cycle. Martinez et al. (2009b) concludes that “despite the significant
amount of material used, its final impact is reduced because of the 90% material
recycling in the phase of dismantling and disposal of the turbine”. Tremeac and Meunier
(2009) state: “The reason why dismantling and removal yields impact reductions is that
recycling is used to a high extent”. This end-of-life approach for recycling is supported
by the metals industry (Atherton, 2006).
It could be questioned how certain it is that the materials will in fact be recycled
in 20 years, or more. For some materials making up large parts of a wind turbine, i.e.
steel, copper, aluminum and other metals, it is highly likely that the materials will be
recycled in the future, but it is not certain. The economics of recycling scrapped wind
plants are also uncertain and it is entirely possible that the cost of dismantling and
extracting the recyclable parts will be prohibitively high in the future, especially for wind
farms located in remote or off-shore areas. For example, the Tehachapi Pass in California
contains “bone yards” of abandoned wind turbine hardware that has been lying around
without being recycled (Pasqualetti et al., 2002). Even if decommission is usually
mandatory in operating permits, the total costs of decommissioning may not be covered
due to price inflation, low capacity, unexpected circumstances (e.g., hurricane
destruction), or a combination of such events (Kaiser and Snyder, 2012). It is possible
that recycling can become uneconomic compared to abandonment under certain
conditions, which is important to remember as decommissioning is dependent on a
number of highly uncertain parameters that can have significant direct or indirect impacts
on cost. Thus, material may be “frozen in” and unavailable despite their theoretical
recyclability. Material recovery at the end of the life cycle cannot be guaranteed as
expressed by Crawford (2009), who also stresses that the environmental credit should
rather be given to products using the recycled material.
Since actual resource use does not appear to be of any great concern in the
reviewed studies, the crediting of recycling mostly have impact on the other things
assessed, such as energy performance and different emissions. However, the exact
implications of the recycling on final results are generally difficult to follow in the
assessments, although some tries to provide more details. In Martinez et al. (2009a)
recycling significantly improves the environmental impact of the wind turbine. For the
tower, environmental impact is said to improve with 52% due to recycling crediting. In
essence this means that the apparent environmental impact of building the tower – mainly
consisting of steel, is cut in half by end-of-life recycling crediting that might happen after
more than 20 years. Guezuraga (2012) states that when no recycling of materials is
considered, energy requirements are increased by 43.4 %, indicating that energy
performance would be 43.4% worse if this view of recycling is not used. In Martinez et
al. (2009b), “Characterization results” are presented as 10 different impact factors in a
table, for maintenance, tower, foundation, rotor, nacelle and a total. Another table
describes “Environmental impact prevented by recycling”, for the same parts of the
turbine. For many of the impact factors, the impact prevented by recycling seems to be a
large part of the total and for the two impact factors, Abiotic depletion and Photochemical
oxidation, the environmental impact prevented by recycling is larger than the total value
in the Characteration results-table. This could be interpreted, as building the turbine
would actually decrease depletion of abiotic resources, which is intuitively impossible.
This raises questions of how this looks like in other assessments which does not
present details how the end-of-life recycling approach affect the energy use and other
impacts over the life cycle. Better and more transparent descriptions on how the assumed
future recycling affect the final results are called for if LCA is supposed to give
reasonable perspectives on sustainability issues. Clearly, there is a significant issue here
and readers could easily be misled unless they are cautious.
If natural resource demand of wind power are to be investigated, in a greater
context over time, other issues with the end-of-life recycling methods rises. Wind power
is often projected to grow at an extremely high pace, causing an increased demand for
resources during the construction of the new turbines, no matter how much will be
recycled at the end of their life cycles. The materials will still be locked up in the wind
turbine during its economic life, typically around 20 years or more, before it potentially
can be recycled.
Jacobson and Delucci (2011) states that Earth has somewhat limited reserves of
economically recoverable iron ore, over a 100–200 year perspective at current recovery
rates, but also mention that most of the steel will be recycled. What is not mentioned is
that the steel consumption is already rising fast. ESTP (2009) projects the global steel
consumption to be over 2000 Mt by 2050, compared to just below 1400 Mt in 2010. This
growth, coupled with the fact that recyclable steel has often been held up for many
decades before finally being recycled, makes the total part of steel production coming
from recycled steel is fairly low, only around 45% in Europe (ESTP, 2009). Such real
world recycling shares appears to be in significant disagreement with some of the very
high recycling percentages used in the reviewed studies.
5. Concluding discussion
There is still a matter of controversy concerning the environmental impact of wind
energy, as Tremeac and Menuier (2009) pointed out. For several reasons, some of which
are addressed in this paper, there is a wide spread in results from different wind power
assessments, and even what kind of results that are presented. Compiling 72 studies,
Lenzen and Munksgaard (2002) found energy intensities of 0.014 to 1.016 kWhin/kWhel
while CO2 intensities varied from 7.9 to 123.7 g/ kWhel. Kubiszewski et al. (2010)
compiled 50 EROI studies and found values ranging from 1.0 to 125.8 with an average of
approximately 18. It is difficult to see that the higher figures describe the same concepts
as the lower ones. It should be added that many of the results in these studies are old, and
that LCA methodology has evolved since. However, a large spread in results is still seen
in the fairly new studies reviewed in this paper (Table 3).
The critique expressed here is not directed towards existing ISO-standards
concerning LCA and does not question whether or not the reviewed assessments follow
these standards. The critique is not directed towards the specific assessments or authors,
but rather tries to address a need for discussion on how environmental impact, energy
performance and natural resource use of renewable energy resources should be assessed.
The impacts are usually derived from different methods presenting different kinds of
environmental impacts. What should be asked is, which environmental impacts are
actually relevant for energy producing facilities like wind turbines, and what is the best
methods to assess this. For future long term planning, the most relevant factors of interest
for wind turbines are probably use of resources, both energy and materials.
5.1 Improving the treatment of energy
There is significant problem that EROI or EPBT is sometimes presented as primary
energy using thermal equivalents, and sometimes using direct equivalents, making
comparisons very difficult, especially since is sometimes difficult to even interpret if the
conversion were done. As an example, Lee et al. (2006) and Lee and Tzeng (2008)
presents an EPBT of 1.3 months – equivalent an EROI of 185 – widely superior to all
other reviewed studies. It seems like they use direct energy payback time without any
conversion to thermal equivalents, but still compares their result to Schleisner (2000),
who converts produced electricity to primary energy. It can be seen as quite odd that an
energy performance many times better than Schleisner (2000) – and literally all other
previous LCAs on wind energy – but this is not reflected upon. Instead, it is claimed that
performance of wind power systems implemented in Taiwan is among the best in the
world (Lee et al. 2006). Drawing these conclusions without analyzing other reasons for
the variations, such as methodological differences, should be considered highly
questionable. This is just one of example how a LCA study can make flawed and even
misleading comparisons and conclusions, and one should be cautious in drawing
conclusions from the results. However, the purpose of this paper is not to compare the
different results, but merely to look at how wind energy LCAs are commonly done.
Regarding energy use during the life cycle, we find no consensus on how different
energy carriers should be treated. How this is done is generally not clearly described in
published studies either. The total amount of primary energy used is often presented, and
in some cases this is also divided into different energy carriers. However, energy carriers
used varies between studies making comparisons difficult. For electricity, national
generation mixes are typically used, if anything is mentioned at all. How much of the
total energy used was originally electrical energy is not plainly presented in any of the
reviewed studies, making it difficult to investigate the impact of using of different
electricity mixes. Guezuraga et al. (2012) showed that switching generation mix could
alter the results by around 50%, indicating the importance of this factor.
Another example is Schleisner (2000), converting electricity into primary energy
that is supposed to be used by a “conventional power plant” at an efficiency of 40%. This
way of thinking basically takes for granted that the electricity produced will substitute
electricity from a “conventional power plant” despite the fact that LCA methodology
simply does not contain the tools to assess the impact of the produced electricity within a
real world energy-economic system. Similarly, other assessments draws conclusions
about how much emission will be saved due to the new production. Instead of being used
as built-in assumptions in an LCA, this kind of estimations should probably be left for
more advanced modeling approaches. One such example is the Life Cycle Sustainability
Analysis (LCSA) framework and related approaches that broadens the scope of LCA to
also include sector and economy levels, thus giving more integrated and holistic
assessments (Guinée et al., 2011).
There is simply no consensus on how produced electrical energy should be
treated. Some converts the electricity produced in to primary energy using thermal
equivalents, while some do not. If the primary energy conversions are to be used it is
extremely important that it is explained that the conversion were made and how it was
done so that the result is communicated correctly. Among the reviewed studies, we note
significant confusion on these concepts and even faulty comparisons. It is not acceptable
that published LCA studies mixes up fundamental concepts from energy analysis. As
long as different assessments do not use the same method and do not describe which
methods that are used properly, this will add to the difficulties of comparing different
assessments to each other and make some results more or less useless.
5.2 Improved handling of non-energy resources
The need for non-energy resources does not seem to be seen as an important factor in
most studies, and is usually not considered or discussed in any detail. When they are,
intricate impact methods expressing resource depletion in antimony equivalents per kg is
sometimes used even though this likely will be challenging to grasp for laymen and
planners. Material resource use is a trivial issue for LCA according to Weidema (2000).
In contrast, Finnveden (2005) suggests that resource use, although it should not be
included as an impact factor in the LCIA, could be included in the LCA and states that
LCA potentially can be a useful tool for discussing both environmental and resource
aspects of products.
Another significant problem is the use of end-of-life recycling crediting. It can be
argued, for many reasons, that environmental effects of recycling that may occur in 20
years should not be credited the environmental impacts apparent today. However, most of
the reviewed studies credit future recycling in some way. The implications of the
recycling crediting on the results are often difficult to interpret, but for some that presents
it, the effect appears to be significant. For instance, energy use in Guezuraga et al. (2012)
is increased by 43.3% when no recycling of materials is considered.
The amount of refined materials that is needed to build a wind turbine is often
presented, but usually often including recycling or substitution crediting. LCA do not
have the necessary tools to model real world mineral exploitation or the mechanism of
economic substitution, making this situation identical to the shortcomings seen for
electricity. If the LCI reflected actual resource use for the product, without end-of-life
credits or assumed substitution, the LCA would be improved. With such presentations, it
would also be possible to better explore the consequences of other assumptions about
recycling and substitution.
From a purely environmental position the actual resource depletion might not be
the most important factor, but from a sustainability viewpoint it can be extremely
important information required for making the correct choices about future development
energy and natural resources. When it comes to evaluating the impact of decisions
concerning renewable energy sources, use of material resources can be of major future
importance, and LCA methods are likely to be among the most appropriate way to
address this. One possible way to deal with some of the issues with natural resource use
is to use exergy in a life cycle perspective, which has been proposed by scientists in the
last couple of decades. The life cycle exergy analysis (LCEA) method is an example of
this (Wall, 2011). How, or even if, natural resource use and depletion should be included
in LCA of wind energy appears to be largely unsettled, and a continued discussion on this
is strongly encouraged.
5.3 Final recommendations
The most troublesome part we found is the lack of transparency regarding fundamental
and underlying assumptions, calculations and conversions done in the reviewed LCAs.
Mitigating this issue will not only improve clarity, but is also likely to strengthen the
credibility of LCA methodology. The LCA society should clearly strive for better
agreement on which methods that are to be used for evaluating renewable energy
resources. This is not just desirable, but crucial, to be able to accurately evaluate and
present the environmental performance of wind energy. Also, the use of natural
resources, like REEs, should be clearly mentioned in the assessments to enable evaluating
of possible bottlenecks in future production. There are some initiatives to address these
issues via LCSA (Guinée et al., 2011), but strong international collaboration is a must to
avoid ending up with a multitude of different approaches and methods.
We would like to thank two anonymous reviewers for helpful comments. Ehri GbegbajeDas from PE International and Peter Garrett and Klaus Rønde from VESTAS have our
gratitude for providing assistance and clarifications regarding the Vestas LCA-study.
This study has been supported by the STandUP for energy collaboration initiative.
EPBT = energy payback time
EROI = energy return on (energy) investment
= input-output
LCA = life cycle assessment
LCI = life cycle inventory
LCIA = life cycle impact assessment
LCSA = life cycle sustainability analysis
PCA = process chain analysis
PEROI = primary energy return on (energy) investment
PEPBT = primary energy payback time
REE = rare earth elements
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