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Assessing the lifecycle greenhouse gas emissions from solar PV and
wind energy: A critical meta-survey
Daniel Nugent
a
, Benjamin K. Sovacool
a,b,
n
a
Institute for Energy & the Environment, Vermont Law School, VT 05068-0444, USA
b
Center for Energy Technology, School of Business and Social Sciences, Aarhus University, AU-Herning, Birk Centerpark 15, DK-7400 Herning, Denmark
HIGHLIGHTS
This article screens 153 lifecycle studies of wind and solar energy.
Wind energy emits 0.4 g CO
2
-eq/kWh to 364.8 g and a mean of 34.11 g.
Solar PV emits 1 g CO
2
-eq/kWh to 218 g and a mean of 49.91 g.
article info
Article history:
Received 24 July 2013
Received in revised form
14 October 2013
Accepted 16 October 2013
Available online 12 November 2013
Keywords:
Solar photovoltaics (PV)
Wind energy
Lifecycle assessment
abstract
This paper critically screens 153 lifecycle studies covering a broad range of wind and solar photovoltaic
(PV) electricity generation technologies to identify 41 of the most relevant, recent, rigorous, original, and
complete assessments so that the dynamics of their greenhouse gas (GHG) emissions profiles can be
determined. When viewed in a holistic manner, including initial materials extraction, manufacturing, use
and disposal/decommissioning, these 41 studies show that both wind and solar systems are directly tied
to and responsible for GHG emissions. They are thus not actually emissions free technologies. Moreover,
by spotlighting the lifecycle stages and physical characteristics of these technologies that are most
responsible for emissions, improvements can be made to lower their carbon footprint. As such, through
in-depth examination of the results of these studies and the variations therein, this article uncovers best
practices in wind and solar design and deployment that can better inform climate change mitigation
efforts in the electricity sector.
&2013 Elsevier Ltd. All rights reserved.
1. Introduction
Herman Scheer, a former German Parliamentarian and influen-
tial renewable energy advocate, once stated that “[o]ur depen-
dence on fossil fuels amounts to global pyromania…[a]nd the only
fire extinguisher we have at our disposal is renewable energy”
(Connolly, 2008). Scheer is famous for his work in creating
Germany's renewable energy feed-in-tariff scheme and the ensu-
ing adoption of solar photovoltaic and wind energy projects across
the country. Although there are a number of options to reduce
global dependence on fossil fuels that Scheer could have referred
to, renewable sources of energy such as wind turbines and solar
panels were his solution. This leaves at least one primary question
to be resolved: how can we most effectively use the fire
extinguisher?
To provide some answers, this study considers one of the most
important aspects of our fossil fuel pyromania, the climate change
implications of electricity generation. It assesses how two promi-
nent renewable energy resources, solar photovoltaics (PV) and
wind turbines, emit greenhouse gases (GHG), and it also offers
suggestions for how such technologies can best be utilized or
improved to mitigate climate change. By critically evaluating the
current literature regarding lifecycle GHG emissions stemming
from the full range of PV and wind electricity generation technol-
ogies, this study seeks to determine what the average lifecycle
emissions are, where the emissions falls in terms of lifecycle
stages, and what factors cause overall GHG variation in the
literature, and can therefore be used to create the most effective
climate change mitigation options.
Our assessment reveals the following. Within the “best”sample
of 41 articles evaluated, the average lifecycle greenhouse gas
emissions for wind energy were 34.1 g CO
2
-eq/kWh, whereas solar
PV averaged 49.9 g CO
2
-eq/kWh. Essentially, these measures
represent the amount of GHGs released in grams for each kWh
of electricity that the technology provides, illustrated in Fig. 1.
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/enpol
Energy Policy
0301-4215/$ - see front matter &2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.enpol.2013.10.048
n
Corresponding author at: Vermont Law School, Institute for Energy & the
Environment, PO Box 96, 164, Chelsea Street, South Royalton, VT 050 68-0444, USA.
Tel.: þ1 802 831 1053; fax: þ1 802 831 1158.
E-mail addresses: sovacool@vt.edu,BSovacool@vermontlaw.edu (B.K. Sovacool).
Energy Policy 65 (2014) 229–244
Author's personal copy
As that figure reveals, cultivation and fabrication are responsible
for the largest share of emissions for both technologies, followed
by construction and operation. Decommissioning practices often
recycle materials from both systems back into future production
processes, thus most studies argue that this constitutes an emis-
sions “sink”that lowers the greenhouse gas profile for both types
of systems.
To make its case, the article proceeds as follows. It starts by
introducing readers to the specific lifecycle stages of both onshore
and offshore wind turbines and solar photovoltaic panels. It then
explains the research methods utilized by the authors to distill
from 153 studies 41 of the most relevant, recent, peer-reviewed,
original, and complete assessments. The next part of the article
presents the findings from this selection process before explaining
the factors behind the disparity in estimates for both wind and
solar energy systems, and offering salient conclusions for techno-
logical entrepreneurs and energy policy analysts.
2. Explaining lifecycle stages
Generally, a lifecycle analysis determines a particular facet
(functional unit) of an object, process, or product over the entire
course of that subject's existence (Dale, 2013). For this particular
study, that subject is both wind and solar photovoltaic electricity
generators, and the functional unit by which both are examined is
the GHG intensity in terms of grams of CO
2
-equivalent emissions
per kilowatt-hour (CO
2
-eq/kWh) produced. Assessing the emis-
sions of both PV and wind leads to a particularly broad categor-
ization of what constitutes a lifecycle stage. Nonetheless, the
literature suggests that four of those stages are salient: material
cultivation and fabrication, construction, operation, and decom-
missioning. This section discusses each in turn.
2.1. Material cultivation and fabrication
In general, the material cultivation and fabrication stage
represents the broadest group as it incorporates the full range of
resource extraction, processing of materials, and the amalgama-
tion of final products. Although details vary based upon the type of
PV module, for instance (thin film, mono, poly, or multi-crystalline,
dye-sensitized, quantum dot, and so on), material cultivation
encompasses mining, refining and purification all of the silicon
and/or other required metals and minerals for the cells, glass,
frame, inverters, and other required electronics. Petroleum extrac-
tion for plastics, natural gas extraction used for heating, and
effectively any other material extraction and processing needed
to create the PV module and finished electronics are also included.
Finally, the wiring, encapsulation and any other processes by
which the modules and electronics are fabricated and finished
(up until the point of transportation to the site of operation) are all
included in this part of the stage for PV. Applying essentially the
same concept to wind energy means metal and petroleum extrac-
tion for steel, plastics, internal wiring, etc., are included. Further-
more, composition and production of the blades, gears (although
there are also gearless turbines), rotors, nacelle, turbine, and tower
are all part of this stage.
2.2. Construction
A second stage involves the on-site construction of the gen-
erator and transportation of materials to the site. For PV, encom-
passes transporting the panels, and installing them along with the
balance-of-system (BOS), including mounting structures, cabling
and interconnection components, and inverter (although the exact
BOS assumptions vary by study). GHG emissions for this stage thus
include the processing of BOS materials and fossil fuels burned in
transporting and assembling the system. For wind power, trans-
portation and BOS includes a significant amount of cement and
iron rebar to support structures, as well as cabling and construc-
tion of substations, when necessary.
2.3. Operation and maintenance
Operation is the third stage, and perhaps the most straightfor-
ward. Operation of solar PV includes maintenance, perhaps some
minor replacements when necessary, cleaning of the modules, and
any other processes that occur while the panels are in use.
Essentially the same applies for wind, including regular main-
tenance and cleaning, possible replacement parts such as blades
and gear components, and required material inputs such as
hydraulic oil and oil filters used to lubricate turbines.
2.4. Decommissioning
Decommissioning is the final stage that essentially involves the
deconstruction processes, disposal, recycling and (possibly) land
reclamation. Because recycling is effectively a means of mitigating
future GHG production, many of the studies we reference below
consider this stage to decrease the total GHGs produced over the
lifecycle of the generator. For instance, reclamation is not a
standard practice for wind energy (the pads are often left or
reused), and a majority of the steel towers, plastics, and fiberglass
blades are recyclable. Accordingly, the process carries with it some
significant offsetting of future emissions.
3. Research methods and selection criteria
To ensure that only the “best”peer-reviewed scientific litera-
ture was selected, as many on-topic studies as possible were
collected by searching eight academic databases—Jstor, Science-
Direct, EbscoHost, Energy Citations Database, Web of Science,
Water Resources Abstracts, Science Abstracts, and ProQuest
abstracts (including Sustainability Science Abstracts and Engineer-
ing Abstracts)—between January 2013 and April 2013. The follow-
ing terms were searched within the title, abstract, or keywords of a
study: “lifecycle,”“life-cycle,”“life,”“cycle,”“analysis,”“LCA (life-
cycle analysis),”“GHG,”“greenhouse gas,”“green-house gas,”
“green house gas,”“carbon dioxide,”“CO2,”“solar,”“PV,”“wind,”
“energy,”“electricity,”“renewable,”and “resources.”Generally
some variation of the terms lifecycle, greenhouse gas, and solar
and/or wind constituted the most effective searches.
These searches resulted in 153 lifecycle studies. To narrow
within this broad base to a more robust sample, we filtered the
literature to ensure that only the most relevant, modern, accurate
and original findings were incorporated into this study. Fig. 2
-3.3%
13.0%
19.0%
71.3%
-19.4%
23.9%
24.0%
71.5%
-20% 0% 20% 40% 60% 80%
Decommissioning
Operation
Construction
Cultivation and Fabrication
Wind
Solar PV
Fig. 1. Breakdown of lifecycle greenhouse gas emissions for wind energy and solar
PV (% of total).
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–24 4230
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shows that, through this process, the application of five selection
criteria whittled our sample down to only 41 of the “best”studies.
The following subsections detail this selection process.
3.1. Relevance
The first exclusionary step entailed removing a total of 58
articles based upon relevance. These studies, shown to the left in
Table 1, did not specifically address lifecycle GHG emissions of
either wind or solar, or else did not provide necessary information,
such as total emissions and total electricity produced, that could
be used to easily find that value. While there were many
comprehensive and competent studies among those excluded for
this reason, they primarily focused on other measures such as the
efficiency or effectiveness of PV and wind, oftentimes considering
total costs and rates of return, total energy input and energy-
payback times, and even other environmental measures such as
toxicity, carcinogen output, and water consumption, but not
greenhouse gas emissions.
3.2. Recentness
The second exclusionary condition was that of recentness,
which was responsible for the omission of the 10 articles shown
in Table 2. Due to the rapid technological progress that has
occurred in the efficiency, sizing, and implementation of PV and
wind systems over the last decade, a 10 year publication window
extending to 2003 was constructed, effectively blocking out all
material published beforehand. However, as evidenced in
Tables 5 and 8, the earliest retained piece of literature was
published in 2004 (the only 2004 inclusion), with only three
2005 studies, and only 12 of the total 41 predating 2008. Although
unintentional, more than 70% of the studies are actually within a
five year window.
Fig. 2. Selection process for determining the best lifecycle studies for wind and solar energy. Note: Articles excluded for “relevance”refer to those articles that failed to
provide any lifecycle GHG intensity estimates. Those excluded by “date”signifies an article published prior to 2003. Those excluded for “peer-review”could not be shown to
have undergone any type of review prior to publication. Those excluded for “originality”refer to articles which provided no original GHG intensity analysis and merely relied
on estimations contained in prior studies. Articles excluded for “completeness”only considered CO
2
lifecycle emissions, not the full range of GHGs in terms of CO
2
-eq.
Table 1
Lifecycle studies excluded for relevance.
Source Technology
Akyuz et al. (2011) Wind, solar PV
Amor et al. (2010) Wind, solar PV
Appleyard (2009) Solar PV
Ardente et al. (2005) Solar PV
Barrientos Sacari (2007) Solar PV
Belfkira et al. (2008) Wind, solar PV
Blanc et al. (2012) Wind
Branker et al. (2011) Manufacturing
Browne (2010) Wind
Burger and Gochfeld (2012) Wind, solar PV
Chel et al. (2009) Solar PV
Crawford (2009) Wind
Delucchi and Jacobson (2011) Wind, solar PV
Espinosa et al. (2011b) Solar PV
Espinosa et al. (2012) Solar PV
Fthenakis (2004) Solar PV
Fthenakis et al. (2009a) Solar PV
Granovskii et al. (2007) Wind, solar PV
Gustitus (2012) Wind
Himri et al. (2008) Wind
Huang et al. (2012) Solar PV
Jacobson and Delucchi (2011) Wind, solar PV
Kaldellis et al. (2012) Wind, solar PV
Kammen (2011) Solar PV
Katzenstein and Apt (2009) Wind, solar PV
Kreiger et al. (2013) Solar PV
Kubiszewski et al. (2010) Wind
Limmeechokchai and Suksuntornsiri (2007) Wind, solar PV
Lindstad et al. (2011) Shipping
Lundahl (1995) Wind, solar PV
Marimuthu and Kirubakaran (2013) Wind, solar PV
Martinez et al. (2009b) Wind
Martinez et al. (2010) Wind
Martinez et al. (2012) Wind, solar PV
Mason et al. (2006) Solar PV
Matsuhashi and Ishitani (2000) Solar PV
McCubbin and Sovacool (2013) Wind
Mendes et al. (2011) Solar PV
Mohr et al. (2009) Solar PV
Muller et al. (2011) Wind, solar PV
Nandi and Ghosh (2010a) Wind
Nandi and Ghosh (2010b) Wind
Oke et al. (2008) Solar PV
Ou et al. (2011) Wind, solar PV
Pearce (2002) Solar PV
Pieragostini et al. (2012) Lifecycle Methodology
Rashedi et al. (2012) Wind
Raugei and Frankl (2009) Solar PV
Rubio Rodriguez et al. (2011) Wind
Silva (2010) Wind, solar PV
Sioshansi (2009) Energy technology
Tokimatsu et al. (2006) Nuclear
Tripanagnostopoulos et al. (2005) Solar PV
Vadirajacharya and Katti (2012) Wind, solar PV
Velychko and Gordiyenko (2009) GHG inventories
Vuc et al. (2011) Wind, solar PV
Whittington (2002) Wind, solar PV
Zhai et al. (2011) Wind, solar PV
Table 2
Lifecycle studies excluded for recentness.
Source Technology g CO
2
/kWh
Huber and Kolb (1995) Solar PV –
Kato et al. (2001) Solar PV 149
Kemmoku et al. (2002) Wind, solar PV –
Kreith et al. (1990) Solar PV –
Lenzen and Munksgaard (2002) Wind –
Norton et al. (1998) Solar PV –
Schleisner (2000) Wind 9.7–16.5
Sorensen (1994) Wind, solar PV –
Van de Vate (1997) Wind, solar PV –
Voorspools et al. (2000) Wind, solar PV –
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–244 231
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3.3. Peer review
Our third step involved excluding studies that were not
formally peer-reviewed. Peer review was thought critical to
ensuring the integrity of the analysis. The only literature examined
beyond peer reviewed journals came from conference proceed-
ings, which were then checked for peer review by a scientific
committee in order to pass this standard. In all, only one
conference report Noori et al. (2012) was unable to be
verified and was removed from the sample for not meeting this
condition.
3.4. Originality
The fourth restriction was to exclude 28 studies shown in
Table 3 that were not a primary source. Effectively, all articles that
did not provide new and original CO
2
-eq/kWh information were
eliminated to avoid reliance on sources more than once (so as to
not skew the analysis), and also to ensure that the other exclu-
sionary criteria were not subverted (e.g., the secondary source
could be based on primary information that was not peer
reviewed). Other articles were excluded if they included a GHG
intensity estimate as a part of a different type of analysis and thus
relied on other sources for the numbers, or amalgamated other
lifecycle studies and gave a range or average, not significantly
unlike this study. A few very detailed studies done in conjunction
with the National Renewable Energy Laboratory's (NREL) “Life
Cycle Harmonization Project”were included despite this literature
compilation approach. Examples include Hsu et al. (2012),Kim
et al. (2012) and Dolan and Heath (2012), not to be confused with
the NREL factsheets excluded for originality in Table 3. These
included studies did much more than simply find a range or
average g CO
2
-eq/kWh estimate, and instead recalculated esti-
mates from other studies by harmonizing the conditions that the
studies assumed, for example by inputting consistent life expec-
tancies, wind speeds or solar irradiance.
3.5. Completeness
Afinal factor used to screen the literature was for failure to
consider the entire range of GHGs, which then led to the removal
of 15 articles shown in Table 4. Although these articles generally
met the previous requirements, they only attempted to quantify
the CO
2
lifecycle emissions attributed to wind and/or solar PV. In
the interests of focusing this study on the entirety of GHGs (in
order to assess the totality of the global warming potential of wind
and solar PV), these articles were excluded.
4. Assessing the greenhouse gas intensity of wind energy
After removing a total of 112 studies based upon our five
selection criteria, 41 studies remained which are relevant, pub-
lished in the past 10 years, peer-reviewed, provided original
estimates of total GHG intensity, and incorporated all greenhouse
gases. These studies were then disaggregated into those looking at
wind and solar PV, with Table 5 presenting those related to wind
energy. These studies were “weighed”equally; that is, they were
not adjusted for their methodology, time of release within the past
ten years, or how rigorously they were peer reviewed or cited in
the literature. Additionally, the estimates were not harmonized
for divergent variables or assumptions inherent in their analysis.
The studies in Table 5 are quite global in nature, spanning at least
five continents specifically, and including several studies that were
global.
Statistical analysis of these 22 studies and 39 estimates reveals
a range of greenhouse gas emissions over the course of wind's
lifecycle at the extremely low end of 0.4 g CO
2
-eq/kWh and the
extremely high end of 364.8 g CO
2
-eq/kWh. Accounting for the
average values of emissions associated with each part of wind
energy's lifecycle, the mean value reported is 34.1 g CO
2
-eq/kWh
—numbers reflected in Fig. 3 and Tables 6 and 7.AsFig. 1 already
depicted in the introduction, cultivation and fabrication are
Table 3
Lifecycle studies excluded for lack of originality.
Source Technology g CO
2
/kWh
Arvesen and Hertwich (2012) Wind 6–34
Bensebaa (2011) Solar PV 30
Chaurey and Kandpal (2009) Solar PV –
Dones et al. (2004) Wind 10–20
Solar PV 39–73
Dotzauer (2010) Wind 9–10
Solar PV 32
Dufo-Lopez et al. (2011) Wind, solar PV –
Evans et al. (2009) Wind 25
Solar PV 90
Fthenakis et al. (2008) Solar PV 24, 30–45,
39–110
Fthenakis and Kim (2011) Solar PV 38
Georgakellos (2012) Wind 8.20
Solar PV 104
Goralczyk (2003) Wind, solar PV –
Graebig et al. (2010) Solar PV –
Hardisty et al. (2012) Wind, solar PV –
Kannan et al. (2007) Solar PV 217
Kenny et al. (2010) Solar 21–59
NREL (National Renewable Energy
Laboratory) (2012)
Solar PV 40
NREL (National Renewable Energy
Laboratory) (2013)
All electricity
generation
–
Pacca et al. (2007) Solar PV 34.3–50
Padey et al. (2012) Wind 4.5–76.7
Peng et al. (2013) Solar PV 10.5–50
Raadal et al. (2011) Wind 17.5
Sherwani et al. (2010) Solar PV 15.6–280
Tyagi et al. (2013) Solar PV 9.4–2820
Van der Meulen and Alsema (2011) Solar PV –
Varun et al. (2009a) Wind 9.7–123.7
Solar PV 53.4–250
Varun et al. (2009b) Wind 16.5–123.7
Solar PV 9.4–30 0
Weisser (2007) Wind 18
Solar PV 56
Yang et al. (2011) Wind .56
Table 4
Lifecycle studies excluded for failure to consider all GHGs.
Source Technology g CO
2
/kWh
Garcia-Valverde et al. (2009) Solar PV 131
Ito et al. (2008) Solar PV 9–16
Ito et al. (2009) Solar PV 51.5–71
Ito et al. (2010) Solar PV 43–54
Kleijn et al. (2011) Wind 15
Solar PV 60
Krauter and Ruther (2004) Solar PV 11–75
Lee and Tzeng (2008) Wind 3.6
Lenzen and Wachsmann (2004) Wind 2–81
Li et al. (2012) Wind 69.9
McMonagle (2006) Solar PV 0–59
Pehnt et al. (2008) Wind 22
Sherwani et al. (2011) Solar PV 55.7
Sumper et al. (2011) Solar PV –
Wang and Sun (2012) Wind 4.97–8.21
Zhai and Williams (2010) Solar PV 21
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–24 4232
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Table 5
Total lifecycle GHG emissions and factors for 22 qualified wind energy studies.
Source Location Life
(years)
Onshore/
offshore
System/turbine
capacity
Hub height
(m)
Rotor diameter
(m)
Other assumptions Total estimate
(g CO
2
-eq/kWh)
Ardente et al. (2008) Italy 20 Onshore 11660 kW turbines 55 50 14.8
Chen et al. (2011) Guangxi, China 20 Onshore 24 1.25 MW
turbines
55 31 7 m/s avg. wind speed 0.56
Dolan and Heath (2012) Global 20 Both –– .25 capacity factor 11
Fleck and Huot (2009) –20 Onshore 5 400 W turbines 30 1.17 Off-grid, with battery bank, .17 capacity factor 364.83
Guezuraga et al. (2012) Global (German, Chinese,
Denmark manufacturing)
20 Onshore 1.8 MW gearless
turbine
–– 8.82
2 MW geared turbine 105 90 7.4 m/s avg. wind speed 9.73
Hondo (2005) Japan 30 Onshore 30 0 kW turbines –– .2 capacity factor 29.5
Kabir et al. (2012) Alberta, Canada 25 Onshore 20 5 kW turbines 36.6 5.5 .23 capacity factor 42.7
520 kW turbines 36.7 9.45 .22 capacity factor 25.1
100 kW turbine 37 21 .24 capacity factor 17.8
Khan et al. (2005) Newfoundland, Canada 20 Onshore 500 kW system –– Turbine, no fuel cell storage 16.86
Turbine with fuel cell storage 59.31
Mallia and Lewis (2013) Ontario, Canada 20 Onshore –– Avg. Canadian electricity mix (210 g CO
2
-eq/kWh) 10.69
Manish et al. (2006) India - Onshore 18500 kW turbines –– 2003 global electricity mix, .1–.3 capacity factor 12–40
Martinez et al. (2009a) Munilla, Spain 20 Onshore 2MW turbine 70 80 6.58
Mithraratne (2009) Production UK, Installation New
Zealand
20 Onshore 1.5 kW turbines 10 2 Roof mounted, .04 .064 capacity factor,
New Zealand electricity mix (224 g CO
2
-eq/kWh),
5.5–6.3 m/s avg. wind speed
138–220
Oebels and Pacca (2013) North Eastern Brazil 20 Onshore 141.5 MW turbines 80 –Brazilian electricity mix (64 g CO
2
-eq/kWh), .3425 capacity
factor, 7.8 m/s avg. wind speed
7.1
Padey et al. (2013) Europe - Onshore –– – 12.9
Pehnt (2006) Germany - Onshore 1.5 MW turbine –– 566 g CO
2
-eq/kWh electricity mix 11
Offshore 2.5 MW turbine –– 566 g CO
2
-eq/kWh electricity mix 9
Querini et al. (2012) Global 20 Onshore 2MW turbine –– 12
Songlin et al. (2011) Fuzhou, China –– 2MW turbine –– 0.43
Tremeac and Meunier
(2009)
Southern France 20 Onshore 4.5 MW turbines 124 113 15.8
Production Finland, Installation
France
20 Onshore 250 W wind turbines –– Finnish electricity mix 46.4
Wagner et al. (2011) German North Sea 20 Offshore –– 32
Weinzettel et al. (2009) –20 Floating
Offshore
40 floating 5 MW
turbines
100 (above
sea level)
116 0.89
Wiedmann et al. (2011) UK 30 Offshore 2 MW farm –– Process lifecycle analysis, .3 capacity factor 13.4
Integrated hybrid lifecycle analysis, .3 capacity factor 28.7
IO-based hybrid lifecycle analysis, .3 capacity factor 29.7
Zimmermann and
Gößling-Reisemanna
(2012)
Germany 20 Onshore 2.3MW system 98 80 7. 9
84 80 7.5 m/s avg. wind speed 12.5
98 80 7.72 m/s avg. wind speed 12
108 80 7.9 m/s avg. wind speed 11 .2
98 80 7.9 m/s avg. wind speed 10.8
108 80 8.15m/s avg. wind speed 10.1
98 80 8.14 m/s avg. wind speed 9.8
108 80 8.57 avg. wind speed 8.3
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–244 233
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responsible for about 71% of wind's emissions, followed by con-
struction (24%), operation (slightly less than 24%), and decom-
missioning, which offset 19.1 percent of wind's emissions.
5. Assessing the greenhouse gas intensity of solar PV
Sticking with the same selection process, Table 7 presents the
23 most relevant, recent, peer-reviewed, original, and complete
studies for solar PV. These studies, similar to those for wind
energy, were weighted equally. Estimates were also not harmo-
nized for different assumptions or variables. The studies in Table 8
are also quite global in nature, spanning three continents and/or
the globe.
Statistical analysis of these 23 studies and 57 estimates reveals
a range of greenhouse gas emissions over the course of solar PV's
lifecycle at the extremely low end of 1 g CO
2
-eq/kWh and the high
end of 218 g CO
2
-eq/kWh. Accounting for the average values of
emissions associated with each part of solar PV's lifecycle, the
mean value reported is 49.9 g CO
2
-eq/kWh numbers reflected in
Fig. 4 and Tables 9 and 10 though the number of selected
studies providing estimates for operation and maintenance (2) and
decommissioning (5) is low. As Fig. 1 also depicted in the
introduction, cultivation and fabrication are responsible for about
71% of solar PV's emissions, followed by construction (19%),
operation (13%), and decommissioning, which offset 3.3% of
emissions.
6. What causes the disparity in wind and solar estimates?
Though the tables and figures above do a satisfactory job
documenting the lifecycle emissions associated with wind energy
n = 16 n = 14 n = 12
n = 13
-75
-25
25
75
125
175
225
275
Cultivation and
Fabrication
Construction Operation Decommissioning
g CO2-eq/kWh
Mean
Median
Range
Fig. 3. Lifecycle greenhouse gas emissions for wind energy by lifecycle stage.
Table 6
Summary statistics of qualified studies reporting projected greenhouse gas intensity for wind energy.
Cultivation and fabrication Construction Operation Decommissioning Total
(n¼16) (n¼14 ) (n¼12) (n¼13 ) (n¼39)
Mean 42.98 14.43 14.36 11.64 34.1
Median 11.99 8.26 2.37 3.27 12
Mode –9–– 12
Std. Dev. 76.95 21.17 26.3 18.76 67.23
High 286.02 78.85 83.6 0.5 364.8
Low 0.15 0.15 0.02 59.4 0.4
Percentage of Total (%) 71.48 24.00 23.88 19.36 100
Note that the “total”column equals the mean for all lifecycle studies that made it past our screen, not necessarily those that broke emissions down by specific lifecycle stages.
“n”also refers to number of estimates, not necessarily number of studies.
Table 7
Detailed statistics of qualified studies reporting lifecycle equivalent greenhouse gas intensity for wind energy.
Source Cultivation and fabrication Construction Operation Decommissioning Total
Chen et al. (2011) 0.15 0.42 0.02 –0.56
Fleck and Huot (2009) 286.02 78.85 –– 364.83
Guezuraga et al. (2012) 7.89 ––– 8.82
7.59 ––– 9.73
Hondo (2005) 13.7 7.4 8.3 –29.5
Kabir et al. (2012) 30.74 9.11 14.8 11.96 42.7
12.01 12.55 3.82 3.27 25.1
11.97 10.13 0.92 5.22 17.8
Mallia and Lewis (2013) ––0.74 0.27 10.69
Martinez et al. (2009a) 6.96 2.01 0.35 2.75 6.58
Mithraratne (2009) 98 24.1 52.4 37.2 138
156.2 37.4 83.6 59.4 220
Oebels and Pacca (2013) 5.31 1.75 0.04 –7.1
Songlin et al. (2011) 0.27 0.15 ̄
–0.43
Tremeac and Meunier (2009) ––0.8 3.6 15.8
––
̄
–29.5 46.4
Wagner et al. (2011) ––6.5 0.4 32
Wiedmann et al. (2011) 9.5 3 –0.43 13.4
22.5 4.8 –0.5 28.7
18.8 10.3 –0.01 29.7
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–24 4234
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Table 8
Total lifecycle GHG emissions and factors for 23 qualified solar PV studies.
Source Location Life
(years)
Irradiance
(kWh/m
2
)
Tech Mounting Assumptions Estimate
(g CO
2
-eq/kWh)
Alsema and de Wild-
Scholten (2004)
Southern Europe –– Ribbon-Si –28
Netherlands/Germany –– Ribbon-Si –48
Southern Europe –– Multi-Si Roof mount 73
Netherlands/Germany –– Multi-Si Roof mount 124
Alsema et al. (2006) Production US, Installation Southern
Europe
30 (15
inverter)
1700 CdTe Ground mount 9% efficiency 25
Southern Europe 30 (15
inverter)
1700 Ribbon-Si Roof mount 11.5% efficiency 29.5
Mono-Si Roof mount 14% efficiency 35
Multi-Si Roof mount 13.2% efficiency 32
Beylot et al. (2014) - 30 1700 Multi-Si 301tilt, fixed aluminum
mount
5 MWp, 14% module efficiency 53.5
301tilt, fixed wood mount 5 MWp, 14% module efficiency 38
301tilt, single axis tracking 5 MWp, 14% module ef ficiency 37.5
301tilt, dual axis tracking 5 MWp, 14% module efficiency 42.8
Bravi et al. (2011) Europe 20 1700 Micromorph 221roof mount 125 Wp module, 8.74% efficiency,
513g CO2/kWh European electricity mix
20.9
Desideri et al. (2013) Sicily, Italy 30 1600–1800 Mono-Si 301tilt, ground mounted
single-axis tracking
13.85% module efficiency, 2 MWp 47.9
de Wild-Scholten et al.
(2006)
Southern Europe 30 (15
inverter)
1700 Multi-Si on-roof Phonix mounting
structure
11.4 kWp, 13.2% module efficiency 38
on-roof Schletter roof hooks 11.4kWp, 13.2% module efficiency 35.5
in-roof Schletter mounting
structure
11.4 kWp, 13.2% module efficiency 32
in-roof Schweizer mounting
structure
11.4 kWp, 13.2% module efficiency 32.5
ground Phonix mount 11.4 kWp, 13.2% module efficiency 41
ground Springerville mount 11.4 kWp, 13.2% module efficiency 37
Espinosa et al. (2011a) Manufacturing Denmark,
Installation Southern Europe
15 1700 Transparent organic polymer,
indium-tin-oxide (ITO)
- 2% module efficiency, 2008 Denmark energy mix
(420.88 g CO
2
-eq/kwh)
37.77
3% module efficiency, 2008 Denmark energy mix
(420.88 g CO
2
-eq/kwh)
56.65
Fthenakis and Alsema
(2006)
Europe 30 1700 Multi-si On-roof mount european electricity mix 13.2% efficiency 37
CdTe On-roof mount european electricity mix, 8% efficiency 21
Ribbon-Si On-roof mount 30
mono-Si on-roof mount 45
Production US, Installation Europe 30 1700 CdTe ground mount US electricity mix, 9% efficiency 25
Fthenakis and Kim.
(2006)
United States 30 1800 CdTe Ground mount 25 MWp, 9% efficiency 24
Fthenakis et al. (2009b) Ohio, USA –1700 CdTe –10.9% efficiency, US electricity mix
(750 g CO
2
-eq/kWh)
12.75
Garcia-Valverde et al.
(2010)
Southern Europe 15 1700 Organic/plastic –5% module efficiency 109.84
Glockner et al. (2008) Europe 30 1700 Multi-Si On-roof mount Schletter
mounting
Siemens Si processing, 13.2% module efficiency 30
Elkem Solar Si processing, 13.2% module efficiency 23
Hondo (2005) Japan 30 –Poly-Si On-roof mount 3 kWp, 0.15 capacity factor, 10% efficiency 53.4
Hsu et al. (2012) Global 30 1700 c-Si –45
mono-Si –14% module efficiency 40
Multi-Si –13.2% module efficiency 47
c-Si Ground mount 48
c-Si Roof mount 44
Jungbluth (2005) Switzerland 30 1100 Poly-Si On-roof mount 3 kWp, 79 g CO
2
-eq/kWh electricity mix 39–110
Kannan et al. (2006) Singapore 25 1635 Mono-Si 2.7 kWp 217
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–244 235
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and solar PV systems from our “best”sample of studies, substan-
tial disparities do exist, and this section of the study explains how
at least eight separate factors play a role in these differences:
(1) resource inputs and technology, (2) transportation, (3) manu-
facturing, (4) location, (5) sizing and capacity, (6) longevity,
(7) optional equipment, and (8) calculation methods.
6.1. Resource inputs and technology
The material inputs required for wind generation necessarily
vary in the literature based upon physical size (capacity and hub
height), the location and design of the plant (onshore versus
offshore and interconnection distances), and even based upon
the type of technology used (floating turbines, turbines with and
without gearboxes, etc.). Guezuraga et al. (2012) compares two
turbines, one 2 MW geared turbine and one 1.8 MW gearless
turbine, and found significantly higher stainless steel, reinforced
concrete and total mass calculations (1538 t) for the former, and
higher copper requirements, but overall lower mass (360 t) for the
latter. Intuitively, these sorts of differences alter the GHG intensity
of the manufacturing and construction lifecycle stages. Also,
despite presumably greater material inputs required by offshore
wind installations to reach the seabed and the general presump-
tion that they are generally larger turbines to take advantage of
higher wind speeds, offshore estimates in the literature show
decreased emissions intensity. While there was a much larger
estimate sample for onshore (31 compared to 6), and some
obvious outliers, offshore estimates showed a lower mean inten-
sity illustrated by Fig. 5.
Similarly, PV technologies vary substantially in their emissions
profiles, given that they require somewhat different material inputs.
Our sample of studies included crystalline silicon technologies
such as mono-crystalline (mono-Si), poly-crystalline (poly-Si),
multi-crystalline (multi-Si) and ribbon multi-crystalline (ribbon-Si),
as well as several thin-film technologies such as amorphous
silicon (a-Si), cadmium telluride (CdTe) and copperindium
galliumdiselenide (CIGS). The sample also included other PV types
such as micromorph (a-Si and micro-Si hybrid), organic/plastic cells
(including indium-tin-oxide, dye sensitized and others), and cad-
mium selenide quantum-dot photovoltaics (CdSe QDPV). All of these
technologies have distinct material and processing requirements,
Table 8 (continued )
Source Location Life
(years)
Irradiance
(kWh/m
2
)
Tech Mounting Assumptions Estimate
(g CO
2
-eq/kWh)
Aluminum/concrete roof
mount
Kim et al. (2012) Global 30 2400 a-Si Ground mount 6.3% efficiency 20
CdTe Ground mount 10.9% efficiency 14
CIGS Ground mount 11.5% efficiency 26
a-Si On-roof mount 6.3% efficiency 21
CdTe On-roof mount 10.9% efficiency 14
CIGS On-roof mount 11.5% efficiency 27
Manish et al. (2006) India 20 –– – 10-15% efficiency 50–130
Pehnt (2006) Germany 25 –Poly SOG-Si –566 CO
2
-eq/kWh electricity mix 104
Querini et al. (2012) Global 30 1204 Mono-Si 45 degree fixed mount 92
Reich et al. (2011) –30 1300 c-Si –no F-gas emissions, renewable electricity mix
(1 g CO
2
-eq/kWh), 15% efficiency
1
coal electricity mix without CCS
(1,000 g CO
2
-eq/kWh), 15% efficiency
218
Sengul and Theis (2011) Europe 30 1700 CdSe QDPV Ground mount 14% efficiency 5
Veltkamp and de Wild-
Scholten (2006)
Southern Europe 5 1700 Glass glass (DSC) dye
sensitizes
–8% efficiency 106.25
10 170 0 –8% efficiency 52.5
20 1700 –8% efficiency 17.5
n = 26 n = 26 n = 2
n = 5
-10
0
10
20
30
40
50
60
70
80
90
100
Cultivation and
Fabrication
Construction Operation Decommissioning
g CO2-eq/kWh
Mean
Median
Range
Fig. 4. Lifecycle greenhouse gas emissions for solar PV by lifecycle stage.
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–24 4236
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leading to different solar conversion efficiencies in the final product,
and thus an exceptional range of emissions possibilities for PV as a
whole—statistics reflected in Table 11.Ta ble 11 shows mono-Si to
have the highest average estimated emissions and CdSe QDPV ranks
ashavingthelowestemissions,thoughthesamplesizesofthe
studies behind these claims are small.
6.2. Transportation
While transportation a subcomponent of our construction
lifecycle stage might not seem like a major GHG producing aspect
of either wind or solar PV, there is significantvariationinthe
literature. For wind, the highest transportation estimate accounted
for 28.3% of total emissions (Mallia and Lewis, 2013), whereas the
average percentage share of transportation is significantly lower, at
only 11.8%, and the lowest estimates fall to as small as 0.2% (Chen
et al., 2011). There are a number of factors that can explain this
variation. First, assessments of smaller turbines that include battery
backup and additional optional equipment, potentially manufactured
and transported separately from different locations, and overall
producing less lifetime energy than large multi-megawatt turbines,
show a higher than average share of transportation GHGs. For
Table 9
Summary statistics of qualified studies reporting projected greenhouse gas emissions for solar PV.
Cultivation and fabrication Construction Operation Decommissioning Total
(n¼26) (n¼26) (n¼2) (n¼5) (n¼57)
Mean 33.67 8.98 6.15 1.56 49.9
Median 30.25 5.1 6.15 1.1 37.8
Mode 16, 21.3, 33, 36 2 –2.2 14, 21, 25, 30, 32, 37, 38, 45, 48
Standard Deviation 20.57 10.15 8.7 4.68 43.3
High 95.31 38.2 12.3 2.2 218
Low 12.1 107. 2 1
Percentage of total (%) 71.30 19.00 13.00 3.30 100
Note that the “total”column equals the mean for all lifecycle studies that made it past our screen, not necessarily those that broke emissions down by specific lifecycle stages.
“n”also refers to number of estimates, not necessarily number of studies.
Table 10
Detailed statistics of qualified studies reporting lifecycle equivalent greenhouse gas emissions for solar PV.
Source Cultivation and fabrication Construction Operation Decommissioning Total
Alsema et al. (2006) 25.4 4.1 –– 29.5
28.7 3.3 –– 32
31.8 3.2 –– 35
18.75 6.25 –– 25
Beylot et al. (2014) 21.3 38.2 –6.1 53.5
21.3 15.6 –1.1 38
20.2 23.2 –2.2 37.5
16 24.6 –2.2 42.8
de Wild-Scholten et al. (2006) 37 1 –– 38
33.5 2 –– 35.5
33 1–– 32
33 0.5 –– 32.5
36 5 –– 41
36 1 –– 37
Fthenakis and Alsema (2006) 32.5 4.5 –– 37
16 5 –– 21
19 6 –– 25
Glockner et al. (2008) 28.1 2 –– 30
20.9 2 –– 23
Hondo (2005) 28.3 9.8 12.3 –53.4
Jungbluth (2005) 33.8–95.31 5.19–14.66 0 –39-110
Querini et al. (2012) 85.6 6.3 –7.2 9 2
Veltkamp and de Wild-Scholten (2006) 75 31.3 –– 106.25
36.9 15.6 –– 52.5
12.1 5.3 –– 17.5
n = 6
n = 31
Fig. 5. Differences in greenhouse gas intensity for onshore and offshore wind
turbines.
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–244 237
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example, Fleck and Huot (2009) find a large 78.85 g CO
2
-eq/kWh,
equating to 21.5% of lifecycle intensity, resulting from transportation
for very small 400 W turbines with battery backup. Further trans-
portation discrepancies could arise between onshore and offshore
turbines as they necessarily entail different transportation processes,
types (boat, airplane, rail, truck) and distances involved.
PV lifecycle studies seemingly focused significantly less on
defining the GHG intensity of transportation, which is a clear
weakness of the literature as a whole. Although the same theore-
tical implications as considered for wind systems should apply, the
only individual estimate specifically for transportation was that of
Querini et al. (2012), which found 6.3 g CO
2
-eq/kWh accounting
for 6.9% of the total emissions profile.
6.3. Manufacturing
Fabrication and manufacturing are energy intensive processes
which may partially depend on direct fossil fuel use, generally for
heating processes, but also significantly rely on electricity inputs.
One assumption found throughout wind and PV literature relates
to the electricity mix of the locale, considering the types of
electricity generators (coal, natural gas, nuclear, renewables)
which supply the local grid. Depending upon how carbon inten-
sive these sources are, wind and solar estimates vary.
In the case of wind, Guezuraga et al. (2012) showed that the
same manufacturing process in Germany would result in less than
half of the total emissions that such a process would entail in
China. This was primarily due to China's significantly greater
dependence on black coal for electricity production in comparison
with Germany's much greater reliance on natural gas and nuclear
power. Oebels and Pacca (2013) also attributed significant dis-
parity to the location of manufacturing, noting that the Brazilian
electricity mix, being as low as 64 g CO
2
-eq/kWh (as much as eight
times lower than the global average), had a significant effect on
their low overall calculation (7.1 g CO
2
-eq/kWh). This contrasts
with Pehnt (2006) which used a 566 g CO
2
-eq/kWh energy mix
and returned a 9–11 g CO
2
-eq/kWh wind calculation, a 55%
increase to Oebels and Pacca (2013).
For PV, this trend again applies as PV manufacturing also
depends upon electricity to compose finished modules. Some
energy mix assumptions made in the literature include a Danish
grid intensity of 420.88 g CO
2
-eq/kWh (Espinosa et al., 2011a) and
a566gCO
2
-eq/kWh for Germany (Pehnt, 2006). One study that
pays explicit attention to this factor, Reich et al. (2011), concludes
that the source of the electricity mix can affect the GHG intensity
of a PV installation anywhere from zero g CO
2
-eq/kWh (for an all
renewable and nuclear mix) to 200 g CO
2
-eq/kWh (for coal-only
mixes). Manufacturing can also see emissions intensity variation
based upon the particular type of PV technology considered and its
relevant processing steps. For example, quartz extraction from
sand and then processing and refinement are needed to create PV
grade silicate for some panels, whereas others such as CIGS may
not need silicates at all. Other influential factors include the type
of PV technology. For amorphous, multi, and mono PV systems,
silicates may need to be converted into different products, such as
ingots, wafers, or other components, to form the finished panel
(Glockner et al., 2008). Accordingly, the amount of energy and
GHG emissions attributable to all of these processes can lead to
significant variation.
6.4. Location
Emissions efficiency is directly tied to geographic location and
the solar and wind resource base. Essentially, the more of the
resource, the more power generation and therefore the lower the
GHG intensity. For wind turbines, wind is subject to signification
spatial variation, both globally and locally, and also to temporal
variation, in terms of seasonal and daily fluctuations. These factors
strongly influence the total amount of electricity generated and
thus are important variables assumed in the literature to calculate
the GHG intensity of wind turbines. Most global average wind
speed maps shows that oceans, especially in the far North and
South, have higher wind speed averages, along with mountainous
and coastal areas (3Tier Inc., 2011b). Furthermore, local topogra-
phy plays a role in wind speeds and availability, as mountains,
manmade structures, and even vegetation (for smaller turbines)
can affect airflow. Zimmermann and Gößling-Reisemanna (2012)
pay particular attention to this factor and show how different hub
heights on the same sited turbine leads to different average wind
speeds, from 7.5 m/s to 8.57 m/s, which then leads to fluctuation in
overall CO
2
-eq/kWh, from 8.3 g to 12.5 g. Despite the critical
implications that wind speed can have between otherwise similar
turbines, this factor is clearly not the most important considera-
tion (as compared to sizing, on/offshore and lifetime) as the un-
harmonized statistics taken from the literature do not show an
obvious trend.
The location of PV installations has the same implications. Solar
resources vary both globally and locally across the world, and
again vary on a daily and seasonal basis. Shading problems caused
by local geography, vegetation, and structures can thus play a role
on solar PV performance (3Tier Inc., 2011a). Therefore, though
most studies presumed a solar irradiance value of 1700 kWh/m
2
/
yr, some in our sample went as low as 1100 kWh/m
2
/yr whereas
others assumed 2400 kWh/m
2
/yr (more consistent with the
Table 11
Differences in greenhouse gas intensity based on solar PV material inputs.
PV technology Mean Median nMode Standard deviation High Low
Mono-Si 79.5 46.5 6 –70.4 217.0 35.0
Multi-Si 44.3 37.5 17 32, 37, 38 23.3 124.0 23.0
Poly-Si 78.7 78.7 2 –35.8 104.0 53.4
Ribbon 33.9 29.8 4 –9.5 48.0 28.0
Total c-Si 55.3 40.5 34 30, 32, 37, 38, 45, 48 47.1 218.0 1.0
a-si 20.5 20.5 2 –0.7 21.0 20.0
CIGS 26.5 26.5 2 –0.7 27.0 26.0
CdTe 19.4 21.0 7 14, 25 5.6 25.0 12.8
Total thin-film 20.9 21.0 11 14 5.2 27.0 12.8
Organic ITO 47.2 47.2 2 –13.4 56.7 37.8
Dye sensitized 58.8 52.5 3 –44.7 106.3 17.5
Total organic 63.4 54.6 6 –37.2 109.8 17.5
CdSe QDPV 5.0 5.0 1 –– 5.0 5.0
Micromorph 20.9 20.9 1 –– 20.9 20.9
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–24 4238
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Sahara or the American Southwest). Fig. 6 illustrates how solar
irradiance has a direct effect on greenhouse gas intensity.
6.5. Sizing and capacity
The literature reveals differences in emissions intensity based
upon the physical and nameplate capacity sizes of each system,
with a positive trend as sizes increase. Higher capacity wind
turbines, both with taller hub heights and larger rotor diameters,
correspond to lower GHG intensities. Tremeac and Meunier (2009)
compared a 4.5 MW turbine to a 250 W version and found the
smaller to have a GHG intensity equal to approximately three
times greater than the larger turbine. Kabir et al. (2012) calculates
that 20 5 kW turbines result in an emissions intensity of 42.7 g,
520 kW turbines have an emissions intensity of 25.1 g, and one
100 kW turbine has a mere 17.8 g of CO
2
-eq/kWh, implying that
“bigger is better.”Figs. 7 and 8plot the relationship between
greenhouse gas emissions intensity and nameplate capacity and
hub height, respectively.
PV, perhaps oddly, also follows the sizing advantages of wind
energy. (We say “oddly”because PV is a modular technology that
is supposed to work the “same”regardless of whether ten panels
or 100 panels are being used). There do appear to be economy of
scale advantages that larger PV installations benefit from, possibly
due to efficiency gains in logistics and transportation, and with
larger systems being able to access a wider (and more stable) solar
resource. Per the logarithmic average shown in Fig. 9, there is a
clearly downward trend as installed capacity increases from small
distributed generation scale installations to larger utility- and
merchant-scale power plant projects.
6.6. Longevity
Longevity is a fairly obvious factor influencing GHG intensity.
Yet it is also an imprecise one because there are a number of
unknown considerations, such as how well maintained the gen-
erators are, how well they are manufactured, the physical and
natural conditions at the installation site, and how quickly the
installations and their interconnections degenerate. Furthermore,
because most wind and solar systems have not (yet) been
deployed for full lifespans, many estimates are little more than
educated guesses.
For the wind literature, lifetime estimates vary in 5–10 year
increments between the maximum of 30 years and the minimum
of 20 years. Despite the fact that Padey et al. (2012) was excluded
for its reliance on secondary sources, it is one of the only studies
which specifically looks at the effects of life expectancy on the
GHG intensity of an otherwise similar turbine, and shows exactly
50% decreases in GHG intensity for doubled life expectancy
estimates, and 66% reductions for tripled estimates. This generally
makes sense as doubling life expectancy should nearly double
total output, however it does not seem to account completely for
n= 6
n= 1
n= 35
n= 1
n = 2
n = 1
n= 2
0
50
100
150
200
250
1000 1250 1500 1750 2000 2250 2500
g CO2-eq/kWh
Irradiance (kWh/m2)
Average intensity
estimate
Linear trend
Fig. 6. Differences in greenhouse gas intensity for solar PV based on irradiance.
n = 4
n = 2
n = 2
n = 2
0
50
100
150
200
250
300
350
400
0.1 1 10 100 1000 10000
g CO2-eq/kWh
Nameplate Turbine Capacity (kW)
Average intensity
estimate
Fig. 7. Differences in greenhouse gas intensity for wind energy based on nameplate
capacity. Note: to avoid excessive data labels, nvalues are not provided for data
points that represent individual estimates from the literature. Instead, only data
points that represent an average GHG intensity from multiple estimates in the
literature are labeled with the appropriate nvalue, and the data points are
presented in red for specificity.
n = 9 n = 3
n = 2
n = 4
n = 2
n = 2
0
50
100
150
200
250
300
350
400
1241059880553730
g CO2-eq/kWh
Height / Diameter (m)
Hub height
Rotor diameter
Moving avg. (hub height)
Moving avg. (rotor
diameter)
Fig. 8. Differences in greenhouse gas intensity for wind energy based on hub
height and rotor diameter. Note: to avoid excessive data labels, nvalues are not
provided for data points that represent individual estimates from the literature.
Instead, only data points that represent an average GHG intensity from multiple
estimates in the literature are labeled with the appropriate nvalue, and the data
points are presented in black for specificity.
n = 1
n = 4
n = 1
n= 6
n= 3
n= 1
0
50
100
150
200
250
1 10 100 1000 10000 100000
g CO2-eq/kWh
Installed System Capacity (kWp)
Average intensity
estimate
Logarithmic trend
Fig. 9. Differences in greenhouse gas intensity for solar PV based on installed
system capacity.
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–244 239
Author's personal copy
increased maintenance and any grid curtailment or degradation of
the turbine. As a whole, our sample of the wind literature does
show a clear trend, where 20 year assumptions result in an
average of 40.69 g CO
2
-eq/kWh, 25 years decreases the mean
intensity to 28.53 g CO
2
-eq/kWh, and 30 years drops it to
25.33 g CO
2
-eq/kWh.
The same trend is confirmed by our sample of PV literature,
which tended to presume systems operated for 30 years. However,
Veltkamp and de Wild-Scholten (2006) showed that a 5 year
operating lifetime resulted in an emissions intensity of
106.25 g CO
2
-eq/kWh, whereas a 20 year lifetime saw emissions
drop to 17.5 g CO
2
-eq/kWh—emphasizing the importance of main-
tenance. When our sample of literature is aggregated as a whole, a
linear trend line shows a slight decrease in GHG intensity as
lifetime increases, which would clearly be more distinct if the
217 g CO
2
-eq/kWh provided by Kannan et al. (2006) in the 25 year
PV categoy were harmonized. Fig. 10 details these effects both for
wind and solar PV.
6.7. Storage and mounting
One clear factor influencing lifecycle estimations involved
optional energy storage. For example, Khan et al. (2005) found
that a turbine integrated with fuel cell electricity storage out-
putted 59.31 g CO
2
-eq/kWh, and Fleck and Huot (2009) found a
small wind turbine with battery backup to generate 364.83 g CO
2
-
eq/kWh. These results of course are well above the mean or
median wind GHG intensity numbers in the literature. At least
one piece of literature, Browne (2010), did attempt to factor in
backup power plants potentially needed to supplement wind
systems due to intermittency, however this study was excluded
for failure to account for GHG intensity (see Table 1). Otherwise,
none of the studies included in further analysis appeared to
consider this issue.
Also and perhaps peculiarly the PV literature did not
discuss the need for supplemental production, nor did it investi-
gate battery backup. The PV literature instead tended to focus on
the type of mounting that the system required. Many types of roof
mounts appear in the literature, including Schletter hooks, Phonix
mounting structures and in-roof options (as opposed to on-roof).
Fixed ground mounting is also considered in some studies, with
various material options including woods and metals (Beylot et al.,
2014). Finally, both single-axis and dual-axis tracking options are
considered in the literature, which track the sun over the course of
the day to maximize exposure and increase productivity per day.
According to one study, even given all of the same conditions and
components otherwise, ground mounting results in a solar foot-
print of 53.5 g CO
2
-eq/kWh whereas tracking lowers the footprint
to 37.5 g CO
2
-eq/kWh, clearly a substantial difference (Beylot et al.,
2014). Regardless, the statistics compiled into Table 12 do suggest
that fixed ground mounting is generally much lower in terms of
GHG intensity than roof mounting, which are in turn slightly
better than tracking systems (though the sample of studies with
data on tracking was very small).
6.8. Calculation methods
Lastly, although not technically related to the “real”GHG
emissions intensity of a wind turbine or solar panel, the particular
methods utilized in each study were also a cause for variation.
Authors from our sample relied on various lifecycle techniques
including CML methods (named based upon its founding institu-
tion, the Centre for Environmental Studies at the University of
Leiden), IO (inputoutput), hybrid methods, International Orga-
nization of Standardization (ISO) methods, and so on. Further-
more, they relied on a variety of different software including
different versions of SimaPro and GaBi, as well as different
lifecycle and materials databases, such as the popular EcoInvent
Database. The best evidence that these different methods result in
differing wind estimates is represented in Wiedmann et al. (2011),
wherein process analysis, integrated hybrid analysis, and IO hybrid
analysis are examined. That study comes to three very different
conclusions ranging from 13.4 g CO
2
-eq/kWh to 29.7 g CO
2
-eq/
kWh, all stemming from the particular method used. In the PV
literature, none of the studies in our sample specifically addressed
this issue, though one article excluded for completeness, Zhai and
Williams (2010), contrasted process and hybrid lifecycle methods,
finding an end calculation difference of 8 g CO
2
/kWh, equivalent to
a 38.1% difference in emissions.
7. Conclusions
This study has screened 153 lifecycle studies of greenhouse gas
equivalent emissions for wind turbines and solar panels to identify
a subset of the 41 most relevant, current, peer-reviewed, original,
and complete assessments. It finds a range of emissions intensities
for each technology, from a low of 0.4 g CO
2
-eq/kWh to a high of
364.8 g CO
2
-eq/kWh for wind energy, with a mean value of
34.11 g CO
2
-eq/kWh. For solar energy, it finds a range of 1 g CO
2
-
eq/kWh to 218 g CO
2
-eq/kWh, where the mean value is
49.91 g CO
2
-eq/kWh. Thus, wind and solar energy are in no way
“carbon free”or “emissions free,”even though, as Table 13
indicates, they can certainly be called “low-carbon.”Based upon
these estimates, we make three conclusions.
The first, and perhaps most blatant conclusion, is that life-
cycle studies of greenhouse gas emissions associated with the
wind and solar energy lifecycles similartothosefornuclear
Table 12
Differences in greenhouse gas intensity for solar PV based on mounting.
Roof
mount
Ground
mount
n
Dual axis
tracking
Single axis
tracking
Mean 48.5 34.5 42.8 42.7
Median 33.8 26 42.8 42.7
n24 13 1 2
Mode 21, 30, 32 25 ––
Std. dev. 44.5 21.9 –7.4
High 217 92 42.8 47.9
Low 14 5 42.8 37.5
n
Includes any “fixed mounting”described in the literature not specified
as roof.
n = 41
n = 2
n = 4
n = 3
n = 1
n = 1
n = 4
n =3
n = 26
0
20
40
60
80
100
120
140
160
180
30 year25 year20 year15 year10 year5 year
g CO2-eq/kWh
PV
Wind
Linear trend
(PV)
Linear trend
(wind)
Fig. 10. Differences in greenhouse gas intensity for wind energy and solar PV based
on longevity.
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–24 4240
Author's personal copy
power (Sovacool, 2008)need to become more methodologi-
cally rigorous. Of the original 153 articles, 38% were studies that
failed to consider greenhouse gas emissions intensity when
considering lifecycle impacts. More than 25% of these 153 studies
were either outdated, non-peer reviewed, or unoriginal, and
another 10% did not consider all greenhouse gases. This left us with
only about one-quarter of the available literature. Even within this
smaller base of selective literature, the types of lifecycle stages and
the ways in which they were defined were dissimilar, and embo-
died varying assumptions related to a multitude of factors such as
resource inputs, manufacturing and fabrication, sizing and capacity,
and longevity, among others. Moreover, these studies raise a
pressing concern regarding energy storage. On the one hand,
storage can alleviate some of the intermittency issues that prevent
wind and solar from gaining a greater market share. On the other
hand, our analysis suggests that adding storage can increase the
GHG intensity of both solar PV and wind energy systems. So if the
choice is to be smaller amounts of wind/solar (without storage) and
more fossil fuels, or larger amounts of wind/solar (with storage) and
less fossil fuels then which option has the overall lower GHG
emissions? The current literature leaves this salient question all
but unaddressed.
Second, specific configurations of both wind and solar bring
with them particular greenhouse gas advantages and disadvan-
tages. A 2 MW wind turbine without battery backup and a 30 year
lifetime results in an incredibly low emissions profile of 0.4 g CO
2
-
eq/kWh. Yet a tiny 400 W, 30 m high, 1.17 m rotor, onshore wind
turbine with battery backup and a short 20 year lifetime results in
a high emissions profile of 364.8 g CO
2
-eq/kWh, approaching that
of natural gas. Similarly, a solar PV system produced without F-
gasses using an all renewable energy mix was found to have an
emissions intensity as low as 1 g CO
2
-eq/kWh, whereas a solar PV
system produced with F-gasses on a completely coal fired energy
mix without carbon capture and storage had an emissions inten-
sity of 218 g CO
2
-eq/kWh. These, along with a number of other
findings, suggest that the “best”solar and wind systems, those that
have the lowest lifecycle greenhouse gas emissions, are those with
the attributes characterized by Fig. 11.
Third, and perhaps most important, by looking at these
disparities, and drawing from these two conclusions, a number
Low Wind
GHG
Intensity
Increase
Hub Height
and Rotor
Diameter
Increase
Turbine
Capacity
Increase
Lifespan
Exclude
Battery
Backup
Use
Renewable
Energy
Mixes
Increase
Wind
Speed
Build
Offshore
Low Solar
PV GHG
Intensity
Thin-film
(CdTe) or
CdSe
QDPV
Increase
System
Capacity
Increase
Lifespan
Use
Renewable
Energy
Mixes
Increase
Irradiance
(Desert)
Ground
Mounting
Fig. 11. Low GHG attributes of wind energy and solar PV systems.
Table 13
Comparative lifecycle estimates for sources of electricity.
Technology Capacity/configuration/fuel Mean estimate (g Co
2
e/kWh)
Hydroelectric 3.1 MW, Reservoir 10
Biogas Anaerobic Digestion 11
Hydroelectric 300 kW, Run-of-River 13
Solar Thermal 80 MW, Parabolic Trough 13
Biomass Forest Wood Co-combustion with hard coal 14
Biomass Forest Wood Steam Turbine 22
Biomass Short Rotation Forestry Co-combustion with hard coal 23
Biomass Forest Wood Reciprocating Engine 27
Biomass Waste Wood Steam Turbine 31
Wind Various sizes and configurations 34
Biomass Short Rotation Forestry Steam Turbine 35
Geothermal 80 MW, Hot Dry Rock 38
Biomass Short Rotation Forestry Reciprocating Engine 41
Solar Photovoltaic Various sizes and configurations 50
Nuclear Various reactor types 66
Natural Gas (Conventional) Various combined cycle turbines 443
Natural Gas (Fracking) Combined cycle turbines using fuel from hydraulic fracturing 492
Natural Gas (LNG) Combined cycle turbines utilizing LNG 611
Fuel Cell Hydrogen from gas reforming 664
Diesel Various generator and turbine types 778
Heavy Oil Various generator and turbine types 778
Coal Various generator types with scrubbing 960
Coal Various generator types without scrubbing 1,050
Note: Wind and solar PV numbers taken from this study. Hydrofracking numbers taken from Hultman et al. (2011), who argue that shale gas has emissions 11% greater than
ordinary natural gas. All other numbers taken from Sovacool (2008).
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229–244 241
Author's personal copy
of important concepts are revealed about how to most effectively
utilize wind and PV to combat climate change. It would appear
that wind energy is generally a better option for bulk power, and
when it comes to this technology, size is key—bigger truly is better
(though not too large as to negate the benefits of decentralization).
Utility and merchant-power-plant sized turbines with larger rotors
and higher nameplate capacities, as well as those placed higher
and out to sea to take advantage of stronger wind speeds, are
generally the best performing options (from an emissions stand-
point). For solar PV, the GHG intensity benefits seem to lie in more
in the use of cadmium telluride, CdSe QDPV, and micromorph
technologies, sited in deserts, with ground mounting and possibly
single or dual-axis tracking. The literature also suggests that
battery and fuel cell electricity storage have a substantially
negative implication for emissions intensity of wind systems,
and despite the lack of information available for PV, the same
logical concerns apply, making grid connection without storage
possibly better options (from a greenhouse gas standpoint, again).
Better understanding, and researching, these sorts of factors will
be critical to enhancing the ability for wind energy and solar PV to
effectively mitigate greenhouse gas emissions.
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