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Assessing the lifecycle greenhouse gas emissions from solar PV and wind energy: A critical meta-survey


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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.
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Assessing the lifecycle greenhouse gas emissions from solar PV and
wind energy: A critical meta-survey
Daniel Nugent
, Benjamin K. Sovacool
Institute for Energy & the Environment, Vermont Law School, VT 05068-0444, USA
Center for Energy Technology, School of Business and Social Sciences, Aarhus University, AU-Herning, Birk Centerpark 15, DK-7400 Herning, Denmark
This article screens 153 lifecycle studies of wind and solar energy.
Wind energy emits 0.4 g CO
-eq/kWh to 364.8 g and a mean of 34.11 g.
Solar PV emits 1 g CO
-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
Solar photovoltaics (PV)
Wind energy
Lifecycle assessment
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 proles 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 inuen-
tial renewable energy advocate, once stated that [o]ur depen-
dence on fossil fuels amounts to global pyromania[a]nd the only
re 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 re
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 bestsample
of 41 articles evaluated, the average lifecycle greenhouse gas
emissions for wind energy were 34.1 g CO
-eq/kWh, whereas solar
PV averaged 49.9 g CO
-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:
Energy Policy
0301-4215/$ - see front matter &2013 Elsevier Ltd. All rights reserved.
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:, (B.K. Sovacool).
Energy Policy 65 (2014) 229244
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As that gure 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 sinkthat lowers the greenhouse gas prole for both types
of systems.
To make its case, the article proceeds as follows. It starts by
introducing readers to the specic 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 ndings 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
-equivalent emissions
per kilowatt-hour (CO
-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 nal products. Although details vary based upon the type of
PV module, for instance (thin lm, mono, poly, or multi-crystalline,
dye-sensitized, quantum dot, and so on), material cultivation
encompasses mining, rening and purication 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 nished electronics are also included.
Finally, the wiring, encapsulation and any other processes by
which the modules and electronics are fabricated and nished
(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 signicant 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 lters used to lubricate turbines.
2.4. Decommissioning
Decommissioning is the nal 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 berglass
blades are recyclable. Accordingly, the process carries with it some
signicant offsetting of future emissions.
3. Research methods and selection criteria
To ensure that only the bestpeer-reviewed scientic litera-
ture was selected, as many on-topic studies as possible were
collected by searching eight academic databasesJstor, 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 ltered the
literature to ensure that only the most relevant, modern, accurate
and original ndings were incorporated into this study. Fig. 2
-20% 0% 20% 40% 60% 80%
Cultivation and Fabrication
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) 22924 4230
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shows that, through this process, the application of ve selection
criteria whittled our sample down to only 41 of the beststudies.
The following subsections detail this selection process.
3.1. Relevance
The rst exclusionary step entailed removing a total of 58
articles based upon relevance. These studies, shown to the left in
Table 1, did not specically 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 nd 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
efciency 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 efciency, 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
ve year window.
Fig. 2. Selection process for determining the best lifecycle studies for wind and solar energy. Note: Articles excluded for relevancerefer to those articles that failed to
provide any lifecycle GHG intensity estimates. Those excluded by datesignies an article published prior to 2003. Those excluded for peer-reviewcould not be shown to
have undergone any type of review prior to publication. Those excluded for originalityrefer to articles which provided no original GHG intensity analysis and merely relied
on estimations contained in prior studies. Articles excluded for completenessonly considered CO
lifecycle emissions, not the full range of GHGs in terms of CO
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
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 9.716.5
Sorensen (1994) Wind, solar PV
Van de Vate (1997) Wind, solar PV
Voorspools et al. (2000) Wind, solar PV
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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 scientic
committee in order to pass this standard. In all, only one
conference report Noori et al. (2012) was unable to be
veried and was removed from the sample for not meeting this
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
-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 signicantly
unlike this study. A few very detailed studies done in conjunction
with the National Renewable Energy Laboratory's (NREL) Life
Cycle Harmonization Projectwere 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 nd a range or
average g CO
-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
Anal 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
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 ve
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 weighedequally; 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
ve continents specically, and including several studies that were
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
-eq/kWh and the
extremely high end of 364.8 g CO
-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
numbers reected 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
Arvesen and Hertwich (2012) Wind 634
Bensebaa (2011) Solar PV 30
Chaurey and Kandpal (2009) Solar PV
Dones et al. (2004) Wind 1020
Solar PV 3973
Dotzauer (2010) Wind 910
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, 3045,
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 2159
NREL (National Renewable Energy
Laboratory) (2012)
Solar PV 40
NREL (National Renewable Energy
Laboratory) (2013)
All electricity
Pacca et al. (2007) Solar PV 34.350
Padey et al. (2012) Wind 4.576.7
Peng et al. (2013) Solar PV 10.550
Raadal et al. (2011) Wind 17.5
Sherwani et al. (2010) Solar PV 15.6280
Tyagi et al. (2013) Solar PV 9.42820
Van der Meulen and Alsema (2011) Solar PV
Varun et al. (2009a) Wind 9.7123.7
Solar PV 53.4250
Varun et al. (2009b) Wind 16.5123.7
Solar PV 9.430 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
Garcia-Valverde et al. (2009) Solar PV 131
Ito et al. (2008) Solar PV 916
Ito et al. (2009) Solar PV 51.571
Ito et al. (2010) Solar PV 4354
Kleijn et al. (2011) Wind 15
Solar PV 60
Krauter and Ruther (2004) Solar PV 1175
Lee and Tzeng (2008) Wind 3.6
Lenzen and Wachsmann (2004) Wind 281
Li et al. (2012) Wind 69.9
McMonagle (2006) Solar PV 059
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 4.978.21
Zhai and Williams (2010) Solar PV 21
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Table 5
Total lifecycle GHG emissions and factors for 22 qualied wind energy studies.
Source Location Life
Hub height
Rotor diameter
Other assumptions Total estimate
(g CO
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
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
–– 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
-eq/kWh) 10.69
Manish et al. (2006) India - Onshore 18500 kW turbines –– 2003 global electricity mix, .1.3 capacity factor 1240
Martinez et al. (2009a) Munilla, Spain 20 Onshore 2MW turbine 70 80 6.58
Mithraratne (2009) Production UK, Installation New
20 Onshore 1.5 kW turbines 10 2 Roof mounted, .04 .064 capacity factor,
New Zealand electricity mix (224 g CO
5.56.3 m/s avg. wind speed
Oebels and Pacca (2013) North Eastern Brazil 20 Onshore 141.5 MW turbines 80 Brazilian electricity mix (64 g CO
-eq/kWh), .3425 capacity
factor, 7.8 m/s avg. wind speed
Padey et al. (2013) Europe - Onshore –– – 12.9
Pehnt (2006) Germany - Onshore 1.5 MW turbine –– 566 g CO
-eq/kWh electricity mix 11
Offshore 2.5 MW turbine –– 566 g CO
-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
Southern France 20 Onshore 4.5 MW turbines 124 113 15.8
Production Finland, Installation
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
40 oating 5 MW
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
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
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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
-eq/kWh and the high
end of 218 g CO
-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
-eq/kWh numbers reected 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
6. What causes the disparity in wind and solar estimates?
Though the tables and gures above do a satisfactory job
documenting the lifecycle emissions associated with wind energy
n = 16 n = 14 n = 12
n = 13
Cultivation and
Construction Operation Decommissioning
g CO2-eq/kWh
Fig. 3. Lifecycle greenhouse gas emissions for wind energy by lifecycle stage.
Table 6
Summary statistics of qualied 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 totalcolumn equals the mean for all lifecycle studies that made it past our screen, not necessarily those that broke emissions down by specic lifecycle stages.
nalso refers to number of estimates, not necessarily number of studies.
Table 7
Detailed statistics of qualied 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 ̄
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
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Table 8
Total lifecycle GHG emissions and factors for 23 qualied solar PV studies.
Source Location Life
Tech Mounting Assumptions Estimate
(g CO
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
30 (15
1700 CdTe Ground mount 9% efciency 25
Southern Europe 30 (15
1700 Ribbon-Si Roof mount 11.5% efciency 29.5
Mono-Si Roof mount 14% efciency 35
Multi-Si Roof mount 13.2% efciency 32
Beylot et al. (2014) - 30 1700 Multi-Si 301tilt, xed aluminum
5 MWp, 14% module efciency 53.5
301tilt, xed wood mount 5 MWp, 14% module efciency 38
301tilt, single axis tracking 5 MWp, 14% module ef ciency 37.5
301tilt, dual axis tracking 5 MWp, 14% module efciency 42.8
Bravi et al. (2011) Europe 20 1700 Micromorph 221roof mount 125 Wp module, 8.74% efciency,
513g CO2/kWh European electricity mix
Desideri et al. (2013) Sicily, Italy 30 16001800 Mono-Si 301tilt, ground mounted
single-axis tracking
13.85% module efciency, 2 MWp 47.9
de Wild-Scholten et al.
Southern Europe 30 (15
1700 Multi-Si on-roof Phonix mounting
11.4 kWp, 13.2% module efciency 38
on-roof Schletter roof hooks 11.4kWp, 13.2% module efciency 35.5
in-roof Schletter mounting
11.4 kWp, 13.2% module efciency 32
in-roof Schweizer mounting
11.4 kWp, 13.2% module efciency 32.5
ground Phonix mount 11.4 kWp, 13.2% module efciency 41
ground Springerville mount 11.4 kWp, 13.2% module efciency 37
Espinosa et al. (2011a) Manufacturing Denmark,
Installation Southern Europe
15 1700 Transparent organic polymer,
indium-tin-oxide (ITO)
- 2% module efciency, 2008 Denmark energy mix
(420.88 g CO
3% module efciency, 2008 Denmark energy mix
(420.88 g CO
Fthenakis and Alsema
Europe 30 1700 Multi-si On-roof mount european electricity mix 13.2% efciency 37
CdTe On-roof mount european electricity mix, 8% efciency 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% efciency 25
Fthenakis and Kim.
United States 30 1800 CdTe Ground mount 25 MWp, 9% efciency 24
Fthenakis et al. (2009b) Ohio, USA 1700 CdTe 10.9% efciency, US electricity mix
(750 g CO
Garcia-Valverde et al.
Southern Europe 15 1700 Organic/plastic 5% module efciency 109.84
Glockner et al. (2008) Europe 30 1700 Multi-Si On-roof mount Schletter
Siemens Si processing, 13.2% module efciency 30
Elkem Solar Si processing, 13.2% module efciency 23
Hondo (2005) Japan 30 Poly-Si On-roof mount 3 kWp, 0.15 capacity factor, 10% efciency 53.4
Hsu et al. (2012) Global 30 1700 c-Si 45
mono-Si 14% module efciency 40
Multi-Si 13.2% module efciency 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
-eq/kWh electricity mix 39110
Kannan et al. (2006) Singapore 25 1635 Mono-Si 2.7 kWp 217
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229244 235
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and solar PV systems from our bestsample 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 (oating 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 signicantly 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
proles, 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-lm 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
Tech Mounting Assumptions Estimate
(g CO
Aluminum/concrete roof
Kim et al. (2012) Global 30 2400 a-Si Ground mount 6.3% efciency 20
CdTe Ground mount 10.9% efciency 14
CIGS Ground mount 11.5% efciency 26
a-Si On-roof mount 6.3% efciency 21
CdTe On-roof mount 10.9% efciency 14
CIGS On-roof mount 11.5% efciency 27
Manish et al. (2006) India 20 –– 10-15% efciency 50130
Pehnt (2006) Germany 25 Poly SOG-Si 566 CO
-eq/kWh electricity mix 104
Querini et al. (2012) Global 30 1204 Mono-Si 45 degree xed mount 92
Reich et al. (2011) 30 1300 c-Si no F-gas emissions, renewable electricity mix
(1 g CO
-eq/kWh), 15% efciency
coal electricity mix without CCS
(1,000 g CO
-eq/kWh), 15% efciency
Sengul and Theis (2011) Europe 30 1700 CdSe QDPV Ground mount 14% efciency 5
Veltkamp and de Wild-
Scholten (2006)
Southern Europe 5 1700 Glass glass (DSC) dye
8% efciency 106.25
10 170 0 8% efciency 52.5
20 1700 8% efciency 17.5
n = 26 n = 26 n = 2
n = 5
Cultivation and
Construction Operation Decommissioning
g CO2-eq/kWh
Fig. 4. Lifecycle greenhouse gas emissions for solar PV by lifecycle stage.
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 22924 4236
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leading to different solar conversion efciencies in the nal product,
and thus an exceptional range of emissions possibilities for PV as a
wholestatistics reected in Table 11.Ta ble 11 shows mono-Si to
have the highest average estimated emissions and CdSe QDPV ranks
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 signicantvariationinthe
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 signicantly 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 qualied 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 totalcolumn equals the mean for all lifecycle studies that made it past our screen, not necessarily those that broke emissions down by specic lifecycle stages.
nalso refers to number of estimates, not necessarily number of studies.
Table 10
Detailed statistics of qualied 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) 33.895.31 5.1914.66 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
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229244 237
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example, Fleck and Huot (2009) nd a large 78.85 g CO
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 signicantly less on
dening 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 specically for transportation was that of
Querini et al. (2012), which found 6.3 g CO
-eq/kWh accounting
for 6.9% of the total emissions prole.
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 signicantly 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 signicantly 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 signicant dis-
parity to the location of manufacturing, noting that the Brazilian
electricity mix, being as low as 64 g CO
-eq/kWh (as much as eight
times lower than the global average), had a signicant effect on
their low overall calculation (7.1 g CO
-eq/kWh). This contrasts
with Pehnt (2006) which used a 566 g CO
-eq/kWh energy mix
and returned a 911 g CO
-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 nished modules. Some
energy mix assumptions made in the literature include a Danish
grid intensity of 420.88 g CO
-eq/kWh (Espinosa et al., 2011a) and
-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
-eq/kWh (for an all
renewable and nuclear mix) to 200 g CO
-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 renement are needed to create PV
grade silicate for some panels, whereas others such as CIGS may
not need silicates at all. Other inuential 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 nished panel
(Glockner et al., 2008). Accordingly, the amount of energy and
GHG emissions attributable to all of these processes can lead to
signicant variation.
6.4. Location
Emissions efciency 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 signication
spatial variation, both globally and locally, and also to temporal
variation, in terms of seasonal and daily uctuations. These factors
strongly inuence 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 airow. 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 uctuation in
overall CO
-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
yr, some in our sample went as low as 1100 kWh/m
/yr whereas
others assumed 2400 kWh/m
/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-lm 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) 22924 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
-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 oddlybecause PV is a modular technology that
is supposed to work the sameregardless of whether ten panels
or 100 panels are being used). There do appear to be economy of
scale advantages that larger PV installations benet from, possibly
due to efciency 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 inuencing 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 510 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 specically 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
1000 1250 1500 1750 2000 2250 2500
g CO2-eq/kWh
Irradiance (kWh/m2)
Average intensity
Linear trend
Fig. 6. Differences in greenhouse gas intensity for solar PV based on irradiance.
n = 4
n = 2
n = 2
n = 2
0.1 1 10 100 1000 10000
g CO2-eq/kWh
Nameplate Turbine Capacity (kW)
Average intensity
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 specicity.
n = 9 n = 3
n = 2
n = 4
n = 2
n = 2
g CO2-eq/kWh
Height / Diameter (m)
Hub height
Rotor diameter
Moving avg. (hub height)
Moving avg. (rotor
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 specicity.
n = 1
n = 4
n = 1
n= 6
n= 3
n= 1
1 10 100 1000 10000 100000
g CO2-eq/kWh
Installed System Capacity (kWp)
Average intensity
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) 229244 239
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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
-eq/kWh, 25 years decreases the mean
intensity to 28.53 g CO
-eq/kWh, and 30 years drops it to
25.33 g CO
The same trend is conrmed 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
-eq/kWh, whereas a 20 year lifetime saw emissions
drop to 17.5 g CO
-eq/kWhemphasizing 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
-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 inuencing 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
-eq/kWh, and Fleck and Huot (2009) found a
small wind turbine with battery backup to generate 364.83 g CO
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
-eq/kWh whereas tracking lowers the footprint
to 37.5 g CO
-eq/kWh, clearly a substantial difference (Beylot et al.,
2014). Regardless, the statistics compiled into Table 12 do suggest
that xed 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 realGHG
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
-eq/kWh to 29.7 g CO
kWh, all stemming from the particular method used. In the PV
literature, none of the studies in our sample specically addressed
this issue, though one article excluded for completeness, Zhai and
Williams (2010), contrasted process and hybrid lifecycle methods,
nding an end calculation difference of 8 g CO
/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 nds a range of emissions intensities
for each technology, from a low of 0.4 g CO
-eq/kWh to a high of
364.8 g CO
-eq/kWh for wind energy, with a mean value of
34.11 g CO
-eq/kWh. For solar energy, it nds a range of 1 g CO
eq/kWh to 218 g CO
-eq/kWh, where the mean value is
49.91 g CO
-eq/kWh. Thus, wind and solar energy are in no way
carbon freeor emissions free,even though, as Table 13
indicates, they can certainly be called low-carbon.Based upon
these estimates, we make three conclusions.
The rst, 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.
Dual axis
Single axis
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
Includes any xed mountingdescribed in the literature not specied
as roof.
n = 41
n = 2
n = 4
n = 3
n = 1
n = 1
n = 4
n =3
n = 26
30 year25 year20 year15 year10 year5 year
g CO2-eq/kWh
Linear trend
Linear trend
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) 22924 4240
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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 dened 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, specic congurations 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 prole of 0.4 g CO
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 prole of 364.8 g CO
-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
-eq/kWh, whereas a solar PV
system produced with F-gasses on a completely coal red energy
mix without carbon capture and storage had an emissions inten-
sity of 218 g CO
-eq/kWh. These, along with a number of other
ndings, suggest that the bestsolar 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
Hub Height
and Rotor
Low Solar
(CdTe) or
Fig. 11. Low GHG attributes of wind energy and solar PV systems.
Table 13
Comparative lifecycle estimates for sources of electricity.
Technology Capacity/conguration/fuel Mean estimate (g Co
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 congurations 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 congurations 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) 229244 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 keybigger truly is better
(though not too large as to negate the benets 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 benets 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.
Akyuz, E., Oktay, Z., Dincer, I., 2011. Energetic, environmental and economic aspects
of a hybrid renewable energy system: a case study. Int. J. Low-Carbon Technol.
6, 4454.
Alsema E.A., de Wild-Scholten, M.J., 2004. Environmental life cycle assessment of
advanced silicon solar cell technologies. In: 19th European Photovoltaic Solar
Energy Conference, Paris, France, June 711.
Alsema, E.A., de Wild-Scholten, M.J., Fthenakis, V.M, 2006. Environmental impacts
of PV electricity generationa critical comparison of energy supply options. In:
21st European Photovoltaic Solar Energy Conference, Dresden, Germany,
September 48.
Amor, M.B., Lesage, P., Pineau, P.O., Samson, R., 2010. Can distributed generation
offer substantial benets in a Northeastern American context? A case study of
small-scale renewable technologies using a life cycle methodology. Renewable
Sustainable Energy Rev. 14, 28852895.
Appleyard, D., 2009. Light cycle: recycling PV materials. Renewable Energy World
MarchApril, 2835.
Ardente, F., Beccali, G., Cellura, M., Brano, V.L., 2005. Life cycle assessment of a solar
thermal collector. Renewable Energy 30, 10311054.
Ardente, F., Beccali, G., Cellura, M., Brano, V.L., 2008. Energy performances and life
cycle assessment of an Italian wind farm. Renewable Sustainable Energy Rev.
12, 20 0217.
Arvesen, A., Hertwich, E.G., 2012. Assessing the life cycle environmental impacts of
wind power: a review of present knowledge and research needs. Renewable
Sustainable Energy Rev. 16, 59946006.
Barrientos Sacari, J.L., 2007. Environmental and Energy Impacts of Grid-connected
Photovoltaic Systems in Massachusetts. ProQuest Dissertations and Thesis,
Sustainability Science Abstracts.
Belfkira, R., Barakat, G., Nichita, C., 2008. Sizing optimization of a stand-alone
hybrid power supply unit: wind/PV system with battery storage. Int. Rev. Electr.
Eng. 3 (5).
Bensebaa, F., 2011. Solar based large scale power plants: what is the best option?
Prog. Photovoltaics: Res. Appl. 19, 240246.
Beylot, A., Payet, J., Puech, C., Adra, N., Jacquin, P., Blanc, I., Beloin-Saint-Pierre, D.,
2014. Environmental impacts of large-scale grid-connected ground-mounted
PV installations. Renewable Energy 61, 26.
Blanc, I., Guermont, C., Gschwind, B., Menard, L., Calkoen, C., Zelle, H., 2012. Web
tool for energy policy decision-making through geo-localized LCA models: a
focus on offshore wind farms in Northern Europe. In: EnviroInfo 201226th
International Conference on Informatics for Environmental Protection.
Branker, K., Jeswiet, J., Kim, I.Y., 2011. Greenhouse gases emitted in manufacturing a
producta new economic model. CIRP Ann. Manuf. Technol. 60, 5356.
Bravi, M., Parisi, M.L., Tiezzi, E., Basosi, R., 2011. Life cycle assessment of a
micromorph photovoltaic system. Energy 36, 42974306.
Browne, G., 2010. There is no Free Lunch (Except for Wind Farms Owners). 111 .
Burger, J., Gochfeld, M., 2012. A conceptual framework evaluating ecological
footprints and monitoring renewable energy: wind, solar, hydro, and geother-
mal. J. Energy Power Eng. 4, 303314.
Chaurey, A., Kandpal, T.C., 2009. Carbon abatement potential of solar home systems in
India and their cost reduction due to carbon nance. Energy Policy 37, 115125.
Chel, A., Tiwari, G.N., Chandra, A., 2009. Simplied method of sizing and life cycle cost
assessment of building integrated photovoltaic system. Energy Build. 41, 11721180.
Chen, G.Q., Yang, Q., Zhao, Y.H., 2011. Renewability of wind power in China: a case
study of nonrenewable energy cost and greenhouse gas emission by a plant in
Guangxi. Renewable Sustainable Energy Rev. 15, 23222329.
Connolly, K., 2008. Endless Possibility. The Guardian, April 15, 2008. Web.
Crawford, R.H., 2009. Life cycle energy and greenhouse emissions analysis of wind
turbines and the effect of size on energy yield. Renewable Sustainable Energy
Rev. 13, 26532660.
Dale, M., 2013. A comparative analysis of energy costs of photovoltaic, solar
thermal, and wind electricity generation technologies. Appl. Sci. 3 (2), 325337.
de Wild-Scholten, M.J., Alsema, E.A., ter Horst, E.W., Fthenakis, V.M., 20 06. A cost
and environmental impact comparison of grid-connected rooftop and ground-
based PV systems. In: 21st European Photovoltaic Solar Energy Conference,
Dresden, Germany.
Delucchi, M.A., Jacobson, M.Z., 2011. Providing all global energy with wind, water,
and solar power, Part II: Reliability, system and transmission costs, and policies.
Energy Policy 39, 1170119 0.
Desideri, U., Zepparelli, F., Morettini, V., Garroni, E., 2013. Comparative analysis of
concentrating solar power and photovoltaic technologies: technical and envir-
onmental evaluations. Appl. Energy 102, 765784.
Dolan, S.L., Heath, G.A., 2012. Life cycle greenhouse gas emissions of utility-scale
wind power. J. Ind. Ecol. 16, 136154.
Dones, R., Heck, T., Hirschberg, S., 2004. Greenhouse gas emissions from energy
systems, comparison and overview. Encycl. Energy 3, 7795.
Dotzauer, E., 2010. Greenhouse gas emissions from power generation and con-
sumption in a Nordic perspective. Energy Policy 38, 701704.
Dufo-Lopez, R., Bernal-Agustin, J.L., Yusta-Loyo, J.M., Dominguez-Navarro, J.A.,
Ramirez-Rosado, I.J., Lujano, J., Aso, I., 2011. Multi-objective optimization
minimizing cost and life cycle emissions of stand-alone PVwinddiesel
systems with batteries storage. Appl. Energy 88, 40334041.
Espinosa, N., Garcia-Valverde, R., Krebs, F.C., 2011a. A life cycle analysis of polymer
solar cell modules prepared using roll-to-roll methods under ambient condi-
tions. Solar Energy Mater. Solar Cells 95, 12931302.
Espinosa, N., Garcia-Valverde, R., Krebs, F.C., 2011b. Life-cycle analysis of product
integrated polymer solar cells. Energy Environ. Sci. 4, 15471557.
Espinosa, N., Hosel, M., Angmo, D., Krebs, F.C., 2012. Solar cells with one-day energy
payback for the factories of the future. Energy Environ. Sci. 5, 51175132.
Evans, A., Strezov, V., Evans, T.J., 2009. Assessment of sustainability indicators for
renewable energy technologies. Renewable Sustainable Energy Rev. 13,
Fleck, B., Huot, M., 2009. Comparative life-cycle assessment of a small wind turbine
for residential off-grid use. Renewable Energy 34, 26882696.
Fthenakis, V., Alsema, E., 2006. Photovoltaics energy payback times, greenhouse gas
emissions and external costs: 2004-early 2005 status. Prog. Photovoltaics Res.
Appl. 14, 275280.
Fthenakis, V., Wang, W., Kim, H.C., 2009a. Life cycle inventory analysis of the
production of metals used in photovoltaics. Renewable Sustainable Energy Rev.
13, 4 93517.
Fthenakis, V.M., 2004. Life cycle impact analysis of cadmium in CdTe PV production.
Renewable Sustainable Energy Rev. 8, 303334.
Fthenakis, V.M., Kim, H.C., Alsema, E., 2008. Emissions from photovoltaic life cycles.
Environ. Sci. Technol. 42, 21682174.
Fthenakis, V.M., Kim, H.C., 2011. Photovoltaics: life-cycle analyses. Solar Energy 85,
Fthenakis, V.M., Kim, H.C., Held, M., Raugei, M., Krones, J., 2009b. Update of PV
energy payback times and life-cycle greenhouse gas emissions. In: 24th
European Photovoltaic Solar Energy Conference, 2125 September 2009.
Fthenakis V.M., Kim. H.C., 2006. Energy use and greenhouse gas emissions in the
life cycle of CdTe Photovoltaics. In: Material Research Society Symposium Proc.
895, 6.16.6.
Garcia-Valverde, R., Cherni, J.A., Urbina, A., 2010. Life cycle analysis of organic
photovoltaic technologies. Prog. Photovoltaics Res. Appl. 18, 535558.
Garcia-Valverde, R., Miguel, C., Martinez-Bejar, R., Urbina, A., 2009. Life cycle
assessment study of a 4.2 kW
stand-alone photovoltaic system. Solar Energy
83, 14341445 .
Georgakellos, D.A., 2012. Climate change external cost appraisal of electricity
generation systems from a life cycle perspective: the case of Greece. J. Cleaner
Prod. 32, 124140.
Glockner, R., Odden, J.O., Halvorsen, G., Tronstad, R., de Wild-Scholten, M.J., 2008.
Environmental life cycle assessment of the Elkem Solar metallurgical process
route to solar grade silicon with focus on energy consumption and greenhouse
gas emissions. Silicon Chem. Solar Ind. 9, 18.
Goralczyk, M., 2003. Life-cycle assessment in the renewable energy sector. Applied
Energy 75, 205211.
Graebig, M., Bringezu, S., Fenner, R., 2010. Comparative analysis of environmental
impacts of maizebiogas and photovoltaics on a land use basis. Solar Energy 84,
Granovskii, M., Dincer, I., Rosen, M.A., 2007. Greenhouse gas emissions reduction by
use of wind and solar energies for hydrogen and electricity production:
economic factors. Int. J. Hydrogen Energy 32, 927931.
Guezuraga, B., Zauner, R., Polz, W., 2012. Life cycle assessment of two different
2 MW class wind turbines. Renewable Energy 37, 3744.
Gustitus, S.A., 2012. Life Cycle Assessment of Taller Wind Turbines with Four
Different Tower Designs. Wind Energy Science, Engineering and Policy:
National Science Foundation Research Experiences for Undergraduates, 2/1
Hardisty, P.E., Clark, T.S., Hynes, R.G., 2012. Life cycle greenhouse gas emissions
from electricity generation: a comparative analysis of Australian energy
sources. Energies 5, 872897.
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 22924 4242
Author's personal copy
Himri, Y., Rehman, S., Draoui, B., Himri, S., 2008. Wind power potential assessment
for three locations in Algeria. Renewable Sustainable Energy Rev. 12,
Hondo, Hiroki, 2005. Life cycle GHG emission analysis of power generation
systems: Japanese case. Energy 30, 20422056.
Hsu, D.D., O'Donoughue, P., Fthenakis, V., Heath, G.A., Kim, H.C., Sawyer, P., Choi, J.,
Turney, D., 2012. Life cycle greenhouse gas emissions of crystalline silicon
photovoltaic electricity generation. J. Ind. Ecol. 16, 122135.
Huang, B., Yang, H., Mauerhofer, V., Guo, R., 2012. Sustainability assessment of low
carbon technologies-case study of the building sector in China. J. Cleaner Prod.
32, 244250.
Huber, W., Kolb, G., 1995. Life cycle analysis of silicon-based photovoltaic systems.
Solar Energy 54, 153163.
Hultman, Nathan, Rebois, Dylan, Scholten, Michael, Ramig, Christopher, 2011. The
greenhouse impact of unconventional gas for electricity generation. Environ.
Res. Lett 6 (044008), 9.
Ito, M., Kato, K., Komoto, K., Kichimi, T., Kurokawa, K., 2008. A comparative study on
cost and life-cycle analysis for 100 MW very large-scale PV (VLS-PV) systems in
deserts using m-Si, a-Si, CdTe, and CIS modules. Prog. Photovoltaics Res. Appl.
16, 1730.
Ito, M., Komoto, K., Kurokawa, K, 2010. Life-cycle analyses of very-large scale PV
systems using six types of PV modules. Curr. Appl. Phys. 10, S27S273.
Ito, M., Komoto, K., Kurokawa, K., 2009. A comparative LCA study on potential of
very-large scale PV systems in Gobi Desert. In: Photovoltaic Specialists Con-
ference, Tokyo, Japan.
Jacobson, M.Z., Delucchi, M.A., 2011. Providing all global energy with wind, water,
and solar power, Part I: Technologies, energy resources, quantities and areas of
infrastructure, and materials. Energy Policy 39, 1154116 9.
Jungbluth, N., 2005. Life cycle assessment of crystalline photovoltaics in the Swiss
ecoinvent database. Prog. Photovoltaics Res. Appl. 13, 429446.
Kabir, R., Rooke, B., Dassanayake, M., Fleck, B.A., 2012. Comparative life cycle
energy, emission, and economic analysis of 100 kW nameplate wind power
generation. Renewable Energy 37, 133141.
Kaldellis, J.K., Zarakis, D., Stavropoulou, V., Kaldelli, El., 2012. Optimum wind-and
photovoltaic-based stand-alone systems on the basis of life cycle energy
analysis. Energy Policy 50, 345357.
Kammen, D., 2011. An Assessment of the Environmental Impacts of Concentrator
Photovoltaics and Modeling of Concentrator Photovoltaic Deployment Using
the SWITCH Model.
Kannan, R., Leong, K.C., Osman, R., Ho, H.K., 2007. Life cycle energy, emissions and
cost inventory of power generation technologies in Singapore. Renewable
Sustainable Energy Rev. 11, 702715.
Kannan, R., Leong, K.C., Osman, R., Ho, H.K., Tso, C.P., 2006. Life cycle assessment
study of solar PV systems: an example of a 2.7 kW
distributed solar PV system
in Singapore. Solar Energy 80, 555563.
Kato, K., Hibino, T., Komoto, K., Ihara, S., Yamamoto, S., Fujihara, H., 2001. A life-
cycle analysis on thin-lm CdS/CdTe PV modules. Solar Energy Mater. Solar
Cells 67, 279287.
Katzenstein, W., Apt, J., 2009. Air emissions due to wind and solar power. Environ.
Sci. Technol. 43, 253258.
Kemmoku, Y., Ishikawa, K., Nakagawa, S., Kawamoto, T., Sakakibara, T., 2002. Life
Cycle CO
Emissions of a Photovoltaic/Wind/Diesel Generating System.
Kenny, R., Law, C., Pearce, J.M., 2010. Towards real energy economics: energy policy
driven by life-cycle carbon emission. Energy Policy 38, 19691978 .
Khan, F.I., Hawboldt, K., Iqbal, M.T., 2005. Life cycle analysis of windfuel cell
integrated system. Renewable Energy 30, 157177.
Kim, H.C., Fthenakis, V., Choi, J., Turney, D., 2012. Life cycle greenhouse gas
emissions of thin-lm photovoltaic Electricity generation. J. Ind. Ecol. 16,
Kleijn, R., van der Voet, E., Kramer, G.J., van Oers, L., van der Giesen, C., 2011. Metal
requirements of low-carbon power generation. Energy 36, 54605648.
Krauter, S., Ruther, R., 2004. Considerations for the calculation of greenhouse gas
reduction by photovoltaic solar energy. Renewable Energy 29, 345355.
Kreiger, M.A., Shonnard, D.R., Pearce, J.M., 2013. Life cycle analysis of silane
recycleing in amorphous silicon-based solar photovoltaic manufacturing.
Resour. Conserv. Recycl. 70, 4449.
Kreith, F., Norton, P., Brown, D., 1990. A comparison of CO
emissions from fossil
and solar power plants in the United States. Energy 15, 11811198 .
Kubiszewski, I., Cleveland, C.J., Endres, P.K., 2010. Meta-analysis of net energy return
for wind power systems. Renewable Energy 35, 218225.
Lee, Y.M., Tzeng, Y.E., 2008. Development and life-cycle inventory analysis of wind
energy in Taiwan. J. Energy Eng., 5357 .
Lenzen, M., Munksgaard, J., 2002. Energy and CO
life-cycle analyses of wind
turbinesreview and applications. Renewable Energy 26, 339362.
Lenzen, M., Wachsmann, U., 2004. Wind turbines in Brazil and Germany: an
example of geographical variability in life-cycle assessment. Appl. Energy 77,
119 130.
Li, X., Feng, K., Siu, Y.L., Hubacek, K., 2012. Energy-water nexus of wind power in
China: the balancing act between CO
emissions and water consumption.
Energy Policy 45, 440448.
Limmeechokchai, B., Suksuntornsiri, P., 2007. Embedded energy and total green-
house gas emissions in nal consumptions within Thailand. Renewable
Sustainable Energy Rev. 11, 259281.
Lindstad, H., Asbjornslett, B.E., Stromman, A.H., 2011. Reductions in greenhouse gas
emissions and cost by shipping at lower speeds. Energy Policy 39, 3456346 4.
Lundahl, L., 1995. Impacts of climate change on renewable energy in Sweden.
Ambio 24, 2832.
Mallia, E., Lewis, G., 2013. Life cycle greenhouse gas emissions of electricity generation
in the province of Ontario, Canada. Int. J. Life Cycle Assess. 18, 377391.
Manish, S., Pillai, I.R., Banerjee, R., 2006. Sustainability analysis of renewables for
climate change mitigation. Energy Sustainable Dev. 10, 2536.
Marimuthu, C., Kirubakaran, V., 2013. Carbon payback period for solar and wind
energy project installed in India: a critical review. Renewable Sustainable
Energy Rev. 23, 8090.
Martinez, E., Sanz, F., Pellegrini, S., Jimenez, E., Blaco, J., 2009a. Life-cycle assess-
ment of a 2-MW rated power wind turbine: CML method. Int. J. Life Cycle
Assess. 14, 5263.
Martinez, E., Jimenez, E., Blanco, J., Sanz, F., 2010. LCA sensitivity analysis of a multi-
megawatt wind turbine. Appl. Energy 87, 22932303.
Martinez, E., Sanz, F., Pellegrini, S., Jimenez, E., Blanco, J., 2009b. Life-cycle
assessment of a multi-megawatt wind turbine. Renewable Energy 34, 667673.
Martinez, P.E., Pasquevich, D.M., Eliceche, A.M., 2012. Operation of a national
electricity network to minimize life cycle greenhouse gas emissions and cost.
Int. J. Hydr. Energy 37, 1478614795.
Mason, J.E., Fthenakis, V.M., Hansen, T., Kim, H.C., 2006. Energy payback and life-
cycle CO
emissions of the BOS in an optimized 3.5 MW PV installation. Prog.
Photovoltaics Res. Appl. 14, 179190 .
Matsuhashi, R., Ishitani, H., 2000. Evaluation of energy technologies to realize
sustainable systems. Electr. Eng. Jpn 130, 2531.
McCubbin, D., Sovacool, B., 2013. Quantifying the health and environmental
benets of wind power to natural gas. Energy Policy 53, 429441.
McMonagle, R., 2006. The Environmental Attributes of Solar PV in the Canadian
Context. Canadian Solar Industries Association: Environmental Attributes of PV
in Canada. (2.2).
Mendes, R.P., Goncalves, L.C., Gaspar, P.D., 2011. Contribution for a better under-
standing of the technological sustainability in electrical energy production
through photovoltaic cells. J. Energy Power Eng. 5, 542547.
Mithraratne, N., 2009. Roof-top wind turbines for microgeneration in urban houses
in New Zealand. Energy Build. 41, 10131018.
Mohr, N., Meijer, A., Huijbregts, M.A.J., Reijnders, L., 2009. Environmental impact of
thin-lm GaInP/GaAs and multicrystalline silicon solar modules produced with
solar electricity. Int. J. Life Cycle Assess. 14, 225235.
Muller, S., Brown, A., Olz, S., 2011. Renewable Energy: Policy Considerations for
Deploying Renewables. International Energy Agency.
Nandi, S.K., Ghosh, H.R., 2010a. Prospect of wind-PV-hybrid power system as an
alternative to grid extension in Bangladesh. Energy 35, 30403047.
Nandi, S.K., Ghosh, H.R., 2010b. Techno-economical analysis of off-grid hybrid
systems at Kutubdia Island, Bangladesh. Energy Policy 38, 976980.
Noori, M., Kucukvar, M., Tatari, O., 2012. Environmental footprint analysis of on-
shore and off-shore wind energy technologies. In: 2012 IEEE International
Symposium on Sustainable Systems and Technology.
Norton, B., Eames, P.C., Lo, S.N.G., 1998. Full-energy-chain analysis of greenhouse
gas emissions for solar thermal electric power generation systems. Renewable
Energy 15, 131136 .
NREL (National Renewable Energy Laboratory), 2012. Life Cycle Greenhouse Gas
Emissions from Solar Photovoltaics. NREL Publication.
NREL (National Renewable Energy Laboratory), 2013. Life Cycle Greenhouse Gas
Emissions from Electricity Generation. NREL Publication.
Oebels, K.B., Pacca, S., 2013. Life cycle assessment of an onshore wind farm located
at the northeastern coast of Brazil. Renewable Energy 53, 6070.
Oke, S., Kemmoku, Y., Takikawa, H., Sakakibara, T., 2008. Inuence of system
operation method on CO
emissions of PV/solar/heat/cogeneration system.
Electr. Eng. Jpn. 164, 5463.
Ou, W., Xiaoyu, Y., Zhang, X., 2011. Life-cycle energy consumption and greenhouse
gas emissions for electricity generation and supply in China. Appl. Energy 88,
Pacca, S., Sivaraman, D., Keoleian, G.A., 2007. Parameters affecting the life cycle
performance of PV technologies and systems. Energy Policy 35, 33163326.
Padey, P., Blanc, I., Le Boulch, D., Zhao, X., 2012. A simplied life cycle approach for
assessing greenhouse gas emissions of wind electricity. J. Ind. Ecol. 16, S28S38.
Padey, P., Girard, R., le Boulch, D., Blanc, I., 2013. From LCAs to simplied models: a
generic methodology applied to wind power electricity. Environ. Sci. Technol.
47, 12311238.
Pearce, J.M., 2002. Photovoltaicsa path to sustainable futures. Futures 34,
Pehnt, M., Oeser, M., Swider, D.J., 2008. Consequential environmental system
analysis of expected offshore wind electricity production in Germany. Energy
33, 747759.
Pehnt, M., 2006. Dynamic life cycle assessment (LCA) of renewable energy
technologies. Renewable Energy 31, 5571.
Peng, J., Lu, L., Yang, H., 2013. Review on life cycle assessment of energy payback
and greenhouse gas emission of solar photovoltaic systems. Renewable
Sustainable Energy Rev. 19, 255274.
Pieragostini, C., Mussati, M.C., Aguirre, P., 2012. On process optimization consider-
ing LCA methodology. J. Environ. Manage. 96, 4354.
Querini, F., Dagostino, S., Morel, S., Rousseaux, P., 2012. Greenhouse gas emissions
of electric vehicles associated with wind and photovoltaic electricity. Energy
Procedia 20, 391401.
Raadal, H.L., Gagnon, L., Modahl, I.S., Hanssen, O.J., 2011. Life cycle greenhouse gas
(GHG) emissions from the generation of wind and hydro power. Renewable
Sustainable Energy Rev. 15, 34173422.
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 229244 243
Author's personal copy
Rashedi, A., Sridhar, I., Tseng, K.J., 2012. Multi-objective material selection for wind
turbine blade and tower: Ashby's approach. Mater. Des. 37, 521532.
Raugei, M., Frankl, P., 2009. Life cycle impacts and costs of photovoltaic systems:
current state of the art and future outlooks. Energy 34, 392399.
Reich, N.H., Alsema, E.A., van Sark, W.G.J.H.M., Turkenburg, W.C., Sinke, W.C., 2011.
Greenhouse gas emissions associated with photovoltaic electricity from crystal-
line silicon modules under various energy supply options. Prog. Photovoltaics
Res. Appl. 19, 603613.
Rubio Rodriguez, M.A., De Ruyck, J., Roque Diaz, P., Verma, V.K., Bram, S., 2011. An
LCA based indicator for evaluation of alternative energy routes. Appl. Energy
88, 630635.
Schleisner, L., 2000. Life cycle assessment of a wind farm and related externalities.
Renewable Energy 20, 279288.
Sengul, H.S., Theis, T.L., 2011. An environmental impact assessment of quantum dot
photovoltaics (QDPV) from raw material acquisition through use. J. Cleaner
Prod. 19, 2131.
Sherwani, A.F., Usmani, J.A., Varun, Siddhartha, 2010. Life cycle assessment of solar
PV based electricity generation systems: a review. Renewable Sustainable
Energy Rev. 14, 540544.
Sherwani, A.F., Usmani, J.A., Varun, Siddhartha, 2011. Life cycle assessment of
50 kW
grid connected solar photovoltaic (SPV) system in India. Int. J. Energy
Environ. 2, 4956.
Silva, C.G., 2010. Renewable energies: choosing the best option. Energy 35,
Sioshansi, F.P., 2009. Carbon constrained: the future of electricity generation. Electr.
J., 6474.
Songlin, T., Xiliang, Z., Licheng, W., 2011. Life cycle analysis of wind power: a case of
Fuzhou. Energy Procedia 5, 18471851.
Sorensen, B., 1994. Life-cycle analysis of renewable energy systems. Renewable
Energy 5, 12701277.
Sovacool, BK., 2008. Valuing the greenhouse gas emissions from nuclear power: a
critical survey. Energy Policy 36 (8), 29402953.
Sumper, A., Robledo-Garcia, M., Villafala-Robles, R., Bergas-Jane, J., Andres-Peiro,
J., 2011. Life-cycle assessment of a photovoltaic system in Catalonia (Spain).
Renewable Sustainable Energy Rev. 15, 38883896.
3 Tier Inc., 2011a. Global Mean Solar Irradiance. 3 Tier: Renewable Energy Risk
3 Tier Inc., 2011b. Global Mean Wind Speed at 80 m. 3 Tier: Renewable Energy Risk
Tokimatsu, K., Kosugi, T., Asami, T., Williams, E., Kaya, Y., 2006. Evaluation of
lifecycle CO
emissions from the Japanese electric power sector in the 21st
century under various nuclear scenarios. Energy Policy 34, 833852.
Tremeac, B., Meunier, F., 2009. Life cycle analysis of 4.5 MW and 250 W wind
turbines. Renewable Sustainable Energy Rev. 13, 21042110.
Tripanagnostopoulos, Y., Souliotis, M., Battisti, R., Corrado, A., 2005. Energy, cost
and LCA results of PV and hybrid PV/T solar systems. Prog. Photovoltaic Res.
Appl. 13, 235250.
Tyagi, V.V., Rahim, N.A.A., Rahim, N.A., Selvaraj, J.A.L., 2013. Progress in solar PV
technology: research and achievement. Renewable Sustainable Energy Rev. 20,
Vadirajacharya, Katti, P.K., 2012. Rural electrication through solar and wind hybrid
system: a self sustained grid free electric power source. Energy Procedia 14,
Van de Vate, J.F., 1997. Comparison of energy sources in terms of their full energy
chain emission factors of greenhouse gases. Energy Policy 25, 16.
Van der Meulen, R., Alsema, E., 2011. Life-cycle greenhouse gas effects of introdu-
cing nanocrystalline materials in thin-lm silicon solar cells. Prog. Photovol-
taics Res. Appl. 19, 453463.
Varun, Bhat, I.K., Prakash, R., 2009a. LCA of renewable energy for electricity
generation systemsa review. Renewable Sustainable Energy Rev. 13,
Varun, Prakash, R., Bhat, I.K., 2009b. Energy, economics and environmental impacts
of renewable energy systems. Renewable Sustainable Energy Rev. 13,
Veltkamp, A.C., de Wild-Scholten, M.J., 2006. Dye sensitized solar cells for large-
scale photovoltaics; the determination of environmental performances. Renew-
able Energy, Chiba, Japan.
Velychko, O., Gordiyenko, T., 2009. The use of guide to the expression of uncertainty
in measurement for uncertainty management in National Greenhouse Gas
Inventories. Int. J. Greenhouse Gas Control 3, 514517 .
Voorspools, K.R., Brouwers, E.A., D'haeseleer, W.D., 200 0. Energy content and
indirect greenhouse gas emissions embedded in emission-free' power plants:
results for the Low Countries. Appl. Energy 67, 307330.
Vuc, G.H., Borlea, I., Jigoria-Oprea, D., Vintan, M., 2011. Optimal energy storage
capacity for a grid connected hybrid wind-photovoltaic generation system.
J. Sustainable Energy 2 (4).
Wagner, H.J., Baack, C., Eickelkamp, T., Epe, A., Lohmann, J., Troy, S., 2011. Life cycle
assessment of the offshore wind farm alpha ventus. Energy 36, 24592464.
Wang, Y., Sun, T., 2012. Life cycle assessment of CO
emissions from wind power
plants: methodology and case studies. Renewable Energy 43, 3036.
Weinzettel, J., Reenaas, M., Solli, C., Hertwich, E.G., 2009. Life cycle assessment of a
oating offshore wind turbine. Renewable Energy 34, 742747 .
Weisser, D., 2007. A guide to life-cycle greenhouse gas (GHG) emissions from
electric supply technologies. Energy 32, 15431559.
Whittington, H.W., 2002. Electricity generation: options for reduction in carbon
emissions. Philos. Trans. Math. Phys. Eng. Sci. 360, 16531668.
Wiedmann, T.O., Suh, S., Feng, K., Lenzen, M., Acquaye, A., Scott, K., Barrett, J.R., 2011.
Applicaation of hybrid life cycle approaches to emerging energy technologiesthe
case of wind power in the UK. Environ. Sci. Technol. 45, 59005907.
Yang, Q., Chen, G.Q., Zhao, Y.H., Chen, B., Li, Z., Zhang, B., Chen, Z.M., Chen, H., 2011.
Energy cost and greenhouse gas emissions of a Chinese wind farm. Procedia
Environ. Sci. 5, 2528.
Zhai, P., Williams, E.D., 2010. Dynamic hybrid life cycle assessment of energy and
carbon of multicrystalline silicon photovoltaic systems. Environ. Sci. Technol.
44, 79507955.
Zhai, Q., Cao, H., Zhao, X., Yuan, C., 2011. Cost benet analysis of using clean energy
supplies to reduce greenhouse gas emissions of global automotive manufacturing.
Energies 4, 14781494.
Zimmermann, T., Gößling-Reisemanna, S., 2012. Inuence of site specic para-
meters on environmental performance of wind energy converters. Energy
Procedia 20, 402413.
D. Nugent, B.K. Sovacool / Energy Policy 65 (2014) 22924 4244
... Therefore, the total emissions over time in all phases of the lifespan of solar power systems from production to disposal or decommissioning should be considered. Nugent and Sovacool (2014) demonstrated the breakdown of PV's total lifecycle greenhouse gas emissions in percentages, where fabrication or manufacturing were reported to have maximum emission contributions of 71.3% [75]. ...
... Therefore, the total emissions over time in all phases of the lifespan of solar power systems from production to disposal or decommissioning should be considered. Nugent and Sovacool (2014) demonstrated the breakdown of PV's total lifecycle greenhouse gas emissions in percentages, where fabrication or manufacturing were reported to have maximum emission contributions of 71.3% [75]. ...
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Fuel cells are essential components of a large portfolio for developing a competitive, secure, and sustainable clean energy economy as they possess the ability to efficiently convert a variety of fuels into electricity. They convert chemical energy from fuels into electricity through chemical reactions with an oxidizing agent. Fuel cells are highly efficient and can produce electricity with very little pollution. They are used in a variety of applications, including powering buildings and vehicles, and as a backup power source. However, the infrastructure for fuel cells is still not fully developed and the cost of fuel cells is currently high, hindering their widespread adoption. This article discusses various advanced fuel cell types with descriptions of their working principles and applications. It provides some insights on the requirements of solar-derived chemical fuel cells as well as some novel materials for the fabrication of solar-derived chemical fuel cells. Discussions on the limitations of solar-derived fuel cells were provided in relation to environmental hazards involved in the use of these cells.
... The main services that a smart home can offer are convivence, energy utilization, secure environment, and health assistance [2]. By living in a world of devices, recent research also considered the management of municipal solid waste for a better recycling [3], renewable energy from solar panels [4], and in the healthcare domain, namely illnesses assessment, such as Alzheimer [5]. ...
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A smart home can be considered a place of residence that enables the management of appliances and systems to help with day-today life by automated technology. In the current paper is described a prototype that simulates a context-aware environment, developed in a designed smart home. The smart home environment has been simulated using three agents and five locations in a house. The context-aware agents behave based on predefined rules designed for daily activities. Our proposal aims to reduce operational cost of running devices. In the future, monitors of health aspects belonging to home residents will sustain their healthy life daily.
... Wind turbine (WT) is quickly becoming a favorite renewable power source, thanks to a set of merits, such as availability and eco-friendly nature. The wind energy conversion system (WECS) allows reducing greenhouse gas emissions [1][2][3]. The WECS based on a doubly fed induction generator (DFIG) is now the most frequently utilized in wind farms; it can operate in different modes with variable and wide speed range. ...
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The purpose of this study is to enhance the accuracy of direct power/torque control (DPC/DTC) applied to back-to-back converters supplying a doubly fed induction generator (DFIG) based wind power system. Two solutions are proposed. The first one is to increase the degree of freedom of the DTC and DPC control by implementing three-level back-to-back converters. Fuzzy logic control is the second solution to enhance the performances of both conventional direct power/torque control, leading in a decrease of the DFIG's torque/flux ripples and the active/reactive powers ripples supplied by the grid side converter, consequently, reduce the grid currents' total harmonic distortion (THD). The MATLAB/Simulink environment is used to evaluate the wind power generation system performances. The collected findings show that the fuzzy direct control (FDC) technique outperforms conventional direct control (CDC) when used for two-level back-to-back converters.
... For example, Lozano-Minguez et al. demonstrates wind power will become environmentally friendly as the size of wind turbines increases [56], and Caduff et al. calculates GHG payback time for 4161 wind turbine locations determined as the function of hub height and blade diameter of the wind turbine [57]. Nugent and Sovacool explores the relationship between GHG intensity and hub height, showing that GHG intensity of wind turbines with 30 m of rotor diameter is around 200 g CO 2 eq/kWh, but those with 124 m of rotor diameter tend to be zero [58], similar to the wind turbines with rotor diameters of 120-130 m in this study (8.40-15.26 g CO 2 eq/kWh). ...
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Presented in this study is a comparative life cycle assessment of 60 wind plant systems’ GHG intensities (49 of onshore and 11 of offshore) in China with regard to different geographical location, turbine technology and management level. As expected, geographical location and turbine technology affect the results marginally. The result shows that the life-cycle GHG intensities of onshore and offshore cases are 5.84–16.71 g CO2eq/kWh and 13.30–29.45 g CO2eq/kWh, respectively, which could be decreased by 36.41% and 41.30% when recycling materials are considered. With wind power density increasing, the GHG intensities of onshore cases tend to decline, but for offshore cases, the larger GHG intensity is as the offshore distance increases. The GHG intensities of onshore cases present a decreasing trend along with the technical advancement, and offshore counterparts is around 65% higher than the onshore cases in terms of wind turbines rated at more than 3 MW. The enlarging of offshore turbine size does not necessarily bring marginal benefit as onshore counterparts due to the increasing cost from construction and maintenance. After changing the functional unit to 1 kWh on-grid electricity (practical), the highest GHG intensities of Gansu province increase to 17.94 g CO2eq/kWh, same as other wind resource rich provinces, which significantly offsets their wind resource endowment. The results obtained in this study also highlight the necessity for policy interventions in China to enhance resource exploration efficiency and promote robust and sustainable development of the wind power industry.
Recently, solar photovoltaic (PV) technology has shown tremendous growth among all renewable energy sectors. The attractiveness of a PV system depends deeply of the module and it is primarily determined by its performance. The quantity of electricity and power generated by a PV cell is contingent upon a number of parameters that can be intrinsic to the PV system itself, external or environmental. Thus, to improve the PV panel performance and lifetime, it is crucial to recognize the main parameters that directly influence the module during its operational lifetime. Among these parameters there are numerous factors that positively impact a PV system including the temperature of the solar panel, humidity, wind speed, amount of light, altitude and barometric pressure. On another hand, the module can be exposed to simultaneous environmental stresses such as dust accumulation, shading and pollution factors. All these factors can gradually decrease the performance of the PV panel. This review not only provides the factors impacting PV panel's performance but also summarizes the degradation and failure parameters that can usually affect the PV technology. The major points are the following: 1) Total quantity of energy extracted from a photovoltaic module is impacted on a daily, quarterly, seasonal, and yearly scale by the amount of dust formed on the surface of the module. 2) Climatic conditions as high temperatures and relative humidity affect the operation of solar cells of more than 70% and lead to a considerable decrease in solar cells efficiency. 3) The PV module current can be affected by soft shading while the voltage does not vary. In the case of hard shadowing, the performance of the photovoltaic module is determined by whether some or all of the cells of the module are shaded. 4) Compared to more traditional forms of energy production, PV systems offer a significant number of advantages to the environment. Nevertheless, these systems can procure greenhouse gas emissions, especially during the production stages. In conclusion, this study underlines the importance of considering multiple parameters while evaluating the performance of photovoltaic modules. Environmental factors can have a major impact on the performance of a PV system. It is critical to consider these factors, as well as intrinsic and other intermediate factors, to optimize the performance of solar energy systems. In addition, continuous monitoring and maintenance of PV systems is essential to ensure maximum efficiency and performance.
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This paper aims to review one of the least used, but no less important, approaches in the assessment of the environmental implications of electricity generation: the Economic Input-Output Life Cycle Assessment (EIO-LCA). This methodology is a top-down approach intertwined with the environmental satellite accounts provided by the national statistical office. Through the use of economic input-output (IO) tables and industrial sector-level environmental and energy data, the EIO-LCA analysis allows for broad impact coverage of all sectors directly and indirectly involved with electricity generation. In this study, a brief overview of this methodology and the corresponding assumptions is presented, as well as an updated review of the different applications of the EIO-LCA approach in electricity generation, suggesting a possible classification of the many studies developed in this context. The different ways of overcoming the problem of disaggregation in the electricity sector are also addressed, namely by considering different IO table formats (i.e., symmetric or rectangular tables). This is a particularly relevant feature of our review, as the way in which electricity generation is modeled can result in different calculations of the costs and benefits of environmental policies. In this context, this paper further contributes to the literature by explaining and providing examples of distinct approaches to modeling the electricity sector in IO models on a detailed level.
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In India, more than 200 million people live in rural areas without access to grid-connected power. A convenient & cost-effective solution would be hybrid power systems which can reduce dependency on grid supply, improve reliability. For a typical domestic load a solar –wind hybrid system is designed with charge controller to charge a conventional battery. To optimize system efficiency, a simple algorithm is developed for system sizing. Total cost of unit is calculated using life cycle cost analysis and payback peri
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The development in solar PV technology is growing very fast in recent years due to technological improvement, cost reductions in materials and government support for renewable energy based electricity production. Photovoltaic is playing an important role to utilize solar energy for electricity production worldwide. At present, the PV market is growing rapidly with worldwide around 23.5 GW in 2010 and also growing at an annual rate of 35-40%, which makes photovoltaic as one of the fastest growing industries. The efficiency of solar cell is one of the important parameter in order to establish this technology in the market. Presently, extensive research work is going for efficiency improvement of solar cells for commercial use. The efficiency of monocrystalline silicon solar cell has showed very good improvement year by year. It starts with only 15% in 1950s and then increase to 17% in 19705 and continuously increase up to 28% nowadays. The growth in solar photovoltaic technologies including worldwide status, materials for solar cells, efficiency, factor affecting the performance of PV module, overview on cost analysis of PV and its environmental impact are reviewed in this paper.
This paper utilized wind speed data over a period of almost 10 years between 1977 and 1988 from three stations, namely Adrar, Timimoun and Tindouf to assess the wind power potential at these sites. The long-term annual mean wind speed values along with the wind turbine power curve values were used to estimate the annual energy output for a 30MWinstalled capacity wind farm at each site. A total of 30 wind turbines each of 1000kW rated power were used in the analysis. The long-term mean wind speed at Adrar, Timimoun and Tindouf was 5.9, 5.1 and 4.3 m/s at 10m above ground level (AGL), respectively. Higher wind speeds were observed in the day time between 09:00 and 18:00 h and relatively smaller during rest of the period. Wind farms of 30MW installed capacity at Adrar, Timimoun and Tindouf, if developed, could produce 98,832, 78,138 and 56,040MWh of electricity annually taking into consideration the temperature and pressure adjustment coefficients of about 6% and all other losses of about 10%, respectively. The plant capacity factors at Adrar, Timimoun and Tindouf were found to be 38%, 30% and 21%, respectively. Finally, the cost of energy (COE) was found to be 3.1, 4.3 and 6.6 US cents/kWh at Adrar, Timimoun and Tindouf, respectively. It was noticed that such a development at these sites could result into avoidance of 48,577, 38,406 and 27,544 tons/year of CO2 equivalents green house gas (GHG) from entering into the local atmosphere, thus creating a clean and healthy atmosphere for local inhabitants.
In this paper a methodology for calculation of the optimum size of a stand-alone hybrid wind/photovoltaic (wind/PV) system is developed. The collection of six months of data of wind speed, solar irradiance and ambient temperature recorded for every hour of the day were used. These data and the manufacturer's specifications of the wind turbines and the PV modules were used to calculate the average hourly power generated by the components of the hybrid system. The mathematical modeling of the principal elements of the hybrid wind/PV system is exposed showing the main sizing variables and a deterministic algorithm is used to minimize the life cycle cost of the system while guaranteeing the satisfaction of the load demand.
Renewables portfolio standards (RPS) encourage large-scale deployment of wind and solar electric power. Their power output varies rapidly, even when several sites are added together. In many locations, natural gas generators are the lowest cost resource available to compensate for this variability, and must ramp up and down quickly to keep the grid stable, affecting their emissions of NOx and CO2. We model a wind or solar photovoltaic plus gas system using measured 1-min time-resolved emissions and heat rate data from two types of natural gas generators, and power data from four wind plants and one solar plant. Over a wide range of renewable penetration, we find CO2 emissions achieve ∼80% of the emissions reductions expected if the power fluctuations caused no additional emissions. Using steam injection, gas generators achieve only 30−50% of expected NOx emissions reductions, and with dry control NOx emissions increase substantially. We quantify the interaction between state RPSs and NOx constraints, finding that states with substantial RPSs could see significant upward pressure on NOx permit prices, if the gas turbines we modeled are representative of the plants used to mitigate wind and solar power variability.