ArticlePDF Available

Meta-Analysis of Net Energy Return for Wind Power Systems


Abstract and Figures

This analysis reviews and synthesizes the literature on the net energy return for electric power generation by wind turbines. Energy return on investment (EROI) is the ratio of energy delivered to energy costs. We examine 119 wind turbines from 50 different analyses, ranging in publication date from 1977 to 2007. We extend on previous work by including additional and more recent analyses, distinguishing between important assumptions about system boundaries and methodological approaches, and viewing the EROI as function of power rating. Our survey shows an average EROI for all studies (operational and conceptual) of 25.2 (n = 114; std. dev = 22.3). The average EROI for just the operational studies is 19.8 (n = 60; std. dev = 13.7). This places wind in a favorable position relative to fossil fuels, nuclear, and solar power generation technologies in terms of EROI.
Content may be subject to copyright.
Meta-analysis of net energy return for wind power systems
Ida Kubiszewski
, Cutler J. Cleveland
, Peter K. Endres
Gund Institute for Ecological Economics, University of Vermont, 617 Main Street, Burlington, VT 05405, USA
Department of Geography and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, USA
JW Great Lakes Wind, LLC, 1900 Superior Avenue, Suite 333, Cleveland, OH 44114-4420, USA
article info
Article history:
Received 10 December 2008
Accepted 29 January 2009
Available online 25 February 2009
Energy return on investment (EROI)
Wind energy
Net energy
Input/output analysis
This analysis reviews and synthesizes the literature on the net energy return for electric power gener-
ation by wind turbines. Energy return on investment (EROI) is the ratio of energy delivered to energy
costs. We examine 119 wind turbines from 50 different analyses, ranging in publication date from 1977 to
2007. We extend on previous work by including additional and more recent analyses, distinguishing
between important assumptions about system boundaries and methodological approaches, and viewing
the EROI as function of power rating. Our survey shows an average EROI for all studies (operational and
conceptual) of 25.2 (n¼114; std. dev ¼22.3). The average EROI for just the operational studies is 19.8
(n¼60; std. dev ¼13.7). This places wind in a favorable position relative to fossil fuels, nuclear, and solar
power generation technologies in terms of EROI.
!2009 Elsevier Ltd. All rights reserved.
1. Introduction
Wind energy is one of the fastest growing energy systems in the
world. Global installed annual wind capacity grew by more than 31
percent from 1997 to 2007 as seen in the global annual installed
wind power capacity graph created by the Global Wind Energy
Council (Fig. 1), and will approach 100,000 MW by the end of 2008
[1]. The surge in wind energy is due to a combination of factors,
including reduction in the cost of wind turbines, volatile and high
prices for conventional forms of energy, the demand for non-
carbon forms of energy to mitigate the effects of climate change,
and favorable policies such as feed-in tariffs in Europe and
renewable portfolio standards in the United States. Despite the
impressive growth, wind energy still accounts for a small fraction of
total installed power generation.
Global electricity use is projected to double from 2005 to 2030,
with its share of final energy consumption rising from 17 to 22
percent [2]. How will the increase in demand be met, particularly in
light to the increasing pressure to reduce carbon emissions? A
variety of alternatives are proposed, including wind, biomass,
various forms of solar power, nuclear, fossil fuel systems with
carbon sequestration, among others. A variety of techniques are
available to compare power generation systems, including life cycle
analysis (LCA), learning or experience curves, and various forms of
economic and financial analysis.
Another technique for evaluating energy systems is net energy
analysis, which seeks to compare the amount of energy delivered to
society by a technology to the total energy required to find, extract,
process, deliver, and otherwise upgrade that energy to a socially
useful form. Energy return on investment (EROI) is the ratio of
energy delivered to energy costs [3]. In the case of electricity
generation, the EROI entails the comparison of the electricity
generated to the amount of primary energy used in the manufac-
ture, transport, construction, operation, decommissioning, and
other stages of facility’s life cycle (Fig. 2). Comparing cumulative
energy requirements with the amount of electricity the technology
produces over its lifetime yields a simple ratio for energy return on
investment (EROI):
EROI [ðcumulative electricity generatedÞ=
ðcumulative primary energy requiredÞ
This analysis reviews 119 wind turbines from 50 different
analyses, ranging in publication date from 1977 to 2007. We extend
the work of Lenzen and Munksgaard [4] by including additional and
more recent analyses, distinguishing between important assump-
tions about system boundaries and methodological approaches,
and viewing the EROI as function of power rating. Our survey shows
average EROI for all studies (operational and conceptual) of 25.2
(n¼114; std. dev ¼22.3). The average EROI for just the operational
studies is 19.8 (n¼60; std. dev ¼13.7). This places wind in
*Corresponding author. Tel.: þ1 860 729 1126; fax: þ1 802 656 2995.
E-mail addresses: (I. Kubiszewski),
(C.J. Cleveland), (P.K. Endres).
Contents lists available at ScienceDirect
Renewable Energy
j o u rn a l h o me p a g e : ww w . e l s e v i e r . c o m / l o c at e / r e n e n e
0960-1481/$ – see front matter !2009 Elsevier Ltd. All rights reserved.
Renewable Energy 35 (2010) 218–225
a favorable position relative to fossil fuels, nuclear, and solar power
generation technologies in terms of EROI.
2. Importance of net energy
Economies with access to energy sources with a large energy
surplus have greater potential for economic expansion and/or
diversification than those with access to lower quality fuels [5]. The
history of the expansion of human civilization and its material
standard of living is directly linked to successive access to and
development of fuel sources with increasingly greater energy
surpluses [6]. The transitions from animate energy sources such as
plant, biomass, and draft animals, to wind and water power, to
fossil fuels and electricity enabled increases in per capita output
due to increases in the quantity of fuel available to produce non-
energy goods. The transition to higher surplus fuels also enabled
social and economic diversification as decreasing amounts of
energy were used in the energy securing process, meaning more
fuel was available to support non-extractive activities.
An EROI ¼1 is an absolute cutoff point for an energy source, the
point at which as much energy is used to deliver a unit of energy as
that unit yields. The EROI for crude oil has declined over time, and
may continue to do so as the resource base is depleted [7]. Smaller,
deeper, and more remote fields require more energy to develop.
Alternatives to crude oil such as ethanol from corn and coal
liquefaction deliver a lower EROI because a significant amount of
energy is needed to process the feedstock itself (corn or coal) [8].
Economic growth and rising standards of living may be more
difficult to maintain than they were 50 years ago when wealth was
produced by the massive energy surplus associated with the
discovery of the Earth’s great oil fields in the first half of the
twentieth century.
EROI is a tool of net energy analysis, a methodology that seeks to
compare the amount of energy delivered to society by a technology
to the total energy required to find, extract, process, deliver, and
otherwise upgrade that energy to a socially useful form. Net energy
analysis was developed in response to the emergence of energy as
an important economic, technological, and geopolitical force
following the energy price increases of 1973–74 and 1980–81.
Interest in net energy analysis was rekindled in recent years
following another round of energy price increases, growing
concern about energy’s role in climate change, and the debate
surrounding the remaining lifetime of conventional fossil fuels,
especially crude oil. It typically is assessed along with material
flows in life cycle analysis (LCA) of energy systems (e.g., [9]).
3. Methodological issues
3.1. System boundary
The choice about system boundaries is perhaps the most
important decision made in net energy analysis, and, for that
Fig. 2. Energy outputs and energy costs of a power generation facility.
Fig. 1. Global annual installed wind power capacity (Source: Global Wind Energy Council,, retrieved 9 September 2008.)
I. Kubiszewski et al. / Renewable Energy 35 (2010) 218–225 219
matter, in other analytical approaches a well. One of the most
critical differences among the diverse studies is the number of
stages in the life cycle of an energy system that are assessed and
compared against the cumulative lifetime energy output of the
system. These stages include the manufacture of components,
transportation of components to the construction site, the
construction of the facility itself, operation and maintenance over
the lifetime of the facility, overhead, possible grid connection costs,
decommissioning, and recycling of component materials. Energy
systems have external costs as well, most notably environmental
and human health costs, although these are difficult to assess in
monetary and energy terms. External costs are excluded from our
3.2. Methodology
Two individual types of net energy analysis techniques are used
to calculate the net energy derived from wind power: process
analysis and input–output analysis. A third type, called hybrid
analysis, is a combination of the two. Process analysis assesses the
energy used directly in each successive step of the production of
a good or service. The energy input–output approach is more
comprehensive than process analysis and is analogous to and
derived from the input–output matrix used in standard economic
analyses. The assumptions, strengths, and weaknesses of the two
approaches have been discussed elsewhere in detail (e.g., [10,11]).
3.3. Operating characteristics
Many analyses must make important assumptions regarding the
operating characteristics of wind turbines. These include power
rating, assumed lifetime, and capacity factors. Changes in the
assumptions made about these factors, or deviations in actual
operating conditions from assumed conditions can have a signifi-
cant impact on results.
3.4. Conceptual versus empirical studies
Some studies use the theoretical or ideal operating character-
istics of a wind turbine that are derived from simulated or assumed
costs and operating conditions, e.g., a wind turbine of a given power
rating, costing a certain dollar amount, in a location with an
assumed wind power density, withan assumed capacity factor, and
so on. Of course, actual operating conditions always deviate from
assumed conditions. Empirical analyses rely on actual costs, oper-
ating conditions, and energy outputs, and thus provide a better
metric of an energy system’s contribution to a nation’s energy
supply. This article focuses primarily on empirical studies based on
actual operational data.
4. Results
Table 1 provides the detailed technical results of the wind
studies. The data include year and location of the study, key tech-
nical assumptions such as load factor, power rating and lifetime,
system boundaries, the type of net energy method used, certain
environmental variables, and EROI. The table also distinguishes
between studies based on actual performance of a wind system and
conceptual studies based on theory or simulations.
The average EROI for all studies (operational and conceptual) is
25.2 (n¼114; std. dev ¼22.3). The average EROI for just the oper-
ational studies is 19.8 (n¼60; std. dev ¼13.7).
5. Discussion
5.1. EROI and power rating
One of the striking features of the studies is that the average
EROI generally increases with the power rating of the turbine
(Fig. 3). Fig. 3 solely looks at operational wind turbines with
a power rating below 1 MW. Turbines above 1 MW were not
included due to lack of reliable data.
The results found in Fig. 3 may be due to a combination of
factors, including larger wind turbines creating economies of scale,
more efficient technology, greater rotor diameters increasing the
load factor, and higher hub heights providing access to greater wind
speeds. These factors are derived from those found to be important
in the creation of experience curves which show a decrease of wind
turbine price over time [12].
First, smaller wind turbines represent older, less efficient tech-
nologies. The new turbines nearing the megawatt (MW) range
embody many important technical advances that improve the
overall effectiveness of energy conversion. Such developments
include improved aerodynamic profiles, increasing the peak effi-
ciency approximately 8% between the early 1980 and early 1990
[13]. Although larger turbines require greater initial energy
investments in materials, the increase in power output due to
improvements more than compensates for this over the lifetime of
the turbine.
Second, larger turbines have a greater rotor diameter, which
determines swept area, probably the most important design
element that affects generating power potential. High annual
energy output will be difficult to obtain if the rotor diameter limits
the ability for the turbine to capture the wind power at lower wind
speeds, even if turbine power rating is respectable. Again, larger
rotors require greater initial energy investments in materials, but
the increase in power output more than compensates for this.
Fig. 4 demonstrates how an increase in rotor diameter produces
an increase in EROI. These conclusions are consistent with the
finding that commercial wind farms have moved towards larger
turbines that are less expensive on a levelized basis with regard to
installation, operation, and maintenance. The greater cost efficiency
of larger turbines is largely attributed to economies of scale and
learning by doing. Accordingly, under a similar assumption, larger
turbines have a greater EROI.
Another reason that larger turbines have a larger EROI is the
well known ‘‘cube rule’’ of wind power, i.e., the power available
from the wind varies as the cube of the wind speed. Thus, if the
wind speed doubles, the power of the wind increases eight times.
New turbines are taller than in earlier technologies, and thus
extract energy from the higher winds that exist at greater heights.
Fig. 5 shows the affect of wind speed on EROI. Surface roughness –
determined mainly by the height and type of vegetation and
buildings – reduces wind velocity near the surface. Over flat, open
terrain in particular, the wind speed increases relatively quickly
with height. EROI at location with high wind speeds is also often
affected due to limited accessibility to those areas. The installation
of wind turbines on mountaintops or far off shore, areas with
greatest wind speeds, significantly increases the input energy
required in transportation, construction, and connection to the
5.2. Comparison with other power systems
The EROI for wind turbines compares favorably with other
power generation systems (Fig. 6). Coal accounts for about 40% of
global electricity generation [2] and has an EROI of about 8.0. It is
a mature technology where technical improvements are not likely
I. Kubiszewski et al. / Renewable Energy 35 (2010) 218–225220
Table 1
Metadata analysis of wind power systems.
Ref Year of
Location Operational/
Power rating
factor (%)
Energy payback
time (yr)
Scope as
diameter (m)
Hub height
Wind speed
[4] 1977 USA c 43.5 1500 30 50.4 I/O BCEMT 2 blades 60 50 10.5
[4] 1980 UK c 12.5 1000 25 18.3 I/O CM on 46 18.4
[4] 1980 UK c 6.1 1000 25 18.3 I/O CM 46 18.4
[4] 1981 USA o 1.0 3 20 26.8 I/O CMO 4.3 20 10.1
[4] 1983 Germany o 2.3 2 15 45.7 I/O CM
[4] 1983 Germany o 3.4 6 15 45.7 I/O CM
[4] 1983 Germany o 5.0 12.5 15 45.7 I/O CM
[4] 1983 Germany o 8.3 32.5 15 45.7 I/O CM
[4] 1983 Germany o 1.3 3000 20 45.7 I/O CM 2 blades 100 100
[4] 1990 Denmark o 71.4 95 20 25.2 PA M!3 blades on 19 22.6
[4] 1990 Denmark o 47.6 8.81 150 25 30.1 PA M
[4] 1990 Germany o 32.3 300 20 28.9 PA CMT 3 blades 32 34 11.5
[4] 1991 Germany o 18.9 45 20 33.5 PA M 12.5
[4] 1991 Germany o 32.3 225 20 39.9 PA M 27
[4] 1991 Germany c 27.0 300 20 39.9 PA M 32
[4] 1991 Germany c 22.2 3000 20 34.2 PA M 80
[4] 1991 Germany o 11.8 30 20 14.4 PA CGMOT 2 blades 12.5 14.8 13
[4] 1991 Germany o 20.4 33 20 29.4 PA M 2 blades 14.8 22 11
[4] 1991 Germany o 14.7 95 20 20.5 PA CGMT 3 blades on 19 22.6
[4] 1991 Germany o 19.6 95 20 20.5 PA M 3 blades 19 22.6
[4] 1991 Germany o 16.7 100 20 20.9 PA M 2 blades 34 24.2 8
[4] 1991 Germany o 20.4 150 20 25.6 PA M 3 blades 23 30 13
[4] 1991 Germany o 27.0 165 20 23.2 PA M 3 blades 25 32 13.5
[4] 1991 Germany o 18.9 200 20 21 PA M 3 blades 26 30 13
[4] 1991 Germany o 15.6 265 20 19 PA M 2 blades 52 30.5 8.5
[4] 1991 Germany o 20.8 450 20 20 PA GM 3 blades 35 36 18
[4] 1991 Germany o 15.4 3000 20 30.4 PA GM 2 blades 100 100 12
[4] 1991 Japan o 4.0 71.7e 100 20 31.5 I/O CMT
[4] 1992 Germany o 11.2 0.3 20 38.8 PA CDMOT 3 blades 1.5 11.6 9
[4] 1992 Germany c 37.0 300 20 41.9 PA CDGMOT 3 blades 32 34
[4] 1992 Japan o 2.9 95.6e 100 20 31.5 I/O CMOT
[4] 1992 Japan o 30.3 33.7 100 30 28 I/O CMOT 30 13
[4] 1992 Japan o 18.5 100 30 40 I/O CMOT 1983 30 10
[4] 1993 Germany o 21.7 11e 300 20 22.8 PA CDMOT
[4] 1994 Germany o 18.2e 500 20 27.4 I/O CM
[4] 1994 Germany o 45.5 300 20 22.8 PA MO(D)
[4] 1994 Germany o 14.7 8.1 500 20 36.5 PA M 2/3 blades 39 41
[4] 1995 UK o 23.8 9.1 350 20 30 PA M 3 blades 30 30 15
[4] 1996 Switzerland o 3.1 52 30 20 7.9 PA CDGMOT 2 blades 12.5 22 11.4
[4] 1996 Switzerland o 5.0 28 150 20 7.6 PA CDGMOT 3 blades 23.8 30
[4] 1996 Germany o 14e 1000 20 18.5 PA CMO 3 blades 54 55
[4] 1996 Germany o 22e 1000 20 18.5 I/O CMO 3 blades 54 55
[4] 1996 UK o 25 6600 20 29 I/O CDMO
[4] 1996 Japan o 2.3 123.6e 100 30 20 I/O CMO
[4] 1996 Japan o 2.2 123.7e 100 20 18 I/O CMO 1984 30
[4] 1996 Japan o 5.8 47.4e 170 20 22.5 I/O CMO 27
[4] 1996 Japan o 8.5 34.9e 300 20 18 I/O CMO 28
[4] 1996 Japan o 11.4 24.1e 400 20 18 I/O CMO 31
[4] 1996 Germany o 8.3 17 100 20 31.4 PA CMO 3 blades 20 30
[4] 1996 Germany c 28.6 10 1000 20 36.2 PA CMO 3 blades 60 50
[4] 1997 Denmark o 8.3 15 20 20.5 I/O CMO 1980 10 18
[4] 1997 Denmark o 8.1 22 20 19.9 I/O CMO 1980 10.5 18
[4] 1997 Denmark o 10.0 30 20 19 I/O CMO 1980 11 19
[4] 1997 Denmark o 15.2 55 20 20.6 I/O CMO 1980 16 20
[4] 1997 Denmark o 27.0 600 20 26.5 I/O BCDEGMOT 3 blades 47 50 15
(continued on next page)
I. Kubiszewski et al. / Renewable Energy 35 (2010) 218–225 221
Table 1 (continued)
Ref Year of
Location Operational/
Power rating
factor (%)
Energy payback
time (yr)
Scope as
diameter (m)
Hub height
Wind speed
[4] 1997 Denmark c 33.3 1500 20 38.4 I/O CMO 3 blades off 64 55 17
[4] 1997 Denmark o 50.0 15.9 400 20 22.8 PA M(O)
[4] 1998 Argentina c 5.9 42 2.5 20 22 PA CMT(O)
[4] 1998 Argentina c 8.3 29 30 20 22 PA CMT(O)
[4] 1998 Argentina c 12.5 18 225 20 22 PA CMT(O)
[4] 1998 Germany o 23.8 500 20 29.6 PA CGMOT 3 blades 40.3 44
[4] 1998 Germany o 15.4 500 20 29.6 I/O CGMOT 3 blades 40.3 44
[4] 1998 Germany o 21.7 1500 20 31 PA CGMOT 3 blades 66 67
[4] 1998 Germany o 14.1 1500 20 31 I/O CGMOT 3 blades 66 67
[4] 1999 Germany c 26.3 1500 20 31 PA CDGMOT 66 67
[4] 1999 India c 31.3 1500 20 45.9 PA CDGMOT E-66 66 67
[21] 1999 USA o 23.0 14.4 342.5 30 24 I/O (B)CDMOT Kenetech KVS-
on 32.9 36.6
[21] 1999 USA o 17.0 20.2 600 20 31 I/O (B)CDMOT Tacke 600e on 46.0 60.0 6.1
[21] 1999 USA o 39.0 8.9 750 25 35 I/O (B)CDMOT Zond Z-46 on 46.0 48.5
[4] 2000 Denmark o 51.3 16.5 500 20 40 0.39 MTCGOD 3-blades off 39 40.5 16
[4] 2000 Denmark o 76.9 9.7 500 20 40 0.26 MTCGOD on 41.5
[22] 2000 Italy o 7.7 36.15 2500 I/O MCO
[4] 2000 Belgium o 30.3 9.2e 600 20 34.2 PA DM(O)
[4] 2000 Belgium o 27.8 7.9e 600 20 34.2 I/O DM(O)
[4] 2001 Japan o 6.3 39.4 100 25 34.8 I/O CMT 30 30
[4] 2001 Brazil o 14.5 500 20 29.6 I/O CGMOT 3 blades; E-40 40.3 44
[23] 2002 USA c 80.0 8.16e TCO
[24] 2003 Canada c 123.5 10 500 20 PA MCTOD
[24] 2003 Canada c 125.8 7.1 500 20 PA MCTOD
[24] 2003 Canada c 109.6 3.7 500 20 PA MCTOD
[25] 2004 Germany c 8.4 45 500 PA-I/O MTCOD Enercon E-40 on 40.3 44 7.5
[25] 2004 Germany c 7.8 48 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Germany c 6.2 61 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Germany c 4.7 81 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Germany c 4.9 77 500 PA-I/O MTCOD Enercon E-40 on 40.3 65 7.5
[25] 2004 Germany and
c 22.5 15 500 PA-I/O MTCOD Enercon E-40 on 40.3 44 7.5
[25] 2004 Germany and
c 21.2 16 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Germany and
c 16.4 20 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Germany and
c 12.0 27 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Germany and
c 12.4 26 500 PA-I/O MTCOD Enercon E-40 on 40.3 65 7.5
[25] 2004 Germany and
c 27.7 8 500 PA-I/O MTCOD Enercon E-40 on 40.3 44 7.5
[25] 2004 Germany and
c 25.7 8 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Germany and
c 20.0 10 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Germany and
c 15.6 13 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Germany and
c 16.4 12 500 PA-I/O MTCOD Enercon E-40 on 40.3 65 7.5
[25] 2004 Brazil c 32.7 3 500 PA-I/O MTCOD Enercon E-40 on 40.3 44 7.5
[25] 2004 Brazil c 30.0 3 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Brazil c 24.0 3 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Brazil c 18.9 4 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Brazil c 18.9 4 500 PA-I/O MTCOD Enercon E-40 on 40.3 65 7.5
[25] 2004 Brazil c 40.0 2 500 PA-I/O MTCOD Enercon E-40 on 40.3 44 7.5
I. Kubiszewski et al. / Renewable Energy 35 (2010) 218–225222
to significantly improve generation efficiency, and thus the EROI
will remain fairly stable. Adding carbon sequestration technology
to coal combustion will increase the energy cost of power genera-
tion. Hydropower has a relatively high EROI (about 12), but on
a global scale it has a modest potential for expansion. The average
EROI for hydropower is based on a literature review of published
life cycle energy assessments (N¼7). Similar literature reviews
were done for coal (N¼12), solar thermal (N¼9) and geothermal
(N¼11) power generation systems.
The comparison with nuclear power is complicated by a number
of factors. The system boundary looms large for nuclear power
because the fuel cycle has many steps, and because many of the
important stages are upstream (mining, milling, enrichment) or
downstream (decommissioning, waste disposal) from the genera-
tion stage. The data presented in Fig. 3 are from Lenzen’s [14]
comprehensive survey of the life cycle energy and greenhouse gas
emissions of nuclear energy based on 52 unique analyses. The
complete sample yields an average EROI ¼15.8, but with a very
large standard deviation (28.0). The 52 studies exhibit a wide range
in the number of stages that are assessed, which explains some of
the huge variation in EROI. Most of the studies with EROI in the
upper range shown in Fig. 3 exclude multiple stages of the fuel
cycle, and thus generate unrealistically high EROI. Excluding those
outliers produces an average EROI ¼9.1, but still with a large
standard deviation (8.0). Readers should also note that two-thirds
of the analyses in Lenzen’s nuclear review date from 1980 or earlier,
and thus do not represent nuclear power plants currently being
built, or any plants that will be built in the future. Suffice it to say
that there remains significant uncertainty regarding the energy
costs associated with nuclear power.
The EROI for wind is demonstrably higher then the current EROI
for photovoltaic (PV) power generation. A literature (N¼62) review
of LCAs and net energy assessments for PV systems from 1997
through 2007 produced an average EROI of about 6.5 (s.d. ¼4.7).
The vast majority of these studies were simulations that assumed
specific lifetimes, locations, module efficiencies, solar intensities,
and other operating characteristics. Like the wind and nuclear
analyses, the PV studies exhibit a wide range in terms of scope, with
decommissioning and recycling stages often excluded. Ceteris par-
ibus, the ongoing improvements in PV module efficiency will tend
to improve the EROI over time.
5.3. Challenges facing wind energy
Does the high EROI for wind power presented here guarantee
that wind will assume a major role in the world’s power generation
system? There are a number of issues surrounding wind energy
that require resolution before that happens. These issues have been
discussed in detail elsewhere, and are summarized here:
%The dramatic cost reductions in the manufacture of new wind
turbines that has characterized the past two decades may be
slowing [15] due to a variety of economic, financial, and tech-
nical reasons. Recently this is particularly true in light of the
rising energy and commodity prices, which are slowly esca-
lating turbine costs. The rising global demand for turbines is
also driving prices upward.
%The uncontrolled, intermittent nature of wind poses unique
challenges to grid management relative to operator-controlled
(baseload) resources such as coal, gas, or nuclear generation
%Much of the wind resource base is located in remote locations,
so costs exist in getting wind-generated electricity from the
local point-of-generation to a potentially distant load center.
[25] 2004 Brazil c 40.0 2 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Brazil c 32.7 2 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Brazil c 25.7 3 500 PA-I/O MTCOD Enercon E-40 on 40.3 55 7.5
[25] 2004 Brazil c 25.7 3 500 PA-I/O MTCOD Enercon E-40 on 40.3 65 7.5
[26] 2004 Germany c 14.8 5000 20 50 0.33 PA MTCOD Repower
Systems AG
off 126.5 95 9.2
[27] 2004 Germany c 70.0 500 20 0.29 PA MCTO Enercon E-40 on 40.3 44
[27] 2004 Germany c 53.0 500 20 0.38 PA MCTO Enercon E-40 on 40.3 55
[27] 2004 Germany c 38.0 500 20 0.53 PA MCTO Enercon E-40 on 40.3 65
[27] 2004 Germany c 64.0 1500 20 0.32 PA MCTO Enercon E-66 on 66 67
[27] 2004 Germany c 50.0 1500 20 0.4 PA MCTO Enercon E-66 on 66 67
[27] 2004 Germany c 39.0 1500 20 0.52 PA MCTO Enercon E-66 on 66 67
[28] 2005 Japan c 29.5 300 30 20 PA-I/O CMO
[28] 2005 Japan c 20.3 400 30 20 PA-I/O CMO
[29] 2006 Italy o 19.2 14.8e 7260 20 I/O MTCOD on 50 55
[30] 2006 Germany c 30.0 10.2 1500 MTCOD
[30] 2006 Germany c 32.7 8.9 2500 MTCOD
[30] 2006 Germany c 29.4 10.2 1500 MCOTD on
[30] 2006 Germany c 32.3 8.9 2500 MCOTD off
Notes: I/O ¼Input–output-based analysis, PA ¼Process analysis, c ¼conceptual, o ¼operating, B ¼Business management, M ¼Manufacture, T ¼Transport, C ¼Construction, G ¼Grid connection, O ¼Operation and maintence,
D¼Decommissioning, e ¼CO
equivalents including CH
and N
O, () ¼partly covered.
I. Kubiszewski et al. / Renewable Energy 35 (2010) 218–225 223
%The remoteness of the wind resource base also generates
increased costs of developing land with difficult terrain or that
which is increasingly removed from development infrastruc-
ture (such as major roads, rivers, or rails capable of trans-
porting the bulky and heavy construction equipment). Little is
known about the extent of these costs.
%At about 6 or 7 MW per square kilometer of net power
potential, wind plants are necessarily spread-out over a signif-
icant land area [17]. Thus, wind plants must compete with
alternative uses of these land resources. This is especially true
when the land is a significant source of aesthetic and/or
recreational value.
%Government subsidies have spurred the development of wind
energy [18]. But subsidies are always subject to political whims,
and thus constitute a significant issue for the wind industry,
creating uncertainty for long-term planning and preventing
faster market development.
%There is also concern about the impacts of wind energy on
birds and bats [19]. Considerable additional research on oper-
ational wind facilities is required to provide a comprehensive
assessment of the potential magnitude of these risks.
None of these challenges are necessarily insurmountable.
Indeed, some of them may be relatively modest in cost terms when
fully assessed. The point here is simply that an EROI is crucial but is
not independently a sufficient condition for the continued wide-
spread expansion of wind energy.
5.4. Difficulties in calculating EROI
Our analysis illustrates the longstanding observation that EROI
is sensitive to the choice of system boundaries [10,20]. Studies
using the input–output analysis have an average EROI of 12 while
those using process analysis an average EROI of 24. Process analysis
typically involves a greater degree of subjective decisions by the
analyst in regard to system boundaries, and may be prone to the
exclusion of certain indirect costs compared to input–output
analysis [10].
Operational wind turbines offer the best opportunity to calcu-
late real EROI, as concrete data for input and output parameters can
be used. However, practical obstacles interfere with data avail-
ability. For example, data retrieval related to turbine transport or
construction material/volume becomes complicated by the
involvement of multiple contractors, inconsistencies in record
keeping, and other factors. Additionally, wind turbine developers or
owners/operators may be unwilling to provide data due to confi-
dentiality and competitive restrictions (this is especially true for
production data), or the time required to collect information. These
Fig. 3. EROI for operational wind turbines below 1 MW as a function of power rating in kilowatts.
Fig. 4. EROI for operational wind turbines below 1 MW as a function of rotor diameter
in meters.
Fig. 5. EROI for operational wind turbines below 1 MW as a function of wind speed in
meters per second.
I. Kubiszewski et al. / Renewable Energy 35 (2010) 218–225224
constraints give rise to the need for estimation, which increases the
level of uncertainty even for operational turbines.
6. Conclusions
This analysis reviews the extant literature on the net energy
return from wind energy systems, ranging in date from 1977 to
2007. Our survey shows average EROI for all studies (operational
and conceptual) of 25.2 (n¼114; std. dev ¼22.3). The average EROI
for just the operational studies is 19.8 (n¼60; std. dev ¼13.7). This
places wind in a favorable position relative to other forms of power
generation, and suggests that wind energy could yield significant
economic and social benefits relative to other power generation
systems. Ongoing technical progress in wind energy technology
will undoubtedly lead to further energy cost reductions. However
technical progress and a high EROI are not sufficient conditions for
the continued rapid expansion of wind energy. A number of social,
economic, environmental and regulatory issues need resolution.
[1] US, China & Spain lead world wind power market in 2007. Global Wind Energy
Council; 2007.
[2] World energy outlook. Paris, France: International Energy Agency; 2008.
[3] Cleveland CJ, Costanza R, Hall CAS, Kaufmann R. Energy and the US economy:
a biophysical perspective. Science 1984;225:890–7.
[4] Lenzen M, Munksgaard J. Energy and CO
life-cycle analyses of wind turbines
– review and applications. Renewable Energy 2002;26:339–62.
[5] Cottrell WF. Energy and society: the relation between energy, social change,
and economic development. New York: McGraw-Hill; 1955.
[6] Hall CA, Kaufmann RK, Cleveland CJ. Energy and resource quality: the ecology
of the economic process. New York: Wiley-Interscience; 1986.
[7] Cleveland CJ. Net energy from the extraction of oil and gas in the United States.
Energy 2005;30:769–82.
[8] Cleveland CJ. Ten fundamental principles of net energy. In: Saundry P, editor.
Encyclopedia of Earth. Washington, DC: Environmental Information Coalition,
National Council for Science and the Environment; 2007.
[9] Pacca S, Sivaraman D, Keoleian GA. Parameters affecting the life cycle
performance of PV technologies and systems. Energy Policy 2007;35:3316–26.
[10] Bullard CW, Penner PS, Pilati DA. Net energy analysis – handbook for
combining process and input–output analysis. Resources and Energy
[11] Spreng DT. Net-energy analysis and the energy requirements of energy
systems. New York, New York: Praeger; 1988.
[12] Neij L. Use of experience curves to analyse the prospects for diffusion and
adoption of renewable energy technology. Energy Policy 1997;25:1099–107.
[13] Gipe P. Wind energy comes of age. New York: Wiley; 1995.
[14] Lensen M. Life cycle energy and greenhouse gas emission of nuclear energy:
a review. Energy Conversion and Management 2008;49(8):2178–99.
[15] Junginger M, Faaij A, Turkenburg WC. Global experience curves for wind
farms. Energy Policy 2005;33:133–50.
[16] Ostergaard PA. Ancillary services and the integration of substantial quantities
of wind power. Applied Energy 2006;83:451–63.
[17] Smil V. 21st century energy: some sobering thoughts. OECD Observer; 20 06.
[18] Mulder A. Do economic instruments matter? Wind turbine investments in the
EU(15). Energy Economics 2008;30:2980–91.
[19] Kunz TH, Arnett EB, Erickson WP, Hoar AR, Johnson GD, Larkin RP, et al.
Ecological impacts of wind energy development on bats: questions,
research needs, and hypotheses. Frontiers in Ecology and the Environment
[20] Mulder K, Hagens NJ. Energy return on investment: toward a consistent
framework. AMBIO: A Journal of the Human Environment 2008;37:74–9.
[21] White SW, Kulcinski GL. Net energy payback and CO
emissions from
wind-generated electricity in the Midwest. Madison, WI: Fusion Technology
Institute; 1998.
[22] Brown MT, Ulgiati S. Emergy evaluations and environmental loading of elec-
tricity production systems. Journal of Cleaner Production 2002;10:321–34.
[23] Gagnon L, BE
`langer C, Uchiyama Y. Life-cycle assessment of electricity
generation options: the status of research in year 2001. Energy Policy
[24] Khan FI, Hawboldt K, Iqbal MT. Life cycle analysis of wind-fuel cell integrated
system. Renewable Energy 2005;30:157–77.
[25] Lenzen M, Wachsmann U. Wind turbines in Brazil and Germany: an example
of geographical variability in life-cycle assessment. Applied Energy
[26] Tryfonidou R, Wagner HJ. Multi-megawatt wind turbines for offshore use:
aspects of life cycle assessment. International Journal of Global Energy Issues
[27] Wagner H-J, Pick E. Energy yield ratio and cumulative energy demand for
wind energy converters. Energy 2004;29:2289–95.
[28] Hondo H. Life cycle GHG emission analysis of power generation systems:
Japanese case. Energy 2005;30:2042–56.
[29] Ardente F, Beccali M, Cellura M, Lo Brano V. Energy performances and life
cycle assessment of an Italian wind farm. Renewable and Sustainable Energy
Reviews 2008;12:200–17.
[30] Pehnt M. Dynamic life cycle assessment (LCA) of renewable energy technol-
ogies. Renewable Energy 2006;31:55–71.
Nuclear (1)
Nuclear (2)
Fig. 6. EROI for power generation systems. Nuclear (1) represents the average and
standard deviation for the entire sample of analyses reviewed by Lenzen [14]. Nuclear
(2) omits the extreme outliers from Lenzen’s survey, and thus represents a better
assessment of what the EROI for nuclear is likely to be. See text for description of
further sources.
I. Kubiszewski et al. / Renewable Energy 35 (2010) 218–225 225
... Furthermore, recently enacted research funding from the US DoE is focused on extending the lifetime of existing PV through improved encapsulation and lower degradation [268]. A large meta-analysis of the published estimates for the EROI of wind electricity up to the year 2010 [269] indicated an average EROI of 20, if the electricity output is converted to primary energy equivalents. Since then, more recent studies have pointed to even better net energy performance, with average primary energy weighted EROIs ranging from 28 [270] to 34 [271], with maximum values up to 58 [271]. ...
Full-text available
Research on 100% renewable energy systems is a relatively recent phenomenon. It was initiated in the mid-1970s, catalyzed by skyrocketing oil prices. Since the mid-2000s, it has quickly evolved into a prominent research field encompassing an expansive and growing number of research groups and organizations across the world. The main conclusion of most of these studies is that 100% renewables is feasible worldwide at low cost. Advanced concepts and methods now enable the field to chart realistic as well as cost- or resource-optimized and efficient transition pathways to a future without the use of fossil fuels. Such proposed pathways in turn, have helped spur 100% renewable energy policy targets and actions, leading to more research. In most transition pathways, solar energy and wind power increasingly emerge as the central pillars of a sustainable energy system combined with energy efficiency measures. Cost-optimization modeling and greater resource availability tend to lead to higher solar photovoltaic shares, while emphasis on energy supply diversification tends to point to higher wind power contributions. Recent research has focused on the challenges and opportunities regarding grid congestion, energy storage, sector coupling, electrification of transport and industry implying power-to-X and hydrogen-to-X, and the inclusion of natural and technical carbon dioxide removal (CDR) approaches. The result is a holistic vision of the transition towards a net-negative greenhouse gas emissions economy that can limit global warming to 1.5°C with a clearly defined carbon budget in a sustainable and cost-effective manner based on 100% renewable energy-industry-CDR systems. Initially, the field encountered very strong skepticism. Therefore, this paper also includes a response to major critiques against 100% renewable energy systems, and also discusses the institutional inertia that hampers adoption by the International Energy Agency and the Intergovernmental Panel on Climate Change, as well as possible negative connections to community acceptance and energy justice. We conclude by discussing how this emergent research field can further progress to the benefit of society.
... The present research considers only alternate forms of green energy, i.e., solar energy, which were known to be citizens with a stronger propensity towards this technology. It is to shed light on the reality that the regeneration of the energy industry and the substitution of fossil energy by green energy are developmental phases correlated with technical transition and the development of markets (Kubiszewski et al., 2010). However, market awareness, which is the first point of reference in the quest for facts prior to the process of decisionmaking, hasn't gained as much consideration as is appropriate. ...
Full-text available
The goal of this study is to examine green energy goods and the relationship of the consumer concern for the atmosphere and perceived danger associated with the mediating role of the customer buying intentions; the point of view of international tourists in Jordan. The sampling technique was accompanied by a cross-sectional, quantitative, and explanatory design, whereby 340 individuals were chosen using a random population method reflecting the tourists of the Jordan for a two-week span beginning on 11/11/2020. By circulation of a paper questionnaire and with the aid of some tourist guides, the person sample was achieved. Moreover, through two stages, the structural model was being analyzed. In the first stage (direct effect) the effect (PK-appropriate CPI) was not important, so H1 was not sponsored. However, the result was important with regard to (PK ΔPR), and hypothesis 2a was supported. (PK ΔEC) were not significant, so hypothesis 2a was not supported. Furthermore, the direct impact of product danger and environmental problems on purchasing intentions were explored, resulting in negative/positive and important findings (PR over CPI, P < 0.05; and environmental concerns over purchase intentions, P > 0.05), so H3awere was endorsed, however, H3b was not supported.
... Modern renewables have an EROI approaching or in some cases exceeding that of fossil fuels [40][41][42], but there is a serious hitch. These fuels are intermittent. ...
Full-text available
The Limits to Growth was a remarkable, and remarkably influential, model, book and concept published 50 years ago this year. Its importance is that it used, for essentially the first time, a quantitative systems approach and a computer model to question the dominant paradigm for most of society: growth. Initially, many events, and especially the oil crisis of the 1970s, seemed to support the idea that the limits were close. Many economists argued quite the opposite, and the later relaxation of the oil crisis (and decline in gasoline prices) seemed to support the economists’ position. Many argued that the model had failed, but a careful examination of model behavior vs. global and many national data sets assessed by a number of researchers suggests that the model’s predictions (even if they had not been meant for such a specific task) were still remarkably accurate to date. While the massive changes predicted by the model have not yet come to pass globally, they are clearly occurring for many individual nations. Additionally, global patterns of climate change, fuel and mineral depletion, environmental degradation and population growth are quite as predicted by the original model. Whether or not the world as a whole continues to follow the general patterns of the model may be mostly a function of what happens with energy and whether humans can accept constraints on their propensity to keep growing.
... In other words, this means that currently by using one unit of energy, it is possible to return between 4 and 18 energy units of a refined fossil fuel for societal use. On the other hand, both solar photovoltaic (PV) and wind energy seem to be mature technologies in terms of energy returns [12] and are still expected to slowly increase their EROI [13][14][15]. Nevertheless, the greatest part of the global solar potential is expected to have, in terms of EROI, an extraction efficiency lower than 9 [16], while the electricity provided by large hydropower plants show EROI values higher than 5 [17]. ...
Full-text available
Energy return on investment (EROI) is a ratio of the energy obtained in relation to the energy used to extract/produce it. The EROI of fossil fuels is globally decreasing. What do the declining EROIs of energy sources imply for society as a whole? We answer this question by proposing a novel EROI measure that describes, through one parameter, the efficiency of a society in managing energy resources over time. Our comprehensive societal EROI measure was developed by (1) expanding the boundaries of the analysis up to the useful stage; (2) estimating the amount of energy embodied in the energy-converting capital; (3) considering non-conventional sources such as the muscle work of humans and draught animals; and (4) considering the influence of imported and exported energy. We computed the new EROI for Portugal as a case study. We find a considerably lower EROI value, at around 3, compared to those currently available, which is stable over a long-time range (1960–2014). This suggests an independence of EROI from economic growth. When estimated at the final stage, using conventional methods (i.e., without applying the four novelties here introduced), we find a declining societal EROI. Therefore, our results imply that the production of new and more efficient final-to-useful energy converting capital has historically kept societal EROI around a stable value by offsetting the effects of the changing returns of energy sources at the primary and final stages. This will be crucial in the successful transition to renewables.
One of the reasons behind the low rate of expansion of power production driven by wind turbines is the premature failure of their gearbox bearings, which fail within the first quarter of their designed life. The leading causes of fatigue damage in wind turbine gearbox bearings (WTGBs) are not well recognized despite the massive studies in this field. The damage initiates as subsurface microcracks, propagating to a macro scale and reaching the contact surface. For that, studying the microcracks in the early initiation stage (1–15 μm) significantly impacts the bearing damage trigger. This study is based on evaluating the role of non-metallic inclusions and voids on crack initiation, revealing the varying parts of (maximum shear stress, Von-Mises stress, and traction force) in crack initiation. The results indicated that the effect of inclusion is relatively limited on cracking initiation regardless of their sizes, other than voids, which probably have a considerable role in crack initiation and propagation. The three factors (Von-Mises stress, maximum shear stress, and traction force) played significant roles in the initiation of cracks. However, there is an urgent need to improve the design and manufacturing of WTGBs to decrease the formation of voids and carbides and revise the design standards for both (bearing life and contact stress) to consider the actual relative effects of the severe operating events.
As a part of the global clean energy transition, the increased deployment of ground-mounted PV systems depends on the availability of land. In some regions, scarce land resources can lead to competition between agriculture and PV land use, threatening both food and energy security. Agrivoltaics is a method to combine agricultural and electricity production on the same unit of land, which significantly increases land-use efficiency and has the potential to contribute towards mitigation of related land-use conflicts. Additionally, agrivoltaics is expected to stabilize agricultural yields in regions that are vulnerable to the effects of climate change by providing weather protection and shading and might contribute to strengthen and vitalize rural economies and livelihoods. Adolf Goetzberger and his colleague Armin Zastrow were the first to propose the concept of agrivoltaics in the early 1980s. However, it was only about 10 years ago that agrivoltaics gained traction. In Japan, pioneer Akira Nagashima analyzed crop growth below PV modules within the first research pilot systems in 2004 and promoted the technology under the heading of “solar sharing” which led to the first governmental support scheme implemented in 2012. In 2014, China installed the first large-scale agrivoltaic systems and, still, today remains the country with the largest installed capacity in the world. France was the first European country to systematically support agrivoltaics with regular tenders starting in 2017. This development was largely driven by the research of Christian Dupraz at the French Institut National de la Recherche Agronomique and the Sun’Agri R&D program. Other countries that implemented or plan to implement governmental supporting schemes are the United States of America in the state of Massachusetts, South Korea, India, Israel, Germany, and Italy. An overview of the policies of those countries can be found in Section 5.7. In 2021, agrivoltaics emerged as a market-ready technology with a globally installed capacity of more than 14 GWp. In most subtropical and semiarid regions, however, agrivoltaics remains widely unknown even though the technical potential appears to be very high especially in these regions.
The energy transition is one of the greatest challenges of our time. While photovoltaics (PVs) became the cheapest technology for generating electricity in many regions, the rising development of ground-mounted PV requires large areas and, hence, competes with other land use forms such as agriculture. Agrivoltaics enables dual use of land for both agriculture and PV power generation considerably increasing land-use efficiency, allowing for an expansion of PV capacity on agricultural land while maintaining farming activities. In recent years, agrivoltaics has experienced a dynamic development mainly driven by Japan, China, France, and Germany. In this chapter, we provide an overview of the current state of agrivoltaics starting with a definition and classification of typical systems. Section 5.2 sheds light on basic agricultural implications in agrivoltaic systems such as light availability, further microclimatic impacts, and crop selection. In Section 5.3, we address typical technical structures and agricultural applications distinguishing between interspace PV and overhead PV systems. Section 5.4 outlines relevant characteristics of PV modules used for agrivoltaics including standard crystalline silicon and thin-film cell technologies as well as emerging module technologies. Section 5.5 provides an economic analysis of agrivoltaic systems based on a location in southern Germany and Section 5.6 summarizes the most relevant facts about the preliminary German standard DIN SPEC 91434 published in April 2021. In Section 5.7, we present the results of a case study on societal implications conducted in southern Germany within the research project APV-RESOLA. Section 5.8 provides brief country profiles of the existing policies around the world while Section 5.9 concludes and outlines perspectives of agrivoltaics.
Full-text available
Abstract Depletion and technological change exert opposing forces on the cost of delivering energy to society. One technique for evaluating the costs of energy systems is net energy analysis, which compares,the quantity of energy delivered to society by an energy system to the energy used directly and indirectly in the delivery process, a quantity called the energy return on investment (EROI). Such an investigationinvolves aggregating different energy flows. A variety of methods have been proposed, but none has received universal acceptance. This paper shows that the method,of aggregation has crucial effects on the results of the analysis. It is argued that that economic,approaches such as the index or marginal product method,are superior because they account for differences in quality among,fuels. The thermal equivalent and quality-corrected EROI for petroleum extraction in the U.S. show the same general pattern: a rise to a maximum in the early 1970s, a sharp decline throughout the 1970s, a recovery in the 1980s, and then another modest decline in the 1990s. However, the quality-corrected EROI is consistently much lower than the thermal equivalent EROI, and it declines faster and to a greater extent than the thermal-equivalent EROI. The results indicate that quality corrections have important effects on the results of energy analyses
Full-text available
A series of hypotheses is presented about the relation of national energy use to national economic activity (both time series and cross-sectional) which offer a different perspective from standard economics for the assessment of historical and current economic events. The analysis incorporates nearly 100 years of time series data and 3 years of cross-sectional data on 87 sectors of the United States economy. Gross national product, labor productivity, and price levels are all correlated closely with various aspects of energy use, and these correlations are improved when corrections are made for energy quality. A large portion of the apparent increase in U.S. energy efficiency has been due to our ability to expand the relative use of high-quality fuels such as petroleum and electricity, and also to relative shifts in fuel use between sectors of the economy. The concept of energy return on investment is introduced as a major driving force in our economy, and data are provided which show a marked decline in energy return on investment for all our principal fuels in recent decades. Future economic growth will depend largely on the net energy yield of alternative fuel sources, and some standard economic models may need to be modified to account for the biophysical constraints on human economic activity.
The current situation of the environment and conventional oil resources makes the transition to new energy sources unavoidable. However, there are first principles to remember before implementing them. For instance, the scale of the shift in energy must be considered. If humans replace only half of all fossil fuels by renewable energies in the next decades, there will be a need to displace coal and hydrocarbons flows of about six Terawatts, which is a gargantuan shift. Another consideration is that of energy density, in which renewable energy sources must match current energy sources to be economical and popular in the long run. Intermittency is another consideration, in which high base load factors for current energy supply is enough, while renewable energies like wind and solar are intermittent, making them unable to deliver high load factors. Another consideration is geographical distribution, in which not all renewable energy sources are available or as available as fossil fuel, as in the case of solar and wind.
Collateral impacts of LULUCF projects, especially those concerning social and environmental aspects, have been recognised as important by the Marrakech Accords. The same applies to the necessity of assessing and, if possible, of quantifying the magnitude of these impacts. This article aims to define, clarify and structure the relevant social, economic and environmental issues to be addressed and to give examples of indicators that ought to be included in the planning, design, implementation, monitoring, and ex post evaluation of LULUCF projects. This is being done by providing a conceptual framework for the assessment of the sustainability of such projects that can be used as a checklist when dealing with concrete projects, and that in principle is applicable to both Annex I and non-Annex I countries. Finally, a set of recommendations is provided to further develop and promote the proposed framework.
At a time of growing concern over the rising costs and long-term environmental impacts of the use of fossil fuels and nuclear energy, wind energy has become an increasingly important sector of the electrical power industry, largely because it has been promoted as being emission-free and is supported by government subsidies and tax credits. However, large numbers of bats are killed at utility-scale wind energy facilities, especially along forested ridgetops in the eastern United States. These fatalities raise important concerns about cumulative impacts of proposed wind energy development on bat populations. This paper summarizes evidence of bat fatalities at wind energy facilities in the US, makes projections of cumulative fatalities of bats in the Mid-Atlantic Highlands, identifies research needs, and proposes hypotheses to better inform researchers, developers, decision makers, and other stakeholders, and to help minimize adverse effects of wind energy development.
Experience curves are used to analyse the prospects for diffusion and adoption of renewable energy technologies, with special emphasis on wind turbines and photovoltaic (PV) modules. The analysis shows that the possibility of cost reductions of renewable energy technologies is greater than for conventional energy technologies, However, large investments are necessary to make wind turbines and PV modules economically competitive with conventional power plants. The results indicate that the prospects for diffusion and adoption of wind turbines and PV modules will increase if policy instruments are used to bring about diffusion.
This paper assesses modeling parameters that affect the environmental performance of two state-of-the-art photovoltaic (PV) electricity generation technologies: the PVL136 thin film laminates and the KC120 multi-crystalline modules. We selected three metrics to assess the modules’ environmental performance, which are part of an actual 33 kW installation in Ann Arbor, MI. The net energy ratio (NER), the energy pay back time (E-PBT), and the CO2 emissions are calculated using process based LCA methods. The results reveal some of the parameters, such as the level of solar radiation, the position of the modules, the modules’ manufacturing energy intensity and its corresponding fuel mix, and the solar radiation conversion efficiency of the modules, which affect the final analytical results. A sensitivity analysis shows the effect of selected parameters on the final results. For the baseline scenario, the E-PBT for the PVL136 and KC120 are 3.2 and 7.5 years, respectively. When expected future conversion efficiencies are tested, the E-PBT is 1.6 and 5.7 years for the PVL136 and the KC120, respectively. Based on the US fuel mix, the CO2 emissions for the PVL136 and the KC120 are 34.3 and 72.4 g of CO2/kW h, respectively. The most effective way to improve the modules’ environmental performance is to reduce the energy input in the manufacturing phase of the modules, provided that other parameters remain constant. Consequently, the use of PV as an electricity source during PV manufacturing is also assessed. The NER of the supplier PV is key for the performance of this scheme. The results show that the NER based on a PV system can be 3.7 times higher than the NER based on electricity supplied by the traditional grid mix, and the CO2 emissions can be reduced by 80%.
The increased urgency of dealing with mitigation of the looming climate change has sparked renewed interest in the nuclear energy option. There exists a substantial stream of research on the amount of embodied energy and greenhouse gas emissions associated with nuclear generated electricity. While conventional fossil fuelled power plants cause emissions almost exclusively from the plant site, the majority of greenhouse gas emissions in the nuclear fuel cycle are caused in processing stages upstream and downstream from the plant. This paper distils the findings from a comprehensive literature review of energy and greenhouse gas emissions in the nuclear fuel cycle and determines some of the causes for the widely varying results.The most popular reactor types, LWR and HWR, need between 0.1 and 0.3 kWhth, and on average about 0.2 kWhth for every kWh of electricity generated. These energy intensities translate into greenhouse gas intensities for LWR and HWR of between 10 and 130 g CO2-e/kWhel, with an average of 65 g CO2-e/kWhel.While these greenhouse gases are expectedly lower than those of fossil technologies (typically 600–1200 g CO2-e/kWhel), they are higher than reported figures for wind turbines and hydroelectricity (around 15–25 g CO2-e/kWhel) and in the order of, or slightly lower than, solar photovoltaic or solar thermal power (around 90 g CO2-e/kWhel).