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5
Environmental Impacts of Renewable Electricity Generation
Environmental impacts are an inherent part of power production and energy use.
Power generated from renewable energy sources has a smaller environmental footprint
than power from fossil fuel sources, which is arguably the major impetus for moving
away from fossil fuels to renewables. However, although the types and magnitude of
environmental effects differ substantially from fossil fuel sources and from one
renewable source to another, using renewables does not avoid impacts entirely, An
understanding of the relative environmental impacts of the various power sources is
essential to the development of sound energy policy.
This chapter reviews and compares the environmental impacts of various fossil
fuel and renewable sources of electricity. It applies life cycle analyses in discussing
impacts that occur typically on regional or larger scales, such as air, water, and global
warming pollution. This chapter then addresses local impacts that are often considered
and assessed as part of the siting and permitting processes.
LARGE-SCALE IMPACTS FROM LIFE CYCLE ASSESSMENT
Life cycle assessment (LCA) attempts to estimate the overall energy usage and
environmental impact from the energy produced by a given technology by assessing all
the life stages of the technology: raw materials extraction, refinement, construction, use,
and disposal. Here, LCA is used to compare the relative impacts of various fossil-fuel-
based and renewable sources of electricity. Environmental impacts considered here
include emissions of carbon dioxide, sulfur oxides, nitrogen oxides, particulate matter,
land use, and use and consumption of water. For completeness, net energy production is
also considered. To place all analyses on a common footing, impacts are expressed in
terms of emission or usage rate per kilowatt-hour (kWh). Finally, it should be noted that
developing complete LCAs of electricity sources is beyond the scope of this panel. There
are, however, a wide range of earlier assessments, and these form the basis of this
section.
A major complication in comparing LCAs is that there is no set standard for
carrying out such analyses. While it is the goal in using LCAs to cover technologies from
cradle to grave in a systematic way, there is variability in the assumptions, boundaries,
and methodologies used in these assessments. Therefore, caution should be used in
comparing LCAs; each is an approximation of a technology’s actual impact. Discussion
of the attributes and assumptions used in life cycle analysis is found in Appendix E.
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The renewable energy technologies are wind, solar, geothermal, hydroelectric,
tidal, biopower, and storage. Appendix F contains LCA studies for coal, natural gas, and
nuclear technologies as a benchmark against which to assess the performance of
renewables. LCA information for solar energy is limited to photovoltaic (PV)
technologies, and no LCA studies were reviewed for concentrating solar power (CSP)
technologies such as solar trough, power tower, or dish engine technologies. No LCA
information is included for enhanced geothermal systems. The life cycle impacts
considered here include net energy usage; atmospheric emissions of greenhouse gases
expressed in units of carbon dioxide (CO2) equivalents (CO2e);1 atmospheric emissions
of sulfur dioxide (SO2), nitrogen oxides (NOx), and particulate matter;2 land use; and
water withdrawals and consumption. To provide a sense of the variability of the LCAs
found in the literature, the maximum, minimum, and average energy usage and
environmental impact for each technology are shown in figures discussed below in this
chapter.
Energy
Energy input and output calculations, the basic building blocks to any life cycle
evaluation of greenhouse gas emissions, can be used to evaluate the energy intensity and
resource consumption of the energy technology itself. The literature is replete with
assessments of life cycle energy usage from renewable and non-renewable sources of
electricity. However, these assessments adopt a wide range of energy metrics, making
internal comparisons problematic. Spitzley and Keoleian (2005) describe eight distinct
energy metrics defined in the literature.
Energy metrics should therefore be used with cautions and caveats. No single
metric defines the ideal energy generation technology without an accompanying
statement of the core value for assessment. For example, a metric such as capacity factor
will effectively measure for intermittence or dispatchability. A metric such as price per
unit of energy produced measures economic value according to conventional accounting,
financing, and cost-accounting assumptions.
This review focuses on two of the more commonly used energy metrics: (1) net
energy ratio (NER), which quantifies how much net energy a technology produces over
its life cycle, and (2) energy payback time (EPBT), which defines how long it takes for a
given energy technology to recoup the lifetime energy invested in its development once
the technology starts generating electricity. These metrics offer insight into the overall
energy and environmental performance of generation technologies, especially in making
macro-level resource acquisition and development decisions.
1
Equivalent carbon dioxide emissions (CO2e) are the amount of greenhouse gas emissions expressed
as carbon dioxide, taking into account the global warming potential of non-carbon dioxide greenhouse
gases (e.g., methane and nitrous oxides).
2
All energy technologies are included in the CO2 section even if CO2 emissions were low. Other
pollutants with emissions less than 100 mg/kWh are not included in the data and discussion. Studies used
to compile CO2 data often make up a different dataset from the studies used to compile other emissions.
Often LCA studies focus only on CO2. This required building another data set for other emissions.
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Net Energy Ratio
The NER is defined as the ratio of useful energy output to the grid to the fossil
fuel energy consumed during the lifetime of the technology. As such, it is critical to
assessing whether or not a renewable energy source reduces our use of fossil fuel.
Renewable energy sources generally have an NER value greater than one. For
fossil fuel energy technologies, the NER is commonly referred to as the life cycle
efficiency. However, there is some inconsistency in the literature on how the NER is
defined when the energy technology itself is based on a fossil fuel. In these cases, some
researchers include only indirect (external) energy inputs and not the (primary) energy
inherent in the fuel (Meier, 2002; White, 2006; Denholm and Kulcinski, 2003). However,
this interpretation of the ratio is not an accurate reflection of the total resource
consumption of the energy technology in question. For example, the energy consumed by
combusting coal in a coal-fired plant is not included in this alternate use of the term. In
cases where the primary energy of the fuel is not included in the energy inputs, the NER
is more accurately defined as an external energy ratio (EER). The EER is also widely
referred to in the literature as the energy payback ratio.
For renewable energy sources such as wind and solar, the NER and EER are very
similar, since the energy technology’s use of fuel (e.g., wind or solar radiation) does not
deplete the energy resource. For the purposes of this text, the ratio is referred to as the
EER when primary fossil energy inputs are not included.
Figure 5-1 illustrates the range of NERs and EERs found in the literature. NER
values are influenced by a number of factors, including plant capacity factor, plant life
expectancy, choice of plant materials (e.g., steel versus concrete for wind towers), and
fuel mix during material construction. For wind and solar technologies, the location and
the strength of the resource at that location also constitute an important variable. For
example, a wind farm sited in a location with higher average wind speeds will generate
more energy than a wind farm sited at a location with lower average wind speeds. In the
same way, solar installations in areas with greater solar radiation will typically have
higher NERs. Additional factors for PV technologies include position of module, solar
conversion efficiency of module, and manufacturing energy intensity.
Figure 5-1 shows that NERs for renewable technologies tend to be higher than for
conventional energy technologies, because they consume fewer resources. Of the
technologies reviewed, wind has the highest NERs, with values that range from 11 to 65.
The lower values tend to be for relatively small wind farms with low-capacity turbines
and slower winds. Net energy ratios of 47 and 65 were reported for two large wind farms
with higher-capacity turbines and higher average wind speeds. The NER for wind is very
dependent on assumptions related to the frequency of blade and turbine replacement,
because so much life cycle energy is consumed in material manufacturing for this
technology.
Figure 5-1 also indicates a relatively high NER for hydroelectric power, but this
should be interpreted with caution, as it is based on only one LCA study (with a NER of
31) for a large reservoir facility in the United States with a 50-year lifetime. NERs for
biopower reported here range from 10 to 16, based on analysis of four power plants that
use cropping to supply biomass. Biopower from waste would be expected to have higher
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NERs, but no LCAs for this fuel stock appear to be available at this time. No NER data
was reviewed for geothermal, tidal or energy storage technologies.
While the NERs for solar PV plotted in Figure 5-1 tend to be relatively low, rapid
innovation should improve this ratio in the coming years. For example, Pacca et al.
(2007) developed an optimal case for multi-crystalline and thin film (a-Si) PV
technologies (using the highest possible solar insolation and conversion efficiency, the
least possible manufacturing energy, and maximum plant life) to evaluate the future
potential of this technology and found that PV NERs improved to 43 and 132,
respectively.
Unlike renewable sources, conventional energy technologies have NERs of less
than 1. Their EERs, however, tend to be comparable to or even greater than the EERs for
solar PV and biopower. Of the three non-renewable sources of energy considered here,
nuclear has the highest average EER.
Energy Payback Time
The energy payback time (EPBT) is a measure of how much time it takes for an
energy technology to generate enough useful energy to offset energy consumed during its
lifetime. As such it provides an indication of the temporal fossil fuel needs and emissions
as an energy infrastructure is transformed from a carbon-intensive to a low-carbon
system.
In the LCA literature, the EPBT is most commonly applied to wind and PV
technologies as an additional measure of the economic viability of these newer
technologies. Wind EPBT of 0.26 and 0.39 years were reported for two large wind farms
with higher-capacity turbines and higher average wind speeds (Schleisner, 2000). The
lower value is for a land-based wind farm, while the higher value reflects the additional
materials needs for offshore installations. EPBT values for PV range from 7.5 years to
less than 1 year. As illustrated in Figure 5-2, this range in EPBT for PV largely reflects a
downward trend in time as each successive generation becomes less energy intensive.
The EPBT of less than 1 year is from analysis of a hypothetical future generation of PV.
The length of the EPBT has important implications for how long it will take to
displace fossil fuel sources of energy with renewable sources. Consider a simple
example. Suppose it takes four units of fossil fuel energy to produce one unit of energy
with a renewable energy technology (such as a wind turbine), and suppose that the unit of
renewable technology displaces one unit of fossil fuel energy. Thus, the EPBT for the
technology is 4 years. The renewable technology does not begin to displace fossil fuel
energy used per year until 4 years after its initial deployment.
However, the preceding example omits the reality that low-carbon technologies
will be deployed over time, so that the energy costs of each successive installation
accumulate and effectively extend the time it takes before the energy benefits of the
renewable technology are realized. For example, suppose that one unit of the renewable
technology discussed above is deployed each year for a period of 5 years. In this
scenario, the break-even point between the expenditure of fossil fuel energy and
displacement of the same does not occur until 1 year after the completion of the
deployment or 6 years after the first unit is deployed (see Figure 5-3). By the same token,
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large-scale deployment of renewable technologies with long EPBTs, such as PV, will
likely not begin to provide a net displacement of fossil fuel energy until some years after
the deployment has begun. Since CO2 emission reductions depend on displacing fossil
fuel energy, this means that the greenhouse gas emissions reductions from using
renewable energy may not be realized for quite some time after the deployment begins.
On the other hand, in terms of greenhouse gas emissions, adding new capacity using
renewables is preferable to adding new capacity using CO2-emitting fossil fuel sources
regardless of the EPBT because of the lifetime commitment to fossil fuel use made by
such plants.
Greenhouse Gas Emissions
Concern about climate change and greenhouse gas (GHG) emissions is a major
driver in the push for use of renewable energy sources. This section reviews the LCAs of
GHG or CO2e for relevant renewable and non-renewable sources of electricity. Figure 5-
4 illustrates the range of estimates of CO2e emissions that appear in the literature. Table
5-A-1 (in the annex at the end of the chapter) provides a compilation of studies that
estimate life cycle emission of GHG in CO2e.
Not surprisingly, renewables are estimated to have significantly less CO2e
emissions than coal and gas; most estimates of emissions from nuclear power use are
similar in magnitude to those from the use of renewables. Adding carbon capture and
sequestration (CCS) to coal and gas systems, however, significantly reduces the relative
advantage renewables have in terms of carbon and energy savings. This relative
advantage is also modestly reduced by adding energy storage to a renewable technology.
Solar Photovoltaic
Of the renewable technologies included in this review, solar photovoltaic (PV)
technologies have the highest CO2e emissions, ranging from 21 to 71 g CO2e/kWh. CO2e
emissions from PV are sensitive to innovations in conversion efficiencies and to the
energy mix used to generate electricity during manufacturing. Older systems have
conversion efficiencies as low as 5 percent. In 2007, efficiencies had increased to 8 to 13
percent depending on the type of PV used. A study of newer PV systems dating from
2004-2006 by Fthenakis et al. (2008) puts CO2e emissions at the lower end of the range
(21-54 g CO2e/kWh). By 2010 conversion efficiencies for CdTe PV are expected to
increase from 9 to 12 percent, and efficiencies for crystalline silicon modules are
expected to increase to 16 percent in the next few years, lowering emissions even further
(Fthenakis and Kim, 2007).
Biopower
The CO2e emissions from biopower are affected not only by the feedstocks used,
but also by the yield, fertilizer, and fuel used to cultivate and harvest the feedstock, as
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well as the specifics of the power plant itself (Mann and Spath, 1997). Most CO2e values
for biopower range from 15 to 52 g CO2e/kWh for biomass derived from cultivated
feedstocks. Spath and Mann (2004) claim that biopower can actually lead to “negative”
CO2e emissions (i.e., act as a greenhouse gas sink). Their estimate of a negative emission
of -410 g CO2e/kWh for biopower was based on using waste residues as the feedstock
and giving credit for the avoided GHG emissions that would have occurred as a result of
normal waste disposal. Negative emissions of -667 g CO2e/kWh and -1368 g CO2e/kWh
were estimated for biopower combined with carbon capture and sequestration (CCS)
using crops and residues, respectively. However, none of these studies considered CO2
emissions from initial land conversion, which can be considerable (Searchinger et al.,
2008; Fargione et al., 2008).
Wind
Among the renewable energy technologies, that for wind is estimated to be among
the lowest life cycle emitters of greenhouse gases, with emissions ranging from 2 to 29 g
CO2e/kWh. The high value corresponds to a wind farm with a 20 percent generating
capacity (Hondo, 2005). This capacity factor is lower than the range of capacity factors
(24 to 40 percent) used in other studies. The two lowest values of 1.7 and 2.5 g
CO2e/kWh are for two larger wind farms (with 50 or more 500-kW turbines) set in an
area with good wind production (Class 6 and 4 wind areas, respectively) (Spitzley and
Keoleian, 2005). While wind speed is a key factor in determining life cycle CO2e
emissions, other variables such as generation capacity per unit of materials are also
important. For example, Berry et al. (1998) found that a U.K. wind farm with 103 lower-
capacity turbines (250kW) located in an area with higher average wind speeds (Class 7)
emitted 9 g CO2e/kWh. This result, while still very low, is more than three times higher
than that seen for the U.S. farm with 50 500-kW turbines located in an area with Class 4
winds.
In spite of producing very low life cycle carbon emissions, wind is often
discounted as a viable source of electricity because of its intermittent availability.
Addressing this limitation, Denholm (2004) evaluated CO2 emissions from wind
generation with different storage options. The study found that a combination of wind
and pumped hydropower storage (PHS) emitted only 24 g CO2e/kWh, which is within the
range of CO2 emissions for wind technology alone. A combination of wind and
compressed air energy storage (CAES) technology showed a higher value of 105 g
CO2e/kWh, but still far less than emissions seen with fossil fuel electricity generation.
The life cycle data from Denholm (2004) demonstrate that current technologies for
storage are capable of overcoming the limitations of wind generation intermittency
without significant carbon emissions.
Geothermal
The total for CO2e emissions from geothermal electricity generation incorporates
the emissions associated with production of the facility and emissions during operation.
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The latter emissions depend on both the reservoir gas composition and whether the gas is
vented to the atmosphere during electricity generation. In 2003, only 14 percent of
geothermal facilities were closed-loop binary systems that did not vent gases to the
atmosphere (Bloomfield et al., 2003). The analysis presented here considers
hydrothermal plants and does not discuss enhanced geothermal systems.
The panel’s review found only one LCA study of geothermal technologies that
considered emissions from both facility construction and operation. Hondo (2005)
reported a value of 15 g CO2e/kWh for a double-flash geothermal facility. Other data
from non-LCA literature show a range of CO2e emissions from 0 to 740 g CO2e/kWh for
reservoir emissions only.
Hydropower
Most studies conclude that the life cycle emissions of CO2e from conventional
hydropower technologies are quite small. For example, Hondo (2005) reported a value of
11 g CO2e/kWh for a river system with a small reservoir. Spitzley and Keoleian (2005)
evaluated a large-capacity, efficient U.S. reservoir system located in a semiarid region
and estimated an emission rate of 26 g CO2e/kWh that did not include emissions from
flooded biomass. A limitation of most LCAs of hydroelectric generation is that they do
not consider the CO2 and CH4 emissions that arise from the flooding of large quantities of
biomass when the facility is first developed. Some studies suggest that these emissions
may be significant for large and/or inefficient tropical hydroelectric projects that flood
large quantities of biomass (Fearnside, 1995, 2002) or hydroelectric reservoirs sited on
more temperately located peat lands (Gagnon and van de Vate, 1997). Ranges in the
literature for carbon emissions from tropical reservoirs can be several hundred to several
thousand grams CO2e/kWh, but they do not reflect normalized life cycle emissions.
Gagnon et al. (1997) addressed this issue by deriving a theoretical life cycle emission
value of 237 g CO2e/kWh for a hydroelectric reservoir located in Brazil. In this
calculation, Gagnon et al.(1997) assumed that 100 percent of the flooded biomass would
decay completely over 100 years and that 20 percent of the biomass carbon would be
emitted as methane. This calculation does not include emissions from turbines and
spillways. More study is needed of the impact of flooded biomass on life cycle emissions
associated with hydroelectric plants.
Hydrokinetic (Tidal/Wave)
No LCA data were reviewed for tidal or wave electricity-generating technologies,
which are still very much in the pilot or demonstration stage. One source reported a value
of 25 g CO2e/kWh for the steel used to manufacture turbines for tidal generation
installations (CarbonTrust, 2008). One would expect LCA emissions to be low and to
occur primarily during material manufacturing and plant construction.
Storage
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Storage is not a generating system, but it can be combined with generating
technologies to provide backup power for intermittent and peak power needs. Storage
options reviewed in the LCA literature included pumped hydropower storage (PHS),
compressed air energy storage (CAES), and battery energy storage (BES) (Denholm and
Kulcinski, 2003; Denholm, 2004). The estimate for PHS was a low 3 g CO2e/kWh. When
transmission and distribution (T&D) were included, the estimate increased to 6 g
CO2e/kWh. A variety of BES technologies were reviewed with values ranging from 33 to
81 g CO2e/kWh. A subset of the BES data with values from 33 to 50 g CO2e/kWh
includes T&D. CAES had the highest emission values, 291 and 292 g CO2e/kWh,
primarily because it relies on natural gas.3 The second example includes T&D.4
SO2 Emissions
Figure 5-5 shows the range from the literature for life cycle SO2 emissions from
power sources. Wind, hydropower, and nuclear technologies have extremely low life
cycle SO2 emissions, less than 100 mg/kWh.
Solar Photovoltaic
Rates of SO2 emissions associated with electricity generation from PV are most
affected by the energy intensity of the manufacturing process and the efficiency of the PV
material, as well as the energy mix used to manufacture the PV material and the solar
insolation at the site where the PV is installed. SO2 emissions for PV installations in
Europe range from 73 to 215 mg/kWh and include a range of PV technologies (single
crystalline, multi-crystalline, amorphous silicon, copper-indium-gallium-diselenide
[CIGS] and cadmium telluride [CdTe]) with conversion efficiencies of 6 to14 percent,
and insolation rates of 1700 to 1740 kWh m2/yr over assumed lifetimes of 20 to 30 years.
SO2 emissions shown from studies in the United States have a wider range of values,
from 158 to 540 mg/kWh. The high value of 540 mg SO2/kWh is from an older U.S. PV
installation with lower insolation rates and a greater reliance on coal for electricity
generation compared to that of Europe (Spitzley and Keoleian, 2005). Fthenakis et al.
(2008) compared 2004-2006 PV technologies for similar systems using the average U.S.
and European inventory data and electricity mix. For the European cases, SO2 emission
values ranged from 73 to 146 mg/kWh, whereas for the U.S. cases the values ranged from
158 to 378 mg/kWh.
Interestingly, studies suggest that the most efficient PV material is not necessarily
the best for minimizing emissions. For example, cadmium telluride (CdTe) technologies
have the lowest conversion efficiencies (9 percent) yet produce lower SO2 emissions
because less energy is consumed during CdTe manufacturing than with other PV
technologies that have higher conversion efficiencies (11.5 to 14 percent) (Fthenakis et
al., 2008). This relationship may change as technology innovations decrease energy
consumption during manufacturing.
3
Natural gas is used to re-heat the air coming from the cavern in diabatic CAES.
4
Most LCA studies cited here do not include T&D.
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Biopower
For biopower, reported values for SO2 emissions range from 40 to 940 mg/kWh.
Mann and Spath (1997) suggest that much of this variation arises from differences in
power plant efficiency. The low end of the range, from 40 to 45 mg/kWh, is from two
European studies cited by Mann and Spath (1997). The four remaining studies, with
values ranging from 302 to 940 mg/kWh, are from the United States. Cases with results
in the mid range include two hypothetical IGCC plants with different fuels. Both plants
are based on models developed by the National Renewable Energy Laboratory (NREL).
A plant using hybrid poplar as fuel has an estimated SO2 emission rate of 302 mg/kWh
(Mann and Spath, 1997), and a willow feedstock plant has an estimated SO2 emission rate
of 370 mg/kWh (Spitzley and Keoleian, 2005).5 Mann and Spath include no special
emission controls on combustion plants and assume that all SO2 in the biomass is
converted to emissions. The other U.S. examples include a direct-fired boiler and a high-
pressure IGCC system, based on Electric Power Research Institute (EPRI) models, that
emit SO2 at rates of 930 and 940 mg /kWh, respectively (Spitzley and Keoleian, 2005).
The base models developed by NREL and EPRI have very different emission profiles for
plant combustion (Heller et al., 2004); the EPRI plant is assumed to emit approximately
three times more SO2 than is assumed for the NREL plant.
Geothermal
No LCA data were found that included SO2 emissions for geothermal
technologies. Data from Green and Nix (2006) show reservoir-only emissions ranging
from 0 to 160 mg/kWh
NOx Emissions
Figure 5-6 illustrates the range of life cycle NOx emissions estimated from various
electrical generation technologies. Among these technologies, hydroelectric, wind,
geothermal, and nuclear technologies have low estimated NOx emissions (<100 mg/kWh)
and are not discussed in detail in this section.
As a rule, energy sources based on combustion have significantly higher levels of
NOx emissions than those that do not involve combustion. The NOx produced from
combustion arises from two sources: the oxidation and volatilization of the nitrogen
contained in the fuel, and the high-temperature reactions involving atmospheric nitrogen
and oxygen. The production of NOx from atmospheric sources can be reduced or even
completely eliminated by carrying out the combustion under high-oxygen conditions, so-
5
Spitzley and Keoleian (2005) attributed incorrect SO2 and NOx emission data to the base model IGCC
plants from Heller et al. (2004). SO2 and NOx emission results cited here have been corrected to be
consistent with Heller et al. (2004).
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called oxy-fuel combustion. Because of a lack of LCAs, the levels of NOx emissions
described here do not reflect the performance of these systems.
Solar
NOx emission data for PV technologies range from 40 to 260 mg/kWh. This range
largely reflects the differing mixes of grid energy used to produce the PV material as well
as the conversion efficiencies and life expectancies of the PV facility. The high value of
260 mg/kWh is for an older U.S. PV installation with lower insolation; the greater
reliance on coal for electricity generation in the United States as compared to Europe
leads to greater life cycle emissions in the United States (Spitzley and Keoleian, 2005).
NOx values from European studies ranged from 40 to 99 mg/kWh. A study by Fthenakis
et al.(2008) demonstrates how the carbon intensity of the grid can affect emissions from
PV technologies. They compared 2004-2006 PV technologies for similar systems using
the average U.S. and European inventory data and electricity mix. For the European
cases, NOx values ranged from 40 to 82 mg/kWh, whereas for the U.S. cases reported
values ranged from 79 to 188 mg/kWh.
Biopower
Of all the renewable electricity technologies, biopower can have the highest NOx
emissions, with estimates ranging from 290 to 820 mg/kWh. Mann and Spath (1997)
found that NOx emissions are most sensitive to variations in crop yield, feedstock fuel
used, and power plant efficiency, and that most NOx emissions in the biopower life cycle
(about 70 percent) are from combustion. Whether the feedstock is a fossil fuel or is
biomass, the amount of NOx produced during combustion depends on the nitrogen
content of the fuel and the temperature of combustion. The higher the temperature, the
more NOx is produced. As a result, production of electricity from biopower produces
NOx at rates comparable to that of fossil fuels.
Emissions of Particulate Matter
Figure 5-7 illustrates the range of estimated life cycle emissions of particulate
matter (PM) from various renewable and non-renewable energy sources. PM emissions
tend to be low (<100 mg/kWh) for all the energy technologies considered here, with the
exception of coal, natural gas, and PV. However, many LCAs do not report on emissions
of PM.
Solar Photovoltaic
Five LCAs for PM emissions from PV were found by the panel. Only one, a U.S.
study, reported results higher than 100 mg/kWh: Spitzley and Keoleian (2005) reported a
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value of 610 mg PM/kWh for an older U.S. PV installation with lower insolation rates
and a relatively large reliance on coal in electricity from the grid. The European data, on
the other hand, showed emissions of PM ranging from 6 to 55 mg/kWh.
Land Use
Some have proposed that land use may be a limiting factor for the use of
renewable energy technologies (Pimentel et al., 2002; Grant, 2003), supporting this
argument with non-LCA land-use data based on calculations of power plant size and
quantity of electricity generated. Other studies have focused on one aspect of an energy
technology (e.g., reservoir size for hydropower) to derive a land-use estimate. These
estimates of land use can be misleading because they fail to present an accurate
understanding of the entire life cycle land-use requirements of a technology. The LCA
land-use data discussed here is from Spitzley and Keoleian (2005), whose land-use metric
accounts for the total surface area occupied by the materials and products of an energy
technology, including the time of land occupation over the total life cycle energy
generated. Figure 5-8 shows the results of this 2005 study’s estimates of LCA land use
for renewables and other electricity-generating technologies. Key assumptions in the
Spitzley and Keoleian analysis are (1) exclusion of fuels and materials with insignificant
land acquisition requirements compared to other life cycle stages, and (2) inclusion of
end-of-life land disposal requirements for nuclear fuel only. The Spitzley and Keoleian
analysis does not allow for distinctions for intensity of land use.
A key factor affecting land use is the generating efficiency of the technology per
unit area. By design, technologies using high energy density power sources use less land
to produce more electricity at the point of generation than do the more diffuse renewable
technologies. For this reason, analyses such as the ones cited here find that renewables
have relatively large land-use requirements. To operate fossil fuel and nuclear plants,
however, the fuel must first be extracted or mined. Most LCAs, including those used in
this study, do not account for that process in their assessment of land-use requirements.
Moreover, the land used by some diffuse renewable electricity technologies usually
allows for multiple uses, or the technology makes use of sites that also serve an alternate
purpose (e.g., PV installations on roofs or sides of buildings, wind turbines on farms, and
hydroelectric reservoirs that provide flood control, recreation, and water supply).
Figure 5-8 shows that studies found in the literature give natural gas, coal, and
nuclear technologies low land-use values: 0.45 m2/(MWh/yr), 4.4 to 5.8 m2/(MWh/yr),
and 6.5 m2/(MWh/yr), respectively (without counting resource extraction). Of the
renewable energy technologies, solar has the lowest land-use values, ranging from 9 to
14.3 m2/(MWh/yr). The lowest estimated value for PV is for an installation in Phoenix
where higher insolation rates yield more energy potential per unit area.
The two wind farms in the Spitzley and Keoleian (2005) study report land-use
values of 69 and 94 m2/(MWh/yr). The lower land-use value is from the wind farm with
higher wind speed and reflects the greater power generation potential per unit area and
per unit of equipment. Additionally, only about 1 percent of wind farm land is used by
the turbines and associated facilities, thus allowing for multiple uses (e.g., grazing and
agriculture).
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Denholm, P., and G. Kulcinski. 2003. Net Energy Balance and Greenhouse Gas
Emissions from Renewable Energy Storage System. ECW Report Number 223-1.
Energy Center of Wisconsin, Madison, Wis. June.
DiPippo, R. 2007. Geothermal Power Plants: Principles, Applications, Case Studies and
Environmental Impact. Edition 2. Butterworth-Heinemann, Oxford, U.K.
DOE (U.S. Department of Energy). 2006. Energy Demands on Water Resources: Report
to Congress on the Interdependency of Energy and Water. Washington, D.C.
December.
DOE. 2007. Estimating Freshwater Needs to Meet Future Thermoelectric Generation
Requirements: 2007 Update. DOE/NETL-400/207/1304. Washington, D.C.
September 24.
Dones, R., T. Heck, M.F. Emmenegger, and N. Jungbluth. 2005. Life cycle inventories
for the nuclear and natural gas energy systems, and examples of uncertainty
analysis. International Journal of Life Cycle Assessments 10:10-23.
Dziegielewski, B., T. Bik, U. Alqalawi, S. Mubako, N. Eidem, and S. Bloom. 2006.
Water Use Benchmarks for Thermoelectric Power Generation. Research Report
of the Department of Geography and Environmental Resources. Southern Illinois
University, Carbondale, Ill. August 15.
Ecobilan. 2001. TEAM/DEAM. Sustainable Business Solutions, PricewaterhouseCoopers,
Bethesda, Md.
EIA (Energy Information Administration). 2007. Annual Energy Outlook 2007. EIA
Report No. DOE/EIA-0383(2006). DOE, Washington, D.C. February.
EPRI (Electric Power Research Institute). 2003. Potential Health and Environmental
Impacts Associated with the Manufacture and Use of Photovoltaic Cells: Final
Report. No. 1000095. Palo Alto, Calif.
Barker, B. 2007. Running dry at the power plant. EPRI Journal, Summer, pp. 26-35.
European Commission. 1997a. External Costs of Electricity Generation in Greece.
Brussels, Belgium.
European Commission. 1997b. ExternE National Implementation Denmark. Brussels,
Belgium.
European Commission. 1997c. ExternE National Implementation France. Brussels,
Belgium.
European Commission. 1997d. ExternE National Implementation Germany. Brussels,
Belgium.
Fargione, J., J. Hill, D. Tilman, S. Polasky, and P. Hawthorne. 2008. Land clearing and
the biofuel carbon debt. Science 319:1235-1238.
Fearnside, P.M. 1995. Hydroelectric dams in the Brazilian Amazon as sources of
‘greenhouse’ gases. Environmental Conservation 22:7-19.
Fearnside, P.M. 2002. Greenhouse gas emissions from a hydroelectric reservoir (Brazil’s
Tucurui Dam) and the energy policy implications. Water, Air, and Soil Pollution
133:69-96.
Feeley III, T.J., T.J. Skoneb, G.J. Stiegel, Jr., A. McNemar, M. Nemeth, B. Schimmoller,
J.T. Murphy, and L. Manfredo.. 2008. Water: A critical resource in the
thermoelectric power industry. Energy 33:1-11.
156
OCR for page 157
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
Frankl, P., Corrado, A., and S. Lombardelli. 2004. Photovoltaic (PV) Systems. Final
Report. ECLIPSE (Environmental and Ecological Life Cycle Inventories for
present and future Power Systems in Europe), European Commission. January.
Franklin Associates, visited April 7, 2008, http://www.fal.com/lifecycle.html.
Fthenakis, V.M. 2004. Life cycle impact analysis of cadmium in CdTe PV production.
Renewable and Sustainable Energy Reviews 8:303–334.
Fthenakis, V.M., and H.C. Kim. 2007. Greenhouse-gas emissions from solar electric- and
nuclear power: A life cycle study. Energy Policy 35:2549-2557.
Fthenakis, V.M., H.C. Kim, and E. Alsema. 2008. Emissions from photovoltaic life
cycles. Environmental Science and Technology 44:2168-2174.
Gagnon L., and J. van de Vate. 1997. Greenhouse gas emissions from hydropower: The
state of research in 1996. Energy Policy 25:7-13.
Gagnon, L., and J. Van de Vate. 1997. Greenhouse gas emissions from hydropower: The
State of research in 1996. Energy Policy 25(1):7-13.
GAO (General Accountability Office). 2005. Wind Power: Impacts on Wildlife and
Government Responsibilities for Regulating Development and Protecting Wildlife.
GAO-05-906. Washington, D.C.
Goodwin P., K. Jorde, C. Meier, and O. Parra. 2006. Minimizing environmental impacts
of hydropower development: transferring lessons from past projects to a proposed
strategy for Chile. Journal of Hydroinformatics 8:253-270.
Grant, P.M. 2003. Hydrogen lifts off—with a heavy load. Nature 424:129-130.
Green, B.D., and R.G. Nix. 2006. Geothermal—The Energy Under Our Feet:
Geothermal Resource Estimates for the United States. NREL/TP-840-40665.
National Renewable Energy Laboratory, Golden, Colo. November.
Green, J. 2008. Overview: Zoning for Small Wind Turbines. Presentation for ASES
Small Wind Division Webinar, January 17, 2008. Available at
http://www.windpoweringamerica.gov/pdfs/workshops/2008/sw_zoning_overvie
w.pdf .
Heller, M.C., G.A. Keoleian, and T.A. Volk. 2003. Life cycle assessment of a willow
bioenergycropping system. Biomass and Bioenergy 25:147-165.
Heller, M.C., G.A. Keoleian, M.K. Mann, and T.A.Volk. 2004. Life cycle energy and
environmental benefits of generating electricity from willow biomass. Renewable
Energy 29:1023-1042.
Hendriks, C. 1994. Carbon Dioxide Removal from Coal-Fired Power Plants. Kluwer
Academic Publishers, Dordrecht, Netherlands.
Hondo, H. 2005. Life cycle GHG emission analysis of power generation systems:
Japanese case. Energy 30:2042-2056.
Kansas Energy Council. 2005. Wind Energy Siting Handbook: Guideline Options for
Kansas Cities and Counties. Topeka, Kan
Keith, D.W., J.F. DeCarolis, D.C. Denkenberger, D.H. Lenschow, S.L. Malyshev,
S. Pacala, and P.J. Rasch. 2004. The influence of large-scale wind power on
global climate. Proceedings of the National Academies of Sciences
101(46):16115-16120.
Keoleian, G.A., and G.M. Lewis. 2003, Modeling the life cycle energy and
environmental performance of amorphous silicon BIPV roofing in the U.S.
Renewable Energy 28:271-293.
157
OCR for page 158
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
Laws, E.A. 2000. Aquatic Pollution: An Introductory Text. Wiley-Interscience, New
York, N.Y.
Lochbaum, D. 2007. Got Water? Issue Brief. Union of Concerned Scientists, Cambridge,
Mass. December 4,
Mann, M., and P. Spath. 1997. Life Cycle Assessment of a Biomass Gasification
Combined-Cycle System. NREL/TP-430-23076. National Renewable Energy
Laboratory, Golden, Colo.
Marland, G., and M. Obersteiner. 2008. Large-scale biomass for energy, with
considerations and cautions: an editorial comment. Climatic Change 87:335-342.
Meier, P. 2002. Life cycle Assessment of Electricity Generation Systems and
Applications for Climate Change Policy Analysis. Ph.D. dissertation, University
of Wisconsin, Madison, Wis.
Morrison, M.L., and K. Sinclair. 2004. Wind energy technology, environmental impacts
of. Pp. 435-448 in Encyclopedia of Energy. Volume 6. Elsevier, St. Louis, Miss.
MSNBC. 2008. Drought could shut down nuclear power plants⎯Southeast water
shortage a factor in huge cooling requirements. January 23, 2008, Available at
http://www.msnbc.msn.com/id/22804065/.
Mudd, G.M., and M. Diesendorf. 2008. Sustainability of uranium mining and milling:
Toward quantifying resources and eco-efficiency. Environmental Science and
Technology 42:2624-2630.
New York Department of Environmental Conservation. 2008. Guidelines for Conduction
Bird and Bat Studies at Commercial Wind Energy Projects. Albany, N.Y.
NRC (National Research Council). 2007. Environmental Impacts of Wind-Related
Projects. The National Academies Press, Washington D.C.
Odeh, N.A., and T.T. Cockerill. 2008. Life cycle GHG assessment of fossil fuel power
plants with carbon capture and storage. Energy Policy 38:367-380.
ORNL (Oak Ridge National Laboratory). 1993. Hydropower⎯ORNL Review. Vol. 26,
No. 3&4. Oak Ridge, Tenn. Available at
http://www.ornl.gov/info/ornlreview/rev26-34/text/hydmain.html.
Pacca, S., and A. Horvath. 2002. Greenhouse gas emissions from building and operating
electric power plants in the Upper Colorado River Basin. Environmental Science
and Technology 36:3194-3200.
Pacca, S., D. Sivaraman, and G.A. Keoleian. 2007. Parameters affecting the life cycle
performance of PV technologies and systems. Energy Policy 35:3316-3326.
Pimentel, D., M. Herz, M. Glickstein, M. Zimmerman, R. Allen, K. Becker, J. Evans,
B. Hussain, R. Sarsfeld, A. Grosfeld, and T. Seidel. 2002, Renewable energy:
Current and potential issues. BioScience 52:1111-1120.
Pre Consultants. 2007a. Available at http://www.pre.nl/eco-indicator99.
Pre Consultants. 2007b. Available at http://www.pre.nl/ecoinvent/.
Schleisner, L. 2000. Life cycle assessment of a wind farm and related externalities.
Renewable Energy 20:279-288.
Searchinger, T., R. Heimlich, R.A. Houghton, F. Dong, A. Elobeid, J. Fabiosa,
S. Tokgoz, D. Hayes, and T. Yu. 2008. Use of U.S. croplands for biofuels
increases greenhouse gases through emissions from land-use change. Science
319:1238-1240.
158
OCR for page 159
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
Serchuk, A. 2000. The Environmental Imperative for Renewable Energy: An Update.
Renewable Energy Policy Project, Washington, D.C.
Silverstein, K. 2008. Transmission Developers Jolted. EnergyCentral.com, January 14,
2008. Available at
http://www.energycentral.com/centers/energybiz/ebi_detail.cfm?id=447.
Spath, P., and M. Mann. 2000. Life Cycle Assessment of a Natural Gas Combined-Cycle
Power Generation System. NREL/TP-570-27715. National Renewable Energy
Laboratory, Golden, Colo. September.
Spath, P, and M. Mann. 2004. Biomass Power and Conventional Fossil Systems with and
without CO2 Sequestration⎯Comparing the Energy Balance, Greenhouse Gas
Emissions and Economics. NREL/TP-510-32575. National Renewable Energy
Laboratory, Golden, Colo. January.
Spath, P., M. Mann, and D. Kerr. 1999. Life Cycle Assessment of Coal-fired Power
Production. NREL/TP-570-25119. National Renewable Energy Laboratory,
Golden, Colo. June.
Spitzley D., and G.A. Keoleian. 2005. Life Cycle Environmental and Economic
Assessment of Willow Biomass Electricity: A Comparison with Other Renewable
and Non-Renewable Sources. Report # CSS04-05R (March 2004, revised
February 10, 2005). Center for Sustainable Systems, University of Michigan, Ann
Arbor. Mich.
Storm van Leeuwen, J.W. 2008. Nuclear power⎯The energy balance energy insecurity
and greenhouse gases. An updated version of “Nuclear power⎯The energy
balance” by J.W. Storm van Leeuwen and P. Smith, published in 2002. Available
at http://www.stormsmith.nl/.
USGS (United States Geological Survey). 2004. Estimated Use of Water in the United
States in 2000. USGS Circular 1268. Available at
http://pubs.usgs.gov/circ/2004/circ1268/pdf/circular1268.pdf
Vattenfall AB. 2004. Certified Environmental Product Declaration of Electricity from
Vattenfall´s Nordic Hydropower. Registration No. S-P-00088. Vattenfall AB
Generation Nordic, Stockholm, Sweden. February. Available at
http://www.environdec.com/reg/088/.
Vattenfall AB. 2005. Certified Environmental Product Declaration of Electricity from
from Forsmark Nuclear Power Plant. Registration No. S-P-00021. Vattenfall AB
Generation Nordic, Stockholm, Sweden. June. Available at
http://www.environdec.com/reg/021/.
Viel, J.A. 2007. Use of Reclaimed Water for Power Plant Cooling. Report ANL/EVS/R-
07/3. Environmental Science Division, Argonne National Laboratory, Argonne,
Ill.
Vinluan, F. 2007. Drought could force shutdown of nuclear, coal plants. Triangle
Business Journal, November 23.
White, S. 1998. Net Energy Payback and CO2 Emissions from Helium-3 Fusion and
Wind Electrical Power Plants. Ph.D. dissertation #UWFDM-1093. Fusion
Technology Institute, University of Wisconsin, Madison, Wis.
White, S. 2006. Net energy payback and CO2 emissions from three midwestern wind
farms: An Update. Natural Resources Research 15:271-281.
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ANNEX
TABLE 5-A-1 Estimates of Life Cycle Greenhouse Gas Emissions in CO2e (g/kWh) for
Electricity Generation Technologies
Technology CO2 Notes
Solar
39 Meier 2002. 8 kW, a-Si. 20% capacity, 30 year lifetime. 157m2.
Colorado.
70 w/BES Denholm 2004. Storage (10-50% capacity, 20 yr lifetime) added to Meier
(2002) PV system with T&D.
53 Hondo 2005. 15% capacity 30 yr lifetime. Rooftop 3kW pSi 10 MW/yr.
system efficiency 10%.
44 or 26 Future scenarios Hondo 2005. 1% capacity, 30 yr lifetime. Case 1 pSi w/
production rate 1GW/yr 10% system efficiency. Case 2 a-Si 1GW/yr
with 8.6% system efficiency.
55 European Commission 1997d. ExternE. Germany. 4.8 kW mc-Si.
(technology from 1990). 25 yr lifetime.
51 European Commission 1997d. ExternE. Germany. 13 kW. mc-Si. (tech.
from 1993). 25 yr lifetime.
43 Frankl et al. 2004. ECLIPSE. Italy. 1 kW. sc-Si. 25 yr lifetime. 13%
conversion efficiency. Insolation 1740 kWhh/m2/yr.
51 Frankl et al.2004. ECLIPSE. Italy. 1 kW. mc-Si. 25 yr lifetime. 10.7%
conversion efficiency. Insolation 1740 kWhh/m2/yr.
44 Frankl et al. 2004. ECLIPSE. Italy. 1kW. a-Si. 20 yr lifetime. 6%
conversion efficiency. Insolation 1740 kWhh/m2/yr.
45 Frankl et al. 2004. ECLIPSE. Italy. 1 kW. CIGS. 20 yr lifetime. 9%
conversion efficiency. Insolation 1740 kWhh/m2/yr.
66 Spitzley and Keoleian (S&K) 2004. Data from Keoleian & Lewis 2003.
2kW, a-Si. 20 yr lifetime. Detroit. 6% conversion efficiency. Insolation
1380 kWhh/m2/yr. technology from 1900s.
44 S&K 2005. 2kW, a-Si. 20 yr lifetime. Phoenix.
71 S&K 2005. 2kW, a-Si. 20 yr lifetime. Portland, OR.
35 Fthenakis et al. 2008. UTCE, ribbon Si, 11.5% conversion efficiency,
(This case and the next seven all have same assumptions for following
parameters: solar insolation 1700 kWh/m2/yr, performance ratio of .8, 30
yr. lifetime. Did not include a case with manufactured with crystal clear
project energy mix (natural gas and hydroelectric).
43 Fthenakis et al. 2008. UTCE, mcSi, 13.2% conversion efficiency.
44 Fthenakis et al. 2008. UTCE, s-Si, 14% conversion efficiency.
21 Fthenakis et al. 2008. UTCE, CdTe, 9% conversion efficiency.
44 Fthenakis et al. 2008. U.S., ribbon Si, 11.5% conversion efficiency.
52 Fthenakis et al. 2008. U.S., mcSi, 13.2 % conversion efficiency.
54 Fthenakis et al. 2008. U.S., s-Si, 14% conversion efficiency.
26 Fthenakis et al. 2008. U.S., CdTe, 9% conversion efficiency.
15 White 1998. 25 yr lifetime. Capacity 24% actual. Class 2 to 4 wind.
Wind
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Technology CO2 Notes
Includes replacement of all blades. NOTE: White (2006) updated LCA
on actual performance and found similar results—14. Wind results
specific to site—hard to generalize and dependent on energy used to
produce materials—in the United States—coal. Wind dismantling
assumed to be same as construction. No recycling of metals taken into
account.
24 w/ PHS Denholm 2004. PHS (10-50% capacity).
105 w/CAES Denholm 2004. CAES (70-85% capacity, 30 yr lifetime).
29 (20 Hondo 2005. 20% capacity both. 300kW (future case 400 kW)
future)
7 European Commission 1997d. ExternE. Germany. 0.25 MW. 20 yr
lifetime. Recycle metals.
7 Chataignere et al. 2003. ECLIPSE. Europe. 0.6 MW. 20 yr lifetime.
1995-98 technology. onshore.
12 Chataignere et al. 2003. ECLIPSE. Europe. 1.5 MW 20 yr. onshore
9 Chataignere et al. 2003. ECLIPSE. Europe. 2.5 MW 20 yr. offshore
14.5 European Commission 1997b. ExternE. Denmark. 0.5 MW turbine,
onshore.
22 European Commission 1997b. ExternE. Denmark. 0.5 MW turbine,
offshore.
8 European Commission 1997a. ExternE. Greece. onshore.
9 Berry et al.1998. 0.3 MW onshore.
1.7 S&K 2005. Turbine data from Schleisner 2000. 30 yr lifetime. 25 MW.
Class 6 wind. 36% capacity.
2.5 S&K 2005. Turbine data from Schleisner 2000. 30 yr lifetime. 25 MW.
Class 4 wind. 24% capacity.
49 Mann and Spath 1997. IGCC with 80% capacity 30 yr lifetime. Assumes
Biopower
95% carbon closure. Biopower from energy crops. 600 MW via several
small plants.
-667 Spath and Mann 2004. Added CO2 capture and storage (CCS) to Mann
and Spath (1997) example from above.
-410 Spath and Mann 2004. 0.6 GW direct-fire boiler with biomass from
waste streams.
-1368 Spath and Mann 2004. 0.6 GW direct-fire boiler with biomass from
waste streams with CCS.
18 European Commission 1997c. ExternE. France. Cropping.
15 Berry et al. 1998. Biopower source mainly willow and poplar. Lp IGCC.
49 S&K 2005. 30 yr lifetime. 113 MW. Hybrid poplar based on Mann and
Spath 1997. Lp IGCC
40 S&K 2005. 20 yr lifetime. 75 MW Willow based on Heller et al., 2003.
Hp IGGC. EPRI model.
39 S&K 2005. 20 yr lifetime. 113 MW. Willow based on Heller et al., 2003.
Lp IGGC. NREL model.
52 S&K 2005. 20 yr lifetime. 50 MW Willow based on Heller et al., 2003.
Direct fire. EPRI model.
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Technology CO2 Notes
Geothermal
15 Hondo 2005. 60% capacity, 30 yr lifetime. Double flash type.
47-97* Serchuk 2000. Only includes reservoirs emissions not LCA.
91* Bloomfield et al. 2003. A weighted average of all geothermal capacity
(including binary plants with no CO2 emissions) per unit of electricity
produced. (not LCA)
122* Bertani and Thain 2002. A weighted average of existing plant operation
per unit of electricity produced not LCA. Actual range 4-740 g CO2
e/kWh from 85 plants in 11 countries.
Hydroelectric
20 Gagnon et al. (1997) present summary of a hydropower LCA survey
using data from Finland, Canada, China, Japan, and Switz. Range in data
15 to 165 g CO2e /kWh; average 20 g CO2e /kWh. 100 yr lifetime.
Includes data from river run and reservoir systems, alpine and prairie,
small and large plants. Emissions very dependent on climate, topography,
size of reservoir, construction materials, type of ecosystem flooded.
Lowest case: 15 CO2e from large reservoir in cold climate where
emissions from flooded biomass drop to 0 at year 50. Worst case was in
Finland where flooded peat land. LCA includes plant construction and
decaying biomass from reservoir. A Brazilian reservoir is mentioned that
due to very large size and low generation capacity has an estimated CO2e
of 237 (Fearnside’s estimate is even higher).
11 Hondo 2005. 45% capacity, 30 yr lifetime. Assumed river run w/small
reservoir and not include CO2 from flooded biomass.
26 S&K 2005. 50 yr lifetime. 1296 MW. Large reservoir type. Used data
from Pacca and Horvath (2002).
Tidal
25-50 Preliminary, not rigorous. NOTE: production of steel for turbine is 25
g/kWh of CO2. ETSU (1999) from Carbon Trust website.
Coal
974 White 1998. 75% capacity 40 yr lifetime. Average U.S. plant with SO2
control.
1050 Denholm 2004. With T&D based on White 1998.
975 Hondo 2005. 70% capacity, 30 yr lifetime. Average Japanese plant with
SCR and FGD.
1042 Spath et al. 1999. Average, 360 MW, 60% capacity, 1995, 30 yr lifetime.
FGC and ESP (same as baghouse?).
960 Spath et al. 1999. NSPS, 425MW, 60% capacity, 1995, 30 yr lifetime.
Same as average but with low NOx burners or staged combustion for
increased removal of airborne pollutants.
757 Spath et al. 1999. Future LEBS, 404MW, 60% capacity, 30 yr lifetime,
1995. Unspecified technologies used to decrease emissions.
681 Spath and Mann 2004. Biomass residue co-fired w/coal.
43 Spath and Mann 2004. Biomass residue co-fired w/coal w/CCS.
847 Spath and Mann 2004. Coal based on Hendrik 1994.
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Technology CO2 Notes
247 Spath and Mann 2004. Coal w/CCS.
861 Odeh and Cockerill 2008. IGCC.
167 Odeh and Cockerill 2008. IGCC w/CCS via selexol.
984 Odeh and Cockerill 2008. Subcritical pc with SRC, ESP, FGD.
879 Odeh and Cockerill 2008. Supercritical pc with SRC, ESP, FGD.
255 Odeh and Cockerill 2008. Supercritical pc (same as above) w/ CCS via
MEA.
Gas
469 Meier 2002. 75% capacity over 30 yr lifetime. Average 620 MW, NGCC.
Assumed CH4 release rate of 1.4% (can range from 1-11%) Missouri
plant.
500 Denholm 2004. NGCC w/T&D based on Meier 2002.
608 Hondo 2005. 70% capacity, 30 yr lifetime, LNG-fired.
518 Hondo 2005. 70% capacity, 30 yr lifetime, LNGCC.
499 Spath and Mann 2000. average case NGCC with SCR.
245 Spath and Mann 2004. Added CCS to Spath and Mann 2000.
488 Odeh and Cockerill 2008. NGCC.
200 Odeh and Cockerill 2008. NGCC w/CCS via MEA.
Nuclear
15 White 1998. PWR. 75% capacity, 40 yr lifetime. Enrichment by gas
centrifuge (not what is normally used in United States.). Data for
construction, operations, decommissioning and waste disposal from
others. Only fuel considered in land reclamation. Spent fuel disposal data
30 yrs old.
25 White (2006) updates value to reflect 100% enrichment by gas
diffusion—25.
16 Denholm 2004. W/T&D based on White 1998.
Hondo 2005. Disposal costs not included only 50 yrs dry storage for
24 (22)
spent fuel. Assumes 67% enrichment in United States. Analysis very
sensitive to enrichment conditions, e.g., values range from 30 v 10 g
CO2/kWh if all U.S.A v. all Japan enrichment. 70% capacity, 30 yr
lifetime. Accounted for CH4 leakage during resource extraction. Not
include decommissioning land for mining and milling just electricity to
mine and mill. LLW stored w/o maintenance in near surface waste
disposal sites.
NOTE: future case 22 w/ recycling includes HLW disposal but not
disposal transport. Lower due to enrichment savings. Includes one time
MOX reprocessing of spent fuel.
20 European Commission 1997d. ExternE. Germany. Capacity 1375MW.
PWR
3 Vattenfall 2004. Sweden. Industry EDP. PWR and BWR. Two sites. 85%
capacity, 40 yr lifetime.
108 Storm van Leeuwen and Smith 2008. Average lifetime baseline case. 30
years at 82% capacity. Very detailed LCA.
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Technology CO2 Notes
24 Fthenakis and Kim 2007. Baseline case represents average United States.
55 Fthenakis and Kim 2007. Worst case, poor ores typical of Australia
(0.05% U), most energy for enrichment from coal requiring
3000kWh/SWU of energy, EIO method for construction.
16 Fthenakis and Kim 2007. Best case, rich Canadian ores (12.7% U), 20%
energy for enrichment from coal, rest U.S. grid mix requiring
2400kWh/SWU of energy, process analysis for construction.
Storage
PHS (pumped hydro storage)
3 Denholm and Kulcinski 2003. 74% efficient (?=capacity), 60 yr lifetime.
Assumes dams and reservoirs permanent
5.6 Denholm 2004 with T&D. 74% efficient, 60 yr lifetime. Assumes dams
and reservoirs permanent.
CAES (compressed air energy storage)
291 Denholm and Kulcinski 2003. 40 yr lifetime
292 Denholm 2004. With T&D. 65% efficient, 40 yr lifetime. Excludes
primary electricity generation. Based on a 2.7GW proposed facility in
OH. Assumes negligible leaks, no energy intensive maintenance on
cavern. Uses natural gas to compress air.
BES (battery energy storage)
80.5 Pb-acid Denholm and Kulcinski 2003. 20 yr lifetime.
64.9 V redox
50.4 Pb-acid Denholm 2004. With T&D. 20 yr lifetime, excludes the stored electricity.
Assumes large system w/energy/power ratio of 8 hrs. Pb-acid oversized
30% due to limited depth of discharge. VRB 75%, PSB 63%, Pb-acid
66% efficient.
32.6 PSB
40.2 V redox
NOTES: a-Si, amorphous silicon; BES, battery energy storage; CAES, compressed air energy storage;
CCS, carbon capture and sequestration; CIGS, copper indium gallium selenide; mc-Si, multicrystalline;
PB-acid, lead acid; pc-Si, polycrystalline; PHS, pumped hydro storage; PSB, sodium-bromide/sodium
polysulphide (Regenesys) - polysulfide battery; sc-Si, single crystalline; T&D, transmission and
distribution; V redox - VRB - Vanadium acid - Vanadium - redox battery
All studies listed use LCA method. Not all studies are comparable. Denholm (2004) includes all life cycle
costs plus transmission and distribution emissions in LCA (most LCA don't include T&D)
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