5
Environmental Impacts of Renewable Electricity Generation

Environmental impacts are an inherent part of electricity production and energy use. Electricity 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 electric 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. To place all analyses on a common footing, impacts are expressed in terms of emission or usage rate Environmental Impacts of



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5 Environmental Impacts of Renewable Electricity Generation E nvironmental impacts are an inherent part of electricity production and energy use. Electricity 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 sub- stantially from fossil-fuel sources and from one renewable source to another, using renewables does not avoid impacts entirely. An understanding of the relative envi- ronmental impacts of the various electric power sources is essential to the develop- ment 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 dis- cussing 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 assess- ing all the life stages of the technology: raw materials extraction, refinement, con- struction, use, and disposal. Here, LCA is used to compare the relative impacts of various fossil-fuel-based and renewable sources of electricity. To place all analyses on a common footing, impacts are expressed in terms of emission or usage rate 

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Electricity from Renewable Resources  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 technolo- gies from cradle to grave in a systematic way, there is variability in the assump- tions, boundaries, and methodologies used in these assessments. Therefore, cau- tion should be used in comparing LCAs; each is an approximation of a technolo- gy’s actual impact. Discussion of the attributes and assumptions used in life-cycle analysis is found in Appendix E. The renewable energy technologies are wind, solar, geothermal, hydroelec- tric, 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 pho- tovoltaic (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 emis- sions 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 maxi- mum, 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 for any life-cycle evaluation of greenhouse gas emissions, can be used to evaluate the energy inten- 1Equivalent 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). 2All energy technologies are included in the CO section even if CO emissions were low. Oth- 2 2 er 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 data set 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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Environmental Impacts of Renewable Electricity Generation  sity and resource consumption of the energy technology itself. The literature is replete with assessments of life-cycle energy usage from renewable and non-renew- able 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 con- ventional 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, 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 tech- nologies, especially in making macro-level resource acquisition and development decisions. 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

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Electricity from Renewable Resources  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 radia- tion) 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 technolo- gies, 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 will a wind farm sited at a 7.0 3.1 16.0 1.0 70 0.9 (with External Energy Ratio) Net Energy Ratio (with External Energy Ratio) 0.8 Average 60 Net Energy Ratio 0.7 Maximum 0.6 Minimum With CCS 0.5 50 EER Average 0.4 0.3 40 0.2 0.1 30 0.0 Coal Gas Nuclear 20 10 0 Hydro Solar Wind Biopower Coal Gas Nuclear Energy Technology FIGURE 5.1  Net energy ratio (NER) and external energy ratio (EER) for various renew- able and non-renewable energy sources.  R 5.1 Source: Developed from data provided in Denholm (2004), Denholm and Kulcinski (2003),  Meier (2002), Pacca et al. (2007), Spath et al. (1999), Spath and Mann (2000), Spitzley and  Keoleian (2005), and White (1998, 2006).

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Environmental Impacts of Renewable Electricity Generation  location with lower average wind speeds. In the same way, solar installations in areas with greater solar radiation will typically have higher NERs. Additional fac- tors 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 aver- age 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 NERs, but no LCAs for this fuel stock appear to be available at this time. No NER data were 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 multicrystalline 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 NERs 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

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Electricity from Renewable Resources 00 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 gener- ation 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 sup- pose that the unit of renewable technology displaces one unit of fossil-fuel energy. Future Future 2004 Technology Year 2004 2004 2004 2004 2004 2000? 1997 0 1 2 3 4 5 6 7 8 Payback Time (Years) FIGURE 5.2  Estimated PV energy payback time decreases as a function of the vintage  year of the technology.  Source: Developed from data provided in Fthenakis et al. (2006), Keoleian and Lewis  R 5.2 (2003), Meier (2002), and Pacca et al. (2007).

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Environmental Impacts of Renewable Electricity Generation 0 30 Units of Fossil-Fuel Energy Displaced Fossil-Fuel Energy to Produce Renewable Technology Energy Generated by Renewable Technology Net Fossil-Fuel Energy Displaced 20 10 0 –10 –20 –30 1 2 3 4 5 6 7 8 Year of Deployment FIGURE 5.3  Simple illustrative example of total fossil-fuel energy expended (red),  renewable energy generated (green), and net fossil-fuel energy displaced (blue) for a  R 5.3 scenario when one unit of a renewable technology with an energy payback time of   4 years is deployed each year over a 5-year period. 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 tech- nologies 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 expen- diture 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, large-scale deployment of renew- able 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

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Electricity from Renewable Resources 0 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 elec- tricity. 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 cap- ture and storage (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 renew- able technology. Solar Photovoltaic Of the renewable technologies included in this review, solar 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 sys- tems have conversion efficiencies as low as 5 percent. In 2007, efficiencies had increased to 8–13 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 emis- sions 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 percent to 12 percent, and efficiencies for crystalline silicon modules are expected to increase to 16 per- cent in the next few years, lowering emissions even further (Fthenakis and Kim, 2007).

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Environmental Impacts of Renewable Electricity Generation 0 80 60 Grams CO2 e/kWh 40 20 -1368 -248 0 –20 –40 Hydro Solar Tidal Wind Geothermal Biomass 1500 Average Maximum 1000 Energy Technology Minimum Grams CO2 e/kWh 500 0 –500 with CCS –1000 –1500 Hydro Solar Tidal Wind Geothermal Biomass Coal Gas Nuclear Storage BES Storage CAES Storage PHS Energy Technology FIGURE 5.4  Life-cycle emissions of greenhouse gases (in CO2 equivalents) for various  sources of electricity. Average, maximum, and minimum emissions are shown for each  R 5.4 technology based on a review of the literature. Note that the inset provides a smaller  scale and more details for sources that are not distinguishable in the main figure. Note:  Values for biomass, coal, and natural gas include data for carbon capture and storage  (CCS).  Source: Developed from data provided in Berry et al. (1998), Chataignere et al. (2003),  Denholm (2004), Denholm and Kulcinski (2003), European Commission (1997a,b,c,d),  Frankl et al. (2004), Fthenakis and Kim (2007), Hondo (2005), Mann and Spath (1997),  Meier (2002), Odeh and Cockerill (2008), Spath et al. (1999), Spath and Mann (2000,  2004), Spitzley and Keoleian (2005), Storm van Leeuwen and Smith (2008), Vattenfall AB  (2004), and White (1998, 2006).

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Electricity from Renewable Resources 0 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 feed- stock, as 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 storage 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, 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 (24–40 percent) used in other studies. The two lowest val- ues 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 fac- tor 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 (250 kW) 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

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Environmental Impacts of Renewable Electricity Generation 0 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 electric- ity generation. The life-cycle data from Denholm (2004) demonstrate that current technologies for storage are capable of overcoming the limitations of wind genera- tion 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. 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 hydro- electric 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 temper- ately 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

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Electricity from Renewable Resources  ANNEx TABLE 5.A.1 Estimates of Life-Cycle Greenhouse Gas Emissions in CO2 Equivalent (g/kWh) for Electricity Generation Technologies Technology CO2 Notes Solar Meier 2002. 8 kW, a-Si, 20% capacity, 30 yr lifetime. 157 m2. Colorado. 39 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 3 kW, p-Si, 10 MW/yr, system efficiency 10%. 44 or 26 Future scenarios Hondo 2005. 1% capacity, 30 yr lifetime. Case 1, p-Si w/ production rate 1 GW/yr, 10% system efficiency. Case 2, a-Si, 1 GW/yr, 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 (technology from 1993), 25 yr lifetime. 43 Frankl et al. 2004. ECLIPSE. Italy. 1 kW, sc-Si, 25 yr lifetime, 13% conversion efficiency. Insolation 1740 kWh/m2/yr. 51 Frankl et al. 2004. ECLIPSE. Italy. 1 kW, mc-Si, 25 yr lifetime, 10.7% conversion efficiency. Insolation 1740 kWh/m2/yr. 44 Frankl et al. 2004. ECLIPSE. Italy. 1 kW, a-Si, 20 yr lifetime, 6% conversion efficiency. Insolation 1740 kWh/m2/yr. 45 Frankl et al. 2004. ECLIPSE. Italy. 1 kW, CIGS, 20 yr lifetime, 9% conversion efficiency. Insolation 1740 kWh/m2/yr. 66 Spitzley and Keoleian 2004. Data from Keoleian and Lewis 2003. 2 kW, a-Si. 20 yr lifetime. Detroit. 6% conversion efficiency. Insolation 1380 kWh/m2/yr (technology from 1900s). 44 Spitzley and Keoleian 2005. 2 kW, a-Si, 20 yr lifetime. Phoenix, Arizona. 71 Spitzley and Keoleian 2005. 2 kW, a-Si, 20 yr lifetime. Portland, Oregon. 35 Fthenakis et al. 2008. UTCE, ribbon Si, 11.5% conversion efficiency. (This case and the next seven all have the same assumptions for the following parameters: solar insolation of 1700 kWh/m2 per yr, performance ratio of .8, 30 yr lifetime. Did not include a case with crystal-clear project energy mix (natural gas and hydroelectric).) 43 Fthenakis et al. 2008. UTCE, mc-Si, 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., mc-Si, 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.

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Environmental Impacts of Renewable Electricity Generation  TABLE 5.A.1 Continued Technology CO2 Notes Wind 15 White 1998. 25 yr lifetime. Capacity 24% actual. Class 2 to 4 wind. 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 future) Hondo 2005. 20% capacity both. 300 kW (future case 400 kW). 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. 1995-1998 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 Spitzley and Keoleian 2005. Turbine data from Schleisner 2000. 30 yr lifetime, 25 MW, Class 6 wind, 36% capacity. 2.5 Spitzley and Keoleian 2005. Turbine data from Schleisner 2000. 30 yr lifetime, 25 MW, Class 4 wind, 24% capacity. Biopower 49 Mann and Spath 1997. IGCC with 80% capacity, 30 yr lifetime. Assumes 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 Spitzley and Keoleian 2005. 30 yr lifetime. 113 MW, hybrid poplar based on Mann and Spath 1997. Lp IGCC. 40 Spitzley and Keoleian 2005. 20 yr lifetime. 75 MW, willow based on Heller et al. 2003. Hp IGGC. EPRI model. 39 Spitzley and Keoleian 2005. 20 yr lifetime. 113 MW, willow based on Heller et al. 2003. Lp IGGC. NREL model. 52 Spitzley and Keoleian 2005. 20 yr lifetime. 50 MW, willow based on Heller et al. 2003. Direct fire. EPRI model. continued

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Electricity from Renewable Resources  TABLE 5.A.1 Continued 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 Switzerland. 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 peat land flooded. 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 did not include CO2 from flooded biomass. 26 Spitzley and Keoleian 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. ETSO (1999) from Carbon Trust website.

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Environmental Impacts of Renewable Electricity Generation  TABLE 5.A.1 Continued Technology CO2 Notes 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, 425 MW, 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, 404 MW, 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 Hendriks 1994. 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 pulverized coal with SRC, ESP, FGD. 879 Odeh and Cockerill 2008. Supercritical pulverized coal with SRC, ESP, FGD. 255 Odeh and Cockerill 2008. Supercritical pulverized coal (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 to 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. continued

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Electricity from Renewable Resources  TABLE 5.A.1 Continued Technology CO2 Notes Nuclear 15 White 1998. PWR. 75% capacity, 40 yr lifetime. Enrichment by gas centrifuge (not 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. With T&D based on White 1998. 24 (22) Hondo 2005. Disposal costs not included, only 50 yr dry storage for spent fuel. Assumes 67% enrichment in United States. Analysis very sensitive to enrichment conditions, e.g., values range from 30 to 10 g CO2/kWh if all U.S. versus all Japan enrichment. 70% capacity, 30 yr lifetime. Accounted for CH4 leakage during resource extraction. Did 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 1375 MW. 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. 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 3000 kWh/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 2400 kWh/SWU of energy, process analysis for construction. Storage PHS 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 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 2.7 GW proposed facility in Ohio. Assumes negligible leaks, no energy intensive maintenance on cavern. Uses natural gas to compress air.

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Environmental Impacts of Renewable Electricity Generation  TABLE 5.A.1 Continued Technology CO2 Notes BES 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 hr. 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 Note: a-Si, amorphous silicon; BES, battery energy storage; CAES, compressed air energy storage; CCS, carbon capture and storage; CIGS, copper indium gallium selenide; FGC, flue gas clean-up; FGD, flue gas desulphurization; LEBS, low emission boiler system; mc-Si, multicrystalline silicon; MEA, monoethanolamine; PB-acid, lead acid; pc-Si, polycrystalline silicon; PHS, pumped hydro storage; PSB, sodium-bromide/sodium-polysulfide battery; sc-Si, single-crystalline silicon; SCR, selective catalytic reduction; T&D, transmission and distribution; V redox, vanadium acid; VRB, vanadium redox battery. All studies listed use LCA method. Not all studies are comparable. Denholm (2004) includes all life-cycle costs plus T&D emissions in LCA (most LCAs do not include T&D).

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