Mark Z.
Jacobson
*
Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305-4020, USA. E-mail: jacobson@stanford.edu; Tel: +1 (650) 723-6836
First published on 1st December 2008
This paper reviews and ranks major proposed energy-related solutions to global warming, air pollution mortality, and energy security while considering other impacts of the proposed solutions, such as on water supply, land use, wildlife, resource availability, thermal pollution, water chemical pollution, nuclear proliferation, and undernutrition. Nine electric power sources and two liquid fuel options are considered. The electricity sources include solar-photovoltaics (PV), concentrated solar power (CSP), wind, geothermal, hydroelectric, wave, tidal, nuclear, and coal with carbon capture and storage (CCS) technology. The liquid fuel options include corn-ethanol (E85) and cellulosic-E85. To place the electric and liquid fuel sources on an equal footing, we examine their comparative abilities to address the problems mentioned by powering new-technology vehicles, including battery-electric vehicles (BEVs), hydrogen fuel cell vehicles (HFCVs), and flex-fuel vehicles run on E85. Twelve combinations of energy source-vehicle type are considered. Upon ranking and weighting each combination with respect to each of 11 impact categories, four clear divisions of ranking, or tiers, emerge. Tier 1 (highest-ranked) includes wind-BEVs and wind-HFCVs. Tier 2 includes CSP-BEVs, geothermal-BEVs, PV-BEVs, tidal-BEVs, and wave-BEVs. Tier 3 includes hydro-BEVs, nuclear-BEVs, and CCS-BEVs. Tier 4 includes corn- and cellulosic-E85. Wind-BEVs ranked first in seven out of 11 categories, including the two most important, mortality and climate damage reduction. Although HFCVs are much less efficient than BEVs, wind-HFCVs are still very clean and were ranked second among all combinations. Tier 2 options provide significant benefits and are recommended. Tier 3 options are less desirable. However, hydroelectricity, which was ranked ahead of coal-CCS and nuclear with respect to climate and health, is an excellent load balancer, thus recommended. The Tier 4 combinations (cellulosic- and corn-E85) were ranked lowest overall and with respect to climate, air pollution, land use, wildlife damage, and chemical waste. Cellulosic-E85 ranked lower than corn-E85 overall, primarily due to its potentially larger land footprint based on new data and its higher upstream air pollution emissions than corn-E85. Whereas cellulosic-E85 may cause the greatest average human mortality, nuclear-BEVs cause the greatest upper-limit mortality risk due to the expansion of plutonium separation and uranium enrichment in nuclear energy facilities worldwide. Wind-BEVs and CSP-BEVs cause the least mortality. The footprint area of wind-BEVs is 2–6 orders of magnitude less than that of any other option. Because of their low footprint and pollution, wind-BEVs cause the least wildlife loss. The largest consumer of water is corn-E85. The smallest are wind-, tidal-, and wave-BEVs. The US could theoretically replace all 2007 onroad vehicles with BEVs powered by 73000–144000 5 MW wind turbines, less than the 300000 airplanes the US produced during World War II, reducing US CO2 by 32.5–32.7% and nearly eliminating 15000/yr vehicle-related air pollution deaths in 2020. In sum, use of wind, CSP, geothermal, tidal, PV, wave, and hydro to provide electricity for BEVs and HFCVs and, by extension, electricity for the residential, industrial, and commercial sectors, will result in the most benefit among the options considered. The combination of these technologies should be advanced as a solution to global warming, air pollution, and energy security. Coal-CCS and nuclear offer less benefit thus represent an opportunity cost loss, and the biofuel options provide no certain benefit and the greatest negative impacts.
Mark Z. Jacobson | Jacobson is Professor of Civil and Environmental Engineering and Director of the Atmosphere/Energy Program at Stanford University. He has received a B.S. in Civil Engineering (1988, Stanford), a B.A. in Economics (1988, Stanford), an M.S. in Environmental Engineering (1988 Stanford), an M.S. in Atmospheric Sciences (1991, UCLA), and a PhD in Atmospheric Sciences (1994, UCLA). His work relates to the development and application of numerical models to understand better the effects of air pollutants from energy systems and other sources on climate and air quality and the analysis of renewable energy resources and systems. Image courtesy of Lina A. Cicero/Stanford News Service. |
Broader contextThis paper reviews and ranks major proposed energy-related solutions to global warming, air pollution mortality, and energy security while considering impacts of the solutions on water supply, land use, wildlife, resource availability, reliability, thermal pollution, water pollution, nuclear proliferation, and undernutrition. To place electricity and liquid fuel options on an equal footing, twelve combinations of energy sources and vehicle type were considered. The overall rankings of the combinations (from highest to lowest) were (1) wind-powered battery-electric vehicles (BEVs), (2) wind-powered hydrogen fuel cell vehicles, (3) concentrated-solar-powered-BEVs, (4) geothermal-powered-BEVs, (5) tidal-powered-BEVs, (6) solar-photovoltaic-powered-BEVs, (7) wave-powered-BEVs, (8) hydroelectric-powered-BEVs, (9-tie) nuclear-powered-BEVs, (9-tie) coal-with-carbon-capture-powered-BEVs, (11) corn-E85 vehicles, and (12) cellulosic-E85 vehicles. The relative ranking of each electricity option for powering vehicles also applies to the electricity source providing general electricity. Because sufficient clean natural resources (e.g., wind, sunlight, hot water, ocean energy, etc.) exist to power the world for the foreseeable future, the results suggest that the diversion to less-efficient (nuclear, coal with carbon capture) or non-efficient (corn- and cellulosic E85) options represents an opportunity cost that will delay solutions to global warming and air pollution mortality. The sound implementation of the recommended options requires identifying good locations of energy resources, updating the transmission system, and mass-producing the clean energy and vehicle technologies, thus cooperation at multiple levels of government and industry. |
Indoor plus outdoor air pollution is the sixth-leading cause of death, causing over 2.4 million premature deaths worldwide.1 Air pollution also increases asthma, respiratory illness, cardiovascular disease, cancer, hospitalizations, emergency-room visits, work-days lost, and school-days lost,2,3 all of which decrease economic output, divert resources, and weaken the security of nations.
Global warming enhances heat stress, disease, severity of tropical storms, ocean acidity, sea levels, and the melting of glaciers, snow pack, and sea ice.5 Further, it shifts the location of viable agriculture, harms ecosystems and animal habitats, and changes the timing and magnitude of water supply. It is due to the globally-averaged difference between warming contributions by greenhouse gases, fossil-fuel plus biofuel soot particles, and the urban heat island effect, and cooling contributions by non-soot aerosol particles (Fig. 1). The primary global warming pollutants are, in order, carbon dioxide gas, fossil-fuel plus biofuel soot particles, methane gas,4,6–10 halocarbons, tropospheric ozone, and nitrous oxide gas.5 About half of actual global warming to date is being masked by cooling aerosol particles (Fig. 1 and ref. 5), thus, as such particles are removed by the clean up of air pollution, about half of hidden global warming will be unmasked. This factor alone indicates that addressing global warming quickly is critical. Stabilizing temperatures while accounting for anticipated future growth, in fact, requires about an 80% reduction in current emissions of greenhouse gases and soot particles.
Fig. 1 Primary contributions to observed global warming from 1750 to today from global model calculations. The fossil-fuel plus biofuel soot estimate4 accounts for the effects of soot on snow albedo. The remaining numbers were calculated by the author. Cooling aerosol particles include particles containing sulfate, nitrate, chloride, ammonium, potassium, certain organic carbon, and water, primarily. The sources of these particles differ, for the most part, from sources of fossil-fuel and biofuel soot. |
Because air pollution and global warming problems are caused primarily by exhaust from solid, liquid, and gas combustion during energy production and use, such problems can be addressed only with large-scale changes to the energy sector. Such changes are also needed to secure an undisrupted energy supply for a growing population, particularly as fossil-fuels become more costly and harder to find/extract.
This review evaluates and ranks 12 combinations of electric power and fuel sources from among 9 electric power sources, 2 liquid fuel sources, and 3 vehicle technologies, with respect to their ability to address climate, air pollution, and energy problems simultaneously. The review also evaluates the impacts of each on water supply, land use, wildlife, resource availability, thermal pollution, water chemical pollution, nuclear proliferation, and undernutrition.
Costs are not examined since policy decisions should be based on the ability of a technology to address a problem rather than costs (e.g., the U.S. Clean Air Act Amendments of 1970 prohibit the use of cost as a basis for determining regulations required to meet air pollution standards) and because costs of new technologies will change over time, particularly as they are used on a large scale. Similarly, costs of existing fossil fuels are generally increasing, making it difficult to estimate the competitiveness of new technologies in the short or long term. Thus, a major purpose of this paper is to provide quantitative information to policy makers about the most effective solutions to the problem discussed so that better decisions about providing incentives can be made.
The electric power sources considered here include solar photovoltaics (PV), concentrated solar power (CSP), wind turbines, geothermal power plants, hydroelectric power plants, wave devices, tidal turbines, nuclear power plants, and coal power plants fitted with carbon capture and storage (CCS) technology. The two liquid fuel options considered are corn-E85 (85% ethanol; 15% gasoline) and cellulosic-E85. To place the electric and liquid fuel sources on an equal footing, we examine their comparative abilities to address the problems mentioned by powering new-technology vehicles, including battery-electric vehicles (BEVs), hydrogen fuel cell vehicles (HFCVs), and E85-powered flex-fuel vehicles. We examine combinations of PV-BEVs, CSP-BEVs, wind-BEVs, wind-HFCVs, geothermal-BEVs, hydroelectric-BEVs, wave-BEVs, tidal-BEVs, nuclear-BEVs, CCS-BEVs, corn-E85 vehicles, and cellulosic-E85 vehicles. More combinations of electric power with HFCVs were not compared simply due to the additional effort required and since the options examined are the most commonly discussed. For the same reason, other fuel options, such as algae, butanol, biodiesel, sugar-cane ethanol, or hydrogen combustion; electricity options such as biomass; vehicle options such as hybrid vehicles, heating options such as solar hot water heaters; and geoengineering proposals, were not examined.
In the following sections, we describe the energy technologies, evaluate and rank each technology with respect to each of several categories, then provide an overall ranking of the technologies and summarize the results.
Table 1 gives estimated upper limits to the worldwide available energy (e.g., all the energy that can be extracted for electricity consumption, regardless of cost or location) and the technical potential energy (e.g., the energy that can feasibly be extracted in the near term considering cost and location) for each electric power source considered here. It also shows current installed power, average capacity factor, and current electricity generated for each source.
Technology | Available energy/PWh yr−1 | Technical potential energy/PWh yr−1 | Current installed power (GW) | Worldwide capacity factor of technology in place | Current electricity generation/TWh yr−1 |
---|---|---|---|---|---|
a Extractable power over land. Assumes the surface area over land outside of Antarctica is 135000000 km2, 160 W solar panels with an area of 1.258 m2 each, a globally-averaged capacity factor for photovoltaics of 15%, and a reduction of available photovoltaic area by one-third to allow for service and panels to be angled to prevent shading by each other. The technical potential is estimated as less than 20% of the total to account for low-insolation and exclusion areas. b Data21 for 2007. About 90% of the installed PV was tied to the grid. c A PV capacity factor range of 0.1–0.2 is used based on running PVWatts12 over many locations globally. The 3 yr averaged capacity factor of 56 rooftop 160 W solar panels, each with an area of 1.258 m2, at 37.3797 N, 122.1364 W was measured by the author as 0.158. d Calculated from installed power and an assumed capacity factor of 15%. e The available energy is calculated by dividing the land area from (a) by the range of km2 MW−1 for CSP without storage given in ESI† and multiplying the result by a mean CSP capacity factor of 19%. A technical potential for installed CSP is 630–4700 GW.16 This was converted to PWh yr−1 assuming a capacity factor of 19%. f The installed power and electricity generation are from ref. 16. The low capacity factor is derived from these two. The high capacity factor is from ref. 22. Neither includes storage. g The number is the actual power wind turbines would generate, from ref. 23. Assumes electric power is obtained from 1500 kW turbines with 77 m diameter rotors and hub heights of 80 m, spaced 6 turbines per square kilometer over the 12.7% of land worldwide outside of Antarctica where the wind speed exceeds 6.9 m s−1. The average global wind speed over land at such locations is 8.4 m s−1 at 80 m hub height. The technical potential is estimated by assuming a 35% exclusion area beyond the 87% exclusion already accounted for by removing low-wind-speed areas over land worldwide (Table 2). A calculated exclusion area over the mid-Atlantic Bight is 31%.24 h Data were for 2007.25 i The low value is the current global average.14 The high value is from ESI†. The 2004–2007 average for wind turbines installed in the US is 0.33–0.35.26 j Calculated from installed power and low capacity factor. k Ref. 13,16. l This range is the technical potential.27 m Data were for 2005.14 n Calculated from installed power and electricity generation. o Calculated in ESI†. p See text. q Ref. 28. r Data were for 2005.29 s Low available energy is for once-through thermal reactors; high number is for light-water and fast-spectrum reactors, which have very low penetration currently. Low number of years is for known reserves. High number is for expected reserves.16 t Coal reserves were 930 billion tons in 2006.30 With 2400 kWh ton−1 and 60% (or 11 PWh yr−1) of annual electricity produced by coal, coal could last 200 yr if coal used did not increase. u Ref. 31,32. | |||||
Solar PV | 14900a | <3000a | 8.7b | 0.1–0.2c | 11.4d |
CSP | 9250–11800e | 1.05–7.8e | 0.354f | 0.13–0.25f | 0.4f |
Wind | 630g | 410g | 94.1h | 0.205–0.42i | 173j |
Geothermal | 1390k | 0.57–1.21l | 9m | 0.73n | 57.6m |
Hydroelectric | 16.5m | <16.5 | 778m | 0.416n | 2840m |
Wave | 23.6k | 4.4k | 0.00075k | 0.21–0.25o | 0.0014j |
Tidal | 7p | 0.18p | 0.26k | 0.2–0.35q | 0.565r |
Nuclear | 4.1–122 for 90–300 yrs | <4.1–122 | 371m | 0.808n | 2630m |
Coal-CCS | 11 for 200 yrt | <11 | 0 | 0.65–0.85u | 0 |
For land-based wind energy costs without subsidy to be similar to those of a new coal-fired power plant, the annual-average wind speed at 80 meters must be at least 6.9 meters per second (15.4 miles per hour).33 Based on the mapping analysis,23 15% of the data stations (thus, statistically, land area) in the United States (and 17% of land plus coastal offshore data stations) have wind speeds above this threshold (globally, 13% of stations are above the threshold) (Table 2). Whereas, the mean wind speed over land globally from the study was 4.54 m s−1, that at locations with wind speeds exceeding 6.9 m s−1 (e.g., those locations in Table 2) was 8.4 m s−1. Similarly, the mean wind speed over all ocean stations worldwide was 8.6 m s−1, but that over ocean stations with wind speeds exceeding 6.9 m s−1 was 9.34 m s−1.
Region | % Stations > 6.9 m s−1 |
---|---|
Europe | 14.2 |
North America | 19 |
United States over land | 15 |
United States over land and near shore | 17 |
South America | 9.7 |
Oceania | 21.2 |
Africa | 4.6 |
Asia | 2.7 |
Antarctica | 60 |
Global over land | 13 |
Although offshore wind energy is more expensive than onshore wind energy, it has been deployed significantly in Europe. A recent analysis indicated that wind resources off the shallow Atlantic coast could supply a significant portion of US electric power on its own.24Water depths along the west coast of the US become deeper faster than along the east coast, but another recent analysis indicates significant wind resources in several areas of shallow water offshore of the west coast as well.34
Technology | Lifecycle | Opportunity cost emissions due to delays | War/terrorism (nuclear) or 500 yr leakage (CCS) | Total |
---|---|---|---|---|
Solar PV | 19–59 | 0 | 0 | 19–59 |
CSP | 8.5–11.3 | 0 | 0 | 8.5–11.3 |
Wind | 2.8–7.4 | 0 | 0 | 2.8–7.4 |
Geothermal | 15.1–55 | 1–6 | 0 | 16.1–61 |
Hydroelectric | 17–22 | 31–49 | 0 | 48–71 |
Wave | 21.7 | 20–41 | 0 | 41.7–62.7 |
Tidal | 14 | 20–41 | 0 | 34–55 |
Nuclear | 9–70 | 59–106 | 0–4.1 | 68–180.1 |
Coal-CCS | 255–442 | 51–87 | 1.8–42 | 307.8–571 |
The addition of CCS equipment to a coal power plant results in an additional 14–25% energy requirement for coal-based integrated gasification combined cycle (IGCC) systems and 24–40% for supercritical pulverized coal plants with current technology.32 Most of the additional energy is needed to compress and purify CO2. This additional energy either increases the coal required for an individual plant or increases the number of plants required to generate a fixed amount of electricity for general consumption. Here, we define the kWh generated by the coal-CCS plant to include the kWh required for the CCS equipment plus that required for outside consumption. As such, the g CO2e kWh−1 emitted by a given coal-CCS plant does not change relative to a coal plant without CCS, due to adding CCS; however, either the number of plants required increases or the kWh required per plant increases.
The study that performed the land use calculation in the most detail,61 determined the effect of price changes on land use change with spatially-distributed global data for land conversion between noncropland and cropland and an econometric model. It found that converting from gasoline to ethanol (E85) vehicles could increase lifecycle CO2e by over 90% when the ethanol is produced from corn and around 50% when it is produced from switchgrass. Delucchi,58 who treated the effect of price and land use changes more approximately, calculated the lifecycle effect of converting from gasoline to corn and switchgrass E90. He estimated that E90 from corn ethanol might reduce CO2e by about 2.4% relative to gasoline. In China and India, such a conversion might increase equivalent carbon emissions by 17% and 11%, respectively. He also estimated that ethanol from switchgrass might reduce US CO2e by about 52.5% compared with light-duty gasoline in the US. We use results from these two studies to bound the lifecycle emissions of E85. These results will be applied shortly to compare the CO2e changes among electric power and fuel technologies when applied to vehicles in the US.
The time between planning and operation of a technology includes the time to site, finance, permit, insure, construct, license, and connect the technology to the utility grid.
The time between planning and operation of a nuclear power plant includes the time to obtain a site and construction permit, the time between construction permit approval and issue, and the construction time of the plant. In March, 2007, the U.S. Nuclear Regulatory Commission approved the first request for a site permit in 30 yr. This process took 3.5 yr. The time to review and approve a construction permit is another 2 yr and the time between the construction permit approval and issue is about 0.5 yr. Thus, the minimum time for preconstruction approvals (and financing) is 6 yr. We estimate the maximum time as 10 yr. The time to construct a nuclear reactor depends significantly on regulatory requirements and costs. Because of inflation in the 1970s and more stringent safety regulation on nuclear power plants placed shortly before and after the Three-Mile Island accident in 1979, US nuclear plant construction times increased from around 7 yr in 1971 to 12 yr in 1980.63 The median construction time for reactors in the US built since 1970 is 9 yr.64 US regulations have been streamlined somewhat, and nuclear power plant developers suggest that construction costs are now lower and construction times shorter than they have been historically. However, projected costs for new nuclear reactors have historically been underestimated64 and construction costs of all new energy facilities have recently risen. Nevertheless, based on the most optimistic future projections of nuclear power construction times of 4–5 yr65 and those times based on historic data,64 we assume future construction times due to nuclear power plants as 4–9 yr. Thus, the overall time between planning and operation of a nuclear power plant ranges from 10–19 yr.
The time between planning and operation of a wind farm includes a development and construction period. The development period, which includes the time required to identify a site, purchase or lease the land, monitor winds, install transmission, negotiate a power-purchase agreement, and obtain permits, can take from 0.5–5 yr, with more typical times from 1–3 yr. The construction period for a small to medium wind farm (15 MW or less) is 1 year and for a large farm is 1–2 yr.66 Thus, the overall time between planning and operation of a large wind farm is 2–5 yr.
For geothermal power, the development time can, in extreme cases, take over a decade but with an average time of 2 yr.27 We use a range of 1–3 yr. Construction times for a cluster of geothermal plants of 250 MW or more are at least 2 yr.67 We use a range of 2–3 yr. Thus, the total planning-to-operation time for a large geothermal power plant is 3–6 yr.
For CSP, the construction time is similar to that of a wind farm. For example, Nevada Solar One required about 1.5 yr for construction. Similarly, an ethanol refinery requires about 1.5 yr to construct. We assume a range in both cases of 1–2 yr. We also assume the development time is the same as that for a wind farm, 1–3 yr. Thus, the overall planning-to-operation time for a CSP plant or ethanol refinery is 2–5 yr. We assume the same time range for tidal, wave, and solar-PV power plants.
The time to plan and construct a coal-fired power plant without CCS equipment is generally 5–8 yr. CCS technology would be added during this period. The development time is another 1–3 yr. Thus, the total planning-to-operation time for a standard coal plant with CCS is estimated to be 6–11 yr. If the coal-CCS plant is an IGCC plant, the time may be longer since none has been built to date.
Dams with hydroelectric power plants have varying construction times. Aswan Dam required 13 yr (1889–1902). Hoover Dam required 4 yr (1931 to 1935). Shasta Dam required 7 yr (1938–1945). Glen Canyon Dam required 10 yr (1956 to 1966). Gardiner Dam required 8 yr (1959–1967). Construction on Three Gorges Dam in China began on December 14, 1994 and is expected to be fully operation only in 2011, after 15 yr. Plans for the dam were submitted in the 1980s. Here, we assume a normal range of construction periods of 6–12 yr and a development period of 2–4 yr for a total planning-to-operation period of 8–16 yr.
We assume that after the first lifetime of any plant, the plant is refurbished or retrofitted, requiring a downtime of 2–4 yr for nuclear, 2–3 yr for coal-CCS, and 1–2 yr for all other technologies. We then calculate the CO2e emissions per kWh due to the total downtime for each technology over 100 yr of operation assuming emissions during downtime will be the average current emission of the power sector. Finally, we subtract such emissions for each technology from that of the technology with the least emissions to obtain the “opportunity-cost” CO2e emissions for the technology. The opportunity-cost emissions of the least-emitting technology is, by definition, zero. Solar-PV, CSP, and wind all had the lowest CO2e emissions due to planning-to-operation time, so any could be used to determine the opportunity cost of the other technologies.
We perform this analysis for only the electricity-generating technologies. For corn and cellulosic ethanol the CO2e emissions are already equal to or greater than those of gasoline, so the downtime of an ethanol refinery is unlikely to increase CO2e emissions relative to current transportation emissions.
Results of this analysis are summarized in Table 3. For solar-PV, CSP, and wind, the opportunity cost was zero since these all had the lowest CO2e emissions due to delays. Wave and tidal had an opportunity cost only because the lifetimes of these technologies are shorter than those of the other technologies due to the harsh conditions of being on the surface or under ocean water, so replacing wave and tidal devices will occur more frequently than replacing the other devices, increasing down time of the former. Although hydroelectric power plants have very long lifetimes, the time between their planning and initial operation is substantial, causing high opportunity cost CO2e emissions for them. The same problem arises with nuclear and coal-CCS plants. For nuclear, the opportunity CO2e is much larger than the lifecycle CO2e. Coal-CCS's opportunity-cost CO2e is much smaller than its lifecycle CO2e. In sum, the technologies that have moderate to long lifetimes and that can be planned and installed quickly are those with the lowest opportunity cost CO2e emissions.
The ability of a geological formation to sequester CO2 for decades to centuries varies with location and tectonic activity. IPCC32 summarizes CO2 leakage rates for an enhanced oil recovery operation of 0.00076% per year, or 1% over 1000 yr and CH4 leakage from historical natural gas storage systems of 0.1–10% per 1000 yr. Thus, while some well-selected sites could theoretically sequester 99% of CO2 for 1000 yr, there is no certainty of this since tectonic activity or natural leakage over 1000 yr is not possible to predict. Because liquefied CO2 injected underground will be under high pressure, it will take advantage of any horizontal or vertical fractures in rocks, to try to escape as a gas to the surface. Because CO2 is an acid, its low pH will also cause it to weather rock over time. If a leak from an underground formation occurs, it is not clear whether it will be detected or, if it is detected, how the leak will be sealed, particularly if it is occurring over a large area.
Here, we estimate CO2 emissions due to leakage for different residence times of carbon dioxide stored in a geological formation. The stored mass (S, e.g., Tg) of CO2 at any given time t in a reservoir resulting from injection at rate I (e.g., Tg yr−1) and e-folding lifetime against leakage τ is
S(t) = S(0)e−t/τ + τI(1−e−t/τ) | (1) |
L(t) = I−S(t)/t | (2) |
If 99% of CO2 is sequestered in a geological formation for 1000 yr (e.g., IPCC,32 p. 216), the e-folding lifetime against leakage is approximately τ =100000 yr. We use this as our high estimate of lifetime and τ = 5000 yr as the low estimate, which corresponds to 18% leakage over 1000 yr, closer to that of some observed methane leakage rates. With this lifetime range, an injection rate corresponding to an 80–95% reduction in CO2 emissions from a coal-fired power plant with CCS equipment,32 and no initial CO2 in the geological formation, the CO2 emissions from leakage averaged over 100 yr from eqn 1 and 2 is 0.36–8.6 g CO2kWh−1; that averaged over 500 yr is 1.8–42 g CO2kWh−1, and that averaged over 1000 yr is 3.5–81 g CO2kWh−1. Thus, the longer the averaging period, the greater the average emissions over the period due to CO2 leakage. We use the average leakage rate over 500 yr as a relevant time period for considering leakage.
Here, we detail the link between nuclear energy and nuclear weapons and estimate the emissions of nuclear explosions attributable to nuclear energy. The primary limitation to building a nuclear weapon is the availability of purified fissionable fuel (highly-enriched uranium or plutonium).68 Worldwide, nine countries have known nuclear weapons stockpiles (US, Russia, UK, France, China, India, Pakistan, Israel, North Korea). In addition, Iran is pursuing uranium enrichment, and 32 other countries have sufficient fissionable material to produce weapons. Among the 42 countries with fissionable material, 22 have facilities as part of their civilian nuclear energy program, either to produce highly-enriched uranium or to separate plutonium, and facilities in 13 countries are active.68 Thus, the ability of states to produce nuclear weapons today follows directly from their ability to produce nuclear power. In fact, producing material for a weapon requires merely operating a civilian nuclear power plant together with a sophisticated plutonium separation facility. The Treaty of Non-Proliferation of Nuclear Weapons has been signed by 190 countries. However, international treaties safeguard only about 1% of the world's highly-enriched uranium and 35% of the world's plutonium.68 Currently, about 30000 nuclear warheads exist worldwide, with 95% in the US and Russia, but enough refined and unrefined material to produce another 100000 weapons.69
The explosion of fifty 15 kt nuclear devices (a total of 1.5 MT, or 0.1% of the yields proposed for a full-scale nuclear war) during a limited nuclear exchange in megacities could burn 63–313 Tg of fuel, adding 1–5 Tg of soot to the atmosphere, much of it to the stratosphere, and killing 2.6–16.7 million people.68 The soot emissions would cause significant short- and medium-term regional cooling.70 Despite short-term cooling, the CO2 emissions would cause long-term warming, as they do with biomass burning.62 The CO2 emissions from such a conflict are estimated here from the fuel burn rate and the carbon content of fuels. Materials have the following carbon contents: plastics, 38–92%; tires and other rubbers, 59–91%; synthetic fibers, 63–86%;71 woody biomass, 41–45%; charcoal, 71%;72 asphalt, 80%; steel, 0.05–2%. We approximate roughly the carbon content of all combustible material in a city as 40–60%. Applying these percentages to the fuel burn gives CO2 emissions during an exchange as 92–690 Tg CO2. The annual electricity production due to nuclear energy in 2005 was 2768 TWh yr−1. If one nuclear exchange as described above occurs over the next 30 yr, the net carbon emissions due to nuclear weapons proliferation caused by the expansion of nuclear energy worldwide would be 1.1–4.1 g CO2kWh−1, where the energy generation assumed is the annual 2005 generation for nuclear power multiplied by the number of yr being considered. This emission rate depends on the probability of a nuclear exchange over a given period and the strengths of nuclear devices used. Here, we bound the probability of the event occurring over 30 yr as between 0 and 1 to give the range of possible emissions for one such event as 0 to 4.1 g CO2kWh−1. This emission rate is placed in context in Table 3.
In 2007, 24.55% of CO2 emissions in the US were due to direct exhaust from onroad vehicles. An additional 8.18% of total CO2 was due to the upstream production and transport of fuel (ESI†). Thus, 32.73% is the largest possible reduction in US CO2 (not CO2e) emissions due to any vehicle-powering technology. The upstream CO2 emissions are about 94.3% of the upstream CO2e emissions.58
Fig. 2 compares calculated percent changes in total emitted US CO2 emissions due to each energy-vehicle combination considered here. It is assumed that all CO2e increases or decreases due to the technology have been converted to CO2 for purposes of comparing with US CO2 emissions. Due to land use constraints, it is unlikely that corn or cellulosic ethanol could power more than 30% of US onroad vehicles, so the figure also shows CO2 changes due to 30% penetration of E85. The other technologies, aside from hydroelectric power (limited by land as well), could theoretically power the entire US onroad vehicle fleet so are not subject to the 30% limit.
Fig. 2 Percent changes in US CO2 emissions upon replacing 100% of onroad (light- and heavy-duty) vehicles with different energy technologies and assuming all CO2e has been converted to CO2. Numbers are derived in ESI† and account for all factors identified in Table 3. For all cases, low and high estimates are given. In all cases except the E85 cases, solid represents the low estimate and solid+vertical lines, the high. For corn and cellulosic E85, low and high values for 30% (slanted lines) instead of 100% (slanted+horizontal lines) penetration are also shown. |
Converting to corn-E85 could cause either no change in or increase CO2 emissions by up to 9.1% with 30% E85 penetration (ESI†, I37). Converting to cellulosic-E85 could change CO2 emissions by +4.9 to −4.9% relative to gasoline with 30% penetration (ESI†, J16). Running 100% of vehicles on electricity provided by wind, on the other hand, could reduce US carbon by 32.5–32.7% since wind turbines are 99.2–99.8% carbon free over a 30 yr lifetime and the maximum reduction possible from the vehicle sector is 32.73%. Using HFCVs, where the hydrogen is produced by wind electrolysis, could reduce US CO2 by about 31.9–32.6%, slightly less than using wind-BEVs since more energy is required to manufacture the additional turbines needed for wind-HFCVs. Running BEVs on electricity provided by solar-PV can reduce carbon by 31–32.3%. Nuclear-BEVs could reduce US carbon by 28.0–31.4%. Of the electric power sources, coal-CCS producing vehicles results in the least emission reduction due to the lifecycle, leakage, and opportunity-cost emissions of coal-CCS.
Here, we examine the effects of the energy technologies considered on air pollution-relevant emissions and their resulting mortality. For wind, solar-PV, CSP, tidal, wave, and hydroelectric power, air-pollution relevant emissions arise only due to the construction, installation, maintenance, and decommissioning of the technology and as a result of planning-to-operation delays (Section 4b). For corn and cellulosic ethanol, emissions are also due to production of the fuel and ethanol-vehicle combustion. For non-binary geothermal plants (about 85% of existing plants) emissions also arise due to evaporation of NO, SO2, and H2S. The level of direct emissions is about 5% of that of a coal-fired power plant. For binary geothermal plants, such emissions are about 0.1% those of a coal-fired power plant. For nuclear power, pollutant emissions also include emissions due to the mining, transport, and processing of uranium. It is also necessary to take into the account the potential fatalities due to nuclear war or terrorism caused by the proliferation of nuclear energy facilities worldwide.
For coal-CCS, emissions also arise due to coal combustion since the CCS equipment itself generally does not reduce pollutants aside from CO2. For example, with CCS equipment, the CO2 is first separated from other gases after combustion. The remaining gases, such as SOx, NOx, NH3, and Hg are discharged to the air. Because of the higher energy requirement for CCS, more non-CO2 pollutants are generally emitted to the air compared with the case of no capture when a plant's fuel use is increased to generate a fixed amount of electric power for external consumption. For example, in one case, the addition of CCS equipment for operation of an IGCC plant was estimated to increase fuel use by 15.7%, SOx emissions by 17.9%, and NOx emissions by 11%.32 In another case, CCS equipment in a pulverized coal plant increased fuel use by 31.3%, increased NOx emissions by 31%, and increased NH3 emissions by 2200% but the addition of another control device decreased SOx emissions by 99.7%.32
In order to evaluate the technologies, we estimate the change in the US premature death rate due to onroad vehicle air pollution in 2020 after converting current onroad light- and heavy-duty gasoline vehicles to either BEVs, HFCVs, or E85 vehicles. Since HFCVs eliminate all tailpipe air pollution when applied to the US vehicle fleet19,18 as do BEVs, the deaths due to these vehicles are due only to the lifecycle emissions of the vehicles themselves and of the power plants producing electricity for them or for H2 electrolysis. We assume lifecycle emissions of the vehicles themselves are similar for all vehicles so do not evaluate those emissions. We estimate deaths due to each electricity-generating technology as one minus the percent reduction in total CO2e emissions due to the technology (Table 3) multiplied by the total number of exhaust- plus upstream-emission deaths (gas and particle) attributable to 2020 light- and heavy-duty gasoline onroad vehicles, estimated as ∼15000 in the US from 3-D model calculations similar to those performed previously.73 Thus, the deaths due to all BEV and HFCV options are attributed only to the electricity generation plant itself (as no net air pollution emanates from these vehicles). Because the number of deaths with most options is relatively small, the error arising from attributing CO2e proportionally to other air pollutant emissions may not be so significant. Further, since CO2e itself enhances mortality through the effect of its temperature and water vapor changes on air pollution,73 using it as a surrogate may be reasonable.
For nuclear energy, we add, in the high case, the potential death rate due to a nuclear exchange, as described in Section 4d, which could kill up to 16.7 million people. Dividing this number by 30 yr and the ratio of the US to world population today (302 million : 6.602 billion) gives an upper limit to deaths scaled to US population of 25500 yr−1 attributable to nuclear energy. We do not add deaths to the low estimate, since we assume the low probability of a nuclear exchange is zero.
The 2020 premature death rates due to corn- and cellulosic-E85 are calculated by considering 2020 death rate due to exhaust, evaporative, and upstream emissions from light- and heavy-duty gasoline onroad vehicles, the changes in such death rates between gasoline and E85. Changes in deaths due to the upstream emissions from E85 production were determined as follows. Fig. 3 shows the upstream lifecycle emissions for multiple gases and black carbon from reformulated gasoline (RFG), corn-E90, and cellulosic-E90.58 The upstream cycle accounts for fuel dispensing, fuel distribution and storage, fuel production, feedstock transmission, feedstock recovery, land-use changes, cultivation, fertilizer manufacture, gas leaks and flares, and emissions displaced. The figure indicates that the upstream cycle emissions of CO, NO2, N2O, and BC may be higher for both corn- and cellulosic E90 than for RFG. Emissions of NMOC, SO2, and CH4 are also higher for corn-E90 than for RFG but lower for cellulosic-E90 than for RFG. Weighting the emission changes by the low health costs per unit mass of pollutant from Spadaro and Rabl74 gives a rough estimate of the health-weighed upstream emission changes of E90 versus RFG. The low health cost, which applies to rural areas, is used since most upstream emissions changes are away from cities. The result is an increase in the corn-E90 death rate by 20% and the cellulosic-E90 death rate by 30% (due primarily to the increase in BC of cellulosic-E90 relative to corn-E90), compared with RFG. Multiplying this result by 25%, the estimated ratio of upstream emissions to upstream plus exhaust emissions (Section 4e) gives death rate increases of 5.0% and 7.5% for corn- and cellulosic-E90, respectively, relative to RFG. The changes in onroad deaths between gasoline and E85 were taken from the only study to date that has examined this issue with a 3-D computer model over the US.75 The study found that a complete penetration of E85-fueled vehicles (whether from cellulose or corn) might increase the air pollution premature death rate in the US by anywhere from zero to 185 deaths yr−1 in 2020 over gasoline vehicles. The emission changes in that study were subsequently supported.76
Fig. 3 Upstream lifecycle emissions of several individual pollutants from corn-E90 and cellulosic-E90 relative to reformulated gasoline (RFG).58 |
An additional effect of corn- and cellulosic ethanol on mortality is through its effect on undernutrition. The competition between crops for food and fuel has reduced the quantity of food produced and increased food prices. Other factors, such as higher fuel costs, have also contributed to food price increases. Higher prices of food, in particular, increase the risk of starvation in many parts of the world. WHO1 estimates that 6.2 million people died in 2000 from undernutrition, primarily in developing countries. Undernutrition categories include being underweight, iron deficiency, vitamin-A deficiency, and zinc deficiency. As such, death due to undernutrition does not require starvation. When food prices increase, many people eat less and, without necessarily starving, subject themselves to a higher chance of dying due to undernutrition and resulting susceptibility to disease. Here, we do not quantify the effects of corn-E85 or cellulosic-E85 on mortality due to the lack of a numerical estimate of the relationship between food prices and undernutrition mortality but note that it is probably occurring.
Fig. 4 indicates that E85 may increase premature deaths compared with gasoline, due primarily to upstream changes in emissions but also due to changes in onroad vehicle emissions. Cellulosic ethanol may increase overall deaths more than corn ethanol, although this result rests heavily on the precise particulate matter upstream emissions of corn- versus cellulosic-E85. Due to the uncertainty of upstream and onroad emission death changes, it can be concluded that E85 is unlikely to improve air quality compared with gasoline and may worsen it.
Fig. 4 Estimates of future (c. 2020) US premature deaths per year from vehicles replacing light- and heavy-duty gasoline onroad vehicles and their upstream emissions assuming full penetration of each vehicle type or fuel, as discussed in the text. Low (solid) and high (solid+vertical lines) estimates are given. In the case of nuclear-BEV, the upper limit of the number of deaths, scaled to US population, due to a nuclear exchange caused by the proliferation of nuclear energy facilities worldwide is also given (horizontal lines). In the case of corn-E85 and cellulosic-E85, the dots are the additional US death rate due to upstream emissions from producing and distributing E85 minus those from producing and distributing gasoline (see text) and the slanted lines are the additional tailpipe emissions of E85 over gasoline for the US.75 |
Fig. 4 also indicates that each E85 vehicle should cause more air-pollution related death than each vehicle powered by any other technology considered, except to the extent that the risk of a nuclear exchange due to the spread of plutonium separation and uranium enrichment in nuclear energy facilities worldwide is considered. This conclusion holds regardless of the penetration of E85. For example, with 30% penetration, corn-E85 may kill 4500–5000 people yr−1 more than CSP-BEVs at the same penetration. Because corn- and cellulosic-E85 already increase mortality more than any other technology considered, the omission of undernutrition mortality due to E85 does not affect the conclusions of this study. Emissions due to CCS-BEVs are estimated to kill more people prematurely than any other electric power source powering vehicles if nuclear explosions are not considered. Nuclear electricity causes the second-highest death rate among electric power sources with respect to lifecycle and opportunity-cost emissions. The least damaging technologies are wind-BEV followed by CSP-BEV and wind-HFCV.
In the case of wind, wave, and tidal power, spacing is needed between turbines or devices to reduce the effect of turbulence and energy dissipation caused by one turbine or device on the performance of another. One equation for the spacing area (A, m2) needed by a wind turbine to minimize interference by other turbines in an array is A = 4D × 7D, where D is the rotor diameter (m).11 This equation predicts that for a 5 MW turbine with a 126 m diameter rotor, an area of 0.44 km2 is needed for array spacing. Over land, the area between turbines may be natural habitat, open space, farmland, ranch land, or used for solar energy devices, thus it is not wasted. On ridges, where turbines are not in a 2-D array but are lined up adjacent to each other, the spacing between the tips of turbine rotors may be one diameter, and the space required is much smaller since the array is one- instead of two-dimensional. Over water, wind turbines are also frequently closer to each other in the direction perpendicular to the prevailing wind to reduce local transmission line lengths.
Fig. 5 shows the ratio of the footprint area required for each technology to that of wind-BEVs. The footprint area of wind-BEVs is 5.5–6 orders of magnitude less than those of corn- or cellulosic-E85, 4 orders of magnitude less than those of CSP- or PV-BEVs, 3 orders of magnitude less than those of nuclear- or coal-BEVs, and 2–2.5 orders of magnitude less than those of geothermal-, tidal-, or wave-BEVs. The footprint for wind-HFCVs is about 3 times that for wind-BEVs due to the larger number of turbines required to power HFCVs than BEVs. As such, wind-BEVs and wind-HFCVs are by far the least invasive of all technologies over land. The relative ranking of PV-BEVs with respect to footprint improves relative to that shown in the figure (going ahead of CCS-BEV) if >80% (rather than the 30% assumed) of all future PV is put on rooftops.
Fig. 5 Ratio of the footprint area on land or water required to power all vehicles in the US in 2007 by a given energy technology to that of wind-BEVs. The footprint area is the area of the technology touching the ground, the ocean surface, or the ocean floor. Also shown are the ratios of the land areas of California and Rhode Island to the footprint area of wind-BEVs. Low and high values are shown for each technology/state. |
Fig. 6 compares the fractional area of the US (50 states) required for spacing (footprint plus separation area for wind, tidal, wave, nuclear; footprint area for the others) needed by each technology to power US vehicles. The array spacing required by wind-BEVs is about 0.35–0.7% of all US land, although wind turbines can be placed over land or water. For wind-HFCVs, the area required for spacing is about 1.1–2.1% of US land. Tidal-BEVs would not take any ocean surface or land area but would require 1550–3700 km2 of ocean floor for spacing (5–6% that of wind) or the equivalent of about 0.017–0.04% of US land. Wave-BEVs would require an array spacing area of 19000–32000 km2 (about 50–59% that of wind), or an area equivalent to 0.21–0.35% of US land. Solar-PV powering US BEVs requires 0.077–0.18% of US land for spacing (and footprint), or 19–26% of the spacing area required for wind-BEVs. Similarly, CSP-BEVs need about 0.12–0.41% of US land or 34–59% of the spacing required for wind-BEV.
Fig. 6 Low (solid) and high (solid+lines) fractions of US land area (50 states) required for the spacing (footprint plus separation area for wind, tidal, wave, and nuclear; footprint area only for the others) of each energy technology for powering all US vehicles in 2007. Also shown are the fractions of US land occupied by California and Rhode Island. Multiply fractions by the area of the US (9162000 km2) to obtain area required for technology. |
A 100 MW geothermal plant requires a land area of about 0.33 km2. This translates to about 0.006–0.008% of US land for running all US BEVs, or about 1.1–1.6% the array spacing required for wind-BEVs. Powering all onroad vehicles in the US with nuclear power would require about 0.045–0.061% of US land for spacing, or about 9–13% that of wind-BEVs. The land required for CCS-BEVs is 0.03–0.06% of the US, or about 7.4–8.2% of the array spacing required for wind-BEVs. The land required for hydro-BEVs is significant but lower than that for E85. Hydro-BEV would require about 1.9–2.6% of US land for reservoirs. This is 3.7–5.4 times larger than the land area required for wind-BEV spacing. Corn and cellulosic ethanol require by far the most land of all the options considered here. Running the US onroad vehicle fleet with corn-E85 requires 9.8–17.6% of all 50 US states, or 2.2–4.0 States of California. Cellulosic-E85 would require from 4.7–35.4% of US land, or 1.1–8.0 States of California, to power all onroad vehicles with E85.
In sum, technologies with the least spacing area required are, in increasing order, geothermal-BEVs, tidal-BEVs, wave-BEVs, CCS-BEVs, nuclear-BEVs, PV-BEVs, CSP-BEVs, wave-BEVs, and wind-BEVs. These technologies would all require <1% of US land for spacing. Corn-E85 and cellulosic-E85 are, on the other hand, very land intensive. The spacing area required for wind-BEVs is about 1/26 that required for corn-ethanol (E85) and 1/38 that required for cellulosic ethanol (E85), on average. The spacing area for PV-BEVs is about 1/3 that of wind-BEVs.
Fig. 7 Low (solid) and high (solid+lines) estimates of water consumption (Gigagallons year−1) required to replace all US onroad vehicles with other vehicle technologies. Consumption is net loss of water from water supply. Data for the figure are derived in ESI†. For comparison, the total US water consumption in 2000 was 148900 Ggal yr−1.87 |
The covering of land with a building or paved road, or the surface mining of land effectively destroys habitat. For example, between 1992 and 2002, 381000 acres (1542 km2) of US forest habitat were destroyed by mountaintop removal due to coal mining.88 With coal-CCS, mountaintop removal will increase as coal consumption expands to meet new energy demand and power CCS equipment.
The conversion of land from natural vegetation to cropland, needed for the production of biofuels, similarly reduces available habitat, particularly when pesticides are used to protect crops. This effect is greatest when rich ecosystems, such as a tropical or other forests are destroyed either directly for biofuel farming or indirectly when biofuel farming in other areas causes cattle ranchers or soy farmers to move and clear rainforest areas. Even when agricultural land is converted from one type of crop to another, biota may be lost. For example, when switchgrass replaces a non-biofuel crop, switchgrass' lignocellulose is removed to produce ethanol, so microorganisms, which normally process the lignocellulose, cannot replenish soil nutrients, reducing biota in the soil. On the other hand, good selection of land use for growing biofuel crops could reduce impacts of the crops on the local ecosystem.60
Dams for hydroelectric power reduce salmon population by reducing access to spawning grounds. To mitigate this problem, fish ladders are usually installed. Because sediment builds up behind a dam, water leaving a dam contains little sediment.89 This can lead to scavenging of sediment from riverbeds downstream, causing loss of riverbank habitat. On the other hand, the flooding of land with water behind a dam reduces habitat for land-based wildlife but increases it for aquatic wildlife there. Similarly, the addition of structures to the ocean increases the surface area of artificial reefs, increasing the presence of fish life in these areas.90 The use of dams for peaking power also affects the diurnal variation of water flow down a river. This can affect downstream ecosystems negatively in some cases although the effect may vary significantly from river to river.
In ranking the relative impacts of land use change due to the technologies on wildlife, we consider the footprint of the technology on land based on Fig. 5, but take into account whether the land was converted to water, agricultural land, land-based buildings/structures, or ocean-based structures, or mined on its surface and what the previous land use might have been. In the case of solar-PV, for example, the impacts are proportional to the footprint area in Fig. 5 (which already excludes rooftops), but less proportional to footprint than other energy sources since much of PV in the near future will be located in arid regions with less wildlife displaced than for other technologies, which will be situated on more biodiverse land. CSP will similarly be located in more arid land. As a result, the rankings of CSP and PV with respect to wildlife in Table 4 are higher than their respective footprint rankings.
Weight (%) | Wind-BEV | Wind-HFCV | PV-BEV | CSP-BEV | Geo-BEV | Hydro-BEV | Wave-BEV | Tidal-BEV | Nuc-BEV | CCS-BEV | Corn-E85 | Cel-E85 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a Based on Table 1, Fig. 5, and discussion in Section 11. b Based on Fig. 2. c Based on Fig. 4. d Based on Fig. 5. e Based on Fig. 6, except that tidal was placed ahead of geothermal since the spacing area on the sea floor is not so relevant (although footprint area is). f Based on Fig. 7. g Based primarily on footprint area, air pollution emissions, and collision risk, as described in Section 8. h Based on discussion of thermal pollution in Section 8. i Based on the discussion of chemical pollution and radioactive waste in Section 8. j Based on discussion in Section 9. k Based on discussion in Section 10. | |||||||||||||
aResourceabundance | 10 | 2 | 3 | 1 | 4 | 7 | 10 | 6 | 5 | 9 | 8 | 11 | 12 |
bCO2e emissions | 22 | 1 | 3 | 5 | 2 | 4 | 8 | 7 | 6 | 9 | 10 | 12 | 11 |
cMortality | 22 | 1 | 3 | 5 | 2 | 4 | 8 | 7 | 6 | 10 | 9 | 11 | 12 |
dFootprint | 12 | 1 | 2 | 8 | 9 | 5 | 10 | 4 | 3 | 6 | 7 | 11 | 12 |
eSpacing | 3 | 8 | 9 | 5 | 6 | 2 | 10 | 7 | 1 | 4 | 3 | 11 | 12 |
f Water consumption | 10 | 1 | 6 | 5 | 9 | 4 | 11 | 1 | 1 | 7 | 7 | 12 | 10 |
gEffects on wildlife | 6 | 1 | 3 | 5 | 2 | 4 | 8 | 7 | 6 | 9 | 10 | 11 | 12 |
hThermal pollution | 1 | 1 | 2 | 4 | 8 | 3 | 7 | 6 | 5 | 12 | 11 | 10 | 9 |
i Water chemical pollution/radioactive waste | 3 | 1 | 3 | 5 | 2 | 4 | 8 | 7 | 6 | 10 | 9 | 12 | 11 |
jEnergy supply disruption | 3 | 3 | 4 | 2 | 6 | 7 | 11 | 5 | 1 | 12 | 8 | 9 | 9 |
kNormal operating reliability | 8 | 10 | 1 | 10 | 5 | 6 | 2 | 10 | 9 | 7 | 8 | 3 | 3 |
Weighted average | 2.09 | 3.22 | 5.26 | 4.28 | 4.60 | 8.40 | 6.11 | 4.97 | 8.50 | 8.47 | 10.6 | 10.7 | |
Overall rank | 1 | 2 | 6 | 3 | 4 | 8 | 7 | 5 | 9-tie | 9-tie | 11 | 12 |
Air-pollution-relevant emissions harm animals as much as they damage humans.91 Such emissions also damage plants and trees by discoloring their leaves, stunting their growth, or killing them.92–94 To account for air pollution effects on wildlife and ecosystems, we use the information from Fig. 4, which shows the effects of the energy technologies on human air pollution mortality, as a surrogate.
The effects on bird and bat deaths due to each energy technology should also be considered. Energy technologies kill birds and bats by destroying their habitat, polluting the air they breathe and the water they drink, and creating structures that birds and bats collide with or are electrocuted on. Loss of habitat is accounted for here by considering the footprint of each technology on the ground. Fig. 5 indicates that a large penetration of wind turbines for BEVs or HFCVs will result in 2.5 orders-of-magnitude less habitat loss based on footprint than geothermal power and 3 orders-of-magnitude less than Nuc-BEVs or CCS-BEVs. In particular, mountaintop removal during coal mining is historically responsible for the decline in several bird species, including the Cerulean Warbler, the Louisiana Waterthrush, the Worm-Eating Warbler, the Black-and-White Warbler, and the Yellow-Throated Vireo.88 Although CSP and PVs require more footprint than most other technologies, both will be located primarily in deserts or, in the case of PV, also on rooftops, reducing their effects on habitat. The large footprint requirements for corn and cellulosic ethanol will cause the largest loss in bird habitat, such as wetlands, wet meadows, grassland, and scrub.88
With regard to air pollution, the low air pollution emissions and human mortality associated with wind-BEVs (Fig. 4) suggest it will have the least effect on respiratory- and cardiovascular-related bird and bat mortality. Corn- and cellulosic-E85 will have the greatest impact, followed by CCS-BEVs and nuclear-BEVs.
Because significant concern has been raised with respect to the effect of wind turbines on birds and bat collisions, we examine this issue in some detail. With regards to structures, wind turbines in the US currently kill about 10000–40000 birds annually, 80% of which are songbirds and 10%, birds of prey.88 For comparison, 5–50 million birds are killed annually by the 80000 communication towers in the US.88 Birds are attracted by their lights and collide with them or their guy wires during night migration. Also, 97.5–975 million birds are killed by collision with windows and hundreds of millions of birds are killed by cats in the US each year.88 Finally, in 2005, 200 million birds were lost to the Avian Flu worldwide.95 A recent report determined that less than 0.003% of anthropogenic bird deaths in 2003 were due to wind turbines in four eastern US states.96 If 1.4–2.3 million 5 MW wind turbines were installed worldwide to eliminate 100% of anthropogenic CO2 emissions (ESI†), the number of bird deaths worldwide due to wind would be about 1.4–14 million, less than 1% of the global anthropogenic bird loss. However, such a conversion would simultaneously eliminate global warming, air pollution human and animal mortality due to current energy use.
A related issue is the effect of tidal turbine rotors on sea life. Because tidal turbine rotors do not turn rapidly, they should not endanger sea life significantly. Further, with tidal turbine configurations that use a duct to funnel water,97 it may be possible to put a grating in front of the duct to prevent medium- and large-sized fish from entering the duct. The turbines may enhance sea communities by serving as artificial reefs as offshore wind turbines do.90
Some additional effects of energy technologies include thermal and chemical pollution, radioactive waste disposal, and feedbacks of technologies to the atmosphere. Thermal pollution reduces dissolved oxygen in water directly and indirectly by enhancing algae blooms. A reduction in dissolved oxygen harms fish, amphibians, and copepods. Thermal pollution also increases the rate of aquatic life metabolism, increasing competition for food. The energy technologies considered here that impact the temperature of water in lakes and rivers the most are CSP, nuclear, coal-CCS, and ethanol – the first three directly and the last through its lifecycle requirement of coal and nuclear electricity. The remaining technologies affect thermal pollution proportionally to their lifecycle CO2e emissions, most of which come from thermal power plants as well, but such lifecycle energy requirements are small.
Chemical waste pollution into surface and groundwater also impacts wildlife. Ethanol factories produce sewage-like effluent containing syrup, ethanol, chloride, copper, and other contaminants, produced during fermentation and distillation.98 Coal-CCS releases acids (SO2 and NOx) and mercury into the air that deposit to lakes and rivers as acid deposition. Some CCS technologies produce liquid wastes that are discharged to lakes or rivers and solid wastes that are incinerated. Both coal- and uranium-mining operations result in the release of chemicals into ground and surface waters. Other energy options are assumed to emit chemical waste proportionally to their lifecycle emissions.
Nuclear power plants produce fuel rods that are usually stored on site for several years in cooling ponds pending transport to a permanent site. The local storage of this “high-level waste” may preclude the future re-use of some nuclear power plant land for decades to centuries. In the US, a planned permanent site since 1982 has been Yucca Mountain. However, studies are still being carried out to determine whether storage at this site poses a long-term hazard.99 Nuclear power plants also produce low-level waste, including contaminated clothing and equipment.
Finally, a question that frequently arises is the effect of a large penetration of wind turbines on local and global meteorology. This issue can be examined correctly only with high-resolution computer modeling. To date, no resolved study covering the large scale has been performed. The modeling studies that have been performed are too coarse for their results to be relied on. A back-of-the-envelope calculation of the effects that accounts for the upstream and downstream velocity of a turbine and the global mean of measured winds over land indicates that, if 10 million 1.5 MW wind turbines were used to power all the world's energy (electric plus nonelectric), the combined energy loss from the slower winds among all wakes worldwide in the boundary layer (about 1 km) would be <1%.100
Severe weather, earthquake, fire, flood, or terrorist activity can take out some distributed-energy devices, but it is unlikely that an entire wind or solar PV farm could be disrupted by one of these events. With respect to severe weather, the survival wind speed for most wind turbines is around 60–65 m s−1, within range of the wind speeds in a Category 4 hurricane of 58.5–69 m s−1. Most tornados are less than 100 m across. An F4 (92.5–116 m s−1) tornado can reach 0.5–1.6 km wide. An F5 (wind speeds 117–142 m s−1) can reach 1.6–5 km wide. Although the chance that a Category 4–5 hurricane or an F4–F5 tornado hits a wind turbine is small, efforts could be made to strengthen turbines in at-risk areas.
In the case of centralized power sources, the larger the plant, the greater the risk of terrorism and collateral damage. In the case of nuclear power, collateral damage includes radiation release. In the case of hydroelectric power, it includes flooding. In the case of ethanol and coal-CCS, it includes some chemical releases. Whereas, nuclear power plants are designed to withstand tornados and other severe whether, the other power plants are not. However, nuclear power plants are vulnerable to heat waves. Because nuclear power plants rely on the temperature differential between steam and river or lake water used in the condenser, they often cannot generate electricity when the water becomes too hot, as occurred during the European heat wave of 2004, when several nuclear reactors in France were shut down.
Because nuclear power plants are centralized, release radiation if destroyed, and may shut down during a heat wave, we deem them to be the most likely target of a terrorist attack and prone to energy supply disruption among all energy sources. Large hydroelectric power plants are the second-most likely to be targeted by terrorists. Because they are a centralized power source and susceptible to reduced capacity during a drought, they are also considered to be the second-most vulnerable to disruption. Ethanol factories, coal-CCS, geothermal, and CSP plants are all centralized so are also subject to disruption and attack, but less so than nuclear or hydroelectricity. The greater potential for chemical releases from an ethanol plant makes it more risky than the other energy sources. CSP plants are generally smaller than coal-CCS plants, so are less likely to result in a disruption if disabled. The distributed-energy sources are the least likely to be disrupted. Among these, tidal power may be the most protected from severe weather whereas wave power, the most vulnerable. Solar PVs are least likely to be sited in locations of severe storms, so will be disrupted less than wind. Wind-BEV supply is more secure than wind-HFCV supply since fewer turbines are required in the former case.
Coal-CCS, nuclear, geothermal, and hydroelectric power are more reliable than the resources listed above but have scheduled and unscheduled outages. For example, nuclear power plants have unscheduled outages during heat waves (Section 9). Further, the average coal plant in the US from 2000–2004 was down 6.5% of the year for unscheduled maintenance and 6.0% of the year for scheduled maintenance.102 This compares with a total down time for modern wind turbines of 0–2% over land and 0–5% over the ocean.90 Solar-PV panels similarly have a downtime of near 0–2%. A difference between the outages of centralized and distributed plants is that when individual solar panels or wind turbines are down, for example, only a small fraction of electrical production is affected, whereas when a nuclear or coal plant is down, a large fraction is affected. Nuclear plants in the US have become more reliable in the last decade. In 2006, the overall capacity factor for nuclear in the US was 89.9%103 compared with 80.8% worldwide (Table 1). Hydroelectric power plants are more reliable than most other centralized plants (e.g., with unscheduled outage rates of <5%;102 however, because they are often used for peaking, their average capacity factors are low (Table 1). Geothermal capacity factors in the US are generally 89–97%,27 suggesting a reliability similar to nuclear power. Like nuclear, the globally-averaged capacity factor of geothermal is lower than its US average (Table 1). The overall outage rate of CSP plants in the Mojave desert have been reported as 3.3–4.0% for 1997–2001, except for 2000 when the outage rate was 7.1%.104
Whether or not intermittency affects the power supply depends on whether effort to reduce intermittency are made. Five methods of reducing intermittency or its effects are (a) interconnecting geographically-disperse naturally-intermittent energy sources (e.g., wind, solar, wave, tidal), (b) using a reliable energy source, such as hydroelectric power, to smooth out supply or match demand, (c) using smart meters to provide electric power to vehicles in such a way as to smooth out electricity supply, (d) storing the electric power for later use, and (e) forecasting the weather to plan for energy supply needs better. These are discussed briefly, in turn.
Fig. 8 Example of powering 80% of California's July electricity with load-matching renewables in 2020. The renewables include wind (26425 MW installed, 8443 MW generated), solar-PV without storage (39828 MW installed, 12436 MW generated), geothermal (4700 MW installed, 4324 MW generated), and hydroelectric (13500 MW installed – the current installation, 9854 MW generated). Hydroelectric is used to fill in gaps, as it currently does in California. Other baseload sources are assumed to supply 20% of electricity. The top line is the monthly-averaged power demand estimated for July, 2020, from California Energy Commission data. January demand is much lower (peaking at 37000 MW) and is met by higher wind production offsetting lower solar production. Figure from ref. 111 using wind data from model calculations at five locations in California.34 |
We rank each energy technology combination in terms of intermittency based on the scheduled and unscheduled downtime of the electric power source, whether the downtime affects a large or small fraction of electric power generation, the natural intermittency of the electric power source, and whether the technology combination includes a storage medium. For example, all cases considered involve combinations of the technology with either BEVs, HFCVs, or E85. Since BEVs are charged over a several-hour period, the instantaneous electricity production is not so important when the aggregate production over the period is guaranteed. With HFCVs, the hydrogen fuel is produced by electrolysis and can be stored for months to years. Thus, neither instantaneous nor weekly or seasonal fluctuations are necessarily disadvantageous. Since E85 can be stored, intermittency of its production is similarly not so much of an issue. Based on the low downtime of wind turbines, the fact that downtime affects only a small portion of the source, and the fact that intermittency is irrelevant for the production of hydrogen, we rank wind-HFCVs as the most reliable of all potential energy technology combinations. Because of the low outage rate and the ability to turn hydroelectric power on and off when it is in spinning reserve mode within 15–30 s, hydro-BEVs are ranked the second-most reliable of all energy technology combinations.
Because E85 can be stored, its production is generally independent of short-term intermittency. However, because ethanol plants are subject to fluctuations in crop supplies due to variations in weather and are more susceptible than hydroelectric power or wind turbines to planned or unplanned outages, corn- and cellulosic-E85 are tied for third. The remaining combinations involve production of electricity for BEVs. CSP-BEVs are ranked fifth because of CSP's ability to store energy in thermal storage media on-site and their low overall outage rate (<5%). Although geothermal, nuclear, and coal-CCS can supply electricity in winter better than CSP-BEVs, the outage rates for the former technologies are higher, thus they are ranked 6th–8th, respectively. Tidal power is somewhat predictable, thus tidal-BEVs are ranked 9th. Wind-BEVs, PV-BEVs, and wave-BEVs are more intermittent.114,115 If wind peaks at night, such as over land in many places, PV can match daytime peak loads better than wind114 (e.g., Fig. 8). However, for powering BEVs, most demand will be at night. Further, offshore wind and wave power generally peak during the time of peak demand. As such, we rank PV-BEVs, wind-BEVs, and wave-BEVs the same in terms of reliability. As discussed, the reliability of the intermittent technologies can be improved or ensured with the four methods discussed in this section; the rankings do not reflect such potential improvements.
The rankings for each category are referenced in the footnote of the table and were discussed previously, except not completely with respect to resource availability. With respect to resource availability, we consider the technical potential of the resource from Table 1, whether the spread of the technology to a large scale is limited by its footprint land area from Fig. 5, and the difficulty of extracting the resource. Based on these criteria, PV-BEVs are ranked the highest in terms of resource because solar-PV has the greatest overall resource availability without the need to extract the resource from the ground and is not limited by area for supplying a substantial portion of US power. Wind-BEVs and wind-HFCVs are ranked second and third, respectively, since wind is the second-most-abundant natural resource, wind does not require extraction from the ground, and wind's footprint area is trivial. CSP-BEVs are ranked fourth due to the great abundance of solar. They are behind PV-BEVs and the wind technologies due to the greater footprint required for CSP-BEVs. Wave- and tidal-BEVs follow due to the renewable nature of their resource and their small footprint. Geo-BEVs are next, since they require extraction from the ground and the resource (heat from the earth) can dissipate at a given location although it will replenish over time. CCS-BEVs and nuclear-BEVs follow due to their abundant, although limited resource, but with the need to extract the resource from the ground, transport it, and process it. Hydro-BEVs are limited by the land required for reservoirs. Similarly, corn-E85 and cellulosic-E85 are limited by their significant land requirements, with cellulosic ethanol potentially requiring more land than corn ethanol (Fig. 5 and 6).
From the overall rankings in Table 4, four general tiers of technology options emerge based on distinct divisions in weighted average score of the technology. Tier 1 (<4.0), includes wind-BEVs and wind-HFCVs. Tier 2 (4.0–6.5) includes CSP-BEVs, geo-BEVs, PV-BEVs, tidal-BEVs, and wave-BEVs. Tier 3 (6.5–9.0) includes hydro-BEVs, nuclear-BEVs, and CCS-BEVs. Tier 4 (>9) includes corn- and cellulosic-E85.
Wind-BEVs rank first in seven out of 11 categories, including the two most important, mortality and climate damage reduction. Although HFCVs are less efficient than BEVs, wind-HFCVs still provide a greater benefit than any other vehicle technology. The Tier 2 combinations all provide outstanding benefits with respect to climate and mortality. The Tier 3 technologies are less desirable. However, hydroelectricity, which is cleaner than coal-CCS or nuclear with respect to climate and health, is an excellent load balancer. As such, hydroelectricity is recommended ahead of the other Tier 3 power sources, particularly for use in combination with intermittent renewables (wind, solar, wave). The Tier 4 technologies are not only the lowest in terms of ranking, but provide no proven climate or mortality benefit and require significant land and water.
The rankings in Table 4 are not significantly sensitive to moderate variations in the weightings. For example, increasing the weighting of mortality by 3% and decreasing that of CO2e emissions by 3% does not change any overall ranking. Similarly, increasing the weighting of normal operating reliability by 3% and decreasing that of water supply by 3% does not change any ranking. Larger changes in weightings do not change the rankings at the top or bottom. They can result in some shifting in the middle, but not significantly.
Fig. 9 Thousands of 5 MW wind turbines needed, placed in locations where the mean annual wind speed is 7.0 m s−1 (high number) to 8.5 m s−1 (low number), to displace 100% of US CO2 from each source. Onroad vehicles include light and heavy-duty vehicles and are assumed to be replaced by BEVs. See ESI† for calculations. The corresponding sources of the 5970 MT-CO2 emitted in the US in 2007 are onroad vehicles: 24.6%, coal electricity: 32.8%, oil electricity: 0.91%, natural gas electricity: 6.1%, and other: 35.7%. |
Wind-BEVs performed best in seven out of 11 categories, including mortality, climate-relevant emissions, footprint, water consumption, effects on wildlife, thermal pollution, and water chemical pollution. The footprint area of wind-BEVs is 5.5–6 orders of magnitude less than that for E85 regardless of ethanol's source, 4 orders of magnitude less than those of CSP-BEVs or PV-BEVs, 3 orders of magnitude less than those of nuclear- or coal-BEVs, and 2–2.5 orders of magnitude less than those of geothermal, tidal, or wave BEVs.
The intermittency of wind, solar, and wave power can be reduced in several ways: (1) interconnecting geographically-disperse intermittent sources through the transmission system, (2) combining different intermittent sources (wind, solar, hydro, geothermal, tidal, and wave) to smooth out loads, using hydro to provide peaking and load balancing, (3) using smart meters to provide electric power to electric vehicles at optimal times, (4) storing wind energy in hydrogen, batteries, pumped hydroelectric power, compressed air, or a thermal storage medium, and (5) forecasting weather to improve grid planning.
Although HFCVs are less efficient than BEVs, wind-HFCVs still provide a greater benefit than any other vehicle technology aside from wind-BEVs. Wind-HFCVs are also the most reliable combination due to the low downtime of wind turbines, the distributed nature of turbines, and the ability of wind's energy to be stored in hydrogen over time.
The Tier 2 combinations all provide outstanding benefits with respect to climate and mortality. Among Tier 2 combinations, CSP-BEVs result in the lowest CO2e emissions and mortality. Geothermal-BEVs require the lowest array spacing among all options. Although PV-BEVs result in slightly less climate benefit than CSP-BEVs, the resource for PVs is the largest among all technologies considered. Further, much of it can be implemented unobtrusively on rooftops. Underwater tidal powering BEVs is the least likely to be disrupted by terrorism or severe weather.
The Tier 3 technologies are less beneficial than the others. However, hydroelectricity is an excellent load-balancer and cleaner than coal-CCS or nuclear with respect to CO2e and air pollution. As such, hydroelectricity is recommended ahead of these other Tier 3 power sources.
The Tier 4 technologies (cellulosic- and corn-E85) are not only the lowest in terms of ranking, but may worsen climate and air pollution problems. They also require significant land relative to other technologies. Cellulosic-E85 may have a larger land footprint and higher upstream air pollution emissions than corn-E85. Mainly for this reason, it scored lower overall than corn-E85. Whereas cellulosic-E85 may cause the greatest average human mortality among all technologies, nuclear-BEVs cause the greatest upper-estimate risk of mortality due to the risk of nuclear attacks resulting from the spread of nuclear energy facilities that allows for the production of nuclear weapons. The largest consumer of water is corn-E85. The smallest consumers are wind-BEVs, tidal-BEVs, and wave-BEVs.
In summary, the use of wind, CSP, geothermal, tidal, solar, wave, and hydroelectric to provide electricity for BEVs and HFCVs result in the most benefit and least impact among the options considered. Coal-CCS and nuclear provide less benefit with greater negative impacts. The biofuel options provide no certain benefit and result in significant negative impacts. Because sufficient clean natural resources (e.g., wind, sunlight, hot water, ocean energy, gravitational energy) exists to power all energy for the world, the results here suggest that the diversion of attention to the less efficient or non-efficient options represents an opportunity cost that delays solutions to climate and air pollution health problems.
The relative ranking of each electricity-BEV option also applies to the electricity source when used to provide electricity for general purposes. The implementation of the recommended electricity options for providing vehicle and building electricity requires organization. Ideally, good locations of energy resources would be sited in advance and developed simultaneously with an interconnected transmission system. This requires cooperation at multiple levels of government.
Footnote |
† Electronic supplementary information (ESI) available: Derivation of results used for this study. See DOI: 10.1039/b809990c |
This journal is © The Royal Society of Chemistry 2009 |