Efficiency of electricity generation

The projected efficiency of electricity generation in the year 2000 (Table 6a) has been replaced with year-by-year projections of generation efficiency by fuel type. The EIA (Annual Energy Outlook 1996, 1996) projects consumption of and generation from coal, petroleum, natural gas, and biomass, by utilities, non-utilities, and co-generators, for every year from 1994 to 2015. I divide the BTU equivalent of the net generation by the higher-heating value of the fuel input to obtain the net generation efficiency. (I ignore fuel used to generate electricity for internal use by non-utilities and co-generators.) The model looks up the calculated efficiency for the target year, and uses it in all calculations of emissions from electricity generation.

The projected efficiencies are generally higher than the originally assumed fixed values. Thus, this change has caused a significant decrease (>5%) in CO2-equivalent emissions from electricity fuelcycles, including the EV fuelcycle.

National average mix of fuels used to generate electricity

The projected national-average mix of fuels used to generate electricity in the year 2000 (Table 6b) has been replaced with year-by-year projections of electricity generation by fuel type. The EIA (Annual Energy Outlook 1996, 1996) projects generation by utilities, non-utilities, and co-generators, from coal, petroleum, natural gas, nuclear power, geothermal, hydropower, waste, biomass, solar thermal, solar photovoltaic, wind, and other sources, for every year from 1994 to 2015. I use these data to calculate generation shares by fuel type. (I allocate total natural gas generation to natural-gas boilers and natural turbines, on the basis of installed capacity, and ignore generation by non-utilities and co-generators for their own use.) The model looks up the calculated average fuel mix for the target year, and applies this average mix to natural gas compressors, hydrogen compressors and liquefiers, and "generic" electricity end uses.

This change has a negligible effect on the results.

Marginal mix of power used to recharge electric vehicles

There are two major changes regarding the marginal mix of power used to recharge EVs. First, the national marginal recharging power mix (Table 6b) has been changed. Second, the model now has the marginal recharging power mix in each of six regions of the U. S., as well as for the whole U. S. The six are the regional power systems of the Electric Power Research Institute (EPRI): Northeast (mainly New England), East Central (Ohio and neighboring states), Southeast (Tennessee and North Carolina and south), West Central (mainly Minnesota and neighboring states, South Central (Texas, Oklahoma, Louisiana, Arkansas), and West (the Rocky Mountain states and west). A macro, "EVs_by_region," calculates and presents g/mi results by stage of fuelcycle for each of the six regions and the whole U. S..

The basis for these changes is the analysis of EV charging in Yao et al. (1993). Yao et al. (1993) describe the method:

Yao et al. (1993) presented electricity use and recharging mix for weekdays and weekend days, in each region (Table XI). Given those results, I calculated the overall recharging mix (weekdays and weekends combined) in each region and for the U. S. as a whole (Table XI).

High-renewables generation scenario

I have added a high-renewables generation scenario, in which less fossil fuel, and more biomass, solar, hydro, wind, and geothermal power is used than in the conventional scenarios. These high-renewables generation mixes are used in the hydrogen and biomass fuelcycles, on the grounds that any large-scale production of renewable transportation fuel is likely to be complemented by a shift to renewable fuels for electricity generation. Thus, for example, the generation mix for power used to compress synthetic gas for transportation has more renewable fuel, and less fossil fuel, than the generation mix for power used to compress fossil natural gas. In the high-renewable scenarios renewable is about 30% of the generation mix, as compared with about 10% in the conventional scenarios.

Uncontrolled emissions from utility boilers

I have updated the factors for uncontrolled emissions from coal and fuel-oil utility boilers (Table D.4) with values from the fifth edition of AP-42 (EPA, 1995) (the changes are insignificant). Emission factors for aldehydes (formaldehyde), PM10, and PM2.5 (EPA, 1995, AP-42) have been added.

AP-42 presents the emission factors for NG boilers in units of lbs/106 SCF. To convert these units to lbs/106 BTU, one must divide by BTU/SCF of NG. In the original version of the model, I used 1031 BTU/SCF (HHV) which is the average heat content of NG in the U. S. However, the fifth edition of AP-42 (EPA, 1995) states that the emission factors are based on a HHV of 1000 BTU/SCF. The model now uses this instead of 1031 BTU/SCF.

Presently, the emission factors for coal-fired plants are those for dry-bottom boilers firing pulverized bituminous coal, and the emission factors for oil-fired plants are those for "normally" fired (as opposed to tangentially fired) utility boilers using number 6 oil. There actually are many different combustion technologies for oil and especially coal-burning plants, and also several types of coal and oil. Ideally, one would represent the actual mix of fuel types and combustion technologies (and emission controls) in use now and projected to be in use in the future. I may do this in a future round of revisions to the model.

Emission-reduction factor due to emission controls

In the GHG model, stack emissions from power plants are estimated simply as:

where:

EMs,kWh = emissions from the stack, per unit of power output (g/kWh)

EMu,input = uncontrolled emissions per unit of fuel input (g/106-BTU; see discussion above)

ER = the emission reduction factor due to emission controls; equal to the ratio of controlled emissions to uncontrolled emissions, on average

EFF = the efficiency of electricity generation (kWh/106-BTU)

Originally, I specified one value of ER for approximately the year 2000. Now the model estimates ER for SO2 and NO2 emissions for each year from 1994 to 2015, on the basis of emissions projections by the EIA.

The EIA’s AEO 1996 (1996) projects total emissions of SO2 and NO2 from utility and non-utility generators in the U. S. from 1994 to 2015. With these projections, and the EIA’s projections of fuel input to power plants and of the sulfur content of coal, one can estimate the ER implicit in the EIA’s projections of SO2 emissions:

This method assumes that SO2 emissions from natural-gas fired plants are not controlled (which is reasonable given the extremely low level of uncontrolled emissions), and that SO2 emissions from oil and coal plants are controlled to the same degree. It accounts for emissions reductions due to the projected decline in the sulfur content of coal as well as reductions due to the use of sulfur removal from the flue gases.

Similarly, I estimate the ER implicit in the EIA’s projections of NO2 emissions:

This method results in ERs of 0.60, which implies an average effective reduction of 40%, which seems reasonable. I apply this to uncontrolled NO2 emissions from all fossil-fuel combustion.

These base-year (1994) values applied to AP-42 uncontrolled emission factors approximately reproduce the EPA’s estimates of total PM and PM10 emissions from power generation in 1994 (EPA, National Air Pollutant Emission Trends 1900-1994, 1995). (The EIA does not project PM emissions.) I then assume that the reduction factors for coal and oil decline by 1.5% per year.

I assume that VOC, CO, CH4, and N2O emissions are uncontrolled throughout the entire period.

Nuclear fuelcycle

Several changes have been made to the nuclear fuelcycle:

1). The model now looks up SWUs/MWh-generated, tons-U3O8/gWh-generated, and MWh-for-enrichment/MWh-generated for the target year, from the year-by-year projections of Table I.3 of Volume 2 (A SWU is a separate work unit.).

2). The model has been updated with the EIA’s most recent projections of nuclear generation and SWUs through the year 2015 (EIA, Nuclear Power Generation and Fuel Cycle Report 1996, 1996).

3). I also have projected that MWh-enrichment/SWU declines from 2.4 (the fixed assumption in the original report) in 1998 to 2.16 by 2015, as more and more enrichment is done by centrifuge rather than diffusion.

4). To estimate emissions from standby-diesel generators, the model now uses the emission factors for large rather than small stationary diesel engines. (EPA’s AP-42 states that large stationary diesel engines are used for standby generation, and to operate emergency cooling-water pumps at nuclear power plants.) (This change is utterly insignificant.) Also, the fuel consumption of the standby generators, in gallons-diesel fuel per million BTU of nuclear power generated, has been made into a separate input variable.

Greenhouse-gas emissions at hydropower facilities

Flooded land at hydropower facilities can produce greenhouse-gas emissions, as inundated soils and organic matter degrade and their carbon content becomes mineralized to CO2 and CH4. (These emissions are analogous to emissions of CO2 and CH4 from natural processes in pristine lakes and wetlands.) The emissions, in grams-CO2 equivalent/kWh-generated, can be estimated simply as the product of the emission rate per unit area (g-CO2-equivalent/ha), and the areal intensity of power generation (ha/kWh). However, it is difficult to estimate any sensible average worldwide or U. S. emission rate, because areal emissions have been measured only at but a few sites in Canada, and the areal intensity of generation varies by orders of magnitude (Gagnon and van de Vate, 1997). Gagnon and van de Vate (1997) speculate that the worldwide average might be on the order of 20 g-CO2-equivalent/kWh, including emissions from construction, which appear to be on the order of 5 g/kWh.

On the basis of information presented in Gagnon and Van de Vate (1997) and Delucchi and Lipman (1996), I assume average emissions of 0.2 g-CH4/kWh, and 15 g-CO2/kWh, excluding emissions from construction, which are not counted for any power generation facilities.

Feedstock and process energy use of methanol, ethanol, and SNG plants

The original estimates of the use of feedstock and process energy by methanol, ethanol, and SNG plants in the year 2000 have been replaced with estimates of: i) inputs of specific fuels and feedstocks, per unit of output, in a base year (1994); and ii) the annual percentage change in the inputs through the terminal year of 2015. This method allows the calculation of feedstock and energy use in any year, but anchors the calculation to the presumably reasonably well-known data on the feedstock and energy use of current-technology plants. (Of course, this does not eliminate uncertainty in projecting energy use; rather, it locates the uncertainty in a single, explicit parameter: the annual percentage change.)

Table XII presents and documents the new parameter values. (Original reference data in Tables J.1, J.3, J.4, K.7, and K.11; original calculated energy use in Table 3.) The estimates for ethanol and methanol from wood, and ethanol from corn, have been updated on the basis of a review of recent literature.

I emphasize that mine are meant to be projections of actual energy use and emissions, not best-case or worst-case scenarios. Marland (1994) properly points out that some of the differences between past estimates of GHG emissions from the corn-to-ethanol fuelcycle are due to the difference between assuming "best practice" (e.g., the use of the most efficient conversion technology) and "typical practice" (the use of the average conversion technology). Here, I wish to project what is most likely to occur, not what might occur under the best circumstances.

Note that I have added grass as a feedstock for the production of ethanol The energy inputs and outputs of the grass-to-ethanol process are taken from NREL’s detailed evaluation of the biomass fuelcycle (Riley and Schell, 1992). I also have added biodiesel from soybeans, with the input/output parameters estimated on the basis of the data reviewed in Appendix A to this report.

Finally, I have added emissions from the lifecycle of chemicals (sulfuric acid, lime, nitrogen, phosphate, solvents, catalysts, miscellaneous chemicals) used in the wood/ethanol, grass/ethanol fuelcycles, soy/biodiesel fuelcycles, and corn/ethanol fuelcycles.

In the soy/biodiesel process, a petroleum solvent, n-hexane, is used to extract the oil from the soybeans (see Appendix A to this report for details). The small fraction of this solvent that is lost to evaporation is counted as fuel consumption, and as a VOC emission.

Feedstock and process energy use of corn-to-ethanol plants

Fuel ethanol can be produced by dry milling or by wet milling. As regards the estimation of GHG emissions, dry-mill plants differ from wet-mill plants in several key respects, and as a result it is important to determine at the outset how much future incremental ethanol supply will come from dry mills, and how much will come from wet mills. I argue that most future incremental supply will come from dry mills.

Dry-mill plants produce ethanol (about 2.7 gallons/bushel), and distillers’ dried grains and solubles (DDGS) as a byproduct. Wet-mill plants produce corn oil, corn gluten meal, corn gluten feed, and, from the starch of corn, high-fructose corn syrup or ethanol (at about 2.5 gallons/bushel). Note that not only do wet-mill plants produce more products than do dry-mill plants, they produce ethanol optionally, whereas dry-mill plants do not. This means that dry-mill plants are built expressly to supply ethanol, and would not be built were there no anticipated demand for the ethanol, whereas wet-mill plants typically are built to supply other products, and in many if not most cases would be built regardless of the market for ethanol (Madson, personal communication, 1997).

Now, in 1992, wet mill plants did produce 872.0 million gallons of fuel ethanol, whereas dry mill plants produced only 174.2 million gallons (Bureau of the Census, 1992 Census of Manufactures, Industrial Organic Chemicals, 1995). However, much of the wet mill capacity was put in place in the 1980s in order to produce high-fructose corn-syrup to replace sucrose in soft drinks (Madson, 1997). Moreover, over the past decade or so, as demand for fuel ethanol has increased roughly fourfold (ERS, Feed Situation and Outlook Yearbook, 1997), the majority of new ethanol plants have been dry mills (Madson, personal communication, 1997) -- probably because, as noted above, dry mill plants are built specifically to supply ethanol, whereas wet mill plants are built mainly to supply the other products (corn oil, corn meal, corn gluten feed, and high-fructose corn syrup). It therefore seems plausible that any increase in demand for ethanol will be supplied mainly by new dry mills, and for this reason, I formally analyze only dry-mill production in the GHG emissions model.

Still, there is no doubt that wet mills will supply at least some of a large increase in demand for ethanol, because in response to an increase in demand, some existing wet mills will switch from producing corn syrup to producing ethanol, and a few new wet mills might even be built. Consequently, it is important to at least sketch out the GHG effects of producing ethanol from wet mills. I do that here.

Energy use at ethanol plants. The energy efficiency of corn-to-ethanol plants has improved substantially over the past 15 years, and as a result new dry milling plants use less energy per gallon of ethanol than I assumed in the original report. On the basis of two recent reviews, discussed next, I have made new assumptions for energy use at corn-to-ethanol plants.

For wet mills, the energy consumption is that of the processes specific to ethanol production. It appears that Madson uses higher heating values.

Their estimates result in an average overall energy use of 0.60 BTU/BTU-ethanol, and a state-of-the-art energy use of 0.40 BTU/BTU-ethanol. (They apparently use lower heating values.) This is similar to the estimate of Conway et al. (1994) that efficient dry-mill and wet-mill corn-to-ethanol plants consume 0.50 BTU-coal per BTU ethanol produced. These energy-use requirements generally are lower than those of Table K.11 of Appendix K, supporting the contention of Lorenz and Morris (1995) and Madson (1997) that ethanol plants have become more efficient. Lorenz and Morris, and Madson, also believe that the efficiency will continue to improve.

My assumptions, shown in Table XII, are based on the data cited above. I pick the %/change per year so that by 2015 the resultant energy-use values approach those estimated for the more efficient technologies.

 In the original model, I assumed that coal supplied 100% of the thermal energy at dry mill plants. However, environmental regulations and in some cases straight economics now favor natural gas over coal, with the result that most new dry mill plants use natural gas (Madson, personal communication, 1997). Therefore, I have changed the mix of fossil fuels used to provide thermal energy at dry mill plants from 100% coal to mainly natural gas. This results in a 5% decrease in fuelcycle CO2-equivalent emissions.

Finally, I have added chemical use at corn-to-ethanol plants. In note h to Table K.7 of Volume 2, I refer to an ethanol dry mill plant designed to consume 3.7 tons/day of chemicals to treat wastewater. The plant was designed to produce about 0.16.106-gal/day, giving a chemical consumption of 23.3 tons-chemicals/106-gal ethanol, or 0.047 lbs/gallon. This is the same as the chemical usage at biomass-to-ethanol plants, which seems reasonable. The GHG emissions associated with the 0.05 lbs/gal chemical consumption increase fuelcycle CO2-equivalent emissions by about 2%.

Coproducts of the corn-to-ethanol conversion process: conceptual background

Ethanol is made from the starch of the corn. The rest of the corn -- the protein, the oil, and the fiber -- is made into other products, such as distillers dried grains and solubles (DDGS). Because only a portion of the corn is made into ethanol, it is tempting to assign to ethanol, according to some allocation rule, only a portion of the total emissions from the corn farming stage through the ethanol production stage. Unfortunately, such allocation schemes, whether according to the market value of the various products, their energy content, or some other rule, do not represent any reality we might wish to model. It is not true, for example, that if we increase production of ethanol from corn, we will get only some fraction of the emissions from corn through ethanol production. Rather, if we increase ethanol production, and hence increase corn production, we will get all of the emissions associated with corn through ethanol production. But -- and here is where consideration of the other products of the ethanol plant (call them "co-products) is relevant -- we also get less emissions in the co-product market, because we presumably will make less of the co-product substitutes.

Thus, as I point out in Appendix K of Volume 2 (DeLuchi, 1993), the correct approach is conceptually simple: estimate emissions in the world with and without ethanol production. Quoting from Appendix K (pp. K-16 to K-17):

This means that, in principle, it is not correct to estimate what we might call a "co-product displacement credit" by deducting or excluding some of the energy (for example, energy to dry coproducts) used in the corn to ethanol process. All energy and emissions from the ethanol production process must be counted and assigned to ethanol. The displacement credit should be calculated by estimating the emissions foregone in a world without the ethanol production.

The trick, of course, is to estimate what would happen in the co-product markets in the world without ethanol production. What would be produced were the co-products of the corn-ethanol process not available? How much would be produced? It also is important to know what would happen in agricultural markets were extra corn not demanded for ethanol production.

Most analyses of the "co-product displacement credit" have assumed that the DDGS from dry-mill plants (recall from above that I consider only dry mill plants) displaces soy protein (e.g., Marland and Turhollow, 1990; Conway et al., 1994). However, the DDGS co-product is a more complete feed than is soy protein because it has more fat and fiber (Madson, personal communication, 1997; Madson states also that the DDGS protein is more digestible than is soy protein). Madson claims that DDGS is used mainly in feedlots, to fatten up cattle, and that the substitute for this is whole corn feed, not soy protein. This seems reasonable, and this analysis therefore assumes that the DDGS displaces whole corn feed. (Note that this assumption probably is favorable to the corn-ethanol energy and emissions balance, because one kg of DDGS displaces more than a kg of whole corn feed, but less than kg of soy protein.) The formal relationship is quantified below.

The effects of corn production on agricultural markets might be important, but are too complex to be modeled here. The shift in demand for corn, as a result of the extra demand in the ethanol production sector, will increase the price, which will reduce demand for corn for other uses, by an amount depending partly on the slope of the supply curve. A USDA study cited by Harding Lawson Associates (HLA, 1992) estimates that each additional 1 billion gallons of ethanol produced per year will increase the price of corn by $0.08 to $0.28/bushel (for the past 20 years the price has been in the range of $2-3/bu). However, the increase in the price of corn will increase demand for corn substitutes, and reduce the demand for complements, by amounts depending on the cross-price elasticity of demand. The overall effect on agricultural markets and ultimately greenhouse-gas emissions is not clear.

GHG emissions displaced by the DDGS coproduct of dry-mill ethanol plants

The net co-product displacement emissions credit is equal to emissions from the production and transport of the corn feed displaced by the DDGS, less the emissions from the transport of the DDGS to end users. The emissions from the production and transport of the displaced corn feed depend on the amount of DDGS produced, the equivalency between DDGS and corn feed, the emission factors for corn production and transport, and other factors. Formally:

CD: lbs of shelled corn (at 56 lbs/bu) equivalent as feed to one lb of DDGS. According to industry consultant Madson (1997), 1.0 lbs of DDGS, plus 0.4 lbs of roughage such as straw, replace 1.4 lbs of bone-dry whole-corn feed. To account for the undoubtedly minor amount of GHG emissions associated with the provision of the 0.4 lbs of roughage, without having to explicitly include roughage in the GHG model, I will assume that 1.05 lbs of DDGS and 0 lbs of roughage are equivalent to 1.4 lbs of bone-dry whole corn feed, or 1.4/0.85 = 1.65 lbs of 15% moisture corn (which is the basis of 56 lbs/bushel metric used in this analysis). Thus, one lb of DDGS is equivalent as feed to 1.65/1.05 = 1.57 lbs of corn (at 56 lbs/bu).

NDF: the net displacement fraction. This is the fraction, of the total lbs of DDGS produced, that actually displaces existing or "old" feed, such that 1-NDF is the fraction that supplies new demand. Not all of the byproduct will displace feed previously produced from other sources; some will be additional, new supply that will satisfy an increased demand for feed. As shown in Figure 1, the byproduct DDGS will shift the supply curve out, from S* to S: at any given price, the amount of feed supplied will increase by the amount of DDGS marketed as a byproduct of ethanol production. But in general, the equilibrium quantity of feed consumed will not increase by the amount of DDGS made available to the market, because the equilibrium price of feed will decline. Hence, some portion of the marketed byproduct DDGS will displace marginal high-cost supply, and some will satisfy additional demand stimulated by the lower price.

The balance between displacement and additional supply depends on the slope of the demand curve. Consider the extreme or boundary conditions. If demand is completely inelastic, there will be no change in consumption, and all of the marketed byproduct DDGS from ethanol plants will displace feed produced from other sources. On the other hand, if demand is completely elastic, there will be no change in price, and all of the byproduct DDGS will be additional consumption. Most likely, reality will lie between these two extremes, as indicated in Figure 1. In Figure 1, the amount of byproduct DDGS marketed is equal to Q-Q’. As a result of the shift in the supply curve from S* to S, the price declines from P* to P, and the equilibrium quantity increases from Q* to Q. The difference between the total byproduct quantity marketed, Q-Q’, and the equilibrium increase in quantity, Q-Q*, is the amount of previously produced [high-cost] feed displaced, Q*-Q’. In Figure 1, the amount of displaced feed is about half of the total amount of DDGS produced.

The amount of feed displaced, Q*-Q’, can be estimated as:

NDF . (Q-Q’)

where:

NDF is the ratio Q*-Q’ to (Q-Q’

Thus, if demand is relatively inelastic, NDF is close to 1; if demand is relatively elastic, NDF is close to 0. We wish to know, then, whether demand for feed elastic or inelastic. The Economic Research Service asserts that "food and industrial demand for feed grain is largely inelastic, with little or no substitution possibilities" (ERS, Feed Situation and Outlook Yearbook, 1997). On the other hand, the same ERS report, and the World Agricultural Outlook Board (1997) projections, indicate that demand for feed grains is fairly sensitive to price. I will assume that demand is only moderately elastic.

Theoretically, however, the story does not end here, because any net expansion of feed consumption -- might itself displace production of other kinds of food. In general, a reduction in the price of feed will reduce consumption of feed substitutes, by an amount depending on the cross-price elasticity of demand. Allowing qualitatively for such effects, and assuming only a moderately elastic demand, I assume that NDF = 0.75; that is, that 75% of the byproduct DDGS displaces previously produced feed (or feed substitutes), and that 25% satisfies additional consumption with no further substitution.

DDGS*: lbs of DDGS per bushel of corn processed. Data cited in Tables K.7 and K.8 of Volume 2 (DeLuchi, 1993) indicate 3,000 to 3,500 tons DDGS/106-gal, or about 15-18 lbs/bu-corn, depending mainly on the ethanol yield. (The higher the ethanol yield per bushel, the lower the DDGS yield per bushel.) Industry consultant Madson (personal communication, 1997) confirms this range: today, the DDGS yield ranges from 16 lbs/bu, at 2.6 gal/bu, to 14 lbs/bu at 2.78 gal/bu. (The greater the ethanol output, the less the DDGS output.) The following formula reproduces the figures reported by Madson, and is used in the model:

where:

DDGSY = the DDGS yield (lbs/bu)

YE = the ethanol yield (gal/bu; Table XII)

Fusel oil credit. The corn-to-ethanol conversion process produces small amounts of aldehydes and higher alcohols. In Volume 2 (DeLuchi, 1993), I assumed that this so-called fusel oil was used as a supplementary boiler fuel. However, according to industry consultant Madson (personal communication, 1997), the fusel oil is left in the fuel alcohol, and is included in the gallon/bushel yield figures reported by Madson and others. Therefore, I have made two changes to the model:

1). I have added an "yes/no" switch to the calculation of the fusel oil credit: "yes" means that the fusel oil is used as a boiler fuel, "no" means it is used as product. The switch now is set to "no", with the result that fuelcycle GHG emissions increase by about 2% over the original estimates. However, the original model did not reduce the yield by the amount of fusel oil diverted to boilers. The present model does, and now the difference between using fusel oil in the boiler and keeping it in the methanol product is only 1% of fuelcycle GHG emissions.

2). If the switch is set to "yes," so that the fusel oil is used as a boiler fuel, then the amount of fusel oil used is deducted from the reported gal/bu yield. I assume that the fusel oil is a mix of propanol and butanol.

Ammonium sulfate credit. In the original model, I assumed that ammonia was used to scrub sulfur from coal and produce ammonium sulfate, which then was used as a fertilizer for corn. It turns out, however, that limestone is used, and that the resultant sludge is disposed of (Madson, personal communication, 1997). Because of this, and because in any event I neglected to include the emissions from the manufacture of ammonia (which emissions probably would cancel the emissions saved as a result of using the ammonium sulfate as fertilizer), I have removed the ammonium sulfate credit from the model. To account for emissions from use of limestone to scrub sulfur, I have added to emissions from coal-fired industrial boilers the same limestone-related emissions estimated for coal-fired utility boilers (see Appendix D, Volume 2 [DeLuchi, 1993]).

The co-product displacement credit for wet-mill plants

It will be apparent from the discussion above that the proper way to analyze GHG emissions from a corn/wet-mill/ethanol fuelcycle depends in the first instance on whether the wet mill plant would have been built had there been no ethanol policy. If a wet mill plant is built specifically to supply ethanol, and would not have been built had there been no incremental demand for ethanol, then fuelcycle GHG emissions are analyzed as in the dry-mill case: one first estimates total "gross" emissions from the production, transport, and processing of all the corn input to the wet mill plant, and then deducts the GHG emissions that would have been generated by the production displaced by the co-products (corn meal, corn oil, and corn gluten) of the wet-mill process. (Because there are several co-products, the analysis of the co-product displacement credit is complicated.) In this case, one starts with total emissions from the processing of all corn input because one would not have processed any of the corn had there been no ethanol policy.

If, however, an ethanol policy induces an existing wet-mill plant (one that would exist, or would have been built, and would be in operation regardless of the ethanol policy) to switch starch conversion from corn syrup to ethanol, then the GHG emissions attributable to the ethanol policy are equal to:

1) the emissions from the starch-to-ethanol conversion process in the wet -mill plant, plus the emissions from transporting and using the ethanol product, minus:

2) the emissions from the now abandoned starch-to-corn-syrup conversion step in the wet-mill plants and the emissions from transporting and using the corn syrup, plus:

3) the emissions from the production, transport, and use of whatever is made to replace the corn syrup formerly produced.

If, to a first approximation, the emissions from the conversion of starch to ethanol (in #1) are canceled by the emissions foregone from the conversion of starch to corn syrup (in #2), and if emissions foregone from the transport and use of the corn syrup (in #2) at least cancel the emissions from the transport and use of whatever replaces the foregone corn syrup (#3), then the net GHG emissions attributable to the ethanol policy are the emissions from transport and end use of ethanol, plus the emissions from the production of stuff to replace the corn syrup formerly made. This makes sense: if an ethanol policy has no effect on the use of corn, and no effect on the output of wet mill plants other than to switch starch from corn syrup to ethanol, then the only things changed in the ethanol-policy world are the transport and use of the ethanol, and the production of whatever makes up for the loss of corn syrup. These emissions will total to much less than the emissions from the corn/dry-mill/ethanol process, because there are no net emissions from corn farming or ethanol production. (To put it yet another way, ethanol in this scenario is almost a "free" byproduct.) I estimate that fuelcycle GHG emissions (including emissions from end use, but not from vehicle manufacture) from switching wet mill plants to ethanol production are on the order of 100-150 g-CO2-equivalent/mi -- well less than half of the emissions in the dry mill case.

Co-products of wood-to-alcohol production

Von Sivers and Zacchi (1996) state that wood-to-ethanol plants produce marketable chemicals, lignin fuel, and electricity, in addition to ethanol, and estimate that the $/gallon-ethanol value of these coproducts is as much as 50% of the $/gallon production cost of ethanol. However, in the wood-to-ethanol process assumed here (Table XII; Lynd, 1996a), the lignin is used within the plant as a boiler fuel, and there is no significant chemical co-product. As discussed elsewhere, the excess power produced is given an appropriate GHG emissions credit.

In the absence of data to the contrary, we assume that there are no significant co-products from wood-to-methanol plants either.

Electricity displaced by electricity exported from wood-to-ethanol and grass-to-ethanol plants

The GHG model now requires that you to specify the mix of electricity that is displaced by the excess power generated by wood-to-ethanol plants. (In the previous version, the model assumed that the U.S. average power mix was displaced.) The excess power made available to the market will displace electricity generated at a high variable cost. Compared to the national average mix, the high-variable-cost mix has a relatively large amount of gas, and a small amount of nuclear and hydro. I have assumed that the displaced power mix is 50% coal, 40% gas, and 10% oil.

Also, the model now asks you to specify the fraction of the byproduct power that actually displaces generated power. Not all of the byproduct power will displace power previously generated from other sources; some will be additional, new supply that satisfies an increased demand. The effect is illustrated in Figure 1, and is discussed above in regards to the DDGS co-product of ethanol production from corn. As discussed there, the balance between displacement and additional supply depends in the first instance on the slope of the demand curve. If demand is relatively inelastic, the net displacement factor NDF (the ratio of Q*-Q’ to Q-Q’) is close to 1; if demand is relatively elastic, NDF is close to 0. I will assume that demand is moderately elastic, an assumption consistent with the EIA’s Analysis of Electricity Prices in a Competitive Environment (EIA, 1997).

Theoretically, however, the story does not end here, because any additional electricity consumption most likely will affect energy use in other sectors. For example, some of the additional consumption of electricity might reflect a switch from gas to electricity for heating or cooking. In this case, the exported power displaces gas indirectly, rather than previously generated power directly. In general, the reduction of the price of electricity will reduce consumption of substitutes for electricity, by an amount determined by the cross-price elasticity of demand.

Co-products of the soy-diesel production process

The soy-diesel manufacturing process produces substantial amounts of glycerine and soy meal, along with fuel (see Appendix A to this report). Ahmed et al. (1994) assume that the soy meal is used in place of barley as an animal feed (see Appendix A to this report). The situation with glycerine is more complicated, because there are many of sources of glycerine, and hundreds of uses (Economic Research Service, 1993, 1996).

For two reasons, it is difficult to estimate the GHG emissions displaced by the co-products of the biodiesel production process. First, as just noted, there are two major co-products, one of which, glycerine, can be made from a variety of sources, and is used in many applications. Also, it is not clear that the other major co-product, soy meal, necessarily replaces barley feed, as Ahmed et al. (1994) assume.

Second, the extra demand for soy oil, to be made into biodiesel, will affect the markets for a variety of farm products. Raneses et al. (1996) use the Food and Agriculture Policy Simulator (FAPSIM) to track the economic impacts of biodiesel production over a broad range of agricultural commodities. They simulate the production of biodiesel by shifting the demand for soybean oil. This shift increases the price of soy oil; the price increase, in turn, causes a decrease in demand for soy oil in other uses, but an increase in demand for raw soybeans used by processors, because of the greater profitability brought about by the higher price of the oil. As more soybeans are crushed, more soy meal is produced, and as a result the price of soy meal falls. Because meal is a major input to the production of livestock, the decline in the price of soy meal leads to an increase in production of livestock. Also, the lower price of soy meal causes livestock producers to feed more soy meal and less corn; as a result, corn production declines.

All of this is too complicated to model here. Instead, as a crude, first-cut approximation of the emissions displaced by soy diesel co-products, I rely on the estimates of Ahmed et al. (1994; Appendix A to this report) of the energy required to make the displaced products displaced by the soy diesel coproducts. Formally:

The estimated displaced emissions, GHGD, or deducted from "gross" (pre-credit) emissions from the biodiesel production stage.

The assumptions shown here result in a displacement "credit" that is a little more than half of the gross (pre-credit) emissions from the biodiesel production cycle excluding end-use combustion in vehicles (feedstock recovery through product distribution). However, the parameters ED, NDF, and EE are quite uncertain, and different assumptions can lead to significantly different results.

Hydrogen produced from biomass: process energy requirements

I assume 1.30 BTUs-wood/BTU hydrogen, and 0.065 BTU-electricity/BTU-hydrogen, in the biomass-to-hydrogen path, partly on the basis of data in Katofsky (1993).

Hydrogen produced from water: energy efficiency of electrolysis

I project the energy efficiency of water electrolyzers with equation B-1 (see above), with the following parameter values:

I base my estimates of MV and BV on the statements by Ogden and Nitsch (1993) that present electrolyzers are 73% efficient (higher heating value), and that "future" electrolyzers will be 90% efficient.

CH4 emissions from methanol plants

I have increased the CH4 emission rate from NG-to-methanol plants (Table A.1), on the basis of a reconsideration of my original data, and new data from Heath (1991) and Ecotraffic AB (1992). See Delucchi and Lipman (1996) for further discussion.

Emission factors for alcohol-fuel production

I have made minor changes to emission factors for coal-to-methanol, wood-to-methanol, and corn-to-ethanol plants, on the basis of a reconsideration of my original data, and new data from Ismail and Quick (1991), Ecotraffic AB (1992), EPA (1994), and Darrow (1994). PM, PM10, and SO2 emission factors have been added. See Delucchi and Lipman (1996) for further discussion.

Formerly, emissions from ethanol plants were estimated by multiplying the energy content of feedstock used as a process fuel by the g/106-BTU-feedstock-input emission factors for wood fluidized bed combustors. Now, VOC, NO2, CO, PM, and SO2 emissions are estimated by multiplying the total 106-BTU-feedstock-input of the plant by total plant emission factors in g/106-BTU-feedstock-input. These total-plant emission factors are from NREL (Riley and Schell, 1992) Emissions from grass-to-ethanol plants are estimated in the same way, also using NREL (Riley and Schell, 1992) estimates. Emissions of CH4 are estimated by scaling VOC emissions by the CH4/VOC ratio for wood-waste combustion (see below), and emissions of N2O are estimated by scaling the NOx emissions by the N2O/NOx ratio for wood-waste combustion.

Emission factors for wood-waste combustion in boilers