Energy intensity of natural gas transmission

The energy required to transport natural gas by pipeline, and the total amount of gas leaked from compressors, joints, and other parts of the system, are related to the distance of transmission. Because of this, and because the average gas transmission distance might differ between end uses (e.g., the average transport distance to a methanol production plant probably will be less than the average distance to a CNG station), the model now estimates the energy intensity of natural gas transmission as a function of transmission distance. Given the transmission distance for each natural-gas end use (electric utilities, industry, methanol production, etc.) relative to the distance for the transportation (CNG or LNG) (relative distances specified by user), and the overall transmission energy intensity for all end uses (calculated by dividing the EIA’s Annual Energy Outlook projection of total pipeline fuel use in the target year by the EIA’s projection of total end use consumption in the target year), the model calculates the BTU-compressor-fuel/BTU-shipped intensity for each end use. The data and results are shown in Table XVIII.

Leaks of natural gas

Partly on the basis of the results of a recent EPA/GRI study (1996), I have changed the calculation of CO2-equivalent emissions of gas leaks from natural-gas systems. (See Delucchi and Lipman [1996] for a review of studies of leakage from natural-gas systems.)

Although at a general conceptual level the estimation of CO2-equivalent emissions of gas leaks is straightforward, there are, as always, niggling details to get straight in the calculation. In general, the CO2-equivalent emission of gas leaks is equal to the CO2-equivalency factor for natural gas multiplied by the amount of natural gas leaked. The CO2-equivalency of natural gas is a function of the composition of the gas, and the CO2-equivalency factors for the components of the gas. The amount of natural gas leaked depends on unit leakage rates for each stage, gas input and output for each stage, the allocation of leaks to multiple products, and other factors. The unit leakage rates are derived, with some adjustments, from the EPA/GRI (1996) detailed study of methane leaks from the natural gas industry. Formally:

Table XIX shows the parameters used in the estimation of the amount of gas lost.

Note that the calculation involves more than just applying the EPA/GRI summary finding that gas leaks amounted to 1.4% of gross production in 1992. In fact, there are at least seven reasons why this overall 1.4% differs from the correct leakage rate estimated here.

First, the overall 1.4% emission rate depends on the emission rate and the throughput weight (input/output factor) for each stage, and hence will change if the throughput weights change. For example, the 1.4% is the result of a certain amount of gas being processed at NGL plants. In the future, a greater or lesser fraction of marketed production might go to NGL plants, depending on whether the raw produced gas requires more or less processing, with the result that the overall emission rate will change, all else equal. (Note that the processing segment now is treated explicitly, as a separate segment.) I address this by estimating emission rates and input/output factors separately for each stage.

Second, EPA/GRI estimate emissions of methane only; they do not estimate emissions of the other minor constituents of natural gas, including carbon dioxide and ethane (an indirect greenhouse gas). I address this by scaling estimated volume emissions of methane by the ratio of total gas volume to methane volume.

Third, the total overall 1.4% emission rate includes methane emissions from combustion, which I have estimated separately. Hence, I count here only venting and fugitive emissions.

Fourth, the EPA/GRI study inappropriately excludes emissions from foreign (primarily Canadian) transmission systems shipping gas to the U.S., and inappropriately includes emissions from U. S. systems exporting gas. Because pipeline imports are an order of magnitude greater than exports (EIA, Natural Gas Annual 1995, 1996), the net effect is to understate emissions from transmission systems that deliver gas to U. S. consumers. The magnitude of the underestimation depends on the length and quality of the foreign transmission systems, among other factors.

Fifth, the 1.4% leakage rate incorporates the average rate for transmission systems. However, any particular NG fuelcycle being analyzed will involve transmission distances greater or less than the average, and consequently will have more or less than the average leakage from transmission systems, because losses from the transmission system are related to the length of the transmission system. I estimate leakage from the transmission system as a function of distance.

Sixth, as old leaky equipment is replaced by new equipment, leakage rates will decline (EPA/GRI,1996). My assumptions are shown in Table XIX.

Seventh, the production and processing stages produce natural gas liquids as well as natural gas. It seems reasonable to assign some of the gas leakage to the NGL product. I have allocated total emissions to NG and NGLs according to the total energy content of the production of each.

Note that I assume that the loss rates for the transmission and distribution stages are proportional to the length of the pipelines (EPA/GRI, 1996), with the result that the leakage rate from a NG-to-methanol transmission system is less than the rate from a NG-to-CNG system, because of the shorter assumed transmission distance in the methanol system.

Emission factors for gas-turbine and gas-engine pipeline compressors

I have input the EPA’s (Compilation of Air Pollutant Emission Factors, AP-42, fifth edition, 1995) revised factors for uncontrolled emissions of CH4, CO, and NMOCs from pipeline compressors (Table A.1). (The CH4 emission factors are consistent with those used in the EPA/GRI’s (1996) comprehensive analysis of methane emissions from the natural gas system.) I assume that NOx emissions from the population of gas-turbine compressors are 25% below AP-42’s revised factor for uncontrolled emissions, and that emissions from the population of engines are 50% below AP-42’s revised factor for uncontrolled emissions. I assume that more engines than turbines are controlled because uncontrolled NOx emissions from turbines are nearly 10 times lower than uncontrolled emissions from engines to begin with.

Work and energy use of gas-turbine and gas-engine compressors

Table G.5 of Vol. 2 presents data from a survey of transmission companies, and from a review of the literature, which show that the installed horsepower capacity of compressor engines is about 4 times higher than the installed capacity of compressor turbines. Consistent with this, the recent EPA/GRI (1996) detailed analysis of methane emissions from the U. S. gas system estimates that in 1992 engines provided 80% of compressor horsepower-hour work, and turbines 20% (not counting work provided by electric compressors). Allowing that electric compressors provided 5% of total compressor work (Table G.5), I assume that in 1992, engines provided 76% of the total work, turbines 19%, and electric motors 5%. I then assume that the share of turbines increases slightly.

Because emissions are estimated per BTU input to the compressor, rather than per unit of work provided, these work-output shares must be converted to energy- input shares. To do this, I assume that turbines use 1.33 times as much energy per horsepower-hour as do engines, and that electric motors use 0.25 times as much energy per horsepower-hour as do engines (DeLuchi, 1993).

In the current version of the model, I assume that the installed hp-hour capacity of turbines is slightly less than the installed capacity of engines, but that engines use energy more efficiently, so that total energy use by turbines equals total energy use by engines. Specifically, I assume that 49.4% of pipeline energy is used in turbines, 49.4% in engines, and 1.3% in electricity-driven compressors (Table 4). The EPA states that "for reciprocating engines, two stroke designs contribute approximately two-thirds of installed capacity" (p. 3.2-1). I assume that 2/3 of the energy used by reciprocating engines is used in 2-cycle lean burn engines, that 1/6 is used in 4-cycle lean-burn engines, and that 1/6 is used in 4-cycle rich-burn engines. This is consistent with the emission estimates in the EPA/GRI (1996) report.

Note on natural gas storage

Natural gas is stored in depleted oil and gas reservoirs, salt caverns, and aquifers in order to buffer seasonal or weekly variations in gas demand. Gas is added to storage during periods of low demand, and withdrawn during periods of high demand. In 1995, 2.6 TCF of natural gas were added to storage facilities, and 3.0 TCF were withdrawn (EIA, Natural Gas Annual 1995, 1996). Total storage capacity is expected to increase by about 10% by the year 2000 (EIA, The Value of Underground Storage in Today’s Natural Gas Industry, 1995).

It takes energy to move natural gas in and out of storage facilities. This energy -- or at least the portion that is provided by using natural gas a fuel -- is counted as pipeline fuel in the EIA’s statistics. Form EIA-176, "Annual Report of Natural and Supplemental Gas Supply and Disposition," asks respondents to report the amount of gas "used in pipeline, storage, and/or distribution operations" (EIA, Natural Gas Annual 1995, 1996, p. 217, survey p. 4). (Virtually all storage sites operated by pipeline and distribution companies [EIA, The Value of Underground Storage in Today’s Natural Gas Industry, 1995].) Hence, the energy requirements of storage operations should be included in the EIA’s projections of pipeline fuel in its Annual Energy Outlook.

Distribution of coal, crude oil, and petroleum products: general method

In the original report, the energy used to distribute coal, crude oil, and petroleum products was calculated, presented (Table E.1a), and input to the model on the basis of historical data on tons and ton-miles of shipments of coal, oil, and products, by mode, in 1987. This was different from the method used to calculate the energy used to distribute methanol, ethanol, and LPG (Table E.1b). For those fuels, distribution energy for mode M was calculated per ton of fuel shipped by mode M, as the product of: an assumed average length of haul by mode M (miles), energy intensity (BTU/ton-mile-shipped by mode M), and a modal usage factor (tons of fuel shipped by mode M per ton of fuel produced). Now, I have changed the basis of the calculation for coal, crude oil, and petroleum products to be the same as the basis of the calculation for ethanol, methanol, and LPG. Thus, for all fuels, distribution energy is calculated per ton of fuel produced, as the product of miles, BTU/ton-mile, and tons shipped by M per ton produced:

This unifies the input, presentation, and interpretation of data. Most of the primary data sources are the same as those used in DeLuchi (1993).

Note that the mileage, LH1W, is the one-way distance, not the round-trip distance. This, of course, is because the fuel in question (say, coal), is shipped only one way; hence, to calculate ton-miles, one multiplies tons by the one-way shipping distance. Now, if the ship returns empty, then the energy used in the empty backhaul must be counted in the total energy E in the calculation of energy intensity EI:

So, if the carrier returns empty, then E includes the energy used on the empty backhaul; if the carrier returns with another product, E includes only the energy used to haul fuel F one way. As noted in Appendix E of DeLuchi (1993), virtually all ships return empty.

International waterborne shipment of crude oil, and petroleum products: estimated tons-shipped/ton-produced, and average length of haul

The estimates of tons shipped per ton produced (parameter TS/TP in the equation above) and average length of haul (parameter LH1W in the equation above) for international waterborne shipment of crude oil and petroleum products have been refined and updated. TS/TP now is calculated on the basis of EIA projections of imports, by region or country, and of domestic petroleum supply, through the year 2015:

The average length of haul, LH1W, now is calculated on the basis of the distance from each exporting country to each major domestic port, and the amount of petroleum imported from each region or country:

Domestic waterborne shipment of crude oil and petroleum products: estimated tons-shipped/ton-produced, and average length of haul

The estimates of tons shipped per ton produced (parameter TS/TP) and average length of haul (parameter LH1W) for domestic waterborne shipment of crude oil and petroleum products have been updated on the basis of 1994 and 1995 data from the Army Corps of Engineers’ Waterborne Commerce (1995) and the EIA’s Petroleum Supply Annual 1994 (1995). Table XX shows the 1994 data for tonnage shipped, ton-miles shipped, and tons produced, by commodity, and the calculated TS/TP and LH1W. On the basis of the calculated values shown in Table XX, I have input new values for TS/TP and LH1W.

In consideration of these updated values for petroleum products, I have changed some of the assumptions for TS/TP and LH1W for methanol transport as well.

Domestic waterborne shipment of coal, crude oil, and petroleum products: energy intensity

In Appendix E of the original report, I assumed that vessels that carry coal domestically have an energy intensity (parameter EI) of 500 BTU/ton-mile, and that vessels that carry petroleum domestically have an energy intensity of around 200 BTU/ton-mile (Table E.1a). My assumption for coal vessels was based on estimates in Table E.2, and my assumption for petroleum was based on estimates of the energy intensity of tankers of different sizes.

I have revisited these assumptions, and looked more closely at the types of vessels that carry each type of commodity. Data from the Army Corps of Engineers’ Waterborne Commerce (1995) indicate that barges haul most coal (87.5% of all coal ton-miles by water), and a significant fraction of domestic petroleum products, but essentially no crude oil (Table XX). Moreover, the average domestic petroleum-product tanker apparently is smaller than I had assumed: according to the EIA (The Energy Information Administration’s Assessment of Reformulated Gasoline, 1994), a typical U. S.-flag petroleum-product tanker operating in U. S. waters is less than 50,000 dwt, whereas in Table E.5 of Volume 2, I assumed that the bulk of petroleum-product tankers in domestic service are in the 60,000 or 90,000 dead-weight-ton (dwt) size class.

Barges have an energy intensity of on the order of 280 to 480 BTU/ton-mile (Rose, 1979; Booze-Allen Hamilton, 1977); here, I assume 350 BTU/ton-mile. Smaller tankers are more energy intensive than larger tankers (Table E.5). Shifting the distribution of tankers carrying petroleum products towards the lighter dwt classes increases the weighted-average BTU/ton-mile by about 10%, to 213 BTU/ton-mile. The size distribution and hence average EI of crude oil tankers remains unchanged.

Given this, I now calculate a weighted average energy intensity (EI) for domestic waterborne commerce, equal to the EI for barges multiplied by the fraction of ton-miles by barge, plus the EI for tankers multiplied by the fraction of ton-miles shipped by tankers. The EI factors are given above, and the ton-mile fractions are based on the data in Waterborne Commerce (Army Corps of Engineers, 1995)

Pipeline shipment of crude oil and petroleum products: estimated tons-shipped/ton-produced, and average length of haul

In Volume 2 (DeLuchi, 1993) , I estimated shipping parameters for crude oil and petroleum products on the basis of ton-mile data from the Association of Oil Pipelines. I have estimated new ton/ton and average length of haul parameters on the basis of data reported by the Bureau of Transportation Statistics (1996) and other sources (Table XX). The average length of pipeline shipment for petroleum products (but not for crude oil) appears to be substantially less than I had assumed originally. I assume that light products have the same TS/TP and average shipping length as heavy products.

Truck shipment of petroleum products: ton miles and average length of haul

A minor part of emissions of greenhouse gases from the oil fuelcycle is emissions from trucks that transport petroleum products. When the original report was written, there were no reliable data on ton-miles of travel by trucks carrying petroleum products. In Volume 2, I used five different data sources and methods to estimate ton-miles of travel by trucks carrying petroleum products (pp. H-38 to H-40; Table E.1a). I based my final estimate on data from the 1982 Truck Inventory and Use Survey (Bureau of the Census, 1985), which reported miles but not ton-miles by trucks carrying petroleum products, and noted in Table E.1a that the estimate was "rough".

Recently, somewhat better data have become available. The most recent Truck Inventory and Use Survey (TIUS) , for 1992, (Bureau of the Census, 1995) now reports truck miles of travel by trucks carrying petroleum products, in 14 weight categories (Table XXII). The weight categories refer to average total weight when loaded. With these data and some assumptions, one can calculate ton-miles of product and average weight of product for 1987 (which in the original report was the base year for all of the calculations of feedstock and product transportation and distribution requirements).

To do this calculation, one must make four sets of estimates or assumptions. First, one must estimate the actual average loaded weight within each of the 14 weight classes. All of the classes except the first and last are small enough that the midpoint of the class must be a reasonable approximation. However, the first class is "less than 6,001 lbs), and the last is "130,001 or more". I assume that trucks less than 6,001 lbs weigh 5,000 lbs, and that trucks more than 130,001 lbs weigh 150,000 lbs. (Table XXII). I expect that the resulting estimated average loaded weights are very close to the true average weights.

Second, one must deduct the weight of the empty vehicle from the reported assumed average weight, to get the weight of the product carried. I have made a separate assumption for each weight class. The assumed empty vehicle weight ranges from 3,700 lbs to 33,000 lbs, and is a progressively smaller fraction of the loaded weight (Table XXII). There probably is a 10% to 30% error in my assumptions.

Third, one must estimate the fraction of total miles with the average load. I assume that half of the total truck miles are empty, and that half are with the average load.

With the data of Table XXII, I calculate that trucks that carried petroleum products in 1992 had an average product load of 13.8 tons, and transported the products 42.2 billion ton-miles, excluding empty back-haul mileage which as I said I presume to be half of total truck mileage.

Finally, one must estimate the average product load per petroleum truck in 1987 given the average product load calculated for 1992. I assume that the average load was the same in 1987 as in 1992. In 1987, petroleum trucks traveled 5.078 billion truck miles total or, I assume, 2.55 billion miles loaded. Multiplying by 13.8 tons of product per truck yields 35.1 billion ton-miles, a little more than one-quarter of the value assumed in the original report. On this basis, I have cut in fourth the assumed average length of haul. The result is consistent with the EIA’s (Alternatives to Traditional Transportation Fuels, 1994) citation of an estimate by the National Petroleum Council of a one-way haul of 40 miles.

Truck distribution of methanol, ethanol, and LPG: average length of haul

In the original report, I based my assumptions about the length of haul (parameter LH1W in the equation above) and tons-shipped/ton-produced (parameter TS/TP) for methanol, ethanol, and LPG on a qualitative consideration of plant siting with respect to end users, and with respect to my estimate of ton-miles by trucks carrying petroleum products. However, as explained immediately above, my revised estimate of ton-miles by trucks carrying petroleum products is about one-quarter of the value originally assumed in Volume 2 (DeLuchi, 1993). This suggests that I implicitly overestimated the average haul by a factor of about four, assuming that virtually all petroleum products are transported by truck at some point. Indeed, I now calculate that the average haul in 1987 was on the order of 40 miles one-way (on the basis of 843 million tons of products supplied in 1987). Therefore, I have greatly reduced all of the assumed one-way haul lengths by truck for methanol, ethanol, and LPG.

Train, water, truck, and pipeline transport of coal

The EIA, the Bureau of the Census, the Army Corps of Engineers (ACE), and the Interstate Commerce Commission (ICC) independently collect data on the shipment of coal by rail, water, truck, and pipeline. Table XXI summarizes ton and ton-mile data from these surveys, for 1993 and 1995. I use these data to estimate tons-shipped per ton produced (TS/TP), and the average length of haul one way (LH1W).

The Census’ Commodity Flow Survey reports tons and ton-miles by all modes; the EIA’s Coal Distribution survey reports tons by all modes; the ACE’s Waterborne Commerce reports tons and ton-miles by water; and the ICC’s Waybill reports tons and ton-miles by rail. For three reasons, I base my estimates of TS/TP on the EIA’s tonnage data: the EIA’s is a comprehensive survey of coal producers and distributors; the EIA data agree with the ACE data on water transport, and the ICC data on rail transport; and the EIA transport data can be used with the EIA’s production data to produce a measure of tons-shipped/ton-produced (TS/TP). These TS/TP estimates are summarized at the bottom of Table XXI.

Pipelines. Originally, I estimated tons shipped and average length for the Black Mesa slurry pipeline, and then made an ad-hoc adjustment to shipping distance to account for all distribution by tramway and conveyor belt. It turns out, however, that much more coal tonnage is shipped by tramway and conveyor than by slurry pipeline, albeit for a much shorter distance. In Table XXI, I estimate TS/TP and average shipment length for the pipeline, tram, and conveyor combined. On the basis of these data, I entered new assumptions for pipeline/tramway/conveyor transport of coal.

In Table E.1a I assumed 600 BTU-electric/ton-mile for the Black Mesa coal slurry pipeline. As discussed in note e to Table E.2, only about half of this energy is required to actually move the coal; the rest is used to prepare the coal and to de-slurrify it. It is doubtful that tramways and conveyor belts, which as just noted carry much more coal than do long-distance coal-slurry pipelines, consume nearly as much energy per ton-mile as do coal-slurry pipelines. I assume an average of 250 BTU-electric/ton-mile (in the base year) for all pipeline, tramways, and conveyor belts.

Distribution of ethanol and methanol from biomass

Upstream NMHC emissions from gasoline marketing

Two changes have been made here. First, "upstream" NMHC emissions from gasoline marketing (excluding emissions from vehicle refueling, but including emissions from refilling storage tanks at service stations), have been reclassified as "fuel distribution" emissions rather than as "vehicular" emissions (Table 5, Table 9, and Table B.2). Second, the original emission factor of 4 grams-VOCs/gallon for all years has been replaced with the following projection over the period 1994 to 2010:

This evaporative loss rate is relevant to the analysis of GHG emissions in two ways. First, the VOC emissions themselves contribute to tropospheric ozone formation and hence to global warming. Second, the fuel lost must be made up by increasing throughput (at all stages except refueling), and this entails increased use of process energy and hence increased GHG emissions. However, both of these -- the effect of VOC emissions on ozone, and the effect of fuel loss on process-energy use -- are relatively minor.

Gas leaks from gaseous-fuel stations

I assume the following loss rates (NG-lost/NG-throughput; note that the loss is of natural gas, not methane per se):

Electricity use at liquid bulk-storage facilities and service stations

The original version of the model did not include emissions from fuel and electricity use at bulk liquid storage facilities (bulk plants and bulk terminals) or from electricity use at service stations to pump liquid fuels. I have now estimated these emissions and incorporated them into the model. I assume that gasoline, diesel fuel, LPG, methanol, and ethanol will be stored at bulk storage facilities, as gasoline is now. (Compressed and liquefied gaseous fuels, I assume, will be delivered directly to stations via pipeline.) I estimate the present emission rate per gram (not gallon; work is related to the mass of the fuel pumped) of gasoline throughput at bulk terminals and plants, and assume that it will apply to all of liquid fuels just mentioned. I also estimate electricity consumption for pumping at gasoline service stations, per gram of gasoline dispensed, and again assume that the rate will apply to all of the liquid fuels just mentioned. (Emissions from compression and liquefaction of gaseous fuels of course already are included in the model.) I do not include emissions associated with energy use for all other functions at service stations (such as heating and lighting), because presumably this energy use will be more or less independent of the type of fuel delivered (although one could argue that the different storage requirements of different fuels will result in different numbers of buildings and different amounts of energy for heating and lighting).

Emissions from storage facilities and service stations are calculated as the product of energy usage per unit of output and emissions per unit of energy usage. Energy usage is calculated from data on expenditures for energy, which are shown in the Table XXIII below. Calculated energy usage per unit of output is shown in Table XXIV("Energy Use Per Unit of Activity"), and the emission factors are shown in Table XXV ("Emission Factors for Natural Gas and Diesel-Fuel Use by Buildings"). (I assume that the 1987 energy intensities apply to future years.) The table on energy use per unit of activity (Table XXIV) includes all energy used at service stations -- not just pumping energy -- even though the model includes for service stations only pumping energy, because those are the original data, and because it is interesting to see total energy use in any event. (Also, at a later date I might incorporate total energy use into the model.) In the next paragraph I discuss how I estimate the portion of total service-station electricity use that is for pumping fuel.

As mentioned above, the estimate shown in Table XXIV, 0.10 kWh/gallon, includes power for lighting and other building functions as well as power to pump gasoline. Presumably, alternative-fuel stations will use the same amount of electricity for lighting and heating and other functions besides pumping as gasoline stations do, so in order to estimate energy use at alternative-fuel stations, I need to separate pumping power use from other power use at gasoline stations. Data from EIA surveys (EIA, Commercial Buildings Characteristics 1992, 1994; Energy End-Use Intensities in Commercial Buildings, 1994) show that in 1989, mercantile and service buildings, which include gasoline stations, consumed 34,500 BTUs of electricity per square foot of floor space, and 27,500,000 BTUs of electricity per employee, for cooling, ventilation, lighting, cooking, office equipment, and refrigeration -- everything except things like pumping gasoline. Multiplying these figures by the total square footage (assuming 1500 ft2 per establishment multiplied by the number of establishments) or the total number of employees in SIC 554 in 1987 (Bureau of the Census, 1987 Census of Retail Trade, 1991) results in an estimate of 2 to 7 billion kWh of electricity, for everything other than pumping at gasoline stations. SIC 554 actually consumed 10 billion kWh in 1987 for all purposes including pumping fuel. Therefore, if this calculation is valid, 3 to 8 billion kWh of electricity were used to pump gasoline in 1987. On the basis of this, I assume that 0.065 kWh/gallon is used to pump gasoline, out of the total electricity consumption of 0.10 kWh/gallon. (This then is converted to kWh/gram, which as noted above is the basis of the calculations in the model.) This results in an energy efficiency of 99.9% for the pumping stage, which seems reasonable.

Calculated emissions from the use of energy at bulk storage facilities have been added to the stage formerly called "Fuel distribution," now renamed "Fuel distribution and storage". Calculated emissions from the use of energy at service stations (for pumping) have been added to the stage formerly called "Compression and liquefaction," now renamed "Fuel dispensing". The input energy usage data (corresponding to Tables 3 and 4 of Volume I of the original report) are in new rows similarly renamed.

CNG compression energy

I have reduced the compression energy requirement for CNG from 0.05 BTU-electric/BTU-CNG to 0.022, on the basis of calculations of compressor-work requirements using engineering equations, and actual energy-use data from CNG stations operated by PG&E (1993) (Table 3).

Hydrogen compression energy

The energy required to compress hydrogen is estimated as a function of the storage pressure on board the vehicle. The user now specifies the hydrogen storage pressure, and the model then calculates the electricity use of the hydrogen compressor with the following simple expression:

Emission factors for trains, engines, industrial boilers, etc.

See Delucchi and Lipman (1996) for further details regarding the emission factors discussed below.

Organic compounds

Formerly, I referred to organic compounds mostly as "hydrocarbons" (HCs), and organic compounds excluding methane as "nonmethane hydrocarbons" (NMHCs). Now, in keeping with the terminology adopted by the EPA, I refer to total organic compounds (TOCs) and non-methane organic compounds (NMOCs). Organic compounds include aldehyde emissions, except as noted. NMOCs exclude only methane; i.e., they include ethane. They therefore are not the same as volatile organic compounds (VOCs), which exclude ethane as well as methane. I do not use VOCs anywhere in this report.

PM and SO2 emissions

PM and SO2 emissions from all combustion sources (vehicles, boilers, trains, ships, etc.), and from some non-combustion sources (e.g., catalytic crackers in petroleum refineries) have been added. Most of the PM emissions factors are from EPA’s AP-42; SO2 emissions are calculated on the basis of the sulfur content. However, several sources of fugitive dust (e.g., coal mining, agricultural operations, and roads) are not included.

Control of emissions from trains, ships, boilers, engines, etc.

In the original version of the model, the user made a direct estimate of the average in-use emission factor for the trains, engines, industrial boilers, etc. (Table A.1); the estimate was not built up from separate estimates of the uncontrolled emission rate and the extent and effectiveness of emission controls. Any effects of emission controls were built into, or written in with, the directly input emission factor -- for example, by dividing an uncontrolled emission rate by two.

Now, the model has a set of factors for uncontrolled emissions, and a separate set of control factors, for trains, tankers, scrapers, loaders, off-road trucks, tractors, well equipment, industrial engines, pipeline engines and turbines, industrial boilers, and building heaters. The control factors are the average ratio of: [in-use emissions from the real mix of controlled and uncontrolled sources] / [uncontrolled emissions]. Thus, for these sources, the in-use emission factors of Table A.1 now are calculated by multiplying uncontrolled emissions by control factors.

Industrial boilers

In the original version of the model, emissions from industrial boilers (used in a variety of fuel cycles) were estimated as follows:

coal: use the emission factors for utility boilers;

NG: use AP-42 (fourth edition) factors for small industrial boilers; assume HC and CO uncontrolled, NOx controlled to level estimated by DeLuchi et al. (1992);

refinery gas: use emission factors for NG;

fuel oil: use AP-42 (fourth edition) factors for industrial boilers firing #5 or #6 fuel oil; assume HC and CO uncontrolled, NOx controlled to level estimated by DeLuchi et al. (1992);

crude oil: use emission factors for fuel oil;

petroleum coke: use factors from AP-42, third edition.

LPG: not included in model.

 

The revised version features a number of minor changes to these factors:

Coal. The model no longer automatically uses the emission factors for utility boilers. Now, the user must input separate uncontrolled-emission factors and control factors for industrial boilers using coal. Presently, the uncontrolled-emission factors are those for dry-bottom, wall-fired, pulverized-coal boilers, which are used commonly by industry as well as by utilities. Emission factors for PM, PM10, PM22.5, SOx, and aldehydes have been added. The control factors are based on the analysis in DeLuchi et al. (1992). Also, 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])

NG. The NG factors remain the same: those for small industrial boilers (between 10 and 100 106 BTU/hour). Factors for PM, PM10, PM22.5, and SOx have been added. The control factors are based on the analysis in DeLuchi et al. (1992).

Refinery gas: The emission factors now are calculated on the basis of the assumed composition of the refinery gas. In essence, there is a separate set of emission factors for each component of refinery gas (CH4, LPG, H2S, and H2). The factors for each component are weighted by the energy share of the component (so that if methane is 40% of refinery gas on an energy basis, then the methane emission factors get a weight of 0.40), and the weighted factors are summed for all of the constituents to produce a weighted-average emission factor. Each set of emission factors (one set for each of the components, CH4, LPG, H2S, and H2) is estimated as NGp . Kp-c, where NGp is the emission factor for pollutant P from natural-gas-fired industrial boilers, and Kp-c is emissions of P from component C (say, LPG) relative to emissions of P from natural-gas combustion. Thus, all emission factors are estimated relative to the natural-gas factors. The relative emission factors (Kp-c) are shown in Table XXVI. Sulfur emissions are calculated on the basis of the sulfur content of the gas, due to H2S.

Fuel oil. The fuel-oil factors remain the same: those for industrial boilers firing #5 or #6 fuel oil. Factors for PM, PM10, PM22.5, SOx, and aldehydes have been added. The control factors are based on the analysis in DeLuchi et al. (1992).

Crude oil. Uncontrolled-emission factors (in g/106 BTU) for CH4, TOCs, CO, and NOx still are assumed to be the same as those for fuel oil. SOx emissions are calculated on the basis of the sulfur content. PM, PM10, and PM2.5 emissions are calculated on the basis of the sulfur content of the fuel, using the relationships defined for fuel oil (EPA, 1995, AP-42). The control factors are assumed to be the same as those for fuel oil.

Petroleum coke. The fifth edition of AP-42 does not have factors for petroleum coke, so the uncontrolled emission factors from the third edition remain in the model. Emission factors for PM and SOx have been added. The control factors are based on the analysis in DeLuchi et al. (1992).

LPG: add emission factors from AP-42, fifth edition.

Trains

The original emission factors for trains (Table A.1) were from a table, dated 1973, in the EPA’s emission-factor handbook, AP-42 Volume 2, "Mobile Sources". In the early 1990s the EPA updated the emission factors for trains, as part of a general update of non-road emission factors required by the 1990 Clean Air Act Amendments. The updated emission factors are reported in Appendix F of the EIA’s Model Documentation Report (1994)

Ships

In the GHG model, the original emission factors for ships were those for coastal vessels, as reported in the EPA’s emission-factor handbook. Recently, the EIA (Model Documentation Report , 1994, Appendix F) estimated a weighted-average emission factor for river, lake, and ocean-going vessels. I have adopted these weighted average emission factors (which, in any case, are not much different from the emission factors for coastal vessels specifically).

Indirect energy use

Appendix E cites Rose’s (1979) citation of estimates of the ratio of "indirect" to "direct" energy for trains, ships, trucks, and pipelines, where indirect energy is that required to manufacture, repair, and service the mode, and direct energy is that consumed directly by the mode. I use these estimates to calculate "indirect" GHG emissions related to the use of trains, ships, trucks, and pipelines.

In the revised model, I have added indirect GHG emissions related to agricultural machinery and heavy offroad mobile equipment. Fluck’s (1985) detailed analysis provides data that can be used to calculate the indirect/direct energy ratio for agricultural machinery. According to Fluck (1985), agricultural machines used 1.149 EJ directly in 1978, and consumed 0.362 EJ per year in manufacture, and 0.200 EJ per year for maintenance and repair. This indicates an indirect/direct ratio of (0.362+0.200)/1.149 = 0.489, quite comparable to Rose’s (1979) estimate of 0.429 for trucks, which seems reasonable. Because these independent estimates for agricultural machinery and for trucks are close, I assume a ratio of 0.45 for scrapers, wheeled loaders, and off-road trucks.

I also have reduced the indirect/direct ratio for trains and ships, because with a simple calculation I am unable to get within an order of magnitude of Rose’s (1979) estimates (1.1 for trains, 0.9 for ships). Data from the EIA’s Manufacturing Consumption of Energy 1991 1994) and the Census’ 1991 Annual Survey of Manufacturers (1992) indicate that in 1991, the manufacture of railroad equipment consumed at most 6 trillion BTU of primary energy. (Data for 1986 indicate the same order of magnitude.) The transport of railroad equipment consumed on the order of 0.6 trillion BTU in 1993 (1.15 billion ton-miles [Bureau of the Census, 1993 Commodity Flow Survey, 1996] multiplied by my assumed average of 500 BTU/ton-mile). Assuming that maintenance, repair, servicing, and terminal operations consumed roughly as much as did manufacture and transport (to a first approximation, this appears to be true for motor vehicles and farm equipment), the grand total indirect energy consumption was 13 trillion BTU. In 1991, freight rail consumed 410 trillion BTU of energy directly (Davis and McFarlin, 1996; consumption averaged about 440 trillion BTU from 1982 to 1994). This implies an indirect/direct energy ratio of about 0.03! An analogous calculation for ship transport gives a similar result.

There are three likely explanations of the discrepancy between my estimates, which are less than 0.05, and Rose’s estimates, which are around 1.0: 1) Rose’s (1979) source overestimates indirect energy; 2) my accounting of indirect energy is incomplete; 3) I have underestimated maintenance, repair, servicing, and terminal-operation energy. I believe that all are true, and so have assumed that the true ratio is of the order of magnitude between my estimates and Rose’s: about 0.20. This results in a 1-2% decrease in total fuelcycle emissions for gasoline.

GHG emissions are calculated from these indirect/direct energy ratios in the manner outlined in Appendix E. The addition of indirect emissions from the use of agricultural machinery increases fuelcycle emissions from the biomass pathways by a nontrivial amount: for example, by about 2% in the corn-ethanol cycle.

Note that I assume that the "direct" energy in the indirect/direct energy ratio includes any direct energy that is used as part of the "indirect" activities: for example, diesel fuel used by trucks used to transport trucks from plant to dealer.

Other

I corrected minor key-in errors for the emission factors for gasoline tractors (Table A.1).

In the original version of the model, I assumed that natural gas used in the recovery stage for any feedstock was used in industrial boilers. Now, natural gas in feedstock recovery is assigned to natural-gas engines, rather than natural-gas boilers. (According to the EPA/GRI [1996] study, all NG used in NG recovery is used in compressor engines.) This results in an increase in emissions from all fuelcycles, because engines emit more CH4 than do boilers. Similarly, I have switched the use of natural gas at NGL plants from boilers to compressor engines and turbines, in the proportion indicated by EPA/GRI (1996).