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Appendix A
Methodology

Data availability were not consistent over the sectors. The data used for the residential analysis (RECS), for example, were sufficient to undertake this analysis. However, other sectors such as the transportation sector had limited data availability. Several sources of data had to be used to develop energy-intensity indicators. Also, adjustments were made where possible to eliminate influences that were not related to energy efficiency. It was necessary to develop primary energy estimates from the energy supply sector. Additionally, a composite was used to create an economy-wide energy-intensity indicator. This appendix documents these methodologies and other methodologies that could be used but were not used in this report. It provides this documentation individually for each of the following sectors: residential, commercial, transportation, manufacturing, and the U.S. economy as a whole. For each sector, the methodologies are presented in alphabetic order for ease of location.


Residential Sector

Degree-Day-Adjusted Estimates

A degree-day-adjusted estimate of annual site energy consumption explains what would have been consumed if the weather had conformed to normal or the 30-year average.(98) For the residential sector, space heating and cooling are degree-day adjusted and added to the other unadjusted end uses, water heating and appliances.

It is calculated by the following method:

  • Obtain heating degree-day (HDD) factors--the variation between HDD for a specific year and normal HDD, e.g., if HDD equals 5,219 and normal HDD equals 6,043 then the HDD factor equals 6043/5219 = 1.158
  • Obtain cooling degree-day (CDD) factors--the variation between CDD for a specific year and normal CDD, e.g., if CDD equals 609 and normal CDD equals 630 then the CDD factor equals 630/609 = 1.034
  • Adjust the amount of each major fuel used for space heating by multiplying heating consumption by the respective HDD
  • Adjust the amount of electricity and natural gas used for air-conditioning by the respective CDD factor
  • Add these weather-adjusted consumption estimates with the other end-use energy estimates, water heating and appliances.

Table A.1 presents an example for the South Census region. Degree-day-adjusted total residential energy consumption for the United States is the sum of the adjusted energy consumption by type of housing unit over all four Census regions.


Commercial Building Sector

Degree-Day-Adjusted Estimates

For the commercial buildings sector, space heating, cooling, and ventilation are degree-day adjusted and added to the remaining unadjusted end-use consumption.

Table A.1. Degree-Day-Adjusted Residential Site Energy Consumption, South Census Region, 1990

End-Use Adjustment Type of Housing Unit
Mobile Home Single-Family Multifamily Total
Detached Attached 2 to 4 Units 5 or More Units
a. Space Heat (Trillion Btu) 45 810 34 46 40 975
b. HDD Factor 1.251 1.251 1.251 1.251 1.251 1.251
c, Adjusted Space Heat (Trillion Btu) (a * b) 56 1,014 43 58 50 1,220
d. Air-Conditioning (Trillion Btu) 15 253 19 19 38 344
e. CDD Factor .928 .928 .928 .928 .928 .928
f. Adjusted Air-Conditioning (Trillion Btu) (d * e) 14 235 18 18 35 319
g. Appliances (Trillion Btu) 38 659 38 40 60 835
h. Water Heating (Trillion Btu) 18 337 19 30 46 450
I. Total Adjusted Consumption (Trillion Btu)
    (c + f + g + h)

126

2,245

118

146

191

2,824
   Sources: Energy Information Administration, Office of Energy Markets and End Use, 1990 Residential Energy Consumption Survey, Public-use Data Files. Normal and annual cooling and heating degree-days provided by the U.S. Department of Commerce, National Oceanic and Atmospheric Administration.

End-Use Estimate Calculation. In order to adjust for the influence of weather, end-use estimates were needed for the commercial buildings sector. The only CBECS end-use consumption estimates by energy source were available are for the 1989 CBECS. End-use data were needed for the other CBECS years. It was assumed that the percent shares for the end uses by energy source, principal building activity, and Census region were the same for all the CBECS years as they were in 1989.

Total energy consumption in each Census region by principal building activity was multiplied by these shares to obtain end-use consumption for the other CBECS years. Table A.2 provides an example for office buildings in the Northeast in 1992. As an illustration, the total consumption of electricity in 1992 was 125 trillion Btu. The percent share of electricity used for space heating in 1989 was 2.4 percent. One hundred twenty five trillion Btu * .024 = 3 trillion Btu used for electric space heat in 1992.

Degree-Day Adjusted Estimate Calculation

The following method is used to calculate the degree-day adjusted estimate:

  • Obtain HDD factors--the variation between HDD for a specific year and normal HDD, e.g., if HDD equals 6,193 and normal HDD equals 6,043 then the HDD factor equals 6,043/6,193 = .976
  • Obtain CDD factors--the variation between CDD for a specific year and normal CDD, e.g., if CDD equals 412 and normal CDD equals 609 then the CDD factor equals 609/412 = 1.478
  • Adjust the amount of each major fuel used for space heating by multiplying heating consumption by the respective HDD factor
  • Adjust the amount of electricity and natural gas used for air-conditioning by the respective CDD factor
  • Adjust the amount of electricity used for ventilation by the respective CDD factor
  • Add these weather-adjusted consumption estimates with the remaining end-use energy estimates.

Table A.2 also presents an example of the calculation of the degree-day adjusted consumption estimates. As an illustration, an estimated 8 trillion Btu were used by office buildings in the Northeast Census region in 1992 for air- conditioning. The CDD factor is 1.478. In 1992, 1.478 * 8 = 11 trillion Btu of degree-day adjusted electricity was used for air-conditioning.

All office buildings in the Northeast used 238 trillion Btu of energy in 1992, adjusted for weather effects.

Degree-day adjusted commercial buildings total energy consumption for the United States is the sum of the adjusted energy consumption by principal building type over all four Census regions.

Table A.2. Calculation of End-Use Energy Consumption in Northeast Office Buildings, 1992

Energy Source End Use Total
Consumption, 1992

(Trillion Btu)
Space Heating Air- Conditioning Ventilation Other Total
Percent Share, 1989            
Electricity 2.4 6.0 17.9 73.8 100.0 125
Natural Gas 63.6 2.0 0.0 34.4 100.0 32
Fuel Oil 89.6 0.0 0.0 10.4 100.0 37
District Heat 65.3 9.5 0.0 25.2 100.0 30
Estimated End-Use Consumption 1992
   (Trillion Btu)

76

11

22

115

--

224
Electricity 3 8 22 92 -- 125
Natural Gas 20 1 0 11 -- 32
Fuel Oil 33 0 0 4 -- 37
District Heat 20 3 22 8 -- 30
Degree-Day Factors .976 1.48 1.48 -- -- --
Degree-Day Adjusted End-Use Consumption, 1992 (Trillion Btu)
75

16

33

115

--

238
Electricity 3 11 33 92 -- 139
Natural Gas 20 1 0 11 -- 32
Fuel Oil 32 0 0 4 -- 36
District Heat 19 4 0 8 -- 31
   Sources: Energy Information Administration, Office of Energy Markets and End Use, 1989 and 1992 Commercial Buildings Energy Consumption Survey, Public-Use Data Files. Normal and annual cooling and heating degree-days provided by the U.S. Department of Commerce, National Oceanic and Atmospheric Administration.


Occupancy-Adjusted Estimates

Adjustments were made to eliminate vacant or mostly vacant buildings from the estimates for the survey years. Adjustments were made to both total site energy consumption and demand indicators for each principal building activity and Census region.

Demand Indicator Adjustment. Adjustments were made to the following demand indicators: floorspace, buildings, floorspace-hours and employees. Vacant buildings that were classified as vacant as well as those that were more than 50 percent vacant at least 3 months during the survey year were removed. An example is presented in Table A.3.

Table A.3. Occupancy-Adjusted Total Commercial Floorspace in the Northeast by Principal Building Activity, 1992

Principal Building Activity Total Floorspace
(Million Square Feet)
More than 50 percent
for 3 Months
During Survey Year
Occupied
Assembly 1,229 122 1,107
Education 1,968 239 1,729
Food Sales/Service 565 68 497
Health Care (inpatient) 348 0 348
Health Care (outpatient) 38 0 38
Laboratory 77 0 77
Lodging 616 60 556
Mercantile/Services 2,798 26 2,772
Office 2,525 85 2,440
Other, excluding Vacant 496 9 487
Public Order/Safety 269 3 266
Vacant 08 708 0
Warehouse 1,763 92 1,672
All Buildings 13,400 1,412 11,988
   Source: Energy Information Administration, Office of Energy Markets and End Use, 1992 Commercial Buildings Energy Consumption Survey, Public-Use Files.

Occupied Commercial Buildings Total Site Energy Consumption Adjustment. The total site energy consumption is adjusted by major fuel for each principal building activity (PBA) in each Census region by removing the site energy used in vacant buildings and buildings that were more than 50 percent vacant for at least 3 months (Table A.4).

Occupied and Degree-Day Commercial Buildings Site Energy Consumption Adjustment. This methodology adjusts for both vacancy and weather. First, the occupied site energy consumption is determined as explained above. Then the occupied site energy consumption is adjusted for weather. An example of this methodology was presented in Table A.2.

Table A.4. Occupancy-Adjusted Total Commercial Site Electricity Consumption in the Northeast, by Principal Building Activity, 1992

Principal Building Activity Total Site Energy Consumption
(Trillion Btu)
All Buildings Vacant Buildings Occupied Buildings
Assembly 24 2 22
Education 42 4 38
Food Sales/Food Service 37 < 1 36
Health Care (Inpatient) 24 -- 24
Health Care (Outpatient) 1 -- 1
Laboratory 3 -- 3
Lodging 21 1 20
Mercantile/Services 77 < 1 77
Office 125 3 122
Other 12 < 1 12
Public Order 8 < 1 8
Vacant 7 7 0
Warehouse 38 < 1 38
Total 419 -- 401
-- = No cases.

   Source: Energy Information Administration, Office of Energy Markets and End Use, 1992 Commercial Buildings Energy Consumption Survey, Public-Use Data Files.

To determine the U.S. total occupied and degree-day total site energy consumption use the following method:

  • Adjust at the PBA level by Census region for vacancy and obtain occupied total site energy consumption by PBA within each Census region (Table A.3)
  • At the level of Step 1, using 1989 percent shares, obtain the end-use estimates for the occupied buildings (Table A.2)
  • At the same level degree-day, adjust the end-use estimates using the appropriate degree-day factor (Table A.2)
  • Sum up end-use estimates for each major energy source by PBA in each Census region
  • Sum across major energy sources to obtain the total occupancy and degree-day adjusted total site energy consumption for each Census region PBA
  • Sum across the PBA to obtain the Census region totals
  • Sum across the Census regions to obtain U.S. occupancy and degree-day adjusted total site energy consumption.


Transportation Sector

Domestic Air Energy Use for Passenger and Freight: Derivation

The available data for aviation energy demand is for both the passenger and freight transportation sectors combined. In many aircrafts, freight is carried in the hull of the craft while passengers ride in the cabin. Although the passenger-miles traveled by air cannot be separated into passenger and freight components, the revenues (in millions of dollars) received for different types of air travel can be separated.

The relative share of revenue dollars was used to estimate the portion of energy consumed for passenger and freight movements by air. Table A.5 presents the methodology.

Table A.5. Calculating U.S. Domestic Passenger and Freight Air Travel Energy Consumption

Indicators Units 1985 1988 1991
U.S. Domestic Passenger  
   Revenue per Passenger Mile Cents 12.21 12.31 13.22
   Passenger Miles Million 277,836 334,291 338,085
   Passenger Revenues Million Dollars 33,924 41,151 44,695
U.S. Domestic Freight  
   Freight Revenue per Ton Mile Cents 102.23 111.31 103.50
   Freight Ton Miles Million 6.71 9.33 9.96
   Freight Revenues Million Dollars 6,860 10,385 10,309
Total Operating Revenues Million Dollars 37,629 50,155 56,165
Air Travel Energy Use Trillion Btu 1,366 1,609 1,542
   Air Passenger Trillion Btu 1,231 1,320 1,227
   Air Freight Trillion Btu 134 289 315
   Notes: Passenger and freight revenues may not add to total operating revenues, due to calculations and differences in data sources. Air passenger energy use calculated as the multiple of its revenue ratio and total air travel energy use, e.g., in 1985 the equation is (33,924/37,629)*1,366 = 1,231. Air freight energy use calculated as the remainder after air passenger energy use is subtracted from total air travel energy use, e.g., in 1985 the equation is (1,366 - 1,231) = 134.

   Sources: Department of Transportation, Bureau of Transportation Statistics, National Transportation Statistics (September 1993), Tables 1, 4, and 6. Eno Transportation Foundation Inc., Transportation in America 1994, pp. 44 and 49.

Site Energy Consumption Conversion: Electricity consumption is reported for pipelines and passenger rail in the Oak Ridge National Laboratory Transportation Energy Data Book. The primary electricity volumes were converted to site electricity by applying conversion factors. See "Primary Conversion Factors" in the economy section in this appendix. Table A.6 presents the methodology.

Table A.6. Calculating U.S. Transportation Site Electricity Consumption

Electricity Variables 1985 1988 1991
Primary Electricity (Trillion Btu)    
   Pipeline 239.0 244.8 243.4
   Passenger Rail 55.4 59.9 59.5
Electricity Conversion Factor 3.292 3.311 3.30
Site Electricity (Trillion Btu)
    Pipeline 72.6 73.9 73.8
   Passenger Rail 16.8 18.1 18.0
   Note: Site electricity is calculated as primary electricity divided by the conversion factor, e.g., in 1985 the equation for pipelines is (239.0/3.292) = 72.6.

   Sources: Department of Energy, Oak Ridge National Laboratory (ORNL), Transportation Energy Data Book (ORNL-6798), Editions 11 and 14, Table 2.6 and unpublished 1985 data from ORNL.


Industrial Sector

Capacity-Utilization Rate

The capacity-utilization rate equals the seasonally adjusted index of industrial production divided by a capacity index (sustainable practical capacity, i.e., the greatest level of output a plant can maintain within a realistic work schedule). The Federal Reserve Board weights the capacity indexes by value-added proportions.

Capacity-Adjusted Value of Production Method. This method adjusts the value of shipments for changes in capacity after the value of shipments has been adjusted for changes in inventories (value of production). See "Inventory Adjustment" in this section of the appendix. The method is as follows:

  1. Divide the 26-year average capacity-utilization rate (average) by the annual reported rate (t) to calculate factors for each major industry group (Table A.7. presents these rates by SIC)
  2. Multiply this factor by the value of production estimate in constant 1987 dollars.

Constant-dollar Capacity-Adjusted Value of Production t

Capacity-Utilization Rate Ave
_____________________

Capacity-Utilization Rate t

*   

Constant-dollar Value of Production


Table A.7. Capacity-Utilization Rate: 26-Year Average and Annual for 1985, 1988, and 1991, by SIC

SIC



Major Industry Group
Capacity Utilization Rates
1967-1993 1985 1988 1991
20 Food and Kindred Products 82.3 81.0 81.8 81.4
21 Tobacco Manufactures NA NA NA NA
22 Textile Mill Products 86.2 83.0 88.7 83.3
23 Apparel and Other Textiles Products 81.1 80.4 82.8 77.6
24 Lumber and Wood Products 83.1 84.6 89.7 79.3
25 Furniture and Fixtures 81.7 79.8 84.2 74.2
26 Paper and Allied Products 89.7 89.7 93.2 88.4
27 Printing and Publishing 86.5 87.0 89.9 79.7
28 Chemicals and Allied Products 80.0 77.1 83.9 80.6
29 Petroleum and Coal Products 85.5 78.6 85.2 86.0
30 Rubber and Miscellaneous Plastics 83.6 85.3 87.7 80.3
31 Leather and Leather Products 81.9 75.6 79.5 78.5
32 Stone, Clay, and Glass Products 77.9 75.6 82.2 73.2
33 Primary Metal Industries 80.1 74.0 87.5 77.9
34 Fabricated Metal Products 77.2 74.9 81.1 73.2
35 Industrial Machinery and Equipment 80.8 73.4 80.5 72.6
36 Electronic and Other Electric Equipment 80.4 80.7 83.7 78.0
37 Transportation Equipment 74.9 78.8 79.4 73.4
38 Instruments and Related Products 82.0 83.6 80.5 77.2
39 Miscellaneous Manufacturing Industries 75.6 67.7 79.5 74.6
NA = Not Available

Sources: U.S. Department of Treasury, Federal Reserve Board (Table provided by Charles Gilbert, 10/12/94). Federal Reserve Statistical Release (August 15, 1994), Table 3 (average).


Inventory Adjustment

Changes in inventories need to be considered when using a demand indicator such as the value of shipments. If inventories are being drawn down, the value of shipments will overestimate the actual value of production. If inventories are being built, then the value of shipments will underestimate the value of production.

Inventory-adjusted Value of Shipments or Value of Production. The inventories used in the adjustment are year-end inventories at cost or market value, deflated to 1987 constant dollars using value of shipments implicit price deflators reported by the U.S. Department of Commerce, Bureau of Economic Analysis. The following steps were followed to adjust the value of shipments for the effects of changes in inventories:

  1. Determine the implicit price deflator by dividing current dollar into constant dollar value of shipments for each major industry group in each year
  2. Multiply this deflator by the year-end inventories of the year preceding and the current year
  3. Add to the deflated value of shipments the deflated current year inventory and subtract the deflated prior year inventory.

Value of Shipments Deflator t = Constant-dollar Value of Shipments t / Current-dollar Value of Shipments t

Constant-dollar Value of Production t =

Constant-dollar

Value of Shipments t

+  

Value of Shipments

Deflator t

* (Current-dollar)

Inventories t

- (Value of Shipments Deflator t-1 * (Current-dollar) Inventories t-1

Table A.8. Value of Production Methodology Example

Calculations Preceding Year (t-1) Current Year (t)
a. Value of Shipments (million 1987 dollars) 307,345 319,212
b. Value of Shipments (million current dollars) 255,723 303,270
c. Implicit Price Deflator (a/b) 1.20 1.05
d. Year-End Inventories (current million dollars) 24,397 24,023
e. Year-End Inventories (million 1987 dollars) (c*d) 29,276 25,224
f. Value of Production (million 1987 dollars) (319,212 + 25,224 - 29,276 = 315,160) -- 315,160
Note: This example is for illustrative purposes only. Although, MECS-weighted value of shipments data (adjusted to 1987 SIC) were used throughout Chapter 6, "Industrial Sector," confidentiality does not permit EIA to release value of shipments data that have been revised by MECS weights.

U.S. Economy

Energy-Weighted Index

One way for removing effects such as geography and housing unit type effects is to index from a "two-way" disaggregation of characteristics to develop an index for total U.S. housing.

As an example, for the Northeast Census region in the residential sector, start with consumption per household for each of the five housing types indexed to 1 in the first year as follows:

Northeast Census Region 1984 1987 1990

Mobile Homes 1 x x

Single-Family Detached 1 x x

Single-Family Attached 1 x x

Multifamily (2-4 Units) 1 x x

Multifamily (5 or More Units) 1 x x

Compute an energy-weighted index (preferably Tornqvist index) of the individual energy-intensity indices for 1987 and 1990 for each of the Census regions. This index will be devoid of mix issues relating to housing type and geography. While there will still be other behavioral and/or structural effects in the five disaggregated indices, a layered procedure that moves down the chain to include measures that disaggregate end-uses (for example, an index of space-heating consumption per square foot, not household, since housing sizes are changing, or an index of water heating per occupant rather than per household, since persons per household are changing, etc...) and to exploit all of the detail available from the energy consumption surveys is a "good measure" of energy intensity. These effects might be relatively minor over the 3 surveys years being compared, but without the calculations such as these, only qualitative judgments as to the potential effects can be made. This proposed approach is for all intents and purposes has been used for the transportation and economy composites. Indices can be developed for each sector and weighted by shares of total energy consumption in the economy.

Primary Conversion Factors for Total Site Electricity

Primary energy estimates include losses in the generation, transmission, and distribution of electricity. In this report, conversion factors are developed to account for these losses. Total site electricity estimates are multiplied by these conversion factors to obtain primary electricity estimates.

The methodology for developing the conversion factors and obtaining primary electricity estimates is shown in the following steps:

Step 1. Calculate gross inputs:

Convert utility-site generation by energy source and region in kWh to equivalent gross-generation estimates in kWh (including generator or shaft losses) by multiplying site generation by the appropriate gross/site ratio plus the transmission and distribution losses estimated at 8 percent by the Department of Energy, Office of Energy Management.

For each year, the gross-generation estimates, by Census region, are multiplied by the appropriate annual heat rate for each energy source to obtain gross inputs for electricity generation by utilities such that:

Gross Inputs = (Net Generation * (Gross/Site Ratio + T&D Losses) * Heat Rate) /1000.

Data on energy sources used by nonutilities and net exporters to produce electricity purchased by U.S. utilities are not available. Since electricity from these sources is primarily produced from either fossil fuels or hydro resources, the heat rates of fossil-fueled steam generators are applied to the purchased energy. Table A.9 presents an example of the calculation of gross inputs for the Northeast Census region in 1992.

Table A.9. Example of Calculating Gross Inputs for the Northeast in 1992

Calculation Inputs Generation Energy Sources
Fossil Fuels Nuclear Hydropower Geothermal Other Nonutility Purchases Net
Imports
a. Site Generation (Billion kWh) 218.4 144.4 30.6 0 0.5 52.5 12.1
b. Gross/Site Ratio 1.07 1.06 1.01 1.06 1.07 1.07 1.07
c. T&D Losses 0.08 0.08 0.08 0.08 0.08 0.08 0.08
d. Heat Rates (Btu/kWh) 10,302 10,678 10,302 20,955 10,302 10,302 10,302
e. Gross Inputs (Trillion Btu) (a * (b + c)* d)/1000 2,588 1,757 344 0 6 622 143
   Note: Fossil fuel heat rate is used also for hydropower, nonutility purchases, net imports, and other.

   Sources: Energy Information Administration, Office of Coal, Nuclear, Electric, and Alternative Fuels, Electric Power Annual 1993 (DOE/EIA-0348(93), Tables 13, 61 and 62. Energy Information Administration, Office of Energy Markets and End Use, Monthly Energy Review (DOE/EIA-0035(94/08)), Table A8; Production Annuelle Brute et Nette d'Electricity, p.16.

Step 2. Add utility-plant use to electricity sales for each sector.(99)

Step 3. All regional estimates of electricity sales and plant use in kWh are converted to Btu using 3,412 Btu per kWh.

Step 4. Divide gross energy inputs by the total site electricity (sales and plant use) for each Census region to obtain conversion factor.

Step 5. Site electricity consumption for each end-use sector and Census region are multiplied by the primary electricity conversion factors to obtain the corresponding primary electricity estimates. The data source is the consumption survey data as developed in each of the sector chapters.

There are three main advantages to using the method described above:

  1. Generation, transmission and distribution losses are accounted for
  2. Includes electricity consumption that was generated by both utilities and nonutilities
  3. Since the heat rate used varies from year to year and by fuel source, changing efficiencies over time are captured.

Regional Manufacturing Estimates: The 1985 estimate of total inputs for heat, power, and generation was based on EIA's revised 13,631 trillion Btu total. This total was distributed regionally by using regional shares based on Table 3 in the EIA publication, Manufacturing Energy Consumption Survey: Consumption of Energy, 1985 (DOE/EIA-0512(85).

The analysis of the manufacturing sector used revised 1985 estimates to match the revised 1987 SIC standards. Electricity was not considered separately. Therefore, electricity consumption for heat, power, and generation in Btu were calculated from the kilowatthour estimates in Table 3 of Manufacturing Energy Consumption Survey: Consumption of Energy, 1985 and Manufacturing Energy Consumption Survey: Consumption of Energy, 1988 (DOE/EIA-0512(88)) by multiplying the estimates by 3,412 Btu per kWh. Natural gas consumption for heat, power, and generation in Btu were calculated from the cubic-foot estimates in the same tables. The natural gas estimates for the respective surveys were multiplied by 1,031 Btu/cubic feet.

Both the 1991 electricity and natural gas estimates in Btu were provided in Table A.4 of Manufacturing Energy Consumption Survey: Consumption of Energy, 1991, DOE/EIA-512(91) report.

Regional Passenger Transportation Estimates: Passenger transportation data are not available by Census region. On the advice of Oak Ridge National Laboratory, it was decided that since household vehicles account for over 70 percent of the total energy for passenger travel, regional shares based on the Residential Transportation Energy Consumption Survey (RTECS) would be an adequate proxy for passenger transportation energy estimates.(100)

There has been very little change in the RTECS regional distribution across survey years. For 1985, 1988, and 1991, the total U.S. passenger transportation energy estimates were multiplied by the percent shares by Census region to obtain regional energy consumption estimates for passenger travel. The percent shares were based on the 1988 RTECS. The 1988 shares were: 17 percent for the Northeast; 25.2 percent for the Midwest; 36.0 percent for the South, and 21.8 percent for the West.

Regional Freight Transportation Estimates: Freight transportation data are not available by Census region. On the advice of Argonne National Laboratory, it was decided that since most of the energy source used for freight transportation was fuel oil, EIA data could be used instead of Department of Transportation, Bureau of Transportation Statistics data.

EIA's Petroleum Marketing Division annually surveys State-level distillate fuel oil consumed by trucks, rail, and marine vehicles and residual fuel oil consumed by rail and marine vehicles, bench marking to the petroleum product supplied data published by EIA's Petroleum Supply Division. The report, Fuel Oil and Kerosene Sales, provides State-level data on the number of gallons consumed, which were converted to trillion Btu using Oak Ridge National Laboratory conversion factors, in order to derive percent shares.

Since there has been little fluctuation in regional distribution year-to-year, it was determined to apply 1988 regional percent shares to all annual estimates. The regional percent shares used were: 12.5 percent for the Northeast; 20.2 percent for the Midwest; 42.0 percent for the South, and 25.3 percent for the West.

Quantity Index. A quantity index measures changes in quantity over time. The Index of Industrial Production, developed by the Federal Reserve Board, is a quantity index.

The weighted aggregate quantity index is computed similar to the weighted aggregate price index.

The Price Index. The price index is a weighted average of expenditures, as a percentage of expenditures existing in a base year. A price index may be calculated for a single good or can be calculated as an aggregated price index for a "basket" of several goods. Price indices can be unweighted or weighted. The unweighted aggregated approach is heavily influenced by those goods with higher prices which dominate the index. To reduce this sensitivity of the unweighted index, a weighted price index is used. Each good in an weighted aggregate price index is be weighted according to its importance.

One way to weight a price index is to use base-year quantities, the Laspeyres price index.

Laspeyres price indices are calculated by comparing the current and base year cost of a basket of goods of fixed composition. As an example, the "basket" can be several "goods" such as energy, clothing, food, housing, etc. that we find in the "basket" used to calculate the Consumer Price Index (CPI) or one "good" such as the major energy sources that is used to calculate the energy component of the CPI. The Producer Price Index (PPI) developed and maintained by the Bureau Labor Statistics, is also a base-weighted price index.

Laspeyres base-weighted price index (ratio of today's cost using base-year quantities to the base-year cost of the goods) equals:

ptiqoi / poiqoi * 100

where the base-year quantities of the various goods = qoi ,

the base-year prices of the various goods = poi , and

present prices of the various goods = pti .

Quantity Index. Similar to the Laspeyres price index, quantities for each item are measured in the base year o and year t with qoi and qti, representing these quantities for item I (e.g.,end use or energy source). The quantities are then weighted by a fixed price (wti) such as value added, value of shipments, etc. where the quantity index I equals

qtiwti / qoiwti * 100


In some quantity indexes, the weight for item I is the base-period price (poi).

End Notes

98Heating degree-days, cooling degree-days, and normal degree-days are defined under "General Terminology" in the Glossary.

99Electricity sales data are obtained from EIA's Electric Power Annual 1993 (DOE/EIA-0348(93), Table 26. Plant use electricity is the difference between gross inputs and site electricity.

100Business fleets are operated very differently from household vehicles, but adequate data are not available on fleet vehicles.


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