OKDES
REGIONAL SOCIOECONOMIC IMPACTS OF
ALTERNATIVE ENERGY SCENARIOS FOR THE
OHIO RIVER BASIN ENERGY STUDY REGION
PHASE
OHIO RIVER BASIN ENERGY STUDY
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October 1980
REGIONAL SOCIOECONOMIC IMPACTS OF
ALTERNATIVE ENERGY SCENARIOS FOR THE
OHIO RIVER BASIN ENERGY STUDY REGION
by
Steven I. Gordon
Anna S. Graham
Department of City and Regional Planning
The Ohio State University
Columbus, Ohio U3210
Prepared for
Ohio River Basin Energy Study (ORBES)
Grant No. EPA R805589
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20U60
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Table of Contents
Section Title Page
1.0 Introduction . 1
2.0 Scenarios h
3.0 Impacts on Employment 7
3.1 Employment for Power Plant Construction
and Operation 7
3.2 Labor Supply in Construction by Skill 7
3.3 Labor Demand in Coal Mining 10
3.U Employment Impacts of Power Plants 13
3.U.I Total Labor Demand Impacts 13
3.U.2 Labor Demand by Skill Impacts 18
3.5 Employment Impacts of Coal Mining 18
k.O Population Impacts 30
U.I General Migration Trends 30
U.2 Population Impacts of Power Plants 36
If.3 Population Impacts of Coal Mining Ul
5.0 Impacts on Public Services 53
5.1 Water and Sewer Systems 53
5.2 Other Public Services 55
6.0 Policy Implications 57
6.1 Siting Policies 57
6.2 Ameliorative Policies 57
6.2.1 Service Subsidies 58
6.2.2 Tax Policies 58
iii
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Table of Contents (Cont'd)
Section Title
6.2.3 Land Use and Related Local Policies 59
6.2.U Administrative Actions 59
References 61
Appendix A 6^
References to Appendix A 76
Appendix B 77
References to Appendix B Ill
Appendix C 112
References to Appendix C 132
Appendix D 135
iv
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List of Tables
Table Number Title Page
1 Basic Description of ORBES Scenarios 5
2 Outputs From the ORBES Labor Impact Model .... 8
3 Supply of Skilled Labor Data and Estimates
for Three Categores....ORBES - I960 to 1990 ... 11
k Total Man-Years Required by Scenario
1975 - 1995 1^
5 Supply & Demand for Boilermakers, Electricians
and Pipefitters. By Scenario, 1980 & 1990. ... 19
o Total ORBES Coal Mining Employment
Increase by Scenario 20
7 Growth in Mining Employment in ORBES Coal
Counties to the Year 2000, Scenario 1 22
8 Growth in Mining Employment in ORBES Coal
Counties to the Year 2000, Scenario 2 23
9 Growth in Mining Employment in ORBES Coal
Counties to the Year 2000, Scenario 2A 2k
10 Growth in Mining hinployment in ORBES Coal
Counties to the Year 2000, Scenario 2B 25
11 Growth in Mining Itoployment in ORBES Coal
Counties to the Year 2000, Scenario 3 26
12 Growth in Mining Employment in ORBES Coal
Counties to the Year 2000, Scenario h 27
13 Growth in Mining Employment in ORBES Coal
Counties to the Year 2000, Scenario 5 28
l4 Percent Change in Employment Within Each
Region From 1965 to 1970 for Various Sectors. . . 33
15 1995 Estimated Employment for ORBES Region. ... 3^
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List of Tables (Cont'd)
Table Number Title Page
16 Maximum Number of Construction Workers and
Associated Population Increases, 1975 - 2000
By Scenario & Group
1? Average Potential Mining Employments Increase
as a Percentage of 1970 Population,
ORBES Coal Counties ................ 51
A-l Construction Schedules Used in ORBES Labor
Impact Model ......... .......... 66
A-2 Percentage of Workers in Eight Skill Categories
Nuclear and Coal- Fired Units ............ 67
A-3 Planned Unit Characteristics for Fabricated
Counties .............. ....... 69
A-k Total Number of Worker-Years for Each Unit
and Ratios Used to Serve Them ........... 70
A-5 Total Construction and Operation Worker
Requirements for Each Generating Unit 2nd
County, Total Number of County in Migrants ..... 71
A-6 Regional Totals of Construction Requirements
by Type of Unit and Totals Operation Workers. . . . 7^
A-7 Regional Totals of Construction Workers
Required by Skill Category ............. 75
B-l County Potential Socioeconomic linpact In
Argonne and Oak Ridge National Laboratory Studie . 80
B-2 Variables Used in the Taxonomy of Candidate
Counties ..................... 90
B-3 Descriptive Statistics on Groupings Derived
Using All Variables ................ 91
B-U Group Statistics for Selected Variables Using
Alternative Classification Schemes ......... 92
B-5 Description of the Classification of Candidate
Counties and Potential for Socioeconomic Impacts. . 9^
VI
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List of Tables (Cont'd)
Table Number Title
B-6 Comparison for ORBES Impact Classifications
with AMI 101
B-7 Comparison of ORBES Impact Classifications
with ORNL 102
B-8 ORBES Candidate County Groupings 103
vii
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List of Illustrations
Figure Number Title
1 Flowchart of Socioeconomic Impact Analysis 2
2 Construction Workers Required, 1975-3995
Scenarios 1, Ib, 2b, 3, 6, 7 16
3 Construction Workers Required, 1975-1995
Scenarios la, 2, 2a, k, 5 17
1* ARC Migration Regions . 31
5 Scenario No. 1: Manufacturing Shift 35
6 Scenario No. 1: Construction Shift 37
7 Scenario No. 1: Service Shift 38
8 Scenario No. 1: Financial Shift 39
9 Six County Groups for Synergistic Impact
Evaluation 1*0
10 Scenario No. 1: Net Migration 1*7
11 Scenario No. 1*: Net Migration 1*8
12 Scenario No. 5: Net Migration 1*9
B-l County Impact Groups Using All Variables 95
B-2 County Impact Groups Using Population
Variables 96
B-3 County Impact Groups Using Housing
Variables 97
B-l* County Impact Groups Using Income Variables. ... 98
B-5 County Impact Groups Using Employment
Variables 99
viii
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1.0 Introduction
The Ohio River Basin Energy Study (ORBES) has as its purpose the
analysis of the impacts of alternative energy futures in the ORBES
region. The purpose of this report is to describe the projected
socioeconomic impacts of the ORBES energy futures, defined as eleven
scenarios, on the region.
We begin the report with a description of the scenarios and the
potential future conditions they attempt to describe. The scenarios
were delineated in a manner which would allow the comparison of impacts
associated with various economic growth assumptions, energy policies,
environmental policies, and energy conversion technologies. The
scenarios encompass conditions from the mid-1970's to the year 2000.
Given the scenarios, we describe the impacts of the scenario
assumptions on socioeconomic conditions in ORBES. Essentially we are
asking the question, if the scenario should occur, what will be the
social consequences? Here, we devote a chapter to each of five major
measures of socioeconomic impact. These are shown in Figure 1 and
discussed below. It should be noted that many other potential measures
of socioeconomic impact exist. We restricted ourselves to these
measures because of the limitations of the data, the state-of-the-art
in socioeconomic impact and analysis, and limited study resources.
Each chapter discusses the method or methods used to estimate impacts
and then compares and contrasts tb.e impacts across scenarios. The
chapters are further broken down into a discussion of the impacts due
to power plant siting and those due to coal mine expansion.
Appendix B presents a slightly different analysis, discussing a general
socioeconomic impact method.
Finally, we discuss the policy implications of the major impact
findings. For each major impact, we note, where.applicable, those
governmental policies which might mitigate or exacerbate the given
impact. This is intended to give policy-makers insights into the
potential consequences of their decisions from a socioeconomic stand-
point. Of course, no decision should be based on these factors alone
but should instead analyze the full range of environmental, energy,
social, economic, and health consequences of a policy. The reader is
referred to the ORBES final report for this overall discussion •[!].
Figure 1 provides an overview of the socioeconomic impact analysis.
Scenarios describe energy, economic, and environmental policies and
conditions for the future in ORBES. These, in turn, are translated
into quantitative representations of energy demand and supply. The
ORBES project then focuses mainly on the impacts of power plants and
coal mines. Siting models allocate the demand to counties. For the
power plants, this is in terms of the amount of electricity generated
in 650 MWE coal plants or 1000 MWE nuclear plants. For coal mines,
this is in terms of amount of new coal mining activity by number of
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Figure 1
Flowchart of Socioecpnomic Impact Analysis
OKBES Scenarios
Pover Plant Impacts
Power Plant Siting
Model
^
\
Description
Economic Growth,
Policy C
Energy Suppl
of Energy,
Environmental
onditions
y and Demand
Siting by County
This Report
Coal Mine Impacts
Coal Mine Siting Model
Siting by County
ORBES Labor Impact
Model
Total County Employment
and Employment by Skill
in Power Plants (by Scenario)
Impacts on Employment(
\ Impacts on Population
Impacts on
Public Services
Sim to Subregions for
Assessment
|Sum to Region for Assessment|
Base Year Population,
Employment, Housing
Conditions
Estimates of Future
Labor Supply, Socioeeoncoic
Conditions
Migration Model
Classification of Counties
for Potential Socioeconcaic
Impacts
Census of Coal Mines
Data on Employment
by Type of Mine
JL
Mining
County Employment
Impacts on Employment
J
General Migration Trends
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tons mined per year.
Given these pieces of information for each scenario, we begin the
socioeconomic impact analysis process. For power plants, we developed
an impact model called the ORBES Labor Impact Model (OLIM) to project
total county employment over time by scenario. This employment
projection is compared to current levels of employment and estimates of
the supply of skilled labor to obtain potential employment impacts.
New employees are translated into population to obtain impacts on
population and public services. These analyses begin .at the
county level but are then summed to subregional and regional levels
to give a better picture of the magnitude and distribution of the
impacts. In addition, the base year data are used to classify each of
the candidate power plant counties into groups with similar potential
for each of the types of impacts.
A similar procedure is followed for coal mining employment impacts.
Here, a set of employment multipliers is developed using existing
data. County level and regional employment changes are forecast
using a range of multipliers. The mining employment data are also
used in conjunction with a set of other forecasts to look at general
migration trends in ORBES.
Each box in Figure 1 below the dotted line essentially represent
a section of this report. Referral to this flowchart may help the
reader to place each section in perspective.
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2.0 Scenarios
The ORBES scenarios are based on a set of regionally based
economic models.* The scenarios look at combinations of assumed energy
conversion technologies, environmental control standards, and economic
growth levels. The scenarios are keyed in time to a base period in
the mid-1970's through the year 2000.
Table 1 provides a summary of the scenarios and those that are
analyzed in this report. Scenarios are first constructed in terms of
fuel emphasis. One set of scenarios emphasizes fossil fuels, a second,
nuclear fuel, and a third, alternative fuels. The base case scenario
is scenario 2. This is essentially a "business as usual" (BAU)
scenario where there is a continuation of current environmental
policies, current emphasis on coal fired power plants, and a projection
of relatively high economic growth. Within the fossil fuel category,
all scenarios represent a conventional coal plan except for scenario k
where a natural gas emphasis is assumed. Both the coal and nuclear
scenarios have a scenario which emphasizes exports of electricity --
scenario 2a and 2b respectively.
The economic growth rates for the scenarios also varies. For many
scenarios, a high growth rate is assumed. This corresponds to a 2.k7%
annual increase in ORBES GRP (Gross Regional Product) and 3.26fo nation-
wide and is based on historic experience. The low growth rate for
scenario 5 is assumed to be only 2.1$ per year between 1976 and 2000.
The most complex of the assumptions are related to environmental
controls. Two environmental control levels for air, water, and land
were assumed. These were the strict and base case levels. Strict
controls for air quality mean that the stringent emission standards in
state implementation plans (SlPs) for urban areas would be applied
throughout the state. The base case controls apply these same controls
in urban areas only while current rural standards in the SIPs are
maintained. New source performance standards are applied to all new
sources under both types of conditions.
Base case conditions for water aean current standards for industrial
and municipal facilities. Strict controls involve the extensive
recirculation of water and a reduction in base case effluents of 95%.
Strict controls for land resources involves interim and permanent
performance standards under the Surface Mining Control and Reclamation
Act of 1977. Base case controls for land are pre-1977 federal standards.
*See [l] for further discussion. This section is taken, in part,
from that report.
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TABLE 1
BASIC DESCRIPTION OF ORBES SCENARIOS
Scenario
Fossil fuel emphasis
1
la
Ib
Ic
Id
2
2a
2a2
2d
2i
4
5
Sa
6
7
7a
Nuclear fuel emphasis
2b
2bl
2c
Technology
conventional,
coal emphasis
conventional,
coal emphasis
conventional,
coal emphasis
conventional,
coal emphasis
conventional
coal emphasis
conventional,
coal emphasis
conventional,
coal-fired exports
conventional,
coal-fired exports
conventional,
coal emphasis
conventional,
coal emphasis
conventional,
natural gas emphasis
conventional ,
coal emphasis
conventional,
coal emphasis
conventional,
coal emphasis
conventional,
coal emphasis •
conventional,
coal emphasis
conventional,
nuclear-fueled
exports '
conventional,
nuclear- fueled
exports
conventional,
nuclear emphasis
Environmental
controls
strict
strict (very
strict air quality) ,
dispersed siting
strict (very strict
air quality) , con-
centrated siting
strict (strict
agricultural land
protection), dis-
persed siting
strict (strict
agricultural land
protection) , con-
centrated siting
base case
base case
base case, plants
on Ohio main stem with
once-through cooling
base case (lax air
quality standards)
base case, plants
on Ohio main stem
with once-through
cooling
base case
base case
base case
base case
base case
base case (least
emissions dispatch)
base case
base case, plants
on Ohio main stem
with once-through
cooling
base case
Economic growth Socioeconomic impacts
analyzed
high
high
high
high
high
high
high
high
high
high
high
low
very high
high (very low
energy growth)
high (high elec-
trical energy
growth)
high (high elec-
trical energy
growth)
high
high
high
Yes
Yes
Yes
No
Np
Yes
Yes
No
No
No
Yes
Yes
No
Yes
Yes
No
Yes
No
No
Alternative fuel emphasis
3
alternative
base case
high
Yes
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These combinations produce 7 major scenarios and 13 subscenarios
as shown in Table 1. Table 1 also shows that only selected subscenarios
are investigated in this report. Differences between the socioeconomic
impacts of the scenarios evaluated and not evaluated were found to be
minimal.
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3.0 Impacts on Employment
The socioeconomic impact analysis begins with the siting of both
power plants and coal mines. This siting is described elsewhere and
will not be repeated here [2,3l. Each siting gives us the total number
of plants or mines for each county in the ORBES region between now and
the year 2000 for each scenario. In the case of power plants, we also
know the on-line date or date on which operation would have to begin
in order for the scenario electrical energy demand to be met. For
coal mines, we have no such time distribution but only scenario by
scenario year 2000 totals. In each case, however, we can estimate the
total new employees required for construction and operation of the
facility. This can be compared to total estimated supply of workers
to get the relative impact of each scenario on employment. Power plant
construction employment demand can also be broken out into several
critical skill categories for further examination.
The sections below first explicate our methods for calculating
expected labor supply and total employment. Then, we delineate the
employment impacts of each scenario. The scenarios are compared in
terms of these impacts.
3.1 Employment for Power Plant Construction and Operation
Given the distribution and timing of power plant construction, the
next step is to calculate the employment induced by these activities.
For this purpose, we calibrated the ORBES Labor Impact Model (OLIM).
This model takes the schedule of on-line dates and megawatt sizes of
generating units for a given scenario and translates them into a
schedule of construction and operation labor requirements. The
population migration impacts of these demands are also calculated by
the model.
OLIM is fully documented in Appendix A of this report and so it
will not be discussed in detail here. What is of note at this point
are the outputs of OLIM. Table 2 lists these outputs. For each
county where a power plant is sited in a particular scenario (host
county), the model generates the construction and operation work force
and an estimate of total inmigrants to the county. At the regional
level, the model gives total workers demanded by year and a breakdown
of these demands by skill. Our impact analysis begins with these
outputs.
3.2 Labor Supply in Construction by Skill
The ORBES Labor Impact Model (OLIM) provides estimates of
regional power plant labor demand for eight skill categories: boiler-
makers, pipefitters, electricians, laborers, ironworkers, carpenters,
operating engineers and other skilled workers. (See Appendices A and
C for a detailed explantion of data sources and methods used to
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TABLE 2
Outputs From the ORBES Labor Impact Model
Scale
Item
Description
County
Regional
Construction workers
Operation workers
Construction workers
immigrating
Total inmigrants
Power plants
Total workers
Workers by skill
Workers demanded in each county where
there is siting for each year between
1975 and 2000.
Workers to operate the plant(s) after
the construction is completed. Listed
on an annual basis.
Number of construction workers expected
to migrate into the host county rather
than to commute to work.
The sum of inmigrating construction
and operation workers.
The type (coal or nuclear), size, and
number of plants sited in each county
Demand for construction and operation
workers for ORBES for each year
between 1975 and 2000.
Construction workers demanded in each
of eight skill categories by year.
8
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derive this skill breakdown of labor demand). Total labor demand is
almost useless for attempting to estimate possible labor shortages
associated with energy development. Very highly-trained, skilled
workers are required to build power plants. Shortages are relevant
only within skill groups such as those listed above. Unfortunately,
labor supply information is not available or inconsistent for five out
of the eight skill groups included as output from OLIM. The remaining
three — boilermakers, pipefitters and electricians — are among the
four skill categories with the largest labor demands that are required
for power plant construction. State level data for these three groups
was taken from the 1970 U.S. Census of Population [U]. Comparisons
with demand required further adjustments of the employment data.
These are discussed below.
Although state level data is a fairly good representation of
employment in Illinois, Indiana, Kentucky, Ohio, and West Virginia
(employment for ORBES portions of states would most likely include
that available in the non-ORBES portion since construction workers are
very mobile) the data for Pennsylvania would significantly overestimate
the workers available for ORBES - Pennsylvania. Both the size of the
non-ORBES portion and the average distance between the two portions
of Pennsylvania indicate that the state's employment would be an
inappropriate estimate of the supply available to the ORBES portion.
Population data for 1970 [5] was used to estimate the proportion of
employment that was attributed to ORBES-Pennsylvania. The Pennsylvania
estimates were summed with the state level data for the five other
states to produce a regional employment for the three skill categories.
It should be noted here, that ideally a labor supply model by
skill would give the best estimates of future supply and thus a closer
estimate of labor shortages. However, neither supply data nor a
supply model were available. Checks with labor unions and government
agencies lead us to the conclusion that the Census employment data
are the only estimating tool currently available. Therefore, we
estimate labor supply by skill based on these employment data.
Projections of supply were necessary to compare with the demand
estimates which are output from OLIM as annual requirements, 1975 to
2000, for each scenario. It was not possible to employ vigorous
projection methods because of data and time limitations. Instead, a
simple linear projection to 1980 and 1990 was made using the I960 to
1970 growth rate. This method assumes that the 1960-1970 rate remains
constant over the three decades (1960-1990). This assumption is
appropriate as a baseline with which to compare our projections.
However, it is not a "prediction" of what will take place in the labor
market.
The labor demand for boilermakers, electricians and pipefitters
estimated by OLIM does not incorporate demand created by any activities
other than power plant construction. The "supply" (employment) data
include supply of skills for all purposes. To adjust "supply" so that
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only potential power plant workers are included several assumptions
had to be made. First, we assumed that the number of skilled workers
predicted by the model for 1975 was a reasonable estimate of the
proportion of the 1975 supply of skills that were available for power
plant construction. In other words, we assume that in 1975 supply
and demand of labor for the three skill categories was in equilibrium.
Second, we assumed that the proportion of power plant workers in
each skill group remain constant over the projected period. Any
change in the proportion over time would have been arbitrarily chosen
since there was no justification for any other method. Making these
assumptions yields a set of "supply" and demand data for skilled
workers in power plants. Any shortage of workers does not imply an
overall shortage in the industry but instead implies a shift of
these skilled workers away from other industries toward building power
plants. Unless more skilled workers are trained or there is a decline
in demand for such workers in other industries, such a shortage means
construction delays either in power plant construction or in other
construction. Data and models currently available do not allow an
estimate of conditions in the overall labor market.
Given these assumptions, the final adjustments to the employment
data were accomplished by the following procedures:
1. OLIM was used to estimate 1975 construction worker requirements
by skill for ORBES. Information concerning the power plants
under construction in 1975 was taken from [6].
2. The 1975 supply of labor in the skill categories, boilermakers,
electricians and pipefitters, was determined by making a
linear interpolation between the actual 1970 data and the
1980 projected supply.
3. The 1975 estimate of skilled power plant workers was divided
by the appropriate 1975 supply estimate (for each of the
three skill categories) to yield a proportion or percentage
of supply in each skill category.
h. These percentages were applied to the 1980 and 1990 projected
supply to obtain an estimate of the supply of skilled workers
available for power plant construction.
The resulting figures of estimated supply for ORBES are shown in Table 3.
3.3 Labor Demand in Coal Mining
A computer model was not used for the calculation of labor demand
in coal mining. However, a similar procedure was followed to arrive
at mining employment estimates. The most critical question in these
calculations involves the estimate of future labor productivity in
coal mines. Rather than use one or more disparate estimates of
10
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TABLE 3
Supply of Skilled Labor Data and Estimates for Three Categories
ORBES - 1960 to 1990
Skill
Category
Boilermakers
Electricians
Pipefitters
Actual Supply
1960 1.9.70
6138 6755
73068 97230
65677 75936
Projected Supply
1980 1990
7430 8173
129413 172249
87782 101475
Adjusted for Power
Plant Workers
1980 1990
2348 2583
2718 3617
3687 4262
Source: U.S. Census Bureau, 1970 U.S. Census of Population
11
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productivity, we base our work on actual productivity data.
The source of our data is the Keystone Coal Mine Census tape (7).
This computer tape contains information on the location (county), type
of mine employment levels, and production of most mines in the ORBES
region for 1976-1977. As such, the data reflect the full range of
productivity now occurring in the region. On one hand, we would expect
older mines using older technologies to show a higher level of
employment per unit of coal produced. The newest mines or mines with
the newest technologies would show the lowest employment needs per
unit of coal produced or the highest productivity. The future will
continue to be a mix of older and newer mines.
Productivity will vary according to the technology used, the rate
of capital investment in new equipment, and any labor difficulties
the industry might experience. Rather than try to forecast each of
these variables, we decided to use a range of productivity estimates
based on data from the Census of Coal Mines.
First, we tabulated data on all coal mines in ORBES by type (deep
versus strip), employment levels, and production. There are a large
number of very small, inefficient mines in the region. They make
up only a small part of regional production and are not likely to
be important in the future. Thus, we eliminated these from further
consideration.
Next, we looked at the range of productivity estimates from the
remaining mines. In order to do this, we standardized production to
the unit of 1 million tons per year by apportioning employment upwards
or downwards as necessary relative to the actual annual production
of each mine to 1 million tons. This yielded a frequency distribution
of mines by productivity across the region reflecting all the
differences in currently available technology, capital, and labor.
The maximum and minimum of these estimates should encompass the "real"
productivity the ORBES region will experience between now and 2000.
Unfortunately, a rather large range of productivity is found in
current ORBES mines. For deep mines the figures range from 150 to
1185 employees per year per million tons mined. For strip mines the
range is 105 to 360 employees per year. The wide range in existing
deep mines presents a problem in trying to project coal mine impacts.
However, these ranges are still used to project the coal mining
employment changes in ORBES in the year 2000.
In order to put these figures in perspective, we might compare
them with one industry estimate of productivity. For a continuous
deep mine operation, the total employment is estimated to be 187
persons/million tons/year (8,15). This might be considered the "best"
currently available technology in ORBES in terms of productivity.
This is 25$ higher than our low estimate and only 16% of our high
12
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estimate based on current data. Conventional mining techniques,
currently more prevalent, and possibly in wide use through 2000 have
a much lower productivity. The mix of technologies will determine
where the final average lies. It appears from this admittedly limited
comparison that our high estimate for deep mining is probably too high
and that the midpoint of the range (668) is probably closer to the
"real" productivity.
For strip mines, industry figures indicate 133 employees for a
1 million ton per year mine. This is 27% above our low estimate and
is 37% of our high estimate. This range is less problematic since
it is much narrower.
Given the many unknowns concerning mining technology and pro-
ductivity, it seems appropriate to analyze the impacts on employment
using the ranges given above bearing in mind the relationship between
the low and high estimates, industry figures, and an average figure.
Using three multipliers, the minimum, maximum, and average of
these ranges, we calculated mining employment growth as a function of
the number of new mines and their related production from the coal
mine siting work. (See 33 for siting description and data). These
data were only provided for scenario 1, 2, 2a, 2b, 3j U» and 5 so
that these are the only scenarios analyzed with respect to coal mine
employment and related impacts.
3.14- Employment Impacts of Power Plants
3.U.I Total Labor Demand Impacts
The overall employment impacts were calculated for each of the
scenarios indicated in Table 1. The impacts are given in Table h.
Scenario 7, the scenario based on NERC energy growth assumptions
represents the largest single impact. This is , of course, because of
the extremely large number of power plants which would have to be
built in order to achieve this level of growth.
The next highest employment impacts are in the "energy-by-wire"
or wheeling scenarios 2a and 2b. The policy of producing electrical
energy in ORBES and transferring it to the Eastern United States would
require the construction of a larger number of plants than in many
scenarios. Even so, the labor demands remain significantly lower
than for scenario 7. Scenario 2b exhibits a slightly higher labor
demand because of the longer time period and greater amount of labor
used in nuclear power plant construction.
Next in the total man-years required are the strict, environmental
controls scenarios. The main reason for this is the larger number of
plants with scrubbers. These units are labor intensive especially in
operation. As is indicated in Appendix B, a scrubber facility for a
13
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Table 4
Total Man-Years Required by Scenario
1975 - 1995
Scenario Total Man-Years Required
1 (high growth, strict controls) 349,309
1A (very strict air, dispersed) 356,642
IB (very strict air, concentrated) 346,637
2 (high growth, lax controls) 326,534
2A (coal exports) 394,083
2B (nuc exports) 412,219
3 (alternate) 267,437
4 (natural gas) 203,742
5 (coal, low growth) 288,533
6 (high eco. growth, low energy growth) 185,286
7 (high eco. growth, NERC energy growth) 433,032
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typical 650 MW coal plant can require 27 - 5^ full time workers per
year. This translates into a large number of man years across the
ORBES region since the strict scenarios imply that all plants have
scrubbers. In the "lax" scenarios, only the so-called conjured plants
(those not announced by the utilities) have scrubbers. The larger
labor benefits for strict controls are interesting in light of the
dispute over flue gas desulfurization systems. The combined labor
benefits deriving from utility employment and the fact that the high
sulfur coals in ORBES would be competitive and keep miners employed
should be compared with the costs of building such systems. To data,
only the air pollution benefits and capital costs have been explored
in any depth.
Following these scenarios in labor demand is scenario 2, lax,
high growth. This scenario essentially represents current environ-
mental standards and high economic growth.
The final scenarios exhibit conditions of low energy growth
conditions, alternate energy use, or a natural gas emphasis and thus
require a significantly lower amount of labor for power plant construc-
tion and operation. However, these figures are misleading in terms of
the overall labor/energy policy tradeoffs being made. The reason for
this is the large labor requirements associated with retrofitting
buildings to conserve enough energy to meet the constraints of the low
growth scenario or to provide the labor for alternative energy systems.
Quantitative estimates made of the amount of labor required for
these purposes are very tentative and untested. Several estimates
have been made however.* In testimony before the Congressional
Subcommittee on Energy, several experts appear to agree that solar
energy and conservation practices will generate more jobs than the
provision of conventional energy.
There may be no negative tradeoff, in terms of jobs, between
alternate energy or conservation scenarios and conventional energy
production even though this is implied by Table U.
Another way of comparing the impacts of the power plant construc-
tion and operation on labor is to look at the time distribution of
labor demands. Figures 2 and 3 display this for most of the scenarios.
Here, one can see that the most extreme growth in demand is associated
with scenarios 7, 2a, and 2b. In each case, the scenario forecast of
electrical generating capacity increases produces a dramatic change
in employment demand between 1980 and 1990. By far, the greatest
increase occurs with Scenario 7. Such rapid changes imply a short term
labor shortage followed by a surplus as experienced workers have a
choice of jobs and then few choices. Since these numbers are region-
*See 9-
15
-------
27500
Scenario 7
25000
/
/ Scenario Ib
W
**• • Scenario 6
5000
1975
1980
1985
Year
1990
1995
Figure_2. Construction Workers Required, 1975-1995
Scenarios i, ib, 2b, 3, 6, 7
16
-------
25000
22500-
20000'
17500"
15000'
o
12500
\
/ Scenario 2
/ Scenario 5
vx/
V
10000 •
7500'
.* *. Scenario A
5000-
1975
1980
1985
YEAR
1990
1995
Figure 3
Construction Workers Required, 1975-1995
Scenarios la, 2, 2a, 4, 5
17
-------
wide averages, they do not imply a "boom and bust" situation locally.
However, they may be indicative of some potential regional problems
Except for Scenario 7, we do not believe that any major labor shortages
will occur as a result of the scenario growth projections. For
Scenario 7> these shortages may prove critical as is illustrated
below.
3.U.2 Labor Demand by Skill Impacts
As was indicated above, OLIM calculates the labor demand for
eight skill categories. Three of these can be compared with the supply
in the region that would occur if historical trends continued (see
Table 3). Table 5 shows this comparison for the scenarios analyzed.
Here, one can see that scenario 7 clearly becomes the most critical in
terms of labor shortages. By 1990, all three categories exhibit
potential shortages. Shortages also occur for electricians with
scenarios 1, 2a, and 2b and for pipefitters for 2b. However, these
are generally of much lower magnitude than the shortages for scenario 7.
The implications of these findings is that construction delays,
increases in costs, inmigration of labor from other regions, or
shortages in these skills in other industries might accompany the
growth in electrical generating capacity forecast by scenario 7. It
is not possible with available data and models to forecast which of
these impacts might occur.
Overall, labor shortages by skill do not seem to be a major
problem resulting from the power plant construction imbedded in the
ORBES scenarios. The shortages that might occur would produce some
short term problems but at present these do not appear extensive
enough to warrant the development of ameliorative policies.
3.5 Employment Impacts of Coal Mining
The growth of electrical generating capacity with an emphasis on
coal implies a large potential impact on the coal industry. The ORBES
scenarios assume that ORBES region coal will be used almost exclusively
in ORBES region power plants. This in turn implies that western coal
will make no further inroads in the ORBES region and that policies
concerning the burning of high sulfur coal, the use of scrubbers, etc.
are given as one of the ways described by our scenarios.
Given these assumptions, we analyzed the employment impacts of
siting the requisite number of new mines to meet ORBES coal demands
as discussed in section 3.3.
We use the minimum, maximum, and average labor productivity
values given above (3.3) to project the mining employment impacts. The
scenario implications of this range for ORBES are illustrated by Table
6. Here, one can see that for the seven scenarios analyzed, scenario 2a
18
-------
Table 5
Supply f, Demand for Boilermakers, Electricians and Pipefitters
By Scenario, 1980 and .1990
Boilermakers
Supply
Demand
Scenario l
1A
IB
2
2A
2B
3
4
5
6
7
1980
2348
2152
!
2315
2315
1980
1976
2179
2109
1179
2050
1171
2008
1990
2583
2579
2563
2425
2437
3565
2806
1619
1700
2039
1463
4225
Electricians
1980
2718
2296
2448
2448
2042
2131
2310
2256
1304
2201
1327
2162
1990
3617
2435
2420
2293
2302
3356
3011
1539
1591
1931
1371
3945
Pipefitters
1980
3687
3251
3417
3417
2732
3071
3234
3207
1948
3146
2056
3104
1990
4262
2726
2710
2569
2581
3730
4243
1749
1748
2177
1508
4302
Notes (1) Supply of skilled labor was estimated by a) calcu-
lating the percentage of workers in each skill
category that were estimated to be working at power
plant sites in 1975 (using OLIM and Generating Unit
Inventory) and b) applying this proportion to
projections of skilled labor in 1980 and 1990.
C2) Underlined numbers indicate potential skill shortage
situations.
19
-------
Table 6
Total ORBES Coal Mining Employment
Increase by Scenario
Total ORBES Mining Increase as a % of 1970
Employment Increase Mining Employment
Scenario
1
2
2A
2B
3
1+
5
Low Estimate
109,11*6
107.159
118,098
107,^23
91,983
70,105
98,159
High Estimate
701,228
688, U56
759,171
690,159
590,962
^50,U01
630,639
Low Est. %
76.5
75.2
82.8
75.3
64.5
49.2
68.8
High Est. %
491.8
482.8
532.4
484.0
414.4
315.9
442.3
Note: ORBES 1970 Mining Employment = 142,593. Only available data
included miners other than coal miners.
Source: U.S. Bureau of the Census, 1970 Census of Population.
20
-------
implies the largest increase in ORBES mining employment.* This is, of
course, because of the coal based power generation assumption with a
large proportion of the electrical energy exported from the region.
Scenarios 1, 2, and 2b are all next in magnitude followed by scenarios
5, 3j and k. The other conventional scenarios, 1, 2, and 2b all
require a similar demand for coal and thus a similar amount of labor.
Scenario 5 is next with a lower projected rate of economic growth.
Scenario 3 is still lower with an emphasis on natural gas while
scenario ^ requires much less coal with an alternative fuel emphasis.
The implications of these figures are first of all that under
all of the conditions hypothesized by the ORBES scenarios, a substantial
growth of the regions coal industry would occur. Differences across
scenarios result from the rate of penetration of alternative fuels,
lower economic growth, and/or lower energy growth.
Tables 7-13 show these potential employment impacts in greater
detail. These tables show the number and percentage of ORBES counties
that would fall in various growth categories using our minimum,
maximum, and average potential labor productivity figures. Here, one
can see that the higher coal mining growth scenarios, 1, 2, 2a, and 2b,
will place fewer counties in a low employment growth situation and
many counties in a situation where employment grows by 25% or more.
This growth would in turn bring indirect economic benefits to the coal
mining counties in terms of service availability, service employment,
local tax receipts, etc. In some counties, an extreme rate of growth
might also bring some "boom town" type of growth effects. Since very
few studies have been performed which monitor the impacts of large
growth rates on small communities, there is not general agreement on
the amount of growth which might produce a "boom town". Gilmore and
Duff (16, p. 6) that "a five percent growth rate is about all that a
small community can absorb." Gilmore (17) cites 15% growth as the
indicator of a boom-town situation. This figure is also used in the
Natural Coal Utilization Assessment at Oak Ridge National Laboratory (18)
Looking back at Tables 7-13, we see that even under an average
labor assumption, a large number of counties exhibit a mining labor
force growth of 25% or more. For example, in scenario 1 (Table 7),
only 9 of the 152 ORBES candidate mining counties have a projected
average mining labor force growth of less than 25%. Translating this
into a proportion of base year population, 31 counties would have a
lrboom-town" growth rate of >15%. This assumes that the newcomers bring
no families. If one assumes the average family size to remain what it
was according to the 1970 Census, 3.3 persons per household, then even
more counties would surpass the >15% growth criterion. In general,
*Since coal mines were not sited for scenario 7, the employment
impacts could not be analyzed.
21
-------
ro
ro
Table 7
Growth in Mining Employment in ORBES
Coal Counties to the Year 2000, Scenario 1
% Growth in
Mining Employment (l)
0
0.1-9.9
10.0-24.9
25.0-49.9
50.0-74.9
75.0-99.9
100.0-149.9
150.0-199.9
200 and over
Using Minimum
Potential Labor
Number of Percentage
Counties of Counties
0
9
13
34 •
38
16
15
6
21
0
5.9
8.6
22.4
25.0
10.5
9.9
3.9
13.8
Using Maximum
Potential Labor
Number of Percentage
Counties of Counties
0
5
3
1
8
6
11
3
115
0
3.3
2.0
.7
5.3
3.9
7.2
2.0
75.7
Using Average
Potential Labor
Number of Percentage
Counties of Counties
0
7
2
8
12
6
12
23
82
0
4.6
1.3
5.3
7.9
3.9
7.9
15.1
54.0
(l) Calculated as projected year 2000 employment divided by 1976 employment x 100%.
-------
ro
oo
Table 8
Growth in Mining Employment in ORBES
Coal Counties to the Year 2000, Scenario 2
% Growth in
Mining Employment (l)
0
0.1-9.9 -
10.0-2^.9
25.0-^9.9
50.0-7^.9
75.0-99.9
100.0-1^9.9
150.0-199.9
200 and over
Using Minimum
Potential Labor
Number of Percentage
Counties of Counties
0
9
16
32
38
19
13
6
19
0
5.9
10.5
21.1
25.0
12.5
8.6
3.9
12.5
Using Maximum
Potential Labor
Number of Percentage
Counties of Counties
0
6
3
1
8
6
11
3
llU
0
3.9
2.0
.7
5.3
3.9
7.2
2.0
75.0
Using Average
Potential Labor
Number of Percentage
Counties of Counties
0
7
2
9
11
7
lU
23
79
0
U.6
1.3
5.9
7.2
k.6
9.2
15.1
52.0
(l) Calculated as projected year 2000 employment divided by 1976 employment x 100%.
-------
ro
Table 9
Growth in Mining Employment in OKBES
Coal Counties to the Year 2000, Scenario 2A
^ Growth in
Mining Employment (l)
0
0.1-9.9
10.0-2U.9
25.0-119.9
50.0-7U.9
75.0-99.9
100.0-1^9.9
150.0-199.9
200 and over
Using Minimum
Potential Labor
Number of Percentage
Counties of Counties
0
9
11
33
36
20
13
5
25
0
5.9
7.2
21.7
23.7
13.2
8.6
3.3
16.5
Using Maximum
Potential Labor
Number of Percentage
Counties of Counties
0
5
3
1
7
5
13
3
115
0
3.3
2.0
.7
U.6
3.3
8.6
2.0
75.7
Using Average
Potential Labor
Number of Percentage
Counties of Counties
0
7
2
8
9
9
7
22
88
\J^J
0
k.6
1.3
5.3
5.9
5.9
4.6
14.5
57.9
(1) Calculated as projected year 2000 employment divided by 1976 employment x 100%.
-------
Table 10
Growth in Mining Employment in ORBES
Coal Counties to the Year 2000, Scenario 2B
% Growth in
Mining Employment (l)
0
0.1-9.9
10. 0-21*. 9
25.0-1*9.9
50.0-74.9
75.0-99.9
100.0-1^9.9
150.0-199.9
200 and over
Using vjiniffium
Potential Labor
Number of Percentage
Co-unties of Counties
0
9
15
33
38
20
12
5
20
0
5.9
9.9
21.7
25.0
13.2
7.9
3.3
13.2
Using Maximum
Potential Labor
Number of Percentage
Counties of Counties
0
5
3
2
8
6
11
3
llU
0
3.3
2.0
1.3
5.3
3.9
7.2
2.0
75.0
Using Average
Potential Labor
Number of Percentage
Counties of Counties
0
7
2
9
11
7
1U
1U
88
0
U.6
1.3
5.9
7.2
k.6
9.2
9.2
57.9
(1) Calculated as projected year 2000 employment divided by 1976 employment x 100%.
-------
ro
Table 11
Growth in Mining Employment in ORBES
Coal Counties to the Year 2000, Scenario 3
% Growth in
Mining Employment (l)
0
0.1-9.9
10.0-24.9
25.0-^9.9
50.0-7^.9
75.0-99.9
100.0-1^9.9
150.0-199.9
200 and over
Using Minimum
Potential Labor
Number of Percentage
Counties of Counties
0
9
22
38
38
15
9
5
16
0
5.9
1^.5
25.0
25.0
9.9
5.9
3.3
10.5
Using Maximum
Potential Labor
Number of Percentage
Counties of Counties
0
5
3
U
11
6
8
10
105
0
3.3
2.0
2.6
7.2
3.9
5.3
6.6
69.1
Using Average
Potential Labor
Number of Percentage
Counties of Counties
0
8
2
13
12
5
21
28
63
0
5.3
1.3
8.6
7.9
3.3
13.8
ia.k
la. 5
(1) Calculated as projected year 2000 employment divided by 1976 employment x 100$.
-------
Table 12
Growth in Mining Employment in ORBES
Coal Counties to the Year 2000, Scenario
% Growth in
Mining Employment (l)
0
0.1-9.9
10.0-2U.9
25.0-^9.9
50.0-7^.9
75.0-99.9
100. 0-1*19. 9
150.0-199.9
200 and over
Using Minimum Using Maximum
Potential Labor Potential Labor
Number of Percentage Number of Percentage
Counties of Counties Counties of Counties
0
12
32
57
20
6
9
h
12
0
7.9
21.1
37.5
13.2
3.9
5.9
2.6
7.9
0
7
2
8
13
6
13
22
81
0
k.6
1.3
5.3
8.6
3.9
8.6
1^.5
53.3
Using Average
Potential Labor
Number of Percentage
Counties of Counties
0
. 8
6
17
8
" 16
33
22
lj-2
0
5.3
3.9
11.2
5.3
10.5
21.7
1^.5
27.6
(1) Calculated as projected year 2000 employment divided by 1976 employment x 100%.
-------
ro
00
Table 13
Growth in Mining Employment in ORBES
Coal Counties to the Year 2000, Scenario 5
% Growth in
Mining Employment (1)
0
0.1-9.9
10.0-214-. 9
25.0-^9.9
50.0-7^.9
75.0-99.9
100.0-1^9.9
150.0-199.9
200 and over
Using Minimum
Potential Labor
Number of Percentage
Counties of Counties
0
9
20
36
35
19
11
3
19
0
5.9
13.2
23.7
23.0
12.5
7.2
2.0
12.5
Using Maximum
Potential Labor
Number of Percentage
Counties of Counties
0
5
3
2
10
8
8
8
108
0
3.3
2.0
1.3
6.6
5.3
5.3
5.3
71.1
Using Average
Potential Labor
Number of Percentage
Counties of Counties
0
8
l
13
9
6
21
25
69
0
5.3
.7
8.6
5.9
3.9
13.8
16.5
^5.^
(1) Calculated as projected year 2000 employment divided by 1976 employment x 100$.
-------
for the ORBES coal counties, this turns out to be those counties with
a coal mine employment growth of 200$ or more. Thus, we will use
this category as an indicator of potential boom-town conditions in
ORBES coal counties.
Comparing scenarios, using average labor productivity, we see
that scenarios 2A and 2B have the largest number of counties with
growth over 200$ (88) followed closely by scenarios 1 and 2. Scenarios
3 and 5 have fewer counties in this situation, 63 and 69 respectively,
with the minimum number, ^2 or 28% of the counties, coming in scenario
k. Even if we are extremely optimistic about productivity and use the
minimum figures, over 10$ of the counties might experience boom-town
conditions under most scenarios.
Thus, some efforts toward ameliorating negative socioeconomic
impacts are indicated. Obvious economic benefits occur with a coal
emphasis but these benefits also bring some potential economic and
social costs. These negative effects could be ameliorated to some
degree through careful planning. Economic costs occur to coal mining
with an alternative energy emphasis. As was discussed above, there are
also labor benefits in other industries accruing to these technologies.
However, these labor demands will have a different geographic distri-
bution. Thus, the energy technology decisions affect not only the
quantity of jobs created but their location as well.
These tradeoffs must also be weighed against the costs and
benefits in terms of capital costs, the environment, human health,
and etc. Some of these comparisons are made in the ORBES summary
report (1).
29
-------
U.O Population Impacts
The population impacts of the ORBES scenarios must be viewed
at the subregional rather than the regional scale. The reason for
this is simply because the impacts of population growth are only really
meaningful for smaller areas. Several thousand migrants mean nothing
to a region the size of ORBES but are quite significant in a community
with 10,000 persons.
Our population impact analysis looks first at general, internal
migration trends associated with industrial, commercial, and coal
mining developments. Then, we focus more specifically on direct
impacts from power plants and coal mines by scenario and at a sub-
regional level of analysis.
U.I General Migration Trends
Implicitly, the ORBES scenarios assume that all industrial and
commercial activities other than coal mining and power plant siting
will remain in the same locations where they exist in the base year.
This assumption is made primarily because of the difficulty of deriving
a method to make such allocations. For the purpose of our migration
analysis, we chose to examine the impacts of alternative future
industrial and commercial location decisions on general internal
migration trends.
In order to perform such an analysis, it was necessary to derive
a model of internal migration in ORBES. This was accomplished using
multiple linear regression techniques with data obtained from the
Appalachian Regional Commission (ARC) and the U.S. Census. These data
showed migration flows and other related conditions for M* subregions
approximating the boundaries of ORBES. A full discussion of the model
and its derivation is given elsewhere (19). The remainder of this
section reports the findings associated with the use of this migration
model.
Figure k shows the migration regions for which data were available
from the ARC. For the purposes of ORBES, these regions do not entirely
make sense. However, data availability dictated that we use them and
it appears that this geographic breakdown is sufficient for our purposes.
In order to simulate the migration impacts of continued trends
in the various economic sectors, we first derived a set of "shift"
factors showing changes in the proportion of ORBES region employment
in each sector residing in each region. These shift factors reflect
the historical trends in industrial and business location across the
ORBES region. It may be, for example, that over the recent past,
manufacturing has shifted its location from one part of the region to
another. These shifts, in turn, mean a change in the location of
employment, population, and pollution residuals. Using the shift
30
-------
ORBES REGION
FIGURE k
ARC MIGRATION REGIONS
OUTSIDE ORBES REGION
-------
factors to model future movements of industries implies that the same
factors that have caused changes of location in the past will continue
into the future. The shift factors for ORBES are shown in Table lU.
Here, one can see that the economy of ORBES is indeed shifting frojn
one place to another.
Our first set of simulation runs assumes that these trends
continue into the future at the same rate. Thus, our five year rate
is projected forward to the year 2000 to give the new employment
distribution by region which would occur if trends continued. Our
total figures for ORBES are derived from the 1-0 model and a set of
employment/output ratios reported in (19). Table 15 shows these
employment forecasts.
The migration model we implemented has as its independent
variables the unemployment rate, median family income, distance between
region centroids and total employment in each region. The model then
calculates the migration flows from each region to every other region.
From this, we can derive the net migration for each region. Unfortun-
ately, several of the independent variables, particularly unemployment
and income, cannot be derived from other ORBES models. Thus, we had
to estimate these variables using other means. The result of this
problem is that we had to make a somewhat arbitrary choice as to the
unemployment and income effects of various population shifts. For our
purposes, we felt that a region's unemployment rate would go down and
median family income up as a significant number of new jobs came into
the region. We used several rates for each and several decision
criteria as to when the rates would change. Our findings indicate
that the relative magnitude and direction of flow indicated by our
model is generally correct but that the absolute values are probably
not. For this reason, we report here only the general flow trends and
not the absolute numbers.
The first simulation calculated the change in manufacturing
employment using the 1965-70 shift trends. The results of this
simulation are shown in Figure 5. A shift in manufacturing employment
at the 1965-70 rate appears to result in a shift of population away
from most of the major population areas to smaller urban areas and to
rural regions. The exceptions to this are the Indianapolis, Indiana
and Lexington, Kentucky regions which are still forecast to have net
inmigrants. This finding seems consistent with recent urban-rural
migration trends, reports of older industries in urban areas closing,
and reports of new industries in less populated areas. Examples
include the closing of Youngstown Sheet and Tube and U.S. Steel in
Youngs town, the building of a new Volkswagen assembly plant in New
Stanton, Pennsylvania and the plans for a major steel facility in
Conneaut, Ohio. Should this trend continue, the implication for ORBES
is that changes in population related to energy growth will be
reinforced by changes in the location of manufacturing concerns. Thus,
the combined impacts may in fact be larger than we may anticipate.
32
-------
Table 14
HtRCtMT LHANtefc IN t*rU»TntM *lTnll»
F-KUM i»65 TU 1V7G (-CK VARIOUS
KEGiUtt
CO
(JO
1
2
3
7
8
11
12
13
1*
1*
it>
17
lit
IV
21
Zi
23
2*
25
2«
70
n
72
73
7»
77
78
80
81
62
63
8*
B9
81
BB
69
V»
lil
1*2
133
15*
155
lt>O
0.1
-0.2
-O.i
-O.i
0.0
1.0
-O.3
O.i
-0.3
0.7
-0.3
-0.1
-O.O
-0.1
2.0
-O.i
-O.I
-0.0
-O.O
-0.2
-0.0
0.0
O.i
-0.1
-0.2
-O.O
-0.0
0.1
-O.O
0.1
-O.O
-0.1
-2.1
O.o
-0.3
0.1
-O.i.
0.0
0.0
0.0
O.b
-O.O
O.O
CUKllKUCTlO.
0.8
0.1
-0.1
0.2
-1.2
O.O
0.1
0.0
0.1
-0.1
6.3
0.3
0.0
0.0
-0.0
-O.I
0.0
0.0
-0.0
0.0
0.6
-0.8
-0.2
-O.i.
O.O
0.2
O.i
0.0
O.»
-O.3
0.»
-0.7
-O.b
-0.3
-0.1
0.5
-0.8
-0.0
0.1
0.3
-0.1
-O.O
MANUFACIOHlNO
-1.3
-0.1
-0.2
0.1
-O.I
-O.3
-0.0
0.0
0.1
0.1
-0.1
0.0
-O.O
-O.3
0.0
0.0
0.1
0.1
-O.O
0.1
0.1
0.2
0.3
0.1
-0.1
0.1
0.2
0.2
0.0
-0.1
-0.3
-0.1
0.1
-0.2
-0.0
0.1
-0.0
0.7
0.3
0.2
0.2
0.1
0.0
HnCILEiALE
-0.1
-O.O
-O.i
-0.0
-0.0
-0.2
-0.0
-O.O
0.0
-0.0
-O.2
0.0
-O.i
0.0
-0.1
0.1
C.I
-O.O
0.0
C.I
O.I
0.7
-0.3
-O.O
0.1
-O.O
0.1
0.0
0.0
o.«
-0.3
-O.I
-0.0
-0.3
-0.3
-O.U
-0.0
0.1
0.1
0.1
0.2
0.1
-0.0
KETAIL
FlfcANClAL
-0.1
-0.1
-0.1
-0.1
-O.I
-0.0
-0.0
0.0
-O.O
-0.1
-O.I
-0.1
-O.i
-O.2
-O.I
-0.0
0.1
0.0
-O.O
0.0
0.0
-0.0
0.4
0.6
0.2
0.1
-0.1
0.1
-0.0
0.3
-0.1
-0.1
-0.1
-0.2
-0.3
0.0
0.1
0.2
-O.O
0.0
0.0
-0.1
0.0
-0.!)
-0.2
-0.0
-c.
-0.
-0.
0.0
0.0
0.
0.
-0.
0.0
-0.0
-0.0
-0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.2
0.2
0.6
0.1
-O.O
-O.O
0.1
0.0
0.1
-0.2
0.0
-0.1
-0.1
0.1
0.0
O.O
0.0
0.1
0.0
0.1
0.0
-0.0
CUnblNti.
WHOLESALE
HelAlk.
t-lNAM-lAL
-0.7
-0.2
-O.i
-0.2
-0.2
-0.3
-O.O
O.i
0.0
o.c
-O.H
-0.0
-u.3
-0.1
-O.i
0.1
0.1
0.0
0.0
0.1
0.1
0.!>
O.i
1.2
0.*
O.C
-O.O
0.2
O.O
0.7
-0.6
-O.i
-O.2
-O.c
-0.6
0.0
0.1
0.3
0.2
0.2
0.3
-0.0
-0.0
-O.tt
-0.0
O.O
-O.O
-0.0
-0.1
-0.0
0.0
O.O
0.0
O.O
O.O
-O.I
-c.»
-O.O
0.1
-0.0
-0.0
O.O
0.2
-O.O
0.2
o.i
0.5
-C.*
-O.O
-O.O
0.1
-O.O
0.2
-o.i
0.1
0.1
0.3
-0.1
-0.0
O.u
-O.i
-0.1
0.0
O.i
O.i
-0.0
-------
Table IS
1995 Estimated Employment for ORBES Region
REGION
1
2
3
7
8
11
12
13
1*
15
16
17
18
19
21
22
23
24
25
28
70
71
72
73
75
77
78
80
81
82
83
a*
85
87
88
dV
99
151
152
153
154
155
160
CONSTRUCTION MANUFACTURING
93390
18060
8534
3516
10421
O
4213
4182
6563
10980
7480
7903
18029
4021
3276
2423
1539
249
7359
1677
60742
31215
43317
56027
10239
10380
16905
1028
50638
&28B1
23382
17Z8U
»5373
4735
13630
35543
16622
9320
11427
8440
151
624527
83143
92022
42812
53058
98303
855
9611
31994
70829
57406
39464
35384
52595
10371
24378
19634
9937
1346
15999
13240
442392
331225
2580*3
5bl780
126807
B6134
160474
8698
285459
297341
174186
112861
145628
329165
30186
65O33
260014
63056
45571
60292
44869
1617
UTILITIES
83389
13857
9868
5036
8154
11864
445
2614
5122
8125
8712
5068
9075
18046
4272
3300
2651
1149
107
5746
881
57155
26214
37653
52663
10221
5906
15876
1015
41484
17830
16465
14376
21310
46411
4852
10149
30721
8968
8205
5777
6598
130
WHOLESALE
102543
9329
6456
3200
5746
8547
123
1198
3192
6600
8362
4662
5184
14542
5567
2916
2852
1153
294
7047
1404
78867
32172
4S858
55179
8039
9438
23297
V14
65601
18442
17V03
15670
15991
55078
3614
10037
42943
10805
7368
7022
8510
726
RETAIL
264100
41595
28751
11193
20968
36425
470
10423
17877
24695
27048
14187
18174
36564
15967
9039
13111
5095
1246
25721
6208
171340
119741
146701
185728
37955
28450
56483
4410
135093
Vb»«2
74602
49123
72911
182384
17901
44345
96058
341^7
28393
26898
23838
1146
FINANCIAL
88530
7684
7033
1935
4255
8447
78
1958
3591
5111
7153
3203
3935
11308
4137
2486
2002
1070
182
5189
2184
64813
27590
60201
42476
6294
6845
18439
828
63920
22574
1649V
11270
19039
59661
3933
7331
361OB
11932
5693
59B1
6692
211
SERVICES
264879
26691
25263
9369
19742
29558
162
4000
10884
17830
19359
11689
16317
31878
13695
8166
7810
2408
712
22277
4335
162504
100455
126671
143153
26218
19457
39025
2840
107*88
54039
41853
4002:*
59110
14tt394
10938
26797
88381
32388
15961
16929
17307
585
-------
FIGURE 5
ORBES REGION
SCENHRIO NO. 1 : MRNUFFICTURING SHIFT
CO
OUT5IDF ORBES REGION
LESS THRN -1000.0
-1000.0 THRU -1.0
0.0 THRU 750.0
GHERTF.R TURN 750.0
-------
However, these impacts may be more easily ameliorated than otherwise
might be the case because growth in some areas will be more stable.
Figure 6 shows a similar migration forecast using the same
criteria for the construction industry. There are several differences
however. The Cincinnati area is expected to have net inmigration
rather than net outmigration. Similarly, Portsmouth, Ohio, Central
Illinois, the South Bend, Indiana area, and Northwestern Pennsylvania,
and Southern West Virginia will all have a reversal in migration.
This implies that historically, construction unrelated to manufacturing
has been occurring in these areas and has induced inmigration.
Figures 7 and 8 show similar distributions using services and
finance sectors respectively as the forecasting variables. Here again,
there are minor differences but no major changes.
What these results indicate is that a general shift of population
away from major metropolitan areas to rural areas has been occurring
in the recent past in conjunction with shifts in employment. Should
these trends continue into the future, they may have some effect on the
direct population impacts of coal mines and power plants since the
population changes brought about by these developments are additive to
these general trends discussed above.
h.2 Population Impacts of Power Plants
Using OLIM, we were able to simulate the population migration
impacts of power plant construction and operation. In order to assess
these potential impacts, we summarized the model output for six groups
of contiguous counties where plants were sited for the various scenarios.
These groups are illustrated in Figure 9. The purpose of this aggregation
is first to allow consideration of the many potential locations of the
existing labor supply and of areas where inmigrants might settle. It
is unlikely that all labor will either come from the county where the
plant is being built or settle in that county. Commuting across' county
boundaries is relatively easy as long as the distances remain reasonable.
The second reason for looking at these six groups of counties is to
determine whether power plant construction in several counties over
the same period would create any significant potential synergistic
impacts. Here, synergistic is being defined as those population impacts
which are the combination of impacts of several plants being built at
one time in the same area. This is in contrast to the typical
Environmental Impact Statement which only looks at one project at a time.
It is unlikely that one plant taken alone will induce enough inmigrants
to have a significant local impact. However, several plants under
construction simultaneously in adjacent counties could produce more
significant impacts.
Our results indicate that the population impacts of power plant
construction and operation are generally not significant although they
36
-------
FIGURE 6
ORBES RFC I ON
SCENRRIO NO. i : CONSTRUCTION SHIFT
CO
I I OUTSIDE ORBES REGION
IB LESS THflN -1000.0
H -1000.0 THRU -1.0
HI 0.0 THRU 750.0
O GRERTER THRN 750.0
-------
FIGURE 7
ORBES REGION
SCE*mftHJ NO. ! : SERVICE SHIFT
CD
I I OUTSIDE ORBES REGION
El] LESS THRN -1000.0
Ljj -1000.0 THRU -1.0
LJ 0.0 THRU 750.0
^J GREfllER THRN 750.0
-------
FIGURE 8
ORBES REGION
SCENRRIO NO. 1 : FINRNCIRL SHIFT
u>
VD
I I OUTSIDE ORBES REGION
113 LESS THPN -1000.0
BJ -1000.0 THRU -1.0
LJ 0.0 THRU 750.0
LJ GRERTER THRN 750.0
-------
FIGURE 9
SIX COUNTY GROUPS FOR
SYNERGISTIC IMPACT EVALUATION
GROUPS
GROUPS
GROUP 4
GROUP 3
_ GROUP 2
0 GROUP 1
PREPARED FOR OHIO RIVER BASM ENERGY STUDY
BY CACIS/UCC, MARCH, 1980
-------
could become so if a large number of workers chose to settle in the
same communities. Table 1 shows that for each scenario, inmigrants
induced by power plant construction and operation are always less than
5% of the county group 1970 population.1 Since the county group
population during the impact period of 1980-2000 will be even greater,
the percentages would actually be smaller.2
Looking at Table 16, one can see that the largest population
impacts occur with scenarios 1A and IB where a large number of plants
.end up sited in county groups 1 and h. Still, these remain less than
5$ of the 1970 population. It is interesting to note that groups 1
and k almost always end up with a larger concentration of plants in
the shortest time period and therefore also the greatest migration
impacts.
Another way of viewing the population impacts is by looking at
the number of construction and operation workers as a percentage of
the 1970 county group labor force. Here, the proportions are greater
going up to a maximum of 15.h% for group k in scenarios 1A and IB.
This illustrates the economic benefits as measured by employment and
related income growth. Here again groups 1 and k are most heavily
impacted.
4.3 Population Impacts of Coal Mining
The population impacts of new coal mining employment demands
can be viewed in several ways. First, we may look at the
sub-regional impacts of coal mining employment changes on migration.
Figures 10, 11, and 12 show the induced migration from three ORBES
scenarios where the amount and distribution of coal mining employment
changes are significant. Using the migration model discussed above
(see ref. 19 , we simulated the impacts of mining employment changes
assuming all other sectors would remain relatively unchanged. An
increase in mining employment over 1,000 persons was simulated as
reducing unemployment and increasing local income.
As one can see by Figures 10-12, the migration model is not
sensitive to these changes in coal mining employment. This is to say
that there are only minor differences in the predicted net migration
across scenarios. The major reason for this is that the model regions
tend to be quite large, many encompassing several coal mining counties.
Even though the overall coal demand varies significantly from scenario
to scenario, the subregional changes tend to be equal relative to the
recall that Gilmore and Duff (17) cite this as the amount of
change a small community can readily absorb.
2
Please see the Appendices for an explanation of how the
calculations in Table 16 were made.
-------
Table 16
Maximum Number of Construction Workers and Associated Population Increases,
1975 - 2000 By Scenario 5 Group
Scenario
1
3
1A
Group
1
2
3
4
5
6
1
2
3
4
5
6
Maximum
Workers
3735
4248
2356
4304
3604
2157
3498
3904
2468
3780
3721
2157
Workers as a
\ of '70 Labor Force
11.9
1.3
0.6
12.7
10.0
3.9
9.6
1.3
0.6
15.4
9.2
3.9
Maximum
Inmigrants
2196
3677
2911
3197
1696
1456
3755
2209
2421
2707
2488
5524
Inmigrants
Plus Families as
% of '70 Population
2.5
.4
.3
3.2
1.6
.9
3.6
0.3
0.2
3.6
2.1
1.0
-------
Table 16 (Cont'd)
S
Scenario
IB
2
Group
1
2
3
4
5
6
1
2
3
4
5
6
Maximum
Workers
3498
3904
2468
3780
4077
1963
5416
4018
4288
3081
3445
3072
Workers as a
% of '70 Labor Force
9.6
1.3
0.6
15.4
10.1
3.8
9.0
1.2
0.9
4.0
3.7
1.5
Maximum
Inmigrants
4734
2209
2911
3686
3956
1946
3985
3197
3564
3859
3067
3454
Inmigrants
Plus Families as
\ of '70 Population
4.6
0.3
0.3
4.9
3.3
1.3
2.2
0.4
0.3
1.6
1.2
0.6
-------
Table 16 (Cont'd)
Inmigrants
Scenario Group
2A 1
2
3
4
5
6
2B 1
2
3
4
5
6
Maximum
Workers
5416
4159
7239
4468
3259
5154
5416
3635
4472
4611
3602
5157
Workers as a
% of '70 Labor Force
10.2
1.3
1.6
5.8
3.5
2.6
9.0
1.1
1.0
5.9
3.9
2.6
Maximum
Inmigrants
4027
3124
4122
5645
3661
5524
4036
3051
4198
3694
3172
4068
Plus Families as
% of '70 Population
2.5
0.4
0.4
2.4
1.4
1.0
2.3
0.4
0.4
1.6
1.2
0.7
-------
Table 16 (Cont'd)
Scenario
3
4
Group
1
2
3
4
5
6
1
2
3
4
5
6
Maximum
Workers
2403
3635
4438
3153
3008
2915
2648
3463
3458
2281
3008
2075
Workers as a
% of ' 70 Labor Force
4.6
1.2
1.0
6.7
4.9
2.0
8.5
1.2
0.8
4.8
4.9
1.6
Maximum
Inmigrants
2304
2646
3388
2952
2437
2475
1588
2498
2898
2218
2245
1111
Inmigrants
Plus Families as
% of '70 Population
1.5
0.4
0.3
2.1
1.4
0.6
1.8
0.3
0.3
1.6
1.3
0.3
-------
Table 16 fCont'dl
Scenario
5
6
7
Group
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
Maximum
Workers
4652
3554
4251
2997
3602
3259
2648
2544
2985
2281
2728
1127
5813
4764
7654
4207
3930
5122
Workers as a
% of '70 Labor Force
7.7
1.2
1.0
5.7
3.9
1.8
8.1
0.9
0.7
8.1
4.5
0.9
9.4
1.5
1.6
5.4
4.2
2.5
Maximum
Inmigrants
3265
2437
3388
3197
2927
3069
1583
1433
2408
1973
1703
621
4525
3380
3877
3931
3172
3803
Inmigrants
Plus Families as
% of '70 Population
1.8
0.3
0.3
2.0
1.1
0.6
1.8
0.2
0.2
2.4
1.0
0.2
2.6
0.4
0.3
1.7
1.2
0.7
-------
FIGURE 1X3
ORBES REGION
SCENRRIO NO. 1 : NET MIGRflTION
LJ OUTSIDE ORBES REGION
m LESS THHN -1000.0
B
fei -1000.0 THRU -1.0
§1 0.0 THRU 750.0
O GREOTER THRN 750.0
-------
FIGURE U
ORBES REGION
SCENARIO NO. 14 : NET MIGRflTION
OUTSIDE ORBES REGICIN
LESS THON -1000.0
-1000.0 THRU -1.0
0.0 THRU 750.0
GREflTER THflN 750.0
-------
FIGURE 12
ORBES REGION
SCENRRIO NO. 5 : NET MIGRRTION
EH OUTSIDE ORBES REGION
lill LESS THflN -1000.0
D -1000.0 THRU -1.0
HI 0.0 THRU 750.0
B GRERTER THRN 750.0
-------
change criteria. The employment change criterion would have to be put
at over 10,000 or more new employees in order to significantly effect
model results. We feel that is artificially high and that instead,
other measures of potential migration should be used.
A second measure of population impact related to potential
migration is shown in Table 17. Here, population change is viewed at
the county level with the indicator being the number of counties
experiencing various amounts of employment increase as a percentage
of base year county population. Several notable trends are exhibited
here. First, one must note that in every case, the majority of the
152 coal mining counties do not have employment increases greater than
5.0$ of the population. This in turn implies that in most counties no
dramatic shifts will take place that strain local services or create
a "boom-town" effect.
There are, however, always a large number of counties in which
dramatic increases do occur. These are the counties where the
employment increases are 5$ or 15$ or more of the base year population.
Here, the scenarios also exhibit some differences. Scenarios 1 and 2A
have the maximum population impact with almost k3.h% of the counties
in the more than 5$ category, and 21.0 and 22.4$ in the more than 15$
category. Scenario 2 follows with hl.h and 21.0 in these same
categories.
The remaining scenarios have many fewer counties in these high
potential growth categories with scenario 4 exhibiting the smallest
impacts followed by scenario 3 and scenario 5. Scenario 7 was not run
for this part of our analysis.
The implications of these large amounts of growth is a greater
potential for boom-town types of impacts. This term implies a situation
in which growth outstrips the ability of local communities to provide
housing, public services, schools, health facilities and etc. The
potential for these impacts in the ORBES region is generally much lower
than in areas in the Western United States. However, it is apparent
that several areas in ORBES may experience such impacts.
Some effort should be made to ameliorate these impacts. This
could be done by anticipating the opening of large new mines and making
nomies available to local communities to upgrade their services before
their capacity is exceeded.
-------
VJl
H
Table 17
Average Potential Mining Employments Increase as a
Percentage of 1970 Population, OKBES Coal Counties
Percent Increase
Scenario
Number of
Counties %
1
Counties
Scenario
Number of
Counties %
2
Counties
Scenario
Number of
Counties %
2A
Counties
0.00-4.99
5.00-9.99
10.00-lU.99
15.00-19.99
20.00 or greater
Summary
Increases 5.0 or
greater
Increases 15.0 or
greater
86
20
14
9
23
66
31
56.6
13.2
9.2
5.9
15.1
43.4
21.0
89
17
14
9
23
63
31
58.6
11.2
9.2
5.9
15.1
41 .1*
21.0
86
17
15
8
26
66
3^
56.6
11.2
9.9
5.3
17.1
43.4
22.4
-------
vn
ro
Table 1?
(continued)
Percent Increase
Scenario
Number of
Counties %
3
Counties
Scenario
Number of
Counties %
4
Counties
Scenario
Number of
Counties %
5
Counties
0.00-4.99
5.00-9.99
10.00-14.99
15.00-19.99
20.00 or greater
Summary
Increases 5.0 or
greater
Increases 15.0 or
greater
93
21
10
10
18
59
28
61.2
13.8
6.6
6.6
11.8
38.8
18.4
100
20
11
10
11
52
21
65.8
13.2
7.2
6.6
7.2
34.2
13.8
92
19
11
10
20
60
30
60.5
12.5
7.2
6.6
13.2
39.5
19.7
-------
5.0 Impacts on Public Services
5.1 Water and Sewer Systems
With the inmigration of power plant workers and the associated
increases in the housing stock are new demands on public services.
Two of the most important public services for population expansion are
the public water and sewer systems. Both systems have physical
capacities which limit the amount of water or sewerage they can handle
on any given day. When these public systems are available, several
alternatives exist, each with its own drawbacks.
Many counties in the ORBES region have never had public sewer
systems. These county residents rely on septic tanks, cesspools or
privies for sewage disposal. Depending on soil characteristics, depth
to water table and the amount of waste disposed by these methods, water
quality can be severely affected. The capital investment necessary to
install or expand a public sewer system is often beyond the budget and
the taxing capacity of small rural counties. If an influx of population.
does occur in a county with an insufficient public sewer system the
area must be able to either absorb the effects of alternative sewer
systems or the effects of public outlay for new services in the form
of an increased tax burden.
Public water systems are much more prevalent in ORBES than public
sewer systems. Alternatives to public water systems are private wells
and cisterns. When public water systems are at or near capacity the
amount and pressure of water available to all consumers may be decreased.
One effect of low supply is the disincentive that it provides for
businesses and industries that may have located in the county. If
excess capacity is available it remains the resident's responsibility
(in most cases) to pay the costs of new hook-up lines to their
residence. The installation or expansion of public water systems would
require capital investments by county or local jurisdictions. Funding
would come from the purchase of bonds with the help of a tax levy.
The burden for the supply of services to meet new demands would fall
on both existing and new residents.
From the ORBES Labor Impact Model (see Appendix A for a descrip-
tion of model inputs and outputs) the number of inmigrants for each
county, for each scenario is derived by year of the scenario. Given
information on water and sewer system capacities and use we should be
able to make some statements regarding county level impacts for these
public services. However, this information is not available for all
counties, nor is it in a comparable or consistent form. In fact, data
on local public sewer systems is almost non-existent. For the Site-
Specific Study (20) we attempted to put together data on water system
capacities and average daily use for the seven case study counties.
Even this small data collection task could not be completed. However,
some data were available for Jasper County, Illinois (21), Jefferson
53
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County, Indiana (22), Adams County, Ohio (23), Beaver County,
Pennsylvania (2k) and Mason County, West Virginia (25). We had planned
to use the information on the case study counties to make some general-
izations about the remaining ORBES counties (using the classification
techniques described in Appendix B of this report). County level
data on water capacity and average daily use revealed considerable
excess capacity for all the case study counties. This seemed unlikely
until we realized there were three problems with this approach:
l) water capacity was either undefined or inconsistently defined (i.e.
water treatment plant capacity, pumping station capacity or total
ground water dependable pumpage) 2) average daily use is not the
appropriate variable, rather the peak or 'maximum daily use' should be
used, 3) using county level data does not reveal potential demand-
supply problems for local water systems within the county. The first
two of these problems could not be resolved for most of the case study
counties. We were able to look at individual local water systems
within several of the counties. At that level, two systems appeared to
be at or near 'capacity.' For example, the New Haven-Hartford-Mason
service area in Mason County was reported as having a daily excess of
20,000 gallons per day (25): Using the 'rule of thumb' estimate of 100
. gallons of water required per person per day this water system could
handle only 200 additional residents. The Cresville Heights water
system in Beaver County is reported as serving 10,500 users with .85 mgd
capacity. These figures indicate that, at capacity, only 81 gallons
per day is available per person -- well below the 'rule of thumb1 as
mentioned above. An influx of new users would further reduce the amount
of water available per person for all users in this local service area.
The most complete data source on water systems that was available
to us was that produced by the Ohio Department of Natural Resources (23).
From this report we gathered data for all Ohio counties on maximum
daily use and plant system capacity. Again, we estimated excess
capacity. We hypothesized that there would be a relationship between
excess capacity and population size of the county. That is, we expected
small counties to have less excess capacity than more populated counties.
We could then use the relationship defined for Ohio counties in
generalizing to all ORBES counties. Using 1975 population data (26) for
this correlation analysis we were unable to define a significant
relationship between population size and excess water capacity. At the
county level there was no evidence of any lack of capacity. Locally,
for individual systems within counties, potential problem areas were
evident.
In general, what we can say is that both sewer and water impacts
will be very localized and difficult to predict. In particular we need
to know the exact localities that will be affected by the growth of
new housing, the system excess capacities and the plans that may have
already been made for installation or expansion of these systems. The
impacts of new public service demands such as public water and sewer
services can take the following forms:
-------
1) installment or expansion of facilities with increased sewer
or water charges
2) expansion of septic tanks, cesspools and privies with
associated potential decrease in water quality
3) decreased water available to all users — leading to
decreased water pressure, disincentives for new business
or industry to locate there
k) little or no change in water quantity or quality because
of excess capacity or because of the magnitude of new
service demands is small
Clearly, we cannot predict which of these impacts may occur
in the future given the lack of data and the uncertainty of future
population movements. This section should point out though that any
major shifts in population could result in several environmental and
economic problems. It does not appear that power plants require
enough labor to be the primary driving force behind such impacts.
However, new coal mines with large labor demands may indeed result in
severe service shortages and their requisite problems. Only careful
planning for such expansion can serve to avoid or at least mitigate
some of these problems.
5 .2 Other Public Services
There are several other local public services that can be
adversely impacted by energy development projects. These include
schools, health services, social services, police and fire services,
garbage collection, and transportation services. As was the case with
sewer and water, the nature and extent of these impacts depends upon
existing level of service, excess system capacity, etc. These impacts,
if they occur will be local rather than regional in scope. Their
quantitative definition was not undertaken for the same reasons as
those outlined above for sewer and water.
One additional local impact associated with these which may have
regional significance is the fiscal impact of service demands. Our
site specific report (20) illustrated that the timing and distribution
of revenues from power plant siting may not be congruent with the
costs and locations of service demands. In particular, most local
assessment practices will yield a minimal amount of community property
tax income at the time when the peak employment and related public
service demands occur. In addition, commuting of workers across
municipal boundaries will produce service demands in jurisdictions
different from those where taxes on the energy project are collected.
The result may be that local impacts will be exacerbated. There may
be several ways of ameliorating this problem some of which involve
the sharing of tax base and of service costs across larger geographic
55
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areas. The policies which may be implemented to ameliorate these
impacts are discussed in the final chapter of this report.
56
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6.0 Policy Implications
Given the nature and extent of potential socioeconomic impacts,
it is important to conclude this report with a review of some of the
policies that may avoid or ameliorate some of those impacts. Before
discussing these policies it is important to note that the socioeconomic
impacts although important, may not be equal in weight to environmental,
national security, or other considerations associated with energy
development and its impacts. The relative weights of the various issues
must be left for decision in the political arena. What we discuss
below are those policies that might be followed to ameliorate socio-
economic impact if the actual, future energy, environmental and/or
economic conditions approach those of our scenarios and thus would
lead to those impacts discussed in previous chapters.
6.1 Siting Policies
In our opinion, siting will continue to be predominantly
influenced by physical, environmental, and cost constraints. For this
reason, we do not feel that a siting policy based on the avoidance of
socioeconomic impacts is entirely practical. However, it may well be
that choices will arise among sites that are essentially equal in
physical, environmental, and cost terms but quite different in terms
of potential socioeconomic impacts. Under these circumstances it
would be feasible to choose those sites for energy facilities where
adverse socioeconomic impacts are minimized and positive impacts are
maximized.
Implementation of this policy could take many forms:
l) Leaving siting decisions in private hands (i.e., private
utilities) but giving a stronger emphasis to socioeconomic consider-
ations in the site review, EIS, and related processes.
2) Forming some type of oversight agency for siting which
utilized socioeconomic criteria (as well as others) in making siting
decisions.
Various combinations of these approaches might also be undertaken.
Discussion of the legal and institutional aspects involved in such
siting is beyond the scope of this report. Readers are referred to
the ORBES Hiase II Final Report for other discussion on this matter (l).
6.2 Ameliorative Policies
Given that a siting decision has already been made and that there
may be some adverse socioeconomic impacts, there are an additional set
of ameliorative policies which might be implemented. Although a few
could be implemented at the federal or regional level, most would take
state and/or local actions. These policies are discussed in turn below.
57
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6.2.1 Service Subsidies
One of the major ways the state and federal government could help
to offset the impacts of energy development would be by giving direct
aid to those areas which are most impacted by sudden growth. Several
programs of this nature are already in existence. For example, the
U.S. Department of Energy provides monies to energy "boom town" areas
to help pay for the costs of increased public services demanded over a
short period of time. The Department of Housing and Urban Development
has also given special housing assistance in such cases.
Within ORBES, however, there will probably be few such "boom
towns". A more general and persistent problem will arise in communities
where there will be short term, significant impacts on public service
demand, low tax revenues while the project is under construction, and
no available forms of assistance. Under these circumstances, several
types of programs could be used to aid communities at the time of peak
service demand. These might include short term, low interest loans
to help pay for service costs, or direct subsidies. Subsidies could
be made either through new programs or by giving higher priorities for
assistance under existing programs to communities that are impacted.
Alternatively, a policy could be formulated that forced the
utility company and thus indirectly its customers, to pay more of the
front end, indirect costs of energy facility development. Such a
program would probably be less popular from the viewpoint of pushing
up the cost of utility bills which are already increasing apace.
6.2.2 Tax Policies
Alternatives to helping offset the local impacts of energy
development revolve around tax policies. Here, both the timing and
distribution of tax receipts are critical. In the long run, local tax
receipts from a power plant greatly exceed the costs for public
cervices. However, during construction this is not the case. One tax
policy that could ameliorate this problem is one of prepayment of taxes
by utilities to pay the cost for services during the peak construction
period. This has been tried in one or two unique cases but has not
been widely implemented.
Similarly, the tax receipts do not always come to all the
communities being impacted simply because of the boundaries of taxing
districts. One method of circumventing this problem is that of tax
base sharing. This policy has been implemented in Minnesota with
respect to all property taxes. Essentially, the program involves
redistributing tax receipts not only to the host community for
facilities but also to surrounding communities that are impacted in
terms of schools, sewer, water, police, and other public services.
This provides a more equitable spatial distribution of costs and benefits
58
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and helps to ameliorate many of the social impacts of large scale
developments such as energy facilities.
6.2.3 Land Use and Related Local Policies
The local impacts of energy development are often exacerbated
because of their occurrence in rural communities with little or no
control over land use and building codes. This means that new
development can often locate anywhere in the community regardless of
its impacts on service costs, the conflicts it may produce with
existing uses, and thus its impacts on local health and welfare. Under
these conditions, communities could choose to institute some form
of land use controls to help prevent such impacts. However, the
zoning, subdivision, building, and other codes that would need to be
put into place require some degree of experience and knowledge as well
as a significant administrative cost. Most rural communities find out
too late that such policies would be of benefit to them. Alternatively,
they put them in place but are unable to provide for adequate enforce-
ment resulting in the same levels of community impacts.
For these reasons, it is important to provide technical assistance,
monies to offset administrative costs, and other incentives to help
local communities deal with these problems. The only alternatives to
such a policy would be to maintain the status quo or have some other
level of government undertake the responsibility for land use controls.
The latter is probably politically infeasible while the former fails
to deal with the socioeconomic impacts of land development.
6 .2.^ Administrative Actions
Aside from the possible implementation of new policies and pro-
grams, much can be done under current operating procedures to prevent
and ameliorate adverse socioeconomic impacts associated with energy
development. These actions really involve tighter control on current
regulatory and administrative procedures affecting the socioeconomic
impacts.
The first of these administrative actions involves a more careful
and more timely property tax assessment of energy facilities. Assess-
ment procedures and practices vary widely across the region. In some
cases, local assessors do not revalue energy facility sites until the
third or fourth year of construction. This practice means that the
local community foregoes the extra income it might otherwise receive.
A similar problem occurs with regard to the amount of the
assessment. Our efforts to obtain data on the tax burden associated
with typical power plants in ORBES revealed that most local assessors
do not know, that the state assessment offices are either unwilling or
unable to provide the information, and that the utilities are generally
unwilling to provide the information. Under these circumstances, it is
59
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impossible to obtain a picture of the accuracy, timeliness, and fair-
ness of these assessments. Thus, some effort should be made to
tighten up this process and to put the information in a more easily
accessible form.
Finally, we must note the administrative problems associated with
some types of land use controls. For zoning and subdivision regula-
tions, it is frequently possible for developers to obtain variances.
To the extent that this adversely'impacts the community, the regula-
tions become ineffectual. Local communities that adopt such regula-
tions must make an effort to carefully evaluate variance requests in
order to avoid these impacts. With building regulations, special
ordinances for trailer parks, signs, etc. the problem is more fre-
quently one of inadequate inspection and enforcement. Communities
where growth has occurred slowly in the past are frequently unprepared
to handle the administrative activity associated with rapid development.
Such preparations must be made if adverse impacts are to be avoided.
60
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REFERENCES
(1) ORBES Core Team, Ohio River Basin Energy Study, Main Report.
Washington: Office of Research and Development,
U.S. EPA, 1980.
(2) Gary L. Fowler et. al. The Ohio River Basin Energy Facility
Siting Model (ORBES Phase II, forthcoming).
(3) Donald Blome, Coal Mine Siting Procedure for ORBES (ORBES Phase II,
forthcoming) .
U.S. Bureau of the Census, 1970 Census, of Population; General
Characteristics of the U.S. Population, Washington: U.S.
Government, 1970.
(5) U.S. Bureau of the Census, 1970 Census of Population,
Characteristics of the Population, Washington, D.C.:
U.S. Government, 1970.
(6) Steven Jansen, University of Illinois at Chicago Circle,
"Electrical Generating Unit Inventory, 1976-1986: Illinois,
Indiana, Kentucky, Ohio, Pennsylvania, and West Virginia, "
ORBES Phase II, Grant No. EPA R805588-01 (Washington, D.C.,
November 1978).
(7) Coal Age Mining Informational Services, Coal Age/Keystone Census
of Coal Mines, New York: Coal Age Mining Informational
Services, September 1978. Tape contains 1976-1977 data.
(8) Donald Blome, letter of December 7j 1979 siting information from
a mining engineer for Skelly and Loy, Lexington, Kentucky:
Institute for Mining and Minerals Research, University of
Kentucky.
(9) James W. Benson, Council on Economic Practices. Hearings before
the Subcommittee on Energy, Joint Economic Committee Congress
of the United States. Second session. March 1978.
Washington: U.S. Government, 1978, p. 28.
(10) Duane Chapman, Cornell University. Hearings before the Sub-
committee on Energy, Joint Economic Committee Congress of
the United States. Second session. March 1978.
Washington: U.S. Government, 1978, p. 51.
(11) Wilson Clark, Assistant to the Governor of California. Hearings
before the Subcommittee on Energy, Joint Economic Committee
Congress of the United States. Second session. March 1978.
Washington: U.S. Government, 1978, p. 95.
61
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(12) F. Ray Marshall, Secretary of Labor. Hearings before the Sub-
committee on Energy, Joint Economic Committee Congress of the
United States. Second Session. March 1978. Washington:
U.S. Government, 1978, p. 136.
(13) Richard Grossman and Gail Daneker, Jobs and Energy. Washington,
B.C.: Environmentalists for Full Employment, 1977, p. 1^.
(1*0 Robert DeGrasse, Jr., Alan Bernstein, David McFadden, Randall
Schatt, Natalie Shiras, Emerson Street, Creating Solar Jobs.
Mountain View, California: Mid-Peninsula Conversion Project,
1978, p. 11-12.
(15) T.S. Wellman and J.A.L. Campbell, "A Case for Conventional
Mining," Mining Congress Journal, November 1979, pp. 23-26.
(16) J.S. Gilmore (1976) "Boom Towns May Hinder Energy Resource
Development," Science. 197(^227): p.^35-5^0. Feb. 13, 1976.
(17) J.S. Gilmore and M.K. Duff (1975) Boom Town Growth Management: A
Case Study of Rock Springs - Green River, Wyoming. Boulder,
Colo: Westview Press.
(18) Oak Ridge National Laboratory, National Coal Utilization
Assessment. Oak Ridge, Tenn:ORNLTM-6122October 1978,
Chap. 9.
(19) Steven I. Gordon and Christopher Badger, Migration in the ORBES
Region (ORBES Phase II, August, 1980.
(20) Steven I. Gordon and Anna S. Graham, Site Specific Socioeconomic
Impacts; Seven Case Studies in the ORBES Region. Columbus,
Ohio: Department of City and Regional Planning, The Ohio
State University, Sept. 1979.
(21) Communications with Illinois Institute of Natural Resources,
Springfield, Illinois.
(22) Communications with Region 12 Development Commission, Versailles,
Indiana.
(23) Ohio Department of Natural Resources, Division of Water,
"Inventory of Municipal Water-Supply Systems by County,
Ohio," Ohio Water Inventory Report No. 2k, 1977.
Green International Inc., Comprehensive Water Quality Management
Plan, for Pennsylvania Department of Environmental Resources,
May 1976.
62
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(25) Communications with Region II Planning and Development
Commission, Huntington, West Virginia.
(26) U.S. Bureau of the Census, Current Population Reports,
Series P-25, 1977.
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APPENDIX A
The QRBES Labor Impact Model
The ORBES Labor Impact Model (OLIM) takes the schedule of on-line
dates and megawatt sizes of generating units for a given scenario and
translates them into a schedule of construction and operation labor
requirements with associated migration figures. Requirements for
operation of the model are very simple: scenario specific information
about the size, type and on-line date for each generating unit and
migration assumptions for three commuting zones around the host county.
Implicit inputs in the model are: ratios of workers per megawatt,
distribution of workers over a schedule and a skill breakdown of
workers required. These inputs are interior to the model but can be
modified with relative ease. Outputs of the model include: a county-
by-county listing of construction workers, operation workers, and
number of inmigrating workers by year of the scenario and for QRBES
as a whole, a listing of construction and operation workers by year
as well as a breakdown of the construction workers into eight skill
categories. The inputs and outputs of OLIM will be discussed below.
Input Requirements
The first set of input requirements are the assumptions concerning
the proportion of construction workers that will migrate to the host
county. Three proportions vary depending on the proximity of the
centroid of the host county to the nearest SMSA: the host is an SMSA
county, within 50 miles of the centroid of the nearest SMSA county,
and greater than 50 miles from the nearest SMSA county. Generally,
increasing proportions of workers will be assumed to migrate with
increasing distance from an SMSA. In most of the ORBES scenario runs
5, 10 and 30 percent were the proportions used for these three
categories. These are congruent with values in the literature (1,2).
All operation workers are assumed to migrate to the county.
The second set of input requirements are detailed information
concerning each generating unit in the scenario. This information
includes:
a) state and county identification code
b) whether the county is an SMSA county,within 50 miles of the
centroid of the nearest SMSA or greater than 50 miles from
the nearest SMSA
c) the type of unit: coal-fired less than 1000 MW, coal-fired
1000MW or greater or nuclear unit
d) the size of the unit in megawatts
e) the on-line date projected for the unit
f) whether the unit is a single unit (no other plants existing
or planned for the site) or part of a multiple unit site
g) whether scrubbers are planned for the unit or not
6k
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The state-county identification code combines a one-digit code (1-6 for
ORBES states in alphabetical order) with the county FIPS code, a census-
designated code which has been used for county identification throughout
the ORBES study. The distances from centroids of counties to centroids
of SMSA counties was roughly estimated using straight line distances on
U.S. Geological Survey state maps. The remaining information is simply
derived from the scenario information provided by the siting study (3)
and the generating unit inventory prepared by Steve Jansen (k).
Implicit Inputs
The implicit inputs to the model are those parameters (factors,
ratios^ proportions) which are exogeneously determined but entered as
part of the model for simplicity's sake. The first set of implicit
inputs are ratios of construction manpower requirements per megawatt.
Ratios were derived for the following types of energy facilities:
coal, single unit, no scrubbers 3.53 workers/MW
coal, part of multiple unit, no scrubbers 2.97
coal, single unit, scrubbers U.23
coal, part of multiple unit, scrubbers 3.56
nuclear U.98
Ratios were also derived for computing operating work force requirements;
these are:
coal, scrubbers .21 workers/MW
coal, no scrubbers .12 workers/MW
nuclear .09 workers/MW
The exact methods and data sources used to derive these ratios is
included in a memo from S. Gordon dated June 19, 1979 included in this
report as Appendix B.
Construction schedules are included in the model for three types
of units: coal-fired, less than 1000 MW; coal-fired 1000 MW or greater;
and nuclear units. These schedules are listed on Table A-l.
The third set of implicit inputs concerns the breakdown of
construction requirements into skill categories. The percentage of
workers in each skill category is included in the model for nuclear
construction requirements and coal-fired construction requirements.
Eight skill categories are utilized for both types of plants: boiler-
makers, electricians, pipefitters, laborers, operating engineers,
carpenters, ironworkers and other skilled workers. The derivation of
the percentages and the data sources used are outlined in detail in
Appendix B. The percentages are listed on Table A-2.
Output from the model includes county tables, one for each county
hosting a planned power plant, and two tables for the ORBES region as a
65
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Table A-l
Construction Schedules Used in ORBES Labor Impact Model
Construction Percent of Total Work Force by Year
Unit Type Period 1234567
coal, < 1000 MW 5 yrs. 2.7 15.4 41.0 36.9 4.0
coal, >_ 1000 MW 6 yrs. 3.0 11.5 27.9 34.0 21.2 2.3
nuclear 7 yrs. 1.9 11.5 23.0 28.5 21.4 11.6 2.1
66
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Table A-2
Percentage of Workers in Eight Skill Categories
Nuclear and Coal-Fired Units
Skill Category Coal Nuclear
Boilermakers 16.6% 7.2
Pipefitters 16.9 28.7
Electricians 15.5 12.5
Laborers 12.1 17.4
Iron Workers 8.2 9.7
Carpenters 6.9 7.9
Operating Engineers 7.9 7.9
Other 15.9 8.7
67
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whole. All output lists the results for each year of the scenario. The
first two columns of the county tables list the construction workers
and operation workers required for each year of the scenario. For each
county, workers for all units concurrently under construction are summed
together for the annual listing. The same is done for concurrent
operating workers within the same county. Also listed on the county
tables are two columns of figures which indicate a) the number of
construction workers that are expected to migrate to the county, and
b) the total number of workers (construction and operation) that are
expected to migrate.
The regional tables produced by the model provide a) the total
number of construction workers required annually in the ORBES region,
b) the total number of workers required annually in the ORBES region,
and c) an annual breakdown of total construction workers by the eight
skill categories mentioned above.
To illustrate how the model works we have fabricated a two-county
region with planned generating units for a scenario lasting from 1980 to
1995. County I has two units planned: a nuclear unit to be on-line
in 1990 and a coal-fired unit to be on-line in 1988. County I is
within 50 miles of an SMSA. The characteristics of the planned units
are listed on Table A-3. County II, a rural county located more than
50 miles from an SMSA, has two units planned: two coal-fired units on
the same site with on-line dates of 1989 and 1992 respectively. Unit
characteristics are listed in Table A-3. Together with the unit
characteristics, we need to specify our migration assumptions for input
to the model. These assumptions are 5 percent for SMSA counties, 10
percent for those counties within 50 miles of an SMSA and 30 percent
for those outside this range.
The first step in the model is to compute the total number of
construction worker-years needed to complete the unit. The appropriate
worker-years per megawatt ratio and total worker-years for each unit
is listed on Table A-U. Also listed in the table are the ratios used
to compute total operation workers.
The next step is to allocate the total number of worker-years to
a schedule based on the specific unit's characteristics. Then the annual
requirements are summed to the county level and number of inmigrants are
computed using the assumptions as input to the model. For County I,
10 percent of the construction work force is assumed to move to the
county, 30 percent for County II. One hundred percent of operation
workers are assumed to be inmigrants. Construction and operation
worker requirements per unit, county sums and number of inmigrants are
shown on Table A-5. Notice that the seven-year schedule was used for
the nuclear unit, the six-year schedule for the coal unit which was
greater than 1000 MW and the five-year schedule for the two coal 800 MW
units.
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Table A-3
Planned Unit Characteristics for Fabricated Counties
County I County II
Type of Unit Nuclear Coal-Fired Coal-Fired I Coal Fired II
Size (MW) 1000 13000 . 800 800
On-line date 1990 1988 1989 1992
Multiple unit Single Single Unit 1 of plant Unit 2 of plant
status
Environmental No Scrubbers Scrubbers Scrubbers
vo controls
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Table A-4
Total Number of Worker-Years for Each Unit and
Ratios Used to Serve Them
County I
County II
Nuclear Coal Coal I Coal II
Construction worker
per MW ratio 1
Total no. of worker
years
Operation worker
per MW ratio
Total Number of
Operation worker years
4
4
.98
.980
.09
90
4.23
5499
.21
273
3.56
2949
.21
168
3.56
2848
.21
168
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Table A-5
Total Construction and Operation Worker Requirements for Each
Generating Unit 2nd County, Total Number of County in Migrants
County I
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
Nuclear
Construction
workers
95
573
1145
1419
1066
578
105
Operation
workers
90
90
90
90
90
90
Coal
Construction Operation
170
632
1534
1870
1166
126
273
273
273
273
273
273
273
273
Construction
170
727
2107 '
3015
2585
1192
578
105
Total County I
Operation
273
273
363
363
363
363
363
363
Inciigrants
17
73
211
302
259
119
331
284
363
363
363
363
363
363
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Table A-5 (Cont.)
County I
Nuclear
Coal
Total County I
Construction
workers
Operation
workers
Construction Operation Construction Operation Inmigrants
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
77
439
1168
1051
114
168
168
168
168
168
168
168
77
439
1168
1051
114
168
168
168
168
77
439
1168
1128
553
1168
1051
114
168
168
168
336
336
336
336
23
132
350
338
166
518
483
202
336
336
336
336
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The final computations in the model involve regional totals of
construction and operation workers and the breakdown of construction
requirements into skill categories. The original totals are simply the
sum of the county totals of construction and operation workers. These
totals are shown on Table A-6. In order to apply the percentages for
skiLL categories we need a breakdown of the regional total of construc-
tion workers into those working at nuclear unit sites and those working
at coal unit sites. This breakdown is also shown on Table A-6. The
appropriate skill percentages are applied to the coal and nuclear
construction requirements to yield the final table produced by the
model. This table is shown for our fabricated region as Table A-7.
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Table A-6
Regional Totals of Construction Requirements by Type of
Unit and Totals Operation Workers
Construction
Operation
Coal
Construction
Nuclear
Construction
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
170
111
2184
3454
3753
2320
1131
1273
1051
114
273
441
531
531
699
699
699
699
170
632
1611
2309
2334
1254
553
1168
1051
114
95
573
1145
1419
1066
578
105
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Table A-7
Regional Totals of Construction Workers Required by Skill Category
Iron Operation
Boilermakers Pipefitters Electricians Laborers Workers Carpenters Engineers
Other
1980
1981
1982
1983
1984
1985
1986
1987
11988
1989
1990
1991
1992
1993
1994
1995
28
112
308
465
489
285
134
202
174
19
29
134
436
719
801
518
259
227
177
19
26
110
322
501
539
327
158
194
163
18
21
93
295
478
529
337
168
159
127
14
14
61
188
300
329
206
101
106
86
9
12
52
156
249
273
171
84
89
73
8
13
58
172
272
296
183
90
100
83
9
27
108
306
467
494
292
138
195
167
18
-------
References
1) Tennessee Valley Authority, Regional Planning Staff, "Brown's
Ferry Nuclear Plant Construction Employment Impact, July,
1973" Knoxville, Tennessee, May, 1974.
2) Tennessee Valley Authority, Regional Planning Staff, "Cumberland
Steam Plant Construction Employment Impact, April, 1973."
Knoxville, Tennessee, July, 1973.
3) ORBES Siting Report, forthcoming.
4) Steven D. Jansen, Electrical Generating Unit Inventory 1976-1986.
Prepared for ORBES, Washington D.C.: uTs.E.P.A., November, 1978.
76
-------
APPENDIX B
A Classification of QRBES Counties for Potential Socioeconomic Impacts
Several studies performed a taxonomy or classification of
counties in order to forecast the potential socioeconomic impacts
associated with major new developments such as energy facilities. The
basic premises behind such a classification can be summarized as
follows :
1) Rural areas supply fewer services to their residents and/or
services of lower overall frequency or quality than do urban
areas . ' Rural areas also have a lower availability of
housing.
2) Rural areas tend to have less slack in their service
capacities than urban areas.
3) Rural areas have a smaller resident labor pool and fewer
skilled laborers than urban areas.
k) Labor demanded for energy facility construction and
operation is largely skilled, is concentrated in urban areas
and thus must migrate or commute to rural areas where such
projects are undertaken. This labor demands urban services.
5) The greatest potential impacts on service demands, housing,
local taxes and revenues, social structure etc. (i.e.
socioeconomic impacts) will occur in those areas that are
most rural, furthest from urban labor centers, provide the
fewest services, and have the smallest populations, and
available housing stock.
For very undeveloped areas of the country, almost «n of these
generalizations are true. However, ORBES is somewhat unique in that
its rural areas are often quite close to highly urbanized, manufactur-
ing oriented centers. In addition, many federal and state programs
have subsidized the replacement or development of many basic urban
services such as highways, sewer and water, health and social services,
housing rehabilitation, etc. These programs include those of U.S.E.P.A.,
the Appalachian Regional Commission, the Department of Housing and
Urban Development, and the Department of Health Education and Welfare
with their related, state counterparts. The result is that several
of the generalizations in the above list do not seem to hold across
the board. That is, not all services have capacity problems, not all
rural areas have housing shortages (in fact some urban areas have
worse such problems) etc.
77
-------
For these reasons, we feel that many of the attempts at class-
ifying counties based on potential socioeconomic impacts have general-
ized to the point of not being very useful. This chapter first reviews
some of these past attempts. We then go on to report our own attempt
at classification with an eye toward greater specificity.
Classification Efforts
It follows from the discussion above, that classification of
counties based on similarities in demographic, economic, and social
attributes will yield groups of counties with similar propensities to
be impacted. This type of classification work can be traced back to
so-called "urban ecology" studies undertaken by geographers, sociolo-
gists, and others in the 1960's and early 1970's. Brian Berry per-
formed many such factorial ecology or social area analysis studies.
Berry and Rees (l) utilized this approach to differentiate urban
subpopulations in Calcutta based on social rank, stage in the life
cycle, ethnic segregation, and other variables. Similarly Abler,
Adams and Gould classify households, housing, units, and urban census
tracts in American cities (2).
More recently, the same techniques have been utilized to classify
the nature of the environment and quality of life in major U.S. cities.
Urban Systems Research and Engineering (3) uses factor analysis to
group 262 SMSA's (Standard Metropolitan Statistical Areas) based on
200 variables measuring ambient environmental quality, urban form and
the physical environment, pollution residuals and demographic charac-
teristics. The method is used to identify representative cities to be
used for further study reflecting the characteristics of different
groups. Once the classification is completed, one implicitly notes
those areas where the environment is "bad" as reflected by the environ-
mental quality variables. What is good or bad is based somewhat on
scientific evidence of the health impacts of certain pollutants but
is also a matter of personal judgement.
If the variables selected for such a classification represent
some accepted measure of potential socioeconomic impact, then the
results could theoretically be applied to delineate areas where the
most adverse impacts might occur. Based on this premise, Argonne
National Laboratory used a classification scheme to group counties
where energy facilities might be sited (U). The variables chosen for
this analysis were:
1) The size and age/sex composition of the population
2) The population density of the county and surrounding areas.
3) The amount of service employment relative to basic (or
industrial) employment in the county.
h) The size and location of nearby regional trade centers.
One might note that these variables are attempts to measure
78
-------
potential impacts related to the basic premises of classification for
impact analysis cited above. The size, age/sex composition and
population density of the area all reflect the size of the local labor
market vis-a-vis workers for energy facility construction and operation.
The amount of service versus industrial employment attempts to measure
both the sensitivity of the area to increased demands on services and
to the direct economic impacts of energy facilities. Finally, the
size and location of nearby regional trade centers measures the
available labor market in the vicinity of the potential new energy
facilities. The closer the area to existing, large trade centers, the
fewer people that need to migrate into the impact county versus
commuting from their existing residence and therefore the lower the
potential adverse socioeconomic impacts. The further away or smaller
are such trade centers, the greater the number of immigrants making
demands on local services and housing.
In operation, ANL used the following variables in their analysis:
l) Population density at the time of impact;
2) Population density of the county and surrounding areas;
3) Distance in miles to the nearest regional trade center;
U) Relationship between basic and service employment.
The potential impacts of coal development on candidate counties was
derived from a classification based on these variables. A multivariate
Euclidean distance algorithm was used to put counties into one of three
groups. A "high probability of adverse socioeconomic impact from
energy development" is associated with the first group of counties
(30, p. 8-16). Less chance of adverse impacts is associated with the
second group of counties because they have moderate assimilative
capacities. The third group can accomodate large increases in coal
development without major impacts.
Table B-l shows the county groupings for those states studied
by ANL that are also in ORBES. These groupings will be compared later
to those derived by other means.
A parallel project by Oak Ridge National Laboratories took a
different approach to socioeconomic impact analysis. For their direct
impact assessment, Oak Ridge researchers took an approach similar to
ours as reported in previous chapters of this report. Using
assumptions related to power plant construction and operation work
force, mining employment, and proportions of workers that migrate into
the county, they calculated the population growth induced by energy
development. They then calculated the growth rate relative to the
base year population. As one of their indicators of socioeconomic
impact, they identified those counties with more than a 15$ growth rate
as having a high probability of social impact, 5% - 15% as a moderate
probability, and less than 5$>» as a low probability.
79
-------
Table B-l
County Potential Socioeconomic Impact
In Argonne and
Oak Ridge National Laboratory Studies
ILLINOIS
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
County
Bond
Bureau
Calhoun
Cuss
Christian
Clinton
Douglas
Edgar
Fayette
Franklin
Fulton
Gallatin
Greene
Grunely
Hamilton
Jackson
Jefferson
Jersey
Kankakee
ANL Group or ORNL
Service Base Index
High Impact
Moderate
High
High
Moderate
Moderate
. High
Moderate
High
Moderate
Moderate
High
High
Moderate
High
Low
Moderate
High
Low
80
-------
Table B-l (cont'd)
ILLINOIS - (cont'd)
County
20. Knox
21. LaSalle
22. Lawerence
23. Livingston
24. Macoupin
25. Madison
26. Marshall
27. Menard
2 8. Montgomery
29. Morgan
30. Peoria
31. Perry
32. Putnam
33. St. Clair
34. Saline
35. Sangamon
36. Shelby
37. Vermillion
38. Washington
39. White
40. Williamson
ANL Group or ORNL
Service Base Index
Low
Low
High
Moderate
Low
Low
High
High
Moderate
Moderate
Low
High
High
Low
Moderate
Low
Moderate
Low
High
High
Low
81
-------
OHIO
TableB-1 (cont'd)
County
1. Athens
2. Belmont
3. Brown
4. Carroll
5. Columbians
6. Coshocton
7. Gallia
8. Guernsey
9. Harrison
10. Hocking
11. Holmes
12. Jackson
13. Jefferson
14. Lawrence
15. Mahoning
16. Meigs
17. Miami
18. Monroe
19. Morgan
20. Muskingham
21. Noble
22. Perry
23. Pickaway
ANL Group or ORNL
Service Base Index
Low
Low
Moderate
Moderate
Low
Moderate
Moderate
Moderate
High
High
Moderate
Moderate
Low
Low
Low
High
Low
High
High
Low
High
Moderate
Moderate
82
-------
Table B-l (cont'd)
OHIO - (cont'd)
Coxlnty
24. Ross
25. Scioto
26. Stark
27. Tuscarawas
28. Vinton
29. Washington
30. Wayne
ANL Group or ORNL
Service Base Index
Low
Low
Low
Low
High
Low
Low
INDIANA
1. Allen
2. Clay
3. Elkhart
4. Floyd
5. Fountain
6. Franklin
7. Gibson
8. Greene
9. Harrison
10. Jasper
11. Knox
12. Morgan
13. Owen
14. Parke
83
Low
Moderate
Low
Low
High
High
Moderate
Moderate
High
High
Moderate
Low
High
High
-------
Table B-l (cont'd)
INDIANA - (cont'd)
County
15. Pike
16. Posey
17. Spencer
18. Starke
19. Sullivan
20. Switzerland
21. Vermillian
22. Vigo
23. Warrick
ANL Group or ORNL
Service Base Index
High
Moderate
High
High
High
High
High
Low
Moderate
KENTUCKY
1. Boone
2. Boyd
3. Breathkitt
4. Carroll
5. Elliott
6. Floyd
7. Hancock
8. Harlan
9. Hopkins
10. Knott
11. Leslie
12. Letchen
184
213
36
109
41
68
87
71
118
199
60
39
-------
Table B-l (cont'd)
KENTUCKY - (cont'd)
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
WEST
1.
2.
3.
4.
5.
6.
7.
8.
County
Lewis
Livingston
Martin
Mason
McLean
Meade
Mahlenberg
Ohio
Perry
Pike
Trimble
Union
Webster
VIRGINIA
Harbour
Boone
Braxton
Brooke
Clay
Fayette
Gilmer
Lewis
ANL Group or ORNL
Service Base Index
61
65 & 66
38
112
67
109
96
77
71
71
70
104
89
74
67
89
157
45
83
48
96
85
-------
Table B-l (cont'd)
WEST VIRGINIA - (cont'd)
County
9. Lincoln
10. Logan
11. McDowell
12. Marshall
13. Mason
14. Mingo
15. Nicholas
16. Pleasants
17. Pocahontas
18. Putnan
19. Raleigh
20. Tyler
21. Upshur
22. Webster
23. Wetzel
2 4. Wyoming
ANL Group or ORNL
Service Base Index
56
96
79
136 & 137
94
75 & 85
85
88
54
146
107
87
98
46
122
89
86
-------
Another OENL indicator of the amount of impact was derived by
calculating a service base index score relative to six socioeconomic
variables. This index was derived by first obtaining a weight for
each variable using a factor analysis of the variables on a sample
of 267 counties in their study region. The resulting weights are
really a classification of the "importance" of each variable in
explaining differences among the 267 counties. The index is
K
where
I. = the index value for the county, j = 1, ... 267
j
w. = the weight of the ith variable, where i ranges from 1-6
X.. = the level of the ith variable in the jth county
ij
X. = the mean or average level of its variable
Sd. = the standard deviation of the ith variable
K = a constant that is added to attach a certain level of the
index to a desired point of comparison. (5, p. 9-35, 9-36
As implemented, the index was set up such that the value would be
zero if all the X.. are zero and the value would be 135 if the value
of all the X.. equal the mean. The interpretation of the index is that
those counties with values below the mean have a relatively lower
ability to absorb growth. The variables used in the index are:
1970 population (xlO3)
percentage urban population, 1970
median family income, 1970
SMSA county (yes or no, 1 or 0)
Population density, 1970
retail wholesale service trade, 10 $ (1972).
Although the index is put forward as another indicator of
potential impacts, the authors caution that it is not a complete index
and thus should not be too heavily relied upon.
87
-------
Table B-l also indicates values of the service base index for
counties in the ORBES region studied by ORNL.
Classification of ORBES Candidate Counties
Rather than use only the four or six variables employed in
previous studies as measures of potential socioeconomic impact, we
have used 25 variables in five different categories as proxies for
potential socioeconomic impact. These are shown in Table B-2. The
reason for utilizing such a large number of variables is to attempt
to better measure the potential impact. We wish to avoid over-
generalization as much as possible. By employing a large number of
variables, there is a higher probability that we will include those
that are critical in each particular situation.
A two step statistical technique was used to classify the
candidate counties. In the first step, the variables are grouped
using factor analysis. This serves to create a new variable set,
called factors, which put the initial variables into groups with
similar characteristics. The results of this step yielded five new
factors which explained 90$ of the original variance. These factors
are uncorrelated, a prerequisite for the next step. Each of the
counties could now be represented by a set of factor scores showing
the relationship between each county and each factor.
In the second step of the analysis, the candidate counties were
placed in groups using a distance algorithm called H-group (32).
The final result was the placement of the candidate counties into
four groups.
Another statistical technique was used in order to test the
efficiency of the first method. Here, the original variables for all
candidate counties were input to a discriminant analysis program.
The discriminant analysis program derived three linear discriminant
functions (mathematically analagous to factor analysis) and tested
the ability of the functions to correctly classify the candidate
counties. Of the llU candidate counties, only seven were found to be
"incorrectly" classified. After changing these seven to the correct
group, the analysis was repeated resulting in discriminant functions
placing 96$ of the counties into the correct group.
Using either method of classification then, the vast majority of
candidate counties were placed into groups which represent their
difference with respect to the socioeconomic variables. Table B-2
shows the variables input for this analysis. Variables on population,
income, housing, employment, and natural resources were used in the
analysis. The percent land in forest variable did not seem to
differentiate any counties and so was dropped after the initial runs.
-------
The results of the overall analysis are shown in Table B-3 and
Figure B-l.
Table B-3 describes the size and content of each group. In
looking at these data, it becomes apparent that although the means of
each group are somewhat distinct across many variables, the ranges of
the groups yield overlaps among members of different groups. For
example, the percent older houses variable has a distinct, mean
difference across the groups with values of 62.9, 60.0 58.2, and kO.O
percent for groups 1 - U respectively. Initially, one would think
this indicates that the fewest older housing units lie in group 4 with
progressively more until one reaches group 1. This would then lead
one to conclude that the potential for housing problems vis-a-vis the
market ability to respond to sudden new demands, might be lower in
these counties. However, when we look at the range associated with
this variable for group members, we see that groups 1, 2, and 3 all
have some members with similar values of percent older homes.
Similar overlap problems occur with many variable as a result of
the averaging that takes place in the classification process. For
this reason, the classification does not yield a distinct set of
groups for which impact interpretations can be made. In order to
circumvent this problem, we reformulated the classification based on
four different groups of variables — population, housing, income,
and employment. The results are shown in table B-4 and figures B-2
through B-5. Table B-U shows the mean values for each variable using
each classification scheme. It is immediately apparent that major
differences in results are associated with the choice of classification
variables. A much more distinct pattern of differences occurs for each
group of variables when that group is used as the sole means of
classification. For example, the percent old housing variable has
means of 6^.8, 61.6, 5^.2, and ko.0% for groups 1-k respectively when
the classification is based on housing. The differences among groups
narrow when other variables are used in the classification — 62.6,
61.5> 58.^, and kk.9 when income variables are used; 62.5» 58.9» 58.0,
and 4o.O when population variables are used; 62.7, 60.1, 57.6, and
kO.O when employment variables are used. What these differences in
classification mean is that to the extent that these census variables
are proxies for potential impacts, some counties have different impact
potentials for housing, employment, income, and population.
Table B-5 shows our interpretation of these potential impacts for
the three major groups. Group k is almost always a set of large urban
counties where we would expect all of the socioeconomic impacts of
energy facility siting to be relatively insignificant. Looking at
table B-5 one can see that these are very distinct differences in the
potential impacts on the groups for different variables. For example,
group 1 counties are smallest in population and thus have the potential
for high impacts on population due to the siting of major energy
facilities. On the other hand, many of these rural counties also have
89
-------
Table 3-2
Variables Used in the Taxonony of Candidate Counties
Variable Type
Population
Income
Housing
Employment
Natural Resources
Variable
Total 1970 Population
Net Migration 1970-76
Total Urban Population
Population Density 1975
Median Family Income
% Families Below Poverty
Level
% Persons on Public
Assistance - Aid to
Dependent Children
% Persons on Public
Assistance - Old Age
Median Effective Buying
Income
% of Housing Units Built
before 1939
% of Housing Units Built
1960-70
% of Housing Units With
Public Water
% of Housing Units with
Public Sewer
% of Housing Units Vacant
Year Round
Total Housing Units
•J Housing Units Lacking
Some Plumbing
% Housing Units with 71.51
Persons per Room
Total Employment 1970
% Workers Bnployed in
Agriculture
% Workers Employed in
Services
% Workers Employed in
IVHrHng
% Workers Employed in
Manufacturing
% Workers Employed as
Craftsmen
% Land in Forest
Source
1970 Census
1970 Census and
Census Estimates
Derived from Census
Comments
Population Q-lh + 65
and Over Divided by
Population 15-6^
1970 Census
1975 Census Population
Estimates
1970 Census
City and County
Data Book, 1972
City and County
Data Book, 1972
Sales Management, 1975
1970 Census
1970 Census
Measure of
Housing Age
Measure of Service
Measure of Service
Measure of Vacancy
Measure of Housing
Quality
Measure of Crowding
1970 Census
1970 Census
1970 Census
1970 Census
1970 Census
1970 Census
90
-------
Table B-3
Descriptive Statistics on Groupings Derived Using All Variables
Variable
% Older Houses
% New Houses
°lo Houses Served by
Public Sewers
% Houses Vacant
% Lacking Some Plumbing 12.3
°lo Families Below Poverty 11.2
% Net Migration '70-'76 1.6
Dependency Ratio
Total Urban Population
(1000-s)
Total Population (10001
Median Family Income 81*63.0
Total Employment (1000
"lo Manufacturing Workers 2l*.0
% Agricultural Workers 15.3
% Mining Employees
Group
N=22
Mean
62.9
15.8
1*6.9
6.5
12.3
11.2
1.6
71.2
8.0
131.1
M53.0
111.1*
2l*.0
15.3
2.3
1
Range
26.2
10.6
58.5
10.2
26.6
11.1*
23.5
15.0
120.8
156.3
31*18. o
57.3
'28.3
2k. 6
. 11.9
Group
N-l*8
Mean.
60.0
16.0 .
38.0
8.2
18.2
15.6
3.5
68.2
18.7
1*9.1
7667.6
16.1*
32.0
5.3
k.k
2
Range
23.1
6U. 9
16.0
^7.0
30.5
35.8
U0.9
159.6
206.7
57^6.0
73.6
50.2
16.5
21*. 2
Group
N=l*2
Mean
58.2
19.2
1*7.8
7.2
15.2
12.3
2.2
68.3
16.2
3^.0
8099.3
12.3
32. k
8.2
1.9
3
Range
58.8
32.8
83.9
8.7
33.0
31.2
25.1
23.9
77.0
102.6
1*890.0
36.3
27.9
26.1*
11.3
Group
N=2
Mean
1*0.0
2l*. 5
85.2
U.8
3.k
8.6
-7.0
63.7
773.0
809.5
10153.0
3H.2
32.3
0.5
0.1
1*
Range
10.8
k.6
15.5
0.5
0.6
0.6
3.3
1.7
230.1
229.0
667.0
85.1
O.l*
0.1
0.1
Sources: 1970 Census of Population and Housing and
1976 Population Estimates of Bureau of the Census
-------
VQ
tV)
Table B-4
Group Statistics for Selected Variables Using Alternative Classification Schemes
Classification Based on Housing
Classification Based on Income
Variable
% Old Housing
% New Housing
% Housing Vacant
% Wet Migration '70-'76
Total Urban Population
(1000's)
Median Family Income
Total Employment (1000's) 11.4
% Manufac. Employees
% Agricultural Employees 11.3
Group
N=28
Mean
64.8
14.7
6.2
1.6
8.0
£0.
11.4
30.5
11.3
1 Group
N=46
Mean
61.6
15.4
8.7
3.4
15.4
7420.
14.7
30.4
8.6
2 Group
N=38
Mean
54.2
21.2
7.0
2.5
21.5
8328.
14.7
30.9
5.9
3 Group
N=2
Mean
40.0
24.4
4.8
-7.0
773.0
10153.
3H.2
32.3
0.5
4 Group 1
N=21
Mean
62.6
15.3
6.1
1.4
15.3
8481.
13.0
25.7
13.5
Group
N=44
Mean
61.5
15.6
8.2
2.8
15.1
7473.
14.8
31.2
5.7
2 Group
N=42
Mean
58.4
19.2
7.9
3.4
12.5
8023.
10.3
31.9
9.2
3 Grou]
N=7
Mean
44.9
22.3
3.9
-1.9
255.4
10112.0
117.4
34.5
2.3
-------
Table B-4 (Cont'd)
Classification Based on Population
Classification Based on Utaployment
Variable
% Old Housing
% New Housing
% Housing Vacant
% Net Migration '70-' 76
Total Urban Population
(1000' s)
Median Family Income
Total Employment (1000' s)
% Manufacturing Bnployment
% Agricultural Bnployees
Group 1
N=38
Mean
62.5
16.4
6.8
1.0
9.5
8359.
11.4
28.5
12.6
Group 2
N=47
Mean
58.9
16.6
8.0
3.2
21.6
7667.
17.7
31.8
4.4
Group 3
N=27
Mean
58.0
19.3
7.5
4.1
13.9
8015.
10.7
31.5
9.2
Group 4
N=2 .
Mean
40.0
24.5
4.8
-7.0
773.0
10152.
3H.2
32.3
0.5
Group 1
N=29
Mean
62.7
16.2
7.7
3.2
5.4
7498.
7.0
23.9
17.8
Group 2
N=46
Mean
60.1
16.4
7.3
2.0
24.3
8039.
19.2
33.5
3.4
Group 3 Group 4
N=37 N=2
Mean
57.6
18.9
7.5
3.1
13.0
8301.
12.7
32.2
7.1
Mean
40.0
24.5
4.8
-7.0
773.0
10152.
3H.2
32.3
0.5
-------
Table B-5
Description of the Classification of Candidate Counties
and Potential for Soc Loeconotnic Impacts
Variable 'type Group
Population
Housing
Income
Employment
3
1
3
1
Group Descriptions
Potential for
Impact
Smallest populations, density,
most rural
Largest populations, density,
most urban, lowest dependency ratio
Medium size, density, dependency
ratio
Fewest units, lowest vacancy, many
with public sewer, water
least crowded units
Largest # units, largest vacancy,
most crowding, fewer with public
sewer, water
Medium # units, vacancy, crowding,
most with public sewer, water
Fewest below poverty, largest median
income, largest buying income,
fewest old age on assistance,
largest ADC*
Highest families below poverty, High
lowest median income, lowest buying
income, medium # persons on public
assistance
Median income between year 2 & 3» Medium
families below poverty, ADC,
buying income, highest old age
public assistance
Most people in agriculture, lowest
workforce, lowest in manufacturing,
services, craftsman
Fewest in agriculture, most manu-
facturing, mining, total employees,
medium in services
Medium in agriculture, total
employees, craftsmen, manufacturing,
highest in services, lowest in mining
High
Low
Medium
Medium to High
Low
Medium to High
Low
Highest- Induced
migration but lowest
employment benefits
Lowest, induced
migration, highest
employment benefits
Medium
-------
FIGURE B-l
COUNTY IMPACT GROUPS USING
ALL VARIABLES
vo
VJl
GROUP 4
GROUPS
_ GROUP 2
G3 CROUP i
PREPARED FOR OHIO RIVES BASK ENERGY STUDY
BY CACIS/JCC. MARCH. 1980
-------
FIGURE B-2
COUNTY IMPACT GROUPS USING
POPULATION VARIABLES
GROUP*
GROUP 3
GROUP 2
GROUP t
MtPARED FOR OHIO RIVER BASK ENERGY STUDY
BY CAGIS/UKX. MARCH. t»W
-------
FIGURE B- 3
COUNTY IMPACT GROUPS USING
HOUSING VARIABLES
PREPARED FOR OHIO RIVER BASK ENERGY STUDY
8YCAQS/UCC. MARCH. I960
-------
FIGURE B-i|
COUNTY IMPACT GROUPS USING
INCOME VARIABLES
VQ
00
GROUP 4
GROUP 3
GROUP 2
GROUP 1
PREPARED FOR OHO RIVER BASM CNOCY STUDY
rr CActs/uicc. MARCH. I*M
-------
FIGURE B- 5
COUNTY IMPACT GROUPS USING
EMPLOYMENT VARIABLES
GROUP 4
GROUP 3
GROUP 2
GROUP 1
PREPARED FOR OHIO RIVER BASH ENERGY STUDY
BYCAOS/UICC. MARCH. 1980
-------
the fewest families below poverty level and largest median incomes
therefore making the income impacts (which might be considered positive)
lower in these counties.
It is useful to compare our results with those of ANL and ORNL.
This is shown in tables B-6 and B-7. Here, we have tabulated, for
those counties that both sets of projects evaluated, the amount of
agreement or disagreement among the classification. In table B-6,
we see that the level of agreement is poor for housing and income,
pretty good for population, and somewhat inbetween for employment
impacts. Argonne National Labs classified 13 counties that also
happen to be OKBES candidate counties in the high potential impact
category. Of these, only 2 were classified in the high category for
housing impact potential according to our classification. On the other
hand, 8 were put in the high impact category for population. Similar
conclusions can be drawn for moderate and low categories. Table B-7
shows similar comparisons to ORNL groupings based on their service
base index.
This analysis shows that the classification of a large number of
counties based on a small number of variables greatly oversimplifies
local conditions and probably gives an overgeneralized picture of
potential impacts. Even our classification, though more involved, has
a limited reliability since the variables used are not the only
potential measures of impacts but only a set which is readily available.
One must also recognize that these data are getting old being from the
1970 Census and that local conditions could have changed radically
since then.
In conclusion, we might recommend our own classification system
as a method of focusing on the first cut, general regional socio-
economic impacts of energy facility siting. More reliable, more
recent, and more detailed local data will still have to be used to
make accurate local impact assessments.
100
-------
Table B-6
Comparison for ORBES Impact Classifications
with ANL
ORBES County Impact Potentials
ANL Impacts
Level
High
Mode-
rate
Nunber
13
10
Housing
H M L
2 0 11
802
Income
H
5
If
M
' 6
5
L
2
1
Population
H M L
8
3
2
3
3
If
Employment
H M L
5
2
If
5
if
3
Low 11 506 533 22? 236
-------
Table B-7
Comparison of ORBES Impact Classifications
with ORML
ORBES County Impact Potentials
ORNL Impacts
Level Number
Housing
H M L
Income
H M L
Population
H M L
Employment
H M L
High 9 k 1 k 0 4 5 630 702
Moderate 4 0 0 k 0 k 0 211 121
Low 2 002 020 020 110
-------
Fips Code State
County
Table B-8
ORBES Candidate County Groupings
Group Using
All Variables Housing Income Population Employment
17039
17047
17057
17059
17073
17079
17099
17125
17131
17149
17153
17155
17167
17169
17171
Illinois DeWitt
11 Edwards
" Fulton
11 Gallatin
" Henry
11 Jasper
" La Salle
" Mason
" Mercer
Pike
11 Puluski
11 Putram
" Sangamon
" Schuyler
" Scott
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
3
1
1
1
1
1
1
2
1
3
1
1
1
1
1
2
1
4
1
1
1
1
2
1
4
1
1
1
1
1
1
1
1
1
3
1
1
1
1
1
1
1
3
1
3
1
1
1
3
1
1
1
1
1
1
1
1
-------
Tables-8 (conf d)
Fips Code State
County
All Variables Housing Income Population Employment
17191
17193
17199
17203
18025
18029
18043
18047
18051
18055
18061
18073
18077
18093
18115
18123
Illinois Wayne
" White
11 Williamson
11 Woodford
Indiana Crawford
" Dearborn
11 Floyd
" Franklin
" Gibson
11 Greene
" Harrison
" Jasper
" Jefferson
" Lawrence
11 Ohio
it Perry
1
1
3
1
2
3
3
2
3
3
2
1
3
3
3
3
1
1
3
1
2
1
3
2
3
3
2
2
3
3
3
3
1
1
2
1
3
2
1
3
1
3
3
1
2
3
3
2
1
1
3
1
2
3
3
1
1
3
2
1
3
3
3
2
1
1
3
3
3
2
3
1
3
2
3
1
3
2
3
3
-------
TableB-8 .(cont'd)
Fips Code State
18125
18129
18131
18147
18149
18153
6 18155
18173
18177
21005
21015
21023
21027
21037
21041
County
All Variables Housing Income Population Employment
Indiana
it
ii
ii
ii
M
It
II
II
Kentucky
ii
"
"
"
"
Pike
Posey
Puluski
Spencer
Starke
Sullivan
Switzerland
Warrick
Wayne
Anderson
Boone
Bracken
Breckinridge
Campbell
Carroll
2
1
1
3
2
2
1
3
1
3
3
3
3
3
3
2
1
1
3
2
2
1
3
1
3
3
3
3
3
3
2
3
2
1
2
2
2
3
3
3
3
2
2
3
3
3
1
3
3
2
3
3
3
1
3
3
3
3
3
3
3
1
1
1
1
1
3
2
1
3
3
3
2
3
3
2
3
1
1
3
2
1
2
3
3
3
1
1
2
3
-------
Table B-8(cont'd)
Fips Code State
County
All Variables Housing Income Population Employment
21077
21091
21103
2111
21135
21161
21163
21185
21223
21233
39001
39009
39013
39015
39025
Kentucky Gallatin
" Hancock
11 Henry
" Jefferson
11 Lewis
" Mason
" Meade
11 Oldham
" Trimble
" Webster
Ohio Adams
11 Athens
" Belmont
11 Brown
" Clermont
2
3
3
4
2
3
3
2
3
3
2
2
3
2
2
2
3
2
4
2
3
3
3
2
3
2
3
3
2
2
2
3
3
4
2
3
3
3
3
3
2
2
3
2
4
3
3
3
4
2
1
2
3
3
3
2
2
2
3
2
3
2
1
4
1
1
3
3
1
1
1
3
2
3
2
-------
Table B-8 (cont'd)
Fips Code State
County
All Variables Housing Income Population Employment
39025
39031
39033
39045
39047
39059
g 39061
39065
39067
39071
39075
39081
39083
39087
39097
39107
Ohio Clermont
" Coshocton
" Crawford
Fairfield
11 Fayette
11 Guernsey
11 Hamilton
" Hardin
11 Harrison
" Highland
" Holmes
" Jefferson
" Knox
" Lawrence
" Madison
" Mercer
2
3
3
3
3
3
4
3
2
3
3
2
3
2
3
3
2
1
3
3
1
3
4
1
2
2
2
1
1
2
3
1
4
3
2
3
3
3
4
3
3
3
2
4
3
2
2
3
2
3
1
3
1
2
4
1
2
1
1
2
1
2
1
1
2
3
3
3
3
3
4
3
2
3
1
2
3
2
1
3
-------
Table B-8 (cont'd)
Fips Code State
County
All Variables Housing Income Population Employment
39111
39115
39117
39119
39121
39127
g 39131
39145
39159
39165
39167
42005
42007
42019
42031
Ohio Monroe
" Morgan
" Morrow
" Muskingam
11 Noble
" Perry
lf Pike
11 Scioto
11 Union
" Warren
" Washington
Penn. Armstrong
" Beaver
" Butler
" Clarion
2
2
2
2
2
2
2
2
3
2
3
2
2
2
2
2
2
1
2
2
2
2
2
1
3
3
2
2
2
2
2
2
2
2
2
2
2
2
3
4
3
2
1
2
2
2
2
1
2
2
2
2
2
1
1
2
2
2
2
2
3
2
2
2
2
2
3
2
3
2
2
2
2
2
2
-------
Table B-8 (cont'd)
Fips Code State
County
All Variables Housing Income Population Employment
42033
42047
42059
42063
42065
42073
g 42085
to
42111
42121
42125
54009
54011
54019
54035
54053
Penn. Clearfield
" Elk
" Greene
" Indiana
" Jefferson
" Lawrence
" Mercer
11 Somerset
11 Venango
" Washington
W. Vir. Brooke
" Cabell
" Fayette
11 Jackson
" Mason
2
2
2
2
2
2
2
2
2
2
3
3
2
2
2
2
1
2
2
1
2
2
2
1
2
1
3
2
3
2
2
2
2
2
2
2
2
2
2
2
3
3
2
2
2
2
2
2
2
2
3
2
2
2
2
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
2
2
2
-------
Table B-8 (cont'd)
Fips Code State
County
All Variables Housing Income Population Employment
g
54059
54069
54073
54091
54095
54099
54103
54107
W. Vir.
it
tt
it
it
it
M
it
Mingo
Ohio
Pleasants
Taylor
Tyler
Wayne
Wetzel
Wood
2
3
2
3
2
2
2
3
3
3
3
2
3
2
3
3
2
1
2
2
3
2
3
3
2
3
2
2
2
2
2
2
2
3
2
2
2
3
2
2
-------
REFERENCES
APPENDIX B
(1) Brian J.L. Berry and Philip H. Rees, "The Factorial Ecology of
Calcutta," The American Journal of Sociology , Vol. 74,
No. 5, March 1969, PP.
(2) Ronald Abler, John S. Adams, and Peter Gould, Spatial Organization.
Englewood Cliffs, N.J.: Prentice Hall, 1971. Chapter 6.
(3) Urban Systems Research and Engineering. Classification of
American Cities for Case Study Analysis by Elizabeth Cole,
et al. Report for the Office of Research and Development,
U.S. EPA. Washington, B.C.: Urban Systems Research and
Engineering, July 1976.
(4) Argonne National Laboratory, An Integrated Assessment of Increased
Coal Use in the Midwest; Impacts and Constraints. Argonne,
INAL/AA-11 (draft report), October 1977.
(5) Oak Ridge National Laboratory, National Coal Utilization
Assessment. Oak Ridge, Tenn.: Oak Ridge National Laboratory,
October 1978.
(6) Veldman, O.J. (ed.), Fortran Programming for the Behavioral
Sciences. New York: Holt, Rinehart, and Winston, 1967.
Ill
-------
Appendix C
Memo from S. Gordon and A. Graham to Core and Management
teams concerning QRBES Labor Bnpact Model, June 19, 1979.
112
-------
The Ohio State University Department of City
and Regional Planning
289 Brown Hall
190 West 17th Avenue
Columbus, Ohio 43210
Phone 614 422-6046
June 19, 1979
MEMORANDUM
TO: ORBES Core and Management Teams
FROM: Steve Gordon and Anna Graham
SUBJECT: ORBES Labor Impact Model
I. Introduction
The purpose of this memo is to explicate the methods and
data sources used to develop the ORBES labor impact model and
to demonstrate how our manpower estimates compare with other
modeling efforts.
Our requests to the Advisory Committee for actual manpower
data were answered only by Jene L. Robinson of the Illihois Power
Company (abstracts of existing reports), Dana Limes of Columbus
and Southern Ohio Electric (portions of EIS's and Conesville
scrubber operation employment) and J.J. Albert of ECAR (man-years
per megawatt figures for four plants and other information - see
attached correspondence). Other sources of data used to develop
the labor impact model are:
.Environmental Reports
.Environmental Impact Statements
.Published Reports and Handbooks
.B. von Rabenau's ORBES Support Study (forthcoming)
.The Energy Supply Planning Model (ESPM), Bechtel Corp.
.Construction Manpower Demand System (CMOS), U.S. Dept.
of Labor
The complete data base with references is shown in Tables 1-3.
The data taken from ECAR, ESPM and CMOS were used to develop
our impact model and to compare with our model results. Specifically,
we have compared:
1) ECAR's estimates of man-year per megawatt of net
capability for scrubber and non-scrubber coal plants,
and nuclear plants with the estimates used in our model
for the same types of plants;
113
-------
Table 1
Available Data on Manpower Requirements for Coal-Fired Electric Power Plants
Plant Name Source
Conesvilie Limes (8)
East Bend 1 & 2 E1S (9)
Gavin Rabenau (26d)
Ghent 1 Rabenau (26c)
Ghent 2 Rabenau (26c)
Ghent 3 i 4 Rabenau (26c)
Klllen Rabenau (26b)
Merom Gordon and Darling (14)
£. New Haven FEIS (20)
Pleasants FEIS (11)
Rockport ER (1)
Seward 7 ER (13)
Spurlock 2 FEIS (12)
Trimble FEIS (23)
Notes: a. Nameplate MW and on-line dates for individual units taken from Electrical Generating Unit Inventory 1976-1986, by Steven D. Jansen for
ORBES, November 1978.
b. Total person-years was derived by multiplying the average number of workers per year times the construction period.
c. Total MW for this plant taken from Environmental Report for Seward Generating Station. Unit 7 by General Public Utilities Corporation,
October 1977.
imeplate
MWa
1995
1200
2600
550
550
1100
1200
980
1300
1252
2600
690C
500
2340
Number
Units
6
2
2
1
1
2
2
2
1
2
2
1
1
4
Years Lag
Time3
-
4
1
-
-
2
3
1
-
1
1
-
-
-
Scrubbers
part
no
no
no
no
no
no
yes
?
?
no
yes
yes
yes
Operation M
Total Person
412
80
150
120
150
140
335
245
350
an]
y
21
07
13
12
12
11
13
36
15
-------
Table 1 (continued)
Available Data on Manpower Requirements for Coal-Fired Electric Power Plants
Construction Manpower
Total Person yrs./MW
Year 1 Year 2 Year 3
Construction Schedule
Year 4 Year 5 Year 6 Year 7 Year 8 Year 9
Plant Name
Conesville
East Bend 1 and 2
Gavin
Ghent 1
Ghent 2
Ghent 3 and 4
Killen
Merom
New Haven
Pleasants
Rockport
Seward 7
Spurlock 2 1100b 2.20
Trimble
Notes: b. Total person-years was derived by multiplying the average number of workers per year times the construction period.
7139
1382
1103
2518
2530
2330
3016
4123b
8404
2.75
2.51
2,01
2.29
2.11
1.94
3.08
3.29
3.23
229
31
12
56
130
30
48
466
1215
190
65
307
300
75
400
756
2958
560
286
834
400
125
730
2225
2383
559
587
947
400
150
875
2988
354
42
153
365 9
400 400
350 450
825 138
1819 150
250 250
350 350
100
-------
Table 2 (part I)
Available Data on Manpower Requirements for Nuclear Electric Power Plants
Plant Name
Erieb
Limerick
Marble Hill
Susquehanna
ZionC
3-Mile Island
Plant Name
Erie
Limerick
Marble Hill
Susquehanna
Zion
3-Mile Island
Source
Ohio Edison (2)
Isard (15a)
Rabenau (26c)
PP&L (17)
Isard (15b)
Rabenau (26a)
Construction
Total
14764b
8810
8215
11950
6441°
13400
Nameplate
MW5
2400
2130
2260
2100
2196
1745
Manpower
PY/MW
6,15b
4.14
3.63
5,69
2.93C
7.68
Number Years Operation
Units Lag3 Total
2 2 253
2 2 125
2 2 155
2 2
2 1 186
2 4
Table 2 (part II)
Manpower
py/MW
.11
.06
.07
,08
Construction Schedule
Year 1 Year 2 Year 3 Year 4 Year 5
372 1693 2380 2615 2658
100 1100 2460 2500 1900
7 180 923 1820 2154
300 1800 2300 2500 2400
169 674 1174 1843 1363
600 1500 2500 2000 1500
Year 6 Year 7
2208 1967
600 150
1864 244
1500 800
1058 160
2000 1500
Year 8 Year 9
817 54
Year 10
250
900
100
500
400
Notes: a. (same as on Table 1)
b. Schedule figures and total person-years are yearly peaks and not averages.
c. An additional 20% manpower was added to the original manpower figures to account for supervisory personnel.
-------
Table 3
Data Available from ECAK, U.S. Dept. of Labor and Bechtel Corporation
ECAR
Plant A - 2 coal-fired units with scrubbers on a new site 4.0 person-years per net MW capacity
Plant B - 2 coal-fired units without scrubbers on a new site 3.23 person-years per net MW capacity
Plant C - 2 coal-fired units without scrubbers on existing site 2.17 person-years per net MW capacity
Plant D - 2 coal-fired units without scrubbers on existing site 2.72 person-years per net MW capacity
Plant E - 2 nuclear units on a new site 3.64 person-years per net MW capacity
CMOS, U.S. Department of Labor
1) 600 MW coal-fired plant with scrubbers 9.64 workhours per kilowatt (1977)
10.43 workhours per kilowatt (1981)
2) 600 MW coal-fired plant without scrubbers 7.99 workhours per kilowatt (1977)
8.64 workhours per kilowatt (1981)
3) 1243 MW coal-fired plant with scrubbers 8.10 workhours per kilowatt (1977)
8.76 workhours per kilowatt (1981)
4) 1243 MW coal-fired plant without scrubbers 6.73 workhours per kilowatt (1977)
7.28 workhours per kilowatt (1981)
ESPM. Bechtel Corporation
1) 800 MW coal-fired low Btu plant 5700 thousand workhours
2) 800 MW coal-fired high Btu plant 4800 thousand workhours
Sources: ECAR correspondence, March 26, 1971
U.S. Dept. of Labor, Forecasts of Cost. Duration and Manual Man-Hour Requirements for Construction of Electric
Generating Plants 1977-1981, Construction Manpower Demand System, January 1978.
Bechtel Corporation, Energy Supply Planning Model, Vol. I and II.
PB245 382, PB245 383, (Springfield, Va.: NTIS) 1975.
-------
MEMORANDUM
ORBES Core and Management Team
June 19, 1979
Page 2
2) ESPM's total work-hour estimates for an 800 MW coal
plant with model results for this size plant; and
3) CMDS's work-hour per kilowatt estimates for a 600
MW and 1243 MW plant with our model results.
These comparisons show that the ORBES labor impact model (with
regard to coal data) is fairly consistent with the ECAR data,
underestimates labor requirements based on the CMDS model, and
slightly underestimates manpower based on the ESPM. There are
several problems involved in making these comparisons due to
unknown assumptions concerning plant characteristics, the
incompatibility of some known characteristics, and the time frame
for which the manpower requirements in the other models were
derived. The basic data base used to derive our model labor
requirements are taken from ER's and EIS's - utility estimates of
construction labor demand. This may explain why the ECAR estimates
are closer to our model estimates than ESPM or CMDS. The utility
estimates of manpower requirements are consistently lower than
those of Bechtel (ESPM) or USDOL (CMDL). Our conclusion was that
the ORBES labor impact model underestimates labor requirements and
that it is necessary to increase the person-year per megawatt
estimates used in the model. This increase has been achieved by
averaging the model, CMDS and ESPM estimates.
II. Construction Manpower Requirements
The manpower required to construct an electric generating
power plant is a function of many factors. Some of these factors
are: the plant design, available infrastructure, transportation
access, size of the plant, pollution control equipment, water
supply and waste removal systems, labor and materials supply, and
any legal, political or social constraints. We have derived man-
power estimates that vary according to the type of plant (coal or
nuclear), the size of the plant (in megawatts), whether the plant
contains a single or multiple unit(s) (advantage of sharing costs
of site preparation, infrastructure, transportation, water supply
and waste removal systems), and the use of scrubbers. By averaging
across the schedules of plants on Tables 1 and 2, and by incorporating
some of the information provided on Table 3, we should be able to
average across all the plant designs and construction conditions
that are associated with these plants.
An estimate of person-years (py) per nameplate megawatt (MW)
was made for the following conditions:
Type 1. coal fired single unit no scrubbers
Type 2. coal fired multiple units no scrubbers
Type 3. coal fired single unit scrubbers
Type 4. coal fired multiple units scrubbers
Type 5. nuclear any number of units
118
-------
MEMORANDUM
ORBES Core and Management Team
June 19, 1979
Page 3
Although we suspect that the requirements for small units of
power stations (less than 400 MW) would be higher per megawatt
than the average-sized units (400 to 1000 MW), we have no evidence
that this is the case. There are no data available for these
units, and, therefore, the model does not take these variations
into account.
Coal Units Without Scrubbers
Data for a single unit coal-fired plant without scrubbers
were not available. However, we were able to determine, from the
information given as part of the Construction Manpower Demand System
(CMOS, see Table 3), that a 600 MW plant would require 19% more
manpower per megawatt than a 1243 MW plant. Assuming that the
1243 plant is a multiple unit plant and the 600 MW plant, a single
unit plant, we have applied the 19% increase to our estimate for
a multiple unit coal-fired plant without scrubbers. The basis for
these estimates are:
Rockport 2.23 py/MW (person-years per megawatt)
Killen 2.11 py/MW
Ghent 3&4 2.26 py/MW
Gavin 2.75 py/MW
The average ratio for these plants is 2.59 py/MW. The ratio we
will use for single unit plants is then 3.08 py/MW (or 2.59 X 1.19):
Type 1. coal fired single unit no scrubbers 3.08 py/MW
Type 2. coal fired multiple unit no scrubbers 2.59 py/MW
Coal Units With Scrubbers
Data on plants with scrubbers are also scarce. Our two
representative plants, Spurlock 2 (2.20 py/MW) and Merom (3.08
py/MW), are not consistent with our non-scrubber estimates because
they are too low. The CMOS data on Table 3 indicate a 20.3 to 20.7%
increase in manpower required for plants with scrubbers. Data from
ECAR can also be used to estimate this percentage increase. However,
because ECAR's py/MW figures are for net capacity rather than name-
plate, we must first convert their figures to be comparable with
ours. Data on Ghent units (non-scrubber) and Seward 7 (scrubbers)
will be used to determine the loss of capacity for these two types
of plants:
Ghent units-non-scrubber-gross rating 550 MW
net rating 525 MW
loss of capacity 5%
Seward 7-scrubber-gross rating 690 MW
net rating 625 MW
loss of capacity 9%
119
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MEMORANDUM
ORBES Core and Management Team
June 19, 1979
Page 4
ECAR's plant A (see Table 3), the scrubber plant, and plant B,
the non-scrubber plant, will be assumed to be 1200 MW gross rating.
By using the appropriate capacity loss figures above, plant A has
a net rating of 1092 and plant B, 1140 MW. The total manpower
required for each would be:
plant A 4.0 py/net MW * 1092 MW - 4368 py
plant B 3.23 py/net MW * 1140 MW - 3682 py.
To convert to a py/gross MW figure:
plant A 4368 py/1200 gross MW = 3.64 py/MW
plant B 3682 py/1200 gross MW = 3.07 py/MW.
Finally, the percentage increase in manpower requirements for plant
A over B (scrubbers over non-scrubbers) is 18.6%, very close to the
CMDS estimates of 20.3-20.7%. The average of these three figures,
19.9%, is used to compute the py/MW estimates for single and
multiple unit coal-fired plants with scrubbers:
Type 3. coal fired
Type 4. coal fired
single unit with scrubbers 3.69 py/MW
multiple unit with scrubbers 3.11 py/MW.
Nuclear Units
The nuclear manpower estimates were derived by averaging data
from four nuclear plants on Table 2:
Marble Hill
3 Mile Island
Susquehanna
Zion
3.63 py/MW
7.68 py/MW
5.69 py/MW
2.93 py/MW
4.98 py/MW.
Average
The ratio used in the ORBES labor impact model is therefore:
Type 5. nuclear units 4.98 py/MW
Comparisons with CMDS, EPSM and ECAR
Although we have no exact figures for the number of work hours
per person-year, we were able to compute an estimate of 1825 work
hours (wh) per person-year from data on the Erie plant. This is
equivalent to 36.5 hours per week for 50 weeks, which seems to be
reasonable, or at least in the ball park. Using 1825 wh/py as a
conversion factor we can compare EPSM's total manpower estimates
with our model estimates:
120
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MEMORANDUM
ORBES Core and Management Team
June 19, 1979
Page 5
ORBES Labor Impact Model
800 MW coal
non-scrubber
scrubber
non-scrubber
scrubber
EPSM
single
single
multiple
multiple
800 MW coal
low Btu 5,700,000 wh
high Btu 4,800,000 wh
2464 py
2952 py
2072 py
2488 py
3123 py
2630 py
3.08 py/MW
3.69 py/MW
2.59 py/MW
3.11 py/MW
3.90 py/MW
3.29 py/MW
The EPSM model estimates appear to be slightly higher than
ours. There may be several reasons for this:
1) our conversion factor was too low
2) the EPSM estimates are rounded to the nearest hundred
thousand worker hours which may indicate very rough estimates
and probably overestimates of labor requirements, and
3) the assumptions concerning plant characteristics are
not known and may be significant.
Using the same assumptions, we can compare CMOS estimates of
manpower requirements with the labor impact model results:
ORBES Labor Impact Model
600 MW coal
1243 MW coal
non-scrubber
scrubber
non-scrubber
scrubber
single
single
1848 py
2214 py
multiple 3219 py
multiple 3866 py
3.08 py/MW
3.69 py/MW
2.59 py/MW
3.11 py/MW
CMOS (1977)
600 MW coal
1243 MW coal
non-scrubber
scrubber
non-scrubber
scrubber
7.99 wh/kw
9.64 wh/kw
6.73 wh/kw
8.10 wh/kw
2628 py 4.38 py/MW
3168 py 5.28 py/MW
4587 py 3.69 py/MW
5517 py 4.44 py/MW
The CMDS estimates seem extremely high. Note, for instance, that
the only plants listed on Table 1 requiring greater than 4,000
person-years are Rockport and Gavin. These two plants are both 2600
MW plants, greater than twice the size of the 1243 MW plant above.
Thus, it appears that CMDS overestimates labor requirements. One
must consider the fact that the CMDS model is "forecasting" labor
requirements to 1977. The estimates of person-year per megawatt
used in the ORBES labor impact model are derived from actual and
expected manpower requirements for plants built between 1974 and
1999.
121
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MEMORANDUM
ORBES Core and Management Team
June 19, 1979
Page 6
According to the Construction Manpower Demand System, labor
requirements per megawatt are increasing with time. Ratios are
presented for two years, 1977 and 1981 (See Appendix). We do not
know if the manpower estimates reported by the utilities and used
to derive the ratios for the ORBES model were developed based on
current or projected requirements per megawatt. However, even
if we backfit the CMDS ratios to 1969 the results are still higher
than the ORBES model results, for example:
600 MW coal non-scrubber 3.67 py/MW
1243 MW coal non-scrubber 3.08 py/MW.
The ECAR data is presented in Table 3. These figures are for
2-unit plants, differentiated according to 'new1 or 'existing1 sites.
ECAR labor requirements are listed per megawatt net capability
rather than nameplate (as we have used in the ORBES labor impact
model). The difference between nameplate and net ratings was shown
in the previous section on scrubber plants. The ORBES labor impact
model differentiates between single unit plants and multiple unit
plants: - single unit plants are those that contain only one unit
and are on a site to themselves - a new site.
- multiple unit plants include all those units which are
on a site that is currently or will be used for additional units.
For the model, units are considered separately due to the wide
variation in lag time between units. The ECAR labor requirement
ratios for 2-unit plants on an existing site would be too low to
compare with ours directly and the labor requirement ratios for
new sites could be too high (some plants have more than 2 units
on a site). For comparison purposes we have listed the ECAR ratios
for nameplate megawatt ratings below:
ECAR
plant A coal fired 2-unit scrubbers new site 3.64 py/MW
plant B coal fired 2-unit no scrubbers new site 3.07 py/MW
plant C coal fired 2-unit no scrubbers existing site 2.06 py/MW
plant D coal fired 2-unit no scrubbers existing site 2.58 py/MW
The average ratio of plants B, C and D will be used to compare with
the averaged non-scrubber ratios in the ORBES model. This ECAR non-
scrubber average is 2.57 py/MW. Considering that the difference
between the ratios for a new and an existing site is approximately
24% (from ECAR data above), the contrived ratio for a scrubber plant
on an existing site would be 2.77 py/MW (76% of 3.64). The average
of the ECAR scrubber ratios is then 3.21 py/MW.
ECAR
1) coal-fired two-unit average non-scrubber ratio 2.57 py/MW
2) coal-fired two-unit average scrubber ratio 3.21 py/MW
122
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MEMORANDUM
ORBES Core and Management Team
June 19, 1979
Page 7
The comparable ORBES labor impact model averages are listed below:
ORBES Labor Impact Model
coal-fired
coal-fired
1) coal-fired
coal-fired
coal-fired
2) coal-fired
single unit
multiple unit
average
single unit
multiple unit
average
non-scrubbers
non-scrubbers
non-scrubber
scrubber
scrubber
scrubber
3.08 py/MW
2.59 py/MW
2.84 py/MW
3.69 py/MW
3.11 py/MW
3.40 py/MW
The labor impact model averages are slightly higher than those of
ECAR but they are quite close.
The ratio used in the ORBES model for nuclear units is 4.98
py/MW. ECAR's only example of nuclear plant has a ratio of 3.64.
The wide discrepancy here might be expected since the variation
between the ratios of plants used to compute the model ratio was ,
extremely great as well (2.93 to 7.68 py/MW). We have no other
comparisons for nuclear plants.
Conclusions
Both the CMDS and the ESPM manpower estimates for coal fired
plants are higher than those of ECAR or the ORBES impact model.
Both ECAR and the ORBES impact model estimates were derived primarily
or entirely from manpower data provided by utilities themselves. It
is hypothesized that utilities may be consistently underestimating
manpower requirements. We think it is necessary to revise our
model estimates for coal plants to account for this apparent bias in
our data. To do this we first computed a combined ratio for the
labor impact model, CMDS, ESPM, and CMDS plus ESPM: f.
model
3.08
3.69
2.59
3.11
com-
bined
ratio 3.12 py/MW
ESPM
3.90
3.29
CMDS
4.38
5.28
3.69
4.44
CMDS + ESPM
3.60
4.45
3.60 py/MW 4.45 py/MW
4.03 py/MW
The average of the combined ratios for the model (3.12) and CMDS +
ESPM (4.03) was 3.58 py/MW. This average is 14.6% higher than the
original combined ratio for the model so the components of the
combined ratio will be adjusted upward by this amount. Finally, the
ratios used in the ORBES labor impact model for coal-fired units
are:
123
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MEMORANDUM
ORBES Core and Management Team
June 19, 1979
Page 8
Type 1 single unit non-scrubber 3.53 py/MW
Type 2 multiple unit non-scrubbers 2.97 py/MW
Type 3 single unit scrubbers 4.23 py/MW
Type 4 multiple unit scrubbers 3.56 py/MW
Note that these figures are now comparable to those of the ESPM
and roughly halfway between those of CMDS and ECAR. The ratio
used in the ORBES model for nuclear units will remain the same
because it was decided that one comparison was not enough to
require revision. This ratio is:
Type 5 nuclear units
III. Construction Schedules
4.98 py/MW.
The length of time it takes to build a plant also varies,
not only because of plant characteristics but because of outside
influences such as labor and material supply, strikes, government
regulations or citizen opposition. The best we can do here is to
review our data base for appropriate construction periods. Units
of a plant are considered separately due to the variation in lag
time between construction of each individual unit (0-5 years).
The construction periods chosen are:
a) coal fired units
b) coal fired units
c) nuclear units
less than 1000 MW 5 years
1000 MW or more 6 years
all sizes 7 years
The distribution of person-years over the construction period
was derived by taking the average of the distributions of repres-
entative plants (see table 4).
Table 4
Distribution of Person-Years for Construction Periods of
Coal and Nuclear Power Plants
Construction
Period
5 years
6 years
7 years
Plant
Name
Ghent 1
Gavin
Average
Rockport
Ghent 3&4
Merom
Average
Limerick
Zion
Average
Percent of Total Workforce by Year
123456
40.4 3.0
33.4 5.0
36.9 4.0
2.2
3.2
2.7
5.5
2.2
1.6
3.1
1.1
2.6
1.9
13.7
17.0
15.4
9.0
12.2
13.2
11.5
12.5
10.5
11.5
40.5
41.4
41.0
26.5
33.1
24.2
27.9
27.9
18.2
23.0
35.5
37.6
29.0
34.0
21.6
14.5
27.3
21.2
1.8
0.4
4.6
2.3
28.4 21.6
28.6 21.2
28.5 21.4
6.8
16.4
11.6
1.7
2.5
2.1
124
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MEMORANDUM
ORBES Core and Management Team
June 19, 1979
Page 9
IV Operation and Maintenance Employment
The operation and maintenance employment is also derived by
using a ratio of person-years per megawatt. The ratio used for
all coal units without scrubbers is .12 py/MW, the average of
the following:
Rockport .13 py/MW
Killen .13 py/MW
Ghent 3&4 .09 py/MW.
For coal plants with scrubbers the ratio is .21 person-years per
megawatt, taken from the average of:
Seward 7 .36 py/MW
Trimble .15 py/MW
Merom .12 py/MW.
For purposes of comparison, Dana Limes of C&SOE provided us
with the operation manpower requirements for the scrubber system
of a unit at the Conesville plant. For a gross rating of 800
MW the scrubber system required approximately 19 operators per
shift and 13 administrative and maintenance personnel (not
including sludge stabilization personnel). This can be restated
as 70 person-years (assuming 3 shifts) or .09 py/MW.
In a report on FGD system costs by Battelle Columbus
Laboratories (6, p. 76), Louisville Gas and Electric data for the
Cane Run plant show that 1.5 persons per shift per 100 MW of
scrubber capacity is needed for operation of its scrubber, excluding
supervisors and lime unloading. So, at a minimum, 4.5 workers
per 100 MW or .045 py/MW are required to operate the scrubber
system of the plant for three shifts a day.
Manpower requirements for operation of a scrubber system
will vary with the type of system, the amount of scrubber material
required, the sludge or waste disposal methods utilized, etc.
Since our scenarios do not specify the exact scrubber methods to
be used in the plants, an average figure will be sufficient. The
C&SOE and Battelle data indicate that at least .045 to .09 py/MW
is needed to run a scrubber system. Our average of .21 py/MW for
the total operation workforce of a scrubber plant is .09 py/MW
greater than the ratio used for non-scrubber plants (.12 py/MW).
The ratio used in the labor impact modeL. therefore, appears to
be reasonable.
The ratio used for nuclear plants is the average of:
Marble Hill .07 py/MW
Erie .11 py/MW
Average .09 py/MW.
To summarize, three ratios were estimated for operation
125
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MEMORANDUM
ORBES Core and Management Team
June 19, 1979
Page 10
and maintenance personnel requirements:
1) coal-fired no scrubbers .12 py/MW
2) coal-fired scrubbers .21 py/MW
3) nuclear .09 py/MW
V. Construction Skill Requirements
The labor impact model, in addition to estimating the total
manpower requirements for power plant construction, also provides
an estimate of the regional labor demand by skill for each year
of the scenario. Seven skill categories (plus the category 'other')
were chosen for this purpose. The percentage of total workforce
that each skill represents is shown on Table 5. The skill break-
down for coal units was taken from data on the Gavin plant (25d)
and from ECAR (correspondence attached); for nuclear units, the
Zion plant data was used (15b).
Table 5
Skill Categories for Coal and Nuclear Power Plants
As a Percent of Total Workforce
Skill
Category Coal Nuclear
Boilermakers 16.6% 7.2%
Pipefitters 16.9 28.7
Electricians 15.5 12.5
Laborers 12.1 17.4
Iron Workers 8.2 9.7
Carpenters 6.9 7.9
Operating Engineers 7.9 7.9
Other 15.9 8.7
Total 100.0% 100.0%
VI. Summary
To summarize we have put together several tables showing
the ORBES labor impact model results when applied to the ORBES
"standard1 units of a coal or nuclear plant. There are five
tables, one for each of the following conditions:
Table 6 Type 1. coal fired single unit no scrubbers 650 MW
Table 7 Type 2. coal fired multiple unit no. scrubbers 650 MW
Table 8 Type 3. coal fired single unit scrubbers 650 MW
Table 9 Type 4. coal fired multiple unit scrubbers 650 MW
Table 10 Typo 5. nuclear single unit 1000 MW
SG/AG/br
cc: Owen Lentz and J.J. Albert (ECAR), Dana Limes (C&SOE), Dane
Mazzitti (AEP), John Barcalow and Jene L. Robinson (Illinois Power Co.)
encl. 126
-------
11.
Table 6
Type 1. Coal-fired, Single Unit, Non-scrubber, 650 MW
Total Manpower Requirements:
3.53 py/MW * 650 MW = 2295 py
Construction Schedule:
Year 1 Year 2 Year 3 Year 4 Year 5
62 353 941 847 92
Operation and Maintenance Manpower:
.12 py/MW * 650 MW = 78 py
Construction Skill Requirements
Boilermakers 381
Pipefitters 388
Electricians 356
Laborers 278
Iron Workers '188
Carpenters 158
Operating Engineers 181
Other 365
Total 2295
12?
-------
12.
Table 7
Type 2. Coal-fired, Multiple Unit Plant, No Scrubbers, 650 MW
Total Manpower Requirements:
2.97 py/MW * 650 MW = 1931 py
Construction Schedule:
Year 1 Year 2 Year 3 Year 4 Year 5
52 297 792 713 77
Operation and Maintenance Manpower:
.12 py/MW * 650 MW = 78 py
Construction Skills:
Boilermakers 321
Pipefitters 326
Electricians 299
Laborers 234
Iron Workers 158
Carpenters 133
Operating Engineers 153
Other 307
128
-------
13,
Table 8
Type 3. Coal-fired, Single Unit, Scrubbers, 650 MW
Total Manpower Requirements:
4.23 py/MW * 650 MW = 2750 py
Construction Schedule:
Year 1 Year 2 Year 3 Year 4 Year 5
74 424 1127 1015 110
Operation and Maintenance Manpower:
.21 py/MW * 650 MW = 137 py
Construction Skills:
Boilermakers 457
Pipefitters 465
Electricians 426
Laborers 333
Iron Workers 226
Carpenters 190
Operating Engineers 217
Other 436
129
-------
14,
Table 9
Type 4. Coal-fired, Multiple Unit Plant, Scrubbers, 650 MW
Total Manpower Requirements:
3.56 py/MW * 650 MW = 2314 py
Construction Schedule:
Year 1 Year 2 Year 3 Year 4 Year 5
62 356 949 854 93
Operation and Maintenance Manpower:
.21 py/MW * 650 MW = 137 py
Construction Skills:
Boilermakers 384
Pipefitters 391
Electricians 359
Laborers 280
Iron Workers 190
Carpenters 160
Operating Engineers 183
Other 367
130
-------
Table 10
Type 5. Nuclear, 1000 MW
Total Manpower Requirements:
4.98 py/MW * 1000 MW = 4980 py
Construction Schedule:
Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7
95 . 573 1145 1419 1066 578 104
Operation and Maintenance Manpower:
.09 py/MW * 1000 MW = 90 py
Construction Skills:
Boilermakers 359
Pipefitters 1429
Electricians 623
Laborers 867
Iron Workers 483
Carpenters 393
Operating Engineers 393
Other 433
131
-------
References
( 1) American Electric Power Service Corporation and Indiana and Michigan
Electric Company
"Environmental Information Report for a New Fossil Fuel
Power Plant Near Rockport", n.d.
(2) "Application for Erie Nuclear Plant, Units 1 and 2" submitted
by Ohio Edison Company to the Ohio Power Siting Commission, n.d.
( 3) Bailey, R.E. and J.J. Ziff
"An Estimation of the Local Tax and Labor Impact from
the Construction and Operation of a 1100 Megawatt Electrical
Power Plant (PWR)", unpublished paper, Nuclear Engineering
Department and the Cooperative Extension Service of Purdue
University, March 1974.
(4) Bechtel Corporation
Energy Supply Planning Model Vol. I and II,
PB 245 382, PB 245 383, Springfield Va.: NTIS, 1975.
(5) Berkshire County Regional Planning Commission
Evaluation of Power Facilities; A Reviewer's Handbook
PB 239 221, Springfield Va.: NTIS, April 1974.
(6) Bloom, S.G.; H.S. Rosenberg; D.W. Hissong and J.H. Oxley
Analysis of Variations in Costs of FGD Systems, Final
Report, Battelle Columbus Laboratories, October 1978.
( 7) Communications with J.J. Albert, ECAR.
(8) Communications with Dana Limes, C&SOE.
(9) Environmental Impact Statement for East Bend Generating Station,
Units 1 and 2.
Q.O) Final Environmental Impact Statement for Killen Electric
Generating Station, Units 1 and 2, Dayton Power and Light Company,
August 1977.
(11) Final Environmental Impact Statement for Pleasants Power Station,
Units 1 and 2, Willow Island, Pleasants County, West Virginia,
Allegheny Power System, January 1975.
(12) Final Environmental Impact Statement for Spurlock Station, Unit 2.
(13) General Public Utilities Corporation
Environmental Report for Seward Generating Station, Unit 7,
Volumes I-III, October 1977.
(14) Gordon, I. and D. Darling
The Economic Impact of the Hoosier Energy Plant on Sullivan
County, Indiana. CES Paper No. 14, West Lafayette Indiana:
Cooperative Extension Service, Purdue University, November,
1976.
132
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References (continued)
(L5) Isard, W. ; T. Reiner; R. Van Zele and J. Stratham
Regional Economic Impacts of Nuclear Power Plants
National Center for Analysis of Energy Systems, Brookhaven
National Laboratory Associated Universities, Inc. BNL
50562, Springfield, Va.: NTIS, December, 1976.
:' a) Environmental Impact Statement for the 2-reactor plant
at Limerick,
b) Alice W. Shurcliff, "Local Economic Impact of Nuclear
Power Plants," November 1975.
CL6) Jansen, Steven D.
Electrical Generating Unit Inventory 1976-1986. for ORBES,
November 1978.
(17) Pennsylvania Power and Light Company
"A Monitoring Study of Community Impacts for the
Susquehanna Steam Electric Station", June 1976.
(18) Purdy, B.J.; E. Peelle; B.H. Bronfman and D.J. Bjornstad
A Power Licensing Study of Community Effects at Two
Operating Nuclear Power Plants Final Report Oak Ridge
National Laboratory for the U.S. Nuclear Regulatory
Commission, ORNL/NUREG/TM-22, September 1977.
(19) Stenehjem, E.J. and J.E. Metzger
A Framework for Projecting Employment and Population
Changes Accompanying Energy Development Phase I Argonne
National Laboratory; Argonne Illinois, August 1976.
a) Environmental Report, Susquehanna Steam Electric
Station, Pennsylvania Power and Light Company
(20) U.S. Army, Engineering Division
Final Environmental Impact Statement Project 1301
New Power Plant on the Ohio River, New Haven, West
Virginia, March 1977.
(21) U.S. Atomic Energy Commission, Directorate of Licensing
Final Environmental Statement related to the Beaver
Valley Power Station, Unit 1, July 1973.
(22) U.S. Department of Labor
Forecasts of Cost, Duration and Manual Man-Hour
Requirements for Construction of Electric Generating
Plants 1977-1981, Construction Manpower Demand System,
Employment Standards Administration, January 1978.
133
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References (continued)
(23) U.S. Environmental Protection Agency, Region IV
Final Environmental Impact Statement for Proposed
Issuance of a New Source National Pollutant Discharge
Elimination System Permit to Louisville Gas and Electric
Company, Trimble County Generating Station, Trimble
County, Kentucky, EPA 904/9-78-017, October 1978.
( 24) U.S. Environmental Protection Agency, Region IV
Final Environmental Impact Statement for Kentucky
Utilities Company, Ghent Generating Station, Units 3
and 4, Ghent Kentucky, EPA 904/9-78-014, July 1978.
( 25) U.S. Nuclear Regulatory Commission, Office of Nuclear Reactor
Regulation
Draft Environmental Statement Related to Construction
of Marble Hill Nuclear Generating Station Units 1 & 2,
Public Service of Indiana, NUREG-0048, March 1976.
( 26) von Rabenau, B.
Preliminary draft of Chapter II, "Scheduling of Construction
and Operations Labor Force for Energy-Related Facilities,"
of forthcoming support study for ORBES entitled: Induced
Migration and Labor Force Impacts of Energy Facility
Development in the ORBES Region, 1979.
a) Communications with G.J. Truffer, Metro Edison Company.
b) Communications with W.H. Bush, Dayton Power & Light Co.
c) Communications with R.M. Whinston, Kentucky Utilities.
d) Communications with W.J. Hardman, Ohio Power Company.
e) Communications with D.L. Oder, Public Service of Indiana,
-------
Appendix D
Materials on Other Labor Impact Models
135
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OWKN LENTZ, Kn-rutii'v Managtr
EXECUTIVE OFFICE: P O BOX 1O2. CANTON. OHIO 447O1
PHONE (216) 456-2488
May 7, 1979
Mr. Steve Gordon
Ohio State University
Department of City 5 Regional Planning
289 Brown Hall
190 West 17th Avenue
Columbus, Ohio 43210
Dear Steve:
This is in response to your letter of April 2, 1979.
First let me say that my comment that there were no "real life"
equivalents in ECAR must be viewed in its proper perspective.
I initiated my data gathering effort following your request for a
review of the information contained in Table IV of your May 22,
1978, memorandum entitled, "Analysis of The Impacts of the NEP
Scenario." My comment applied to the 1,000 MW unit size which
was selected for that particular analysis and was not meant to
reflect on current ORBES scenarios. You may recall that when I
contacted you in early June 1978 for additional information on the
data sources used for your scenario, you indicated that precise
construction manpower figures would have little if any impact on
your results. Thus, I did not feel that there was any urgent
need for the information which I was attempting to develop.
The information which you forwarded on June 14, 1978
identified the sources for the alternative plant schedules used
in your analysis, although it did not identify which source went
with which plant development. All of the sources identified in
your memorandum were not available to me, but I was successful in
obtaining the information provided by Mr. R. M. Winston, Jr. of
Kentucky Utilities with respect to the Ghent Plant. As noted in
my March 26 letter, it appeared that the data set which you dev-
eloped for the Ghent station was based on the unit gross rating of
556 MW rather than the 525 MW net rating.
Answers to the specific questions raised in your April 2,
1979, letter are as follows:
1. The figures which I provided are based on the total
net rating for the two-unit developments. As noted during
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136
-------
Steve Gordon
May 7, 1979
Page Two
our recent telephone conversation, you should expect a minimum
difference of five percent in the net rating for two otherwise
identical units when one unit is equipped with a scrubber and
the other is not. In addition, the unit with the scrubber may
require certain other facilities that are unique to the site.
This could include, for example, facilities for the unloading,
storing, and handling of limestone, as well as special sludge
handling facilities. The auxiliary power requirements for
these facilities at some sites may be substantial.
2. When I undertook this task, it was my intent to deter-
mine whether or not the numbers which appeared in your May 22,
1978, memorandum were reasonable. As such, I did not consider
a plot of the manpower requirements during the very early
stages and the final stages of construction as being particularly
significant for my purposes. Thus the graph which I provided
was intended to illustrate significant differences during the
construction period and did not indicate some of the early work
at new site developments where very few construction workers
were involved. I took this liberty because comparable data
was not specifically identified at the existing site developments
although I was assured that it was reflected in the total man-
power figures. Thus, your original information on construction
periods was correct.
3. I do not have any data for single unit plants, nor do
I have any information on units in the 100 to 400 MW size
range. It would be reasonable to expect, however, that the
man-years per megawatt for the smaller units would be somewhat
higher than that shown for the larger size units.
4. Plant A is the only unit equipped with scrubbers.
Plants B, C, D, and E do not have scrubbers
5. The only plant for which information was available with
respect to craft breakdowns was Plant D. Therefore, I can only
speculate about the breakdown for the other plants in this
sample. I can say, however, that the craft requirements for a
particular plant are a function of the plant design. A plant
which utilizes steam-driven boiler feed pumps would require
more boiler makers and pipe fitters than would a plant which
utilizes electric motor driven boiler feed pumps. The latter
plant, in turn, would have a greater requirement for electricians
than for boiler makers and pipefitters. I feel confident that
the differences which you noted can be attributed to such factors.
Plants are different and you should expect that the craft require-
ments will also be different.
Very truly yours,
'A'
Albert
Staff Engineer
JJArdlw
cc: J. J. Stukel, ORBES Project Office
-------
The Ohio Start* University Department of CNy
and Regional Planning
289 Brown Hall
190 West 17th Avenue
Columbus, Ohio 43210
Phone 614 422-6046
April 2, 1979
J.J. Albert
Staff Engineer
ECAR
P.O. Box 102
Canton, Ohio 44701
Dear Mr. Albert:
Thank you for your letter of March 26 detailing the manpower
requirements for power plant construction. We have a number of
questions regarding these data. First, we question your
assumption that the plants envisioned in the ORBES scenarios
have no "real life" equivalents in ECAR. In reviewing the data
you have supplied in the context of the ORBES generic plants,
it would appear that they are indeed extremely similar. Can
you explain in more detail why you feel that our scenarios are
not representative? We have attached a description of our
generic plants.
Several other more specific questions arise in reviewing
your figures:
1) Plant A and B are similar plants with the major
difference that plant A has scrubbers and plant B does
not and that plant B has four additional months between
on-line dates. Your figures indicate a 23.8* increase
in manpower/MW for plant A, Our contact with Columbus
and Southern Ohio Electric and review of impact statements
shows this to be a quite a bit larger difference than we
would have expected. Can you give us a better idea where
these figures were derived, their degree < r reliability,
and any potential sources of difference between plants
leading to a range of differences around your figures?
2) Though we do not know the total megawatt size of
the plants used for deriving your figures (this would, by
the way, be quite helpful) we assume that they are large
units (approx. 400-800 MW each). The construction periods
as noted on your graph show, for coal-fired two unit plants,
a construction period of 19-22 quarters, 57-66 months or
approximately 5-5 1/2 years. Thus, construction period
for a single unit plant would be in the neighborhood of
4 years. Can we assume these are correct? Analysis of
our generating unit inventory, reviewed by each utility
138
-------
J.J. Albert
April 2, 1979
Page 2
in ORBES, shows that many units required a five year period
for a single unit and 6 years for a two unit plant. This
is illustrated by Attachment 1. Please comment on the
relationship between these data and your own.
3) Do you have any data for smaller single unit plants
(100-400 MW in size)? Are the man years/MW required
higher for these smaller units than for the average
sized unit (i.e. 401-800 MW)?
4) Should we assume that Plants C and D have no scrubbers?
5) Your craft breakdown data differ slightly from those of
two other plants for boiler makers and electricians. This
is shown in Attachment 2. What are the possible reasons for
these differences? Is it because Plant D was built on an
existing site? Would there be any but minor differences
in the distribution of crafts for your other example plants?
We would appreciate a prompt reply to these questions
that we may incorporate the data you have supplied into ou
so
our analysis
Sincerely,
Steven I. Gordon
Asst. Professor
SIG/br
encl.
cc: J.J. Stukel
Owen Lentz
139
-------
Attachment 1
Coal-Fired Plants Reviewed
Plant
Name
Cheswick
Killen
Mont.our
Ghent 1
Ghent 2
Ghent 3 6
Gavin
Merom
Spurlock
New Haven
Pleasants
Patriot
#Units
1
2
2
1
1
4 2
2
2
#2 1
1
2
2
Interval
(years)
-
3
1
-
-
2
1
1
-
-
1
?
Construction
Period (Years)
5
8-9
6
5
5
6-7
5
5-6
4 1/2
4 1/2
7
9-10
Scrubbers
no?
no
no?
no
no
no
?
yes
7
no
?
yes
Nameplate
MW
570
1200
1625
550
550
1100
2600
980
500
1300
1252
1300
Source
• a
a.b
a
a,b
a,b
a,b
a
b,c
b
d
b
a,c
Sources: a Preliminary data collected by B. v. Rabenau for ORBES Support
Study on Induced Migration.
b Final Environmental Impact Statements.
c John Gordon and David Darling, The Economic Impact of the
Hoosier Energy Plant on Sullivan County, Indiana.CES Paper
No.14, November,1976,Purdue University.
d Draft Environmental Impact Statement
e Steven D. Jansen, Electrical Generating Unit Inventory
1976-1986 Ohio River Basin Energy Study Re£ion Phase II
March, 1978, Preliminary Report"
-------
Attachment 2
Boilermakers and Electricians as a Percentage
of Construction Workforce
Boilermakers Electricians
19% 14%
14.9% 18.8%
14.0% 18.2%
Source: B. v. Rabenau, "Chapter II - Scheduling of Construction and
Operations Labor Force for Energy-Related Facilities" of ORBES
Support Study still in progress.
-------
ORBES Phase II Standard Units
Coal Fired Unit
--650 MWe installed capacity
--198 meter (650 foot) stack height
--30.47 meters per second (100 feet per second) exit
velocity
--338 K (65 C, 150 F) exit gas temperature
--7.8 meter (25.6 foot) stack diameter
--10,200 Btu per kilowatt hour heat rate
--if 2 units, a common stack is used
--1.2 pounds of S02 per 1,000,000 Btu (for siting purposes)
--0.1 pounds of particulates per 1,000,000 Btu (for siting
purposes)
Nuclear-Fueled Unit
--1,000 MWe installed capacity
--both pressurized and boiler water reactors will be
considered in a ratio of nine to one
--material and requirements as specified in the
Teknekron standard plants handed out at the Core
Team meeting of 5/4-5/78 (Nashville); this includes
major raw materials input, major finished product
output and air, water and solid wastes
--in conformancc with existing regulatory constraints
Source: Minutes of Core Team Meeting, Columbus, Ohio January 4-5, 1979
-------
AR
OWEN LENTZ, Executive Manager
EXECUTIVE OFFICE: P. O. BOX IO2. CANTON. OHIO 447O1
PHONE (216) 456-2488
March 26, 1979
Mr. Steve Gordon
Ohio State University
Department of City § Regional Planning
289 Brown Hall
190 West 17th Avenue
Columbus, Ohio 43210
Dear Steve:
I contacted a number of utilities in the ECAR region,
per your request, to obtain information that would be suitable
for developing realistic construction manpower estimates for
the Ohio River Basin Energy Study CORBES). It was obvious
that there have been no "real life" plant developments in ECAR
of the type envisioned in the ORBES scenarios so I was forced
to concentrate my efforts on obtaining representative data
that had been reduced to a common base so that significant
differences could be readily identified. The information
deemed suitable for this purpose was obtained from various
sources within the ECAR member systems. It was necessary to
supplement the initial data response in order to assure a
uniform base and to verify the significant differences.
I also reviewed the information which you included in
your memorandum dated June 14, 1978. It appears that the
manpower rate that you developed from the data provided for
the Ghent Station of Kentucky Utilities is based on the unit
gross rating of 556 MW. ECAR records show that the net dem-
onstrated rating for the first two units at Ghent is 525 MW
each. Since the electrical requirement for plant auxiliary
equipment is charged to the plant operation and since the elec-
trical demand for these auxiliaries is a function of the plant
design, the difference between net and gross ratings is variable
and can be significant. All of the construction manpower figures
which I have developed are based on the net rating.
The information that was available for this analysis
was for two-unit plant developments and each of these develop-
ments had significant differences in terms of the facilities
provided. The results have been summarized as follows:
MEMBERS OF EAST CENTRAL AREA RELIABILITY COORDINATION AGREEMENT
App.il.ichi.iii Power Company The Cincinnati r,as «, Elnclric Company . The Clevrl.md Klertnr. Illummahnr. Company ColurnK. ,
SoullH'in Ohm I'lec.lrir. Company Consumer; Power Company • The Dayton Power & Until Company Thr Dolroil Cclison rjomn an
Duiiuesne Lir.hl Comp.my \ asl Kentucky Rural I lectric: Cooperative Indiana «. Michigan I'li-rlni Company Indiana Kenlui ^.. p,, ly
Corporation Indianapolis rower «. I ir.'il Company Kentucky rower Company Kentucky utilities Company Louisville Gas .-' fi
Company Mononi;ahela l-ower Company Northern Indiana Piihhr Service Company Ohio Edison Company nll'ii
Company • Ohio Valley Electric Corporation Pennsylvania Power Company The I'olomac Edison Company i i
Company ol Indiana, Inc. • Southern Indiana das and Electric Company • The Toledo Edison Company Webl " „ ,
-i it o . rcnn HOW
-------
March 23, 1979
Page Two
Plant A - 4.0 construction man-years per megawatt of net capability.
Two coal-fired units at a new site with 12-month interval
between operating dates of the units. These units are
equipped with cooling towers and scrubbers. Facilities
which must be provided at a new site include such items
as coal unloading, coal handling, water intake structures,
ash and sludge disposal areas, potable water supply,
sanitary facilities, laboratory and office equipment,
building crane, and maintenance equipment. Site dev-
elopment requirements include such items as grading,
access roads, and landscaping.
Plant B - 3.23 construction man-years per megawatt of net capability.
Two coal-fired units at a new site with 16-month interval
between operating dates of the units. These units have
cooling towers but do not have scrubbers. The new site
development requirements are comparable to those of
Plant A.
Plant C - 2.17 construction man-years per megawatt of net capability.
Two coal-fired units at an existing site with 12-month
interval between the operating dates of the units.
These units have a once-through cooling cycle and utilize
the same coal unloading facilities as the existing
units. This development did require limited additions
to the existing coal handling and ash disposal facilities.
Plant D - 2.72 construction man-years per megawatt of net capability.
Two coal-fired units at an existing site with 18-month
interval between operating dates of the units. These
units have cooling towers and did require limited additions
to the existing coal handling facilities.
Plant E - 3.64 construction man-years per megawatt of net capability
Two nuclear units at a new site with 16-month interval
between operating dates of the units. These units have
cooling towers and the manpower figure includes the site
development requirements associated with a nuclear plant.
The attached figure depicts the distribution of the manpower
requirements during the construction period. These plots must be
interpreted in light of the significant differences that were
identified above. Remember, too, that the manpower requirements
are based on a two-unit installation. This inherently provides
some opportunity for more efficient use of manpower by crafts than
-------
Steve Gordon
•March 23, 1979
Page Three
can be realized with a one-unit project. I have also included a
table which gives an estimated breakdown of the construction man-
power, by crafts, for the Plant D development.
I trust that this information will prove adequate for
your requirements. I apologize for taking so long to respond to
your request but the press of normal work duties did not permit
an earlier completion.
Very truly yours,
J. J. Albert
Staff Engineer
JJA:dlw
cc: J. J. Stukel, ORBES Project Office
0. A. Lentz, ECAR
-------
ESTIMATED CONSTRUCTION MANPOWER REQUIREMENTS BY CRAFTS
% OF
CRAFT TOTAL MANHOURS
Carpenters 6*
Laborers 7%
Operating Engineers 7%
Iron Workers 11%
Boiler Makers 191
Pipe Fitters 181
Electricians 14%
Millwrights 4%
Insulators 4%
Other 10%
100%
-------
ECAR
GENERATING STATION
CONSTRUCTION MANPOWER REQUIREMENTS
* (2-UNIT DEVELOPMENTS)
P.
ca
u
o>
c
o
U
a
V)
O
U
6 8 10 12 14 16 18 20 22 24 26 28 30
2 4
-------
CMDS Information
Forecasted Work-Hours per Kilowatt in the
Construction of Coal-fired Power Plants, United States
With Scrubbers
_ _ _ .. _ — Without Scrubbers
1977 1978 1979 1980 1981
Source: U.S. Department of Labor, Employment Standards Administration, Forecast
or Cost, Duration, and Manual Man-Hour Requirements for Construction of Electric
Generatina Plants. 1977-1981. Jan. 1978.
-
-------
ESPM Infornjation
FAC /rr INVESTMENT RESOURCES
50 SOLID WASTE COLLECTION/SEPARATION PT.C335 T/D)
si OIL-FIREC PO>«E« PLANT teoo WJE)
52 RECONVERSION OF OIL PLANT TO CCAL (250 *H)
53 COAL FIREO PC»ER PLAN'T-LO» 8UI (POO M*E)
5a COAL FIRIP PC»ER PLANT-HIGH «TU (800 »<»E)
55 COAL/»ASU POER PLA*T-LU PTU COAL oso M«t)
56 COAL/*A3TE PG*ER PLA^T-HI BTU COAL (350 MKt)
50
51
CONSTRUCTION LABOR REQUIREMENTS (THOUSAND PERSON-HOURS)
53 CHEMICAL ENGINEERS
54 CIVIL ENGI'EERS
55 ELECTRICAL ENGINEERS
56 M£CM»NICAL E*GPEf9S
57 DINING ENGINEERS
58 NUCLEAR ChGIMF'S
5« GEOLOGICAL EN'.P-EfRS
60 PETROLEUM ENGU.EE»S
61 OTHER E«GPEE»3
62 ENGINEERS TOTAL
63 DESIGNERS t DPAFTS'EN
64 SUPERVISORS t "AN'AGtRS
66 NON«V»KUAL, TECHNICAL TOT*j.
67 NON-KANUAL, NOK-TECHMCAL
70 f»ON-»-ANU/.L TOTAL
71 PIPEFITTERS
72 PIPEFITTEK/fcElDEWS
73 ELECTRICIANS
74 BOILERMAHtRS
J 75 BOILE*M»KER/*ELPERS
i 76 IRON WORKERS
) 77 CARPENTERS
78 ECUIPKEKT OPERATORS
79 LINE"fcv
80 TEAPSTCRS i LABORERS
81 OTHER
82 MANUAL TOTAL
85 CONSTPLCTION LABOR TOT*L
0,000
2,200
2.000
5,000
0.000
0,000
0,300
0,000
1.1PO
11.000
0.700
3, POO
19.500
2.500
22.000
0.000
0.000
?,eoo
0,000
5,000
26.700
7,000
3.600
0.000
17.«00
3.600
72,100
90.100
0.000
108.000
108,000
P « . o a o
O.POO
P , o >.' 0
o.ono
0.000
C.OOO
310. 000
136.000
60,000
500.000
260,ot;p
Per. ODO
72(/.ooo
320,000
500,000
500,000
160,000
252.000
252.000
J 0 C- . 0 0 0
O.DOO
432.000
216.000
36 00, nop
4000.000
02/28/79
53
55
56
0,000
2,200
2.000
5,000
0.000
0,000
0,300
0,000
1.1PO
11.000
0.700
3, POO
19.500
2.500
22.000
0.000
0.000
?,eoo
0,000
5,000
26.700
7,000
3.600
0.000
17.«00
3.600
72,100
90.100
0.000
108.000
108,000
P « . 0 •'! 0
O.POO
P , o , ) 0
0.010
0.000
C.OOO
sao.ooo
136.000
60,000
500.000
260,oi;p
Per. ooo
72(/.ooo
320,000
500,000
500,000
160,000
252.000
252.000
1 o <:< . o o o
O.DOO
432.000
216.000
36 00, nop
4000.000
0.000
0,600
0.400
0.3CO
0 , 0 1) 0
o,oon
0.000
0,000
0,000
1.300
0,500
C.2PO
2,000
l.POO
3.01)0
0,500
c.ooO
2.000
0.300
0,100
?,000
2.300
1 .000
0 , 0 " 0
3,600
1.6CO
15.000
Ifi.oco
0.000
2oe.ono
152.000
llfl.noo
O.ooo
O.OPO
o.oro
0.000
O.POO
478.000
192.000
90,000
7hO.OPO
360.000
1 120.000-
916.000
012.000
601.000
«i"7.000
2*9.000
3?1 ,OPC
321 .000
2*9.000
O.OPO
509,000
275.000
0500.000
5730.000
C.OOO
17P ,000
130.000
101, OOP
0,000
0,000
O.OPO
0.000
0,000
409.0PP
160. POO
77.000
650.000
310.000
9*0.000
76P.OOO
341,000
53e,oop
57*;. OOP
192.000
2')9.000
26a.OOO
192.000
O.OOQ
461.000
230.000
3*00.009
oRor.ooo
O.OOQ
210.000
ISO. 000
110,000
0.000
0.000
O.POO
0.000
0.000
470. 0"0
190.00ft
PO.OPO
700.000
160.000
9 0 o . 0 P 0
010. OPO
190.000
2PO.OOO
300,000
100.000
150.000
150.0',0
IPP.OOO
O.POO
200.000
120.000
?OOg.OOO
29*0.000
0.000
190,000
130.000
IIP. COP
O.OOP
0.000
0.000
0,000
P. COO
03P.OOO
J70.000
£0.000
660.000
120. CCC
POP. POO
350,000
160.000
240.000
260,000
9o,000
12C.OOP
130.000
90,000
0,000
160,000
lOP.ooo
1700.000
2500. POD
Source: Bechtel Corp., Energy Supply Planning Model NTIS PB 245 382, August 1975.
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