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Susitna-Watana Hydroelectric Project Document
ARLIS Uniform Cover Page
Title:
Social conditions and public goods and services study, Study plan Section
15.6 : Initial study report
SuWa 207
Author(s) – Personal:
Author(s) – Corporate:
Northern Economics, Inc. and Veritas Economic Consulting
AEA-identified category, if specified:
Draft initial study report
AEA-identified series, if specified:
Series (ARLIS-assigned report number):
Susitna-Watana Hydroelectric Project document number 207
Existing numbers on document:
Published by:
[Anchorage : Alaska Energy Authority, 2014]
Date published:
February 2014
Published for:
Alaska Energy Authority
Date or date range of report:
Volume and/or Part numbers:
Study plan Section 15.6
Final or Draft status, as indicated:
Draft
Document type:
Pagination:
v, 63, 14 p.
Related work(s):
Pages added/changed by ARLIS:
Notes:
All reports in the Susitna-Watana Hydroelectric Project Document series include an ARLIS-
produced cover page and an ARLIS-assigned number for uniformity and citability. All reports
are posted online at http://www.arlis.org/resources/susitna-watana/
Susitna-Watana Hydroelectric Project
(FERC No. 14241)
Social Conditions and Public Goods
and Services Study
Study Plan Section 15.6
Initial Study Report
Prepared for
Alaska Energy Authority
Prepared by
Northern Economics, Inc. and Veritas Economic Consulting
February 2014 Draft
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TABLE OF CONTENTS
Executive Summary ...................................................................................................................... v
1. Introduction............................................................................................................................ 1
2. Study Objectives .................................................................................................................... 1
3. Study Area .............................................................................................................................. 2
4. Methods and Variances in 2013 ............................................................................................ 2
4.1. Data Collection and Analysis ......................................................................................... 2
4.2. Variances ........................................................................................................................ 3
5. Results ..................................................................................................................................... 3
5.1. Description of Existing Socioeconomic Conditions ...................................................... 4
5.1.1. Demographic Characteristics .................................................................................. 5
5.1.2. Economy ................................................................................................................. 7
5.1.3. Housing ................................................................................................................. 13
5.1.4. Local Infrastructure and Public Services .............................................................. 14
5.1.5. Local Government Finances ................................................................................. 17
5.1.6. Ecosystem Services ............................................................................................... 17
5.1.7. Quality of Life....................................................................................................... 28
5.2. REMI Model Development .......................................................................................... 36
6. Discussion ............................................................................................................................. 36
7. Completing the Study .......................................................................................................... 37
8. Literature Cited ................................................................................................................... 37
9. Tables .................................................................................................................................... 45
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LIST OF TABLES
Table 5.1.1.1-1. Population Size and Density in Study Area ....................................................... 45
Table 5.1.1.2-1. Age Characteristics in Study Area ..................................................................... 46
Table 5.1.1.3-1. Racial and Ethnic Composition in Study Area ................................................... 47
Table 5.1.2.1-1. Employment and Per Capita Income in Study Area ........................................... 48
Table 5.1.2.2-1. Annual Unemployment and Labor Force Participation Rate in Study Area ...... 49
Table 5.1.2.2-2. Monthly Unemployment Rate in Study Area ..................................................... 49
Table 5.1.2.3-1. Poverty Rate in Study Area. ............................................................................... 50
Table 5.1.2.4-1. Current Resident Labor Force and Workforce Composition in Study Area ...... 51
Table 5.1.2.5.1-1. Construction Industry Employment, Income, and Output in Study Area ....... 52
Table 5.1.2.5.2-1. Air Transportation Industry Employment, Income, and Output in Study
Area ....................................................................................................................................... 53
Table 5.1.2.5.2-2. Water Transportation Industry Employment, Income, and Output in Study
Area1 ..................................................................................................................................... 53
Table 5.1.2.5.2-3 Truck Transportation Industry Employment, Income, and Output in Study
Area1 ..................................................................................................................................... 53
Table 5.1.2.5.2-4. Rail Transportation Industry Employment, Income, and Output in
Study Area ............................................................................................................................ 54
Table 5.1.2.5.3-1. Recreation and Tourism Industry Employment, Income, and Output in
Study Area ............................................................................................................................ 54
Table 5.1.2.5.5-1. Oil and Gas Industry Employment, Income, and Output in Study Area ......... 54
Table 5.1.2.5.6-1. Utilities Industry Employment, Income, and Output in Study Area ............... 55
Table 5.1.3-1. General Housing Characteristics in Study Area .................................................... 55
Table 5.1.3-2. Vacant Housing Characteristics in Study Area ..................................................... 56
Table 5.1.4.1-1. Police and Fire Protection Services in Study Area ............................................. 57
Table 5.1.4.1-2. Medical Services in Study Area ......................................................................... 57
Table 5.1.4.2-1. Number of Schools, Grade Levels, and Enrollment in Study Area.................... 58
Table 5.1.4.2-2. Revenue per Average Daily Membership and Funding Sources........................ 58
Table 5.1.4.3-1. Utility Providers in Study Area .......................................................................... 59
Table 5.1.4.3-2. Percentage of Household Income Spent on Gas, Electricity, and
Heating Fuel .......................................................................................................................... 60
Table 5.1.5-1. Local Tax Revenue Sources in Study Area ........................................................... 60
Table 5.1.5-2. Local Government Revenues by Source in Study Area ........................................ 61
Table 5.1.5-3. Local Government Operating Expenditures by Category in Study Area .............. 62
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Table 5.1.6.1.3-1. Timber Harvest in Selected Natural Resource Management Units of the
Matanuska-Susitna Borough ................................................................................................. 63
Table 5.1.6.1.4-1. Property Values of Selected Land Parcels in the Study Area ........................ 63
APPENDICES
Appendix A: Random Utility Model Methodology
Appendix B: Technical Memorandum on Long-Term Model Assumptions
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LIST OF ACRONYMS, ABBREVIATIONS, AND DEFINITIONS
Abbreviation Definition
ADOL&WD Alaska Department of Labor and Workforce Development
AEA Alaska Energy Authority
ANCSA Alaska Native Claims Settlement Act
ARRC Alaska Railroad Corporation
CIRI Cook Inlet Region Inc.
FERC Federal Energy Regulatory Commission
ILP Integrated Licensing Process
ISR Initial Study Report
PRM Project River Mile
Project Susitna-Watana Hydroelectric Project
REMI Regional Economic Models Inc.
RSP Revised Study Plan
RUM Random Utility Model
SPD study plan determination
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EXECUTIVE SUMMARY
Social Conditions and Public Goods and Services Study 15.6
Purpose This study assesses potential changes in population, housing, public goods
and services, and other quality of life factors resulting from the construction
and operation of the Project and potential changes in regional economic
conditions resulting from the nonpower effects of the Project.
Status Data collection efforts in 2013 made good progress in meeting overall study
objective of describing, using text and appropriate tables and graphics,
existing socioeconomic conditions within the study area. Completion of the
description of existing socioeconomic conditions will require continuing
integration with other study reports.
Study
Components
• Identify recreation outcomes that are likely to occur under with-
Project conditions.
• Assess currently available recreation data.
• Identify and apply existing recreation utility functions from the
literature.
• Identify recreation demand by appropriately combining existing utility
functions with site characteristics under without-Project and expected
with-Project conditions.
• Identify aggregate demand using population data and participation
rates.
• Document results of the social conditions and public goods and
services study in initial and updated study reports.
2013 Variances Based on information presented in the Transportation Resources Study (ISR
Study 15.7), which describes the primary sources and destinations of Project-
related road and railroad traffic, Seward, Point MacKenzie, Whittier, Wasilla,
and Houston were added to the list of potentially affected communities in the
study area.
Steps to
complete the
Study
As explained in the cover letter to this draft ISR, AEA’s plan for completing
this study will be included in the final ISR filed with FERC on June 3, 2014.
Highlighted
Results and
Achievements
Data have been collected to describe socioeconomic conditions in the study
area.
Progress was made in developing the REMI model assumptions for
comparing future socioeconomic conditions with and without the Project.
The recreation utility functions for the Random Utility Model have been
identified, and these functions are being combined with existing recreational
site characteristics.
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1. INTRODUCTION
On December 14, 2012, the Alaska Energy Authority (AEA) filed with the Federal Energy
Regulatory Commission (FERC or Commission) its Revised Study Plan (RSP) for the Susitna-
Watana Hydroelectric Project No. 14241 (Project), which included 58 individual study plans
(AEA 2012). Included within the RSP was the Social Conditions and Public Goods and Services
Study, Section 15.6. RSP Section 15.6 on assessing potential changes in population, housing,
public goods and services, and other quality of life factors resulting from the construction and
operation of the proposed Project and potential changes in regional economic conditions
resulting from the non-power effects of the Project. RSP Section 15.6 provided goals, objectives,
and proposed methods for data collection.
On February 1, 2013, FERC staff issued its study determination (February 1 SPD) for 44 of the
58 studies, approving 31 studies as filed and 13 with modifications. RSP Section 15.6 was one of
the 31 studies approved with no modifications.
Following the first study season, FERC’s regulations for the Integrated Licensing Process (ILP)
require AEA to “prepare and file with the Commission an initial study report describing its
overall progress in implementing the study plan and schedule and the data collected, including an
explanation of any variance from the study plan and schedule.” (18 CFR 5.15(c)(1)) This Initial
Study Report on the Social Conditions and Public Goods and Services Study has been prepared
in accordance with FERC’s ILP regulations and details AEA’s status in implementing the study,
as set forth in the FERC-approved RSP (referred to herein as the “Study Plan”).
2. STUDY OBJECTIVES
The study objectives are established in RSP Section 15.6.1 and include the following:
• Describe, using text and appropriate tables and graphics, existing socioeconomic
conditions within the study area.
• Evaluate the effects of on-site manpower requirements, including the number of
construction personnel who currently reside within the study area, who would commute
to the site from outside the study area, or who would relocate temporarily within the
study area.
• Estimate total worker payroll and material purchases during construction and operation.
• Evaluate the impact of any substantial immigration of people on governmental facilities
and services, and describe plans to address the impact on local infrastructure.
• Determine whether existing housing within the study area is sufficient to meet the needs
of the additional population.
• Describe the number and types of residences and businesses that might be displaced by
the Project access road and transmission corridors.
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• Describe, based on other studies, what bio-physical attributes of the Susitna River system
may change as a result of the Project and what those changes might mean to commercial
opportunities related to fishing, logging, agriculture, mining, and recreational activities,
recreation and subsistence use values, quality of life, community use patterns, non-use
environmental values, and social conditions of the area.
3. STUDY AREA
As established by RSP Section 15.6.3, the study area includes communities in the Denali
Borough and Matanuska-Susitna Borough that are located in relatively close proximity to the
components of the Project such as the dam and powerhouse, impoundment area, or road and
transmission line corridors.
4. METHODS AND VARIANCES IN 2013
4.1. Data Collection and Analysis
Except as described in Section 4.1.1 below, AEA implemented the methods as described in the
Study Plan with no variances.
Existing demographic, economic, and fiscal conditions in the study area were described based on
data published by a variety of state, and federal sources such as the U.S. Census Bureau, Alaska
Department of Labor and Workforce Development, U.S. Bureau of Economic Analysis, and the
Alaska Department of Commerce, Community and Economic Development. Data are presented
for the five Alaska boroughs and ten communities in the study area and for the State of Alaska.
In addition, the Regional Economic Models Inc. (REMI) model updated for 2012 was used to
provide additional information on those economic sectors in the study area that are most likely to
be affected by Project construction and operations. These sectors include the construction,
transportation, recreation and tourism, commercial fishing, oil and gas, and electric utilities
sectors.
With respect to development of the REMI model assumptions for comparing future
socioeconomic conditions with and without the Project, an information collection process was
initiated aimed at developing a consensus about reasonably foreseeable future activities in
Alaska under the with and without scenarios.
Secondary sources were used to describe the existing local infrastructure and public services
within the study area. Financial reports for governments of boroughs and incorporated
communities in the study area have been collected to determine current local government
revenues and expenditures. The fiscal data collected are current through 2012.
Discussions were initiated with preparers of the Transportation Resources Study (Study 15.7) to
coordinate the collection of information on the primary sources and destinations of Project-
related road and railroad traffic.
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Primary and secondary data has started to be compiled to develop and refine the Random Utility
Model (RUM) that will be used to estimate Project-related changes in demand and social welfare
associated with various recreational activities (sport fishing, boating, hunting, and snow
machining). Data collection efforts include the Alaska Outdoor Recreation Survey conducted by
the Recreation Resources Study (Study 12.5). In addition, the recreation utility functions have
been identified, and these functions are being combined with existing site characteristics. A
description of the RUM methodology is provided in Appendix A.
Discussions were initiated with preparers of the Recreation Resources Study (Study 12.5) to
coordinate the collection of information on the recreation resources and facilities that currently
support both commercial and non-commercial recreation in the Susitna River watershed and
estimates of current levels of recreational use in the region. Descriptions of existing commercial
farming, logging, and mining operations in the study area were prepared based on secondary
literature. Discussions were initiated with preparers of the Analysis of Fish Harvest Study (Study
9.15) to coordinate the collection of information on baseline harvest levels and harvest locations
of commercial fishing operations.
An interactive map-based database available on the Matanuska-Susitna Borough’s website was
used to obtain the current values of selected land parcels in the study area. This database contains
the current assessed value of properties in the borough and identifies the locations of the
properties and their total acreage.
A preliminary list of the specific attributes of the Susitna River corridor and upper watershed that
may have non-use value has been compiled based on information in the Pre-Application
Document and other secondary sources.
Information on the values, attitudes, and lifestyle preferences of residents in Talkeetna, Trapper
Creek, Cantwell, Chase, and the area north of Chase was collected through secondary sources. In
addition, a list of potential key informants was prepared for the quality of life interviews to be
conducted in the next study season.
4.2. Variances
Based on information presented in the Transportation Resources Study (Study 15.7), which
describes the primary sources and destinations of Project-related road and railroad traffic,
Seward, Point MacKenzie, Whittier, Wasilla, and Houston were added to the list of potentially
affected communities in the study area. The inclusion of these communities is necessary to fully
achieve the study objective of describing the effects of the movement of Project construction
equipment, materials, and workers on local government public services, including police and
medical services.
5. RESULTS
As described in Section 4 above, efforts in 2013 focused primarily on collecting data on current
conditions in the socioeconomics study area to provide context for potential socioeconomic
effects of the proposed Project. The preliminary results of this effort appear in Section 5.1. In
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addition, the study team made progress in developing the REMI model. The preliminary results
of this effort appear in Section 5.2.
5.1. Description of Existing Socioeconomic Conditions
In 2013, the study team collected data to describe current demographic characteristics of the
study area, including population size and density, age composition, and race/ethnicity
characteristics. In addition, sufficient data have been gathered to discuss the existing economy of
the study area in terms of per capita income, employment, unemployment, and the number and
composition of the workforce. These general economic development descriptions have been
augmented with information on those economic sectors that are most likely to be affected by
Project construction and operations. These sectors include the construction, transportation,
recreation and tourism, commercial fishing, oil and gas, and electric utilities sectors. The current
health and viability of each sector is measured in terms of employment, earnings, and output.
Information has been compiled on the existing local infrastructure and public services within the
study area, as presented in the subsections that follow. These services include law enforcement
agencies; fire departments; hospitals and other medical facilities; schools; solid waste disposal;
sewer and water; and other utilities. Local government finances are described in terms of the
sources and levels of revenues collected in 2012 by borough and community governments within
the study area, and in terms of local government expenditures in 2012, such as operating costs
for schools, public safety, and public utilities.
Drawing on analytical frameworks in the existing literature, the benefits associated with
ecosystem services potentially affected by the Project are classified into two main categories: use
benefits and non-use, or “passive use”, benefits. Use benefits include in-river recreation (fishing,
river rafting and boating), near-river recreation (hunting, snow machining), commercial natural
resource extractive uses (fishing, farming, logging, mining), and aesthetic enjoyment (as
reflected in property values). Non-use benefits include existence benefits and bequest benefits.
With respect to current use benefits generated by ecosystem services occurring in the Susitna
River corridor and upper watershed, the Recreation Resources Study (Study 12.5) is providing a
description of the recreation resources and facilities that currently support both commercial and
non-commercial recreation in the Susitna River watershed and estimates of current levels of
recreational use in the region. Data collection efforts include the Alaska Outdoor Recreation
Survey conducted by the Recreation Resources Study (Study 12.5). This information will also be
used to develop and refine the Random Utility Model (RUM) that will estimate Project-related
changes in demand and social welfare associated with various recreational activities. To date, the
recreation utility functions for the RUM have been identified, and these functions are being
combined with existing recreational site characteristics.
The Analysis of Fish Harvest Study (Study 9.15) will describe baseline harvest levels and
harvest locations for commercial fisheries for Susitna River-origin resident and anadromous fish.
For other commercial extractive uses, such as agriculture, mining, and logging, adequate
information has been collected from secondary sources to describe the trends of these activities
in the study area. Land prices have been used to reflect the value of property attributes, including
access to aesthetically pleasing ecosystem services. The appraised value of land parcels in the
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vicinity of the proposed Project dam site and impoundment area are compared to the values of
selected parcels in more accessible and developed areas. The per-acre value of a parcel was
calculated by dividing the appraised value by the gross acreage.
A preliminary description of the specific attributes of the Susitna River corridor and upper
watershed that may have non-use value has been prepared based on information in the Pre-
Application Document and other secondary sources.
For the quality of life analysis information on the values, attitudes, and lifestyle preferences of
residents in Talkeetna, Trapper Creek, Cantwell, Chase, and the area north of Chase has been
collected using secondary sources. The first part of the quality of life analysis provides a
historical overview of the development of each of the communities selected for study. The
second part defines existing conditions in the communities in terms of five quality of life
indicators: rural character, pace of life, self-sufficient lifestyle, community image, and
community cohesiveness. The information compiled to date will be supplemented with data
collected by interviews conducted with key informants in the next study season.
The first phase of this socioeconomic study, presented below, is limited to a description of
existing socioeconomic conditions in the study area. This description provides context for
potential socioeconomic effects of the proposed Project.
5.1.1. Demographic Characteristics
This section contains a discussion of the demographic characteristics of the study area, including
population size and density, age composition, and race/ethnicity characteristics.
5.1.1.1. Population Size and Density
Table 5.1.1.1-1 provides a summary of population statistics for Alaska and the boroughs and
communities within the study area. In 2010, the population of Alaska was 710,231, and 535,628
people, or about 75 percent of the state’s population, resided in the Railbelt, which includes all
the boroughs in the study area. Approximately 73 percent of the total population of the study
area lived in the Municipality of Anchorage and the Fairbanks North Star Borough.
Population growth in the study area between 2000 and 2010 was most substantial in the
Matanuska-Susitna Borough. The population growth rate in the borough was far higher than the
average Alaska growth rate of 13.1 percent between 2000 and 2010. The Matanuska-Susitna
Borough is in many ways an Anchorage suburb, with almost a third of the borough’s residents
commuting to Anchorage daily for work (Fried 2013a). While the Municipality of Anchorage
has net gains from the rest of the state, it has consistently lost population to the Matanuska-
Susitna Borough. During the 2000-2008 period, 14.5 percent of the average annual movement
out from the Municipality of Anchorage was to the Matanuska-Susitna Borough. The movement
to the Matanuska-Susitna Borough reached its peak in 2005-2006; after that, migration to the
borough declined and migration from the borough increased (Williams 2010).
Most of the growth in the Matanuska-Susitna Borough has occurred in a core area that begins
with Palmer and runs along the Parks Highway through Wasilla, Meadow Lakes, and Knik-
Fairview and ends in Houston (Fried 2013a). Nevertheless, some of the more distant
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communities, including the study area community of Talkeetna, also experienced above-average
growth. The combination of data on school enrollment, age, and population growth, suggest most
Talkeetna-area growth is tied to older people, retirees, and second home owners. However,
growth was below average in Trapper Creek, and Chase saw a marked decrease in population
between 2000 and 2010. Residents of many rural villages located off the road system in Interior
Alaska appear to be migrating to Fairbanks or to Southcentral Alaska in search of better
employment opportunities (Williams 2010).
Recently, Point MacKenzie has experienced rapid population growth with the opening of the
Goose Creek Correctional Center, a 1,536-bed, medium-security prison. A group of around 125
inmates were housed in the facility when it opened in mid-2012. The facility became fully
operational in 2013, with a total inmate population of about 1,075 (Alaska Department of
Corrections 2012).
In the Denali Borough, the population is centered around the Parks Highway. The population of
the Borough remained relatively unchanged between 2000 and 2010.
The highest population density in the study area in 2010 was in the Municipality of Anchorage
with an average of 171.9 persons per square mile, which was the highest population density in
Alaska. The lowest density of people in the study area was in the Denali Borough, which had a
density of 0.1 persons per square mile. Denali National Park and Preserve accounts for 70
percent of the Denali Borough’s land area, and nearly all the borough’s residents live along a 70-
mile stretch of the Parks Highway (Fried 2009).
5.1.1.2. Age Characteristics
Table 5.1.1.2-1 shows age characteristics of boroughs and communities in the study area in 2010.
The Denali Borough had the highest proportion of working-age adults. Generally, rural areas of
Alaska, such as the Denali Borough, have a smaller percentage of working-age adults because of
the limited employment opportunities. However, three of the Denali Borough’s five identified
communities have high-paying, year-round employers that provide jobs for most of the
borough’s resident work force: Anderson has the Clear U.S. Air Force Station, Healy has
Usibelli Coal Mine, Inc. and Golden Valley Electric Association, and McKinley Park has the
U.S. National Park Service (Fried 2009).
With respect to communities in the study area, Fairbanks had a lower median age (27.9 years)
than the state median age in 2010 due to the presence of the University of Alaska Fairbanks’
student population. All other communities, with the exception of Point Mackenzie, had a median
age higher than that of the state. Families with young children are less prevalent in many rural
Alaska communities, perhaps because of limited employment opportunities.
5.1.1.3. Race/Ethnicity Characteristics
Table 5.1.1.3-1 describes the ethnic and racial composition of the boroughs and communities
within the study area in 2010. The two most urbanized areas, Fairbanks and the Municipality of
Anchorage, both have sizeable minority populations—37.5 percent and 36.8 percent,
respectively. These percentages are close to the state average of 35.4 percent. The borough with
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the lowest minority population was the Denali Borough, with 10.6 percent. All of the smaller
communities in the study area had minority populations below the state average.
5.1.2. Economy
This section contains a discussion of the local and regional economy, including per capita
income, employment, unemployment, and the number and composition of the workforce. In
addition, this section augments these general economic development descriptions with
information on those economic sectors in the study area that are most likely to be affected by
Project construction and operations. The current health and viability of these sectors are
measured in terms of such factors as employment, output, and earnings
5.1.2.1. Employment and Income
Table 5.1.2.1-1 summarizes resident employment in the boroughs and communities within the
study area as measured by the number of jobs. The total 2011 employment in the affected
boroughs was 327,655 jobs, representing about 72 percent of the statewide employment. A large
portion (44 percent) of Alaska’s employment is concentrated in the Municipality of Anchorage,
with 202,153 jobs in 2011. Elsewhere in the study area, employment is concentrated in the
Fairbanks North Star Borough, Matanuska-Susitna Borough, and Kenai Peninsula Borough, with
much smaller employment totals in the Denali Borough.
Table 5.1.2.1-1 also describes the per capita personal income distribution of the boroughs within
the study area in 2011. The per capita income in the Matanuska-Susitna Borough, Fairbanks
North Star Borough and Kenai Peninsula Borough was less than Alaska’s per capita income. The
borough with the highest per capita income was the Denali Borough, at $60,191. Three of the
Denali Borough’s five identified communities have high-paying, year-round employers that
provide jobs for most of the borough’s resident work force: Anderson has the Clear U.S. Air
Force Station, Healy has Usibelli Coal Mine, Inc. and Golden Valley Electric Association, and
McKinley Park has the U.S. National Park Service (Fried 2009). The relatively high per capita
income in the Municipality of Anchorage reflects the more robust economic conditions generated
in the state’s most urbanized area.
In study area communities with economies strongly tied to Denali National Park and Preserve—
in particular, Talkeetna and Trapper Creek—the per capita income was lower than the rest of the
borough and state because of the seasonality of work.
5.1.2.2. Unemployment
Table 5.1.2.2-1 shows that a marked variation in unemployment rates existed within the study
area in 2012. The Municipality of Anchorage and Fairbanks North Star Borough had
unemployment rates lower than the state average of 7.0 percent. The remaining affected
boroughs had unemployment rates higher than state averages, with the highest being in the
Denali Borough (10.2 percent).
Unemployment is especially high in small, rural villages, particularly during the winter when
there is little alternative market-based activity (U.S. Department of the Interior 2002). With the
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exception of Fairbanks, all the communities within the study area had unemployment rates
higher than the state average.
It is likely that unemployment data underestimate the number of people who would like to work,
particularly in more rural communities, because the unemployment rate includes only persons
who are looking for work. In many rural Alaska communities, the number of employment
opportunities is limited. Consequently, some people may no longer be actively searching for
employment (U.S. Department of the Interior 2002; Robinson 2009). In addition, other people
such as homemakers, retirees, and full-time students are often members of this group, as well as
people engaged full-time in subsistence activities. As shown in Table 5.1.2.2-1, the average
percentage of the working-age (16 years old and over) population that was not in the labor force
from 2007 through 2011 was particularly high in the Kenai Peninsula Borough. With the
exception of Fairbanks, all the communities within the study area had high percentages of adults
who were not in the workforce in comparison to the state as a whole.
Several of rural Alaska’s predominant industries, particularly seafood harvesting and processing,
tourism, construction, and timber, are highly seasonal and result in total employment for the
summer exceeding that in the winter by a large percentage. As shown in Table 5.1.2.2-2, the
unemployment rate fluctuated substantially in the Denali Borough, which is heavily dependent
on tourism and has the most seasonal work force of any borough in the state (Fried 2009), while
the unemployment rate in large urbanized areas such as the Municipality of Anchorage and the
Fairbanks North Star Borough showed relatively little seasonal variation.
5.1.2.3. Poverty Rate
Table 5.1.2.3-1 shows the average percent of people who are in poverty in the boroughs and
communities within the study area. The Matanuska-Susitna Borough has a slightly higher
percentage of people living below the poverty line than the state percentage. Both Trapper Creek
and Talkeetna have substantially higher percentages of people living below the poverty line than
the state percentage.
5.1.2.4. Workforce Number and Composition
Table 5.1.2.4-1 summarizes characteristics of the existing Alaska resident workforce in boroughs
and communities within the study area. The total 2011 resident workforce in the affected
boroughs was 380,635 individuals, representing 76 percent of the statewide workforce. A large
portion (41 percent) of Alaska’s workforce is concentrated in the Municipality of Anchorage,
with 208,016 working-age (16 years old and over) residents in 2011. Elsewhere in the study area
the resident workforce is concentrated in the Fairbanks North Star Borough, Matanuska-Susitna
Borough, and Kenai Peninsula Borough, with much a smaller workforce total in the Denali
Borough. The study area community with the largest resident labor force in 2011 was Fairbanks
(18,314).
Employment by industry data compiled by the Alaska Department of Labor and Workforce
Development (ADOL&WD) (2013a) for 2011 show that employment in the Municipality of
Anchorage and Matanuska-Susitna Borough is dominated by trade and educational and health
service jobs.
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The Denali Borough economy depends heavily upon the tourist trade, primarily related to Denali
National Park and Preserve. While tourism is not classified as an economic sector, tourist activity
can be gauged by examining the size of the leisure and hospitality sector. About 22 percent of the
resident wage and salary employment was in the leisure and hospitality sector in 2011, compared
to 9 percent statewide. During the summer each year, the sector’s share of employment in the
borough becomes even higher due to an influx of large numbers of nonresident employees.
Nearly all these seasonal workers are employed at hotels or restaurants, or a combination of the
two. The remaining workers typically work for river rafting and boating companies, sled dog
operations, guided fishing and hunting outfits, or for companies providing other forms of outdoor
recreation (Fried 2009).
With respect to study area communities, Talkeetna, in particular, has an economy supported by
the seasonal tourist industry. The community is popular for its recreational fishing, hunting, river
rafting and boating, flightseeing, skiing, and dog mushing. In addition, it serves as the staging
area for climbers attempting to scale Denali and other peaks in the Alaska Range. The tourism
primarily booms in the summer months, May through September, with very few year-round jobs
existing in the immediate area (Alaska Department of Commerce 2013c).
To a large extent the economies of the road-connected communities of Talkeetna, Trapper Creek,
and Cantwell are organized around providing services to highway travelers and visiting
recreationalists. However, many residents in these communities and in the Chase area, which is
off the road system, are also engaged in “non-market” activities such as gardening and
subsistence hunting, fishing, and gathering, along with activities that do not fit well within
standard economic reporting systems, such as trapping and the manufacture and sale of arts and
crafts (National Park Service 2006). The economies of these households have been characterized
as “semi-subsistence” in that they are based upon use of local natural resources subsidized and
supported by a cash income derived from seasonal employment (Chase Citizen's Planning
Advisory Committee 1993).
The employment by industry data also reflect the continued importance of the public sector in
rural Alaska economies. For example, employment for all non-federal government positions in
Cantwell accounted for more than one-third of the community’s total employment in 2011. The
small private sector in the community is based upon services to public sector employees and to
the seasonal visitors to the general recreation area.
5.1.2.5. Specific Economic Sectors
This section augments the general economic development descriptions above with information
on those economic sectors in the study area that are most likely to be affected by Project
construction and operations. These sectors include the construction, transportation, recreation
and tourism, commercial fishing, oil and gas, and electric utilities sectors. The current health and
viability of these sectors are measured in terms of employment, earnings, and output.
5.1.2.5.1. Construction
Construction employment in Alaska fell each year between 2006 and 2011, but in 2012 it
changed course and grew substantially. Alaska’s construction employment started falling one
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year before the industry declined nationwide and three years before Alaska lost jobs across all
industries. The industry likely started to soften before the national recession started in 2007 due
to the end of a housing boom in the Matanuska-Susitna Borough. Public construction had been
the bright spot in an otherwise dimming industry, and is likely largely responsible for the
industry’s turnaround in 2012. Alaska’s fiscal year 2012 capital budget was $2.8 billion, which
buoyed the construction industry across the state (Schultz 2013). Table 5.1.2.5.1-1 presents an
overview of the construction industry in the study area in terms of employment, income, and
output (sales). In 2012, construction employment statewide was approximately 25,000, with
about 42 percent of those jobs occurring in Anchorage.
5.1.2.5.2. Transportation
Alaska’s transportation industry is one of the state’s larger employers. Transportation plays a
much bigger role in Alaska’s economy than it does in the rest of the nation because the vast
distances and lack of highways makes it considerably more difficult to move people or goods in
the state. Nationally, only three percent of all private wage and salary employment is tied to
transportation, versus almost six percent in Alaska (Fried and Keith 1999).
Alaska’s transportation industry is also unusually diverse (Fried and Keith 1999). It encompasses
the air, water, rail, and truck transportation sectors. The air transportation sector accounts for
around half of all transportation employment in Alaska versus less than one-third nationally
(Fried and Keith 1999). Table 5.1.2.5.2-1 presents an overview of the air transportation industry
in the study area in terms of employment, income, and output (sales). As of 2012, more than
5,600 jobs existed in the industry statewide.
In addition, no state in the continental U.S. depends on water transportation as much Alaska does
(Fried and Keith 1999). Water transportation may be one of the smaller transportation sectors in
terms of employment, but it handles the greatest tonnage of freight coming into the state. The
Port of Anchorage, which is an enterprise department under the Municipality of Anchorage, is
the largest port in the state, handling 90 percent of all consumer goods sold in the Railbelt and
serving approximately 80 percent of the state’s population (Fried and Keith 1999). Table
5.1.2.5.2-2 presents an overview of the water transportation industry in the study area in terms of
employment, income, and output (sales).
In contrast to air and water transportation, trucking’s share of the transportation industry in
Alaska is considerably smaller than in the nation as a whole due to the absence of a network of
interstate highways. Nevertheless, trucking businesses are major employers in the state,
accounting for more than 4,000 jobs in 2012. Table 5.1.2.5.2-3 presents an overview of the truck
transportation industry in the study area in terms of employment, income, and output (sales).
The Alaska Railroad Corporation (ARRC), a public corporation, owns and operates the Alaska
Railroad for the State of Alaska. The ARRC employs relatively few people, but it plays an
important role in moving people, materials, and equipment from Seward and Whittier n the south
through Anchorage to Fairbanks in the north. Dock and handling yards are maintained by the
ARRC at the ports of Anchorage, Seward, and Whittier for handling freight reaching Alaska by
ship and barge (Fried and Keith 1999). Customers can load their goods onto a railcar in the
Lower 48 and it will be transferred to Alaska and Railbelt communities via the contracted barge
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services that operate from Seattle and Prince Rupert, British Columbia. Table 5.1.2.5.2-4
presents an overview of the rail transportation industry in the study area in terms of employment,
income, and output (sales).
5.1.2.5.3. Recreation and tourism
Recreation-related businesses are a part of the recreation and tourism industry, which is one of
the largest private sector employers in Alaska. Table 5.1.2.5.3-1 presents an overview of the
recreation and tourism industry in the study area in terms of employment, income, and output
(sales). In 2012, an estimated 43,600 people were employed in the industry, with a combined
income of $1.8 billion. About 46 percent of those jobs were located in Anchorage. These
numbers include companies categorized under scenic and sightseeing transportation; amusement,
gambling, and recreation; accommodation; and food services and drinking places.
The Parks Highway connecting Anchorage and Fairbanks, Alaska’s two largest cities, has had a
large impact on the recreation and tourism industry in the study area since the highway’s
completion in 1971. The highway facilitated the dramatic rise in visitors to Denali National Park
and Preserve. This popular tourist destination quadruples the Denali Borough’s 1,000 winter jobs
to more than 4,000 in the summer to cater to the 400,000 visitors that go through the borough
along the Parks Highway or on the Alaska Railroad to get to the park. The borough’s five
identified communities, including the study area community of Cantwell, have become providers
of support services to visitor traffic on the highway (Fried 2009).
The study area communities of Talkeetna and Trapper Creek are also important gateway
communities to Denali National Park and Preserve. Tourism and recreation form the main basis
of Talkeetna’s present economy. In Trapper Creek, a few businesses, associated with tourism and
highway services, provide some employment.
Existing tourist facilities in the region beginning at the Talkeetna junction of the Parks Highway
and extending to the southern boundary of the national park include approximately 900 rooms.
Almost 700, or 60 percent, of these rooms are contained in two hotels—the Talkeetna Alaskan
Lodge and Princess Mount McKinley Wilderness Lodge (Center for Alaska Economic
Development 2011). The opening of the lodges, which serve the package tour industry, has
driven much of the tourism growth in Talkeetna and Trapper Creek. The Princess Mount
McKinley Wilderness Lodge opened 20 miles north of Trapper Creek in 1997, while Cook Inlet
Region, Inc. opened its Talkeetna Alaskan Lodge near Talkeetna in 1998. The two lodges have
since become two of the largest private sector tourism-related employers in the Matanuska-
Susitna Borough (National Park Service 2006). They generate substantial revenues—$12 million
in 2007—that impact the regional economy through operations, employment, and bed taxes
(Center for Alaska Economic Development 2011). Moreover, construction of the hotels, together
with an influx of major tour companies, led to rapid expansion of tourism in the Talkeetna-
Trapper Creek area. The number of visitors to the area increased from about 40,000 in the early
1990s to well over 100,000 by the early 2000s (Agnew::Beck Consulting 2002).
Table 5.1.2.5.3-1 presents an overview of the recreation and tourism industry in the study area in
terms of employment, income, and output (sales).
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5.1.2.5.4. Commercial fishing
From 2001 to 2011, between 3,000 and 4,000 harvesting jobs were created by the Cook Inlet
commercial salmon fisheries each year. It is estimated that the Cook Inlet drift-net fishery
generated $18.2 million in income for permit holders and crew members in 2011, and the set-net
fishery generated $13.1 million, for a total of $31.3 million. In 2011, it is estimated that 1,617
Cook Inlet jobs were attributable to processing salmon. Both salmon harvesting and processing
jobs are typically of a 2-3 month duration, coinciding with the length of the salmon fishing
season. The ex-vessel value of the Cook Inlet salmon fisheries was $56.4 million in 2011, while
the total processed product value (wholesale value) exceeded $94.5 million (Northern Economics
2013).
5.1.2.5.5. Oil and gas
Although Alaska’s oil production has trended downward for the past two decades, oil industry
employment has been on the rise as a result of increased labor needs for harder-to-reach oil as
well as the drive to extract more oil under the recent high price regime. As Alaska’s facilities
age, additional labor is required for repair and maintenance as well as extraction (Schultz 2013).
Between 2002 and 2012, the oil industry’s payroll grew by 106 percent, considerably more than
the 56 percent growth for all industries (Fried 2013b). Table 5.1.2.5.5-1 presents an overview of
the oil and gas industry in the study area in terms of employment, income, and output (sales).
Direct jobs in this sector stood at more than 4,000 in early 2012. While this figure is a small
percentage of Alaska’s total wage and salary employment, average earnings in the oil and gas
industry are more than two-and-a-half times the average for all Alaska industries. Its payroll
impact is therefore more pronounced—the industry paid $1.7 billion in 2012, or 10 percent of
wage and salary payroll (Fried 2013b).
Direct employment in the oil and gas sector is concentrated in the North Slope Borough, with
smaller numbers of workers in the Municipality of Anchorage, Kenai Peninsula Borough, and
Fairbanks North Star Borough. However, industry draws workers from all over the state. For
example, eight percent of the Matanuska-Susitna Borough’s working residents commuted to the
North Slope in 2011, and brought home $223 million in wages (Fried 2013b).
Furthermore, the oil and gas industry indirectly creates thousands of other private and public
sector jobs in Alaska through support industries, including catering, accommodations,
transportation, engineering, and logistics, and through government revenues—oil funds over half
the state budget and about 90 percent of the state’s general fund. It is estimated that around one-
third of all jobs and personal income in the state can be traced to the oil and gas industry
(Goldsmith 2007).
5.1.2.5.6. Electric utilities
Five electric utilities—Chugach Electric Association, Homer Electric Association, Matanuska
Electric Association, Golden Valley Electric Association, and Anchorage Municipal Light &
Power—serve a majority of Alaska’s population and most of the state’s commercial customers.
These utilities collectively serve almost 200,000 metered locations all along the Railbelt.
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In addition, utility companies are important employers in the state, creating more than 2,000 jobs
in 2012. Table 5.1.2.5.6-1 presents an overview of the utilities industry in the study area in terms
of employment, income, and output (sales). Counted in these numbers is employment for entities
that provide all types of utilities, including electric power generation, transmission, and
distribution; natural gas distribution; and water, sewage, and other systems.
5.1.2.5.7. Alaska Native Claims Settlement Act Corporations and Non-Profit Corporations
Native regional and village corporations created under the Alaska Native Claims Settlement Act
(ANCSA) of 1971 play a major role in Alaska’s economy and an even more important role in
their individual regions by creating jobs as well as earning profits (Harrington 2012). A portion
of these profits goes to shareholders in the form of dividends. Cook Inlet Region Inc. (CIRI) is
the Native regional corporation that owns subsurface rights to land near Project components
(dam and powerhouse, impoundment area, or road and transmission line corridors). CIRI also
owns a possible resource area for sand, rock, and gravel for Project construction and owns
stretches of at least one of the routes proposed for a Project access road and power lines. In
addition, CIRI Alaska Tourism Corporation, a subsidiary of CIRI, operates the 212-room
Talkeetna Alaskan Lodge in Talkeetna (CIRI Alaska Tourism 2013).
CIRI also served as de facto trustee for its village corporations’ surface rights to land near the
Project until those corporations finalized their ANCSA entitlement land selections in the area in
2013. Tyonek Native Corporation, the Tyonek tribal member’s village corporation, already held
title to surface estate at the proposed dam site prior to the settlement. In addition, the majority of
the lands of Knikatnu, Inc., the Knik tribal member’s village corporation, are near the Project
(Lochner 2012).
5.1.3. Housing
A housing unit is defined by the U.S. Census Bureau as a house, apartment, group of rooms, or
single room occupied or intended for occupancy as separate living quarters. There were a total of
228,493 housing units in the boroughs within the study area in 2010, about half of which were in
the Municipality of Anchorage (Table 5.1.3-1). The Denali Borough had the lowest occupancy
rate (45 percent), while the Municipality of Anchorage and Fairbanks North Star Borough, two
of most urbanized areas in the study area, had the highest occupancy rates (95 percent and 77
percent, respectively). Housing market conditions in these urban areas tend to be tighter than in
the rural areas. Median monthly rent in the boroughs within the study area was generally less
than the median monthly rent in Alaska, but rent was higher than the median state rent in the
Municipality of Anchorage. The Denali Borough had the lowest median monthly rent at $572.
As shown in Table 5.1.3-2, of the vacant housing units within the study area, the large majority
were for seasonal, recreational, or occasional use. Temporary housing is also available in the
form of daily, weekly, and monthly rentals in motels, hotels, campgrounds, and recreational
vehicle parks. The availability of these accommodations may vary, particularly during any tourist
season, local event, or because of demand for housing by other industries (e.g., mining).
The community in the study area with the largest number of housing units is Fairbanks, with
13,056 total units in 2010. The number of housing units in other study area communities was
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considerably smaller. For example, there are 200 housing units in Cantwell. The proportion of
units vacant is especially high in Point Mackenzie, Chase, and Trapper Creek because of the
large number of seasonal and recreational units in those communities. Motels and other types of
temporary housing in study area communities are concentrated in those communities with large
tourist industries, such as Talkeetna and Fairbanks. During the summer months, the population of
Talkeetna swells with students and others seeking employment in the seasonal tourism industry,
and these seasonal employees often occupy all available housing in the town center area.
Consequently, the vacancy rate in the summer is very low (National Park Service 2006;
Talkeetna Chamber of Commerce 2012).
5.1.4. Local Infrastructure and Public Services
This section contains a discussion of the existing local infrastructure and public services within
the study area. A wide range of public services and facilities are offered, with concentrations in
the larger cities. Where services are not available at the community level, they are available from
the borough or state. Services provided in the study area include law enforcement agencies; fire
departments; hospitals and other medical facilities; schools; solid waste disposal; sewer and
water; and other utilities.
The provision of public services and infrastructure across Alaska is expensive, particularly in
rural areas. For example, the costs to construct public buildings—including schools, health
clinics and hospitals—in remote areas is on the order of twice as much per square foot as in
Anchorage (Foster and Goldsmith 2008). The higher cost per square foot for rural buildings is
due to a combination of higher input costs, especially freight costs (barge and air); limited supply
of specialty labor (mechanical, electrical); challenging foundation conditions—including areas
with abundant permafrost; weather delays; remote logistics; and the high cost of fuel. Moreover,
the harsh winter climate of Alaska shortens the useful life of roads and public buildings.
5.1.4.1. Law Enforcement/Fire/Medical Services
A listing of police and fire protection services in the boroughs and communities within the study
area is provided in Table 5.1.4.1-1. Police services in the study area are only provided by local
police departments in Fairbanks and the Municipality of Anchorage; law enforcement in other
parts of the study area is primarily the responsibility of the Division of Alaska State Troopers
under the Alaska Department of Public Safety. The Division is composed of posts that provide
patrol, enforcement, and search and rescue to all areas of the state and a central headquarters
(Alaska Department of Public Safety 2013). The Division has four bureaus: the Alaska Bureau of
Investigation investigates major crimes; the Alaska Bureau of Alcohol and Drug Enforcement
enforces laws against bootlegging and illegal drug distribution throughout Alaska; the Alaska
Bureau of Judicial Services is responsible for prisoner transports and providing security for
Alaska courts; and the Alaska Bureau of Highway Patrol is responsible for highway safety
(Alaska Department of Public Safety 2013).
The Municipality of Anchorage, Matanuska-Susitna Borough, and Fairbanks have fire
departments staffed with career firefighters. Generally, these departments are responsible for all
structural firefighting within their jurisdictional boundaries. Volunteers provide fire protection
services in Talkeetna, Cantwell, and Seward. Wildland fire management in Alaska is an
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interagency effort involving the U.S. Bureau of Land Management, Alaska Fire Service; Alaska
Department of Natural Resources, Division of Forestry; and the U.S. Forest Service. The Alaska
Interagency Coordination Center located at Fort Wainwright serves as the focal point for initial
attack resource coordination, logistics support, and predictive services for all state and federal
agencies involved in wildland fire management and suppression in Alaska. In addition, the
Alaska Interagency Coordination Center is the focal point for coordinating and providing support
for all-hazard emergency response activities for federal landholding agencies in Alaska (Alaska
Interagency Coordination Center 2013). The U.S. Bureau of Land Management Alaska Fire
Service provides wildland fire suppression services for all U.S. Department of the Interior and
Native corporation lands in Alaska (Alaska Fire Service 2013).
A listing of medical services in the communities in the study area is provided in Table 5.1.4.1-2.
There are five major hospitals in the study area—three in the Municipality of Anchorage, one in
Fairbanks, and one in the Matanuska-Susitna Borough community of Palmer. Health clinics are
located in the majority of other communities in the study area, but trauma cases, as well as
serious illness cases, must be sent to hospitals. Transport in emergency situations is usually by
air (i.e., airplane or helicopter). Medical facilities in the Municipality of Anchorage, Palmer, and
Fairbanks provide air medical services. Most communities provide emergency medical services,
which, in many cases, are delivered by local fire departments. A number of regional and
community organizations administer health and social service programs for Alaska Natives.
5.1.4.2. Schools
Information regarding the number of schools in communities within the study area, and the grade
levels and student enrollment at those schools, is shown in Table 5.1.4.2-1. The highest number
of schools is in the Municipality of Anchorage and Fairbanks. The 123 schools in these
population centers had 59,623 students enrolled in 2011. Alaska schools vary greatly in size.
High schools in Anchorage may serve more than 2,000 students. Schools in other urban areas
such as Fairbanks, the Kenai Peninsula, or the Matanuska-Susitna Borough may serve hundreds
and are similar to schools in small cities in the rest of the United States. In contrast, many
schools in rural communities such as Cantwell are small, some with 30 or fewer students at a
variety of grade levels (Alaska Teacher Placement 2013). The State of Alaska does not provide
state funds for schools with fewer than 10 students. Talkeetna and Trapper Creek middle and
high school students are bused to Susitna Valley School at milepost 98 of the Parks Highway.
There are no local schools in Chase or Point Mackenzie; children are home-schooled or attend
schools in other areas. The State of Alaska provides parents with the option of home-schooling
their children. Under state law, children schooled at home by their parents or guardians are
exempt from the compulsory school attendance law. Parents are not required to register with the
state or their local school district, and no testing or other requirements are placed on home-
schools not funded with public dollars. The Alaska Department of Education and Early
Development oversees the regulation of correspondence schools available to home-school
families. This department listed 26 correspondence schools on its web site. Of the total, 14 of the
schools are available to students from all over the state, while 12 of the schools serve students in
individual school districts (Alaska Department of Education and Early Development 2010).
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Table 5.1.4.2-2 presents the revenue per average daily membership and funding sources in
school districts with affected communities. Average per-student cost in Alaska is higher than in
any other state, reflecting the costs associated with maintaining educational services among often
extremely widely geographically dispersed communities (U.S. Department of the Interior 2002).
As shown in Table 5.1.4.2-2, the revenue per average daily membership was highest in the Kenai
Peninsula Borough School District and lowest in the Matanuska-Susitna Borough School
District. State law establishes a formula by which a guaranteed level of funding known as “basic
need” is determined for each of Alaska’s school districts. This formula is weighted in favor of
small, isolated sites. It takes into consideration the total number of students enrolled in the entire
district, the number of students in each school within the district, regional cost differentials
(“district cost factors”), special needs funding, intensive services funding, and enrollment in
correspondence programs. The components of public school funding are state aid, required local
contribution, federal Title VIII impact aid, special revenues and other sources. Federal impact aid
provides funds to school districts for children with parents living and/or working on federal
property “in lieu of local tax revenues.” Municipalities with taxing power are required to provide
their coterminous school district with the local contributions to assure the equivalent of a 2.65
mill tax levy on the full and true value of the taxable real and personal property in the district;
and not to exceed 45 percent of the district’s basic need for the preceding fiscal year (Alaska
Department of Education and Early Development 2012).
5.1.4.3. Utilities
Table 5.1.4.3-1 documents the provision of local utilities (water, sewer, solid waste, electric,
natural gas) to communities within the study area by identifying the local communities’ service
providers by utility type. Like many rural communities in Alaska, Trapper Creek, Chase, and
Point Mackenzie do not have community piped potable water or sewage treatment systems.
Water in these communities is typically provided by individual household wells, and sewage
treatment facilities consist of individual septic systems or communal sewage lagoons. Some
households may lack flush toilets and running water. Refuse in communities within the study
area is generally hauled to the borough or private landfills. Most rural communities have Class
III landfills that do not meet the requirements of the federal Resource Conservation and
Recovery Act (Colt et al. 2003).
Alaska’s electrical energy infrastructure differs from that in the rest of the United States in that
there is no extensive infrastructure of transmission interties that span the state or connect to the
grid in Canada or the Lower 48. The electrical needs of communities in the study area are
currently served by seven regulated public utilities that extend from Fairbanks to Anchorage and
the Kenai Peninsula. These utilities include four cooperatives (Golden Valley Electric
Association, Chugach Electric Association, Matanuska Electric Association, and Homer Electric
Association), two municipal utilities (Anchorage Municipal Light & Power and City of Seward
Electric System), and one independent power producing utility (Aurora Energy, LLC). These
utilities account for nearly all of the electricity generated in the state. ENSTAR Natural Gas
supplies natural gas produced in Cook Inlet to many residences and businesses in Southcentral
Alaska.
When the low level of per-capita income in rural Alaska is taken into account, residents of
communities in Alaska that are remote and off the road system typically pay about 14 percent of
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their household income for gas, electric, and heating fuel, while Anchorage residents pay about 3
percent (Table 5.1.4.3-2). Most remote rural places rely on fuel oil for both heating homes and
generating electricity. Fuel oil is far more expensive than natural gas, which is available in
Anchorage and a few other communities (Goldsmith 2008). Moreover, the day-to-day operating
costs of community water, sewer, and electric utility systems in rural Alaska are high. With a
small customer base and limited income, many—if not most—of these utility systems are not
self-supporting (Colt et al. 2003).
5.1.5. Local Government Finances
Table 5.1.5-1 identifies sources of tax revenues in the boroughs within the study area. With the
exception of the Denali Borough, all of the boroughs within the study area collect local taxes,
primarily in the form of property taxes or sales taxes. The Denali Borough relies upon bed taxes
and severance taxes on coal extraction.
Table 5.1.5-2 presents the sources and levels of revenues collected in 2012 by borough and
community governments within the study area. Property taxes accounted for a large share of
revenues in the Matanuska-Susitna Borough, Fairbanks North Star Borough, and Municipality of
Anchorage. In addition, a substantial percentage of local government revenues comes in the form
of transfers from the state, primarily as direct state funding of local education programs, and
from the federal government. A large portion of state funding is derived from state oil revenues.
Local government expenditures of boroughs and communities in the study area are shown in
Table 5.1.5-3. Education accounts for a large proportion of local government spending. Alaska
school districts get about two-thirds of their operating money from the state, and the rest comes
from a combination of local and federal money (Goldsmith 2008).
5.1.6. Ecosystem Services
This section examines the relationships between ecosystem services that are valued by society
and human actions (positive or negative). Ecosystem services are biophysical components of
nature, such as a river, lake, mountain vista, fish population, or forest, that benefit humans by
enhancing their welfare or well-being (Boyd and Banzhaf 2007; Fisher et al. 2009). For example,
a fish population in a stream habitat with scenic surroundings is a combination of ecosystem
services that can generate recreational benefits for angles. Visually available natural resources in
proximity to the fishing area are a service because they contribute to the aesthetic enjoyment of
the angling experience. The target fish population is a service because the possibility of a catch is
also important to the experience (Boyd and Banzhaf 2007).
Ecological services are site-specific, with important place-based quality differences.
Consequently, the level of benefits derived from a given ecosystem service is specific to that
service. Spatial context matters for another reason as well, this one related to the economic value
of the benefits of given ecosystem service (Boyd 2011). In general, the value of any ecosystem
service is higher the scarcer it is—a recreational fish population is more valuable if it is the only
population in a region; it would be worth less if recreational fishing opportunities in the region
are plentiful. The value of an ecosystem service also depends on how many people depend on the
fish population, which is a function of where they are in relation to the population. Fish stocks
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important to recreation are more valuable when more people can enjoy them; therefore, sport
fishing areas closer to highly populated areas or located in frequently visited tourist areas may
likely generate a different set of values than more isolated areas. Further, many ecosystem
services are more valuable if they are bundled with certain manmade assets. These assets are
called “complements” because they complement the value of the ecosystem service. For
example, roads, trails, docks, and boat ramps are spatially configured complements that can
increase the value of a recreational fish population (Boyd and Banzhaf 2007; Boyd 2011).
Drawing on the analytical frameworks developed by King and Mazzotta (2000), Black et al.
(1998) and Colt (2001), the benefits associated with ecosystem services potentially affected by
the Project can be classified into two main categories: use benefits and non-use, or “passive use”,
benefits.
Use benefits include:
• In-river recreation (fishing, river rafting and boating)
• Near-river recreation (hunting, snow machining)
• Subsistence
• Commercial natural resource extractive uses (fishing, farming, logging, mining)
• Aesthetic enjoyment (as reflected in property values)
Non-use benefits include:
• Existence benefits
• Bequest benefits
Use benefits derived from ecosystem services are those benefits that humans realize through
direct or indirect use of an ecosystem and its services (Black et al. 1998; King and Mazzotta
2000). In the case of the Project, some of the relevant use benefits are associated with river-based
ecosystem services that are traded in conventional markets with buyers and sellers and
established prices. For example, commercial extraction of Susitna River-origin salmon produces
marketed benefits that accrue to the fishermen as additional profits and to consumers as taste and
nutritional satisfaction. Other use benefits generated by ecosystem services are not traded in
conventional markets. A recreational angler, for instance, may value the activity of catching a
salmon in a scenic tributary of the Susitna River despite the fact that he or she pays no explicit
price for that activity. Other possible recreational benefits derived from ecosystem services that
are not bought and sold in markets include sport hunting, river kayaking, snow machining, and
hiking. In addition, subsistence activities, which by definition are not part of the market
economy, are valued in Alaska for their nutritional, economic, cultural, and social benefits.
Whereas use benefits are based on actual use of an ecosystem and its services, non-use benefits
are benefits that are not associated with actual use. For example, some individuals may simply
value the knowledge that a given ecosystem service is intact (existence benefits), and that it will
be conserved for future generations (bequest benefits) (Black et al. 1998).
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Economic valuation and economic impact analysis are two widely used but distinctly different
economic measures. As discussed in Section 5.1.2.5, economic impact analysis provides
information on how policy changes affect economic activity, as measured in terms of
sales/output, income, and employment, in communities, counties, or even at the state or national
level. In contrast, economic valuation is a measure of net changes in human well-being or
welfare—any human action that increases welfare is a benefit and any action that decreases
welfare is a cost.
Economic welfare includes what economists call consumer surplus and producer surplus.
Consumer surplus is the net value consumers receive from a good or service over and above
what they actually pay for the good or service. Producer surplus (also called economic rent) is
the difference between what producers actually receive when selling a good or service and the
amount they would be willing to accept for the good or service. While not an exact measure of
social welfare, the sum of the consumer and producer surplus that results from a change in the
level of benefits generated by an ecosystem service provides a useful approximation of the
service’s net value.
The consumer and producer surplus associated with the benefits generated by an ecosystem
service can be evaluated in dollar terms, at least in principle, using an array of methods
developed by economists. However, no single method can capture the total value of the many,
disparate benefits provided by a complex natural asset such as the Susitna River watershed.
Moreover, although the values of some benefits derived from ecosystem services can be readily
monetized, the values of others can be done so only with great difficulty and uncertainty
(Johnston et al. 2002). For example, estimation of consumer surplus is relatively straightforward
if the benefits are traded in traditional markets with market prices. The benefits derived from
salmon caught in a commercial fishery is one example, and while benefits from recreational
activities typically are not produced and traded in the private market economy, exceptions exist,
including those associated with guided fishing, hunting, river rafting , and snow machining trips.
However, as discussed above, the benefits of some ecosystem services accrue directly to people
without passing through the market economy. There are two general types of approaches for
estimating gains (or losses) in welfare or well-being associated with changes in the level of
benefits. The first approach, which is to conduct primary research, can be subdivided into
indirect (revealed preference) and direct (stated preference) methods. Indirect methods include
the travel cost method, while direct methods include the contingent valuation method. Both
indirect and direct methods require the collection of new data, which may be costly and time-
consuming. Consequently, some economists have adopted the second approach, commonly
called benefit transfer, whereby existing valuation information for a benefit derived from an
ecosystem service in one location is used to estimate the value of a benefit from an ecosystem
service in another location.
As noted previously, converting the benefits of ecosystem services to a common comparable unit
(dollars) often represents a major challenge to economics. However, the valuation of the benefits
of ecosystem services in monetary terms is not required to enable the explicit inclusion of
ecosystem services in decision making; other valuation metrics can be included in describing and
measuring the benefits of ecosystem service. The following sections discuss the benefits of
various ecosystem services that could be potentially affected by the proposed Project. Monetary
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measures are included to the fullest extent that they can be usefully estimated, as well as
qualitative measures of the benefits that are difficult to quantify but, nevertheless, important to
consider.
5.1.6.1. Use Benefits
This section describes current use benefits generated by ecosystem services occurring in the
Susitna River corridor and upper watershed. These use benefits can derive from market-related
activities or non-market activities, and activities that are extractive or non-extractive.
5.1.6.1.1. Recreational benefits
The Recreation Resources Study (Study 12.5) will provide a description of the recreation
resources and facilities that currently support both commercial and non-commercial recreation in
the Susitna River watershed and estimates of current levels of recreational use in the region.
5.1.6.1.2. Commercial natural resource extraction benefits
This section describes natural resources in the vicinity of Project components (i.e., dam and
powerhouse, impoundment area, and road and transmission line corridors) that are related to
commercial extractive uses, including fishing, agriculture, mining, and logging.
Commercial fishing
The Susitna River and its tributaries are important producers of salmon for the Cook Inlet
commercial fishery and also support high levels of sport fishing. The Analysis of Fish Harvest
Study (Study 9.15) describes baseline harvest levels and harvest locations for commercial
fisheries for Susitna River-origin resident and anadromous fish. This section will be completed
after data from this study become available.
Agriculture
The area around some Project components, such the dam and impoundment area, is not
conducive to grazing or agriculture because of the remote, mountainous location, high average
elevation and scarcity of well-drained soils without permafrost.
However, small- or medium-sized commercial agricultural development occurs in the Trapper
Creek area and near the Talkeetna River within areas of fairly level terrain and suitable soil
conditions (Alaska Department of Natural Resources 2011; Alaska Department of Natural
Resources 2012a). Most farmers fall into a “truck farmer” category, selling their goods in local
markets which advertise locally-grown products, especially root vegetables and potatoes;
weekend farmers’ markets, such as the Denali View Farmers’ Market in Trapper Creek, also
draw customers within the region during the summer. Generally, farm production is small in
volume (with higher unit costs), has low to medium market values, and may have a shelf life that
further limits sales areas.
There are also areas near the railhead at Chulitna and Gold Creek with soils suitable for
agricultural development. However, due to limited access, commercial agricultural development
in these areas is not feasible at this time. The small agriculture uses and development currently
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occurring in these areas is restricted to supporting local residents’ rural lifestyles (RWS
Consulting 2010).
Mining
Project components lie within the 5.7 million-acre Valdez Creek Mining District. This district
has a long history of mining, with gold being the principal metal recovered. Nearly all gold
production has come from placer deposits in the drainage of Valdez Creek, a tributary of the
upper Susitna River (Kurtak et al. 1991). While some mining activity continues in the district,
there is much less than in the past. However, there are still concentrations of state mineral leases
and federal patented mining claims in areas such as Valdez Creek.
The U.S. Bureau of Mines’ most recent mineral resource assessment of the Valdez Creek Mining
District was conducted in 1987-1990 (Kurtak et al. 1991). The assessment concluded that three
sites in the district had high mineral development potential. The highest of these was the Valdez
Creek Mine, which contained high grades and resources well delineated by drilling. This was
followed by placers on Yacko Creek and Gold Creek (East), which have lower grades and
unproven resources. A total of 51 sites were given a moderate rating, 84 low, 69 unknown, and
18 unevaluated (Kurtak et al. 1991).
Various placer gold mining operations have been conducted in the Valdez Creek drainage
beginning in the early 1900s (Kurtak et al. 1991). In 1992, the Valdez Creek Mine was the
largest placer gold mine in North America and was operated by Cambior Alaska, Incorporated.
Surface pit mining operations were used to extract gold between 1983 and 1995. Placer gold
production from the mine during this period was several times greater than the total previous
production for the entire district. Reclamation was conducted concurrently with mining where
possible, with final reclamation completed one year after the Valdez Creek Mine ceased
production in 1995 (Kell 1991; King 1997). Since then there have been no further large-scale
operations in the Valdez Creek drainage. An unpaved access road leading from the Denali
Highway to the Valdez Creek Mine site and several mining claims further upstream remains
open (King 1997). In 2008, CA Gold, LLC began a placer gold mining operation in the Rusty
Creek drainage, a tributary of Valdez and White Creeks (CA Gold 2013). As with most gold
produced in Alaska, the gold is processed on site and transported by air from the mining
operation because of the high value by weight.
The Broad Pass coal field is located about 160 miles south of Fairbanks along the west side of
the Parks Highway and is considered a northeastern extension of the Cook Inlet/Susitna coal
province, which contains Alaska’s most accessible and second largest coal resource base.
Identified resources in the Broad Pass coal field are estimated at 50 million short tons (Diel
1991). Between 1940 and 1954, the W.E. Dunkle Mine near the West Fork of the Chulitna River
produced subbituminous coal from the Costello Creek coal basin, a small inlier of the Broad Pass
coal field (National Park Service 1988; Kurtak et al. 1991). No further production has occurred
in the coal field. Coal-bearing continental rocks of Pliocene age crop out along Watana Creek
about 40 miles southeast of Broad Pass. The coal is reported as both subbituminous and lignite;
however, its thickness and extent is not known (Diel 1991).
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Oil and gas potential in the vicinity of Project components is unknown but expected to be limited
because appropriate sedimentary rocks to generate and reservoir hydrocarbons are absent
(Alaska Department of Natural Resources 2011).
Forestry
Forest resources in the area surrounding Project components consist of large, mostly
undeveloped forested stands with spruce and hardwoods such as birch, aspen and cottonwood.
Most timber stands in the area originated from fire, whether due to lightening or, more likely,
from development and early operations of the Alaska Railroad (Todd and Jewkes 2006).
Commercial use of forest resources in the study area has been limited to relatively small logging
operations in the lower basin of the Susitna River. Vegetation in the upper basin is almost
entirely undisturbed, cannot be accessed by roads, and is not addressed in the most current
forestry and land management plan for state land in the region (Alaska Department of Natural
Resources 2011).Table 5.1.6.1.3-1 shows the location and scale of current forest uses on
Matanuska-Susitna Borough land in the vicinity of Project components. The table describes the
amount of timber available for harvest in selected natural resource management units during the
2013–2017 period. At the request of the Trapper Creek Community Council the borough is
proposing timber harvests in the Susitna River Corridor Unit, which is located within the
community council’s area. The harvests are expected to help meet the needs of small commercial
logging operators and the need for personal use firewood, wood products for crafts and
woodworking, and house logs. Trapper Creek would also benefit from the opportunity for jobs,
sales of fuelwood, and other ancillary items associated with timber harvest activities. The
borough is also proposing timber harvests in the Rabideux Creek and Parks Highway Units, but
all commercial and personal use firewood sales are eliminated from the units. These units are
dedicated as a Susitna Valley High School fuel wood forest to supply the fuel wood needs of the
school’s proposed wood heat project (Matanuska-Susitna Borough Community Development
Department 2013).
With respect to current forest uses on State of Alaska land close to Project components, the
Susitna Matanuska Area Plan (Alaska Department of Natural Resources 2011) provides a broad-
scale analysis of the types of land uses appropriate on different areas of state land in the Susitna
Valley. According to the plan, forestry is an allowed use on approximately 683,000 acres of the
8.6 million acres in the planning area. As detailed in the 2014-2018 five-year schedule of timber
sales, a large portion the proposed sales in the planning area is from the West Petersville Block
near Trapper Creek (Curran 2013). However, the sales in that block were designed for a large-
scale wood chipping operation that had ceased operating in the Matanuska-Susitna Borough by
2007. Moreover, when the chipping operation was still active in the area it had opted not to buy
timber in the same block during a previous sale because it was uneconomical based on the price
of the timber and the distance to its wood chipping facilities at Port MacKenzie (Bauman 2006b).
The deep-water port at Point Mackenzie provides a shipping facility with the equipment to
directly load wood chips or other products onto ships or barges for shipment to markets in
Alaska, the Lower 48 or Asia. However, wood chips are a low unit value commodity very
sensitive to the cost of transportation from the standing forest to the port facility (Metz 2007).
It is likely that the majority of the timber from timber sales on state land in the Susitna Valley
will be sold for house logs and the commercial firewood business. House logs are higher quality
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products used by custom home or cabin firms; there are relatively few of these products per acre
and lower-quality timber generally dominates area forests. Personal use firewood demand
continues to be high in Southcentral Alaska due to high fuel prices. Housing starts in the
Matanuska-Susitna Valley are down, so the availability of firewood from private land clearing is
also down. This has put more demand on the state to produce firewood for both personal use and
commercial markets. The high demand presents a major challenge due to limited access into
areas with firewood volume (Alaska Department of Natural Resources 2012b).
Currently, there is only limited commercial harvest of forest resources on private land near
Project components. For example, private forested land in the Talkeetna area that has been
converted to other uses (e.g., gravel pit development and subdivision clearing) has been the
source for a large portion of the logs needed by a birch bowl mill in Fairbanks (Jandreau 2013),
and a birch syrup producer harvests sap on a 12-acre homestead near Quiet Lake, located about
30 miles east of Talkeetna (Kahiltna Birch Works 2013). CIRI land managers have stated an
interest in selling forest products associated with land clearing and fire mitigation activities.
However, a possible economic disincentive is a provision of ANCSA which would require CIRI
to contribute 70 percent of its revenues from timber harvest to a common pool shared with other
Native regional corporations. Native village corporations are not subject to the 70 percent
revenue-sharing provision, and they have been more active in timber harvesting. For example,
Knikatnu, Inc. has harvested timber in conjunction with a local program of shareholder training,
(Northern Economics 2009), and the village corporation is currently cutting 30 acres for a land
use conversion (Jandreau 2013).
The relatively low value of Southcentral Alaska’s forests is an economic hurdle for any long-
term forest management program in the region. Due to a high defect rate, the paper birch and
spruce forest in the Susitna Valley historically has had little economic value in comparison to the
state’s coastal Sitka spruce-hemlock forests or the interior spruce-hardwood forests north and
west of the Alaska Range (Northern Economics 2007; Northern Economics 2009; Alaska
Department of Natural Resources 2010). Moreover, limited local demand reduces the ability to
pay for higher capital cost equipment, such as mechanical feller-bunchers, log skidders, firewood
processors, and sawmills. These factors have made it difficult to create more than the few jobs
offered by firewood suppliers, log home builders and very small sawmills (Alaska Department of
Natural Resources 2010).
On the other hand, growing interest in biomass for energy may provide wider opportunities for
use of Southcentral Alaska forests. This increased demand for lower-quality wood should have a
positive impact on the ability of local mills to economically access higher quality timber they
require to meet local manufacturing and raw lumber demands. The purchaser of a timber sale can
sort higher-quality logs coming to a landing, sell the saw logs and house logs to local mills and
process the remainder for firewood, chips, or other wood products (Alaska Department of
Natural Resources 2012c).
5.1.6.1.3. Aesthetic benefits and property values
Aesthetics generally refer to the identification of visual and auditory resources and the quality of
what can be seen or heard, or overall visual and auditory perception of the environment. People
are attracted to the aesthetic provided by an ecosystem service such as woodlands, hills and
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valleys, or water features, such as rivers or lakes, and are often willing to pay a premium to
experience this aesthetic pleasure. The Aesthetic Resources Study (Study 12.6) describes
existing aesthetic (e.g., visual, auditory) conditions in the study area.
Ecosystem services with aesthetic qualities are not just important for periodic human enjoyment,
but can also have economic importance by influencing real estate prices. For example, when
people purchase a home near an aesthetically gratifying ecosystem service, the housing price is
related to the value of the characteristics of the house and property, including the consumer
surplus derived from the ecosystem service. Moreover, the value of aesthetically-pleasing real
estate used for commercial purposes, such as a tourist lodge or resort, reflects the producer
surplus accruing to the property owner.
However, a beautiful vista yields use value only when people have access to it. Currently, there
are no access roads to the area around the proposed Project dam site and impoundment area. This
isolation, combined with a lack of public utilities and available private property, has created few
opportunities for human settlement. Commercial development has been limited to a small
number of fly-in lodges that have capitalized on the pristine qualities of nearby lakes and
streams. For additional information on transportation assets in the study area, see the
Transportation Resources Study (Study 15.7).
Given that land prices reflect the value of property attributes, including access to aesthetically
pleasing ecosystem services, it is instructive to examine the appraised value of land parcels in the
vicinity of the proposed Project dam site and impoundment area and compare those values to the
values of parcels in more accessible and developed areas. The database provided by the MSB
(Matanuska-Susitna Borough 2013) was used to describe the current values and key
characteristics of selected land parcels. This database contains the current assessed value of
properties in the borough and identifies the locations of the properties and their total acreage.
Table 5.1.6.1.4-1 summarizes these key features for various parcels. The per-acre value of a
parcel was calculated by dividing the appraised value by the gross acreage.
As shown in Table 5.1.6.1.4-1, the appraised value of undeveloped land parcels in the vicinity of
the proposed Project dam site and impoundment area are relatively low. Undeveloped land
bordering Stephan Lake also has a low value; however, the value of a lakeshore parcel improved
with a commercial fishing/hunting lodge is substantially higher (structural components are not
included in the reported parcel land value). The value of this parcel is comparable to that of a
parcel along Christiansen Lake that is located on a borough-maintained road but off the electrical
grid. Christiansen Lake is located about three miles from downtown Talkeetna. A parcel fronting
on Christiansen Lake near a paved road and with electric and telephone access has a substantially
higher appraised value.
5.1.6.2. Non-Use Benefits
As discussed above, non-use benefits are associated with the gain in a person’s welfare or well-
being without actually “using” an ecosystem service. They encompass the existence benefits of
knowing the service exists and bequest benefits of knowing that future as well as current
generations may enjoy the service.
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There are no known data on individuals’ non-use benefits for ecosystem services occurring in the
Susitna River corridor and upper watershed, and directly measuring these benefits was beyond
the scope of this analysis. Having identified that the non-use benefits are unknown, the issue that
arises is how significant is this omission—in other words, what is the likely magnitude of the
value of these benefits? To address this question, the current analysis presents relevant
information or studies that might indicate the significance of the non-use benefits of the
ecosystem services offered by the Susitna River corridor and upper watershed. First, the
literature pertaining to indicators of significant non-use benefits is briefly reviewed. Next, the
analysis examines how non-use benefits were measured in other contexts. Particularly relevant
are two studies described by Colt (2001) that explored non-use benefits associated with Alaska
wilderness areas. Lastly, the analysis describes how indicators of significant non-use benefits
may apply to ecosystem services derived from the Susitna River corridor and upper watershed.
5.1.6.2.1. Literature Review
The notion of non-use benefits was first proposed by Krutilla (1967) who observed that people
might be willing to pay to preserve a particular natural asset (i.e., ecosystem service) even if they
knew they would never visit it because, as he put it, they “obtain satisfaction from mere
knowledge that part of wilderness North America remains even though they would be appalled
by the prospect of being exposed to it.” By way of example, Krutilla describes “an area with
some unique attribute of nature—a geomorphologic feature such as the Grand Canyon, a
threatened species, or an entire ecosystem or biotic community essential to the survival of the
threatened species.” Krutilla also notes that non-use benefits are not generated by only natural
assets; they apply equally to cultural heritage assets (cf., Navrud and Ready 2002; Noonan
2003).
Krutilla emphasizes that the uniqueness of a natural or cultural asset need not be absolute for his
arguments to hold. What is essential he argues is that there be “no adequate substitutes” for the
asset. Subsequent researchers have noted that non-use benefits may be present for natural assets
more common than, say, an endangered species, but assets with unique and irreplaceable
character likely have the highest non-use benefits (Madariaga and McConnell 1987; Harpman et
al. 1994; Crowards 1997).
Further, Bishop and Welsh (1992) maintain that a person’s lack of knowledge of a natural asset
does not mean that the individual holds no non-use value for the asset, but may simply indicate
that there have not been past opportunities or motivations for the individual to gather specific
information about the asset. So, even if, as Randall (1986) suggests, “individuals place no value
on resources of whose existence or usefulness they are entirely unaware”, this does not deny that
such individuals could suffer a loss of well-being on learning of their loss (Crowards 1997).
In principle, non-use benefits for a particular natural asset may be held by anyone because they
may be held by individuals who never visit or otherwise use the asset. Moreover, there is no
reason to expect non-use benefits to decline with increasing distance of individuals from the
asset since, unlike the case for use benefits, there is no relationship between benefits and costs of
access (Bateman et al. 2005). However, Hanley et al. (2003) suggest that there may be a cultural
identity or “ownership” dimension to non-use benefits, with those who live closer to a natural
asset expressing relatively higher non-use benefits. For example, the average Alaskan may hold
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stronger non-use benefits for Alaska wildlife sites than the average resident of the Lower 48,
because Alaskans view their state’s natural resources with a higher degree of ownership or
attachment. Notwithstanding these higher regional values, if interest in the affected resource is
widespread, then even a small per-person or per-household value can be large when extrapolated
across the entire population holding non-use benefits.
In summary, the literature emphasizes that non-use benefits are most likely to be greater where
the natural asset in question is unique and/or where adverse impacts are irreversible, and where
the asset is widely acknowledged to be of significance. As stated in the U.S. Department of
Interior’s proposed rules for natural resource damage assessment:
…an injury to a common natural resource with many substitutes (e.g., a typical small stream),
may not generate large nonuse values, particularly for those residing outside the area where the
injury occurred, even if the recovery takes a long time. However, a permanent injury to a unique
resource (e.g., the Grand Canyon) may generate significant nonuse values, even for those
residing in areas far removed geographically from the site where the injury occurred (U.S.
Department of the Interior 1991, cited by Harpman et al. 1994)
5.1.6.2.2. Non-Use Benefits of Alaska Wilderness
Colt (2001) used existing studies to estimate the possible magnitude of non-use benefits for
wildlife habitat and wilderness areas in Alaska. He calculated both a lower and upper bound
estimate. For the lower bound estimate, Colt examined the non-use benefits used to quantify
damages in the litigation arising from the Exxon Valdez oil spill in Prince William Sound, one of
Alaska’s premier ecosystems with a diversity of wildlife and habitat. In the lawsuit filed by the
State of Alaska and U.S. Department of Justice the state conducted a survey to estimate the value
that the American public placed on the natural integrity of Prince William Sound. It did not focus
on people who lost recreational or business opportunities but looked instead at people who might
not have and might not ever visit Alaska but who were nonetheless upset by the damage caused
by the spill. Based on the responses, the Alaska government estimated $2.8 billion in lost non-
use benefits as a result of the approximately 1,200 miles of Alaska coastline that were damaged
by the spill (Carson et al. 1992). Since this figure is a one-time payment, Colt converted it to an
annualized value—about $3 per U.S. household per year.
To form an upper bound estimate, Colt examined the study by Goldsmith et al. (1998), which
considered the existing literature on non-use benefits in relation to the ecological values of three
wildlife refuges in the Bristol Bay area. These refuges comprise 13.2 million acres, with 5.5
million acres of designated wilderness. Goldsmith et al. concluded that the existence value to
U.S. households of these refuges ranged between $25 and $50 per U.S. household per year.
Colt observes that these lower and upper per household values are very high when extrapolated
across the entire U.S. population that potentially holds these values. As a partial measure of the
total non-use benefits of Alaska’s public protected areas, he estimates that U.S. households
receive up to $30 billion worth of value per year from the continued preservation of these areas
in their undeveloped state. Colt concludes that while this estimate is highly uncertain, the
available evidence supports the conclusion that Alaska’s unique, pristine natural environment
produces billions of dollars of non-use value every year for U.S. citizens.
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Colt also notes that the non-use value of Alaska’s functioning ecosystems far outweighs the use
value. Goldsmith et al. (1998) report a similar finding in their analysis of the economic value of
Bristol Bay area refuges, and they explain their study results as follows:
This huge disparity exists partly because the remoteness of the refuges, the expense of visiting
them and the harsh weather during much of the year all limit refuge use. But a more important
reason is that Americans as a whole place a high value on refuge lands in Alaska, whether they
ever visit them or not. Alaska is still, in the minds of many Americans, the last part of the country
with huge untouched areas and prime fish and wildlife habitat. As a result, the “existence value”
of the Bristol Bay refuges is very high.
5.1.6.2.3. Potential Study Area Non-Use Value
From a national, regional, and local perspective, the ecosystem services offered by the Susitna
River corridor and upper watershed may be sufficiently unique and important that the non-use
value of these services is comparable to those for wilderness in other regions of Alaska.
A preliminary list of the specific attributes of the Susitna River corridor and upper watershed that
may have non-use value is presented below based on information in the Pre-Application
Document and other secondary sources.
• The Susitna River is among the four most important Alaska rivers used by salmon species
when spawning, and the river and its tributaries support the second largest salmon-
producing system within Cook Inlet. In 2008, Susitna River sockeye salmon were
established as a stock of yield concern by the Alaska Board of Fisheries. In 2011, the
Willow and Goose Creeks Chinook salmon stocks were found to be a yield concern, and
the Alexander Creek Chinook salmon stock was designated a stock of management
concern (Alaska Energy Authority 2011).
• The upper Susitna River drainage supports one of the northernmost populations of wild
rainbow trout in North America (Alaska Energy Authority 2011).
• Wetlands cover large portions of the Susitna River basin, including riparian zones along
the mainstem Susitna River, sloughs, and tributary streams. Wetlands generally support a
greater diversity of wildlife species per unit area than most other habitat types in Alaska
(Alaska Energy Authority 2011).
• The Susitna Flats State Game Refuge supports spectacular spring and fall concentrations
of migrating waterfowl and shorebirds. As many as 100,000 waterfowl use the Susitna
refuge to feed, rest, and conduct their final courtship prior to nesting. (Alaska Department
of Fish and Game 1988).
• The Nelchina caribou herd near the upper Susitna River is the third largest caribou herd
in Alaska and the largest herd south of the Brooks Range. The herd is the only large
caribou herd in the state accessible by the road system from the major population centers
of Fairbanks and Anchorage. Accessibility to human population centers makes the herd
particularly vulnerable to overharvesting by sport and subsistence hunters (Alaska Energy
Authority 2011).
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• The 11-mile long Devil’s Canyon section on the upper Susitna River is widely
recognized as one of the most formidable and challenging stretches of whitewater in
North America and has been called the “Mt. Everest” of whitewater kayaking. It joins the
Grand Canyon of the Stikine and Turnback Canyon on the Alsek as one of North
America’s legendary Class V rapids (Alaska Energy Authority 2011).
The rapids in Devil Canyon are so exceptionally violent and spectacular as to constitute a
nearly unique aesthetic and recreational resource. Most Alaskan rivers occupy broad
glacially scoured valleys, and whitewater beyond Class III is rare. Only three major
whitewater rivers are known in Alaska: the Susitna and the Bremner in the Southcentral
Region, and the Alsek in the Southeast (Jones & Jones 1975).
• Of the five stocks of beluga whales in Alaska, the Cook Inlet stock is the only one listed
as endangered under the Endangered Species Act. The Susitna River mouth and delta are
vital habitats for the Cook Inlet beluga whale (Alaska Energy Authority 2011).
5.1.7. Quality of Life
The quality of life analysis focuses on potential fundamental changes in lifestyle due to the
influx of workers during the construction phase of the Project and increased residential and
commercial development during the operations phase. Only the residents of the communities in
relatively close proximity to Project components, including the dam and powerhouse,
impoundment area, and road and transmission line corridors, would potentially experience these
effects. These communities are Talkeetna, Trapper Creek, Cantwell, and Chase and other nearby
“railroad communities.”
People frequently justify the location of their home due to the perceived quality of life the area
provides. However, the quality of life variable is often difficult to define, as it is inherently
intangible and subjective. Quality of life can be described as the personal satisfaction (or
dissatisfaction) with the attributes of the area in which one lives, including environmental,
economic, cultural, or social conditions (National Park Service 2006).
The first part of the quality of life analysis provides a historical overview of the development of
each of the communities selected for study. The second part defines existing conditions in the
communities in terms of five quality of life indicators: rural character, pace of life, self-sufficient
lifestyle, community image, and community cohesiveness.
5.1.7.1. Community Overviews
5.1.7.1.1. Talkeetna
Located at the confluence of the Susitna, Chulitna, and Talkeetna Rivers, Talkeetna is reportedly
the site of a Dena’ina (Tanaina) village; Talkeetna is a Dena’ina word meaning “river of plenty”
(Braund and Lonner 1982). The Dena’ina preferred to locate their village, camp, and activity
sites at the confluence of streams containing anadromous fish runs, especially where clear
tributaries joined with turbid streams and rivers (Matanuska-Susitna Borough Planning
Department 1998) . The area developed into a mining town and Alaska Commercial Company
trading post in 1896. A gold rush to the Susitna River brought prospectors to the area, and by
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1910 Talkeetna was a riverboat steamer station, supplying miners and trappers in the Cache
Creek, Iron Creek, and Broad Creek districts. In 1915, Talkeetna was chosen as the headquarters
for the Alaska Engineering Commission, which built the Alaska Railroad, and the community
population peaked near 1,000 (Alaska Department of Commerce 2013c). Talkeetna was chosen
as district headquarters for rail construction because of its accessibility by river. Freight was sent
up the river by steamer before being off-loaded at Talkeetna. Construction of the railroad
brought new people into the area, including surveyors, engineers, teamsters and packers,
foremen, timekeepers, storekeepers, blacksmiths, timber men, and carpenters (Matanuska-
Susitna Borough Planning Department 1998).
The 1918 influenza epidemic and 1923 completion of the railroad resulted in a sharp drop in
Talkeetna’s population. However, it continued to survive with a combination of miners, haulers
and packers for the mines, trappers, and homesteaders. The town remained a supply center for
area miners until 1940, when most of the mining operations shut down due to the onset of the
Second World War and a decline in the production of many of the richest mines. Although a
small recovery in the local mining industry occurred in the late 1940s, nearly all mining activity
had ended by the mid 1960s because of increased operational costs and a fixed gold price
(Matanuska-Susitna Borough Planning Department 1998; Talkeetna Historical Society 2013a).
In 1964, Talkeetna connected to the Parks Highway by the 14-mile Talkeetna Spur Road,
opening up the area to vehicle access and development (Talkeetna Historical Society 2013a).
State land disposals and homestead programs helped the community grow through the 1970s and
1980s (Alaska Department of Commerce 2013c). In addition, the construction in 1969 of the
COMSAT Station (now the Bartlett Earth Station) five miles north of Talkeetna created well-
paid, year-round jobs, which strengthened the local economy (Matanuska-Susitna Borough
Planning Department 1999). As people began to settle along the Talkeetna Spur Road, the
community’s development began to change from a compact settlement located along the
riverfront to a more scattered and populated rural hinterland whose population depended on the
community for a range of community services, including electricity, fire protection, and medical
and education facilities (Matanuska-Susitna Borough Planning Department 1999).
5.1.7.1.2. Trapper Creek
In the early twentieth century, a road house located on the Trapper Creek side of the Susitna
River served as a stopping point for freighters and miners. However, because it had the railroad,
Talkeetna, and not Trapper Creek, became the center of mining activity (Braund and Lonner
1982). In 1920, the Alaska Road Commission started construction of a wagon road, known now
as the Petersville Road, to Cache Creek from Talkeetna (Alaska Department of Commerce
2013c). Federal homesteading began in the area in 1948. The usable agricultural land in the
Trapper Creek area attracted the first homesteaders in the mid-1950s (Braund and Lonner 1982).
In 1959, the “Fifty-Niners,” a group of settlers from Detroit, Michigan, moved to Talkeetna and
then on to Trapper Creek to find homesteads. They lived in trailers and tents before building log
cabins on land cleared by earlier settlers, primarily along the Petersville Road and Trapper
Creek. Most of these homesteaders worked on the construction of the Parks Highway, which
opened as far as the Petersville Road in 1967 and was completed in 1971 (Matanuska-Susitna
Borough Planning Department 1998; Agnew::Beck Consulting 2006; Alaska Department of
Commerce 2013c; Trapper Creek 2013a).
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Since the 1960s, homesteads, state land disposal program parcels, and scattered subdivisions
have provided the residential land base for Trapper Creek (Braund and Lonner 1982). In 1981,
for example, two state residential subdivisions were approved—Peters Creek Subdivision, which
platted about 100, mainly 5-acre lots located in the Jake Lake area and spread over three miles
southward from the Petersville Road, and Kenny Creek Subdivision, which has approximately
200, typically 5-acre lots located from the Petersville Road up to 4.5 miles northward (Alaska
Department of Land and Natural Resources 2012).
Trapper Creek, like Talkeetna, became more diverse and complex with the highway connection,
arrival of new residents, and expansion of public services. Unlike Talkeetna, however, Trapper
Creek does not have a clearly recognizable townsite. Instead, it presently has a cluster of
buildings (residential and commercial) at the junction of the Parks Highway and the Petersville
Road (Braund and Lonner 1982; Trapper Creek 2013b). The majority of the population lives
near this intersection, and most of the land adjacent to the first two miles of the 40-mile long
Petersville Road is privately owned (Matanuska-Susitna Borough Planning Department 1998).
Currently, the road is paved to approximately mile 10, and year-round state maintenance ends
near mile 14 (Alaska Department of Land and Natural Resources 2012).
Most of the growth in population in the Trapper Creek area is tied to an increase in retired people
moving into the area. Trapper Creek is also a popular location for backcountry cabins and second
homes, most of which are owned by residents of Anchorage, Palmer, Wasilla, and other
population centers close to the area (Braund and Lonner 1982; National Park Service 2006;
Trapper Creek 2013b).
5.1.7.1.3. Cantwell
The Native Village of Cantwell is the only federally recognized Alaska Native village in the
study area. In 1980, Yedatene Na Corporation, Cantwell’s Native village corporation, merged
with the Native regional corporation, Ahtna, Inc. Under the terms of the merger agreement,
Ahtna, Inc. assumed the management of all former village corporation lands (Ahtna 2013).
The first known inhabitants of the Cantwell area were Athabascans, the Ahtna. In 1903, gold was
discovered on Valdez Creek and a small community of miners and Ahtna gathered to form the
first relatively permanent settlement in the area. Eventually, Ahtna from Valdez Creek settled in
Cantwell, where their descendents live today. In 1916, Cantwell was established as a
construction camp for the Alaska Railroad and soon after became a jumping off point for miners
and freight going to the Valdez Creek mine. In 1919, John Carlson and Jack West built a store at
Cantwell, which they operated for prospectors, miners, and trappers working in the area
(Simeone 2002). In the early 1920s, the Alaska Road Commission established a sled route
between Cantwell and Valdez Creek. By the mid 1930s, the trail had been upgraded to a gravel
road. This road would later become the Denali Highway (Bacon 1975).
By 1950, the population of Cantwell had swelled to 67 due, at least in part, to an influx of Ahtna
families who moved from Valdez Creek to Cantwell to work as laborers on the railroad.
Cantwell was linked to Alaska’s highway system in 1957, with the completion of the Denali
Highway (Simeone 2002). In 1971, the completion of the Parks Highway connected the
community to Anchorage and Fairbanks.
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After the Parks Highway became the principal access to Denali National Park and Preserve,
Cantwell reoriented itself toward the highway and in the process began to spread out. While a
few people still reside near the railroad, almost all of the businesses, including a restaurant, gas
stations, a bed and breakfast, and the post office are now located at the intersection of the Parks
and Denali Highways. This area, called “downtown,” also includes a number of residences, the
offices of the Native Village of Cantwell, and a large parking lot built to accommodate
recreational vehicles driven by tourists. A second group of homes, referred to as Cantwell
Heights, is located three miles from the highway in a relatively new subdivision that is on the
west side of the railroad tracks. A third group of houses, called the Drashner Subdivision, is
located on a lake three miles up the Denali Highway. More homes are dispersed along the Parks
Highway between mile 207 and mile 217 and along the first three miles of the Denali Highway
(Simeone 2002).
5.1.7.1.4. Chase and Other Railroad Communities
Chase and other nearby “railroad communities” are off the road system and are only served by
flag stop on the Alaska Railroad. Chase derives its name from Nancy Chase, the daughter of
R.D. Chase, who was a special disbursing agent for the Alaska Engineering Commission at
Anchorage. In 1919, the railroad station at mile 236.2 of the Alaska Railroad was originally
named “Nanchase.” A creamery was constructed at nearby Curry in 1927. This created a market
for milk produced in the Matanuska Valley. Most of the butter was sold through hotels operated
by the Alaska Railroad. In 1933 the creamery equipment was transferred to the Matanuska
Experiment Station in Palmer (Alaska Department of Commerce 2013c). In addition, Curry was
a railroad station with a hotel which, for more than 25 years, accommodated overnight
passengers and crew when the train took two days to travel between Anchorage and Fairbanks.
As late as 1958, Curry was still used as a crew change point. However, a fire and a trend towards
larger sections on the railroad led to the virtual desertion of the stop, and between Talkeetna and
Gold Creek very little remains from the early railroad days (Braund and Lonner 1982).
Most of the land between Talkeetna and Gold Creek is owned by the State of Alaska. However,
over 900 parcels passed into private control in the 1970s and 1980s through the state’s land
disposal programs (Chase Citizen's Planning Advisory Committee 1993; Alaska Department of
Commerce 2013c). While some individuals acquired land for seasonal recreational use or as an
investment, speculating that land values would increase in the future with the demand for
recreational and settlement sites, others obtained land in order to live full-time on their parcels
(Stanek et al. 1988). The majority of the people who settled in the area in the 1970s and 1980s
were young people in their twenties or thirties, many with young children (Braund and Lonner
1982). They were motivated by a desire to live a life with a slower pace than that of the city, to
live “close to nature,” and to seek a “healthier lifestyle” removed from the “pollution of
industrialization.” These settlers believed that living in an area of low population density
promoted cooperative social relationships. Additionally, they sought a perceived self-reliant way
of life based on hunting, fishing, and growing their own foods (Stanek et al. 1988). Many of
them as well as others who came later to the area were committed to remain on their land and
learn the necessary skills to live in a remote wilderness area (Braund and Lonner 1982).
Today, the majority of residents of the Chase area live within two miles of the railroad, but some
residents live along various lakes and streams several miles from the railroad tracks. The
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Hurricane Turn train, one of America’s last flag stop trains and which is currently operated by
the Alaska Railroad Corporation, delivers individuals to their remote residences in the area as it
has for the past nine decades (Alaska Railroad Corporation 2012).
5.1.7.2. Quality of Life Indicators
5.1.7.2.1. Rural Character
Residents of the selected study area communities have chosen to live in these communities
because of the remoteness and peaceful character of the area. As discussed in Section 5.1.1.1,
there are low human population levels in the area. Large tracts of undeveloped land surround
these communities; the natural environment dominates the landscape. Forests, streams, wildlife,
and viewsheds are highly valued by locals. The wild character of the area, or the feeling of
Alaska as the “last frontier,” is something that residents identify with and strive to maintain. The
legacy value of the area, or the ability for future generations to enjoy the area essentially
unchanged from its present condition, is important to local residents (National Park Service
2006). The Talkeetna Comprehensive Plan states, for example, that “…most choose to live [in
Talkeetna] because of [the] simpler lifestyle and the high quality of living it affords. People are
here because they value clean air and water, space to live, a safe place to raise children, a sense
of community, and wilderness out the backdoor” (Matanuska-Susitna Borough Planning
Department 1999).
5.1.7.2.2. Pace of Life
During most of the year, the pace of life within the selected study area communities is slow and
tranquil. Residents typically value the low numbers of cars in the planning area and freedom
from traffic congestion for the majority of the year (National Park Service 2006).
The summer months bring vast increases in the numbers of cars and people to the area, which in
turn greatly affects the pace of life for residents of communities on the highway system (National
Park Service 2006). The population of Talkeetna, in particular, substantially increases with
tourists as well as with students and others seeking seasonal employment (Talkeetna Chamber of
Commerce 2012). For residents living along the Petersville Road in Trapper Creek, there is also
a marked increase in traffic and congestion on weekends during the winter months from
snowmachiners. The road is a “snowmachiners’ mecca,” and the point of origin for a large trail
network in the area (Matanuska-Susitna Borough Planning Department 1998). Similarly, during
winter months, “…pullouts along the Parks [Highway] near Cantwell often are crowded with
empty snowmachine trailers, their owners playing in deep snow on wide-open tundra” (Alaska
Department of Natural Resources 2012d).
The sparse permanent population and intense seasonal public use of the area has resulted in a
local debate regarding the access issue. There has been a diversity of opinion among residents
regarding constructed access; some prefer a roadless system to preserve the rustic and private
atmosphere of their lots, while others would like to have year-round, maintained road access to
their property (Alaska Department of Land and Natural Resources 2012). Despite differences of
opinion about access and other specific issues, there is considerable common ground among
residents regarding the community characteristics and values they want to preserve into the
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future, and as discussed below in Section 5.1.7.2.4, residents are engaged in discussions to
develop workable solutions to development issues.
5.1.7.2.3. Self-sufficient Lifestyle
Residents of the selected study area communities do not have all of the modern conveniences of
living in a large city. The independence and resourcefulness that is associated with this type of
lifestyle is something the residents value, and keeps them living in these communities. Residents
pride themselves on being creative and being able to survive with the resources at hand (National
Park Service 2006). For instance, many Talkeetna residents have made a conscious choice to live
their chosen lifestyle off the grid, either doing without running water, electricity, or central
heating, or using modern technology (e.g., solar panel electric systems) to provide those
amenities in a self-sufficient manner (Talkeetna Historical Society 2013b).
The ability to provide for oneself or one’s family by subsisting on the land is vital to most local
residents’ quality of life (National Park Service 2006). Some households lead a semi-subsistence
lifestyle based on hunting, fishing, and gardening. As one Cantwell resident remarked: “We
don’t have Safeway, Carrs, or Fred Meyer where we live. Our grocery store is here where we
choose to live” (Simeone 2002). In the following excerpt Stanek et al. (1988) describe the “semi-
subsistence” economy of Chase and other nearby railroad communities, which includes both a
subsistence and cash income component:
This combination allows [residents of Chase, Gold Creek-Chulitna, and Hurricane-
Broad Pass] to live in an area that is marginal to the economic opportunities found in
more densely populated parts of the southcentral Alaska. Even the contrast between the
three study communities and the road-connected areas just to the south around Trapper
Creek and Talkeetna is notable. The economy of this latter area is organized around
providing services to highway travelers and visiting recreationalists … Most households
in the Trapper Creek - Talkeetna area use and harvest wild foods, but harvest quantities
arc relatively low. In contrast, harvests at Chase, Gold Creek-Chulitna, and Hurricane-
Broad Pass are much higher and approach those of other communities off the road
system such as Skwentna and Tyonek. Especially when the large harvest of garden
produce at Chase is considered, it is likely that most of these households are producing
much of their own food supplies. This economic pattern is a product of the relatively high
availability of wild resources, a low population density, a marginal cash economy, and a
value orientation conducive to living in a relatively remote area.
Given the limited number of full-time jobs in the selected study area communities, many
households derive cash income from trapping, small-scale gold mining, arts and crafts sales, and
seasonal or part-time employment outside the area. According to an inhabitant of Trapper Creek,
“people typically need cottage industry, subsistence, and seasonal work to get by. You need to be
willing to do a bit of everything” (Trapper Creek 2013b). The choice residents have made to
accept the limited employment and income opportunities in their communities in exchange for
the fishing, hunting, and other outdoor activities that are readily available is illustrated in the
following passage:
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Residents’ incomes are stretched further because of the distance (and price of fuel)
residents have to travel to obtain goods and services not available locally. Trapper Creek
incomes may, on average, be lower than state averages, but residents have the advantage
of direct access to world-class scenery, recreation, and subsistence resources. They enjoy
a sense of freedom and remoteness that more affluent communities cannot buy. Many
Trapper Creek residents have large gardens, subsistence fish and hunt, and have
wilderness out their backdoors. Trapper Creek, like most Alaskan rural communities, has
limited government services. This combination of local features makes it apparent why
residents stay, despite limited cash based economic opportunities and the higher costs of
living. It appears that Trapper Creek self selects residents who love the area, who want
to be off the beaten path, and who are resourceful enough to try and make a go of
generating an income in an area with limited employers (Trapper Creek 2013b).
A concern among many local residents is the negative effect that the growing number of visitors
to the area could have on their self-sufficient lifestyle. Because the area is accessible from large
population centers via the Parks and Denali Highways, competition over fish and wildlife
resources in the area is becoming increasingly intense. Pressure from urban hunters has,
according to some local residents, caused game populations to dwindle. One consequence of this
problem is that many Cantwell residents now hunt almost exclusively on Denali National Park
and Preserve lands, which are closed to urban residents. As members of a resident zone
community, Cantwell residents can conduct subsistence activities on land added to Denali
National Park and Preserve under the Alaska National Interest Lands Conservation Act (Simeone
2002; National Park Service 2013a). However, subsistence hunters accessing the park on off-
road vehicles are restricted to specific corridors located near Cantwell (National Park Service
2013b).
5.1.7.2.4. Community Image
An identifiable community image is another important quality of life indicator. Several of the
communities in the area have a rich history; maintaining the historic identity of the area is
important to residents. An identifiable community center (or business district) contributes to the
community image. Maintaining their existing community images is important to local residents,
including the perception of clean and safe communities (National Park Service 2006).
Many of the buildings in Talkeetna’s contemporary downtown date from the mining and railroad
era of the early 1900s. In recognition of the significance of this history, in 1993, a four-block
area of the downtown was designated on the National Register of Historic Places as a National
Historic District (Talkeetna Historical Society 2013a). Preservation of the contributing historic
structures in the downtown historic remains a high priority for the community (Matanuska-
Susitna Borough Planning Department 1999). To retain Talkeetna’s rural historic village
characteristics, local residents successfully organized to halt a number of development projects,
including an early state proposal to route the Parks Highway through the community, and a
federal proposal in 1992 to construct a large National Park Service facility in the town
(Talkeetna Historical Society 2013b). Furthermore, Talkeetna residents completed a community
comprehensive plan in 1998 in which they articulated their vision to ensure that the community
continued to be an “end of the road” village, maintaining its small-town atmosphere, sense of
community, and high quality of life.
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As discussed in Section 5.1.2.4, tourism is the main industry in Talkeetna, and protecting and
preserving the wilderness and natural resources while maintaining recreation and an ecologically
sound tourism economy are also key values (Matanuska-Susitna Borough Planning Department
1999; Talkeetna Historical Society 2013b). Since 1978, the Talkeetna Chamber of Commerce
has promoted a healthy tourist and recreation industry in the area as well as encouraged new
businesses to locate in the community (Braund and Lonner 1982). For example, in 2002, the
Chamber of Commerce, together with the Talkeetna Community Council, facilitated the
production of a Talkeetna Community/Tourism Plan (Agnew::Beck Consulting 2002). As a
result of the planning effort and community’s desire to mitigate side effects of rapid tourism
growth, a chapter of the document was dedicated to implementing special land use district
regulations in the downtown area and along the Talkeetna Spur Road.
Over the last several years, the residents of Trapper Creek and Chase have also responded to the
challenges and opportunities posed by growth. In the fall of 2003, with the assistance of the
Matanuska-Susitna Borough and the National Park Service, the community organized a first ever
community wide gathering called “Trapper Creek Tomorrow.” In late 2004, the community, with
assistance of the Borough, started the preparation of a Comprehensive Plan (Agnew::Beck
Consulting 2006). In 1992, the Matanuska-Susitna Borough Planning Department assisted the
Chase Citizens’ Planning Advisory Committee, which included local residents, land owners, and
persons with business interests in the Chase area, in preparing a comprehensive plan for the
management and development of lands in the area. The plan’s preface asserts “that the residents
have a right to preserve—to the extent consistent with State and Borough law—their subsistence,
wilderness lifestyle” (Chase Citizen's Planning Advisory Committee 1993).
5.1.7.2.5. Community Cohesiveness
Even though the selected communities in the study area have relatively small populations, and
the houses are fairly spread out from one another, there is still a strong sense of community.
Neighbors know one another and are willing to lend a helping hand to one another in times of
need. This familiarity and cooperation with neighbors is important to local residents and
distinguishes the small rural communities from larger urban communities (National Park Service
2006).
For Cantwell residents this cohesiveness is long-established and is partially rooted in traditional
Alaska Native beliefs in the social and cultural importance of subsistence. As one Cantwell
resident noted, “The community sustains itself on people passing meat back and forth” (Simeone
2002). This community tradition of sharing of subsistence resources and of the knowledge, skills,
and equipment required to harvest those resources has been passed from generation to generation
(National Park Service 2005).
For other communities, the development of a sense of cohesiveness is relatively recent. For
example, it was the influx of new settlers into the Trapper Creek area after completion of the
Parks Highway that “cemented” Trapper Creek’s existence as a community and engendered a
sense of cohesion among its residents (Agnew::Beck Consulting 2006; National Park Service
2006). In 1979, Trapper Creek residents formed the Trapper Creek Community Council, a five-
member elected advisory council intended to bring local issues into the open, afford residents the
opportunity for maximum participation in community self-government, and influence higher
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levels of government related to community development and services (Braund and Lonner
1982). The council continues to be active in promoting the general well being of the community
(Trapper Creek Community Council 2013). For instance, there is an emerging consensus in
Trapper Creek that the community needs to find new ways to increase tourism and develop job
and business opportunities, but do so in a manner that benefits local residents and retains the
qualities that make the community unique (Agnew::Beck Consulting 2006).
Over the years local residents have demonstrated their cohesiveness on some issues and diversity
on other issues. In 2006, for example, a proposed 1,286-acre state timber sale in the West
Petersville Road area of the MSB prompted a lawsuit on behalf of the Trapper Creek Community
and area land owners (Bauman 2006b; Bauman 2006a). An Oklahoma-based firm that produced
wood chips at a large-scale facility at Port MacKenzie and exported the chips to overseas
markets was expected to be the main bidder. Local residents said they saw no direct benefit to
their communities from the sale. However, some issues have been more divisive. The
aforementioned 1992 proposal by the National Park Service to construct a large visitor center in
Talkeetna received opposing opinions from area residents as illustrated by the following excerpt
from a magazine article covering the controversy:
Sandra Jacques, co-owner of Jake's Adventure Co., a Talkeetna-based rafting, hunting
and fishing service, agrees with [Steve Mahay, owner of Mahay's Riverboat Service] that
the proposed visitor center would not change the area significantly. “They (visitors to the
center) would come into town, spend their money and leave,” she says. Talkeetna
resident Doug Smith vehemently opposes the center and adjacent hotel. He points out
that the community has no zoning and that strip development along the spur road could
be disastrous. He also feels the large influx of summer visitors would turn the town into a
“mob scene” (Maschmeyer 1992).
5.2. REMI Model Development
In 2013, progress was made in developing the REMI model assumptions for comparing future
socioeconomic conditions with and without the Project by conducting interviews with industry
and government representatives who have experience and expertise in the state’s leading
industries and economic policy areas. This information appears in Appendix B. In addition,
preliminary discussions were held with Alaska Department of Labor and Workforce
Development staff to determine the availability of Alaska residents with the skill sets and
experience necessary to construct and operate the Project based on the number of persons being
trained for these positions each year.
6. DISCUSSION
Data collection efforts in 2013 made good progress in meeting overall study objective of
describing, using text and appropriate tables and graphics, existing socioeconomic conditions
within the study area. Completion of the description of existing socioeconomic conditions will
require continuing integration with other study reports, including the following:
• The description of current commercial fishing operations will require data on the baseline
harvest levels and harvest locations for commercial fisheries for Susitna River-origin
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resident and anadromous fish collected by the Analysis of Fish Harvest Study (Study
9.15).
• Descriptions of the recreation resources and facilities that currently support both
commercial and non-commercial recreation in the Susitna River watershed and estimates
of current levels of recreational use in the region will be provided in the Recreation
Resources Study (Study 12.5).
• The description of the specific attributes of the Susitna River corridor and upper
watershed that may have non-use value will be revised as information in other study
reports becomes available.
• The quality of life analysis will be supplemented with data collected by key informant
interviews and by surveys and focus groups conducted for the Recreation Resources
Study (Study 12.5) and Aesthetic Resources Studies (Study 12.6).
7. COMPLETING THE STUDY
[As explained in the cover letter to this draft ISR, AEA’s plan for completing this study will be
included in the final ISR filed with FERC on June 3, 2014.]
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9. TABLES
Table 5.1.1.1-1. Population Size and Density in Study Area
Population
Population Density
(persons per square mile)
2000 2010
Percent Change
2000-2010 2000 2010
STATE OF ALASKA 626,932 710,231 13.3 1.1 1.2
Matanuska-Susitna Borough 59,322 88,995 50.0 2.4 3.6
Trapper Creek 423 481 13.7 1.2 1.3
Chase 41 34 -17.1 0.4 0.3
Talkeetna 772 876 13.5 18.6 32.7
Point Mackenzie 111 529 376.6 0.8 3.5
Houston 1,202 1,912 59.1 53.7 85.4
Wasilla 5,469 7,831 43.2 -- 632.4
Denali Borough 1,893 1,826 -3.5 0.1 0.1
Cantwell 222 219 -1.4 1.9 1.9
Fairbanks North Star Borough 82,840 97,581 17.8 11.2 13.3
Fairbanks 30,224 31,535 4.3 948.7 995.0
Municipality of Anchorage 260,283 291,826 12.1 153.4 171.9
Kenai Peninsula Borough 49,691 55,400 11.5 3.1 4.0
Seward 2,699 2,693 -0.2 196.0 190.8
Other--Whittier 182 220 20.9 14.5 17.9
Source: U.S. Census Bureau (2013b)
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Table 5.1.1.2-1. Age Characteristics in Study Area
Age (2010)
Under 16 16-64 65 and Over Median Age
State of Alaska 23.4 68.9 7.7 33.8
Matanuska-Susitna Borough 25.5 66.6 7.9 34.8
Trapper Creek 16.8 70.3 12.9 48
Chase 0 94.1 5.9 52
Talkeetna 17.4 72.5 10.2 45.4
Point Mackenzie 15.9 76.0 8.1 32.8
Houston 24.0 67.4 8.6 35.4
Wasilla 26.1 63.7 10.3 32.2
Denali Borough 19.9 72.6 7.5 41.5
Cantwell 17.4 68.9 13.7 42.7
Fairbanks North Star Borough 23 70.4 6.5 31
Fairbanks 23.8 68.9 7.3 27.9
Municipality of Anchorage 23 69.7 7.2 32.9
Kenai Peninsula Borough 20.6 68 11.3 40.6
Seward 13.1 77.5 9.5 38.3
Other--Whittier 12.3 75.5 12.3 48
Source: U.S. Census Bureau (2013b)
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Table 5.1.1.3-1. Racial and Ethnic Composition in Study Area
Race/Ethnicity (2010)
Percent
White1
Percent
Black or
African
American2
Percent
Alaska
Native and
American
Indian2
Percent
Native
Hawaiian
and Other
Pacific
Islander2
Percent
Asian2
Percent
Some
Other
Race2
Percent
Hispanic
or
Latino3
Percent
Minority4
State of Alaska 66.7 4.7 19.5 1.6 7.1 2.1 5.5 35.4
Matanuska-Susitna
Borough 84.9 1.8 10.1 0.6 2.4 1.2 3.7 16.8
Trapper Creek 86.5 0.4 6.4 0 1 0 1 13.9
Chase 100 0 0 0 0 0 0 0.0
Talkeetna 91.4 0.7 6.4 0.5 1.1 0.3 1.8 9.2
Point Mackenzie 67.7 4.5 23.3 0.4 0.6 0.6 3.2 34.2
Houston 82.2 1.3 13.9 0.3 1.3 1.6 3.3 19
Wasilla 83.4 2.4 9.6 0.5 3.2 1.7 4.3 18.1
Denali Borough 89.6 0.6 6.4 0.1 2.2 1.2 2.3 10.9
Cantwell 77.2 0.5 20.5 0 0 1.8 1.4 21.9
Fairbanks North
Star Borough 77 6.1 10.9 0.8 4.3 2.1 5.8 25.4
Fairbanks 66.1 11.7 14 1.4 5.3 3.4 9 37.5
Municipality of
Anchorage 66 7.7 12.4 2.8 10.3 3.1 7.6 36.8
Kenai Peninsula
Borough 84.6 1.0 11.6 0.5 2.1 0.9 3.0 16.8
Seward 68.5 4.2 22.9 1.0 3.3 1.3 3.6 32.5
Other--Whittier 69.5 0.9 15 3.6 10.9 0.9 5 33.2
1 Alone
2 Alone or in combination with one or more other races
3 Of any race
4 Minority = Total – (White Alone + Some Other Race Alone + Two or More Races, White and Some Other Race) +
(Hispanic, White Alone + Hispanic, Some Other Race Alone)
Source: U.S. Census Bureau (2013b)
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Table 5.1.2.1-1. Employment and Per Capita Income in Study Area
Number of Workers/Jobs (2011)1 Per Capita Personal Income ($) (2011)1
State of Alaska 454,201 45,665
Matanuska-Susitna Borough 33,143 41,905
Trapper Creek 167 28,383
Chase 17 46,962
Talkeetna 470 30,390
Point Mackenzie 83 38,072
Houston 718 33,539
Wasilla 3,366 38,349
Denali Borough 2,085 60,191
Cantwell 95 37,044
Fairbanks North Star Borough 59,014 42,626
Fairbanks 10,380 33,944
Municipality of Anchorage 202,153 50,958
Kenai Peninsula Borough 31,270 41,772
Seward 972 33,699
Other--Whittier 106 31,851
1Community data are for Alaska residents only.
Source: U.S. Department of Commerce, Bureau of Economic Analysis (2013); Alaska Department of Labor and
Workforce Development, Research & Analysis Division (2013b)
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Table 5.1.2.2-1. Annual Unemployment and Labor Force Participation Rate in Study Area
Unemployment Rate1 Percent Not in Labor Force (2007-2011)
State of Alaska 7.0 28.3
Matanuska-Susitna Borough 8.0 33.6
Trapper Creek 7.3 39.8
Chase 0 44.7
Talkeetna 10.9 37.5
Point Mackenzie 22 85.1
Houston 14.6 33.6
Wasilla 12.4 35.2
Denali Borough 10.2 23.7
Cantwell 15.5 30.2
Fairbanks North Star Borough 6.2 26.6
Fairbanks 8.1 28.3
Municipality of Anchorage 5.4 25.6
Kenai Peninsula Borough 8.4 36.4
Seward 9.2 45.8
Other--Whittier 14.6 28.4
1 State and borough data are for 2012. Community data are for 2007-2011.
Source: U.S. Census Bureau (2013a); Alaska Department of Labor and Workforce Development, Research &
Analysis Division (2013b)
Table 5.1.2.2-2. Monthly Unemployment Rate in Study Area
Unemployment Rate (2012)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
State of Alaska 8.1 8.1 7.8 7.2 6.9 7.3 6.6 6.2 6.1 6.1 6.5 7
Matanuska-Susitna Borough 9.7 9.9 9.6 8.3 7.7 8.4 7.6 6.9 6.7 6.4 7.2 7.7
Denali Borough 24.4 25 22.6 18.5 7.6 4.7 4 3.9 4.6 15.7 19.3 20.6
Fairbanks North Star
Borough 7.5 7.4 7.2 6.5 6 6.6 6 5.5 5.2 5.1 5.6 6.1
Municipality of Anchorage 6 6 5.9 5.6 5.6 6.1 5.6 5.2 5 4.7 4.8 5.1
Kenai Peninsula Borough 10.6 10.6 10 8.7 8 8.1 7.2 6.8 7.2 7.4 8 8.5
Source: Alaska Department of Labor and Workforce Development, Research & Analysis Division (2013b)
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Table 5.1.2.3-1. Poverty Rate in Study Area.
Percent of Individuals Living in Poverty1
State of Alaska 10.8
Matanuska-Susitna Borough 11.6
Trapper Creek 24.1
Chase 0.0
Talkeetna 15.9
Point Mackenzie 0.0
Houston 13.5
Wasilla 12.8
Denali Borough 5.8
Cantwell 6.0
Fairbanks North Star Borough 9.5
Fairbanks 10.5
Municipality of Anchorage 8.5
Kenai Peninsula Borough 10.6
Seward 4.1
Other--Whittier 18.0
1 State and borough data are for 2011. Community data are for 2007-2011.
Source: U.S. Census Bureau (2013c); U.S. Census Bureau (2013a)
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Table 5.1.2.4-1. Current Resident Labor Force and Workforce Composition in Study Area
Working Age Residents (2011) Top Industries by Employment (2011)
State of Alaska 501,704
Trade, Transportation and Utilities (21%)
Local Government (15%)
Educational and Health Services(14%)
Matanuska-Susitna Borough 65,562
Trade, Transportation and Utilities (21%)
Educational and Health Services(15%)
Local Government (12%)
Trapper Creek
411
Trade, Transportation and Utilities (29%)
Local Government (20%)
Leisure and Hospitality (16%)
Chase
39
Leisure and Hospitality (35%)
Natural Resources and Mining (35%)
Trade, Transportation and Utilities (12%)
Talkeetna 813
Trade, Transportation and Utilities (33%)
Leisure and Hospitality (17%)
Local Government (12%)
Point Mackenzie 186
Construction (24%)
Trade, Transportation and Utilities (19%)
Local Government/Educational and Health Services (12%)
Houston 1,384
Trade, Transportation and Utilities (20.6%)
Educational and Health Services (15.3%)
Construction (12.7%)
Wasilla 5,933
Trade, Transportation and Utilities (22%)
Educational and Health Services (16.8%)
Construction (11.5%)
Denali Borough 1,453
Leisure and Hospitality (25%)
Natural Resources and Mining (16%)
Local Government (16%)
Cantwell 174
Leisure and Hospitality (22%)
Local Government (21%)
State Government (13%)
Fairbanks North Star Borough 63,061
Trade, Transportation and Utilities (22%)
State Government (13%)
Educational and Health Services (13%)
Fairbanks 18,314
Trade, Transportation and Utilities (24%)
Educational and Health Services (15%)
Leisure and Hospitality (13%)
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Working Age Residents (2011) Top Industries by Employment (2011)
Municipality of Anchorage 208,016
Trade, Transportation and Utilities (22%)
Educational and Health Services(15%)
Leisure and Hospitality (12%)
Kenai Peninsula Borough 42,543
Trade, Transportation and Utilities (20%)
Educational and Health Services(15%)
Local Government (14%)
Seward 1,586
Trade, Transportation and Utilities (24%)
Leisure and Hospitality (16%)
State Government (12%)
Other--Whittier
183
Leisure and Hospitality (20.8%)
Local Government (20.8%)
Trade, Transportation and Utilities (19.8%)
Source: Alaska Department of Labor and Workforce Development (2013a)
Table 5.1.2.5.1-1. Construction Industry Employment, Income, and Output in Study Area1
Region
Employment
(Thousands)
Compensation (Millions of
Current Dollars)
Output (Millions of Fixed
(2005) Dollars)
State of Alaska 24.908 1.538 2.897
Fairbanks North Star Borough 3.671 0.256 0.465
Kenai Peninsula Borough 2.163 0.080 0.186
Municipality of Anchorage 10.570 0.802 1.406
Matanuska Susitna Borough 3.412 0.130 0.299
Denali Borough 0.015 0.001 0.001
1 Data are for 2012
Source: Regional Economic Models Inc. (REMI) Policy Insight Model (2013)
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Table 5.1.2.5.2-1. Air Transportation Industry Employment, Income, and Output in Study Area1
Region Employment (Thousands)
Compensation (Millions of Current Dollars)
Output (Millions of Fixed (2005) Dollars)
State of Alaska 5.678 0.399 1.388
Fairbanks North Star Borough 0.603 0.034 0.129
Kenai Peninsula Borough 0.165 0.009 0.035
Municipality of Anchorage 2.674 0.238 0.766
Matanuska Susitna Borough 0.119 0.005 0.022
Denali Borough 0.003 0.000 0.001
1 Data are for 2012
Source: Regional Economic Models Inc. (REMI) Policy Insight Model (2013)
Table 5.1.2.5.2-2. Water Transportation Industry Employment, Income, and Output in Study Area1
Region Employment (Thousands)
Compensation (Millions of Current Dollars)
Output (Millions of Fixed (2005) Dollars)
State of Alaska 1.094 0.101 0.472
Fairbanks North Star Borough 0.000 0.000 0.000
Kenai Peninsula Borough 0.111 0.007 0.037
Municipality of Anchorage 0.257 0.022 0.105
Matanuska Susitna Borough 0.013 0.000 0.002
Denali Borough 0.000 0.000 0.000
1 Data are for 2012
Source: Regional Economic Models Inc. (REMI) Policy Insight Model (2013)
Table 5.1.2.5.2-3 Truck Transportation Industry Employment, Income, and Output in Study Area1
Region
Employment
(Thousands)
Compensation
(Millions of
Current Dollars)
Output
(Millions of Fixed
(2005) Dollars)
State of Alaska 4.126 0.226 0.679
Fairbanks North Star Borough 0.925 0.051 0.153
Kenai Peninsula Borough 0.185 0.009 0.027
Municipality of Anchorage 2.347 0.134 0.399
Matanuska Susitna Borough 0.105 0.003 0.011
Denali Borough 0.000 0.000 0.000
1 Data are for 2012
Source: Regional Economic Models Inc. (REMI) Policy Insight Model (2013)
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Table 5.1.2.5.2-4. Rail Transportation Industry Employment, Income, and Output in Study Area1
Region
Employment
(Thousands)
Compensation (Millions of
Current Dollars)
Output (Millions of Fixed
(2005) Dollars)
State of Alaska 0.001 0.000 0.000
Fairbanks North Star Borough 0.000 0.000 0.000
Kenai Peninsula Borough 0.000 0.000 0.000
Municipality of Anchorage 0.001 0.000 0.000
Matanuska Susitna Borough 0.000 0.000 0.000
Denali Borough 0.000 0.000 0.000
1 Data are for 2012
Source: Regional Economic Models Inc. (REMI) Policy Insight Model (2013)
Table 5.1.2.5.3-1. Recreation and Tourism Industry Employment, Income, and Output in Study Area1
Region Employment (Thousands)
Compensation (Millions of Current Dollars)
Output (Millions of Fixed (2005) Dollars)
State of Alaska 43.564 1.784 2.145
Fairbanks North Star Borough 5.166 0.181 0.254
Kenai Peninsula Borough 4.083 0.111 0.168
Municipality of Anchorage 19.921 0.925 0.973
Matanuska Susitna Borough 3.198 0.110 0.119
Denali Borough 1.081 0.059 0.077
1 Data are for 2012
Source: Regional Economic Models Inc. (REMI) Policy Insight Model (2013)
Table 5.1.2.5.5-1. Oil and Gas Industry Employment, Income, and Output in Study Area1
Region Employment (Thousands)
Compensation
(Millions of Current Dollars)
Output
(Millions of Fixed (2005) Dollars)
State of Alaska 4.273 0.770 4.965
Fairbanks North Star Borough 0.000 0.000 0.000
Kenai Peninsula Borough 0.332 0.046 0.311
Municipality of Anchorage 2.033 0.385 2.484
Matanuska Susitna Borough 0.000 0.000 0.000
Denali Borough 0.000 0.000 0.000
1 Data are for 2012
Source: Regional Economic Models Inc. (REMI) Policy Insight Model (2013)
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Table 5.1.2.5.6-1. Utilities Industry Employment, Income, and Output in Study Area1
Region
Employment
(Thousands)
Compensation
(Millions of
Current Dollars)
Output
(Millions of Fixed
(2005) Dollars)
State of Alaska 2.256 0.243 1.232
Fairbanks North Star Borough 0.446 0.046 0.236
Kenai Peninsula Borough 0.253 0.030 0.151
Municipality of Anchorage 0.620 0.093 0.451
Matanuska Susitna Borough 0.185 0.020 0.101
Denali Borough 0.024 0.003 0.013
1 Data are for 2012
Source: Regional Economic Models Inc. (REMI) Policy Insight Model (2013)
Table 5.1.3-1. General Housing Characteristics in Study Area
Total Units
(2010)
Occupied Units
(%) (2010)
Median Value of Owner Occupied Units ($) (2007-
2011)
Median Gross Rent ($)
(2007-2011)
Number of Vacant Housing
Units (2010)
State of Alaska 306,967 84.1 235,100 1,017 48,909
Matanuska-Susitna Borough 41,329 77 216,500 969 9,505
Trapper Creek 499 45.1 100,000 743 274
Chase 209 8.6 120,500 -- 191
Talkeetna 744 60.3 156,800 700 295
Point Mackenzie 257 43.6 308,700 -- 145
Houston 973 75.1 172,100 800 242
Wasilla 3,277 90.4 217,900 922 315
Denali Borough 1,771 45.5 179,500 572 965
Cantwell 200 52 162,500 725 96
Fairbanks North Star Borough 41,783 87.2 212,800 1,105 5,342
Fairbanks 13,056 88.3 197,900 1,120 1,522
Municipality of
Anchorage 113,032 95 276,200 1,058 5,700
Kenai Peninsula Borough 30,578 72.5 200,000 812 8,417
Seward 1,124 82.6 192,000 815 196
Other--Whittier 280 40.7 49,000 688 166
Source: U.S. Census Bureau (2013b)
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Table 5.1.3-2. Vacant Housing Characteristics in Study Area
Number of
Vacant Units (2010)
Units for Sale Units for Rent
Vacant for Seasonal, Recreational, or Occasional
Use Other Vacant
% (2010)
State of Alaska 48,909 5.9 13.8 57.0 19.2
Matanuska-Susitna Borough 9,505 5.6 6.2 71.8 14.5
Trapper Creek 274 4 1.1 84.7 9.1
Chase 191 0.5 0 96.3 2.1
Talkeetna 295 1.4 5.8 74.2 15.6
Point Mackenzie 145 0.7 0.7 89.7 9
Houston 242 2.5 2.4 13.8 5.9
Wasilla 315 1.6 3.7 1.4 2.5
Denali Borough 965 2.4 5.2 77.1 14
Cantwell 96 6.3 13.5 64.6 15.6
Fairbanks North Star Borough 5,342 9.5 28.1 31.4 27.8
Fairbanks 1,522 15.2 51.8 12.0 18
Municipality of Anchorage 5,700 14.9 30 26.3 22.7
Kenai Peninsula Borough 8,417 4.8 7.8 72.3 13
Seward 196 5.6 15.8 54.6 23.5
Other--Whittier 166 2.1 17.9 37.1 1.1
Source: U.S. Census Bureau (2013b)
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Table 5.1.4.1-1. Police and Fire Protection Services in Study Area
Area Police Department Alaska State Trooper Post
Nearest Law Enforcement Facility Fire Department Volunteer Firefighters
Trapper Creek No No
Talkeetna State
Troopers Post No No
Chase No No
Talkeetna State
Troopers Post No No
Talkeetna No Yes
No Yes
Point Mackenzie No No
Palmer State
Troopers Post No No
Houston Yes No
Palmer State
Troopers Post No Yes
Wasilla Yes No
Palmer State
Troopers Post No Yes
Cantwell No Yes
No Yes
Fairbanks Yes Yes
Yes No
Anchorage Yes Yes
Yes Yes
Seward Yes Yes
Yes Yes
Whittier Yes No
Girdwood State
Troopers Post Yes Yes
Source: Alaska Department of Commerce, Community and Economic Development (2013c)
Table 5.1.4.1-2. Medical Services in Study Area
Hospitals
Health Clinics and Federally
Qualified Health Centers Emergency Medical Services
Trapper Creek No Sunshine Community Health
Center
Trapper Creek EMS
Chase No No No
Talkeetna No Sunshine Community Health
Center
Talkeetna Fire
Point Mackenzie No No No
Houston No No No
Wasilla Yes Mat-Su Health Services; Mat-Su
Public Health Center; Providence
Matanuska Health Care
Matanuska-Susitna Borough
EMS
Cantwell No Cantwell Clinic Cantwell Volunteer Ambulance
Fairbanks Fairbanks Memorial Hospital;
Bassett Army Community
Hospital
Interior Community Health
Center; Chief Andrew Isaac
Health Center and others
Chena-Goldstream Fire and
Rescue; Fairbanks Fire
Department; and others
Anchorage Alaska Native Medical Center;
Alaska Regional Hospital;
Providence Alaska Medical
Center; Anchorage Military
Anchorage Neighborhood Health
Center
Anchorage Fire Department
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Hospital
Seward No North Star Health Clinic-
Chugachmiut; Seward Public
Health Center
Bear Creek Fire/EMS
Department; Seward Volunteer
Ambulance Corps.
Whittier No Whittier Clinic; Whittier
Community Health Center
Whittier Volunteer Fire/EMS
Department
Source: Alaska Department of Commerce, Community and Economic Development (2013c)
Table 5.1.4.2-1. Number of Schools, Grade Levels, and Enrollment in Study Area
Number of Schools Grades Served1 Enrollment (FY 2013)
Matanuska-Susitna Borough 45 P-12 17,494
Trapper Creek 1 P-6 42
Chase -- -- --
Talkeetna 1 P-6 113
Point Mackenzie -- -- --
Houston 3 P-12 1,189
Wasilla 22 P-12 9,804
Denali Borough 4 P-12 899
Cantwell 1 K-12 22
Fairbanks North Star Borough 35 P-12 14,406
Fairbanks 27 P-12 10,760
Municipality of Anchorage 96 P-12 48,863
Kenai Peninsula Borough 43 P-12 9,256
Seward 3 P-12 601
Other--Whittier 1 P-12 39
1P = Pre-Elementary; K = Kindergarten.
Source: Alaska Department of Education and Early Development (2013)
Table 5.1.4.2-2. Revenue per Average Daily Membership and Funding Sources
School District
Revenue Per Average Daily
Membership ($) (2012) Share of Funding by Source (2012)
Matanuska-Susitna Borough School
District 13,687 20.2% local, 71.9% state, 0.4% federal,
1.1% other, and 6.3% special revenue
Denali Borough School District 12,368 23.4% local, 71.9% state, 0.2% federal,
1.4% other, 3.1% special revenue
Fairbanks North Star Borough School
District 15,728 20.7% local, 64.2% state, 6.3% federal,
0.5% other, 8.3% special revenue
Anchorage School District 14,477 28.0% local, 50.5% state, 2.9%
federal,0.9% other, 9.7% special revenue
Kenai Peninsula Borough School District 16,273 29.6% local, 62.6% state, 0.4% federal,
0.6% other, 6.8% special revenue
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Valdez City School District (Valdez-
Cordova Census Area) 22,911 52.5% local, 41.0% state, 0.1% federal,
0.6% other, 5.8% special revenue
Source: Alaska Department of Education and Early Development (2013)
Table 5.1.4.3-1. Utility Providers in Study Area
Community Piped Water System Operator
Community Piped Sewage System Operator
Landfill Facility Operator Electric Utility Operator Natural Gas Utility Operator
Matanuska-Susitna Borough
Trapper Creek No No
Transfer facility
that also serves
Denali State Park
Matanuska
Electric
Association
No
Chase No No No
Matanuska
Electric
Association-
Golden Valley
Electric
Association
Intertie
No
Talkeetna Borough Borough Private
Matanuska
Electric
Association
No
Point Mackenzie Private Private No
Matanuska
Electric
Association
No
Houston No No Borough
Matanuska
Electric
Association
No
Wasilla City, Private City, Private Borough
Matanuska
Electric
Association
ENSTAR
Denali Borough
Cantwell No No Borough transfer
facility
Golden Valley
Electric
Association
No
Fairbanks North Star Borough
Fairbanks Private Private Borough; Ft.
Wainwright
Golden Valley
Electric
Association
Fairbanks Gas
Municipality of Anchorage City, Private City Private,
Municipality
Chugach Electric
Association;
Anchorage
Municipal Light
and Power
ENSTAR
Kenai Peninsula
Borough
Seward City City Borough Seward Electric
System No
Other--Whittier City City Private Chugach Electric
Association ENSTAR
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Source: Alaska Department of Commerce, Community and Economic Development (2013c)
Table 5.1.4.3-2. Percentage of Household Income Spent on Gas, Electricity, and Heating Fuel
Percentage of Household Income Spent on Gas, Electricity, and Heating Fuel
United States 4.1
State of Alaska 4.7
Anchorage 3.2
Remote Rural Alaska 14.4
Source: Saylor et al. (2008)
Table 5.1.5-1. Local Tax Revenue Sources in Study Area
Area Type of Area Property Tax3 Oil and Gas Property Tax Sales Tax Special Tax
Matanuska-
Susitna Borough
Second-Class
Borough1 9.691 mills Yes No
5% Bed Tax;
5.74% Cigarette &
Tobacco Tax
Denali Borough Home-Rule
Borough2 No -- No
Severance Tax
$.05/yard gravel-
$.05 ton-coal; 7%
Bed Tax
Fairbanks North
Star Borough
Second-Class
Borough1 12.97 mills Yes No
8% Bed Tax; 8%
Tobacco Tax; 5%
Alcohol Tax
Municipality of
Anchorage
Unified Home-Rule
Municipality2 15.57 mills Yes No
12% Bed Tax; 8%
Car Rental Tax;
113.2 mill Cig &
55% Tobacco Tax
Kenai Peninsula Borough Second-Class
Borough1 4.5 mills Yes 3.0% No
1 First-class/second-class borough: A main difference between a first-class and second-class borough is the
authority to assume powers. A first-class borough may exercise any power not prohibited by law on a non-area-wide
basis (i.e., in the area of the borough outside cities) by adopting an ordinance. A second-class borough, however,
must gain voter approval for the authority to exercise many non-area-wide powers.
2 Home rule borough/city: A city or borough that has all the legislative powers not prohibited by law or charter.
Typical area-wide powers include education, planning, animal control, fireworks control, health and environmental
protection, library, mass transit, zoning, taxicab, rights-of-way use, parking, and sewers. Non-area-wide powers
include building safety and police.
3 Mills, or millage rate, is the measure of a tax per $1,000 of assessed value.
Source: Alaska Department of Commerce, Community and Economic Development (2013a); Alaska Department of
Commerce, Community and Economic Development (2013d)
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Table 5.1.5-2. Local Government Revenues by Source in Study Area
Property Tax O&G Property Tax Other Taxes Other Fees and Charges
Inter-governmental Transfers
Other General Fund Revenues
Non-General Fund Revenues
Enterprise/ Business Funds Total
$ Thousands (2012)
Matanuska-Susitna Borough 76,243 109 4,224 3,417 24,457 237 64,162 4,293 177,141
Trapper Creek -- -- -- -- -- -- -- -- --
Chase -- -- -- -- -- -- -- -- --
Talkeetna -- -- -- -- -- -- -- -- --
Point Mackenzie -- -- -- -- -- -- -- -- --
Houston 413 0 0 83 276 11 674 0 1,457
Wasilla 0 0 0 2,007 2,137 116 1,561 6,014 11,835
Denali Borough 0 0 2,685 0 1,076 27 260 352 4,400
Cantwell -- -- -- -- -- -- -- -- --
Fairbanks North Star
Borough 90,342 9,002 3,622 2,002 16,196 2,135 32,311 14,057 169,667
Fairbanks 13,971 157 5,546 7,787 4,997 866 18,249 598 52,171
Municipality of Anchorage 488,741 3,964 45,962 50,970 48,818 5,711 143,628 288,274 1,076,070
Kenai Peninsula
Borough 26,244 3,748 0 0 9,749 2,349 37,272 156,157 235,519
Seward 1,033 0 346 2,708 1,953 109 4,444 35,910 46,504
Other--Whittier 436 9 251 253 531 90 1,129 1,770 4,469
Source: Alaska Department of Commerce, Community and Economic Development (2013b); Alaska Department of Commerce, Community and Economic
Development (2013a)
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Table 5.1.5-3. Local Government Operating Expenditures by Category in Study Area
Area
Transportation and Public Works Education Public Welfare
Health (including utilities) Public Safety Environment & Housing Government Administration Debt Service Other
$ Thousands (2012)
Matanuska-Susitna Borough 4,177 48,048 0 0 7,236 3,545 16,998 0 0
Trapper Creek -- -- -- -- -- -- -- -- --
Chase -- -- -- -- -- -- -- -- --
Talkeetna -- -- -- -- -- -- -- -- --
Point Mackenzie -- -- -- -- -- -- -- -- --
Houston 260 0 0 0 264 0 315 47 17
Wasilla 2,167 0 0 0 6,161 1,835 2,791 420 65
Denali Borough 0 2,070 0 0 0 0 1,162 0 6
Cantwell -- -- -- -- -- -- -- -- --
Fairbanks North Star
Borough 6,223 47,359 0 0 1,829 12,235 19,570 0 2,436
Fairbanks 7,530 0 0 0 14,319 0 9,899 885 719
Municipality of Anchorage 43,955 231,070 0 12,204 202,327 35,482 20,618 55,615 47,084
Kenai Peninsula
Borough 5,574 43,898 0 0 598 0 14,878 0 0
Seward 1,766 0 0 0 3,260 1,678 2,652 809 0
Other--Whittier 315 0 0 0 714 0 677 14 0
Source: Alaska Department of Commerce, Community and Economic Development (2013a)
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Table 5.1.6.1.3-1. Timber Harvest in Selected Natural Resource Management Units of the Matanuska-Susitna Borough
Unit Name
Operable Net Cubic
Feet Available For Harvest (rounded)
Proposed Harvest (Cubic Feet)
2013 2014 2015 2016 2017
Chulitna River
(South Subunit) 4,104,000 -- -- -- -- --
Mile 233 5,384,000 -- -- -- -- --
Moose Creek 1,302,000 61,848 -- -- -- --
Parks Highway 2,604,000 -- -- 42,950 TBD --
Rabideux Creek 2,726,000 -- 67,002 67,002 TBD 67,002
Susitna River Corridor 5,087,000 549,760 -- -- -- 1,023,298
Whiskers Creek South 13,097,000 -- -- -- -- --
Source: RWS Consulting (2010); Matanuska-Susitna Borough Community Development Department (2013)
Table 5.1.6.1.4-1. Property Values of Selected Land Parcels in the Study Area
Location Parcel ID Owner
Gross
Acreage
Land Appraised
Value (2013) $/Acre
Parcel borders Susitna River
downstream from mouth of
Watana Creek 2446
Tyonek Native
Corporation 583.36 $160,400.00 $275
Parcel borders Susitna River
and lower portion of Watana
Creek 45432
Ninilchik Native
Association 583.36 $160,400.00 $275
Parcel borders north shore of
Stephan Lake 62264
Tyonek Native
Corporation 595.7 $148,900.00 $250
Parcel borders west shore of
Stephan Lake and includes a
commercial fishing/hunting
lodge
24052 Stephan Lake
Holdings LLC
5.00 $10,000.00 $2,000
Parcel borders Christiansen
Lake near Talkeetna with road
access 41027
Matanuska-Susitna
Borough 38.45 $85,300.00 $2,218
Parcel borders Christiansen
Lake near Talkeetna with road,
electric, and telephone access 36573 Private 5.48 $99,600.00 $18,175
Source: Matanuska-Susitna Borough (2013)
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APPENDIX A: RANDOM UTILITY MODEL METHODOLOGY
Estimating Changes in Recreation Demand and
Social Welfare Resulting from the Susitna-Watana
Hydroelectric Project
Preliminary, Interim-Draft Report — For Review and
Discussion Purposes Only
Prepared for:
Northern Economics, Inc.
Prepared by:
Jason C. Kinnell
Matthew F. Bingham
Grant Crownfield
Dawn M. Woodard
Sara G. Hickman
December 10, 2013
Office: 919.677.8787 Preliminary, Interim Draft Report Economic Consulting Fax: 919.677.8331 For Review and Discussion Purposes Only VeritasEconomics.com
V e r i t a s1851 Evans RoadCary, NC 27513
Social Welfare Evaluation – Watana Hydroelectric Project December 2013
Preliminary, Interim-Draft Report
For Review and Discussion Purposes Only i Economic Consulting
V e r i t a s
Table of Contents
Section Page
1. Introduction ............................................................................................................ 1
1.1 Relevant Background and Methodology Overview ...................................................... 1
1.2 Steps in Developing the Structural Recreation Demand Model ................................... 4
1.2.1 Step 1—Identifying Recreation Outcomes ....................................................... 4
1.2.2 Step 2—Assess Currently Available Recreation Data ...................................... 4
1.2.3 Step 3—Identify Recreation Utility Functions ................................................... 5
1.2.4 Step 4—Identify Recreation Demand .............................................................. 5
1.2.5 Step 5—Identify Aggregate Demand ............................................................... 5
1.3 Summary of Results .................................................................................................... 6
1.4 Report Organization .................................................................................................... 6
2. Conceptual Mathematical Foundations and Information ................................... 8
2.1 Overview of Mathematical Structure for Valuing Changes in the
Susitna River System .................................................................................................. 8
2.2 System Structure and Parameter Specification ......................................................... 11
2.3 Modeling Changes in Recreation Demand and Social Welfare ................................. 12
2.3.1 System Structure for Recreation .................................................................... 12
2.3.2 Information Requirements for Recreation ...................................................... 14
2.4 Modeling Changes in Fishing Demand—A Conceptual Example .............................. 15
3. Fishing .................................................................................................................. 20
3.1 Information Requirements for the Fishing Demand Model......................................... 20
3.1.1 2013 Alaska Outdoor Recreation Survey ....................................................... 22
3.1.2 2010 Alaska Sport Fishing Survey ................................................................. 23
3.1.3 2010 U.S. Census ......................................................................................... 24
3.1.4 Guide Logbook .............................................................................................. 24
3.1.5 2008–2013 ADF&G Emergency Orders ........................................................ 24
3.1.6 Carson, Hanemann, and Wegge (2009) ........................................................ 24
3.1.7 Alaska Fishing: The Ultimate Guide, Deluxe 3rd Edition ................................ 25
3.1.8 Supplemental Data Sources .......................................................................... 25
3.2 Characterization of Baseline Fishing Conditions ....................................................... 25
3.2.1 Background on Alaska Anglers’ Trip-Taking Behavior ................................... 25
3.2.2 Fishing Demand Model Structure and Variables ............................................ 27
3.2.3 Specifying the Affected Population—Population Size and Annual Trips ........ 29
3.2.4 Specifying Relevant Fishing Sites for the Affected Population ....................... 30
3.2.5 Model Variables ............................................................................................ 33
3.3 Calibrated Baseline Trips .......................................................................................... 35
3.4 Changes in Social Welfare Under With-Project Conditions ....................................... 35
3.5 Watana With-Project Conditions and Trips and Welfare Implications ........................ 38
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4. Bibliography ......................................................................................................... 40
Appendix A Fishing Sites and Site Characteristic Data ........................................... 49
Appendix B 2013 Alaska Outdoor Recreation Survey ............................................. 52
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List of Figures
Figure Page
Figure 1.1: Location of the Proposed Susitna-Watana Hydroelectric Project .............................. 2
Figure 2.1: Mathematical Notation for the Susitna River System ................................................ 9
Figure 2.2: The Site Demand Curve and Consumer Surplus .................................................... 17
Figure 2.3: Increase in Consumer Surplus from Increase in Catch ........................................... 19
Figure 3.1: Data Needs and Corresponding Sources ............................................................... 21
Figure 3.2: Choice Structure for Resident Angler Sport Fishing Demand ................................. 27
Figure 3.3: Area of Affected Population—Southcentral Areas J–P and Areas I and U .............. 29
Figure 3.4: Policy and Substitute Sites for Affected Population ................................................. 32
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List of Tables
Table Page
Table 1.1 Summary of Results .................................................................................................... 6
Table 3.1 Components of Estimating Changes in Fishing Behavior .......................................... 22
Table 3.2 Components of Estimating Consumption Rates ........................................................ 23
Table 3.3 Affected Angling Population Evaluated in the Fishing Demand Model ....................... 30
Table 3.4 Estimated Average Annual Trips to Affected Policy Sites .......................................... 35
Table 3.5 Baseline (Without-Project) Conditions: Species Ratings for the Downstream Sites .. 38
Table 3.6 With-Project Conditions: Species Ratings for the Downstream Sites ........................ 39
Table 3.7 Summary of Fishing Results ...................................................................................... 39
Table A.1 Selected Alaska Fishing Sites and Site Characteristic Data ...................................... 50
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1. Introduction
This report presents a preliminary, interim draft of the Alaska Energy Authority’s (AEA)
efforts to characterize changes in recreation demand and social welfare for the recreator
populations that are most likely to be affected by the Susitna-Watana Hydroelectric Project
(Project). AEA is developing recreation demand models to estimate changes in recreation
demand and social welfare associated with the Project. The report describes the methodology
that AEA is using to develop the demand models for the following four recreation activities that
may be affected by the Project:
• Fishing
• Recreational boating
• Snow machining and
• Hunting.
The report also presents a detailed description of the Fishing Demand Model that AEA
has developed using existing and new survey data. Because this report is a preliminary, interim
draft, it does not include the results from the demand models; however, it does describe the
policy scenarios and the structure of the results the models are designed to address.
Section 1.1 presents project background and describes its relationship to the
methodology needed to assess changes in recreation demand and social welfare. Section 1.2
presents an overview of the steps that AEA is conducting to develop the recreation demand
models and assess the changes in social welfare resulting from the Project. Section 1.3
presents an overview of the results that the models will produce, and Section 1.4 presents an
overview of the remainder of the report.
1.1 Relevant Background and Methodology Overview
AEA is considering a hydroelectric project on the Susitna River at Watana Creek. Figure
1.1 shows the location of the proposed dam for the hydroelectric project. Undertaking the
hydroelectric project would have a number of implications for Alaska’s outdoor recreators.
Constructing the dam would require creating new access roads and associated construction
efforts. Construction efforts could be disruptive in the short run. For example, blasting and
construction noise could impact hunting opportunities while the building of coffer dams and short
term river re-routing could impact fishing. As the construction project winds down and the dam
begins its normal operations different effects would occur.
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Figure 1.1: Location of the Proposed Susitna-Watana Hydroelectric Project
Susitna
River
Proposed Susitna-Watana Dam
V e r i t a s
Economic Consulting
Study Area for
Proposed Susitna-
Watana Dam Area
Enlarged
Proposed Susitna-
Watana Dam
Legend
Susitna
River
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In the long run the access provided during the project (and possibly enhanced
subsequently) would provide a new way into Game Management Unit 13, one of Alaska’s most
popular game management units. This improved access would allow hunters an easier way to
reach Game Management Unit 13, but also could have detrimental effects on big game in the
area, potentially affecting harvest rates.
Above the dam, what was previously free-flowing river would become a 49-mile-long , 2-
mile wide lake. Because the dam would separate the river, salmon passage upstream would be
affected. Downstream, river flow would change. The way in which flow will change will depend
on how the dam is operated. Generally speaking, dams not operated as run-of-the-river would
be expected to cause changes in water flow, temperature, and turbidity. These changes could
impact fisheries production. It has also been noted that dam operation may decrease ice pack
stability on the Susitna, potentially limiting snow machining opportunities and changing winter
accessibility. The degree of these effects will depend on any mitigation measures such as
restoration activities or dam management that includes controlling water temperature and flow.
To estimate the welfare changes of the hydroelectric project, AEA is developing a
mathematical representation of outdoor recreation preferences for the population expected to be
affected by the Project. The mathematical representation identifies recreation preferences
across site characteristics that occur in both the Baseline (Without-Project) and With-Project
Conditions. Important modeling features include fusing behavioral (choice-based) preference
functions to spatially represented population data. This fusing process produces integrated
partial equilibrium models that are used to simulate conditions under Baseline (Without-Project)
and With-Project Conditions. The differences between these two states determine the social
welfare changes associated with the Project.1
Preference functions are used to identify how outdoor recreators tradeoff the
characteristics of alternative recreation sites when they choose how and where to recreate.
Using fishing as an example, when anglers take a trip, they have a choice of which site to visit.
The sites from which they can choose have numerous characteristics such as the distance from
their home, fishing quality, facility amenities (e.g., presence of a boat launch), and water-body
characteristics and surroundings (e.g., fresh versus saltwater, level of crowding, and
remoteness of the surroundings).
Preference functions include the (nonmarket) price of outdoor recreation as the costs
recreators incur in traveling from their homes to recreation sites. These “prices” vary according
1 As described in EPA’s Guidelines for Conducting Economic Analysis, equilibrium modeling using the With- and
Without-Impact approach is central to all sound welfare estimation processes (EPA 2010).
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to recreator locations. When new recreation sites are created or existing sites have their
features changed, the preference functions allow interpreting the value of the quality change in
terms of travel costs. Recreators respond to site quality changes by reallocating their trips so as
to maximize the value of their recreation experience. For example, if a high-quality fishing site
is closed, an angler who typically visits that site might travel farther to achieve a similar
recreation experience. This recreator would incur higher travel and time costs. Alternatively,
the angler could choose a closer, but lower quality experience or forgo angling trips while the
site is closed. In these cases, angler costs go down, but the value they receive from their
angling opportunities is also diminished.
1.2 Steps in Developing the Structural Recreation Demand Model
AEA took the following steps to develop the spatial, travel-cost-based representations of
recreation demand that it uses to evaluate changes in fishing, recreational boating, snow
machining and hunting:
• Step 1—Identify important recreation conditions and outcomes.
• Step 2—Assess available recreation data.
• Step 3—Identify relevant recreation utility functions.
• Step 4—Identify recreation demand by combining utility functions and site
characteristics (under Baseline and W ith-Project Conditions)
• Step 5—Identify aggregate demand using population data and recreation
participation rates.
1.2.1 Step 1 —I dentifying Recreation Outcomes
In this step, AEA identifies recreation outcomes that are likely to occur under With-
Project Conditions. Example outcomes to be considered include those that affect the quality of
a recreation trip, such as changes in access, solitude, crowding, and harvest. Each section of
the report that presents the individual recreation demand models describes policy-relevant
variables that AEA identified to be able to evaluate changes in the demand for each recreation
activity and the effect on social welfare.
1.2.2 Step 2—Assess Currently Available Recreation Data
In this step, AEA gathers population data and recreation participation rates to define and
estimate the size and characteristics of the affected population. For example, to identify the
angler population that will most likely be affected by the project, AEA reviewed and compiled
information from the 2010 Census Bureau, ADF&G’s 2010 Sport Fishing Survey, annual fishing
license sales, the 2013 Alaska Outdoor Recreation Survey, and recreation participation rates
from Alaska’s Statewide Comprehensive Outdoor Recreation Plan (SCORP) (AKDNR 2009).
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1.2.3 Step 3 —Identify Recreation Utility Functions
In this step, AEA first identifies recreation utility functions and then spatially calibrated
specifications of these functions to capture important recreator behavioral responses that are
expected to occur under With-Project Conditions (e.g., changes in trip-taking behavior as a
result of changes to the fishery). The accuracy of this methodology is limited only by the ability
to calibrate an already estimated utility function to a different population using appropriate
economic methodologies (Smith, van Houtven, and Pattanayak 2002).
1.2.4 Step 4—Identify R ecreation D emand
In this step, AEA identifies recreation demand by combining the existing utility functions
with site characteristics under Baseline (Without-Project) and expected With-Project
Conditions. The representation of recreation opportunities in a demand system allows
identifying an individual’s (or like-minded and located group’s) likelihood of visiting a site under
the specified site characteristics. Under this approach, Baseline (Without-Project) Conditions
and site visits are used to identify the econometric model. Under With-Project Conditions, site
characteristics will be different from Baseline (Without-Project) Conditions to reflect expected
outcomes (i.e., changes in site availability, access, and harvest).
With the new set of opportunities and site characteristics under W ith-Project Conditions,
AEA conducts participation and site-choice simulations to identify expected changes in the
affected population’s visits across the directly affected project site and all relevant substitute
sites included in the recreation demand model. This allows estimating visitation at sites under
conditions that are not currently occurring at those sites. In addition, because these forecasts
arise from simulations of a structural demand system, it is possible to recover sophisticated
economic metrics such as changes in respondents’ consumer surplus (i.e., willingness to pay)
by making comparisons across Baseline (Without-Project) and With-Project specifications of the
demand models. Consumer surplus provides the appropriate measure of changes in social
welfare.
1.2.5 Step 5 —Identify Aggregate Demand
In this step, AEA identifies aggregate demand using 2010 Census data and participation
rates to reflect changes in demand and social welfare for the entire affected population (i.e.,
anglers, hunters, boaters, and snow machiners). AEA also scales the results of the evaluation
for the residential population to reflect changes in demand and social welfare for the non-
resident population based on rates of resident to non-resident participation for each recreation
activity.
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1.3 Summary of Results
Table 1.1 presents the structure of the summary results that AEA will produce from its
evaluation of recreation demand and social welfare changes resulting from the Project. The
evaluation covers the 30-year time period from 2015 through 2045. The changes in recreation
demand and social welfare are estimated as differences between Baseline (Without-Project)
and With-Project Conditions over this 30-year time period. The results present the number of
affected recreators (both resident and non-resident), the average annual trips they take under
Baseline (Without-Project) Conditions, the difference in the average annual trips they take
between Baseline (Without-Project) and With-Project Conditions, and the total and annual
change in social welfare associated with this change in trips.
Table 1.1
Summary of Results
Category
Recreation Activity
Fishing Paddling Hunting
Snow-
machining Boating
Evaluation Time Period 2015 – 2045
Number of Affected Recreators
Resident
Non-Resident
Total
Average Total Annual Trips by the
Affected Population
Resident
Non-Resident
Total
Annual Change in Trips
[ Increase / (Decrease) ]
Total Change in Social Welfare
[ Increase / (Decrease) ]
Annual Change in Social Welfare
[ Increase / (Decrease) ]
1.4 Report Organization
The report contains 3 sections and 2 appendices. Section 2 presents a detailed
description of the methods that AEA used and the information required to model changes in
recreation demand and social welfare associated with the creation of the Susitna-Watana
Hydroelectric Project. The section uses changes in social welfare resulting from changes in
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fishing demand as a conceptual example. Section 3 describes the Fishing Demand Model that
AEA developed to assess changes in angling demand and social welfare resulting from the
Project. Appendix A presents the site characteristics included in the Fishing Demand Model,
and Appendix B presents the 2013 Alaska Outdoor Recreation Survey.
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2. Conceptual Mathematical Foundations and Information
This section presents an overview of the conceptual mathematical methodology. This
conceptual methodology informs the modeling approach and information requirements AEA
uses to estimate changes in recreation demand and social welfare under the With-Project
Conditions. The approach is a general-purpose, policy-modeling framework developed by
Vining (1984) that Bingham and Kinnell 2012) have adapted as a dam-management policy
framework . Vining’s general methodology is at a higher level than the specific recreation
economics that are relevant for this portion of the Watana project. However, the methodology is
entirely consistent with sophisticated recreation demand modeling techniques and provides an
elegant overarching framework for the complex ecologonomic systems that characterize the
interface between dams and fisheries.
Section 2.1 presents a generalized overview, and Section 2.2 presents the application of
the structure to assessing changes in recreation demand and social welfare. Section 2.3
presents an example using changes in fishing demand.
2.1 Overview of Mathematical Structure for Valuing Changes in the Susitna
River System
Simulation modeling is used to conduct the evaluation. Figure 2.1 presents a flow
diagram representation of the Susitna River system with the addition of mathematical notation.2
The mathematical notation, characterizes the Susitna River system as (S,Θ). In this framework
S represents the integrated physical, hydrologic, ecological, environmental, and socioeconomic
relationships that link dam development and management alternatives with socioeconomic
outcomes.
The selection of dam development alternatives directly and indirectly affects numerous
physical, hydrologic, ecological, and environmental characteristics of the Susitna River system.
Of these characteristics, those that are relevant to local socioeconomic conditions are
represented by Θ. Prime notation is used to represent level of control. Factors that can be
directly controlled are typically closely coupled to alternative-related physical characteristics,
such as the existence and operational status of the dams and presence or absence of fish
passage technology.3 Relevant, indirectly controllable hydrologic, ecologic, and environmental
2 The model’s conceptual foundation follows Vining’s (1984) public policy modeling approach that “the real thing that
is being reacted to and talked about,” in this case recreators’ trip-taking decisions and well being, “does in fact
exist; it lies there in time and space ready to be depicted” (page 13). 3 “Closely coupled” refers to changes that can be known with certainty.
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characteristics are represented by Θ ' and Θ ".4 Consequently, the specification of a resource
characteristic as Θ means that it is both relevant to socioeconomic processes and either directly
or indirectly related to the physical status of the Susitna River.
Figure 2.1: Mathematical Notation for the Susitna River System
In this figure, “S” mathematically denotes the Susitna River system. The character Θ represents
relevant system parameters over which decision-makers have some control. Socioeconomic
processes are represented by π and measured outcomes related to these processes are
denoted β.
Socioeconomic processes that are impacted by changes to Θ are represented by π.
These are specific, continually occurring collections of events. A particular person choosing
how to spend a day off for recreation is an example of a socioeconomic process as is a real
estate transaction from listing to sale.5 Because of the number and complexity of these
socioeconomic processes, their complete properties are rarely observed. Consequently,
quantitatively assessing the performance of these systems requires the use of indicators. For
example, the number of trips taken to the Susitna is an indicator that informs an assessment of
the Susitna River’s performance as a socioeconomic asset with respect to recreation.6 In the
mathematical structure, these indicators are identified as β.
4 The use of prime notation to represent degree of control (and thus degree certainty) recognizes that expert
judgment and reduced form modeling (as opposed to detailed structural modeling) may be used to identify changes
to the Θ. 5 Mathematically this is represented with subscripting by i for time periods and j for individuals and superscripting by
R for recreation . 6 The collection of additional information can refine this assessment. For example, information on distances traveled
provides information about values.
Susitna-Watana-0005
Ѕ Susitna River System
Quantified Impacts
on Area Residents
β
π
Economic
Process
π
Social
Process
Dam Development
and Management
OptionsΘ
Hydrologic
Θ′
Ecological and
Environmental
Θ″
πijR
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To ensure that they are both mathematically tractable and useful for policy analysis,
indicators must have the following qualities:
1. They are generated through socioeconomic activities.
2. They are real numbers that can be measured.
3. Evaluating their statistical properties conveys a sense of system performance.
The selection of performance indicators for the Susitna River is informed by assessing
these properties. Recreational pressure provides an example. Recreational pressure estimates
clearly meet requirements 1 and 2 because the number of trips taken to the Susitna River over
a particular time period is a measurable quantity that is generated through a socioeconomic
process.
With respect to requirement 3, although pressure estimates are based on actually
occurring events, they are typically random variables.7 An evaluation of the statistical properties
of recreational pressure does indeed provide an indication of system performance. For
example, an estimate of recreational pressure that is “high” combined with an estimate of
variation in pressure that is “low” could indicate “good” performance. When possible, this
evaluation is furthered by identifying the historical statistical properties of the indicator.
Specifically, historical pressure estimates and associated value estimates provide norms upon
which performance of the system as currently operating can be evaluated.8
With this structure informing the identification of relevant conditions, required information
for socioeconomic modeling of the system includes the following:
1. Recreation site attributes—Θ
2. Dam creation and operation characteristics—Θ
3. Recreational use patterns and values—β
This exposition identifies information needed to evaluate the current status of the Susitna River
as a socioeconomic asset. The identification of changes to these indicators associated with
dam-management alternatives via mathematical simulation requires additional information as
identified in the following subsections.
7 “Random variable” refers to estimates of real numbers. Random variables are identified by frequency distributions
containing parameters that indicate central tendency and degree of certainty of the estimate. 8 Value estimates are also a β. Subscripting β by t represents the inclusion of these temporal considerations.
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2.2 System Structure and Parameter Specification
The backdrop for the Susitna-Watana Hydroelectric Project on recreation is one of state
policy-making. The identification of variables influencing socioeconomic processes (Θ) and
indicators of the performance of these processes (β) underlie hopes of improving the
performance of the system. Alternatives are evaluated through the simulation of changes in β
(as opposed to actual changes in Θ and observations of β). For this reason, identifying
expected changes in β requires mathematically modeling the relationship between Θ and β.
This modeling task consists of identifying and specifying circumstances (Θ, S) that generate
both the variation currently being observed in β and the variation expected in β under potential
dam development and management alternatives.
Given an acceptable specification of the mathematical structure that generates
observables of interest, the system (Θ, S) must then be parameterized. This is accomplished
by identifying those parameters associated with S and those control variables associated with Θ
within the appropriate mathematical structures. Because numerous statistical models have
been estimated, it is often possible to identify parameters from existing studies.
A mathematical form that is particularly important for evaluating changes across multiple
characteristics is based on hedonic price theory as developed by Rosen (1974). The hedonic
concept decomposes the total utility of a multi-attribute good into values that are implicitly
associated with each characteristic.9 The characteristics of a fishing trip include distance from
home to the site, availability of a boat launch, and expected catch, among other things. Hedonic
forms are employed in the mathematical modeling of recreation pressure and value and
property value. Advantages of this specification include both conceptual correctness and ability
to simulate socioeconomic outcomes β under policy-relevant specifications of site
characteristics Θ.
The accuracy of transferred functions depends upon the analyst’s ability to calibrate
previously estimated functions to different geographic areas and populations (Smith, van
Houtven, and Pattanayak 2002). Calibration needs are minimized by relying on scientifically
sound studies that evaluate socioeconomic processes similar to those under consideration. In
some cases, available functions are estimated in situations that are dissimilar to the situation
being evaluated. In others, there is no statistical model available. In such cases, a combination
9 Rosen’s original application addressed residential property values. Since then hedonic forms have been used to
evaluate most multi-attribute goods, including less obvious applications such as pharmaceuticals and wine.
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of expert judgment and mathematical calibration is employed to both refine the parameters and
link the recreation models.
Until this point, the mathematical system has been presented as deterministic. In fact,
the system is best characterized as being inherently uncertain. For this reason, specifying
uncertainty in system parameters is important. When system parameters are transferred
directly from a high-quality empirical study of the same process and population, uncertainty
estimates are directly transferred.
When the uncertainty in a parameter is not directly available, it is specified and updated
via statistical calibration. Once specified in this manner, downstream uncertainties can be
evaluated by using Monte Carlo analysis. This approach can appropriately account for
uncertainty in transferred parameters, calibrated parameters, and those parameters that are
specified based on professional judgment.
2.3 Modeling Changes in Recreation Demand and Social Welfare
Complexities in recreational demand modeling arise because the use of recreation
resources does not take place in a traditional marketplace and because there are numerous
types of recreation taking place, including several different types of fishing, hunting, boating,
and snow machining. Section 2.3.1 describes the structure for evaluating changes in recreation
demand and social welfare. This description is generic across all recreation types. The section
containing each recreation demand model presents the unique model specification for each
activity. Section 2.3.2 presents the information requirements needed to develop this demand
specification.
2.3.1 System Structure for Recreation
The mathematical structure for recreation is the probabilistic site choice model. This
modeling structure, based on choice theory, has the advantages of being professionally
accepted, useful for policy-simulation predictions, consistent with economic theory, and capable
of identifying trip values and changes in social welfare.10
These models identify the probability of a specific outcome (in this case, the selection of
a recreation site), conditioned on the site characteristics of all relevant choices for recreators
(e.g., distance from the site to the angler’s home, expected catch, etc.). In the site choice
framework, a recreator chooses a site by comparing characteristics across all sites. The
mathematical structure is presented in Equation 2.1 below.
10 The statistical basis for choice theory is the standard conditional logit model (McFadden 1974; McFadden 1981).
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where Vij = f (Θ, S) (2.1)
This equation represents the probability that on any particular recreation choice
occasion, a recreator (identified by i) will choose to visit a particular site (identified by j). Note
that this likelihood, identified by Pi(j), is determined on the basis of both site characteristics (Θ)
and parameters representing the value the recreator holds for those site characteristics (S).
This mathematical construct identifies visitation likelihood. However, the probability that
a recreator will visit a site is not an observable β that can be used to evaluate the performance
of the system. Pressure is a closely related and commonly employed β. To estimate pressure
for any given site j, Pi(j) is summed over all recreators’ choice occasions.11
The motivation underlying the identification of the mathematical structure that generates
recreation trips is to allow evaluation of changes in trips (and other metrics as well) that are
associated with policy decisions. The hedonic decomposition of recreation sites into site
characteristics and the representation of these site characteristics in the site-choice framework
allow an evaluation of important information, including changes in visitation probability, changes
in site pressure, and changes in resource value. This is accomplished by developing an
equivalent mathematical structure with appropriately altered Θ for policy alternatives and finding
the difference in trips between this policy simulation model and the base case. Equation 2.2
presents the mathematics for an individual.
(2.2)
Aggregating over individuals identifies changes in trips for each site due to the policy that
changes .
Estimates of changes in economic value improve the ability to assess resource
performance. The distance from an individual’s home to a site is a critical variable in a site-
choice model because it represents the fuel cost and travel time required to visit any site. When
11 In the simulation context, this is accomplished by multiplying the likelihood of selecting each site (Equation 1) by
the total number of trips.
Pi(j) =
j =1
exp(Vij)
exp(Vik )
J∑
Annual Choice Occasionsi
j =1
exp(Vij)
exp(Vik )
J∑
j =2
exp(Vij)
exp(Vik )
J∑–
where Vij = f (Θ,S)Vij = f (Θ,S)
Θ Θto
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distance is converted to travel cost, the site-choice framework supports the calculation of
monetary changes in value associated with changes in site characteristics. The mathematical
form used to identify dollar-based changes in value associated with a policy that changes
is the difference between the utility levels scaled by the relative impact of travel costs.
Equation 2.3 presents the mathematical structure used to evaluate the change in annual value
that a recreator attributes to the policy that changes . φ is the parameter that accounts
for the impact of travel costs on site choice.
(2.3)
CVi refers to the compensating variation or dollar-valued, willingness to pay that
recreator i has for the change from . This is the amount of money that would make the
recreator indifferent between the two states of the world specified under .12
2.3.2 Information Requirements for Recreation
Operationalizing the powerful site-choice framework requires a significant amount of
information. The information that must be identified includes
1. the population of affected recreators,
2. relevant site characteristics for both the site being evaluated and potential substitute
sites,
3. assessment of recreator preferences, and
4. travel costs from recreator origins to sites.
The first step identifies information relevant to the local population of recreators.
Identifying the affected population of recreators and their characteristics is critical. Based on
publicly available information about typical travel distances, the likely users of the affected site
are identified within a defined geographic scope. Publicly available information on population
densities, license purchases, and other measures of recreation participation are then used to
assess the size and total number of annual trips across each affected recreator population.
The next step involves identifying substitute sites and site characteristics for each
recreation activity. Available information on recreation in the area and typical travel distances
are used to develop an appropriate area for substitute sites. For the Susitna River assessment,
12 This information is useful for evaluating changes via a utilitarian perspective, such as benefit-cost analysis (Dower
1989).
Θ Θto
Θ Θto
CVi = Annual Tripsiφi
ln –lneVij()eV ij()j =1
J∑
j =1
J∑
where Vij = f (Θ,S)Vij = f (Θ,S)
Θ Θto
Θ Θand
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its substitute sites should reflect similar and nearby rivers and inland lakes. Very popular sites
by activity within the evaluated area are included as substitute sites along with the policy
relevant sites. The least frequented sites within the affected area, because they have a
negligible effect on aggregate recreation demand, need not be included.13 Once the
representative substitute sites are selected, publicly available sources are used to collect the
site characteristics for the affected and substitute sites.
The last critical piece of information is distances from all recreator origins (ZIP codes) to
all specified policy and substitute sites. The distance traveled to a site directly influences the
travel cost to each site for each angler. These distances are calculated using the most recent
version of a popular transportation routing software called PC*Miler (ALK Technologies 2010).
Travel costs reflect both direct costs and travel time costs. Direct costs are calculated by
multiplying the round-trip miles by the standard per mile reimbursement. The average hourly
wage of each ZIP Code within the affected population is calculated by dividing household
income from the U.S. Census by 2,000 work hours per year. Travel time in minutes is also
calculated by PC*Miler. The round-trip time estimate is multiplied by one-third of the average
hourly wage rate to reflect the opportunity cost of time. The travel cost included in the model is
the sum of the direct travel cost and the opportunity cost of time. Because some sites are not
accessible by road, direct costs are calculated using the costs to fly to those sites. For sites that
are only accessible by boat, the direct costs are calculated from the ZIP Code to the launch
nearest the site.
2.4 Modeling Changes in Fishing Demand—A Conceptual Example
Evaluating changes in recreation demand and social welfare under the With-Project
Conditions requires developing a mathematical model that identifies the relationship between
trips, travel costs, and other site quality features. The concept of an individual’s demand for
recreation (e.g., fishing trips) to sites is the foundation for the economic valuation of a
recreational impact resulting from a project or an event because it provides the basis for
measuring recreators’ willingness to pay (WTP) for the recreation opportunity. The demand
function describes the maximum number of trips a person would be willing to take at each price
over a given time period. It also describes the WTP for another trip—the so called marginal
value of a trip—given the number of trips. For a nonmarket service like recreational fishing,
“price” is the cost of taking a trip to that site. This cost may include transportation costs, the
opportunity cost of time, entrance fees, and other trip-related costs. When these costs are
13 This varies by activity.
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considered as an implicit price, an individual’s demand function can be interpreted as his
maximum WTP for an additional trip at each quantity of trips. Differences across demand
functions under Baseline (Without-Project) and With-Project Conditions are used to identify
changes in social welfare.
Using fishing as an example recreation activity, an important task is identifying affected
anglers’ demand curves for sites in the fishery. To be consistent with welfare economic
concepts, the estimation of demand for fishing trips to a site considers the number and quality of
substitute sites. The characteristics of each fishing site, such as total catch, presence of
facilities like a boat ramp, and distance to the site from the angler’s home distinguish one site
from another. Because each geographic area contains a unique set of sites and anglers, the
most precise way to identify each site’s demand curve is by observing the behavior of anglers
that could potentially use the site of interest. With information about the trips of anglers and the
costs and site characteristics of substitute sites, it is possible to econometrically identify site-
demand curves. The data required to do so are trip-taking behavior (i.e., number of trips to
sites) and site characteristic information, including catch and travel costs. This information may
be collected by on-site interviews, phone surveys, or other methods.
Economists have long used random utility models (RUMs) to facilitate the estimation of
demand curves (Bockstael, Hanemann, and Strand 1986; Bockstael, McConnell, and Strand
1991; Feenberg and Mills 1980; Caulkins, Bishop, and Bouwes 1986; Bockstael, Hanemann,
and Kling 1987; Morey, Shaw, and Rowe 1991, Bingham et. al 2011).14 The RUM is based on
welfare theory and posits that individuals make choices that maximize their utility, subject to
constraints. A fishing area is divided into discrete sites, each site being a plausible destination
for fishing. In this framework, anglers choose which sites to visit, based on costs and fishing
opportunities at the sites. Because anglers trade off factors, such as the cost of getting to the
site against the quality of the fishing opportunity, this approach can evaluate the relative
influence of these variables as revealed by anglers’ decisions. Incorporating the relevant
substitute sites allows evaluating the importance of site characteristics at each of these sites to
identify the site-demand curves, which form the foundation for appropriately estimating changes
in social welfare that result from changes in site attributes, such as catch improvements.
Site-demand curves are identified from data on anglers’ trip taking using a RUM.
Identification of demand curves allows the simulation of choice behaviors for a specific site and
14 RUMs are also widely accepted in other areas of the economics profession. RUMs have been used in
transportation (Beggs, Cardell, and Hausman 1981; Hensher 1991), housing (McFadden 1997), and electricity
demand estimation (Cameron 1985).
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embodies trade-offs between money (the costs of travel) and fishing opportunities, measured in
terms of catch. This links WTP to changes in catch.
Figure 2.2 shows an example econometrically estimated demand curve for recreational
fishing trips.15 Because the demand curve represents the angler’s marginal WTP for each
fishing trip to the site, the angler’s total WTP for all fishing trips to this site is the entire area
under the demand curve.
Figure 2.2: The Site Demand Curve and Consumer Surplus
The downward slope of the curve indicates that anglers are willing to pay less for each
additional trip. Thus, the second trip has a lower value than the first and so forth. To maximize
his own welfare, an angler does not take a trip to the site when the cost of doing so exceeds its
value. In the figure above, the angler’s round-trip travel cost is $25.16 The first, second, third,
and fourth trips are valued at more than travel cost (demand curve above $25). The fifth (and
higher) trip is valued at less than travel cost. Therefore, the angler maximizes his utility by
taking four trips. In the figure, the area bounded by four trips and $25 shows the individual
15 There is a demand curve for every combination of sites and trip origination location. In a RUM, the choice among
sites depends on the costs and fishing opportunities at all sites, and so each site’s demand curve depends on costs
and fishing opportunities to all sites, even those the angler does not visit. 16 Travel cost consists of direct expenditures and the value of time going to and from the site.
Susitna-Watana-0006
TripsTravel Cost ($)250
225
200
175
150
125
100
75
50
25
0 0 1 2 3 4 5 6 7 1089
Consumer surplus
Number of trips
taken at $25 per trip
Trip cost
Baseline (Without-Project) Demand
Legend
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expenditures on recreational fishing trips to the site, which are calculated as 4 * $25 = $100
(cost per trip x number of trips). The gray area above the per-trip cost and below the demand
curve is the difference between what an angler pays for fishing trips to a site and the value
(WTP) that the angler has for those trips.
This surplus value a consumer retains; the difference between what a consumer is
willing to pay and what a consumer has to pay (cost) is measured to determine the consumer’s
welfare. The value of this area is called consumer surplus, and it is the dollar measure of the
satisfaction received from trips to the site. It can also be viewed as the amount of income that is
equivalent to the satisfaction of the fishing alternative. Consumer surplus is widely accepted as
the appropriate measure of the social value of environmental goods (Zerbe and Dively 1994).
Consumer surplus is identified by calculating the area under the demand curve and above
expenditures.
As the example shows, in order to estimate the total number of trips expected to occur
as a result of the project and determine the value of those trips, the analysis requires the entire
demand curve to truly support evaluations of outcomes under conditions that do not currently
exist. In addition, there may be cases for specific recreation activities where the project causes
a decrease in current trips of that activity to the study area and recreators switch to a different
location or chose a different activity. These cases require both developing the estimate of
current demand as illustrated in Figure 2.2 and estimating how much that curve shifts to the left
if site conditions for the recreation activity decrease in quality as a result of the project
(improvements in quality would result in shifts to the right).
Figure 2.3 provides an example of conditions at the current site improving under the
With-Project scenario. In this example, consider the case where fishing is currently occurring at
the site and catch for an individual species improves as a result of a project.17 Figure 2.3
depicts the process where there is a single species pursued by the angler and the catch rate for
that species has improved as a result of the impoundment’s creation. In the figure, the red
demand curve reflects Baseline (Without-Project) catch. The blue curve depicts demand when
the site has the higher With-Project catch. This new demand curve is to the right of the
Baseline curve. For each level of visitation, the trip is more valuable (higher WTP) because of
the higher catch. Consequently, the angler takes more trips to the site (five trips rather than
four) and these trips have a higher value.
17 An alternative example could consider the case where catch below the dam decreases under With-Project
conditions. That example would lead to a decrease in social welfare and would be illustrated as the inverse of the
example presented here—i.e., demand under the With-Project conditions would shift left instead of right and there
would be a decrease in consumer surplus between the Baseline (Without-Project) and With-Project conditions.
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Figure 2.3: Increase in Consumer Surplus from Increase in Catch
The change in consumer surplus is the difference between the Baseline (Without-
Project) and With-Project demand curves. It is the measure of the change in the individual
angler’s welfare resulting from the increase in catch. Thus, the increase in welfare from the
improvement in catch at that site for that angler is represented by the shaded area in Figure 2.3.
The increase in trips (from 4 to 5 for this angler) is reflected in decreased trips to other sites.
Also, when the choice of whether or not to fish is modeled, there can be an increase in the total
amount of fishing as anglers substitute away from other activities.18 Changes in aggregate
social welfare are derived by summing the welfare changes for affected sites over all anglers
who would visit the site with the increased catch. This approach constitutes the best available
method for calculating consumer-surplus-based values of changes in social welfare that arise
from recreational changes.
18 In Bingham et al. (2011), approximately 1 to 2 percent of trips resulting from site-quality improvements are new
trips.
TripsTravel Cost ($)250
225
200
175
150
125
100
75
50
25
0 0 1 2 3 4 5 6 7 1089
Change in
consumer surplus
Number of trips
taken at $25 per trip
Trip cost
Susitna-Watana-0007
Baseline (Without-Project) Demand
Legend
With-Project Demand
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3. Fishing
To assess changes in social welfare resulting from changes in fishing conditions under
With-Project Conditions, the analysis relies upon first establishing Baseline (Without-Project)
Conditions and models that can be subjected to counterfactual experiments under the With-
Project Conditions. To accomplish this, AEA developed a Fishing Demand Model that
integrates information on the angler population most likely to be affected by the hydroelectric
project and their trip-taking preferences and behavior.
Section 3.1 presents the information requirements and data sources that AEA used to
develop the Fishing Demand Model. Section 3.2 then presents the process of using the Fishing
Demand Model to characterize the baseline fishing conditions of the population most likely to be
affected under With-Project Conditions. Section 3.3 presents the process of using the Fishing
Demand Model to evaluate the With-Project Conditions and changes in social welfare resulting
from the differences between the Baseline and With-Project Conditions.
3.1 Information Requirements for the Fishing Demand Model
The Fishing Demand Model combines information from a number of existing sources in
a manner that most accurately predicts the angling preferences and behaviors of the angling
population most likely to be affected by the project. Figure 3.1 illustrates the specific data needs
to develop the Fishing Demand Model and the corresponding available data sources that AEA
used to develop the model.
As the left-hand box of Figure 3.1 shows, the major categories of data needs focus on
information reg arding each of the following:
• Anglers most likely to be affected by the project and their demographic
characteristics
• Trips these anglers take
• Characteristics of the sites they visit
• Angler preferences.
The right-hand box of Figure 3.1 shows the available data sources that provide this information.
The data sources are separated into the following major and supplemental categories:
• Major Data Sources
♦ 2013 Alaska Outdoor Recreation Survey
♦ 2010 Alaska Sport Fishing Survey
♦ 2010 U.S. Census
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Figure 3.1: Data Needs and Corresponding Sources
Data Needs Data Sources
1.2013 Alaska Outdoor Recreation Survey
•Current trips to Study Area and substitute sites
•Number of anglers
•Origin of trips to the Study Area and substitute
sites
•Demographics
•Resident and non-resident angling behavior
2. 2010 Alaska Sport Fishing Survey
•Current trips to Study Area and substitute sites
•Actual catch and harvest rates by species
•Geographic extent of the market
•Resident and non-resident angling behavior
3. U.S. Census
•Population in geographic extent of the market
•Demographics
4. Guide Logbook
•Actual catch and harvest rates by species
5.2008–2013 ADF&G Emergency Orders
•Catch and harvest improvements by species
6. Alaska Fishing: The Ultimate Angler’s Guide, Deluxe
3rd Edition
•Catch and harvest improvements by species
7. Carson, Hanemann, and Wegge (2009)
•Angler preferences
People
•Number of anglers who may potentially and
who actually fish in Study Area and substitute
sites
•Demographics of potential and actual Study
Area anglers
Major
Trips
•Number of fishing trips taken
•Distribution of trip locations
(within and beyond the Study Area)
Sites
•Characteristics of all trip locations
1.Alaska Statewide Comprehensive Outdoor
Recreation Plan: 2009–2014
2.2011 National Survey of Fishing, Hunting, and
Wildlife-Associated Recreation: Alaska
Supplemental
Fishing Demand Model
Susitna-Watana-0003
Angler Preferences
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♦ Guide Logbook Data
♦ 2008-2013 ADF&G Emergency Orders (EOs)
♦ Alaska Fishing: The Ultimate Angler’s Guide, Deluxe 3rd Edition (Limeres and
Pedersen 2011).
• Supplemental Data Sources
♦ Alaska Statewide Comprehensive Outdoor Recreation Plan: 2009–2014
♦ U.S. Fish and Wildlife Survey’s 2011 National Survey of Fishing, Hunting, and
Wildlife-Associated Recreation: Alaska.
Table 3.1 summarizes the role that each data component provides in the analysis and is
followed by a brief summary of each source.
Table 3.1
Components of Estimating Changes in Fishing Behavior
Category Factors
Alaska
Outdoor
Recreation
Survey
(2013)
Alaska
Sportfishing
Survey
(2010)
U.S.
Census
(2010)
Guide
Logbook
Data
(2010)
ADF&G
Emergency
Orders
(2005-2013)
Alaska
Fishing
(2005)
Market Geographic extent of the market X X
Sites X X
Substitute sites for AK anglers X X
Anglers X X
Evaluated ZIP Codes X
Demographics X X
Trips Number of current trips to the Study
Area and substitute sites
X X X
Origin of trips X
Angler characteristics X
Catch Species caught X X X X X
Quantity of fish caught X X X X
Quantity of fish kept X X
Future
(Without-
Project)
Characterizing future Baseline
(Without-Project) Conditions X X X X
3.1.1 2013 Alaska Outdoor Recreation Survey
The 2013 Alaska Outdoor Recreation Survey (AKORS) collected information about
outdoor recreators and trips in Alaska. The survey has two objectives for the analysis. The first
is that it provides time-specific information on the number and location of trips taken by the
recreators most likely to be affected by the Project. This allows us to calibrate trips under the
Baseline (Without-Project) Conditions. The second objective is that it allows the option of
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estimating Alaska-specific utility functions to be available in the future if such analysis is
determined to be needed.
The outdoor recreation activities included in the study are snow machining, sport fishing,
recreational boating, and hunting. The survey was sent to households throughout Alaska.
The survey was administered in two waves to cover different time periods of recreator
trip-taking. Table 3.2 provides the time period covered for each recreational activity for each
administration of the survey. Appendix B presents the first of two 2013 AKORS mail surveys.
Table 3.2
Components of Estimating Consumption Rates
Administration Recreational Activity Time Period
1 Snow Machining January–April 2013
Sport Fishing May–October 2012
Recreational Boating May–October 2012
Hunting June–November 2012
2 Snow Machining May–October 2013
Sport Fishing May–October 2013
Recreational Boating May–October 2013
Hunting May–October 2013
As part of the survey, respondents were asked if they participate in recreation activities.
If yes, respondents were asked to provide trip information for each of their recreational trips.
Respondents provided the location of each trip, the access point for each location (e.g., a boat
ramp), and the number of days at each location.
The AKORS also collects demographic data (e.g., age, gender, race, education, and
income) for all respondents (recreators and non-recreators).
3.1.2 2010 Alaska Sport Fishing Survey
The Alaska Department of Fish and Game (ADF&G) administers an annual household
survey to households with at least one sport fishing licensed angler or one Alaska resident who
holds a Permanent Identification (PID) or Disabled Veteran (DAV) permit. The survey is
administered to residents and nonresidents and gathers information about respondents’ fishing
activities. Data collected include fishing sites, number of people who fished, use of a charter or
guide, and species caught and kept. Resident and nonresident sportfishing anglers, days, and
species-specific harvest are calculated for three regions: Southeast Alaska, Southcentral
Alaska, and the Arctic-Yukon-Kuskokwim.
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3.1.3 2010 U.S. Census
AEA collected and compiled demographic data (e.g., age, gender, race, education, and
income) on the population from the 2010 U.S. Census for every ZIP code evaluated in the
analysis.
3.1.4 Guide Logbook
The Guide Logbook Data contains data on harvest for guided fishing trips.
3.1.5 2008–2013 ADF&G Emergency Orders
The ADF&G issues Emergency Orders (EOs) in order “to open and close sport fishing
seasons or areas; to increase or decrease sport fish bag limits; or to modify methods of sport
fish harvest” (ADF&G). Such emergency orders include fishery closures and restrictions such
as reduced bag limits, gear and bait restrictions, schedule restrictions, length restrictions, and
area restrictions. EOs also include season extensions and liberalizations such as bag limits,
gear and bait liberalizations, schedule liberalizations, and area liberalizations. The EOs from
2005 through 2013 provide insight into site availability under Baseline (Without-Project)
Conditions.
3.1.6 Carson, Hanemann , and Wegge (2009)
Mathematically modeling fishing site-choice in Alaskan waters requires identifying both
site characteristics and parameterization of the relative importance that anglers attach to each
of these characteristics. Carson, Hanemann, and Wegge (2009) present an econometrically-
estimated preference function that identifies the relative importance that anglers place on each
site characteristic.
Carson, Hanemann, and Wegge (2009) analyzed 29 sport-fishing sites and 12 species
groups (along with “no target” species) to study recreational fishing demand in Alaska. The
statistical model estimated in Carson, Hanemann, and Wegge (2009) is a repeated nested logit
that identifies anglers fishing choices over a 22-week time period. Carson, Hanemann, and
Wegge (2009) use the repeated nested logit to delineate potential differences in angler
preferences with respect to each of the following:
1. Whether to go fishing in a particular week
2. Fishing experience (Salmon, Freshwater, Saltwater, and No Target Species)
3. Individual target species (e.g., King, Silver, Red or Pink Salmon)
4. Which site to fish for the particular species.
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The model output is a coefficient for each level of nest and site characteristic. Each
coefficient reflects the importance of that characteristic on angler decision making and welfare.
These coefficients play a key role in the approach used in this assessment.
3.1.7 Alaska Fishing: The Ultimate Guide, Deluxe 3rd Edition
The angler preference function presented in Carson, Hanemann, and Wegge (2009)
uses an angler site selection choice function to determine site selection probabilities. Data from
Alaska Fishing is used to specify these variables. These variables differ from species to species
and involve either site or run time dimensions. For example, in Southcentral Alaska, silver
salmon is available from late June through the middle of October, with the peak season being
late July to early September.
3.1.8 Supplemental Data Sources
In addition to the sources described above, the analysis uses two supplemental data
sources to inform the number of anglers:
• The Alaska Statewide Comprehensive Outdoor Recreational Plan (SCORP) 2009–
2014
• The U.S. Fish and Wildlife’s (USFWS) 2011 National Survey of Fishing, Hunting, and
Wildlife-Related Recreation: Alaska.
The Alaska SCORP includes the results of a 600 resident household survey that collected data
on outdoor recreation activity. The USFWS 2011 National Survey included fishing participation
in Alaska by residents and nonresidents.
3.2 Characterization of Baseline Fishing Conditions
Baseline fishing conditions are the expected conditions without the hydroelectric project.
Characterizing the baseline fishing conditions requires considering current and future
recreational fishing. Understanding the baseline fishing conditions requires assessing the
angling preferences and trip taking behaviors of the recreational angling population whose
welfare is most likely to be affected by the creation of the hydroelectric project.
3.2.1 Background on Alaska Anglers’ Trip -Taking Behavior
Alaska contains some of the worlds’ most abundant and diverse fisheries. This richness
is spread out over plentiful rivers, countless streams, and a coastline longer than the rest of the
United States. Like many anglers, Alaskan anglers tend to be aware of historically good fishing
sites. However, Alaskan fishing is unique in many ways. Sport fishing is a primary activity for
many Alaskans. During periods of extended summer daylight, Alaskans can fish for trout at
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dozens of places, catch several different types of salmon or target the world-class halibut fishery
along with many other saltwater species including lingcod and rockfish.
Of the 20 or so important Alaskan gamefish, more than half are salmonids. These fish
prefer clean, cold, highly oxygenated waters and thrive in Alaska’s rivers and streams. The
Pacific salmon—including Chinook, silver, sockeye, chum, and pink—are hatched in cold water
rivers and streams, but spend much of their life in the Pacific where they exploit r ich marine fish
reserves to grow quickly. Each year, millions of Pacific salmon return to Alaska’s numerous
rivers, lakes, and streams to spawn. The annual return of salmon supports coastal ecosystems,
and also underlies Alaskan angling behaviors.
Salmon availability is driven by run timing. Run timing can vary dramatically by species,
time and location. For example, the first runs of Chinooks (the dominant and iconic species)
can begin as early as mid-April in the Southeast and Southcentral regions. These runs can
peak in early June and have fish typically in the 12 to 30 pound range but maxing out at perhaps
50 pounds. Mid-season Chinook runs occur throughout Alaska, peak in mid-June and can
produce 60–70 pound Chinooks. The late runs occur in a few temperate lake drainages such
as the Kenai River, begin in mid-June, and peak in mid-July. These runs are commonly full of
30 to 50 pound fish and 90 pounders are not uncommon.
Many of these fish travel hundreds of miles after arriving at the coast. Thus “early run”
Chinooks arriving at the mouth of the Yukon in mid-April might arrive in Fairbanks in early July
(Limeres and Peterson Ultimate Alaska Fishing Guide). The initial arrival of Chinook is driven
by environmental conditions and are somewhat predictable; however, once they arrive,
availability at inland sites becomes more predictable (Mundy and Evenson 2011).
Pacific salmon are a tremendously important economic, ecological, and cultural resource
in Alaska and these partially predictable pulses of availability underlie the history and culture of
Alaskan fishing. Although some people pick a site and fish for whatever is there, many target a
specific fish species and then look for the best site and time (Carson, Hanemann and Wegge
2009). Many Alaskan’s have the flexibility to take off work to take advantage of salmon runs.
Anglers tend to be highly aware of angling opportunities and seek those with historically good
catch. Publications such as Alaska Fishing guide provide general information, and ADFG puts
out weekly announcements describing fishing conditions and restrictions. Fishing takes place
over the entire year, but the great majority occurs over May through September.
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3.2.2 Fishing Demand Model Structure and Variables
To model Alaskan fishing behaviors via choice simulation, AEA is fortunate to have an
existing sophisticated choice model structure of Alaskan fishing. This model (Carson
Hanemann and Wegge 2009) incorporates important features of Alaskan fishing in econometric
modeling and simulations. The econometric model was developed from weekly (May 1 to
September 30) data on sport fishing activities of 1,063 respondents.19 This model follows the
discrete choice RUM framework that allows forecasting trips under various conditions. Those
conditions can include marginal changes (such as changes in expected catch and the opening
or closing of entire sites). Importantly, the approach used to forecast trips under various
conditions is consistent with utility theory. This means that welfare theoretic predictions of
changes in social welfare can also be produced to support cost-benefit analysis.
The particular type of discrete choice model is the nested logit. Nested logit models
enable decision-making sequentially. The structure of the Carson Hanemann and Wegge
(CHW ) model is depicted in the figure below.
Figure 3.2: Choice Structure for Resident Angler Sport Fishing Demand
19 This study was published in 2009, but the data were collected in 1986. Although that has been a long time, basing
the study approach on old data from Alaska was judged superior to using a study with newer data from a location
not as similar as the Alaska fishery.
Susitna-Watana-0004
Sport fishing trips this weekT
Salmon Freshwater species Saltwater species No target species
0 1 2 3
King Red Silver Pink
Site 1 Site 2 Site 1 Site 2Site 1 Site 2 Site 1 Site 2
Site 1 Site 2 Site 1 Site 2 Site 1 Site 2Site 1 Site 2 Site 1 Site 2
Rainbow trout Dolly Varden Grayling OtherLake trout Site 1 Site 2Site 1 Site 2
Halibut OtherRazor clams
Site 1 Site 2Site 1 Site 2 Site 1 Site 2
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As can be seen in the figure, the angler is modeled to first choose how many trips to
take in a particular week. At the next level, the angler chooses whether to target salmon,
freshwater species, saltwater species or no target. Anglers with no target then choose a site
based on site characteristics. For all other anglers, before selecting a site, they choose a
specific species. Anglers pursuing salmon choose from among kings, reds, silvers and pinks.
Freshwater anglers can select from rainbow trout, Dolly Varden, lake trout, grayling and all other
species. Saltwater anglers can pick halibut, razor clams, and “other”. Following the selection of
a specific species, all anglers choose a fishing site.
A couple of important features of the nested logit model that are helpful in gaining a
holistic perspective on its operation include the fact that
1. Lower level choices are conditioned on upper level probabilities
2. Upper level probabilities are based on the inherent “quality” of trip/species
group/species and the quality of the sites that occur in the lowest levels.
The implications of Feature 1 are that an angler who chooses to fish selects the type of fishing
and that the probabilities of those fishing types sum to one (e.g. salmon = 0.35, freshwater
= 0.15, saltwater species = 0.3, no target = 0.2). These probabilities are sequentially nested,
meaning that the same process occurs at every level. The import of Feature 2 is that this sort of
model allows changes in top level probabilities based on changes in lower level site
characteristics. Thus, relatively high salmon catch in a particular week flow upward to produce
more trips in that week.
The approach for modeling using the CHW results is to adopt the nested choice-based
mathematical structure and to specify it over recent information for populations and sites. For
populations, rather than using survey participant locations and site locations to calculate travel
cost, the choice model is geospatially fused to the affected areas of Alaska. This is
accomplished by “replacing” survey participants and survey weights with ZIP codes and anglers
per ZIP code.20 In addition to being necessary (the original survey data is not available) this
updates the population specification to account for changes in population densities that have
occurred since the data was collected. Although anglers may travel great distances to enjoy
Alaskan fishing, it is necessary to limit the modeling to include anglers who are reasonably close
and whose decision-making can be expected to be similar to the anglers modeled by CHW .
20 Trip and economic welfare simulations using econometrically estimated choice models are typically done using the
survey dataset. Because AEA does not have the data from CHW, “replacing” is only conceptual.
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3.2.3 Specifying the Affected Population—Population Size and Annual Trips
The CHW article focuses on resident anglers and summer sport fishing. This
combination of anglers and activity comprised the majority (more than 80%) of Southcentral
Alaskan sport fishing trips in Southcentral Alaska in 1986. Because of the nature of the Pacific
salmon fishery, these trips are also the most responsive to short run changes in site-quality
conditions. This population is also the most likely to be affected by changes in the Study Area.
Therefore, for this analysis, the area of the Affected Population includes anglers residing in all
the ZIP Codes contained in ADF&G’s Southcentral Alaska Areas J-P, I, and U—illustrated in
Figure 3-2 from Jennings, Sundet, and Bingham (2011).
Source: Jennings, Sundet, and Bingham (2011)
Figure 3.3: Area of Affected Population—Southcentral Areas J–P and Areas I and U
The area of the affected population includes all the anglers residing in ZIP Codes contained in
ADF&G’s Southcentral Alaska Areas (J–P) plus Area I and U.
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To estimate the number of anglers residing in these ZIP Codes, AEA first collects data
on the population residing in each of these ZIP Codes. AEA then uses data on rates of angling
participation and trip taking in Alaska from each of the following to determine both the number of
anglers residing in each ZIP Code and the annual number of fishing trips they take:
• ADF&G license sales
• 2013 Alaska Outdoor Recreation Survey
• the USFWS national survey
• the Alaska SCORP.
Table 3.3 summarizes the results of this data collection and compilation for the affected
population.
Table 3.3
Affected Angling Population Evaluated in the Fishing Demand Model
Data Component
Total Number of ZIP Codes in the Affected Area
Total Population Residing in those ZIP Codes
Total Anglers Residing in those ZIP Codesa
Average Annual Trips by Affected Angling Population
Average Trips per Angler
Annual Daysb
Average Days per Angler
Average Days per Trip
Sources:
a The range of the angler population is based on the Alaska Statewide Comprehensive Outdoor
Recreation Plan (SCORP); the number of licenses sold in 2011 from the Alaska Department of Fish
and Game (ADFG) Statewide License Sales (ADFG 2012a); estimates from the U.S. Fish and
Wildlife Services’ 2011 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation for
Alaska (USFWS 2013); and the 2013 Alaska Outdoor Recreation Survey.
b The range of the angler days is based on 2011 Sportfishing survey from the Alaska Department of
Fish and Game (ADFG 2012b); estimates from the U.S. Fish and Wildlife Services’ 2011 National
Survey of Fishing, Hunting, and Wildlife-Associated Recreation for AK (USFWS 2013); and the 2013
Alaska Outdoor Recreation Survey.
3.2.4 Specifying Relevant Fishing Sites for the Affected Population
In addition to the population of affected anglers, choice-based models of recreational
angling demand require specifying the sites the population is likely to visit. CHW include 29
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sites. For the current effort, this list of sites was adapted to reflect evolution in site popularity
and the project focus.21 Objectives for the fishing site list are to
1. include Watana area and sites that could have changes under With-Project
Conditions
2. include a reasonably high percentage of trips from the 2013 Alaska Outdoor
Recreation Survey
3. develop a list that is manageable when considering both the number of sites and
weeks to be modeled. At the end of this process there was significant but not
complete overlap with the CHW sites.
To create the site list AEA relies on the 2013 Alaska Outdoor Recreation Survey and
additional information including Alaska fisheries and catch information. The 2013 Alaska
Outdoor Recreation Survey data include 6,881 trips to 262 where anglers took at least one trip
between May and October 2012 (the data are currently available only for the first wave of the
survey administration). After approximately 25 sites, trip rates drop off dramatically. Some of
this is the nature of Alaskan fishing which has varied opportunities; however, some of this is not
true variation, but arises because survey respondents provide different names for the same site.
AEA conducted text-string matching to combine sites with 10 or more trips that are to the same
place, but were named differently by survey respondents.
After consolidating some sites in this manner, AEA finds that 70 percent of survey trips
occur at 27 sites. These sites had 40 or more trips in the 2013 Alaska Outdoor Recreation
Survey data. ADF&G’s regional survey (Estimates of Participation, Catch, and Harvest in
Alaska Sport Fisheries During 2010) indicates that based on days fished and species harvested
in 2010 there were an additional 21 sites worth considering for inclusion. Of these, AEA could
not locate sufficient site data to support modeling five of them. The remaining 16 were added to
the site list making the list total 43. This site list accounts for approximately 80 percent of trips
from the 2013 Alaska Outdoor Recreation Survey data.
After completing this evaluation, AEA then considered regional coverage. The regional
evaluation indicated that by selecting sites with 40 or more trips, AEA gets good regional
coverage. Regions with sites included in the site list account for over 97 percent of trips. Figure
3.4 shows the area containing the policy and substitute sites modeled for this affected
population. This area is composed of the ADF&G Areas I, J–P, Q, S, T, U and part of Y (see
Figure 3.3 for an illustration of these areas from Jennings, Sundet, and Bingham 2011).
21 CHW was a general-purpose model built to support fishery management.
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Figure 3.4: Policy and Substitute Sites for Affected Population
The outlined area includes the policy and substitute sites for the affected angling population
evaluated in the Fishing Demand Model. The affected angling population includes all anglers
residing in the ZIP Codes contained in ADF&G’s Southcentral Alaska Areas J-P, I, and U.
Fairbanks
Dillingham
Aniak
Cantwell
Galena
Proposed
Dam
Legend
Policy and Substitute Sites for Affected Population
Outlined Area includes the Policy and Substitute Sites for the
Affected Angling Population Evaluated in the Fishing Demand Model
Fishing Demand Model’s
Area of Analysis
Anchorage
Paxson
Juneau
Nome
Point Hope
Prudhoe
Bay
Barrow
Fort
Yukon
Cold Bay
Valdez
Talkeetna Glennallen
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3.2.5 Model Variables
Choice models rely on coefficients to identify the value of site characteristics. The CHW
econometric model contains several variables that are included in the simulation model created
for this effort. These include the price variable (Travel Cost), expected catch related variables
(Site Rating, Species Rating, and Harvest) as well as the additional site characteristic variables
(Developed, Crowd, and Cabin).
In the CHW model, travel cost is specific by respondent and site. The simulation model
does not contain survey respondent data. In the geospatial fusing approach that is applied in
the simulation model for this effort, the survey respondents and their weights are replaced with
ZIP codes and angling populations. Then, distances are identified using PCMiler and travel
costs are calculated in a process identical to CHW, but updated to reflect current costs. Also,
because the vehicle ownership information available to CHW was not available for this study,
average vehicle operations costs were applied.
In the CHW model, Site Rating varies by species and week. It begins as a range from 1
(very poor) to 8 (excellent). This number is then normalized by dividing each week’s rating by
the average rating over all weeks to create the final variable. Although CHW note that the
process of creating the initial (1–8) Site Rating variable for their econometric model was
complicated, no details on its construction are provided. To specify the Site Rating variable
specification in the simulation model, AEA relies on information in Alaska Fishing the Ultimate
Angler’s Guide. By species, the Guide ranks sites as excellent fishing, good fishing, fishable,
and rare. To create the initial Site Rating variable, these qualitative descriptions are
transformed to data as 8, 4, 1, and 0. The weekly variation is included by interacting this
species-level rating to create a site and species level rating based on weekly run timing
information from the Guide. For example, a site rated 4 for a particular species only receives
that rating during weeks the Guide anticipates runs for that species to occur at that site. This is
then normalized following the CHW process.
The CHW variable Fish Rating is a quality index that operates across the CHW macro
species groups (Salmon, Freshwater, Saltwater). It is used for modeling trips with no target
species and is specific at the site and macro species level for Freshwater and Saltwater macro
groups and at the weekly level for salmon. The index ranges from 0 (not available) to 4
(excellent). In the simulation model, for freshwater and saltwater species, this is specified
based on availability information from the Guide. For salmon, the weekly index is created based
on the summed and normalized species-level information.
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The final variable related to catch expectations is Harvest which is specific at the site
level. Harvest measures the total number of “species caught (in thousands)” in the prior year.
Here again, CHW provide no additional details. However, because the total number of species
caught does not number in the thousands AEA presumes this could be clarified to say either
“the total number of all species caught” or “the total number of fish caught”. In the simulation
model, this is specified as the total harvest of all species at each site in 2010. This information
is available from the Alaska Department of Fish and Game (Jennings, Sundet, and Bingham
2011).
Site characteristic variables that do not relate to expected catch include the Developed,
Crowd, and Cabin variables. In the CHW econometric model the Developed variable is a
dummy variable that is 1 if the site has boat and tourist facilities and 0 otherwise. This variable
was specified for the simulation model based on site-level research from various sources.
In CHW, the Crowd variable is specific by site, week, and respondent. To begin, a 0
represents “not crowded”, a 1 is “somewhat crowded” and a 2 is “very crowded”. This quantity
is then interacted with a survey response that indicates each respondent’s tolerance for
crowding to result in a variable that is specific by site and survey respondent and is (for
example) 0 if there is either no crowding or high crowding and the respondent indicates a
preference for crowding. Respondent preferences are not available for the simulation model.
However, the Guide provides an indication of crowding at the site level. For preliminary
specifications, this information is used to specify the crowding variable with the idea that it would
be dropped or improved depending on how it impacts the model calibration process.
The CHW variable Cabin is a dummy variable which takes on the value 1 if the individual
respondent owns or has regular access to a private cabin at a site and 0 otherwise. Like the
Crowding variable the Cabin variable requires individual specific information that is only
available from the original survey. There is however information about cabin availability. For
the preliminary specification, the number of cabins to expected visitors is used to calculate a
probability of any visitor having a cabin. Like the crowding variable, the usefulness of this
approach will be assessed in the calibration process.
Table A.1 in Appendix A provides a summary of some of the site-characteristic data for
each of the sites included in the Fishing Demand Model. Because some of the variables are
indexed by individual (travel cost) or time (site rating), they are not included in the table.
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3.3 Calibrated Baseline Trips
The Fishing Demand Model combines data on travel costs, harvest, and site quality with
the coefficients from the Carson, Hanemann, and Wegge (2009) to allocate the estimated
annual trips by the affected angling population to the policy and substitute sites (illustrated in
Figure 3.4 and listed in Appendix A). Total trips to the policy relevant sites are calibrated to
correspond to the best available visitation information for these sites from the 2013 Alaska
Outdoor Recreation Survey and ADF&G’s 2010 survey data. This process results in the
following distribution of trips to affected policy sites (Table 3.4).
Table 3.4
Estimated Average Annual Trips to Affected Policy Sites
Location Average Annual Trips under Baseline
(Without-Project) Conditions
Total
The remaining trips are calibrated to be distributed among the substitute sites using the
best available visitation information from the 2013 Outdoor Recreation Survey and ADF&G’s
2010 survey data.
3.4 Changes in Social Welfare Under With-Project Conditions
Baseline (Without-Project) Conditions are represented by specifying the data to be used
by the choice model as previously described and then calibrating the model to produce the
appropriate number of trips for each week, species group, species, and site. Having Baseline
Conditions specified in this way, it is possible to change the data so as to quantitatively
represent With-Project Conditions. Estimates of With-Project trips are then produced by
evaluating this With-Project Fishing Demand Model.
Along with trip predictions, each specification (i.e., Baseline and With-Project Conditions)
produces estimates of social welfare. Social welfare in this model represents the value of all
sites under that specification. It is calculated as the sum of the consumer surplus across sites.
Changes in social welfare resulting from changes in the recreational fishery are calculated as
the difference in consumer surplus across Baseline and With-Project states.
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Consumer surplus is evaluated using demand functions. Demand functions describe the
maximum number of trips a person would be willing to take at each price over a given time
period. For a nonmarket service like recreational fishing, “price” is the cost of taking a trip to
that site. This cost may include transportation costs, the opportunity cost of time, entrance fees,
and other trip-related costs.
Developing these estimates of demand and changes in consumer surplus requires
estimating changes in angler utility under the With-Project Conditions. In mathematical terms,
an individual angler’s utility, Uipwj (the well-being they receive from a fishing trip), is treated as a
random variable composed of a deterministic component and a random component. The utility
associated with a recreation trip to site j of waterbody type w after making participation decision
p by angler i can be expressed as:
(1)
where Vipwj is the deterministic part of the utility function and εipwj represents the random terms,
which are assumed to be jointly distributed according to the generalized extreme value (GEV)
distribution. V is a function of site characteristics (here p and w omitted for brevity):
jnnnjnnnijTCijSRWBVTCV∑∑++=21ααβ (2)
where
TCij = Travel cost to site j by individual i
WBVjn = Waterbody-related variables for site j
SRjn = Species rating for species n at site j.
The probability of site choice can be expressed as a product of conditional probabilities.
The probability of making participation decision p choosing waterbody type w and site j is as
follows (i is suppressed for the sake of notational simplicity):
(3)
The first probability is the probability of choosing site j conditional on making participation
decision p and choosing waterbody type w and is expressed as the following:
(4)
The second probability in equation (3) is the probability of choosing waterbody type w
conditional on making participation decision p and is expressed as the following:
Uipwj = Vipwj + εipwj
Prob pwj = Prob j|wp x Prob w|p x Prob p
Prob j|wp= exp (Vj)
∑j exp (Vj)
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(5)
where
(6)
The third probability in equation (3) is the probability of making participation decision p, that is,
whether to go fishing or not:
(7)
where
(8)
Utility changes from site improvements and additions are calculated as
(9)
where
(10)
Changes in economic well-being are calculated as changes in consumer surplus or
compensating variation (the amount of money required to restore utility to its initial level). This
is accomplished by scaling the utility change by the marginal utility of money in the following
manner:
(11)
where the marginal utility of money is identified as βTC, the travel cost coefficient. D0 and D1
represent angling demand under the Baseline and With-Project states.
The Fishing Demand Model calculates the welfare impact under With-Project Conditions
for Alaskan angling residents. However, the 2010 Alaska Sport Fishing Survey indicates that
approximately 58 percent of licensed anglers are nonresidents, and the USFWS 2011 National
Survey indicates that 61 percent of anglers taking trips to Alaska are nonresidents. Although
nonresidents make up over 60 percent of the number of anglers, they take on average 10 trips
fewer than residents per year.
Prob w|p= exp (γwIw)
∑w exp (γwIw)
Ipw = ln ∑j exp(Vj)
Probp= exp (θpQp)
∑p exp (θpQp)
Q p = ln ∑w exp (γw Ipw)
ΔU = (ln D1 –ln D0)
D = ∑w∑j exp
γw∑p θp θpVjγw()
CV =x (ln D1 –ln D0)βTC
–1
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To account for welfare impacts to non-resident anglers, AEA scales the resident welfare
impacts based on relative rates of resident to nonresident participation in each benefit category.
The relative rates of resident to nonresident participation (anglers and trips) are calculated using
data from the 2010 Alaska Sport Fishing Survey and the USFWS 2011 National Survey.
3.5 Watana With-Project Conditions and Trips and Welfare Implications
Predicting changes in total trips, species targeted, site-level pressure, and economic
welfare requires mathematically specifying site characteristics under With-Project Conditions.
Information requirements include differences in site conditions across modeled site
characteristics between Baseline and With-Project Conditions. Ultimately, this will be
accomplished with input from AEA regarding the final proposed characteristics of the
hydroelectric project. For this assessment, affected sites can be categorized as upstream and
downstream from the location of the potential dam.
In the Baseline, the Watana site, which represents areas above the dam, are essentially
nonexistent. Fishing quality there is low. Moreover, because there is no road or suitable
airplane landing spot there is no access. Below Watana Creek are 7 sites which could be
affected under the With-Project Conditions. Two of these are along the Talkeetna (a major
Susitna tributary). The Chase Trail site is at the confluence of the Chase Trail creek and the
Talkeetna. The Chunilna Creek site is at the confluence of Chunilna Creek (also called Clear
Creek) and the Talkeetna. The remaining 5 sites are downstream on the Susitna and are
located at the confluence of the Susitna and Sunshine Creek, Montana Creek, Kashwitna River,
Deshka River, and Willow Creek. In the Baseline, the Species Ratings of the downstream sites
are specified as shown in Table 3.5.
Table 3.5
Baseline (Without-Project) Conditions: Species Ratings for the Downstream Sites
Site King Red Pink Chum Silver Rainbow Grayling
Deshka River 8 4 8 2 8 4 4
Montana Creek 8 0 8 4 8 4 4
Sunshine Creek 8 0 8 4 8 4 4
Kashwitna River 8 0 8 4 8 4 4
Willow Creek 8 0 8 4 8 4 4
Chase Trail 8 4 8 8 8 8 4
Chunilna Creek 8 4 8 8 8 8 4
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Under With-Project Conditions, species ratings at the new impoundment are specified to
be identical to Lake Louise. Travel costs to the new site are specified based on distance for
road travel and availability of air travel. With-Project Conditions below the dam are currently
specified as being slightly degraded for fishing as seen in the table below. All of these are
preliminary and will be informed by discussions with the project team.
Table 3.6
With-Project Conditions: Species Ratings for the Downstream Sites
Site King Red Pink Chum Silver Rainbow Grayling
Deshka River 7 3 7 1 7 3 3
Montana Creek 7 0 7 3 7 3 3
Sunshine Creek 7 0 7 3 7 3 3
Kashwitna River 7 0 7 3 7 3 3
Willow Creek 7 0 7 3 7 3 3
Chase Trail 7 3 7 7 7 7 3
Chunilna Creek 7 3 7 7 7 7 3
Table 3.7 presents the structure of the summary results that AEA will produce from its
evaluation of recreation demand and social welfare changes resulting from the Project. The
evaluation covers the 30-year time period from 2015 through 2045. The changes in fishing
demand and social welfare are estimated as differences between Baseline (Without-Project)
and With-Project Conditions over this 30-year time period. The results present the number of
affected anglers, the average annual trips they take under Baseline (Without-Project)
Conditions, the difference in the average annual trips they take between Baseline (Without-
Project) and With-Project Conditions, and the total and annual change in social welfare
associated with this change in trips.
Table 3.7
Summary of Fishing Results
Category Result
Evaluation Time Period 2015–2045
Number of Affected Recreators
Average Total Annual Trips by the Affected Population
Annual Change in Trips [ Increase / (Decrease) ]
Total Change in Social Welfare [ Increase / (Decrease) ]
Annual Change in Social Welfare [ Increase / (Decrease) ]
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Social Welfare Evaluation – Watana Hydroelectric Project December 2013
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Appendix A
Fishing Sites and Site Characteristic Data
Susitna-Watana Data Collection and Compilation December 2013
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Table A.1
Selected Alaska Fishing Sites and Site Characteristic Data
Example Site Characteristic Variables used in the Fishing Demand Model
Total Harvest, 2010 (not including subsistence fishing)
Site Name Sportfish Caught in Waterbody Latitude Longitude Total Harvest
King
(Chinook)
Salmon
Red
(Sockeye)
Salmon
Silver
(Coho)
Salmon
Pink
(Humpy)
Salmon
Rainbow Trout,
Land-Locked
Salmon
Dolly Varden,
Arctic Char
Lake
Trout
Arctic
Grayling
Other
Fresh-
water Halibut
Razor
Clams
Other
Salt-
water
Anchor Point Hardshell and razor clams, other saltwater 59.774121 -151.867371 4,075 364 10 2,863 48 790
Ship Creek Anchorage Chum, king, pink, and silver salmon; Dolly Varden 61.223452 -149.888967 2,287 1,095 743 377 15 57
Bird Creek, Anchorage Chum, pink, and silver salmon; Dolly Varden 60.973172 -149.466602 2,584 974 1,433 29 148
Birch Lake Fairbanks Arctic char, grayling, king and silver salmon, and rainbow
trout 64.315199 -146.646066 3,299 2,021 648 108 522
Chena Lakes Recreation Area Arctic grayling, rainbow trout, Dolly Varden, and other
freshwater 64.768836 -147.222762 3,547 2,662 319 8 558
Chunilna Creek King, red, silver, and pink salmon; rainbow trout, Dolly
Varden, arctic grayling, and other freshwater 62.37 -150.008889 2,008 485 124 1141 12 26 29 82 109
Clam Gulch King, red, silver, and pink salmon; Dolly Varden, Halibut,
razor clams, and other saltwater 60.239425 -151.400878 411,412 2,310 1,623 2,272 644 278 74,226 325,440 4,619
Clearwater Creek King, red, and silver salmon; and other freshwater 62.199166 -151.607778 1,507 143 61 334 969
Copper Center, Klutina River Dolly Varden, grayling, king and red salmon, rainbow trout,
whitefish 61.953829 -145.321667 1,4397 964 12,946 8 66 268 57 88
Deep Creek Marine Dolly Varden; king, pink, red, and silver salmon; other
saltwater 60.03053 -151.680543 2,006 249 52 1,484 12 209
Deshka River King, pink, red, and silver salmon; northern pike; rainbow
trout; whitefish 61.695774 -150.314433 10,646 3,381 33 5,690 77 122 29 67 1,247
Finger Lake Ramp Arctic grayling, land-locked Chinook salmon, Dolly Varden,
northern pike, rainbow trout 61.608892 -149.264867 3,431 1,520 345 8 1,558
Jim Creek Chum, pink, red, and silver salmon; Dolly Varden 61.525498 -148.940749 11,086 2,440 8,442 57 147
Kachemak Bay at Homer Spit Clams; capelin; halibut; king, pink, red, and silver salmon;
lingcod; rockfish (sea bass) 59.607683 -151.431198 93,405 5,098 6,076 4,656 1,133 230 57,619 5,825 12,768
Kashwitna River Arctic grayling; chum, king, pink, red, and silver salmon;
Dolly Varden; northern pike; rainbow trout 61.923685 -150.071032 1,058 214 149 660 9 26
Kasilof River at Kasilof Beach Coho, king, pink, and red salmon; Dolly Varden; steelhead 60.386517 -151.295256 11,330 3,549 4,470 2,327 553 134
Kenai River Cooper Landing Arctic grayling; Dolly Varden; lake and rainbow trout; Pacific
salmon: chum, king, pink, red, and silver; whitefish 60.492597 -149.822622 38,796 404 32,182 4,864 448 368 438 28 45 19
Kenai River Soldotna at Centennial
Campground
Arctic grayling; Dolly Varden; lake and rainbow trout; other
saltwater; Pacific salmon: chum, king, pink, red, and silver;
smelt; whitefish
60.481906 -151.098789 295,130 8,754 226,484 39,164 10,976 1,339 1,781 25 6,607
Kepler Lake Palmer AK Grayling, rainbow trout 61.554562 -149.220174 1,957 1,916 41
Knik Arm Eklutna Tailrace Chum, king, pink, red, and silver salmon; Dolly Varden 61.476354 -149.162296 4,692 168 689 3,233 343 259
Kodiak, Chiniak Bay
Broad whitefish; chum, king, pink, red, and silver salmon;
Dolly Varden; Dungeness and Tanner crab; halibut; lingcod;
other saltwater; Pacific cod; rockfish
57.785625 -152.407992 5,532 322 565 1,284 244 14 258 1 1,153 1,691
Kvichak River, Kaskanak Flats King, red, silver, and pink salmon; rainbow trout, Dolly
Varden, Lake trout, Halibut, and razor clams 59.317783 -156.07044 1,522 52 884 261 59 26 27 75 94 45
Lake Creek Burbot; chum, king, pink, red, and silver salmon; Dolly
Varden; grayling; northern pike; rainbow trout 61.909879 -150.905001 7,157 1,644 407 4,572 175 154 11 115 79
Lake Louise Arctic grayling, burbot, lake trout, whitefish 62.287869 -146.541699 3,507 579 1,286 1,642
Little Susitna River Public Use Facility
near Point Mac
Burbot; chum (dog), king (Chinook), pink (humpy), red
(sockeye), and silver (coho) salmon; Dolly Varden; northern
pike; rainbow trout; whitefish
61.559705 -149.213662 13,516 889 1,257 10,662 292 203 189 24
Ninilchik River Dolly Varden; king, pink, red, and silver salmon 60.052839 -151.666567 1,211 358 81 711 13 48
Susitna-Watana Data Collection and Compilation December 2013
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V e r i t a s
Table A.1, Selected Fishing Sites in Alaska, continued
Example Site Characteristic Variables used in the Fishing Demand Model
Total Harvest, 2010 (not including subsistence fishing)
Site Name Sportfish Caught in Waterbody Latitude Longitude Total Harvest
King
(Chinook)
Salmon
Red
(Sockeye)
Salmon
Silver
(Coho)
Salmon
Pink
(Humpy)
Salmon
Rainbow Trout,
Land-Locked
Salmon
Dolly Varden,
Arctic Char
Lake
Trout
Arctic
Grayling
Other
Fresh-
water Halibut
Razor
Clams
Other
Salt-
water
Montana Creek Campground Chum, king, pink, red, and silver salmon; Dolly Varden 62.104086 -150.05831 4,013 371 134 2,498 506 53 451
Nushagak River, Ekwok King, red, silver, and pink salmon; rainbow trout, Dolly
Varden, lake trout, arctic grayling, and other freshwater 59.346378 -157.478646 2,806 502 368 480 68 37 186 7 72 1,085
Peters Creek King and silver salmon 62.176944 -150.877778 236 36 200
Prince William Sound Valdez fishing pier Pink salmon, other saltwater 61.124144 -146.362135 126,335 1,815 1,473 78,005 16,489 12,348 16,205
Prince William Sound, Whittier Passage
Canal
Broad whitefish; chum, king, pink, red, and silver salmon;
cutthroat trout; Dolly Varden; halibut; lingcod; other
saltwater; rockfish; shark; shrimp
60.776993 -148.68948 56,570 619 7,620 10,624 1,598 42 14,703 21,364
Quartz Lake Fairbanks Dolly Varden, land-locked salmon, rainbow trout 64.199012 -145.827284 4,765 2,952 194 1,619
Resurrection Bay Seward
Broad whitefish; chum, king, pink, red, and silver salmon;
Dolly Varden, halibut; other saltwater; rockfish; shark;
shrimp; steelhead
60.115999 -149.439443 30,973,539 2,158 4,515 30,906,658 2,357 30 27,952 29,869
Russian River Hamiltons Place Dolly Varden; other freshwater; pink, red, and silver salmon;
rainbow trout 60.486488 -150.002614 35,519 32,745 2,581 51 97 45
Salcha River State Recreational Site Arctic grayling, burbot, chum and king salmon, northern pike,
whitefish 64.469779 -146.930128 1,868 143 1,556 169
Sheep Creek King, red, pink, and silver salmon; rainbow trout, Dolly
Varden, arctic grayling, and other freshwater 61.936318 -147.674946 2,476 153 15 1,641 88 288 41 14 236
Sunshine Creek King, red, pink, and silver salmon; and other freshwater 62.176111 -150.125555 1,323 56 17 1,123 56 71
Talachulitna River King, red, pink, and silver salmon; rainbow trout, Dolly
Varden, arctic grayling, and other freshwater 61.876222 -151.414167 2,094 323 424 681 12 59 193 165 237
Talkeetna River near Chase Trail Burbot; chum, king, pink, red, and silver salmon; Dolly
Varden; rainbow trout 62.327416 -150.112043 5,944 528 3,258 1,588 81 59 193 237
Tangle Lakes North Burbot, grayling, lake trout, whitefish 63.044674 -146.026798 3,599 657 2,656 286
Willow Creek/Little Willow Creek Chum, king, pink, and silver salmon; Dolly Varden; rainbow
trout 61.775666 -150.162281 4,790 387 15 3,492 335 219 15 327
Yukon River, Birch-Beaver Creek System King, red, and silver salmon; Dolly Varden, Lake trout, arctic
grayling, and other freshwater 66.563314 -145.274725 5,908 161 20 575 364 23 1,778 2,987
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Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Feburary 2014 Draft
APPENDIX B: TECHNICAL MEMORANDUM ON LONG-TERM
MODELING ASSUMPTIONS
Memorandum
Date: December 13, 2013
To: The Project File
From: Marcus Hartley, Patrick Burden, and Leah Cuyno
Re: Draft: Technical Memorandum on Long-term Modeling Assumptions (LTMAs)
This technical memorandum summarizes our long-term modeling assumptions (LTMAs) which
will form the basis for the analyses of socioeconomic impacts under the “With Watana Dam”
and the “Without Watana Dam” scenarios. In general, the LTMAs create a qualitative
framework within which the quantitative economic impact models and analyses will be
developed. The description of future events or activities provided in this memorandum is general
in nature, without any specific amounts or terms provided except for a few of the key
assumptions directly related to the proposed Watana project. These long-term sets of
assumptions represent two logical futures of the Alaska economy. Choosing any one of the
assumptions may preclude use of another assumption. W hile some of the assumptions may be
mutually exclusive in this regard, they are not necessarily independent from each other as
assumptions about events that occur later in time are path-dependent and the selection of an
earlier assumption may preclude certain activities in later years.
Sources of LTMAs
The LTMAs are the result of an information collection process aimed at deriving a consensus of
the most probable economic future for Alaska. The LTMAs reflect the combined information from
published reports, project proponents, statements from industry and government
representatives, and opinions from other stakeholders. In addition to a review of published
reports and news articles, the study team interviewed more than 30 Alaskan stakeholders with
experience and expertise in the state’s leading industries and policy areas. These interviews
took place from August–November 2013 and their collective responses played a significant role
in shaping many of the LTMAs. The list of persons interviewed, and the businesses and
organizations that they represent, are listed in the table at the end of the document. Ultimately,
Northern Economics was responsible for assessing the likelihood of the future outcomes
identified by these sources and compiling the information into the consistent set of assumptions
presented in the memorandum.
Organization of the LTMAs
There are 25 LTMAs organized into different categories. The categories start at the national level
(LTMAs 1–3), then move on to describe Alaska oil and gas production and prices (LTMAs 4–9).
From there a description of the future power generation infrastructure in the Railbelt is provided
(LTMA 10), followed by assumptions on other major industries in the state (LTMAs 11–16). The
State of Alaska’s fiscal assumptions are described in LTMAs 17–20, followed by assumptions on
large transportation (road and port) projects (LTMA 20–21). Finally, the memo describes
assumptions on statewide population, labor availability, and rural issues (LTMAs 22–25).
Appendix B - Page 1
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1 U.S. Economy
No Action / Without Watana Dam
The REMI model generates a baseline forecast that incorporates a time series of historical data
about the U.S. economy over the last three decades. The REMI model’s baseline forecast covering
the entire project timeline (2013–2060) will be used in the Without Watana Dam or No Action
analysis.
With Watana Dam
This set of assumptions will include additional economic activity from construction and operation of
Watana Dam.
2 U.S. Oil Prices
No Action / Without Watana Dam
EIA forecasts for oil prices out to 2040 will be taken from the 2013 Annual Energy Outlook. The EIA
assumes increased prices as the world economy recovers from the recent recession. By 2040, oil is
expected to cost $163 per barrel (Brent1 crude oil price in 2011 dollars). Oil prices from 2041 to 2060
will be extrapolated based on the trend of EIA prices from 2031–2040.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario.
3 Federal Spending and Permitting Activities in Alaska
No Action / Without Watana Dam
Federal per capita spending will remain at current levels in real terms through the remainder of the
study period. Permitting policies are also assumed to remain generally constant with those in place
today.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario.
1 In the 2013 Annual Energy Outlook, the Brent crude oil price is tracked as the main benchmark for world oil prices.
The WTI crude oil price has recently been discounted relative to other world benchmark crude prices. The
divergence between WTI and other world crude oil prices over the last few years has made WTI a less reliable
indicator of U.S. average refiner crude oil costs and petroleum product prices (EIA). Note that Alaska North Slope oil
is delivered aboard tankers almost exclusively to West Coast refineries. It competes against foreign oils priced off
Brent for space in the refineries. Lately, however, West Coast refineries have also been bringing in crude oil by rail
from the Midwest and Canada.
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4 Alaska On-shore Oil Production
No Action / Without Watana Dam
Oil production from currently producing on-shore fields continues to decline and will follow the
forecasts of the Alaska Department of Revenue annual production through 2022 (the endpoint of
ADOR’s forecasts). Beyond 2022, production from these existing fields will continue to decline at a
rate equal to the projected rate of decline from 2013–2022 (an average annual decline of 8
percent).
The “2013 More Alaska Production Act” (2013 MAP Act) reforming Alaska’s oil and gas tax regime
is expected to create incentives that will result in an increase in oil production. The ADOR projects
that new oil would increase total ANS oil production by 10 percent in 2014, and about 27 percent by
2022. The study will also assume that the construction of the Alaska-LNG project will further induce
onshore oil production.
The following future activities/development are assumed to take place in the North Slope:
• Liberty is developed and comes on line in 2021 with peak production in 2023.
• Point Thomson condensates production will commence in 2016. With the start-up of Alaska-
LNG project in 2025 condensate production increases significantly.
• Permitting delays push first production in NPRA to 2017. Production peak occurs in 2027.
• The development of the Trans-NPR-A pipeline (TNP) to move oil from the Chukchi to TAPS
will spur additional development of previously marginal fields in the NPR-A. These marginal
fields will contribute an average of 70,000 bod from 2030–2060.
• Some of the North Slope shale oil fields will be sanctioned in 2015 and subsequently
developed with first oil production in 2022. However, regulatory and capital constraints as
well as technical and cost issues result in limited field development until 2030. After that
date, shale oil begins to add significant oil production to total North Slope production and
TAPS throughput for the remainder of the study period.
• The combination of the 2013 MAP Act and later on the construction of the Alaska-LNG
project will lead to oil production from previously marginal or sub-economic oil fields
beginning in 2016.
• Development in ANWR will not be permitted during the study period.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario.
5 Alaska OCS Oil Production
No Action / Without Watana Dam
OCS oil production from the Chukchi Sea and the Beaufort Sea will begin in 2030 and 2034,
respectively. Oil produced in the Beaufort Sea will be transported through TAPS. Oil produced in the
Chukchi Sea will be transported through an onshore pipeline across the NPR-A to TAPS with
construction beginning in 2027. There are no changes from the current rules for federal OCS
royalties; the State of Alaska will not receive any portion of the royalties from OCS activity that are
paid to the federal government. OCS production will create a significant number of jobs both in the
oil and gas sector and the support sectors.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario.
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6 TAPS
No Action / Without Watana Dam
With increased production from the 2013 MAP Act, induced production related to Alaska LNG, and
most importantly development of large OCS oil fields in the Beaufort and Chukchi Sea, the owners
of TAPS make the necessary investments to keep the pipeline open and flowing. With throughput
from the OCS expected to continue through the study period, and with the development of the
shale oil plays, TAPS is reauthorized to operate for another 30 years in 2033.
With Watana Dam
The study will use the same primary assumptions as in the Without Watana Dam Scenario.
7 North Slope/Arctic OCS Natural Gas Production
No Action / Without Watana Dam
Prior to the Alaska LNG project, natural gas will be produced in sufficient quantities to meet localized
demand in the NSB and for field consumption. Long-term purchase agreements with one or more of
the Fairbanks natural gas utilities results in the construction of a small-scale (16 to 25 mmcfd)
modular LNG plant on the North Slope that begins production in 2016. When the Alaska LNG project
begins operations, the small LNG plant supplies LNG to industrial users on the North Slope.
The Alaska LNG project is sanctioned and export of gas (LNG) starts in 2027. An average of 3.0
bcfd of ANS natural gas will be supplied to the Alaska LNG pipeline starting in 2027 through the end
of the study period. Several off-take points are built along the route to supply natural gas to
communities with large populations or large industrial users that can justify the capital cost of the
facilities (e.g., Livengood gold mine). The study assumes that most of the NGLs (liquid petroleum
gases) associated with ANS gas will also be exported; with some propane distribution to
communities on the road system.
The route of the Alaska LNG pipeline will transit from Livengood south along the Tanana River to
Nenana and will not parallel the existing road system. As a result, a spur pipeline will be required to
bring gas from an off-take point to Fairbanks. This spur pipeline is not part of the Alaska LNG project
but will be another construction project to incorporate into the assumptions.
Prudhoe Bay and Point Thomson will be primary gas sources for the Alaska LNG project during the
early years of operation. Later, gas production from other fields will begin to meet Alaska LNG
needs, primarily from NPR-A, and the Foothills of the Brooks Range. Gas production from Beaufort
Sea OCS will begin in 2043 and will be transported to markets via the Alaska LNG project. Some
Chukchi OCS gas is used for field use with the balance re-injected and not fully developed until after
the study period.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario except that the
Livengood gold mine is assumed to use electric power from Watana after transmission lines are
built.
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8 Cook Inlet Natural Gas Production
No Action / Without Watana Dam
Natural gas production in Cook Inlet recovers as a result of state incentive programs and long-
term contracts with Southcentral gas and electric utilities.
Gas production from Cook Inlet continues at levels sufficient to meet regional utility needs
throughout the study period, although the natural gas storage facilities must be expanded in
2015 to ease winter peak demand issues.
Some Southcentral utilities purchase gas from the Alaska LNG project when it begins
operation to seek diversity in their fuel supplies. The ConocoPhillips LNG facility in Nikiski re-
opens in 2014 and operates through 2030; the facility continues to operate on a seasonal
basis beyond 2030.
The Agrium fertilizer facility in Nikiski also reopens (in 2015) and operates using a single train
through 2030.
Other discussions related to in-state use of natural gas are described under Prices for Users
of Natural Gas in Alaska and Mining.
With Watana Dam
Watana reduces the demand for gas from Southcentral electrical utilities, which leaves
enough supply of Cook Inlet gas for both the ConocoPhillips LNG plant and Agrium to remain
in operation using Cook Inlet Gas. The Agrium plant closes in 2030, but the Conoco LNG
Plant operates through 2040 and then seasonally after that year.
With Watana, the natural gas storage facilities are expanded again in 2022 to ease winter
peak demand issues.
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9 Prices for Users of Natural Gas in Alaska
No Action / Without Watana Dam
Natural gas prices for consumers in Alaska will be higher than Lower 48 prices in order to generate
adequate returns to local gas producers that operate in a high-cost Alaskan environment. In general,
prices paid by consumers for natural gas will not be subsidized by the state and will equal the sum of
the wellhead value of the gas plus transportation costs. Prior to the beginning of operations of the
Alaska LNG project, the wellhead value of the gas will be linked to the sales price of ANS oil sold on
the West Coast and the ratio of $5.71 per mmBtu (the current prevailing value for Cook Inlet gas) of
gas to $100 per barrel oil, with a floor of $5.00 per mmBtu. According to AIDEA project documents
for the Interior Energy Plan (2013), natural gas prices in Fairbanks prior to the operations of the
Alaska LNG project are expected to range between $14.50 and $17 for the end user.
After the Alaska LNG project is operating, ANS gas will be purchased by utilities on long term
contracts (20+ years). The cost of natural gas to Southcentral Alaska customers will be a blend of
ANS and Cook Inlet pricing, and it is anticipated that ANS gas prices will be higher than prices for
natural gas from Cook Inlet production. It is assumed that the wellhead value of ANS gas will be the
netback price from LNG sold in Asia.
With Watana Dam
When Watana comes online, demand for Cook Inlet (CI) gas by utilities will decline, and result in
Cook Inlet gas becoming a smaller percentage of the total gas supply. Cook Inlet gas prices may not
drop because production will be negatively affected by the decline in demand. Since ANS gas is
priced higher than CI gas, the blended gas price in Southcentral increases. Other assumptions in the
Without Watana Dam scenario hold.
10 Electrical Generation Infrastructure
No Action / Without Watana
HEA’s Soldotna LM6000 turbine comes online in 2014, and MEA’s Eklutna Generation Station
(EGS) comes online in 2015. ML&P’s George M. Sullivan Plant 2 Generation Replacement Project
comes online in 2016 with a 120-MW capacity.
The Healy Clean Coal plant comes online in 2015. In Fairbanks, GVEA converts one of their North
Pole generator units to natural gas in 2016 with the availability of LNG from the North Slope.
Proposed upgrades to the existing Railbelt electrical transmission system are completed in 2020.
Beginning in 2027, the availability of propane and LNG from the Alaska LNG project leads to the
replacement of diesel powered generation plants use by Copper Valley Electric Association (CVEA)
and other Railbelt communities on the road system that are not served by the Railbelt transmission
system.
No additional thermal generation plants are developed in the Railbelt, although aging plants are
replaced with similar-sized but more efficient gas-fired generators as maintenance costs increase.
Some relatively small renewable energy projects are brought online, but the goal to generate 50
percent of electricity from renewable sources by 2025 is not achieved.
The Mount Spur geothermal project is built as a private/public partnership and comes online in 2026.
LNG and propane from Alaska LNG are shipped to rural Alaska to replace high-cost diesel
generators in communities that have year-round road or marine access.
With Watana
Power from Watana dam becomes available in 2024. The goal of generating 50 percent of electrical
power from renewable sources is met. Because energy from Watana is available, the state elects
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not to partner with the developer of the Mt. Spur geothermal project and plans are shelved.
Additional transmission lines connect CVEA to the Railbelt transmission system in 2028 and to Tok
in 2030.
Unless otherwise discussed here the Without Watana Dam scenario holds.
11 Alaska In-state Oil Refining and Imports of Petroleum
Fuels
No Action / Without Watana Dam
In-state refineries are assumed to continue to operate at current levels through 2026. With the
opening of Alaska LNG in 2027, and the availability of low cost natural gas, refineries in North Pole
(Flint Hills and Petro Star) convert to natural gas as their primary source of energy. This situation
results in cost savings for the refineries and operations at current levels through the end of the study
period. However, the cost of petroleum imports is higher than production from in-state refiners which
means there is no noticeable reduction of in-state gasoline or distillate prices.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario.
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12 Mining
No Action / Without Watana Dam
Mining activity expands with development of several large prospects and expanded resource
utilization at existing operations. In general, mine developers determine that they cannot afford to
wait for the state to develop energy infrastructure and therefore provide their own infrastructure in a
way that allows future flexibility if new energy sources become available. The major new mining
projects are described separately below, but other smaller mining operations also come on line
during the study period.
1) The Donlin Creek Mine begins production in 2019. The project would require 150 megawatts
of electricity to power the mill and facilities. The power would be produced using on-site
natural gas fired generation. The gas is transported to Donlin Creek via a gas pipeline from
Cook Inlet. Revitalized production of Cook Inlet natural gas (see LTMA #8) generates
sufficient gas supply until the opening of Alaska LNG in 2027. The mine operates for 27+
years (from 2019–2046 and produces a total of 30 million ounces of gold.
2) Pebble begins production in 2040, after permitting delays. The mine has a smaller footprint
than currently envisioned, but is still able to access known mineral resources. The mine
utilizes natural gas as its primary energy source. The gas is transported to the mine via a
sub-sea pipeline from Anchor Point to Insikin Bay and then a 90-mile pipeline that runs from
Iniskin Bay to the mine. The mine operates throughout the remainder of the study period.
The copper and gold are exported via the port facility in Iniskin Bay.
3) Livengood mine comes on line in 2028, two years after the opening of Alaska LNG. A gas
off-take point at Livengood enables the mine to generate its own electricity and to use co-
generated steam in the milling process. The mine would produce 16 million ounces of gold
during the study period.
4) Red Dog Mine expands operations to adjacent resource deposits and operates through
2045.
5) Coal exports increase from the Usibelli Mine through the Port of Seward.
6) Smaller unspecified mines with road/port access and access to energy will generate
additional mining jobs each year from 2013–2060. These have the effect of replacing jobs
from older mines that are reaching the end of their production cycles.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario with the following
difference: Livengood mine comes on line in 2024, two years after the opening of Watana Dam.
Livengood builds a transmission line from GVEA’s distribution system to access electricity. The mine
would produce 16 million ounces of gold during the study period.
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13 Fisheries
No Action / Without Watana Dam
Harvest volumes of most species are expected to stay within the ranges of the last 10 years.
Revenues from seafood are expected to increase as demand in Asia continues to grow and wild-
caught seafood attains a premium over farm-raised seafood in the marketplace. Trends associated
with global climate change continue with some northward movement of fish stocks and densities.
The industry is able to adapt to the gradual changes, as stocks that were formerly found in more
southerly waters are now more abundant in Alaska waters. Commercial fish harvests in the Chukchi
and Beaufort Seas continue to be prohibited.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario. Assumptions on
Watana’s impact on salmon stocks in Cook Inlet or on recreation fishing in the main stem of the
Susitna River will await more information from fisheries related studies.
14 Tourism
No Action / Without Watana Dam
Growth in Alaska’s tourism industry continues, but at a lower rate than in the past decade due to
competition from other global tourist destinations, and a limited number of communities that can
meet the needs of the cruise ship industry. The growth rate in the tourism sectors is constrained to
two-thirds of the prior decade’s growth rates.
With Watana Dam
Watana Dam is assumed to have no net impact on the number of out-of-state visitors to Alaska.
There may be in-state distributional impacts resulting from enhancement of certain sites as a result
of the dam. The studies on recreational impacts will inform these assumptions when they become
available.
15 Air Transportation
No Action / Without Watana Dam
Air cargo support in Alaska will continue to grow, but at lower rates than in prior decades. Tourism
accounts for a substantial portion of air transportation activity and future growth rates are
constrained to two-thirds of the prior decade’s growth rates.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario.
16 Economic Diversification
No Action / Without Watana Dam
A liquid petroleum gas (LPG) handling facility and marine terminal is developed at Nikiski to export
the LPGs to the Pacific Rim countries. The facility uses gas liquids from the Alaska LNG project as
inputs. Some of the propane is shipped to rural Alaska ports with ice-free access.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario.
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17 State Revenues
No Action / Without Watana Dam
The State of Alaska will continue to depend on revenues from the oil industry. Long-term
projections on state revenues will therefore depend on assumptions regarding future oil
production and prices (as stated in the LTMAs above).
The “2013 More Alaska Production Act” is expected to incentivize additional investments in
exploration and development of oil and gas resources that would result in additional oil and
gas production, and State revenues.
When significant volumes of OCS oil begin to flow through TAPS, the value of TAPS will
increase substantially. Prior to the value increase, the legislature is assumed to rewrite the
existing oil and gas property tax statutes to limit the local government take of the shared tax
and increase the amount available to the state. The oil and gas property tax mill rate is also
assumed to increase at the same time.
However, despite near and medium-term assumptions regarding new fields coming on line
that would slow down the decline rate of producing oil fields, in the long run, it is anticipated
that the State of Alaska will need to create additional revenue sources from new taxes in order
to fund government services.
The fiscal model will determine the level of taxes that would have to be generated in order to
balance the operating budget. The operating budget will be consistent with assumptions
stated in the LTMA regarding state spending. Any taxes that are implemented would be
considered temporary and would be eliminated or reduced if operating budget surpluses are
generated. The timing of imposition of these taxes, if they are required, would be determined
by initial model runs specific to the Without Watana Dam Scenario.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario, except the
timing of imposition of taxes, if required, would be determined by initial model runs specific to
the With Watana Dam Scenario.
18 Permanent Fund and Permanent Fund Dividends
No Action / Without Watana Dam
As mandated by the Alaska Constitution, 25 percent of state oil and gas royalties continue to be paid
into the Permanent Fund (PF) and the principal balance of the PF continues to grow. Earnings from
the PF continue to be paid as dividends (PFDs) unless it is determined by initial model runs that
state budget deficits have fully depleted the Constitutional Budget Reserve (CBR). At that time, all
PFD payments are eliminated and investment earnings from the PF are used to balance the state
budget. PFDs would resume only if the PF earnings are not required to balance the budget.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario.
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19 Spending by the State of Alaska
No Action / Without Watana Dam
Unless constrained by budget deficits, the state operating budget plus the capital budget and
receipts from the federal government is set to equal state revenues less dedicated contributions to
the Permanent Fund, the CBR, Education Fund, and other accounts.
In years when the CBR is needed to balance the budget, the operating budget is reduced by two
percent per year, and in years after the CBR is depleted and a budget deficit is facing the state, the
operating budget, plus a modest capital budget of $200 million (in 2013 $ and adjusted for inflation in
future years), is set to equal total revenues. In years when there is a budget surplus, the state capital
budget is assumed to be approximately 75 percent of the available surplus (total revenues less
operating budget), with 25 percent going into the CBR.
Future capital projects include:
• Railbelt transmission upgrades
• North Slope LNG facility for Fairbanks
• Port MacKenzie rail
• Port of Anchorage upgrade
• State investment in Alaska LNG pipeline (including at least some of off-take points and a
pipeline to supply gas to Fairbanks.)
• Road projects (see State Funded Road projects)
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario. In addition, the
State of Alaska will subsidize the cost of the Watana Dam construction. The project will be funded in
a manner similar to the Bradley Lake hydroelectric project. Project financing assumptions will be
further fine-tuned in collaboration with the Alaska Energy Authority.
20 State Funded Road Projects
No Action / Without Watana Dam
Because of the recognition that the state government needs to spend within its means, only those
new road projects that appear to generate positive economic development will be built. In general,
the state will require these road projects to be funded through private/public partnerships and local
improvement programs. The only road projects that are foreseeable under these conditions is an
upgrade of the road from Iniskin Bay to Pebble, the Umiat Road on the North Slope, and the road to
Ambler.
Following construction of the Alaska LNG project, the Parks Highway, and the Dalton Highway and
the Glenn Highway between Anchorage and Palmer are refurbished to repair construction related
damage.
With Watana Dam
The road projects assumed under the “Without Watana” Scenario will be undertaken. In addition, a
new road providing access to Watana Dam and the Watana Reservoir will be developed in 2018.
This road may or may not be accessible to the public; public access will be determined in the
decision-making process.
INITIAL STUDY REPORT SOCIAL CONDITIONS AND PUBLIC GOODS AND SERVICES STUDY (15.6)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Appendix B - Page 12 February 2014 Draft
21 Port Projects
No Action / Without Watana Dam
Port of Seward improvements will be completed in 2020 to support coal exports. An expanded LNG
port will be developed at Nikiski by 2022. The port in Iniskin Bay will be built to support development
of the Pebble Mine in 2035. A port on the Chukchi Sea coastline will be developed in 2026 to
support OCS and TNP development. The Port of Anchorage expansion will be completed in 2018
(this is included in the list of State-funded projects).
With Watana Dam
All port projects assumed under the Without Watana Dam Scenario will be built.
22 Statewide Population growth
No Action / Without Watana Dam
Statewide population is an output of the REMI model; no specific assumptions regarding population
will be made.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario. Population will
be determined independently for the With Watana Dam Scenario.
23 Rural and Urban Changes
No Action / Without Watana Dam
Population for modeled boroughs and census areas will be an output of the REMI model. Borough
and census area totals from the model will be allocated down to the community level using existing
trends, but modified by any of the model assumptions that are specific to individual communities.
Other assumptions that affect community populations include:
• State funding of schools in communities as long as 10 students remain.
• Revenue sharing formulas that are currently in place will remain unchanged.
• Bypass mail subsidies continue.
With Watana Dam
The study will use the same assumptions as in the Without Watana Dam Scenario.
24 Resident v. Non-Resident Labor
No Action / Without Watana Dam
The trends of resident versus non-resident labor over the past 10 years will continue through the
study period and any differences by major industry groups will be utilized.
With Watana Dam
Specific assumptions regarding resident and non-resident workforce for construction and
operation of the Watana Dam, in-migration, and similar topics will be developed in concert with
ADOLWD. Otherwise, the study will use the same assumptions as in the Without Watana Dam
Scenario.
INITIAL STUDY REPORT SOCIAL CONDITIONS AND PUBLIC GOODS AND SERVICES STUDY (15.6)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Appendix B - Page 13 February 2014 Draft
25 Subsistence
No Action / Without Watana Dam
Subsistence activities are not addressed in the REMI or the fiscal model.
With Watana Dam
Subsistence activities are not addressed in the REMI or the fiscal model.
INITIAL STUDY REPORT SOCIAL CONDITIONS AND PUBLIC GOODS AND SERVICES STUDY (15.6)
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Appendix B - Page 14 February 2014 Draft
Persons Interviewed
Person Interviewed Company or Organization Title
Mr. Phil Steyer Chugach Electric Association Government Affairs Manager
Mr. Lee Thibert Chugach Electric Association Vice President, Regulatory
Affairs
Mr. Arthur Miller Chugach Electric Association Director, Regulatory Affairs
Mr. Mark Fouts Chugach Electric Association Marketing Director
Mr. Cory Borgeson Golden Valley Electric President & CEO
Mr. Brad Janorschke Homer Electric Association General Manager
Mr. Joe Griffith Matanuska Electric Association General Manager
Mr. James Posey Anchorage Municipal Light and Power General Manager
Mr. Ed Fogels Alaska Department of Natural Resources Deputy Commissioner
Mr. Kevin Banks Alaska Department of Natural Resources Petroleum Market Analyst
Ms. Karen Matthias Council of Alaska Producers Executive Director
Mr. Jeff Cook Flint Hills Refinery Refinery Manager
Mr. Dan Dickinson Dan Dickinson, CPA CPA
Ms. Cindi Bettin Alaska Mental Health Trust Authority Senior Lands Manager
Mr. Glen Haight Alaska Department of Commerce, Community, &
Economic Development Executive Director
Mr. Andrew Halcro Anchorage Chamber of Commerce
Ms. Deantha Crockett Alaska Miners Association Executive Director
Mr. JR Wilcox Cook Inlet Energy President
Mr. Neal Fried Alaska Department of Labor & Workforce Development Economist
Mr. Scott Goldsmith University of Alaska, Institute of Social & Economic
Research Economist
Mr. Larry Persily Federal Pipeline Coordinator
Ms. Colleen Starring ENSTAR Natural Gas Company President
Mr. Curtis McQueen Eklutna, Inc. CEO
Mr. James Hemsath Alaska Industrial Development & Export Authority Deputy Director
Ms. Sarah Leonard Alaska Travel Industry Association President
Mr. Bill Popp Anchorage Economic Development Corporation President & CEO
Mr. Robert Wilkinson Copper Valley Electric Association CEO
Mr. Jim Dodson Fairbanks Economic Development Corporation President & CEO
Mr. Kurt Gibson Hillcorp Vice President, AK Midstream
Mr. Jim Jansen Lynden Transportation
Mr. John Parrott Ted Stevens Anchorage International Airport Manager
Ms. Lorali Simon Usibelli Coal Vice President, External Affairs
Mr. Scott Jepsen ConocoPhillips Alaska, Inc. Vice President, External Affairs
Mr. Tim Buller Agrium US Inc. Senior Specialist, Engineer