Clarkson Nanos Galloo Island Wind Farm Study

Baseline Research
Exploring the impact of the proposed

Galloo Island energy project
ONTARIO MEDICAL
ASSOCIATION
Martin Heintzelman
Clarkson University
Stephen Bird
Clarkson University
William Olson
Clarkson University
conducted for the Town of Henderson

submitted by the Nanos Clarkson University
Summary Report Project 2010-101
Research Collaboration
Project 2016-676, January 2016
Nik Nanos
Nanos Research
Daniel Kolundzic
Nanos Research
Town of Henderson January 2016

Nanos Clarkson Research Collaboration Report Project 2015-676
Table of Contents
1.0
Executive Summary ...............................................................................................4
Key Findings ...................................................................................................................................................................... 5
2.0
Town of Henderson Property Value Analysis in regards to Proposed Galloo
Island Facility ...................................................................................................................6
2.1
Introduction ........................................................................................................................................................... 6
2.2 Existing Literature on Property Value Impacts ....................................................................................................... 7

Figure 1: Wolfe Island Study ........................................................................................................................................ 9
2.2.1 Methodology ....................................................................................................................................................... 9
2.2.2 Data ................................................................................................................................................................... 13
2.2.3 Wolfe Island Results .......................................................................................................................................... 13
Table 1: Summary Statistics for Wolfe Island Sample ................................................................................................ 15
Table 2: Wolfe Island Results ..................................................................................................................................... 16
2.2.4 Application to Town of Henderson ................................................................................................................... 16
Table 3: Projected Property Values ............................................................................................................................ 17
Table 4: Projected Effects on Average Parcels w/ Turbine View ................................................................................ 19
3.0
Jobs and Tourism Analysis for Galloo Island Wind Installation ........................20
3.2 Background ............................................................................................................................................................... 20

Figure 2. Town of Henderson, with local environmental amenities and Galloo Island shown;
source: Google Maps ................................................................................................................................................. 20
3.3 Jobs and Economic Development Literature Review................................................................................................ 21
3.4 Jobs and Economic Development Impact Analysis: The JEDI Model ........................................................................ 23
3.4.1 Jurisdictional Impact and Assumptions ............................................................................................................. 18
Table 5. Population Base for Job and Jurisdiction Analysis ........................................................................................ 24
Figure 3. 50 mile radius from Galloo; Source: Google Maps ...................................................................................... 25
Figure 4. County Breakdown around Galloo Island .................................................................................................... 25
3.4.2 JEDI Analysis ...................................................................................................................................................... 20
Table 6. Construction Jobs and Economic Activity Scenarios: Direct and Indirect Effects ......................................... 29
Table 7. Yearly Operations and Maintenance Jobs and Economic Activity Scenarios (20 yrs.) .................................. 30
3.5 Tourism Analysis ....................................................................................................................................................... 32
4.0 Viewshed Analysis & Methodology ..........................................................................36

4.1 Introduction .............................................................................................................................................................. 36
4.2 Data Sources and Processing .................................................................................................................................... 37
4.3 GIS Methodology and Results ................................................................................................................................... 38
4.4 Interactive Map Viewer on ArcGIS Online ................................................................................................................ 38
4.5 List of Figures ............................................................................................................................................................ 40
Figure 5. Henderson Overview with Satellite Imagery ............................................................................................... 41
Figure 6. Henderson Overview with Topography ...................................................................................................... 42
Figure 7. Henderson Town Visibility 20m Forested ................................................................................................... 43
Figure 8. Henderson Town Visibility 13m Forested ................................................................................................... 44
Figure 9. Henderson Town Bare Earth Visibility ......................................................................................................... 45
Figure 10. Henderson Bay Detail ................................................................................................................................ 46

Figure 11. Henderson SE Detail .................................................................................................................................. 47

Figure 12. Henderson NE Detail ................................................................................................................................. 48
Figure 13. Henderson NW Detail ............................................................................................................................... 49
5.0 Research Note ...........................................................................................................50

6.0 Report Summary & Conclusions .............................................................................50
7.0
References ............................................................................................................51
7.1
Property Value Analysis ........................................................................................................................................ 51
7.2
Jobs & Tourism Analysis ....................................................................................................................................... 52
Appendix A ..................................................................................................................................................................... 56
Appendix B ..................................................................................................................................................................... 59
Appendix C ..................................................................................................................................................................... 62

January 20, 2016
Attention: John Culkin, Town Supervisor

Town of Henderson
12105 Town Barn Road
Henderson, NY 13650
Dear Mr. Culkin,
Re: Analysis of the proposed Galloo Island wind energy project
We are pleased to submit the following report on the potential impact of a proposed wind
energy facility on Galloo Island which was commissioned, funded, and conducted on
behalf of the Town of Henderson.
The report is comprised of the following elements:
a property value analysis;
a jobs and tourism analysis; and,
a viewshed analysis.
Together, the analysis conducted by the research team is intended to support the
decision-making process on the proposed project. The research was conducted
independently by the project team based on our current understanding of the project and
its configuration.
Martin Heintzelman
Stephen Bird
Nik Nanos
Dan Kolundzic
Clarkson University
Clarkson University
Nanos Research
Nanos Research

1.0 Executive Summary

The Town of Henderson, New York has been confronted with a challenging development
issue. The proposed development of a wind energy facility on Galloo Island has raised
concerns by local residents and commercial operators in the Town of Henderson.
Uncertainties regarding both social and economic impacts of the proposed development
have motivated the Towns leadership to undertake a study of the development proposals
impacts in order to support the local decision-making process
With this in mind, the Town of Henderson has secured the Nanos Clarkson Research
Collaboration energy consultant team of experts to assist in determining a series of
impacts from the proposed development. The following report provides an overview of
property value and economic impacts, as well as a viewshed analysis for the Town of
Henderson from the proposed wind energy facility development.
The Galloo Island Wind Energy Facility (henceforth GIWEF) Project was first informally
proposed in September 2014 by Albany based Hudson Energy Development LLC under a
subsidiary Hudson North Country Wind 1 LLC (henceforth HNCW). Its formal Program
Involvement Plan Application occurred in Summer 2015. Its plan comprises 29 turbines
located on the privately owned island for an expected 102 MW output. The turbines will
be 575 feet high, with blade lengths of 210 feet (Hudson North Country Wind 1, LLC
2015). On Dec. 18, 2015, HNCW sold the project to Apex Clean Energy LLC of
Charlottesville, VA.1
A key concern of many residents of the Town of Henderson is that the Galloo Island wind
facility will negatively impact both property values in the town, as well as economic
activity through tourism. These concerns are exacerbated by the fact that the usual
benefits which typically accrue to counter potential negative impacts of this type of
development, such as payments-in-lieu-of-taxes (PILOTS) or lease payments, are not
eligible for residents of the Town of Henderson. As such, the impact of the proposed
development on the Town of Henderson is uncertain and requires clarification. This study
does not examine environmental benefit / cost impacts for the region as they are beyond
the scope of the report as designated by the Town of Henderson.
See Hudson North Country Wind 1 LLC letter to PSC Secretary notifying of GIWF sale to APEX at
http://documents.dps.ny.gov/public/MatterManagement/CaseMaster.aspx?MatterSeq=48345&MNO=15-F0327.
Key Findings
The Nanos Clarkson Research Collaboration has undertaken a series of analyses, enclosed
within the subsequent report, specifically a property value analysis, an economic and jobs
analysis, as well as a viewshed analysis.
While methodologies (and qualifiers) for the
various analyses are highlighted within the report along with report details, the overall
general findings can be summarized as follows in terms of the anticipated impacts:
likely negative land valuations for the Town of Henderson;
likely positive economic effects to the region, but not commensurate to the Town
of Henderson;
likely minor positive effects on jobs and economic impacts to the Town of
Henderson; and,
likely minor negative effect on tourism
These findings are elaborated in more detail within the subsequent report. It should be
noted that this study does not examine environmental benefit/cost impacts for the region
as they are beyond the scope of the report as designated by the Town of Henderson.
Finally, the report includes a series of view-shed analyses for the Town of Henderson in
relation to the proposed Galloo Island development. In addition to the enclosed data and
documents, the 3-D viewer can be accessed at: http://arcg.is/20Y5VEc in order to provide a
more variable tool for analysis and evaluation.2
Disclaimer: The Nanos Clarkson Research Collaboration provides analytic services to stakeholders in the
energy and environmental arenas and the views and analysis expressed are the authors'. They do not
necessarily represent the policies or views of Clarkson University.
2.0 Town of Henderson Property Value Analysis in

regards to Proposed Galloo Island Facility
2.1
Introduction
One concern of many residents of the Town of Henderson is that the Galloo Island wind
facility will negatively impact the value of their properties in the town. This section looks
at that issue using an analysis of the impacts of the Wolfe Island wind facility in northern
Jefferson County.
Building on that analysis, the team projected impacts on residential
properties in the Town of Henderson.

There are a number of factors related to wind power generation facilities which may result
in changes to local property values. For example, both noise and views are often a cause
of concern for local homeowners with a potential new project.
Wind turbines have
become quieter in recent years, but there remain concerns about low frequency noise that
dissipates slowly over distance (Bolin, Bluhm, Eriksson, & Nilsson, 2011). In addition,
some people raise concerns about health effects, primarily related to noise, although the
scientific literature has not found a solid link between the two (Council of Canadian
Academies, Expert Panel on Wind Turbine Noise and Human Health, 2015).
Visually, wind turbines are, at the least, large human-made structures that represent a
significant change to the landscape. In addition, if wind turbines are improperly sited,
there can be more acute visual disamenities such as shadow flicker, when rotating
shadows move over a parcel.
Flicker is very unlikely to be an impact in Henderson,
however, given the relatively large distance of the Town from the turbines. Another visual
impact is the array of blinking red lights that sit atop the wind turbine hubs.
Acting counter to these negative impacts are other impacts that may generate a positive
effect on property values.
These include the benefits from payments-in-lieu-of-taxes
(PILOTS) and payments to individual landowners. The first of these would be expected to
reduce taxes or increase local public services, or both, while the second would at least
have a multiplier effect on the local economy. However, in the case of Henderson, neither
of these effects is likely since it is our understanding that Henderson will not be getting
PILOT money nor will any Henderson property owners be receiving lease payments. The
fact that Henderson landowners may be affected by visual disamenities from the Galloo
Island facility, while not receiving any payment as compensation, provides reason to
believe that the impact on property values may be negative.

2.2
Existing Literature on Property Value Impacts
There exists a growing scientific literature on the impacts of wind turbines on property
values. This literature is not conclusive and a main conclusion from a detailed read of this
literature is that the specific context and policy parameters matter tremendously in
driving property value impacts. Amongst the first studies of this issue are Sims & Dent
(2007) and Sims, Dent, & Oskrochi (2008). Neither of these studies finds any significant
impact on property values in a study of facilities in the United Kingdom. Unfortunately,
these studies are based, on small samples and in areas with significant confounding
factors which make interpretation difficult.
The largest studies of wind turbines and property values have been done by Ben Hoen and
his coauthors (Hoen, Wiser, Cappers, Thayer, & Sethi, 2011; Hoen et al., 2015). These
studies overcome the small sample size problems of many studies in this literature by
using a pooled dataset of property transactions nearby to a large number of wind facilities
around the country.
They also find no significant impact on property values, but an
admitted weakness of their study is exactly its strength by using multiple sites, their
estimates represent an average effect that may be hiding significant impacts in particular
sub-samples of their data.
Two other more recent papers also find no significant impact. Vyn & Mccullough (2014)
looks at a large wind facility in Ontario while Lang, Opaluch, & Sfinarolakis (2014) look at
small facilities in Rhode Island.
Both studies are carefully done and have reasonable
sample sizes. A weakness of the paper by Lang et al. (2014) for the purposes of applying
to the Henderson case is the fact that the facilities they study are mostly sites with
individual large turbines or a small number of residential-scale turbines, while the Galloo
Island facility is proposed to have a larger number of very large turbines.
There have also been a few recent studies that do find significant impacts on property
values.
Heintzelman & Tuttle (2012) is the first study to report significant negative
impacts on property values using data from the areas around three large wind facilities in
Northern New York. Importantly, they only find these negative impacts in two of three
study areas which brings to the fore the idea that impacts are likely to vary in different
areas, and that using large samples of facilities from a large geographic area may be
inappropriate.
Sunak & Madlener (2012) find negative impacts using proximity measures and digital
viewshed modelling for a region of Germany with a small wind facility with only 9
turbines. Jensen, Panduro, & Lundhede (2014) use similar methodology and a large
dataset in close proximity to wind turbines to find significant negative impacts separately
from both proximity and view. Finally, Gibbons (2015) focuses on visibility of turbines

and finds significant and large negative impacts on property values in the United
Kingdom.

Figure 1: Wolfe Island Study
Our analysis of the likely impacts of the Galloo Island wind farm on property values in
Henderson is based upon an analysis of the impacts of the Wolfe Island wind farm on
properties in Jefferson County, NY.
Wolfe Island is a Canadian island in the St. Lawrence
River near its junction with Lake Ontario. The wind turbines are visible from parcels in
Jefferson County, NY, on the island itself, and on the Canadian mainland. Wolfe Island
and the surrounding area is a good case study for application to Galloo Island for a
number of reasons. First, the impacted area is in the same county as Henderson, with
similar socioeconomics and topography.
Second, in both cases the turbines are on
islands on the water some distance from the affected properties on the shoreline. Third,
no affected U.S. municipalities or landowners receive any compensation as a result of the
Wolfe Island facility, meaning that, like in the case of Henderson, there are no
confounding factors to counteract any negative impacts from disamenities. While Galloo
Island is a U.S. island, it is a part of the Town of Hounsfield, meaning that any
compensation paid will be paid to Hounsfield, not Henderson. An important qualification
however, in using this comparison site is that while the turbines on Galloo Island are
This analysis is based upon, but not the same as, unpublished work by Heintzelman, Vyn, and Guth (2015).
That study looks at properties in both countries whereas the analysis described here focuses only on
Jefferson County.
expected to be visible in the Town of Henderson, they are considerably further away from
Henderson than those on Wolfe Island are to properties in Cape Vincent.

10
2.2.1 Methodology
We use a standard hedonic property value analysis to study the impacts of the Wolfe
Island facility. Hedonic analysis goes back at least to Rosen (1974), who posits a model
where consumers derive utility from the attributes of a good rather than the good itself.
This allows researchers to use consumption decisions by a sample of consumers in a
market with differentiated goods, which vary along a number of dimensions, to estimate
the consumers marginal willingness-to-pay for changes in the attributes of the good.
This turns out to be an excellent model of property markets since parcels and homes vary
along a large number of dimensions, many of which are easily observed by the researcher.
In addition, most property markets are reasonably competitive in the economic sense
since there are usually many properties for sale at any given time and a number of people
simultaneously looking to purchase a home.
With Rosens (1974) model in mind, hedonic analysis uses data on a sample of sales
transactions (generally including price, date of sale, and a number of parcel attributes) to
estimate the impacts of individual attributes on price.
It uses regression analysis to
control for all observable factors, thus allowing for an all else equal analysis of how each
factor affects price.
There are a few problems that often arise in hedonic analysis which can be controlled
using a fixed effects approach. First, is omitted variables bias, which occurs whenever an
unobserved variable (say, neighborhood quality) is correlated both with parcel prices, and
at least one included explanatory variable. When this happens, the estimated effect of the
included variables will be biased and inaccurate.
Another, related, problem is
endogeneity, when prices and an explanatory variable are co-determined as might happen
if, all else equal, wind turbines are more likely to be sited in areas with lower value land.
In this case, the analysis may mistake the cause of the siting (lower property values) for an
effect of the siting.
Fixed effects analysis helps to curb the impacts of both of these
issues by estimating fixed area effects which control for all factors which are
homogeneous across a small geographic area.
This reduces the number of omitted
variables (and particularly those related to geography) and reduces the scope for possible
endogeneity.
One tension that arises when using fixed effects analysis is that the smaller the
geographic area chosen for the fixed effects, the more control the analyst has for these
problems, but also the less power the analyst has to estimate the effects of included
variables. Unfortunately, there is no foolproof way to know at which level to control for
See (Freeman, Herriges, & Kling, 2014) and (Taylor, 2003) for comprehensive descriptions of the hedonic
method.
11
these issues. In this analysis we use municipality fixed effects because of the relatively
small number of parcels with a view of turbines.
A related problem is spatial autocorrelation which occurs when error terms (variation in
prices that is left unexplained by included variables) for transactions nearby to each other
are correlated.
This can be controlled by allowing for this correlation and calculating
standard errors appropriately, which we do by allowing error terms to be clustered within

municipalities.

12
2.2.2 Data
Data for this study comes from several sources, as described in Heintzelman, Vyn, and
Guth (2015). We use data on 5,631 single-family residential parcels in Jefferson County,
NY. Data on NY transactions come from the New York State Office of Real Property
Taxation Services (NYSORPTS). This data includes sale price, sale date, and parcel
identifying information. Transaction data is then merged with parcel and home
characteristics data from the assessment process, also from NYSORPTS. We then bring in
parcel shapefile (GIS) data which we acquired from the Jefferson County Assessor's Office.
With this spatial data we calculate a number of distance and spatial variables in ArcGIS.
Table 1 presents summary statistics for this sample.
With this data in hand, we conducted a preliminary viewshed analysis to identify parcels
within five miles of the turbines that had a potential view of the turbines.
A pair of
students then visited each of these identified parcels to confirm the view from each
parcel. The students recorded the number of visible turbines as well as whether or not
these views were full or partial. However, given the small number of parcels (26) with a
view in our dataset, we simplify the analysis to only focus on whether or not each parcel
had any view of one or more turbines. We use a log-linear functional form in following
the bulk of the hedonic literature (Cropper, Deck, & McConnell, 1988). We also include
year fixed effects to control for sample-wide price trends. Unfortunately, we are unable to
accurately control simultaneously for both distance to turbines and view, which is an
important limitation.
2.2.3 Wolfe Island Results

Results for the hedonic study of the Wolfe Island wind farm are shown in Table 2. The
primary variable of interest is the variable representing parcel transactions with a view of
turbines, after the turbines were constructed.
We see that parcels with a view of the
turbines sell for a positive premium (approximately 10%) before the turbines are built, but
that this premium is more than eroded by a strong negative impact
after turbine
construction. The estimated coefficient of -0.164 that describes this effect implies a 15%
decrease in property values for homes with a view after the turbines are built. We also
calculate a 95% confidence interval for this effect, which tells us that, given the observed
data, there is a 95% chance that the true effect is a decrease of between 5.1% and 23.9%.
So, while we cant be confident that the effect is exactly negative 15%, we are reasonably
confident that there was a negative impact.
Seasonal and waterfront homes both sell at significant premiums while other attributes of
the homes have the expected signs. We see a positive premium in the period between
announcement and construction for homes with a view, which may have to do with
13
general appreciation for these water view homes. Our spatial controls are not significant
but provide important controls for distances to larger communities with shopping and
other man-made amenities.
Importantly, there is no general post-construction effect
across the sample. Instead those affects appear to be limited to those parcels with a view.

14
Table 1: Summary Statistics for Wolfe Island Sample
Price ($US)
Mean
Std. Dev.
Min
Max
139,349.30
100,036.80
3,500.00
2,000,000.00
Parcel w/ View of Turbines
0.005
0.068
Parcel Sold between Approval and Construction
0.216
0.411
Parcel Sold After Construction
0.398
0.49
Parcel w/ View of Turbines AND Sold between Approval and

Construction
0.001
0.035
Parcel w/ View of Turbines AND Sold After Construction
0.003
0.052
Seasonal Home
0.093
0.291
Waterfront Home
0.113
0.316
Mobile Home
0.037
0.19
Lotsize (Acres)
5.145
20.958
0.00573
391.433
Bathrooms
1.421
0.576
5.5
Bedrooms
2.992
0.938
Fireplace
0.174
0.379
Air Conditioning
0.021
0.143
Quality=2
0.128
0.334
Quality=3
0.76
0.427
Quality=4
0.093
0.291
Quality=5
0.001
0.03
1551.206
596.219
136
6074
Age
69.203
51.83
225
Number of Storeys
1.458
0.442
Distance to University (Miles)
15.644
7.658
32.4344
Distance to School (Miles)

Distance to Hospital (Miles)
2.913
10.387
2.91
6.776
0
0
17.9056
34.0026
Living Area (sq.ft.)

15
Table 2: Wolfe Island Results

Coef.
Std. Err.
P>|t|
Parcel w/ View of Turbines
0.106
0.062
1.72
0.097
Parcel Sold between Approval and Construction
-0.032
0.042
-0.77
0.45
Parcel Sold After Construction
-0.034
0.057
-0.61
0.55
Parcel w/ View of Turbines AND Sold between Approval and Construction
0.203
0.058
3.5
0.002
Parcel w/ View of Turbines AND Sold After Construction
-0.164
0.053
-3.07
0.005
Seasonal Home
0.08
0.034
2.32
0.028
Waterfront Home
0.658
0.082
8.05
Mobile Home
-0.209
0.051
-4.06
Lotsize (Acres)
0.0005
0.0005
0.325
Bathrooms
0.11
0.017
6.45
Bedrooms
0.003
0.02
0.15
0.879
Fireplace
0.152
0.026
5.9
Air Conditioning
0.143
0.046
3.13
0.004
Quality=2
0.394
0.104
3.78
0.001
Quality=3
0.769
0.093
8.24
Quality=4
0.94
0.09
10.42
Quality=5
0.698
0.108
6.45
Living Area (sq.ft.)
0.0003
0.00003
10.8
Age
-0.005
0.001
-4.9
0.00002
0.00001
3.05
0.005
Number of Storeys
0.07
0.031
2.22
0.034
Distance to University (Miles)
0.005
0.005
1.03
0.311
Distance to School (Miles)
-0.003
0.008
-0.39
0.696
Distance to Hospital (Miles)
-0.011
0.006
-1.69
0.102
Age Squared
Municipality Fixed Effects
Yes
Year Fixed Effects
Yes
R-Squared
0.4625
2.2.4 Application to Town of Henderson

The analysis of the Wolfe Island case study provides important evidence suggesting that
the Galloo Island wind farm will likely negatively impact property values for those parcels
that are likely to have a view of the turbines, although it is again important to note that
the Galloo Island turbines will be considerably further away from the mainland than those
on Wolfe Island, despite prominent views. The central estimate of the hedonic analysis
suggests a likely 15% reduction in property values for homes with a view, after the
turbines are built.
We now combine the above analysis with the viewshed analysis
conducted in GIS to estimate the aggregate effects on property values in the Town of
16
Henderson. To do this we use parcel attribute data on 1,453 single-family residential

parcels in the Town of Henderson together with an estimate from the viewshed analysis of
whether each of these parcels will view the turbines. We plug this parcel attribute data
into the estimated hedonic model to project two values for each parcel with and without
the turbines. Because our data ends in 2013, this projection is done in 2013 US dollars,
as if the homes were selling in 2013, but with the Galloo Island turbines constructed.
These estimates are sensitive to assumptions made in the viewshed analysis and, in
particular, the assumed height of the forest canopy. For this reason, we calculate two
estimates according to a 13m and 20m assumed canopy heights.
These projections,
aggregated to the town level, are presented in Table 3.
Table 3: Projected Property Values

20m Canopy Height
13m Canopy Height
Aggregate
Average
Aggregate
Average
Projected Value w/ Turbines
$298,950,891.57
$205,747.34
$294,918,808.00
$202,972.34
Projected Value w/o Turbines
$338,816,107.10
$233,183.83
$338,109,916.64
$232,697.81
Projected Change in Value
-$39,865,215.53
-$27,436.49
-$43,191,108.64
-$29,725.47
Projected % Change in Value
-$11.77
-$12.77
We see in these projections that the average home is expected to lose between 11.77%
and 12.77% of its value if the Galloo Island turbines are built. Importantly, however, this
average includes homes both with and without a view. Homes with a view will face the
bulk of the value loss. In aggregate, this analysis suggests a total value loss for the Town
of Henderson of between $39.8M and $43.2M. These estimates are all calculated using
the central estimate of the post-turbine impact for homes with a view from the hedonic
analysis of -15%.
Because this central estimate is uncertain, our projections are also
uncertain. So these estimated aggregate impacts could be considerably larger or smaller

in actuality.
While these numbers are large, and suggest a real loss to the people of Henderson, it is
important to note that these losses do not affect peoples wealth all at once. Instead, for
residents who are planning to stay in Henderson for a number of years, they will not
actually be significantly affected until such time as they choose to sell. In addition, there
is a strong suggestion in the literature (Hoen et al. 2015) that these affects may be shortlived. As people adapt and get used to having the turbines in their landscape, and as
This is a benign assumption. With additional data on appreciation in values since 2013 we could adjust
these numbers to 2015, but this would not affect the relative changes projected to be caused by the
turbines.
17
many Americans become more familiar with wind energy, negative property value impacts
may dissipate.

18
Table 4: Projected Effects on Average Parcels w/ Turbine View

Baseline Price
% Effect
Low Estimate
Central Estimate
High Estimate
-5.10%
-15.00%
-23.90%
Mean
$276,954.50
-$14,156.35
-$41,593.40
-$66,165.92
Median
$221,393.00
-$11,316.36
-$33,249.10
-$52,891.98
To illustrate the statistical uncertainty associated with our projections, Table 4 presents
projected impacts for parcels with a Turbine view for low, medium, and high estimates of
the post-turbine impacts.
We calculate the mean and median projected price for the
parcels in our Henderson dataset projected to have a turbine view.
Using these
projections, we use the low and high ends of the 95% confidence interval as well as the
central estimate of the impact and apply these to the mean and median prices for this
selection of parcels. This analysis suggests that the mean dollar-value impact on homes
with a view could be as low as $14,156.25 and as high as $66,165.92. It is worth noting,
however that these two extremes are bounds on likely impacts, and are highly unlikely to
occur. Finally, the real uncertainty associated with our estimates is likely to be somewhat
larger than that suggested by the analysis of statistical uncertainty presented because of
the greater distance from Henderson to Galloo Island than from Cape Vincent to Wolfe
Island. On its own, our expectation is that this greater distance should make the realized
impacts lower in magnitude than the projections provided above.

19
3.0 Jobs and Tourism Analysis for Galloo Island

Wind Installation
3.2 Background
The Town of Henderson is a small Town situated on the south eastern coast of Lake
Ontario in the Thousand Islands area of New York State. Census data shows the town has
a total area of 53 square miles of which approximately 12 square miles (22%) is water. The
west boundary of the town is Lake Ontario. 2010 Census data shows the towns
population at 1360. The town is located in Jefferson County (population: 116,229),
southwest of the City of Watertown. It is also located just north of the city of Syracuse and
Onondaga County.
Figure 2. Town of Henderson, with local environmental amenities and Galloo Island
shown; source: Google Maps
Hendersons primary industry is tourism. These activities are split between seasonal
residents and tourist visitors. Seasonal residents own extensive plots of cottages,
residences, and some year-round homes. Other tourist activities focus on day visitors,
visitors to the local parks, and extensive activities focused around Henderson Harbor.
Water activities include fishing trips, day trips, general boating and sailing, and lakeside
activities along the coast. Henderson is exceptionally well-placed for these activities
because of the quality of its environmental amenities for these various tourist activities
and its close location to the cities of Watertown (19 miles) and Syracuse (65 miles). It has
20
three New York State Parks (Robert G. Wehle, Westcott Beach, and Southwick Beach) and a
Wildlife Management area, all of which are water based.
Within Henderson, there is a high proportion of owned land, with few rentals. However,
many
of
the
owned
properties
are
owned
by
seasonal
residents
(Henderson
Comprehensive Land Use Plan Committee 2004). The year round residents of Henderson
have generally low income, with a large majority of property taxes paid for by upper and
middle income residents with seasonal housing on the coast (Henderson, Fishers Landing
among Poorest Communities in State 2015).
The Galloo Island Wind Energy Facility (henceforth GIWEF) Project was first informally
proposed in September 2014 by Albany based Hudson Energy Development LLC under a
subsidiary Hudson North Country Wind 1 LLC (henceforth HNCW) and was sold to Apex
Clean Energy in December 2015. Its formal Program Involvement Plan Application
occurred in Summer 2015. Its plan comprises 29 (originally planned as 31) turbines
located on the privately owned island for an expected 102 MW nameplate capacity. The
turbines will be 575 feet high, with blade lengths of 210 feet (Hudson North Country Wind
1, LLC 2015).
The following analysis is divided into three sections. The first provides a specific overview
of relevant scholarly literature on jobs and economic development for wind farms. It then
proceeds with a Jobs and Economic Development Impact (JEDI) analysis specified to the
Galloo Context as much as possible. The third section provides an overview of the
research on wind farm impacts on tourism activities.
3.3 Jobs and Economic Development Literature Review

An extensive literature exists that addresses the economic impacts of wind farms, and
specifically the question of job development and wind farms. This section is primarily
focused on the question of job development with a secondary section on tourism impacts.
Our land valuation analysis is a separate part of this report.
As a general rule most energy infrastructure development has significant employment and
economic development benefits for communities and jurisdictions. These must always be
balanced against potential negative impacts and externalities that can include negative
6
Note that in addition to the readings discussed in this section, several of which contain extensive reviews
of the academic literature, we also consulted several additional readings that support our general
conclusions. These include the following: (Westerberg, Jacobsen, and Lifran 2015; Lilley, Firestone, and
Kempton 2010; Slattery, Lantz, and Johnson 2011; Brown et al. 2012; Wolsink 2013; Frantl and Kunc 2011;
Broekel and Alfken 2015; Stiftung Offshore-Windenergie (German Offshore Wind Energy Foundation) 2013)
21
environmental impacts, devaluation of land, and loss of environmental benefits.

Calculated on a broad basis, wind development has real economic benefits to states and
broad regional areas. Loomis and Carter (2011) show that wind development in Illinois
has significantly greater number of jobs per MWH compared to coal or natural gas
development (Loomis and Carter 2011). A wide range of work (including the development
of the JEDI model, used in this analysis) shows broad ranges of economic benefits at the
regional or state level.
The two most significant factors affecting economic benefit are the presence of in-state
turbine development and manufacturing (either whole or in component parts), and local
ownership models (Lantz and Tegen 2008). The sourcing of turbines for Apex is unknown
at this time. Apex Ownership for Galloo is based in Virginia and will be out of state.
The key concern for wind implementation is the potential for mismatched benefits and
costs. Many authors note a significant dynamic in the siting of most wind developments
and community acceptance. This is that communities are more willing to accept the
detrimental visual impacts (and potential for land or other associated devaluation) in
return for property tax payments, PILOTs (Payments In Lieu of Taxes), and the localized
economic benefits of lease payments to individual land owners (Lantz and Tegen 2008;
McKeown, Adelaja, and Calnin 2011).
In fact when jurisdictions do not receive these forms of economic benefit, there is a
substantial literature showing that affected populations have a high degree of willingness
to pay to move turbines farther from shore, or to not be built at all (Snyder and Kaiser
2009). This is particularly the case for offshore wind projects which often have no
localized leases and may not have tax benefits.
Willingness to pay, contingent valuation, and choice experiment research are different
forms of similar economic assessments that determine the cost to the public of a
specific activity. It is a well-established and respected way to understand the costs that a
community perceives from an activity. It is particularly relevant for thinking through the
potential costs of impact for Henderson. Ladenburg and Dubgaard estimated that citizens
were willing to pay 46, 96, and 122 Euros per year per household in order to move a
theoretical wind farm (relative to an 8 km baseline) to 12, 18, or 50 km away from the
coast (2007). A similar analysis from Cape Cod found that 22% of respondents were
willing to pay a onetime cost of $286 for windmills not be built at all, with an average net
willingness to pay of $75 (Haughton, Giuffre, and Barrett 2003).

22
Clearly, without specific localized economic benefits many publics understand the
implementation of visual disamenities from wind as a net economic cost prior to being
built. A full literature review of these types of studies shows that wind developments close
to shore, particularly when visible, generate significant welfare losses that can be
reduced by siting further offshore (Ladenburg and Lutzeyer 2012, 1). In particular, their
review shows that residents are most motivated to pay for removal or greater distance in
the 8-12 km or less range. Galloo is located 11 km from the coastal area of Henderson
near Wehle State Park. Obviously moving the facility in the case of Galloo would require
siting the project in water further from shore rather than on the island.
3.4 Jobs and Economic Development Impact Analysis: The JEDI Model
In the early 2000s the U.S. Department of Energy and the National Renewable Energy
Laboratory (DOE/NREL) developed a spreadsheet-based wind model called the Jobs and
Economic Development Impact Model, or JEDI for short (Goldberg, Sinclair, and Milligan
2004). This tool uses input-output analysis to determine expected economic outcomes
from a wind development, including direct jobs outcomes, and associated jobs from
supply chain, induced impacts, and other associated activity. 7 The models base empirical
assumptions are imputed from data on dozens of completed wind projects and have been
peer-reviewed (McKeown, Adelaja, and Calnin 2011; Lantz and Tegen 2008; Lantz and
Tegen 2009).
That said, the results from any JEDI analysis should be considered as speculative given
that every wind farm development has its own specific and unique circumstances. For this
reason we develop several different scenarios with the JEDI model as detailed below.
3.4.1 Jurisdictional Impact and Assumptions
A critical concern for assessing job impacts of wind infrastructure (or any form of
economic development) is determining the jurisdictional impact. For instance, the JEDI
model used in our analysis is generally considered to have State level impacts in its
output. Even then, economic benefits may be even more diffuse and extend beyond state
borders to other states or other countries. Generally, economic development models
assess both direct and secondary impacts (in JEDI these are characterized as supply chain
and induced impacts). In the jobs category, direct impacts would be any job directly
linked to the development or operation of the wind farm. Other related or induced
impacts would include administrative positions, or jobs associated with increased
economic activity through the local area and the state.
A complete outline of the methodology for the JEDI modelling tool is included in this report as Appendix A.
Readers are also directed to the source itself.
23
In the case of a small local town such as Henderson or more broadly the two local
counties (Oswego and Jefferson), affiliated jobs and economic activity could be occurring
in Watertown, Albany, or New York City. For that matter, some of the related jobs could be
associated with banking services in another country, or the economic impacts from the
purchase of turbines in another state or another country. Thus, the challenge is to make
reasonable and appropriate assumptions about which impacts will actually be local.
For the purposes of the JEDI analysis, we develop a series of scenarios based on the JEDI
output for the state, and we similarly apply the same assumptions to the job claims made
by HNCW. We use a 50 mile radius (a reasonable commuting distance) around Galloo
Island as the primary area from which we expect local direct jobs (i.e. workers) would be
sourced. We expect local area jobs to be located primarily in Jefferson and Oswego
Counties. Oswego County is included because the transmission line interconnection will
be put in place between Galloo Island and the City of Oswego.
Table 5. Population Base for Job and Jurisdiction Analysis

Henderson
Jefferson County
Oswego County
50% of Oswego and Jefferson Counties
Henderson Proportion of Oswego and Jefferson @ 50%
population
Watertown (city)
Oswego (city and town)
1360
119,103
120,913
120,008
1,360 / 120,008 = 0.0113 or
1.13%
27,590
25,908 (17,988 + 7,920)
All 2014 U.S. Census estimates (except Henderson 2010 Census count)

24
Figure 3. 50 mile radius from Galloo; Source: Google Maps
Figure 4. County Breakdown around Galloo Island

http://www.ezilon.com/maps/images/usa/new-york-county-map.gif

25
The 50 mile jurisdiction covers slightly more than 50% of both Oswego and Jefferson
Counties. To assess Hendersons expected proportion of the jobs from a population basis
we use 50% of the population of those counties as the base population for local job
benefits. Hendersons proportion of this base population is 1.13% (data shown in Table 6
above). We also include a best-case 2.26% scenario (i.e. a doubling or 100% increase of
the baseline population proportion) and a worst-case 0.57% scenario (50% of the
baseline). Since Henderson is so much smaller, and isolated from the city centers that
most of the jobs are likely to accrue to, a proportional approach is likely to be slightly
optimistic.
Another important aspect of the analysis is that the county/regional option in JEDI is not
used because we do not have access to specific county level data needed for the inputs to
the county level analysis. Further, benefits from Galloo can be expected to accrue to two
counties rather than one. Discussions with other consultants and experts in input/output
analysis led us to pursue the 50 mile benefit jurisdiction as a more appropriate and
realistic analysis for specific regional and locational benefits. This is also one of the
reasons we pursued scenario analysis.
3.4.2 JEDI Analysis

Full results of the JEDI analysis are shown in Appendix B (Detailed Wind Farm Project Data
Costs) and C (State Level Impacts) of this report. Scenarios are developed as follows.
First we develop sets of scenarios for each distinct time period of the wind developments
life. The first time period is construction and is defaulted to JEDIs average 2 year period
for construction. The second time period is for the twenty year period of wind farm
operation.
Next, we develop three sets of scenarios as follows for each time period. Set 1 assumes
that Jefferson and Oswego Counties receive 100% of the economic benefits and jobs
(direct and indirect) from the wind development. Set 2 assumes an 80% benefit for the
counties, with the remaining 20% of benefits accruing to other parts of New York State or
possible out of state or country. We believe that the 80% model is most realistic during the
construction period because the jobs will be temporary in nature, some proportion will
likely be specialized, and that certain accounting and legal activities will likely occur
beyond the county regions. HNCW is based in Albany, and we reasonably expect that
some economic activity will occur outside of the immediate county region. Finally, we have
a least optimal set which assumes 40% of benefits occur beyond the county regions. For
the long term job and economic benefits through the operation of the wind farm we

26
prefer to use the 100% benefit option. While fewer jobs and economic activity occur
during the O&M period, it is most likely that at least 90-100% of these jobs and activity
will occur in the local area. For both sets of analysis we use rose highlighting to denote
our expectation for the most likely scenario.

27
Each of the three sets of analyses uses job numbers derived from JEDI and HNCW (the
developer). The jobs analysis is divided into direct jobs and also associated (i.e. derived or
indirect) jobs. HNCW has significantly higher numbers for job creation than the JEDI model
predicts. We hope that HNCW would be willing to provide its analysis for the job derivation
numbers it predicts. We note specifically that HNCW uses the term up to 8 full time jobs
for operations and up to 120 temporary construction jobs. (emphasis added, Hudson
North Country Wind 1, LLC 2015, 4) That said it may be reasonable for HNCW to expect
slightly higher numbers because the development is on an island and this may have
slightly higher needs for human capital.
Finally, we develop scenarios specific to Hendersons expected benefit that are
proportional to the county population. Hendersons proportion is 1.13% of the 50%
combined populations of Oswego and Jefferson counties that lie within the expected
commuting range of the development. Again, we believe the most reasonable expectation
for Henderson is a model that is directly proportional to the expected overall population
benefit (i.e. the middle case 1.13% model). Arguably, many of the construction jobs will
likely go to workers based in Oswego and Watertown. We expect specialized jobs to be
sourced beyond the commuting range from Syracuse, Utica, Rochester, and beyond.

28
Table 6. Construction Jobs and Economic Activity Scenarios: Direct and Indirect Effects
JEDI Direct Construction:
JEDI Construction Related:
Developer Direct Construction:
Developer Inferred Construction Related: (Using JEDI Ratio 5.95:1):
66
393
120
714
Direct Construction and Construction Related Jobs (First 2 year period only)
Jefferson &
Oswego 100%
JEDI: Direct Construction
Henderson
0.57%
Henderson
1.13%
Henderson
2.26%
66.00
0.38
0.75
1.49
JEDI: Direct Plus Supply Chain and Induced Impacts
393.00
2.24
4.44
8.88
HNCW: Direct Construction
120.00
0.68
1.36
2.71
HNCW: Direct Plus Supply Chain and Induced Impacts

(inferred via JEDI)
714.00
4.07
8.07
16.14
Earnings (millions)
29.90
0.17
0.34
0.68
Output (millions)
65.00
0.37
0.73
1.47
Value Added (millions)
40.30
0.23
0.46
0.91
Jefferson &
Oswego 80%
Henderson
0.57%
Henderson
1.13%
Henderson
2.26%
52.80
0.30
0.60
1.19
314.40
1.79
3.55
7.11
96.00
0.55
1.08
2.17
571.20
3.26
6.45
12.91
Earnings (millions)
23.92
0.14
0.27
0.54
Output (millions)
52.00
0.30
0.59
1.18
32.24
0.18
0.36
0.73

(inferred via JEDI)
Jefferson &
Oswego 60%
Henderson
0.57%
Henderson
1.13%
Henderson
2.26%
39.60
0.23
0.45
0.89
235.80
1.34
2.66
5.33
72.00
0.41
0.81
1.63
428.40
2.44
4.84
9.68
Earnings (millions)
17.94
0.10
0.20
0.41
Output (millions)
39.00
0.22
0.44
0.88
24.18
0.14
0.27
0.55

(inferred via JEDI)
*Optimal scenarios are shaded in light red in Table 6 of 80% benefit to the county regions during construction.
Note: Jobs are single job years and monetary values are millions of dollars.

29
Table 7. Yearly Operations and Maintenance Jobs and Economic Activity Scenarios (20 yrs.)
JEDI Direct O&M:
JEDI O&M Related:
Developer Direct O&M:
Inferred O&M Related (Using JEDI Ratio 2.83:1):
6
17
8
23
Jefferson &
Oswego 100%
Henderson
0.57%
Henderson
1.13%
Henderson
2.26%
6.00
0.03
0.07
0.14
17.00
0.10
0.19
0.38
8.00
0.05
0.09
0.18
23.00
0.13
0.26
0.52
Earnings (millions)
1.40
0.01
0.02
0.03
Output (millions)
3.60
0.02
0.04
0.08
2.80
0.02
0.03
0.06

(inferred via JEDI)
Jefferson &
Oswego 80%
Henderson
0.57%
Henderson
1.13%
Henderson
2.26%
4.80
0.03
0.05
0.11
13.60
0.08
0.15
0.31
6.40
0.04
0.07
0.14
18.40
0.10
0.21
0.42
Earnings (millions)
1.12
0.01
0.01
0.03
Output (millions)
2.88
0.02
0.03
0.07
2.24
0.01
0.03
0.05

(inferred via JEDI)
Jefferson &
Oswego 60%
Henderson
0.57%
Henderson
1.13%
Henderson
2.26%
3.60
0.02
0.04
0.08
10.20
0.06
0.12
0.23
4.80
0.03
0.05
0.11
13.80
0.08
0.16
0.31
Earnings (millions)
0.84
0.00
0.01
0.02
Output (millions)
2.16
0.01
0.02
0.05
1.68
0.01
0.02
0.04

(inferred via JEDI)
*Optimal scenarios are shaded in light red in Table 7 of 100% benefit during the operations period.
Note: Jobs are single job years and monetary values are millions of dollars.

30
Analysis and Discussion

Our analysis is based on our optimal scenarios (shaded in light purple in Tables 6 and 7)
of 80% benefit to the county regions during construction, and 100% benefit during the
operations period. Under these most likely scenarios, the Jedi and HNCW job output data
show the potential for 4-8 jobs to the town of Henderson during the two year
construction period.8 During the twenty year operational period Henderson can expect 01 job(s). Barring the introduction of analysis from HNCW showing otherwise, we project
job creation to occur on the lower end of this scale, closer to the JEDI modelling numbers.
Thus our most likely expectation is actually a range of 4-6 temporary construction jobs
and likely no long term operational jobs.
Similarly, the JEDI model shows that Henderson can expect to see proportional yearly
earnings at approximately $270,000, economic output at $590,000, and added value of
$360,000 during the construction period. During the twenty year period of operation
Henderson can expect associated benefits of $20,000 in yearly earning, $40,000 in
economic output, and $30,000 in added value economic activity on an annual basis.
Several important points accompany the analysis of these most likely scenarios. First,
these analyses are predictive and reflect the best range of inputs but they should still be
cited or considered with care. Second, they do not reflect potential economic costs
associated with loss of land value or tourism activity. Finally, as discussed in the review
section, normally communities benefit from local lease payments, property tax payments,
and/or PILOTs. Further, the hosting communities benefit from these forms of economic
inputs to an extensive degree. They can be the balancing factor that offsets viewshed
impacts and the potential costs to a community from those impacts in terms of economic
devaluation.
A particular issue for Henderson is that it will not benefit economically from local lease
payments, nor property taxes or PILOTs. For this reason we believe the generalized
economic outputs of the JEDI model are likely optimistic or overstated in terms of direct
benefits to the town. Henderson is in the difficult position of carrying a large majority of
negative impacts, while reaping a very small proportion of local economic development.
Remember that because Galloo is an island this may explain HCNWs higher numbered job analysis
compared to the JEDI analysis.
9
It may be that Hounsfield, the town of jurisdiction, will reap lesser economic benefits than average given
that leases are consolidated by a single absentee landowner.
31
3.5 Tourism Analysis

Wind farms can potentially affect tourism in a variety of ways. They can be perceived as an
industrial development in areas of rich beauty that are attractive to tourists for activities
such as fishing, boating, beach activities, or hiking.
Riddington et al. summarize the potential problem succinctly:
many people find that man-made structures such as
pylons and wind turbines reduce the attractiveness of a
landscape. It is logical to assume that reduced quality of an
important feature will inevitably reduce demand to some
degree which in turn may result in either reduced prices for
tourism services or reduced numbers of tourists or both.
Any loss of expenditure will lead to a reduction in economic
activity and result in a loss of income and jobs. (Riddington
et al. 2010, 237)
In more rare circumstances, they can also occasionally be perceived as an eco-tourism
benefit, although this is almost always in combination with other kinds of eco-tourism. In
general, there is mixed evidence on the impact of wind developments in coastal areas.
The following discussion outlines some of the evidence.
Distances from a living area to a wind farm are a critical component of assessing impacts.
Distances to the center of Galloo in the Henderson coastal areas range from 6.8 miles
(Wehle State Park) to 12.2 Miles (Westcott Beach State Park; or approximately 10-19
kilometers). As discussed in the previous section on economic valuation by residents,
most negative perceptions occur at the 8-12 km range and under (Lantz and Tegen 2009;
Westerberg, Jacobsen, and Lifran 2013).
Finally, a critical concern in interpreting the vast majority of literature on tourism impacts
is that most of it is prospective (forward-looking) not retrospective (looking to the past).
This means that our understanding is not empirically based. Most of the literature that
tests prospective economic impact literature show that the vast majority of the time both
positive and negative impacts are less than predicted. Thus the reported impacts (negative
and positive) discussed in the following review are likely overstated. In addition, predicted
impacts are likely to dissipate over time, as has been discussed in the literature on
property values (Hoen et al., 2015).
Westerberg et al. conducted choice experiments in the coastal Mediterranean area of
Languedoc Roussillon in France. Their research showed that tourists and beachgoers had

32
negative reactions to all choice variations for wind implementation under a 12 kilometer
radius (Westerberg, Jacobsen, and Lifran 2013). Alternately, they also found that wind
developments created in partnership with ecotourism opportunities created support for
wind development. These recreational activities included diving around the artificial reefs
created by offshore wind, and sightseeing tours to the wind farm areas. Importantly,
Westerberg et al conclude that

33
disamenity costs decline as distance from coast increases

(Krueger et al., 2011). Our results indicate that the impact of
wind farm disamenity costs on tourism revenues tends to
zero, somewhere between 8 and 12 km. The study also
showed that there is large heterogeneity in the tourists
preferences. (Westerberg, Jacobsen, and Lifran 2013, 182)
Riddington et al review an extensive set of literature based in the UK, Scotland, and Wales
(2010). The results they report are mixed. Many of the studies they cite are often biased
by the motivations of the funding agency or institution (these studies show both negative,
positive, and non-effects) and have minor issues in research design. I review several of
the most important here, as cited in Riddington. First they discuss research by Hanley and
Nevin (1999) that shows a small but significant willingness to pay of 15 per person on
average to have views without turbines. They also review an NFO/System Three (2002)
study that shows 29% tourist opposition to wind development. Both these studies have
significant research design flaws that may bias these results (Riddington et al. 2010, 239
240).
Riddington also review positive literature by MORI Scotland (2002) that showed 43%
positive response to area wind farms and only 8% negative in tourist surveys during or
after their visit. This is important because it represents one of the few retrospective
analyses that exist in the literature. Riddingtons characterization of the MORI study
continues:
When asked about the impact on the likelihood of visiting
Argyll in future, 91% said it made no difference, 4% said they
are more likely to return and 2% said they were less likely to
return. As so many studies show, there was strong interest
in visiting a wind farm if opened to the public. If a wind farm
had a visitor centre, 80% would be interested in going, with
54% very interested and 19% not interested. The majority
of tourists who knew about the wind farms came away with
a more positive image of the area because of their presence
(Riddington et al. 2010, 240).
Lastly, Riddington et al. cite survey work by the Welsh Tourist Board from 2003 that shows
78% with neutral or positive views and 21% negative. 68% stated their interest in wind
farm tours or a visitor center (2010, 240).

34
The Riddington et al. analysis used GIS research and survey work to determine net
economic impact on tourist areas in Scotland. They calculate a significant but very small
net job loss of less than 0.1% of employment.
Alternately Landry et al assessed the impact of coastal wind turbines on local tourism and
recreation for residents in northern coastal counties of North Carolina. They used
telephone and web survey data to determine wind development effects on trip behavior
and site choice. Most of the respondents stated their support of offshore wind energy
development. Their research found no evidence of aversion to wind farms 4 miles out in
the ocean. (2012, 93).
Finally, an extensive analysis done for the Government of Wales more recently by
Regeneris had several findings (Regeneris Consulting 2013). First, the overall effect of
wind farms on tourism in Wales was negligible. The exception was for unique markets
noted for their tranquility, remoteness, and natural scenery. In these markets, the
potential for minor negative impacts existed. Similarly, they noted the possibility of
positive visitor impacts, primarily by wind farm association with other activity or via direct
tourism.
Analysis and Discussion
As should be obvious at this point, the literature on tourism impacts is heterogeneous and
varied in nature.
10
However, the overall summation of the evidence seems to show that
tourism impacts on coastal areas in and around Henderson are likely to be non-existent
or very minimal in nature. This would include boating, beach activities, and other
associated on-shore activities. Overall, this is primarily because the distance from the
island to the shore amenities is beyond the distance in which affects seem to occur which
negatively affect tourist perceptions. Given the location of the turbines on the island
(rather than in water), we can also expect minor effects on boating and fishing according
to the literature. Finally, the possibility exists for the development of eco-tourism
opportunities which may help reinforce positive perceptions of wind farm activity. Such an
option is not a part of the developers plan, nor is it clear that the development of this
type of activity would lead to positive economic or tourist perception outcomes.
10
Note that in addition to the readings discussed above, several of which contain extensive reviews of the
academic literature, we also consulted several additional readings that support our general conclusions.
These include the following: (Westerberg, Jacobsen, and Lifran 2015; Lilley, Firestone, and Kempton 2010;
Slattery, Lantz, and Johnson 2011; Brown et al. 2012; Wolsink 2013; Frantl and Kunc 2011; Broekel and
Alfken 2015; Stiftung Offshore-Windenergie (German Offshore Wind Energy Foundation) 2013)
35
4.0 Viewshed Analysis & Methodology

4.1 Introduction
Viewshed analysis is a technique used within Geographic Information Systems software
(GIS) for predicting areas on a landscape that will be visible or invisible to observers with
known locations and elevations.
It uses elevation data in a raster, or cell-based, data
format to evaluate the visibility potential of objects on the Earth's surface. The function
creates individual line-of-sight calculations from multiple points (wind turbines) to every
location in the study area and in the case of multiple points, will add up the values to give
a cumulative total of objects visible from each cell across the study area. In the case of
the proposed Gallo Island Wind Project, the total number of turbines was 29, so the
viewshed values range from 0 - 29.
Visibility of distant objects decreases with distance due to many factors, including
meteorological conditions, the curvature of the earth, atmospheric refraction, terrain and
physical obstructions and the eyesight of individuals viewing the objects. The parameters
considered for this viewshed analysis included terrain obstructions, forest vegetation,
earth curvature and atmospheric refraction. An atmospheric refraction coefficient of .013
was used for all viewshed calculations.
The curvature of the earth begins to affect
visibility of objects at around 3 miles, and the proposed turbines would be completely
invisible at a distance of 35 miles over open water.
The study area considered for the viewshed focused on the Town of Henderson, but also
included the areas around Galloo Island out to a distance of about 30 miles.
Visual
impacts of distant objects, regardless of height, rapidly decreases beyond about 15 miles.
Distance buffers (rings) were created around the proposed turbines at 5, 10 and 15 miles
and are shown in Figures 5 and 6. As can be seen on the map, the majority of Henderson
Town is within the 5 - 15 mile distance band. For the purpose of providing visibility
information for the entire town of Henderson, the viewshed was run out to a distance of
approximately 30 miles, even though the visual significance of the turbines would be very
small.
The viewshed (or Visibility) analysis was carried out using ArcGIS Desktop 10.3.1 and the
3D Analyst Extension.

36
4.2 Data Sources and Processing

Proposed wind turbine locations and tower height values were provided by Neil Habig of
Hudson Energy Development. Turbine parameters used in the analysis are shown below:
Tower Heights: 110 meters (361 feet)
Rotor Diameter: 130 meters (427 feet)
Blade Tip Height: 175 meters (574 feet)
Total Turbines: 29
Digital Elevation Models (DEMS) were obtained from the United States Geological Survey
(USGS) National Elevation Dataset (NED) at a spatial resolution of 10 meters (33 feet). This
is the highest resolution data available for the study area.
The turbines were plotted on
the DEM data to obtain the base elevations of the towers. These values ranged from 76 89 meters above sea level (249 - 291 feet). A value of 1.7 meters (5.6 feet) was added to
all cells in the study area to represent the approximate height of a person standing on the
ground.
Visibility potential depends heavily on vegetation, buildings and local ground conditions.
Building footprint data is unfortunately not available for Jefferson County, so the viewshed
models do not take existing structures such as houses and commercial buildings into
account. The lack of this data will tend to over-estimate the visibility potential of the
turbines, especially in urban areas, as buildings will block the view from ground level.
Forest canopy data is available from the National Land Cover Database 2011 (NLCD) at a
spatial resolution of 30 meters and was downloaded from the Multi-Resolution Land
Characteristics Consortium (MRLC) website. The forested areas were extracted from the
NLCD, re-sampled to 10 meter resolution and then added to the heights of the Digital
Elevation Model. The forest data does not include the actual heights of the tree canopy,
just the presence of continuous forest areas, so assumptions have to be made about the
actual heights of the trees.
Two forest canopy viewsheds were created, one using
estimated canopy heights of 13m and one using forest heights of 20m. The NLCD data
does not contain information about individual trees or smaller shrubs, which can also
impact the visibility.
Boundary and transportation data for Jefferson County was downloaded from the New
York State GIS Clearinghouse.

37
4.3 GIS Methodology and Results

Once all of the data was downloaded and processed into a common coordinate system,
the viewshed parameters were applied and the viewshed function was run using the
terrain data only, without vegetation. This is known as a denuded or bare-earth viewshed
and predicts visibility across the study area without consideration of forest canopy. This
method tends to exaggerate the potential visibility but is useful as a reference point (see
Figure 9).
To account for the screening effects of forest canopy, NLCD forest areas were added to
the DEM elevations for two simulations, one with 13 meter forest heights and another with
20 meter forest heights. The forest areas are then classified as not being able to see any
turbines, assuming that observers in these areas would not have a view of the proposed
turbines. This could lead to areas that are classified as zero turbines visible, especially in
forested areas that directly face Gallo Island. These visibility maps are shown in Figure 7
and Figure 8.
Additional detail maps are provided at a 1:24,000 scale to show visual impact potential
for individual parcels (shown in Figures 10 through 13).
The results of the viewshed analysis were then added to the tax parcel polygons using the
Zonal Statistics function.
This provided a value that indicates the average number of
turbines visible to a given parcel.
This step was necessary because parcel size varies
greatly across the town and large parcels contain areas that have high and low visibility
values.
The average turbine visibility values were then used to assess the impacts on
property values in section 2 above.
4.4 Interactive Map Viewer on ArcGIS Online

The URL to access an Interactive Map Viewer of some of the GIS analyses is at:
http://arcg.is/20Y5VEc. Animations of the view shed analysis can be found at:
https://www.youtube.com/watch?v=KHNV6SwQHg0&feature=youtu.be.
Some notes regarding the Interactive Map Viewer:
1. The 3D trees are added randomly to all areas considered "Forest" by the USGS. It
would be difficult to fill the areas completely with trees because that would take
thousands of individual points and would make the product far too cumbersome. The
trees are sized to be 20 meters tall. Turbines are 175m to blade tip.
2. All layers can be turned on and off and zoomed into, however the 3D space doesn't
allow two base layers to appear neatly simultaneously. So if a user tries to see the USGS
topo together with imagery, it can create some strange effects.

38
3. The resolution of the terrain surface is such that the draped layers (everything else)
may not exactly conform to the surface. You may see a tree floating above or cutting
under the surface and roads slightly offset. This cannot be altered, as it is due to the
native resolution of the digital elevation model, which also comes from the USGS.
Interactive Map Viewer Functionality:
1. Mouse buttons control the view (left = Pan, middle=Zoom, right=Orbit)
2. Environment settings can be changed in upper right (time of year and day, shadows,
etc.)
3. Three views were added at ground level and are shown at bottom middle of screen
4. The viewer requires a WebGL enabled browser, which can be confirmed at:
https://en.wikipedia.org/wiki/WebGL.

39
4.5 List of Figures

1. Henderson Overview with Satellite Imagery - [Figure 5]
2. Henderson Overview with Topography - [Figure 6]
3. Henderson Town Visibility 20m Forested - [Figure 7]
4. Henderson Town Visibility 13m Forested - [Figure 8]
5. Henderson Town Bare Earth Visibility - [Figure 9]
6. Henderson Bay Detail - [Figure 10]
7. Henderson SE Detail - [Figure 11]
8. Henderson NE Detail - [Figure 12]
9. Henderson NW Detail - [Figure 13]

40
Figure 5. Henderson Overview with Satellite Imagery

41
Figure 6. Henderson Overview with Topography

42
Figure 7. Henderson Town Visibility 20m Forested

43
Figure 8. Henderson Town Visibility 13m Forested

44
Figure 9. Henderson Town Bare Earth Visibility

45
Figure 10. Henderson Bay Detail

46
Figure 11. Henderson SE Detail

47
Figure 12. Henderson NE Detail

48
Figure 13. Henderson NW Detail

49
5.0 Research Note

The Nanos Clarkson Research Collaboration team has made every attempt to ensure the
accuracy and reliability of the information provided in this report. However, the
information is provided "as is" without warranty of any kind. The Nanos Clarkson Research
Collaboration does not accept any legal responsibility or liability for the public use or
interpretation of the data. The data prepared is based on the latest available information
in the public domain without prejudice.
The Nanos Clarkson Research Collaboration, as well as individual team members, shall not
be liable for any loss or damage of whatever nature (direct, indirect, consequential, or
other) whether arising in contract, tort or otherwise, which may arise as a result of your
use of (or inability to use) this report, or from your use of (or failure to use) the
information on this report.
6.0 Report Summary & Conclusions

The Nanos Clarkson Research Collaboration team has undertaken the preceding analyses
based on the best available public data.
While the analytical methodologies (and
qualifiers) for the various analyses have been highlighted within the preceding report, the
overall general findings can be summarized as follows in terms of the anticipated impacts:
likely negative land valuations for the Town of Henderson;
likely positive economic effects to the region, but not commensurate to the Town
of Henderson;
likely minor positive effects on jobs and economic impacts to the Town of
Henderson; and,
likely minor negative effect on tourism.
Finally, the reports series of view-shed analyses for the Town of Henderson in relation to
the proposed Galloo Island development are intended as a tool or aid in the planning and
decision-making process. Some of the items can be found online in the Interactive Map
Viewer identified in the preceding analysis.

50
7.0 References
7.1
Property Value Analysis
Bolin, K., Bluhm, G., Eriksson, G., & Nilsson, M. E. (2011). Infrasound and low frequency
noise from wind turbines: exposure and health effects. Environmental Research
Letters, 6(3), 035103. http://doi.org/10.1088/1748-9326/6/3/035103

Council of Canadian Academies, Expert Panel on Wind Turbine Noise and Human Health.
(2015). Understanding the evidence: wind turbine noise. Retrieved from
http://www.deslibris.ca/ID/246309
Cropper, M. L., Deck, L. B., & McConnell, K. E. (1988). On the Choice of Funtional Form for
Hedonic Price Functions. The Review of Economics and Statistics, 70(4), 668675.
http://doi.org/10.2307/1935831
Gibbons, S. (2015). Gone with the wind: Valuing the visual impacts of wind turbines
through house prices. Journal of Environmental Economics and Management, 72,
177196. http://doi.org/10.1016/j.jeem.2015.04.006
Heintzelman, M. D., & Tuttle, C. M. (2012). Values in the Wind: A Hedonic Analysis of Wind
Power Facilities. Land Economics, 88(3), 571588.
http://doi.org/10.3368/le.88.3.571
Hoen, B., Brown, J. P., Jackson, T., Thayer, M. A., Wiser, R., & Cappers, P. (2015). Spatial
Hedonic Analysis of the Effects of US Wind Energy Facilities on Surrounding
Property Values. Journal of Real Estate Finance and Economics, 51(1), 2251.
http://doi.org/10.1007/s11146-014-9477-9
Hoen, B., Wiser, R., Cappers, P., Thayer, M., & Sethi, G. (2011). Wind Energy Facilities and
Residential Properties: The Effect of Proximity and View on Sales Prices. Journal of
Real Estate Research, 33(3), 279316.

http://doi.org/10.5555/rees.33.3.16133472w8338613
III, A. M. F., Herriges, J. A., & Kling, C. L. (2014). The Measurement of Environmental and
Resource Values: Theory and Methods. Routledge.

Jensen, C. U., Panduro, T. E., & Lundhede, T. H. (2014). The Vindication of Don Quixote:
The Impact of Noise and Visual Pollution from Wind Turbines. Land Economics,
90(4), 668682. http://doi.org/10.3368/le.90.4.668

51
Lang, C., Opaluch, J. J., & Sfinarolakis, G. (2014). The windy city: Property value impacts of
wind turbines in an urban setting. Energy Economics, 44, 413421.
http://doi.org/10.1016/j.eneco.2014.05.010
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure
competition. The Journal of Political Economy, 3455.
Sims, S., & Dent, P. (2007). Property stigma: Wind farms are just the latest fashion. Journal
of Property Investment and Finance, 25(6), 626651.

http://doi.org/10.1108/14635780710829315
Sims, S., Dent, P., & Oskrochi, G. R. (2008). Modelling the impact of wind farms on house
prices in the UK. International Journal of Strategic Property Management, 12(4),
251269. http://doi.org/10.3846/1648-715X.2008.12.251-269
Sunak, Y., & Madlener, R. (2012). The Impact of Wind Farms on Property Values: A
Geographically Weighted Hedonic Pricing Model (SSRN Scholarly Paper No. ID

2114216). Rochester, NY: Social Science Research Network. Retrieved from
http://papers.ssrn.com/abstract=2114216
Taylor, L. O. (2003). The Hedonic Method. In P. A. Champ, K. J. Boyle, & T. C. Brown (Eds.),
A Primer on Nonmarket Valuation (pp. 331393). Springer Netherlands. Retrieved

from http://link.springer.com/chapter/10.1007/978-94-007-0826-6_10
Vyn, R. J., & Mccullough, R. M. (2014). The Effects of wind turbines on property values in
Ontario: Does public perception match empirical evidence? Canadian Journal of
Agricultural Economics, 62(3), 365392. http://doi.org/10.1111/cjag.12030
7.2
Jobs & Tourism Analysis
Broekel, Tom, and Christoph Alfken. 2015. Gone with the Wind? The Impact of Wind
Turbines on Tourism Demand. Energy Policy 86: 50619.
Brown, Jason P., John Pender, Ryan Wiser, Eric Lantz, and Ben Hoen. 2012. Ex Post
Analysis of Economic Impacts from Wind Power Development in U.S. Counties.
Energy Economics 34 (6): 174354. doi:10.1016/j.eneco.2012.07.010.

Frantl, Bohumil, and Josef Kunc. 2011. Wind Turbines in Tourism Landscapes: Czech
Experience. Annals of Tourism Research 38 (2): 499519.
doi:10.1016/j.annals.2010.10.007.

52
Goldberg, M., K. Sinclair, and M. Milligan. 2004. Job and Economic Development Impact
(JEDI) Model: A User-Friendly Tool to Calculate Economic Impacts from Wind
Projects. NREL/CP-500-35953. National Renewable Energy Laboratory.
http://www.researchgate.net/profile/M_Milligan/publication/228428843_Job_and
_Economic_Development_Impact_(JEDI)_Model_A_UserFriendly_Tool_to_Calculate_Economic_Impacts_from_Wind_Projects/links/54403e8
60cf2be1758cffea4.pdf.
Haughton, Jonathan Henry, Douglas Giuffre, and John Barrett. 2003. Blowing in the Wind:
Offshore Wind and the Cape Cod Economy. Beacon Hill Institute at Suffolk
University. http://bhiweb.beaconhill.org/BHIStudies/BHIWindFarmStudy102803a.pdf.
Henderson Comprehensive Land Use Plan Committee. 2004. Comprehensive Land Use
Plan for the Town of Henderson. Henderson NY.
http://townofhendersonny.org/content/EconDev/Home/:field=documents;/conten
t/Documents/File/1011.pdf.
Henderson, Fishers Landing among Poorest Communities in State. 2015. Watertown
Daily Times. Accessed December 18.

http://www.watertowndailytimes.com/article/20151211/NEWS03/151208022.
Hudson North Country Wind 1, LLC. 2015. Public Involvement Program Plan (Final): Galloo
Island Wind Energy Facility. Hounsfield, NY.
http://documents.dps.ny.gov/public/MatterManagement/CaseMaster.aspx?Matter
Seq=48345&MNO=15-F-0327.
Ladenburg, Jacob, and Alex Dubgaard. 2007. Willingness to Pay for Reduced Visual
Disamenities from Offshore Wind Farms in Denmark. Energy Policy 35 (8): 4059
71. doi:10.1016/j.enpol.2007.01.023.
Ladenburg, Jacob, and Sanja Lutzeyer. 2012. The Economics of Visual Disamenity
Reductions of Offshore Wind farmsReview and Suggestions from an Emerging
Field. Renewable and Sustainable Energy Reviews 16 (9): 67936802.
doi:10.1016/j.rser.2012.08.017.
Landry, Craig E., Tom Allen, Todd Cherry, and John C. Whitehead. 2012. Wind Turbines
and Coastal Recreation Demand. Resource and Energy Economics 34 (1): 93111.
doi:10.1016/j.reseneeco.2011.10.001.

53
Lantz, E., and S. Tegen. 2008. Variables Affecting Economic Development of Wind
Energy. NREL Report No. CP-500-43506. National Renewable Energy Laboratory.
http://www.nrel.gov/docs/fy08osti/43506.pdf.
. 2009. Economic Development Impacts of Community Wind Projects: A Review and
Empirical Evaluation. NREL/CP-500-45555. National Renewable Energy
Laboratory. http://www.nrel.gov/docs/fy09osti/45555.pdf.
Lilley, Meredith Blaydes, Jeremy Firestone, and Willett Kempton. 2010. The Effect of Wind
Power Installations on Coastal Tourism. Energies 3 (1): 122.
Loomis, Jacob, and Jason Carter. 2011. Wind Development Provides the Most Jobs of the
Various Generation Sources in Illinois. Center for Renewable Energy Working Paper
11-02. Center for Renewable Energy Illinois State University.
http://renewableenergy.illinoisstate.edu/downloads/publications/1102%20Workin
gPaperWindDevProvidesMostJobs030211.pdf.
McKeown, Charles, Adesoji Adelaja, and Benjamin Calnin. 2011. On Developing a
Prospecting Tool for Wind Industry and Policy Decision Support. Energy Policy,
Special Section on Offshore wind power planning, economics and environment, 39
(2): 90515. doi:10.1016/j.enpol.2010.11.015.
Regeneris Consulting. 2013. Study into the Potential Economic Impact of Wind Farms and
Associated Grid Infrastructure on the Welsh Tourism Sector. Government of Wales.
http://www.renewableuk-cymru.com/wpcontent/uploads/2014/04/140404economic-impacts-of-wind-farms-ontourism-en.pdf.
Riddington, Geoff, David McArthur, Tony Harrison, and Hervey Gibson. 2010. Assessing
the Economic Impact of Wind Farms on Tourism in Scotland: GIS, Surveys and
Policy Outcomes. International Journal of Tourism Research 12 (3): 23752.
doi:10.1002/jtr.750.
Slattery, Michael C., Eric Lantz, and Becky L. Johnson. 2011. State and Local Economic
Impacts from Wind Energy Projects: Texas Case Study. Energy Policy, Clean
Cooking Fuels and Technologies in Developing Economies, 39 (12): 793040.
doi:10.1016/j.enpol.2011.09.047.
Snyder, Brian, and Mark J. Kaiser. 2009. Ecological and Economic Cost-Benefit Analysis of
Offshore Wind Energy. Renewable Energy 34 (6): 156778.
doi:10.1016/j.renene.2008.11.015.

54
Stiftung Offshore-Windenergie (German Offshore Wind Energy Foundation). 2013. The

Impact of Offshore Wind Energy on Tourism: Good Practices and Perspectives for
the South Baltic Region. http://www.offshorestiftung.com/60005/Uploaded/Offshore_Stiftung%7C2013_04SBO_SOW_tourism_s
tudy_final_web.pdf.
Westerberg, Vanja, Jette Bredahl Jacobsen, and Robert Lifran. 2013. The Case for
Offshore Wind Farms, Artificial Reefs and Sustainable Tourism in the French
Mediterranean. Tourism Management 34 (February): 17283.
doi:10.1016/j.tourman.2012.04.008.
. 2015. Offshore Wind Farms in Southern Europe Determining Tourist Preference
and Social Acceptance. Energy Research & Social Science 10 (November): 16579.
doi:10.1016/j.erss.2015.07.005.
Wolsink, Maarten. 2013. Wind Power Wind Power: Basic Challenge Concerning Social
Acceptance Wind Power Social Acceptance. In Renewable Energy Systems, 1785
1821. Springer. http://link.springer.com/10.1007/978-1-4614-5820-3_88.

55
Appendix A
JEDI Wind Model Methodology

56
Appendix A. JEDI Wind Model Methodology

The following appendix is a verbatim quote taken from Goldberg, Sinclair, and Milligan
(2004) paper on the JEDI Wind Model.
Methodology
Basic information about a wind project (at minimum the projects state, county, or region;
the year of construction; and the size of the facility), the model calculates the project cost
(i.e., specific expenditures) as well as the number of jobs, income (i.e., wages and salary),
and economic activity that will accrue to the state, county, or region being analyzed.
Although JEDI was originally designed to provide state level analysis, the model also
includes a User Add-in Location feature. This feature allows users to import specific
county or region level multipliers and personal expenditure patterns to localize the
analysis to a location other than the state level.
To evaluate these impacts, input-output or multiplier analysis is used. Input-output
models were originally developed to trace supply linkages in the economy. For example,
they show how purchases of wind turbines not only benefit turbine manufacturers, but
also the fabricated metal industries and others businesses supplying inputs to those
manufacturers. The benefits that are ultimately generated by expenditures for wind plants
depend on the extent to which those expenditures are spent locally and the structure of
the local economy. Consistent with the spending pattern and location-specific economic
structure (state, county, or region), different expenditures support a different level of
employment, income, and output. Input-output analysis is a method of evaluating and
summing the impacts of a series of effects generated by an expenditure (i.e., input). To
determine the total effect of developing a wind power plant, three impacts are examined
for each expenditure. These include direct effect, indirect effect, and induced effect.
Direct effect: Direct effects are the on-site or immediate effects created by an
expenditure. In constructing a wind plant, it refers to the on-site jobs of the contractors
and crews hired to construct the plant. It also includes the jobs at the turbine
manufacturing plants and the jobs at the tower and blade factories.
Indirect effect: Indirect effects refer to the increase in economic activity that occurs when
a contractor, vendor or manufacturer receives payment for goods or services and in turn
is able to pay others who support their business. For instance, this impact includes the
banker who finances the contractor; the accountant who keeps the contractors books;

57
and the steel mills and electrical manufacturers and other suppliers that provide the
necessary materials.
Induced effect: Induced effects refer to the change in wealth and income that is induced
by the spending of those persons directly and indirectly employed by the project. This
would include spending on food, clothing, or day care by those directly or indirectly
employed by the project, retail services, public transit, utilities, cars, oil, property &
income taxes, medical services, and insurance, for example.
The sum of these three effects yields a total effect that results from a single expenditure.
To accomplish this analysis at the state level, state-specific multipliers and personal
expenditure patterns are used to derive the results. These state-by-state multipliers for
employment, wage and salary income and output (economic activity), and personal
expenditure patterns were adapted from the IMPLAN Professional model using year 2000
data, the most current data available last year.
The changes in expenditures from investments in developing wind power plants are
matched with their appropriate multipliers for each sector affected by the change in
expenditure. Consistent with an analysis of this type and scope, the assumptions play an
important role in influencing the results. Thus, to accommodate the greatest level of
flexibility in user skill level and availability of specific detailed project information, the
model is designed to incorporate model default values or new values entered by the user.
The default values represent a reasonable expenditure pattern for constructing and
operating a wind power plant in the United States and the share of expenditures spent
locally. The default expenditure pattern is based on a review of numerous wind resource
studies.
Currently, not every project will follow this exact default pattern for expenditures.
Project size, location, financing arrangements, and numerous site-specific factors
influence the construction and operating costs. Similarly, the availability of local resources
(including skilled labor and materials) and the availability of locally manufactured power
plant components will have a significant effect on the costs and the economic benefits
that accrue to the state or local region. To the extent the user has and can incorporate
project-specific data and the share of spending that is expected to occur locally, the more
localized the impact analysis will be.

58
Appendix B
JEDI Model Detailed Wind
Farm Project Data Costs

59
Appendix B. JEDI Model Detailed Wind Farm Project Data Costs
Detailed Wind Farm Project Data Costs
NEW YORK
Cost
Local
Share
Turbines
$79,223,991
0%
$0
Blades
$18,547,423
0%
$0
Towers
$20,534,646
0%
$0
Transportation
$14,175,530
0%
$0
Construction Costs
Equipment
Equipment Total
$132,481,590
$0
Balance of Plant
$0
Materials
$0
Construction (concrete rebar, equip, roads and site prep)
$19,143,590
90%
$17,229,231
Transformer
$2,165,537
0%
$0
Electrical (drop cable, wire, )
$2,282,622
100%
$2,282,622
HV line extension
$4,169,589
70%
$2,918,712
Materials Subtotal
$27,761,338
$0
Labor
$0
Foundation
$1,542,541
95%
$1,465,414
Erection
$1,747,146
75%
$1,310,360
Electrical
$2,546,118
70%
$1,782,283
Management/supervision
$1,321,186
0%
$0
Misc.
$6,802,950
50%
$3,401,475
Labor Subtotal
$13,959,941
$0
Development/Other Costs
$0
HV Sub/Interconnection
$0
Materials
Labor
Engineering
$1,315,666
90%
$1,184,099
$403,014
10%
$40,301
$1,790,292
Legal Services
Development/Other Subtotal
Balance of Plant Total
Sales Tax (Materials & Equipment Purchases)
Total Project Costs
$0
100%
$975,709
$0
100%
$0
$456,524
100%
$456,524
Land Easements
Site Certificate
0%
$975,709
$4,941,205
$0
$46,662,485
$0
$12,269,661
100%
$191,413,737
$12,269,661
$45,316,391
Wind Plant Annual Operating and Maintenance Costs

Cost
Local

60
Share
Labor Costs
Personnel
Field Salaries
$351,051
100%
$351,051
Administrative
$55,130
100%
$55,130
Management
$137,826
100%
$137,826
Labor/Personnel Subtotal
$544,007
$0
Materials and Services
$0
Vehicles
$42,908
100%
$42,908
Misc. Services
$16,734
80%
$13,387
$8,367
100%
$8,367
$33,468
100%
$33,468
$321,810
0%
$16,734
100%
$16,734
Tools and Misc. Supplies
$108,772
100%
$108,772
Spare Parts Inventory
$953,200
2%
$19,064
Fees, Permits, Licenses

Misc. Materials
Insurance
Fuel (motor vehicle gasoline)
Materials and Services Subtotal
$1,501,993
Sales Tax (Materials & Equipment Purchases)

Other Taxes/Payments
Total (with Sales Tax and Other Taxes/Payments)
Debt Payment (average annual)
$92,810
100%
$92,810
$0
100%
$0
$21,102,314
$0
Equity Payment - Corporate
$0
$2,138,810
Equity Payment - Individuals
$0
$6,775,440
$0
0%
$0
100%
$0
0%
$0
Property Taxes
$774,923
100%
$774,923
Land Lease
$306,900
100%
$306,900
Total Annual Operating and Maintenance Costs
$31,098,387
$1,961,340

61
Appendix C
State Level Impacts JEDI
Analysis

62
Appendix C. State Level Impacts JEDI Analysis
11
Jobs
Earnings
Output
Value Added
During construction period

Total Impacts
393
$29.9
$65.0
$40.3
During operating years (annual)

Total Impacts
17
$1.4
$3.6
$2.8
Output
Value Added
Jobs
Earnings
During construction period

Project Development and Onsite Labor Impacts
Construction and Interconnection Labor
Construction Related Services
Total
Turbine and Supply Chain Impacts
Induced Impacts
Total Impacts
61
5
66
213
115
393
$4.7
$0.6
$5.3
$16.2
$8.4
$29.9
$5.7
$39.4
$19.9
$65.0
$5.4
$21.1
$13.7
$40.3
During operating years (annual)

Onsite Labor Impacts
Local Revenue and Supply Chain Impacts
Induced Impacts
Total Impacts
6
6
5
17
$0.5
$0.5
$0.4
$1.4
$0.5
$2.1
$1.0
$3.6
$0.5
$1.6
$0.7
$2.8
11
Primary expected impacts would likely accrue primarily to Jefferson, Oswego, and Onondaga Counties,
though many benefits would extend far afield. For instance to the rest of New York State, out of state, and
for some benefits, out of country.
63

Clarkson Nanos Galloo Island Wind Farm Study

Uploaded by

Document Information

Copyright

Available Formats

Share this document

Share or Embed Document

Sharing Options

Did you find this document useful?

Is this content inappropriate?

Copyright:

Available Formats

Clarkson Nanos Galloo Island Wind Farm Study

Uploaded by

Copyright:

Available Formats

Baseline Research

Exploring the impact of the proposed

conducted for the Town of Henderson

Town of Henderson January 2016

Executive Summary ...............................................................................................4

Key Findings ...................................................................................................................................................................... 5

2.2 Existing Literature on Property Value Impacts ....................................................................................................... 7

3.2 Background ............................................................................................................................................................... 20

4.0 Viewshed Analysis & Methodology ..........................................................................36

Town of Henderson January 2016

Figure 11. Henderson SE Detail .................................................................................................................................. 47

5.0 Research Note ...........................................................................................................50

Property Value Analysis ........................................................................................................................................ 51

Jobs & Tourism Analysis ....................................................................................................................................... 52

Town of Henderson January 2016

January 20, 2016

Attention: John Culkin, Town Supervisor

a property value analysis;

a jobs and tourism analysis; and,

Town of Henderson January 2016

1.0 Executive Summary

While methodologies (and qualifiers) for the

likely negative land valuations for the Town of Henderson;

likely minor negative effect on tourism

2.0 Town of Henderson Property Value Analysis in

Building on that analysis, the team projected impacts on residential

properties in the Town of Henderson.

Wind turbines have

Flicker is very unlikely to be an impact in Henderson,

These include the benefits from payments-in-lieu-of-taxes

Town of Henderson January 2016

Existing Literature on Property Value Impacts

They also find no significant impact on property values, but an

Both studies are carefully done and have reasonable

Town of Henderson January 2016

Town of Henderson January 2016

Figure 1: Wolfe Island Study

Wolfe Island is a Canadian island in the St. Lawrence

Second, in both cases the turbines are on

Town of Henderson January 2016

It uses regression analysis to

Another, related, problem is

Fixed effects analysis helps to curb the impacts of both of these

This reduces the number of omitted

This can be controlled by allowing for this correlation and calculating

standard errors appropriately, which we do by allowing error terms to be clustered within

Town of Henderson January 2016

2.2.3 Wolfe Island Results

We see that parcels with a view of the

Importantly, there is no general post-construction effect

Town of Henderson January 2016

Table 1: Summary Statistics for Wolfe Island Sample

Parcel w/ View of Turbines

Parcel Sold between Approval and Construction

Parcel Sold After Construction

Parcel w/ View of Turbines AND Sold between Approval and

Parcel w/ View of Turbines AND Sold After Construction

Distance to University (Miles)

Distance to School (Miles)

Living Area (sq.ft.)

Town of Henderson January 2016

Table 2: Wolfe Island Results

Parcel w/ View of Turbines

Parcel Sold between Approval and Construction