A Triple Bottom Line Assessment of 

Traditional and Green Infrastructure 

Options for Controlling CSO Events 

in Philadelphia's Watersheds 

Final Report 

Prepared for: 

Howard M. Neukrug, Director, Office of Watersheds, 

City of Philadelphia Water Department 

under contract to Camp Dresser and McKee 
A Triple Bottom Line Assessment of
 

Traditional and Green Infrastructure
 

Options for Controlling CSO Events
 

in Philadelphia's Watersheds
 

Final Report
 

Prepared for: 

Howard M. Neukrug, Director, Office of Watersheds,
 

City of Philadelphia Water Department
 

under contract to Camp Dresser and McKee
 

Prepared by: 

Stratus Consulting Inc.
 

P.O. Box 4059
 

Boulder, CO 80306-4059
 

303-381-8000
 

Contact: 

Robert S. Raucher 

August 24, 2009 

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Contents 

List of Figures............................................................................................................................ vii
 

List of Tables ...............................................................................................................................ix
 

Executive Summary ................................................................................................................. S-1
 

Chapter 1 Introduction...................................................................................................... 1-1
 

1.1 Background ........................................................................................................ 1-1
 

1.2 Objectives .......................................................................................................... 1-1
 

1.3 Report Organization........................................................................................... 1-2
 

Chapter 2 Relevant Watersheds and CSO Control Options.......................................... 2-1
 

2.1 Philadelphia’s CSO Watersheds ........................................................................ 2-1
 

2.2 CSO Control Options......................................................................................... 2-3
 

Chapter 3 General Methodology and Data...................................................................... 3-1
 

3.1 Overview of the TBL Approach ........................................................................ 3-1
 

3.2 Key Inputs to the TBL Analysis......................................................................... 3-1
 

3.3 General Overview of Methods and Key Assumptions....................................... 3-2
 

Chapter 4 The Benefits and External Costs of PWD’s CSO Control Options............. 4-1
 

4.1 Recreational Use and Values (creekside and non-creekside)............................. 4-1
 

4.2 Enhanced Aesthetics (reflected in residential property values) ......................... 4-2
 

4.3 Heat Stress-Related Premature Fatalities Avoided ............................................ 4-2
 

4.4 Water Quality and Aquatic Habitat Enhancements and Values......................... 4-3
 

4.5 Wetland Enhancement and Creation.................................................................. 4-4
 

4.6 Poverty Reduction Benefits of Local Green Infrastructure Jobs........................ 4-4
 

4.7 Energy Use and Related Changes in Carbon and Other Emissions ................... 4-5
 

4.8 Air Quality Pollutant Removal from Added Vegetation.................................... 4-6
 

4.9 Construction- and Maintenance-Related Disruption Impacts............................ 4-7
 

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Stratus Consulting Contents (Final, 8/24/2009)
 

Chapter 5 Summary of Results......................................................................................... 5-1
 

5.1 Benefits of LID CSO Control Options............................................................... 5-1
 

5.2 Benefits and External Costs of Example CSO Options..................................... 5-3
 

5.3 Detailed Results by Watershed .......................................................................... 5-4
 

Chapter 6 Key Uncertainties and Sensitivity Analyses .................................................. 6-1
 

Appendices 

A Recreational Use and Values
 

B Property Values, as Enhanced by the LID Options
 

C Heat Stress and Related Premature Fatalities Avoided
 

D Water Quality and Aquatic Habitat Enhancements and Values
 

E Wetland Enhancement and Creation
 

F Poverty Reduction Benefits of Local Green Infrastructure Jobs
 

G Energy Usage and Related Changes in Carbon and Other Emissions
 

H Air Quality Pollutant Removal from Added Vegetation
 

I Construction- and Maintenance-Related Disruption Impacts
 

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Figures 

5.1	 City-wide net benefits for LID options by watershed .................................................... 5-2
 

5.2	 Shares of City-wide present value benefits of key CSO options:
 

Cumulative through 2049 .............................................................................................. 5-2
 

5.3	 City-wide present value benefits/external costs of the LID and tunneling
 

CSO control options, over 40-year project period (2009 USD)..................................... 5-3
 

5.4	 Benefits less external costs for key CSO options in the Tacony-Frankford
 

Creek Watershed.......................................................................................................... 5-10
 

5.5	 Benefits less external costs for key CSO options in the Cobbs Creek
 

Watershed .................................................................................................................... 5-10
 

5.6	 Benefits less external costs for key CSO options in the Schuylkill
 

River Watershed........................................................................................................... 5-11
 

5.7	 Benefits less external costs for key CSO options in the Delaware
 

River Watershed........................................................................................................... 5-11
 

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Tables
 

5.1	 City-wide present value benefits of key CSO options: Cumulative
 

through 2049 (2009 million USD)................................................................................. 5-5
 

5.2	 City-wide natural unit benefits of key CSO options: Cumulative through 2049........... 5-5
 

5.3	 Present value benefits of key CSO options in Tacony-Frankford Watershed:
 

Cumulative through 2049 (2009 million USD) ............................................................. 5-6
 

5.4	 Present value benefits of key CSO options in Cobbs Creek Watershed:
 

Cumulative through 2049 (2009 million USD) ............................................................. 5-6
 

5.5	 Present value benefits of key CSO options in Schuylkill River Watershed:
 

Cumulative through 2049 (2009 million USD) ............................................................. 5-7
 

5.6	 Present value benefits of key CSO options in Delaware River Watershed:
 

Cumulative through 2049 (2009 million USD) ............................................................. 5-7
 

5.7	 Natural unit benefits of key CSO options in Tacony-Frankford Watershed:
 

Cumulative through 2049 .............................................................................................. 5-8
 

5.8	 Natural unit benefits of key CSO options in Cobbs Creek Watershed:
 

Cumulative through 2049 .............................................................................................. 5-8
 

5.9	 Natural unit benefits of key CSO options in Schuylkill River Watershed:
 

Cumulative through 2049 .............................................................................................. 5-9
 

5.10	 Natural unit benefits of key CSO options in Delaware River Watershed:
 

Cumulative through 2049 .............................................................................................. 5-9
 

6.1	 Sensitivity analysis: Discount rates................................................................................ 6-2
 

6.2	 Sensitivity analysis: Social cost of carbon..................................................................... 6-3
 

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Executive Summary 

Objectives 

The City of Philadelphia Water Department (PWD) is considering a wide array of options for
 

controlling Combined Sewer Overflow (CSO) events in its four relevant watershed areas. The
 

options range from traditional infrastructure-based approaches (e.g., storage tunnels) to more
 

innovative “green infrastructure” approaches based largely on Low Impact Development (LID)
 

elements (e.g., tree planting, permeable pavement, green roofs).
 

PWD is especially interested in gaining a more complete understanding of the Triple Bottom
 

Line (TBL) implications of the green and traditional infrastructure approaches in terms of their
 

respective ability to provide environmental, social, public health, and other values. Accordingly,
 

this report provides a TBL-oriented benefit-cost assessment of the CSO control alternatives
 

under consideration by PWD. The focus here is on the benefits and external costs of the
 

alternatives. Ultimately, the TBL benefit results from this report, and the engineering cost
 

information from Camp, Dresser and McKee (CDM), will be combined to provide insights as to
 

the estimated net benefits of the alternatives.
 

Key Findings 

The key finding of this TBL assessment is that the LID-based green infrastructure approaches
 

provide a wide array of important environmental and social benefits to the community, and that
 

these benefits are not generally provided by the more traditional alternatives. Tables S.1 and S.2
 

provide a summary of the numeric findings for two of the CSO control options under
 

consideration: the 50% LID, or green infrastructure option [meaning runoff from 50% of
 

impervious surface in the City of Philadelphia (the City) is managed through green
 

infrastructure], and the 30’ Tunnel option (a system of storage tunnels with an effective diameter
 

of 30 ft, serving all watersheds). These options were chosen to demonstrate the difference in net
 

benefits between green and traditional infrastructure. The reporting of these results is not
 

intended to indicate that a final PWD decision will be based on these two alternatives.
 

The results shown below reflect benefits (and external costs) accrued over the 40-year study
 

period (from 2010 to 2049). Table S.1 describes the outcomes in terms of the physical outcomes
 

obtained, and the second table provides the estimated monetary value for these outcomes, in
 

present value terms.
 

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Stratus Consulting Executive Summary (Final, 8/24/2009)
 

Table S.1. City-wide natural unit benefits of key CSO options: Cumulative through 2049a
 

Benefit categories 50% LID option 30’ Tunnel optionb
 

Additional creekside recreational user days 247,524,281 

Additional non-creekside recreational user days 101,738,547 

Reduction in number of heat-related fatalities 196 

Annual willingness to pay (WTP) per household for water quality 

and aquatic habitat improvementsc $9.70−$15.54 $5.63−$8.59 

Wetlands created or restored (acres) 193 

Green collar jobs (job years) 15,266 

Change in particulate matter (PM2.5) due to increased trees (μg/m3

) 0.01569 

Change in seasonal ozone due to increased trees (ppb) 0.04248 

Electricity savings due to cooling effect of trees (kWh) 369,739,725 

Natural gas savings due to cooling effect of trees (kBtu) 599,199,846 

Fuel used (vehicles for construction and operation and 

maintenance) (gallons) 493,387 1,132,409 

Sulfur dioxide (SO2) emissions (metric tons) (1,530)d 1,452 

Nitrogen oxides (NOx) emissions (metric tons) (38) 6,356,083 

Carbon dioxide (CO2) emissions (metric tons) (1,091,433) 347,970 

Vehicle delay from construction and maintenance (hours of delay) 346,883 796,597 

a. The 50% LID and 30’ Tunnel options were chosen as example alternatives to illustrate the differences
 

between green and traditional infrastructure approaches. This does not imply that a final decision has been
 

made by PWD regarding the implementation of these options.
 

b. 28’ Tunnel option in Delaware River Watershed.
 

c. WTP per household in Philadelphia, MA, including Bucks, Chester, Delaware, Montgomery, and
 

Philadelphia counties.
 

d. Parentheses indicate negative values.
 

Relevant TBL Benefit Categories 

A summary of the key benefit (and external costs) categories included in this TBL assessment is
 

provided below. Most of these benefits accrue only with the LID-oriented green infrastructure
 

options, and not under the traditional infrastructure alternatives.
 

Recreation. Under the LID-based options, streamside recreational opportunities will be 

increased as a result of stream restoration and riparian buffer improvements. Recreation will also 

improve in non-creekside parts of the City due to the general increase in vegetated and treed 

acreage in the City. These recreational benefits are not anticipated under the traditional 

infrastructure approaches. 

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Stratus Consulting Executive Summary (Final, 8/24/2009)
 

Table S.2. City-wide present value benefits of key CSO options: Cumulative through 2049 

(2009 million USD) 

Benefit categories 50% LID option 30’ Tunnel optiona 

Increased recreational opportunities $524.5 

Improved aesthetics/property value (50%) $574.7 

Reduction in heat stress mortality $1,057.6 

Water quality/aquatic habitat enhancement $336.4 $189.0 

Wetland services $1.6 

Social costs avoided by green collar jobs $124.9 

Air quality improvements from trees $131.0 

Energy savings/usage $33.7 $(2.5) 

Reduced (increased) damage from SO2 and NOx emissions $46.3 $(45.2) 

Reduced (increased) damage from CO2 emissions $21.2 $(5.9) 

Disruption costs from construction and maintenance $(5.6) $(13.4) 

Total $2,846.4 $122.0 

a. 28’ Tunnel option in Delaware River Watershed. 

Increased Community Aesthetics, Reflected in Higher Property Values. Trees and plants 

improve urban aesthetics and community livability and studies show that property values are 

higher when trees and other vegetation are present. 

Heat Stress Reduction. Green infrastructure (trees, green roofs, and bio-retention areas) creates 

shade, reduces the amount of heat absorbing materials and emits water vapor – all of which cool 

hot air. This cooling effect will be sufficient to reduce heat stress-related fatalities in the City 

during extreme heat wave events. 

Water Quality and Aquatic Ecosystem Improvements. The traditional infrastructure options 

(e.g., plant expansions, tunnels) are aimed at reducing the number of overflow episodes, but do 

little to directly improve the physical riparian area environment (i.e., riparian and aquatic 

ecosystems and habitat areas) or otherwise enhance living resources in many of the City’s 

watershed environments. In contrast, the LID options, in conjunction with the related watershed 

restoration efforts, are expected to generate important improvements to these living natural 

resources. 

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Stratus Consulting Executive Summary (Final, 8/24/2009)
 

Wetland Creation and Enhancement. The watershed restoration and related efforts, as 

associated with the LID options, are expected to create or enhance over 190 acres of wetlands in 

the relevant watersheds. These added and enhanced wetland acres will provide a range of 

services in the urban area watersheds. 

Poverty Reduction from Local Green Jobs. Specialized labor is required for construction of 

conventional stormwater management solutions (e.g., boring, tunneling). Such skilled laborers 

might typically be already employed in the construction field. Green infrastructure creates the 

opportunity to hire local unskilled – and otherwise unemployed – laborers for landscaping and 

restoration activities. Thus the benefits of providing these local green jobs include the avoided 

costs of social services that the City would otherwise provide on behalf of the same people if 

they remained unemployed. 

Energy Savings and Carbon Footprint Reduction. Green space helps lower ambient 

temperatures and, when incorporated on and around buildings, helps shade and insulate buildings 

from wide temperature swings, decreasing the energy needed for heating and cooling. In 

addition, diverting stormwater from wastewater collection, conveyance, and treatment systems 

reduces the amount of energy needed to pump and treat the water. Reduced energy demands in 

buildings, and increased carbon sequestration by added vegetation, result in a lower carbon 

footprint (reduced CO2 emissions). 

Air Quality Improvement. Trees and vegetation also improve air quality by filtering some 

airborne pollutants (e.g., particulate matter and ozone). Likewise, reduced energy consumption 

results in decreased emissions (e.g., SO2 and NOx) from power generation facilities. These air 

quality improvements can reduce the incidence and severity of respiratory illness. 

Construction- and Maintenance-Related Disruption. All of the CSO options will result in 

some level of disruption due to construction and/or program activities. Social costs of disruption 

can include traffic delays, limited access to places of business, increased noise and pollution, and 

other inconveniences. Under all of the CSO alternatives, construction activities will likely result 

in occasional delays and increased travel times for passenger and commercial vehicle travelers in 

Philadelphia; however the level of disruption will be considerably less for the LID options than 

many of the traditional infrastructure alternatives. 

Page S-4 

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1. Introduction 

1.1 Background 

There are numerous ways of managing stormwater runoff and combined sewer overflow (CSO)
 

events in urban areas. These include traditional engineering approaches that rely largely on
 

physical infrastructure such as large-scale concrete collection and storage systems
 

(e.g., excavating and building large diameter tunnels), and pumping collected stormwater to
 

wastewater treatment plants for treatment and discharge. Alternatively, there are more “natural”
 

and environmentally friendly approaches that rely more on “green infrastructure,” or Low Impact
 

Development (LID) techniques, to help divert, store, and promote infiltration of stormwaters so
 

that they help restore and enhance natural systems rather than overload traditional wastewater
 

collection and treatment facilities. There are various possible levels and combinations of the
 

traditional and green approaches that can be considered.
 

Both the traditional and green infrastructure approaches to stormwater and CSO management can
 

be very expensive to retrofit within older urban areas (e.g., costing several billion dollars for a
 

city like Philadelphia). Both approaches can also generate important environmental, social, and
 

other benefits to local watersheds and urban-area communities. However, the green
 

infrastructure, LID-oriented approaches may generate a broader and more valuable array of
 

environmental, public health, and social benefits than do traditional CSO control strategies. In
 

order to gain a clearer appreciation of which option (or mix of approaches) may be most valuable
 

to a community, it is important to assess the types and levels of benefits associated with the
 

alternative approaches. These benefits can then be compared to the costs of each option, so that
 

community leaders can discern which approach will yield the largest net benefit to the
 

community (where net benefits refer to present value benefits minus present value costs).
 

1.2 Objectives 

The Philadelphia Water Department (PWD) currently is giving serious consideration to a wide 

array of options for controlling CSO events. PWD is especially interested in gaining a more 

complete understanding of the Triple Bottom Line (TBL) implications of green infrastructure 

approaches, and of more traditional approaches, in terms of their respective environmental, 

social, and other values. PWD, in concert with its engineering support contractor − Camp, 

Dresser and McKee (CDM) − retained Stratus Consulting to evaluate the benefits and external 

costs (i.e., costs beyond engineering cost estimates for building and operating the various control 

options) associated with a number of alternative approaches for controlling CSO events in the 

City of Philadelphia (the City). 

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Stratus Consulting	 Introduction (Final, 8/24/2009)
 

Accordingly, this report provides a TBL-oriented benefit-cost assessment of the CSO control 

alternatives under consideration by PWD. The focus here is on the benefits and external costs of 

the alternatives. CDM is developing a separate report to describe the engineering design and 

performance aspects − and engineering cost estimates − for the alternatives. Ultimately, the TBL 

benefit results from this report, and the engineering cost information from CDM, will be 

combined to provide insights as to the estimated net benefits of the alternatives. 

Throughout this report, we refer to the green infrastructure CSO control options as LID-based
 

approaches. We categorize the different options based on different levels of implementation
 

(e.g., the 50% LID option would manage runoff from 50% of impervious surfaces in
 

Philadelphia through green infrastructure). Green infrastructure and LID are used
 

interchangeably throughout the following chapters and appendices.
 

We also refer to the traditional infrastructure options according to different levels of
 

implementation. For example, throughout the report we draw upon the “30’ Tunnel” option as an
 

example alternative. This option includes a system of storage tunnels serving all watersheds with
 

an effective diameter of 30 ft. Alternative tunneling options (e.g., 15’, 20’, 25’, and 35’ options),
 

are also being evaluated by PWD and the impacts of all alternatives are examined here.
 

1.3	 Report Organization 

This report is structured as follows:
 

�	 First, this main portion of the report provides a brief overview of the four PWD 

watershed areas addressed by the policy options, as well as abbreviated descriptions of 

the 16 CSO control options being considered for each area. More detailed descriptions of 

the watersheds and CSO control options are provided in the main body of the PWD Long 

Term CSO Control Plan Update (LTCPU). 

�	 Second, a general description is provided of the data and methods used to conduct our 

TBL-oriented benefit-cost assessment of the alternatives. Also provided is an overview of 

the types of benefits and external costs we address within this assessment. 

�	 Third, more detailed descriptions are offered of the estimated levels of benefits (and 

external costs) for each major benefit-cost category. An overview of the methods, data, 

and limitations associated with these estimates is also provided. (Detailed categoryspecific appendices, described below, furnish additional detail on the methods, data, 

findings, and limitations of the analysis for each type of benefit or external cost). 

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Stratus Consulting	 Introduction (Final, 8/24/2009)
 

�	 Fourth, summaries are provided of the benefit estimates for two of the prominent CSO 

control options under consideration, aggregated across the four watershed areas. These 

summaries thus provide a city-wide overview of the physical and economic magnitude of 

benefits (and external costs) for two highlighted CSO control alternatives. The two 

highlighted CSO control options are the LID-50% option (reflecting a green 

infrastructure approach), and the 30’ Tunnel option (reflecting a more traditional 

infrastructure approach). 

�	 Fifth, a suite of detailed tables are provided that indicate watershed-specific estimates for 

each benefit and external cost category, for each CSO control option evaluated. 

�	 Sixth, the key uncertainties inherent in this type of TBL-oriented benefit-cost analysis are 

discussed, and the results of several sensitivity analyses are provided to provide insights 

as to the level of stability of the estimates to alternative input values and assumptions. 

The main body of this report is then followed by a series of detailed technical appendices – one
 

for each benefit or external cost category assessed. These appendices describe the methods, data,
 

findings, and caveats relevant to each endpoint, and also contain relevant reference citations. The
 

appendices correspond to the following categories of assessed impacts:
 

�	 Appendix A: Recreational use and values (both creekside and non-creekside) 

�	 Appendix B: Property values, as enhanced by the LID options 

�	 Appendix C: Heat stress and related premature fatalities avoided 

�	 Appendix D: Water quality and aquatic habitat enhancements and values 

�	 Appendix E: Wetland enhancement and creation 

�	 Appendix F: Poverty reduction benefits of local green infrastructure jobs 

�	 Appendix G: Energy usage and related changes in carbon and other emissions 

�	 Appendix H: Air quality pollutant removal from added vegetation 

�	 Appendix I: Construction- and maintenance-related disruption impacts. 

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2. Relevant Watersheds and CSO Control Options 

PWD’s CSO program area covers about 40,500 acres (63 square miles) within the City. The
 

boundaries of the CSO area fall within the watersheds of Tacony-Frankford Creek, Cobbs Creek,
 

the Lower Schuylkill River, and the tidal portion of the Delaware River (Delaware Direct
 

Watershed). The City’s CSO program is managed on a watershed-basis and our analysis of CSO
 

control options includes the evaluation of management alternatives in each of the four CSO
 

watersheds.
 

The following sections provide a brief description of each CSO watershed and outlines the
 

different CSO control options being considered by PWD.
 

2.1 Philadelphia’s CSO Watersheds 

The Tookany/Tacony-Frankford Watershed 

The Tookany/Tacony-Frankford Watershed encompasses approximately 20,000 acres, or
 

29 square miles, within the north central portion of Philadelphia County and the southeastern
 

portion of Montgomery County. The creek is referred to as Tookany Creek until it enters
 

Philadelphia County at Cheltenham Avenue. It is then called Tacony Creek from the
 

Montgomery County border until it meets with the historical Wingohocking Creek in Juniata
 

Park. The section of stream from Juniata Park to the Delaware River is referred to as Frankford
 

Creek.
 

The hydrology of the Tacony-Frankford system is highly modified. Most of the tributary system
 

of Tacony Creek has been converted into sewers. Below what is now Juniata Park, the Tacony
 

joins with buried tributaries to form Frankford Creek. In order to deal with flooding associated
 

with large influxes of stormwater, the Frankford Creek was channelized and straightened in
 

concrete a number of years ago. The concrete channel prevents interaction between Frankford
 

Creek and the groundwater system and eliminates streambed habitat needed to support aquatic
 

life. The area surrounding Frankford Creek is highly industrialized and much of the creek is
 

inaccessible.
 

The Philadelphia County portion of the watershed accounts for about 62% (12,200 acres) of total
 

watershed land area, and PWD’s CSO program area covers almost all of this. The population
 

within this part of the watershed is approximately 285,000, which results in an average
 

population density of about 23 persons per acre. There are about 6.3 miles of stream along
 

Tacony-Frankford Creek targeted for improvements under the different CSO control options
 

(mainstem creek).
 

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Cobbs Creek Watershed 

Cobbs Creek is a subwatershed of the larger Darby-Cobbs Watershed, which encompasses
 

approximately 80 square miles of land that drain to the mouth of Darby Creek or below, to its
 

confluence with the Delaware Estuary. Cobbs Creek drains approximately 14,500 acres or 27%
 

of the total Darby-Cobbs Watershed area. The upper portions and headwaters of Cobbs Creek,
 

including East and West Branch Indian Creek, contain portions of Philadelphia, Montgomery,
 

and Delaware Counties. The lower portion of Cobbs Creek Watershed, including the lower
 

mainstem and Naylors Run, drain parts of Philadelphia and Delaware Counties. Cobbs Creek
 

discharges to Darby Creek.
 

The Philadelphia County portion of the Cobbs Creek Watershed is about 3,600 acres, and falls
 

almost entirely within PWD’s CSO program area. This area encompasses about 11.5 miles of
 

stream, including about 8.2 miles of mainstem creek and 3.3 miles of major tributaries. The
 

population of the Philadelphia County portion of the watershed is about 107,000 (U.S. Census
 

Bureau, 2000), which yields a population density of almost 30 persons per acre. Similar to the
 

Tacony-Frankford Watershed, Cobbs Creek is very urbanized and its hydrologic system has been
 

highly modified.
 

Lower Schuylkill River Watershed 

The Schuylkill River Watershed includes portions of 11 counties, and encompasses an area of
 

approximately 2,000 square miles. The river travels approximately 130 miles from its headwaters
 

at Tuscarora Springs in Schuylkill County to its mouth at the Delaware River in Philadelphia.
 

The Schuylkill River is the largest tributary to the Delaware River and is a major contributor to
 

the Delaware Estuary.
 

The Philadelphia County portion of the Schuylkill River Watershed is approximately
 

23,000 acres. About half of this area falls within PWD’s CSO area, which includes the tidal
 

portion of the Schuylkill River, or the approximately 7 miles of river upstream of the confluence
 

with the Delaware River.
 

Much of the land outside of the Schuylkill River CSO area is characterized by large open space
 

areas and recreational amenities (e.g., East and West Fairmount Parks and Boathouse row).
 

However, in the lower portion of the watershed, which coincides with the CSO boundaries, there
 

is a significant amount of industrial land uses.
 

Within the CSO area, there are numerous active and inactive rail lines directly adjacent to the
 

river, including the large and active East Side Yard for CSX Transportation Corporation
 

(CSXT). Several major road corridors also run adjacent to and through the river, including I-95,
 

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I-76 (Schuylkill Expressway), I-676, Route 291/Passyunk Avenue, Grays Ferry Avenue,
 

University Avenue, South Street, Walnut Street, Chestnut Street, and Market Street.
 

The population of the Philadelphia County portion of the Lower Schuylkill River Watershed is
 

about 353,000 (U.S. Census Bureau, 2000), which yields a population density of about 16
 

persons per acre, on average. The majority of residents (about 82%) live within the CSO area,
 

where population density is almost 30 persons per acre.
 

Lower Delaware River (Delaware Direct Watershed) 

The 300-mile long Delaware River winds its way through four states on the eastern coast of the
 

United States, encompassing 42 counties and 838 municipalities. The river serves a variety of
 

important residential, commercial, and industrial functions, including fishing, transportation,
 

power cooling, and recreational purposes. The river also serves as an important source of
 

drinking water for PWD and other utilities in the regions through which it passes.
 

The Delaware Direct Watershed encompasses the lower 20 miles of the Delaware River, before
 

it discharges to the ocean. The watershed is located entirely within the City. About 70% of total
 

land area in the watershed falls within PWD’s CSO boundaries, which includes the tidal portion
 

of the Delaware River, or about 15.6 stream miles.
 

The population of the Delaware Direct Watershed is approximately 500,000 and close to 99% of
 

residents live within the CSO area. Like all the CSO watersheds, this area is highly urbanized,
 

however, it does not support the level of industrial activity as seen within the Schuylkill River
 

CSO area. Residential and commercial uses account for about 63% of total land uses in the
 

watershed, while industrial uses account for close to 9%.
 

2.2 CSO Control Options 

For each watershed, PWD has developed a suite of CSO control options based on four primary
 

approaches, including:
 

� Low-Impact Development 

� Tunneling 

� Transmission, Plant Expansion and Treatment 

� Transmission and Satellite Treatment. 

LID (green infrastructure approaches) 

For each watershed, PWD has developed a range of LID CSO control options (e.g., 25, 50, 75,
 

and 100% of runoff from impervious surfaces managed through green infrastructure),
 

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Stratus Consulting Watersheds and CSO Control Options (Final, 8/24/2009)
 

representing different levels of implementation. The LID approach focuses on restoring a more
 

natural balance between stormwater runoff and infiltration, reducing pollutant loads, and
 

controlling runoff rates at levels that minimize stream bank erosion. A variety of controls are
 

incorporated into the different LID options, including disconnection of impervious cover,
 

bioretention, subsurface storage and infiltration, green roofs, swales, and tree canopy. Land-


based measures are a key part of this approach because they provide benefits to the community
 

beyond water quality improvement (e.g., recreational opportunities, improved aesthetics, and
 

increased home values).
 

The LID options also include a variety of water-based approaches to CSO control, including bed
 

and bank stabilization and reconstruction, aquatic habitat creation, plunge pool removal,
 

improvement of fish passage, and floodplain reconnection. The ultimate goal of this component
 

of the LID program is to restore designated uses and ultimately remove CSO streams from the
 

state’s list of impaired waters. Similar to the land-based approaches described above, stream
 

restoration will provide a number of benefits beyond water quality improvement.
 

Traditional Infrastructure-based Management Measures 

The Tunneling, Transmission, Plant Expansion and Treatment, and Transmission and Satellite
 

Treatment options for CSO control include traditional storage, conveyance, and treatment
 

measures within the collection and treatment system. For each watershed, PWD has developed a
 

number of variations based on these three infrastructure-based approaches. For example, in each
 

watershed, a range of different Tunneling options is currently being evaluated, along with a
 

range of options for both Satellite Treatment and Plant Expansion.
 

The traditional infrastructure-based measures have two main drawbacks. First, as noted above,
 

the LID-oriented measures provide several important environmental, social, and public health
 

benefits to the community beyond water quality improvement. Traditional infrastructure-based
 

measures typically do not provide these benefits.
 

Second, traditional infrastructure-based measures may not address the root causes of impairment
 

in Philadelphia’s urban streams, where the primary causes of impairment are modified flow
 

patterns and habitat degradation. Infrastructure-based measures are typically focused on
 

removing loads of specific pollutants rather than restoring natural flow conditions and habitat. As
 

such, they may assist in meeting some specific water quality parameters (e.g., reducing the
 

number of overflow events), but do not necessarily support or enhance/restore the living
 

resources (i.e., the aquatic and riparian ecosystems) of the watersheds.
 

To obtain maximum benefits and CSO control, PWD is currently considering many of the
 

traditional infrastructure options (particularly the Plant Expansion options), in combination with
 

LID measures. Traditional infrastructure options are expected to play an important role in
 

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developing cost-effective and feasible solutions. For more detailed information on the suite of
 

CSO management options currently being considered by PWD, see LTCPU.
 

References 

U.S. Census Bureau. 2000. 2000 Census of Population Social and Economic Characteristics,
 

Philadelphia, Bucks, Chester, Delaware and Montgomery Counties, Pennsylvania.
 

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3. General Methodology and Data 

3.1 Overview of the TBL Approach 

The TBL approach reflects the fact that society and its enterprises – including the institutions
 

that work specifically in the public interest (e.g., water and wastewater utilities) – typically are
 

engaged in activities intended to provide the greatest total value to the communities they serve.
 

These values extend well beyond the traditional financial bottom line that portrays only cash
 

flows (i.e., revenues and expenditures) of a standard financial analysis. PWD and similar utilities
 

that serve the public interest also need to consider their stewardship and other responsibilities,
 

and to thus account for how they may generate values that contribute towards the “social” and
 

“environmental” bottom lines. Hence, a more complete and meaningful accounting of PWD
 

activities needs to provide a TBL perspective that reflects all three bottom lines: financial, social,
 

and environmental.
 

In many ways, this TBL perspective is very similar to how an economist would define a
 

comprehensive benefit-cost analysis that attempts to account for the full range of internal and
 

external costs and benefits of an activity (project, or program), including nonmarket outcomes.
 

The TBL approach provides an organizing framework within which the broad array of benefits
 

and costs can be portrayed and communicated. This TBL approach should include both those
 

outcomes that can be quantified and reasonably well monetized in dollar terms, as well as
 

outcomes that are less amenable to reliable valuation and instead require qualitative discussion.
 

Accordingly, this TBL assessment of the benefits and external costs of the various relevant CSO
 

control options for Philadelphia relies to a large extent on the tools and methods deployed by
 

natural resource economists to estimate market and nonmarket values for a broad array of
 

relevant environmental and social impacts. The sections below, and the more technically-


oriented appendices, provide additional detail for the broad range of impacts that are assessed in
 

this TBL evaluation of the PWD’s CSO control options.
 

3.2 Key Inputs to the TBL Analysis 

As noted above, the TBL analysis evaluates CSO control options that have been defined by PWD
 

and CDM. Accordingly, most of the key physical inputs to our analyses (e.g., number and
 

general location of trees planted, the number of stream miles impacted, the types of vehicles used
 

on various construction and maintenance activities, power requirements associated with
 

construction, the timing of various project activities) were provided by CDM.
 

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Stratus Consulting General Methodology and Data (Final, 8/24/2009)
 

3.3 General Overview of Methods and Key Assumptions 

Key assumptions and basic methodological approaches used for the overall TBL analysis are
 

detailed below. Assumptions and methods associated with each specific benefit and external cost
 

category are discussed in the subsequent section.
 

External costs and benefits. As part of our analysis, we evaluate the “external” or ancillary costs 

and benefits associated with each of the CSO options (i.e., costs that are not included in 

traditional engineering estimates of the expense to build and operate facilities). External costs 

include, for example, time spent and fuel lost in construction-related traffic delays, and air 

quality impacts associated with construction and implementation activities (including the carbon 

footprint of concrete requirements under the traditional infrastructure alternatives). Under the 

LID alternatives, many of the air quality and energy impacts result in ancillary benefits in the 

form of carbon sequestration, air pollutant removal, and energy savings due to the cooling effect 

and other impacts provided by adding trees and other vegetation. 

General methods for quantifying and/or valuing outcomes. The benefit and external cost 

estimates are derived from standard approaches as developed and used by environmental impact 

and valuation professionals and organizations. Many of the key methods, models and data are 

developed and deployed routinely by the U.S. Environmental Protection Agency (EPA) and 

other relevant federal agencies. For example, the air quality impacts of added trees is based on a 

model developed and applied by the U.S. Forest Service for Philadelphia. The resulting estimates 

of projected changes in ambient air quality (i.e., ozone and particulate matter concentrations) is 

then analyzed using EPA’s Environmental Benefits Mapping and Analysis Program (BenMAP), 

which estimates reductions in health risks and associated monetary values for the given change 

in ambient air quality. Similar reliance on well established federal and other models, methods, 

and data underlie most of the key benefit estimates derived in this study. 

Time path for realizing benefits. Results presented below represent the discounted sum of 

annual values over the 40-year planning horizon (2010–2049). For each benefit and cost 

category, we applied a time path over which the different benefits and costs accrue. Our 

timelines are based on implementation, construction, and maintenance schedules provided by 

CDM, as well as on a tree growth model that applies to benefits dependent on the number of 

additional trees to be planted in the watershed. For example, the benefits associated with air 

pollutant removal from trees will not be fully realized in the first year of project implementation. 

Our analysis takes into account the percentage of trees planted each year as well as the rate at 

which the trees grow and mature (assumed here to be 20-years after they are planted). 

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Present value estimates. Our monetary results are in present value terms [2009 U.S. dollars 

(USD)] and are based on an inflation rate of 4% and a nominal discount rate of 4.875% applied 

over the 40-year planning horizon. Later in this report, we present the results of sensitivity 

analyses that were conducted to evaluate the impact of using alternative escalation and discount 

rates. 

Additivity versus double-counting. The benefits presented below are additive, meaning they can 

be added together to generate a total value. However, the results of the property value analysis 

are likely to include some overlap and double-counting of benefits measured under several of the 

other benefit categories. For example, the anticipated energy savings enjoyed at tree-shaded 

properties are likely to be capitalized into the property values for those residences (depending on 

the extent to which current and prospective owners take anticipated energy costs into account 

when valuing properties). Likewise, enhanced greenspace-related recreational opportunities in 

the neighborhood are also likely to be capitalized (at least in part) in property values. At the same 

time, the property value analysis does reflect some unique values that are not embodied in the 

other estimated categories (e.g., aesthetics). Thus, the interpretation of the property value 

estimates needs to be carefully considered. For the purposes of this analysis, we include 50% of 

the estimated property value benefits to avoid this potential double-counting. 

Omissions, biases, and uncertainties. Analyses of social and environmental benefits invariably 

require the use of assumptions and approaches (e.g., benefits transfer) that interject uncertainty 

about the accuracy or comprehensiveness of the empirical results. Throughout our analysis, and 

as detailed in the appendices, we have attempted to be explicit and reasonable about what 

assumptions and approaches we are adopting. We also provide summaries in each appendix of 

the key omissions, biases, and uncertainties (OBUs) that we believe are embedded in our work, 

and describe how the results of the analysis would likely have been impacted (e.g., whether 

benefits would have increased, decreased, or changed in an uncertain direction) if the omission 

or data limitation had been avoidable. 

Sensitivity analyses. In conjunction with the OBU issues, we conducted several sensitivity 

analyses to explore how changing some of the key assumptions would impact our findings. The 

results of these sensitivity analyses are summarized in Chapter 6 (and are also described in 

relevant appendices). 

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4.	 The Benefits and External Costs of PWD’s 

CSO Control Options 

The TBL analysis of benefits and external costs is organized according to a series of benefit
 

categories. The general approach and results for each category are described below. Considerable
 

additional detail can be found in the associated appendices. It is important to note that not all
 

options generate every type of benefit described below. Likewise, some options create external
 

costs (negative benefits, such as added energy consumption and carbon emissions) within some
 

of the categories.
 

4.1	 Recreational Use and Values (creekside and non-creekside) 

The green infrastructure, or LID-based, options include stream restoration and riparian buffer
 

improvements, which will result in an anticipated increase in creekside (i.e., near stream)
 

recreational opportunities in green areas along and adjacent to the impacted waters. Most of this
 

added activity is anticipated for land-based, near water activities such as jogging, biking,
 

walking, picnicking, and so forth. Little or no increases are expected in in-stream recreation
 

(direct water contact or angling is not anticipated or encouraged in some relevant watershed
 

areas).
 

Under the LID options, recreational opportunities will also improve in non-creekside areas, due
 

to the general increase in vegetated and treed acreage in the relevant portions of the City. These
 

non-creekside recreational benefits also are included in the analysis.
 

The more traditional infrastructure approaches (e.g., tunnels) are not expected to generate any
 

appreciable changes in these types of recreational levels or values. While these approaches are
 

aimed at reducing CSO overflow events – which will yield some water quality improvement –
 

these options do not result in improved streamside or urban landscape conditions. Thus, there are
 

no projected recreational benefits estimated for these options.
 

Total recreational benefits associated with improvements made under the LID options are a
 

function of the additional recreational trips (“user days”) taken as a result of these improvements,
 

and the benefit (or direct use value) derived from each trip. To estimate additional recreational
 

use and associated direct use benefits, we relied on a recent report prepared for the Philadelphia
 

Parks Alliance by the Trust for Public Lands. The 2008 report, How Much Value Does the City
 

of Philadelphia Receive from its Park and Recreation System? (Parks Report), provides
 

visitation data and direct use values for a variety of recreational uses and activities at
 

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Philadelphia’s parks. We tailored these data to individual watersheds based on conversations
 

with park staff, detailed watershed and park management plans, and on-site visits
 

Based on these methods and data, we estimate an increase of nearly 350 million outings over the
 

40-year period (i.e., 2010–2049) for the 50% LID option. Over 70% of these outings are for
 

near-stream activities, and the balance are non-creekside. The monetized present value of these
 

added activities over the 40-year period amounts to over $520 million (these and all other dollar
 

values described in this report are in 2009 USD, unless otherwise noted). Additional detail is
 

provided in Appendix A.
 

4.2 Enhanced Aesthetics (reflected in residential property values) 

Trees and plants improve urban aesthetics and community livability, and several empirical
 

studies show that property values are higher when trees and other vegetation are present in urban
 

neighborhoods. Applying a benefits transfer approach to interpret the relevant body of LID-


related published hedonic valuation literature, coupled with neighborhood-specific baseline
 

property values, we derive an estimated aggregate increase in property values for each LID
 

option and impacted city area. The literature used includes a Philadelphia-specific study
 

published by Wachter and Wong (2006).
 

For the 50% LID option applied city-wide to all four watershed areas, the estimated value of
 

enhanced residential property values amounts to over $1.1 billion. We reduce this by 50% to
 

avoid potential double-counting with several of the other benefit categories, since our objective
 

here is to capture aesthetics-related benefits only. The resulting $575 million in present value
 

benefits only accounts for residential properties; enhanced values for nonresidential properties
 

are not included in this analysis. Additional detail is provided in Appendix B.
 

4.3 Heat Stress-Related Premature Fatalities Avoided 

The City has endured several excessive heat events (EHEs), with numerous documented cases of
 

premature fatality attributed to heat stress in some summer periods (e.g., over 100 premature
 

fatalities attributed to heat stress in the EHEs of 1993). The episodes have been studied
 

extensively by the City, the federal Centers for Disease Control (CDC), and EPA.
 

Green infrastructure (trees, green roofs, and bio-retention areas) – such as would be implemented 

under the LID-oriented options − creates shade, reduces the amount of heat absorbing materials 

and emits water vapor – all of which cool hot air and reduce the urban heat island (UHI) effect. 

This cooling effect will be sufficient to actually reduce heat stress-related fatalities in the City 

during extreme heat wave events. 

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Applying the standard methods developed and applied for relevant federal agencies, our analysis
 

(supported by Dr. Larry Kalkstein and his associates) links increases in vegetated areas to
 

potential reductions in summer temperatures and, ultimately, to projected cases of heat stress
 

fatalities avoided. City-wide, we estimate 196 premature fatalities avoided over the 40-year
 

project planning horizon, for the 50% LID option.
 

Standard EPA methods and values (i.e., value of statistical life, VSL, estimates) were then used
 

to monetize these reductions in premature fatalities. For the 50% LID option, the present value of
 

the reduced risk of premature fatality from heat stress amounts to nearly $1.1 billion. This
 

estimate does not include the avoided medical costs and reduced suffering of morbidity impacts
 

(i.e., the costs associated with those individuals who would otherwise suffer adversity from heat
 

stress, but would not be projected to die from the impact). As such, the omission of morbidity
 

events means that our premature mortality-oriented estimates are probably a lower-bound of the
 

total public health benefit attributable to the LID options. Additional detail is provided in
 

Appendix C.
 

4.4 Water Quality and Aquatic Habitat Enhancements and Values 

A core objective of any CSO control option is to improve water quality and aquatic ecosystems
 

in the impacted watersheds. The traditional infrastructure options (e.g., plant expansions,
 

tunnels) are aimed at reducing the number of overflow episodes, but do little to directly improve
 

the physical riparian area environment (i.e., riparian and aquatic ecosystems and habitat areas) or
 

otherwise enhance living resources in many of the City’s watershed environments. In contrast,
 

the LID options, in conjunction with the related watershed restoration efforts, are expected to
 

generate improvements to these natural resources.
 

To estimate the value of these improvements, a benefits transfer approach was applied, drawing
 

on a meta analysis of nonuse value estimates associated with different potential baseline levels
 

and improvements in water quality. A primary objective of this meta-analysis was to develop a
 

tool (regression model), based on existing (primary) studies, that could be used to predict what
 

individual households would be willing to pay for improvements in water quality to a specified
 

level. Using the regression tool, we were able to apply information related to the Philadelphia
 

CSO control options (e.g., demographic data and expected water quality/habitat improvements
 

under each option) to estimate total willingness to pay (WTP) for water quality improvements.
 

Due to differences in demographics and location (distance from the resource), we separately
 

evaluated WTP for households within Philadelphia and nearby households outside of the City.
 

The households outside of Philadelphia included in this analysis fall within the greater
 

Philadelphia Metropolitan Area (MA; including Bucks, Chester, Delaware and Montgomery
 

counties).
 

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The results for the 50% LID option indicate an estimated annual WTP of approximately $10 to
 

$15 per household per year, when the water quality and related habitat enhancements are fully
 

realized. Over the 40-year analysis period, this amounts to an estimated city-wide value of over
 

$330 million. Additional detail is provided in Appendix D.
 

4.5 Wetland Enhancement and Creation 

Under the LID options, watershed restoration and related efforts are expected to create or
 

enhance over 190 acres of wetlands in the relevant watersheds. We monetized these added and
 

enhanced wetland acres according to the range of services they are expected to provide in the
 

urban area watersheds, using a benefits transfer approach based on the relevant published
 

literature of wetland values.
 

For the 50% LID option, these added wetland acres and related services are estimated to provide
 

over $1.6 million in added value city-wide, in present value terms, over the 40-year project
 

planning period. Additional detail is provided in Appendix E.
 

4.6 Poverty Reduction Benefits of Local Green Infrastructure Jobs 

Jobs associated with large civil works projects, such as CSO control options, are not typically
 

counted within an economically sound benefit-cost analysis. This is because the labor retained in
 

such projects typically would be gainfully employed in other ventures (private or public
 

investments), meaning that there typically is a transfer of employment across potential activities
 

rather than a real net gain in jobs. Therefore, in this analysis of PWD’s CSO control options, we
 

are not counting jobs under any of the options as new employment creation benefits.
 

However, there are some relevant considerations to be taken into account for some of the CSO
 

control options. Specifically, there are likely to be social benefits (e.g., avoided social costs)
 

when jobs can be steered to local citizens who are typically unemployed (or under-employed)
 

due to a lack of education and training and other social circumstances.
 

Specialized labor is required for construction of conventional stormwater management solutions
 

(e.g., boring, tunneling). Such skilled laborers might typically be already employed in the
 

construction field. In contrast, green infrastructure projects, as embodied in the LID options,
 

creates the opportunity to hire unskilled – and otherwise unemployed – laborers for landscaping
 

and restoration activities. Thus the benefits of providing these green jobs include the avoided
 

costs of social services that the City would provide on behalf of the same people if they remained
 

unemployed. These “green infrastructure jobs” therefore have the unique capability to provide
 

not just employment, but a crucial stepping stone to help people escape from poverty. The
 

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benefits of providing “green infrastructure jobs” include the avoided costs of social services that
 

the City would provide on behalf of the same unskilled people if they remained unemployed,
 

outside the workforce, and trapped in poverty.
 

For the 50% LID option, we project over 15,000 job years will be created for low-skilled local
 

workers, over the 40-year period, across the four watershed areas. Based on the avoided costs of
 

social services linked to these added job years, we estimate a present value benefit of nearly
 

$125 million. For addition detail, see Appendix F.
 

4.7	 Energy Use and Related Changes in Carbon and 

Other Emissions 

Green space helps lower ambient temperatures and, when incorporated on and around buildings,
 

helps shade and insulate buildings from wide temperature swings, decreasing the energy needed
 

for heating and cooling. In addition, diverting stormwater from wastewater collection,
 

conveyance and treatment systems reduces the amount of energy needed to pump and treat the
 

water, which in turn reduces emissions of greenhouse gases (GHG, including carbon dioxide,
 

CO2) and other air pollutants (e.g., sulfur dioxide, SO2, and nitrogen oxides, NOx) from power
 

plants. Reduced energy demands in buildings, and increased carbon sequestration by added
 

vegetation, also result in a lower carbon footprint (reduced CO2 emissions).
 

Our analysis calculates the amount of energy consumption added (or reduced) by the various
 

CSO control options, and calculates the value of the added energy costs (or the energy cost
 

savings), at current energy prices. The energy use levels include, for example, the home energy
 

cost savings provided by the shading offered by trees added under the LID options. Also
 

included is the increased consumption of motor fuel associated with construction-related vehicle
 

traffic delays imposed by any of the options. Some CSO control options generate net energy
 

savings (i.e., the LID options), and others result in a net increase in energy use and costs
 

(e.g., the tunnel options). It is important to note that our analysis includes only those energy costs
 

that are external to engineering cost estimates. The cost of fuel used by construction and
 

maintenance vehicles, and electricity costs associated with excavation and other construction
 

activities are reflected in the cost estimates developed by CDM.
 

In addition to the direct expense of added energy consumed (or savings from use of less energy),
 

we also assess the level of CO2 emissions added (or reduced or sequestered) by each option.
 

Thus, for example, the LID options reduce CO2 emissions at power plants by providing energy
 

savings at shaded homes, plus the added trees sequester some CO2 as well. These reductions
 

more than offset the added emissions associated with implementation-related activities, such as
 

added vehicle fuel use during the installation of green infrastructure. The net savings in
 

emissions are valued using a “social cost of carbon” estimate derived from the Intergovernmental
 

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Panel on Climate Change (IPCC) of the climate change damages contributed by each metric ton
 

(MT) of CO2 equivalent (CO2e) emitted. The value used is $12/MT.
 

In contrast, traditional infrastructure options tend to increase net CO2 emissions, because they
 

require extensive excavation activity and concrete, and also required added energy use in
 

pumping and treating the collected and stored stormwaters. Again, the direct cost of the energy
 

used in constructing and operating the traditional infrastructure approaches are not included in
 

our cost estimates, because they are internal costs that are reflected in the capital and operation
 

and maintenance (O&M) costs developed for each of those CSO control options (i.e., the energy
 

cost is included in the engineering cost estimates provided by CDM). However, in our work, we
 

do include the external costs associated with the added energy use required by these options.
 

Finally, the changes in energy use also change the amount of SO2 and NOx emitted from power
 

plants. These changes in emissions are estimated based on region-specific data from EPA, and
 

assigned monetary values based on EPA methods that reflect the average health benefit (or cost)
 

associated with each ton of emission reduced (or added).
 

For the 50% LID option, our analysis indicates a net energy savings over the 40-year planning
 

period of nearly 370 million kilowatt hours (kWh) of electricity and nearly 600 million British
 

thermal units (Btus) of natural gas. The 50% LID option will result in close to 0.5 million gallons
 

of “wasted” motor fuel consumed by vehicles delayed by construction activities. Emissions
 

reductions over that period include over 1,500 MT of SO2, 1.1 million MT of CO2, and a small
 

reduction in NOx emissions of 38 MT.
 

The monetized present value of these changes from the 50% LID option amount to nearly
 

$34 million for energy savings, over $21 million for reduced CO2 emissions, and over
 

$46 million for reduced net damages from SO2 and NOx emissions. For additional detail, see
 

Appendix G.
 

4.8 Air Quality Pollutant Removal from Added Vegetation 

Trees and vegetation improve air quality by filtering some airborne pollutants (particulate matter
 

and ozone). Likewise, reduced energy consumption results in decreased emissions (SO2 and
 

NOx) from power generation facilities (as described and evaluated in the previous section). These
 

air quality improvements can reduce the incidence and severity of respiratory illness.
 

To evaluate the air quality impacts of added trees, we used a model developed by the U.S. Forest
 

Service, for application in Philadelphia. We analyzed the resulting estimates of projected
 

changes in ambient air quality (i.e., ozone and particulate matter concentrations) using software
 

developed by the EPA to calculate the avoided health effects from the contribution of trees to
 

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reducing ozone and PM2.5 concentrations, and to estimate the economic value of the avoided
 

health effects. EPA’s BenMAP (Ver. 3.0.15), was used to conduct this analysis.
 

The avoidable air pollution-related health effects estimated in this analysis are:
 

�	 Premature mortality (from ozone and PM2.5) 

�	 Onset of irreversible chronic bronchitis (PM2.5) 

�	 Heart attacks (non-fatal acute myocardial infarctions) (PM2.5) 

�	 Hospital admissions (non-fatal) for respiratory and cardiovascular conditions (from ozone 

and PM2.5) 

�	 Emergency room visits for asthma (from ozone and PM2.5) 

�	 Respiratory symptoms (days of illness) (from ozone and PM2.5) 

�	 Work loss days (PM2.5) and school absence (ozone). 

The quantified estimates are then monetized using standard EPA dollar values for each
 

applicable adverse health endpoint.
 

For the 50% LID option, applied across the four watershed areas, we estimate that after full
 

implementation and tree maturation, the health effects avoided will include between 1 and
 

2.4 premature fatalities avoided per year, 1.2 heart attacks avoided per year, and over 700 cases
 

of other respiratory illness days avoided per year. The present value of the associated monetized
 

benefits is over $130 million over the 40-year period. Additional detail is provided in
 

Appendix H.
 

4.9	 Construction- and Maintenance-Related Disruption Impacts 

All of the CSO options will result in some level of disruption due to construction and program
 

activities. Social costs of disruption can include traffic delays, limited access to places of
 

business, increased noise and pollution, and other inconveniences. Under all of the CSO
 

alternatives, construction activities will likely result in occasional delays and increased travel
 

times for passenger and commercial vehicle travelers in Philadelphia. Travel time delays can be
 

caused by:
 

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�	 General traffic slowdowns associated with an increase in the number of trucks and 

construction equipment on the road 

�	 Slowdowns from trucks entering and exiting construction or landscaping sites 

�	 Lane or road closures associated with construction in the roadway or road right-of-way. 

In addition to the value of “lost” time spent in traffic, construction-related delays can result in
 

increased costs associated with additional fuel used by vehicles as a result of slower speeds and
 

occasional vehicle stops and idling.
 

Using standard methods and data for estimating traffic delays and associated fuel use and time
 

loss, we estimated the 40-year present value of these external costs for each CSO control option.
 

City-wide, the present value of these external costs for the 50% LID option is $5.6 million, and
 

for the 30’ Tunnel option, it is more than 200 times larger, at over $13.4 billion. Additional detail
 

is provided in Appendix I.
 

References 

Wachter, S.M. and G. Wong. 2006. What is a tree worth? Green-city strategies and housing
 

prices. Real Estate Economics 36(2):2008.
 

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5. Summary of Results 

The following sections summarize the benefits and external costs of the CSO control options
 

currently being considered by PWD. We first present the results of our analysis on a City-wide
 

basis, highlighting the benefits and costs across the CSO watersheds. More detailed tables,
 

providing benefits and costs in each watershed by category, are provided at the end of this
 

chapter.
 

Again, it is important to note that throughout the following sections, we refer to the green
 

infrastructure CSO control options as LID-based approaches. We categorize the different options
 

based on different levels of implementation (e.g., the 50% LID option would manage runoff
 

from 50% of impervious surfaces in Philadelphia through green infrastructure). Green
 

infrastructure and LID are used interchangeably throughout the next chapter and appendices.
 

We also refer to the traditional infrastructure options according to different levels of
 

implementation. For example, throughout the report we draw upon the “30’ Tunnel” option as an
 

example alternative. This option includes a system of storage tunnels serving all watersheds with
 

an effective diameter of 30 ft. Alternative tunneling options (e.g., 15’, 20’, 25’, and 35’ options),
 

are also being evaluated by PWD and the impacts of all alternatives are examined here.
 

5.1 Benefits of LID CSO Control Options 

Figure 5.1 presents the total net benefits (defined here as benefits minus the external costs of
 

construction disruption) for the LID CSO control options over the 40-year project evaluation
 

period. City-wide, total present value benefits range from about $1,935 million (2009 USD)
 

under the 25% LID option to more than $4,466 million under the 100% LID option.
 

The relative make up of total benefits by watershed is consistent across LID options. As shown
 

in Figure 5.1, the Tacony-Frankford Creek Watershed accounts for about 20 to 22% of total
 

benefits under each option. Cobbs Creek makes up about 8 to 11%, while the Schuylkill and
 

Delaware River Watersheds account for about 25 to 27% and 42 to 44% of total net benefits,
 

respectively.
 

Figure 5.2 shows the breakdown of total City-wide benefits by benefit category for the 50% LID
 

option. As shown, reduced heat-stress fatalities, increased property values, and increased
 

recreational opportunities make up the majority of total benefits. These categories account for
 

37, 20, and 18% of total benefits, respectively.
 

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Stratus Consulting Summary of Results (Final, 8/24/2009)
 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

3,500 

4,000 

4,500 

5,000 

Millions $

Delaware 

Schuylkill 

Cobbs 

Tacony 

25% 50% 75% 100% 

LID option 

Figure 5.1. City-wide net benefits for LID options by watershed. 

Increased recreational 

opportunities 

< 1% 

2% 

Improved aesthetics/property 

1% value 

5% Reduction in heat stress 

4% 18% mortality 

Water quality/aquatic habitat 

enhancement 

12% Wetland services 

Social costs avoided by green 

collar jobs 

Air quality improvements from 20% trees 

Energy savings 

Reduced damage from SO2 

and NOx emissions 

37% Reduced damage from CO2 

emissions 

Figure 5.2. Shares of City-wide present value benefits of key CSO options: 

Cumulative through 2049. 

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The benefits associated with improved water quality and aquatic habitat also account for a
 

substantial portion of total benefits (12%), while net energy savings, reduced NOx and SO2
 

emissions, and carbon sequestration all account for less than 2%. “Green jobs” and air quality
 

improvements due to pollutant removal from trees, both account for about 5% of total benefits.
 

The percent breakdown of benefit categories shown in Figure 5.2 is consistent across the LID
 

options.
 

5.2 Benefits and External Costs of Example CSO Options 

To show a more direct comparison of benefits and external costs of the different CSO control
 

options, Figure 5.3 provides City-wide estimates for the LID and tunneling CSO Control options.
 

These options were chosen to demonstrate the difference in net benefits between green and
 

traditional infrastructure. The reporting of these results is not intended to indicate that a final
 

PWD decision will be based on these two alternatives.
 

100% 

75% 

50% 

25% 

35' Tunnel 

30' Tunnel 

25' Tunnel 

20' Tunnel 

15' Tunnel 

0 1,000 2,000 3,000 4,000 5,000 

Millions $ 

Figure 5.3. City-wide present value benefits/external costs of the LID and tunneling 

CSO control options, over 40-year project period (2009 USD). 

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Stratus Consulting Summary of Results (Final, 8/24/2009)
 

As shown in Figure 5.3, on a City-wide basis, the net external costs of the tunneling options
 

ranges from about $61.6 million under the 15’ Tunneling option, to more than $140 million
 

under the 35’ Tunneling option. This compares to the range of net present value benefits for the
 

LID options of $1,935 million to $4,466 million, as reported in Section 5.1 above.
 

Table 5.1 shows City-wide estimates for total net benefits (benefits minus external costs) of the
 

50% LID and 30’ Tunnel options over the 40-year project period. This comparison is intended to
 

provide a bit more detail into the break down of the individual options. The ratio of the external
 

costs of the tunneling options to the net benefits of the LID options varies considerably by
 

watershed. Section 5.3 provides a comparison of the costs and benefits of these different options
 

for each watershed.
 

As discussed earlier in this report, the physical unit measures associated with the monetary
 

values presented above are an important component of our discussion of total benefits. For the
 

LID options, for example, physical unit measures include the number of lives saved as a result of
 

reduced heat stress, the number of new recreational visitor days, and the energy and carbon
 

savings associated with increased vegetated area, among others.
 

Table 5.2 presents City-wide estimates for the physical unit measures associated with the 50%
 

LID and 30’ Tunneling options. The measures shown below can be directly tied to the monetary
 

values provided in Table 5.1.
 

5.3 Detailed Results by Watershed 

The following tables provide detailed results for the CSO control options being evaluated in each
 

of the CSO watersheds. Tables 5.3–5.6 show the present value estimates (2009 USD) for each
 

benefit/external cost category, while Tables 5.7–5.10 provide the physical unit measures
 

associated with these values. Finally, for comparison purposes, Figures 5.4–5.7 provide a visual
 

depiction of the present value net benefits/external costs for the tunneling versus LID options
 

within each watershed. The tables and figures included in the following pages include options in
 

the Delaware River Watershed.
 

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Stratus Consulting Summary of Results (Final, 8/24/2009)
 

Table 5.1. City-wide present value benefits of key CSO options: Cumulative through 2049 

(2009 million USD) 

Benefit categories 50% LID option 30’ Tunnel optiona 

Increased recreational opportunities $524.5 

Improved aesthetics/property value (50%) $574.7 

Reduction in heat stress mortality $1,057.6 

Water quality/aquatic habitat enhancement $336.4 $189.0 

Wetland services $1.6 

Social costs avoided by green collar jobs $124.9 

Air quality improvements from trees $131.0 

Energy savings/usage $33.7 $(2.5) 

Reduced (increased) damage from SO2 and NOx emissions $46.3 $(45.2) 

Reduced (increased) damage from CO2 emissions $21.2 $(5.9) 

Disruption costs from construction and maintenance $(5.6)b $(13.4) 

Total $2,846.4 $122.0 

a. 28’ Tunnel option in Delaware River Watershed. 

b. Parentheses indicate negative values. 

Table 5.2. City-wide natural unit benefits of key CSO options: Cumulative through 2049 

Benefit categories 50% LID option 30’ Tunnel optiona 

Additional creekside recreational user days 247,524,281
 

Additional non-creekside recreational user days 101,738,547
 

Reduction in number of heat-related fatalities 196
 

Annual WTP per household for water quality and aquatic habitat
 

improvementsb $9.70−$15.54 $5.63−$8.59 

Wetlands created or restored (acres) 193 

Green collar jobs (job years) 15,266 

Change in particulate matter (PM2.5) due to increased trees (μg/m3

) 0.01569 

Change in seasonal ozone due to increased trees (ppb) 0.04248 

Electricity savings due to cooling effect of trees (kWh) 369,739,725 

Natural gas savings due to cooling effect of trees (kBtu) 599,199,846 

Fuel used (vehicles for construction and O&M) (gallons) 493,387 1,132,409 

SO2 emissions (metric tons) (1,530) 1,452 

NOx emissions (metric tons) (38) 6,356,083 

CO2 emissions (metric tons) (1,091,433) 347,970 

Vehicle delay from construction and maintenance (hours of delay) 346,883 796,597 

a. 28’ Tunnel option in Delaware River Watershed.
 

b. WTP per household in Philadelphia, MA, including Bucks, Chester, Delaware, Montgomery, and
 

Philadelphia counties.
 

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Table 5.3. Present value benefits of key CSO options in Tacony-Frankford Watershed: 

Cumulative through 2049 (2009 million USD) 

Benefit categories 50% LID option 30’ Tunnel optiona 

Increased recreational opportunities $161.2 

Improved aesthetics/property value (50%) $85.0 

Reduction in heat stress mortality $249.9 

Water quality/aquatic habitat enhancement $23.7 $13.3 

Wetland services $0.3 

Social costs avoided by green collar jobs $27.0 

Air quality improvements from trees $28.3 

Energy savings/usage $7.3 $(0.4) 

Reduced (increased) damage from SO2 and NOx emissions $10.0 $(8.8) 

Reduced (increased) damage from CO2 emissions $4.6 $(1.1) 

Disruption costs from construction and maintenance $(1.2) $(2.2) 

Total $596.0 $0.8 

a. 28’ Tunnel option in Delaware River Watershed. 

Table 5.4. Present value benefits of key CSO options in Cobbs Creek Watershed: 

Cumulative through 2049 (2009 million USD) 

Benefit categories 50% LID option 30’ Tunnel optiona 

Increased recreational opportunities $100.2 

Improved aesthetics/property value (50%) $24.8 

Reduction in heat stress mortality $89.8 

Water quality/aquatic habitat enhancement $30.6 $17.2 

Wetland services $0.3 

Social costs avoided by green collar jobs $8.6 

Air quality improvements from trees $9.0 

Energy savings/usage $2.3 $(0.5) 

Reduced (increased) damage from SO2 and NOx emissions $3.2 $(6.5) 

Reduced (increased) damage from CO2 emissions $1.5 $(1.0) 

Disruption costs from construction and maintenance $(0.4) $(2.8) 

Total $270.0 $6.5 

a. 28’ Tunnel option in Delaware River Watershed. 

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Table 5.5. Present value benefits of key CSO options in Schuylkill River Watershed: 

Cumulative through 2049 (2009 million USD) 

Benefit categories 50% LID option 30’ Tunnel optiona 

Increased recreational opportunities $90.1 

Improved aesthetics/property value (50%) $193.7 

Reduction in heat stress mortality $297.1 

Water quality/aquatic habitat enhancement $86.2 $48.5 

Wetland services $0.3 

Social costs avoided by green collar jobs $28.9 

Air quality improvements from trees $30.4 

Energy savings/usage $7.8 $(0.6) 

Reduced (increased) damage from SO2 and NOx emissions $10.7 $(14.2) 

Reduced (increased) damage from CO2 emissions $4.9 $(1.7) 

Disruption costs from construction and maintenance $(1.3) $(3.4) 

Total $748.9 $28.5 

a. 28’ Tunnel option in Delaware River Watershed. 

Table 5.6. Present value benefits of key CSO options in Delaware River Watershed: 

Cumulative through 2049 (2009 million USD) 

Benefit categories 50% LID option 30’ Tunnel optiona 

Increased recreational opportunities $173.0 

Improved aesthetics/property value (50%) $271.2 

Reduction in heat stress mortality $420.9 

Water quality/aquatic habitat enhancement $195.8 $110.0 

Wetland services $0.7 

Social costs avoided by green collar jobs $60.4 

Air quality improvements from trees $63.4 

Energy savings/usage $16.3 $(0.9) 

Reduced (increased) damage from SO2 and NOx emissions $22.4 $(15.7) 

Reduced (increased) damage from CO2 emissions $10.3 $(2.1) 

Disruption costs from construction and maintenance $(2.7) $(5.1) 

Total $1,231.6 $86.2 

a. 28’ Tunnel option in Delaware River Watershed. 

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Stratus Consulting Summary of Results (Final, 8/24/2009)
 

Table 5.7. Natural unit benefits of key CSO options in Tacony-Frankford Watershed: 

Cumulative through 2049 

Benefit categories 50% LID option 30’ Tunnel optiona 

Additional creekside recreational user days 80,527,887 

Additional non-creekside recreational user days 22,714,215 

Reduction in number of heat-related fatalities 46 

Annual WTP per household for water quality and aquatic habitat 

improvementsb $9.70−$15.54 $5.63−$8.59 

Wetlands created or restored (acres) 35 

Green collar jobs 3,303 

Electricity savings due to cooling effect of trees 79,771,661 

Natural gas savings due to cooling effect of trees 129,277,877 

Fuel used (vehicles for construction and O&M) 106,449 184,336 

SO2 emissions (metric tons) (330) 283 

NOx emissions (metric tons) (8) 1,082,609 

CO2 emissions (metric tons) (235,478) 63,986 

Disruption delay from construction and maintenance 74,840 129,672 

a. 28’ Tunnel option in Delaware River Watershed.
 

b. WTP per household in Philadelphia, MA, including Bucks, Chester, Delaware, Montgomery, and
 

Philadelphia counties.
 

Table 5.8. Natural unit benefits of key CSO options in Cobbs Creek Watershed: 

Cumulative through 2049 

Benefit categories 50% LID option 30’ Tunnel optiona 

Additional creekside recreational user days 50,478,407 

Additional non-creekside recreational user days 8,629,946 

Reduction in number of heat-related fatalities 17 

WTP per household for water quality and aquatic habitat 

improvementsb $9.70−$15.54 $5.63−$8.59 

Wetlands created or restored (acres) 39.93 

Green collar jobs 1,050 

Electricity savings due to cooling effect of trees 25,475,530 

Natural gas savings due to cooling effect of trees 41,285,620 

Fuel used (vehicles for construction and O&M) 33,995 235,991 

SO2 emissions (metric tons) (105) 208 

NOx emissions (metric tons) (3) 1,256,965 

CO2 emissions (metric tons) (75,201) 59,809 

Disruption delay from construction and maintenance 23,901 166,009 

a. 28’ Tunnel option in Delaware River Watershed.
 

b. WTP per household in Philadelphia, MA, including Bucks, Chester, Delaware, Montgomery, and
 

Philadelphia counties.
 

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Table 5.9. Natural unit benefits of key CSO options in Schuylkill River Watershed: 

Cumulative through 2049 

Benefit categories 50% LID option 30’ Tunnel optiona 

Additional creekside recreational user days 40,371,870 

Additional non-creekside recreational user days 22,991,914 

Reduction in number of heat-related fatalities 55 

Annual WTP per household for water quality and aquatic 

habitat improvementsb $9.70−$15.54 $5.63−$8.59 

Wetlands created or restored (acres) 30 

Green collar jobs 3,535 

Electricity savings due to cooling effect of trees 85,676,380 

Natural gas savings due to cooling effect of trees 138,847,060 

Fuel used (vehicles for construction and O&M) 114,328 285,414 

SO2 emissions (metric tons) (355) 456 

NOx emissions (metric tons) (9) 1,653,470 

CO2 emissions (metric tons) (252,908) 98,814 

Disruption delay from construction and maintenance 80,380 200,775 

a. 28’ Tunnel option in Delaware River Watershed.
 

b. WTP per household in Philadelphia, MA, including Bucks, Chester, Delaware, Montgomery, and
 

Philadelphia counties.
 

Table 5.10. Natural unit benefits of key CSO options in Delaware River Watershed: 

Cumulative through 2049 

Benefit categories 50% LID option 28’ Tunnel optiona 

Additional creekside recreational user days 76,146,118 

Additional non-creekside recreational user days 47,402,472 

Reduction in number of heat-related fatalities 78 

Annual WTP per household for water quality and aquatic 

habitat improvementsb $9.70−$15.54 $5.63−$8.59 

Wetlands created or restored (acres) 88 

Green collar jobs 7,379 

Electricity savings due to cooling effect of trees 178,816,154 

Natural gas savings due to cooling effect of trees 289,789,289 

Fuel used (vehicles for construction and O&M) 238,615 426,667 

SO2 emissions (metric tons) (740) 505 

NOx emissions (metric tons) (18) 2,363,038 

CO2 emissions (metric tons) (527,847) 125,361 

Disruption delay from construction and maintenance 167,762 300,141 

a. 28’ Tunnel option in Delaware River Watershed.
 

b. WTP per household in Philadelphia, MA, including Bucks, Chester, Delaware, Montgomery, and
 

Philadelphia counties.
 

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100% 

75% 

50% 

25% 

35' Tunnel 

30' Tunnel 

25' Tunnel 

20' Tunnel 

15' Tunnel 

0 100 200 300 400 500 600 700 800 900 1,000 

Millions $ 

Figure 5.4. Benefits less external costs for key CSO options in the Tacony-Frankford 

Creek Watershed. 

100% 

75% 

50% 

25% 

35' Tunnel 

30' Tunnel 

25' Tunnel 

20' Tunnel 

15' Tunnel 

0 50 100 150 200 250 300 350 400 

Millions $ 

Figure 5.5. Benefits less external costs for key CSO options in the Cobbs Creek 

Watershed. 

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Stratus Consulting Summary of Results (Final, 8/24/2009)
 

15' Tunnel 

20' Tunnel 

25' Tunnel 

30' Tunnel 

35' Tunnel 

25% 

50% 

75% 

100% 

0 500 1,000 1,500 2,000 

Millions $ 

Figure 5.6. Benefits less external costs for key CSO options in the Schuylkill River 

Watershed. 

15' Tunnel 

20' Tunnel 

25' Tunnel 

30' Tunnel 

35' Tunnel 

25% 

50% 

75% 

100% 

0 500 1,000 1,500 2,000 

Millions $ 

Figure 5.7. Benefits less external costs for key CSO options in the Delaware River 

Watershed. 

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6.	 Key Uncertainties and Sensitivity Analyses 

As detailed in the appendices of this report, there are a number of uncertainties (e.g., discount
 

rate, social cost of carbon) and potential sources of variability (e.g., changes in energy costs)
 

surrounding our analysis. To explore the impacts of these uncertainties on our overall results, we
 

implemented a series of sensitivity analyses. The results of these analyses are discussed below.
 

Sensitivity analysis involves systematically changing the value of a key input or variable to see
 

how it affects the outcome of the analysis. The change in results shows how sensitive the project
 

outcome is to changes in individual factors. Sensitivity analysis is often performed by varying a
 

particular input by equal amounts greater to and less than the current value (e.g., +/- 50%). The
 

ultimate purpose of sensitivity analysis is to understand which assumptions are important to the
 

choice of a particular policy or project option, and what those assumptions would have to be to
 

change the decision on which option to pursue.
 

As part of the sensitivity analysis, we have explored the effect of a number of key assumptions
 

on our overall results, including:
 

�	 Discount rate. It is common practice to perform a sensitivity analysis on the discount rate 

used to determine the present value of costs and benefits. We therefore evaluated the 

benefits and external costs of the CSO options under alternative discount rate scenarios. 

Under the first scenario, we raised the nominal discount rate to 6.5%, (up from 4.875% in 

the current analysis) to reflect a 2.5% real discount rate, given the cost escalator 

(i.e., general inflation rate) of 4%. As a second scenario, we lowered the real discount 

rate to 0% (because of intergenerational equity aspects associated with the LID options). 

This entails lowering the nominal discount rate to 4% (i.e., setting discount rate to same 

value as the price escalator). Table 6.1 shows the results of this analysis for the 50% LID 

and 30’ Tunnel options. 

As shown in Table 6.1, under the 50% LID option, net benefits decrease by 27% citywide when the discount rate is increased to 6.5% (i.e., future benefits are “discounted” at
 

a higher rate). Under the 4% discount rate scenario, benefits increase by about 21% citywide from the baseline analysis (where the discount rate is equal to 4.875%).
 

Under the 30’ Tunneling option, relative impacts are larger and more varied across
 

watersheds. For example, in the Tacony-Frankford Watershed, increasing the discount
 

rate to 6.5% results in a 66% decrease in net benefits. In dollar terms, this represents a
 

decrease of about $550,000. The large percentage decrease is due to the relatively low net
 

benefits associated with this option in the Tacony-Frankford Watershed. City-wide, net
 

benefits decrease by 34% and increase by 27% under the 6.5% and 4% discount rate
 

scenarios, respectively.
 

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Stratus Consulting	 Uncertainties and Sensitivity Analysis (Final, 8/24/2009)
 

Table 6.1. Sensitivity analysis: Discount rates
 

Present value net benefits (millions, 2009 USD) % change from baseline estimate* 

Discount rate 4.875% 6.5% 4.0% 6.5% 4.0% 

50% LID option 

Tacony $596.0 $416.2 $737.0 -30% 24% 

Cobbs 

Schuylkill 

Delaware 

City-wide 

$270.0 

$748.9 

$1,231.6 

$2,846.4 

$185.6 

$551.9 

$895.1 

$2,048.7 

$335.7 

$903.8 

$1,495.4 

$3,471.9 

-31% 

-26% 

-27% 

-27% 

24% 

21% 

21% 

21% 

30’ Tunnel option 

Tacony 

Cobbs 

$0.8 

$6.5 

$0.3 

$3.7 

$1.3 

$8.7 

-66% 

-42% 

59% 

34% 

Schuylkill 

Delaware 

City-wide 

$28.5 

$86.2 

$122.0 

$18.9 

$57.2 

$80.1 

$36.0 

$108.6 

$154.6 

-34% 

-34% 

-34% 

26% 

26% 

27% 

�	 Social cost of carbon. There is currently quite a debate among experts and in the 

literature regarding the true social cost of carbon. For our analysis, we assume a cost of 

$12 per ton (MT), as reported by the IPCC. To evaluate how an increase in the social cost 

of carbon would impact our results for the different CSO control options, we conducted a 

sensitivity analysis comparing benefits and external costs with a higher social cost of 

carbon of $48 versus the IPCC’s average of $12. The $48 per ton is about half of the 

high-level estimates reported by the IPCC (which include values of $85 to $98 per MT). 

Table 6.2 shows the results of this analysis for the 50% LID and 30’ Tunnel options. 

More detailed results are included in Appendix G of this report. 

As shown below, changing the social cost of carbon does not significantly impact the net
 

benefits of the 50% LID option on a percentage basis. This is because the benefits
 

associated with carbon sequestration and reduced emissions make up a very small
 

component of the total net benefits (e.g., < 1% under the 50% LID option). In dollar
 

terms, the change in net benefits under the 50% LID option amounts to more than
 

$63 million.
 

Under the 30’ Tunnel option, the impact of an increased social cost of carbon has a much larger
 

relative effect on overall results. City-wide, net benefits decrease by about 15% with an increase
 

in the social cost of carbon from $12/MT to $48 MT. In dollar terms, this change amounts to
 

about $18 million.
 

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Stratus Consulting	 Uncertainties and Sensitivity Analysis (Final, 8/24/2009)
 

Table 6.2. Sensitivity analysis: Social cost of carbon
 

Present value net benefits 

(millions, 2009 USD) % change from 

Social cost of carbon $12/MT $48/MT baseline estimate 

50% LID option 

Tacony $596.0 $609.7 2.30% 

Cobbs $270.0 $274.3 1.62% 

Schuylkill $748.9 $763.6 1.97% 

Delaware $1,231.6 $1,262.3 2.50% 

City-wide $2,846.4 $2,910.0 2.23% 

30’ Tunnel option 

Tacony $0.8 $(2.5) (400.25)% 

Cobbs $6.5 $3.5 (45.54)% 

Schuylkill $28.5 $23.4 (18.06)% 

Delaware $86.2 $79.9 (7.35)% 

City-wide $122.0 $104.3 (14.53)% 

�	 Electricity prices. Electricity and other fossil fuel-based energy prices are expected to 

increase if a federal climate policy is introduced. Energy prices can also increase in the 

future due to a number of other factors (as evident by the price volatility seen in recent 

years). For our analysis, we assume a conservative estimate of $0.10 per kWh of 

electricity. This assumption affects the benefits associated with electricity savings under 

the LID CSO control options (electricity costs associated with power use within any CSO 

control option are not included in our analysis, because they are included in engineering 

cost estimates). 

To evaluate the impact of our assumption for the current rate of electricity, we conducted
 

a sensitivity analysis that doubled this rate (e.g., up to $0.20 per kWh). The analysis
 

shows that the rate of electricity has a very small impact on net benefits of the LID
 

options. In all cases, net benefits increased by close to 1% as a result of the additional
 

savings that would occur with higher electricity rates.
 

�	 WTP for water quality improvements. As reported in Appendix D, we conducted a 

sensitivity analysis to evaluate how WTP per household fluctuates in response to changes 

in baseline water quality and the level of water quality/habitat improvement (as defined 

by the WQ10). The results of this sensitivity analysis (reported in Appendix D) indicate 

that within the reasonable range of assumptions related to these variables, WTP per 

household does not vary appreciably as these input values change, but seem to follow a 

reasonable progression. WTP is more sensitive to the actual improvement in water 

quality as opposed to the baseline index value used in the analysis. 

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A. Recreational Use and Values 

The LID CSO control options currently being evaluated by PWD would provide (and enhance)
 

recreational amenities within PWD’s CSO watersheds. The LID options include a substantial
 

increase in vegetated acreage (including “treed” acreage) throughout the City. Much of this
 

“green” acreage would be in the form of trees planted along streets in residential areas or will be
 

planted in areas that are currently vacant or abandoned. This “greening” of Philadelphia would
 

increase enjoyment and participation in neighborhood activities such as walking, biking or
 

jogging on sidewalks, bench sitting, and/or other general outdoor recreation.
 

In addition, under all of the LID options, PWD would implement a stream restoration program
 

intended to improve aquatic habitat in affected water bodies. The program is focused on physical
 

in-stream improvements (primarily within the main stem water body associated with each
 

watershed), as well as on improvement and expansion of riparian areas. In some watersheds, this
 

would include improving riparian lands located within Fairmount Park and/or other open space
 

areas. Activities in these areas might include trail construction and restoration, removal of
 

invasive species, and other activities that would improve access along streams and rivers within
 

the combined sewer area. In other areas, access to water bodies would be improved through key
 

land and trail connections, enhancing recreational use in these areas.
 

The following sections outline Stratus Consulting’s methodology for estimating the benefits
 

associated with the increased recreational opportunities that will be available under the LID
 

options for CSO control. Estimates of total benefits within each watershed are also provided. As
 

described below, this analysis addresses “direct use” benefits only. Nonuse values associated
 

with increased recreational opportunities are addressed in a subsequent analysis (see
 

Appendix D).
 

A.1 General Methodology 

To estimate total benefits of increased recreational activity under the LID options, we separately
 

evaluated the benefits derived from improvements made as part of the stream restoration
 

program (which are planned for implementation under all of the LID options) and those
 

associated with a general increase in vegetated acreage throughout the CSO watersheds. For the
 

purposes of this analysis, we refer to these benefits as “creekside” and “non-creekside” benefits,
 

respectively.
 

The following sections describe the general methodology used to evaluate creekside and noncreekside recreational benefits. Subsequent sections provide more detailed descriptions of how
 

our analyses were tailored to each watershed.
 

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Stratus Consulting Appendix A (Final, 8/24/2009)
 

A.1.1 Recreational use 

As a first step to our analyses, we estimated the additional recreational use expected to occur
 

under the different LID options in each watershed. To do this, we relied heavily on a recent
 

report prepared for the Philadelphia Parks Alliance by the Trust for Public Lands. The 2008
 

report, How Much Value Does the City of Philadelphia Receive from its Park and Recreation
 

System? (Parks Report), provides visitation data for a variety of recreational uses and activities at
 

Philadelphia’s parks.1
 

The Parks Report provides data for visitation to parks in Philadelphia in general, and does not
 

report recreational use at individual parks. We therefore used a per-acre estimate (number of
 

visits per acre of Philadelphia park land) to evaluate potential changes in recreational activity
 

under the different LID options in each watershed. We tailored these per-acre estimates to
 

individual watersheds based on conversations with park staff, detailed watershed and park
 

management plans, and on-site visits. We also made assumptions related to per-acre recreational
 

use in non-park areas (e.g., on residential streets). Assumptions related to per-acre use in each
 

watershed are described in detail in subsequent sections.
 

Finally, the recreational use values reported in the Parks Report are for Philadelphia residents
 

only. Our estimates therefore do not include recreational use (or benefits) for non-Philadelphia
 

residents.
 

A.1.2 Direct use values 

The total recreational benefits associated with improvements made under the LID options are a
 

function of the additional recreational trips (“user days”) taken as a result of these improvements,
 

and the benefit (or direct use value) derived from each trip.
 

Because recreational activities are not traded in the market (i.e., there is no fee for participation),
 

it can be difficult to establish the direct use values associated with them. However, economists
 

have developed a number of techniques for valuing “non-market” goods and resources, such as
 

recreation. For example, economists have often determined the value of a recreational experience
 

based on the consumer’s WTP for the recreational experience in the private marketplace.
 

1. The number of park visits reported in the Parks Report were determined via a professionally conducted
 

telephone survey of 600 Philadelphia residents. (The random-digit-dialed survey had an accuracy level of plus
 

or minus 4%.) Residents were asked to answer for themselves; for those adults with children under the age of
 

18, a representative proportion were also asked to respond for one of their children.
 

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Stratus Consulting Appendix A (Final, 8/24/2009)
 

For this analysis, we were able to rely on direct use values for specific recreational activities, as
 

reported in the Parks Report. The model used to quantify these values is based on the “Unit Day
 

Value” method as documented in Water Resources Council recreation valuation procedures by
 

the U.S. Army Corps of Engineers. The Unit Day Value model counts park visits by specific
 

activity, and assigns each activity a dollar value, based on WTP for park activities. For example,
 

playing in a playground is worth $3.50 each time to each user. Running, walking, or
 

rollerblading on a park trail is worth $4.00. For a more detailed description of how direct use
 

values were calculated, see the Parks Report.
 

A.2 Non-creekside Recreation 

To estimate benefits associated with a general increase in vegetated acreage (including treed
 

acreage), we relied on inputs from CDM regarding the planned increase in vegetated acreage
 

under the LID options for each watershed. We modified the number of vegetated acres provided
 

by CDM to reflect only those acres that would result in additional or enhanced recreational
 

activity. For example, we subtracted out the estimated number of acres expected to be planted in
 

green roofs (also an input provided by CDM).
 

In addition to accounting for green roofs, we also subtracted the number of vegetated acres
 

estimated for implementation in parking lots. To do this, we assumed that the vegetated acreage
 

would be distributed based on the current pattern of impervious surface area in each watershed.
 

For example, in the Tacony-Frankford Watershed, approximately 17% of impervious area (not
 

including roofs) can be attributed to parking lots. We therefore assumed that 17% of the
 

vegetated acreage planned under each alternative would be planted in parking lots. Thus, after
 

accounting for green roofs, 17% of the remaining vegetated acreage planned for the Tacony-


Frankford Watershed would not result in recreational benefits.
 

For the Schuylkill River Watershed, we also subtracted the number of acres identified in the
 

Schuylkill River Master Plan (EDAW, 2003) as being available for recreational development
 

(150 acres). This area was evaluated as part of the creekside recreational analysis. We assumed a
 

similar area, on a per-stream mile basis, would be available for recreational development along
 

the Delaware River and accounted for this in our analysis.
 

Table A.1 shows the planned increase in vegetated acreage assumed to result in recreational
 

benefits for the LID CSO options in each watershed.
 

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Table A.1. Planned increase in vegetated acreage assumed to result in recreational 

benefits under the LID options 

Tacony-Frankford Cobbs Creek Schuylkill Delaware 

25% LID 231 87 126 236 

50% LID 822 312 832 1,715 

75% LID 1,169 445 1,247 2,584 

100% LID 1,404 534 1,528 3,171 

Our next step was to estimate the number of recreational visits, or “user days,” per acre for
 

specific recreational activities that would occur as a result of the increases in vegetated acreage.
 

We used visitation data for specific activities (e.g., walking the dog, walking on sidewalks/trails,
 

and picnicking or bench sitting) from the Parks Report as the basis for this estimate. We then
 

assumed that on a per-acre basis, the vegetated acreage planted under the LID options would
 

support about 10% of the recreational activity seen at an average park in Philadelphia.
 

Table A.2 presents the annual additional recreational activity (in terms of “user days”) under the
 

LID CSO options in each watershed, assuming full program implementation. Table A.3 shows
 

total additional recreational user days over the 40-year project evaluation period. The estimates
 

shown in Table A.3 take into account the LID implementation timeline provided by CDM.
 

Table A.2. Additional non-creekside recreational user days under LID CSO control 

options each year (at full program implementation) 

Tacony-Frankford Cobbs Creek Schuylkill Delaware 

25% LID 310,000 117,300 169,200 317,300 

50% LID 1,104,100 419,500 1,117,600 2,304,100 

75% LID 1,571,300 597,500 1,676,300 3,472,900 

100% LID 1,886,700 717,000 2,053,400 4,261,400 

Table A.3. Additional non-creekside recreational user days under LID CSO control 

options over 40-year project period 

Tacony-Frankford Cobbs Creek Schuylkill Delaware 

25% LID 6,376,780 2,413,061 3,481,727 6,528,626 

50% LID 22,714,215 8,629,946 22,991,914 47,402,472 

75% LID 32,326,746 12,292,929 34,486,588 71,448,114 

100% LID 38,815,401 14,751,738 42,245,022 87,670,535 

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A.2.1 Direct use value of additional recreational visits 

To estimate the monetary value of additional recreational activity, we applied direct use values
 

from the Parks Report for the recreational activities described above. We used 50% of the direct-


use values reported in the Parks Report to account for differences in the value of recreational
 

activities in parks versus non-park areas (i.e., walking on a sidewalk).
 

To estimate total benefits over the 40-year project life, we scaled annual benefits based on the
 

LID implementation timelines provided by CDM. Table A.4 shows the present value benefits
 

associated with non-creekside recreational activity expected to occur under the LID CSO options
 

in each watershed.
 

Table A.4. Direct-use benefits associated with non-creekside recreational visits under LID 

CSO control options (present value estimates for 40-year project period) 

Tacony-Frankford Cobbs Creek Schuylkill Delaware 

25% LID $4,499,952 $1,702,843 $2,456,977 $4,684,956 

50% LID $16,028,916 $6,089,960 $16,224,881 $34,016,111 

75% LID $22,812,265 $8,674,846 $24,336,416 $51,271,313 

100% LID $27,391,164 $10,409,972 $29,811,370 $62,912,556 

A.3 Creekside Recreation 

The following sections describe Stratus Consulting’s approach for estimating recreational
 

benefits associated with the stream restoration component of the LID CSO options. For this
 

evaluation, we adapted our methodology to account for differences in current and expected
 

changes in recreational use in each watershed. Further, the stream restoration program is
 

assumed to be implemented under all of the LID alternatives, therefore total benefits are the
 

same at each level of LID (25–100%).
 

Our methodology and assumptions are based on an extensive review of watershed and park
 

management/master plans (documented at the end of this appendix), on-site visits with PWD
 

staff, and discussions with Fairmount Park representatives.
 

A.3.1 Tacony-Frankford Watershed 

Tacony Creek Park, a unit of the Fairmount Park System, accounts for the majority of creekside
 

recreational lands in the Tacony-Frankford Watershed. The park consists of 302 acres of land
 

(including Juniata Park Golf Course) that form a narrow corridor of park along Tacony Creek
 

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from the Montgomery/Philadelphia County border through Juniata Park. The park offers
 

2.5 miles of creekside trails and is reportedly used by residents for picnicking, running, walking,
 

and fishing. Although an illegal activity, people do swim in the Tacony Creek Park section of the
 

creek. Unsanctioned uses of the park include all terrain vehicle (ATV) use, dumping, graffiti, and
 

drug activity.
 

Below Juniata Park Golf Course, the Tacony joins with now buried tributaries to form Frankford
 

Creek. In order to deal with flooding and large influxes of stormwater, Frankford Creek has been
 

completely channelized in concrete. The concrete channel prevents interaction between
 

Frankford Creek and the groundwater system and eliminates streambed habitat needed to support
 

aquatic life. The area surrounding Frankford Creek is highly industrialized and much of the creek
 

is inaccessible.
 

Stream restoration activities in the Tacony-Frankford Watershed are focused on in-stream
 

restoration and riparian area improvements along the 2.6 miles of stream through Tacony Creek
 

Park and the 3.5 miles of Frankford Creek (south of Juniata Park through to the Frankford’s
 

confluence with the Delaware River). Major improvements related to recreational use include
 

trail construction and restoration, expanded riparian areas, and improved access to the Tacony-


Frankford main stem. Implementation of the Frankford Creek Greenway (as described in the
 

Frankford Greenway Master Plan) is expected to include 3.1+ miles of trail and a number of
 

recreational amenities.
 

Baseline recreational use 

We first established a baseline estimate for current recreational activity in Tacony Creek Park.
 

We limited the baseline to activity within the park because it is currently the only area in the
 

Philadelphia County portion of the watershed that provides direct access to the main stem creek.
 

Our baseline estimate of recreational activity relies on survey data from the Tacony-Frankford
 

River Conservation Plan (RCP), and qualitative descriptions from Fairmount Park Staff and the
 

Tacony Creek Park Natural Lands Restoration Master Plan. We also used the Parks Report to
 

help determine the mix of recreational activities occurring in the park.
 

The RCP survey reports stream-related recreational activity for the entire watershed (including
 

tributaries). We therefore used geographic information systems (GIS) land use data to estimate
 

the percentage of creek-related recreational activity that occurs along the Tacony main stem in
 

Tacony Creek Park. We estimate that Tacony Creek Park currently supports about 70% of total
 

creek-related recreation in the watershed. The remaining 30% is assumed to occur in tributaries
 

and other areas of the watershed not relevant to our analysis.
 

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Table A.5 shows the inputs and data sources used to establish a baseline estimate for recreational
 

use along the creek. As shown below, the majority of residents in the Tacony-Frankford
 

Watershed report that they rarely, if ever, spend recreational time along the creek. Conversations
 

with park staff also indicate that this park gets very little use.
 

Table A.5. Assumptions and inputs used to establish baseline recreational use along 

Tacony-Frankford Creek 

Data source 


General inputs 

2007 watershed population 285,405 EPA BenMap 2007; Tacony-Frankford Integrated 

(Philadelphia County portion) Watershed Management Plan (IWMP) 

Percent of population less than 18 years old 26% 2000 Census 

Recreational activities along the creek 

Percent of watershed residents under the age of
 

18 that recreate along the creek
 

Percent of watershed residents over the age of
 

18 that recreate along the creek
 

Average number of visits per year (both groups)
 

12% Tacony-Frankford RCP survey data as reported in
 

the Tacony-Frankford IWMP
 

39% Tacony-Frankford RCP survey data as reported in
 

the Tacony-Frankford IWMP
 

3 Tacony-Frankford RCP survey data as reported in
 

the Tacony-Frankford IWMP
 

Mix of recreational activities 

Walk along creek 53% Tacony-Frankford RCP survey data as reported in 

the Tacony-Frankford IWMP 

Other non-contact activities 38% Tacony-Frankford RCP survey data as reported in 

the Tacony-Frankford IWMP; Parks Report 

Fishing 8% Tacony-Frankford RCP survey data as reported in 

the Tacony-Frankford IWMP 

Based on the assumptions and inputs shown above, we estimate that Tacony-Frankford Creek
 

supports approximately 192,320 recreational visits to the creek each year. This amounts to about
 

$406,000 in annual direct-use benefits.
 

Additional recreational visits under LID options 

To estimate total creekside recreational benefits in the Tacony-Frankford Watershed, we
 

separately evaluate recreational use under the LID CSO control options in the following
 

locations:
 

� Tacony Creek Park 

� Juniata Creek Golf Course 

� The planned Frankford Creek Greenway. 

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Tacony Creek Park. As a first step to our analysis of recreational activity in Tacony Creek 

Park, we calculated the average number of per-acre visits to all Philadelphia parks for specific 

activities expected to occur in Tacony Creek Park. These activities include: 

� Visits to playgrounds and tot lots 

� Picnicking or bench-sitting 

� Walking on trails 

� Walking dog in park 

� Birdwatching/nature 

� Bicycling on trails 

� Running on park trails 

� Fishing. 

We then assumed that under the LID/stream restoration improvements, Tacony Creek Park
 

would likely support about 40% of the per-acre visitation experienced at an average park in
 

Philadelphia. To estimate total visitation to the park, we therefore applied 40% of the average
 

number of recreational visits per acre of park land in Philadelphia to the 174 acres of Tacony
 

Park (excluding Juniata Park Golf Course). Our 40% assumption is based on the relative “local”
 

nature of the park (e.g., compared to the regional appeal of East and West Fairmount parks),
 

surrounding neighborhood demographics, and discussions with Fairmount Park representatives.
 

Based on these assumptions, we estimate that approximately 2.1 million people would visit
 

Tacony Park each year under the LID options (at full program implementation). This includes
 

the baseline estimate of individuals who already visit the park, as well as visits from individuals
 

who would have visited a park elsewhere in Philadelphia if the improvements along Tacony
 

Creek had not taken place. These factors are accounted for in our estimate of total benefits, as
 

described below.
 

Juniata Park Golf Course. We based our estimate of additional visits to Juniata Park Golf 

Course on data reported in the Juniata Park Golf Course Land Use and Feasibility Study 

(EDAW, 2008). This report indicates that odors associated with CSO events in Tacony Creek are 

one of many limiting factors for increasing visitation to the course. 

EDAW reports that there are currently about 11,350 rounds of golf played at Juniata Park each
 

year (2007 estimate). This compares to an average of 28,375 rounds reported for other public
 

courses in Philadelphia, or 40% of average use. We assume that under the LID options, use
 

might increase to about 50% of the average use at other courses, or to 14,190 rounds of golf (an
 

additional 2,800 rounds).
 

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Based on an average of 3 golfers per round, we estimate that as a result of the CSO
 

improvements, approximately 8,500 individuals will golf at Juniata Park Golf Course that
 

otherwise would not have. This includes individuals who would have golfed elsewhere in the
 

City (and are therefore not included in the overall benefit estimates reported below).
 

We use a conservative estimate for increase in use of the course as a result of CSO
 

improvements because the park is plagued by non-CSO related problems such as graffiti and
 

vandalism. In addition, Juniata Park is smaller than many other public courses and does not have
 

the same historic or regional appeal as some of the other more well-used courses (e.g., Cobbs
 

Creek Golf Course).
 

Frankford Creek Greenway. The planned Frankford Creek Greenway is a massive public 

works project that would include 3.1+ miles of trail construction along Frankford Creek and 

would restore much of Frankford Creek to its natural stream bottom. To estimate the number of 

visits to the new greenway, we relied on the same methodology described above for our analysis 

of increased use at Tacony Creek Park. 

We first estimated the total area (acres) of the greenway, based on 3.5 stream miles and an
 

assumed greenway width from the stream zone. Based on our assumptions, we estimate that the
 

greenway would be approximately 190 acres. We then estimated per-acre visitation for activities
 

expected to occur along the greenway.
 

With the exception of fishing and playgrounds/tot lots, the activities within the greenway were
 

assumed to be the same as those included in the Tacony Creek Park analysis. We did not include
 

fishing as a specific recreational activity because the concrete walls on the side of the stream
 

channel are assumed to prevent direct contact with the stream. Additionally, it is unclear whether
 

playgrounds and tot lots would be included as part of the greenway (they were not described in
 

the Frankford Greenway Master Plan). As with the Tacony Creek Park analysis, we assumed that
 

the Frankford Greenway would support about 40% of the recreational use of an average park in
 

Philadelphia, on a per-acre basis.
 

Based on these inputs, we estimate that more than 1.9 million individuals will visit the greenway
 

each year, once it is fully constructed.
 

Total additional recreational visits. We assume that under the LID options, approximately 70% 

of the recreational visits reported above would be “new” visits, meaning they would not have 

occurred if the LID stream restoration program had not been implemented. This assumption 

implies that the remaining 30% of recreational visits would have occurred at parks or golf 

courses elsewhere in the City if the LID improvements had not taken place. Although there is a 

marginal benefit associated with these visits (otherwise individuals would continue to visit the 

other parks), these benefits are not included in our analysis. 

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Table A.6 provides a summary of total additional recreational visits in the Tacony-Frankford
 

Watershed under the LID options. The number of additional visits is reported on an annual basis
 

(assuming full program implementation) as well as in terms of total visits over the 40-year
 

project period. Total visits over the project period were determined based on the stream
 

restoration implementation timeline provided by CDM.
 

Table A.6. Summary of total additional recreational visits in the TaconyFrankford Watershed under LID options 

Additional visits to Tacony Creek Park under LID options (minus baseline) 1,934,000 

Visits to Frankford Greenway 1,910,000 

Additional (person) visits to Juniata Park Golf Course 8,500 

Percent of visits that are new recreational visits 70% 

Additional annual recreational user days s 2,696,800 

Additional recreational user days over 40-year project period 80,527,887 

Direct use value of additional recreational visits 

To estimate the monetary value of additional creekside recreational visits under the LID CSO
 

control options, we applied direct-use values from the Parks Report, weighted by specific
 

recreational activity. Based on these values, we estimate that the increased recreational activity
 

will result in approximately $6.1 million each year (2009 USD), at full program implementation.
 

This amounts to more than $145 million in direct use benefits over the 40-year project period, in
 

present value terms (2009 USD). Present value estimates were determined based on the stream
 

restoration implementation timeline provided by CDM.
 

A.3.2 Cobbs Creek Watershed 

Cobbs Creek Park, located on the western edge of Philadelphia, accounts for the majority of
 

recreational/park land in the Cobbs Creek Watershed. The Park’s 220 acres encompass nearly
 

13 miles of stream that eventually drain to the Delaware River. The main stem, which is
 

8.2 miles, accounts for the majority of total stream length. The remaining stream length is made
 

up of tributaries such as Indian Creek, and smaller, un-named streams.
 

For the purposes of this analysis, we focus solely on recreational use along the Cobbs Creek
 

mainstem, as this will be the focus of PWD’s stream restoration program. All improvements
 

along the creek are expected occur within Cobbs Creek Park, which borders the creek throughout
 

most of the CSO area. No additional recreational amenities are planned (i.e., nothing similar to
 

the Frankford Creek Greenway). Stream restoration program activities are expected to result in
 

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improved water quality, restored and expanded trails, and improved access to the creek via
 

expanded riparian areas.
 

To estimate recreational use along Cobbs Creek, we employed a methodology similar to the
 

methodology used for our analysis of the Tacony-Frankford Watershed. Our methodology and
 

results are described below.
 

Baseline recreational use 

In the absence of data for current recreational use at Cobbs Creek Park, we relied on the per-acre
 

baseline use established for Tacony Creek Park. We applied this baseline estimate to the
 

220 acres of Cobbs Creek Park, assuming that per-acre use is about 15% higher at Cobbs Creek
 

Park than at Tacony Creek Park. This assumption was based on on-site visits and qualitative
 

descriptions of each park. Based on our per-acre use application (with the 15% adjustment), we
 

estimate that currently, Cobbs Creek Park supports about 280,000 visits each year.
 

Additional recreational visits to Cobbs Creek under the LID options 

Similar to our analysis of recreational benefits in Tacony Creek Park, we calculated the average
 

number of per-acre visits to all Philadelphia parks for specific activities expected to occur in the
 

park under the LID options. We assumed the same mix of recreational activities for Cobbs Creek
 

as we did for Tacony Creek Park.
 

We applied the per-acre estimates for specific recreational activities to Cobbs Creek Park and
 

assumed that under the LID/stream restoration improvements, Cobbs Creek Park would likely
 

support about 40% of the per-acre visitation experienced at an average park in Philadelphia. This
 

assumption is based on the relative “local” nature of the park (e.g., compared to the regional
 

appeal of East and West Fairmount parks), surrounding neighborhood demographics, and
 

discussions with Fairmount Park representatives.
 

Based on these assumptions, we estimate that approximately 2.7 million people would visit
 

Cobbs Creek Park each year under the LID options (at full program implementation). This
 

includes the baseline estimate of individuals who already visit the park, as well as visits from
 

individuals who would have visited a park elsewhere in Philadelphia if the improvements along
 

Cobbs Creek had not taken place.
 

To estimate the number of additional visits under the LID options, we subtract out the baseline
 

visits and assume that about 70% of the total visits are new visits (rather than visits that would
 

otherwise have taken place at other city parks). Based on these assumptions, we estimate that
 

improvements under the LID options will result in approximately 1.7 million additional visits
 

each year, at full program implementation. This amounts to an additional 50.5 million visits over
 

the 40-year project period, based on the implementation timeline provided by CDM.
 

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Direct use value of additional recreational visits 

To estimate the monetary value associated with these increased visits, we applied direct-use
 

values from the Parks Report, weighted by specific recreational activity. We estimate that
 

improvements under the LID options will result in approximately $3.9 million recreation-related
 

benefits each year, at full program implementation. This amounts to $94 million in present value
 

benefits (2009 USD) over the 40-year project period.
 

A.3.3 Schuylkill River Watershed 

Our analysis of recreational benefits in the Schuylkill River Watershed relies on the information
 

and data reported in the Tidal Schuylkill River Master Plan (EDAW, 2003). The study area of
 

the Master Plan includes the eight-mile stretch of the tidal Schuylkill River (and adjacent land)
 

from the Fairmount dam to the Delaware River. This area consists of a significant amount of
 

industrial land uses that are adjacent to residential, open space, institutional, and other public
 

uses such as the Philadelphia International Airport.
 

There are numerous active and inactive rail lines in the area, including the large and active East
 

Side Yard for CSXT. Several major road corridors also run adjacent to and through the study
 

area including I-95, I-76 (Schuylkill Expressway), I-676, Route 291/Passyunk Avenue, Grays
 

Ferry Avenue, University Avenue, South Street, Walnut Street, Chestnut Street, and Market
 

Street.
 

Land use data reveal that over half of the Master Plan study area (54.75%) is currently devoted to
 

manufacturing, utilities, parking, and transportation (rail and street rights-of-way). Another 29%
 

of land is categorized as wooded, vacant, or water (water associated with industrial uses, not the
 

river and canals). Only 2.52% is currently categorized as recreation and 2.81% as residential of
 

all types.
 

The Master Plan proposes a number of major public investments in the revitalization of the tidal
 

Schuylkill River. These investments include greenway and trail improvements, including
 

neighborhood linkages to the river and “streetscapes,” as well as infrastructure improvements.
 

Based on the Master Plan’s full implementation, the potential development program for the study
 

area could include the development of:
 

� Over 3,270 residential units 

� Over 1,600,000 square feet of retail uses 

� Over 11,300 square feet of restaurants 

� Over 1,000,000 square feet of office space 

� Over 2,000,000 square feet of flex/industrial space 

� Over 100,000 square feet of cultural facilities 

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� Over 150 acres of new green space and park land 

� Over 8 miles of new multi-purpose trails 

� Marinas and boat storage for about 400 boats. 

Improvements made as part of the LID CSO control options in the Lower Schuylkill River will
 

play a role in the implementation of the Tidal Schuylkill River Master Plan. For our analysis of
 

recreational benefits, we focus on the development opportunities described above that can be
 

directly tied to LID CSO control implementation. Based on our understanding of the LID
 

options, this includes the implementation of 150 “creekside” acres of new open space and park
 

land, including trails and streetscape improvements, and the opportunities for new marinas and
 

boat storage. The benefits associated with these improvements are described in the following
 

sections.
 

Additional recreational visits associated with new green space 

To evaluate recreational benefits, we first estimate per-acre visitation for specific recreational
 

activities associated with the additional open space and park land, based on the Parks Report. We
 

then assume that recreational areas in the Lower Schuylkill River would support about 60% of
 

the use of an average Philadelphia Park. This is higher than the 40% estimate used for the
 

Tacony and Cobbs Creek parks due to the park’s more regional nature. However, due to the
 

abundance of recreational opportunities just upstream of the CSO area (e.g., East and West
 

Fairmount parks, Boathouse Row) and the heavy industrial nature of the area, this area will
 

likely see less use than many other parks in the region.
 

Additionally, we also assume that only about 50% of recreational visits to the Lower Schuylkill
 

open space areas will be “new” visits (i.e., visits would not have taken place at another park in
 

the region). This is also based on the abundance of recreational opportunities located just
 

upstream of the Schuylkill CSO area.
 

Based on these assumptions, we estimate that the improvements identified in the Schuylkill
 

River Master Plan (associated with green space, trails, and pedestrian linkages only) will amount
 

to about 1.3 million new recreational visits per year, assuming full program implementation. This
 

amounts to about 40.2 million new visits over the 40-year project period, taking into account the
 

stream restoration implementation timeline provided by CDM. Our analysis assumes no baseline
 

level of visitation to this area due to its highly industrial nature and current land uses.
 

Additional recreational visits for boating and fishing 

In addition to the benefits associated with new green space, the Master Plan identifies
 

opportunities for the development of marinas and boat storage for about 400 boats. We include
 

this in our analysis of recreational benefits because it can be directly tied to improvements in
 

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water quality as well as the implementation of aesthetic and recreational amenities
 

(e.g., additional open space) under the LID CSO control options.
 

To estimate the number of new trips to the Lower Schuylkill River for fishing and boating, we
 

rely on original survey data from the Parks Report, provided by the Trust for Public Lands.2 We
 

used these data to determine the number of average trips per year taken by Philadelphians who
 

engage in fishing and/or boating. We then assume an average of 3 people per boat/fish trip and
 

that about 60% of the trips taken on the Lower Schuylkill River would be “new trips”
 

(i.e., would not have taken place elsewhere). Based on these assumptions, we estimate an
 

additional 4,400 trips each year at full program implementation. This amounts to about
 

131,600 trips over the 40-year project period.
 

Direct use value of additional recreational visits 

Similar to our analysis of Tacony and Cobbs Creek watersheds, we used direct-use values for
 

specific recreational activities from the Parks Report to determine total benefits. Based on these
 

values, we estimate the annual value of new recreational visits resulting from the implementation
 

of 150 acres of open space, including trails and pedestrian linkages to the river, to be about
 

$3.1 million (2009 USD) at full program implementation. Based on the implementation timeline
 

provided by CDM, this amounts to more than $73.4 million in present value benefits
 

(2009 USD) over the 40-year project period. Increased participation in boating and fishing in the
 

Lower Schuylkill will provide an additional $19,172 in annual direct-use benefits, or a total of
 

$460,000 in present value benefits over the 40-year project period.
 

A.3.4 Delaware River Watershed 

In absence of specific data for the Delaware River Watershed, we assume that on a per-stream
 

mile basis, the LID CSO control options for the Delaware River will include the same amount of
 

open/green space area as planned for the Schuylkill River.
 

As noted above, there are about 150 acres (or about 21 acres per stream-mile) of open/green
 

space planned for the Lower Schuylkill area, which encompasses about 8.7 miles of river.
 

Applying this to the 15.6 miles of the Delaware River within PWD’s CSO area, we estimate
 

there will be about 341 acres of new open/green space under the LID CSO options. Similar to the
 

Schuylkill Watershed, this additional acreage is separate from the vegetated acreage planned for
 

areas throughout the watershed, as reported in the section on “non-creekside” recreational
 

2. The raw survey data is unweighted and does not account for differences in demographic characteristics of
 

the study population and the population of Philadelphia County.
 

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benefits. For our evaluation of non-creekside benefits, we subtracted out the open/green space
 

acreage planned for the area along the river.
 

We used the same methodology as described for the Schuylkill River to estimate the recreational
 

benefits associated with this new area. Based on this methodology, we estimate that
 

implementation of the stream restoration program under the LID CSO control options will result
 

in about 2.6 million additional creekside recreational visits each year, at full program
 

implementation. This amounts to about 76.1 million visits over the 40-year project period, taking
 

into account the project implementation timeline.
 

In terms of direct use benefits, additional recreational visits to the Delaware River will result in
 

an annual benefit of $5.8 million (2009 USD), at full program implementation. Over the 40-year
 

project period, this amounts to $139 million in present value benefits (2009 USD).
 

A.4 Summary of Results 

Tables A.7 and A.8 provide a summary of total recreational benefits associated with the LID
 

CSO control options. Table A.7 shows the additional number of recreational visits and the direct-


use benefits, in present value terms, associated with additional non-creekside recreation.
 

Table A.8 shows the same results for the creekside recreational analysis.
 

Table A.7. Summary of additional recreational visits under the LID CSO control options, 

over the 40-year project period 

Tacony Cobbs Schuylkill Delaware
 

Non-creekside recreation 

25% LID 6,376,780 2,413,061 3,481,727 6,528,626 

50% LID 22,714,215 8,629,946 22,991,914 47,402,472 

75% LID 32,326,746 12,292,929 34,486,588 71,448,114 

100% LID 38,815,401 14,751,738 42,245,022 87,670,535 

Creekside recreationa 80,527,887 50,478,407 40,371,870 76,146,118 

a. Applies to all LID options. 

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Table A.8. Summary of monetized recreational benefits under the LID CSO control options, 

over the 40-year project period (present valuea

) 

Tacony Cobbs Schuylkill Delaware
 

Non-creekside recreation
 

25% LID $4,499,951 $1,702,843 $2,456,977 $$4,684,956
 

50% LID $16,028,916 $6,089,960 $16,224,881 $34,016,111
 

75% LID $22,812,264 $8,674,846 $24,336,416 $51,271,313
 

100% LID $27,391,163 $10,409,972 $29,811,370 $62,912,556
 

Creekside recreationb $145,154,937 $94,100,602 $73,900,681 $138,970,735
 

a. Present value estimates presented in 2009 USD, assuming a 4% inflation rate and 4.875% discount rate.
 

b. Applies to all LID options.
 

A.5 Omissions, Biases, and Uncertainties 

To estimate the total recreational benefits under the LID alternatives, it was necessary to make a
 

number of assumptions in the absence of specific data. In addition, a number of data omissions
 

and uncertainties surrounding the analysis have been identified throughout this report. Table A.9
 

provides a summary of these assumptions and uncertainties and their likely impact on our
 

estimation of recreational benefits.
 

Table A.9. Omissions, biases, and uncertainties 

Likely 

impact on 

Assumption/methodology net benefitsa Comment/explanation 

Only “new” visits are - Our analysis only includes visits that would not have occurred 

included in the analysis elsewhere if the LID improvements had not been implemented. 

However, there is a marginal benefit associated with the trips that 

would have occurred in another location (or the individuals would 

continue to make trips to this location under the LID alternatives). 

Given the relatively low direct-use values, the exclusion of these 

benefits does not likely make a significant impact on overall 

benefits. 

Further, the percentage of total visits that are “new” is based on 

qualitative discussions and on-site visits. A degree of uncertainty 

surrounds these assumptions. 

Page A-16 

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Stratus Consulting Appendix A (Final, 8/24/2009)
 

Table A.9. Omissions, biases, and uncertainties (cont.)
 

Assumption/methodology 

Likely 

impact on 

net benefitsa Comment/explanation 

Non-Philadelphia residents 

are not included in the 

analysis 

+/++ The Parks Report includes park visitation data for Philadelphia 

residents only. Non-Philadelphia residents are therefore not included 

in our analysis due to lack of data on how often they visit 

Philadelphia Parks. Inclusion of these visitors would increase 

overall benefits, most likely in the Schuylkill and Delaware River 

watersheds, which have a more regional appeal. 

Direct use values do not 

take into account the 

quality of the recreational 

experience 

U If the quality of recreational visits to CSO watersheds is higher (or 

lower) than for visits to an average park in Philadelphia, users might 

experience a higher (or lower) value per outing. Locational factors 

(e.g., proximity to existing parks or neighborhood demographics) 

may also affect the quality of the recreational experience. 

The direct-use values used 

in this analysis are low 

compared to similar studies 

+ The direct use values in the Parks Report are relatively low. 

However, in Philadelphia, recreational values are not expected to 

amount to as much as those in more remote areas. In the City, most 

people do not have to travel far to reach the parks, and residents 

spend a shorter time recreating once they get to the park. 

Further, based on qualitative descriptions of parks in the watershed, 

the quality of the experience seems to be lower than in other areas 

used in many valuation studies. 

Analysis relies on average 

per-acre visitation estimates 

for all parks in Philadelphia 

U- Our analysis assumes that parks/recreational land in CSO 

watersheds support a certain percentage of recreational use of an 

average park in Philadelphia on a per-acre basis. This is based on 

on-site visits, review of park master plans, and discussions with park 

staff. Increasing/decreasing this assumption would impact net 

benefits. 

Locational factors (e.g., proximity to existing parks or neighborhood 

demographics) and the amount of contiguous land in improved areas 

may also affect per-acre use. 

On-the ground 

implementation 

U There is a large degree of uncertainty surrounding planned activities 

under the LID options (e.g., location in the watershed) and how 

these activities will affect recreational use. It is therefore difficult to 

estimate the benefits associated with them. Our estimates are 

intended to provide an approximation of total benefits, based on our 

understanding of program implementation and the best available 

data for current recreational activity in Philadelphia. 

a. Indicating how addressing the assumption or overcoming the omission would probably impact the analysis,
 

using the following key: + would likely increase net benefits; ++ would increase net benefits significantly;
 

U direction of change in net benefit is uncertain; - would diminish net benefits; -- would diminish net benefits
 

significantly.
 

Page A-17 

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Stratus Consulting Appendix A (Final, 8/24/2009)
 

Bibliography 

Academy of Natural Sciences (Patrick Center), Natural Lands Trust and the Conservation Fund.
 

2001. Schuylkill Watershed Conservation Plan. Prepared for Pennsylvania Department of
 

Conservation and Natural Resources and the William Penn Foundation.
 

EDAW. 2003. The Tidal Schuylkill River Master Plan: Creating a New Vision. Prepared for the
 

Schuylkill River Development Corporation. Funded in part by the Department of Conservation
 

and Natural Resources, Bureau of Recreation and Conservation; William Penn Foundation; US
 

Army Corps of Engineers; PWD, OOW; National Park Service, RTC; Pennsylvania Dept. of
 

Community and Economic Development; Philadelphia Dept. of Commerce; US EPA, Region III.
 

EDAW. 2008. Juniata Golf Course Land Use and Feasibility Study. Prepared for Fairmount
 

Parks Conservancy.
 

Fairmount Park Commission. 1999. Tacony Creek Park Master Plan, Natural Land Restoration
 

Master Plan, Park-Specific Master Plans.
 

Heritage Conservancy and NAM Planning & Design. 2003. Tookany Creek Watershed
 

Management Plan (River Conservation Plan). Funded by the Rivers Conservation Program of
 

the Pennsylvania Department of Conservation and Natural Resources.
 

Pennsylvania Department of Environmental Protection, Bureau of Watershed Management.
 

1999. Unified Watershed Assessment Report.
 

Philadelphia Water Department. 2004. Tacony-Frankford River Conservation Plan, Final
 

Report. Tookany-Tacony-Frankford Watershed Partnership.
 

Philadelphia Water Department. 2005. Tookany/Tacony-Frankford Integrated Watershed
 

Management Plan. Tookany/Tacony-Frankford Watershed Partnership.
 

Philadelphia Water Department and Darby-Cobbs Watershed Partnership. 2004. Cobbs Creek
 

Integrated Watershed Management Plan, Final Report.
 

Philadelphia Water Department Office of Watersheds. 2001. Tacony-Frankford Creek
 

Watershed Assessment (Draft).
 

Trust for Public Lands, Center for City Park Excellence. 2008. How Much Value Does the City
 

of Philadelphia Receive from its Park and Recreation System? Prepared for the Philadelphia
 

Parks Alliance.
 

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B. Property Values, as Enhanced by the LID Options
 

B.1 Summary 

Residential property value benefits are calculated for properties within the four watersheds
 

relevant to this analysis: Cobbs Creek, Delaware Direct, Lower Schuylkill River, and Tacony-


Frankford Creek. Specifically, benefits are quantified separately for properties within PWD’s
 

combined sewer area and those outside of the area; and the analysis is limited to the City.
 

Benefits to properties outside of the combined sewer area and within the Lower Schuylkill River
 

Watershed are excluded from the analysis because this area already has a considerable amount of
 

LID, including East and West Fairmount Parks, and we do not anticipate any significant
 

additional benefits to properties in this area. An estimate is provided for each of the other seven
 

geographic areas using a range of benefits found in the literature. These estimates are meant to
 

account for benefits that accrue to property owners from implementation of the LID options, or a
 

significant aspect of the LID options (e.g., trees), that are unique from other benefit estimates
 

presented in this report. Estimates of property value benefits from the green infrastructure LID
 

options are summarized in Tables B.1 through B.4. Details on the derivation of these estimates
 

are presented below.
 

B.2 Data and Methods 

Estimates are calculated using neighborhood-level property count and price data from the
 

Philadelphia “NIS neighborhoodBase,” a database of spatial and numerical data maintained by
 

the University of Pennsylvania’s Cartographic Modeling Lab (CML, 2005). The total number of
 

properties within a watershed (both within and outside of the combined sewer area) is compiled
 

using GIS data obtained on neighborhood boundaries, watershed boundaries, and combined
 

sewer area boundaries. The neighborhood data contain census housing unit counts, which are
 

used to aggregate counts over several neighborhoods within a given watershed.
 

Using 2007 median sales price data from the NIS neighborhoodBase, a weighted average market
 

value is derived for properties sold within a given geographic area of interest (e.g., within the
 

combined sewer area for a given watershed). Each neighborhood has a portion of the total
 

properties sold for a given geographic area in 2007. Multiplying each of these neighborhood
 

proportions by its median sales price for 2007 and summing over all neighborhoods, we derive a
 

weighted average market value. Using the median selling price data helps to mitigate sensitivity
 

to extreme selling prices, since only a fraction of properties sell within a given year. Moreover, if
 

a certain type of property sold more heavily in 2007, relative to a historical baseline of sales by
 

property type (e.g., condominiums vs. single family homes), the median will be less sensitive to
 

this. It is for these reasons that median selling price is favored over the mean.
 

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Stratus Consulting Appendix B (Final, 8/24/2009)
 

Table B.1. Summary of residential property value benefits from 25% LID program 

elements (2009 USD) 

Within combined Outside combined 

sewer area sewer area Total 

Total residential properties 503,882 48,544 552,426 

Weighted average median sales price $128,307 $152,920 $130,470 

Estimated total market value of affected 

residential properties $16,162,924,000 $1,855,841,000 $18,018,765,000 

Low-end estimate of one-time increase in 

residential property value for 25% LID $161,629,000 $2,941,000 $164,570,000 

Average estimate of one-time increase in 

residential property value for 25% LID $282,851,000 $5,146,000 $287,997,000 

High-end estimate of one-time increase in 

residential property value for 25% LID $404,073,000 $7,352,000 $411,425,000 

Table B.2. Summary of residential property value benefits from 50% LID program 

elements (2009 USD) 

Within combined Outside combined 

sewer area sewer area Total 

Total residential properties 503,882 48,544 552,426 

Weighted average median sales price $128,307 $152,920 $130,470 

Estimated total market value of affected 

residential properties $32,325,848,000 $3,711,682,000 $36,037,530,000 

Low-end estimate of one-time increase in 

residential property value for 50% LID $323,258,000 $5,881,000 $329,140,000 

Average estimate of one-time increase in 

residential property value for 50% LID $565,702,000 $10,292,000 $575,995,000 

High-end estimate of one-time increase in 

residential property value for 50% LID $808,146,000 $14,703,000 $822,850,000 

Page B-2 

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Stratus Consulting Appendix B (Final, 8/24/2009)
 

Table B.3. Summary of residential property value benefits from 75% LID program 

elements (2009 USD) 

Within combined Outside combined 

sewer area sewer area Total 

Total residential properties 503,882 48,544 552,426 

Weighted average median sales price $128,307 $152,920 $130,470 

Estimated total market value of affected 

residential properties $48,488,771,000 $5,567,523,000 $54,056,294,000 

Low-end estimate of one-time increase in 

residential property value for 75% LID $484,888,000 $8,822,000 $493,710,000 

Average estimate of one-time increase in 

residential property value for 75% LID $848,554,000 $15,438,000 $863,992,000 

High-end estimate of one-time increase in 

residential property value for 75% LID $1,212,219,000 $22,055,000 $1,234,274,000 

Table B.4. Summary of residential property value benefits from 100% LID program 

elements (2009 USD) 

Within combined Outside combined 

sewer area sewer area Total 

Total residential properties 503,882 48,544 552,426 

Weighted average median sales price $128,307 $152,920 $130,470 

Estimated total market value of affected 

residential properties $64,651,695,000 $7,423,364,000 $72,075,059,000 

Low-end estimate of one-time increase in 

residential property value for 25% LID $646,517,000 $11,763,000 $658,280,000 

Average estimate of one-time increase in 

residential property value for 25% LID $1,131,405,000 $20,585,000 $1,151,989,000 

High-end estimate of one-time increase in 

residential property value for 25% LID $1,616,292,000 $29,407,000 $1,645,699,000 

The literature suggests a range of benefits from green storm water infrastructure, or LID, from
 

0% to 7%. This implies the average property value will increase anywhere from 0% to 7% due to
 

LID additions to the surrounding landscape. A further discussion of the literature is provided
 

later in this appendix. For the calculations below, we tighten this range to 2–5% for properties
 

within the combined sewer area, with a mean increase of 3.5%, given that most of the studies
 

provide estimates within this inner range.
 

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Stratus Consulting Appendix B (Final, 8/24/2009)
 

In the absence of spatial data that outline the specific location and magnitude of LID
 

installments, we calculate total market value of affected residential properties under four LID
 

scenarios: 25%, 50%, 75%, and 100% LID coverage. Under the 50% scenario, for example, the
 

total market value of affected residential properties for a given area is calculated as 50% of the
 

total number of properties in that area times its weighted average median selling price.
 

Given that LID will be implemented within the combined sewer area, properties in the near
 

vicinity of these changes will capitalize the greatest benefit (i.e., those properties within the
 

combined sewer area). However, properties outside the combined sewer area will arguably
 

accrue some benefit, though perhaps at a diminished rate. A number of studies reflect this
 

“decay” in benefit as distance from the amenity increases (see Correll et al., 1978; Tyrvainen and
 

Miettinen, 2000; Morancho, 2003; Wachter and Wong, 2006). For properties outside the
 

combined sewer area, we adjust the benefit estimates range downward from 2%–5% to 1%–
 

2.5%. This downward adjustment reflects the decay of benefits as indicated by the literature.
 

Calculations for properties both within and outside the combined sewer area assume benefits
 

accrue uniformly among affected properties.
 

Property value estimates from the literature encompass a wide range of benefits associated with
 

LID. Many of these are not distinct from other benefits presented in this report (e.g., anticipated
 

energy cost savings are likely to be capitalized, to some extent, in the increased property values
 

of tree-shaded properties). In theory, changes in property values should reflect associated
 

differences in air quality, water quality, energy usage (often relating to heat stress), flood control,
 

and perhaps other benefits (particularly those qualitative in nature). For example, a property in an
 

area with good air quality should sell for a higher amount relative to another property in an area
 

with low air quality, all else equal. Thus, to simply add property value benefits with the benefits
 

from improved air quality would be double-counting. This applies to most benefit categories in
 

this report. Therefore, only a portion of the literature estimates should be considered unique from
 

other benefits in this report, such as those stemming from aesthetic improvements. To account
 

for this, we adjust estimates from the literature downward by 50% to arrive at a range of 1–2.5%
 

for properties within the combined sewer area and 0.5–1.25% for properties outside the
 

combined sewer area.
 

Tables B.1 through B.4 show the projected benefits under the four LID scenarios, within and
 

outside of the combined sewer area. Under each scenario, the total market value of affected
 

properties is multiplied by the endpoints of the corresponding benefit estimates range, along with
 

the mean. This yields aggregated benefit estimates for increases in property values. For example,
 

the estimated average benefits for properties within the combined sewer area under the 50% LID
 

scenario is a one-time increase of $565.7 million.
 

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Stratus Consulting Appendix B (Final, 8/24/2009)
 

Total property value benefits range, on average, from $282.9 million to $1.13 billion for
 

properties within the combined sewer area and between $5.1 and $20.6 million for properties
 

outside the combined sewer area, depending on the LID scenario. This leads to a total estimate of
 

average benefits ranging from $288.0 million for 25% LID to $1.15 billion for 100% LID.1
 

B.3 Literature Used in the Benefits Transfer 

The “benefits transfer” methodology is used to calculate the above estimates. Due to the high
 

costs of carrying out original research, primarily in terms of time, existing estimates for property
 

benefits associated with LID or specific aspects of LID are applied to the Philadelphia context.
 

As Sample et al. (2003) and Powell et al. (2005) point out, more research is needed in
 

quantifying the benefits of LID; therefore, the pool of studies from which to choose is somewhat
 

small. However, a number of studies were reviewed and six studies were selected as good
 

candidates for a benefits transfer, given their similar context and scope. All six studies estimate a
 

bundle of benefits associated with trees/LID/green storm water management in general. These
 

studies are summarized in Table B.5. A brief summary is offered for each study, along with the
 

estimate itself.
 

Table B.5. Studies used in benefits transfer 

Estimate 

Study Summary of study (% increase in value) 

Ward et al. Estimates effect of LID on adjacent properties relative to those 3.5−5.0% 

(2008) farther away, in King County (Seattle), WA. 

Shultz and Proxies LID effects by looking at differentials for neighborhoods Greenways: 1.1−2.7%; 

Schmitz (2008) with clustered open spaces and greenways, etc., in Omaha, NE. clustered open space: 

0.7−1.1% 

McPherson References an uncited study that looks at the differentials between 3−7% 

et al. (2006) properties with ample trees vs. none or few trees (few details). 

Wachter and Estimates the effect of tree plantings on property values for select 2% 

Wong (2006) neighborhoods in Philadelphia. (intrinsic value of trees) 

Anderson and Uses sales data from Athens-Clarke County (GA) to estimate the 3.5−4.5% 

Cordell (1988) value of trees on residential property. Looks at differences between 

houses with five or more front yard trees and those that have fewer. 

Braden and Uses meta-analysis of studies to estimate several benefit categories 0−5% 

Johnston (2003) related to on-site storm water retention (green approach/LID) for 

managing storm water. 

1. Watershed-specific estimates are provided in Section B.4.
 

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Stratus Consulting Appendix B (Final, 8/24/2009)
 

B.4 Watershed-Specific Results 

The tables that follow (Tables B.6 through B.12) show the property value results, by watershed
 

and LID option. The benefit estimates reported here reflect the 50% reduction in increased
 

property values described above, so as to focus on the aesthetic value of improvements provided
 

by the added vegetation (i.e., reflecting a conservative approach to precluding possible double
 

counting of energy savings and other benefits that might be embedded within the property value
 

estimates).
 

Table B.6. Summary table of estimates (within combined 

sewer area; Tacony-Frankford Creek Watershed) 

LID option (% increase) Low % increase High % increase 

25% $22,160,000.00 $55,399,000.00
 

50% $44,319,000.00 $110,798,000.00
 

75% $66,479,000.00 $166,197,000.00
 

100% $88,639,000.00 $221,596,000.00
 

Table B.7. Summary table of estimates (within combined 

sewer area; Cobbs Creek Watershed) 

LID option (% increase) Low % increase High % increase 

25% $7,010,000 $17,525,000
 

50% $14,020,000 $35,049,000
 

75% $21,030,000 $52,574,000
 

100% $28,040,000 $70,099,000
 

Table B.8. Summary table of estimates (within combined 

sewer area; Delaware Direct Watershed) 

LID option (% increase) Low % increase High % increase 

25% $77,123,000 $192,808,000
 

50% $154,246,000 $385,615,000
 

75% $231,369,000 $578,423,000
 

100% $308,492,000 $771,230,000
 

Page B-6 

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Stratus Consulting Appendix B (Final, 8/24/2009)
 

Table B.9. Summary table of estimates (within combined 

sewer area; Lower Schuylkill River Watershed) 

LID option (% increase) Low % increase High % increase 


25% $55,337,000 $138,342,000
 

50% $110,673,000 $276,683,000
 

75% $166,010,000 $415,025,000
 

100% $221,347,000 $553,367,000
 

Table B.10. Summary table of estimates (outside combined 

sewer area; Tacony-Frankford Creek Watershed) 

LID option (% increase) Low % increase High % increase
 

25% $2,133,000 $5,333,000
 

50% $4,266,000 $10,666,000
 

75% $6,399,000 $15,998,000
 

100% $8,532,000 $21,331,000
 

Table B.11. Summary table of estimates (outside combined 

sewer area; Cobbs Creek Watershed) 

LID option (% increase) Low % increase High % increase
 

25% $81,000 $203,000
 

50% $162,000 $406,000
 

75% $244,000 $609,000
 

100% $325,000 $812,000
 

Table B.12. Summary table of estimates (outside combined 

sewer area; Delaware Direct Watershed) 

LID option (% increase) Low % increase High % increase
 

25% $726,000 $1,816,000
 

50% $1,453,000 $3,632,000
 

75% $2,179,000 $5,447,000
 

100% $2,905,000 $7,263,000
 

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Stratus Consulting Appendix B (Final, 8/24/2009)
 

B.5 Omissions, Biases, and Uncertainties 

To estimate property value benefits under the LID alternatives, it was necessary to make a
 

number of assumptions in the absence of specific data. In addition, a number of data omissions
 

and uncertainties surrounding the analysis have been identified throughout this report.
 

Table B.13 provides a summary of these assumptions and uncertainties and their likely impact on
 

our estimation of property value benefits.
 

Table B.13. Omissions, biases, and uncertainties 

Assumption/ 

methodology 

Likely impact 

on net benefitsa Comment/explanation 

Focuses only on 

residential properties 

++ Property values for commercial, industrial, and other nonresidential properties are excluded from the analysis. Including 

the benefits to these properties would increase net benefits. 

Based on benefits 

transfer approach, using 

range of 2–5% 

U The literature provides estimates for increases in residential 

property values from 0–7% due to LID implementation. We 

narrow this range to 2–5%. A Philadelphia-specific study, 

Wachter and Wong (2006), estimates the benefits to residential 

properties from tree plantings at 2%. Estimates used in this 

benefits transfer are assumed to be, on average, for a similar 

population and scale. Studies were chosen with these 

considerations. 

Estimates are based on 

marginal changes to land 

market 

U Estimates used in the benefits transfer are based largely on 

hedonic analyses, which reflect benefits associated with marginal 

changes in a land market. We assume the aggregation of benefits 

over multiple properties around the City is a marginal change. 

Reducing property value 

benefits to reflect 

potential double-counting 

U To avoid double-counting, we adjust property value benefits 

downward by 50%. This adjustment is ad hoc, but is used to 

estimate unique benefits to residential properties that are not 

estimated in other parts of the report. For example, enhanced 

aesthetics is a unique benefit, while reduced heat stress is not. 

Number of affected 

properties 

U The number of residencies impacted depends on the LID option 

for which benefits are calculated. These range from 25%–100% as 

presented in Tables B.1–B.4. 

Affected properties 

accrue benefits uniformly 

U All affected properties are assumed to accrue benefits uniformly. 

Considerations for baseline conditions or precise locations of LID 

implementations could not be made reliably in the absence of 

better data. 

Page B-8 

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Stratus Consulting Appendix B (Final, 8/24/2009)
 

Table B.13. Omissions, biases, and uncertainties (cont.)
 

Assumption/ Likely impact 

methodology on net benefits Comment/explanation 

Average property price is U, but small The average property price for a given geographic area (used to 

the weighted average of derive total market value for that area) is calculated by taking the 

median prices from the sales price for each neighborhood and multiplying by the share of 

affected neighborhoods residential properties sold within those neighborhoods, summing 

over all neighborhoods. 

a. Indicating how addressing the assumption or overcoming the omission would probably impact the analysis,
 

using the following key: + would probably increase net benefits; ++ would increase net benefits significantly;
 

U direction of change in net benefit is uncertain; - would diminish net benefits; -- would diminish net benefits
 

significantly.
 

Bibliography 

Anderson, L.M. and H.K. Cordell. 1988. Influence of trees on residential property values in
 

Athens, Georgia (U.S.A.): A survey based on actual sales prices. Landscape and Urban Planning
 

15:153–164.
 

Braden, J.B. and D.M. Johnston. 2003. The downstream economic benefits of storm water
 

retention. Journal of Water Resources Planning and Management 130(6):498–505.
 

CML. 2005. Philadelphia NIS neighborhoodBase. The Cartographic Modeling Lab. Available:
 

http://cml.upenn.edu/nbase/. Accessed October 15, 2008.
 

Correll, M.R., J.H. Lillydahl, and L.D. Singell. 1978. The effects of greenbelts on residential
 

property values: Some findings on the political economy of open space. Land Economics
 

54(2):207–217.
 

McPherson, E.G., J.R. Simpson, P.J. Peper, S.L. Gardner, K.E. Vargas, S.E. Maco, and Q. Xiao.
 

2006. Piedmont Community Tree Guide: Benefits, Costs, and Strategic Planting. USDA Forest
 

Service General Technical Report PSW-GTR-200.
 

Morancho, A.B. 2003. A hedonic valuation of urban green space. Landscape and Urban
 

Planning 66(1):35–41.
 

Philadelphia Department of Revenue. 2007. Millage Rate Data. Available:
 

http://www.phila.gov/revenue/. Accessed October 23, 2008.
 

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Powell, L.M., E.S. Rohr, M.E. Canes, J.L. Cornet, E.J. Dzuray, and L.M. McDougle. 2005. Low-


Impact Development Strategies and Tools for Local Governments: Building a Business Case.
 

Report No. LID50T1. LMI Government Consulting. September.
 

PWD. 2008. Public Survey Tacony-Frankford River Conservation Plan. Available:
 

http://www.phillyriverinfo.org/WICLibrary/Tookany-TaconyFrankford%20Integrated%20Watershed%20Management%20Plan%20­

%20Appendix_B_Tacony-Frankford_RCPsurvey.pdf. Accessed October 1, 2008.
 

Sample, D.J., J.P. Heaney, L.T. Wright, C-Y Fan, F-H Lai, and R. Field. 2003. Costs of best
 

management practices and associated land for urban stormwater control. Journal of Water
 

Resources Planning and Management 129(1):59–68.
 

Shultz, S. and N. Schmitz. 2008. How Water Resources Limit and/or Promote Residential
 

Housing Developments in Douglas County. Final Project Report. UNO Research Center, Omaha,
 

NE. Available: http://unorealestate.org/pdf/UNO_Water_Report.pdf. Accessed September 1,
 

2008.
 

The Trust for Public Land. 2008. How Much Value Does the City of Philadelphia Receive from
 

Its Park and Recreation System? A Report by The Trust for Public Land’s Center for City Park
 

Excellence for the Philadelphia Parks Alliance, Philadelphia, PA. June.
 

Tyrvainen, L. and A. Miettinen. 2000. Property prices and urban forest amenities. Journal of
 

Economics and Environmental Management 39:205–223.
 

Wachter, S.M. and G. Wong. 2006. What is a tree worth? Green-city strategies and housing
 

prices. Real Estate Economics 36(2):2008.
 

Ward, B., E. MacMullan, and S. Reich. 2008. The effect of low-impact development on property
 

values. ECONorthwest.
 

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C.	 Heat Stress and Related Premature 

Fatalities Avoided 

This appendix describes the methodology used to evaluate the benefits associated with the
 

reduction in EHEs and heat-related fatalities under the LID CSO control options currently being
 

considered by the PWD. Results of our analysis are also provided.
 

C.1	 Introduction 

EHEs have a well documented history of adverse public health impacts. Relatively recent
 

demonstrations of this heat-health relationship include the loss of roughly 15,000 lives in France
 

during the 2003 European EHE (Koppe et al., 2004; Valleron and Mendil, 2004) and over
 

700 deaths in Chicago, Illinois, in a July 1995 EHE (Kaiser et al., 2007). In addition to causing
 

increased mortality, EHEs have also been associated with a range of morbidity impacts including
 

increased emergency room use (NOAA, 1995) and hospitalizations (Semenza et al., 1999).
 

Philadelphia has its own tragic history of adverse public health impacts from EHEs. Notably, in
 

1991 and 1993, the county coroner determined EHEs were responsible for over 20 and
 

100 deaths, respectively (CDC, 1994; U.S. EPA, 2006). These findings drew significant attention
 

to the heat-health relationship in Philadelphia and resulted in a number of formal responses
 

including:
 

�	 The establishment of Philadelphia’s Heat Task Force to help develop and implement 

EHE notification and response plans. 

�	 Interest from the City in developing a meteorological warning system to predict when 

threatening conditions were expected. This ultimately led to the development of 

Philadelphia’s Heat Watch Warning System, which predicts daily mortality increases 

based on forecast weather conditions (Kalkstein et al., 1996). 

Concern about the heat-health issue continued to build and drive research from the late 1980s
 

through the 1990s. A similar pattern developed with respect to examining how the urban
 

environment can increase the severity and/or duration of residents’ exposure to elevated
 

temperatures. These associated health concerns, combined with interest in reducing the electrical
 

demand within urban areas, helped spur research into what is commonly known as UHI issues,
 

particularly the potential for different mitigation actions (U.S. EPA, 2008a). Within this field,
 

one studied UHI mitigation strategy involves increasing the reflectiveness (i.e., albedo) of urban
 

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Stratus Consulting	 Appendix C (Final, 8/24/2009)
 

surfaces and/or increasing the acreage of urban vegetation (e.g., Hudischewskyj et al., 2001;
 

Sailor, 2003).
 

The LID CSO control options are expected to increase the City’s vegetated acreage. Thus, the
 

envisioned LID programs will mimic urban revegetation programs focused on addressing the
 

UHI. As a result, the LID options are expected to generate ancillary health benefits by reducing
 

urban summer temperatures.
 

This appendix first provides a summary of results from studies that have estimated urban
 

temperature reductions associated with increasing urban vegetation. As described below, these
 

results are used to define a range of plausible scenarios for how the increase in vegetated acreage
 

under the LID CSO control options could affect urban weather conditions in Philadelphia. The
 

meteorological changes defined in these scenarios are then used to estimate the potential benefit
 

of the LID programs in terms of avoided heat-attributable deaths. The appendix concludes with a
 

series of final comments and considerations including a review of potential omissions, biases,
 

and uncertainties in the study methods and results.
 

C.2	 Modeled and Predicted Urban Temperature Reductions from 

Increased Urban Vegetation 

Complex spatial models have been used to estimate how increasing urban vegetation can affect
 

solar energy absorption and ultimately local meteorological values such as temperature and
 

humidity. In these applications, the study area is first divided into grid cells. Each grid cell is
 

then assigned to a land category class that has its own unique combination of attribute values
 

(e.g., solar reflectivity/absorption, moisture, roughness). The impact of a program that increases
 

urban vegetation is then accounted for by recalculating and reassigning attribute values in cells
 

where the policy would be implemented.
 

For example, in the simplest approach, each grid cell would be assigned to one of two land
 

categories, nonvegetated or vegetated. A policy to increase urban vegetation would then describe
 

a percentage increase in vegetation, for example, a 10% increase in the study area. To simulate
 

the effects of this policy, a new set of attribute values would be calculated for all cells initially
 

assigned to the nonvegetated category. These new attribute values would reflect a weighted
 

average of the nonvegetated and vegetated attribute values. In this hypothetical scenario, the new
 

attribute value in previously nonvegetated cells would now be equal to 90% of the original
 

nonvegetated attribute value plus 10% of the vegetated attribute. Values for cells originally
 

categorized as vegetated would remain unchanged in this example. The policy’s impact on urban
 

conditions is then calculated by running an urban meteorology model for the base case and the
 

policy case and calculating the difference between meteorological values of interest
 

(e.g., average daily temperature).
 

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This approach has previously been used to estimate the impact of a 10% increase in urban
 

vegetated acreage for a number of U.S. cities, including Philadelphia (Hudischewskyj et al.,
 

2001; Sailor, 2003), over a limited number of days. In the Hudischewskyj et al. (2001) study, the
 

modeling was limited to considering the period July 14–15, 1995. Sailor (2003) modeled a
 

number of multi-day events from June through August 1991–2001. Table C.1 presents the results
 

of both studies with respect to changes in various air temperature measures.
 

Table C.1. Summary of urban temperature impact results from increasing urban 

vegetation in Philadelphia 

Vegetation Modeled temperature 

Study scenario change result (°F) Notes 

Sailor (2003) 10% increase in 0.39 (average temperature) Average temperature is the 

urban vegetation average of hourly differences 

from increased calculated from 8 a.m. to 7 p.m. 

deciduous broadleaf 0.49 (maximum temperature) Maximum temperature is the tree cover difference between the 

maximum daily temperatures in 

the control and policy cases 

Hudischewskyj 10% increase in 0.70 (maximum temperature 7/14) Difference in maximum surface 

et al. (2001) urban vegetation 

(type of vegetation 0.40 (maximum temperature 7/15) temperatures in base and policy case 

not clearly 

specified) 

The results in Table C.1 suggest that increasing vegetation by 10% in Philadelphia might reduce 

urban temperatures by between 0.40°F and 0.70°F depending on the temperature measure 

(i.e., maximum vs. average temperature). 

A similar study (Columbia University Center for Climate Systems Research et al., 2006) 

evaluated a number of potential changes to the urban landscape in New York City. The study 

estimated that there would be a 0.40°F reduction in temperature at 3 p.m. in New York City if 

6.7% of the total city area represented were to receive shading by adding trees along streets. The 

study also estimated a potential 1.10°F reduction at 3 p.m. if 31% of the city area were converted 

from its current mix of grass areas, streets without trees, and impervious roofs to areas with trees 

and living (i.e., vegetated) roofs. 

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Stratus Consulting Appendix C (Final, 8/24/2009)
 

C.3 The Meteorological Impact of the LID Scenarios 

The green CSO compliance alternatives are expected to reduce daily maximum temperatures in
 

the watershed area as a result of increased shading and replacement of dark paved surfaces with
 

vegetation that absorbs less solar radiation. However, the increase in vegetated acreage is also
 

expected to increase humidity due to increased evapotranspiration. Collectively, this would
 

increase the dewpoint temperature.
 

Depending on the LID option implemented, the resulting increase in vegetated acreage would be
 

equivalent to a 6% to 31% increase in vegetated acreage measured as a percentage of the original
 

impervious acreage across all CSO areas in the watersheds. This is similar to how the vegetated
 

acreage increase was measured in Sailor (2003). The vegetation increase under the LID options
 

is also roughly equivalent to a 4% to 21% increase in vegetated area when measured as a
 

percentage of the total area covered by combined sewers across all watersheds. This is similar to
 

how the change in vegetation was measured in the Columbia University Center for Climate
 

Systems Research et al. (2006) study.
 

Because the increases in vegetation planned for implementation under the LID options are
 

similar to the increases in vegetation evaluated in Sailor (2003) and Columbia University Center
 

for Climate Systems Research et al. (2006), we used these studies to estimate the meteorological
 

changes that would occur under the LID options. Specifically, the values of the temperature
 

reductions in the temperature-only scenarios in Table C.2 bound the temperature change results
 

reported in these earlier studies (see Table C.1 and associated discussion). The scenario results
 

that incorporate changes in temperature and dewpoint are intended to increase the overall reality
 

of the LID option impacts by addressing the expected increase in the dewpoint with the
 

additional vegetation while hopefully providing an additional set of realistic estimates for
 

consideration.
 

Table C.2. Alternative heat and relative humidity scenarios for Philadelphia LID 

compliance heat-mortality modeling 

Reduction in daily max Increase in daytime dew 

Scenario temperature (°F) point temperature (°F) 

1. Temperature only: minimum 0.25 0.00 

2. Temperature only: maximum 1.75 0.00 

3. Temperature and relative humidity: minimum 0.75 0.25 

4. Temperature and relative humidity: maximum 1.25 0.50 

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Stratus Consulting	 Appendix C (Final, 8/24/2009)
 

C.4	 Estimating Future Health Benefits from Reduced EHE 

Temperatures in Philadelphia 

Our current analysis reflects an expansion in scope from our previous work that estimated
 

potential public health benefits for a program that reduced EHE-attributable health impacts in
 

Philadelphia during selected EHEs, by increasing urban vegetation (based on Kalkstein and
 

Sheridan, 2003). Because a similar method is used for this effort, we first begin this section with
 

a review of Kalkstein and Sheridan (2003) to present critical methods. The rest of this section
 

provides an overview of how the meteorological scenario changes for analyses selected in
 

Section C.2 were applied to the available regionally downscaled climate change data and the
 

associated heat-mortality calculation system encompassed in Philadelphia’s Heat Health Watch
 

Warning System.
 

C.4.1	 A review of Kalkstein and Sheridan (2003) 

Kalkstein and Sheridan (2003) used a five-step process to estimate how a hypothetical change in
 

urban temperature could affect heat-attributable mortality by evaluating a subset of summertime
 

days specifically selected because they represented EHE conditions. The study is particularly
 

relevant because Philadelphia was one of the study cities evaluated.
 

In the first step, each selected day was assigned to an air mass category based on available
 

meteorological data. Air mass categories characterize weather conditions based on the values for
 

a set of meteorological variables including temperature, dew point, wind speed, and cloud cover.
 

Specific air mass categories include:
 

�	 Dry moderate (DM): A warm, comfortable air mass that occurs in Philadelphia frequently 

in summer. 

�	 Dry polar (DP): Cooler than DM, but still quite warm in the summertime. Usually occurs 

immediately after the passage of a cold front. 

�	 Dry tropical (DT): The hottest air mass in the summer, with temperatures usually 

exceeding 95 degrees and sometimes topping 100. Little cloud cover and low humidity 

lead to potentially rapid dehydration. 

�	 Moist moderate (MM): A cloudy, mild air mass that may sometimes be associated with 

fog and light rain. 

�	 Moist polar (MP): Usually a winter, rather than summer, air mass, this situation is often 

associated with storms moving up the East Coast. 

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�	 Moist tropical (MT): Very warm and humid air mass, sometimes associated with summer 

thunderstorms. Sticky and uncomfortable, and quite common in summer. 

�	 Moist tropical plus (MT+) and Moist tropical plus plus (MT++): These are particularly 

hot and humid subsets of the MT air mass. Dewpoint temperatures are very high, 

temperatures are in the 90s, and overnight temperatures are the warmest of any air 

masses. These hot, humid conditions have historically led to increased mortality in 

Philadelphia. 

�	 Transition (T): Associated with a frontal passage, when temperature, dewpoint, and other 

meteorological factors are changing rapidly. 

In the second step, the study days with offensive air masses are identified. In short, those air
 

masses that have daily mortality values that are consistently larger than longer-term averages are
 

labeled offensive. The identification of offensive air masses relies on the evaluating time series
 

data over multiple years to evaluate the relationships between daily mortality totals and air mass
 

categories. In Philadelphia, the offensive air mass categories include: DT, MT+, and MT++.
 

In the third step, the heat-attributable mortality for each offensive air mass day is calculated.
 

These calculations are completed using mortality algorithms developed using an iterative process
 

to identify the regression equation that provides the best explanation of the observed difference
 

in mortality from the longer term trends (i.e., the heat-attributable mortality). In this iterative
 

process, meteorological variables and factors such as the timing of the offensive air mass day
 

within the summer season and the persistence of the EHE are evaluated as potential explanatory
 

variables.
 

The fourth step repeats the process for the study day while also accounting for the predicted
 

change in temperature as a result of the increased urban vegetation. In the fifth step, the
 

difference in mortality from the two scenarios is calculated and reported to indicate the impact of
 

the increased urban vegetation.
 

Kalkstein and Sheridan (2003) found that the impact of increased vegetation varied according to
 

the EHE event, and often day-to-day. Overall, the study reported a net reduction in the estimate
 

of heat-attributable deaths with the increase in urban vegetation. However, the mortality
 

reductions were not evenly distributed across days and some days showed an increase in the
 

mortality estimates. The strength of the conclusions and ability to generalize the results across
 

longer time periods are constrained by the limited number of summertime days and EHEs
 

considered.
 

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Stratus Consulting	 Appendix C (Final, 8/24/2009)
 

C.4.2	 New study of increased vegetation with climate change 

To develop a more detailed assessment of the potential heat-health impacts of the LID scenarios,
 

the possible changes in temperature and relative humidity presented in Table C.2 were evaluated
 

using the same general approach as in Kalkstein and Sheridan (2003) and described above in
 

Section C.4.1. However, because the LID programs are expected to take a number of years for
 

the vegetation targets to be fully achieved, the meteorological data used for the evaluation was
 

provided by regionally downscaled General Circulation Model (GCM) results from a
 

compilation of the A1 family of climate change emissions scenarios.
 

The downscaled meteorological results are produced for each day, from April 1 through
 

August 31, in a representative year using a deterministic method that incorporates linear monthly
 

regressions to help adjust the GCM results and ensure the probability distributions for the values
 

for a baseline period in the 1990s are generally consistent with observed values during this time.
 

This approach has been used for similar assessments of potential future heat impacts
 

(e.g., Hayhoe et al., 2004). To try and capture inter-annual variability and provide results at
 

different points in the LID project lifecycle, downscaled results were calculated for two future
 

decades: 2020–2030 and 2045–2055. To help provide a point of reference, similar calculations
 

were made for the 1990–2000 period.
 

The results of this evaluation are presented in Tables C.3 and C.4 in terms of the estimated
 

number of heat-attributable deaths and offensive air mass days in each decade using the
 

downscaled GCM data alone (the control results), and when accounting for the temperature and
 

dewpoint temperature changes being evaluated for the LID scenarios.
 

Looking at the results a number the general conclusions can be drawn:
 

�	 Any measurable cooling provided by implementing an LID scenario is likely to provide 

some reduction in EHE-attributable mortality 

�	 EHE-attributable mortality reductions are roughly proportional to the relative magnitude 

of the assumed temperature change 

�	 The health benefits of the LID scenario implementation are relatively constant across the 

different decades, comparing the lives lost in the scenario to the control with the 

exception of the 1.75°F temperature reduction which has a noticeable increase in lives 

saved moving from the 2020s to the 2045–2055 period 

�	 EHEs are likely to become an increasing risk to public health in Philadelphia without 

continued adaptation. 

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Stratus Consulting Appen

Table C.3. Estimated heat-attributable deaths assuming alternative temperature and dewpoint impacts fr

Scenario Scenario Scenario Scenario Scenario Scenar

Year Control Year Control 1 2 3 4 Year Control 1 2 

Total surplus heat-related mortality 

1990 75 2020 90 85 66 79 75 2045 121 118 86 

1991 70 2021 50 47 34 39 36 2046 117 114 90 1992 32 2022 52 48 36 41 38 2047 98 91 75 

1993 47 2023 155 150 122 135 127 2048 94 87 64 

1994 120 2024 128 122 105 112 109 2049 138 130 111 

1995 53 2025 61 55 43 51 47 2050 85 79 62 

1996 69 2026 98 95 74 83 79 2051 171 165 149 

1997 93 2027 86 83 63 77 71 2052 72 63 47 

1998 56 2028 54 49 41 46 45 2053 105 97 74 

1999 116 2029 117 105 83 93 91 2054 89 87 73 2000 60 2030 47 45 33 40 37 2055 147 143 110 

Mean 72 Mean 85 80 64 72 69 Mean 112 107 85 

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Stratus Consulting Appen

Table C.4. Estimated offensive air mass days assuming alternative temperature and dewpoint impacts frovarious time periods
 

Scenario Scenario Scenario Scenario Scenario Scenar

Year Control Year Control 1 2 3 4 Year Control 1 2 

Total number of offensive days 

1990 54 2020 59 56 49 53 52 2045 73 72 60 

1991 44 2021 43 41 35 36 35 2046 62 62 53 1992 32 2022 37 35 32 33 32 2047 61 58 53 1993 33 2023 76 75 69 72 69 2048 57 54 44 1994 67 2024 61 58 55 55 55 2049 74 71 67 1995 44 2025 46 44 37 40 38 2050 56 53 45 1996 45 2026 62 61 52 56 54 2051 76 74 70 1997 51 2027 61 61 52 59 55 2052 47 44 35 

1998 41 2028 38 35 32 33 34 2053 60 58 51 1999 64 2029 65 62 56 57 57 2054 55 55 49 2000 42 2030 42 42 37 39 38 2055 79 78 69 

Mean 47 Mean 54 52 46 48 47 Mean 64 62 54 

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Underlying most of the mortality estimates and most of the summary results identified above is
 

the actual mortality algorithm that was incorporated for the offensive air mass days. This
 

algorithm is presented as Equation 1.
 

Equation 1. Daily heat-attributable mortality 

Daily heat attributable mortality = [-22.904+(1.79 × DIS)+(1.198 × Tmax) –
 

(0.054 × Julian)] / 4.722
 

where:
 

DIS = day in sequence value, where 1 is the first day of an offensive air mass,
 

2 is the second consecutive day, etc.
 

Tmax = daily maximum temperature in °C
 

Julian = time of year variable, with April 1 =1, April 2 = 2 ... August 31 = 153
 

4.722 scalar = adjustment value used so that the GCM 1990 control scenario mortality 

estimates match actual heat attributable mortality estimates for the decade. 

The mortality algorithm shows why, because Tmax is the only meteorological variable in the
 

equation, the mortality results can generally be sorted by in terms of the associated temperature
 

changes. It also demonstrates why, with a coefficient value on maximum temperature of roughly
 

1, the results are generally proportional to the assumed temperature changes. However, this
 

emphasis on the maximum temperature in the mortality algorithm overlooks that the assumed
 

changes in dewpoint temperature do play an important role in the results as they influence the air
 

mass categories a day is assigned to and thus, in some cases, whether it falls into an offensive or
 

non-offensive category.
 

Perhaps the most important feature of both the mortality and EHE day estimates in Tables C.3
 

and C.4 is to note the significant variability within the year-by-year results for a scenario and
 

across scenarios. Expressed as a percentage of the mean values for estimated EHE-attributable
 

deaths, the standard deviation of the decadal results is roughly 45% in the 2020–2030 estimates
 

and roughly 30% in the period 2045–2055. Within years, results for scenarios can be roughly
 

2–3 times as large when comparing the largest estimates to the smallest. In short, while the
 

results show the benefits of pursuing an LID program in terms of reducing EHE-attributable
 

mortality in Philadelphia, predicting the exact nature of benefits in any given time period is
 

complicated and becomes increasingly uncertain if narrower time windows are considered.
 

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C.5 Application to Philadelphia LID Option Scenarios 

We used the temperature and relative humidity changes identified in Table C.2 to estimate
 

changes in heat-related mortality under the LID alternatives. First, based on estimated increases
 

in vegetated acreage, we assumed that Scenarios 1 and 3 represent a range of the changes that
 

would occur under the 25% LID option. We also assumed that changes under the 100% LID
 

option are best represented by Scenarios 2 and 4.
 

Based on these assumptions, we estimated the average number of lives each year, for three
 

10-year periods: 2020–2029, 2030–2039, and 2040–2049 under the 25% and 100% LID options.
 

We then scaled the percent of benefits realized each year based on the timeline for program
 

implementation provided by CDM and the effective tree model developed by Stratus Consulting
 

(see Appendix H). We assume that no heat-reduction benefits are realized prior to 2020.
 

To estimate the number of lives saved under the 50% and 75% LID options, we scaled results for
 

the 25% and 100% LID options based on the level (percentage) of LID for each option. We then
 

estimated the monetary value associated with the number of lives saved under each LID option
 

based on EPA’s recommended VSL ($7,000,000). Table C.5 presents the results of this analysis
 

on a City-wide basis.
 

Table C.5. City-wide benefits associated with reduced urban 

temperatures under the LID alternatives 

Present value of lives saved 

(based on EPA’s 

Number of lives saved, recommended VSL) 

CSO option over 40-year period (millions, 2009 USD) 

25% LID 137 $739.4
 

50% LID 196 $1,057.6
 

75% LID 255 $1,375.9
 

100% LID 314 $1,694.1
 

To estimate benefits for each watershed, we allocated the City-wide estimates shown above
 

based on watershed population. Table C.6 presents the present value benefits (for 40-year project
 

period, 2009 USD) associated with reduced heat-related fatalities, by watershed.
 

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Table C.6. Present value benefits associated with reduced heat-related fatalities under LID 

CSO options, allocated by watershed (millions, 2009 USD) 

% of total 

population in 

CSO option CSO watersheds Tacony Cobbs Schuylkill Delaware 

25% LID 8% $174.7 $62.8 $207.7 $294.2
 

50% LID 24% $249.9 $89.8 $297.1 $420.9
 

75% LID 28% $325.1 $116.8 $386.5 $547.5
 

100% LID 40% $400.3 $143.8 $475.9 $674.2
 

C.6	 Omissions, Biases, and Uncertainties Associated with Health 

Benefit Conclusions 

The following sections provide a summary of the impact of critical assumptions and calculation
 

approaches used to develop the results of this analysis.
 

C.6.1	 Accuracy of any single temperature and dewpoint scenario result 

Well-understood basic physical principles underlie the assumption that significantly increasing
 

the vegetated acreage in Philadelphia through an LID program should reduce ambient
 

temperatures and increase the relative humidity and dewpoint temperature. The extent of this
 

change, however, is uncertain.
 

Past experiments calculate possible values using complex integrated models that also take the
 

unrealistic step of instantaneously changing the nature of a significant portion of an urban area.
 

The more realistic scenario is that these changes occur and are fully realized over time. What
 

complicates calculating the associated impact of these changes is that they are also likely to be a
 

function of other changes in the urban landscape. This uncertainty prevents assigning a likely
 

direction of bias in the current estimates.
 

What the results and the mortality algorithm make clear though is that larger temperature
 

reductions will, all else equal, increase the health benefit of LID implementation.
 

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Stratus Consulting Appendix C (Final, 8/24/2009)
 

C.6.2 Uncertainty of climate change 

Philadelphia has a long history of being adversely affected by EHEs. All else equal, climate
 

change is likely to increase the public risks and impacts associated with future EHEs as shown in
 

the results. However, while acceptance of climate change impacts continues to grow there is still
 

considerable uncertainty over what the future climate will look like.
 

In particular, researchers have begun to note how several climate change-related impacts that
 

were anticipated to begin appearing later in the century may have already begun and how the
 

pace of climate change may be more rapid than previously anticipated. In this study, further
 

warming would increase the number of EHE days. This would increase the mortality estimates
 

across the control and LID scenarios and may have little impact on the estimate of lives saved
 

with the LID scenarios. More importantly, increased warming could fundamentally alter the
 

nature of the EHE-mortality relationship in Philadelphia. If tolerance/infrastructure thresholds
 

are crossed in an increasingly warm climate before the population can adapt there is the chance
 

that the mortality estimates presented could be conservative.
 

C.6.3 Changing population size, demographics and response to heat 

Heat is a well-recognized public health threat in Philadelphia and the City has an active and
 

aggressive education, notification, and response program to address EHE conditions. The current
 

estimates assume that the future rate of EHE-attributable deaths in response to EHE conditions
 

will remain unchanged. To the extent future heat programs become more effective or factors that
 

make those most currently vulnerable to EHEs become less of an issue (e.g., better access and
 

use of air conditioning), the current heat mortality estimates could be overstated. However, the
 

potential benefits of the LID program, all else equal, could remain unchanged in this situation if
 

the impact is relatively small. In addition, these estimates hold the City’s population at a constant
 

size for all time periods evaluated. The bias introduced as a result will result in an overstatement
 

of impacts, all else equal, if the future population is expected to decline compared to 2000 levels.
 

Results would similarly be understated if future populations are expected to grow relative to
 

2000 levels.
 

Heat has and will continue to be a public health threat in Philadelphia. By offering the potential
 

to reduce urban temperatures, the envisioned LID scenarios directly address the fundamental
 

nature of the risk associated with EHE conditions and hold the potential to help prevent lives
 

being lost to future EHEs.
 

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C.6.4 The benefits of nonfatal heat stress cases avoided are not included 

This analysis has focused solely on the number of premature fatalities avoided due to the impact
 

that LID options are projected to have on urban temperatures and heat stress deaths. The cooling
 

anticipated from the green infrastructure approaches also will generate public health benefits for
 

individuals who would otherwise suffer nonfatal heat stress-related episodes. For example, the
 

LID approaches will reduce the number of nonfatal heat stress episodes, thereby reducing the
 

pain, suffering, medical expenses, and other losses incurred by individuals who otherwise would
 

have become ill or temporarily disabled by heat stress. Thus, the total anticipated value of
 

reduced heat stress is underestimated here, because it focuses exclusively on mortality events and
 

omits morbidity episodes.
 

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D.	 Water Quality and Aquatic Habitat 

Enhancements and Values 

Under all of the CSO control options currently being evaluated by the PWD, water quality will
 

be improved in streams and rivers within the City’s CSO service area. Under the LID CSO
 

options, stream restoration, wetlands, and increased vegetated area will also result in substantial
 

aquatic habitat enhancements.
 

As described below, individuals in Philadelphia not only benefit from the direct use of these
 

improved resources (e.g., through recreation), but also from knowing that these resources exist at
 

a given level of quality. In environmental economics, this is referred to as “nonuse” value.
 

The following sections provide further background on nonuse values and outline Stratus
 

Consulting’s methodology for estimating nonuse values for improved water quality and aquatic
 

habitat under the different CSO control options. Estimates for the benefits associated with these
 

improvements are also provided.
 

D.1	 Nonuse Values and Benefit Transfer 

The different CSO control options yield different types and levels of water quality-related
 

benefits. For example, stream restoration and water quality improvements under LID options will
 

result in recreational benefits for many Philadelphia residents (see Appendix A). Recreational
 

benefits accrue to individuals who actually participate in recreational activities, and are therefore
 

quantified based on “use values” associated with different types of stream-side recreation.
 

For most residents in the greater Philadelphia area (including those who rarely or never
 

participate in stream-related recreational activities), the different CSO control options will also
 

result in some level of “nonuse” benefits. These nonuse benefits stem from the inherent value
 

that individuals place on environmental goods and resources (in this case, water quality and
 

habitat improvements). A frequently discussed basis for nonuse value is the desire to maintain
 

the functioning of specific ecosystems.
 

In environmental economics, nonuse values are often referred to as existence and bequest values
 

(King and Mazzotta, 2005). Existence value is the benefit generated today by knowing that a
 

resource exists even if no use of the resource is anticipated. Bequest value is the value
 

individuals gain from the preservation of the resource for use by their heirs. The term nonuse
 

value is typically used in a more general manner to encompass both of these constructs
 

(Harpman et al., 1994).
 

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Nonuse values can only be estimated using techniques called “stated preference” methods.
 

Contingent valuation (CV) has been the most commonly used stated preference method for
 

estimating nonuse value, although more sophisticated variants (such as conjoint or choice set
 

approaches) are now sometimes applied. In its simplest terms, CV is a survey-based technique
 

used to elicit the maximum amount (in dollar terms) that an individual would be willing to pay
 

for a resource (or an improvement to a resource) of a specified quality. Stated preference
 

methods for conducting economic analysis are so named because values are obtained based on
 

the stated preferences of individual survey respondents. An original stated preference study
 

typically requires a significant amount of time and financial resources, because there are several
 

important design and sampling features that need to be developed and pre-tested to ensure the
 

reliability of the values derived from the survey instrument. For this reason, researchers often use
 

the benefits transfer approach to estimate “willingness to pay” values.
 

Bergstrom and De Civita (1999, p. 79) offer the following definition of benefits transfer:
 

Benefits transfer can be defined practically as the transfer of existing economic
 

values estimated in one context to estimate economic values in a different context
 

.... In the case of natural resource and environmental policies and projects,
 

benefits transfer involves transferring value estimates from a “study site” to a
 

“policy site” where sites can vary across geographic space and or time.
 

Benefits transfer is commonly used in economics, and there is a well-developed literature on
 

how to correctly apply this method (e.g., Rosenberger and Loomis, 2003). Federal guidelines for
 

economic analysis discuss how and when benefits transfer should be applied (U.S. EPA, 2000;
 

U.S. OMB, 2003).
 

In the present case, we use benefits transfer to estimate average WTP per household in the
 

greater Philadelphia Metropolitan Area (MA) for water quality and aquatic habitat improvements
 

under each of the CSO control options. Our estimates are based on a meta-analysis, conducted by
 

Van Houtven et al. (2007), of 131 WTP estimates from 18 studies (21 publications) conducted
 

between 1977 and 2003. The WTP estimates included in the meta-analysis were all derived using
 

stated preference methods.
 

D.2 Methodology 

As noted above, to estimate WTP values for water quality and aquatic habitat improvements in
 

Philadelphia, we relied on a meta-analysis of water quality valuation studies conducted by Van
 

Houtven et al. (2007). A primary objective of the meta-analysis was to develop a tool (regression
 

model), based on existing (primary) studies that could be used in benefits transfer analysis to
 

predict WTP estimates for different policy scenarios. The following sections summarize the
 

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methodology used to conduct the meta-analysis and the assumptions made to transfer results of
 

the analysis to Philadelphia.
 

D.2.1	 Meta-analysis: data collection and common influences on WTP estimates 

The studies included in the Van Houtven et al. analysis were limited to stated preference studies
 

conducted in the United States and to studies that described water quality in terms that could be
 

converted to a common 10-point scale. Once studies that met these criteria were selected, the
 

authors identified common variables across the studies that were likely to influence WTP
 

estimates. In general, these variables can be categorized as follows:
 

�	 The water quality “commodity.” The authors converted the water quality changes 

evaluated in each study into a common metric. To do this, they constructed a 10-point 

water quality index, WQI10. This index is based in part on the water quality ladder 

(WQL) developed by Vaughan (1986) as a way of conveying water quality to the general 

public, particularly survey respondents. Vaughan defined the ladder such that, for 

example, a water quality index value of 2.5 (out of 10) was “boatable,” 5.1 was 

“fishable,” and 7.0 was “swimmable.” Many researchers (e.g., Desvousges et al., 1987 

and others) have used Vaughan’s WQL to obtain WTP estimates for changes in the 

“steps” of the ladder. Van Houtven et al.’s WQI10 maps water quality characteristics not 

specifically related to recreational use (e.g., habitat suitability) to the WQL. Figure D.1 

shows a schematic of Vaughan’s original WQL. Table D.1 shows some specific water 

quality measures associated with the different use levels identified. 

�	 Study population characteristics. WTP relates primarily to individuals’ preferences, 

which are determined at least in part by personal characteristics. For example, individuals 

who are active recreational users of water resources are also likely to have stronger 

preferences for improving freshwater quality. Thus, users typically place higher values on 

water quality changes than nonusers, all else equal. 

Further, individual values for water quality changes reflect both their willingness and
 

their ability to pay. The economic conditions that affect an individual’s perceived ability
 

to pay for water quality changes can be captured (at least in part) through personal or
 

household income. If water quality is a normal good, then increasing income is expected
 

to have a positive effect on WTP.
 

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10
 

9
 

8
 

7
 

6
 

5
 

4
 

3
 

2
 

1
 

0
 

Becomes acceptable for drinking without treatment;
 

Character uniformly excellent for ingestion and
 

all other uses
 

Becomes acceptable for swimming;
 

Suitable for water-contact sports; acceptable for
 

public water supply with appropriate treatment
 

Becomes acceptable for game fishing;
 

Good fish and wildlife habitat
 

Becomes acceptable for rough fishing;
 

Satisfactory habitat for some wildlife and some
 

common food fish indigenous to the region
 

Becomes acceptable for boating;
 

Suitable for pleasure craft navigation
 

Figure D.1. Vaughan’s (1986) water quality ladder. 

Table D.1. Water quality characteristics for 5 classes of water use 

Fecal coliform Dissolved oxygen 5-day BOD Turbidity 

(no./100 mL) (mg/L) (mg/L) (NTU) pH 

Acceptable for drinking 

without treatment 0 7.0 0 5 7.25 

Acceptable for swimming 200 6.5 1.5 10 7.25 

Acceptable for game fishing 1,000 5.0 3 50 7.25 

Acceptable for rough fishing 1,000 4.0 3 50 7.25 

Acceptable for boating 2,000 3.5 4 100 4.25 

Source: Russell et al., 2001. 

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�	 Valuation method. The magnitude of the value estimates for water quality changes is 

also expected to depend on the way in which the estimates were derived. As noted above, 

all of the WTP estimates included in the analysis are based on stated preference methods 

(either the CV method or conjoint analysis). However, a number of methodological 

differences have the potential to influence WTP. One potentially significant difference is 

the type of value elicitation format used (e.g., open-ended vs. dichotomous choice 

questions). WTP may also be influenced by whether the stated preference survey is 

conducted in person, over the phone, through a mailed questionnaire, or in another 

format. These variables are controlled for in the Van Houtven et al. (2007) analysis. 

�	 Other study characteristics. WTP estimates may also be influenced by the overall 

quality of the methods and results of the study. Two potential indicators of study quality 

are the survey response rate and the publication outlet. Higher response rates and 

publication in peer-reviewed outlets are generally considered to reflect better quality 

studies. However, the publication selection process may result in estimation bias if, for 

example, reviewers and editors are more inclined to accept higher value estimates or if 

analysts are less likely to submit lower estimates (Stanley, 2001). Thus, while the 

expected effect of these characteristics on WTP is indeterminate, it is important to control 

for them in meta-analysis. 

D.2.2	 Meta-regression analysis 

To evaluate societal preferences for water quality changes, Van Houtven et al. (2007)
 

incorporated data from the 18 selected water valuation studies (based on the categories described
 

in Section D.2.1 above) into a meta-regression analysis.
 

Table D.2 describes the specific variables used to estimate the author’s final regression models.
 

The two primary variables of interest are WTP2000 (dependent variable), which is the estimated
 

mean WTP per household for a defined change in water quality [converted to 2000 dollars using
 

the consumer price index (CPI)], and WQI10CHANGE, which captures the corresponding change
 

in water quality in terms of the WQI10.
 

The authors estimated the model using three different functional forms – linear, semi-log, and
 

log-linear. Although all three of these forms are reasonable for approximating the relationship
 

between WTP and the other variables, the log-linear approach has at least two conceptual
 

advantages. First, it implies that, as changes in water quality approach zero, WTP also
 

approaches zero. Second, it implies that the marginal effect of a water quality change on WTP
 

depends on income. The semi-log model shares this second advantage; however, it also implies
 

that if WTP increases with larger improvements in water quality, then it does so at an increasing
 

rate.
 

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Table D.2. Variables included in Van Houtven et al. meta-regression analysis
 

Variable Description
 

WTP2000 Annual WTP for water quality change (in 2000 dollars)
 

WQI10CHANGE Water quality change (based on 10-point WQI)
 

WQ_REC_USE = 1 if the water quality change described in the study includes a reference to
 

recreational use support (e.g., suitable for recreational fishing)
 

WQI10BASE Baseline level of water quality from which water quality improves
 

ESTUARY = 1 if the water quality change occurs in an estuary
 

LOCAL_FWATER = 1 if the water quality change is restricted to freshwater in the local area (i.e., within
 

a single waterbody, county, or metro area)
 

MIDWEST = 1 if the affected waterbodies are in the Midwest region of the United States
 

SOUTH = 1 if the affected waterbodies are in the Southern region of the United States
 

INCOME2000 Average household income (in thousands of 2000 dollars)
 

INCOME_APPROX = 1 if average household income was approximated based on local Census data
 

PERCENT_USER Percent of the sample population that are users of the affected water resource
 

PUBLISHED = 1 if the study is published in a peer-reviewed book or journal
 

OPEN_ENDED = 1 if the value was estimated from an open-ended valuation question
 

RESPONSE_RATE Response rate for the survey used in the study
 

IN_PERSON = 1 if the survey used in the study was administered with an in-person interview
 

STUDY_YR73 = Year SP survey was fielded (minus 1973)
 

Source: Van Houtven et al., 2007.
 

Van Houtven et al. report two similar model specifications for each functional form. The first is a
 

full model with all of the main explanatory variables included, while the second is a restricted
 

model using a more parsimonious specification. The restricted models exclude variables that are
 

not individually significant at 0.10 level or less (based on t-statistics). As shown in Table D.2,
 

the dropped variables include ESTUARY, LOCAL_FWATER, MIDWEST, SOUTH,
 

OPEN_ENDED, and the interacted variable for INCOME2000 and INCOME_APPROX. Due to
 

their conceptual and economic importance in the model, all water quality variables were retained
 

in the restricted models regardless of their statistical significance.
 

Table D.3 shows the results of log-linear (full and restricted) models estimated by Van Houtven
 

et al. The log-linear model is shown because this is the functional form we decided to use for our
 

benefits transfer analysis. Although the numbers presented below are not inherently intuitive
 

(because they are in logged form), the magnitude and sign of the coefficients provide a relative
 

idea of how the different variables influence WTP estimates.
 

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Table D.3. Meta-analysis regression results
 

Model coefficient Model coefficient 

Variables (full model) (restricted model) 

Ln(WQI10CHANGE) 0.343 0.358
 

Ln(WQI10CHANGE)xWQ_REC_USE 0.414* 0.465**
 

WQI10BASE 0.091 0.08
 

ESTUARY 0.025
 

LOCAL_FWATER -0.11
 

MIDWEST 0.329
 

SOUTH -0.052
 

Ln(INCOME2000) 0.964* 0.897*
 

Ln(INCOME2000)xINCOME_APPROX -0.008
 

PERCENT_USER 0.011** 0.011**
 

PUBLISHED 0.960** 0.898**
 

OPEN_ENDED 0.051
 

RESPONSE_RATE -0.014 -0.013*
 

IN_PERSON 0.315 0.43
 

STUDY_YR73 -0.041** -0.029**
 

CONSTANT -0.399 -0.227
 

Note: ** and * respectively denote statistical significance at the 5% (p = 0.05) and
 

10% level (p = 0.10).
 

Source: Van Houtven et al., 2007.
 

As shown in Table D.3, most variables included in the model have a positive influence on WTP
 

estimates (e.g., an individual with higher income will report higher WTP) to relative degrees.
 

The negative effect of STUDY_YR73 indicates that, controlling for income and price effects,
 

estimates of average real (inflation-adjusted) WTP for water quality improvements has declined
 

over time. It is possible that this decline reflects changes in preferences over time; however, it
 

may also be the result of other factors, such as possible changes in publication selection
 

processes (e.g., by authors or editors) or in estimation methods, that tend to favor lower WTP
 

estimates.
 

The effect of RESPONSE_RATE is also negative. The authors report that although there are no
 

strong priors for how response rates should affect the magnitude of WTP estimates, these results
 

suggest that surveys with lower response rates might exclude individuals with lower average
 

WTP for water quality improvements.
 

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For benefits transfer, the model coefficients shown above are multiplied by their respective input
 

variable (the value of which is determined by the specific policy scenario). The sum of these
 

products is then used to estimate WTP2000. For example, WTP estimates for the restricted
 

model would be calculated as follows:
 

Ln(WTP2000) = -0.227 + (0.358 × Ln[WQI10CHANGE]) 

+ (0.465 × Ln(WQI10CHANGE) × WQ_REC_USE) + (0.08 × WQI10BASE) 

+ (0.897 × Ln(INCOME2000)) + (0.011 × PERCENT_USER)
 

+ (0.898 × PUBLISHED) + (0.013 × RESPONSE_RATE) + (0.43 × IN_PERSON)
 

+ (-0.029 × STUDY_YR73)
 

D.2.3	 Benefits transfer 

As noted above, we used the log-linear model specification to predict WTP for the LID and
 

non-LID CSO control options. We first estimated benefits associated with water quality/habitat
 

changes under the 100% LID, 35’ Tunnel, and RTB HR01 alternatives. To estimate benefits
 

associated with the less aggressive alternatives under each option (LID, Tunneling, Satellite
 

Treatment), we scaled downwards based on the scope of the different alternatives. Further, we
 

assumed the level of improvement under the Plant Expansion options to be equal to those of their
 

corresponding LID component (e.g., benefits under the 100% LID + 215 MGD option will be the
 

same as those estimated for the 100% LID option alone).
 

To provide a range of benefit values, we estimated results using both the full and restricted
 

models from Van Houtven et al. We made the following assumptions in applying these models to
 

Philadelphia:
 

�	 Benefits are estimated based on an average baseline water quality for all affected 

waterbodies (i.e., not by individual watershed). This is consistent with most studies 

included in the meta-analysis, which were conducted on a more regional scale. These 

estimates would be difficult to allocate across watersheds. 

�	 We separately evaluate WTP per household for households within the City and 

households within the greater Philadelphia, MA but not within the City limits (including 

households in Bucks, Chester, Delaware, Montgomery counties). A number of factors led 

to this separate evaluation: 

�	 Households outside of the City have much higher incomes (on average) than 

households within Philadelphia. This affects WTP for water quality and 

ecological habitat improvements. 

�	 Distance from the water bodies being improved is expected to decrease WTP to 

some degree. 

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�	 Households outside of the City are expected to have a much higher WTP for 

improvements in the Schuylkill and Delaware rivers (given their regional 

importance), as opposed to the more local Tacony and Cobbs creeks. 

�	 To account for these factors, we scaled WTP estimates for households outside of 

the City by 0.80 to account for distance and then multiplied these estimates by 

0.61 (percent of CSO area stream miles in the Schuylkill and Delaware River
 

watersheds).
 

�	 We assumed the baseline water quality in the affected streams and rivers (Cobbs Creek, 

Tacony Creek, and the tidal portions of the Schuylkill and Delaware rivers) to be 

4.3 units. This score was determined based on knowledge of the WQI and affected
 

streams. At 4.3, the water quality and habitat in the water body is assumed to support
 

some “rough” fishing (not for game species), and is considered boatable.
 

�	 Under the 100% LID option, water quality is expected to improve by 2.5 units, up to 6.8. 

At this level, habitat (and fishing) is greatly improved but water quality levels do not 

allow for swimming. 

�	 Under the most aggressive tunneling and satellite treatment options, water quality is 

assumed to improve by 1.2 units. This accounts for improved water quality but little 

change in aquatic habitat. 

�	 In each case, we assumed that the stream restoration and water quality improvements will 

improve recreational opportunities in most areas (WQ_REC_USE equals 1). Although 

many residents do not use these areas for in-stream recreation, we can estimate the 

nonuse value they hold for these amenities. 

�	 The variable PERCENT_USER is set at 0 because we are looking to capture only nonuse 

values in this part of the analysis. 

�	 The variable INCOME_2000 is set at median household income for the City, which was 

estimated by the Census as $30,746 annually (lower than the 2000 national average). For 

households outside of the City but within the Philadelphia, MA, the model was estimated 

with INCOME_2000 equal to $64,736 (U.S. Census Bureau, 2000). 

�	 The variables ESTUARY and LOCAL_FWATER were both set equal to 0.61 to reflect 

the percent of stream miles within PWD’s CSO boundaries that are considered “tidal” 

rather than freshwater. 

�	 The study year is assumed to be 2009. 

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�	 Finally, consistent with Van Houtven et al., PUBLISHED was set at 0.5 (due to the 

uncertainties regarding whether this variable reflects study quality or publication bias). 

All other variables related to study format were set at the Van Houtven et al. sample 

means. 

Based on these assumptions, Table D.4 shows the inputs used for each CSO control alternative
 

for WTP for households within the City.
 

Table D.4. Meta-regression analysis inputs for Philadelphia CSO control options 

Variable input Variable input 

LID option non-LID option 

WQI10CHANGE 2.5 1.2
 

WQI10CHANGExWQ_REC_USE 2.5 1.2
 

Ln(WQI10CHANGE) 0.916 0.182
 

Ln(WQI10CHANGE)xWQ_REC_USE 0.916 0.182
 

WQI10BASE 4.3 4.3
 

ESTUARY 0.61 0.61
 

LOCAL_FWATER 0.61 0.61
 

MIDWEST 0 0
 

SOUTH 0 0
 

INCOME2000 30.746 30.746
 

INCOME2000xINCOME_APPROX 30.746 30.746
 

Ln(INCOME2000)
 3.426 3.426
 

Ln(INCOME2000)xINCOME_APPROX 3.426 3.426
 

PERCENT_USER 0 0
 

PUBLISHED 0.5 0.5
 

OPEN_ENDED 0.6 0.6
 

RESPONSE_RATE 58.02 58.02
 

IN_PERSON 0.31 0.31
 

STUDY_YR73 36 36
 

Based on these inputs, Tables D.5 and D.6 show the results of the meta-analysis. Table D.5
 

shows estimated WTP in the greater Philadelphia, MA (per household) for water quality
 

improvements under the 100% LID and most aggressive non-LID options. Table D.6 shows total
 

present value estimates (over the 40-year project time period) for all CSO options within each
 

watershed.
 

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Table D.5. Estimated WTP (per household and total annual) for water quality 

improvements under the 100% LID and most aggressive non-LID options 

WTP per WTP per Total Total annual 

household household per annual WTP 

per year year (restricted WTP (restricted 

(full model) model) (full model) model) 

100% LID option 

City/County of Philadelphia $11.48 $18.28 $6,774,451 $10,791,199
 

Philadelphia, MA (excluding
 

Philadelphia County)a $11.41 $17.40 $9,917,607 $15,119,047
 

Total annual WTP $16,692,057 $25,910,246
 

Non-LID (most aggressive options) 

City/County of Philadelphia $6.58 $9.99 $3,886,634 $5,898,359
 

Philadelphia, MA (excluding
 

Philadelphia County)a $6.55 $9.51 $5,689,925 $8,263,918
 

Total annual WTP $9,576,559 $14,162,277
 

Note: Based on 1,459,331 households in Philadelphia, MA (2000 Census). Values adjusted to 2009 current
 

year dollars based on percent increase in CPI from 2000.
 

a. Scaled to account for distance from waterbodies and WTP estimates for Delaware/Schuylkill only.
 

To estimate total benefits associated with the 24 different CSO alternatives, we applied a scalar 

based on the scope of each option compared to the most aggressive LID, Tunneling, or Satellite 

Treatment option. Consistent with our analysis of other benefits, we allocated benefits over the 

40-year project time period based on construction and implementation timelines provided by 

CDM. We assumed that stream restoration and riparian improvements would occur under all the 

LID alternatives (25%−100% LID Options). Thus, at each level of LID, 75% of the maximum 

water quality/ecological habitat benefits will be realized (as a result of the stream restoration 

program). The remaining 25% of maximum benefits will vary based on the level of LID 

implemented. 

To estimate WTP for water quality and ecological habitat improvements for each watershed, we
 

allocated total WTP for households the City by restored stream mile within each affected CSO
 

area. For households outside of Philadelphia County, but within the greater Philadelphia, MA,
 

we allocated total WTP by restored stream mile within the Schuylkill and Delaware River CSO
 

watersheds only. Thus, we assume $0 WTP by these households for improvements to Tacony-


Frankford and Cobb creeks.
 

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Table D.6. Total WTP in the Philadelphia, MA for water quality and ecological habitat 

improvements under different CSO control options (present value 2009 USD) 

Tacony Cobbs Schuylkill Delaware
 

LID options/Transmission and new treatment capacity with LID componenta 

25% LID $21,576,660 $27,912,663 $78,631,310 $178,551,447
 

50% LID $23,664,723 $30,613,888 $86,240,792 $195,830,619
 

75% LID $25,752,787 $33,315,114 $93,850,273 $213,109,791
 

100% LID $27,840,851 $36,016,339 $101,459,755 $230,388,963
 

Tunnel optionsb 

15’ Tunnel $6,646,639 $8,598,429 $24,230,834 $55,021,981
 

20’ Tunnel $8,862,185 $11,464,573 $32,307,779 $73,362,642
 

25’ Tunnel $11,077,731 $14,330,716 $40,384,724 $91,703,302
 

30’ Tunnel $13,293,277 $17,196,859 $48,461,668 $110,043,963
 

35’ Tunnel $15,508,824 $20,063,002 $56,538,613 $128,384,623
 

Transmission and satellite treatment options 

25 Ofs $15,508,824 $20,063,002 $56,538,613 $128,384,623
 

10 Ofs $8,840,029 $11,435,911 $32,227,009 $73,179,235
 

4 Ofs $2,481,412 $3,210,080 $9,046,178 $20,541,540
 

1 Ofs $642,016 $2,985,239
 

a. Analysis assumes that transmission treatment options will be combined with LID components to reach target
 

level of water quality associated with each LID option.
 

b. Tunnel options in Delaware River Watershed are 15, 18, 21, 23, 28, and 31’.
 

Table D.6 shows total WTP (in present value terms) in the greater Philadelphia, MA (including
 

Philadelphia City/County) for water quality and ecological improvements under each CSO
 

control option. The benefit estimates shown below reflect total WTP based on the average WTP
 

estimates per household as reported in Table D.5. Total benefits also reflect the aggregation of
 

WTP by households within the City and those outside of the City but within the Philadelphia,
 

MA.
 

D.3 Sensitivity Analysis 

Stratus Consulting conducted a sensitivity analysis to evaluate how WTP per household
 

fluctuates in response to changes in baseline water quality and the level of water quality/habitat
 

improvement (as defined by the WQ10). The results of this analysis (as summarized in Table D.7)
 

indicate that within the reasonable range of assumptions related to these variables, WTP per
 

household does not vary wildly as these inputs change but seem to follow a reasonable
 

progression. WTP is more sensitive to the actual improvement in water quality as opposed to the
 

baseline index value used in the analysis.
 

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Table D.7. Summary of sensitivity analysis of household WTP for water quality 

improvements 

Household WTP Household WTP 

within Philadelphia within Philadelphia, MA 

Baseline Increase in Endpoint Full Restricted Full Restricted 

Scenario WQI WQI WQI model model model model 

1 4.3 2.5 6.8 $11.48 $18.28 $23.39 $35.65 

2 4.3 1.9 6.2 $ 9.32 $14.59 $19.00 $28.44 

3 4.8 2 6.8 $10.14 $15.84 $20.67 $30.88 

4 4.8 1.4 6.2 $7.74 $11.81 $15.78 $23.02 

5 5 1.8 6.8 $9.54 $14.75 $19.44 $28.77 

6 5 1.2 6.2 $7.02 $10.57 $14.30 $20.61 

7 4.3 1.2 5.5 $6.58 $9.99 $13.42 $19.49 

Numerous studies have examined water quality issues using a variety of techniques including
 

CV (Hurley et al., 1999; Loomis et al., 2000; Whitehead, 2000; Stumborg et al., 2001; Eisen-


Hecht and Kramer, 2002; Brox et al., 2003; Collins et al., 2005). To further validate our results,
 

we reviewed many of these studies in order to obtain a range of current estimates. However, we
 

found very few studies that evaluated water quality improvements within a context similar to the
 

Philadelphia policy case. Very few studies have been conducted in urban areas and most studies
 

include use values, as well as non-use values, in the stated WTP. The estimates for WTP per
 

household reported in Table D.5 therefore reflect the lower end of the range of WTP values
 

reported in most studies. However, we feel that these estimates represent a reasonable WTP per
 

household.
 

D.4 Omissions, Biases and Uncertainties 

In the absence of site-specific data, it was necessary to make a number of assumptions in order to
 

estimate WTP per household for water quality and habitat improvements under the CSO control
 

options. In addition, a number of data omissions and uncertainties surrounding the analysis have
 

been identified throughout this report. Table D.8 provides a summary of these assumptions and
 

uncertainties and their likely impact on total benefits.
 

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Table D.8. Omissions, biases and uncertainties
 

Assumption/ Likely impact 

methodology on net benefitsa Comment/explanation 

Analysis of improvements -­ The inclusion of households in Bucks, Chester, Delaware, and 

in the Schuylkill and Montgomery counties substantially increases total WTP due to 

Delaware River (1) the large number of households in these counties, and 

watersheds include (2) the high average income of households in these counties, 

households in the which is correlated with estimated WTP. In contrast, 

Philadelphia, MA region households in the City have a relatively low average income 

(i.e., more than City and, thus, a lower estimated WTP for water quality/habitat 

residents). improvements. 

No adjustment is made to WTP estimates for these households 

even though they do not live close by. A distance adjustment 

would serve to decrease overall benefits. 

In the absence of a study U There are limitations of using the meta-regression model as a 

specific to the Philadelphia benefits transfer tool. For example, results provide very limited 

area, we relied on a meta­ evidence about how WTP is related to the spatial characteristics 

analysis of WTP for water of water quality changes. The meta-regression does not 

quality/habitat measure how WTP varies with respect to the proportion or 

improvements to estimate amount of waters that are improved or the distance of the water 

total benefits. quality changes from populations. This lack of specificity 

imposes limitations on the precision of policy-relevant benefits 

transfer, since policies almost always impact waterbodies in 

spatially non-uniform ways. 

There are uncertainties U It is difficult to estimate the WQI index improvements in each 

surrounding the baseline watershed under the different CSO options. However, as 

WQI and estimated demonstrated through sensitivity analysis (see Table D.7), this 

improvements under CSO is not likely to have a significant impact on total benefits within 

options. the reasonable range of WQI estimates. 

Additionally, we currently assume that the 

Transmission/Treatment options combined with the LID 

options, will not achieve water quality and habitat benefits 

beyond those that would be achieved through the 

implementation of LID alone. Revising this assumption would 

serve to increase total benefits. 

a. Indicating how addressing the assumption or overcoming the omission would probably impact the analysis,
 

using the following key: + would likely increase net benefits; ++ would increase net benefits significantly;
 

U direction of change in net benefit is uncertain; - would diminish net benefits; -- would diminish net benefits
 

significantly.
 

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Stratus Consulting Appendix D (Final, 8/24/2009)
 

References 

Bergstrom J.C. and P. De Civita. 1999. Status of benefit transfer in the United States and 

Canada: Review. Canadian Journal of Agricultural Economics 47(1):79−87. 

Brox, J.A., R.C. Kumar, and K.R. Stollery. 2003. Estimating willingness to pay for improved 

water quality in the presence of item nonresponse bias. American Journal of Agricultural 

Economics 85(2):414−428. 

Collins, A., R. Rosenberger, and J. Fletcher. 2005. The economic value of stream restoration. 

Water Resources Research 41:1−9. 

Desvousges, W.H., V.K. Smith, and A. Fisher. 1987. Option price estimates for water quality
 

improvements: A contingent valuation study for the Monongahela River. Journal of
 

Environmental Economics and Management 14:248–267.
 

Eisen-Hecht, J.I. and R.A. Kramer. 2002. A cost-benefit analysis of water quality protection in
 

the Catawba Basin. Journal of the American Water Resources Association 38(2):453–465.
 

Harpman, D., M. Welsh, and R. Bishop. 1994. Nonuse Economic Value: Emerging Policy
 

Analysis Tool. U.S. Bureau of Reclamation’s General Investigation Program.
 

Hurley, T.M., D. Otto, and J. Holtkamp. 1999. Valuation of water quality in livestock regions:
 

An application to rural watersheds in Iowa. Journal of Agricultural and Applied Economics
 

31(1):177–184.
 

King, D.M. and M. Mazzotta. 2005. Ecosystem Valuation. Available:
 

http://www.ecosystemvaluation.org/contingent_valuation.htm. Accessed March 2009.
 

Loomis, J., P. Kent, L. Strange, K. Fausch, and A. Covich. 2000. Measuring the total economic
 

value of restoring ecosystem services in an impaired river basin: Results from a contingent
 

valuation survey. Ecological Economics 33:103–117.
 

Rosenberger R.S. and J.B. Loomis. 2003. Benefit transfer. In A Primary Non Market Valuation,
 

P. Champ, K. Boyle, and T. Brown (eds.). Kluwer Academic Press, Boston. pp. 449−482. 

Russell C., W. Vaughan, C. Clark, D. Rodriguez, and A. Darling. 2001. Chapter 7, Annex 7-A:
 

The water quality ladder. In Investing in Water Quality, Measuring the Benefits, Costs and Risks.
 

Inter-American Development Bank. p. 195.
 

Stanley, T.D. 2001. Wheat from chaff: Meta-analysis as quantitative literature review. The
 

Journal of Economic Perspectives 15(Summer):131–150.
 

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Stratus Consulting Appendix D (Final, 8/24/2009)
 

Stumborg, B.E., K.A. Baerenklau, and R.C. Bishop. 2001. Nonpoint Source pollution and
 

present values: A contingent valuation study of Lake Mendota. Review of Agricultural
 

Economics 23(1):120–132.
 

U.S. Census Bureau. 2000. 2000 Census of Population Social and Economic Characteristics,
 

Philadelphia, Bucks, Chester, Delaware and Montgomery Counties, Pennsylvania.
 

U.S. EPA. 2000. Guidelines for Preparing Economic Analyses. U.S. Environmental Protection
 

Agency.
 

U.S. OMB. 2003. Circular A-4. U.S. Office of Management and Budget. Available:
 

http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf. Accessed March 2009.
 

Van Houtven, G., J. Powers, and S. Pattanayak. 2007. Valuing water quality improvements in the
 

United States using meta-analysis: Is the glass half-full or half-empty for national policy
 

analysis? Resource and Energy Economics 29:206–228.
 

Vaughan, W.J. 1986. The water quality ladder. Included as Appendix B in The Use of Contingent
 

Valuation Data for Benefit/Cost Analysis in Water Pollution Control, R.C. Mitchell and R.T.
 

Carson. CR-810224-02. Prepared for U.S. Environmental Protection Agency, Office of Policy,
 

Planning, and Evaluation, Washington, DC.
 

Whitehead, J.C. 2000. Demand-side factors and environmental equity analysis. Society &
 

Natural Resources 13(2000):75–81.
 

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E. Wetland Enhancement and Creation 

As described in the main body of this report, PWD is currently evaluating a number of LID
 

options for controlling CSO events. A major component of these LID alternatives is an
 

aggressive stream restoration program intended to improve water quality and aquatic habitat
 

within affected streams. As part of the stream restoration program, PWD has planned for the
 

enhancement and creation of a number of wetlands within each of the CSO watersheds.
 

Long regarded as wastelands, wetlands are now recognized as important features in the
 

landscape that provide numerous beneficial services to people and to fish and wildlife. Some of
 

these services include improved water quality, groundwater recharge, shoreline anchoring, flood
 

control, and habitat for species. In addition, wetlands, like other natural resources such as
 

streams and lakes, can provide positive amenity values for nearby residents. These include open
 

space, enhanced views, increased wildlife, and a buffer against noise and other forms of
 

pollution.
 

Increased awareness of the value of wetlands has resulted in a number of studies to determine the
 

value of their services. However, determining the value of individual wetlands is difficult
 

because they differ widely and do not all perform the same functions or perform functions
 

equally well. Further, a number of factors can influence how a wetland is valued, including
 

wetland size, location, surrounding environment, characteristics of the surrounding population,
 

and others.
 

Despite these uncertainties, we provide estimates for the benefits associated with the
 

enhancement and creation of wetlands in the CSO watersheds under the LID CSO options. Our
 

analysis is based on a review of the wetland valuation literature. As shown below, our per-acre
 

benefit estimates represent the lower end of the range from most studies. This is because many of
 

the benefits associated with wetlands are captured in the other analyses described in this report
 

(e.g., recreation and water quality – to some extent).
 

The following sections provide a summary of Stratus Consulting’s approach to assigning a value
 

(or range of benefits estimates) to the wetlands planned for implementation as part of the LID
 

CSO control options. The results of this analysis are also provided.
 

E.1 Acres of Wetlands Planned 

The first step to this analysis was to determine the number of wetland acres that would be
 

restored or created in each of the CSO watersheds. For the Schuylkill and Delaware River
 

Watersheds, this information was provided by PWD and CDM.
 

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To obtain estimates for planned wetland acres in the Cobbs Creek Watershed, we relied on a
 

November 2008 report provided by CDM: “Cobbs Creek: A Gateway to Many Places and to
 

Cleaner Water.” This report was completed by CDM in partnership with PWD.
 

In the absence of specific data for Tacony-Frankford Creek, we determined the number of
 

wetland acres per restored stream mile in Cobbs Creek and applied that ratio to the number of
 

restored stream miles planned for the Tacony-Frankford.
 

Table E.1 presents the number of wetland acres planned for enhancement/creation in each CSO
 

watershed as part of the LID CSO stream restoration program.
 

Table E.1. Wetland acres restored and created under LID CSO options 

Tacony-Frankford Cobbs Schuylkill Delaware 

Wetland areas in need of vegetative 

enhancement (acres) 8.4 9.7 26.7 

Wetland creation (acres) 26.3 30.3 30.1 61.3 

Total acres (may not add due to rounding) 34.8 39.9 30.1 88.0 

E.2 Wetland Value 

To assign a range of per-acre values to the wetland acres planned for enhancement or creation,
 

we conducted a literature review of wetland valuation studies. Although a number of these
 

studies have been conducted, we did not find any studies that could be directly applied to the
 

Philadelphia policy case. Very few valuation studies have been conducted in urban areas. In
 

addition, many studies include very high per-acre or WTP estimates based on services that will
 

not be provided by the relatively small number of wetland acres planned in Philadelphia
 

(e.g., flood control is not a relevant service anticipated from the wetlands created or enhanced in
 

this study area).
 

As described below, we therefore relied on estimates from two meta-analyses to obtain an
 

average value per wetland acre. This approach allows us to provide a reasonable, yet
 

conservative estimate for specific wetland functions.
 

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E.2.1 Brief review of wetland valuation literature 

The range of estimates associated with wetland valuation studies is remarkable. For example,
 

Woodward and Wui (2001) report per-acre values from 39 different studies ranging from $5 to
 

$1,877 (updated to 2009 USD). In a recent meta-analysis, Borisova-Kidder (2006) estimated per-


acre values for wetlands in different regions of the United States ranging from $93 to $1,935
 

(2009 USD). The meta-analysis incorporated 72 separate observations of wetland value from
 

33 studies.
 

A broad range of valuation methodologies has been applied to value wetlands. The method most
 

commonly used in the literature has been to observe the market prices of products related to
 

wetland services and then ascribe the total revenue from the sale of such products as the value of
 

the wetland (Brander et al., 2003). This methodology is not applicable to the situation in
 

Philadelphia, where the wetlands planned for implementation are not expected to provide
 

market-related products to any extent.
 

The contingent valuation method (CVM) (see Appendix D) has also been widely used. For
 

example, a common method is to use a hypothetical referendum, where households are asked if
 

they would vote in favor of a particular resource protection action, if it cost their household $X.
 

The amount of $X varies across households, so that a demand curve can be traced. From this
 

demand curve, WTP is calculated. WTP values are commonly reported in dollars per year (or per
 

month or other specified period of time) per household.
 

As expected, different valuation methodologies have been applied to value different wetland
 

services. For example, CVM, hedonic pricing, and the travel cost method (TCM) have been
 

applied to value amenity and recreational values. Replacement cost has largely been used to
 

value the role of wetlands in improving water quality, and the production function approach has
 

been used to value the habitat and nursery services of wetlands. Further, wetland values have
 

been reported in the literature in many different metrics, currencies, and referring to different
 

years (e.g., WTP per household per year, capitalized values, marginal value per acre).
 

To exemplify the differences and range of value estimates associated with wetland valuation
 

studies, Tables E.2 and E.3 present some observations from the literature.
 

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Table E.2. Examples of values of wetlands
 

Value 

(April 2009 USD) Description Source 

$14,047 per wetland 

acre 

Using a discount rate of 3%, this study estimated that present values 

per wetland acre are: commercial fishery = $846; trapping = $401; 

recreation = $181; storm protection = $7,549; total of these values = 

$8,977/acre (1983$). 

Costanza et al., 

1989 

$74 annually per 

household 

This study examined what Ohio residents were willing to pay for 

increased protection of wetlands of the Maumee River and Western 

Lake Erie basins in Ohio. 

De Zoysa, 1995 

$10−$38 per household 

per year 

This study estimated WTP for wetland preservation benefits in 

western Kentucky. 

Dalecki et al., 

1993 

$1,392 per acre per 

year for 30 years 

($381,401 per acre 

over 15 years) 

This study estimated economic benefits of wetlands for wastewater 

treatment use, in terms of savings over conventional wastewater 

treatment methods. 

Breaux et al., 

1995 

$8 and $27 annually 

per household 

This study estimated WTP for preserving the Clear Creek wetland 

in western Kentucky. 

Whitehead and 

Bloomquist, 1991 

$169−$2,688 per acre 

lump sum 

Values reflect the range of restoring wetlands from croplands, by 

estimating easement costs, restoration costs, and the present 

discounted value of perpetual crop production. 

Heimlich, 1994 

$106−$164 annually 

per respondent 

Values reflect what respondents are willing to pay for protection of 

wetlands in New England. 

Stevens et al., 

1995 

$56 annually per 

household 

This study is a meta-analysis of 30 studies. The largest mean WTP 

by wetland function was in terms of flood control ($84), with the 

smallest for water generation ($20). 

Brouwer et al., 

1997 

$657−$11,830 per acre 

for residents of the 

drainage basin, and 

from $9,463 to $80,380 

across residents of the 

State of Michigan. 

The study estimated wetland benefits for Saginaw Bay, Michigan. Cangelosi et al., 

2001 

$4−$1,877 per acre 

annually 

The predicted values per acre of single-service wetlands range from 

$4 for presence of amenities to $1,868 for presence of birdwatching 

opportunities, with most services having predicted values in the 

$275−$600 range (see Table E.3 for breakdown of all values). 

Woodward and 

Wui, 2001 

$93−$1,935 per 

wetland acre 

This range of values is from a meta-analysis of 72 observations of 

wetland values from 33 studies. This range represents predicted 

values for different regions in the United States. 

Borisova-Kidder, 

2006 

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Table E.3. Per acre annual values of wetland services
 

Mean value per acrea 

Service (April 2009 USD) 

Flood $641
 

Quality $681
 

Quantity $207
 

Recreational fishing $583
 

Commercial fishing $1,270
 

Bird hunting $114
 

Bird watching $1,978
 

Amenity $5
 

Habitat $498
 

Storm $387
 

a The predicted values are obtained at the means of year and acre 


variables. It must be emphasized that the values do not represent
 

marginal values and cannot be summed to obtain the value of
 

multiple function wetlands.
 

Source: Woodward and Wui, 2001.
 

E.2.2 Applying wetland value estimates to PWD’s LID options 

As noted above, we relied on two meta-analyses to estimate the value of the wetlands planned
 

for implementation under the LID CSO control options. The meta-analyses allowed us to assign
 

a per-acre value to area of wetlands within each watershed.
 

The first analysis was conducted in 2006 by Borisova-Kidder as part of a Master’s thesis. All of
 

the studies included in this analysis (1) evaluated wetlands within the United States, and
 

(2) allowed for the calculation of wetland value on a per-acre basis. Based on this criteria, the
 

meta-analysis incorporated 72 separate observations of wetland value from 33 studies. The
 

studies include 22 journal articles, seven research reports or academic papers, two chapters in a
 

book, one PhD dissertation, and one Master’s thesis.
 

Rather than apply the results of Borisova-Kidder’s meta-regression analysis, which allows for
 

valuation of wetlands with only one primary function (e.g., flood control, recreation), we use the
 

average value of the 72 estimates included in the study. This amounts to about $303.38 per acre
 

in 2009 USD (adjusted from 2003 USD based on the CPI). We applied this value to obtain a
 

lower bound estimate for the value of each new acre of wetlands created. For restored wetlands,
 

we used half of this amount, or $151.69 (2009 USD).
 

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As an upper bound for per-acre value estimates, we relied on the results of Woodward and Wui’s
 

(2001) meta-regression analysis of the value of a single service wetland. Woodward and Wui’s
 

analysis focuses on two types of variation in wetland values: deviations from the valuation
 

function due to bias or errors in estimation, and variations along the valuation function
 

attributable to different wetland characteristics (e.g., whether it is suitable for flood control,
 

habitat, water quality). These factors were controlled for through a number of variables included
 

in the regression analysis (e.g., through dummy variables for wetland services as well as the
 

valuation method).
 

The dependent variable in Woodward and Wui’s regression model is the natural log of the value
 

per acre of wetland converted to 1990 dollars. In addition to the variables discussed above, the
 

regression analysis includes variables for the year the study was conducted, whether the wetland
 

was a coastal wetland, whether the value was an estimate of producer’s surplus, and whether the
 

results had been published. Three additional dummy variables were included in the analysis to
 

indicate whether the data, theory, or econometrics used in the study were deemed highly
 

questionable (see Woodward and Wui, 2001, for more detail).1
 

The results of the meta-analysis are shown in Table E.3. As an upper bound for the value of
 

wetlands in Philadelphia, we applied the value estimate for a single service wetland providing
 

habitat. We chose to use the single service value for habitat because it represents a middle
 

ground for the single service wetlands evaluated and it excludes values that are accounted for in
 

other areas of our analysis (e.g., recreation) or that are not applicable to the Philadelphia policy
 

case (e.g., flood control). As shown in Table E.3, Woodward and Wui estimate that the value of
 

a single service wetland providing habitat amounts to about $498 per acre (2009 USD). To value
 

restored wetlands (as opposed to newly created wetlands), we applied half this amount on a per-


acre basis.
 

Based on the values described above, Table E.4 shows the range of annual benefit estimates for
 

the new and restored wetlands planned under the LID options within each watershed. Present
 

value estimates for the 40-year project period are also provided. These values were obtained
 

based on the stream restoration timeline provided by CDM. The stream restoration program is
 

expected to be fully implemented by 2025.
 

1. The authors recognize that important variables that determine a wetland’s value are omitted from their
 

model. For example, characteristics of the population near a wetland are particularly likely to influence the 


value placed on the area. However, such data could not be identified in most of the studies included in the 


analysis and these types of variables were therefore not included in the model. According to the authors, while 


the absence of these variables no doubt diminishes the explanatory power of the analysis, it need not bias the 


estimated coefficients if these variables are uncorrelated with the included set (Kennedy, 1986).
 

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Table E.4. Total benefits provided by wetland services under LID options 

(2009 USD) 

Total annual wetland 

benefits (range of Present value 

estimates assuming full wetland benefits 

program implementation) (range of estimates) 

Delaware River (tidal wetlands) 

Wetlands restored $4,055 $6,657 $97,320 $159,751 

Wetlands created $18,585 $30,507 $445,910 $731,964 

Total commitment $22,640 $37,164 $543,230 $891,715 

Schuylkill River (tidal wetlands) 

Wetlands created $9,134 $14,994 $219,170 $359,769 

Total commitment $9,134 $14,994 $219,170 $359,769 

Cobbs Creek 

Wetlands restored $1,465 $2,405 $35,157 $57,711 

Wetlands created $9,183 $15,074 $220,335 $361,681 

Total commitment $10,649 $17,480 $255,492 $419,392 

Tacony-Frankford Creek 

Wetlands restored $1,276 $2,094 $30,608 $50,243 

Wetlands created $7,991 $13,117 $191,728 $314,723 

Total commitment $9,267 $15,211 $222,336 $364,966 

E.3 Omissions, Biases, and Uncertainties 

Although the economic literature on wetland valuation is relatively expansive, very few wetland
 

valuation studies have been conducted in urban areas on wetlands similar to those planned for
 

implementation in Philadelphia. We therefore relied on two meta-analyses reporting wetland
 

value on a per-acre basis. Table E.5 identifies the key issues and uncertainties associated with
 

this approach.
 

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Table E.5. Omissions, biases, and uncertainties
 

Likely impact 

on net 

Assumption/methodology benefitsa Comment/explanation 

Wetland valuation studies are U Although we use an average estimate, as well as an estimate 

remarkably diverse in terms of the derived through meta-regression analysis, the 

values obtained, the wetlands characteristics of the wetlands in Philadelphia may be quite 

evaluated, and the characteristics different than those of wetlands included in the base 

of the studies. estimates. 

This could serve to increase or decrease overall benefits 

depending on the nature of these characteristics, however, 

we feel our estimates provide a reasonable range of benefits 

per acre given that they are intended to exclude the more 

“high-dollar” benefits associated with wetland services such 

as recreation and flood control. 

The wetlands planned under the U It is difficult to determine how this might impact overall 

LID CSO control options are benefits. On one hand, the scarcity of wetlands in the City 

smaller in size than wetlands may result in a higher value associated with them. On the 

evaluated in most studies (and are other hand, larger wetlands can often provide additional 

not contiguous). ecosystem benefits that cannot be supported by wetlands of 

smaller size. 

Our benefits transfer does not take - Several of the wetland valuation studies included in the two 

into account demographic meta-analyses are based on household WTP estimates, 

characteristics of surrounding which are almost always correlated with average household 

communities. income of the study population. Given the relatively low 

average income of households in Philadelphia 

(e.g., compared to the national average), the inclusion of 

demographic characteristics would likely slightly decrease 

the overall benefits. 

a. Indicating how addressing the assumption or overcoming the omission would probably impact the analysis,
 

using the following key: + would likely increase net benefits; ++ would increase net benefits significantly;
 

U direction of change in net benefit is uncertain; - would diminish net benefits; -- would diminish net benefits
 

significantly.
 

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References 

Borisova-Kidder, A. 2006. Meta-analytical estimates of values of environmental services
 

enhanced by government agricultural programs. Dissertation presented in partial fulfillment of
 

the requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State
 

University.
 

Brander, L.M., R.J. Florax, and J.E. Vermaat. 2003. The Empirics of Wetland Valuation: A
 

Comprehensive Summary and Meta-analysis of the Literature. Report number W-03/30. Institute
 

for Environmental Studies, The Netherlands. October 23.
 

Breaux, A., S.C. Farber, and J. Day. 1995. Using natural coastal wetlands systems for
 

wastewater treatment: An economic benefit analysis. Journal of Environmental Management
 

44:285–291.
 

Brouwer R., I.H. Langford, I.J. Bateman, T.C. Crowards, and R.K. Turner. 1997. A Meta-


analysis of Wetland Contingent Valuation Studies. CSERGE Working Paper GEC 97-20. Centre
 

for Social and Economic Research on the Global Environment, University of East Anglia, UK.
 

Cangelosi, A., R. Wither, J. Taverna, and P. Cicero. 2001. Wetlands restoration in Saginaw Bay.
 

In Revealing the Economic Value of Protecting the Great Lakes. National Oceanic and
 

Atmospheric Administration and Northeast-Midwest Institute.
 

Costanza, R., S.C. Farber, and J. Maxwell 1989. Valuation and management of wetland
 

ecosystems Ecological Economics 1:335–361.
 

Dalecki, M.G., J.C. Whitehead, and G.C. Blomquist. 1993. Sample non-response bias and
 

aggregate benefits in contingent valuation: An examination of early, late and non-respondents.
 

Journal of Environmental Management 38:133–143.
 

De Zoysa, A.D.N. 1995. A Benefit Evaluation of Programs to Enhance Groundwater Quality,
 

Surface Water Quality and Wetland Habitat in Northwest Ohio. Dissertation, Ohio State
 

University.
 

Heimlich, R. 1994. Costs of an agricultural wetland reserve. Land Economics 70(2):234-246.
 

Kennedy, P.A. 1986. Guide to Econometrics. 2nd edition. MIT Press, Cambridge, MA.
 

Stevens, T.H., S. Benin, and J.S. Larson. 1995. Public attitudes and economic values for wetland
 

preservation in New England. Wetlands 15:226–231.
 

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Whitehead, J. and G. Blomquist. 1991. Measuring contingent values for wetlands: Effects of
 

information about related environmental goods. Water Resources Research 27(10):2523–2531.
 

Woodward, R.T. and Y.-S. Wui. 2001. The economic value of wetland services: A metaanalysis. Ecological Economics 37:257–270.
 

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F.	 Poverty Reduction Benefits of Local Green 

Infrastructure Jobs 

Benefit-cost analysis of public infrastructure investment projects does not traditionally consider
 

job creation as a category of project benefits. Although creating jobs is universally perceived as
 

beneficial, it is reasoned that jobs created by public investment are no more beneficial than jobs
 

created by the private sector.
 

A public investment project must be funded with revenues drawn from the private sector – in this
 

case, from PWD rate revenues collected from customers. If these funds were instead allowed to
 

remain in private hands and be used for other private purposes, it is argued that an equivalent
 

level of jobs would be supported. Stated another way, any jobs created by public investment are
 

generally created at the expense of jobs in the private sector, so there is no net benefit in the
 

overall level of employment arising from public expenditures (instead, under normal conditions,
 

there is simply a transfer of employment across locations and sectors).
 

The only exception to this reasoning is the special case of a severe economic downturn in which
 

private demand is so depressed that job creation is more assured through public expenditures.
 

Despite the fact that recent economic events actually reflect this special case, the approach
 

adopted here does not attempt to evaluate benefits of job creation in that context. Instead, we
 

examine the value of specific types of job opportunities created within a certain socioeconomic
 

niche.
 

In the popular media, “green jobs” or “green collar jobs” are described as encompassing many
 

diverse job categories that have a bearing on environmental improvement in one way or another.
 

In contrast, this analysis focuses only on the unique character of jobs created in the construction
 

and maintenance of green infrastructure systems installed for purposes of urban stormwater
 

management as part of an aggressive campaign to transform urban landscapes and
 

neighborhoods. “Green infrastructure jobs” as defined here are essentially landscaping jobs,
 

suitable for unskilled laborers and requiring no experience. There are significant social benefits
 

that result from creating these specific types of jobs in an urban setting as part of a greening
 

campaign. Such jobs can serve as a crucial stepping stone out of poverty for otherwise
 

unemployed persons who reside in the very same neighborhoods in which the greening is
 

targeted. The stabilizing and transforming effects of the green infrastructure on these
 

neighborhoods reinforces and supports the benefits of providing employment to this population
 

that is outside the labor force and trapped in poverty.
 

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Stratus Consulting	 Appendix F (Final, 8/24/2009)
 

Traditional infrastructure – consisting of multi-billion dollar concrete tunnels – produces no such 

benefits. When the large construction contractors engage in large-scale traditional infrastructure 

projects, they have pre-negotiated labor agreements with all of the appropriate trade unions that 

enable them to expedite the project with no obstacles to obtaining the required labor when and 

where it is needed. For the most part, traditional stormwater infrastructure requires skilled 

laborers such as those represented by the trade unions. As implied by their status as union 

members, these are also people who are already in the labor force. When a city water department 

implements a traditional infrastructure project in this manner, the net effect is just to bid these 

already employed workers away from other construction projects. 

This appendix presents some additional background on the connections between green
 

infrastructure and poverty. The methodology employed in evaluating the poverty reduction
 

benefits of “green infrastructure jobs” is described, and results are summarized and discussed.
 

F.1	 Urban Poverty and Green Infrastructure 

Most large older cities have been faced with long-standing problems in coping with poverty in 

their midst. Philadelphia is a typical example, as made clear in results of the U.S. Census 

Bureau’s 2005–2007 American Community Survey (U.S. Census Bureau, 2008). 

�	 Median household income in 2007 was $34,767 in Philadelphia, compared to $50,007 for 

the nation as a whole – 30% less household income at the median. 

�	 Using a household income of $25,000 per year as a measure of poverty status, there were 

212,093 households below this level in Philadelphia in 2007 – 38% of all households in 

the City. Nationally, the proportion of households with incomes below $25,000 per year 

was 25%. 

�	 In Philadelphia, 57.8% of people over age 16 were in the labor force, compared to 64.7% 

for the nation as a whole. 

Cities incur many types of costs in coping with poverty. Many types of assistance programs are
 

supported to help people in poverty. But one of the greatest expenditure categories is
 

unfortunately coping with crime, for which the poverty trap is a major causative factor. In this
 

regard, Philadelphia is incurring relatively high costs (Heller, 2008).
 

�	 Philadelphia has the highest incarceration rate of any big U.S. city. The recidivism rate is 

80% and the annual cost per inmate is among the highest at $30,000 per year. 

�	 The City spends about $1 billion per year on the criminal justice system, which is about a 

quarter of the City budget. 

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Stratus Consulting	 Appendix F (Final, 8/24/2009)
 

The growing movement to transform urban landscapes with green infrastructure in the name of
 

stormwater management and energy conservation holds the promise of a number of spillover
 

benefits in reducing poverty. The installation and maintenance of green infrastructure requires
 

large amounts of unskilled labor in what is essentially landscaping work. Large amounts of the
 

work is to be performed in neighborhoods where many unemployed and relatively unskilled
 

people live in poverty. Moreover, the transforming effect of green infrastructure on these
 

neighborhoods can provide a foundation to stabilize troubled communities, reduce crime rates,
 

and set a course for further progress against poverty. In the words of a leading green
 

infrastructure activist, “If you give opportunities to the young men and women of this
 

community to support themselves and their families, the need to build a jail goes away” (Carter,
 

2007).
 

Proof of these broader spillover benefits of green infrastructure is provided in the experience of
 

the “Weed and Seed” program of the Community Capacity Development Office of the
 

U.S. Department of Justice (U.S. DOJ, 2009). Launched in 2003, this program is now being
 

demonstrated in 300 sites across the country. The strategy involves a two-pronged approach: law
 

enforcement agencies and prosecutors cooperate in “weeding out” violent criminals and drug
 

abusers, and public agencies and community-based private organizations collaborate to “seed”
 

much-needed human services, including prevention, intervention, treatment, and neighborhood
 

restoration programs. Through coordinated use of federal, state, local, and private-sector
 

resources, neighborhood restoration strategies focus on economic development, employment
 

opportunities for residents, and improvements to the housing stock and physical environment of
 

the neighborhood. In the period between 2003 and 2006, major crimes decreased 2% within
 

Weed and Seed areas (Baker, 2009).
 

F.2	 Estimating Poverty Reduction Benefits of “Green 

Infrastructure Jobs” 

The methodology for estimating benefits of “green infrastructure jobs” is based on the
 

expectation that providing such jobs to unskilled residents within the targeted neighborhoods will
 

provide these individuals with an important stepping stone on the path out of poverty which
 

would not otherwise exist. The presence of the green infrastructure in these neighborhoods will
 

enhance the opportunity for community stabilization and recovery that can further support
 

progress against poverty.
 

As discussed above, society spends large amounts every year in its efforts to cope with the
 

effects of poverty. If PWD chooses an LID approach to CSO control providing “green
 

infrastructure jobs” to unskilled and unemployed residents who are currently living in poverty,
 

they will be less impoverished and impose a lower level of societal costs. If PWD chooses a
 

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Stratus Consulting Appendix F (Final, 8/24/2009)
 

traditional infrastructure approach, the jobs created will be much less likely to be filled by
 

unskilled workers who are currently not in the workforce, yielding no benefits in reducing the
 

societal cost of poverty.
 

The benefits of “green infrastructure jobs” are estimated by multiplying the total number of jobs
 

created by an assumed per-employee amount of societal costs that will be avoided due to the
 

altered poverty status of the new employee. The number of labor hours required for construction
 

and maintenance of the LID alternatives was estimated as part of the engineering cost analysis. It
 

is further assumed that one-quarter of these hours will be supervisory positions and therefore less
 

likely to result in the hiring of unskilled and otherwise unemployed people. The avoided societal
 

cost of poverty per non-supervisory employee used to value this benefit is estimated to be about
 

$10,000 per year. This figure is derived from a review of different sources, as described below.
 

A 1993 analysis produced by the Institute for the Study of Civic Values reviewed local budget
 

data sources for Philadelphia and produced an estimate of the total public cost of poverty shown
 

in Table F.1. This estimate seems low because it does not include an element relating to coping
 

with crime.
 

Table F.1. Estimate of the cost of poverty in Philadelphia 

Estimated annual cost 

Element (1992 USD millions) 

Income, Medicaid, food stamps 1,000 

Health and social services 400 

Public housing 150 

Community development 100 

Homeless expenditure 15 

Education 200 

Total 2,000 

Source: Schwartz, 1993. 

A 1998 analysis by Wharton researchers also employed a bottom-up approach to identify direct
 

poverty related expenditures in the City’s 1996 budget amounting to about a billion dollars
 

(Summers and Jakubowski, 1996). This study left out additional costs of crime and education,
 

although acknowledging their potential significance. It also omitted direct expenditures by the
 

Federal government that were estimated to be on the order of another billion dollars by Schwartz.
 

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Stratus Consulting Appendix F (Final, 8/24/2009)
 

In a landmark study in the mid-1990s, econometric research was applied to a survey of
 

U.S. cities and demonstrated statistically that a high incidence of urban poverty not only
 

increases direct poverty expenditures of city governments, but also significantly increases the
 

cost of many other seemingly unrelated city services (Pack, 1998). Applying the approaches of
 

Summers and Jakubowski as well as those of Pack to the Philadelphia 2009 city budget, implies
 

that as much as $3.5 billion of the $4 billion total is attributable to poverty. That total still omits
 

additional direct poverty related outlays in Philadelphia by the Federal government.
 

A top-down national analysis of the “avoidable costs of poverty” was developed in a study
 

prepared for the Entergy Corporation (Oppenheim and MacGregor, 2006), yielding the estimates
 

shown in Table F.2.
 

Table F.2. National estimate of the avoidable costs of poverty 

Estimated annual cost 

Element Description (2005 USD millions) 

Crime Cost of criminal activity, including property losses, costs of 

the judicial and correctional system, and security costs. 

660,791 

Health Costs of health care, including costs that are preventable by 

improving health care and costs of low-income health care 

that are spread through society. 

335,841 

Unemployment/ 

underemployment 

Costs of unemployment and underemployment, including 

unemployment compensation, job training, and the multiplier 

effects of lost economic activity. 

222,492 

Anti-poverty 

investments 

Costs of current anti-poverty investments, including costs for 

social services, elderly services, income supports, affordable 

housing, food, education, energy and utility supports, and 

block grants for community services and community 

development. 

270,053 

Total 1,489,178 

Source: Oppenheim and MacGregor, 2006. 

Another top-down analysis developed by the Center for American Progress (Holzer et al., 2007)
 

produced a national estimate of the cost of poverty from a different perspective. Their approach
 

was to compute the costs to society resulting from having children grow up in poverty. They
 

focused on the individual as a means of capturing both lost economic productivity and additional
 

costs associated with higher crime and poorer health later in life. Although this is a different
 

approach to the analysis, it covers many of the same impacts in arriving at an estimate of the
 

total cost of poverty. They summarize their results in terms of the net impact on the U.S. Gross
 

Domestic Product (GDP), as shown in Table F.3.
 

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Stratus Consulting Appendix F (Final, 8/24/2009)
 

Table F.3. Economic costs of poverty in the United States
 

Estimated annual cost 

Element (% of GDP) 

Foregone earnings 1.3 

Crime 1.3 

Health 1.2 

Total 3.8 

Source: Holzer et al., 2007. 

Their indicated percentage of GDP attributable to poverty (3.8%) translates into a national cost
 

estimate of about $500 billion per year which is only about one-third the national cost estimate
 

developed in the previously discussed study for the Entergy Corporation. The differences lie in
 

the approaches used to assign part of the cost of crime to poverty and also in the lack of
 

accounting for the costs of social assistance programs in the work by the Center for American
 

Progress which the Entergy study showed to be 18% of the total. In addition, the authors of the
 

Center for American Progress study stressed that it was their very deliberate analytical objective
 

to produce a lower bound estimate of the cost of poverty. In contrast to their results, another
 

interesting study of the cost of poverty in Ontario (Laurie, 2008) produced an estimate that
 

poverty expenditures accounted for between 5.5 and 6.6% of the provincial GDP.
 

The Philadelphia region (including the suburbs) is the fourth largest urban area in the United
 

States in terms of GDP (PricewaterhouseCoopers, 2006). Apportioning the $500 billion national
 

estimate from the Center for American Progress study on the basis of the Philadelphia share of
 

national GDP yields an estimate of the cost of poverty to the region of $12 billion per year.
 

Apportioning the $500 billion instead on the basis of the share of the nation’s low-income
 

households that lie within the City yields an estimate of about $3 billion per year. If a higher
 

percentage of GDP (e.g., ~6% found in Ontario) is applied, the Philadelphia share of the
 

$500 billion would be closer to $5 billion per year.
 

This latter range of “top-down” estimates is similar to the $2.0 to $3.5 billion per year range
 

derived from the several “bottom-up” estimates for Philadelphia described earlier. However, the
 

bottom-up studies mostly omitted direct Federal expenditures. The Entergy study described
 

above is judged to provide the most complete top-down estimate of the total annual cost of
 

poverty in the United States. Apportioning their $1.5 trillion per year national estimate on the
 

basis of the share of the nation’s low-income households that lie within the City yields an
 

estimate of about $9 billion per year.
 

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Stratus Consulting Appendix F (Final, 8/24/2009)
 

In estimating the spillover benefits of “green collar jobs” in reducing the costs of poverty, it is
 

assumed that currently unemployed people living in poverty would be hired into the unskilled,
 

non-supervisory positions. By the latest Census figures, there are about 227,500 such people
 

residing in the City. If the $12 billion per year estimate of the cost of poverty is correct, it
 

implies an annual cost of $57,000 per unemployed person in Philadelphia. An estimate of
 

$9 billion per year implies about $45,000 per unemployed person per year. An estimate of
 

$5 billion per year implies $25,000 per unemployed person per year. An estimate of $3 billion
 

per year implies $15,000 per unemployed person per year.
 

The benefit assumed here is $10,000 per year in offsets to all the societal costs of coping with
 

poverty. Hence an estimated savings of $10,000 per year is multiplied times the number of work
 

years in “green infrastructure jobs” provided by each LID option.
 

F.3 Results 

Table F.4 presents a summary of the total number of work years in “green infrastructure jobs”
 

provided by each of the LID options in each watershed over the 40-year implementation period.
 

Table F.5 presents a similar summary of the total present value (over 40 years) of the avoided
 

societal cost of poverty attributable to the provision of these “green infrastructure jobs.”
 

Table F.4. Total work years in “green infrastructure jobs” provided by LID alternatives 

LID % Delaware Schuylkill Cobbs Tacony Totals 

25 3,341 1,607 476 1,490 6,914 

50 7,379 3,535 1,050 3,303 15,266 

75 11,307 5,409 1,608 5,040 23,364 

100 14,778 7,081 2,105 6,590 30,554 

Table F.5. Total present value (2009 USD millions) of “green infrastructure jobs” 

provided by LID alternatives 

LID % Delaware Schuylkill Cobbs Tacony Totals 

25 28 13 4 12 57 

50 60 29 9 27 125 

75 93 44 13 41 192 

100 121 58 17 54 251 

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Stratus Consulting Appendix F (Final, 8/24/2009)
 

F.4 Omissions, Biases, and Uncertainties 

The analysis of poverty reduction benefits of “green infrastructure jobs” is straightforward;
 

multiplying the number of work years provided times the estimated amount of avoided social
 

costs. The basis for the estimate of the societal costs of poverty is the largest area of uncertainty
 

in this procedure, as described further in Table F.6.
 

Table F.6. Omissions, biases, and uncertainties affecting valuation of “green 

infrastructure jobs” 

Assumption/ Likely impact 

methodology on net benefitsa Comment/explanation 

It is assumed that LID - If it is not possible to make many of the “green infrastructure 

options can be implemented jobs” available to the target population of unskilled and 

in a manner that makes otherwise unemployed people living in poverty, then the 

most non-supervisory spillover benefits of poverty reduction will be correspondingly 

“green infrastructure jobs” reduced. We have assumed 75% of the job hours can be targeted 

available to the target to the relevant population. 

population. 

The estimated value of the U Despite extensive research on poverty, the total social cost of 

societal costs of poverty is poverty is not as well studied as a concept. We found only a few 

supported by only a half studies. Although they seem to bound a roughly comparable 

dozen studies that were overall order of magnitude, confidence would be enhanced if 

designed for different there were a few more estimates to draw from. 

purposes. 

It is assumed that the U There is evidence that an unskilled job, alone, is inadequate to 

societal costs of poverty are boost a person out of poverty. A skilled job is required. Thus, 

reduced by $10,00 if a “green infrastructure jobs” are just a stepping stone on the path 

targeted recipient obtains a out of poverty. We assumed a $10,000 reduction in the avoided 

“green infrastructure job.” societal costs of coping with poverty. 

a. Indicating how addressing the assumption or overcoming the omission would probably impact the analysis,
 

using the following key: + would probably increase net benefits; ++ would increase net benefits significantly;
 

U direction of change in net benefit is uncertain; - would diminish net benefits; -- would diminish net benefits
 

significantly.
 

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Stratus Consulting Appendix F (Final, 8/24/2009)
 

References 

Baker, F. 2009. Building and Sustaining Safe, Thriving Communities. Presented at the Alliance
 

for Community Trees Green Infrastructure Summit and Urban Trees Forum, Washington, DC.
 

May. Available: http://actrees.org/files/Events/fbaker.pdf. Accessed May 29, 2009.
 

Carter, M. 2007. Jobs not jails. Sustainable South Bronx Magazine, Spring. Available:
 

http://www.ssbx.org/documents/SSBxMagazine.pdf. Accessed May 29, 2009.
 

Heller, K. 2008. A Messed-up Justice System. The Philadelphia Enquirer. June 9.
 

Holzer, H., D. Whitmore Schanzenbach, G.J. Duncan, and J. Ludwig. 2007. The Economic Costs
 

of Poverty: Subsequent Effects of Children Growing Up Poor. Center for American Progress,
 

Washington, DC. Available:
 

http://www.americanprogress.org/issues/2007/01/poverty_report.html. Accessed May 29, 2009.
 

Laurie, N. 2008. The Cost of Poverty: An Analysis of the Economic Costs of Poverty in Ontario.
 

The Atkinson Charitable Foundation and the Metcalf Foundation.
 

http://www.oafb.ca/assets/pdfs/CostofPoverty.pdf. Accessed June 1, 2009.
 

Oppenheim, J. and T. MacGregor. 2006. The Economics of Poverty: How Investments to
 

Eliminate Poverty Benefit All Americans. Report prepared for Entergy Corporation. Available:
 

http://www.democracyandregulation.com/detail.cfm?artid = 99. Accessed May 29, 2009.
 

Pack, J. 1998. Poverty and urban public expenditures. Urban Studies 35(11). Available:
 

http://usj.sagepub.com/cgi/content/abstract/35/11/1995. Accessed June 1, 2009.
 

PricewaterhouseCoopers. 2006. The 150 Richest Cities in the World by GDP in 2005. Report for
 

citymayors.com. Available: http://www.citymayors.com/statistics/richest-cities-2005.html.
 

Accessed May 29, 2009.
 

Schwartz, E. 1993. Delaware Valley Legislators: An Anti-Poverty Agenda for the 90’s. Institute
 

for the Study of Civic Values. Memorandum available:
 

http://www.iscv.org/Opportunity/Poverty/poverty.html. Accessed May 29, 2009.
 

Summers, A. and L. Jakubowski. 1996. The Fiscal Burden of Unreimbursed Poverty
 

Expenditures in the City of Philadelphia: 1985–1995. Working Paper #238. Department of
 

Public Policy and Management and Real Estate, Wharton School of Business, University of
 

Pennsylvania, Philadelphia. Available: http://www.worldcat.org/oclc/83004545. Accessed June
 

1, 2009.
 

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U.S. Census Bureau. 2008. American Community Survey 2007 Data Release. Available:
 

http://www.census.gov/acs/www/index.html. Accessed May 29, 2009.
 

U.S. DOJ. 2009. Weed and Seed Program Description. U.S. Department of Justice. Available:
 

http://www.ojp.usdoj.gov/ccdo/ws/welcome.html. Accessed May 29, 2009.
 

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G.	 Energy Usage and Related Changes in Carbon 

and Other Emissions 

This appendix provides a summary of Stratus Consulting’s approach for estimating the net
 

energy use, and associated external costs, of the CSO control options currently being evaluated
 

by the PWD. As described below, we have identified several key categories related to energy use
 

(and associated emissions) for quantitative assessment, including:
 

�	 Electricity and natural gas savings due to cooling effect under the LID CSO 

control options 

�	 “Wasted” fuel consumed by vehicles stuck in traffic delays caused by construction and 

maintenance activities 

�	 Resulting energy costs and/or cost savings 

�	 Carbon emissions/offsets associated with energy use (including fuel used by construction 

and maintenance vehicles) and/or savings under each option 

�	 Estimated social value of carbon emissions and/or savings 

�	 NOx and SO2 emissions/offsets, and associated health costs, related to energy use and/or 

savings under each option. 

The following sections identify key inputs and assumptions used in our analysis and describe the
 

general methodology employed to evaluate energy-related benefits and external costs. Final
 

results for each CSO watershed are also presented.
 

G.1	 Key Inputs and Assumptions 

To estimate the energy-related benefits and external costs under each CSO control option, we
 

employed standard industry methodology. In the absence of specific data, it was also necessary
 

to make a number of assumptions based on our understanding of the different program
 

components. Key inputs and assumptions are detailed below. Individual assumptions related to
 

specific program components are provided in subsequent sections.
 

� Energy costs. To estimate the monetary benefits of electricity and natural gas savings 

under the LID options, we used PECO estimated electricity rates and natural gas rates 

provided by CDM ($0.10/kWh and $0.0135/MM Btu, respectively). The electricity rates 

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Stratus Consulting	 Appendix G (Final, 8/24/2009)
 

used in this analysis are relatively conservative. Section G.4.2 discusses how our overall
 

results change as electricity rates are increased. To estimate the cost of additional fuel
 

consumed in construction -related traffic delays, we assumed a cost of $2.50 per gallon of
 

gasoline.
 

�	 Energy-related emissions factors. We evaluate emissions of CO2, SO2, and NOx 

associated with net energy use under each CSO option. To do this, we use average air 

pollution emission factors for the State of Pennsylvania’s electricity sector [in terms of 

tons of emissions per megawatt hour (MWh); EIA, 2007]. When the specific generating 

plants cannot be determined for an electricity grid like Philadelphia’s, these estimated 

emissions are used at the state or regional level. The EIA estimates that Pennsylvania’s 

CO2 emission factor is 0.574 MT/MWh. SO2 and NOx factors at Pennsylvania power 

plants are estimated at 0.0041 and 0.00076 MT/MWh, respectively. To estimate 

emissions related to the use of natural gas, we use the CO2 emission factor of 0.0527 MT 

of CO2/MM Btu (EIA, 2007). 

�	 Social cost of carbon. Another input used for this analysis is the dollar value assigned to 

GHG emissions, measured in CO2e. The social cost of carbon is estimated as the 

aggregate net economic value of damages from climate change across the globe, and is 

expressed in terms of future net benefits and costs that are discounted to the present 

(IPCC, 2007). The most recent IPCC Assessment Report contained peer-reviewed 

estimates of the social cost of carbon. The IPCC found an average value of $12 per 

MT CO2, but added that the range around this mean is large. For example, in a survey of 

100 estimates, the values ran from USD $-3 per MT CO2 up to $95 per MT CO2. The 

often-cited Stern Review on the Economics of Climate Change estimates a social cost of 

carbon at $85 per MT CO2 (Stern, 2006). 

For this analysis, the IPCC’s average value of $12 was used when calculating social
 

benefits and costs, which produces conservative estimates for the benefits and costs
 

associated with GHG emissions (a conservative estimate). To determine total costs over
 

the 40-year project period, we escalated the social cost of carbon by 2.4% per year,1
 

above the general rate of inflation.
 

�	 Cooling effect and carbon sinks of green infrastructure. To estimate the benefits 

associated with the cooling effect and carbon sinks under the LID options, we relied on 

previous studies by the U.S. Department of Agriculture (USDA) Forest Service. The 

Urban Forest Effects Model (UFORE) provides estimates of energy savings via shading 

1. The United Kingdom has established an official estimate of the social cost of carbon for use in many of its
 

project evaluations and models the growth rate of the cost at 2.4% per year.
 

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of trees and insulation by green roofs. It also provides carbon storage and sequestration
 

data by species of tree. For our research, we used one type of tree of average size and
 

average storage capabilities for all the cooling and carbon sinks. We also assumed that
 

30% of trees planted would be close enough to buildings to provide shading. Our results
 

can easily be adjusted for specific species of trees.
 

�	 Engineering estimates versus external costs. The amount of energy required for 

excavation and other construction activities serves as a key input into our analysis. 

However, the costs associated with this energy use (i.e., electricity costs and the cost of 

fuel for construction and maintenance vehicles) are not included in our estimate of total 

benefits and external costs. The cost of energy used for these purposes is assumed to be 

included in the engineering cost estimates for each CSO option. However, we estimate 

and include the external costs associated with the energy consumption [e.g., CO2, sulfur 

oxides (SOx), and NOx emissions and costs]. 

G.2	 Methods 

G.2.1	 Estimating the external costs of traditional infrastructure CSO control options 

We first estimated total energy use (electric and gasoline) under each of the non-LID CSO
 

control options. Total electrical energy use was calculated based on power requirements for
 

excavation, building, equipment, and pumping, as provided by CDM. Total fuel use was
 

determined based on the estimated number of vehicle miles traveled (VMT) by construction and
 

maintenance vehicles throughout the course of the project. Total fuel use also took into account
 

the additional fuel used by individuals traveling on Philadelphia roadways as a result of
 

construction-related traffic delays (see Appendix I).
 

Based on estimated total energy use, we were able to estimate total NOx, SO2, and carbon
 

emissions (and associated monetary costs) under each CSO option. The individual components
 

of our analysis are described below.
 

Emissions associated with energy used for excavation, building, equipment, and pumping. 

CDM provided estimates of the power needed for excavation, building, equipment, and pumping
 

under each of the traditional infrastructure CSO control option (i.e., tunneling, plant expansion,
 

and satellite treatment). We used these inputs to estimate total emissions generated under each
 

option.
 

To determine total carbon emissions, we used average air pollution emission factors for the State
 

of Pennsylvania’s electricity sector (0.574 MT of CO2/MWh) (EIA, 2007). We applied these
 

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estimates to total power use required under each option. The monetary cost of these emissions
 

was then estimated based on IPCC’s average estimate for the social cost of carbon ($12/MT).
 

In addition to carbon emissions, we also evaluated the SOx and NOx emissions associated with
 

electricity use under the different CSO control options. This analysis was also based on average
 

air pollution emission factors for Pennsylvania power plants (EIA, 2007). We applied these
 

emission factors (0.00414 MT SO2/MWh and 0.000766 MT NOx/MWh) to total electricity use
 

under each option.
 

We then estimated the human health costs of SO2 and NOx emissions based on EPA-generated
 

national averages. These estimates reflect the change in health risks, and associated values, of a
 

typical ton of emissions for each pollutant (U.S. EPA, 2008b). They do not reflect only benefits
 

in the local area, but take into account long-range transport of the pollution (emissions in one
 

location spread over a wide area).
 

EPA estimates that the health-related costs of SOx emissions from electricity-generating sources
 

ranges from $25,234 to $53,985 per ton. For NOx emissions, these costs range from $2,681 to
 

$5,733 per ton. To determine total costs of SOx and NOx under the CSO control options, we
 

applied the midpoints of these estimates to total emissions.
 

It should be noted that the power requirements provided by CDM for excavation, building, and
 

equipment were provided as totals over the 40-year period, and the power requirements for
 

pumping were provided as annual estimates. It is difficult to estimate energy-related costs far
 

into the future due to a number of significant variables. These include a change in the generation
 

mix for electricity, a change in retail energy prices, changes in both the social costs of carbon
 

emissions and air pollution, and the change in the price of carbon emissions under a federal or
 

regional carbon policy.
 

Emissions associated with fuel used by heavy construction vehicles. To evaluate fuel use and 

emissions associated with construction activities, we relied on CDM’s estimate for the number of 

heavy-duty truck trips under each CSO control option. We estimated the total gallons of diesel 

fuel consumed by heavy-duty trucks based on an average distance of 20 miles per truck trip and 

an average mile per gallon of 6.6 (U.S. EPA, 2007). 

We then calculated CO2, SOx, and NOx emissions associated with heavy-duty vehicles based on
 

emission factors for heavy-duty trucks (lbs CO2/mile) as determined by the South Coast Air
 

Quality Management District (SCAQMD, 2007). The social cost of carbon was used to measure
 

the costs of carbon emissions from these truck trips. For SOx and NOx emissions, we applied the
 

midpoint of EPA’s estimates for health-related costs of SO2 and NOx from mobile sources.
 

EPA’s estimates range from $13,200 to $28,264 and $4,357 to $9,350 for SOx and NOx,
 

respectively.
 

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Emissions associated with fuel use by concrete delivery trucks. To determine the external costs 

associated with heavy-duty trucks used to deliver concrete materials, we used the same approach 

mentioned above. Because the number of concrete trucks under each option was not an input 

provided by CDM, we assumed the number of these trucks to equal half of the number of heavyduty trucks used for excavation and construction. 

Concrete manufacturing. One of the most energy-intensive industrial processes in the world is 

the production of cement, a key ingredient in the large amounts of concrete used in construction 

of traditional CSO infrastructure. The cement manufacturing process uses both electricity and a 

significant amount of fossil fuels directly in a heating process. While the direct energy costs of 

cement manufacturing do not affect this benefit-cost analysis, the carbon and air pollution costs 

that result do play a role. We were able to analyze the energy used and resulting carbon 

emissions and air pollution that result from this process. 

First, using the total cubic feet of concrete (an input provided by CDM), we estimated the
 

amount of cement used for each non-LID scenario based on standard concrete-cement
 

conversion methods. We estimate the energy and emissions associated with the cement
 

manufacturing process for each of the non-LID scenarios based on standard energy/emissions
 

factors (Worrell and Galitsky, 2004, and as described above).
 

Traffic disruption. Under all of the CSO control options, construction and maintenance activities 

will cause traffic delays on Philadelphia roadways. There is an increase in fuel use associated 

with these delays due to increased time spent idling and traveling at slower speeds. The methods 

used to estimate additional fuel used as a result of construction-related delays are detailed in 

Appendix I. However, actual fuel use and associated costs are reported in the energy use/cost 

category in Tables G.1 through G.8. 

We used standard emissions conversion factors, as described above, to estimate tons of CO2,
 

SOx, and NOx emitted into the atmosphere as a result of this additional fuel use.
 

G.2.2 Estimating the external costs and benefits of green infrastructure 

Emissions associated with energy used for excavation. Similar to the traditional infrastructure 

options, the LID options will require large amounts of power (electricity) to excavate areas for 

LID coverage. This input was provided by CDM. We use the same methods as described above 

to estimate the external costs of emissions associated with this energy use. 

Emissions associated with fuel used by construction and operation vehicles. For the 

development of green infrastructure, heavy-duty vehicles will be needed during the construction 

process. For the LID options, we used the same techniques and assumptions described above to 

estimate emissions associated with these vehicles. As part of this analysis, we also included the 

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emissions generated by operations and maintenance vehicles. For these trucks, we assume an
 

average truck trip of 15 miles and an average mile per gallon of 20.2.
 

Table G.1. Energy-related benefits and external costs of CSO control options in the TaconyFrankford Creek Watershed, over 40-year project period (present value, 2009 USD) 

Air quality health-related Carbon footprint 

Energy savings improvements reduction benefit 

(costs) (costs) (cost of increase) 

LID options 

25% LID $2,994,995 $4,380,801 $2,022,051
 

50% LID $7,274,893 $9,989,179 $4,574,863
 

75% LID $10,164,800 $13,920,497 $6,955,968
 

100% LID $12,671,820 $17,492,296 $8,790,891
 

Plant expansion options (excluding LID component)a 

215 MGD ($32,635) ($240,406) ($36,526)
 

298 MGD ($37,299) ($262,233) ($41,239)
 

490 MGD ($55,050) ($600,679) ($79,752)
 

820 MGD ($81,063) ($840,455) ($120,971)
 

Tunneling options 

15’ Tunnel ($124,142) ($4,127,396) ($469,015)
 

20’ Tunnel ($194,652) ($5,461,468) ($644,125)
 

25’ Tunnel ($286,028) ($ 6,988,847) ($851,115)
 

30’ Tunnel ($401,457) ($8,781,757) ($1,098,570)
 

35’ Tunnel ($538,551) ($10,722,019) ($1,376,390)
 

Satellite treatment options 

25 Ofs ($ 2,152) ($108,395) ($12,248)
 

10 Ofs ($8,748) ($443,600) ($49,884)
 

4 Ofs ($36,550) ($1,945,197) ($212,250)
 

1 Ofs ($104,928) ($5,620,441) ($608,916)
 

a. Plant expansion options are not planned for implementation on their own but will be combined with some 


level of LID.
 

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Table G.2. Energy-related benefits and external costs of CSO control options in the Cobbs 

Creek Watershed, over 40-year project period (present value, 2009 USD) 

Air quality health-related Carbon footprint 

Energy savings improvements reduction 

(costs) (costs) (increase) 

LID options 

25% LID $956,469 $1,399,034 $645,753
 

50% LID $2,323,278 $3,190,101 $1,461,008
 

75% LID $3,246,186 $4,445,589 $2,221,428
 

100% LID $4,046,817 $5,586,264 $2,807,421
 

Plant expansion options (excluding LID component)a 

63 MGD ($17,580) ($363,341) ($60,090)
 

233 MGD ($19,353) ($497,537) ($73,871)
 

404 MGD ($19,851) ($539,720) ($82,551)
 

Tunneling options 

15’ Tunnel ($189,398) ($2,946,459) ($409,049)
 

20’ Tunnel ($265,640) ($3,918,187) ($558,469)
 

25’ Tunnel ($389,503) ($5,202,623) ($771,474)
 

30’ Tunnel ($513,954) ($6,450,870) ($979,242)
 

35’ Tunnel ($661,099) ($7,745,230) ($1,206,602)
 

Satellite treatment options 

25 Ofs ($1,500) ($113,581) ($12,967)
 

10 Ofs ($7,119) ($626,085) ($67,436)
 

4 Ofs ($19,703) ($1,889,297) ($197,383)
 

1 Ofs ($33,685) ($3,307,472) ($341,548)
 

a. Plant expansion options are not planned for implementation on their own but will be combined with some 


level of LID.
 

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Stratus Consulting Appendix G (Final, 8/24/2009)
 

Table G.3. Energy-related benefits and external costs of CSO control options in the
 

Schuylkill River Watershed, over 40-year project period (present value, 2009 USD)
 

Air quality health-related Carbon footprint 

Energy savings improvements reduction 

(costs) (costs) (increase) 

LID options 

25% LID $3,216,685 $4,705,069 $2,171,724
 

50% LID $7,813,382 $10,728,581 $4,913,495
 

75% LID $10,917,201 $14,950,896 $7,470,850
 

100% LID $13,609,791 $18,787,081 $9,441,595
 

Plant expansion options (excluding LID component)a 

157 MGD ($15,316) ($349,321) ($57,000)
 

747 MGD ($33,322) ($727,346) ($119,692)
 

1,336 MGD ($49,501) ($988,837) ($172,595)
 

Tunneling options 

15’ Tunnel ($272,527) ($7,429,041) ($842,353)
 

20’ Tunnel ($362,014) ($9,537,170) ($1,100,347)
 

25’ Tunnel ($478,079) ($11,840,716) ($1,394,327)
 

30’ Tunnel ($621,589) ($14,238,048) ($1,715,633)
 

35’ Tunnel ($793,412) ($16,506,514) ($2,045,052)
 

Satellite treatment options 

25 Ofs ($6,648) ($705,765) ($70,845)
 

10 Ofs ($20,567) ($2,224,732) ($220,716)
 

4 Ofs ($51,597) ($5,728,678) ($563,893)
 

1 Ofs ($115,829) ($12,774,988) ($1,256,428)
 

a. Plant expansion options are not planned for implementation on their own but will be combined with some 


level of LID.
 

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Stratus Consulting Appendix G (Final, 8/24/2009)
 

Table G.4. Energy-related benefits and external costs of CSO control options in the
 

Delaware River Watershed, over 40-year project period (present value, 2009 USD)
 

Air quality health-related Carbon footprint 

Energy savings improvements reduction 

(costs) (costs) (increase) 

LID options 

25% LID $6,713,580 $9,820,003 $4,532,630
 

50% LID $16,307,399 $22,391,744 $10,255,012
 

75% LID $22,785,416 $31,204,186 $15,592,497
 

100% LID $28,405,151 $39,210,732 $19,705,661
 

Plant expansion options (excluding LID component)a 

225/130 ($130,530) ($1,259,852) ($179,991)
 

225/250 ($134,230) ($1,690,557) ($207,334)
 

495/950 ($221,295) ($2,439,859) ($278,522)
 

495/1250 ($228,070) ($2,848,114) ($305,397)
 

Tunneling options 

15’ Tunnel ($408,423) ($9,101,348) ($1,115,480)
 

18’ Tunnel ($489,540) ($10,503,691) ($1,304,735)
 

23’ Tunnel ($682,323) ($12,903,993) ($1,670,979)
 

28’ Tunnel ($929,218) ($15,726,970) ($2,112,658)
 

31’ Tunnel ($1,089,041) ($17,824,366) ($2,422,831)
 

Satellite treatment options 

25 Ofs ($8,811) ($438,766) ($49,371)
 

10 Ofs ($24,959) ($1,167,302) ($133,045)
 

4 Ofs ($64,856) ($3,331,932) ($367,354)
 

1 Ofs ($137,147) ($6,784,880) ($750,802)
 

a. Plant expansion options are not planned for implementation on their own but will be combined with some 


level of LID.
 

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Stratus Consulting Appendix G (Final, 8/24/2009)
 

Table G.5. Non-monetized energy-related benefits and external costs of CSO control 

options in the Tacony-Frankford Creek Watershed, over 40-year project period 

Air quality – emissions Energy use 

(reductions) (savings) CO2 

SO2 NOx Natural gas Fuel Electricity emissions 

(MT) (MT) (kBtu) (gallons) (kWh) (reductions) 

LID options 

25% LID (145.05) 3.41 (38,028,191) 59,440 (35,046,202) (105,045)
 

50% LID (330.20) (8.24) (129,277,877) 106,449 (79,771,661) (235,478)
 

75% LID (463.54) 5.56 (183,776,322) 182,578 (111,990,066) (358,536)
 

100% LID (583.72) 16.55 (221,563,669) 247,575 (141,029,264) (453,597)
 

Plant expansion optionsa 

215 MGD 6.67 11.19 14,985 2,361
 

298 MGD 7.29 12.78 17,126 2,666
 

490 MGD 17.39 21.10 25,277 5,155
 

820 MGD 24.95 32.03 37,222 7,819
 

Tunneling options 

15’ Tunnel 133.56 375,913.37 57,002 26,553
 

20’ Tunnel 176.62 561,135.30 89,378 36,885
 

25’ Tunnel 225.74 793,910.46 131,335 49,197
 

30’ Tunnel 283.22 1,082,609.31 184,336 63,986
 

35’ Tunnel 345.42 1,421,147.25 247,285 80,737
 

Satellite treatment options 

25 Ofs 3.55 1.75 988 720
 

10 Ofs 14.57 6.93 4,017 2,868
 

4 Ofs 63.47 28.51 16,783 12,071
 

1 Ofs 183.27 80.61 48,179 34,379
 

a. Plant expansion options are not planned for implementation on their own but will be combined with some 


level of LID.
 

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Stratus Consulting Appendix G (Final, 8/24/2009)
 

Table G.6. Non-monetized energy-related benefits and external costs of CSO control 

options in the Cobbs Creek Watershed, over 40-year project period 

Air quality – emissions Energy use 

(reductions) (savings) CO2 

SO2 NOx Natural gas Fuel Electricity emissions 

(MT) (MT) (kBtu) (gallons) (kWh) (reductions) 

LID options 

25% LID (46.32) 1.09 (12,144,517) 18,983 (11,192,203) (33,547)
 

50% LID (105.45) (2.63) (41,285,620) 33,995 (25,475,530) (75,201)
 

75% LID (148.03) 1.78 (58,690,006) 58,307 (35,764,660) (114,501)
 

100% LID (186.42) 5.29 (70,757,609) 79,064 (45,038,492) (144,859)
 

Plant expansion optionsa 

63 MGD 12.71 12.26 8,072 3,884
 

233 MGD 16.93 14.35 8,886 4,775
 

404 MGD 18.68 15.83 9,115 5,336
 

Tunneling options 

15’ Tunnel 94.69 475,999.78 86,965 24,465
 

20’ Tunnel 126.19 665,540.02 121,974 33,620
 

25’ Tunnel 167.55 962,260.15 178,847 46,873
 

30’ Tunnel 207.52 1,256,965.47 235,991 59,809
 

35’ Tunnel 248.74 1,598,573.93 303,556 74,109
 

Satellite treatment options 

25 Ofs 3.81 1.65 689 739
 

10 Ofs 20.74 7.94 3,269 3,761
 

4 Ofs 62.20 22.23 9,047 10,887
 

1 Ofs 108.61 37.85 15,467 18,756
 

a. Plant expansion options are not planned for implementation on their own but will be combined with some 


level of LID.
 

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Stratus Consulting Appendix G (Final, 8/24/2009)
 

Table G.7. Non-monetized energy-related benefits and external costs of CSO control 

options in the Schuylkill River Watershed, over 40-year project period 

Air quality – emissions Energy use 

(reductions) (savings) CO2 

SO2 NOx Natural gas Fuel Electricity emissions 

(MT) (MT) (kBtu) (gallons) (kWh) (reductions) 

LID options 

25% LID (156) 4 (40,843,047) 63,840 (37,640,331) (112,820)
 

50% LID (355) (9) (138,847,060) 114,328 (85,676,380) (252,908)
 

75% LID (498) 6 (197,379,493) 196,092 (120,279,600) (385,075)
 

100% LID (627) 18 (237,963,870) 265,900 (151,468,287) (487,172)
 

Plant expansion optionsa 

157 MGD 12 11 7,033 3,684
 

747 MGD 26 24 15,300 7,737
 

1,336 MGD 35 35 22,729 11,156
 

Tunneling options 

15’ Tunnel 237 742,003 125,136 47,605
 

20’ Tunnel 305 987,092 166,225 62,539
 

25’ Tunnel 379 1,291,300 219,519 79,755
 

30’ Tunnel 456 1,653,470 285,414 98,814
 

35’ Tunnel 528 2,069,410 364,310 118,737
 

Satellite treatment options 

25 Ofs 23 8 3,053 3,850
 

10 Ofs 72 23 9,444 11,937
 

4 Ofs 186 58 23,692 30,399
 

1 Ofs 415 129 53,185 67,707
 

a. Plant expansion options are not planned for implementation on their own but will be combined with some 


level of LID.
 

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Stratus Consulting Appendix G (Final, 8/24/2009)
 

Table G.8. Non-monetized energy-related benefits and external costs of CSO control 

options in the Delaware River Watershed, over 40-year project period 

Air quality – emissions Energy use 

(reductions) (savings) CO2 

SO2 NOx Natural gas Fuel Electricity emissions 

(MT) (MT) (kBtu) (gallons) (kWh) (reductions) 

LID options 

25% LID (325) 8 (85,243,992) 133,241 (78,559,566) (235,468)
 

50% LID (740) (18) (289,789,289) 238,615 (178,816,154) (527,847)
 

75% LID (1,039) 12 (411,953,001) 409,267 (251,036,931) (803,695)
 

100% LID (1,308) 37 (496,657,118) 554,963 (316,131,196) (1,016,782)
 

Plant expansion optionsa 

225/130 MGD 37 49 59,935 11,634
 

225/250 MGD 49 52 61,634 13,402
 

495/950 MGD 67 77 101,611 18,003
 

495/1,250 MGD 78 80 104,722 19,740
 

Tunneling options 

15’ Tunnel 292 1,089,554 187,535 64,559
 

18’ Tunnel 337 1,298,714 224,781 75,805
 

23’ Tunnel 415 1,770,355 313,301 98,203
 

28’ Tunnel 505 2,363,038 426,667 125,361
 

31’ Tunnel 572 2,754,184 500,053 144,203
 

Satellite treatment options 

25 Ofs 14 7 4,046 2,875
 

10 Ofs 38 19 11,460 7,689
 

4 Ofs 109 50 29,780 20,947
 

1 Ofs 222 103 62,973 42,744
 

a. Plant expansion options are not planned for implementation on their own but will be combined with some 


level of LID.
 

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Stratus Consulting Appendix G (Final, 8/24/2009)
 

Energy savings and emissions offsets: trees. When properly placed, trees can affect energy 

consumption by shading buildings, providing evaporative cooling, and by blocking winter winds 

(USDA, 2007). Using data obtained from the USDA, we calculated the energy savings from 

trees based on average heating and cooling per building. This allowed us to estimate savings in 

energy costs for the entire community of shaded buildings. We also estimated the reduction in 

emissions (offsets) associated with these savings. 

Energy savings and emissions offsets: green roofs. Green roofs provide insulation and shade for 

buildings, thus reducing their need for both heating and cooling costs. Using energy savings 

estimates confirmed by two separate studies, we quantified the energy savings associated with 

green roofs under each LID CSO control option. To estimate electricity savings (from reduced 

cooling), we applied an average savings of 0.39 kWh/sq ft of green roof. For natural gas savings 

(from reduced heating), we used an estimate of 123 MM Btu per building (Doshi, 2005; Green 

Roofs for Healthy Cities, Undated). 

Green sinks − trees. Trees provide a valuable resource for green infrastructure projects by 

removing (sequestering) CO2. Trees therefore act as a carbon sink by removing the carbon and 

storing it as cellulose in their trunk, branches, leaves, and roots while releasing oxygen back into 

the air. The USDA’s UFORE model estimates the CO2 storage capacity for many species of 

trees. For our analysis, we used the storage capacity associated with the average-sized tree from 

the UFORE model as a model for all trees planted under the LID options. We estimated carbon 

stored simply by multiplying the storage capacity of an average tree according to the USDA by 

the number of total trees planted. 

Green sinks − green roof and bioretention. Green roofs and vegetated bioretention areas also 

store large amounts of CO2. The United Kingdom’s Department of Environment, Food and Rural 

Affairs (DEFRA) provides an estimate of sequestered CO2 per 1,000 square meters 

(U.K. DEFRA, 2007). Using this rule of thumb, we calculated CO2 sinks based on the total
 

estimated new green acreage under each LID scenario.
 

G.3 Summary of Results 

Tables G.1 through G.8 show the energy-related benefits and external costs for the different CSO
 

control options in each watershed. Tables G.1 through G.4 show results in physical units
 

(e.g., tons of emissions, energy savings). Tables G.5 through G.8 show the monetary values tied
 

to the physical units in Tables G.1 through G.4. As shown, the largest benefits and costs (in
 

terms of monetary value) under each option can generally be attributed to a reduction of SOx and
 

NOx emissions (or net emissions). Under some of the LID options, reductions in NOx emissions
 

do not completely offset the NOx emissions associated with energy use (thus, there are positive
 

net emissions).
 

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Stratus Consulting Appendix G (Final, 8/24/2009)
 

G.4 Uncertainties and Sensitivity Analysis 

G.4.1 Omissions, biases, and uncertainties 

To estimate energy savings, costs, and emissions under the different CSO control options, it was
 

necessary to make a number of assumptions. In addition, a number of data omissions and
 

uncertainties surrounding the analysis have been identified throughout this report. Table G.9
 

provides a summary of these assumptions and uncertainties and their likely impact on the results
 

of our analysis.
 

Table G.9. Omissions, biases, and uncertainties 

Assumption/ Likely impact 

methodology on net benefitsa Comment/explanation 

Estimates of the + The IPCC evaluated a range of cost estimates and found an average of 

social cost of $12/MT CO2. Many recent estimates of the social cost of carbon are 

carbon are wide found in the upper bound of IPCC’s range, including the Stern estimate 

ranging and of $85. Section G.4.2 shows the results of a sensitivity analysis in 

uncertain which a higher social cost of carbon of $48 is used. 

Electricity prices + A federal climate policy could increase fossil fuel based energy prices 

are conservative at a much higher rate than the estimates provided in this study. 

However, an economy-wide policy that would limit GHG emissions is 

expected, but not a certainty. Section G.4.2 shows the results of a 

sensitivity analysis in which higher electricity rates are used. 

GHG emissions U GHG emission factors from power plants vary from plant to plant and 

associated with from region to region. The actual emissions from the CSO options may 

electricity be higher or lower than the average emissions factor for the State of 

generation in Pennsylvania used in this analysis. The emissions factors used in this 

Pennsylvania vary analysis are the best available option. 

Transportation fuel U An average cost of gasoline and diesel fuel were chosen based on 

costs recent prices and adjusted to rise with inflation. It should be noted that 

fuel prices are volatile and many experts expect fuel prices will rise 

faster than inflation during the life of this project life. These increases 

would be expected to be even larger under a federal climate policy. 

However, technology gains in vehicle efficiency could ease any price 

increases. 

Reduction of - The blocking of wind in the winter and the shading of buildings during 

energy usage from summertime depend on the type of tree planted and the distance and 

the planting of direction from the building. This analysis assumed an estimate of 30% 

trees of total trees planted were properly placed to shade during the summer 

and block wind during the winter. The analysis may be sensitive to this 

assumption. Benefits would be decreased if 30% is too high. 

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Stratus Consulting Appendix G (Final, 8/24/2009)
 

Table G.9. Omissions, biases, and uncertainties (cont.)
 

Assumption/ Likely impact 

methodology on net benefitsa Comment/explanation 

Carbon U Different species of trees at different stages of life are able to sequester 

sequestration from carbon in varying amounts. This analysis used an average sized tree to 

trees are based on calculate total carbon sequestration. A tree growth model was used to 

USDA’s UFORE simulate the different stages of sequestration as the trees grow over 

analysis of the time. 

benefits of 

Philadelphia’s 

urban forest 

a. Indicating how addressing the assumption or overcoming the omission would probably impact the analysis,
 

using the following key: + would likely increase net benefits; ++ would increase net benefits significantly;
 

U direction of change in net benefit is uncertain; - would diminish net benefits; -- would diminish net benefits
 

significantly.
 

G.4.2 Sensitivity analysis 

We conducted a sensitivity analysis to evaluate the effect of electricity rates and the social cost
 

of carbon on our overall results. This sensitivity analysis compares the benefits and external
 

costs of two CSO control options (50% LID and 30’ Tunnel) when a higher social cost of carbon
 

($48/MT CO2) versus the IPCC’s average ($12/MT CO2) is used. Our analysis also evaluates the
 

effect on energy savings under the LID options if a doubling in the price of electricity is
 

assumed. Table G.10 shows the results of this analysis.
 

Table G.10. Sensitivity analysis for city-wide present value benefits of key CSO options: 

Cumulative through 2049 

50% LID option 30’ Tunnel option
 

Social cost of carbon increase Total benefits minus external costs
 

$12 $2,846.4 $122.0
 

$48 $2,910.0 $104.3
 

Percent change in overall results 2.23% -14.53%
 

Electricity rate increase resulting from Energy savings (usage)
 

a enacted federal climate policy
 

$0.1 kWh $2,846.4 $122.0
 

$0.2 kWh $2,874.9 $122.0
 

Percent change in overall results 1.00% 0%
 

a. Our external cost analysis does not include higher electricity costs associated with the engineering costs for
 

the 30’ Tunnel option, but it is assumed that electricity costs would double in this scenario as well. This
 

would be reflected in engineering cost estimates for this option.
 

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Stratus Consulting Appendix G (Final, 8/24/2009)
 

Bibliography 

CBO. 2008. Cost Estimate. S. 2191 America’s Climate Security Act of 2007. Washington, DC, 

April 10, 2008. Available: http://www.cbo.gov/ftpdocs/91xx/doc9120/s2191.pdf. Accessed 

10/31/2008. 

Doshi, H. 2005. Report on the Environmental Benefits and Costs of Green Roof Technology for
 

the City of Toronto. Prepared for the City of Toronto and Ontario Centres of Excellence by
 

Ryerson University. Available: http://www.toronto.ca/greenroofs/pdf/fullreport103105.pdf.
 

Accessed 10/31/2008.
 

EIA. 2007. Voluntary Reporting of Greenhouse Gases Program Fuel and Energy Source Codes
 

and Emission Coefficients. Available:
 

http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2007V1_0_year05_SummaryTable
 

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EIA. 2008. Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate
 

Security Act of 2007. SR/OIAF/2008-01. Washington, DC, April 2008. Available:
 

http://www.eia.doe.gov/oiaf/servicerpt/s2191/pdf/sroiaf(2008)01.pdf. Accessed 10/28/2008.
 

Green Roofs for Healthy Cities. Undated. Available:
 

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Accessed 10/30/2008.
 

IPCC. 2007. Summary for policymakers. In Climate Change 2007: Impacts, Adaptation and
 

Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the
 

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McPherson, E.G., J.R. Simpson, P.J. Peper, S.L. Gardner, K.E. Vargas, S.E. Maco, and Q. Xiao.
 

2006. Piedmont community tree guide: Benefits, costs, and strategic planting. Gen Tech. Rep.
 

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Station, Albany, CA.
 

SCAQMD. 2007. EMFAC 2007 (v 2.3) Emission Factors (On-Road). Diamond Bar, CA. The
 

South Coast Air Quality Management District. Available:
 

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Stern, N.H. 2006. Stern Review: The Economics of Climate Change. Cambridge University
 

Press. Cambridge, UK.
 

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and Rural Affairs. London, UK. January. Available:
 

http://www.defra.gov.uk/environment/climatechange/uk/ukccp/pdf/synthesisccpolicyappraisals.pdf. Accessed October 31, 2008.
 

USDA. 2007. Assessing Urban Forest Effects and Values. Philadelphia’s Urban Forest.
 

Resource Bulletin NRS-7, February 2007. Available: http://nrs.fs.fed.us/pubs/rb/rb_nrs007.pdf.
 

Accessed October 31, 2008.
 

U.S. EPA. 2007. Light-Duty Automotive Technology and Fuel Economy Trends: 1975 Through
 

2007 – Executive Summary. Washington, DC. Available:
 

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http://www.epa.gov/climatechange/downloads/s2191_EPA_Analysis.pdf. Accessed October 31,
 

2008.
 

U.S. EPA. 2008b. Final Ozone NAQQS Regulatory Impact Analysis. Office of Air Quality
 

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H.	 Air Quality Pollutant Removal from 

Added Vegetation 

The LID CSO control options currently being evaluated by the PWD would provide (and
 

enhance) recreational amenities within PWD’s CSO watersheds. Under the LID options, PWD
 

plans to substantially increase vegetated acreage (including “treed” acreage) throughout the City.
 

Expanding the amount of trees and vegetated acres in Philadelphia will help improve
 

Philadelphia’s air quality by removing air pollutants from the atmosphere. Conventional air
 

pollution is a persistent problem for most cities in the United States. Even after decades of
 

concerted federal and state efforts to improve air quality, the majority of the U.S. population
 

lives in areas with ambient air quality above the National Ambient Air Quality Standards
 

(NAAQS). The two air pollutants most damaging to human health are ozone (a gaseous pollutant
 

that is a primary ingredient of smog) and fine particulate matter (PM2.5, aerosol particles less
 

than 2.5 microns in diameter, commonly referred to as soot).
 

The following sections outline Stratus Consulting’s methods for estimating the health benefits
 

associated with the improved air quality due to increasing the number of trees that will be
 

planted under the LID options for CSO control. Estimates of total health benefits deriving from
 

trees planted within each watershed are also provided. Additional benefits of air quality relating
 

to avoiding certain air emissions (such as sulfur dioxide and hydrocarbons) related to
 

construction and changes in vehicle traffic are presented in Appendix G.
 

H.1	 Impacts of Trees on Ozone and Particulate Matter 

Trees and shrubs have an important effect on reducing important air pollutants including ozone
 

and particulate matter. In addition to other benefits, trees reduce air pollution concentrations.
 

Increased plantings of some tree species (especially trees that naturally emit low levels of
 

biogenic volatile organic compounds) can be a viable component of an air pollution control
 

strategy because trees remove small but significant amounts of ozone and particulate matter from
 

the ambient air. Trees thus can help reduce the air pollution exposure levels of the local
 

population, and help urban areas meet air quality goals.
 

Ozone (and other gaseous pollutants) are taken into the leaves of trees through stomata
 

respiration. Once inside the leaf, ozone diffuses into intercellular spaces and reacts with inner-


leaf surfaces (Nowak et al., 2006). Additional ozone and particulate matter are removed from the
 

ambient air by direct interaction with the leaf surface. Although some particles are absorbed into
 

the leaves, most particles are retained on the surface of the leaf, with 50% assumed to be reSC11737
 
Stratus Consulting Appendix H (Final, 8/24/2009)
 

released to the atmosphere. The remainder is washed off during rain events, or deposited during
 

autumn leaf drop, effectively removing the particulate matter from the air.
 

A U.S. Forest Service report on the benefits of the Philadelphia urban forests (USDA, 2007)
 

estimates that the existing forest cover in Philadelphia removes 0.33% of the annual mean ozone,
 

and 0.38% of the annual mean particulate matter (PM10), from Philadelphia’s air. This removal is
 

from the entire amount of trees and shrubs in Philadelphia. There are an estimated 2.1 million
 

trees covering 15.7% of the land area of Philadelphia; an additional 5.9% of the land area is
 

covered by shrubs.
 

These Forest Service estimates of the impact of Philadelphia’s trees are used as the basis for the
 

air pollution-related health analysis, and the subsequent economic benefit analysis, reported in
 

this appendix. This analysis assumes that PM2.5 is reduced by the same proportion (0.38%) as
 

total respirable particulate matter (PM10), and calculates the avoidable health effects from
 

reducing PM2.5 levels.
 

H.2 Philadelphia Air Quality Situation 

Like most major cities in the United States, EPA currently classifies Philadelphia County (and 

the entire greater Philadelphia metropolitan area) as exceeding the current NAAQS for both 

ozone and PM2.5. Recent ozone levels1 in Philadelphia exceed the current ozone standard by 

19%. Philadelphia County’s PM2.5 levels are below the national fine particle standards 

(maximum 2008 monitor value in Philadelphia County had an annual mean of 13.49 μg/m3, 

compared with the NAAQS of 15.0 μg/m3

), although higher PM2.5 levels in adjoining counties 

result in the Philadelphia metropolitan area being classified as a PM2.5 non-attainment area. As a 

designated non-attainment area, Philadelphia must develop and periodically update their State 

Implementation Plan, identifying additional control measures that will allow Philadelphia to 

achieve attainment by 2015, and maintain the level of the standards thereafter. 

Air pollution levels in Philadelphia vary year to year, reflecting variability both in meteorology
 

and economic activity. Non-attainment designations are based on three years of monitoring data
 

to accommodate the year-to-year variability. Philadelphia’s air quality has been generally getting
 

better over time, as numerous federal, state, and local emission control requirements take effect.
 

This analysis of the air pollution impacts of increasing the number of trees in Philadelphia
 

County uses monitor data from 2007 (the most recent complete year at the time of the analysis).
 

In 2007, the highest monitor in Philadelphia County had a second highest 8-hour ozone level (the
 

1. The three-year (2006−2008) ozone fourth highest maximum for eight hour ozone in Philadelphia County is 

89 ppb. The 2008 revision to the ozone NAAQS set the standard (as measured by the same metric) at 75 ppb. 

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averaging time of the NAAQS) of 110 parts per billion (ppb); the lowest monitor was 87 ppb (all 

exceeding the NAAQS). The 2007 annual mean PM2.5 levels at the highest monitor in 

Philadelphia County was 14.83 μg/m3 (below the standard of 15.0 and the lowest monitor 

was 12.77). 

The initial air pollution levels in this analysis are derived from the 2007 ozone and PM2.5 air 

quality monitors in Philadelphia County. In this analysis the county-wide population-weighted 

average annual average PM2.5 level is 13.60 μg/m3

. The county-wide population-weighted seven 

month (April through October) seasonal average of the daily 8-hour maximum ozone is 

42.4 ppm. Changes in the seasonal average of ozone are the determinates of ozone’s impact on
 

human health, rather than changes in peak daily values used to determine attainment of the ozone
 

NAAQS.
 

As described above, increasing the size of the urban forest in Philadelphia County is expected to 

lower the ambient ozone and PM2.5 concentrations. Using the relationship from the Forest 

Service report (USDA, 2007) that the current 2.1 million tree urban forest reduces ozone by 

0.33% and PM2.5 by 0.38%, an increase in the number of trees planted in the 50% LID option2 

would reduce recent (2007) ozone levels by 0.04 ppb, and PM2.5 by 0.02 μg/m3 when the trees 

are fully mature. The benefits analysis assumes that future ozone and PM2.5 levels will be 

reduced by the same amount (for the same number of planted trees). Varying the number of trees 

planted, such as in other LID options, is assumed to proportionally effect the changes in ozone 

and PM2.5 levels. 

H.3 Human Health Effects of Ozone and PM2.5 Exposure 

The adverse health effects of ozone and PM2.5 are well established, and are extensively
 

documented in recent EPA documents such as EPA’s Regulatory Impact Analysis (RIA) for the
 

2008 revisions to the ozone NAAQS (U.S. EPA, 2008b). Adverse human health effects that can
 

be avoided by reducing ambient levels of ozone and PM2.5 include premature mortality and a
 

broad array of respiratory and cardiovascular health effects. Health effects occur not only above
 

the level of the NAAQS, but also below the level of the standards.
 

The avoidable air pollution-related health effects estimated in this analysis are:
 

� Premature mortality (from ozone and PM2.5) 

� Onset of irreversible chronic bronchitis (PM2.5) 

2. The 50% LID option includes planting 637,000 trees if implemented in all four watersheds, or an increase of
 

30% in the total number of trees in Philadelphia County.
 

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�	 Heart attacks (non-fatal acute myocardial infarctions) (PM2.5) 

�	 Hospital admissions (non-fatal) for respiratory and cardiovascular conditions (from ozone 

and PM2.5) 

�	 Emergency room visits for asthma (from ozone and PM2.5) 

�	 Respiratory symptoms (days of illness) (from ozone and PM2.5) 

�	 Work loss days (PM2.5) and school absence (ozone). 

This analysis uses software developed by the EPA to calculate the avoided health effects from
 

the contribution of trees to reducing ozone and PM2.5 concentrations, and to estimate the
 

economic value of the avoided health effects. EPA’s BenMAP (U.S. EPA, 2008a), the
 

Environmental Benefits Mapping and Analysis Program (Ver. 3.0.15), was used to conduct this
 

analysis.
 

H.4	 Methods of Estimating Health Effects of Improvements in Air 

Pollution from an Increase in the Number of Trees 

The fundamental method used in this analysis is to calculate the avoided health effects associated
 

with “rolling-back” the air quality levels recorded by Philadelphia monitors in 2007 by the Forest
 

Service’s estimate of the percentage that trees reduce air pollution. As a first step in the analysis,
 

BenMAP estimated the health effects associated with reducing 2007 monitor levels of both
 

ozone and PM2.5 by 1%. These estimated health effects are proportionally adjusted to estimate
 

the health effects associated with the specific estimated air pollution changes resulting from
 

increasing the amount of urban forest in Philadelphia by the amounts associated with tree
 

planting in each of the LID options.
 

The BenMAP closest monitor algorithm was used to estimate the population-weighted average
 

change in ozone and PM2.5 by assigning the population in Philadelphia (BenMAP forecast for
 

2010 = 1,438,198, based on 2000 tract level Census data and EPA forecasts of county-level
 

population changes) to the closest monitor to their point of residence. There are four EPA
 

monitors in Philadelphia County; all four monitors record ozone and PM2.5 levels.
 

The health effects analysis methods are adopted from the methods used by EPA in the 2008
 

ozone NAAQS RIA (U.S. EPA, 2008b). BenMAP was used to estimate the avoided health
 

effects using a concentration-response function from each of the individual concentration-


response functions that EPA used in the 2008 ozone NAAQS RIA.
 

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Because the benefits calculations are dominated by premature mortality associated with PM2.5,
 

the benefit estimates are made using two different estimates of PM2.5-related adult premature
 

mortality. This use of two estimates creates a high estimate and a low estimate for the benefits.
 

The high estimate is from a concentration-response function derived from a long-term cohort
 

tracking epidemiology study in six eastern U.S. cities (Laden et al., 2006). The low estimate is
 

from a long-term cohort tracking epidemiology study of 50 cities nationwide (Pope et al., 2002).
 

The health analysis estimates the annual cases in Philadelphia of each category of avoided health
 

effects associated with implanting each of the four LID options. Table H.1 presents a
 

representative result; the numbers of avoided cases for implementing the 50% LID option in all
 

four watersheds (e.g., the health benefits of planting 637,000 trees, when the trees reach mature
 

size).
 

Table H.1. Avoided cases in Philadelphia County for the 50% LID option 

implemented in all four watersheds (assuming 2010 population) 

Health effect Avoided cases 

Premature mortality 1.0 deaths/year (low estimate from Pope et al., 2002) 

2.4 deaths/year (high estimate from Laden et al., 2006) 

New cases of chronic bronchitis 0.4 cases/year 

Heart attacks 1.2 cases/year 

Hospital admissions (all types) 1.0 cases/year 

Asthma attacks 23 cases/year 

Respiratory illness days 708 days of illness/year 

Work loss days and school absence 250 days/year 

Varying the number of trees planted, such as in other LID options, is assumed to proportionally
 

effect the health benefits of the changes in ozone and PM2.5 levels.
 

H.5 Economic Valuation of the Avoided Health Effects 

In order to include the air quality-related health effects in a benefit-cost analysis containing other
 

benefit categories (energy savings, cooling effects, etc.), it is useful to estimate the economic
 

value of the health effects. For purposes of air pollution policy analysis, the EPA estimates the
 

value of avoiding a case of each estimated health effect, and these estimates (expressed in terms
 

of 2006 prices and forecasted 2010 income levels) are used in this analysis. The EPA valuation
 

estimates are included in the BenMAP software (U.S. EPA, 2008a), which was used to conduct
 

both the health and valuation analyses.
 

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According to economic theory, the best measure of the value of reducing the risk of an adverse
 

health effect is the average that individuals are WTP to reduce the risk a small amount. EPA’s
 

methods for valuing air pollution health effects use WTP valuation measures wherever possible,
 

relying on periodic EPA reviews of existing economic studies. However, for certain endpoints
 

reliable WTP studies are not available. EPA has developed alternative methods for valuing the
 

health effects without WTP valuations. The alternative methods produce a lower value estimate
 

than a WTP method because they only consider a portion of the total demand (WTP) for
 

avoiding a health risk. For example, hospital admissions are valued using the medical costs
 

incurred during the stay in the hospital; this ignores the pain and suffering components of value
 

that would be included in WTP. Heart attacks are valued using a combination of medical cost
 

information plus the lost stream of income from people not able to re-enter the workforce (or
 

who must work at a reduced level of income) after a heart attack. The heart attack valuation thus
 

also ignores the pain and suffering components of WTP, and does not include lost income for
 

people assumed to be out of the workforce (e.g., retirees and unemployed adults).
 

Background and detailed sources of all values used in this analysis are available in the BenMAP
 

documentation and technical appendices (U.S. EPA, 2008a). The values for each health effect
 

are presented in Table H.2.
 

Table H.2. Values for one case of each health effect 

Health effect Value per case (2006 prices, 2010 income) 

Premature mortality $7,000,000 

Chronic bronchitis $196,000 

Heart attack $141,000 to $233,000 (varies by age) 

Hospital admission $15,000 to $33,000 (varies by cause of hospitalization and age) 

Emergency room visit $336 

Asthma attack $189 

Illness day $18 to $59 (varies by illness) 

Work loss days $143 

School absence $89 

Using the methods described above, the total annual health value implementing the 50% LID
 

option in all four watersheds (an increase of 30% in the number of trees in Philadelphia County)
 

is between $12.5 million (based on the low estimate of PM2.5 adult mortality from Pope et al.,
 

2002) and $20.5 million (with the high estimate of PM2.5 adult mortality from Laden et al.,
 

2006). The corresponding annual benefits per tree planted are between $19 (low estimate) and
 

$45 (high estimate). The mean per tree annual benefit is $32. Varying the number of trees
 

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planted, such as in other LID options, is assumed to proportionally affect the total health benefits
 

of the changes in ozone and PM2.5 levels, but the benefit per tree will remain constant.
 

As described in the following sections however, these benefit estimates are not realized
 

immediately when a tree is planted. The schedule in planting trees, plus the time required for a
 

tree to grow to maturity, significantly reduce the present value of planting each tree due to
 

discounting of the value of the avoided health effects.
 

H.6	 Estimates of Trees Planted, the Timeline for Planting Trees, 

and Time to Reach Maturity 

The number of trees planted under each LID option in each of the four watersheds are presented
 

in Table H.3.
 

Table H.3. Number of trees planted in each watershed under the LID options 

Tacony-Frankford Cobbs Creek Schuylkill Delaware 

25% LID 38,612 12,331 41,470 86,553 

50% LID 137,537 43,923 147,718 308,304 

75% LID 195,743 62,511 210,231 438,776 

100% LID 235,032 75,059 252,429 526,848 

There are two assumptions about trees that influence the benefits estimation: the schedule for
 

tree planting, and the time it takes for trees to grow to maturity. Both of these factors result in the
 

full air quality health benefits of the increased number of trees being realized well after the LID
 

program activities begin.
 

The timeline of program activities provided by CDM shows the total number of trees planted in
 

each LID option will be planted over a 35-year period. Approximately 10% of the trees will be
 

planted over the first 6 years of the planting program, 35% planted over the following 14 years,
 

and 55% planted over the final 15 years. Planting begins in 2010, and is not completed until
 

2045.
 

When initially planted trees are not fully mature, and cannot produce the full air quality
 

improvement benefits immediately. For the purpose of this analysis, each newly planted tree is
 

assumed to take 20 years to reach maturity in terms of improving air quality. Newly planted trees
 

are assumed to grow at a uniform rate (in air quality removal terms) throughout the 20-year
 

growth period. After the 20-year growth period, the air quality improving characteristics of a
 

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planted tree are assumed to remain constant, with urban forestry management practices replacing
 

the trees as necessary to maintain the same effective level of air pollution improvements.
 

The combination of the 35 year planting schedule and the 20-year tree growth assumption results
 

in the full benefits of air quality improvements for an LID option not being realized until
 

55 years after the planting begins. The effect of the time delays in the planted trees reaching their
 

full effect on air quality and human health is reflected in the benefit cost analysis through
 

discounting the value of the health effects from the year the health effects are realized back to the
 

time the LID program begins. The discount rate (4.875%) and project initiation year (2008) are
 

the same as used in all portions of the benefits analysis.
 

H.7 Estimated Economic Benefits 

Table H.4 presents a summary of the present value of the health related benefits deriving from
 

air quality improvements resulting from the trees planted in each LID option.
 

Table H.4. Present value of air quality-related health benefits from tree planting under 

the LID options (USD millions) 

Tacony-Frankford Cobbs Creek Schuylkill Delaware Total 

25% LID $7.9 $2.5 $8.5 $17.8 $36.8 

50% LID $28.3 $9.0 $30.4 $63.4 $131.0 

75% LID $40.2 $12.8 $43.2 $90.2 $186.5 

100% LID $48.3 $15.4 $51.9 $108.3 $223.9 

H.8 Omissions, Biases, and Uncertainties 

To estimate the health benefits from air quality improvements associated with planting trees
 

under the LID alternatives, it was necessary to make a number of assumptions in the absence of
 

specific data. In addition, a number of data omissions and uncertainties surrounding the analysis
 

have been identified throughout this report. Table H.5 provides a summary of these assumptions
 

and uncertainties and their likely impact on our estimation of our air quality related health
 

benefits from tree planting.
 

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Table H.5. Omissions, biases, and uncertainties
 

Likely impact 

Assumption/methodology on net benefits Comment/explanation 

Air quality improvements U The ozone and PM2.5 improvements from increasing the number
 

are based on the Forest of trees in Philadelphia’s urban forest is projected to increase 


Service analysis of the air proportionally as the size of the urban forest is increased.
 

quality benefits of the Changes in species composition of the planted trees may make 


existing Philadelphia urban the relationship nonlinear, making the impact on benefits
 

forest uncertain.
 

Non-Philadelphia residents + Planting trees in Philadelphia County will likely improve air
 

are not included in the quality in adjoining counties as well. Air quality improvements in
 

analysis the densely populated adjoining locations are not included in the 


analysis, and would increase the benefits.
 

Trees are assumed to -- PM2.5 is more toxic than an equal amount of PM10. If trees are 


decrease PM2.5 the same less effective at reducing PM2.5 concentrations than in reducing
 

amount that the USDA PM10, the tree planting will result in smaller PM2.5 changes than
 

UFORE analysis estimated estimated in this report. PM2.5 contributes more to the total
 

PM10 is reduced by the benefit value than ozone, so a smaller change in PM2.5 levels
 

existing Philadelphia urban would reduce benefits more than a comparable degree of change 


forest in ozone.
 

Trees are assumed to have - Over the past several decades air quality levels in Philadelphia 


the same reductions in ozone have been improving steadily since air pollution programs began
 

and PM levels in the future to substantially reduce emissions. This trend will generally
 

as they do now continue as older cars are retired, additional control programs are 


implemented, etc. If air quality is cleaner in the future, the impact
 

of additional trees could be less, resulting in smaller
 

improvements in PM2.5 and ozone levels than modeled here.
 

+ would increase benefits; ++ would increase net benefits significantly; U uncertain direction of change; - would
 

diminish net benefits; -- would diminish net benefits significantly
 

References 

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. Reduction in fine particulate air
 

pollution and mortality: Extended follow-up of the Harvard six cities study. American Journal of
 

Respiratory and Critical Care Medicine 173:667-672.
 

Nowak, D.J., D.E. Crane, and J.C. Stevens. 2006. Air pollution removal by urban trees and 

shrubs in the United States. Urban Forestry & Urban Greening 4(11):5−123. 

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Pope III, C.A., R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 

2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air 

pollution. Journal of the American Medical Association 287(9):1132−1141. 

USDA. 2007. Assessing Urban Forest Effects and Values: Philadelphia’s Urban Forests. U.S.
 

Department of Agriculture, Forest Service, Forest Service Northern Research Station, Resource
 

Bulletin NRS-7. Available: http://www.treesearch.fs.fed.us/pubs/19659. Accessed October 20,
 

2008.
 

U.S. EPA. 2008a. BenMAP, the Environmental Benefits Mapping and Analysis Program. Office
 

of Air Planning and Standards. Available: http://www.epa.gov/air/benmap/download.html.
 

Accessed October 20, 2008.
 

U.S. EPA. 2008b. Final Ozone NAAQS Regulatory Impact Analysis. March 2008. Office of Air
 

Quality Planning and Standards. Available: http://www.epa.gov/ttn/ecas/ria.html. Accessed
 

October 20, 2008.
 

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I.	 Construction- and Maintenance-Related 

Disruption Impacts 

Under all of the CSO options, construction activities will likely result in occasional delays and
 

increased travel times for passenger and commercial vehicle travelers in Philadelphia. Travel
 

time delays can be caused by:
 

�	 General traffic slowdowns associated with an increase in the number of trucks and 

construction equipment on the road 

�	 Slowdowns from trucks entering and exiting construction or landscaping sites 

�	 Lane or road closures associated with construction in the roadway or road right-of-way. 

In addition to the value of “lost” time spent in traffic, construction-related delays can result in
 

increased costs associated with additional fuel used by vehicles as a result of slower speeds and
 

occasional vehicle stops and idling.
 

The following sections outline Stratus Consulting’s approach for estimating the costs associated
 

with travel time delay and additional fuel used under the different CSO options. Cost estimates
 

associated with construction-related delays are also provided.
 

I.1	 Impact of Additional Construction and Maintenance Vehicles 

on Philadelphia Roadways 

To estimate travel time delay caused by an increase in the number of construction and
 

maintenance vehicles on Philadelphia’s roadways, we first estimated the number of miles
 

traveled by these vehicles under the different CSO alternatives. We calculated total VMT based
 

on inputs received from CDM, including the number of heavy truck trips over the construction
 

period and total person-hours of O&M labor per year. We made several assumptions regarding
 

average trip length per vehicle, number of concrete trucks under the non-LID alternatives, and
 

the average number of employees per truck (crew size) for O&M vehicles.
 

Table I.1 shows the inputs and assumptions used to determine additional truck miles traveled
 

under the different CSO options.
 

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Stratus Consulting Appendix I (Final, 8/24/2009)
 

Table I.1. Inputs and assumptions for estimating additional VMT under CSO options
 

LID alternatives
 

Heavy truck trips 

Vehicle trips (heavy trucks/construction) Provided by CDM for each alternative 

Vehicle trips (concrete trucks) For non-LID alternatives, assumed to equal 1⁄2 of 

heavy vehicle construction trips 

Average trip length (miles) 20 

Light truck trips (LID options only) 

Person-hours of O&M labor per year Provided by CDM for each LID alternative 

Working hours per year 2,000 

Persons per truck (crew size) 4 

Number of additional trucks on the road each day Number of employees divided by crew size 

Average trip length (miles) 15 

Using the total VMT by construction and maintenance vehicles under each option, we were able
 

to estimate the travel time delay caused by these vehicles based on methodology developed by
 

the Texas Transportation Institute (TTI) (Schrank and Lomax, 2007). The following sections
 

outline our general approach and provide monetary and non-monetary cost estimates for
 

construction-related impacts under each of the CSO alternatives. Non-monetary estimates are
 

presented in terms of total hours of delay.
 

Step 1: Determine congested peak period VMT. This first step is based on the assumption that 

an increase in the number of construction vehicles on Philadelphia’s roadways will only affect 

vehicles already traveling in congested conditions. Thus, vehicles traveling in uncongested 

conditions would continue to travel at “free-flow” speeds despite the addition of extra vehicles. 

We assume that congestion is typically only experienced during certain times of the day 

(i.e., during “peak” periods). 

Based on the TTI’s Annual Mobility Report (Schrank and Lomax, 2007), peak period travel 

accounts for 50% of DVMT. Further, TTI estimates that in Philadelphia, 63% of peak period 

travel is spent in congested conditions. Thus, approximately 32% (50% × 63%) of DVMT is 

considered to be congested, peak period travel. 

Step 2: Determine VMT impacted. Only a small percentage of congested peak period travel will 

experience traffic delays or slow downs due to an increase in the number of trucks on the road. 

To determine total VMT affected, we assume that for each heavy construction vehicle mile 

traveled, an additional 30 vehicle miles (or 30 vehicles) are impacted. Thus, if 10 million vehicle 

miles are traveled under a given CSO option, we assume that 300 million passenger and/or 

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commercial vehicle miles are traveled at slower speeds. In the absence of specific roadway data,
 

this assumption is intended to serve as a benchmark to provide an order of magnitude of costs.
 

Step 3: Estimate impact on traffic speed. We assume construction vehicle travel to be consistent 

with current traffic patterns, with approximately 42% of travel taking place on highways and 

58% on arterial roads (Schrank and Lomax, 2007). TTI reports that during peak periods, the 

average highway speed in Philadelphia is about 45.6 miles per hour (mph). On arterial roads, the 

average speed is approximately 27.5 mph. We estimate that the speed of affected vehicles will 

decrease by approximately 8 and 10% on highways and arterial roads, respectively (to 42 and 

24.8 mph). Again, in the absence of specific roadway data, this assumption is intended to serve
 

as a benchmark to provide an order of magnitude for potential impacts.
 

Step 4: Estimate travel time and determine annual delay. The fourth step involves calculating 

the amount of time it would take to travel the affected vehicle miles at decreased speeds and at 

current (or baseline) speeds. This calculation yields travel time on an hourly basis and was 

performed separately for arterials and freeways under each scenario. Total annual vehicle delay 

was then determined by comparing travel time under decreased speeds for each alternative to 

travel times at current speeds. 

To determine total person delay, we distinguish between heavy truck travel and passenger
 

vehicle travel. Based on TTI data, we assume that 5% of total travel can be attributed to heavy
 

trucks and that these vehicles typically have only one passenger (the truck driver). Passenger
 

vehicles are assumed to contain an average of 1.25 passengers per vehicle, including the driver
 

(Schrank and Lomax, 2007).
 

Based on the steps described above, we were able to estimate travel time delay caused by
 

construction and implementation activities under each CSO option. Our estimates reflect total
 

delay over the 40-year project period. To estimate annual delay over the project life, we allocated
 

total delay based on construction and implementation timelines provided by CDM.
 

Table I.2 provides total person-delay estimates (accounting for 1.25 persons per passenger
 

vehicle) for the CSO options in each watershed.
 

Table I.2. Total vehicle delay caused by additional construction and maintenance vehicles 

on Philadelphia roadways under PWD’s CSO options (person-hours) 

Tacony Cobbs Schuylkill Delaware 

LID options 

25% LID 41,801 13,349 44,895 93,701 

50% LID 74,840 23,901 80,380 167,762 

75% LID 128,378 40,998 137,881 287,772 

100% LID 174,087 55,596 186,973 390,233 

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Table I.2. Total vehicle delay caused by additional construction and maintenance vehicles 

on Philadelphia roadways under PWD’s CSO options (person-hours) (cont.) 

Tacony Cobbs Schuylkill Delaware
 

Transmission and new treatment capacity (excluding LID component)a 

Level 1 10,541 5,678 4,947 42,162
 

Level 2 12,048 6,251 10,763 43,357
 

Level 3 17,781 6,412 15,989 71,479
 

Level 4 26,184 73,667
 

Tunnel optionsb 

15’ Tunnel 40,098 61,176 88,027 131,922
 

20’ Tunnel 62,873 85,803 116,932 158,123
 

25’ Tunnel 92,388 125,811 154,421 220,393
 

30’ Tunnel 129,672 166,009 200,775 300,141
 

35’ Tunnel 173,954 213,537 256,275 351,764
 

Transmission and satellite treatment options 

25 Ofs 695 485 2,147 2,846
 

10 Ofs 2,826 2,299 6,643 8,062
 

4 Ofs 11,806 6,364 16,666 20,949
 

1 Ofs 33,892 10,880 37,413 44,299
 

a. Levels 1–4 correspond to the different capacity options within each watershed (e.g., for Tacony-Frankford
 

Watershed, Levels 1–4 are 215, 298, 490, and 820 MGD, respectively.
 

b. Tunnel options in Delaware River Watershed are 15, 18, 21, 23, 28 and 31’.
 

I.2 Wasted Fuel 

To calculate wasted fuel due to vehicles moving at slower speeds, we again draw upon
 

methodology developed by TTI. We first calculate average fuel economy based on a linear
 

regression applied to a modified version of fuel consumption reported by Raus (1981), as
 

follows:
 

Average fuel economy = 8.8 + 0.25 (average speed)
 

This equation is applied to average speeds for arterials and freeways. Annual fuel consumed as a
 

result of the delay under each CSO option is then calculated:
 

Annual fuel consumed = Travel delay (vehicle hours) × Average speed /
 

Average fuel economy
 

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The additional fuel use associated with construction-related delay is reported under the “energy
 

usage/savings” category for each CSO alternative (Appendix G). The value of this “wasted” fuel
 

is also reported as part of this category (in terms of total energy costs). However, we can provide
 

an idea of total costs associated with additional fuel used as a result of construction-related delay.
 

At $3.00 per gallon, additional fuel use amounts to about 16% of the total costs estimated for
 

travel time delay, which is reported below.
 

I.3	 The Value of Travel Time Delay Caused by Additional 

Construction Vehicles on the Roadways 

To determine the value of extra time spent in traffic, we applied hourly rates used by the
 

U.S. Department of Transportation and TTI to value an individual’s time. Hourly rates for
 

passenger vehicle travelers are weighted by a standard to account for both leisure and work-


related travel (approximately $16.00 per hour). Heavy truck travel (assumed to be commercial
 

truck travel) represents hourly wage plus fringe benefits (approximately $84 per hour). These
 

values are based on 2005 TTI estimates and inflated by 3% to reflect 2008 values.
 

Table I.3 shows the total value of travel time delay caused by additional vehicles on Philadelphia
 

roadways. The values shown here represent present value estimates for the 40-year project
 

timeline. Similar to hours of delay, these costs were allocated by year based on construction and
 

implementation timelines provided by CDM.
 

Table I.3. Monetary value of total vehicle delay caused by additional construction and 

maintenance vehicles on Philadelphia roadways under PWD’s CSO options (present value, 

2009 USD) 

Tacony Cobbs Schuylkill Delaware 

LID options 

25% LID $677,244 $216,282 $727,374 $1,518,111 

50% LID $1,210,066 $386,441 $1,299,636 $2,712,484 

75% LID $2,077,509 $663,464 $2,231,286 $4,656,943 

100% LID $2,818,088 $899,972 $3,026,684 $6,317,026 

Transmission and new treatment capacity (excluding LID component)a 

Level 1 $177,872 $95,815 $83,479 $711,433 

Level 2 $203,292 $105,483 $181,616 $731,600 

Level 3 $300,043 $108,195 $269,800 $1,206,134 

Level 4 $441,823 NA NA $1,243,061 

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Table I.3. Monetary value of total vehicle delay caused by additional construction and 

maintenance vehicles on Philadelphia roadways under PWD’s CSO options (present value, 

2009 USD) (cont.) 

Tacony Cobbs Schuylkill Delaware
 

Tunnel optionsb 

15’ Tunnel $676,617 $1,032,283 $1,485,367 $2,226,049
 

20’ Tunnel $1,060,923 $1,447,835 $1,973,102 $2,668,168
 

25’ Tunnel $1,558,954 $2,122,931 $2,605,699 $3,718,904
 

30’ Tunnel $2,188,081 $2,801,233 $3,387,882 $5,064,569
 

35’ Tunnel $2,935,292 $3,603,223 $4,324,377 $5,935,660
 

Transmission and satellite treatment options 

25 Ofs $11,731 $8,177 $36,234 $48,025
 

10 Ofs $47,680 $38,799 $112,099 $136,036
 

4 Ofs $199,213 $107,387 $281,222 $353,488
 

1 Ofs $571,892 $183,593 $631,312 $747,498
 

a. Levels 1–4 correspond to the different capacity options within each watershed (e.g., for Tacony-Frankford
 

Watershed, Levels 1–4 are 215, 298, 490, and 820 MGD, respectively.
 

b. Tunnel options in Delaware River Watershed are 15, 18, 21, 23, 28, and 31’.
 

I.4	 Delay Associated with Temporary Lane/Road Closures 

To estimate annual vehicle delay associated with detours and temporary lane and/or road
 

closures, we would ideally know the location and duration of each closure as well as the number
 

of travelers affected and their speed over the impacted area. Because we are uncertain of how
 

these variables might vary under each alternative, we do not include the impact of lane and road
 

closures in our overall analysis.
 

In the absence of this detailed information, we can provide a rough benchmark estimate of
 

annual delay caused by construction activities in the roadway based on the following
 

assumptions:
 

�	 Five percent of travelers are impacted 

�	 Each affected traveler experiences an average of a 5-minute delay per lane/road closure 

and/or detour 

�	 Affected travelers experience the delay twice a day, an average 250 days each year (total 

working days in a year) 

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�	 Vehicles will experience these delays on arterial streets as opposed to freeways 

�	 Travelers can experience delays throughout the day (not just during peak periods) 

�	 Heavy trucks account for approximately 5% of total traffic and typically contain only one 

person (the driver) 

�	 Passenger vehicles have an average of 1.25 persons per vehicle. 

Based on these assumptions, we estimate that increased construction activities under the different
 

CSO options could delay Philadelphia truck drivers and passenger vehicle occupants by an
 

additional 12,200 hours each year (about 15,100 person-hours). If this is assumed to be the
 

average impact each year over the 40-year project, total vehicle delay would amount to about
 

490,000 hours.
 

The key variables here are the percent of travelers affected and the amount of time and frequency
 

that each vehicle is delayed. Again, it is uncertain how these variables might vary across the
 

different options. The assumptions described above are intended to provide a benchmark
 

estimate from which to gauge potential impacts.
 

Table I.4 shows the inputs and the order-of-magnitude estimate associated with this city-wide
 

impact.
 

Table I.4. Inputs and preliminary estimates for total delay caused by 

lane closures and/or detours 

Input/preliminary estimate 

Daily vehicle-miles of travel (1,000s) on arterial roads 48,235 

Arterial road lane miles 8,240 

Total number of vehicles on arterial roads per day 5,850 

Percent of total travelers affected 5% 

Total travelers affected 290 

Daily hours of delay 49 

Number of days delay is experienced 250 

Annual hours of vehicle delay 12,200 

Annual hours of delay for heavy trucks 610 

Annual hours of passenger vehicle person-delay 14,480 

Total annual hours of person delay 15,100 

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This estimate will vary each year depending on the level of activity in any given year. In the
 

absence of this information, it is difficult to estimate the present value of this benefit. Further,
 

due to lack of more detailed information, we were unable to calculate the cost of wasted fuel due
 

to idling and slower speeds associated with this type of delay.
 

I.5 Other Non-quantifiable Impacts 

I.5.1 Neighborhood and business access issues 

In some cases, access to residential areas and local businesses may be made difficult by
 

construction and maintenance activities. In residential areas, access issues can result in increased
 

travel time for residents having to choose alternate routes in traveling to and from their homes.
 

Employees and customers of local businesses may also experience increased travel times from
 

having to choose alternate routes or visit other businesses. Some local businesses may
 

temporarily see a decline in the number of customers visiting their businesses.
 

I.5.2 Temporary construction impacts 

Other public impacts from construction and maintenance can include mitigation or repair of
 

construction-related damage due to tunneling settlement and vibration or equipment damage to
 

private property. Additional impacts may include noise, dust, vibration, and safety issues
 

associated with construction activities. These impacts are typically experienced by residents and
 

businesses within the project area, including those located on streets where detours have been
 

routed. These miscellaneous other social costs will not likely represent a large portion of overall
 

project costs and in the absence of specific data, they are described qualitatively.
 

I.6 Omissions, Biases, and Uncertainties 

As detailed throughout this report, to estimate traffic-related impacts associated with the
 

different CSO control options, it was necessary to make a number of assumptions. Many of these
 

assumptions are based on Philadelphia-specific data (average speeds, annual VMT, etc.) or
 

represent standard industry estimates (e.g., number of person per vehicle, wage rates). Although
 

there is a degree of uncertainty surrounding these assumptions, they are developed based on
 

well-accepted methodology (see Schranx and Lomax, 2007) that has been used to evaluate
 

mobility and traffic patterns in urban areas for a number of years.
 

Additional uncertainties surrounding our analysis of construction-related costs generally stem
 

from a lack of specific data related to on-the-ground implementation of the CSO options
 

(location, expected road closures, etc.). Table I.5 provides a summary of these assumptions and
 

uncertainties and their likely impact on total benefits.
 

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Table I.5. Omissions, biases and uncertainties
 

Likely 

Assumption/ impact on 

methodology net benefitsa Comment/explanation 

Analysis does not ++ Depending on their timing and location, temporary lane and road
 

include the impact of closures could significantly increase the overall costs associated with
 

temporary lane and/or construction disruption, in terms of additional time spent in traffic and
 

road closures during wasted fuel.
 

construction. Further, individual businesses could experience significant impacts if
 

they are located on a closed road. This would not likely result in
 

substantial impacts on a city-wide basis (e.g., residents would continue 


to shop, just in different locations).
 

Analysis assumes miles U It is unclear how this assumption affects our current estimates. If
 

traveled by additional construction vehicles spend more time driving on arterial roads, impacts
 

construction vehicles on would be greater because we assume a larger impact on arterial roads
 

highways versus arterial for each vehicle. (e.g., we estimate that the speed of affected vehicles
 

roads, follows current will decrease by approximately 8 and 10% on highways and arterial
 

traffic patterns. roads, respectively)
 

Analysis includes U To determine total VMT affected, we assume that for each heavy
 

assumption for VMT construction vehicle mile traveled, an additional 30 vehicle miles (or
 

impacted by additional 30 vehicles) are impacted. In the absence of specific roadway data, this
 

construction vehicles. assumption is intended to serve as a benchmark to provide an order of
 

magnitude of costs.
 

a. Indicating how addressing the assumption or overcoming the omission would probably impact the analysis,
 

using the following key: + would likely increase net benefits; ++ would increase net benefits significantly;
 

U direction of change in net benefit is uncertain; - would diminish net benefits; -- would diminish net benefits
 

significantly.
 

References 

Raus, J. 1981. A Method for Estimating Fuel Consumption and Vehicle Emissions on Urban
 

Arterials and Networks. Report No. FHWA-TS-81-210. Office of Research and Development,
 

Washington, DC. April.
 

Schrank, D. and T. Lomax. 2007. The 2007 Urban Mobility Report. Texas Transportation
 

Institute. Texas A&M University System.
 

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