OCS Study

BOEM 2015-052

Forecasting Environmental and Social 

Externalities Associated with Outer Continental 

Shelf (OCS) Oil and Gas Development –

Volume 1: 

The 2015 Revised Offshore Environmental Cost 

Model (OECM)

U.S. Department of the Interior

Bureau of Ocean Energy Management

December 2015
[This page intentionally left blank.]
OCS Study

BOEM 2015-052

Forecasting Environmental and Social 

Externalities Associated with Outer Continental 

Shelf (OCS) Oil and Gas Development –

Volume 1: 

The 2015 Revised Offshore Environmental Cost 

Model (OECM)

Prepared by

Industrial Economics, Inc.

2067 Massachusetts Avenue

Cambridge, MA 02140

with

Applied Science Associates, Inc.

55 Village Square Drive

South Kingstown, RI 02879

Northern Economics

880 H Street, Suite 210

Anchorage, AK 99501

Dr. Nicholas Z. Muller

215 Hillcrest Environmental Center

Middlebury College

Middlebury, VT 05770

SC&A, Inc.

1608 Spring Hill Road, Suite 400

Vienna, VA 22182

U.S. Department of the Interior

Bureau of Ocean Energy Management 

December 2015
DISCLAIMER

Study concept, oversight, and funding were provided by the US Department of the Interior, Bureau of 

Ocean Energy Management, Environmental Studies Program, Washington, DC, under Contract Number 

GS-10F-0093K, order number M14PD00057. This report has been technically reviewed by BOEM and it 

has been approved for publication. The views and conclusions contained in this document are those of the 

authors and should not be interpreted as representing the opinions or policies of the US Government, nor 

does mention of trade names or commercial products constitute endorsement or recommendation for use.

REPORT AVAILABILITY

To download a PDF file of this Environmental Studies Program report, go to the US Department of the 

Interior, Bureau of Ocean Energy Management, Environmental Studies Program Information System

website and search on OCS Study BOEM 2015-052.

CITATION

Industrial Economics, Inc. and SC&A, Inc. 2015. Forecasting Environmental and Social Externalities 

Associated with Outer Continental Shelf (OCS) Oil and Gas Development - Volume 1: The 2015 

Revised Offshore Environmental Cost Model (OECM). U.S. Department of the Interior, Bureau 

of Ocean Energy Management. OCS Study BOEM 2015-052. 
i

Table of Contents

Page

List of Figures................................................................................................................................ iv

List of Tables ...................................................................................................................................v

1 INTRODUCTION ............................................................................................................1

2 MODEL DESCRIPTION .................................................................................................5

2.1 General Cost and Benefit Calculations....................................................................6

2.2 Cost Calculations for the No Action Alternative.....................................................8

2.2.1 Oil spill costs under the No Action Alternative...........................................9

2.2.2 Air quality costs under the No Action Alternative ....................................10

3 RECREATION ...............................................................................................................12

3.1 Overview................................................................................................................12

3.2 Basic calculation – recreational fishing .................................................................12

3.3 Calculation drivers – recreational fishing ..............................................................13

3.3.1 Recreational fishing trips per year.............................................................13

3.3.2 Area of recreational fishing activity ..........................................................13

3.3.3 Area of recreational fishing closure...........................................................13

3.3.4 Duration of recreational fishing closure ....................................................14

3.3.5 Economic value of a recreational fishing trip ............................................14

3.4 Basic calculation – beach use ................................................................................14

3.5 Calculation drivers – beach use .............................................................................14

3.5.1 Beach-use days per year.............................................................................14

3.5.2 Total length of public beach ......................................................................15

3.5.3 Length of beach closure .............................................................................15

3.5.4 Duration of beach closure ..........................................................................15

3.5.5 Economic value of a beach-use day...........................................................15

3.6 References – Recreation ........................................................................................16

4 AIR QUALITY...............................................................................................................17

4.1 Overview................................................................................................................17

4.2 Basic calculation ....................................................................................................18

4.3 Calculation drivers.................................................................................................19

4.3.1 Level of emissions-generating activity ......................................................19

4.3.2 Emission factors.........................................................................................19

4.3.3 Damages per ton ........................................................................................30

4.4 Offshore dollar-per-ton values...............................................................................35

4.5 Onshore energy production dollar-per-ton values .................................................38

4.6 Dollar-per-ton values for alaska onshore pipeline construction ............................38

4.7 References – Air quality ........................................................................................40
ii

5 PROPERTY VALUES – VISUAL DISAMENITIES....................................................45

5.1 Overview................................................................................................................45

5.2 Basic calculation ....................................................................................................45

5.3 Calculation drivers.................................................................................................46

5.3.1 Total residential property value along the coast ........................................46

5.3.2 After-tax discount rate ...............................................................................47

5.3.3 Visibility and distance................................................................................48

5.3.4 Percent damage ..........................................................................................48

5.4 References – Property value: visual disamenity ...................................................50

6 PROPERTY VALUES – OIL SPILLS...........................................................................51

6.1 Overview................................................................................................................51

6.2 Basic calculation – Oil spills..................................................................................51

6.3 Calculation drivers.................................................................................................51

6.3.1 Residential property value along the coast ................................................51

6.3.2 After-tax discount rate ...............................................................................53

6.3.3 Duration of event .......................................................................................53

6.3.4 Length of oiled shore .................................................................................53

6.4 Assumptions...........................................................................................................53

6.5 References – Property value: oil spills..................................................................54

7 SUBSISTENCE HARVESTS ........................................................................................55

7.1 Overview................................................................................................................55

7.2 Basic calculation ....................................................................................................56

7.3 Calculation drivers.................................................................................................56

7.3.1 Subsistence harvest as percentage of total harvest ....................................56

7.3.2 Area or volume of water in which spill impact occurs..............................56

7.3.3 Mortality factors.........................................................................................56

7.3.4 Replacement cost .......................................................................................57

7.4 References – Subsistence.......................................................................................60

8 COMMERCIAL FISHING.............................................................................................61

8.1 Overview................................................................................................................61

8.2 Basic calculation ....................................................................................................62

8.3 Regression coefficients..........................................................................................65

8.4 Examples of model usage ......................................................................................66

8.5 Fishery data sources and allocation methods.........................................................68

8.5.1 Alaska ........................................................................................................68

8.5.2 Pacific Coast ..............................................................................................69

8.5.3 Gulf of Mexico and South Atlantic............................................................72

8.5.4 North and Mid-Atlantic..............................................................................73
iii

8.6 Summary Tables of Fishery Data and Regression Coefficients for Alaska 

Planning Areas.......................................................................................................75

8.7 Summary Tables of Fishery Data and Regression Coefficients for West Coast 

Planning Areas.......................................................................................................87

8.8 Summary Tables of Fishery Data and Regression Coefficients for Gulf of Mexico 

Planning Areas.......................................................................................................91

8.9 Summary Tables of Fishery Data and Regression Coefficients for East Coast 

Planning Areas.......................................................................................................94

8.10 References – Commercial fishing..........................................................................98

9 ECOLOGICAL.............................................................................................................100

9.1 Overview..............................................................................................................100

9.2 Overview of the 2001 model................................................................................102

9.3 Modifications to the 2001 model .........................................................................103

9.4 Ecological damages calculations .........................................................................105

9.4.1 Basic calculation ......................................................................................105

9.4.2 Calculation drivers...................................................................................106

9.5 References – Ecological.......................................................................................111

10 BENEFITS OF OCS OIL AND NATURAL GAS ACTIVITY...................................113

10.1 Overview..............................................................................................................113

10.2 Recreational benefits............................................................................................113

10.3 Energy security benefits.......................................................................................114

10.4 References – Benefits ..........................................................................................117

Appendix A: Oil Spill Modeling for the Offshore Environmental Cost Model

Appendix B: Derivation of Emission Factors for Onshore Oil and Natural Gas Production

Appendix C: Modeling the Impacts of Offshore Emissions on Onshore Air Quality

Appendix D: Physical Effects and Valuation Estimates for the OECM Air Quality Module

Appendix E: Derivation of Arctic and Non-Arctic Alaska Emission Factors

Appendix F: Spatial Distribution of Oil and Natural Gas Substitutes Under the No Action 

Alternative

Appendix G: A Comparison of Ecosystem Service Valuation and Habitat Equivalency Analysis 

Frameworks 
iv

LIST OF FIGURES

Figure 1. Offshore Grid for the OCS Adjacent to the Contiguous United States. ............... 18

Figure 2a. Damages Due to PM2.5 Emissions......................................................................... 36

Figure 2b. Damages Due to SO2 Emissions........................................................................... 36

Figure 2c. Damages Due to NOx Emissions. ......................................................................... 37

Figure 2d. Damages Due to VOC Emissions......................................................................... 37

Figure 3. Relationship Between Percent Reduction in Economic Rent from Properties

and Distance from Shore to Platforms.................................................................. 49

Figure 4. Marine Harvest Profiles for Alaska Planning Areas............................................. 58

Figure 5. Locations of Alaska Groundfish Harvests by Planning Area (2006-2009). ......... 63

Figure 6. International North Pacific Fishery Commission (INPFC) Statistical Areas. ...... 71

Figure 7. Statistical Areas for Gulf of Mexico and South Atlantic Fisheries. ..................... 72
v

LIST OF TABLES

Table 1 Emission Factors for the Gulf of Mexico, Atlantic, and Pacific OCS Regions....... 22

Table 2 Crosswalk between Emissions Drivers and Emissions Category............................ 28

Table 3 Assumptions Regarding Location of Tanker Emissions.......................................... 30

Table 4 Summary of Air Pollution Physical Effects Included in APEEP for the Revised 

OECM...................................................................................................................... 32

Table 5 Unit Values for Economic Valuation of Health Endpoints (2006$)........................ 33

Table 6 Summary of Crop and Materials Prices (2006$) ..................................................... 34

Table 7 Total Residential Property Value per Mile of Coastline from the Coast to Oneeighth Mile Inland.................................................................................................... 47

Table 8 Total Residential Property Value per Meter of Coastline to an Average Property 

Width Inland ............................................................................................................ 52

Table 9 Commercial Fishing Modeling: Data Summary for St. George Basin, Alaska ....... 66

Table 10 Commercial Fishing Modeling: Fishery Impact Coefficients for St. George Basin, 

Alaska ...................................................................................................................... 68

Table 11 Data Provided for Gulf of Mexico and South Atlantic Fisheries............................. 73

Table 12 Maximum Cell Depths to Which Fisheries Were Assigned Landings by Fishery and 

Region...................................................................................................................... 73

Table 13 Federal Fisheries for Which Data Were Requested and Provided in the North and 

Mid-Atlantic Regions............................................................................................... 74

Table 14 Commercial Fishing Modeling: Data Summary for Norton Basin.......................... 75

Table 15 Commercial Fishing Modeling: Fishery Impact Coefficients for Norton Basin ..... 75

Table 16 Commercial Fishing Modeling: Data Summary for Navarin Basin ........................ 76

Table 17 Commercial Fishing Modeling: Fishery Impact Coefficients for Navarin Basin.... 76

Table 18 Commercial Fishing Modeling: Data Summary for St. Matthew Hall.................... 77

Table 19 Commercial Fishing Modeling: Fishery Impact Coefficients for St. Matthew Hall 77

Table 20 Commercial Fishing Modeling: Data Summary for St. George Basin .................... 78

Table 21 Commercial Fishing Modeling: Fishery Impact Coefficients for St. George Basin 78

Table 22 Commercial Fishing Modeling: Data Summary for North Aleutian Basin ............. 79

Table 23 Commercial Fishing Modeling: Fishery Impact Coefficients for North Aleutian 

Basin ........................................................................................................................ 79

Table 24 Commercial Fishing Modeling: Data Summary for Aleutian Basin ....................... 80

Table 25 Commercial Fishing Modeling: Fishery Impact Coefficients for Aleutian Basin... 80

Table 26 Commercial Fishing Modeling: Data Summary for Bowers Basin ......................... 81

Table 27 Commercial Fishing Modeling: Fishery Impact Coefficients for Bowers Basin .... 81

Table 28 Commercial Fishing Modeling: Data Summary for Aleutian Arc........................... 82

Table 29 Commercial Fishing Modeling: Fishery Impact Coefficients for Aleutian Arc ...... 82

Table 30 Commercial Fishing Modeling: Data Summary for Shumagin ............................... 83

Table 31 Commercial Fishing Modeling: Fishery Impact Coefficients for Shumagin........... 83

Table 32 Commercial Fishing Modeling: Data Summary for Kodiak.................................... 84

Table 33 Commercial Fishing Modeling: Fishery Impact Coefficients for Kodiak ............... 84

Table 34 Commercial Fishing Modeling: Data Summary for Cook Inlet .............................. 85

Table 35 Commercial Fishing Modeling: Fishery Impact Coefficients for Cook Inlet.......... 85

Table 36 Commercial Fishing Modeling: Data Summary for Gulf of Alaska........................ 86

Table 37 Commercial Fishing Modeling: Fishery Impact Coefficients for Gulf of Alaska ... 86
vi

Table 38 Commercial Fishing Modeling: Data Summary for Washington/Oregon............... 87

Table 39 Commercial Fishing Modeling: Fishery Impact Coefficients for 

Washington/Oregon ................................................................................................. 87

Table 40 Commercial Fishing Modeling: Data Summary for Northern California................ 88

Table 41 Commercial Fishing Modeling: Fishery Impact Coefficients for Northern 

California ................................................................................................................. 88

Table 42 Commercial Fishing Modeling: Data Summary for Central California .................. 89

Table 43 Commercial Fishing Modeling: Fishery Impact Coefficients for Central 

California ................................................................................................................. 89

Table 44 Commercial Fishing Modeling: Data Summary for Southern California................ 90

Table 45 Commercial Fishing Modeling: Fishery Impact Coefficients for Southern 

California ................................................................................................................. 90

Table 46 Commercial Fishing Modeling: Data Summary for Western Gulf of Mexico ........ 91

Table 47 Commercial Fishing Modeling: Fishery Impact Coefficients for Western Gulf of 

Mexico ..................................................................................................................... 91

Table 48 Commercial Fishing Modeling: Data Summary for Central Gulf of Mexico.......... 92

Table 49 Commercial Fishing Modeling: Fishery Impact Coefficients for Central Gulf of 

Mexico ..................................................................................................................... 92

Table 50 Commercial Fishing Modeling: Data Summary for Eastern Gulf of Mexico ......... 93

Table 51 Commercial Fishing Modeling: Fishery Impact Coefficients for Eastern Gulf of 

Mexico ..................................................................................................................... 93

Table 52 Commercial Fishing Modeling: Data Summary for Straits of Florida .................... 94

Table 53 Commercial Fishing Modeling: Fishery Impact Coefficients for Straits of 

Florida ...................................................................................................................... 94

Table 54 Commercial Fishing Modeling: Data Summary for South Atlantic ........................ 95

Table 55 Commercial Fishing Modeling: Fishery Impact Coefficients for South Atlantic.... 95

Table 56 Commercial Fishing Modeling: Data Summary for Mid-Atlantic .......................... 96

Table 57 Commercial Fishing Modeling: Fishery Impact Coefficients for Mid-Atlantic...... 96

Table 58 Commercial Fishing Modeling: Data Summary for North Atlantic ........................ 97

Table 59 Commercial Fishing Modeling: Fishery Impact Coefficients for North Atlantic.... 97
1

1 INTRODUCTION

The Bureau of Ocean Energy Management (BOEM) is charged with assisting the U.S. Secretary of the 

Interior in carrying out the mandates of the Outer Continental Shelf (OCS) Lands Act (Act), which calls 

for expedited exploration and development (E&D) of the OCS to, among other goals, “reduce dependence 

on foreign sources and maintain a favorable balance of payments in world trade.” The Act also requires 

that BOEM prepare forward-looking five year schedules of proposed OCS lease sales that define as 

specifically as possible the size, timing, and location of the OCS territory(ies) to be offered for lease. As 

part of the development of these “Five Year Programs,” BOEM completes an analysis of the 

environmental and social costs attributable to the exploration, development, production, and transport of 

oil and natural gas anticipated to result from the Program proposal, net of the environmental and social 

costs attributable to the No Action Alternative (NAA) (i.e., the costs associated with energy production 

from sources that would substitute for OCS production in the absence of the Program) and net of any 

benefits (measured as “negative costs”) attributable to OCS oil- and natural gas-related activities.

To estimate the anticipated environmental and social costs attributable to oil and natural gas E&D

activities on the OCS, as specified in an E&D scenario,

1 BOEM utilizes the Offshore Environmental Cost 

Model (OECM). An updated Microsoft (MS) Access-based version has been developed in conjunction 

with development of the 2017-2022 Program. This guide presents the model’s cost calculation 

methodologies as well as descriptions of each calculation driver, including the sources of underlying data 

and any necessary assumptions.

The model currently addresses six cost categories:

1. Recreation: The loss of consumer surplus that results when oil spills interfere with recreational 

offshore fishing and beach visitation.

2. Air quality: Emissions—by pollutant, year, and planning area—and the monetary value of the 

human health and environmental damage caused by these emissions.

3. Property values: Impacts of the visual disamenity caused by offshore oil and natural gas platforms 

and losses in the economic rent of residential properties caused by oil spills.

4. Subsistence harvests: The estimated replacement cost for marine subsistence organisms killed by 

oil spills.

5. Commercial fishing: The costs of fishing area pre-emption caused by the placement of oil and 

natural gas infrastructure (platforms and pipelines).

6. Ecological: Restoration costs for habitats and biota injured by oil spills.

The OECM also is programmed to enable consideration of any benefits that might be attributable to OCS 

activity (e.g., the recreational or ecological benefits associated with platforms that become artificial reefs 

after oil or natural gas production has ceased). A discussion of key potential benefit categories is 

 

1 An E&D scenario defines the incremental level of OCS exploration, development, production, and 

decommissioning activity anticipated to occur within planning areas expected to be made available for leasing in the 

BOEM Five Year OCS Oil and Gas Leasing Program. Elements of an E&D scenario include the number of 

exploration wells drilled, the number of platforms installed, the number of development wells drilled, miles of new 

pipeline constructed, anticipated aggregate oil and gas production, and the number of platforms removed. 
2

provided at the end of this document. The current version of the model does not, however, include any 

benefit calculations due to a lack of data that would support a credible and consistent assessment across 

all BOEM planning areas.

While the six categories of impacts identified above capture most of the environmental and social costs 

associated with E&D activities, they do not reflect all of these costs. Because the OECM was designed to 

capture the most significant costs associated with OCS exploration and development, BOEM considers 

additional non-monetized costs and benefits qualitatively in the supplemental document Economic 

Analysis Methodology for the OCS Oil and Gas Leasing Program for 2017-2022.2 Cateogires of impacts 

not captured in the OECM include the following: 

(a) Select oil spill impacts. While the OECM quantifies spill-related costs associated with animal 

mortality and habitat loss using restoration costs for interim losses as an indicator of monetized 

damages, the model does not quantify any values above the restoration cost at which society may 

value the damaged resource. As described in Appendix G, estimating these values would require 

more detailed data than are currently available. Further, the OECM does not include ecological 

costs associated with the use of dispersants. This would require detailed data on the likelihood of 

their use on a given spill and the likely impacts associated with their use, both of which are highly 

uncertain. Finally, the OECM does not estimate air quality costs associated with response vessel 

activity in the event of an oil spill. For most oil spills, which involve limited volumes of oil, 

emissions from vessel activity are not likely to be substantial. 

(b) Ecological damage from exploration and development operations. As discussed in detail in this 

document, the OECM monetizes ecological damages associated with oil spills but does not 

monetize impacts to marine resources from general exploration and development operations. For 

example, the model does not capture costs to habitats or organisms from waste cuttings or drilling 

muds deposited on the ocean floor near offshore structures during their construction, operation, or 

removal. Similarly, the OECM does not estimate water quality impacts associated with produced 

water discharged from wells or non-oil discharges from platforms and vessels. For each of these 

impact categories, key information that would be required to quantify and monetize impacts are 

not readily available (e.g., sediment damage to benthic communities per well drilled). The OECM 

also does not capture auditory and vessel strike impacts to marine mammals. These impacts are 

unlikely to be substantial, and the data required for their estimation are not currently available 

(e.g., relationship between seismic surveying and marine mammal reproduction). 

(c) Impacts from development of onshore infrastructure. With one exception, the OECM does not 

quantitatively address environmental impacts related to the construction and operation of onshore 

infrastructure to support OCS activities. The model includes air quality impacts from onshore 

pipeline construction associated with development in the Chukchi Sea planning area, but does not 

capture changes in air quality, impacts from reductions in coastal marshland, the value of the 

ecosystem services lost (e.g., flood protection), or impacts to water quality associated with 

onshore infrastructure construction. The estimation of these impacts would require information 

on the level of onshore infrastructure development required under individual E&D scenarios that 

is not currently available.

 

2 A draft version of the Economic Analysis Methodology paper will be published with the 2017-2022 Proposed 

Program and a final version will be published with the 2017-2022 Proposed Final Program. 
3

(d) Greenhouse gas impacts. The OECM estimates greenhouse gas emissions under program 

scenarios and the NAA. While greenhouse gases contribute to ocean acidification and 

eutrophication, the OECM does not monetize these impacts as they relate to greenhouse gases 

under program scenarios and the NAA because the marginal impact of OCS greenhouse gas 

emissions on ocean acidification and eutrophication is likely to be minimal.

(e) Other impacts. Further, although certain passive-use values, such as bequest value, option value, 

existence value, and altruistic value can exist for stakeholders under both an E&D scenario and 

the NAA, they are not included in the OECM, as the monetization of these impacts would require 

detailed survey data specific to the resources affected by a given exploration and development 

scenario. 

Just as there are non-monetized environmental impacts under program scenarios, there are also nonmonetized impacts associated with the NAA. These costs not captured relate to increased onshore energy 

production, including the environmental costs associated with new infrastructure construction. The NAA 

analysis does not account for the ecological costs associated with increased terrestrial oil spills or 

pollution from produced water discharges associated with increased onshore oil and gas production; 

increased emissions and increased oil spill risk associated with transporting onshore oil; air emissions 

associated with the production of biomass energy sources; or ecosystem and health damages related to 

releases from coal and uranium mines. For most of these impacts, either the data required for their 

monetization are not available or their magnitude of impacts is likely to be small.

In addition to these broad categories of impacts not captured in the OECM, the model does not estimate 

the impacts of catastrophic spills, impacts to unique resources such as endangered species, or the broader 

regional economic impacts of oil and natural gas exploration and development activity (e.g., the 

employment this activity supports and the indirect effects that result when employment-related income 

enters a local economy). With respect to catastrophic events and impacts to uniqe resources, the rarity of 

such events and resources makes it problematic to develop statistical representations of impacts 

comparable to the estimated impacts of other environmental effects included in the OECM. 

This document represents an update to the original June 2012 documentation of the Offshore 

Environmental Cost Model (OECM).3 The original version of the document presents the methods and 

data incorporated into the OECM for the purpose of developing BOEM’s Five Year Outer Continental 

Shelf (OCS) Oil and Gas Leasing Program for 2012-2017 (“Program” or “Five Year Program”), and it 

corresponds to the Decision Document for the Program. This update to the OECM documentation 

reflects refinements to the methods and data in the model since publication of the June 2012 model 

documentation. The OECM was revised for the purpose of developing BOEM’s Five Year Outer 

Continental Shelf (OCS) Oil and Gas Leasing Program for 2017-2022. These changes include the 

following:

 Revisions to air emissions factors for the Alaska OCS Region to reflect emission data specific to 

Arctic Alaska (the Chukchi Sea and Beaufort Sea Planning Areas) versus non-Arctic Alaska. 

These updates are reflected in the emission factor discussion in Chapter 4 and Appendix E, the 

latter of which details the estimation of Alaska-specific emission factors.

 

3

See U.S. Department of the Interior, Bureau of Ocean Energy Management, Forecasting Environmental and Social 

Externalities Associated with OCS Oil and Gas Development: The Revised Offshore Environmental Cost Model, 

OCS Study BOEM 2012-025, June 2012.
4

 Updated emissions factors for onshore oil, gas, and coal production to reflect updated data for 

these activities.

 Addition of FPSO installation and removal as emissions-generating activities.

 Expansion of the OECM to capture air impacts associated with constructing an onshore pipeline 

connecting the Chukchi Sea planning area to the Trans-Alaska pipeline.

 Descriptions of the model time horizon were modified to reflect its 75-year projection horizon. 

The model previously assessed impacts over a 50-year period.

 Refinements to the model’s assumptions regarding the spatial allocation of oil and gas imported 

to the U.S. via tanker.

 A minor correction to the OECM’s regression equations for oil spill effects in the Chukchi Sea 

planning area (see below for description of these regression equations).

A principal goal of this guide is to aid the user in navigating the details contained within the revised

model. During that process, users should be aware of the following:

 Many of the impacts that the model estimates are associated with the possibility of oil spills from 

pipelines, tankers, and OCS platforms. Estimating the costs of these impacts depends on the 

output from the SIMAP fate and transport model developed by Applied Science Associates.

Running SIMAP many times to reflect a range of oil spill types, locations, and environmental 

conditions enables the development of regression equations that relate, by planning area, a 

particular effect (e.g., meters of oiled beach) to an estimated volume of oil produced in a 

particular scenario.4 The regression equations and coefficients are the only elements of this part 

of the analysis that are stored within the OECM. More complete descriptions of SIMAP and the 

process for developing the regression equations are provided in Appendix A (Oil Spill Modeling 

for the Offshore Environmental Cost Model).

 Users similarly are limited in their ability to view or modify the calculations associated with the

air quality and commercial fishing categories. In both cases, the model incorporates the results of 

analyses completed outside of the OECM. However, complete descriptions of these external 

analyses, and how their results are incorporated into the model, are presented in this guide. For 

the air quality analysis, the user does have the ability to view and edit one of the key calculation 

drivers: air emission factors.

 

4

SIMAP modeling covers the range of spill volumes that historically have resulted from “routine” exploration, 

development, and production activity. SIMAP results are not meant to be scaled to low probability/potentially high 

consequence events. As a result, OECM results do not reflect the costs that might be associated with such events. 
5

2 MODEL DESCRIPTION

The OECM is built on an MS Access 2003 platform and is compatible with MS Access 2010. As defined 

in the E&D scenario worksheet, OCS platform groups serve as the fundamental unit for estimating costs 

and benefits. Currently, the model estimates costs for six sectors:

 Recreation

 Air quality 

 Property values

 Subsistence use 

 Commercial fishing

 Ecological effects

For the recreation, property value, subsistence use, and ecological sectors, the OECM uses the parameters 

set forth in the E&D scenario worksheet to estimate annual oil production and the location of potential 

spills associated with each platform group. This is represented by the Spill Size & Quantity portion of the 

diagram below.

5

 

 

5 The E&D scenario worksheet includes information on several variables for platforms, including depth and distance 

from shore. Platforms in a given planning area that share the same values across these variables are combined into 

platform groups. 

*Impact equations apply to recreation, property values, subsistence, and ecological effects.

*
6

The OECM feeds this information into SIMAP-generated regressions to estimate the physical impacts of 

oiling, as represented by the Oil Spill Impacts node in the diagram above. Then, using impact equations 

developed for each sector, the OECM employs the SIMAP regression outputs and impact-specific data 

elements to estimate monetized estimates of costs and benefits. The OECM then uses this information in 

its estimation of the total environmental and social costs associated with an E&D scenario. The model 

provides additional flexibility for BOEM to add additional cost sectors and impacts as information 

becomes available. Due to the unique characteristics of the air quality and commercial fishing sectors, the 

OECM employs the output from external modules to estimate impacts associated with OCS production in 

these sectors. 

The description below walks through the series of model steps and calculations used in the general cost 

and benefit calculations that occur within the OECM. A discussion of the calculations associated with the 

energy sources that would serve as substitutes for forgone OCS production under the NAA also is 

provided.

2.1 GENERAL COST AND BENEFIT CALCULATIONS 

The following describes the OECM’s general methods for estimating costs and benefits. These methods 

apply to the first four sectors presented above and would apply to additional sectors added through the 

OECM interface. As stated above, the OECM performs calculations at the platform group level as 

provided in the E&D scenario worksheet. For each platform group, the OECM completes the following 

steps.

6

 

Step 1. Annualize and distribute oil production across potential spill sources

The OECM estimates annual oil production based on each platform group’s anticipated total oil 

production adjusted by the activity and production schedule from the E&D scenario. Annualized oil 

production is then distributed across production and transportation modes (i.e., platform, pipeline, barge, 

and tanker) based on the percentages held in the OECM (see the Oil Transport Assumptions under 

Manage Data).7 The OECM assumes all oil originates at the platform, and therefore attributes 100 

percent of oil production to platforms. 

Step 2. Classify mean spill sizes by oil source and type 

Based on historic oil spill information, the OECM applies the mean spill size per barrel (bbl) of oil 

production or transport for four spill sources: (1) platforms, (2) pipelines, (3) OCS supply vessels, and (4)

tankers; and three oil types: (1) crude and condensate, (2) heavy fuel oil, and (3) diesel. The OECM

classifies mean spill sizes into five or six size classes ranging from very small (1 to 10 bbl) to extra-large 

(10,001 to 100,000 bbl).8 The default OECM size classes and mean spill rates can be found and edited on 

the Oil Spill Data page within the model. 

Step 3. Estimate number of spills for each size class, oil type, and oil source 

 

6 Note that the calculations for those impacts that do not depend on oil spill impact drivers (e.g., visual disamenity 

from platforms) skip directly to Step 7 using the research-based drivers relevant to the specific impact equation. 

7 Little information is available on spills from barges, and the percentage of oil moved through barges is believed to 

be minimal. Therefore, the OECM combines barge spills with pipeline spills. 

8

For OCS platforms, wells, pipelines, and service vessels, the OECM uses mean spill sizes for five size classes. For 

tankers, the model uses mean spill sizes for six size classes.
7

For each combination of size class, oil type, and oil source, the OECM estimates the annual number of 

individual spills that correspond to the mean spill size for each class. To accomplish this, the model 

applies the following equation: 

where:

Pa = Annual oil production adjusted for source of spill (billion barrels of oil [BBO])

Rs = Mean spill rate per size class (bbl/bbl produced or transported)

Cs = Spill class as a percentage of total spills (%)

Xs= Mean spill size per spill class (bbl)

Step 4. Employ SIMAP-generated regressions based on oil type and spill location 

As discussed in detail in Appendix A, Oil Spill Modeling for the Offshore Environmental Cost Model, the 

OECM applies regressions developed using SIMAP to estimate the impacts of oil spills based on a 

volume of oil spilled and distance from shore.9 SIMAP-generated impacts include:

 Length of oiled shoreline ~ rock and gravel (meters [m])

 Length of oiled shoreline ~ sand (m)

 Length of oiled shoreline ~ mudflat and wetland (m)

 Length of oiled shoreline ~ artificial (m)

 Water surface area exposed to oil (m2)

 Surface area of shoreline oiled ~ rock and gravel (m2)

 Surface area of shoreline oiled ~ sand (m2)

 Surface area of shoreline oiled ~ mudflat and wetland (m2)

 Surface area of shoreline oiled ~ artificial (m2)

 Water surface area ~ impacts to shorebirds and waders (km2)

 Water surface area ~ impacts to birds, mammals, and sea turtles (km2)

 Volume of oil water exposed ~ impacts to water column organisms (m3)

For each spill size class, oil type, and oil source, the mean spill size is applied to the SIMAP regression to 

generate measures of the above impacts. For spills originating from OCS platforms, pipelines, and supply 

vessels, the OECM assumes that the spill occurs at the location of the platform. For tanker spills, the 

OECM assumes that one-half of the spills occur in the planning area where production occurs and onehalf of the spills occur in the planning area where oil is brought to shore. In an effort to avoid 

 

9

For most planning areas, the OECM applies a different set of regressions for nearshore areas and offshore areas. 

The boundary line between inshore and offshore differs by planning area.

 

s

a s s

X

P  R C 1,000,000,000
8

significantly over- or under-estimating the spill-related costs, the model assumes that tanker spills would 

occur at the distance specified as the boundary between the nearshore and offshore areas.

Step 5. Multiply regression outputs by the number of spills per size class 

For the combination of spill size class, oil type, and oil source, the OECM multiplies each regression 

output by the number of spills estimated in Step 3. 

Step 6. Sum regression outputs to develop oil spill-related drivers

The OECM sums the resulting impacts (corresponding to those in Step 4) across spill size classes, oil 

sources, and oil types to develop the final oil spill-related drivers for the relevant platform group. 

Step 7. Apply relevant oil spill-related drivers and research drivers in the impact equations

The OECM loops through each sector and impact and applies the relevant oil spill-related and researchbased drivers to estimate annual costs and benefits associated with the platform group. 

Step 8. Calculate present value based on user assumptions

Finally, the OECM converts the annual impacts to present values based on the analysis year and discount 

rate assumptions entered in the E&D scenario and the Update OECM page.

National versus regional allocations

The OECM allows the user to choose between national allocation and regional allocation schemes. 

Because the Secretary of the Interior must be able to view costs and benefits from a national perspective 

and also be able to attribute them to specific program options he or she could select, the OECM’s national 

allocation attributes all of the impacts associated with an E&D scenario to the planning area of 

production. For the NAA, the OECM allocates avoided impacts to planning areas in proportion to their 

combined oil and natural gas production under the E&D scenario. For example, if 35 percent of oil and 

natural gas production under an E&D scenario occurs in the Western Gulf of Mexico, the OECM assigns 

35 percent of NAA impacts to this planning area. 

For the regional allocation, the OECM uses two allocation approaches—one for oil spill-related impacts 

and one for air pollution impacts. For the former, the regional allocation assigns one-half of the impacts 

from tanker spills to the planning area where oil is brought to shore, and the remainder (OCS platforms, 

pipelines, vessels, and one-half of tanker spills) are attributed to the planning area of production.10 This 

approach acknowledges the uncertainty regarding the location of oil spills, as spills could occur anywhere 

between the loading and offloading locations. For air pollution impacts, the regional allocation distributes 

emissions to the location where they are expected to occur, including the domestic onshore environment 

(e.g., for onshore oil and gas production).

2.2 COST CALCULATIONS FOR THE NO ACTION ALTERNATIVE

An assessment of net environmental and social costs depends on monetization of anticipated costs and 

benefits in the absence of Five Year Program activity (i.e., the Secretary decides not to hold any lease

 

10 Although environmental and social costs are not confined to (offshore) planning areas, their attribution to 

planning areas under the regional allocation is a convenient way to provide decision makers with a general idea of 

where such costs are likely to be incurred, especially given the extent to which actual conditions could affect 

specific results. Attribution to planning areas also provides some consistency for comparisons between results 

obtained with the national allocation and those obtained with the regional allocation.
9

sales during the five year period of analysis). The absence of program activity is referred to as the No 

Action Alternative (NAA). The process for calculating these costs begins with the application of the 

Market Simulation Model (MarketSim) to an E&D scenario. MarketSim produces an estimate of the 

energy markets’ response to the forgone production that would have occurred as a result of the program 

proposal or other program options. MarketSim results may show an overall reduction in energy demand 

due to slightly higher prices, but the primary response will be substitution across various segments of the 

energy sector. Three specific responses are considered important enough to measure and evaluate in the 

absence of the program production forecasted in the E&D scenario: (1) an increase in the quantity of oil 

delivered into the U.S. market via overseas tanker; (2) the quantity of natural gas imported into the United 

States via tanker; and (3) an increase in the onshore production of oil, natural gas, and coal within the 

United States.11 These responses are assumed to be the most significant in terms of potential 

environmental effects and resulting costs, namely (1) the impact of oil spills from incoming oil tankers; 

(2) the air quality impacts associated with emissions from incoming tankers (oil and liquefied natural gas

[LNG]); and (3) the incremental emissions associated with onshore oil, natural gas, and coal production. 

Other potential costs are not included in the model due to the combination of the lack of reliable data and 

limited importance to the results. For example, potential impacts associated with the waste water 

generated through onshore oil and gas production are not included in this version of the model due to the 

lack of credible bases for describing them as functions of specific model inputs.

The OECM’s estimates of the net environmental and social costs of a program scenario also reflect the 

impacts avoided due to conservation under the NAA. Relative to the impacts estimated under an E&D 

scenario, the avoidance of impacts under the NAA represents a benefit of the NAA. Because 

conservation involves no production of substitutes for OCS oil and natural gas, the OECM assumes that 

the impacts associated with conservation are nil. Thus, when OECM nets NAA impacts from E&D 

impacts, the impacts of conservation are reflected in these calculations (i.e., as a value of $0 among the 

other NAA impacts). Put differently, for the small fraction of OCS production displaced by conservation, 

the net impact of an E&D scenario is the same as the gross impact associated with the E&D scenario 

itself (i.e., an estimated impact of $0 is netted out of the estimated impact associated with the E&D 

scenario). 

12

2.2.1 Oil spill costs under the No Action Alternative

The methodology for modeling oil spill-related costs under the NAA is as follows.

 The OECM imports the MarketSim estimate of imported crude oil in the absence of the oil production 

assumed to result from the Five Year Program. Note that the relevant MarketSim output is exclusive 

to tankers, and thus does not need to be expressed net of imports that might arrive via pipeline.

 In order to assign potential costs to individual planning areas in the regional allocation of the model, 

the model must make assumptions about the geographic distribution of the volume of imported oil. 

The model user can view and adjust this distribution by selecting the No Action Alternative page 

under Manage Scenarios. Specifically, users may adjust the distribution of imports displaced by 

production in a given region. The approach for deriving default values is presented in Appendix F. 

 

11 The OECM does not estimate impacts associated with pipeline imports because pipeline transportation is unlikely 

to result in significant environmental impacts relative to tankers. Thus, while pipeline oil and natural gas imports 

from Canada may change under the NAA, no impacts are estimated for these specific changes in imports.

12 The OECM does not isolate impacts for the portion of the E&D scenario displaced by conservation under the 

NAA. This comparison in the main text is provided for illustrative purposes only.
10

 To estimate costs associated with the transportation of OCS oil to shore and, in the case of Alaska, to 

the continental United States, the model includes user-specified assumptions regarding where and 

how OCS oil is transported. Model users can view and adjust assumptions with respect to the method 

of shipping oil to shore (i.e., via tanker, barge, or pipeline) in the Exploration and Development 

(E&D) spreadsheet used to develop scenarios for analysis in the OECM. In addition, on the Regional 

Allocation of Costs page under Manage Data, OECM users may specify the percentage of Alaskan 

OCS oil that stays in the State and the percentage that is transported to the contiguous United States. 

The OECM’s assessment of both oil spill and air quality impacts accounts for changes in these 

assumptions.

 To calculate spill-related costs under the NAA, the model uses the same spill probability and spill size 

distribution factors to determine the volume of spilled oil in each of the applicable planning areas, and 

then it applies this volume to each of the cost calculations that have a spill component in the same 

way it would calculate costs associated with a program scenario.13

 Since the OECM distinguishes between nearshore and offshore locations for its assessment of oil spill 

impacts, an assumption is required regarding the location of potential spills. In an effort to avoid 

significantly over- or under-estimating the spill-related costs, the model assumes that spills would 

occur at the distance specified as the boundary between the nearshore and offshore areas. This 

boundary ranges from approximately 29 to 87 miles offshore. 

2.2.2 Air quality costs under the No Action Alternative

The OECM uses two separate approaches to estimate the air quality costs of the NAA: one approach for 

tanker imports of oil and natural gas and a second methodology for increased onshore production of oil, 

natural gas, and coal.

 For oil and natural gas tanker imports, the model applies emissions factors from the literature to 

various tanker activities, including (1) tanker cruising, (2) unloading, (3) Volatile Organic Compound 

(VOC) losses in transit (oil tankers only), and (4) ballasting (oil tankers only). For emissions that 

occur in transit (i.e., tanker cruising and VOC losses), the OECM assumes that emissions are 

distributed across an entire planning area. In contrast, emissions released at port (unloading and 

ballasting emissions) are distributed uniformly across the coastal portion of each planning area. 

Similar to the OECM’s assessment of oil spill costs for tanker imports, the model allows users to 

specify the distribution of imports displaced by production in a given region. 

 To estimate the air quality costs related to increased onshore production of oil, natural gas, and coal, 

the model follows a two-step process.

o First, the OECM estimates the emissions associated with onshore production by applying the 

change in onshore production projected by MarketSim to a series of emission factors specific 

to each fuel (i.e., onshore oil, natural gas, and coal). This yields the change in emissions 

associated with onshore production. 

o Second, the model multiplies emissions resulting from oil, natural gas, and coal production 

by a series of dollar-per-ton values that represent the monetized costs of onshore emissions. 

These values were derived from outputs of the Air Pollution Emission Experiments and 

 

13 The oil spill rates used within the OECM are outlined in Appendix A.
11

Policy (APEEP) analysis model (see Appendix C). For a given pollutant, the dollar-per-ton 

values applied depend on the location of displaced OCS production (see Appendix F).
12

3 RECREATION

3.1 OVERVIEW

The model assesses the impact of OCS oil and natural gas activities by estimating the loss of consumer 

surplus that results when oil spills interfere with two activities that occur in the coastal and marine

environment: (1) recreational offshore fishing, and (2) beach visitation. The model is limited to these two 

general-use categories because they capture the primary recreational uses of coastal and marine resources

that would be affected by OCS activity, and they are the uses for which relevant data are generally 

available on a consistent, national basis. Recreational boating (non-fishing) is a use that also would 

realize an impact. However, the lack of geographically organized activity data (i.e., trips or days per year 

by state or region) precludes the ability to model this potential cost at this time.

The model estimates and values changes in recreational offshore fishing activity for the planning areas on 

the Atlantic and Pacific coasts and in the Gulf of Mexico, as well as three planning areas off Alaska. The 

Gulf of Alaska, Cook Inlet, and Kodiak are assumed to account for nearly all recreational saltwater 

angling activity in the State. The model estimates and values changes in beach use only for the planning 

areas in the Atlantic, Pacific, and Gulf of Mexico OCS Regions.

As described below, the methods for estimating the costs associated with changes in recreational activity 

are essentially the same as those employed in the previous version of the OECM. Specifically, the costs 

are attributable to presumed closures of offshore fishing areas or beaches resulting from oil spills.

Note that the model does not take into account a recreational user’s ability to move to another location in 

response to a spill-related closure, in which case some proportion of the value realized by the user would 

be retained. Note as well the difference between the model’s measure of consumer surplus losses (a 

welfare-based measure of economic value) and the assessment of the regional economic impact of an oil 

spill on recreational activity. Expenditures (as captured in a regional economic impact analysis) provide a 

measure of the relative importance of different industries or sectors, such as recreation, within a local or 

regional economy. However, expenditures do not reveal the underlying value of those activities to 

participants, and when aggregated across all participants, to society as a whole. Value, more specifically 

net economic value or consumer surplus, is measured by what individuals are willing to pay for 

something above and beyond what they are required to spend. This concept of value is recognized as the 

appropriate measure to compare the costs and benefits of policy alternatives.

3.2 BASIC CALCULATION – RECREATIONAL FISHING

The model develops an estimate of costs for each planning area in which OCS activity is projected to 

occur using the following equation:

 T  A365OCV

where:

T = Number of recreational fishing trips per year

A = Area within which recreational fishing activity occurs, assumed to be within 30 miles of the 

planning area coastline (m2)

O = Area of recreational fishing closure resulting from an oil spill (m2)
13

C = Duration of recreational fishing closure (days)

V = Economic value of a recreational fishing trip ($/trip)

For the purpose of the model, the annual number of recreational fishing trips is assumed to be distributed 

evenly across the area within which this activity is assumed to occur.

3.3 CALCULATION DRIVERS – RECREATIONAL FISHING

3.3.1 Recreational fishing trips per year

Estimates of the baseline annual level of recreational fishing trips in each planning area are drawn from 

the most recent National Survey of Fishing, Hunting, and Wildlife-Associated Recreation produced by the 

U.S. Fish and Wildlife Service in cooperation with the U.S. Census Bureau (U.S. DOI, 2006). This report

provides consistent, state-level estimates of saltwater fishing trips and days for resident and nonresident 

populations aged 16 and older. The total for each planning area is the sum of the state-level estimates for 

states associated with that planning area, with the following exceptions.

 The Florida total is assumed to be distributed as: (a) 50 percent – Eastern Gulf of Mexico, (b) 25 

percent – South Atlantic, and (c) 25 percent – Straits of Florida.

 The California total is assumed to be distributed equally among the Southern California, Central 

California, and Northern California Planning Areas.

 The allocation of the Alaska total is based on the relative proportions documented in the 2001 OECM, 

but it only accounts for 90 percent of the total, reflecting a concentration of activity in and adjacent to

three planning areas: (a) Gulf of Alaska – 45 percent, (b) Cook Inlet – 40 percent, and (c) Kodiak – 5 

percent. The remaining 10 percent is assumed to be distributed across the other Alaska planning 

areas, but at very low levels of activity in each one; they are not included in the model based on an 

assumption that activity at this scale cannot reasonably be distinguished from subsistence activity.

3.3.2 Area of recreational fishing activity

The model adopts the previous version of the OECM’s general assumption that recreational fishing 

activity occurs within 30 miles of the coast (Roach et al., 2001). Using a geographic information system

(GIS), an estimate of the relevant area offshore each state was generated by creating a buffer at the 30-

mile mark. Planning area totals are the sum of the measured areas across the states (or partial states) that 

correspond to the planning areas.

3.3.3 Area of recreational fishing closure

The SIMAP model quantifies areas swept by floating oil of varying thicknesses and the fates and 

concentrations of subsurface oil components (dissolved and particulate). Regressions on the results of 

multiple SIMAP iterations in representative regions, simulating a range of oil types, volumes, spill 

distance from shore, and environmental conditions, produce equations that generally relate spill volume to 

water area exposed to oil above an impact threshold. For recreational fishing, the threshold is specified as 

a surface sheen produced by an oil concentration of 1 gram (g)/m2.
14

3.3.4 Duration of recreational fishing closure

Lacking a sound basis for altering the assumption included in the 2001 OECM, the duration of saltwater 

recreational fishing closures associated with oil spills is set at 60 days for all planning areas (Roach et al.,

2001).

3.3.5 Economic value of a recreational fishing trip

Based on a review of recreational valuation literature available at that time, the 2001 OECM model 

employed a range of consumer surplus estimates of $11 to $57 per trip for saltwater fishing. To identify 

relevant studies published since the previous update, searches of the economics and social science 

literature were conducted. Specifically, the goal was to determine if additional information was available 

to (1) refine and/or narrow the ranges of values, and (2) support assignment of region-specific values 

(e.g., East, West, and Gulf coasts). The review was restricted to recently published studies that apply 

current best-practices in recreational demand modeling (i.e., random utility travel cost models). While 

several additional studies were identified, reported values generally fell within the existing ranges. In 

addition, no consistent patterns across regions could be discerned. It is likely that differences in model 

specification and estimation procedures obscure any underlying regional variation in value estimates. As 

such, the existing range of values was retained, the high and low was inflated to current dollars, and the 

resulting average value of $42 was assigned as the oil spill-related per-trip loss for recreational fishing.

3.4 BASIC CALCULATION – BEACH USE

The model develops an estimate of costs for each planning area in which OCS activity is projected to 

occur using the following equation.

T  B 365OCV

where:

T = Number of beach-use days per year

B = Total length of public beach in the planning area (m)

O = Length of beach closure resulting from an oil spill (m)

C = Duration of beach closure (days)

V = Economic value of a beach use day ($/day)

As with recreational fishing trips, the annual number of beach-use days is assumed to be distributed 

evenly across the cumulative length of beach within each planning area.

3.5 CALCULATION DRIVERS – BEACH USE

3.5.1 Beach-use days per year

Lacking newer or more refined data that are consistent and complete across all states/planning areas, the 

model uses the annual beach-use data included in the 2001 OECM (Roach et al., 2001). These data 

describe typical “sandy beach” use in the lower 48 States. Data describing the use of Alaskan beach types 

(e.g., kayak haul-outs on rocky beaches) are not included in the available sources of information. Though 

the relative-use levels in Alaska are assumed to be low, this omission is a limitation of the model in its 

current form.
15

3.5.2 Total length of public beach

The length of public beach in each state was determined in GIS using a shapefile associated with a dataset 

maintained by the U.S. Environmental Protection Agency (U.S. EPA, 2009). This dataset does not 

include beach length information for Alaska. The dataset contains information on Beaches 

Environmental Assessment and Coastal Health (BEACH) Program events indexed to the National 

Hydrography Dataset Reach Addressing Database. The total length of public beach in each planning 

area is the sum of state-level beach lengths across the states (or parts of states) that correspond to the 

planning areas.

3.5.3 Length of beach closure

SIMAP models the fate and transport of oil spilled in the ocean to quantify lengths of oiled shoreline, 

using regional data to separate those impacts by shore type (specifically, rock and gravel; sand; mudflat 

and wetland; and artificial). Regressions on the results of multiple SIMAP iterations in representative 

regions, simulating a range of oil types, volumes, spill distance from shore, and environmental conditions, 

produce equations that generally relate spill volume to the length of shoreline exposed to oil above an

impact threshold. For beach use, the model uses the regression result for the sand shoreline type. The 

impact threshold is specified as a surface sheen produced by an oil concentration of 1 g/m2.

3.5.4 Duration of beach closure

Lacking a sound basis for altering the assumption included in the 2001 OECM, the duration of beach 

closures associated with oil spills is set at 21 days for all planning areas. This generalized estimate of 

closure duration does not capture possible variation in spill impacts across different beach types.

3.5.5 Economic value of a beach-use day

Based on a review of recreational valuation literature available at that time, the 2001 OECM model 

employed a range of consumer surplus estimates of $4 to $19 per person per day for beach use. As with 

recreational fishing, the economics and social science literature were reviewed to identify relevant studies 

published since the previous update, again restricting the search to recently published studies that apply 

current best-practices in recreational demand modeling. While several additional studies were identified, 

reported beach-use values also generally fell within the existing ranges and no consistent patterns across 

regions could be discerned. As such, the existing range of values is retained, and the high and low values 

are inflated to current dollars. The resulting average value of $14 is assigned as the oil spill-related perday-loss for beach use.
16

3.6 REFERENCES – RECREATION

Roach, B., W. Wade, and J. Plater. 2001. Forecasting Environmental and Social Externalities Associated 

with OCS Oil and Gas Development: The Offshore Environmental Cost Model, Volume 2, 

Determinants of Environmental and Social Costs. Minerals Management Service (MMS) OCS Study 

2001-018. Herndon, VA: U.S. DOI, MMS, Environmental Studies Branch.

U.S. Department of the Interior (U.S. DOI), Fish and Wildlife Service. 2006. 2006 National Survey of 

Fishing, Hunting, and Wildlife-Associated Recreation.

U.S. Environmental Protection Agency (U.S. EPA). 2009. BEACH Act: Beaches National Geospatial 

Dataset.
17

4 AIR QUALITY

4.1 OVERVIEW

Exploration and development of the OCS will lead to emissions of sulfur dioxide (SO2), oxides of 

nitrogen (NOx), volatile organic compounds (VOCs), particulate matter (PM)14, and other air pollutants 

that may adversely affect human populations and the environment. To account for these effects, the 

revised OECM includes an air quality module that estimates the emissions—by pollutant, year, and 

planning area—associated with a given E&D scenario and the monetary value of the environmental 

damage caused by these emissions (estimated on a dollar-per-ton basis). The model estimates emissions 

based on a series of emissions factors derived from BOEM data and, for planning areas along the coast of 

the contiguous United States, converts these values to monetized damages using a modified version of the 

APEEP model developed by Muller and Mendelsohn (2006). The model monetizes damages associated 

with emissions in Alaska planning areas using scaled estimates of the monetized damages by scaling the 

APEEP estimates of damages per ton of emissions for the Oregon-Washington Planning Area. The 

geographic unit of analysis within the air quality module is a series of offshore grid cells approximately 

2,500 km2in size, as illustrated in Figure 1. 

The specific air pollution impacts that the OECM examines include:

 adverse human health effects associated with increases in ambient PM2.5 and ozone concentrations,

 changes in agricultural productivity caused by changes in ambient ozone concentrations, and

 damage to physical structures associated with increases in SO2.

Emissions from OCS development also may affect visibility, forest productivity, and recreational activity 

(e.g., visits to National Parks). The OECM does not include these effects, however, as the limited data on 

these impacts in the peer-reviewed literature are not amenable to the streamlined air quality modeling 

framework included in the APEEP model. Because human health effects generally dominate the results 

of more detailed air pollution impact analyses,15 excluding emissions-related changes in visibility, forest 

productivity, and recreational activity from the OECM is unlikely to have a significant impact on the 

model’s results.

 

14 The PM estimates emissions of both fine particulate matter (PM2.5) and coarse particulate matter (PM10). 

15 See U.S. EPA (2011).
18

Figure 1. Offshore Grid for the OCS Adjacent to the Contiguous United States.

4.2 BASIC CALCULATION

To estimate air quality impacts, the OECM first estimates the emissions for a given E&D scenario and 

then estimates the damages associated with these emissions. Equation 1 illustrates the OECM’s 

estimation of emissions:

(1) 

EP,A,Y ,G LA,Y ,G  FP,A

where:

EP,A,Y,G = Emissions of pollutant P from emissions-generating activity A (e.g., number of platforms 

operating) in year Y and offshore grid cell G

LA,Y,G = Level of emissions-generating activity A in year Y and offshore grid cell G

FP,A = Emissions factor (e.g., tons per platform operating) for pollutant P and emissions-generating 

activity A

The timing of emissions in the OECM depends on the specified schedule for various E&D activities (i.e., 

exploration and development, platform construction, platform operations, oil and natural gas extraction, 

and platform removal). The model also uses a series of activity-specific assumptions to specify the 

location of OCS emissions. For example, E&D scenarios specified by BOEM specify the average 

distance from shore for each platform group within a planning area. Using this information, the model 

distributes platform emissions to those offshore grid cells that are located a similar distance from shore.

To estimate the economic damages associated with the emissions estimates generated from Equation 1, 

the OECM applies pollutant-specific dollar-per-ton values, as shown in Equation 2.
19

(2) 

  

G A

P Y G EP Y DP Y G

I

, , , , ,

where:

IP,Y,G = Monetized impacts from emissions of pollutant P in year Y in grid cell G



G A

EP,Y

= Emissions of pollutant P in year Y, summed across all emissions-generating activities and 

offshore grid cells 

DP,Y,G = Damages per ton of pollutant P emitted in year Y and offshore grid cell G

As indicated by Equation 2, the value of the damages caused by a ton of air emissions varies by year. 

This reflects growth in population and income per capita over time.

4.3 CALCULATION DRIVERS

4.3.1 Level of emissions-generating activity

The OECM estimates the level of emissions-generating activity for any given year based on the E&D 

scenarios and schedules developed by model users. The specific activities used by the air quality module 

to assess emissions associated with OCS exploration and development activities include:

 exploration/delineation wells drilled, by year;

 development and production wells drilled, by year;

 production platforms, caissons, and FPSOs installed, by year;

 production platforms, caissons, and FPSOs in operation, by year;

 transport of oil produced on the OCS, by year16;

 miles of pipeline laid, by year;

 production platforms decommissioned, by year; and

 for the NAA, changes in onshore energy production and tanker imports of oil and natural gas, by 

year.

4.3.2 Emission factors

The OECM applies a series of emission factors to the annual estimates of emission-generating activity to 

estimate emissions associated with the following OCS activities: (1) oil and natural gas platform 

operations, (2) exploration and delineation well activity, (3) floating production storage and offloading 

(FPSO) vessel activity17, (4) development and production well activity, (5) helicopter trips, (6) pipelaying vessels, (7) platform installation and removal, (8) support vessels, (9) survey vessels, and (10) 

 

16 All natural gas is assumed to be shipped via pipeline, with no emissions.

17 Absent emissions data specific to FPSOs, the OECM assumes that the emissions factors for exploration wells also 

apply to FPSOs.
20

tankers and/or barges/tugs transporting oil produced on the OCS (with separate emission factors for oil 

produced in Alaska planning areas versus all other planning areas).18 Table 1 summarizes the emission 

factors for each of these activities. For planning areas along the contiguous United States, the values in 

the figure for OCS exploration and development activities are based upon emissions data provided by 

BOEM staff for oil and natural gas operations in the Gulf of Mexico. These emission factors are applied 

to all OCS regions adjacent to the contiguous United States under the assumption that oil and natural gas 

operations in these areas would not differ significantly from operations in the Gulf of Mexico.

19 The 

emission factors for Alaska planning areas are, where possible, based on Alaska-specific emissions data. 

For those activities for which Alaska-specific data were not readily available (e.g., helicopter trips), the 

OECM uses the emission factors derived from Gulf of Mexico emission data. Appendix E includes more 

detailed information on the derivation of emission factors for Alaska planning areas.

Table 1 also presents the emission factors for activities associated with the NAA. These include: (1) 

onshore oil production in the contiguous United States, (2) onshore natural gas production in the 

contiguous United States, (3) coal production in the contiguous United States, (4) importation of oil by 

tanker, and (5) importation of LNG by tanker. The data sources for these emission factors are as follows.

 Onshore oil production: Criteria pollutant emission factors for onshore oil production for the 

contiguous United States were based on the Western Regional Air Partnership’s (WRAP’s) 2002

emissions inventory for oil and gas activities in 12 Western States: Alaska, Arizona, California, 

Colorado, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, and 

Wyoming (WRAP, 2009).20 Excluding oil and natural gas operations in the coastal States of 

Alaska and California that were included in the WRAP inventory, emission factors for onshore 

oil production were developed by dividing the emissions estimates from the WRAP inventory 

(with some adjustments) by U.S. Department of Energy (U.S. DOE) estimates of onshore oil 

production in the 10 States analyzed. These States accounted for approximately 14 percent of 

onshore crude oil production in 2002. More detailed information on the derivation of the onshore 

oil emission factors for criteria pollutants is available in Appendix B.

The greenhouse gas emission factors for oil production were derived from an analysis published 

by the National Energy Technology Laboratory (U.S. DOE NETL, 2014). The NETL analysis 

provides estimates of greenhouse gas emissions, expressed as CO2 equivalents (CO2e), for five 

categories of oil production in 2013: enhanced oil recovery, tight oil, other onshore oil, offshore, 

and Alaska. For the purposes of deriving emission factors, enhanced oil recovery and tight oil 

were considered “unconventional” oil production, whereas other onshore oil was considered 

conventional oil production.21 To estimate greenhouse gas emissions factors for unconventional 

oil production, the CO2e emissions for the enhanced oil recovery and tight oil categories 

combined were divided by total production for these categories in 2013 as reported in the 2015 

 

18 Note that the model allows users to specify how oil is distributed across three modes of transportation to shore in 

non-Alaska OCS regions: (1) pipeline (assumed not to cause emissions), (2) tanker, and (3) tug/barge. The OECM 

estimates emissions related to tanker and tug/barge transport. Emissions associated with pipeline transportation are 

assumed to be zero. The only pipeline-related emissions estimated by the OECM are emissions associated with the 

laying of pipelines.

19 Model users wishing to obtain emissions estimates that reflect emissions factors specific to individual planning 

areas can run the model separately with emissions factors and E&D data tailored to a specific planning area.

20 WRAP also includes the States of Washington and Idaho, but they did not produce crude oil or natural gas in 

2002, so these two States were not considered in this analysis.

21 Because the offshore and Alaska categories include offshore production, the emissions data for these categories 

were not used. 
21

Annual Energy Outlook (AEO) (U.S. DOE EIA, 2015). Similarly, for conventional production, 

the NETL estimates of CO2e emissions for other onshore oil were divided by production for this 

category reported in the 2015 AEO. The CO2e emissions per barrel of conventional and 

unconventional production were distributed to individual greenhouse gases—CO2, CH4, and 

N2O—based on (1) the distribution of the oil sector’s emissions of these pollutants, as reported in 

the U.S. Greenhouse Gas Emissions and Sinks Inventory (U.S. EPA, 2015) and (2) the global 

warming potential of each pollutant.

 Onshore natural gas production: The OECM contains separate emission factors for conventional 

and unconventional natural gas production, drawing from data published by the NETL (2012), the 

U.S. EPA (2012b), and the World Resources Institute (WRI, 2013). Focusing on the year 2010, 

NETL (2012) estimates the emissions, by pollutant, of onshore natural gas production across 

multiple production methods and sources. These include shale gas, coal bed methane, tight gas, 

associated gas, and onshore conventional production. For the purposes of distinguishing between 

conventional and unconventional onshore gas production, unconventional natural gas production 

is assumed to include tight gas, shale gas and coal bed methane production. All other methods, 

including associated gas production, are considered conventional. To estimate emissions per 

billion cubic feet of gas production, pollutant-specific emissions for conventional and 

unconventional sources as reported by NETL were divided, respectively, by conventional and 

unconventional production in 2010, as obtained from EIA (2012). 

Because the 2010 emissions estimates published in NETL (2012) reflect emissions in 2010, they 

do not account for the impact of EPA’s 2012 New Source Performance Standards (NSPS) for the 

oil and gas industry. These standards are projected to lead to substantial reductions in VOC and 

CO2e emissions and slight increases in CO and NOx emissions. To capture these changes, the 

emissions factors for conventional and unconventional gas production described in the previous 

paragraph were adjusted based on information published in EPA’s 2012 regulatory impact 

analysis (RIA) for the NSPS (2012) and a WRI report that examined the emissions impacts of the 

standards (WRI, 2013). EPA’s RIA presents the aggregate changes in emissions, by pollutant, 

expected under the NSPS but does not distinguish between changes from conventional and 

unconventional production sources. WRI (2013) provides an estimate of the distribution between 

methane emissions reductions from conventional gas production versus unconventional under the 

NSPS. This distribution is assumed to apply to other pollutants as well. While the NSPS 

examined in the EPA and WRI documents cover both the oil and gas sectors, information in both 

documents make clear that the standards are primarily aimed at reducing emissions from gas 

production. This is consistent with EPA’s Greenhouse Gas Emissions and Sinks Report, which 

includes emissions reductions related to the NSPS for the natural gas sector but not for the oil 

sector (EPA, 2015). Adjustments for the NSPS were therefore made to the natural gas emission 

factors only.
22

Table 1. Emission Factors for the Gulf of Mexico, Atlantic, and Pacific OCS Regions.

EMISSIONS FACTORS:1

PLANNING 

AREA NOX SO2 PM10 PM2.5 CO VOC CO2 CH4 N2O

Oil and Natural Gas Platform Operations 

(tons/platform/yr)2

All planning 

areas

75.65 2.87 0.75 0.75 63.25 23.8 10035.5 117.88 0.33

Caisson Operations (tons/caisson/yr)2 All planning 

areas

0.00 0.00 0.00 0.00 0.00 0.14 0.00 0.42 0.00

Exploration and 

Delineation Wells

(tons/well, by water 

depth)

 0-60 m All, except 

for Alaska 

planning 

areas3

58.37 1.602 1.85 1.79 11.21 1.73 5,221.9 0.06 0.005

 60-800 m 102.69 2.84 3.24 3.14 19.60 3.08 9,244.00 0.11 0.00

 800-1600 m 264.54 6.00 9.69 9.40 59.37 4.79 19,556.35 0.15 0.89

 >1600 m 518.55 11.37 19.38 18.80 118.98 8.48 37,081.20 0.26 1.54

All depths4

Cook Inlet 50.89 0.1254 1.441 1.304 9.793 4.518 5,309 0.00 0.00

Beaufort and 

Chukchi 242.3 0.3787 12.24 11.04 90.33 18.68 5,309 0.00 0.00

Other Alaska 50.89 0.1254 1.441 1.304 9.793 4.518 5,309 0.00 0.00

Development and 

Production Wells 

(tons/well, by water 

depth)

 0-60 m All, except 

for Alaska 

planning 

areas3

58.37 1.6 1.85 1.79 11.21 1.73 5,221.9 0.06 0.005

 60-800 m 102.69 2.84 3.24 3.14 19.6 3.08 9,244 0.11 0.000

 800-1600 m 264.54 6.00 9.69 9.40 59.37 4.79 19,556.35 0.15 0.89

 >1600 m 518.55 11.37 19.38 18.80 118.98 8.48 37,081.20 0.26 1.54

All depths4

Cook Inlet 50.89 0.1254 1.441 1.304 9.793 4.518 5,309 0 0

Beaufort and 

Chukchi 242.3 0.3787 12.24 11.04 90.33 18.68 5,309 0 0

Other Alaska 50.89 0.1254 1.441 1.304 9.793 4.518 5,309 0 0

Helicopters (tons/platform/yr)2 All planning 

areas

0.27 0.04 0.01 0.01 .78 .59 58.07 0.004 0.004

Pipe-laying Vessels (tons/mile of pipe)2 All planning 

areas

20.31 0.25 0.75 0.72 4.55 0.27 1305.99 0.010 0.039 

Platform 

Construction/Removal 

(tons/platform or 

caisson, by water 

depth)

 <300 ft All, except 

for Alaska 

planning 

areas3

8.74 0.18 0.33 0.32 2.06 0.12 601.73 0.004 0.03

 300-600 ft 25.86 0.55 0.97 0.94 6.10 0.37 1779.50 0.01 0.08

 >600 ft 226.60 4.78 8.53 8.27 53.46 3.22 15,594.87 0.09 0.71

All depths4,5

Cook Inlet 307.5 31.4 6.7 6.3 49.7 12.5 13,910 0.103 0.576

Beaufort and 

Chukchi

800.9 32.1 34.1 30.8 263.3 48.1 13,910 0.103 0.576
23

EMISSIONS FACTORS:1

PLANNING 

AREA NOX SO2 PM10 PM2.5 CO VOC CO2 CH4 N2O

Subsea Construction 

(tons/subsea/year)

 0-197 ft All, except 

for Alaska 

planning 

areas3

58.37 1.60 1.85 1.79 11.21 1.73 5,221.9 0.06 0.005

 197-2625 ft 102.69 2.84 3.24 3.14 19.60 3.08 9,244 0.11 0.00

 2625-5248 ft 264.54 6.00 9.69 9.40 59.37 4.79 19,556.35 0.15 0.89

 >5248 ft 518.55 11.37 19.38 18.80 118.98 8.48 37,081.2 0.26 1.54

All depths4

Cook Inlet 50.89 0.13 1.44 1.3 9.79 4.52 5,308.77 0.00 0.00

Beaufort and 

Chukchi

242.28 0.38 12.24 11.04 90.33 18.68 5,308.77 0.00 0.00

Other Alaska 50.89 0.13 1.44 1.3 9.79 4.52 5,308.77 0.00 0.00

FPSO Operation 

(tons/FPSO/year)6

 0-60 m All planning 

areas

39.52 0.35 0.38 0.38 51.25 30.19 2,759.35 147.92 0.05

 60-800 m 84.66 2.95 0.98 0.98 76.12 74.22 9,964.10 285.79 0.24

 800-1600 m 626.64 60.81 5.15 5.13 177.24 57.03 144,970.15 261.17 2.63

 >1600 m 1275.9 61.16 11.40 11.37 393.68 151.63 320,304 637.18 5.11

FPSO Construction, installation activity at site 

(tons/FPSO)6

All planning 

areas

145.78 0.054 1.56 1.51 42.88 1.18 5591.11 0.03 0.27

FPSO Construction, en route to site (tons per mile 

per FPSO)6

All planning 

areas

0.196 0.025 0.004 0.004 0.055 0.005 6.78 0.00006 0.0003

FPSO Removal, removal activity at site 

(tons/FPSO)6

All planning 

areas

69.16 0.03 0.74 0.72 20.34 0.56 2652.36 0.02 0.13

FPSO Removal, en route to site (tons per mile per 

FPSO)6

All planning 

areas

0.196 0.025 0.004 0.004 0.055 0.005 6.783 0.00006 0.0003

Support Vessels (tons/platform/yr) All, except 

for Alaska 

planning 

areas2

147.40 1.81 5.40 5.24 29.96 3.37 10,916.96 0.06 0.32

Cook Inlet2147.40 1.81 5.40 5.24 29.96 3.37 10,916.96 0.06 0.32

Beaufort and 

Chukchi4,5

2,096.8 15.8 110.8 98.9 873.1 139.7 5,247 0.033 0.249

Other Alaska2147.40 1.81 5.40 5.24 29.96 3.37 10,916.96 0.06 0.32

Survey Vessels (tons/platform or caisson/yr)2 All planning 

areas

6.59 0.08 0.24 0.24 1.48 0.09 423.77 0.002 0.01

Tugs Pulling Barges, 

Non-Alaska OCS 

Regions5

Cruising emissions 

(tons/BBO/mile)

All planning 

areas

183.5 22.2 2.78 2.78 15.2 2.78 9,063 0.0557 0.430

Idling emissions during 

loading (tons/BBO)

All planning 

areas

5,087 863.8 192.0 192.0 1,056 12,473 344,084 2,171 14.9 
24

EMISSIONS FACTORS:

1

PLANNING 

AREA NO

X SO2 PM10 PM2.5 CO VOC CO2 CH4

N

2

O

Idling emissions during 

unloading (tons/BBO)

All planning 

areas

5,087 863.8 192.0 192.0 1,056 6,332 344,084 1,087 14.9 

Oil Tankers, Non-Alaska OCS Regions5Cruising emissions 

(tons/BBO/ mile)

All planning 

areas

62.7 7.61 0.9511 0.9511 5.23 0.9512 3,101 0.190 0.1474

Idling emissions during 

loading (tons/BBO)

All planning 

areas

1,740 295.5 65.7 65.7 361.2 12,346 117,717 2,304 5.1 

Idling emissions during 

unloading (tons/BBO)

All planning 

areas

1,740 295.5 65.7 65.7 361.2 6,206 117,717 1,085 5.1 

Oil Tankers (Alaska 

OCS Region)

5

Cruising emissions 

(tons/BBO/mile)

All planning 

areas

22.8 2.77 0.346 0.346 1.905 18.7 1,129 3.246 0.054

Idling emissions during 

loading (tons/BBO)

All planning 

areas

633.7 107.6 23.9 23.9 131.5 12,363 42,867 2,178 1.9 

Idling emissions during 

unloading (tons/BBO)

All planning 

areas

633.7 107.6 23.9 23.9 131.5 6,194 42,867 1,089 1.9 

LNG Tankers

7 Cruising emissions 

(tons/tillion cubic feet (ft3) of gas/ mile)

All planning 

areas 0.434 1.4 0.095 0.068 0.098 0.006 244.4 NA NA

Loading emissions 

(tons/tillion ft

3

of gas)

All planning 

areas

NA NA NA NA NA NA 19,080 NA NA

Unloading emissions 

(tons/tillion ft

3

of gas)

All planning 

areas

55.4 136.0 9.6 6.5 15.6 0.982 772.3 NA NA

Onshore Oil Production –Contiguous United 

States (tons/million barrels)

8

All planning 

areas

5.0 15.0 0.003 0.001 4.5 209.6 8,609.5 1,446.4 0.00

Onshore Pipeline (tons/pipeline mile)

9 Chukchi Sea 0.64 0.00158 0.0

3 0.04 0.28 0.05 0.01 0.00 0.00

Onshore Gas Production (Conventional)–

Contiguous United States (tons/trillion ft

3

)

10

All planning 

areas

89,070 50 1,280 1,280 8,710 108,100 5,352,900 533,300 70

Onshore Gas Production (Unconventional) –

Contiguous United States (tons/trillion ft

3

)

10

All planning 

areas

80,000 830 760 760 7,390 64,860 3,499,620 352,910 120

Onshore Coal Production – Contiguous United 

States (tons/million short tons)11

All planning 

areas

335.56 153.13 191.34 33.10 80.20 164.76 36,853 3184.26 0.72

Production of Oil Imported via Pipeline 

(tons/million barrels)12

All planning 

areas

0.00 0.00 0.00 0.00 0.00 0.00 66,074 0.00 0.00

Production of Oil Imported via Tanker 

(tons/million barrels) 12

All planning

areas

0.00 0.00 0.00 0.00 0.00 0.00 40,259 0.00 0.00

Production of Gas Imported via Pipeline 

(tons/trillion ft

3

)

13

All planning 

areas

0.00 0.00 0.00 0.00 0.00 0.00 5,709,250 0.000.00

Production of Gas Imported via Tanker 

(tons/trillion ft

3

)

13

All planning 

areas

0.00 0.00 0.00 0.00 0.00 0.00 954,958 0.00 0.00
25

EMISSIONS FACTORS:1

PLANNING 

AREA NOX SO2 PM10 PM2.5 CO VOC CO2 CH4 N2O

Sources/Notes:

1. The emissions listed in this table are nitrogen oxides (NOx), sulfur dioxide (SO2), particulate matter up to 10 micrometers in size (PM10), particulate matter up to 2.5 

micrometers in size (PM2.5), carbon monoxide (CO), volatile organic compounds (VOC), carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).

2. Emissions factors from Wilson et al, 2014.

3. Emissions factors from Eastern Research Group, Inc. 2012.

4. Emissions factors from EPA, 2010b Statement of Basis for Frontier Discoverer in Chukchi and EPA AP-42.

5. Emissions factors from Eric Wolvovsky of BOEM, May 1, 2012, and Billings et al. (2012). 

6. Emissions factors from Billings et al. (2015a and 2015b).

7. Emissions factors from Jaramillo et al. (2007) and Afon and Ervic (2008).

8. CAPs derived from WRAP (2009). GHGs derived from NETL (2014).

9. Derived from the emissions estimates for pipeline construction associated with two of the alternatives (alternatives A and D2) considered in the Environmental Impact 

Statement for Greater Mooses Tooth One Development Project proposed by ConocoPhillips Alaska, Inc. These alternatives pertain to the development of oil and gas 

leases on land managed by the Bureau of Land Management (BLM) in the National Petroleum Reserve in Alaska. See U.S. Department of the Interior, Bureau of Land 

Management (2013, 2014).

10. Derived from NETL, 2012. "Role of Alternative Energy Sources: Natural Gas Technology Assessment".

11. Emissions factors from Argonne National Laboratory (2014). 

12. Emissions factors from U.S. Department of Energy, NETL (2009).

13. Emissions factors from Advanced Resources International, Inc. and ICF International (2008)

27

 Onshore coal production: Emissions per unit production of coal were derived using the Argonne 

National Laboratory’s GREET Model (U.S. DOE ANL, 2014), which is a widely used national 

database for life cycle emissions estimates. The emissions factors are based on the mix of coal 

consumed in the United States. Because 99 percent of coal consumed in the U.S. is produced 

domestically, the data in GREET provide a reasonable representation of the emissions associated 

with U.S. coal production. 

 Oil imports: The OECM also estimates emissions associated with oil imported into the United 

States. Given the global effects of green house gas (GHG) emissions, the model treats these 

emissions from imports separately from NOx, SO2, PM, CO, and VOCs. More specifically, the 

OECM estimates GHG emissions associated with both the production and transport of this oil. 

The model estimates GHG emissions for the full roundtrip journey from the country of 

production to the U.S. and back. For NOx, SO2, PM, CO, and VOCs, the OECM’s estimation of 

tanker emissions is limited to tanker travel in U.S. waters. For oil shipped into the United States 

by tanker, the OECM estimates tanker emissions on a roundtrip basis only in U.S. waters using 

the same emission factors as are used for tankers transporting crude oil from Alaska to the West 

coast of the contiguous 48 States. 

The OECM estimates GHG emissions associated with the production of oil imported into the 

United States based on non-U.S. greenhouse gas emission factors published by the National 

Energy Technology Laboratory (U.S. DOE, NETL, 2009). According to these data, Canadian oil 

production is more GHG-intensive than oil production in other countries that supply oil to the 

United States. Because Canada represents the most significant source of U.S. pipeline imports, 

the OECM uses the Canada-specific GHG emission factors from NETL (2009) for pipeline oil 

imports. For the production of oil imported via tanker, the OECM uses a weighted average of the 

GHG emission factors reported in NETL (2009) for other countries that supply oil to the United 

States, using country-specific exports of oil to the United States as weights. The OECM does not 

estimate NOx, SO2, PM, CO, and VOC emissions associated with the production of oil imported 

into the United States.

 Natural gas imports: Similar to its treatment of oil imports, the OECM estimates GHG emissions 

associated with the production of natural gas imported into the United States and GHG, NOx, 

SO2, PM, CO, and VOCs associated with the transportation of natural gas shipped to the United 

States by tanker. For tanker transport, the OECM’s emission factors for LNG tankers (expressed 

as emissions per trillion cubic feet) are based on LNG tanker emission information (e.g., power 

rating, average speed, etc.) obtained from Jaramillo et al. (2007) and Afon and Ervin (2008). The 

OECM applies these emission factors on a roundtrip basis, though the model’s emissions 

estimates for NOx, SO2, PM, CO, and VOCs reflect LNG tanker travel only in U.S. waters. For 

GHGs, the OECM estimates tanker emissions associated with the full roundtrip journey.

The OECM’s estimation of production-related emissions for natural gas imports is limited to 

GHGs. To estimate these emissions, the OECM applies separate emission factors to natural gas 

pipeline imports from Canada and imports shipped to the United States via tanker. GHG 

emission factors for both were obtained from Advanced Resources International, Inc. and ICF 

International (2008). Similar to the emission factors for non-U.S. oil production, the emission 

factors for non-U.S. natural gas production indicate that natural gas production in Canada is more 

GHG intensive than in other countries that supply natural gas to the United States.
28

As indicated in Table 1, the OECM estimates emissions for each emissions category (e.g., helicopter 

trips) based on the emission factors for that category and an emissions driver that represents the amount 

of emissions-generating activity. For reference, Table 2 provides a crosswalk between the various 

emissions categories and the emissions drivers employed in the OECM.

Table 2. Crosswalk between Emissions Drivers and Emissions Category.

EMISSIONS DRIVERS EMISSIONS CATEGORIES

Number of operational platforms Platform operations

Helicopters

Support vessels

Survey vessels1

Number of operational caissons Caisson operations

Survey vessels1

Number of operational FPSOs FPSO operations

Number of platforms and caissons over the life of the 

program

Platform construction

Platform removal

Number of FPSOs over the life of the program FPSO construction

FPSO removal

Number of exploration & delineation wells Exploration & delineation wells

Number of development & production wells Development & production wells

Pipeline miles installed Pipe-laying vessel emissions

Number of subseas Subsea construction

Number of barrel miles traveled (e.g., 3 million barrels 

traveling 10 miles is 30 million barrel miles)

Cruising emissions – tugs pulling barges, non-Alaska OCS regions

Cruising emissions – oil tankers non-Alaska OCS regions

Cruising emissions – oil tankers Alaska OCS Region

Barrels of oil shipped Idling emissions – oil tankers non-Alaska OCS regions

Loading emissions – oil tankers Alaska OCS Region

Unloading emissions – oil tankers Alaska OCS Region

Trillion cubic feet miles traveled for imported natural 

gas (e.g., 2 trillion cubic feet of gas traveling 100 miles 

is 200 trillion cubic feet miles traveled)

LNG tanker cruising emissions

Trillion cubic feet of natural gas imported LNG tanker unloading emissions

Barrels of onshore oil production Emissions from onshore oil production

Trillion cubic feet of onshore natural gas production Emissions from onshore natural gas production

Tons of onshore coal production Emissions from onshore coal production

Notes:

1. Survey vessel emissions are estimated based on the total number of operational platforms and caissons.
29

A critical element of modeling the impact of emissions in the OECM is developing assumptions about 

where these emissions will occur. The E&D scenarios specified by OECM users provide some insight in 

this regard, as they specify the distance from shore for platform groups within individual planning areas. 

Even with this information, however, the location of various E&D activities is uncertain. The location of 

emissions associated with the NAA is similarly uncertain. For example, if domestic onshore natural gas 

production increases under the NAA relative to a given E&D scenario, the air impacts associated with this 

increased gas production depend on where production increases (e.g., Wyoming, Pennsylvania, etc.). 

To address uncertainty related to the location of emissions, the following approaches were employed for 

allocating emissions geographically.

 Offshore band: For a given platform group with an average distance from shore specified in the 

E&D scenario, some E&D activities are likely to be concentrated near platform locations. 

Among the activities listed in Table 1, this includes (1) platform, caisson, and FPSO operations;

(2) exploration and delineation wells; (3) platform, caisson, and FPSO installation and removal

(excluding en route emissions for FPSO installation and removal), (4) subseas, (5) and survey 

vessel activity. For these E&D activities, emissions for a given platform group were allocated to 

the band of offshore grid cells in the planning area with a distance from shore equal to the 

distance from shore specified for the platform group. Within the offshore band, emissions were 

allocated to grid cells in proportion to their surface area.

 Offshore array of grid cells: While some E&D activities are likely to be concentrated offshore 

near platforms, others are likely to occur over a larger geographic range between platform groups 

and shore. For example, crew boats are likely to log several miles between platforms and port 

facilities. Helicopters, support vessels, pipe-laying vessels, and vessels traveling for FPSO 

installation or removal also are likely to operate over the full distance between platform groups 

and shore. To account for this wider geographic scope of activity, emissions for these activities 

were allocated to the array of grid cells whose distance from shore is less than or equal to the 

distance from shore for the corresponding platform group.

 Tankers: Assumptions about the location of tanker emissions vary by tanker type (e.g., tankers 

delivering imports versus tankers delivering oil from Alaska) and tanker activity (i.e., loading, 

cruising, and unloading). Table 3 summarizes these assumptions.

 Onshore energy production: The OECM allocates onshore oil and gas production under the No 

Action Alternative based on (1) the regions of the country likely to be served by OCS production 

and (2) the sources of domestic crude and natural gas that serve these regions. Appendix F 

contains more detailed information on this approach. For onshore coal production, the model 

does not allocate production to specific locations within the United States. Instead, it applies the 

same dollar-per-ton values to all onshore production of coal. These dollar-per-ton values reflect 

the geographic distribution of onshore coal production across the contiguous United States as 

derived from U.S. DOE production data. Thus, for coal, the OECM implicitly assumes that the 

geographic distribution of onshore production under the NAA is the same as for current onshore 

production. 
30

Table 3. Assumptions Regarding Location of Tanker Emissions.

TANKER TYPE TANKER ACTIVITY SPATIAL ASSUMPTIONS

Tankers delivering 

oil from Alaska

Loading All emissions from loading assumed to originate from the offshore grid cell 

adjacent to the Port of Valdez.

Cruising Cruising emissions (including VOC losses) assumed to be released in the OCS 

grid cells intersected by the shipping routes between Valdez and three ports on 

the West Coast: Port Angeles, Washington; San Francisco, California; and Long 

Beach, California. 

Unloading and 

Ballasting

All emissions from unloading and ballasting assumed to occur in the grid cell of 

the destination port.

Tankers and 

Tugs/Barges in the 

Atlantic, Pacific, 

and Gulf of Mexico

Loading Location uncertain due to uncertainty regarding the location of offshore 

platforms. E&D scenarios specified by model users indicate the distance from 

shore for each platform group. Loading emissions for each platform group are 

therefore distributed across the band of offshore grid cells whose distance from 

shore is equal to that of the platform group.

Cruising Cruising emissions are distributed to those offshore grid cells between shore and 

the band of cells identified for loading. All cruising emissions are assumed to 

occur in the grid cell where oil is produced.

Unloading and 

Ballasting

Given the uncertainty regarding where unloading would occur in each planning 

area, unloading and ballasting emissions are assumed to occur in the grid cells 

along the coast in the planning area where oil is produced.

Tankers – Oil 

Imports

Cruising For a given planning area receiving oil imports, emissions from tanker cruising 

are distributed to all of the grid cells in the planning area, given the uncertainty 

about where oil tankers may travel within each planning area.

Unloading and 

Ballasting

Given the uncertainty regarding where unloading would occur in each planning 

area, unloading and ballasting emissions are assumed to occur in the grid cells 

along the coast in the planning area where oil is delivered.

Tankers – LNG 

Imports

Cruising For a given planning area receiving oil imports, emissions from tanker cruising 

are distributed to all of the grid cells in the planning area, given the uncertainty 

about where oil tankers may travel within each planning area. Based on the 

current LNG port infrastructure, the model assumes that LNG tankers may 

deliver natural gas to eight LNG terminals in five planning areas: the North 

Atlantic, Mid-Atlantic, South Atlantic, Central Gulf of Mexico, and Western 

Gulf of Mexico Planning Areas.

Unloading and 

Ballasting

Unloading and ballasting assumed to occur only in existing LNG terminals.

4.3.3 Damages per ton

As noted above, the OECM uses two approaches for monetizing the damages associated with emissions 

from OCS activities: one approach for planning areas in the Atlantic, Pacific, and Gulf of Mexico OCS 

Regions that will be discussed first below, and a second approach for planning areas in the Alaska OCS 

Region. 

The dollar-per-ton values included in the revised OECM for the non-Alaska planning areas are derived 

from a modified version of the APEEP model, which is a reduced-form integrated air quality assessment 

model designed to estimate county-level dollar-per-ton estimates of the damages associated with PM2.5, 

VOC, NOx, and SO2 emissions.22 To generate dollar-per-ton values for the OECM, the APEEP model 

 

22 The approach described in this section is based on the approach applied in Muller (2014).
31

follows a three-step analytic chain consistent with the methods employed in U.S. EPA regulatory impact 

analyses of air pollution impacts:

1. Air quality: First, the APEEP model estimates the extent to which one ton of emissions of a given 

pollutant affects ambient pollutant concentrations in different locations.

2. Physical effects: Based on the change in air quality estimated for each location, the APEEP model 

employs a series of peer-reviewed dose-response functions to estimate changes in the incidence 

of various adverse physical effects (e.g., premature mortality).

3. Valuation: The APEEP model estimates the monetized value of the change in physical effects 

based on information from the economics literature and other published sources.

Based on these steps, the APEEP model generates dollar-per-ton impact estimates for emissions of NOx, 

SO2, PM2.5, and VOCs. Each of these steps is described in more detail below.

Air quality

The air quality modeling module within the APEEP model originally was designed to estimate the extent 

to which changes in onshore emissions affect air quality in individual (onshore) counties. Developing the 

dollar-per-ton values for the OECM therefore required modifying the APEEP model to assess how 

offshore emissions affect onshore air quality. The approach for estimating the onshore air quality impacts 

for each of the offshore grid cells shown in Figure 1 is as follows. 

1. Statistical assessment of emissions-air quality transfer coefficients. For onshore emissions, the 

APEEP model includes a series of emissions-air quality parameters (i.e., transfer coefficients) 

that represent the relationship between emissions in one county and ambient air quality in 

another. Using these data, a regression analysis was conducted that estimates the value of 

transfer coefficients as a function of both the distance and directional relationship (measured in 

degrees) between an emissions source and a receptor county.

2. Estimate transfer coefficients for each offshore location. Based on the statistical relationships 

estimated in Step 1, the emissions-air quality transfer coefficients for each offshore grid cell were 

estimated. To develop these estimates, the distance and directional relationship between each 

offshore grid cell and each (onshore) county in the contiguous United States was entered into the 

regression equations developed under Step 1. The values generated by these equations represent 

the relationship between emissions in each offshore grid cell and ambient air quality in each 

contiguous U.S county. 

Using the transfer coefficients developed from this methodology, the changes in onshore pollutant 

concentrations associated with changes in offshore emissions were assessed. Additional information on 

the air quality modeling approach is presented in Appendix B.

Physical effects

The county-level changes in air quality derived from the methods outlined above serve as inputs into the 

assessment of pollution-related physical effects in the APEEP model. As outlined in Table 4, these 

effects include adverse health impacts, changes in agricultural productivity, and damage to manmade 

materials. To quantify these physical effects, the APEEP model estimates the number of receptors 

exposed to changes in air pollution and employs a series of peer-reviewed dose-response (D-R) functions 

to estimate impacts for exposed receptors. Appendix D presents additional information on the 

assumptions employed in the modeling of physical effects.
32

Table 4. Summary of Air Pollution Physical Effects Included in the APEEP Model for the Revised OECM.

IMPACT 

CATEGORY POLLUTANT(S) PHYSICAL EFFECT

STUDIES USED FOR DOSERESPONSE

Human Health

PM

Premature mortality (adults aged 29 and older) Pope et al. (2002) and Laden et 

al. (2006)

Infant mortality (age <1 year) Woodruff et al. (1997)

Chronic bronchitis (all ages) All ages: Abbey et al. (1995)

Ozone

Premature mortality (all ages) Ito et al. (2005) and Bell et al.

(2004)

Respiratory hospital admissions (adults aged 65 

and older)

Schwartz (1995)

Respiratory hospital admissions (age <2 years) Burnett et al. (2001)

Asthma-related emergency room visits (all ages) Peel et al. (2005) and Wilson et 

al. (2005)

Minor restricted activity days (ages 18-64) Ostro and Rothschild (1989)

School loss days (ages 5 to 17) Chen et al. (2000)

Agriculture Ozone

Change in yield for corn, cotton, peanuts, wheat, 

grain sorghum, soybeans, kidney beans, and 

tobacco

Lesser et al. (1990)

Material Damage 

SO2

Damage to galvanized steel, painted surfaces, and 

carbonate stone surfaces

Atteraas (1982), Haynie (1986), 

and ICP (1998)

Valuation

To estimate the value of the health, agricultural, and materials impacts outlined above, the APEEP model 

uses a combination of market price data, willingness-to-pay (WTP) values estimated in the peer-reviewed 

literature, and (for certain health impacts) cost-of-illness (COI) estimates derived from studies of 

treatment costs. Tables 5 and 6 summarize these values. 
33

Table 5. Unit Values for Economic Valuation of Health Endpoints (2006$).

HEALTH ENDPOINT

CENTRAL ESTIMATE OF VALUE 

PER STATISTICAL INCIDENCE 

(ADJUSTED FOR INCOME)

WTP OR 

2015 INCOME 2065 INCOME COI NOTES

Premature mortality $8,600,000 $12,000,000 WTP

 Mean Value of Statistical Life (VSL), 

adjusted for income, based on 26 wage-risk 

and contingent valuation studies. A Weibull 

distribution provided the best fit to the 26 

estimates. Note that VSL represents the value 

of a small change in mortality risk aggregated 

over the affected population. This is 

consistent with the VSL approach used in 

U.S. EPA (2010a).

Chronic bronchitis $470,000 $680,000 WTP

 WTP to avoid a case of pollution-related CB 

is calculated as 

WTPx = WTP13 · e-β(13-x), where x is the 

severity of an average CB case; WTP13 is 

WTP for a severe case of CB; and β is the 

parameter relating WTP to severity, based on 

the regression results reported in Krupnick 

and Cropper (1992). This valuation function 

and the rationale behind it are described in 

detail in U.S. EPA (1999). 

Respiratory hospital 

admissions (age 65+)

$25,000 $25,000 

COI

 These COI point estimates (lost earnings plus 

direct medical costs) are based on ICD-9 code 

level information (e.g., average hospital care 

costs and average length of hospital stay) 

reported in Agency for Healthcare Research 

and Quality (2000). As noted in the text, no 

adjustments are made to COI values for 

income growth.

Respiratory hospital 

admissions (age <2

years)

$10,000 $10,000 

Asthma-related 

emergency room visits

$370 $370 COI

 Simple average, adjusted for income, of 

estimates from Smith et al. (1997) and 

Stanford et al. (1999). 

Minor restricted 

activity days

$62 $70 WTP

 Median WTP estimate to avoid one minor 

restricted activity day from Tolley et al.

(1986).

School loss days $89 $89 COI

 Point estimate is based on (1) the probability 

that, if a school child stays home from school, 

a parent will have to stay home from work to 

care for the child, and (2) the value of the 

parent’s lost productivity. Additional 

information on the derivation of this valuation 

estimate is available in Abt Associates, Inc.

(2008).
34

Table 6. Summary of Crop and Materials Prices (2006$).

CROP PRICE

Agriculture Corn $4.08 per bushel

Cotton $0.59 per pound

Peanut $0.20 per pound

Grain Sorghum $7.08 per hundredweight

Soybeans $9.82 per bushel

Spring Wheat $7.31 per bushel

Tobacco $1.65 per pound

Materials Galvanized Steel $750 per ton

Carbonate Stone $115 per square meter

Paint $35 per gal

Sources: 

Agriculture – USDA/NASS (2009)

Materials (galvanized steel and paint) – Morici (2005)

Materials (stone) – Masonry Advisory Council, 2009-2010 Masonry Cost Guide, 

http://www.maconline.org/tech/estimating/cost/cost.html

In economic terms, WTP is the more appropriate measure of the value of avoiding an adverse effect, as it 

reflects the dollar amount necessary such that a person would be indifferent between avoiding the effect 

and receiving the compensation. Where possible, the APEEP model therefore uses WTP values derived 

from the peer-reviewed literature to estimate the value of avoiding adverse health effects associated with 

changes in ambient pollutant concentrations. For some health effects, however, (e.g., hospital 

admissions), WTP estimates are not available from the peer-reviewed literature. In these cases, the 

APEEP model uses the cost of treating or mitigating the illness (COI) as a primary estimate.

The data in Table 5 also show that valuation estimates expressed as WTP values increase over time. This 

reflects projected increases in income. Economic theory maintains that individuals’ willingness to pay for 

goods, including the avoidance of an adverse health effect, increases as real income increases. Given that 

incomes are likely to increase during the 75-year analytic time horizon of the OECM, the APEEP model 

(where possible) uses income-adjusted valuation estimates to assess the value of adverse health effects. 

More detailed valuation estimates for each year in the OECM’s time horizon are available in Appendix D.

Damages per ton in Alaska planning areas 

As outlined above, the air quality analysis for the Atlantic, Pacific, and Gulf of Mexico OCS Regions 

relies on the existing APEEP modeling framework to monetize the air quality impact of offshore 

emissions. A similar model for Alaska, however, is not readily available. Moreover, no studies in the 

literature were identified that could be adapted to estimate the economic damage of emissions in the 

Alaska OCS Region. In the absence of Alaska-specific models or literature, dollar-per-ton values for grid 

cells off the coast of Alaska were derived by scaling values generated by the APEEP model for the 

Washington/Oregon Planning Area. This scaling approach accounts for a given grid cell’s distance from 

shore, as well as the population located near each grid cell, as outlined below: 
35

1. Distance to shore: As shown in the APEEP model results presented in Appendix C for the 

Atlantic, Pacific, and Gulf of Mexico OCS Regions, a grid cell’s distance from shore has a 

significant effect on the extent to which emissions from the grid cell affect onshore air quality. 

To incorporate this relationship into the scaling procedure, Alaska and Washington/Oregon grid 

cells were grouped into a series of 50-kilometer (km) bands. The cells in the band nearest shore 

have an average distance from shore of between 0 and 50 km; the next nearest band is located 

between 50 and 100 km offshore, etc.

2. Develop dollar-per-ton values for each 50-km band: After developing the offshore bands, average 

dollar-per-ton values, by pollutant and year, were developed for each band of grid cells in the 

Washington/Oregon Planning Area. These band-specific values form the basis of scaled dollarper-ton values for Alaska. 

3. Scale band-specific dollar-per-ton values: Based on the distance from shore for each grid cell in 

the Alaska OCS Region, the corresponding 50-km band of grid cells was identified in the 

Washington/Oregon Planning Area. To develop dollar-per-ton estimates for a given Alaska grid 

cell, dollar-per-ton values for the corresponding Washington/Oregon distance band were 

multiplied by the ratio of (1) the population within 750 miles of the Alaska grid cell, and (2) the 

average population within 750 miles for the grid cells in the Washington/Oregon band. The 750-

mile cutoff around each grid cell was based on the APEEP model results presented in Appendix 

C. As noted in the appendix, the effect of distance on air quality, as modeled by APEEP, levels 

off at approximately 750 miles.

4.4 OFFSHORE DOLLAR-PER-TON VALUES

Figures 2a through 2d display the damages ($/ton) due to emissions of PM2.5, SO2, NOx, and VOCs 

corresponding to all of the nearly 1,500 offshore source locations for the lower 48 States. The figures

show several patterns that are important in determining damages. First, sources closer to land cause 

greater damage per ton than sources farther from shore in any region. Second, sources located near large 

cities tend to cause greater damage than sources offshore from rural areas. Third, the importance of 

prevailing winds is clearly evident. For example, sources off the U.S. Northeast coast are located very 

close to large population centers. As such, one would expect these sources to have very high damages per 

ton. While this basically holds, it is interesting to note that the sources with PM2.5-related damages 

between $15,000 and $25,000 (shown in crimson) do not extend far off the East coast. 

In contrast, examine sources in the Gulf of Mexico. This top damage class for PM2.5 encompasses 

sources that extend far out into the Gulf. Yet, nearby populations that would be exposed to emissions 

from sources in the Gulf must be smaller than the populations near offshore sources in the Northeast. 

This difference is due to the prevailing wind direction. In the Gulf, emissions are pushed to the northeast 

over land and cities in the southeastern United States. Similarly, sources off the East coast also have their 

emissions directed to the northeast. However, in marked contrast, the major population centers in the 

Northeast lie to the west of these sources, so a small fraction of emissions are projected to reach onshore 

given the direction of prevailing winds. This reduces the estimated damage per ton of emissions from 

sources in the Atlantic Ocean. Hence, sources that produce the highest damage are concentrated in a 

narrow band along the Eastern seaboard.

Figures 2b through 2d show a similar pattern for emissions of SO2, NOx, and VOCs. In the Gulf of 

Mexico, the transition from high- to low-impact grid cells is much more gradual than in the Northeast.
36

Figure 2a. Damages Due to PM2.5 Emissions.

Figure 2b. Damages Due to SO2 Emissions.
37

Figure 2c. Damages Due to NOx Emissions.

Figure 2d. Damages Due to VOC Emissions.
38

4.5 ONSHORE ENERGY PRODUCTION DOLLAR-PER-TON VALUES

A key element of impacts realized under the NAA is the economic value of the air quality effects 

associated with onshore production of oil, natural gas, and coal. Emissions associated with onshore 

energy production may be estimated based on the fuel-specific emission factors presented in Table 1. To 

monetize the resulting emissions estimates, the OECM relies upon dollar-per-ton impact values, by 

pollutant and year, derived from the APEEP model. The APEEP model produces these values at the 

county level, but the change in onshore energy production for the NAA is specified only at the national 

level. Therefore, two sets of weighted average dollar-per-ton values were developed, one for oil and 

natural gas and a second set of estimates applied to emissions from onshore coal production. For oil and 

natural gas, weighted average values were developed based on county-level employment data for the oil 

and natural gas sector from the U.S. Census Bureau’s 2008 County Business Pattern data. County-level 

coal production published in the U.S. DOE’s (2010) Annual Coal Report 2009, was used to develop 

weighted average dollar-per-ton values for coal production.

Note that the assessment of air quality impacts related to onshore oil production does not capture the 

effects of increased onshore oil and natural gas production in Alaska. Because U.S. DOE’s baseline 

projections do not distinguish between onshore and offshore production in Alaska, the outputs generated 

by MarketSim do not differentiate between changes in onshore and offshore production in Alaska under a 

given E&D scenario.

4.6 DOLLAR-PER-TON VALUES FOR ALASKA ONSHORE PIPELINE CONSTRUCTION

As described above, most of the air quality impacts associated with a given E&D scenario result from 

emissions occurring offshore. The production of oil in the Chukchi Sea planning area, however, would 

require the construction of an onshore pipeline connecting the Chukchi Sea to the Trans-Alaska Pipeline 

System (TAPS).23 The construction of this pipeline would result in air emissions and would potentially 

lead to impacts associated with onshore oil spills (if the pipeline were to leak), visual disamenity, and 

potential erosion impacts from the clearing of land. Of these, air emissions represent the only impact 

category that can be measured and monetized credibly in the OECM. The other categories of impacts are 

likely to be small in monetized terms, and data that would enable the monetization of these other impacts 

in the OECM are not readily available. To quantify the onshore emissions associated with the 

construction of the pipeline, the OECM assumes that the pipeline would transport Chukchi Sea oil 284 

miles from Wainwright to Pump Station 1 of the TAPS near Prudhoe Bay and that its construction would 

occur during the two years immediately preceding production in the Chukchi Sea.24

 

23 The OECM assumes that no additional pipelines would be necessary for oil produced in the Beaufort Sea or Cook 

Inlet Planning Areas. A recent expansion for the Point Thompson Development has extended a pipeline all the way 

to the western border of ANWR near the eastern edge of existing Beaufort Sea Planning Area leases. Future oil 

production from the Beaufort Planning Area could tie into this line and minimize new pipeline installation. In the 

Cook Inlet Planning Area, produced oil would be stored at Nikiski, where smaller tankers could load up for transport 

to the U.S. west coast. Some produced oil would also be processed at the local refinery in Nikiski and converted 

into fuel products for local use. 

24 At the time of publication, BOEM assumed that any connection from producing blocks in the Chukchi Sea to 

TAPS would be completed to transport production from existing leases, prior to any production resulting from the 

new Five Year Program. However, the costs of this new pipeline construction are being estimated to assure that 

they are not omitted should earlier development not be sufficient to complete that pipeline connection.
39

Similar to the OECM’s monetization of offshore emissions in the Alaska OCS Region, the model 

monetizes these onshore emissions by scaling the onshore dollar-per-ton values generated by the APEEP 

model for Washington and Oregon. The specific steps involved in this scaling process are as follows:

1. Population within 750 miles: Estimate the population located within 750 miles of North Slope 

Borough County, Alaska (where the pipeline would be located) and within 750 miles of each 

county in Washington and Oregon. As noted above, the APEEP results presented in Appendix C 

suggest that the effect of distance on air quality levels off at approximately 750 miles.

2. Scale the county-level dollar-per-ton values for Washington and Oregon: For each county in 

Washington and Oregon, the dollar-per-ton values generated by APEEP were scaled in proportion 

to the ratio of North Slope Borough County’s population to the population of each county in 

Washington and Oregon.

3. Average the scaled dollar-per-ton values: For each pollutant, the scaled dollar-per-ton values 

estimated in Step 2 were averaged to create the values applied to North Slope Borough County.
40

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NC: Office of Air Quality Planning and Standards.

http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf.

U. S. Environmental Protection Agency (U.S. EPA). 2010b. Statement of Basis for Proposed Outer 

Continental Shelf Prevention of Significant Deterioration Permit No. R10OCS/PSD-AK-09-01, Shell 

Gulf of Mexico Inc., Frontier Discoverer Drillship, Chukchi Sea Exploration Drilling Program

(January 8, 2010).

http://www.epa.gov/region10/pdf/permits/shell/chukchi_revised_permit_statement_of_basis_010610.

pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2011. The Benefits and Costs of the Clean Air Act

from 1990 to 2020: Final Report – Rev. A (April 2011).

http://www.epa.gov/oar/sect812/feb11//fullreport_rev_a.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2012a. Oil and Natural Gas Sector: Standards of 

Performance for Crude Oil and Natural Gas Production, Transmission, and Distribution. Background 

Supplemental Technical Support Document for the Final New Source Performance Standards (April 

2012). http://www.epa.gov/airquality/oilandgas/pdfs/20120418tsd.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2012b. Regulatory Impact Analysis: Final New 

Source Performance Standards and Amendments to the National Emissions Standards for Hazardous 

Air Pollutants for the Oil and Natural Gas Industry (April 2012).

http://www.epa.gov/ttn/ecas/regdata/RIAs/oil_natural_gas_final_neshap_nsps_ria.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2015. “Inventory of U.S. Greenhouse Gas 

Emissions and Sinks: 1990-2013”. EPA 430-R-15-004. (April 15, 2015).

Western Regional Air Partnership (WRAP) Stationary Sources Joint Forum. 2009. Point and Area Source 

Pivot Tables for Regional Haze Planning Emissions Scenarios, Plan 02d. Accessed May 15, 2010.

http://www.wrapair.org/forums/ssjf/pivot.html.

Wilson, A.M., C.P. Wake, T. Kelly, and J.C. Salloway. 2005. Air Pollution, Weather, and Respiratory 

Emergency Room Visits in Two Northern New England Cities: An Ecological Time-series Study. 

Environmental Research 97(3): 312-321.
44

Wolvovsky, E. 2012. Emission factors provided by Eric Wolvovsky of BOEM, May 1, 2012.

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. The Relationship between Selected Causes of 

Postneonatal Infant Mortality and Particulate Air Pollution in the United States. Environmental 

Health Perspectives 105(6): 608-612.

World Resources Institute (WRI). 2013. Clearing the Air: Reducing Upstream Greenhouse Gas 

Emissions from U.S. Natural Gas Systems. April 2013. http://pdf.wri.org/clearing_the_air_full.pdf
45

5 PROPERTY VALUES – VISUAL DISAMENITIES 

5.1 OVERVIEW

The model estimates the annual losses in economic rent of residential properties due to the visual impact 

of offshore oil and natural gas platforms than can be seen from shore (a “visual disamenity”). Estimates 

of the value of property in the affected area and of the effects of distance of the proposed platforms are 

calculated for each of the 23 planning areas that are within visible distance from land. Due to the size of 

the planning areas, parameters must be generalized over coastlines with varying levels of visibility and 

development. Parameters and effects are determined using literature and data from previous studies.

The model makes the following simplifying assumptions.

 Property values decrease when a platform is visible from a home.

 Property value impacts decline with the distance from a visual disamenity (Bishop and Miller,

2007; Des Rosiers, 2002; Hoen et al., 2011).

 No impacts occur beyond a fixed distance from shore, which varies regionally based on visibility 

information (Ladenburg, 2009).

This model differs from the 2001 OECM’s assessment of the impact of the visual aesthetics of platforms 

in that (1) it uses a logarithmic decay in damages with the distance an oil platform is implemented from 

shore, and (2) the negative effect is considered prominent over longer distances. Importantly, this 

modeling framework is relevant only as a generalized analysis of property value impacts at the planning 

area level. The approach, therefore, is not appropriate for application to smaller geographic regions (e.g., 

a 10-mile segment of densely populated coastline).

5.2 BASIC CALCULATION

The model develops an estimate of damage to residential property values using the equation below. 

Damage in this context represents an annual loss in the economic rent of residential properties from the 

year oil exploration begins through the final year of decommissioning. Note that unlike other impacts 

estimated by the OECM, impacts due to visual disamenities do not follow the level of construction or 

production activity. Instead, the undiscounted annual impacts to property values are constant between the 

start and finish of platform activities.

p

c

Damagep Api

atp rp

d m d



 * *  * *

2 2

 

where:

Ap = Annualized total residential property value ($) over the lifetime of a platform per mile coastline 

from the shore to one-eighth mile inland

rp = Regional visibility (miles)

d = Shortest distance from shore to the platform to a maximum of r (miles)

m = Maximum percent reduction in property value due to disturbance in visual surroundings

cp = Constant of decay, dependent on region

iatp = ( iptp – (iptp*(tf+tsp*(1- tf )))), where:

iatp = After-tax discount rate in each planning area

iptp = Pre-tax discount rate in each planning area

tf = Marginal Federal tax rate (28 percent for individual)
46

tsp = Marginal State tax rate in each planning area

This equation is used to determine annual monetary damage to residential property values due to the 

visual disamenity created by offshore oil platforms. Damage is measured in 2010 U.S. dollars. The 

variables and parameters are described below.

5.3 CALCULATION DRIVERS

5.3.1 Total residential property value along the coast

The parameter A is the total residential property value per mile of coastline from the shore to one-eighth 

mile inland annualized over the lifetime of a platform. This parameter varies across the 23 planning areas 

that are adjacent to the coastlines, and it includes residential property values from assessor and census 

data at the census block group level (DataQuick, 2007-2009; U.S. Census Bureau, 2000b). The coastline 

is split into the 23 planning areas using a BOEM shapefile, and then it is further split by census block 

group using a census shapefile (U.S. Census Bureau, 2000a). Length of coastline and area of each block 

group are measured with GIS. 

To determine total residential property value within each block group, assessor data are used when 

available in the lower 48 States (DataQuick, 2007-2009); otherwise, data from the 2000 census are used 

(U.S. Census Bureau, 2000b). Figure 8 provides an overview of the total residential property value per 

one-eighth mile by one-mile area of coastline within each planning area based on assessor and census 

data. From both sources, total residential housing values per block group are employed, and values are 

converted to 2010 U.S. dollars using an implicit Gross Domestic Product (GDP) price deflator (BEA,

2010). The model assumes that property values will be affected to one-eighth mile back from shore, so 

total housing value divided by eight, times the total area in each block group, yields the average 

residential property value of one linear mile of shoreline that will be adversely affected by offshore visual 

disamenities. When the calculated coastal residential property value is greater than the total value of 

residential properties in the block group reported by assessors or the census (i.e., due to a highly detailed 

coastline), the total value of residential properties in the block group is used. Next, the coastal property 

value per mile of coastline is summed within each planning area and divided by the total miles of 

coastline to determine the weighted average of a one-eighth by one-mile section in each planning area. 

The following assumptions are made in determining residential property value impacts by planning area.

 Residential property value impacts occur up to one-eighth mile from the coast. This assumption 

was retained from the previous version of the OECM (as described in A.T. Kearney, Inc., et al.,

1991) given a lack of data or other information upon which to base an alternative assumption.

 Coastal block groups extend at least one-eighth mile inland.

 Coastal property values are equivalent to average property values over the extent of a coastal 

block group.

 The prior existence of one or more platforms in the region in which new platforms may be 

installed does not affect the property value impact. While this may result in an overestimate of 

the impact in areas of existing activity, such as the Gulf of Mexico, the assumption is that new 

platforms would be located in different viewsheds. 

 The density of residential properties near the coast is the same as the density over the entire block 

group.
47

 Overlapping areas within planning areas are assigned to a single planning area.

Table 7. Total Residential Property Value Per Mile of Coastline from the Coast to One-eighth Mile Inland.

REGION PLANNING AREA

TOTAL RESIDENTIAL PROPERTY VALUE 

PER MILE COASTLINE FROM THE COAST TO 

1/8-MILE INLAND (PARAMETER A IN 2010$)

Atlantic and Gulf

North Atlantic $32,100,000

Mid-Atlantic $8,350,000

South Atlantic $16,900,000

Straits of Florida $56,900,000

Eastern Gulf of Mexico $25,000,000

Central Gulf of Mexico $1,560,000

Western Gulf of Mexico $2,470,000

Pacific

Oregon/Washington $2,980,000

Northern California $1,120,000

Central California $24,900,000

Southern California $36,200,000

Alaska

Beaufort Sea $0.00420

Chukchi Sea $331

Hope Basin $355,000

Norton Basin $122,000

St. Matthew Hall $0.373

St. George Basin $31.7

Aleutian Arc $5.55

North Aleutian Basin $770

Shumagin $9.24

Cook Inlet $263,000

Kodiak $329,000

Gulf of Alaska $305,000

5.3.2 After-tax discount rate

To calculate the annual losses in economic rent (i.e., property value impacts), this analysis applies, in 

each year of the analysis, a discount rate of between 4.20 and 4.45 percent in each of the planning areas. 

This value is the adjusted after-tax current residential mortgage rate (i.e., cost of capital) for the average 

national 30-year fixed interest loan rate between 2005 and 2009 of 5.94 percent (Freddie Mac, 2010). 

The Federal tax rate at the median household income of $50,300 is 25 percent (U.S. Census Bureau,

2009b; Tax Foundation, 2010), and average State taxes weighted by coastline in each planning area vary 

between 0 and 5.58 percent (Federation of Tax Administrators, 2010).
48

As noted above, the after-tax discount rate is determined using the following formula: (Pre-Tax Rate) –

(Pre-Tax Rate)*[(Federal Tax Rate) + (State Tax Rate)*(1 – Federal Tax Rate)]. For example, for the 

North Atlantic Planning Area: 5.94 – 5.94*[0.25 + (0.0558)*(1 – 0.25)] = 4.20.

5.3.3 Visibility and distance

The parameter r represents the maximum visibility by region measured in miles. In prior research, the 

maximum visibility of an offshore wind turbine was estimated to be approximately 31 miles (Ladenburg,

2009). Due to the absence of studies on the maximum visibility distance to oil platforms, 31 miles is 

assumed to represent the maximum distance an offshore platform can be seen under good visibility. 

Visibility by region is known to vary due to haze. Based on visibility data from national parks and 

wilderness areas from 1992 through 2004, visibility on the Pacific coast is generally superior (IMPROVE,

2007). As such, visibility (parameter r) is assumed to be 31 miles on the Pacific coast and in Alaska. 

Based on a ratio of visibility in eastern to western parks and wilderness areas drawn from the above 

dataset, visibility (r) is assumed to be 16 miles on the Atlantic coast and the Gulf of Mexico (IMPROVE,

2007).

The variable d is the distance of the platform to the closest point on shore measured in miles. A 

constraint is placed on d based on visibility, where d must be less than 16 miles in the Atlantic and Gulf 

Regions and 31 miles in the Pacific and Alaska Regions. To analyze the value of property across the total 

area affected, the length of shoreline from which the platform can be seen must be determined. The 

length of affected coastline based on the maximum visibility and distance from shore is 

2 2

2 r  d

, based 

on simple geometry. The total value of properties in the affected region per year is the product of A and 

the distance along the shoreline.

5.3.4 Percent damage 

The parameter m is the maximum percentage impact of a visual disturbance on residential property 

values. Based on previous studies, the maximum loss in property values resulting from a visual 

disturbance ranges from 21 to 25 percent, so an estimate of 23 percent is used here (Hoen et al., 2011;

Des Rosiers, 2002; Sims and Dent, 2005). 

Previous studies have indicated that the impact on property values tends to attenuate with distance of 

households from the visual disturbance. Following a study of the effect of an electrical power plant on 

housing values (Blomquist, 1974), the model assumes that there is a constant negative elasticity between 

distance to platforms and effect on housing values. That is, for a 1 percent decrease in distance, there is 

some constant percentage increase in property value impacts. In the damage formula, the effect of 

distance on damage is d

-c

, where c is a constant value. Because property value impacts will never fall to 

zero in this formulation, the value of c is scaled such that damage is assumed to be 100 percent of the 

maximum impact of 23 percent when d is one mile, and less than 10 percent when d is r miles (16 miles 

in the East and 31 miles in the West). This leads to a decay equation where c = 0.83 in the East and c = 

0.67 in the West. Figure 3 provides a graph of the assumed relationship between distance from shore and 

property value impacts for the East and West coasts. Note that this assumes the relationship between 

distance and economic impact has the same functional form for onshore and offshore structures. If this is 

not the case, for example if the impact of offshore structures is constant as long as the structures are 

visible, then economic impacts would vary from those reported here.

Additionally, the visual impact will decrease with distance along the shore from the closest point to the 

platform (i.e., at the outer edge of the affected segment of shoreline, distance to the platform would be r
49

and the platform(s) would just be visible). To incorporate this effect, the impact on economic rents is 

multiplied by one-half, which cancels the “2” in the 

2 2 2 r  d

term described above. 

Figure 3. Relationship Between Percent Reduction in Economic Rent from Properties and Distance from Shore 

to Platforms. Percent Reduction in Property Value with Distance Offshore

0%

5%

10%

15%

20%

25%

0 5 10 15 20 25 30 35

d (distance in miles)

Damage (%)

West Coast

East Coast
50

5.4 REFERENCES – PROPERTY VALUE: VISUAL DISAMENITY

ArcGIS 9.1.3. Environmental Systems Research Institute, Inc. Redlands, CA.

A.T. Kearney, Inc.; ICF, Inc.; Impact Assessments, Inc.; S. Batie; and P. Sorensen. 1991. Estimating the 

Environmental Costs of OCS Oil and Gas Development and Marine Oil Spills: A General Purpose 

Model. OCS Study MMS 91-043, 2 vols. Herndon, VA: Prepared for U.S. DOI, MMS.

Bishop, I.D. and D.R. Miller. 2007. Visual Assessment of Off-shore Wind Turbines: The Influence of 

Distance, Contrast, Movement and Social Variables. Renewable Energy 32(5): 814-831.

Blomquist, G.C. 1974. The Effect of Electric Utility Power Plan Location on Area Property Value. Land 

Economics 50(1): 97-100.

Bureau of Economic Analysis (BEA) National Economic Accounts. 2010. Implicit Price Deflators for 

Gross Domestic Product. U.S. Department of Commerce. http://www.bea.gov/iTable/index_nipa.cfm.

DataQuick. 2007-2009. Assessor Data. Compiled by DataQuick.

Des Rosiers, F. 2002. Power Lines, Visual Encumbrance and House Values: A Microspatial Approach to 

Impact Measurement. Journal of Real Estate Research 23(3): 275-301. 

http://pages.jh.edu/jrer/papers/pdf/past/vol23n03/05.275_302.pdf.

Hoen, B., R. Wiser, P. Cappers, M. Thayer, and G. Sethi. 2011. Wind Energy Facilities and Residential 

Properties: The Effect of Proximity and View on Sales Prices. Journal of Real Estate Research 33(3): 

279-316. http://emp.lbl.gov/sites/all/files/JOURNAL%20lbnl-2829e.pdf.

IMPROVE (Interagency Monitoring of Protected Visual Environments). 2007. Data from the IMPROVE 

network based on the “New IMPROVE algorithm” (updated August 2007). 

http://vista.cira.colostate.edu/views/Web/IMPROVE/SummaryData.aspx.

Ladenburg, J. 2009. Stated Public Preferences for On-land and Offshore Wind Power Generation – A 

Review. Wind Energy 12(2): 171-181. 

Sims, S. and P. Dent. 2005. High-voltage Overhead Power Lines and Property Values: A Residential 

Study in the UK. Urban Studies 42(4): 665-694. 

http://www.offnews.info/downloads/MH67355810773164.pdf.

U.S. Census Bureau. 2000a. Census Tract Cartographic Boundary Files. U.S. Census Bureau, Geography 

Division, Cartographic Products Management Branch. Created July 2001. 

https://www.census.gov/geo/maps-data/data/cbf/cbf_tracts.html.

U.S. Census Bureau. 2000b. SF1 and SF3 Data. http://www.census.gov/prod/cen2000/doc/sf1.pdf; 

http://www.census.gov/prod/cen2000/doc/sf3.pdf.
51

6 PROPERTY VALUES – OIL SPILLS

6.1 OVERVIEW

The model estimates the annual losses in the economic rent of residential properties caused by oil spills in 

each of the planning areas.

6.2 BASIC CALCULATION – OIL SPILLS

The model develops an estimate of damage to residential property values using the equation below. In the 

equation, impact is defined as the annual loss in economic rent from residential properties that results 

from oil spill events. This is calculated as the product of the property value per linear meter of beach, the 

after-tax discount rate, the fraction of year taken up by the event, and the length of oiled shore in meters.

Impactp = Valuep* iatp *(dp/365)*lp

where:

Valuep = Total coastal property value per meter in each planning area

dp = Duration of event (in days)

lp = Length of beach oiled (m)

iatp = ( iptp – (iptp*(tf+tsp*(1- tf )))), where:

iatp = After-tax discount rate in each planning area

iptp = Pre-tax discount rate in each planning area

tf = Marginal Federal tax rate (25 percent for individual)

tsp = Marginal State tax rate in each planning area

6.3 CALCULATION DRIVERS

6.3.1 Residential property value along the coast

The parameter Value is the total residential property value per meter of coastline from the shore to onehouse width inland. This parameter varies across the 23 planning areas adjacent to the coastlines. In 

order to solve for this parameter, residential property values from assessor and census data are used at the 

census block group level (DataQuick, 2007-2009; U.S. Census Bureau, 2000b). The coastline is split into 

the 23 planning areas using a BOEM shapefile, and then it is further split by census block group using a 

census shapefile (U.S. Census Bureau, 2000a). The length of coastline and area of each block group are 

measured with GIS. 

To determine total residential property value within each block group, assessor data are used when 

available in the lower 48 States (DataQuick, 2007-2009). Otherwise, data from the 2000 census are used 

(U.S. Census Bureau, 2000b). Table 8 provides an overview of the total residential property value per 1-

meter wide by 242-foot long (the length of an average property) area of coastline within each planning 
52

area based on assessor and census data.25 From both sources, total residential housing values per block 

group were employed, and values were converted to 2010 U.S. dollars using an implicit GDP price 

deflator (BEA, 2010). Total housing value divided by the total area in each block group yielded the 

average residential values for a 1-meter by 242-foot area. The model assumes that this is the average 

residential property value of one linear meter of shoreline that will be adversely affected by oil spills. 

When the calculated coastal residential property value was greater than the total value of residential 

properties in the block group reported by assessors or the census (i.e., due to a highly detailed coastline), 

the total value of residential properties in the block group was used. Weighted averaging is used to 

aggregate census block group data to the planning area level.

Table 8. Total Residential Property Value Per Meter of Coastline to an Average Property Width Inland.

REGION PLANNING AREA VALUE (2010 USD)

Atlantic and 

Gulf

North Atlantic $7,620 

Mid-Atlantic $1,980 

South Atlantic $4,010

Straits of Florida $13,500 

Eastern Gulf of Mexico $5,930

Central Gulf of Mexico $369 

Western Gulf of Mexico $587

Pacific

Oregon/Washington $707 

Northern California $266 

Central California $5,910 

Southern California $8,590 

Alaska

Beaufort Sea $9.96E-07

Chukchi Sea $0.0785 

Hope Basin $84 

Norton Basin $29 

St. Matthew Hall $8.85E-05

St. George Basin $0.00752 

Aleutian Arc $0.00132 

North Aleutian Basin $0.183 

Shumagin $0.002 

Cook Inlet $62 

Kodiak $78 

Gulf of Alaska $72 

 

25 Note that this method makes the simplifying assumption that only those properties immediately adjacent to the 

shore and thus directly affected by an oil spill would experience a property value effect. Other, near-coast properties 

potentially could see an affect if those properties’ value is in part derived from proximity to the shoreline.
53

6.3.2 After-tax discount rate

To calculate the annual losses in economic rent from residential property, this analysis applies a discount 

rate of between 4.20 and 4.45 percent in each of the planning areas. This value is the adjusted after-tax 

current residential mortgage rate (cost of capital) for the average national 30-year fixed interest loan rate 

between 2005 and 2009 of 5.94 percent (Freddie Mac, 2010). The Federal tax rate at the median 

household income of $50,300 is 25 percent (U.S. Census Bureau, 2009a; Tax Foundation, 2010) and 

average State taxes weighted by coastline in each planning area vary between 0 and 5.58 percent 

(Federation of Tax Administrators, 2010).

As noted above, the after-tax discount rate is determined using the following formula: (Pre-Tax Rate) –

(Pre-Tax Rate)*[(Federal Tax Rate) + (State Tax Rate)*(1 – Federal Tax Rate)]. For example, for the 

North Atlantic Planning Area: 5.94 – 5.94*[0.25 + (0.0558)*(1 – 0.25)] = 4.20.

6.3.3 Duration of event

Property values are assumed to be lost entirely for the duration of the spill event. Consistent with the 

assumed duration of a beach closure resulting from an oil spill, the duration of shoreline oiling is set at 21 

days for all planning areas.

6.3.4 Length of oiled shore

SIMAP models the fate and transport of oil spilled in the ocean to quantify lengths of oiled shoreline, 

using regional data to separate those impacts by shore type (specifically, rock and gravel; sand; mudflat 

and wetland; and artificial). Regressions on the results of multiple SIMAP iterations in representative 

regions, simulating a range of oil types, volumes, spill distance from shore, and environmental conditions, 

produce equations that generally relate spill volume to the length of shoreline exposed to oil above an 

impact threshold. For property value impacts, the model uses the regression result for all four shoreline 

types. The impact threshold is specified as a surface sheen produced by an oil concentration of 1 g/m2.

6.4 ASSUMPTIONS

Several assumptions are made in determining the driver values for each planning area, including:

 coastal property values are equivalent to average property values over the extent of a coastal 

block group;

 density of residential properties near the coast is the same as the density over the entire block 

group; 

 residential properties from the coast to 242 feet inland are negatively affected by oil spills. This 

value is based on the width of an average parcel size, assuming that this average parcel is square 

(ERS, 2002; U.S. Census Bureau, 2009b); 

 overlapping areas within planning areas are assigned to a single planning area;

 property values are lost entirely for the duration of the spill event; and

 property values along all types of shoreline are affected equally.
54

6.5 REFERENCES – PROPERTY VALUE: OIL SPILLS

ArcGIS 9.1.3. Environmental Systems Research Institute, Inc. Redlands, CA.

Bureau of Economic Analysis (BEA) National Economic Accounts. 2010. Implicit Price Deflators for 

Gross Domestic Product. U.S. Department of Commerce. Accessed August 15, 2010. 

http://www.bea.gov/iTable/index_nipa.cfm.

DataQuick. 2007-2009. Assessor Data. Compiled by DataQuick.

Economic Research Service (ERS). 2002. Urban and Residential Land Uses. Accessed August 25, 2010.

http://www.ers.usda.gov/publications/eib-economic-information-bulletin/eib14.aspx.

Federation of Tax Administrators. 2010. State Income Taxes. Accessed August 25, 2010.

http://www.taxadmin.org/fta/rate/tax_stru.html.

Freddie Mac. 2010. 30-Year Fixed Mortgage Rates Since 1971. Accessed August 25, 2010. 

http://www.freddiemac.com/pmms/pmms30.htm. 

Tax Foundation. 2010. U.S. Federal Individual Income Tax Rates History, 1913-2010. Accessed August 

25, 2010. http://taxfoundation.org/article/us-federal-individual-income-tax-rates-history-1913-2013-

nominal-and-inflation-adjusted-brackets.

U.S. Census Bureau. 2000a. Census Tract Cartographic Boundary Files. U.S. Census Bureau, Geography 

Division, Cartographic Products Management Branch. Created July 2001. Accessed August 15, 2010. 

http://www.census.gov/geo/maps-data/data/tiger-cart-boundary.html. 

U.S. Census Bureau. 2000b. SF1 and SF3 Data. http://www.census.gov/prod/cen2000/doc/sf1.pdf; 

http://www.census.gov/prod/cen2000/doc/sf3.pdf.

U.S. Census Bureau. 2009a. Income, Poverty, and Health Insurance Coverage in the United States: 2008. 

Accessed August 25, 2010. http://www.census.gov/prod/2009pubs/p60-236.pdf. 

U.S. Census Bureau. 2009b. State and County Quickfacts: Housing Units. Accessed August 25, 2010. 

http://quickfacts.census.gov/qfd/states/00000.html.
55

7 SUBSISTENCE HARVESTS

7.1 OVERVIEW

The model assesses the impact of OCS oil and gas activities on subsistence harvests by estimating oil 

spill-related mortality effects among general subsistence species groups. This assumes that all organisms 

killed by oil spills would have been harvested for commercial or subsistence purposes, estimating the 

subsistence component of this lost harvest, and calculating an estimated replacement cost.

The model does not currently assess three potential subsistence harvest-related costs that might be 

attributed to OCS oil and gas activities.

 Resource “tainting.” An oil spill can create a situation in which potentially exposed subsistence 

resources, while unharmed by the spill, would be considered unfit for hunting. Depending on the 

magnitude of the spill, this perception could remain across multiple hunting seasons. While this 

potential cost is important to acknowledge, a method for credibly quantifying a change in 

behavior, as a function of a specific model input, has not been identified.

 Seismic impacts. In the offshore environment, seismic testing and other physical disturbance 

such as drilling during exploration and development might alter the behavioral patterns of whales 

or other marine species valued by subsistence hunters, and thus might interfere with traditional 

harvesting activities. Anecdotal information, cited in the 2007-2012 programmatic

Environmental Impact Statement (EIS), strongly suggests that seismic and drilling activities do 

have an effect on the subsistence harvest of whales and other marine mammals. The EIS, 

however, also indicates that thresholds above which specific changes can be expected to occur do 

not exist in the literature. Thus, there is no empirical basis for modeling an adverse change in 

subsistence harvest success rates.

 Onshore infrastructure. The development of coastal infrastructure to support OCS activity (oil 

and natural gas processing facilities, water treatment plants, pipelines, etc.) might alter or 

otherwise impair habitats upon which subsistence harvests depend. Furthermore, development 

might impede the movement on land of harvesters or target species. Additional research is 

necessary to establish a credible relationship between terrestrial impacts and adverse effects on 

the subsistence harvest of terrestrial species.

The model also is limited to the impact of OCS oil and natural gas activities on subsistence harvests in 

Alaska planning areas, reflecting the significance of this issue in Alaska relative to other regions and the 

availability of Alaskan subsistence harvest data. Alaska planning areas that do not include a coastal 

component (Navarin Bay, Aleutian Basin, and Bowers Basin) are excluded from the analysis. While 

subsistence harvests do occur in other regions of the coastal United States, they are not readily 

characterized. As data that describe the scope and value of these harvests become available, the OECM 

can be updated to incorporate assessments of any impact OCS oil and natural gas exploration and 

production activity might have.

While similar in approach to the assessment of spill-related subsistence costs in the 2001 OECM, the 

methodology in the model is somewhat simplified in comparison to the previous model due to the 

availability of relationships describing mortality as a function of spill volume for four distinct harvest 

categories (whales, other marine mammals, marine invertebrates, and fish), as described below. The 
56

previous model assumed that mortality among all marine subsistence species occurs in the same 

proportion as the mortality rate assumed for marine mammals.

7.2 BASIC CALCULATION

The model develops an estimate of costs for each planning area in which OCS activity is projected to 

occur using the equation

Ai BiCi Di

where:

Ai = Subsistence harvest as a percentage of total harvest of biological group i (specifically whales, 

other marine mammals, marine invertebrates, and fish)

Bi = Area or volume of water in which spill impact occurs (km2of oiled surface area above an impact 

threshold for whales and other marine mammals; m

3

for marine invertebrates and fish)

Ci = Mortality factor for biological group i (kg killed/km2for whales and other marine mammals, 

kg/m3for marine invertebrates and fish)

Di = Replacement cost for biological group i ($/kg)

For each platform/well group within each planning area, the model calculates a replacement cost for the 

spill-related loss in each biological group. The planning area result is the sum across platform/well 

groups.

7.3 CALCULATION DRIVERS

7.3.1 Subsistence harvest as percentage of total harvest

The Alaska Department of Fish and Game (ADFG), Division of Subsistence, reports that subsistence 

harvests, in the aggregate, account for 2 percent of the annual harvest of all fish and game in the State 

(Wolfe, 2000; Fall et al., 2009). Information describing this relationship at the level of specific harvests 

or sub-harvests (e.g., fish or salmon) is not readily available. Therefore, with the exception of whales (for 

which the subsistence harvest is equal to the total harvest), calculation driver Ai, is specified in the model 

as 2 percent.

7.3.2 Area or volume of water in which spill impact occurs

SIMAP quantifies areas swept by floating oil of varying thicknesses and the fates and concentrations of 

subsurface oil components (dissolved and particulate). Regressions on the results of multiple SIMAP 

iterations in representative regions, simulating a range of oil types, volumes, spill distance from shore, 

and environmental conditions, produce equations that generally relate spill volume to water area or water 

column volume exposed to oil above impact thresholds specified in SIMAP.

7.3.3 Mortality factors

SIMAP calculates the oil/hydrocarbon exposure, dose, and resulting percent mortality for organisms in 

the contaminated exposure areas (wildlife) and water volumes (fish, invertebrates). SIMAP applies these 

results to region-specific biological databases, which describe population densities for each of several 
57

organism types, to arrive at mortality factors per unit water area or water volume. For the model, specieslevel data are aggregated into four biological groups.

 Whales: baleen and piscivorous

 Other marine mammals: polar bears, pinnipeds, and sea otters

 Marine invertebrates: crustaceans and mollusks

 Fish: small pelagic fish, large pelagic fish, and demersal fish

An analysis of the ADFG Community Profile Database (ADFG, 2001), which provides the most current 

accounting of subsistence harvests by type, indicates that this taxonomy is consistent with observed 

activity. Figure 4 provides marine harvest profiles, drawn from the ADFG database, for each of the 

Alaska planning areas where subsistence activity is presumed to occur. To avoid overstating costs 

associated with whale harvests, the model includes whale mortality factors only for the planning areas 

that in the aggregate account for more than 99 percent of the total whale harvest, according to the 

information in the ADFG database.26

7.3.4 Replacement cost

Subsistence use of natural resources includes a cultural element that is not well-addressed in the 

economics literature. The standard methods for deriving estimates of economic value are limited in their 

ability to capture the full value associated with subsistence-use activities, as it is very difficult to include 

cultural and other intangible values that would necessarily be part of a total value measure. Replacement 

cost is one method that is commonly used as a substitute measure of value. However, here, too, data 

related to subsistence use are limited. In the model, replacement cost is used as a proxy for the lost 

subsistence harvest and provides some level of compensation for the lost cultural value. The cultural and 

social impacts associated with the loss of the subsistence harvest are difficult to quantify and may not be 

fully reflected in this measure. The model currently utilizes a single per kilogram (kg) replacement cost 

derived from the BP Exploration Good Neighbor Policy for its Northstar Project in the Beaufort Sea 

(Sharpe, 2001). This policy called for the creation of a financial instrument in the amount of $20 million 

to serve as a fund for specific expenditures required to mitigate the impact of an oil spill on an Alaska 

native community’s subsistence harvests. Bowhead whale is the most significant element of this harvest, 

with an estimated annual harvested quantity of 336,000 lbs. To account for other marine subsistence 

resources that would be affected by a spill, BP and the local community agreed on a scaling factor of 1.5, 

resulting in a total estimated annual harvest, subject to replacement, of 504,000 lbs (or 228,610 kg). The 

total cost of all mitigation activities, which include specific items intended to address the cultural 

dimension of the loss (e.g., an annual conference of youth and elders to impart the cultural significance of 

subsistence and promote the retention of local knowledge), was estimated to be $19,454,164 ($2001). 

This implies a replacement cost of approximately $85/kg; inflated to current dollars using the Consumer 

Price Index (U.S. Department of Labor, 2010), the implied cost is approximately $105/kg.

 

26 The planning areas in which whale harvest losses can be calculated are Cook Inlet, North Aleutian Basin, St. 

Matthew Hall, Norton Basin, Hope Basin, Chukchi Sea, and Beaufort Sea.
58

Figure 4. Marine Harvest Profiles for Alaska Planning Areas.

SALMON

MARINE INVERTEBRATES

WHALES

OTHER FISH

OTHER MARINE MAMMALS

North 

Aleutian 

Basin

Aleutian Arc

St. George 

Basin

St. MatthewHall

Gulf of Alaska

Kodiak

Cook Inlet

Shumagin
59

SALMON

MARINE INVERTEBRATES

WHALES

OTHER FISH

OTHER MARINE MAMMALS

Norton Basin

Hope Basin

Chukchi Sea
60

7.4 REFERENCES – SUBSISTENCE

Alaska Department of Fish and Game (ADFG), Division of Subsistence. 2001. Community Profile 

Database for Access 2000, Version 3.12. July 10.

Fall, J.A., C. Brown, M. Turek, N. Braem, J. Simon, W. Simeone, D. Holen, L. Naves, L. HutchinsonScarbrough, T. Lemons, V. Ciccone, T. Krieg, and D. Koster. 2009. Alaska Subsistence Salmon 

Fisheries 2007 Annual Report. Technical Paper No. 346. Anchorage, AK: Alaska Department of Fish 

and Game, Division of Subsistence. http://www.subsistence.adfg.state.ak.us/TechPap/TP346.pdf.

Sharpe, J.P. 2001. Plans for the Provision of Interim Transportation, Provision of Interim Alternate Food 

Supplies, Provision of Interim Counseling and Cultural Assistance, and Provision of IWC Quota 

Restoration Assistances. Attachment 1 to the BP Exploration (Alaska) Inc., Good Neighbor Policy, 

Northstar Project. September 18.

U.S. Department of Labor, Bureau of Labor Statistics. 2010. Consumer Price Index - All Urban 

Consumers. Accessed August 18, 2010. http://www.bls.gov/cpi/#tables.

Wolfe, R.J. 2000. Subsistence in Alaska: A Year 2000 Update. Juneau, AK: Alaska Department of Fish 

and Game, Division of Subsistence.

http://www.subsistence.adfg.state.ak.us/download/download/subupd00.pdf.
61

8 COMMERCIAL FISHING

8.1 OVERVIEW

The Commercial Fisheries Impact (CFI) Model measures the costs of fishing area preemption caused by 

the placement of oil and natural gas infrastructure (platforms and pipelines) in the OCS.27 The model 

assumes that there will be buffer zones around platforms that decrease the area of the ocean available for 

fishing. In most cases, these buffer zones will be a circle with a radius of 805 meters (0.5 miles). The 

model assumes that the buffer zones cause a proportional redistribution of fishing effort within each 

planning area and that the redistribution of effort can lead to cost increases, particularly when effort is 

redistributed from a low-cost area to a high-cost area.

A key element in the model is that the distribution of fishing effort within each planning area is highly 

variable. Fishery data from the National Marine Fisheries Service (NMFS) confirms that in many 

planning areas fishing effort is highly concentrated. If the oil and natural gas infrastructure is placed in an 

area where little or no fishing takes place, the preemption impacts will be zero. If platforms are placed in 

important fishing areas, impacts will be greater.

The model also assumes that the total amount harvested is unaffected by oil and natural gas infrastructure. 

This assumption follows from the fact that nearly all fisheries in federally managed waters are managed 

with annual catch limits that are set at levels well below the harvestable biomass.28

The model also assumes that, in general, seabed pipelines do not affect harvesting. Federal regulations 

require that all seabed pipes that are in waters less than 200 feet deep must be buried. The model, which 

uses the metric system for distances, depths, and areas, assumes that all pipe in waters 60 meters or less 

(196.9 feet) are buried. Buried pipeline is assumed not to affect fisheries. Evidence from interviews with 

harvesters and gear manufacturers around the United States, Norway, and elsewhere around the world 

indicates that unburied pipe also is unlikely to affect fish harvesting, with the exception of dredges used to 

harvest scallops and clams.29 The model for the North Atlantic and Mid-Atlantic Planning Areas includes 

preemption impacts of unburied pipelines on the scallop fisheries and quahog fisheries that occur in 

waters deeper than 60 meters. 

This CFI model is significantly different than the CFI model developed in the 2001 version of the OECM. 

The previous version of the OECM assumed that fish harvests were uniformly distributed throughout a

planning area, and that harvests were reduced in proportion to the amount of the planning area preempted 

by oil and natural gas infrastructure. Thus, if there were 10,000 square miles in a planning area and 

 

27 The CFI model currently operates external to the OECM and generates coefficients that the OECM uses to 

estimate total commercial fishery-related costs, as described later in this section.

28 There do not appear to be significant concerns that platforms cause negative impacts on the biomass of 

commercial fish species. In fact, there is considerable debate about whether platforms actually may increase 

fishable biomasses of certain species. In this case, the decision was to err on the conservative side of the issues and 

assume for the purpose of the model that additional platforms will not increase biomass levels of commercial fish 

species. 

29 In the research for this model, conversations were held on April 25, 2010 with Dr. Gordon Kruse, a recognized 

crab biologist in Alaska, regarding the question of whether seabed pipeline could impact migrations of crab. Dr. 

Kruse indicated that it was very possible that unburied pipeline could affect migrations of king and tanner crab in the 

Bering Sea. Dr. Kruse indicated that to his knowledge there had not been any research directly on the topic, and that 

it would be difficult to estimate an impact without more research and without specific information regarding the 

locations of the pipelines. In the absences of specific information regarding potential impacts of pipelines on crab 

migrations, the decision was not to speculate, but note that there may be additional impacts beyond those reflected in 

the model. 
62

platforms and pipelines preempted 100 square miles, then fish harvests were assumed to be reduced by 1 

percent multiplied by a mobility factor specific to various species. For very mobile species, the mobility 

factor was very low or zero, while for less mobile species the factor was set at a higher level.

Note that the OECM does not currently estimate the impact of oil spills on commercial fishing. While 

spills attributable to OCS activity may affect this industry, especially when the spill is large enough or of 

long enough duration to require the closure of a fishery for some period of time, the ability to model the 

potential costs associated with a specific exploration and development scenario is constrained by a 

number of factors. Producing a credible prediction of spill-related costs would require assumptions such 

as the spill’s biological impact, if any, on future stocks; the relative impact of a spill on different 

commercial species; the timing of a spill and whether it would occur during a period when commercial 

activity is occurring at a particular location; and a spill’s influence on consumer behavior (whether 

demand would change due to real or perceived risks). Making these assumptions and building a 

sufficiently credible model of spill-related costs was beyond the scope of effort to date. Also note that the 

most significant costs would result from low probability/high consequence events that the model is not 

intended to address.

Focusing on potential impacts associated with temporary fishery closures, such impacts are unlikely to be 

significant in the areas included in the Five Year Program. Because most fisheries are managed through 

catch limits, temporary closure of a fishery still would give the industry ample opportunity to reach the 

catch limit for the season. Although the industry may not be able to completely make up for the catch 

losses associated with the temporary closure, historical experience suggests that the industry is able to 

make up for a significant portion of these losses by increasing their catch later in the season. For 

example, data on the impact of the 1987 Glacier Bay spill on the Cook Inlet salmon fishery show that 

catch for drift net fishermen increased in late July and early August to make up for the reduction in catch 

in the immediate aftermath of the spill in mid-July.30 If a spill occurs near the end of the season, fisheries 

management authorities typically have the flexibility to extend the season to minimize catch losses.

8.2 BASIC CALCULATION

This section provides a summary of the methodology used to estimate preemption impacts of oil and 

natural gas infrastructure on commercial fisheries.

Each planning area has been divided into cells comprising 10 minutes of latitude and 10 minutes of 

longitude (10 × 10 cells). The cells within each planning area are classified by the depth of the cell at its 

centroid using published bathymetric data from the National Oceanic and Atmospheric Administration 

(NOAA). Five different depth ranges were used, based on platform and pipeline characteristics 

applicable for infrastructure at those depths. The radius of fishery buffer zones is set at 805 meters for all 

ranges except for Depth Range 3, where the buffer zone increases with depth. The depth ranges and 

platform types associated with each range are listed below.

 Depth Range 1: 0 m – 60 m; Fixed Platforms and Buried Pipelines

 Depth Range 2: 60 m – 150 m; Fixed Platforms 

 Depth Range 3: 150 m – 300 m; Floating Anchored Platforms; radius of buffer zones will be 

equal to 805 meters + 2 × cell depth at centroid.

 Depth Range 4: 300 m – 1,500 m; Tension Leg Platforms 

 

30 See MMS (1990).
63

 Depth Range 5: 1,500 m +; Dynamically Positioned Floating Platforms

Data estimating the value of commercial fisheries harvested from each 10 × 10 cell has been generated 

using available data from NMFS or from other available sources of fishery data. Summary of these data 

by region are provided in the next section of this overview. The remainder of this section uses examples 

from Alaska to describe the model and the way it generates estimates of impacts of oil and natural gas 

infrastructure on commercial fisheries.

Figure 5 shows groundfish harvests in Alaska planning areas. From Figure 5 it is clear that with the 

exception of the St. George (GEO) Basin, Navarin Basin (NAV), and the North Aleutian Basin (NAL), a 

relatively small portion of the Alaska OCS is utilized in the groundfish fisheries. It is expected that the 

distribution of fishing effort in other planning areas is similar.

Figure 5. Locations of Alaska Groundfish Harvests by Planning Area (2006-2009).

Source: Developed for the OECM by Alaska Map Company from data supplied by NMFS.

Preemption impacts are derived from data in the E&D scenario. The model assumes that, if development 

is expected to occur in a planning area, the E&D scenario will provide information about one or more 

groups of platforms. For each group of platforms it is expected that among other information, the 

following will be provided.
64

 Number of platforms in the group

 Average depth of platforms in the group

The CFI model assigns the platforms to one of the five depth ranges described earlier, based on the 

average of the depths indicated in the E&D scenario for each platform group. The model then randomly 

assigns platforms to 10 × 10 cells within the depth range corresponding to the E&D scenario. Assume,

for example, that the E&D scenario indicates the following:

 Group 1: 5 platforms with average depth of 40 meters, and 

 Group 2: 7 platforms with average depth of 55 meters

The model randomly assigns platforms to 12 different 10 × 10 cells in Depth Range 1, as both Group 1 

and Group 2 fall into that range. The model assumes that no more than one new platform can be located 

in a single cell. It should be noted that if a legacy platform (pre-2010) already exists in that cell, then it is 

assumed new platforms may be added.

Once the platforms are assigned, the model calculates the size of the buffer zone required for each 

platform and reduces the fishing area in the cell by an appropriate amount. Fishery values are 

proportionally redistributed in a two-step process. 

1. Reduce the fishery value in each cell with a platform in proportion to the reduction in available 

fishing area in the cell caused by the introduction of the platform—Preliminary Revenue for the 

cell (PRc) = Baseline Revenue for the cell (BRc) × (Cell area – buffer zone) ÷ Cell Area.

2. Increase the fishery value in all cells proportionally such that the total fishery value in all cells in 

the planning area is unchanged, and such that the percentage of the fishery value in each cell is 

equal to the percentage of total revenue after the revenue reduction calculated in step 1—Final 

Revenue (FRc) = PRc × ∑IRc ÷ ∑PRc

Once fishery values are redistributed across the planning area, the model estimates the differences in 

fishing costs that result. Reliable fishing cost data are not generally available, so the model uses an 

assumption that fishing costs are lowest in the cell within the planning area that has the highest revenue. 

Cost differentials in all other cells are estimated as an increasing percentage of revenue up to a 20 percent 

differential.31

In order to the estimate the cost impact of grounds preemption, the estimated fishing cost differentials are 

applied to baseline revenue distribution by fishing area, and then reapplied to the revenue distribution 

after fishing grounds have been preempted due to oil and natural gas infrastructure. The incremental 

difference in cost over all areas between the baseline and post-infrastructure case constitutes the estimate 

of the cost of grounds preemption for the fishery.

The estimation of the fishing cost differentials for the set of platforms in the E&D scenario is highly 

dependent on the location of the platforms within the depth range. If the platforms are located in cells 

 

31The following formulation will be used to estimate the fishing cost differential across 10 × 10 cells:

dCpc = (1 – Rpc ÷ RpMax) × dCpzero; where

dCpc = difference in fishing cost percentage in the cell relative to the fishing cost percentage in the cell 

with the maximum revenue

Rpc = revenue in the cell as a percent of total revenue in the planning area 

RpMax = revenue in the cell with the maximum revenue as a percent of total planning area revenue

dCpzero = difference in fishing cost percentage as revenue in a cell approaches zero; as noted, the model 

assumes this to be 20 percent
65

where very little fishing takes place, the cost impacts will be negligible. On the other hand, if the 

platforms are located in cells where a lot of value is generated, the impacts will be greater. In other 

words, if the model assigns the same number of platforms to a different set of cells, the cost estimate will 

be different. Coefficients incorporated in this version of the OECM result from regressions on thousands 

of simulation iterations32 for each planning area. Each simulation represents a random placement of 

platforms, with the number of new platforms in each depth band ranging from zero up to the number of 

cells in the band. Within each iteration, platforms are assigned randomly to cells in 250 different 

location-configurations, and cost impacts are calculated for each location configuration.33 The result for 

each iteration is the average of the cost differentials calculated over all 250 randomly drawn locationconfigurations for that particular E&D scenario. The model also can report the cost differentials if 

platforms are intentionally assigned to the cells that generate the highest amount of revenue and thus are 

likely to generate a “worst case” scenario in terms of impacts.34

8.3 REGRESSION COEFFICIENTS

The OECM, as currently configured, will generate estimated impacts on commercial fisheries that result 

from BOEM-supplied E&D scenarios through the use of regression coefficients and equations. The 

estimated cost impacts for each simulated scenario were compiled into a regression dataset for each 

planning area with estimated regression coefficients assuming that impacts were of the form Y = aixi + 

bixi

2

. The dependent variable (Y) is the estimated cost impact for each scenario, and xiis the number of 

new platforms in depth range i. Regression coefficients are included for the square of xito account for the 

fact that if the number of new platforms is relatively large, then the incremental impact of additional 

platforms is likely to be diminishing. In most cases, the regression coefficient for the un-squared term (a) 

will be positive while the regression coefficient for the squared term (b) will be negative and smaller in 

absolute magnitude. If this is the case, then the squared terms will provide a dampening effect on the unsquared terms. If increasingly greater numbers of cells have platforms, then the redistribution of effort 

will have an increasingly smaller impact on fishing costs. If large numbers of platforms are placed in 

depths with relatively low fishing revenues, it is possible that the estimated cost impacts may turn 

negative. In these instances, the model assigns a zero-cost outcome to the E&D scenario.

There are also a limited number of instances in which the coefficients for the un-squared terms are 

negative. A full listing of the coefficients is provided later in the document. A negative coefficient in the 

un-squared term implies that while there is fishing activity in the depth range, fishing revenues are low 

relative to other depth ranges in the planning area. Therefore, shifting effort out of the depth range moves 

effort to areas where revenues are higher and costs are lower. This could result in an overall reduction in 

fishing costs for an E&D scenario, particularly if the majority of platforms are placed in the ranges with 

negative coefficients for the un-squared term. Because it is doubtful that displacing fishing effort will 

result in overall cost reductions, the model returns a zero value for any negative cost results. 

 

32 For most planning areas, over 20,000 simulation iterations were generated. Exceptions were the North and MidAtlantic for which 10,000 simulation iterations were run. 

33 Because of the large number of 10 × 10 cells in each planning area, the spreadsheets used to calculate results are 

quite large. For example, the spreadsheet used to interactively calculate the 250 location configurations in the St. 

Matthew Hall Planning Area (with 1,447 cells) is over 23 megabytes in size. Increasing the number of iterations to 

1,000 would make the estimate of cost impacts somewhat more robust because it is more likely that the random 

assignment of platforms will choose a set of platform locations that correspond to important fishing areas. To 

account for the possibility that the mean may be skewed to a lower estimate, the model also includes an estimate of 

costs assuming that the platforms are assigned to the highest ranked areas in each depth range. 

34 This last set of regression coefficients has not been included with this version of the OECM, but it potentially 

could be added at a later date.
66

In part because of the fact that negative coefficients for the un-squared terms occur, but also because 

fewer interactions are likely between depth ranges when all platforms are located in a single depth range, 

a second set of regression coefficients has been generated. This second set of coefficients should be used 

in cases if the E&D scenario places platforms in only one depth range within the planning area. For the 

most part the issue of negative coefficients for un-squared terms is eliminated with this second set of 

coefficients. However, there still is one instance of a negative coefficient. This occurs in the Bowers 

Basin Planning Area off Alaska, with very limited fishing activity. As indicated above, any model 

outcomes that result in a negative cost impact should be treated as a zero-cost scenario.

8.4 EXAMPLES OF MODEL USAGE

Tables 9 and 10 show fishery data inputs and regression coefficients for the St. George Basin Planning 

Area in Alaska. These two tables are provided as examples of the data and model result tables for the 

other planning areas. St. George Basin is home to some of the most prolific fishing grounds in Alaska. 

Table 9 summarizes the data by depth range used to develop the impact model for St. George Basin. The 

table shows: 

1) number of 10 × 10 cells by depth range,

2) water area in terms of millions of hectares within each depth range,

3) number of 10 × 10 cells in each depth range that were assigned fishery revenues,

4) four-year average of total fishery revenues in millions of 2009 dollars in that depth range,

5) number of existing oil and gas platforms in each depth range, and

6) number of cells containing the existing oil and natural gas platforms.

In the St. George Basin, most of the planning area is from 60 to 150 meters in depth, and over two-thirds

of the fishery revenue is generated from cells in that depth range.

Table 9. Commercial Fishing Modeling: Data Summary for St. George Basin, Alaska.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 216 774 102 151 304 1,547

Water Area (HA M) 3.7 14.5 2.0 3.0 6.0 29.2

Cells with Revenue 184 743 102 138 88 1,255

Fishery Revenue (Real $ M) 14.5 213.8 44.8 32.1 0.9 306.1

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes:

HA M is millions of hectares, 

Real $ M is millions of real dollars.
67

Table 10 summarizes the regression coefficients that result from the thousands of simulations run through 

the St. George Basin Planning Area model. The table contains two independent sets of regression 

coefficients. The first set of coefficients assumes that platforms are assigned to multiple depth ranges 

within a given simulation. The number of platforms in each depth range could range from zero to as high 

as the number of cells that exist in the depth range. For most areas, it would be more likely that the 

number of platforms would be relatively small, and therefore potential scenarios in the range from zero to 

nine platforms were over-sampled.

The second set of regression coefficients should be used if the E&D scenario calls for platforms within a 

single depth range. Because the number of interactions between platforms in different depth ranges is 

eliminated, the number of model simulations for these regressions was reduced significantly. Simulations 

were run four times for each number of platforms up to the maximum of 50 platforms in each depth 

range. Note that each simulation for a given number of platforms generates 250 location-configurations,

each with its own cost-impact estimate.

The rows in each section of the table show the results for each depth range (D1-D5). The value of the 

regression coefficients (a and b) are shown in the first two numbered columns after the depth range 

specifications. That is, the regression coefficient for the number of platforms in D1 is -0.6107, and the 

coefficient for the square of platforms in D1 is -0.0199. The last two columns in the table show the pvalues, indicating the statistical significance of the coefficient. P-values less than 5 percent generally are 

considered significantly different than zero. The fact that both coefficients for platforms in D1are 

negative implies that additional platforms in this depth range will dampen the negative impacts of 

platforms in other depth ranges (D3 for example). 

It also should be noted that the estimated impacts are in terms of annual cost impacts in real dollars. 

Thus, a coefficient of 22.0 in D3 implies that adding an additional platform in D3 in conjunction with 

platforms in other depth ranges generates a cost impact of $22 per year to fisheries in the St. George 

Basin. Given that fisheries in St. George Basin are estimated to generate over $300 million per year, it 

appears that the estimated preemption cost impacts of platforms in the St. George Basin are quite small.

The fact that the regression coefficients for both the number and the square of the number of platforms are 

negative underscores the need for the second set of coefficients, to be used if platforms are to be placed in 

only one depth range within the E&D scenario. As seen in the lower section of Table 10, the regression 

coefficient for the number of platforms in D1 is positive while the coefficients for the square are negative. 
68

Table 10. Commercial Fishing Modeling: Fishery Impact Coefficients for St. George Basin, Alaska.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m -0.6107 -0.0199 0.00% 0.00%

D2 60m - 150m 6.5774 -0.0083 0.00% 0.00%

D3 150m - 300m 22.0001 -0.2567 0.00% 0.00%

D4 300m - 1,500m 25.7349 -0.1509 0.00% 0.00%

D5 1,500m + 2.3083 -0.0098 0.00% 0.00%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0153 -0.0004 0.00% 0.00%

D2 60m - 150m 23.0807 -0.1633 0.00% 0.00%

D3 150m - 300m 39.9362 -0.5776 0.00% 0.00%

D4 300m - 1,500m 36.8306 -0.2414 0.00% 0.00%

D5 1,500m + 0 0

Note: Regression coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.

The following is a numerical example of the way the model coefficients should be used under two 

different sets of circumstances.

Example 1: Assume an E&D scenario for the St. George Basin in which two platforms will be placed in 

D1 and five platforms will be placed in D2. In this case, x1 = 3 and x2 = 5. The annual fishery impacts 

would be estimated as Y = (-0.6107 × 2) + (-0.0199 × 22) + (6.5774 × 5) + (-0.0083 × 52) = 31.3785.

Example 2: Assume an E&D scenario for the St. George Basin in which two platforms will be placed in 

D2 and no other platforms will be developed. The annual fishery impacts would be estimated as Y = 

(23.0807 × 2) + (-0.1633 × 22) = 45.5082. 

8.5 FISHERY DATA SOURCES AND ALLOCATION METHODS

Fisheries data were divided into four general regions: (1) Alaska, (2) the Pacific Coast, (3) the Gulf of 

Mexico and South Atlantic, and (4) the Mid- and North Atlantic. The level of detail available for each 

region varied considerably, as did the process of assigning harvests to the 10 × 10 cells. An overview of 

the data sources and processes is provided below.

8.5.1 Alaska

The Alaska Region of NMFS provided data for groundfish by harvests by gear in 10 × 10 cells for all of 

Alaska for the years 2006-2009 (Lewis, 2010). NMFS Alaska has spent a considerable amount of time 

developing the data that combine reports from logbooks, observers, and their standard catch accounting 

system to assign harvests algorithmically to very precise geographic locations. These data were provided 

to Northern Economics by year, fishery, and 10 × 10 cell, as long as more than three vessels contributed 

harvests to the landings; otherwise, the landings were considered confidential. NMFS also provided total 

harvest summaries by year and fishery in larger management areas. It was assumed that landings in cells 

with three or fewer harvesters would be small relative to landings in cells with more harvesters. 

Therefore, these landings could be distributed proportionally to other cells that had landings without 
69

materially affecting the model outcomes. If anything, this process would lead to higher concentrations of 

landings in particular cells, which would have the effect of increasing the potential impact of platforms.

In additional to groundfish, crab and halibut also are harvested in significant quantities in Federal waters 

in which OCS development could occur. Crab data were provided by the Commercial Fishing Entry 

Commission (CFEC) through a specific data request (Huntsman, 2010) by fishery year for 2006-2009. In 

Alaska, crab landings are reported by statistical areas covering one-half of a degree of latitude and one

degree of longitude. Given that these statistical areas are already geographically based, it was a 

straightforward process to subdivide the landings by stat-area into 10 × 10 cells, with each cell receiving a

portion of the landings equal to its share of the water in the stat-area. The CFEC also reported total 

landings by fishery and year. The amount of crab landings that were considered confidential were 

inferred from these data. Confidential harvests were assigned to statistical areas that were adjacent to 

statistical areas that had landings on a pro-rata basis, and these further assigned harvests to cells within 

each stat-area. Because crab data in general were provided at a more aggregated level of geographic 

detail, it has the effect of smoothing overall harvests within planning areas.

Data on halibut landings in Alaska were provided in a manner similar to the crab data by the International 

Pacific Halibut Commission (IPHC) for 2006-2009 (Kong, 2010). In the Bering Sea, halibut landings are 

reported using the same geographically-defined statistical areas. In the Gulf of Alaska, statistical areas

specifically for halibut are used. In general, essentially the same process was used to assign harvests to 

10 × 10 cells with one important twist. It was assumed that within a statistical area, halibut harvested 

were distributed to 10 × 10 cells in proportion to the amount of water area in each cell. In the case of 

halibut, however, information from IPHC indicated that harvests of halibut generally are limited to water 

less than 500 fathoms (914 meters) of depth. Thus, halibut harvests were not assigned to cells in which 

the depth of the centroid was greater than 914 meters.

Once all of the harvests by species were assigned to cells, average ex-vessel harvest values by species and 

year were assigned independently. Ex-vessel values were adjusted to account for inflation to 2009 dollars 

using the U.S. Bureau of Labor Statistics producer price index (PPI) for unprocessed and packaged.

35

 The 

final harvest value assigned to each cell was the average over four years of the annual adjusted value.

It should be noted in this section that salmon harvests were not included in the Alaska data. While 

salmon fisheries are very important in Alaska, accounting for roughly one-third of the ex-vessel value 

(Hiatt et al., 2009), the vast majority of harvests take place inside State waters and therefore would not be 

directly affected by the placement of platforms in Federal waters. Herring fisheries and other shellfish 

(oysters, geoducks, etc.) harvests were excluded for the same reason. 

8.5.2 Pacific Coast

Estimates of groundfish trawl harvest for the Pacific Coast were provided in 10 × 10 cells for 2006-2009 

from two sources. Harvests in the offshore Pacific whiting fishery were provided from observer data by 

NMFS Northwest Fishery Science Center through a special request (Tuttle, 2010). Estimates of harvests 

of shorebased trawl by 10 × 10 cells were developed using logbook data and were provided by the Pacific 

Fisheries Information Network (PacFIN) through a special request (Stenberg, 2010). As for Alaska,

summary totals over all areas by fisheries were also provided. This allowed for calculation of data that 

 

35 PPI obtained from http://data.bls.gov:8080/PDQ/outside.jsp?survey=wp.
70

had been withheld for confidentiality. Confidential harvest amounts then were assigned back to the nonconfidential cell in proportion to the landings in the non-confidential cells.

Assignment of landings of other West Coast fisheries was more problematic than with non-groundfish 

landings in Alaska. In general, geographically specific estimates of non-trawl landings on the Pacific 

Coast are reported only for relatively large areas known as INPFC Areas, established by the International 

North Pacific Fisheries Commission (INPFC) under the International Convention for the High Seas 

Fisheries of the North Pacific Ocean in 1952. Although INPFC has been dissolved, INPFC statistical 

areas remain in use, and are the most geographically precise reporting areas in general use on the Pacific 

Coast. As seen in Figure 6, five INPFC Areas comprise the Exclusive Economic Zone (EEZ) off the 

Pacific Coast. Harvest data from INPFC Areas are reported by PacFIN on an annual basis (PacFIN,

2010a and 2010b).

For non-trawl landings of groundfish, primarily sablefish and rockfish, landings data by INPFC Areas

were combined with fishery specific landings data for 10 × 10 cells for trawls and assigned non-trawl 

landings to 10 × 10 cells in each INPFC Area in proportion to the landings those cells had in trawl 

fisheries for the same species. For example, non-trawl landings of rockfish in the Monterey INPFC Area 

were assigned to the same 10 × 10 cells that had rockfish trawl landings. In this case, areas of high 

abundance of particular species presumably would be used by all gears. 

An exception to the general approach for non-trawl groundfish was in the Conception INPFMC Area. 

Trawling for groundfish has not been allowed in the area for several years, and therefore there were no 10 

× 10 data with which to associate non-trawl landings. In this case, estimates were made of the proportion 

of landings by depth in areas north of the Conception INPFC Areas for those fisheries that occur inside 

the Conception Area. Harvests then were assigned to cells inside the Conception Area in proportion to 

the estimated water areas of cells by depth.

There are other important fisheries on the Pacific Coast including the Dungeness crab, salmon, and 

shrimp trawl fisheries. Both Dungeness crab and salmon fisheries take place primarily inside State 

waters, are unlikely to be displaced by oil and gas platforms, and therefore have not been included in the 

CFI model. 

The shrimp trawl fishery is more likely to be affected because the majority of shrimp harvests occur in 

waters from 300 to 650 feet in depth (CDFG, 2007), which could range farther out into the EEZ. Based 

on this information, landings data by INFPC Areas from PacFIN were assigned to 10 × 10 cells in these 

depth ranges in proportion to water area.
71

Figure 6. International North Pacific Fishery Commission (INPFC) Statistical Areas.
72

8.5.3 Gulf of Mexico and South Atlantic

Information on commercial fish harvests for the Gulf of Mexico and for the South Atlantic were the result 

of a formal data request from BOEM to the Science Director of NMFS Southeast Fisheries Science 

Center (Labelle, 2010). The information was provided in terms of the standard statistical areas in general 

use throughout the region. The statistical areas over which these data were reported are shown in Figure 

7.

Figure 7. Statistical Areas for Gulf of Mexico and South Atlantic Fisheries.

Source: Provided by NMFS SEFSC as part of the BOEM data request (Jamir, 2010).

In the Gulf of Mexico there are 23 statistical areas. Areas 19-23 and 1-7 extend in east-west directions 

outward from the coastline until they meet the statistical area running from north to south. In particular,

Area 18 is the eastern boundary of for Areas 19-23, and Area 8 is the western boundary of Areas 1-7. 

Areas 8-18 extend in a north-south direction from the coastline to international waters in the south. 

In the South Atlantic, statistical areas are linked to specific geographic coordinates. Each statistical area 

covers one degree of longitude and one degree of latitude and is designated based on the coordinates of 

the lower right corner.

NFMS provided harvest data and ex-vessel values from 2006-2009 for the fisheries shown in Table 11.
73

Table 11. Data Provided for Gulf of Mexico and South Atlantic Fisheries.

Region Fishery

Gulf of Mexico Reeffish

Gulf of Mexico, South Atlantic Shrimp

Gulf of Mexico, South Atlantic Coastal Migratory

Gulf of Mexico, South Atlantic Spiny Lobster

Gulf of Mexico, South Atlantic Large Pelagics

Gulf of Mexico, South Atlantic Stone Crab, Red Drum

Gulf of Mexico, South Atlantic Red Drum

South Atlantic Snapper/Grouper

South Atlantic Dolphin/Wahoo

South Atlantic Golden Crab

Note that the sizes of many of the statistical areas, particularly in the Gulf of Mexico, are quite large. 

Therefore, in order to provide more realistic geographic distributions of harvests, the determination was 

made that it would be appropriate to augment the landings data with information on the maximum depth 

at which significant species within each fishery are likely to be found. Depth distributions for significant 

species were taken from the database maintained by AquaMaps at http://www.aquamaps.org/. Table 12

shows the maximum cell depth to which landings were allocated for each of the fisheries within statistical 

areas. For example, if there were 1,000 metric tons of reeffish landings reported for Statistical Area 12 in 

the Gulf of Mexico, then only those cells in the statistical area that had depths of 540 meters or less would 

be assigned reeffish landings. The cells with depths greater than 540 meters would not be assigned 

reeffish harvests. In general, fisheries to be harvested were not constrained in cells with depths greater 

than some minimum. The exception to this rule was for Golden Crab. For that fishery, cells had to have 

a minimum depth of 250 meters to receive an allocation.

Table 12. Maximum Cell Depths to which Fisheries Were Assigned Landings by Fishery and Region.

Region Fishery

Maximum Cell 

Depth (meters)

Gulf of Mexico Reeffish 540

Gulf of Mexico, South Atlantic Shrimp, Coastal Migratory, Spiny Lobster 200

Gulf of Mexico, South Atlantic Large Pelagics 9,850

Gulf of Mexico, South Atlantic Stone Crab, Red Drum 51

South Atlantic Snapper Grouper 540

South Atlantic Dolphin/Wahoo 85

South Atlantic Golden Crab 1400

As with other regions, there are significant harvests of species that are not federally managed. In the Gulf 

of Mexico, for example, there are very significant harvests of oysters and menhaden. Since these 

fisheries are not managed by NMFS, it was assumed that their harvest occurs in water within three miles 

of shore, and therefore that they would not be displaced by new platforms on the OCS.

8.5.4 North and Mid-Atlantic

Data for most of the major fisheries in the North and Mid-Atlantic were provided as a result of a data 

request to NMFS Northeast Fishery Science Center. Dr. Eric Thunberg provided estimates based on 

logbook data and dealer reports of harvests from 10 × 10 cells for 2006-2009 for the fisheries listed in 
74

Table 13. Dr. Thunberg also provided summaries of dealer reports that enable the estimation of ex-vessel 

values within the various fisheries. These were used to assign ex-vessel prices to landings and as a means 

to assign harvests to cells for which fishery-specific data were noted as confidential. While the data 

provided by Dr. Thunberg are relatively comprehensive in terms of fisheries that take place in Federal 

waters, the data do not include landings or values of highly migratory pelagic species, nor did they 

include landings or values of lobster. Other fisheries that are primarily harvested inside three miles also 

were excluded, such as blue crab.

Table 13. Federal Fisheries for which Data Were Requested and Provided in the North and Mid-Atlantic 

Regions.

North Atlantic Mid-Atlantic

black sea bass monkfish gillnet black sea bass

bluefish monkfish trawl bluefish

butterfish other butterfish

dogfish scallop dogfish

fluke scup fluke

groundfish gillnet shrimp monkfish gillnet

groundfish hook skates other

groundfish trawl small mesh multispecies scallop

herring squid shrimp

mackerel surf clam skates

tilefish squid

tilefish

There are significant levels of lobster harvests in Federal waters of the North Atlantic, as well as 

significant harvests of large pelagic species (e.g., bluefin tuna and swordfish) that were not included in 

the Thunberg data. It is believed that these fisheries could be affected by OCS platforms; therefore,

alternative sources of information were found. Estimates of commercial fisheries harvest volumes and 

values by state compiled by the NMFS Office of Science and Technology for 2006-2009 were used as 

source data for lobster and large pelagics in the North and Mid-Atlantic (NMFS, 2010).

Estimated harvests by state were allocated to 10 × 10 cells by latitude. For example, cells with latitude of 

43 degrees and higher were assigned to Maine, while cells from 41.6 degrees to 42.9 degrees were 

assigned to Massachusetts and New Hampshire. Lobster landings reported in the NMFS database for 

Maine were assigned to the 10 × 10 cells in Maine in proportion to each cell’s water area. It should be 

noted that a maximum depth limit was added for lobster harvests. Cells with centroid depths greater than 

100 meters did not receive assignments of lobster harvests. 

Harvests of large pelagic species in the North and Mid-Atlantic were assigned to cells using a similar 

state-based allocation using the NMFS commercial fisheries harvest database (NMFS, 2010). In this 

case, harvests were not constrained to specific depths and were allocated to all cells by state in proportion 

to the water area of the cell.
75

8.6 SUMMARY TABLES OF FISHERY DATA AND REGRESSION COEFFICIENTS 

FOR ALASKA PLANNING AREAS

This section provides fishery data and regression coefficients for the Alaska planning areas. Two tables 

with the same formats as the example for the St. George Basin above are provided for each Alaska 

planning area. Note that there are no commercial fisheries in Federal waters in Hope Basin or Chukchi 

and Beaufort Seas, so fishery data and impact models for those areas are not provided. Tables are 

arranged in a north-to-south and west-to-east progression.

Table 14. Commercial Fishing Modeling: Data Summary for Norton Basin.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 998 130 1,128

Water Area (HA M) 13.5 2.0 15.5

Cells with Revenue 252 0 252

Fishery Revenue (Real $ M) 1.6 0.0 1.6

Existing Platforms 0 0 0

Cells with Platforms 0 0 0

Notes:

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 15. Commercial Fishing Modeling: Fishery Impact Coefficients for Norton Basin.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 0.1850 -0.0001 0.0% 0.0%

D2 60m - 150m 0.2952 -0.0105 0.0% 0.0%

D3 150m - 300m 0.0000 0.0000

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.4889 -0.0047 0.0% 0.0%

D2 60m - 150m 0.0000 0.0000

D3 150m - 300m 0.0000 0.0000

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
76

Table 16. Commercial Fishing Modeling: Data Summary for Navarin Basin.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 17 635 167 107 217 1,143

Water Area (HA M) 0.3 10.6 2.9 1.9 3.9 19.4

Cells with Revenue 12 351 109 53 44 569

Fishery Revenue (Real $ M) 3.6 90.4 36.2 5.2 0.8 136.2

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 17. Commercial Fishing Modeling: Fishery Impact Coefficients for Navarin Basin.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 57.1660 -3.4547 0.0% 0.0%

D2 60m - 150m 5.4190 -0.0086 0.0% 0.0%

D3 150m - 300m 42.5967 -0.3323 0.0% 0.0%

D4 300m - 1,500m -6.3399 -0.1090 0.0% 0.0%

D5 1,500m + 2.0710 -0.0221 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 35.0192 -1.9134 0.0% 0.0%

D2 60m - 150m 21.1382 -0.2104 0.0% 0.0%

D3 150m - 300m 66.7881 -0.7613 0.0% 0.0%

D4 300m - 1,500m 0.0011 0.0000 35.4% 37.2%

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
77

Table 18. Commercial Fishing Modeling: Data Summary for St. Matthew Hall.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 1,145 283 14 1,442

Water Area (HA M) 17.7 4.8 0.2 22.7

Cells with Revenue 282 105 14 401

Fishery Revenue (Real $ M) 5.3 4.7 2.9 12.9

Existing Platforms 0 0 0 0

Cells with Platforms 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 19. Commercial Fishing Modeling: Fishery Impact Coefficients for St. Matthew Hall.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 0.3618 -0.0005 0.0% 0.0%

D2 60m - 150m -2.1276 0.0003 0.0% 18.9%

D3 150m - 300m 259.0227 -0.8606 0.0% 0.0%

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 1.1314 0.0004 0.0% 86.7%

D2 60m - 150m 0.0041 -0.0001 0.0% 0.0%

D3 150m - 300m 264.0219 0.1602 0.0% 57.1%

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
78

Table 20. Commercial Fishing Modeling: Data Summary for St. George Basin.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 216 774 102 151 304 1,547

Water Area (HA M) 3.7 14.5 2.0 3.0 6.0 29.2

Cells with Revenue 184 743 102 138 88 1,255

Fishery Revenue (Real $ M) 14.5 213.8 44.8 32.1 0.9 306.1

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 21. Commercial Fishing Modeling: Fishery Impact Coefficients for St. George Basin.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m -0.6107 -0.0199 0.00% 0.00%

D2 60m - 150m 6.5774 -0.0083 0.00% 0.00%

D3 150m - 300m 22.0001 -0.2567 0.00% 0.00%

D4 300m - 1,500m 25.7349 -0.1509 0.00% 0.00%

D5 1,500m + 2.3083 -0.0098 0.00% 0.00%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0153 -0.0004 0.00% 0.00%

D2 60m - 150m 23.0807 -0.1633 0.00% 0.00%

D3 150m - 300m 39.9362 -0.5776 0.00% 0.00%

D4 300m - 1,500m 36.8306 -0.2414 0.00% 0.00%

D5 1,500m + 0 0

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
79

Table 22. Commercial Fishing Modeling: Data Summary for North Aleutian Basin.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 596 262 858

Water Area (HA M) 9.5 5.0 14.5

Cells with Revenue 406 259 665

Fishery Revenue (Real $ M) 29.4 151.3 180.6

Existing Platforms 0 0 0

Cells with Platforms 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 23. Commercial Fishing Modeling: Fishery Impact Coefficients for North Aleutian Basin.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m -5.6643 0.0006 0.0% 24.5%

D2 60m - 150m 58.1161 -0.0771 0.0% 0.0%

D3 150m - 300m 0.0000 0.0000

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0000 0.0000

D2 60m - 150m 107.0198 -0.8597 0.0% 0.0%

D3 150m - 300m 0.0000 0.0000

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
80

Table 24. Commercial Fishing Modeling: Data Summary for Aleutian Basin.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 1 6 1,305 1,312

Water Area (HA M) 0.0 0.1 24.2 24.3

Cells with Revenue 1 4 5 10

Fishery Revenue (Real $ M) 0.3 0.5 0.0 0.8

Existing Platforms 0 0 0 0

Cells with Platforms 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 25. Commercial Fishing Modeling: Fishery Impact Coefficients for Aleutian Basin.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 0.0000 0.0000

D2 60m - 150m 0.0000 0.0000

D3 150m - 300m 0.0000 0.0000

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0000 0.0000

D2 60m - 150m 0.0000 0.0000

D3 150m - 300m 158.6262 0.0000 0.0%

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
81

Table 26. Commercial Fishing Modeling: Data Summary for Bowers Basin.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 1 1 3 87 1,859 1,951

Water Area (HA M) 0.0 0.0 0.1 1.8 37.1 39.0

Cells with Revenue 0 0 0 5 73 78

Fishery Revenue (Real $ M) 0.0 0.0 0.0 0.0 0.0 0.0

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 27. Commercial Fishing Modeling: Fishery Impact Coefficients for Bowers Basin.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 0.0000 0.0000

D2 60m - 150m 0.0000 0.0000

D3 150m - 300m 0.0116 -0.0041 0.0% 0.0%

D4 300m - 1,500m 0.0023 0.0000 0.0% 0.0%

D5 1,500m + 0.0019 0.0000 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0000 0.0000

D2 60m - 150m 0.0000 0.0000

D3 150m - 300m 0.0000 0.0000

D4 300m - 1,500m -0.0007 0.0000 0.0% 0.0%

D5 1,500m + 0.0027 0.0000 0.0% 19.7%

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
82

Table 28. Commercial Fishing Modeling: Data Summary for Aleutian Arc.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 122 119 86 293 5,289 5,909

Water Area (HA M) 1.8 2.5 1.8 6.2 116.5 128.7

Cells with Revenue 122 116 85 270 933 1,526

Fishery Revenue (Real $ M) 8.6 25.7 15.4 31.0 5.3 86.0

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 29. Commercial Fishing Modeling: Fishery Impact Coefficients for Aleutian Arc.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 15.8745 -1.1003 0.0% 0.0%

D2 60m - 150m 36.1643 -0.1989 0.0% 0.0%

D3 150m - 300m 16.0232 -0.2565 0.0% 0.0%

D4 300m - 1,500m 14.1891 -0.0146 0.0% 0.0%

D5 1,500m + -0.2148 0.0000 0.0% 1.4%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0000 0.0000

D2 60m - 150m 56.9089 -0.5447 0.0% 0.0%

D3 150m - 300m 26.4894 -0.4417 0.0% 0.0%

D4 300m - 1,500m 33.1780 -0.2485 0.0% 0.0%

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
83

Table 30. Commercial Fishing Modeling: Data Summary for Shumagin.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 188 192 84 56 1,406 1,926

Water Area (HA M) 2.6 3.8 1.6 1.1 29.8 38.9

Cells with Revenue 188 192 84 54 39 557

Fishery Revenue (Real $ M) 21.2 22.2 9.1 12.3 1.0 65.9

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 31. Commercial Fishing Modeling: Fishery Impact Coefficients for Shumagin.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 5.2925 -0.0303 0.0% 0.0%

D2 60m - 150m -1.4832 0.0055 0.0% 0.0%

D3 150m - 300m -6.1889 0.0656 0.0% 0.0%

D4 300m - 1,500m 7.2769 -0.0163 0.0% 0.0%

D5 1,500m + 0.1119 -0.0001 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 22.8590 -0.2496 0.0% 0.0%

D2 60m - 150m 8.2160 -0.1190 0.0% 0.0%

D3 150m - 300m 0.0910 -0.0022 1.6% 2.1%

D4 300m - 1,500m 18.2711 -0.0281 0.0% 0.0%

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
84

Table 32. Commercial Fishing Modeling: Data Summary for Kodiak.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 115 189 95 93 1,597 2,089

Water Area (HA M) 1.5 3.5 1.7 1.8 32.0 40.5

Cells with Revenue 115 189 95 85 32 516

Fishery Revenue (Real $ M) 15.6 43.7 22.8 23.4 1.3 106.9

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 33. Commercial Fishing Modeling: Fishery Impact Coefficients for Kodiak.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m -3.1155 -0.2197 0.0% 0.0%

D2 60m - 150m 9.9260 -0.0547 0.0% 0.0%

D3 150m - 300m 3.4458 -0.1301 0.0% 0.0%

D4 300m - 1,500m 33.9909 0.0812 0.0% 0.0%

D5 1,500m + 0.2553 -0.0003 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0004 0.0000 74.8% 75.7%

D2 60m - 150m 23.4770 -0.2662 0.0% 0.0%

D3 150m - 300m 14.2944 -0.2833 0.0% 0.0%

D4 300m - 1,500m 57.3254 -0.0684 0.0% 0.0%

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
85

Table 34. Commercial Fishing Modeling: Data Summary for Cook Inlet.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 231 33 62 1 327

Water Area (HA M) 2.2 0.6 1.1 0.0 3.9

Cells with Revenue 229 33 62 1 325

Fishery Revenue (Real $ M) 7.3 2.6 7.0 0.1 17.0

Existing Platforms 19 0 0 0 19

Cells with Platforms 9 0 0 0 9

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 35. Commercial Fishing Modeling: Fishery Impact Coefficients for Cook Inlet.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 0.0911 -0.1270 51.2% 0.0%

D2 60m - 150m 4.1009 -0.2174 0.0% 0.0%

D3 150m - 300m 53.6277 -0.1231 0.0% 0.0%

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0008 0.0000 68.8% 69.8%

D2 60m - 150m 3.3460 -0.1402 0.0% 0.0%

D3 150m - 300m 112.7147 -0.2888 0.0% 0.0%

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
86

Table 36. Commercial Fishing Modeling: Data Summary for Gulf of Alaska.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 732 241 277 165 2,072 3,487

Water Area (HA M) 5.0 4.2 4.9 3.1 39.8 57.0

Cells with Revenue 600 229 262 132 59 1,282

Fishery Revenue (Real $ M) 21.8 22.0 35.7 35.7 4.2 119.4

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 37. Commercial Fishing Modeling: Fishery Impact Coefficients for Gulf of Alaska.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 30.1512 -1.3129 0.0% 0.0%

D2 60m - 150m -3.7119 -0.0151 0.0% 0.0%

D3 150m - 300m -4.1361 -0.0166 0.0% 0.0%

D4 300m - 1,500m 71.5700 0.1964 0.0% 0.0%

D5 1,500m + 0.4133 -0.0003 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0000 0.0000

D2 60m - 150m 0.3350 -0.0081 0.0% 0.0%

D3 150m - 300m 13.7071 -0.2405 0.0% 0.0%

D4 300m - 1,500m 111.8007 -0.0521 0.0% 3.4%

D5 1,500m + 0.0414 -0.0004 0.0% 0.0%

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
87

8.7 SUMMARY TABLES OF FISHERY DATA AND REGRESSION COEFFICIENTS 

FOR WEST COAST PLANNING AREAS

This section provides fishery data and regression coefficients for West Coast planning areas.

Table 38. Commercial Fishing Modeling: Data Summary for Washington/Oregon.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 224 103 52 127 1,233 1,739

Water Area (HA M) 2.7 2.4 1.2 3.0 29.9 39.3

Cells with Revenue 53 88 47 81 3 272

Fishery Revenue (Real $ M) 1.3 24.4 15.7 39.7 0.2 81.3

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 39. Commercial Fishing Modeling: Fishery Impact Coefficients for Washington/Oregon.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 49.7169 -6.3516 0.0% 0.0%

D2 60m - 150m 13.3141 -0.3552 0.0% 0.0%

D3 150m - 300m 27.7859 -1.4289 0.0% 0.0%

D4 300m - 1,500m 27.8188 0.0261 0.0% 0.0%

D5 1,500m + 0.2841 -0.0004 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0000 0.0000

D2 60m - 150m 30.2198 -0.6552 0.0% 0.0%

D3 150m - 300m 27.5531 -0.9806 0.0% 0.0%

D4 300m - 1,500m 48.0148 -0.2051 0.0% 0.0%

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
88

Table 40. Commercial Fishing Modeling: Data Summary for Northern California.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 26 18 4 48 674 770

Water Area (HA M) 0.2 0.5 0.1 1.3 17.7 19.7

Cells with Revenue 9 16 4 38 0 67

Fishery Revenue (Real $ M) 0.4 2.1 2.1 8.0 0.0 12.5

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 41. Commercial Fishing Modeling: Fishery Impact Coefficients for Northern California.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 0.0000 9.6002 0.0%

D2 60m - 150m -2.3562 -0.7016 0.0% 0.0%

D3 150m - 300m 480.4073 -15.7714 0.0% 0.0%

D4 300m - 1,500m 15.2975 -0.3703 0.0% 0.0%

D5 1,500m + 0.1348 -0.0002 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0000 0.0000

D2 60m - 150m 0.0000 0.0000

D3 150m - 300m 512.8884 -12.0618 0.0% 5.9%

D4 300m - 1,500m 22.3296 -0.4914 0.0% 0.0%

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
89

Table 42. Commercial Fishing Modeling: Data Summary for Central California.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 28 24 4 27 651 734

Water Area (HA M) 0.4 0.6 0.1 0.7 17.8 19.6

Cells with Revenue 15 16 4 21 0 56

Fishery Revenue (Real $ M) 1.0 0.6 0.5 1.7 0.0 3.8

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 43. Commercial Fishing Modeling: Fishery Impact Coefficients for Central California.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 73.4460 -5.3453 0.0% 0.0%

D2 60m - 150m -2.0906 -0.1640 0.0% 0.0%

D3 150m - 300m 111.2114 -12.3236 0.0% 0.0%

D4 300m - 1,500m 1.2845 -0.1046 0.0% 0.0%

D5 1,500m + 0.0247 0.0000 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0000 0.0000

D2 60m - 150m 129.8943 -14.5836 0.0% 0.0%

D3 150m - 300m 2.4434 -0.1020 0.0% 0.0%

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 66.0736 -0.8313 0.0% 24.5%

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
90

Table 44. Commercial Fishing Modeling: Data Summary for Southern California.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 42 23 19 209 1,297 1,590

Water Area (HA M) 0.6 0.6 0.5 6.0 37.4 45.0

Cells with Revenue 42 23 19 196 2 282

Fishery Revenue (Real $ M) 1.5 1.0 0.5 2.4 0.0 5.3

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 45. Commercial Fishing Modeling: Fishery Impact Coefficients for Southern California.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 9.8322 1.7319 0.0% 0.0%

D2 60m - 150m 1.1087 -0.0887 0.0% 0.0%

D3 150m - 300m -1.2919 0.0156 0.0% 0.0%

D4 300m - 1,500m 1.2125 -0.0066 0.0% 0.0%

D5 1,500m + 0.0073 0.0000 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 23.0426 0.1588 0.0% 4.0%

D2 60m - 150m 0.2636 -0.0198 0.0% 0.0%

D3 150m - 300m 0.0304 -0.0020 5.4% 6.5%

D4 300m - 1,500m 1.4216 -0.0073 0.0% 0.0%

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
91

8.8 SUMMARY TABLES OF FISHERY DATA AND REGRESSION COEFFICIENTS 

FOR GULF OF MEXICO PLANNING AREAS

This section provides fishery data and regression coefficients for Gulf of Mexico planning areas. 

Table 46. Commercial Fishing Modeling: Data Summary for Western Gulf of Mexico.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 300 65 21 201 235 822

Water Area (HA M) 7.1 2.0 0.6 6.1 7.2 23.1

Cells with Revenue 238 52 19 177 106 592

Fishery Revenue (Real $ M) 103.4 22.6 8.3 74.4 45.5 254.1

Existing Platforms 544 88 7 10 2 651

Cells with Platforms 113 28 4 8 2 155

Notes: HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 47. Commercial Fishing Modeling: Fishery Impact Coefficients for Western Gulf of Mexico.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 41.1558 0.0856 0.0% 0.0%

D2 60m - 150m 17.1473 -0.4121 0.0% 0.0%

D3 150m - 300m 96.8024 -3.9112 0.0% 0.0%

D4 300m - 1,500m -5.9425 -0.0090 0.0% 0.0%

D5 1,500m + 2.1800 -0.0706 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 58.8368 -0.0017 0.0% 17.5%

D2 60m - 150m 15.4988 -0.3156 0.0% 0.0%

D3 150m - 300m 99.2532 -3.4300 0.0% 0.0%

D4 300m - 1,500m 1.6356 -0.0111 0.0% 0.0%

D5 1,500m + 5.2442 -0.0513 0.0% 0.0%

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
92

Table 48. Commercial Fishing Modeling: Data Summary for Central Gulf of Mexico.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 352 47 15 109 393 916

Water Area (HA M) 6.5 1.4 0.4 3.3 12.1 23.7

Cells with Revenue 268 47 15 109 293 732

Fishery Revenue (Real $ M) 153.5 40.4 26.1 180.3 402.7 803.1

Existing Platforms 2,185 190 13 30 8 2,426

Cells with Platforms 170 40 8 25 6 249

Notes: 

HA M is millions of hectares. 

Real $ M is millions of real dollars.

Table 49. Commercial Fishing Modeling: Fishery Impact Coefficients for Central Gulf of Mexico.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m -166.4307 0.6090 0.0% 0.0%

D2 60m - 150m 23.0788 -2.0727 0.0% 0.0%

D3 150m - 300m 929.2590 -13.1703 0.0% 0.0%

D4 300m - 1,500m 224.2500 -0.0830 0.0% 0.5%

D5 1,500m + 84.9431 -0.0305 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 2.5466 -0.0198 0.0% 0.0%

D2 60m - 150m 153.1490 -0.6535 0.0% 0.0%

D3 150m - 300m 1362.5590 -2.9497 0.0% 0.0%

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
93

Table 50. Commercial Fishing Modeling: Data Summary for Eastern Gulf of Mexico.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 470 130 81 145 474 1,300

Water Area (HA M) 12.3 4.0 2.5 4.4 14.6 37.7

Cells with Revenue 454 130 81 141 332 1138

Fishery Revenue (Real $ M) 64.4 17.7 9.4 22.3 54.4 168.2

Existing Platforms 3 0 0 1 0 4

Cells with Platforms 2 0 0 1 0 3

Notes: 

HA M is millions of hectares. 

Real $ M is millions of real dollars.

Table 51. Commercial Fishing Modeling: Fishery Impact Coefficients for Eastern Gulf of Mexico.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m -0.5223 0.0013 0.0% 0.0%

D2 60m - 150m 0.2414 -0.0059 0.0% 0.0%

D3 150m - 300m -0.9243 0.0027 0.0% 1.8%

D4 300m - 1,500m 2.1622 -0.0080 0.0% 0.0%

D5 1,500m + 0.7573 -0.0020 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0147 -0.0001 0.0% 0.0%

D2 60m - 150m 0.1527 -0.0014 0.0% 0.0%

D3 150m - 300m 0.0908 -0.0014 0.1% 0.2%

D4 300m - 1,500m 6.2401 -0.0071 0.0% 0.0%

D5 1,500m + 2.2147 -0.0045 0.0% 0.0%

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
94

8.9 SUMMARY TABLES OF FISHERY DATA AND REGRESSION COEFFICIENTS 

FOR EAST COAST PLANNING AREAS

This section provides fishery data and regression coefficients for the East Coast planning areas. The 

models for the Mid- and North Atlantic differ from models for other regions in that they assume that 

unburied pipelines can occur in D2 and D3 (from 60-300 m) and create an additional, one-half mile wide 

buffer zone that precludes scallop dredges from operating.

Table 52. Commercial Fishing Modeling: Data Summary for Straits of Florida.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 89 16 31 193 14 343

Water Area (HA M) 2.1 0.5 1.0 6.0 0.4 10.0

Cells with Revenue 51 12 27 90 6 186

Fishery Revenue (Real $ M) 20.1 8.7 29.4 67.2 0.6 126.0

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares., 

Real $ M is millions of real dollars.

Table 53. Commercial Fishing Modeling: Fishery Impact Coefficients for Straits of Florida.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m -17.8965 -7.6203 0.0% 0.0%

D2 60m - 150m 39.8724 -17.8959 0.0% 0.0%

D3 150m - 300m 519.6629 -2.6130 0.0% 0.0%

D4 300m - 1,500m 100.4700 -0.1348 0.0% 0.0%

D5 1,500m + 0.0000 0.0000

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 0.0000 0.0000

D2 60m - 150m 0.0000 0.0000

D3 150m - 300m 591.6000 -3.3874 0.0% 0.0%

D4 300m - 1,500m 136.0181 -0.5571 0.0% 0.0%

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
95

Table 54. Commercial Fishing Modeling: Data Summary for South Atlantic.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 317 18 31 432 124 922

Water Area (HA M) 6.8 0.5 0.9 12.8 3.7 24.8

Cells with Revenue 258 18 29 379 30 714

Fishery Revenue (Real $ M) 44.1 2.5 1.8 33.0 2.7 84.0

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares. 

Real $ M is millions of real dollars.

Table 55. Commercial Fishing Modeling: Fishery Impact Coefficients for South Atlantic.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 13.0727 0.0160 0.0% 0.0%

D2 60m - 150m 10.6494 -0.4229 0.0% 0.0%

D3 150m - 300m 6.9703 -0.4178 0.0% 0.0%

D4 300m - 1,500m -3.5769 0.0053 0.0% 0.0%

D5 1,500m + 4.5347 -0.0707 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 25.8040 -0.0574 0.0% 0.0%

D2 60m - 150m 0.5119 -0.0333 0.0% 0.0%

D3 150m - 300m 0.0000 0.0000

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
96

Table 56. Commercial Fishing Modeling: Data Summary for Mid-Atlantic.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 427 38 17 99 1,523 2,104

Water Area (HA M) 7.9 1.1 0.5 2.8 43.5 55.7

Cells with Revenue 427 38 17 99 1523 2104

Fishery Revenue (Real $ M) 193.8 63.9 0.1 35.1 54.9 347.9

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 57. Commercial Fishing Modeling: Fishery Impact Coefficients for Mid-Atlantic.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 338.5462 0.1483 0.0% 0.0%

D2 60m - 150m 969.9951 -23.2053 0.0% 0.0%

D3 150m - 300m 184.4773 -14.5967 0.0% 0.0%

D4 300m - 1,500m 13.8066 -0.4718 0.0% 0.0%

D5 1,500m + -7.6396 -0.0009 0.0% 0.6%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 485.5860 -0.7344 0.0% 0.0%

D2 60m - 150m 872.2275 -17.9010 0.0% 0.0%

D3 150m - 300m 0.0000 0.0000

D4 300m - 1,500m 0.2734 -0.0068 0.1% 0.1%

D5 1,500m + 0.7569 -0.0171 0.0% 0.0%

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
97

Table 58. Commercial Fishing Modeling: Data Summary for North Atlantic.

Variable 0m - 60m 60m - 150m 150m - 300m 300m - 1,500m 1,500m + Total

Cell by Depth Range 469 320 246 64 1,022 2,121

Water Area (HA M) 8.6 8.2 6.2 1.7 27.4 52.1

Cells with Revenue 469 320 246 64 1022 2121

Fishery Revenue (Real $ M) 924.7 649.6 119.0 34.7 17.7 1,745.8

Existing Platforms 0 0 0 0 0 0

Cells with Platforms 0 0 0 0 0 0

Notes: 

HA M is millions of hectares.

Real $ M is millions of real dollars.

Table 59. Commercial Fishing Modeling: Fishery Impact Coefficients for North Atlantic.

Regression Coefficients P-values (Significant if <5%)

Depth Band Depth Range a b a b

Use these coefficients if platforms will be placed in multiple depth ranges

D1 0m - 60m 42.8581 -0.1396 0.0% 0.0%

D2 60m - 150m 53.5270 -0.3556 0.0% 0.0%

D3 150m - 300m 27.8205 -0.2662 0.0% 0.0%

D4 300m - 1,500m 363.6121 -8.6874 0.0% 0.0%

D5 1,500m + 11.1531 -0.0176 0.0% 0.0%

Use these coefficients if platforms will be placed in only one depth range

D1 0m - 60m 125.1157 -0.7609 0.0% 0.0%

D2 60m - 150m 551.4754 -5.8763 0.0% 0.0%

D3 150m - 300m 63.8554 -0.6173 0.0% 0.0%

D4 300m - 1,500m 0.0000 0.0000

D5 1,500m + 0.0000 0.0000

Note: Regressions coefficients should be used in the regression equation Y = aixi + bixi

2

, where Y are cost impacts to commercial 

fisheries and xi are the number of platforms that will be developed in depth range i. If no p-values are provided and the 

coefficients are shown as zero, then no impacts are estimated for platforms in the depth range.
98

8.10 REFERENCES – COMMERCIAL FISHING

Aquamaps. 2010. Standardized Distribution Maps for 11,500 Species of Fishes, Marine Mammals and 

Invertebrates. Accessed December 9, 2010. http://www.aquamaps.org/.

California Department of Fish and Game (CDFG). 2007. Information Concerning the Pink Shrimp Trawl 

Fishery off Northern California. Report to the California Fish and Game Commission. California 

Department of Fish and Game, Marine Region (December 24, 2007).

Hiatt, T., M. Dalton, R. Felthoven, B. Garber-Yonts, A. Haynie, S. Kasperski, D. Lew, C. Package, L. 

Pfeiffer, J. Sepez, C. Seung, and Northern Economics, Inc. 2009. Stock Assessment and Fishery 

Evaluation Report for the Groundfish Fisheries of the Gulf of Alaska and Bering Sea/Aleutian Island 

Area: Economic Status of the Groundfish Fisheries off Alaska, 2008. Seattle, WA: Alaska Fisheries 

Science Center, Resource Ecology and Fisheries Management Division, Economic and Social 

Sciences Research Program; NMFS; and NOAA. Accessed December 18, 2009. 

http://www.afsc.noaa.gov/REFM/docs/2009/economic.pdf.

Huntsman, D. 2010. Alaska Commercial Fishing Entry Commission. Data as requested on Alaska king 

and tanner crab landings and values by statistical area, 2006-2009. August 4, 2010.

Jamir, T. 2010. NMFS Southeast Science Center. Data on harvests and values by statistical areas in the 

Gulf of Mexico and the South Atlantic, 2006-2009. November 29, 2010.

Kong, T. 2010. International Pacific Halibut Commission. Data as requested on halibut landings by IPHC 

statistical area, 2006-2009. October 6, 2010.

Kruse, G. 2010. Professor; University of Alaska Fairbanks. Personal Communication with M. Hartley, 

Interviewer. April 25, 2010.

Labelle, R. 2010. Acting Associate Director for Offshore Energy and Minerals Management. Data 

request to Dr. Bonnie Ponwith, Science Director NMFS, Southeast Fishery Science Center. 

September 28, 2010.

Lewis, S. 2010. NMFS Alaska Region. Data as requested on Alaska groundfish harvests and values by 10 

× 10 cells, 2006-2009. July 8, 2010.

Minerals Management Service (MMS). 1990. Economic Impacts of the S.S. Glacier Bay Oil Spill. MMS 

90-0081 (Anchorage, Alaska, November 1990).

National Marine Fisheries Service (NMFS). 2010. Annual Landings Statistics, 2006-2009. NMFS, Office 

of Science and Technology, Fisheries Statistics Division. Accessed November and December 2010. 

http://www.st.nmfs.noaa.gov/st1/commercial/landings/annual_landings.html.

PacFIN. 2010a. PacFIN - Pacific Fishery Management Council (PFMC) Gear Group Reports. Report 

#009: PFMC Gear Group Report: Groundfish Landed-catch (Metric Tons) for Each INPFC Area, for 

Year 2009. Accessed October 5, 2010. http://pacfin.psmfc.org/pacfin_pub/pfmc.php.

PacFIN. 2010b. PacFIN - Pacific Fishery Management Council (PFMC) Gear Group Reports. Report 

#074: PFMC Gear Group Report: Commercial Groundfish Estimated Revenue ($1000) for All Areas, 

for Year 2009. Accessed October 5, 2010. http://pacfin.psmfc.org/pacfin_pub/pfmc.php.
99

Stenberg, B. 2010. Pacific Fisheries Information Network (PacFIN). Data as requested from West Coast 

trawl logbooks, 2006-2009. November 15, 2010.

Thunberg, E. 2010. NMFS Northeast Fishery Science Center. Data as requested from logbooks and dealer 

reports on harvests and values by fishery in the North and Mid-Atlantic, 2006-2009. July 20, 2010.

Tuttle, V. 2010. NMFS Northwest Fisheries Science Center. Data as requested on offshore Pacific 

whiting landings by 10 × 10 cells, 2006-2009. July 15, 2010.

U.S. Bureau of Labor Statistics (BLS). 2010. Producer Price Index (PPI) for Unprocessed and Packaged 

Fish. Accessed August 21, 2010. http://www.bls.gov/ppi/.
100

9 ECOLOGICAL 

9.1 OVERVIEW

To assess ecological costs associated with offshore oil and natural gas development, the 2001 version of 

the OECM employed a habitat equivalency analysis (HEA)-based, restoration cost approach to 

determining dollar damages. The revised OECM uses a similar approach. However, it updates (1) the 

restoration cost data used in the OECM, and (2) the way the restoration cost data are applied and damages 

calculated.

Consistent with the standard economic view of natural resources as assets that provide flows of services, 

ecosystems are understood to provide a flow of ecosystem services. These services are valued by society, 

as demonstrated by the willingness to pay (WTP) for their protection and/or enhancement. Changes in 

the quality or quantity of these services, due to ecosystem injuries caused by oil spills and/or 

development, have implications in terms of the value of the benefits they provide. 

One way to estimate the economic value of services adversely affected by offshore oil and natural gas 

development would be to conduct an original economic valuation study or apply dollar values from the 

existing literature. In the context of natural resource damage assessment (NRDA), the use of economic 

valuation techniques to scale the monetary compensation required for the interim loss of natural resource 

services establishes the sum of money that will be available to accomplish additional, “compensatory”

restoration of injured natural resources. In other words, economic valuation determines the amount of 

money available for restoration actions that have not yet been defined. 

Among valuation approaches, stated preference methods are the only tools available for eliciting non-use 

values (i.e., economic value that is not associated with direct use of a resource) from the public. The 

strength of stated-preference methods is the ability to pose to a respondent any hypothetical scenario; the 

method is not limited to observing behaviors under limited actual conditions. However, given the 

national scope of the OECM and the challenge of conducting a large-scale economic valuation study to 

ascertain potential geographic variability of values, such an approach would be incredibly complex and 

financially prohibitive. Stated preference methods also remain controversial when applied to elicit 

values.36 As noted in the U.S. EPA’s guidance document for preparing economic analyses: 

Concerns about the reliability of value estimates that come from CV [contingent 

valuation] studies have dominated debates about the methodology, since research has 

shown that bias can be introduced easily into these studies, especially if they are not 

carefully done. In particular, the concern that CV surveys do not require respondents to 

make actual payments has led critics to argue that responses to CV surveys are biased 

because of the hypothetical nature of the good. Reliability tests on the data that conform 

to expectations from both economic and psychological theory can enhance the credibility 

of a CV survey. Surveys without these tests should be suspect; surveys whose results fail 

the tests may be discredited. (U.S. EPA, 2000, page 83)

This limitation also applies to benefits transfers that apply existing valuation estimates from the stated 

preference literature. 

 

36 The application of survey-based approaches for use-values, such as understanding how, and how often, members 

of a community use a resource, is generally accepted, especially when issues such as recall bias and strategic 

responses are addressed. 
101

In many instances in NRDA, instead of applying economic valuation tools, natural resource trustees will 

identify and scale appropriate compensatory restoration actions. These actions are scaled to make the 

public whole for interim losses of natural resource services. Restoration is intended to compensate the 

public for any and all resource services lost due to injury.37 Restoration costs also can be viewed as costs 

for which the public has demonstrated a willingness to pay, and therefore are believed to be a lower 

bound estimate of WTP, or the “value” of lost services, as long as restoration actions are scaled 

appropriately to match the magnitude of lost resources. In NRDA, when a restoration cost approach is 

taken, dollar damages are the cost to implement the necessary compensatory restoration projects.

Resource economists commonly use HEA, or a variant of HEA called resource equivalency analysis 

(REA) to scale restoration projects. HEA is an analytical tool specifically designed to balance the 

magnitude of restoration (or service credit) with the magnitude of resource loss (or service debit). REA, 

as an extension of HEA, is an analytical tool by which restoration aimed at a specific resource (e.g., fish 

or birds) may be scaled to appropriately compensate for injury to that specific resource. One of the 

primary economic notions behind the use of HEA and REA is that natural resources can and should be 

discounted over time to account for changes in the value the public holds for material goods, or in this 

case, resource services, over time (i.e., the time-value of money). Compounding natural resource service 

losses or gains in the past and discounting future resource services, as one would similarly adjust dollar 

values in any economic analysis, allows for the integration of resource service value over time. In this 

way service credits and debits can be balanced in present value terms using units that incorporate space 

and time (e.g., acre-years of habitat, or in the case of a REA, units such as bird-years). 

In the context of the OECM, an important strength of HEA relative to the economic valuation techniques 

outlined above is that HEA may be broadly applied to large areas with relative ease. Although the cost of 

restoration projects depends on factors such as the existing condition of the area to be restored, these costs 

do not vary significantly between different areas. In addition, because HEA provides a lower bound 

estimate of WTP, there is less uncertainty in the directional bias of HEA-based estimates than from 

estimates derived from primary economic valuation methods or benefits transfer. 

It is important to note that economic valuation and equivalency-based approaches both require a detailed 

understanding of the underlying ecological injury and changes in service flows. That is, these approaches 

are not a substitute for sound injury determination and injury quantification. They are a distinct means, 

however, to establishing the scale of restoration, since they involve valuation of service losses, or at least 

economic tradeoffs. While habitat and resource equivalency do not involve valuation, they ultimately 

involve the development of restoration cost estimates.

The types of assessment approaches that are required to apply economic valuation of ecological changes 

and/or value equivalency approaches are different than those used for habitat and resource equivalency. 

They might include benefits transfer (i.e., application of values from the published literature), or primary 

research involving focus groups and stated preference surveys of the public. As noted above, the cost of 

applying primary economic techniques can be substantial, and these studies can take significant time to 

complete.

 

37 Importantly, this compensation would be in addition to any actions that have been or will be taken to restore the 

injured habitat to its baseline condition (i.e., remedial actions or so-called “primary” restoration). In some cases 

(e.g., smaller oil spills in remote locations that may go unnoticed), no primary restoration will take place, leading to 

longer time periods of injury until resource services are returned to their baseline condition. In the case of the 

OECM, oil spills are assumed to naturally degrade over time or are cleaned up such that adverse impacts resolve 

within 3 to 5 years (French-McCay, 2009).
102

In the context of the OECM, the use of HEA (and REA), in combination with restoration costs (as a lower 

bound estimate of the value the public holds for ecological resources) provides a robust way to quantify 

damages stemming from injuries caused by a range of potential ecological impacts of offshore oil and gas 

development. 

9.2 OVERVIEW OF THE 2001 MODEL

For calculations of damages from adverse ecological impacts of offshore oil and natural gas development, 

the 2001 OECM model relies generally on a HEA (and REA)/restoration cost-based approach. 

Specifically:

 It focuses exclusively on ecological impacts from modeled oil spills. It does not quantify any 

other potential causes of ecological harm associated with offshore oil and natural gas 

development, such as noise and vibration, impacts associated with the physical destruction or 

displacement of resources, etc.

 It uses the NRDAM/CME model (i.e., the NRDA Type A model) to forecast ecological injuries 

stemming from three “average” modeled oil spill scenarios for each region: small spill, large 

platform/pipeline spill, and large tanker spill.

 Outputs from the NRDAM/CME model take the form of acre-years of habitat38 oiled—broken 

down by sand beach, wetland, mudflat, rocky coast, and gravel beach—and total numbers of 

wildlife killed, which is calculated based on the area of habitat oiled and region-specific wildlife 

density information, and is broken down by birds, marine mammals, and reptiles killed. Outputs 

are by region and are single-point estimates for each of the three “average size” spill scenarios.

 Based upon a single, generic, credit HEA for a hypothetical salt marsh restoration project with a 

fixed 25-year lifespan, and a 2:1 compensation ratio that accounts for services provided by the 

habitat prior to restoration, a fixed benefit of 4.23 acre-years is determined and relied upon in 

damages calculations nationwide.

 Per-acre low and high restoration costs to restore each of the habitat and wildlife categories noted 

above are determined from restoration costs spent at similar sites (or to replace similar wildlife 

through restocking) presented in a variety of documents, but they rely heavily on NOAA’s

“Primary Restoration: Guidance Document for Natural Resource Damage Assessment under the 

Oil Pollution Act of 1990” (http://www.darrp.noaa.gov/library/1_d.html). The simplifying 

assumption that the high estimate for restoration costs for salt marsh represents the upper-bound 

cost for all restoration projects is used to justify cost ranges. When high or low cost estimates are 

unavailable in the literature, the assumption that high range costs are approximately five to seven 

times greater than low cost estimates (based on salt marsh data) is used to estimate whichever end 

of the range is missing.

 Per-acre low and high restoration costs (which are assumed to provide 4.23 acre-years of benefits 

per acre of habitat restored; or expressed simply on a per-bird, per-reptile, or per-marine mammal 

basis), are converted to damages estimates based on NRDAM/CME-output estimates of habitat or 

wildlife injured per BBO spilled on a regional basis. Damages are expressed as either low or high 

 

38 An “acre-year” is a measure of the ecological services provided by one acre of habitat over the course of one year. 

Actual output from the model is presented in square-meter-days, a unit that is readily converted to acre-years 

through simple area- and time-conversions. What is relevant about this approach is that injury is expressed on a 

time and area basis.
103

by applying the low or high restoration costs, respectively, and they are expressed on a per-BBOproduced basis when used in OECM calculations.

 Resultant damages are increased by 9 percent to account for NRDA administrative costs; a 

percentage calculated based on cost components from six NRDAs.39

9.3 MODIFICATIONS TO THE 2001 MODEL

In general, an HEA and REA/restoration cost-based approach for assessing ecological damages from 

offshore development continues to be appropriate. However, several important updates are incorporated 

into the revised model.

 Instead of the NRDAM/CME model, Applied Science Associate’s more recent SIMAP model is 

used to forecast the likely scale of ecological injury stemming from oil spills. Further, SIMAP 

has been run iteratively to produce functional relationship equations for predicting the scale of 

injury as a function of volume of oil spilled and season for use in the OECM. Specifically, injury 

is determined by estimating

o aerial extent of surficial oiling of intertidal habitat, and

o on a wildlife-class-by-wildlife-class basis, the biomass of wildlife killed as a result of 

oiling.

 Restoration cost estimates have been updated by

o utilizing restoration cost data beyond those used in the 2001 model,

o applying actual restoration costs as opposed to NRDA settlement amounts, and

o incorporating geographic differences in restoration costs.

 Rather than using a single compensation ratio, the model applies information about the relative 

productivity of habitats and more realistic estimates of restoration project lifetimes and expected 

service benefits (see below).

 Cost estimates exclude administrative cost components.

As in the 2001 model, the revised OECM addresses only those adverse ecological impacts caused by oil 

spills. Although other adverse ecological effects likely occur as a result of offshore development (for 

example, adverse effects from noise and vibration and wildlife kills related to collisions with offshore 

structures have been evaluated in the context of programmatic EISs), reliable methods to quantify such 

impacts on a planning area basis currently are unavailable. The OECM does not assess any impacts 

related to onshore construction and development-related projects.

40

 The model considers only the 

ecological costs of exploration, development, and transportation of OCS resources to shore, as these 

activities are within Department of the Interior jurisidiction. The development of any new onshore 

infrastructure beyond existing infrastructure would be analyzed through future permitting-related 

activities and, given the uncertainties about the scope of onshore infrastructure construction required 

under program scenarios, are not incorporated into the OECM. Finally, estimation of ecological costs in 

 

39 Note that this calculation is made ex post facto, on a regional basis, outside of the actual OECM.

40 The OECM does include the air emissions resulting from the development of an onshore pipeline to transport oil 

produced in the Chukchi Sea. This is discussed in Chapter 4.
104

an international context (i.e., costs that might be realized in non-U.S. jurisdictions due to an increase or 

decrease in U.S. oil or gas imports) is beyond the current scope of this effort.
105

9.4 ECOLOGICAL DAMAGES CALCULATIONS

The calculation of ecological damages in the OECM is performed in five steps.

1) The extent of oiling is estimated using regression equations generated through the process of 

running SIMAP iteratively. These regressions are used to forecast the extent of oiling based on a 

variety of factors, but predominantly oil production.

2) Habitat impacts (extent of intertidal zone oiling) are calculated.

3) Wildlife impacts:

a. Numbers of individual wildlife organisms killed are calculated from wildlife abundance 

in sea and shoreline areas oiled above mortality thresholds.

b. Biomass of biota killed is calculated based on the number of individual organisms killed 

and the average mass of the given organism.

c. Information about the average regional primary productivity of salt marsh habitat and the 

trophic transfer of biomass up the food chain is used to calculate the salt marsh habitat 

acre-equivalent of biomass loss. 

4) Using HEA, the number of acres of salt marsh restoration that is required to replace injured 

habitat from Step 2 above and to replace the acre-equivalent of habitat from Step 3 above is 

calculated. (See discussion of model drivers below for more details.)

5) Impacts are monetized by determining the cost of restoring the required area of salt marsh 

determined by HEA in Step 4.

9.4.1 Basic calculation

The model develops an estimate of ecological damages based on the two equations detailed below, which 

are specific to a given planning area.

First, habitat damages are calculated using the equation

 

where:

O = Area of intertidal habitat over which spill impact occurs (m2 of oiled surface area)

RHEA = Habitat restoration factor (m2of marsh habitat required to be restored per m2of oiled surface 

area)

C = Per-m

2

-restoration cost (dollars per m2to restore marsh habitat)

Second, wildlife damages are calculated using the equation

where:

ORHEA C

(O M R C) i i REAi
106

Oi = Area or volume of spill impact (m2 of oiled surface area for wildlife species or m3of water for 

fish and macro-invertebrates above a mortality threshold for species i)

M = Mortality factor for the mass of species killed per unit area or volume of spill impact (kg lost per 

m

2

or m3of spill impact for species i)

RREAi = Habitat restoration factor (m2of marsh habitat required to be restored per kg lost of species i)

C = Per-m

2

-restoration cost (dollars per m2to restore marsh habitat)

9.4.2 Calculation drivers

Area or volume of water in which spill impact occurs

SIMAP quantifies areas swept by floating oil of varying thicknesses and the fates and concentrations of 

subsurface oil components (dissolved and particulate). Regressions on the results of multiple SIMAP 

iterations in representative regions, simulating a range of oil types, volumes, spill distance from shore, 

and environmental conditions, produce equations that generally relate spill volume to water area or water 

column volume exposed to oil above a specified impact threshold.

Mortality factors

SIMAP calculates the oil/hydrocarbon exposure, dose, and resulting percent mortality for organisms in 

the contaminated exposure areas (wildlife) and water volumes (fish, invertebrates). SIMAP applies these 

results to region-specific biological databases, which describe population densities for each of several 

organism types, to arrive at mortality factors per unit of water area or water volume. For the ecological 

component of the OECM, species-level data are aggregated into the following biological groups.

 Birds: waterfowl, seabirds, wading birds, shorebirds, raptors, and kingfishers

 Whales: baleen and piscivorous

 Other marine mammals: pinnipeds and sea otters

 Marine invertebrates: crustaceans and mollusks

 Fish: small pelagic fish, large pelagic fish, demersal fish

 Polar bears (Alaska planning areas only)

Habitat Equivalency Analysis (HEA) restoration factor

A modified HEA is used to estimate the quantity of restored habitat required to compensate for habitat 

areas injured by oiling. Rather than focusing on intertidal habitat area alone (e.g., acres), area is adjusted 

based on invertebrate production. The approach, equations, and assumptions are described in greater 

detail in NOAA (1997, 1999), LA DEQ et al. (2003), and French McCay and Rowe (2003). 

In the case of the OECM, habitat impacts (debit or loss side of the analysis) are quantified when 

saltmarsh, mangrove, rocky shore, gravel and sand beaches, and mudflat habitats are oiled with sufficient 

oil to adversely affect invertebrates associated with the intertidal habitat. A greater than 0.1 mm thickness 

results in invertebrate injuries. Benthic invertebrate production rates for each habitat type are taken into 

account when determining injury. Time for recovery for intertidal invertebrates (based on a natural 

recovery curve) is estimated as 3 to 5 years (French-McCay, 2009). The total loss of intertidal 

invertebrates from shoreline oiling greater than 0.1 mm thick is calculated as a factor of daily production 
107

rate, taking into consideration the number years to recovery and applying an annual discount rate of 3

percent. 

The area (m2) of salt marsh requiring restoration per m2of habitat oiled is calculated by scaling benthic 

invertebrate production gains afforded by such restoration to losses. Gains in invertebrate production 

provided by an area of restored salt marsh (the credit or gain side of the analysis) are calculated by 

multiplying the kilograms of benthic invertebrate production by the area (m2) of marsh restored. 

This HEA calculation was performed for habitat types in which a benthic invertebrate injury would occur,

such as rocky shore, sand beach, gravel beach, macroalgal bed, fringing mudflat, and fringing wetland. In 

order to get one estimate for intertidal injury per OECM geographic region, a weighted average of the 

area of salt marsh restored per m2oiled for these individual habitats was calculated based on the percent 

of that habitat type present in the entire habitat grid for the particular OECM region.

Resource Equivalency Analysis (REA) restoration factor

In addition to general habitat impacts, REA restoration factors, which are derived using a combined REAtrophic web model, are used to calculate the required area of restored habitat to produce biomass lost due 

to an oil spill. As noted above, the basis for using this model is that restoration should provide equivalent 

quality fish, wildlife, and invertebrate biomass to compensate for lost fish, wildlife, and invertebrate 

production. Equivalent quality implies the same or similar species with an equivalent ecological role. 

Equivalent production or replacement that occurs in the future is discounted to account for the interim 

loss between the time of the injury and the time when restoration provides equivalent ecological services.

Scaling methods used here initially were developed for use in the North Cape oil-spill damage 

assessment, as described in French et al. (2001), French McCay and Rowe (2003), and French McCay et 

al. (2003a). These methods also have been used in several other cases, as well as in 23 successful claims 

submitted by the Florida Department of Environmental Protection to the U.S. Coast Guard, National 

Pollution Funds Center (French McCay et al., 2003b). 

The concept is that the restored habitat leads to a net gain in wildlife, fish, and invertebrate production 

over and above that produced by the location before the restoration. In a manner similar to the HEA 

described above, the size of the habitat on an area basis is scaled to compensate for the injury (interim 

loss), with primary production used to measure the benefits of the restoration. However, in this case, the 

transfer of production up the food web is taken into consideration. Specifically, total injuries in kilograms

are translated into equivalent plant (angiosperm) production as follows. 

1. Plant biomass passes primarily through the detrital food web via detritivores consuming the plant 

material and attached microbial communities. When macrophytes are consumed by detritivores, 

ecological efficiency is low because of the high percentage of structural material produced by the 

plant, which must be broken down by microorganisms before it can be used by the detritivore. 

2. Each species group is assigned a trophic level relative to that of the detritivores. If the species 

group is at the same trophic level as detritivores, it is assumed 100 percent equivalent, as the 

resource injured would presumably have the same ecological value in the food web as the 

detritivores. If the injured resource preys on detritivores or that trophic level occupied by the 

detritivores, the ecological efficiency is that for trophic transfer from the prey to the predator. 

Values for production of predator per unit production of prey (ecological efficiency) are taken 

from the ecological literature, as reviewed by French McCay and Rowe (2003). 
108

3. The equivalent compensatory amount of angiosperm (plant) biomass of the restored resource is 

calculated as kilogram of injury divided by ecological efficiency, which is the product of the 

efficiency of transfer from angiosperm to invertebrate detritivore and efficiency from detritivore 

to the injured resource, accounting for each step up the food chain from detritivore to the trophic 

level of concern. The productivity gained by the created habitat is corrected for less than full 

functionality during recovery using a sigmoid recovery curve. 

4. Discounting at 3 percent per year is included for delays in production because of development of 

the habitat, and delays between the time of the injury and when the production is realized in the 

restored habitat. The equations and assumptions may be found in French McCay and Rowe 

(2003).

Additional data needs for the scaling calculations are as follows.

 Number of years for development of full function in a restored habitat

 Annual primary production rate per unit-area (P) of restored habitat at full function (which may 

be less than that of natural habitats)

 Delay before restoration project begins

 Project lifetime (years the restored habitat will provide services)

In this case, it is assumed that marsh creation or restoration is performed, that the marsh requires 15 years 

to reach full function (based on LA DEQ et al., 2003) and ultimately reaches 80 percent of natural habitat 

productivity, and that the project lifetime is 20 years. The restoration creation project is assumed to begin 

three years after the date of injury. Primary production estimates, which are regionally specific, are 

detailed below.

 North/Mid-Atlantic: Above-ground primary production rates for a New England salt marsh were 

used from Nixon and Oviatt (1973) as 500 g dry weight m-2 yr-1. In addition, benthic microalgal 

production provides another 105 g dry weight m-2(Van Raalte et al., 1976). Thus, estimated total 

primary production rate in salt marshes in this region is 605 g dry weight m-2 yr-1. 

 Gulf of Mexico and South Atlantic: Above-ground primary production rates of salt marsh cord 

grasses in Georgia were used as estimated by Nixon and Oviatt (1973), based on Teal (1962), as 

1,290 g dry weight m-2 yr-1. In addition, benthic microalgal production provides another 105 g 

dry weight m-2(Van Raalte et al., 1976). Thus, estimated total primary production rate in salt

marshes in this region is 1,395 g dry weight m-2 yr-1. 

 Northern, Central, and Southern California: Above-ground primary production rates of salt

marshes in the Central California coast were used as estimated by Continental Shelf Associates

(CSA) (1991) as 3,666 g dry weight m-2 yr-1. In addition, benthic microalgal production provides 

another 312 g dry weight m-2(CSA, 1991). Thus, estimated total primary production rate in salt

marshes in this region is 3,978 g dry weight m-2 yr-1.

 Washington and Oregon: Above-ground primary production rates of salt marshes on the Oregon 

coast were used as estimated by CSA (1991) as 2,636 g dry weight m-2 yr-1. In addition, benthic 

microalgal production provides another 375 g dry weight m-2(CSA, 1991). Thus, estimated total 

primary production rate in salt marshes in this region is 3,011 g dry weight m-2 yr-1.

 Gulf of Alaska: Above-ground primary production rates of salt marshes in the Lower Cook Inlet 

were used as estimated by CSA (1991). The daily rates were applied to a 6-month growing 
109

season, with the annual total being 681 g dry weight m-2 yr-1. In addition, benthic microalgal 

production over a 6-month growing season provides another 1,488 g dry weight m-2(CSA, 1991). 

Thus, estimated total primary production rate in salt marshes in this region is 2,170 g dry weight 

m

-2 yr-1

.

 Northern Alaska: Above-ground primary production rates of salt marshes in the Lower Cook Inlet 

were used as estimated by CSA (1991). The daily rates were applied to a 3-month growing 

season, with the annual total being 341 g dry weight m-2 yr-1. In addition, benthic microalgal 

production over a 6-month growing season provides another 744 g dry weight m-2(CSA, 1991). 

Thus, estimated total primary production rate in salt marshes in this region is 1,085 g dry weight 

m

-2 yr-1

.

For the injured resources, all weights are as wet weight; dry weight is assumed to be 22 percent of wet 

weight (Nixon and Oviatt, 1973). The ratio of carbon to dry weight is assumed to be 0.45 (French et al., 

1996). For the wildlife, body mass per animal (from French et al. [1996] or from Sibley [2003]) is used 

to estimate injury in kilograms, multiplying by number killed and summing each species category. 

Restoration cost factor

Planning area-specific per-acre coastal marsh restoration costs are applied in the OECM. These costs are 

derived through the estimation of salt marsh restoration project costs in the Northeast, the region for 

which the most data are available for coastal marsh restoration costs, and the extrapolation of these costs 

to other geographical regions based on a recent survey of nationwide wetland restoration costs. 

Specifically, data from a meta-analysis of wetland restoration costs by Louis Berger & Associates, Inc.

(1997) is combined with data on seven marsh creation projects provided by Carl Alderson (2010) of the 

NOAA Restoration Center. Costs are extrapolated geographically based on data presented in a report by 

the Environmental Law Institute (ELI) (2007). All costs are updated to 2009 dollars. 

The Louis Berger study presents cost data for two sets of projects. The first set includes 65 restoration 

and creation projects for several wetland types in the States of New York, Rhode Island, Maine, 

Massachusetts, Connecticut, and New Hampshire. Data from the subset of 11 estuarine wetland 

restoration and two creation projects were applied.

41

 Presented costs included three components of 

planning, construction, and monitoring. All three cost components were used to calculate and apply peracre costs for purposes of this analysis. The second set of data presented in the Louis Berger study is for 

projects conducted in the State of Connecticut by the Connecticut Department of Environmental 

Protection. These are primarily inexpensive restoration projects, with lower planning costs. Of 33 

Connecticut projects presented in the report, costs from a subset of 15 that involve estuarine wetland 

restoration, include complete construction costs, and comprise substantial restoration were applied. 

Because data provided for the Connecticut projects do not include monitoring costs, calculation and 

application of a ratio of monitoring to planning and construction costs based on data from Louis Berger 

(1997) were used to estimate monitoring costs for these projects. 

These selected cost data from Louis Berger were combined with data on seven coastal marsh creation 

projects provided by Mr. Carl Alderson (2010) to populate a restoration costs database of 26 restoration 

projects and nine creation projects. Based on these data, average per-acre restoration and average per-

 

41 Costs from one coastal wetland restoration project (Boston’s Logan Airport) were determined to be excessive and 

were not used in the calculations. In addition, data from two combination restoration and creation projects were 

excluded.
110

acre creation costs for coastal marsh habitat in the Northeast were created.

42

 The midpoint of the 

restoration cost and creation cost averages was applied as the per-acre cost estimate for the Northeast. 

In order to account for the variability of restoration costs in different regions of the United States, ELI 

data on wetland compensatory restoration costs for 38 cities and regions in the United States were used. 

Although restoration cost data presented in this document do not apply directly to coastal marsh 

restoration, as they include costs for freshwater wetland restoration, they provide a basis for quantifying 

differences in regional expenditures on habitat restoration. 

To apply these data in the scaling of coastal restoration costs, planning area-specific ratios of restoration 

costs were created based on ELI (2007) data. Specific ratios of restoration costs were created for each 

planning area, relative to restoration costs in the North Atlantic Planning Area.

43

 To establish planning 

area-specific cost estimates for use in calculating ratios, data presented for coastal cities in a given 

planning area were averaged. In instances where data are not available from such cities, the average of 

neighboring planning areas was used. 

The final step in the cost estimation process is to extrapolate the average per-acre marsh restoration cost 

based on the Louis Berger (1997) and Alderson (2010) data using the ratios calculated from ELI (2007) 

data. This determines planning area-specific per-acre coastal marsh restoration costs for application in

the OECM.44

 

42 Although one heavily cited article in the peer-reviewed literature suggests that wetland restoration and creation 

costs do not differ significantly in magnitude (King and Boehlen, 1994), other data clearly suggest that wetland 

creation costs greatly exceed wetland restoration costs.

43 Per-acre restoration costs from New England and New York are averaged to determine a baseline restoration cost 

for the North Atlantic Planning Area.

44 Note that per-acre restoration costs are converted to units of dollars per m2

.
111

9.5 REFERENCES – ECOLOGICAL

Alderson, C. 2010. Personal Communication. NOAA Restoration Center. 

Continental Shelf Associates, Inc. (CSA). 1991. A Comparison of Marine Productivity among Outer 

Continental Shelf Planning Areas. Supplement - An Evaluation of Benthic Habitat Primary 

Productivity.

Environmental Law Institute (ELI). 2007. Mitigation of Impacts to Fish and Wildlife Habitat: Estimating 

Costs and Identifying Opportunities (October). Accessed August 11, 2015. 

http://www.eli.org/sites/default/files/eli-pubs/d17_16.pdf.

Flint, R.W. 1985. Long-term Estuarine Variability and Associated Biological Response. Estuaries 8(2A): 

158-169. (from EAI and ASA, 1987).

French, D.P., C.A. Manen, M. Gibson, and J. Catena. 2001. Quantifying the Scale of Restoration 

Required to Compensate for the Impacts of the North Cape Oil Spill on Fish and Invertebrates. In: 

Proceedings of the 2001 International Oil Spill Conference & Exposition, March 26-29, 2001, Tampa, 

Florida. Washington, DC: American Petroleum Institute.

French, D., M. Reed, K. Jayko, S. Feng, H. Rines, S. Pavignano, T. Isaji, S. Puckett, A. Keller, F.W. 

French III, D. Gifford, J. McCue, G. Brown, E. MacDonald, J. Quirk, S. Natzke, R. Bishop, M. 

Welsh, M. Phillips, and B.S. Ingram. 1996. The CERCLA Type A Natural Resource Damage 

Assessment Model for Coastal and Marine Environments (NRDAM/CME), Technical 

Documentation, Vol. I-V. Final Report, PB96-501788. Submitted to the Office of Environmental 

Policy and Compliance, U.S. Department of the Interior, Washington, DC. [Available from the 

National Technical Information Service, 5285 Port Royal Road, Springfield, VA 22161.]

French McCay, D., N. Whittier, T. Isaji, and W. Saunders. 2003b . Assessment of the Potential Impacts of 

Oil Spills in the James River, Virginia. In: Proceedings of the Twenty Sixth Arctic and Marine Oil 

Spill Program (AMOP) Technical Seminar, Victoria, BC, Canada, June 2003. Ottawa, ON, Canada: 

Environment Canada, Emergencies Science Division, pp. 857-878.

French McCay, D., J. J. Rowe, and N. Whittier. 2003a. Final Report, Estimation of Natural Resource 

Damages for 23 Florida Cases Using Modeling of Physical Fates and Biological Injuries. (23 

volumes). Prepared for Florida Department of Environmental Protection (May 2003). 

French McCay, D.P. and J.J. Rowe. 2003. Habitat Restoration as Mitigation for Lost Production at 

Multiple Trophic Levels. Marine Ecology Progress Series 264: 233-247.

French-McCay, D.P. 2009. State-of-the-Art and Research Needs for Oil Spill Impact Assessment 

Modeling. In: Proceedings of the 32nd AMOP Technical Seminar on Environmental Contamination 

and Response. Ottawa, ON, Canada: Environment Canada, Emergencies Science Division, pp. 601-

653.

King, D.M. and C.C. Bohlen. 1994. A Technical Summary of Wetland Restoration Costs in the 

Continental United States. University of Maryland, Center for Environment and Estuarine Studies

Technical Report UMCEES-CBL-94-048 (April 1994). EPA Report Number 230/R-94-023. 

Solomons, MD.

Louis Berger & Associates, Inc. 1997. Costs for Wetland Creation and Restoration Projects in the 

Glaciated Northeast. Boston, MA: Prepared for the U.S. EPA, Region 1.
112

Louisiana Department of Environmental Quality (LA DEQ), Louisiana Department of Natural Resources, 

Louisiana Department of Wildlife and Fisheries, Louisiana Oil Spill Coordinator’s Office (Office of 

the Governor), National Oceanic and Atmospheric Administration, and U.S. Department of the 

Interior. 2003. The Louisiana Regional Restoration Planning Program, Draft Regional Restoration 

Plan, Region 2 (September). http://www.darrp.noaa.gov/southeast/rrpp-la/pdf/larrp11_18_04.pdf.

Muscatine, L. 1980. Productivity of Zooxanthellae. In: Primary Productivity in the Sea, Brookhaven 

Symposia in Biology, No. 31, Environmental Science Research, Vol. 19, P.G. Falkowski (ed.).

Upton, NY: Plenum Press, pp. 381-402.

National Oceanic and Atmospheric Administration (NOAA). 1997. Scaling Compensatory Restoration 

Actions: Guidance Document for Natural Resource Damage Assessment Under the Oil Pollution Act 

of 1990. NOAA Damage Assessment Center, Silver Spring, MD.

National Oceanic and Atmospheric Administration (NOAA). 1999. Habitat Equivalency Analysis: An 

Overview. NOAA Damage Assessment Center, Silver Spring, MD, 23 p.

Nixon, S.W. and C.A. Oviatt. 1973. Ecology of a New England Salt Marsh. Ecological Monographs

43(4): 463-498.

Raymont, J.E.G. 1980. Plankton and Productivity in the Oceans, 2nd Edition, Vol. 1 Phytoplankton. 

Oxford: Pergamon Press (from EAI and ASA, 1987).

Sibley, D.A. 2003. The Sibley Field Guide to Birds of Eastern North America. New York: Alfred A. 

Knopf, Inc., 432 p.

Teal, J.M. 1962. Energy Flow in the Salt Marsh Ecosystem of Georgia. Ecology 43: 614-624.

U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses. 

EPA 240-R-00-003 (September). Washington, DC: U.S. EPA, Office of the Administrator.

Van Raalte, C.D., I. Valiela, and J.M. Teal. 1976. Production of Epibenthic Salt Marsh Algae: Light and 

Nutrient Limitation. Limnology and Oceanography 21(6): 862-872.
113

10 Benefits of OCS Oil and Natural Gas Activity

10.1 OVERVIEW

As with the cost categories considered for inclusion in the OECM, assessing benefits associated with 

OCS oil and natural gas activity would include: (1) defining an appropriate quantification metric, (2) 

using that metric as a basis for quantitative analysis of the impact of a specific E&D scenario, and (3) 

translating the results of the quantitative analysis into a monetary estimate of a change in social welfare 

value. One potential benefit is the enhancement of recreational fishing and diving opportunities in the 

vicinity of exploration and production platforms, absent access restrictions.

10.2 RECREATIONAL BENEFITS

Recreational fishing combines both private boat fishing and commercial charter or party boat operations 

that engage in fishing for sport or competition. Commercial charter and party boat operations involve 

vessels with licensed captains and crew. Charter operations generally consist of pre-formed groups of 

fishermen, while party boats combine several groups on the same trip and vessel. Recreational diving 

similarly encompasses private, charter, and party trips, but because these trips are comparatively less 

common than fishing trips, there are few data to support reporting statistics in narrower categories.

The most detailed study of the economic impacts of recreational fishing and diving in relation to offshore 

structures is a 2002 Minerals Management Service (MMS)

45 study, Economic Impact of Recreational 

Fishing and Diving Associated with Offshore Oil and Gas Structures in the Gulf of Mexico. This study 

used a combination of in-person and telephone surveys of marinas in the Central and Western Gulf of 

Mexico to estimate the total annual expenditures in 1999 on recreational fishing and diving trips from 

respondents’ self-reported survey responses. Survey estimates were then scaled up to the full population 

values of expenditures using NMFS’s Marine Recreational Fishery Statistics Survey’s estimates of 

recreational fishing and diving in the survey areas. Further, in order to extend the analysis from direct 

expenditures on fishing and diving trips to the full direct, indirect, and induced economic impacts of that 

activity, the study used IMPLAN (IMpact analysis for PLANning) input-output modeling to estimate total 

output, employment, and value added impacts.

The 2002 MMS study found significant use of offshore oil and natural gas structures for recreational 

fishing and diving trips. The percentages of trips to within 300 feet of an offshore or oil/natural gas 

structure for recreational fishing and recreational diving were 21.9 percent and 93.6 percent, respectively. 

These trips accounted for a total economic output of $324.6 million in the coastal counties of the states in 

the study. Private boating accounted for the highest proportion of this amount with a total of $255.2 

million, followed by charter fishing at $45.4 million, recreational diving with $13.6 million, and finally 

party boats with the smallest economic impact at $10.4 million.46

Recognizing the economic and environmental benefits of the artificial reefs created by offshore structures, 

all five Gulf States have instituted artificial reef programs to facilitate permitting, navigational 

requirements, and liability transfer for decommissioned and reefed rigs. These artificial reef programs aim 

to continue the economic and environmental benefits of offshore structures through their productive 

 

45 Predecessor agency to BOEM.

46 No party boat trips were sampled in Alabama and Mississippi. Follow-up interviews were not performed for 

recreational diving in Texas. These categories of expenditures, therefore, are missing from the economic impacts 

analyses.
114

stages and post-abandonment. For example, in the Louisiana Artificial Reef Program, designations of 

“artificial reef planning areas” incorporate input from recreational as well as commercial fishermen, 

recreational divers, and shrimpers. These designations steer reefing activities towards locations that tend 

to decrease burdens on navigation and commercial fishing, while increasing the likelihood that they are 

attractive sites for recreational use. 

While the economic impact study in the Gulf provides evidence of calculable recreational benefits 

associated with offshore structures, existing studies and data are insufficient for quantification, across all 

planning areas, of the benefit of a specific change in offshore activity. Even within the four Gulf States in 

the 2002 MMS survey, there was a considerable amount of disparity in not only the mode but also the 

impacts of the recreational use of offshore structures, making it difficult to support extrapolating from the 

Gulf of Mexico planning areas to other regions.47 Additionally, the effect that artificial reef programs 

have on decommissioning cost decision-making, and hence on extending the period of time during which 

recreational benefits accrue for a given structure, adds a level of unpredictability to the quantification of 

recreational benefits. There are few data with which to model the comparative costs of decommissioning 

options, especially considering geographic differences in decommissioning costs and individual States’ 

specific artificial reef programs. Further, the 2002 MMS study specifically describes the difficulty in 

identifying the characteristics that make certain sites more attractive for recreational use.

10.3 ENERGY SECURITY BENEFITS

The pursuit of energy security has a long history, having gained worldwide impetus after the tripling of 

the international price of crude oil in October 1973. One of the consequences of this price shock “was to 

put energy security and, more specifically, security of oil supply at the heart of the energy policy agenda 

of most industrialized nations” (LaCasse and Plourde, 1995). The run-up in oil prices in recent years has 

again raised the profile of energy security policies. During the first year of the 111th Congress 

(2009/2010), more than 90 bills were introduced with the term “energy security” in the bill text; more 

than 200 such bills were introduced during the 110th Congress. 

Since the 1970s, macroeconomists and energy economists have viewed large changes in the price of oil as 

a contributing source of economic fluctuations both domestically and globally. Policymakers’ interest in 

energy security often reflects this concern that sharp increases in oil prices can lead to economic 

downturns. Moreover, these oil price shocks are often attributed to deliberate attempts by oil-producing 

countries to hold back supply or to supply disruptions caused by geopolitical events in those countries. It 

therefore is argued that reduced import dependence or diversification in the sources of supply would 

insulate oil-importing nations from this source of economic instability. On this basis, a complete 

accounting of the net benefits (costs) attributable to a Five Year Program would include the value 

attributable to the increase in energy security provided by the program-related domestic production 

activity.

The OECM does not currently provide a quantified estimate of energy security benefits, as there is not yet 

a single, widely accepted method for doing so. However, renewed interest among policymakers in the 

economic impacts of price shocks has coincided with a growing academic literature on the measurement 

and attainment of energy security.48 Many papers seek to quantify the security of energy supplies for 

importing countries, using measures such as the degree of import dependence, the extent of diversification 

 

47 Hiett and Milon, pg. 78.

48 Several recent books address the topic of energy independence, including Bryce (2008), Hakes (2008), and 

Sandalow (2008). See Loungani (2009) for a review of these books.
115

in sources of supply, and the distance between sources of supply and the point of consumption (Blyth and 

Lefevre, 2004; Le Coq and Paltseva, 2008, 2009; Gupta, 2008). This section offers an introduction to the 

issue by describing two approaches that might serve as a foundation for future modeling considerations.

LaCasse and Plourde (1995) provide a useful conceptual framework for analyzing energy security. They 

suggest that in the short run, energy security “can be identified with the physical availability of [energy] 

supplies.” Over longer time horizons, energy security is a function not only of the availability of supplies 

but the likelihood of sustained run-ups in energy prices and the associated effects on the macroeconomy. 

LaCasse and Plourde argue that this macro response is not likely to be overly dependent on import 

reliance; instead, “a country’s oil consumption, regardless of its origin, together with the magnitude of 

the price hike, jointly determine the severity of the effects of the shock.” For long-run energy security, 

“the focus of policymakers on import dependence leads down a false trail: the composition of demand is 

a minor issue compared to the use of [energy] as an input.”

Casual empiricism suggests that diversification in sources of energy supply has been increasing. Bryce 

(2008) notes, for instance, that the United States buys crude oil and gasoline from over 40 countries and 

jet fuel from over 25 countries. Canada and Mexico have grown in importance as suppliers, whereas 

countries of the Persian Gulf now supply only about 10 percent of all the oil consumed in the United 

States.

The basic idea behind diversification is borrowed from portfolio theory in finance. Holding other things 

constant, the overall risk to a country’s energy supply is smaller if it has a diversified portfolio of 

suppliers. A diversified portfolio can reduce a country’s vulnerability to supply disruptions from a 

particular source. Moreover, even in the absence of supply disruptions, diversification reduces the market 

power of any one supplier, lowering the “risks of higher prices and/or inferior products and services” 

(Blyth and Lefevre, 2004, p. 18).

Another approach for measuring energy security approaches the issue from a welfare analytics 

perspective. Energy policymakers make decisions on both micro and macro levels. When developing oil

(and natural gas) policy, they need to address a wide range of issues such as the impact of oil 

consumption on aggregate economic activity, the terms of trade for imported oil and petroleum products, 

the welfare effects due to pollution arising from the consumption of oil, and the vulnerability of U.S. 

economic activity to world oil supply disruptions. Brown and Huntington (2009) developed a welfare 

approach for addressing these analytical issues. Under their framework, energy security or vulnerability 

depends on expected losses in U.S. economic activity and increased transfers to foreign oil producers that 

are associated with oil supply disruptions. The welfare function associated with U.S. oil consumption can 

be represented as

Welfare Y O TC PO X E Y E P O          C D M O M     

where domestic welfare (Welfare) depends on the six right-hand components. The first four are expressed 

in terms of domestic benefits and costs, and the last two are related to oil price shocks. Domestic GDP, Y, 

is a function of oil consumption (OC). The total cost of domestic oil production, TCD, and the cost of 

imported oil (price of oil (P), times the quantity imported (OM), and the environmental externalities as a 

result of oil consumption (XO), enter the welfare function negatively. E[ΔY] is the expected GDP loss 

associated with price increases resulting from oil supply disruptions, and E[ΔP]·OM is the expected 

increase in oil import costs associated with the expected oil supply disruptions.
116

While the two approaches described above for quantifying energy security have potential application 

within the OECM or MarketSim framework, incorporating the data that would be necessary to do so (e.g., 

changes in oil or natural gas imports by country of origin or the change in GDP associated with increased 

oil or natural gas prices) is beyond the scope of the current model development effort.
117

10.4 REFERENCES – BENEFITS

Blyth,W. and N. Lefevre. 2004. Energy Security and Climate Change Policy Interactions: An Assessment 

Framework. Paris: International Energy Agency, 88 p.

Brown, S.P.A. and H.G. Huntington. 2009. Estimating U.S. Oil Security Premiums. Stanford University 

Energy Modeling Forum, EMF OP 68 (September), 32 p.

Bryce, R. 2008. Gusher of Lies: The Dangerous Delusions of “Energy Independence.” New York, NY: 

PublicAffairs, 371 p.

Gupta, Eshita. 2008. Oil vulnerability index of oil-importing countries. Energy Policy 36 (3), 

1195–1211. 

Hiett, Robert L. and J. Walter Milon, Economic Impact of Recreational Fishing and Diving Associated 

with Offshore Oil and Gas Structures in the Gulf of Mexico: Final Report. OCS Study MMS 2002-010, 

April 2002.

Kaiser, Mark J. “The Louisiana artificial reef program.” Marine Policy 30 (2006) 605-623.

LaCasse, C. and A. Plourde. 1995. On the Renewal of Concern for the Security of Oil Supply. The

Energy Journal 16(2), pp. 1-24.

Le Coq, Chloe and Elena Paltseva, 2008. Common Energy Policy in the EU: The Moral Hazard 

of the Security of External Supply. SIEPS Report 2008:1. 

Le Coq, Chloe and Elena Paltseva, 2009. Measuring the security of external energy supply in the 

European Union, Energy Policy 37 (2009) 4474–4481.
APPENDIX A: OIL SPILL MODELING FOR THE OFFSHORE 

ENVIRONMENTAL COST MODEL (OECM) 

by 

Deborah French McCay, Jill Rowe, Danielle Reich, Melanie Schroeder, and Eileen 

Graham 

Applied Science Associates, Inc. 

55 Village Square Drive 

South Kingstown, RI 02879 

Tel: +1 401 789 6224 

Fax: +1 401 789 1932 

Email: dfrench@asascience.com 

Prepared for: 

Industrial Economics, Inc. (IEc) 

for Submission to: 

Bureau of Ocean Energy Management (BOEM) 

ASA 09-183 

December 15, 2010 

Revised June 20, 2012 
Oil Spill Modeling for the OECM 

October 25, 2011

A-i 

Table of Contents 

1. Introduction ................................................................................................................... 1

2. SIMAP Modeling Approach ........................................................................................ 2

2.1 Scenarios Modeled .................................................................................................... 3

2.2 Model Input Data ...................................................................................................... 6

2.2.1 Geographical and Model Grid ........................................................................... 6

2.2.2 Environmental Data ........................................................................................... 6

2.2.3 Currents .............................................................................................................. 7

2.2.4 Oil Properties and Toxicity ................................................................................ 7

2.2.5 Shoreline Oil Retention...................................................................................... 7

2.3 Impact Measures ....................................................................................................... 7

2.3.1 Biological Impacts ............................................................................................. 8

2.3.2 Shoreline Recreation and Tourism Impacts ..................................................... 11

2.3.3 Shoreline Cleanup Impacts .............................................................................. 11

2.3.4 Boating/Shipping and Water Surface Cleanup Impacts................................... 12

3. Habitat Equivalency Analysis (HEA) ....................................................................... 12

4. Spill Rate and Volume Analysis ................................................................................ 12

5. Model Results .............................................................................................................. 13

6. Conclusions .................................................................................................................. 14

7. References .................................................................................................................... 15

Sub-appendices 

Sub-appendix A Detailed Model Input Information for Each Location 

Sub-appendix B Properties of Oils used in SIMAP Modeling 

Sub-appendix C Habitat Equivalency Analysis for Habitat Injuries 

Sub-appendix D Spill Rate and Volume Data for OECM Modeling 

Sub-appendix E Digital Regression Files and Biological Databases 

Sub-appendix F Alternative Spill Rate and Volume Data for OECM Modeling 

Provided by BOEM 
Oil Spill Modeling for the OECM 

October 25, 2011

A-ii 

List of Tables 

Table 1. Spill volumes and durations for crude oils. .......................................................... 3

Table 2. Spill volumes and durations for heavy fuel oil and diesel. ................................... 4

Table 3. SIMAP model scenarios from which the OECM model equations were 

developed. ................................................................................................................... 5

Table 4. Impact measures used to estimate consequences. ................................................. 8

Table 5. Combined probability of encounter with the slick and mortality once oiled, if 

present in the area swept by a slick exceeding the thickness threshold. ..................... 9

Table 6. Lethal dose of aromatics as a function of temperature. ...................................... 10

Table 7. Summary of OECM regressions and biological databases. ................................ 13
Oil Spill Modeling for the OECM 

October 25, 2011

A-1 

1. INTRODUCTION 

As part of the update to the Offshore Environmental Cost Model (OECM), Applied Science 

Associates, Inc. (ASA) undertook a separate modeling effort to better understand the potential 

environmental, social, and economic consequences of oil spills. Such spills could occur in the 

context of outer continental shelf (OCS) oil and natural gas exploration and development, or in 

the context of imports that might serve as alternatives to OCS production. As described below, 

the projected consequences are entered into OECM as oil spill model-derived algorithms that 

relate quantity and location of spilled oil (forecast separately) to bio-physical consequence 

metrics. OECM will be applied by the Bureau of Ocean Energy Management (BOEM) to 

understand the potential impact of offshore oil and gas development in all 26 OCS planning 

areas, covering the offshore areas on all marine coastlines of the lower 48 states plus Alaska. 

Thus, the modeling study used to develop OECM equations addressed spills of varying oil types 

and sizes in all of these areas under a wide range of conditions. 

Given the infeasibility of modeling every possible situation that could occur in each of the 26 

planning areas, our technical approach was designed to address the major variables to which oil 

spill consequences are sensitive. In addition, OECM cannot include highly complex oil spill 

modeling within its coding. Thus, our general approach was to: 

• Use an existing, well-vetted and validated oil spill impact model system, SIMAP 

(described in French-McCay, 2004, 2009), to project consequences associated with a 

matrix of potential conditions; 

• Summarize the model output data that quantify areas, shore lengths, and volumes where 

impacts would occur with regression equations that can be applied within OECM; 

• Within OECM, multiply the areas, shore lengths and volumes affected by receptor 

densities and/or costs in the locations of concern; and 

• Allow OECM to be updated with new receptor information, as needed and available, to 

which the regression results can be applied. 

We approached the assessment of oil spill risk by applying the standard technical definition of 

risk that includes both the likelihood (i.e., probability) of spill incidents of various types 

occurring and the impacts or consequences of those incidents. In other words, 

Spill risk = probability of spill x impacts of spill 

The probability of a spill is a combination of the likelihood a spill will occur and the likely sizes 

of spills once they occur. Data to estimate both of these are discussed in this report. 

Impacts of a spill depend on the spill size, oil type, environmental conditions, resources present 

and exposed, toxicity and other impact mechanisms, and population/ecosystem recovery 

following direct exposure. This report describes the approach, model, data inputs, and results of 

the modeling. Inputs include habitat and depth mapping, winds, currents, other environmental 

conditions, chemical composition and properties of the oils most likely to be spilled, 

specifications of the release (amount, location, etc.), toxicity parameters, and biological 

abundance. The input data for modeling impacts are available from government-run websites 
Oil Spill Modeling for the OECM 

October 25, 2011

A-2 

(e.g., winds, temperatures), government reports, published literature, and data libraries that ASA 

has compiled over many years of performing similar modeling. Where feasible, ASA also used 

current data from BOEM-sponsored hydrodynamic modeling studies, which are used by BOEM 

in its oil spill risk assessment modeling analyses. 

In summary, the SIMAP model was used to develop data (i.e., areas, shore lengths and water 

volumes affected as a function of oil type, spill volume, and environmental conditions) that were 

then described using regression analysis. The resulting functions are the basis for estimating oil 

spill-related costs within OECM. The oil impact model was developed for a matrix of potential 

environmental conditions representative of those in all 26 of the planning areas. The results for a 

given set of environmental conditions are applicable to all planning areas where those conditions 

occur at some time of the year. OECM will apply the appropriate regressions for conditions 

occurring in the planning area being modeled, along with the resource density data for that 

planning area. In this way, estimates of potential consequences can be made for all 26 planning 

areas. 

Section 2 describes the modeling approach used for this analysis, including model input data and 

impact measures. Sections 3 and 4 discuss the approach for the Habitat Equivalency Analysis 

(HEA) and spill rate/volume estimation, respectively. Results of the model are described in 

Section 5. Discussion and conclusions are in Section 6. Section 7 contains the references cited. 

Sub-appendices provide the details of the input data and model results, in tables, maps, and other 

figures. 

2. SIMAP MODELING APPROACH 

The modeling approach involved estimating the areas of water surface, lengths of shoreline and 

volumes of water exposed above consequence thresholds (oil thickness or concentrations) for a 

series of oil spill volumes and a matrix of potential conditions that might occur in any of the 26 

planning areas. For a given oil volume spilled in open water under a set of environmental 

conditions (e.g., winds, temperature), the spreading and transport of oil is such that the areas and 

volumes affected are similar regardless of where the spill occurs. Thus, we ran oil spill model 

simulations for a matrix of oil types, environmental conditions, and series of spill volumes, and 

developed regression models fit to the data. This allows prediction of the area of water surface, 

shore length, and volume of water that would be affected for any spill volume, regardless of the 

location of the spill. The resulting regression models will then be included in OECM and used to 

estimate impacts of spills as a function of the planning area, distance from shore, the oil type, 

and the spill volume. 

The oil spill modeling for OECM was performed using SIMAP (French McCay, 2003, 2004), 

which uses wind data, current data, and transport and weathering algorithms to calculate the 

mass of oil components in various environmental compartments (water surface, shoreline, water 

column, atmosphere, sediments, etc.), oil pathway over time (trajectory), surface oil distribution, 

and concentrations of the oil components in water and sediments. SIMAP was derived from the 

physical fates and biological effects submodels in the Natural Resource Damage Assessment 

Models for Coastal and Marine and Great Lakes Environments (NRDAM/CME and 

NRDAM/GLE), which were developed for the U.S. Department of the Interior (USDOI) as the 
Oil Spill Modeling for the OECM 

October 25, 2011

A-3 

basis of Comprehensive Environmental Response, Compensation and Liability Act of 1980

(CERCLA) Natural Resource Damage Assessment (NRDA) regulations for Type A assessments 

(French et al., 1996; Reed et al., 1996). 

SIMAP contains physical fate and biological effects models, which estimate exposure and 

impact on each habitat and species (or species group) in the area of the spill. Environmental, 

geographical, physical-chemical, and biological databases supply required information to the 

model for computation of fates and effects. The technical documentation for the model can be 

found in French McCay (2003, 2004, 2009). 

Modeling was conducted using SIMAP’s stochastic model to determine the range of distances 

and directions oil spills are likely to travel from a particular set of spill sites, given historical 

wind and current speed and direction data for the area. For each model run used to develop the 

statistics, the spill date is randomized, which provides a probability distribution of wind and 

current conditions during the spill. The stochastic model performs a large number of simulations 

for a given set of spill sites, varying the spill time and thus the wind and current conditions, for 

each run. The stochastic modeling outputs provide a distribution of spill results, which can be 

summarized by statistics such as mean and standard deviation. 

Using these statistics from the SIMAP model, the worst case exposure was calculated as the 99th

percentile value for each impact category, location, and oil type. These 99th percentile values 

were then plotted as regressions of exposure area/volume versus spill volume and applied within 

the OECM to predict the areas, shore lengths, and water volumes affected for spills in any 

location (planning area). 

2.1 Scenarios Modeled 

A matrix of 230 scenarios was run in SIMAP to determine mean, standard deviation, and range 

of exposures (areas, shore lengths, and volumes) to floating oil, shoreline stranded oil, and water 

contamination for a range of five spill volumes (see Tables 1 and 2 below). We then used the 

99th percentile results from each scenario to develop regressions of exposures versus volume of 

oil spilled for each of the locations modeled (Table 3). The resulting sets of regressions were 

mapped to each of the 26 planning areas as described in Table 3. 

Table 1. Spill volumes and durations for crude oils. 

Spill Volume (gallons) Duration of release (hours) 

1,000,000 24 

500,000 16 

100,000 10 

10,000 4 

1,000 1 
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Table 2. Spill volumes and durations for heavy fuel oil and diesel. 

Spill Volume (gallons) Duration of release (hours) 

100,000 10 

50,000 5 

10,000 2 

1,000 1 

100 0 
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Table 3. SIMAP model scenarios from which the OECM model equations were developed. 

Region # of Spill Locations Ice in Winter # of Scenarios Spill Sites Oil types Planning areas Represented

Atlantic 2 No 30 Virginia lease area; near Delaware 

(nearshore and offshore)

Light crude, heavy crude, heavy fuel oil 

North Atlantic, Mid-Atlantic, South Atlantic

Straits of Florida 1 No 10 Along straits Light crude, 

heavy fuel oil 

Straits of Florida 

Gulf of Mexico 2 No 30 Central Gulf of Mexico planning 

area (nearshore and offshore)

Light crude, heavy fuel oil, diesel 

Eastern Gulf, Central Gulf, Western Gulf

California 2 No 30 Offshore southern California 

(Santa Maria Basin); Santa Barbara Channel (Santa BarbaraVentura Basin) 

Light (Arab) crude, heavy crude, heavy fuel oil 

Southern California, Central California 

Washington/ Oregon 1 No 30 Mid-Washington (nearshore and 

offshore)

Medium crude, heavy fuel oil, diesel 

Northern California, Washington/ Oregon

Gulf of Alaska 2 No 20 Gulf of Alaska near Yakutat 

(nearshore and offshore) 

Medium crude, heavy fuel oil 

Gulf of Alaska, Kodiak, Shumagin, Aleutian Arc 

Cook Inlet & Shelikof Strait

1 No 15 Cook Inlet planning area Medium crude, 

heavy fuel oil, diesel 

Cook Inlet 

Bering Sea 1 No 15 North Aleutian Basin program 

area

Medium crude, heavy fuel oil, diesel

North Aleutian Basin, St. George Basin, St. Matthew Hall, Bowers Basin, Aleutian Basin, Navarin Basin, Norton Basin 

Chukchi Sea 2 Yes 30 Chukchi sea planning area 

(nearshore and offshore)

Light crude, heavy crude, heavy fuel oil 

Hope Basin, Chukchi Sea 

Beaufort Sea 2 Yes 20 Beaufort sea planning area 

(nearshore and offshore) 

Medium crude, heavy fuel oil 

Beaufort Sea 

Total 17 230 
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2.2 Model Input Data 

Detailed descriptions of input data for each location modeled are provided in Sub-appendix A. A 

general overview of model input data is provided in the sections below. 

2.2.1 Geographical and Model Grid 

For geographical reference, SIMAP uses a rectilinear grid to designate the location of the 

shoreline, the water depth (bathymetry), and the shore or habitat type. The grid is generated 

from a digital coastline using the ESRI Arc/Info compatible Spatial Analyst program. The cells 

are then coded for depth and habitat type. Note that the model identifies the shoreline using this 

grid. Thus, in model outputs, the coastline map is only used for visual reference; it is the habitat 

grid that defines the actual location of the shoreline in the model. 

The intertidal habitats are assigned based on the shore types in digital Environmental Sensitivity 

Index (ESI) maps distributed by NOAA HAZMAT (CD-ROM). These data were gridded using 

the ESRI Arc/Info compatible Spatial Analyst program. Open water areas were defaulted to 

sand bottom, as open water bottom type has no influence on the model results. 

2.2.2 Environmental Data 

The model uses hourly wind speed and direction for the time of the spill and simulation. A long 

term wind record is sampled at random to develop a probability distribution of environmental 

conditions that might occur at the time of a spill. The model can use multiple wind files, 

spatially interpolating between them to determine local wind speed and direction. 

Surface water temperature in the model varies by month, based on data from French et al. 

(1996). The air immediately above the water is assumed to have the same temperature as the 

water surface, this being the best estimate of air temperature in contact with floating oil. Salinity 

is assumed to be the mean value for the location of the spill site, based on data compiled in 

French et al. (1996). The salinity value assumed in the model runs has little influence on the fate 

of the oil, as salinity is used to calculate water density (along with temperature), which is used to 

calculate buoyancy, and none of the oils evaluated have densities near that of the water. 

Suspended sediment is assumed to be 10 mg/L, a typical value for coastal waters (Kullenberg, 

1982). The sedimentation rate is set at 1 m/day. These default values have no significant effect 

on the model trajectory. Sedimentation of oil and polyaromatic hydrocarbons (PAHs) becomes 

significant at about 100 mg/L suspended sediment concentration. 

The horizontal diffusion (randomized mixing) coefficient is assumed as 10 m2

/sec for floating oil 

and 1 m2/sec for surface and deep waters. The vertical diffusion (randomized mixing) 

coefficient is assumed as 0.0001 m2

/sec. These are reasonable values for coastal waters based on 

empirical data (Okubo and Ozmidov, 1970; Okubo, 1971) and modeling experience. 
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2.2.3 Currents 

Currents have significant influence on the trajectory and oil fate, and are critical data inputs. 

Dependent upon geographic location, wind-driven, tidal and background currents are included in 

the modeling analysis. The tidal currents and background (other than tidal) currents are input to 

model from a current file that is prepared for this purpose (see Sub-appendix A for a detailed 

description of currents for each location). 

2.2.4 Oil Properties and Toxicity 

The spilled oil used in OECM consisted of a variety of types, including various crude oils, heavy 

fuel oil (HFO), and diesel. Physical and chemical data on these oils are summarized in Subappendix B. 

The oil’s content of volatile and semi-volatile aliphatics and aromatics (which are also soluble 

and cause toxicity in the water column) is defined and input to the model. The volatile aliphatics 

rapidly volatilize from surface water, and their mass is accounted for in the overall mass balance. 

However, as they do not dissolve in significant amounts, they have limited influence on the 

biological effects on water column and benthic organisms. 

For crude oil, diesel, and heavy fuel oil spills at/near the water surface, monoaromatic 

hydrocarbons (MAHs) do not have a significant impact on aquatic organisms for the following 

reasons. MAH concentrations are less than 3 percent in fresh fuel oils. MAHs are soluble, and 

so some become bioavailable (dissolved). MAH compounds are also very volatile, and will 

volatilize (from the water surface and water column) very quickly after a spill. The threshold for 

toxic effects for these compounds is about 500 ppb for sensitive species (French McCay, 2002). 

MAHs evaporate faster than they dissolve, such that toxic concentrations are not reached. The 

small concentrations of MAHs in the water will quickly be diluted to levels well below toxic 

thresholds immediately after a spill. 

2.2.5 Shoreline Oil Retention 

Retention of oil on a shoreline depends on the shoreline type, width and angle of the shoreline, 

viscosity of the oil, the tidal amplitude, and the wave energy. In NRDAM/CME (French et al., 

1996), shore holding capacity was based on observations from the Amoco Cadiz spill in France 

and the Exxon Valdez spill in Alaska (based on Gundlach, 1987) and later work summarized in 

French et al. (1996). This approach and data were used in the present study. 

2.3 Impact Measures 

To develop regressions for incorporation into OECM, a number of impact measures were 

evaluated, as described in Table 4 and the following sections. All regressions used the 99th

percentile value for each oil type, spill volume, and impact measure. 
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Table 4. Impact measures used to estimate consequences. 

Consequence Impact Measure Impact Threshold 

Impact to wildlife: seabirds, 

waterfowl, marine mammals, 

and sea turtles 

Water surface area exposed to 

floating oil

10 g/m2 (French et al., 1996; 

French McCay, 2009)

Impact to wildlife: shorebirds 

and waders Shore area exposed 100 g/m2

 (French et al., 

1996; French McCay, 2009) 

Impact to water column 

organisms

Aromatic dosage (volume 

exposed to dissolved aromatic 

concentrations) 

Acute: ppb-hrs as a function 

of temperature (French 

McCay, 2002) 

Impact to benthic organisms 

Sediment area exposed to 

dissolved aromatic 

concentrations (assume 10 cm 

deep biological zone) 

Chronic and tainting: 1 ppb 

Acute: 45 ppb (French 

McCay, 2002)

Shoreline recreation and 

tourism Shore length exposed Sheen (1 g/m2

) 

Shoreline cleanup Shore area exposed Sheen 

(1 g/m2) 

Boating/shipping Water surface area exposed to 

floating oil 

Sheen 

(1 g/m2

) 

Water surface cleanup Water surface area exposed to 

floating oil 

Sheen 

(1 g/m2) 

2.3.1 Biological Impacts 

As described in the sections below, birds and other wildlife are affected in proportion to the 

water and shoreline surface area oiled above a threshold thickness for effects. Impacts to fish 

and invertebrates in the water and on the sediments are related to water column and sediment 

pore water concentrations of dissolved aromatics. 

Biological impacts are calculated in OECM as the area or volume affected times the density of 

animals in the location of interest. Densities of biological resources in each planning region are 

available in the Type A model that ASA developed for the Department of the Interior in support 

of the CERCLA NRDA regulations (French et al., 1996); these data sets are included in the 

OECM and provided in Sub-appendix E. Because of this direct multiplication performed within 

the OECM itself, other and updated biological densities may be inserted in the OECM at any 

time (by BOEM or others). Also note that the numbers of animals oiled is directly proportional 

to animal density. Thus, if the density increases by a factor of two, so do the impact results 

calculated by the model. This allows complete flexibility in adding or updating the densities of 

receptors. 

Impacts to Wildlife: Marine Mammals, Sea Turtles, Seabirds and Waterfowl 

Impacts to marine mammals, sea turtles, seabirds, and waterfowl were evaluated as the water 

surface area exposed to floating oil with a thickness of 10 g/m2

 or higher. Regressions were 

developed of area exposed versus spill volume for each oil type. To determine biological density 
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information for each species, we multiplied the annual average number per km2 (from the Type 

A model) by the probability of oiling for that species’ behavior group (Table 5) to estimate the 

number killed per km2

. Estimates for the probabilities shown in Table 5 are derived from 

information on behavior and field observations of mortality after spills (reviewed in French et al., 

1996 and French McCay, 2009). We also multiplied the number killed per km2

 by the mean 

weight per individual of each species to calculate the kilograms killed per km2. This information 

is summarized for each location in the enclosed digital sub-appendix (Sub-appendix E). 

Table 5. Combined probability of encounter with the slick and mortality once oiled, if 

present in the area swept by a slick exceeding the thickness threshold. 

Wildlife Behavior Group Probability 

Dabbling and surface-feeding waterfowl* 99% 

Nearshore aerial divers 35% 

Surface seabirds 99% 

Aerial seabirds 5% 

Wetland wildlife (waders and shorebirds) 35% 

Cetaceans 0.1% 

Furbearing marine mammals 75% 

Pinnipeds, manatee, sea turtles 1% 

*Dabblers, geese, and swans were not included in the modeling because they are not found in significant numbers in areas 

affected by offshore spills. 

Impacts to Wildlife: Shorebirds and Waders 

Impacts to shorebirds and waders were evaluated as the shore area exposed to oil with a 

thickness of 100 g/m2

 or higher. Shore area exposed was calculated by summing the impacts for 

rock, gravel, sand, mudflat, and wetland shore types. We excluded impacts to artificial shore 

types from this total because artificial shorelines are typically not suitable shorebird/wader 

habitat. Regressions were developed of area exposed versus spill volume for each oil type. To 

determine biological density information for each species, we multiplied the annual average 

number per km2 (from the Type A model) by the probability of oiling for that species’ behavior 

group (Table 5, French et al., 1996 and French McCay, 2009) to estimate the number killed per 

km2

. We also multiplied the number killed per km2 by the mean weight per individual of each 

species to calculate the kilograms killed per km2

. This information is summarized for each 

location in the enclosed digital sub-appendix (Sub-appendix E). 

It should be noted that, because of the resolution of the modeling, shorebird/wader impacts are 

likely to be underestimated. In the model, the shore area exposed to oil is averaged based on the 

length of the shore cell in the habitat grid for each location. Because of the geographic extent of 

potential oiling in OECM locations for the spills examined, our habitat grids were large, resulting 

in large individual shore cell lengths (shore cell size information for each location can be found 

in Sub-appendix A). These large shore cells tend to dilute the effect of shore oiling, and thereby 

underestimate shorebird/wader impacts. 
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Impacts to Water Column Organisms 

Contamination in the water column changes rapidly in space and time, such that a dosage 

measure as the product of concentration and time is a more appropriate index of impacts than 

simply peak concentration. Toxicity to aquatic organisms increases with time of exposure, such 

that organisms may be unaffected by brief exposures to the same concentration that is lethal at 

long times of exposure. Toxicity data indicate that the 96-hour LC50 (which may serve as an 

acute lethal threshold) for dissolved aromatics (primarily PAHs) averages about 50 Pg/l (ppb, 

French McCay, 2002). 

Impacts to water column organisms (fish and invertebrates) were evaluated as the volume of 

water exposed to aromatic concentrations above a lethal dose threshold (in ppb-hrs). The lethal 

dose threshold was based on LC50 = 50 ppb at infinite time of exposure (the time to approach 

equilibrium of tissue concentration with ambient concentration) and is a function of temperature 

(Table 6 below, French McCay, 2002). For temperatures not listed in the table, the lethal dose 

was interpolated. 

Table 6. Lethal dose of aromatics as a function of temperature. 

Temperature (°C) Lethal Dose (ppb-hrs) 

25 5000 

20 9000 

15 14000 

10 24000 

2 58000 

To calculate water column organisms impacts, we used two different model outputs, (1) the 

volume of water that had dissolved aromatic concentrations exceeding 1 ppb and (2) the average 

dose of dissolved aromatics, as ppb-hrs in that volume. Using the annual average surface water 

temperature for each location (from French et al., 1996), if the average dose exceeded the lethal 

threshold, then the entire volume of water exceeding 1 ppb is assumed to be exposed to a lethal 

dose (i.e., the kill volume). If the average dose did not exceed the lethal threshold, the kill 

volume is calculated as: 

Volume Killed = (Average Dose)/(Lethal Threshold) * (Volume exceeding 1ppb) 

Regressions were then developed for the water volume killed as a function of the spill volume. 

In OECM, these regressions are multiplied by the total fish and invertebrate injury per unit 

volume killed. Total fish and invertebrate injury per unit volume killed was determined by 

running SIMAP’s biological model for the 99th percentile run of the scenario with the largest 

spill volume of crude oil. This model outputs the total injury in kilograms for each species, 

which we then divided by the volume killed for that run to determine the injury in kilograms per 

unit volume. This information is summarized for each location in the enclosed digital subappendix (Sub-appendix E). 
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It should be noted that these fish and invertebrate impacts were calculated assuming all the 

species were of average sensitivity to dissolved aromatics. Some species will be much more 

sensitive, and impacts to those species would be higher. There would also likely be species less 

sensitive than average. As there are insufficient toxicity data available to quantify the degree of 

sensitivity to aromatics for all species in every planning area, there is considerable uncertainty 

around the results based on average sensitivity. Experience with past modeling efforts indicate 

the uncertainty in the impact estimate related to species sensitivity is on the order of a factor ten 

higher or lower (95% confidence range). As there is a mix of species sensitivity present, the 

uncertainty in the total fish and invertebrate impact would be less than a factor ten. 

Impacts to Benthic Organisms 

Impacts to benthic organisms were planned to be evaluated using the sediment area exposed to 

dissolved aromatic concentrations above an acute threshold. However, after initial model 

testing, it was discovered that the dissolved sediment pore water concentration would not be 

acutely lethal for the spills evaluated. Only sublethal effects of those dissolved aromatic 

concentrations would likely be significant, and SIMAP is only able to evaluate acute lethal 

effects. While literature studies suggest that sublethal effects of the soluble aromatics and other 

hydrocarbons can occur, it was beyond the scope of our current work to perform a model 

evaluation of these potential impacts; thus, we excluded this impact category from further 

analysis. 

2.3.2 Shoreline Recreation and Tourism Impacts 

Impacts to shoreline recreation and tourism were evaluated as the shore length (by shore type) 

exposed to an oil thickness greater than 1 g/m2

. Regressions were developed of shore length 

exposed versus spill volume for each oil type for the following shore type categories: 

x Rock + Gravel; 

x Sand; 

x Mudflat + Wetland; and 

x Artificial. 

2.3.3 Shoreline Cleanup Impacts 

Shoreline cleanup impacts were evaluated as the shore area (by shore type) exposed to an oil 

thickness greater than 1 g/m2

. Regressions were developed of shore area exposed versus spill 

volume for each oil type for the following shore type categories: 

x Rock + Gravel; 

x Sand; 

x Mudflat + Wetland; and 

x Artificial. 
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2.3.4 Boating/Shipping and Water Surface Cleanup Impacts 

Boating/shipping and water surface cleanup impacts were combined into the same category 

because they were evaluated using the same impact measure, that is, the water surface area 

exposed to floating oil with a thickness greater than that of sheen, 1 g/m2

. Regressions were 

developed of water surface area exposed versus spill volume for each oil type. 

3. HABITAT EQUIVALENCY ANALYSIS (HEA) 

In Natural Resource Damage Assessments in the U.S., damages (costs) for biological impacts are 

commonly based on restoration costs to replace the ecological and related services. Habitat 

Equivalency Analysis (HEA) has been used by state and federal trustees to estimate the restored 

habitat required to compensate for habitat and biological resources injured, taking into account 

the time before the project is begun (lag time after the spill and injuries occur), the time for 

development of the restored habitat, the ultimate productivity of services in the new habitat as 

compared to that injured, the duration of the restoration project life, and discounting of future 

habitat services at 3 percent per year. The approach, equations, and assumptions are described in 

NOAA (1997, 1999), LA DEQ et al. (2003), and French-McCay and Rowe (2003). 

A detailed description of the HEA analysis used for OECM is provided as Sub-appendix C. 

4. SPILL RATE AND VOLUME ANALYSIS 

As part of the OECM analysis, Environmental Research Consulting used in-house databases, 

including data provided by BOEM, to summarize the spill risk from offshore exploration and 

production activities (i.e., from platforms, drilling rigs, drill ships, Floating Production, Storage 

and Offloading units, pipelines, and offshore service vessels) and from transport of oil by 

tankers. As part of this analysis, the probability of spillage (i.e., how likely is a spill to occur 

from any particular offshore facility or tanker) was calculated, as well as the probability 

distribution function of the spill volumes of different oil types should a spill occur from one of 

these sources. 

This analysis incorporated two sets of spill volume probability distribution functions for spills, 

developed based on past U.S. spill histories (as in Etkin, 2009). The first set was for spills 

associated with the OCS program (i.e., from offshore platforms/wells and pipelines, as well as 

from vessels servicing the platforms). The second set of probability distribution functions was 

for the volumes of spills associated with the alternative to OCS oil production (i.e., importing 

crude and products by tanker). For each of these spill and oil types, spill volumes were divided 

into the following size classes: very small, small, medium, large, very large, and for tanker spills 

only, extra-large volumes. The results of this analysis are summarized in Sub-appendix D.1

 

 

1

 Subsequent to the completion of this analysis, BOEM instructed the project team to apply the rates reported in 

Sub-Appendix F, which are based on BOEM’s own data and analysis, to spills associated with pipelines, platforms, 

and non-tanker vessels. As its data source, BOEM used the file named “All Petroleum Spills ≥ 1 Barrel from OCS 

Oil & Gas Activities by Size, Category and Year, 1964 to 2010,” which is on the BSEE website at 

http://www.bsee.gov/Inspection-and-Enforcement/Accidents-and-Incidents/Spills----Statistics-and-Summaries1996-2011.aspx (accessed September 15, 2011). OCS production since 2000 is available at: 
Oil Spill Modeling for the OECM 

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These data could be adjusted to reflect future changes (such as changes in tanker traffic, volumes 

of oil cargo being carried, etc.) or to include more (or less) of particular types of incidents as 

required for future analyses. 

5. MODEL RESULTS 

Regression results and biological database tables are provided in the enclosed digital subappendix (Sub-appendix E). Each set of regressions applies to a particular location, distance 

from shore, and biological database, as summarized in Table 7. 

Table 7. Summary of OECM regressions and biological databases. 

Planning Area 

Distance from Shore 

(nautical miles) 

Regression Set 

to Use Biological Database to Use 

Mid-Atlantic 0 - 50 ATL-ON Delmarva Shelf 

50+ ATL-OFF Offshore Mid-Atlantic 

Straits of Florida All SFL Straits of Florida 

Central Gulf of Mexico 0 - 65 CGM-ON LA-No. Texas Shelf 

65+ CGM-OFF Offshore Gulf of Mexico 

Southern California* Santa Barbara Channel SCA-SBVB Santa Barbara Channel 

Other SCA-SMB Central Calif. Offshore 

Washington/Oregon 0 -25 WAS-ON Washington Outer Coast 

25+ WAS-OFF Oregon-Wash. Offshore 

Gulf of Alaska (North Pacific) 0 -75 GOA-ON Yakutat 

75+ GOA-OFF Gulf of Alaska 

Cook Inlet/Shelikof Strait All CIS Shelikof Strait 

Bering Sea All BER So. Bering Sea Shelf 

Chukchi Sea 0 - 40 CHU-ON Chukchi Sea 

40+ CHU-OFF Chukchi Sea 

Beaufort Sea 0 - 40 BEA-ON Beaufort Sea 

40+ BEA-OFF Beaufort Sea 

*Rather than offshore and nearshore scenarios, for Southern California we modeled two locations, (1) the Santa 

Barbara-Ventura Basin, representing spills within the Santa Barbara Channel, and (2) the Santa Maria Basin, 

representing all other Southern California spills. 

 

http://www.boemre.gov/stats/OCSproduction.htm. OCS oil production for the period through 1999 was obtained 

from BOEM queries of its internal Technical Information Management System (TIMS). OECM continues to use the 

tanker spill rates reported in Sub-appendix D. Subsequent to the incorporation of the aforementioned spill rates 

from the databases cited above, BOEM instructed us to use the spill rates and analysis included in the Five Year 

Programmatic EIS. These spill rates and sizes for the 1996-2010 period are obtained from source data supporting 

Table 16. U.S. OCS Petroleum Spills, Overall Spill Size Characterization By Spill Source, 1996 – 2010 from the 

Anderson (2012) paper listed in Sub-appendix F (alternative). 
Oil Spill Modeling for the OECM 

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6. CONCLUSIONS 

The modeling performed herein addresses oil spills associated with OCS development and oil 

imports that effectively occur at or near the water surface. In the SIMAP modeling, we assumed 

the release was at the water surface. For subsurface releases, oil behavior and fate would be 

considerably different than that modeled herein. Because the oil would not be immediately in 

contact with the atmosphere, the soluble and semi-soluble aromatics, the most toxic fractions of 

the oil, would dissolve rather than evaporating (to varying degrees, depending on the compound). 

This would result in considerably more impact to water column biota. The impacts to water 

column biota may be increased by application of dispersants either on the water surface or at the 

source of the release. Sea-bed blowouts are certainly a much more detrimental situation for 

water column biota, and application of dispersants to the release at the source amplifies the 

impact considerably. Thus, the environmental impacts estimated by OECM, as configured 

herein, are not applicable to subsurface (e.g., seabed) releases, and particularly not to crude oil 

blowouts. 

In addition, the spill volumes used to develop the regressions covering water-surface spills span 

the range from small spills to 1 million gallons of crude oil. The largest tanker spill in U.S. 

history, the Exxon Valdez oil spill (EVOS), was 11 million gallons. The EVOS was not a 

catastrophic loss of the entire cargo; the largest “super” tankers used today (Ultra-Large Cargo 

Carriers, ULCCs) transport up to 3.52 million barrels (148 million gallons). While extrapolation 

of the regressions to 11 million gallons might be justifiable as reliable, the model results cannot 

be reliably extrapolated to spills of a size on the order of 148 million gallons. 

Note that for surface spills in the range of volumes studied, the calculated physical spreading and 

transport of oil, exposure doses, and percentages of biota affected would not require updating if 

there are changes in receptors that BOEM would wish to evaluate or if biological densities or 

distributions change. Physical processes are a function of environmental conditions, and the 

model design allows for selection of appropriate environmental conditions in each planning area, 

which in turn will indicate the appropriate regression equations quantifying exposure to employ 

for the planning area of interest. Thus, the SIMAP-modeled exposure data provided in OECM 

will not need to be updated. 

Furthermore, we do not anticipate a need to update the regression models of exposure 

area/volume versus oil type, spill size, and environmental conditions, unless in the future BOEM 

sees the need to develop a more detailed and site-specific model than is described herein. The 

modeling used to develop the regressions incorporated into OECM was generalized to allow 

extrapolation to all potential (surface) spills in all potential locations of 26 planning areas; thus, 

these results will not be accurate for specific spill cases. For such incidents, the environmental 

and biological specifics for the scenario should be used to estimate environmental impacts when 

case-specific spill assessments are performed. 
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7. REFERENCES 

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Consulting for the American Petroleum Institute (API). API Publication 356. 86 pp. 

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W. French III, D. Gifford, J. McCue, G. Brown, E. MacDonald, J. Quirk, S. Natzke, R. 

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Model, OilToxEx. Environmental Toxicology and Chemistry 21(10): 2080-2094, October 

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French McCay, D.P., 2003. Development and Application of Damage Assessment Modeling: 

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French McCay, D.P, 2009. State-of-the-Art and Research Needs for Oil Spill Impact 

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Environmental Contamination and Response, Emergencies Science Division, Environment 

Canada, Ottawa, ON, Canada, pp. 601-653. 

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at multiple trophic levels. Marine Ecology Progress Series 264:235-249. 

Gundlach, E.R., 1987. Oil holding capacities and removal coefficients for different shoreline 

types to computer simulate spills in coastal waters. In: Proceedings of the 1987 Oil Spill 

Conference, pp. 451-457. 

Kullenberg, G. (ed.), 1982. Pollutant Transfer and Transport in the Sea. Volume I. CRC Press, 

Boca Raton, Florida. 227 p. 

LA DEQ et al., 2003. The Louisiana Regional Restoration Planning Program, Draft Regional 

Restoration Plan, Region 2. Louisiana Department of Environmental Quality, Louisiana 

Department of Natural Resources, Louisiana Department of Wildlife and Fisheries, 
Oil Spill Modeling for the OECM 

October 25, 2011

A-16 

Louisiana Oil Spill Coordinator’s Office, National Oceanic and Atmospheric 

Administration, U.S. Department of the Interior, September 2003. 

National Oceanic and Atmospheric Administration (NOAA), 1997. Natural resource damage 

assessment guidance document: scaling compensatory restoration actions (Oil Pollution Act 

of 1990), NOAA Damage Assessment Center, Silver Spring, MD. 

National Oceanic and Atmospheric Administration (NOAA), 1999. Habitat Equivalency 

Analysis: An Overview. NOAA Damage Assessment Center, Silver Spring, MD, 23p. 

Okubo, A., 1971. Oceanic diffusion diagrams. Deep-Sea Research 8: 789-802. 

Okubo, A. and R.V. Ozmidov, 1970. Empirical dependence of the coefficient of horizontal 

turbulent diffusion in the ocean on the scale of the phenomenon in question. Atmospheric 

and Ocean Physics 6(5):534-536. 

Reed, M., D.P. French, S.Feng, F.W. French III, E. Howlett, K, Jayko, W.Knauss, J. McCue, S. 

Pavignano, S. Puckett, H. Rines, R.Bishop, M. Welsh, and J. Press, 1996. The CERCLA 

type a natural resource damage assessment model for the Great Lakes environments 

(NRDAM/GLE), Vol. I - III. Final report, submitted to Office of Environmental Policy and 

Compliance, U.S. Department of the Interior, Washington, DC, by Applied Science 

Associates, Inc., Narragansett, RI, April 1996, Contract No. 14-01-0001-88-C-27.
A-17 

Oil Spill Modeling for the Offshore Environmental Cost Model (OECM) 

SUB-APPENDIX A 

Detailed Model Input Information for Each Location 
A-18 

Table of Contents 

Mid-Atlantic .............................................................................................................................. 22

Straits of Florida ....................................................................................................................... 27

Gulf of Mexico .......................................................................................................................... 32

Southern California ................................................................................................................... 38

Washington/Oregon .................................................................................................................. 43

Gulf of Alaska ........................................................................................................................... 52

Cook Inlet/Shelikof Strait ......................................................................................................... 58

Bering Sea ................................................................................................................................. 64

Chukchi Sea .............................................................................................................................. 69

Beaufort Sea .............................................................................................................................. 77

References ................................................................................................................................. 85
A-19 

List of Tables 

Table A-1. Dimensions of the habitat grid cells used to compile statistics for Mid-Atlantic model 

runs. .......................................................................................................................... 23

Table A-2. Mid-Atlantic nearshore spill sites. ............................................................................. 26

Table A-3. Mid-Atlantic offshore spill sites. ............................................................................... 26

Table A-4. Dimensions of the habitat grid cells used to compile statistics for Straits of Florida 

model runs. ............................................................................................................... 28

Table A-5. Straits of Florida spill sites. ....................................................................................... 31

Table A-6. Dimensions of the habitat grid cells used to compile statistics for Gulf of Mexico 

model runs. ............................................................................................................... 33

Table A-7. Gulf of Mexico nearshore spill sites. ......................................................................... 36

Table A-8. Gulf of Mexico offshore spill sites. ........................................................................... 37

Table A-9. Dimensions of the habitat grid cells used to compile statistics for Southern California 

model runs. ............................................................................................................... 39

Table A-10. Southern California Santa Barbara-Ventura Basin spill sites. .................................. 42

Table A-11. Southern California Santa Maria Basin spill sites. ................................................... 42

Table A-12. Dimensions of the habitat grid cells used to compile statistics for 

Washington/Oregon model runs. .............................................................................. 44

Table A-13. Washington/Oregon nearshore spill sites. ................................................................ 47

Table A-14. Washington/Oregon offshore spill sites. .................................................................. 49

Table A-15. Dimensions of the habitat grid cells used to compile statistics for Gulf of Alaska 

model runs. ............................................................................................................... 53

Table A-16. Gulf of Alaska nearshore spill sites. ......................................................................... 56

Table A-17. Gulf of Alaska offshore spill sites. ........................................................................... 57

Table A-18. Dimensions of the habitat grid cells used to compile statistics for Cook 

Inlet/Shelikof Strait model runs. ............................................................................... 59

Table A-19. Cook Inlet/Shelikof Strait spill sites. ........................................................................ 62

Table A-20. Dimensions of the habitat grid cells used to compile statistics for Bering Sea model 

runs. .......................................................................................................................... 65

Table A-21. Bering Sea spill sites................................................................................................. 68

Table A-22. Dimensions of the habitat grid cells used to compile statistics for Chukchi Sea 

model runs. ............................................................................................................... 70

Table A-23. Chukchi Sea nearshore spill sites. ............................................................................ 72

Table A-24. Chukchi Sea offshore spill sites. ............................................................................... 74

Table A-25. Dimensions of the habitat grid cells used to compile statistics for Beaufort Sea 

model runs. ............................................................................................................... 78

Table A-26. Beaufort Sea nearshore spill sites. ............................................................................ 81

Table A-27. Beaufort Sea offshore spill sites. .............................................................................. 82
A-20 

List of Figures 

Figure A-1. Habitat grid developed for the Mid-Atlantic region. ................................................ 22

Figure A-2. Depth grid developed for the Mid-Atlantic region................................................... 23

Figure A-3. Example of current component data used in modeling for the Mid-Atlantic region. 

Vector length indicates speed in the indicated direction. ........................................ 24

Figure A-4. Spill sites developed for the Mid-Atlantic region. ................................................... 25

Figure A-5. Habitat grid developed for the Straits of Florida region. ......................................... 27

Figure A-6. Depth grid developed for the Straits of Florida region. ........................................... 28

Figure A-7. Example of current component data used in modeling for the Straits of Florida 

region. Vector length indicates speed in the indicated direction. ........................... 29

Figure A-8. Spill sites developed for the Straits of Florida region. ............................................ 30

Figure A-9. Habitat grid developed for the Gulf of Mexico region. ........................................... 32

Figure A-10. Depth grid developed for the Gulf of Mexico region. ............................................. 33

Figure A-11. Example current component data used in modeling for the Gulf of Mexico region. 

Vector length indicates speed in the indicated direction. ........................................ 34

Figure A-12. Spill sites developed for the Gulf of Mexico region. .............................................. 35

Figure A-13. Habitat grid developed for the Southern California region. .................................... 38

Figure A-14. Depth grid developed for the Southern California region. ...................................... 39

Figure A-15. Example current component data used in modeling for the Southern California 

region. Vector length indicates speed in the indicated direction. ........................... 40

Figure A-16. Spill sites developed for the Southern California region. ....................................... 41

Figure A-17. Habitat grid developed for the Washington/Oregon region. ................................... 43

Figure A-18. Depth grid developed for the Washington/Oregon region. ..................................... 44

Figure A-19. Extent of current data used in modeling for the Washington/Oregon region. ........ 45

Figure A-20. Spill sites developed for the Washington/Oregon region. ....................................... 46

Figure A-21. Habitat grid developed for the Gulf of Alaska region. ............................................ 52

Figure A-22. Depth grid developed for the Gulf of Alaska region. .............................................. 53

Figure A-23. Example current component data used in modeling for the Gulf of Alaska region. 

Vector length indicates speed in the indicated direction. ........................................ 54

Figure A-24. Spill sites developed for the Gulf of Alaska region. ............................................... 56

Figure A-25. Habitat grid developed for the Cook Inlet/Shelikof Strait region. .......................... 58

Figure A-26. Depth grid developed for the Cook Inlet/Shelikof Strait region. ............................ 59

Figure A-27. Example current component data used in modeling for the Cook Inlet/Shelikof 

Strait region. Vector length indicates speed in the indicated direction. ................. 60

Figure A-28. Spill sites developed for the Cook Inlet/Shelikof Strait region. .............................. 62

Figure A-29. Habitat grid developed for the Bering Sea region. .................................................. 64

Figure A-30. Depth grid developed for the Bering Sea region. .................................................... 65

Figure A-31. Example current component data used in modeling for the Bering Sea region. 

Vector length indicates speed in the indicated direction. ........................................ 66

Figure A-32. Spill sites developed for the Bering Sea region. ..................................................... 67

Figure A-33. Habitat grid developed for the Chukchi Sea region. ............................................... 69

Figure A-34. Depth grid developed for the Chukchi Sea region. ................................................. 70

Figure A-35. Example current component data used in modeling for the Chukchi Sea region. 

Vector length indicates speed in the indicated direction. ........................................ 71

Figure A-36. Spill sites developed for the Chukchi Sea region. ................................................... 72

Figure A-37. Habitat grid developed for the Beaufort Sea region. ............................................... 77
A-21 

Figure A-38. Depth grid developed for the Beaufort Sea region. ................................................. 78

Figure A-39. Example current component data used in modeling for the Beaufort Sea region. 

Vector length indicates speed in the indicated direction. ........................................ 79

Figure A-40. Spill sites developed for the Beaufort Sea region. .................................................. 81
A-22 

Mid-Atlantic 

Habitat Grid 

The digital shoreline, shore type, and habitat mapping for the Mid-Atlantic region were obtained 

from the Environmental Sensitivity Index (ESI) Atlas databases compiled for the Eastern U.S. 

states of New Jersey to North Carolina by Research Planning, Inc. (RPI). These data are 

distributed by NOAA HAZMAT (Seattle, WA). 

Depth data were based on soundings available from the NOAA NOS Hydrographic Survey Data 

(NOAA, 2009). Grid cells with missing data were then filled with ETOP01 modeled data 

(Amante et al., 2009). ETOP01 is a one arc-minute global relief model of the Earth’s surface 

that integrates land topography and ocean bathymetry. 

The gridded habitat and depth data are shown in Figures A-1 and A-2. 

Figure A-1. Habitat grid developed for the Mid-Atlantic region. 
A-23 

Figure A-2. Depth grid developed for the Mid-Atlantic region. 

Table A-1. Dimensions of the habitat grid cells used to compile statistics for Mid-Atlantic 

model runs. 

Habitat grid OECM-ATLANTIC.HAB 

Grid W edge 79o 58.856’W

Grid S edge 31o 59.649’ N

Cell size (o longitude) 0.013o W 

Cell size (o latitude) 0.013o N 

Cell size (m) west-east 1,233.23 

Cell size (m) south-north 1,454.10 

# cells west-east 991 

# cells south-north 632 

Water cell area (m2) 1,793,233.00 

Shore cell length (m) 1,339.12 

Shore cell width – Rocky shore (m) 2.0 

Shore cell width – Artificial shore (m) 0.1 

Shore cell width – Gravel beach (m) 3.0 

Shore cell width – Sand beach (m) 10.0 

Shore cell width – Mud flat (m) 140.0 

Shore cell width – Wetlands (fringing, m) 140.0 
A-24 

Currents 

Currents were based on the study "Mid-Atlantic Ocean Model Calculations" performed for 

BOEM by Oey and Xu (2010, Princeton University). The hydrodynamic model is the Princeton 

Ocean Model (POM; http://www.aos.princeton.edu/WWWPUBLIC/htdocs.pom/), which 

includes wind, waves, rivers, tides, slope and shelf-break currents, the Gulf Stream, rings and 

eddies, as well as the large-scale Atlantic Ocean influences. The model operates a nesting 

scheme with ECCO (Estimating the Circulation and Climate of the Ocean; an MIT8 JPL-SIO 

consortium model based on the MIT GCM with data assimilation). The hindcast simulation 

(year 1993-2008) was forced by winds from the blended NCEP/QSCAT product and a regional 

high-resolution atmospheric model, surface heat and salt fluxes, weekly discharges from major 

rivers along the east coast, ECCO temperature and salinity fields as initial conditions, ECCO 

density and transport at the eastern PROFS (Princeton Regional Ocean Forecast System) open 

boundary in the Atlantic Ocean and tides. BOEM provided the hindcast data set, and ASA 

subsequently subset surface velocities to the appropriate SIMAP domain for the period 1993 to 

June 2000. 

Figure A-3. Example of current component data used in modeling for the Mid-Atlantic 

region. Vector length indicates speed in the indicated direction. 
A-25 

Winds 

Standard meteorological data were acquired from the National Data Buoy Center Internet site for 

the nearest NDBC buoy, number 44009, “Delaware Bay,” at 38.464°N, 74.702°W. Hourly mean 

wind speed and direction for the time period 12/27/1992 to 2/19/2000 were compiled in the 

SIMAP model input file format. 

Spill Sites 

Spill sites for the Mid-Atlantic region were placed within the Proposed Final Program Area 

(2007-2012), including buffer areas and the non-obstruction zone (Figure A-4). Twenty spill 

sites were placed within the nearshore spill area, and twenty spill sites were placed within the 

offshore spill area. The coordinates of these points are provided in Tables A-2 and A-3. 

Figure A-4. Spill sites developed for the Mid-Atlantic region. 
A-26 

Table A-2. Mid-Atlantic nearshore spill sites. 

Spill Site # Latitude Longitude 

1 36.64093 -75.81953 

2 36.89273 -75.81953 

3 36.64093 -75.58961 

4 36.89273 -75.58961 

5 37.14602 -75.58961 

6 37.39152 -75.58961 

7 36.64093 -75.33350 

8 36.89273 -75.33350 

9 37.14602 -75.33350 

10 37.39152 -75.33350 

11 37.64084 -75.33350 

12 36.64093 -75.10066 

13 36.89273 -75.10066 

14 37.14602 -75.10066 

15 37.39152 -75.10066 

16 37.64084 -75.10066 

17 37.87324 -75.10066 

18 37.39152 -74.85619 

19 37.64084 -74.85619 

20 37.64084 -74.62335 

Table A-3. Mid-Atlantic offshore spill sites. 

Spill Site # Latitude Longitude 

1 36.67137 -74.81720 

2 36.82661 -74.81720 

3 36.99366 -74.81720 

4 37.18119 -74.81720 

5 36.82661 -74.42013 

6 36.99366 -74.42013 

7 37.18119 -74.42013 

8 37.36466 -74.42013 

9 37.52585 -74.42013 

10 36.82661 -73.99588 

11 36.99366 -73.99588 

12 37.18119 -73.99588 

13 37.36466 -73.99588 

14 36.68968 -73.55950 

15 36.82661 -73.55950 

16 36.99366 -73.55950 

17 37.14251 -73.55950 

18 36.66295 -73.11707 

19 36.82661 -73.11707 

20 36.66295 -72.67766 
A-27 

Straits of Florida 

Habitat Grid 

The digital shoreline, shore type, and habitat mapping for the Straits of Florida were obtained 

from the Florida Environmental Sensitivity Index (ESI) Atlas database compiled for the state of 

Florida by the Florida and Wildlife Institute (FWRI). 

Bathymetry data were available from bathymetric contours contained within the GEBCO Digital 

Atlas (GEBCO, 2003). 

The gridded habitat and depth data are shown in Figures A-5 and A-6. 

Figure A-5. Habitat grid developed for the Straits of Florida region. 
A-28 

Figure A-6. Depth grid developed for the Straits of Florida region. 

Table A-4. Dimensions of the habitat grid cells used to compile statistics for Straits of 

Florida model runs. 

Habitat grid OECM-FLSTRAITS.HAB 

Grid W edge 81o 58.520’W

Grid S edge 23o 52.095’ N

Cell size (o longitude) 0.0045 W 

Cell size (o latitude) 0.0045o N 

Cell size (m) west-east 455.97 

Cell size (m) south-north 498.61 

# cells west-east 600 

# cells south-north 993 

Water cell area (m2) 227,352.05 

Shore cell length (m) 476.81 

Shore cell width – Rocky shore (m) 2.0 

Shore cell width – Artificial shore (m) 0.1 

Shore cell width – Gravel beach (m) 3.0 

Shore cell width – Sand beach (m) 10.0 

Shore cell width – Mud flat (m) 140.0 

Shore cell width – Wetlands (fringing, m) 140.0 
A-29 

Currents 

Currents for the Straits of Florida were mainly assembled from CUPOM (Colorado University 

Princeton Ocean Model; see Gulf of Mexico currents description for more detail). As the 

CUPOM model domain ends at 80.85°W (approximately the narrowest section between Cuba 

and Florida), the eastern portion was augmented using currents from POP (Parallel Ocean 

Program). CUPOM currents are available daily from year 1993 to 1999. Hence, the western 

portion of currents vary in time, however currents in the eastern portion were filled with timeaverage of POP currents, thus constant in time. POP is the global ocean circulation model forced 

by observed temperature, salinity, and wind stress (Maltrud et al., 1998). The original simulation 

period extended from 1/1/1985 to 12/31/1995, and produced daily outputs with an average 

horizontal resolution of 1/6 degree. 

Figure A-7. Example of current component data used in modeling for the Straits of Florida 

region. Vector length indicates speed in the indicated direction. 

Winds 

Standard meteorological data were acquired from the National Data Buoy Center Internet site for 

the nearest NDBC buoys with sufficient records, number FWYF1, “Fowey Rocks,” at 25.590°N, 

80.097°W, and number SMKF1, “Sombrero Key,” at 24.627°N, 81.110°W. Hourly mean wind 

speed and direction for the time period 12/31/1995 to 12/28/2008 (FWYF1) and 1/1/1993 to 

11/30/1999 (SMKF1) were compiled in the SIMAP model input file format. 
A-30 

Spill Sites 

Spill sites for the Straits of Florida region were randomly distributed within a small portion of 

the Straits of Florida Planning Area (Figure A-8) to provide a representative set of model results 

for potential release locations the indicated distances from shore. The locations were placed on 

the upstream side of the model grid, so the transport would remain within the grid. A total of 

twenty spill sites were placed within the spill area. The coordinates of these points are provided 

in Table A-5. 

Figure A-8. Spill sites developed for the Straits of Florida region. 
A-31 

Table A-5. Straits of Florida spill sites. 

Spill Site # Latitude Longitude 

1 24.04264 -81.67753 

2 24.30002 -81.59861 

3 24.16146 -81.74880 

4 24.13037 -81.70384 

5 24.02205 -81.60536 

6 24.20515 -81.53698 

7 24.14667 -81.48918 

8 24.10032 -81.55524 

9 24.13743 -81.62534 

10 24.28058 -81.78421 

11 24.10804 -81.79195 

12 24.25780 -81.64454 

13 24.30164 -81.69879 

14 24.27776 -81.51076 

15 24.20202 -81.59468 

16 24.22763 -81.71558 

17 24.04198 -81.75251 

18 24.10701 -81.66911 

19 24.31069 -81.56680 

20 24.03964 -81.49442 
A-32 

Gulf of Mexico 

Habitat Grid 

The digital shoreline used to create the habitat grid was the “Land and Water Interface of the 

Louisiana Coastal Region” from LOSCO, published in 2000. Although, there is a more recent 

shoreline from LOSA published in the year 2002, the 2000 shoreline was a better fit to the other 

habitat GIS data that were used to create the grid. Shore type and habitat mapping were obtained 

from the G-WIS Environmental Sensitivity Index dataset published by the U.S. Minerals 

Management Service (MMS) and the U.S. Geological Survey (USGS) Land Cover Institute 

(LCI). 

Bathymetry data were available from bathymetric contours contained within the GEBCO Digital 

Atlas (GEBCO, 2003). 

The gridded habitat and depth data are shown in Figures A-9 and A-10. 

Figure A-9. Habitat grid developed for the Gulf of Mexico region. 
A-33 

Figure A-10. Depth grid developed for the Gulf of Mexico region. 

Table A-6. Dimensions of the habitat grid cells used to compile statistics for Gulf of Mexico 

model runs. 

Habitat grid OECM-CENTRALGOM.HAB 

Grid W edge 94o 59.638’W

Grid S edge 26o 18.173’ N

Cell size (o longitude) 0.0077o W 

Cell size (o latitude) 0.0068o N 

Cell size (m) west-east 768.73 

Cell size (m) south-north 753.92 

# cells west-east 900 

# cells south-north 600 

Water cell area (m2) 579,564.12 

Shore cell length (m) 761.29 

Shore cell width – Rocky shore (m) 1.0 

Shore cell width – Artificial shore (m) 0.1 

Shore cell width – Gravel beach (m) 2.0 

Shore cell width – Sand beach (m) 10.0 

Shore cell width – Mud flat (m) 20.0 

Shore cell width – Wetlands (fringing, m) 50.0 
A-34 

Currents 

Currents for the Gulf of Mexico were based on a study by Kantha et al. (1999) that produced 

current hindcasts of the Gulf of Mexico using the CUPOM model. The model was developed by 

Dr. Lakshmi Kantha and colleagues at the University of Colorado (CU) with partial support from 

an industry-sponsored study on Climatology and Simulation of Eddies (CASE). It is the CU 

version of the Princeton Ocean Model adapted for the Gulf of Mexico, referred to by the 

acronym CUPOM. The horizontal resolution is 1/12 degree and the vertical resolution is 24 

sigma levels. The model run was for the years 1993 through 1999. The model assimilates 

altimeter data for the region in water depths of 1000 meters or more. It also assimilates satellite 

sea surface temperature data, but uses climatological sea surface salinity. The 6-hourly, 1.125° 

resolution ECMWF wind stresses are used for the wind forcing. The inflow boundary is at 

21.333°N in the Yucatan Channel, with a geophysically balanced inflow prescribed using typical 

monthly temperature and salinity profiles. The outflow boundary is at the Florida Straits; the 

boundary condition is set to be balanced and in phase with the inflow boundary. The data 

assimilation module is the same as in Horton et al. (1997) and Clifford et al. (1997). Details of 

the specifics with respect to the Gulf of Mexico can be found in Kantha et al. (1999). 

Figure A-11. Example current component data used in modeling for the Gulf of Mexico 

region. Vector length indicates speed in the indicated direction. 
A-35 

Winds 

Standard meteorological data were acquired from the National Data Buoy Center Internet site for 

the nearest NDBC buoys with sufficient records, number 42001, “Mid-Gulf,” at 25.900°N, 

89.667°W, and number 42019, “Freeport,” at 27.913°N, 95.353°W. Hourly mean wind speed 

and direction for the time period 1/1/1993 to 11/30/1999 (42001) and 1/1/1993 to 12/14/1999 

(42019) were compiled in the SIMAP model input file format. 

Spill Sites 

Spill sites for the Gulf of Mexico region were randomly distributed within a portion of the 

Central Gulf of Mexico Planning Area (Figure A-12) to provide representative results for the 

entire planning area (and other Gulf of Mexico planning areas). The delineation between the 

nearshore and offshore spill areas was based on the 200 meter depth contour. Twenty-five spill 

sites were placed within the nearshore spill area, and twenty-five spill sites were placed within 

the offshore spill area. The coordinates of these points are provided in Tables A-7 and A-8. 

Figure A-12. Spill sites developed for the Gulf of Mexico region. 
A-36 

Table A-7. Gulf of Mexico nearshore spill sites. 

Spill Site # Latitude Longitude 

1 28.27809 -90.63407 

2 28.32999 -90.57522 

3 28.18947 -90.36839 

4 28.09871 -90.96342 

5 28.34223 -90.38914 

6 28.40739 -90.13332 

7 28.33234 -90.04664 

8 28.09267 -90.83830 

9 28.43589 -90.44322 

10 28.46975 -90.68286 

11 28.41958 -90.91776 

12 28.46293 -90.22270 

13 28.25360 -90.50444 

14 28.22976 -90.96986 

15 28.20711 -90.21975 

16 28.30773 -90.17396 

17 28.22805 -90.16437 

18 28.06677 -90.49718 

19 28.03898 -90.70642 

20 28.22692 -90.72602 

21 28.24542 -90.82115 

22 28.37421 -90.83320 

23 28.47609 -90.01843 

24 28.14758 -90.64695 

25 28.31080 -90.94804 
A-37 

Table A-8. Gulf of Mexico offshore spill sites. 

Spill Site # Latitude Longitude 

1 28.02883 -90.54000 

2 27.97720 -90.65663 

3 27.95121 -90.45292 

4 28.12660 -90.11544 

5 27.84080 -90.63855 

6 28.00041 -90.31300 

7 27.83616 -90.78981 

8 27.72352 -90.22744 

9 27.72410 -90.77993 

10 27.71269 -90.95298 

11 27.65610 -90.13622 

12 27.96562 -90.83508 

13 28.09683 -90.23034 

14 27.88465 -90.76178 

15 27.61588 -90.42420 

16 27.67656 -90.66392 

17 27.71794 -90.09253 

18 27.91786 -90.15377 

19 27.85827 -90.31076 

20 27.61158 -90.87315 

21 27.80592 -90.49699 

22 27.84537 -90.02027 

23 27.89021 -90.95233 

24 28.00849 -90.07113 

25 27.74505 -90.38673 
A-38 

Southern California 

Habitat Grid 

The digital shoreline, shore type, and habitat mapping for Central and Southern California were 

obtained from Environmental Sensitivity Index (ESI) Atlas database compiled for the area by 

Research Planning, Inc. (RPI). These data are distributed by NOAA Hazmat (Seattle, WA). 

Bathymetry data were available from bathymetric contours contained within the GEBCO Digital 

Atlas (GEBCO, 2003). 

The gridded habitat and depth data are shown in Figures A-13 and A-14. 

Figure A-13. Habitat grid developed for the Southern California region. 
A-39 

Figure A-14. Depth grid developed for the Southern California region. 

Table A-9. Dimensions of the habitat grid cells used to compile statistics for Southern 

California model runs. 

Habitat grid OECM-SOUTHERNCA.HAB 

Grid W edge 124o 18.873’W

Grid S edge 32o 33.413’ N

Cell size (o longitude) 0.0073o W 

Cell size (o latitude) 0.0073o N 

Cell size (m) west-east 681.28 

Cell size (m) south-north 808.30 

# cells west-east 997 

# cells south-north 643 

Water cell area (m2) 550,682.75 

Shore cell length (m) 742.08 

Shore cell width – Rocky shore (m) 2.0 

Shore cell width – Artificial shore (m) 0.1 

Shore cell width – Gravel beach (m) 2.0 

Shore cell width – Sand beach (m) 10.0 

Shore cell width – Mud flat (m) 120.0 

Shore cell width – Wetlands (fringing, m) 120.0 
A-40 

Currents 

Mean offshore currents for January, March, May, July, September, and November were 

compiled using data from the California Cooperative Oceanic Fisheries Investigations Atlas No. 

4 (State of California Marine Research Committee, 1966). Data were taken from maps showing 

mean monthly geostrophic flow off the coast of California for the years 1950-1965. These maps 

contain contour lines showing ocean surface topography. The current files were created by 

marking points along each of the contour lines and placing corresponding current vectors at those 

points. The magnitude of the current vectors was determined by measuring the distance between 

adjacent contour lines and estimating the current velocity using a conversion chart provided in 

the atlas. Once these vectors were entered into a grid, a vector spreading algorithm filled in the 

vectors for the remainder of the gridded area. The current velocities are estimates and have an 

error margin of roughly ± 5 cm/s. 

Figure A-15. Example current component data used in modeling for the Southern 

California region. Vector length indicates speed in the indicated direction. 

Winds 

Standard meteorological data were acquired from the National Data Buoy Center Internet site for 

the nearest NDBC buoys with sufficient records, number 46011, “Santa Maria,” at 34.868°N, 

120.857°W, and number 46053, “E. Santa Barbara,” at 34.248°N, 119.841°W. Hourly mean 
A-41 

wind speed and direction for the time period 1/1/1998 to 11/23/2009 (46011) and 4/28/1998 to 

12/31/2009 (46053) were compiled in the SIMAP model input file format. 

Spill Sites 

Spill sites for the Southern California region were randomly distributed within two areas, the 

Santa Maria Basin Draft Proposed Program Area (2010-2015), and a representative portion of 

the Santa Barbara-Ventura Basin Draft Proposed Program Area (2010-2015) (Figure A-16). Ten 

spill sites were placed within the Santa Barbara-Ventura Basin spill area, and twenty spill sites 

were placed within the Santa Maria Basin spill area. The coordinates of these points are provided 

in Tables A-10 and A-11. 

Figure A-16. Spill sites developed for the Southern California region. 
A-42 

Table A-10. Southern California Santa Barbara-Ventura Basin spill sites. 

Spill Site # Latitude Longitude 

1 34.14906 -119.70352 

2 34.18444 -119.60804 

3 34.20766 -119.70123 

4 34.20849 -119.57720 

5 34.13387 -119.61862 

6 34.13745 -119.75179 

7 34.16097 -119.55994 

8 34.18316 -119.74798 

9 34.18931 -119.64070 

10 34.16694 -119.68029 

Table A-11. Southern California Santa Maria Basin spill sites. 

Spill Site # Latitude Longitude 

1 35.21500 -121.06691 

2 34.96865 -121.15132 

3 34.85808 -120.93869 

4 34.51589 -120.89091 

5 34.87350 -121.13914 

6 35.03428 -120.89563 

7 35.60317 -121.45206 

8 34.64221 -120.77935 

9 35.10547 -121.16538 

10 34.70945 -121.06548 

11 35.14592 -121.36015 

12 34.79824 -120.78795 

13 35.54632 -121.24035 

14 35.32874 -121.38281 

15 35.04960 -121.06353 

16 34.43887 -120.74410 

17 35.49837 -121.33215 

18 35.40780 -121.39807 

19 35.35871 -121.16099 

20 35.22194 -120.97173 
A-43 

Washington/Oregon 

Habitat Grid 

The digital shoreline, shore type, and habitat mapping for the outer coast of Washington and the 

Columbia River were obtained from Environmental Sensitivity Index (ESI) Atlas database 

compiled for the area by Research Planning, Inc. (RPI). These data are distributed by NOAA 

Hazmat (Seattle, WA). 

Depth data for the offshore and coastal waters were obtained from Hydrographic Survey Data 

supplied on CD-ROM by the NOAA National Geophysical Data Center. Hydrographic survey 

data consist of large numbers of individual depth soundings. The depth soundings were 

interpolated into the model grid for each area by averaging all soundings falling within a cell. 

The gridded habitat and depth data are shown in Figures A-17 and A-18. 

Figure A-17. Habitat grid developed for the Washington/Oregon region. 
A-44 

Figure A-18. Depth grid developed for the Washington/Oregon region. 

Table A-12. Dimensions of the habitat grid cells used to compile statistics for 

Washington/Oregon model runs. 

Habitat grid OC_SL_HAB-DEPTH.HAB 

Grid W edge 126o 13.958’W

Grid S edge 46o 0.085’ N

Cell size (o longitude) 0.0031o W 

Cell size (o latitude) 0.0031o N 

Cell size (m) west-east 236.87 

Cell size (m) south-north 340.99 

# cells west-east 875 

# cells south-north 993 

Water cell area (m2) 80,769.68 

Shore cell length (m) 284.20 

Shore cell width – Rocky shore (m) 3.0 

Shore cell width – Artificial shore (m) 0.1 

Shore cell width – Gravel beach (m) 4.0 

Shore cell width – Sand beach (m) 15.0 

Shore cell width – Mud flat (m) 210.0 

Shore cell width – Wetlands (fringing, m) 210.0 
A-45 

Currents 

A barotropic hydrodynamic model, HYDROMAP (Isaji et al., 2002) was used to obtain the 

depth-averaged tidal currents for this region. HYDROMAP is a globally re-locatable 

hydrodynamic model, capable of simulating complex circulation patterns due to tidal forcing and 

wind stress. HYDROMAP operates over a spatially-nested, rectangular grid that may have up to 

six step-wise changes in resolution in the horizontal plane. The spatial nesting capability allows 

the model resolution to step up as land or complex bathymetry is approached. The spatial 

nesting of the grid provided the hydrodynamic model with a good resolution on the offshore and 

a fine resolution near the coast, especially in Grays Harbor, Grays Bay, and Willapa Bay. The 

grid used in this study consisted of 22,200 active water cells, with cell size varying from 5 km x 

5 km in the offshore to about 625 m x 625 m near the coast. The tidal forcing for the 5 major 

harmonic constituents (M2, S2, N2, K1, and O1), derived from the Global Ocean Tidal Model 

(TPOX5.1) developed at the Oregon State University (Egbert et al., 1994) was applied along the 

offshore open boundaries. 

Seasonal components (climatic winter and summer) of the offshore currents for the present study 

were assembled from results of the three-dimensional hydrodynamic simulations from a highresolution global ocean circulation model, Parallel Ocean Program (POP). The time-averaged 

daily outputs of the results from POP, for the global ocean at a horizontal resolution of 1/6 

degree, forced by observed temperature and wind stress during 1985-1995 (Maltrud et al., 1998) 

was used to obtain the seasonally averaged currents used in the present study. The seasonal 

currents thus assembled from POP compared well with a schematic of the large-scale boundary 

currents off the U.S. west coast given in Hickey (1998). 

Figure A-19. Extent of current data used in modeling for the Washington/Oregon region. 
A-46 

Winds 

Standard meteorological data were acquired from the National Data Buoy Center Internet site for 

the nearest NDBC buoy with sufficient records, number 46041, “Cape Elizabeth,” at 47.353°N, 

124.731°W. Hourly mean wind speed and direction for the time period 6/9/1987 to 12/31/2004 

were compiled in the SIMAP model input file format. 

Spill Sites 

Spill sites for the Washington/Oregon region were randomly distributed within a portion of the 

Washington/Oregon Planning Area (Figure A-20). The delineation between the nearshore and 

offshore spill areas was based on the 200 meter depth contour. One hundred spill sites were 

placed within the nearshore area, and one hundred spill sites were placed within the offshore 

area. The coordinates of these points are provided in Tables A-13 and A-14. 

Figure A-20. Spill sites developed for the Washington/Oregon region. 
A-47 

Table A-13. Washington/Oregon nearshore spill sites. 

Spill Site # Latitude Longitude 

1 47.51954 -124.74392 

2 47.14469 -124.55124 

3 47.46402 -124.74055 

4 47.11469 -124.81603 

5 47.25659 -124.27336 

6 47.21658 -124.79418 

7 47.43057 -124.71784 

8 47.53395 -124.97780 

9 47.17453 -124.75512 

10 47.13653 -124.71076 

11 47.52575 -124.45525 

12 47.42527 -124.58143 

13 47.09871 -124.19774 

14 47.10118 -124.33207 

15 47.30507 -124.72367 

16 47.26208 -124.78960 

17 47.18656 -124.69440 

18 47.51110 -124.57377 

19 47.31630 -124.31316 

20 47.53642 -124.44312 

21 47.34097 -124.49943 

22 47.24598 -124.24019 

23 47.17088 -124.85868 

24 47.53256 -124.84121 

25 47.37855 -124.71731 

26 47.16981 -124.48679 

27 47.37001 -124.68517 

28 47.34961 -124.37346 

29 47.47395 -124.54357 

30 47.37838 -124.52586 

31 47.28832 -124.46899 

32 47.48241 -124.66634 

33 47.38187 -124.69296 

34 47.28275 -124.54693 

35 47.24095 -124.74290 

36 47.32572 -124.43238 

37 47.08979 -124.56549 

38 47.30931 -124.65400 

39 47.21679 -124.55065 

40 47.50408 -124.67038 

41 47.26376 -124.51271 

42 47.51419 -124.81133 

43 47.46457 -124.41277 

44 47.22730 -124.62976 

45 47.34764 -124.61915 
A-48 

Spill Site # Latitude Longitude 

46 47.44336 -124.40071 

47 47.17141 -124.91648 

48 47.49186 -124.50514 

49 47.37913 -124.49046 

50 47.08402 -124.71409 

51 47.46960 -124.77871 

52 47.27661 -124.47534 

53 47.17651 -124.31404 

54 47.08410 -124.87745 

55 47.31544 -124.43371 

56 47.50229 -124.45415 

57 47.38204 -124.64668 

58 47.11327 -124.42872 

59 47.08097 -124.68835 

60 47.36195 -124.56128 

61 47.27598 -124.70540 

62 47.12388 -124.95523 

63 47.38922 -124.37773 

64 47.17085 -124.25009 

65 47.14845 -124.54034 

66 47.49019 -124.38492 

67 47.16394 -124.39779 

68 47.11732 -124.58139 

69 47.45876 -124.48375 

70 47.33145 -124.75647 

71 47.19720 -124.66769 

72 47.25480 -124.34857 

73 47.52473 -124.63568 

74 47.43333 -124.47281 

75 47.52730 -124.41498 

76 47.26007 -124.78190 

77 47.14743 -124.88243 

78 47.39370 -124.65059 

79 47.18840 -124.71570 

80 47.47344 -124.73762 

81 47.53234 -124.93523 

82 47.28916 -124.80094 

83 47.13418 -124.25604 

84 47.15643 -124.22940 

85 47.24532 -124.45071 

86 47.54648 -124.56244 

87 47.45479 -124.35502 

88 47.48023 -124.61887 

89 47.13718 -124.64353 

90 47.22228 -124.81857 

91 47.41307 -124.54564 

92 47.39449 -124.41689 
A-49 

Spill Site # Latitude Longitude 

93 47.36500 -124.35267 

94 47.11463 -124.89561 

95 47.20847 -124.40985 

96 47.19336 -124.88976 

97 47.08966 -124.23142 

98 47.31746 -124.36319 

99 47.15100 -124.80461 

100 47.21148 -124.24550 

Table A-14. Washington/Oregon offshore spill sites. 

Spill Site # Latitude Longitude 

1 47.33123 -125.32982 

2 47.10728 -124.97417 

3 47.28993 -125.25624 

4 47.45446 -125.22185 

5 47.46040 -125.28207 

6 47.44767 -124.89719 

7 47.27879 -125.26936 

8 47.20694 -125.23301 

9 47.31911 -124.78539 

10 47.46825 -125.26655 

11 47.10385 -125.20255 

12 47.25398 -125.01548 

13 47.21701 -124.95518 

14 47.18103 -124.93181 

15 47.38567 -124.85259 

16 47.40459 -125.31313 

17 47.34821 -125.14237 

18 47.52441 -125.32582 

19 47.30554 -125.04369 

20 47.20840 -125.24732 

21 47.14190 -124.98866 

22 47.32051 -125.07793 

23 47.40507 -124.91441 

24 47.12075 -125.26664 

25 47.29508 -124.93681 

26 47.08208 -125.13957 

27 47.48809 -125.02683 

28 47.11455 -125.00904 

29 47.32316 -124.88726 

30 47.45105 -124.91517 

31 47.43287 -125.12307 

32 47.38469 -125.05274 

33 47.24016 -124.89041 

34 47.42193 -124.80174 

35 47.43725 -125.32122 
A-50 

Spill Site # Latitude Longitude 

36 47.39183 -125.13110 

37 47.38879 -125.07291 

38 47.10170 -125.08943 

39 47.32986 -125.14581 

40 47.24814 -124.92950 

41 47.24030 -125.05951 

42 47.27156 -125.23168 

43 47.25922 -125.19778 

44 47.43632 -124.81578 

45 47.08127 -125.24699 

46 47.23331 -124.99669 

47 47.48611 -124.89601 

48 47.53634 -125.27324 

49 47.48257 -124.86831 

50 47.52805 -124.91932 

51 47.36678 -125.18274 

52 47.43669 -125.06113 

53 47.42066 -124.85287 

54 47.53127 -125.02871 

55 47.16405 -125.19430 

56 47.39476 -125.18604 

57 47.48679 -125.28779 

58 47.08680 -125.04200 

59 47.34051 -125.06537 

60 47.14713 -125.09410 

61 47.19553 -125.17268 

62 47.53995 -125.13768 

63 47.39196 -125.22588 

64 47.41776 -124.97983 

65 47.17424 -125.25464 

66 47.08206 -125.29426 

67 47.19512 -125.04331 

68 47.52045 -125.23509 

69 47.45400 -124.94670 

70 47.09146 -125.25534 

71 47.50001 -125.14747 

72 47.35104 -125.02156 

73 47.49080 -125.17734 

74 47.34455 -124.85385 

75 47.42650 -124.88815 

76 47.52405 -125.08651 

77 47.36182 -124.82298 

78 47.24873 -124.91653 

79 47.16307 -125.10354 

80 47.34474 -124.98050 

81 47.33097 -125.20805 

82 47.38952 -125.30719 
A-51 

Spill Site # Latitude Longitude 

83 47.32093 -125.31789 

84 47.37445 -124.88461 

85 47.45945 -125.10720 

86 47.30859 -125.31901 

87 47.10459 -125.17208 

88 47.35076 -125.28658 

89 47.16434 -125.01922 

90 47.21292 -125.12637 

91 47.46187 -125.03605 

92 47.30620 -125.07710 

93 47.48807 -124.98131 

94 47.35945 -124.92376 

95 47.13437 -125.22563 

96 47.14106 -125.25651 

97 47.09007 -125.01457 

98 47.23720 -125.30891 

99 47.26415 -125.18111 

100 47.17534 -125.31137 
A-52 

Gulf of Alaska 

Habitat Grid 

The digital shoreline, shore type, and habitat mapping for the Gulf of Alaska region were 

obtained from the Environmental Sensitivity Index (ESI) Atlas databases for Prince William 

Sound, Cook Inlet, and Southeast Alaska compiled for the state of Alaska by Research Planning, 

Inc. (RPI). These data are distributed by NOAA Hazmat (Seattle, WA). 

Bathymetry data were available from bathymetric contours contained within the GEBCO Digital 

Atlas (GEBCO, 2003). 

The gridded habitat and depth data are shown in Figures A-21 and A-22. 

Figure A-21. Habitat grid developed for the Gulf of Alaska region. 
A-53 

Figure A-22. Depth grid developed for the Gulf of Alaska region. 

Table A-15. Dimensions of the habitat grid cells used to compile statistics for Gulf of 

Alaska model runs. 

Habitat grid OECM-GULFOFAK.HAB 

Grid W edge 151o 17.549’W

Grid S edge 55o 26.991’ N

Cell size (o longitude) 0.0147o W 

Cell size (o latitude) 0.0147o N 

Cell size (m) west-east 926.39 

Cell size (m) south-north 1,633.48 

# cells west-east 992 

# cells south-north 397 

Water cell area (m2) 1,513,233.25 

Shore cell length (m) 1,230.14 

Shore cell width – Rocky shore (m) 3.0 

Shore cell width – Artificial shore (m) 0.1 

Shore cell width – Gravel beach (m) 6.0 

Shore cell width – Sand beach (m) 20.0 

Shore cell width – Mud flat (m) 300.0 

Shore cell width – Wetlands (fringing, m) 300.0 
A-54 

Currents 

Currents were based on outputs from the NEP ROMS oceanographic model jointly developed by 

NMFS, PMEL and the University Washington (http://www.pmel.noaa.gov/people/dobbins/ 

nep3/index.html#details). NEP ROMS is a 3-dimensional (3D) oceanographic model based on 

the Regional Oceanographic Modeling System (Haidvogel et al., 2000) and covers the northeast 

Pacific with a terrain-following finite difference grid of 42 vertical levels and horizontal grid 

spacing of ~10 km. The program host provided ASA surface (layer 1) velocities subset for the 

period January 1997 to June 2003. 

Figure A-23. Example current component data used in modeling for the Gulf of Alaska 

region. Vector length indicates speed in the indicated direction. 

Winds 

Standard meteorological data were acquired from the National Data Buoy Center Internet site for 

the nearest NDBC buoys with sufficient records: 

x 46001, “Gulf of Alaska,” at 56.300°N, 148.021°W; 

x 46080, “Northwest Gulf,” at 58.035°N, 149.994°W; 

x 46082, “Cape Suckling,” at 59.688°N, 143.399°W; and 

x 46083, “Fairweather Grounds,” at 58.243°N, 137.993°W. 
A-55 

For station 46001, hourly mean wind speed and direction for the time period 6/1/1997 to 

5/31/2003 were compiled in the SIMAP model input file format. The other three stations used 

for this location had sufficient wind records, but did not have data for all the years encompassed 

by the time-stamped currents file. To extend the wind records to match the currents, we used 

data from later years as a proxy for the missing earlier years, as described below. 

For station 46080, the original wind record was late 2002-2009, so data for years 2004-2009 

were relabeled as 1997-2002 (original data were used for year 2003). That is, data for years 

2004, 2005, 2006, 2007, 2008, and 2009 were relabeled as years 1997, 1998, 1999, 2000, 2001, 

and 2002, respectively. Hourly mean wind speed and direction for the time period 7/9/1997 to 

6/1/2003 were then compiled in the SIMAP model input file format. 

For stations 46082 and 46083, data from years 2004-2009 were relabeled as 1997-2002 (original 

data was used for year 2003). Hourly mean wind speed and direction for the time period 

6/1/1997 to 6/1/2003 were then compiled in the SIMAP model input file format. 

Spill Sites 

Spill sites for the Gulf of Alaska region were randomly distributed within a representative 

portion of the Gulf of Alaska Planning Area (Figure A-24). The delineation between the 

nearshore and offshore spill areas was based on the 200 meter depth contour. Twenty-five spill 

sites were placed within the nearshore area, and twenty-five spill sites were placed within the 

offshore area. The coordinates of these points are provided in Tables A-16 and A-17. 
A-56 

Figure A-24. Spill sites developed for the Gulf of Alaska region. 

Table A-16. Gulf of Alaska nearshore spill sites. 

Spill Site # Latitude Longitude 

1 59.25638 -140.28613 

2 59.05296 -139.62432 

3 58.84889 -139.11253 

4 58.60369 -138.76499 

5 58.58578 -139.14629 

6 59.05238 -139.76825 

7 59.40949 -139.75890 

8 58.84560 -139.48058 

9 59.43231 -139.88639 

10 59.10423 -138.78885 

11 58.80299 -138.81081 

12 58.68438 -139.20439 

13 59.21802 -139.29475 

14 59.15781 -140.16484 

15 58.92961 -138.54802 
A-57 

Spill Site # Latitude Longitude 

16 58.83876 -139.87945 

17 58.91929 -138.93752 

18 59.25115 -139.61176 

19 58.87416 -138.38803 

20 59.08766 -140.35988 

21 58.45319 -139.16451 

22 59.29274 -139.83694 

23 59.11175 -139.25354 

24 58.97117 -139.86721 

25 58.98738 -140.29472 

Table A-17. Gulf of Alaska offshore spill sites. 

Spill Site # Latitude Longitude 

1 58.13984 -139.38958 

2 58.77348 -140.12530 

3 58.23517 -139.24711 

4 58.27960 -139.61293 

5 58.47333 -140.97327 

6 58.24460 -140.32702 

7 58.95843 -140.74139 

8 58.39584 -139.89962 

9 58.52728 -139.51145 

10 58.17358 -139.67521 

11 58.59186 -140.38587 

12 58.13068 -140.06509 

13 58.65782 -139.57738 

14 59.00662 -140.66443 

15 58.36217 -140.16284 

16 58.39703 -140.47164 

17 58.70253 -141.10434 

18 58.78795 -140.58091 

19 58.03546 -139.76069 

20 58.48011 -139.64262 

21 58.63312 -140.79054 

22 58.88851 -140.38132 

23 58.50026 -140.86414 

24 58.59998 -140.02921 

25 58.82478 -140.91625 
A-58 

Cook Inlet/Shelikof Strait 

Habitat Grid 

The digital shoreline, shore type, and habitat mapping for the Cook Inlet/Shelikof Strait region 

were obtained from the Environmental Sensitivity Index (ESI) Atlas databases for Aleutians, 

Bristol Bay, Cook Inlet, Kodiak, Prince William Sound and Western Alaska compiled for the 

state of Alaska by Research Planning, Inc. (RPI). These data are distributed by NOAA Hazmat 

(Seattle, WA). 

Bathymetry data were available from bathymetric contours contained within the GEBCO Digital 

Atlas (GEBCO, 2003). 

The gridded habitat and depth data are shown in Figures A-25 and A-26. 

Figure A-25. Habitat grid developed for the Cook Inlet/Shelikof Strait region. 
A-59 

Figure A-26. Depth grid developed for the Cook Inlet/Shelikof Strait region. 

Table A-18. Dimensions of the habitat grid cells used to compile statistics for Cook 

Inlet/Shelikof Strait model runs. 

Habitat grid OECM-COOKINLET.HAB 

Grid W edge 164o 11.938’W

Grid S edge 52o 47.926’ N

Cell size (o longitude) 0.0154o W 

Cell size (o latitude) 0.0154o N 

Cell size (m) west-east 1,034.47 

Cell size (m) south-north 1,710.95 

# cells west-east 993 

# cells south-north 566 

Water cell area (m2) 1,769,930.75 

Shore cell length (m) 1,330.39 

Shore cell width – Rocky shore (m) 3.0 

Shore cell width – Artificial shore (m) 0.1 

Shore cell width – Gravel beach (m) 6.0 

Shore cell width – Sand beach (m) 20.0 

Shore cell width – Mud flat (m) 300.0 

Shore cell width – Wetlands (fringing, m) 300.0 
A-60 

Currents 

Currents were based on outputs from the NEP ROMS oceanographic model jointly developed by 

NMFS, PMEL and the University Washington (http://www.pmel.noaa.gov/people/dobbins/ 

nep3/index.html#details). NEP ROMS is a 3-dimensional (3D) oceanographic model based on 

the Regional Oceanographic Modeling System (Haidvogel et al., 2000) and covers the northeast 

Pacific with a terrain-following finite difference grid of 42 vertical levels and horizontal grid 

spacing of ~10 km. The program host provided ASA surface (layer 1) velocities subset for the 

period January 1997 to June 2003. 

Figure A-27. Example current component data used in modeling for the Cook 

Inlet/Shelikof Strait region. Vector length indicates speed in the indicated direction. 

Winds 

Standard meteorological data were acquired from the National Data Buoy Center Internet site for 

the nearest NDBC buoys/meteorological stations with sufficient records: 

x 46080, “Northwest Gulf,” at 58.035°N, 149.994°W; 

x AUGA2, “Augustine Island,” at 59.378°N, 153.348°W; and 

x DRFA2, “Drift River Terminal,” at 60.533°N, 152.137°W. 

These three stations used for this location had sufficient wind records, but did not have data for 

all the years encompassed by the time-stamped currents file. To extend the wind records to 
A-61 

match the currents, we used data from later years as a proxy for the missing earlier years, as 

described below. 

For station 46080, the original wind record was late 2002-2009, so data for years 2004-2009 

were relabeled as 1997-2002 (original data were used for year 2003). That is, data for years 

2004, 2005, 2006, 2007, 2008, and 2009 were relabeled as years 1997, 1998, 1999, 2000, 2001, 

and 2002, respectively. Hourly mean wind speed and direction for the time period 7/9/1997 to 

6/1/2003 were then compiled in the SIMAP model input file format. 

For stations AUGA2 and DRFA2, data for years 2004-2006 were relabeled as years 1997-1999 

(original data were used for years 2000-2003). Hourly mean wind speed and direction for the 

time period 6/1/1997 to 6/1/2003 were then compiled in the SIMAP model input file format. 

Spill Sites 

Spill sites for the Cook Inlet/Shelikof Strait region were randomly distributed within the entirety 

of the Cook Inlet Planning Area/Proposed Final Program Area (2007-2012) (Figure A-28). 

Twenty-five spill sites were placed within the spill area; the coordinates of these points are 

provided in Table A-19. 
A-62 

Figure A-28. Spill sites developed for the Cook Inlet/Shelikof Strait region. 

Table A-19. Cook Inlet/Shelikof Strait spill sites. 

Spill Site # Latitude Longitude 

1 57.20782 -155.16895 

2 59.67250 -152.86635 

3 59.22465 -152.95397 

4 59.39124 -152.39976 

5 59.65776 -152.17027 

6 59.20755 -153.43753 

7 57.66893 -154.95491 

8 57.08036 -154.81524 

9 58.57756 -152.95397 

10 57.01014 -155.98131 

11 59.05936 -152.03583 

12 57.22846 -155.73861 

13 59.78027 -152.43599 

14 58.95866 -152.75387 

15 58.42016 -153.44840 
A-63 

Spill Site # Latitude Longitude 

16 58.92738 -153.08662 

17 58.28740 -153.84528 

18 57.92627 -153.98005 

19 57.46068 -155.09342 

20 57.86392 -154.51652 

21 60.11031 -152.20362 

22 58.28420 -153.37415 

23 59.48858 -153.12071 

24 57.46238 -155.48558 

25 59.13050 -152.52043 
A-64 

Bering Sea 

Habitat Grid 

The digital shoreline, shore type, and habitat mapping for the Bering Sea region were obtained 

from the Environmental Sensitivity Index (ESI) Atlas databases for Aleutians, Bristol Bay and 

Western Alaska compiled for the state of Alaska by Research Planning, Inc. (RPI). These data 

are distributed by NOAA Hazmat (Seattle, WA). 

Bathymetry data were available from bathymetric contours contained within the GEBCO Digital 

Atlas (GEBCO, 2003). 

The gridded habitat and depth data are shown in Figures A-29 and A-30. 

Figure A-29. Habitat grid developed for the Bering Sea region. 
A-65 

Figure A-30. Depth grid developed for the Bering Sea region. 

Table A-20. Dimensions of the habitat grid cells used to compile statistics for Bering Sea 

model runs. 

Habitat grid OECM-BERING.HAB 

Grid W edge 170o 35.380’W

Grid S edge 54o 22.155’ N

Cell size (o longitude) 0.0132o W 

Cell size (o latitude) 0.0132o N 

Cell size (m) west-east 853.89 

Cell size (m) south-north 1,465.76 

# cells west-east 994 

# cells south-north 442 

Water cell area (m2) 1,251,591.62 

Shore cell length (m) 1,118.75 

Shore cell width – Rocky shore (m) 3.0 

Shore cell width – Artificial shore (m) 0.1 

Shore cell width – Gravel beach (m) 6.0 

Shore cell width – Sand beach (m) 20.0 

Shore cell width – Mud flat (m) 300.0 

Shore cell width – Wetlands (fringing, m) 300.0 
A-66 

Currents 

Currents were based on outputs from the NEP ROMS oceanographic model jointly developed by 

NMFS, PMEL and the University Washington (http://www.pmel.noaa.gov/people/dobbins/ 

nep3/index.html#details). NEP ROMS is a 3-dimensional (3D) oceanographic model based on 

the Regional Oceanographic Modeling System (Haidvogel et al., 2000) and covers the northeast 

Pacific with a terrain-following finite difference grid of 42 vertical levels and horizontal grid 

spacing of ~10 km. The program host provided ASA surface (layer 1) velocities subset for the 

period January 1997 to June 2003. 

Figure A-31. Example current component data used in modeling for the Bering Sea region. 

Vector length indicates speed in the indicated direction. 

Winds 

Sufficient historical buoy records were not available for this region, so standard meteorological 

data were acquired from the National Climatic Data Center Internet site for the nearest weather 

observation stations, Cold Bay Airport at 55.2166°N, 162.7333°W, and St. Paul Island Airport at 

57.1666°N, 170.2166°W. Hourly mean wind speed and direction for the time period 6/1/1997 to 

6/1/2003 were compiled in the SIMAP model input file format. 
A-67 

Spill Sites 

Spill sites for the Bering Sea region were randomly distributed within the entirety of the North 

Aleutian Basin Proposed Final Program Area (2007-2012) (Figure A-32). Twenty spill sites 

were placed within the spill area; the coordinates of these points are provided in Table A-21. 

Figure A-32. Spill sites developed for the Bering Sea region. 
A-68 

Table A-21. Bering Sea spill sites. 

Spill Site # Latitude Longitude 

1 55.89743 -164.35828 

2 55.71157 -163.23842 

3 56.25903 -162.89722 

4 56.31636 -161.47658 

5 56.19551 -161.68012 

6 56.03558 -164.78367 

7 55.90328 -164.55477 

8 55.35427 -164.44818 

9 56.38417 -162.31786 

10 56.11979 -162.78089 

11 55.54512 -164.80593 

12 56.36158 -164.67885 

13 56.24949 -163.66265 

14 55.45074 -163.70036 

15 56.35405 -160.96096 

16 55.66995 -164.09538 

17 56.29373 -164.17680 

18 55.96789 -162.20930 

19 55.72360 -162.81991 

20 55.97550 -163.48478 
A-69 

Chukchi Sea 

Habitat Grid 

The digital shoreline, shore type, and habitat mapping for the Chukchi Sea region were obtained 

from the Northwest Arctic and North Slope Environmental Sensitivity Index (ESI) Atlas 

databases compiled for the state of Alaska by Research Planning, Inc. (RPI). These data are 

distributed by NOAA Hazmat (Seattle, WA). 

Depth data were based on soundings available from the NOAA NOS Hydrographic Survey Data 

(NOAA, 2009). Soundings were interpolated on to the model grid for areas where the depth data 

were missing. 

The gridded habitat and depth data are shown in Figures A-33 and A-34. 

Figure A-33. Habitat grid developed for the Chukchi Sea region. 
A-70 

Figure A-34. Depth grid developed for the Chukchi Sea region. 

Table A-22. Dimensions of the habitat grid cells used to compile statistics for Chukchi Sea 

model runs. 

Habitat grid CHUKCHI-OECM_HABS.HAB 

Grid W edge 176o 36.041’W

Grid S edge 67o 7.309’ N

Cell size (o longitude) 0.029o W 

Cell size (o latitude) 0.029o N 

Cell size (m) west-east 1,249.73 

Cell size (m) south-north 3,214.56 

# cells west-east 898 

# cells south-north 252 

Water cell area (m2) 4,017,342.25 

Shore cell length (m) 2,004.33 

Shore cell width – Rocky shore (m) 3.0 

Shore cell width – Artificial shore (m) 0.1 

Shore cell width – Gravel beach (m) 6.0 

Shore cell width – Sand beach (m) 20.0 

Shore cell width – Mud flat (m) 300.0 

Shore cell width – Wetlands (fringing, m) 300.0 
A-71 

Currents 

Currents were based on data from BOEM’s annual means analysis of the Haidvogel, Hedstrom 

and Francis (2001) coupled ice-ocean model. Offshore of the 10- to 20-meter bathymetry 

contour, the wind-driven and density-induced ocean-flow fields and the ice-motion fields are 

simulated using a three-dimensional coupled ice-ocean hydrodynamic model (Haidvogel, 

Hedstrom, and Francis, 2001). The model is based on the ocean model of Haidvogel, Wilkin, 

and Young (1991) and the ice models of Hibler (1979) and Mellor and Kantha (1989). This 

model simulates flow properties and sea ice evolution in the western Arctic during the years 

1982-1996. The coupled system uses the S-Coordinate Rutgers University Model (SCRUM) and 

Hibler viscous-plastic dynamics and the Mellor and Kantha thermodynamics. It is forced by 

daily surface geostrophic winds and monthly thermodynamic forces. The model is forced by 

thermal fields for the years 1982-1996. The thermal fields are interpolated in time from monthly 

fields. The location of each trajectory at each time interval is used to select the appropriate ice 

concentration. The pack ice is simulated as it grows and melts. The edge of the pack ice is 

represented on the model grid. Depending on the ice concentration, either the ice or water 

velocity with wind drift from the stored results of the Haidvogel, Hedstrom and Francis (2001) 

coupled ice-ocean model is used. A major assumption used in this analysis is that the ice-motion 

velocities and the ocean daily flows calculated by the coupled ice ocean model adequately 

represent the flow components. Comparisons with data illustrate that the model captures the 

first-order transport and the dominant flow (Haidvogel, Hedstrom, and Francis, 2001). 

Figure A-35. Example current component data used in modeling for the Chukchi Sea 

region. Vector length indicates speed in the indicated direction. 

Ice 
A-72 

As mentioned above, ice distribution was included in the model analysis and was treated in the 

same manner as current velocities. The program host provided the model outputs in original 

binary format. ASA subsequently converted them in to NetCDF format for SIMAP model usage. 

Winds 

ASA received wind data files that were used to force the coupled ice-ocean model. The period 

of the wind data extended daily from 1/1/1982 to 12/31/1996. ASA subsequently converted 

them into the SIMAP model input file format. 

Spill Sites 

Spill sites for the Chukchi Sea region were randomly distributed within the Sale 193 Lease Area, 

as well as a nearshore spill area between the lease area and shore (Figure A-36). Fifty spill sites 

were placed within the nearshore spill area, and one hundred spill sites were placed within the 

offshore spill area. The coordinates of these points are provided in Tables A-23 and A-24. 

Figure A-36. Spill sites developed for the Chukchi Sea region. 

Table A-23. Chukchi Sea nearshore spill sites. 
A-73 

Spill Site # Latitude Longitude 

1 68.80251 -166.28573 

2 71.02819 -159.43650 

3 71.34568 -158.60270 

4 69.87216 -163.96762 

5 70.57997 -160.33417 

6 68.52057 -167.11132 

7 69.45986 -164.88673 

8 71.12524 -159.83501 

9 69.43817 -167.28959 

10 71.19232 -157.34682 

11 68.53220 -166.68482 

12 70.78150 -160.89181 

13 69.21929 -165.53565 

14 70.45479 -161.45151 

15 70.44305 -160.96306 

16 69.28126 -164.32847 

17 69.69478 -164.17194 

18 70.78138 -161.61954 

19 69.08198 -166.46510 

20 69.37443 -167.74802 

21 70.63369 -161.33107 

22 69.12355 -168.62024 

23 70.50124 -160.55358 

24 69.35443 -165.40538 

25 70.83822 -159.69201 

26 71.61517 -157.29102 

27 68.97434 -167.11750 

28 70.96058 -157.69149 

29 68.88972 -167.76460 

30 70.32692 -163.15407 

31 69.91732 -162.91041 

32 71.00610 -160.18830 

33 71.37710 -157.98790 

34 68.67538 -166.28326 

35 69.41614 -165.15221 

36 69.39960 -166.29136 

37 69.89731 -163.71011 

38 69.97028 -162.73033 

39 70.37986 -162.67941 

40 69.29252 -164.19189 

41 69.59682 -164.50646 

42 70.19909 -162.56209 

43 71.38619 -156.52521 

44 70.43495 -161.22769 

45 70.15946 -162.65819 

46 68.57023 -167.31760 
A-74 

Spill Site # Latitude Longitude 

47 69.99947 -164.54262 

48 69.19607 -164.71789 

49 69.07496 -164.60936 

50 69.24981 -169.06530 

Table A-24. Chukchi Sea offshore spill sites. 

Spill Site # Latitude Longitude 

1 71.92936 -160.41165 

2 72.43589 -167.28961 

3 72.88615 -159.79134 

4 71.83674 -166.45084 

5 72.82983 -161.76844 

6 71.94995 -159.38638 

7 72.20713 -159.61815 

8 71.45993 -160.64299 

9 71.86930 -162.49988 

10 70.95694 -166.21624 

11 71.90616 -161.31514 

12 70.91950 -162.53362 

13 72.57356 -168.09370 

14 71.07125 -164.86758 

15 71.24247 -168.49455 

16 71.33546 -165.91325 

17 71.46233 -161.66241 

18 71.21312 -164.95780 

19 72.37240 -163.54577 

20 70.14865 -168.22286 

21 70.48523 -168.95809 

22 72.49724 -159.25866 

23 72.01791 -165.05015 

24 72.65726 -157.49342 

25 70.30754 -167.41901 

26 71.99233 -163.38693 

27 70.93487 -163.73603 

28 70.98280 -166.96406 

29 71.30183 -165.62369 

30 72.90926 -167.14628 

31 72.57767 -161.98319 

32 69.81513 -168.64268 

33 72.50634 -160.01427 

34 71.34174 -163.73638 

35 71.27379 -165.57891 

36 72.70301 -162.54825 

37 72.47364 -166.07357 

38 70.72216 -164.85489 

39 69.72906 -167.58467 
A-75 

Spill Site # Latitude Longitude 

40 70.41722 -168.42607 

41 71.88031 -157.05767 

42 71.97243 -168.27634 

43 72.50604 -164.57449 

44 70.10337 -164.85982 

45 71.85271 -167.07432 

46 71.66131 -168.35190 

47 71.33480 -159.96276 

48 72.01163 -164.44316 

49 72.12142 -167.44217 

50 70.96090 -167.48849 

51 69.63964 -168.80214 

52 69.63937 -167.14220 

53 72.42569 -163.51771 

54 72.92037 -166.49295 

55 70.58959 -164.56552 

56 71.72754 -157.61223 

57 72.24041 -160.68334 

58 71.88655 -158.81334 

59 72.03956 -157.91582 

60 72.50384 -164.90532 

61 71.06678 -161.71957 

62 70.50225 -166.09321 

63 69.99500 -165.85293 

64 72.88198 -165.03866 

65 72.80670 -168.03689 

66 72.50975 -162.04557 

67 72.32033 -165.56966 

68 70.84432 -162.25083 

69 71.53586 -163.11424 

70 72.52016 -166.82811 

71 71.80884 -158.14457 

72 70.86805 -164.71200 

73 72.80629 -160.37520 

74 72.00042 -165.83125 

75 72.45025 -157.41283 

76 69.79513 -167.61006 

77 69.96372 -166.01639 

78 71.77068 -159.78731 

79 70.79339 -165.45720 

80 71.47257 -161.02885 

81 72.59875 -165.06284 

82 69.41989 -168.92478 

83 72.81331 -163.20682 

84 71.47801 -164.11186 

85 71.48900 -166.88244 

86 71.04396 -167.91722 
A-76 

Spill Site # Latitude Longitude 

87 70.70016 -164.39985 

88 69.98332 -165.24292 

89 72.83466 -158.23320 

90 70.47644 -167.27234 

91 70.79677 -168.68739 

92 72.82949 -165.98163 

93 71.94684 -159.42457 

94 71.13974 -164.00366 

95 70.73232 -163.15751 

96 71.17573 -163.22685 

97 71.31242 -164.83903 

98 72.30332 -168.46872 

99 72.56200 -168.34908 

100 72.34657 -168.40539 
A-77 

Beaufort Sea 

Habitat Grid 

The digital shoreline, shore type, and habitat mapping for the Beaufort Sea region were obtained 

from the North Slope Environmental Sensitivity Index (ESI) Atlas database compiled for the 

state of Alaska by Research Planning, Inc. (RPI). These data are distributed by NOAA Hazmat 

(Seattle, WA). 

Bathymetry data were available from bathymetric contours contained within the GEBCO Digital 

Atlas (GEBCO, 2003). 

The gridded habitat and depth data are shown in Figures A-37 and A-38. 

Figure A-37. Habitat grid developed for the Beaufort Sea region. 
A-78 

Figure A-38. Depth grid developed for the Beaufort Sea region. 

Table A-25. Dimensions of the habitat grid cells used to compile statistics for Beaufort Sea 

model runs. 

Habitat grid OECM-BEAUFORT.HAB 

Grid W edge 162o 17.630’W

Grid S edge 68o 27.172’ N

Cell size (o longitude) 0.0267o W 

Cell size (o latitude) 0.0267o N 

Cell size (m) west-east 1,087.45 

Cell size (m) south-north 2,960.93 

# cells west-east 992 

# cells south-north 275 

Water cell area (m2) 3,219,849.75 

Shore cell length (m) 1,794.39 

Shore cell width – Rocky shore (m) 3.0 

Shore cell width – Artificial shore (m) 0.1 

Shore cell width – Gravel beach (m) 6.0 

Shore cell width – Sand beach (m) 20.0 

Shore cell width – Mud flat (m) 300.0 

Shore cell width – Wetlands (fringing, m) 300.0 
A-79 

Currents 

Currents were based on data from BOEM’s annual means analysis of the Haidvogel, Hedstrom 

and Francis (2001) coupled ice-ocean model. Offshore of the 10- to 20-meter bathymetry 

contour, the wind-driven and density-induced ocean-flow fields and the ice-motion fields are 

simulated using a three-dimensional coupled ice-ocean hydrodynamic model (Haidvogel, 

Hedstrom, and Francis, 2001). The model is based on the ocean model of Haidvogel, Wilkin, 

and Young (1991) and the ice models of Hibler (1979) and Mellor and Kantha (1989). This 

model simulates flow properties and sea ice evolution in the western Arctic during the years 

1982-1996. The coupled system uses the S-Coordinate Rutgers University Model (SCRUM) and 

Hibler viscous-plastic dynamics and the Mellor and Kantha thermodynamics. It is forced by 

daily surface geostrophic winds and monthly thermodynamic forces. The model is forced by 

thermal fields for the years 1982-1996. The thermal fields are interpolated in time from monthly 

fields. The location of each trajectory at each time interval is used to select the appropriate ice 

concentration. The pack ice is simulated as it grows and melts. The edge of the pack ice is 

represented on the model grid. Depending on the ice concentration, either the ice or water 

velocity with wind drift from the stored results of the Haidvogel, Hedstrom and Francis (2001) 

coupled ice-ocean model is used. A major assumption used in this analysis is that the ice-motion 

velocities and the ocean daily flows calculated by the coupled ice ocean model adequately 

represent the flow components. Comparisons with data illustrate that the model captures the 

first-order transport and the dominant flow (Haidvogel, Hedstrom, and Francis, 2001). 

Figure A-39. Example current component data used in modeling for the Beaufort Sea 

region. Vector length indicates speed in the indicated direction. 

Ice 
A-80 

As mentioned above, ice distribution was included in the model analysis and was treated in the 

same manner as current velocities. The program host provided the model outputs in original 

binary format. ASA subsequently converted them in to NetCDF format for SIMAP model usage. 

Winds 

ASA received wind data files that were used to force the coupled ice-ocean model. The period 

of the wind data extended daily from 1/1/1982 to 12/31/1996. ASA subsequently converted 

them into the SIMAP model input file format. 

Spill Sites 

Spill sites for the Beaufort Sea region were randomly distributed within the Beaufort Sea 

Proposed Final Program Area (2007-2012) (Figure A-40). The delineation between the 

nearshore and offshore spill areas was based on the 200 meter depth contour. Fifty spill sites 

were placed within the nearshore spill area, and one hundred spill sites were placed within the 

offshore spill area. The coordinates of these points are provided in Tables A-26 and A-27. 
A-81 

Figure A-40. Spill sites developed for the Beaufort Sea region. 

Table A-26. Beaufort Sea nearshore spill sites. 

Spill Site # Latitude Longitude 

1 71.09416 -150.55489 

2 71.04977 -149.80843 

3 70.16038 -140.54562 

4 70.72247 -151.93163 

5 71.65489 -155.88458 

6 70.63948 -151.08009 

7 69.81953 -141.04329 

8 70.18409 -142.89296 

9 70.81622 -150.64923 

10 70.46382 -146.51869 

11 70.42556 -145.41750 

12 70.89464 -148.01302 

13 71.24509 -152.04843 

14 71.37994 -155.24213 

15 70.89631 -148.65458 

16 70.58543 -147.94038 

17 70.48751 -143.33026 

18 71.41831 -153.93019 

19 70.72197 -149.63456 

20 70.61578 -144.50381 

21 70.60121 -146.05678 

22 70.77382 -146.96288 

23 71.93125 -155.95195 

24 70.95568 -151.21086 

25 70.81727 -149.14234 

26 71.70420 -154.64078 

27 71.13697 -154.71567 

28 71.07996 -153.94169 

29 71.22144 -153.10001 

30 71.82637 -155.25314 

31 70.64101 -145.14466 

32 71.42288 -152.93646 

33 70.92724 -152.42098 

34 70.75093 -148.50526 

35 71.39407 -155.71131 

36 70.09659 -141.54452 

37 70.56424 -150.19496 

38 70.39671 -144.13115 

39 70.33755 -141.98623 

40 70.56559 -142.69344 

41 70.35553 -144.46766 

42 71.71538 -155.87289 
A-82 

Spill Site # Latitude Longitude 

43 70.27678 -143.59203 

44 70.71670 -145.99497 

45 70.01231 -141.73111 

46 70.69599 -143.26969 

47 70.75724 -150.47266 

48 71.45252 -154.53263 

49 71.07716 -151.98361 

50 70.16182 -145.52020 

Table A-27. Beaufort Sea offshore spill sites. 

Spill Site # Latitude Longitude 

1 72.29496 -147.08120 

2 71.73417 -150.66227 

3 72.13888 -142.85791 

4 70.88100 -141.72560 

5 72.22916 -147.11949 

6 71.75584 -142.73912 

7 72.00947 -149.88702 

8 71.92671 -152.40884 

9 71.49766 -147.36808 

10 72.04667 -144.62675 

11 72.18190 -138.63222 

12 71.35929 -147.70307 

13 70.92243 -141.21316 

14 72.07513 -151.92909 

15 72.25540 -153.79302 

16 72.05159 -149.05832 

17 71.23738 -146.32349 

18 72.26839 -143.42684 

19 71.39175 -149.48182 

20 71.40896 -145.60822 

21 71.45490 -145.03830 

22 70.43023 -141.14996 

23 70.96278 -142.78107 

24 72.21686 -149.48586 

25 71.45134 -142.37002 

26 71.67549 -152.23698 

27 71.73836 -143.42921 

28 72.25394 -151.90851 

29 72.39339 -152.63689 

30 71.34515 -147.19502 

31 71.38384 -141.20408 

32 71.73262 -144.55076 

33 71.02140 -143.47386 

34 70.71095 -140.58864 

35 72.22207 -140.99789 
A-83 

Spill Site # Latitude Longitude 

36 71.39158 -140.75832 

37 72.01116 -139.62887 

38 71.73580 -148.48504 

39 71.36116 -144.46177 

40 71.31775 -141.85878 

41 71.76833 -146.36440 

42 70.74077 -142.41038 

43 71.85086 -141.86780 

44 72.01963 -142.91378 

45 71.96678 -153.09642 

46 72.19083 -144.49330 

47 70.63471 -140.21745 

48 70.83295 -143.07608 

49 71.44030 -149.78383 

50 71.91070 -151.95198 

51 72.27827 -155.45421 

52 71.23903 -149.23539 

53 72.14577 -138.79756 

54 71.58540 -147.05258 

55 71.14155 -147.85344 

56 71.72331 -153.82559 

57 71.43232 -151.56977 

58 71.25138 -150.21080 

59 71.97065 -154.53263 

60 71.38478 -144.85615 

61 71.54977 -150.34206 

62 70.76519 -144.91984 

63 71.89757 -148.05638 

64 72.17640 -148.50076 

65 72.09564 -153.17291 

66 72.33436 -153.16122 

67 70.92831 -143.37199 

68 72.07259 -150.78764 

69 72.25242 -145.78420 

70 71.77156 -139.28180 

71 71.05838 -145.50633 

72 72.00101 -155.51508 

73 71.16242 -141.39111 

74 71.47752 -143.58400 

75 71.62782 -146.14896 

76 71.05028 -144.48335 

77 71.62961 -147.39388 

78 71.15341 -146.36557 

79 72.00669 -146.38091 

80 71.57202 -140.62417 

81 72.07764 -153.11093 

82 71.16951 -142.27154 
A-84 

Spill Site # Latitude Longitude 

83 71.77527 -144.94114 

84 72.03951 -142.36495 

85 72.04944 -150.25811 

86 70.80993 -140.89805 

87 72.12715 -140.95769 

88 72.13630 -151.54441 

89 71.06806 -140.01054 

90 71.63668 -149.41625 

91 72.12962 -147.61933 

92 71.79935 -145.69031 

93 71.34955 -151.59206 

94 71.57533 -142.01904 

95 72.08866 -144.83869 

96 72.38530 -150.51524 

97 72.13353 -153.92568 

98 71.76075 -140.47483 

99 71.55824 -145.19205 

100 71.59166 -146.45027 
A-85 

References 

Amante, C. and B. W. Eakins. 2009. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, 

Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24, 19 pp. 

Clifford, M., C. Horton, J. Schmitz, and L. Kantha. 1997. An oceanographic nowcast/forecast 

system for the Red Sea. J. Geophys. Res., 102, 25,101–25,122. 

Egbert, G. D., A. F. Bennett, and M.G.G. Foreman. 1994. TOPEX/POSEIDON tides estimated 

using global inverse model. J. Geophys. Res. 99(C12), pp.24821-24852. 

Haidvogel, D.B., K.S. Hedstrom, and J. Francis. 2001. Numerical simulation of 

atmosphere/ocean/sea ice interaction in the Arctic Ocean: 1982-1996. OCS Study MMS 

2001-069. 

Haidvogel, D., J. Wilkin, and R. Young. 1991. A semi-spectral primitive equation ocean 

circulation model using vertical sigma and orthogonal curvilinear horizontal coordinates. J. 

Comp. Phys., 94: 151-184. 

Hibler, W.D. 1979. A dynamic thermodynamic sea ice model. J. Phys. Oceanogr., 9: 815-846. 

Hickey, B. M. 1998. Coastal Oceanography of western North America from the tip of Baja 

California to Vancouver Island, The Sea, Vol. 11, edited by Allan R. Robinson, and 

Kenneth H. Brink, John Wiley & Sons. 

Horton, C., M. Clifford, and J. Schmitz. 1997. A real-time oceanographic nowcast/ forecast 

system for the Mediterranean Sea. J. Geophys. Res., 102, 25,123–25,156. 

IOC, IHO and BODC (GEBCO). 2003. Centenary Edition of the GEBCO Digital Atlas, 

published on behalf of the Intergovernmental Oceanographic Commission (IOC) and the 

International Hydrographic Organization (IHO) as part of the General Bathymetric Chart of 

the Oceans; British Oceanographic Data Centre (BODC), Liverpool. 

Isaji T., E. Howlett, C. Dalton, and E. Anderson. 2002. Stepwise-Continuous-VariableRectangular Grid Hydrodynamic Model, Proceedings of the Seventh International 

Conference on Estuarine and Coastal Modeling, Nov 5-7, 2001, St. Petersburg, Florida, 

pp.519-534. 

Kantha, L., J.-K. Choi, R. Leben, C. Cooper, M. Vogel, and J. Feeney. 1999. Hindcasts and 

real-time nowcast/forecasts of currents in the Gulf of Mexico. Offshore Technology 

Conference (OTC ’99), May 3-6, 1999, Houston, TX. 
A-86 

Maltrud, M. E., R. D. Smith, A. J. Semtner, R. C. Malone. 1998. Global eddy resolving ocean 

simulations driven by 1985-1995 atmospheric winds, J. Geophys. Res., 103, C13, 30825-

30853. 

Mellor, G.L. and L. Kantha. 1979. An ice-ocean coupled model. J. Geophys Res., 94: 10, 937-

10, 954. 

National Oceanic and Atmospheric Administration (NOAA). 2009. NOAA NOS Hydrographic 

Survey Data. http://map.ngdc.noaa.gov/website/mgg/nos_hydro/viewer.htm 

Oey, L. Y. and F. H. Xu. 2010. The origin of along-shelf 1 pressure gradient in the Middle 

Atlantic Bight. In preparation. 

State of California Marine Research Committee. 1966. California Cooperative Oceanic 

Fisheries Investigations Atlas No. 4, December 1966. 
A-87 

Oil Spill Modeling for the Offshore Environmental Cost Model (OECM) 

SUB-APPENDIX B 

Properties of Oils used in SIMAP Modeling 
A-88 

Table B-1. Oil properties for Light Crude used in the SIMAP simulations. 

Property Value Reference 

Density @ 25 deg. C (g/cm3) 0.8518 Jokuty et al. (1999)*

Viscosity @ 25 deg. C (cp) 8.0 Jokuty et al. (1999)*

Surface Tension (dyne/cm) 25.9 Jokuty et al. (1999)*

Pour Point (deg. C) -28.0 Jokuty et al. (1999)*

Adsorption Rate to Suspended Sediment 0.01008 Kolpack et al. (1977) 

Adsorption Salinity Coef.(/ppt) 0.023 Kolpack et al. (1977) 

Fraction monoaromatic hydrocarbons (MAHs) 0.01478 Jokuty et al. (1999)*

Fraction 2-ring aromatics 0.003161 Henry (1997) 

Fraction 3-ring aromatics 0.005055 Henry (1997) 

Fraction Non-Aromatics: boiling point < 180oC 0.16522 Jokuty et al. (1999)*

Fraction Non-Aromatics: boiling point 180-264oC 0.185839 Henry (1997) 

Fraction Non-Aromatics: boiling point 264-380oC 0.275945 Henry (1997) 

Minimum Oil Thickness (mm) 0.00001 McAuliffe (1987) 

Maximum Mousse Water Content (%) 75.0 - 

Mousse Water Content as Spilled (%) 0.0 - 

Water content of oil (not in mousse, %) 0 - 

Degradation Rate (/day), Surface & Shore 0.01 French et al. (1996)

Degradation Rate (/day), Hydrocarbons in Water 0.01 French et al. (1996) 

Degradation Rate (/day), Oil in Sediment 0.001 French et al. (1996) 

Degradation Rate (/day), Aromatics in Water 0.01 Mackay et al. (1992) 

Degradation Rate (/day), Aromatics in Sediment 0.001 Mackay et al. (1992) * – Environment Canada’s Oil Property Catalogue (Jokuty et al., 1999) provided total hydrocarbon data for volatile fractions of 

unweathered oil. The aromatic hydrocarbon fraction was subtracted from the total hydrocarbon fraction to obtain the aliphatic 

fraction of unweathered oil.
A-89 

Table B-2. Oil properties for Light Arab Crude used in the SIMAP simulations. 

Property Value Reference 

Density @ 25 deg. C (g/cm3) 0.8641 Environment Canada (2004) 

Viscosity @ 25 deg. C (cp) 32.6 Environment Canada (2004) 

Surface Tension (dyne/cm) 21.6 Environment Canada (2004) 

Pour Point (deg. C) -21.0 Environment Canada (2004) 

Adsorption Rate to Suspended Sediment 0.01008 Kolpack et al. (1977) 

Adsorption Salinity Coef.(/ppt) 0.023 Kolpack et al. (1977) 

Fraction monoaromatic hydrocarbons (MAHs) 0.019571 Environment Canada (2004) 

Fraction 2-ring aromatics 0.001572 Environment Canada (2004) 

Fraction 3-ring aromatics 0.00623 Environment Canada (2004) 

Fraction Non-Aromatics: boiling point < 180oC 0.139429 Environment Canada (2004) 

Fraction Non-Aromatics: boiling point 180-264oC 0.167188 Environment Canada (2004) 

Fraction Non-Aromatics: boiling point 264-380oC 0.13381 Environment Canada (2004) 

Minimum Oil Thickness (mm) 0.00005 McAuliffe (1987) 

Maximum Mousse Water Content (%) 91.1 Environment Canada (2004) 

Mousse Water Content as Spilled (%) 0.0 - 

Water content of oil (not in mousse, %) 0.0 - 

Degradation Rate (/day), Surface & Shore 0.01 French et al. (1996) 

Degradation Rate (/day), Hydrocarbons in Water 0.01 French et al. (1996) 

Degradation Rate (/day), Oil in Sediment 0.001 French et al. (1996) 

Degradation Rate (/day), Aromatics in Water 0.01 Mackay et al. (1992) 

Degradation Rate (/day), Aromatics in Sediment 0.001 Mackay et al. (1992) 
A-90 

Table B-3. Oil properties for Medium Crude used in the SIMAP simulations. 

Property Value Reference 

Density @ 25 deg. C (g/cm3) 0.8714 - 

Viscosity @ 25 deg. C (cp) 23.2 Environment Canada (2004) 

Surface Tension (dyne/cm) 27.3 Environment Canada (2004) 

Pour Point (deg. C) -32.0 Environment Canada (2004) 

Adsorption Rate to Suspended Sediment 0.01008 Kolpack et al. (1977) 

Adsorption Salinity Coef.(/ppt) 0.023 Kolpack et al. (1977) 

Fraction monoaromatic hydrocarbons (MAHs) 0.02192 Environment Canada (2004) 

Fraction 2-ring aromatics 0.003076 Environment Canada (2004) 

Fraction 3-ring aromatics 0.007284 Environment Canada (2004) 

Fraction Non-Aromatics: boiling point < 180oC 0.20408 Environment Canada (2004) 

Fraction Non-Aromatics: boiling point 180-264oC 0.121224 Environment Canada (2004) 

Fraction Non-Aromatics: boiling point 264-380oC 0.186616 Environment Canada (2004) 

Minimum Oil Thickness (mm) 0.00005 McAuliffe (1987) 

Maximum Mousse Water Content (%) 72.9 Environment Canada (2004) 

Mousse Water Content as Spilled (%) 0.0 - 

Water content of oil (not in mousse, %) 0.0 - 

Degradation Rate (/day), Surface & Shore 0.01 French et al. (1996) 

Degradation Rate (/day), Hydrocarbons in Water 0.01 French et al. (1996) 

Degradation Rate (/day), Oil in Sediment 0.001 French et al. (1996) 

Degradation Rate (/day), Aromatics in Water 0.01 Mackay et al. (1992) 

Degradation Rate (/day), Aromatics in Sediment 0.001 Mackay et al. (1992) 
A-91 

Table B-4. Oil properties for Heavy Crude used in the SIMAP simulations. 

Property Value Reference 

Density @ 0 deg. C (g/cm3) 0.9465 Environment Canada (2009) 

Viscosity @ 0 deg. C (cp) 3220.0 Environment Canada (2009) 

Surface Tension (dyne/cm) 30.1 Environment Canada (2009) 

Pour Point (deg. C) -25.0 Environment Canada (2009) 

Adsorption Rate to Suspended Sediment 0.01008 Kolpack et al. (1977) 

Adsorption Salinity Coef.(/ppt) 0.023 Kolpack et al. (1977) 

Fraction monoaromatic hydrocarbons (MAHs) 0.008228 Environment Canada (2009) 

Fraction 2-ring aromatics 0.001613 Environment Canada (2009) 

Fraction 3-ring aromatics 0.003434 Environment Canada (2009) 

Fraction Non-Aromatics: boiling point < 180oC 0.104772 Environment Canada (2009) 

Fraction Non-Aromatics: boiling point 180-264oC 0.091787 Environment Canada (2009) 

Fraction Non-Aromatics: boiling point 264-380oC 0.129966 Environment Canada (2009) 

Minimum Oil Thickness (mm) 0.001 McAuliffe (1987) 

Maximum Mousse Water Content (%) 75.6 Environment Canada (2009) 

Mousse Water Content as Spilled (%) 0.0 - 

Water content of oil (not in mousse, %) 0.0 - 

Degradation Rate (/day), Surface & Shore 0.01 French et al. (1996) 

Degradation Rate (/day), Hydrocarbons in Water 0.01 French et al. (1996) 

Degradation Rate (/day), Oil in Sediment 0.001 French et al. (1996) 

Degradation Rate (/day), Aromatics in Water 0.01 Mackay et al. (1992) 

Degradation Rate (/day), Aromatics in Sediment 0.001 Mackay et al. (1992) 
A-92 

Table B-5. Oil properties for Heavy Fuel Oil used in the SIMAP simulations. 

Property Value Reference 

Density @ 25 deg. C (g/cm3) 0.9749 Jokuty et al. (1999)*

Viscosity @ 25 deg. C (cp) 3180.0 Jokuty et al. (1999)*

Surface Tension (dyne/cm) 27.0 Jokuty et al. (1999)*

Pour Point (deg. C) 7.0 Whiticar et al (1994) 

Adsorption Rate to Suspended Sediment 0.01008 Kolpack et al. (1977) 

Adsorption Salinity Coef.(/ppt) 0.023 Kolpack et al. (1977) 

Fraction monoaromatic hydrocarbons (MAHs) 0.001819 Jokuty et al. (1999)*

Fraction 2-ring aromatics 0.003794 Jokuty et al. (1999)*

Fraction 3-ring aromatics 0.015941 Jokuty et al. (1999)*

Fraction Non-Aromatics: boiling point < 180oC 0.008181 Jokuty et al. (1999)*

Fraction Non-Aromatics: boiling point 180-264oC 0.045206 Jokuty et al. (1999)*

Fraction Non-Aromatics: boiling point 264-380oC 0.097059 Jokuty et al. (1999)*

Minimum Oil Thickness (mm) 0.001 McAuliffe (1987) 

Maximum Mousse Water Content (%) 30.0 NOAA (2000) 

Mousse Water Content as Spilled (%) 0.0 - 

Water content of oil (not in mousse, %) 0.0 - 

Degradation Rate (/day), Surface & Shore 0.01 French et al. (1996) 

Degradation Rate (/day), Hydrocarbons in Water 0.01 French et al. (1996) 

Degradation Rate (/day), Oil in Sediment 0.001 French et al. (1996) 

Degradation Rate (/day), Aromatics in Water 0.01 Mackay et al. (1992) 

Degradation Rate (/day), Aromatics in Sediment 0.001 Mackay et al. (1992) * – Environment Canada’s Oil Property Catalogue (Jokuty et al., 1999) provided total hydrocarbon data for volatile fractions of 

unweathered oil. The aromatic hydrocarbon fraction was subtracted from the total hydrocarbon fraction to obtain the aliphatic 

fraction of unweathered oil.
A-93 

Table B-6. Oil properties for Diesel Fuel Oil used in the SIMAP simulations. 

Property Value Reference 

Density @ 25 deg. C (g/cm3) 0.8291 Jokuty et al. (1999)*

Viscosity @ 25 deg. C (cp) 4.0 Jokuty et al. (1999)*

Surface Tension (dyne/cm) 26.9 Jokuty et al. (1999)*

Pour Point (deg. C) -14.0 Jokuty et al. (1999)*

Adsorption Rate to Suspended Sediment 0.01008 Kolpack et al. (1977) 

Adsorption Salinity Coef.(/ppt) 0.023 Kolpack et al. (1977) 

Fraction monoaromatic hydrocarbons (MAHs) 0.017793 Jokuty et al. (1999)*

Fraction 2-ring aromatics 0.010175 Lee et al. (1992) 

Fraction 3-ring aromatics 0.001976 Lee et al. (1992) 

Fraction Non-Aromatics: boiling point < 180oC 0.042207 Jokuty et al. (1999)*

Fraction Non-Aromatics: boiling point 180-264oC 0.335825 Jokuty et al. (1999)*

Fraction Non-Aromatics: boiling point 264-380oC 0.542024 Jokuty et al. (1999)*

Minimum Oil Thickness (mm) 0.00001 McAuliffe (1987) 

Maximum Mousse Water Content (%) 0.0 Whiticar et al. (1994) 

Mousse Water Content as Spilled (%) 0.0 - 

Water content of oil (not in mousse, %) 0.0 - 

Degradation Rate (/day), Surface & Shore 0.01 French et al. (1996) 

Degradation Rate (/day), Hydrocarbons in Water 0.01 French et al. (1996) 

Degradation Rate (/day), Oil in Sediment 0.001 French et al. (1996) 

Degradation Rate (/day), Aromatics in Water 0.01 Mackay et al. (1992) 

Degradation Rate (/day), Aromatics in Sediment 0.001 Mackay et al. (1992) * – Environment Canada’s Oil Property Catalogue (Jokuty et al., 1999) provided total hydrocarbon data for volatile fractions of 

unweathered oil. The aromatic hydrocarbon fraction was subtracted from the total hydrocarbon fraction to obtain the aliphatic 

fraction of unweathered oil.
A-94 

References

Environmental Technology Centre, Environment Canada. 2004. Spills Technology Databases. 

Oil Properties database. 

 http://www.etc-cte.ec.gc.ca/databases/OilProperties/Default.aspx. 

Environmental Technology Centre, Environment Canada. 2009. Spills Technology Database. Oil 

Properties Database. 

http://www.etc-cte.ec.gc.ca/databases/OilProperties/pdf/WEB_Sockeye_(2000).pdf

French, D., M. Reed, K. Jayko, S. Feng, H. Rines, S. Pavignano, T. Isaji, S. Puckett, A. Keller, F. 

W. French III, D. Gifford, J. McCue, G. Brown, E. MacDonald, J. Quirk, S. Natzke, R. 

Bishop, M. Welsh, M. Phillips and B.S. Ingram. 1996. The CERCLA type A natural 

resource damage assessment model for coastal and marine environments 

(NRDAM/CME), Technical Documentation, Vol. I - V. Final Report, submitted to the 

Office of Environmental Policy and Compliance, U.S. Dept. of the Interior, Washington, 

DC. Available from National Technical Information Service, 5285 Port Royal Road, 

Springfield, VA 22161, PB96-501788. 

Henry, C. 1997. Louisiana State University. Personal communication, September 3, 1997. 

Jokuty, P., S. Whiticar, Z. Wang, M. Fingas, B. Fieldhouse, P. Lambert, and J. Mullin, 1999. 

Properties of Crude Oils and Oil Products. Manuscript Report EE-165, Environmental 

Protection Service, Environment Canada, Ottawa, ON. Emergencies Science and 

Technology Division, Oil Properties Database. 

Kolpack, R.L., Plutchak, N.B. and R. W. Stearns, 1977. Fate of oil in a water environment - 

Phase II, a dynamic model of the mass balance for released oil. University of Southern 

California, prepared for American Petroleum Institute, API Publication 4313, 

Washington, D.C. 53 pp. 

Lee, L.S., M. Hagwall, J.J. Delfino, and P.S.C. Rao. 1992. Partitioning of polycyclic aromatic 

hydrocarbons from diesel fuel into water. Environ. Sci. Technol. 26: 2104-2110. 

Mackay, D., W.Y. Shiu and D.C. Ma, 1992. Illustrated Handbook of Physical-Chemical 

Properties and Environmental Fate for Organic Chemicals. Lewis Publishers, Inc. 

Chelsea, Michigan. 

McAuliffe, C.D. 1987. Organism exposure to volatile/soluble hydrocarbons from crude oil 

spills – a field and laboratory comparison. In: Proceedings of the 1987 Oil Spill 

Conference, American Petroleum Institute, Washington, D.C., pp. 275-288. 

National Oceanic and Atmospheric Administration (NOAA). 2000. ADIOS Oil Database. 

NOAA HAZMAT, Version 2.0. http://response.restoration.noaa.gov
A-95 

Whiticar, S., M. Bobra, M. Fingas, P. Jokuty, P. Liuzzo, S. Callaghan, F. Ackerman and J. Cao. 

1994. A catalogue of crude oil and oil product properties, 1992 edition with 1994 data 

files. 
A-96 

Oil Spill Modeling for the Offshore Environmental Cost Model (OECM) 

SUB-APPENDIX C 

Habitat Equivalency Analysis for Habitat Injuries 
A-97 

Habitat Equivalency Analysis (HEA) has been used by state and federal trustees to estimate the 

restored habitat required to compensate for habitat and biological resources injured, taking into 

account the time before the project is begun (lag time after the spill and injuries occur), the time 

for development of the restored habitat, the ultimate productivity of services in the new habitat as 

compared to that injured, the duration of the restoration project life, and discounting of future 

habitat services at 3% per year. The approach, equations, and assumptions are described in 

NOAA (1997, 1999), LA DEQ et al. (2003), and French-McCay and Rowe (2003). 

HEA with Trophic Web Model 

This model for scaling required compensatory restoration uses HEA with a trophic web model to 

calculate the required area of restored habitat to produce the same biomass as lost due to a spill. 

Scaling methods used here were initially developed for use in the North Cape case, as described 

in French et al. (2001), French McCay and Rowe (2003) and French McCay et al. (2003a). 

These methods have also been used in several other cases, as well as in successful claims for 23 

cases submitted by the Florida Department of Environmental Protection to the U.S. Coast Guard, 

National Pollution Fund Center (French McCay et al., 2003b). 

The habitat restoration model is based on food chain transfers, such that equivalent production at 

the same trophic level as the losses is produced by the restoration project. The approach uses 

energetic efficiencies to scale across trophic levels. Benefits of habitat to each trophic level are 

estimated by assuming that the production of consumers is proportional to prey production 

gained by the restoration of habitat. The habitat restoration model balances the production 

foregone losses with trophically equivalent production, discounting future gains in compensatory 

production relative to present losses such that interest is paid, analogous to economic discounting 

(French and Rowe, 2003). 

The basis for using this model is that restoration should provide equivalent quality fish and 

invertebrate biomass to compensate for the lost fish and invertebrate production. Likewise for 

wildlife, restoration should also replace the wildlife biomass that was lost. Equivalent quality 

implies same or similar species with equivalent ecological role and value for human uses. The 

equivalent production or replacement should be discounted to present-day values to account for 

the interim loss between the time of the injury and the time when restoration provides equivalent 

ecological and human services. 

Habitat creation or preservation projects have been used to compensate for injuries of wildlife, 

fish and invertebrates. The concept is that the restored habitat leads to a net gain in wildlife, fish 

and invertebrate production over and above that produced by the location before the restoration. 

The size of the habitat (acreage) is scaled to just compensate for the injury (interim loss). 

In the model developed by French-McCay and Rowe (2003), the habitat may be seagrass bed, 

saltmarsh, oyster reef, freshwater or brackish wetland, or other structural habitats that provide 

such ecological services as food, shelter, and nursery habitat and are more productive than open 

bottom habitats. The injuries are scaled to the new primary (plant) or secondary (e.g., benthic) 

production produced by the created habitat, as the entire food web benefits from this production. 
A-98 

A preservation project that would avoid the loss of habitat could also be scaled to the production 

preserved. The latter method would only be of net gain if the habitat is otherwise destined to be 

destroyed. In this analysis, we assume only habitat creation projects would be undertaken. 

The approach used here for scaling the size of the needed project is to use primary production to 

measure the benefits of the restoration. The total injuries in kg are translated into equivalent 

plant (angiosperm) production as follows. Plant biomass passes primarily through the detrital 

food web via detritivores consuming the plant material and attached microbial communities. 

When macrophytes are consumed by detritivores, the ecological efficiency is low because of the 

high percentage of structural material produced by the plant, which must be broken down by 

microorganisms before it can be used by the detritivore. Each species group is assigned a trophic 

level relative to that of the detritivores. If the species group is at the same trophic level as 

detritivores, it is assumed 100% equivalent, as the resource injured would presumably have the 

same ecological value in the food web as the detritivores. If the injured resource preys on 

detritivores or that trophic level occupied by the detritivores, the ecological efficiency is that for 

trophic transfer from the prey to the predator. Values for production of predator per unit 

production of prey (i.e., ecological efficiency) are taken from the ecological literature, as 

reviewed by French McCay and Rowe (2003). The ecological efficiencies assumed are in Table 

1. 

Table 1. Assumed ecological efficiencies for one trophic step (French McCay and Rowe, 

2003).

Consumer Prey/food % Efficiency 

Invertebrate or finfish Macrophyte 0.034 

Invertebrate or finfish Microalgae 10 

Invertebrate Microorganisms 20 

Invertebrate or finfish Detritivores 10 

Invertebrate or fish Invertebrate 20 

Invertebrate or fish filter feeder Plankton 20 

Medium (200-1000g) fish piscivore Finfish 10 

Large (>1kg) fish piscivore Finfish 4 

Sea turtles Invertebrates 2 

Birds, mammals, sea turtles (herbivores) Macrophyte 0.03 

Birds, mammals Invertebrate 2 

Birds, mammals (piscivores) Finfish 2 

The equivalent compensatory amount of angiosperm (plant) biomass of the restored resource is 

calculated as kg of injury divided by ecological efficiency. The ecological efficiency is the 

product of the efficiency of transfer from angiosperm to invertebrate detritivore and efficiency 

from detritivore to the injured resource, accounting for each step up the food chain from 

detritivore to the trophic level of concern. Table 2 lists the composite ecological efficiency 

relative to benthic invertebrate production for each trophic group evaluated in the modeling. 
A-99 

The productivity gained by the created habitat is corrected for less than full functionality during 

recovery using a sigmoid recovery curve. Discounting at 3% per year is included for delays in 

production because of development of the habitat, and delays between the time of the injury and 

when the production is realized in the restored habitat. The equations and assumptions may be 

found in French McCay and Rowe (2003). 

Table 2 Composite ecological efficiency relative to benthic invertebrate production by 

trophic group. 

Species Category Trophic Level Ecological Efficiency 

Relative to Benthic 

Detritivores (%) 

Fish and Invertebrates: 

Small pelagic fish planktivorous 20 

Large pelagic fish piscivores/predators 0.8 

Demersal fish bottom feeders 10 

Crustaceans bottom feeders 20 

Mollusks (large benthic 

invertebrates) filter/bottom feeder 100 

Intertidal benthic invertebrates filter/bottom feeder 100 

Birds: 

Waterfowl bottom feeders 2 

Seabirds piscivores 0.4 

Waders piscivores 0.4 

Shorebirds bottom feeders 2 

Raptors piscivores 0.4 

Kingfishers piscivores 0.4 

Other wildlife: 

Herbivorous mammals herbivores 0.03 

Sea turtles invertebrate feeders 2 

Sea otters plankton/benthos 2 

Pinnipeds piscivores 0.04 

Cetaceans (baleen) plankton/benthos 0.4 

Cetaceans (piscivores) piscivores 0.04 

Polar bear Consume piscivores 0.0008 

 

The needed data for the scaling calculations are: 

x number of years for development of full function in a restored habitat; 

x annual primary production rate per unit area (P) of restored habitat at full function (which 

may be less than that of natural habitats); 

x delay before restoration project begins; and 

x project lifetime (years the restored habitat will provide services). 
A-100 

In the regions analyzed for the OECM project, saltmarsh restoration could be undertaken as 

restoration for wildlife, fish and invertebrate injuries. Other wetlands, such as brackish marshes, 

intermediate marshes or freshwater wetlands, could also be restored. Seagrass bed restoration is 

another option. However, this requires good water quality and appropriate environmental 

conditions to be successful. The calculations below are based on (saltmarsh) wetland restoration, 

as this habitat is most frequently used for compensation; thus, it is used for estimating the 

potential restoration needs and Natural Resource Damage Assessment (NRDA) costs. 

Saltmarsh Restoration 

Restoration scaling calculations for saltmarsh were performed following the methods in French 

McCay and Rowe (2003). It is assumed that the saltmarsh requires 15 years to reach full 

function (based on LA DEQ et al., 2003), ultimately reaching 80% of natural habitat 

productivity, the restoration begins in 2013, and the project lifetime is 20 years (LA DEQ et al., 

2003). 

For the Mid-Atlantic OECM location, above-ground primary production rates for a New England 

salt marsh were used from Nixon and Oviatt (1973) as 500 g dry weight m-2 

yr

-1. In addition, 

benthic microalgal production provides another 105 g dry weight m-2 (Van Raalte, et al., 1976). 

Thus, estimated total primary production rate in saltmarshes in this region is 605g dry weight m-2 

yr

-1. 

For the Gulf of Mexico and Straits of Florida OECM locations, above-ground primary 

production rates of saltmarsh cord grasses in Georgia were used as estimated by Nixon and 

Oviatt (1973), based on Teal (1962), as 1,290 g dry weight m-2 

yr

-1. In addition, benthic 

microalgal production provides another 105 g dry weight m-2 (Van Raalte, et al., 1976). Thus, 

estimated total primary production rate in saltmarshes in this region is 1,395g dry weight m-2 

yr

-1. 

For the Southern California OECM location, above-ground primary production rates of 

saltmarshes in the Central California coast were used as estimated by Continental Shelf 

Associates (1991) as 3,666 g dry weight m-2 

yr

-1. In addition, benthic microalgal production 

provides another 312 g dry weight m-2 (Continental Shelf Associates, 1991). Thus, estimated 

total primary production rate in saltmarshes in this region is 3,978 g dry weight m-2 yr-1. 

For the Washington/Oregon OECM location, above-ground primary production rates of 

saltmarshes in the Oregon coast were used as estimated by Continental Shelf Associates (1991) 

as 2,636 g dry weight m-2 

yr

-1. In addition, benthic microalgal production provides another 375 g 

dry weight m-2 (Continental Shelf Associates, 1991). Thus, estimated total primary production 

rate in saltmarshes in this region is 3,011 g dry weight m-2 yr

-1. 

For the Gulf of Alaska, Cook Inlet/Shelikof Strait, and Bering Sea OECM locations, aboveground primary production rates of saltmarshes in the Lower Cook Inlet were used as estimated 

by Continental Shelf Associates (1991). The daily rates were applied to a 6-month growing 

season, with the annual total being 681 g dry weight m-2 

yr

-1. In addition, benthic microalgal 

production over a 6-month growing season provides another 1,488 g dry weight m-2 (Continental 
A-101 

Shelf Associates, 1991). Thus, estimated total primary production rate in saltmarshes in this 

region is 2,170 g dry weight m-2 yr-1. 

For the Chukchi Sea and Beaufort Sea OECM locations, above-ground primary production rates 

of saltmarshes in the Lower Cook Inlet were used as estimated by Continental Shelf Associates 

(1991). The daily rates were applied to a 3-month growing season, with the annual total being 

341 g dry weight m-2 

yr

-1. In addition, benthic microalgal production over a 6-month growing 

season provides another 744 g dry weight m-2 (Continental Shelf Associates, 1991). Thus, 

estimated total primary production rate in saltmarshes in this region is 1,085g dry weight m-2 yr-1. 

For the injured resources, all weights are as wet weight and dry weight is assumed 22% of wet 

weight (Nixon and Oviatt, 1973). The ratio of carbon to dry weight is assumed 0.45 (French et 

al., 1996). For the wildlife, the body mass per animal (from French et al. (1996) or from Sibley 

(2003)) is used to estimate injury in kg (multiplying by number killed and summing each species 

category). 

Restoration for Intertidal Injury 

In addition to the quantifiable injuries in water habitats, there is also be impact to intertidal 

invertebrates if saltmarsh, mangrove, rocky shore, gravel and sand beach, and mudflat habitats 

are oiled with enough oil to impact invertebrates associated with the intertidal habitat (> 0.1mm 

thickness results in invertebrate injuries). Benthic invertebrate production rates for each habitat 

type are taken into account when determining injury (Tables 3 to 9). Time for recovery for 

intertidal invertebrates (based on a natural recovery curve) is estimated as 3-5 years (French 

McCay, 2009). The total loss of intertidal invertebrates from shoreline oiling greater than 

0.1mm thick is calculated as a factor of daily production rate, as a function of # years to 99% 

recovery and annual discount rate (3%). 

For the HEA calculations, the area (m2

) of saltmarsh restored per m2 oiled was calculated by 

scaling benthic invertebrates production lost to that gained, by multiplying the kilograms of 

benthic invertebrate injury per m2

 oiled by the area (m2) restored per kilogram benthic invert 

injured. This was done for all habitats in which a benthic invertebrate injury would occur (i.e., 

rocky shore, sand beach, gravel beach, macroalgal [seagrass or landweed], fringing mudflat and 

fringing wetland). In order to get one estimate for intertidal injury per OECM geographic 

location, a weighted average of the area of saltmarsh restored per m2

 oiled for these individual 

habitats was calculated based on the percent of that habitat type present in the entire habitat grid 

for the particular OECM location.
A-102 

Table 3. Benthic invertebrate production rates by habitat type for Mid-Atlantic location. 

Habitat Injured

Production Rate of 

Habitat (pre-spill)

g C/m2/day 

Production Rate of 

Habitat (pre-spill)

g dry wt/m2/day 

Saltmarsh 0.7471 2.053 

Rocky shore 0.12 0.275 

Macroalgal bed 0.12 0.275 

Artificial/man made 0.1 0.275 

Gravel beach 0.1 0.275 

Sand beach 0.0022 0.006 

Mudflat 0.12 0.275 1

 Nixon and Oviatt, 1973; VanRaale et al., 1976 

2

 Raymont, 1980 

[12.5 g wet weight/g C, Odum, 1971; dry weight is 22% of wet weight, Nixon and Oviatt, 1973] 

Table 4. Benthic invertebrate production rates by habitat type for Straits of Florida 

location. 

Habitat Injured

Production Rate of 

Habitat (pre-spill)

g C/m2/day 

Production Rate of 

Habitat (pre-spill)

g dry wt/m2/day 

Saltmarsh 1.72051 4.731 

Rocky shore 0.12 0.275 

Artificial/man made 0.12 0.275 

Gravel beach 0.12 0.275 

Sand beach 0.0022 0.006 

Mudflat 0.12 0.275 1

 Teal, 1962; Van Raalte et al., 1976 

2 Raymont, 1980 

[12.5 g wet weight/g C, Odum, 1971; dry weight is 22% of wet weight, Nixon and Oviatt, 1973] 
A-103 

Table 5. Benthic invertebrate production rates by habitat type for Gulf of Mexico location. 

Habitat Injured

Production Rate of 

Habitat (pre-spill)

g C/m2/day 

Production Rate of 

Habitat (pre-spill)

g dry wt/m2/day 

Mangrove 1.72051 4.731 

Saltmarsh 0.0722 0.198 

Rocky shore 0.13 0.275 

Artificial/man made 0.1 0.275 

Gravel beach 0.1 0.275 

Sand beach 0.0023 0.006 

Mudflat 0.0082 0.022 

Coral 2.84 7.700 1 Teal, 1962; Van Raalte et al., 1976 2

 Flint, 1985 

3 Raymont, 1980 

4 Muscatine, 1980 

[12.5 g wet weight/g C, Odum, 1971; dry weight is 22% of wet weight, Nixon and Oviatt, 1973] 

Table 6. Benthic invertebrate production rates by habitat type for Southern California 

location. 

Habitat Injured

Production Rate of 

Habitat (pre-spill)

g C/m2

/day 

Production Rate of 

Habitat (pre-spill)

g dry wt/m2

/day 

Saltmarsh 4.9051 13.489 

Rocky shore 0.12 0.275 

Macroalgal bed 0.12 0.275 

Artificial/man made 0.1 0.275 

Gravel beach 0.1 0.275 

Sand beach 0.0022 0.006 

Mudflat 0.12 0.275 1

 Continental Shelf Associates, Inc., 1991 

2

 Raymont, 1990 
A-104 

Table 7. Benthic invertebrate production rates by habitat type for Washington/Oregon 

location. 

Habitat Injured

Production Rate of 

Habitat (pre-spill)

g C/m2

/day 

Production Rate of 

Habitat (pre-spill)

g dry wt/m2

/day 

Saltmarsh 3.71251 10.209 

Rocky shore 0.12 0.275 

Macroalgal bed 0.12 0.275 

Artificial/man made 0.1 0.275 

Gravel beach 0.1 0.275 

Sand beach 0.0022 0.006 

Mudflat 0.12 0.275 1

 Greeson et al., 1979 

2

 Raymont, 1990 

Table 8. Benthic invertebrate production rates by habitat type for Gulf of Alaska, Cook 

Inlet/Shelikof Strait, and Bering Sea locations. 

Habitat Injured

Production Rate of 

Habitat (pre-spill)

g C/m2/day 

Production Rate of 

Habitat (pre-spill)

g dry wt/m2/day 

Saltmarsh 2.6751 7.356 

Rocky shore 0.12 0.275 

Macroalgal bed 0.12 0.275 

Artificial/man made 0.1 0.275 

Gravel beach 0.1 0.275 

Sand beach 0.0022 0.006 

Mudflat 0.12 0.275 1

 Greeson et al., 1979 

2

 Raymont, 1990 

Table 9. Benthic invertebrate production rates by habitat type for Chukchi Sea and 

Beaufort Sea locations.

Habitat Injured

Production Rate of 

Habitat (pre-spill)

g C/m2/day 

Production Rate of 

Habitat (pre-spill)

g dry wt/m2/day 

Saltmarsh 1.33751 3.678 

Rocky shore 0.12 0.275 

Macroalgal bed 0.12 0.275 

Artificial/man made 0.1 0.275 

Gravel beach 0.1 0.275 

Sand beach 0.0022 0.006 

Mudflat 0.12 0.275 1

 Continental Shelf Associates, Inc., 1991 

2

 Raymont, 1990 
A-105 

Table 10. Natural recovery time (in year) by habitat type based on French McCay (2009).

Habitat Injured 

Natural 

Recovery 

Time (years) 

Saltmarsh 5 

Mangrove 5 

Rocky shore 3 

Macroalgal bed 3 

Artificial/man 

made 

3 

Gravel beach 3 

Sand beach 3 

Mudflat 3 

Coral 3 

Table 11 provides a summary for HEA information for all OECM locations. 
A-106 

Table 11. Summary of HEA Information used in the OECM.

Area (m2) of Saltmarsh Restored per kg of Injury, by OECM Location 

OECM Location Code ATL SFL CGM SCA WAS GOA CIS BER CHU BEA Birds: Waterfowl 82.56 46.37 46.37 16.13 18.75 18.75 18.75 18.75 18.75 18.75  Seabirds 412.82 231.87 231.87 80.63 93.75 93.75 93.75 93.75 93.75 93.75  Wading Birds 412.82 231.87 231.87 80.63 93.75 93.75 93.75 93.75 93.75 93.75  Shorebirds 82.56 46.37 46.37 16.13 18.75 18.75 18.75 18.75 18.75 18.75  Raptors 412.82 231.87 231.87 80.63 93.75 93.75 93.75 93.75 93.75 93.75 Other Wildlife: Sea Turtles 82.56 46.37 46.37 82.56 82.56 - - - - -  Sea Otters - - - 80.63 93.75 52.84 52.84 52.84 52.84 52.84  Pinnipeds 4,128.16 - - 806.35 937.46 528.37 528.37 1,056.75 1,056.75 1,056.75  Cetaceans (Baleen) 412.82 231.87 231.87 80.63 93.75 52.84 52.84 105.67 105.67 105.67  Cetaceans (Piscivores) 4,128.16 2,318.70 2,318.70 806.35 937.46 528.37 528.37 1,056.75 1,056.75 1,056.75  Polar Bears - - - - - - - 52,837.31 52,837.31 52,837.31  Herbivorous Mammals 3,091.60 3,091.60 3,091.60 - - - - - - - Fish and Invertebrates: Small Pelagic Fish 8.26 4.64 4.64 1.61 1.87 1.87 1.87 1.87 1.87 1.87  Large Pelagic Fish 206.41 115.94 115.94 40.32 46.87 46.87 46.87 46.87 46.87 46.87  Demersal Fish 16.51 9.27 9.27 3.23 3.75 3.75 3.75 3.75 3.75 3.75  Crustaceans 8.26 4.64 4.64 1.61 1.87 1.87 1.87 1.87 1.87 1.87  Molluscs (Large Benthic  Invertebrates) 8.26 4.64 4.64 0.32 1.87 1.87 1.87 1.87 1.87 1.87 

Area (m2) of Saltmarsh Restored per Area (m2) Oiled, by OECM Location 

 Intertidal Injury 9.375 15.514 0.725 0.757 2.616 0.378 0.480 0.597 2.243 1.765 
A-107 

References 

Continental Shelf Associates, Inc. 1991. A comparison of marine productivity amoung outer 

continental shelf planning areas. Supplement - An evaluation of benthic habitat primary 

productivity. 

Flint, R.W., 1985. Long-term Estuarine Variability and Associated Biological Response. 

Estuaries 8: 158-169. (from EAI and ASA, 1987). 

French, D., M. Reed, K. Jayko, S. Feng, H. Rines, S. Pavignano, T. Isaji, S. Puckett, A. Keller, F. 

W. French III, D. Gifford, J. McCue, G. Brown, E. MacDonald, J. Quirk, S. Natzke, R. 

Bishop, M. Welsh, M. Phillips and B.S. Ingram, 1996. The CERCLA type A natural 

resource damage assessment model for coastal and marine environments (NRDAM/CME), 

Technical Documentation, Vol. I - V. Final Report, submitted to the Office of 

Environmental Policy and Compliance, U.S. Dept. of the Interior, Washington, DC, April, 

1996; Available from National Technical Information Service, 5285 Port Royal Road, 

Springfield, VA 22161, PB96-501788. 

French, D. P., C.A. Manen, M. Gibson, and J. Catena, 2001. Quantifying the Scale of 

Restoration Required to Compensate for the Impacts of the North Cape Oil Spill on Fish and 

Invertebrates. In: Proceedings of the 2001 International Oil Spill Conference & Exposition, 

March 26-29, 2001, Tampa, Florida, American Petroleum Institute, Washington, D.C. 

French-McCay, D.P, 2009. State-of-the-Art and Research Needs for Oil Spill Impact Assessment 

Modeling. In Proceedings of the 32nd AMOP Technical Seminar on Environmental 

Contamination and Response, Emergencies Science Division, Environment Canada, Ottawa, 

ON, Canada, pp. 601-653. 

French McCay, D.P., and J.J. Rowe, 2003. Habitat restoration as mitigation for lost production 

at multiple trophic levels. Marine Ecology Progress Series 264:235-249. 

French McCay, D., J. J. Rowe, and N. Whittier, 2003a. Final Report, Estimation of Natural 

Resource Damages for 23 Florida Cases Using Modeling of Physical Fates and Biological 

Injuries. (23 volumes). Prepared for Florida Department of Environmental Protection, May 

2003. 

French McCay, D., N. Whittier, T. Isaji, and W. Saunders, 2003b. Assessment of the Potential 

Impacts of Oil Spills in the James River, Virginia. In Proceedings of the 26th Arctic and 

Marine Oil Spill Program (AMOP) Technical Seminar, Emergencies Science Division, 

Environment Canada, Ottawa, ON, Canada, p. 857-878. 

LA DEQ et al., 2003. The Louisiana Regional Restoration Planning Program, Draft Regional 

Restoration Plan, Region 2. Louisiana Department of Environmental Quality, Louisiana 

Department of Natural Resources, Louisiana Department of Wildlife and Fisheries, Louisiana 

Oil Spill Coordinator’s Office, National Oceanic and Atmospheric Administration, U.S. 

Department of the Interior, September 2003. 
A-108 

Muscatine, L. 1980. Productivity of Zooxanthellae. In: P.G. Falkowski (ed.), Primary 

Productivity in the Sea, Plenum Press, NY, pp.381-402. 

National Oceanic and Atmospheric Administration (NOAA). 1997. Natural resource damage 

assessment guidance document: scaling compensatory restoration actions (Oil Pollution Act 

of 1990), NOAA Damage Assessment Center, Silver Spring, MD 

National Oceanic and Atmospheric Administration (NOAA). 1999. Habitat Equivalency 

Analysis: An Overview. NOAA Damage Assessment Center, Silver Spring, MD, 23p. 

Nixon, S.W., and C.A.Oviatt. 1973. Ecology of a New England salt marsh. Ecolog. Monogr. 

43:463-504 

Raymont, J.E., 1980. Plankton and Productivity in the Oceans, 2nd Edition, Vol. 1 

Phytoplankton. Pergamon Press, Oxford. (from EAI and ASA, 1987) 

Sibley, D.A., 2003. The Sibley Field Guide to Birds of Eastern North America. Alfred A. Knopf, 

Inc., NY, 431p. 

Teal, J.M. 1962. Energy flow in the salt marsh ecosystem of Georgia. Ecology 43: 614-624. 

[=40% of fresh, as referenced in Nixon, S.W., and C.A. Oviatt. 1973. Ecology of a New 

England salt marsh. Ecological Monographs 43: 463-498)] 

Van Raalte, C.D., I. Valiela, J. M. Teal, 1976. Production of epibenthic salt marsh algae: Light 

and nutrient limitation. Limnol Oceangr 21:862-872. 
A-109 

Oil Spill Modeling for the Offshore Environmental Cost Model (OECM) 

SUB-APPENDIX D 

Spill Rate and Volume Data for OECM Modeling 
A-110 

This sub-appendix summarizes the analysis performed by Environmental Research Consulting 

(ERC) of (1) the probability of oil spillage (i.e., the likelihood that a spill will occur from any 

particular offshore facility or tanker) and (2) the probability distribution function of the spill 

volumes of different oil types (crude, bunker fuel, diesel) should a spill occur from one of these 

sources. In particular, the analysis requires two sets of spillage rates and spill volume probability 

distribution functions (PDFs) for spills. The first set is for spills associated with the OCS 

program (i.e., from offshore platforms/wells [Table 1], pipelines [Table 2], and from vessels 

servicing the platforms [Table 3]). The PDFs include: 

x Crude oil spills from OCS platforms/wells (Table 1); 

x Operational diesel spills from OCS platforms/wells (Table 1); 

x Crude oil spills from offshore pipelines (Table 2); 

x Diesel spills from offshore pipelines (Table 2); and 

x Diesel spills2

 from offshore supply or service vessels (Table 3). 

For each of these spill types, a very small spill volume (with negligible consequences), a small 

volume, a medium volume, a large volume, and a very large (but not worst-case-discharge) were 

determined. 

The second set of PDFs (Table 4) is for the spillage rates and volumes for spills associated with 

the alternative to OCS oil production (i.e., importing crude and products by tanker): 

x Cargo spills for tankers transporting crude oil; 

x Cargo spills for tankers transporting petroleum products;3

x Bunker fuel spills4

 for tankers transporting crude oil and petroleum products; and 

x Diesel fuel spills5

 for tankers transporting crude oil and petroleum products. 

For each of these spill types, a very small spill (with negligible consequences), a small volume, a 

medium volume, a large volume, a very large volume, and an extra-large volume were 

determined. 

 

2

 The smaller vessels that service the offshore platforms are fueled by diesel rather than heavy 

fuel oil. 

3

 Petroleum products will be represented by diesel fuel in the modeling scenarios. 

4

 Heavy fuel oil 

5

 Tankers in the future will most likely be fueled with diesel rather than heavy fuel oil to meet air 

pollution standards. 
A-111 

Table 1. Spill rates and spill size distributions for spills associated with OCS platforms and wells, and are based upon all OCS spills 

from 1964-2010 excluding the catastrophic Deepwater Horizon (DWH) event. 

Oil Source Oil Type6 Spill Rate (bbl spilled per bbl produced) Size Class Spill Size Range (bbl) Mean bbl7 Median bbl % of Spills 

OCS 

Platforms/Wells 

(w/o Deepwater 

Horizon incident) 

Light crude 0.0000038 

Very Small 1 – 10 4 3.5 75.56% Small 11 – 100 29 21 15.85% Medium 101 – 1,000 334 262 5.89% Large 1,001 – 10,000 3,312 3,500 2.18% Very Large 10,001 – 100,000 47,937 20,000 0.51% 

Heavy 

crude 0.0000038 

Very Small 1 – 10 4 3.5 75.56% Small 11 – 100 29 21 15.85% Medium 101 – 1,000 334 262 5.89% Large 1,001 – 10,000 3,312 3,500 2.18% Very Large 10,001 – 100,000 47,937 20,000 0.51% 

Medium 

crude 0.0000038 

Very Small 1 – 10 4 3.5 75.56% Small 11 – 100 29 21 15.85% Medium 101 – 1,000 334 262 5.89% Large 1,001 – 10,000 3,312 3,500 2.18% Very Large 10,001 – 100,000 47,937 20,000 0.51% 

Diesel 0.00000009 

Very Small 1 1 1 7.64% Small 2 – 10 4 4 62.53% Medium 11 - 100 31 24 23.39% Large 101 – 1,000 312 239 5.25% Very Large 1,001 – 3,600 1,941 1,500 1.19% 

6 No heavy fuel oil spills would be expected from platforms. 7 Mean spill volume within the spill range.
A-112 

Table 2. Spill rates and spill size distributions for spills associated with OCS pipelines. 

Oil Source Oil Type8 Spill Rate (bbl spilled per bbl produced) Size Class Spill Size Range (bbl) Mean bbl9 Median bbl % of Spills 

OCS Pipelines 

Light crude 0.00000162 

Very Small 1 – 10 4 3 67.9% Small 11 – 100 28 18 17.6% Medium 101 – 1,000 404 323 9.0% Large 1,001 – 10,000 3,734 3,700 4.7% Very Large 10,001 – 100,000 16,351 15,574 0.8% 

Heavy 

crude 0.00000162 

Very Small 1 – 10 4 3 67.9% Small 11 – 100 28 18 17.6% Medium 101 – 1,000 404 323 9.0% Large 1,001 – 10,000 3,734 3,700 4.7% Very Large 10,001 – 100,000 16,351 15,574 0.8% 

Medium 

crude 0.00000162 

Very Small 1 – 10 4 3 67.9% Small 11 – 100 28 18 17.6% Medium 101 – 1,000 404 323 9.0% Large 1,001 – 10,000 3,734 3,700 4.7% Very Large 10,001 – 100,000 16,351 15,574 0.8% 

Diesel 0.00000003 

Very Small 1 1 1 11.8% Small 2 – 10 4.6 5.5 29.4% Medium 11 - 100 70 97 17.6% Large 101 – 1,000 371 300 29.4% Very Large 1,001 – 3,600 2,547 2,547 11.8% 

8 No heavy fuel oil spills would be expected from pipelines. 9 Mean spill volume within the spill range.
A-113 

Table 3. Spill rates and spill size distributions for spills associated with OCS service vessels. 

Oil Source Oil Type10 Spill Rate (bbl spilled per bbl produced) Size Class Spill Size Range (bbl) Mean bbl11 Median bbl % of Spills 

OCS Vessels 

Light crude 0 

Very Small 1 – 10 - - - Small 11 – 100 - - - Medium 101 – 1,000 - - - Large 1,001 – 10,000 - - - Very Large 10,001 – 100,000 - - - 

Heavy 

crude 0 

Very Small 1 – 10 - - - Small 11 – 100 - - - Medium 101 – 1,000 - - - Large 1,001 – 10,000 - - - Very Large 10,001 – 100,000 - - - 

Medium 

crude 0 

Very Small 1 – 10 - - - Small 11 – 100 - - - Medium 101 – 1,000 - - - Large 1,001 – 10,000 - - - Very Large 10,001 – 100,000 - - - 

Diesel 0.00000136 

Very Small 1 1 1 9.3% Small 2 – 10 4.6 4 61.2% Medium 11 - 100 30 18 24.0% Large 101 – 1,000 230 166 5.5% Very Large 1,001 – 3,600 -12 - - 

10 No crude or heavy fuel oil spills would be expected from offshore supply vessels. Minor spills of other oils (lubricating oil) might occur. 11 Mean spill volume within the spill range. 12 Theoretical worst-case discharge 3,570 bbl. 
A-114 

Table 4. Spill rates and spill size distributions for spills associated with tankers importing oil. 

Oil Source Oil Type Spill Rate (bbl spilled per bbl transport) Size Class Spill Size Range (bbl) Mean bbl Median bbl % of Spills 

Imported Tankers (this will also be used as proxy for domestic tankers) 

Light crude 0.00000287 

Very Small 0.1 – 10 4.5 0.24 89.10% Small 10.1 - 100 35 40 7.92% Medium 101 – 1,000 293 200 1.90% Large 1,001 – 10,000 4,220 3,000 0.64% Very Large 10,001 – 100,000 29,858 20,000 0.41% Extra Large Over 100,000 250,000 250,000 0.03% 

Heavy 

crude 0.00000287 

Very Small 0.1 – 10 4.5 0.24 89.10% Small 10.1 - 100 35 40 7.92% Medium 101 – 1,000 293 200 1.90% Large 1,001 – 10,000 4,220 3,000 0.64% Very Large 10,001 – 100,000 29,858 20,000 0.41% Extra Large Over 100,000 250,000 250,000 0.03% 

Heavy Fuel 

Oil 0.00000067 

Very Small 0.1 – 10 1.1 0.24 91.67% Small 10.1 - 100 33 24 6.27% Medium 101 – 1,000 393 225 1.28% Large 1,001 – 10,000 3,186 2,381 0.57% Very Large 10,001 – 100,000 16,905 15,952 0.14% Extra Large Over 100,000 160,714 160,714 0.07% 

Medium 

crude 0.00000287 

Very Small 0.1 – 10 4.5 0.24 89.10% Small 10.1 - 100 35 40 7.92% Medium 101 – 1,000 293 200 1.90% Large 1,001 – 10,000 4,220 3,000 0.64% Very Large 10,001 – 100,000 29,858 20,000 0.41% Extra Large Over 100,000 250,000 250,000 0.03% 

Diesel 0.00000117 

Very Small 0.1 – 10 0.94 0.19 92.10% Small 10.1 - 100 33 24 5.74% Medium 101 – 1,000 266 167 1.56% Large 1,001 – 10,000 3,790 2,200 0.52% Very Large 10,001 – 100,000 24,881 14,881 0.07% Extra Large Over 100,000 - - - 

Data sources: 
A-115 

1. All Petroleum Spills ≥ 1 Barrel from OCS Oil & Gas Activities by Size Category and Year, 1964 to 2010

(http://www.boemre.gov/incidents/Excel/PetroleumSpillsBySizeCatYearSourceFourTabs.xls) 

2. Accessed from http://www.bsee.gov/Inspection-and-Enforcement/Accidents-and-Incidents/Spills----Statistics and-Summaries1996-2011.aspx [Accessed September 15, 201]3. OCS oil production since 2000 obtained from: http://www.boemre.gov/stats/OCSproduction.htm 4. 1999 and prior OCS oil production obtained from BOEM queries of Technical Information Management System (TIMS)
A-116 

Oil Spill Modeling for the Offshore Environmental Cost Model (OECM) 

SUB-APPENDIX E 

Guide to the Digital Regression Files and Biological Databases 
A-117 

A large number of regression files were created as part of this effort, and are available in this 

Sub-appendix as digital files (*.xlsx). 

Contained in this Sub-appendix are: 

x Regression summaries for each region (coded summary table of coefficients from all 

individual regressions for the region); 

x Supporting graphs for each individual regression, organized by region; and 

x Biological data files corresponding to each region (may be one or multiple files per 

region). 

The digital files/regressions are identified by codes for region, oil type, and offshore/nearshore 

location. Codes are defined in the tables below. 

Table 1. Region Codes 

Code Region 

ATL Mid-Atlantic 

BEA Beaufort Sea 

BER Bering Sea 

CGM Central Gulf of Mexico 

CHU Chukchi Sea 

CIS Cook Inlet/Shelikof Strait 

GOA Gulf of Alaska (North Pacific) 

SCA Southern California 

SFL Straits of Florida 

WAS Washington/Oregon 

Table 2. Offshore/Nearshore Location Codes 

Code Region 

OFF Offshore 

ON Onshore (nearshore) 

SBVB* Santa Barbara-Ventura Basin 

SMB* Santa Maria Basin 

*Applies to Southern California region only 

Table 3. Oil Type Codes 

Code Region 

ALC Arab Light Crude 

DFO Diesel Fuel Oil 

HC Heavy Crude 

HFO Heavy Fuel Oil 

LC Light Crude 

MC Medium Crude 
A-118 

Oil Spill Modeling for the Offshore Environmental Cost Model (OECM) 

SUB-APPENDIX F 

Alternative Spill Rate and Volume Data for OECM Modeling Provided by BOEM* 

* BOEM considers the recently developed rates for platform and pipeline spills using historical data from 1996-

2010 to best reflect the improved technology and safety procedures in use today. Hence, these are the rates used in 

the Five Year Programmatic EIS. To maintain consistency with the PEIS, the OECM (which is not designed to 

estimate the effects of catastrophic events) now also uses the BOEM rates shown in this appendix for spills from 

platforms and pipelines. 
A-119 

The oil spill rates and spill sizes (other than tanker spills) used in the OECM model runs for the 

2012-2017 Five Year Proposed Final Program are the following 1996-2010 rates from Anderson 

2012. 

Platform Spills

Size Class Spill Range Mean 

bbl 

% of 

Spills 

Cumulative Oil 

Production (bbl) 8,000,000,000

Very Small 1-10 3 63.13% Years 1996-2010 

Small 10-100 34 26.84% Spilled (bb) 13,666 

Medium 100-1000 240 9.73% Spill Rate/bbl 0.0000017083

Large 1000-10,000 2,000 0.29% Spill Rate/MMbbl 1.70827500 

Very Large 10,000-100,000 n/a 0.00% 

Platform Diesel Spills 

Size Class Spill Range Mean 

bbl 

% of 

Spills 

Cumulative Oil 

Production (bbl) 8,000,000,000 

Very Small 1 1 22.22% Years 1996-2010 

Small 2-10 4 43.83% Spilled (bbl) 7,781 

Medium 11-100 37 28.40% Spill Rate/bbl 0.0000009726 

Large 101-1,000 390 4.32% Spill Rate/MMbbl 0.97257750 

Very Large 1,001-3,600 1,533 1.23% 

Pipeline Spills

Size Class Spill Range Mean 

bbl 

% of 

Spills 

Cumulative Oil 

Production (bbl) 8,000,000,000

Very Small 1-10 4 67.98% Years 1996-2010 

Small 10-100 31 20.69% Spilled (bbl) 27,320 

Medium 100-1000 383 7.88% Spill Rate/bbl 0.0000034150

Large 1000-10,000 2,771 3.45% Spill Rate/MMbbl 3.41497875 

Very Large 10,000-100,000 n/a 0.00% 
A-120 

Pipeline Diesel Spills 

Size Class Spill Range Mean 

bbl 

% of 

Spills 

Cumulative Oil 

Production (bbl) 8,000,000,000

Very Small 1 1 25.00% Years 1996-2010 

Small 2-10 3 75.00% Spilled (bbl) 11 

Medium 11-100 n/a 0.00% Spill Rate/bbl 0.0000000013

Large 101-1,000 n/a 0.00% Spill Rate/MMbbl 0.00134125 

Very Large 1,001-10,000 n/a 0.00% 

Vessel Spills 

Size Class Spill Range Mean 

bbl 

% of 

Spills 

Cumulative Oil 

Production (bbl) 8,000,000,000

Very Small 1 1 20.59% Years 1996-2010 

Small 2-10 5 44.12% Spilled (bbl) 975 

Medium 11-100 42 32.35% Spill Rate/bbl 0.0000001218

Large 101-1,000 430 2.94% Spill Rate/MMbbl 0.12183750 

Very Large 1,001-10,000 n/a 0.00% 

References 

Anderson, Cheryl McMahon, Melinda Mayes, and Robert LaBelle. 2012 Update of Occurrence 

Rates for Offshore Oil Spills, BOEM 2012-069, Bureau of Ocean Energy Management and 

Bureau of Safety and Environmental Enforcement, Herndon, VA. June, 2012 

<http://www.boem.gov/Environmental-Stewardship/Environmental-Assessment/Oil-SpillModeling/Oil-Spill-Occurence-Rate-for-Oil-Spill-Risk-Analysis-(OSRA).aspx> 
B-1

APPENDIX B: DERIVATION OF CRITERIA POLLUTANT EMISSION 

FACTORS FOR ONSHORE OIL PRODUCTION

To provide additional documentation of the data and methods incorporated into the OECM, this appendix 

provides a detailed description of the data and methods employed to derive the criteria pollutant emission 

factors for onshore oil production.49 These emission factors serve as inputs into the NAA in the OECM 

and help assess the environmental impacts of energy production displaced by production on the OCS.

Before describing the approach in detail, note that there are two main challenges to estimating emissions 

associated with onshore oil production. The first is the need to distinguish between onshore and offshore 

oil production – both in quantifying the amount of fuel produced and in identifying emissions sources. 

The second is the need to allocate total emissions from oil and natural gas production to either oil 

production or natural gas production, given that oil and gas are often jointly produced. Some emissionsproducing activities, such as drilling wells, are integral to the production of both crude oil and natural gas

at the same well, so allocating emissions to each fuel type is not a straightforward process.

As will be discussed below, limitations in the availability of emissions data associated with onshore oil 

production made it necessary to focus only on a subset of ten states: Arizona, Colorado, Montana, North 

Dakota, New Mexico, Nevada, Oregon, South Dakota, Utah, and Wyoming. Using emissions and fuel 

production data from these ten states, an estimate of the rate of emissions associated with each unit of 

onshore crude oil produced nationwide was developed. That is, each emission factor was estimated as the 

ratio of oil-related emissions to oil production in the states analyzed.

The remainder of this appendix proceeds as follows.

1. The first section describes the data sources used to obtain estimates of crude oil production and 

associated emissions from both point and nonpoint sources.

2. This appendix then outlines the methods followed, first, to identify emissions associated with 

the production of oil and natural gas (because the two are often jointly produced), and second,

to apportion these emissions between oil production and natural gas production.

3. Finally, estimates of NOx, SO2, VOC, CO, PM2.5, and PM10 emissions for area and point sources 

associated with production of crude oil are presented. Using these values, emissions per unit of 

crude oil produced onshore were calculated.

Data Sources

In order to develop a per-unit estimate of the emissions caused by onshore production of oil, it was 

necessary to obtain data on the quantity of oil produced and the emissions from point and area sources 

associated with oil production.

 

49 While the data sources described in this appendix would allow for the estimation of emission factors for onshore 

gas production, gas-specific emission factors are developed with more up-to-date data sources, as documented in 

Chapter 4, and are not presented in this appendix. 
B-2

Onshore oil production

Data on onshore oil production were obtained from databases of domestic energy production maintained 

by the DOE’s Energy Information Administration (EIA). EIA’s databases include production data from 

1967 through 2014 and distinguish between onshore production and offshore production. From these 

databases, annual onshore crude oil production data by state were obtained.

50

 These totals are presented 

in Table 1. Note that the data presented in Table 1 are specific to the year 2002 to be consistent with the 

emissions data obtained for the oil sector. With the significant increase in unconventional oil production 

over the past decade, the distribution of onshore oil production has shifted significantly since 2002, with 

sharp increases in market share for North Dakota and Texas and decreases in California, Alaska, and 

Louisiana.

Table 1. Onshore Crude Oil Production in 2002.

STATE

2002 ONSHORE PRODUCTION OF CRUDE OIL 

(THOUSAND BARRELS)

Alabama 8,631

Alaska1359,335

Arizona163

Arkansas 7,344

California1258,010

Colorado117,734

Florida 3,656

Illinois 12,051

Indiana 1,962

Kansas 32,721

Kentucky 2,679

Louisiana 93,477

Maryland 0

Michigan 7,219

Mississippi 18,015

Missouri 95

Montana116,855

Nebraska 2,779

Nevada1553

New Mexico167,041

New York 165

North Dakota130,993

Ohio 6,004

Oklahoma 66,642

Oregon10

Pennsylvania 2,233

South Dakota11,214

Tennessee 275

Texas 411,985

Utah113,676

 

50 Crude oil production data were obtained at http://tonto.eia.doe.gov/dnav/pet/pet_crd_crpdn_adc_mbbl_a.htm. 
B-3

STATE

2002 ONSHORE PRODUCTION OF CRUDE OIL 

(THOUSAND BARRELS)

Virginia 22

West Virginia 1,382

Wyoming154,717

Total Onshore Production 1,499,528

WRAP States (minus California and Alaska) 202,846

Source: Department of Energy’s Energy Information Administration databases. Crude oil production values 

were taken from http://tonto.eia.doe.gov/dnav/pet/pet_crd_crpdn_adc_mbbl_a.htm,

Note:

1. Member of the Western Regional Air Partnership (WRAP)

Emissions

As mentioned above, obtaining data on emissions associated with onshore oil (and natural gas) production 

presented a significant challenge. EPA’s National Emissions Inventory (NEI) is the most comprehensive 

database of nationwide emissions information, but it does not fully capture emissions from area sources 

related to oil production. Emissions from smaller field equipment involved in oil production usually fall 

below EPA’s permitting thresholds and are therefore underrepresented in the NEI. Consequently, 

emissions information was sought from sources that improved on the NEI by adding information about 

area sources related to the production of oil. 

As part of their efforts to comply with the Clean Air Act’s Regional Haze Rule, states in the Western 

Regional Air Partnership (WRAP) formed an Oil and Gas Emissions Workgroup tasked with developing 

a more complete inventory of emissions from oil and natural gas production.51 To develop this inventory, 

WRAP solicited review of the 2002 NEI from individual states, corrected errors, and added an oil and 

natural gas field operations area source inventory. This analysis uses the final version of the 2002 

emissions inventory developed by WRAP’s Stationary Sources Joint Forum, which includes point-source 

and area-source emissions data for NOx, SO2, VOC, CO, PM10, and PM2.5

52

 Because of reliance on 

WRAP emissions inventory, the scope of the analysis is limited to the states that belong to WRAP: 

Alaska, Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, 

South Dakota, Utah, Washington, and Wyoming. Idaho and Washington did not produce crude oil or 

natural gas in 2002, so they were not considered in this analysis. In addition, Alaska and California were 

excluded from consideration, because both states engaged in onshore as well as offshore oil production, 

and it was not possible to identify which emissions were associated only with onshore production. As 

shown in Table 1, the remaining ten WRAP states produced over 200 million barrels of crude oil in 2002. 

These totals represent about 14 percent of total onshore crude oil production in 2002. Of the states not 

included in WRAP, Texas, Oklahoma, and Louisiana are the most significant, together accounting for 38 

percent of onshore crude oil production. 

 

51 “Oil & Gas Emissions Workgroup: About.” Accessed at http://www.wrapair.org/forums/ogwg/index.html. 

52 Both area-source and point-source emissions were obtained at http://www.wrapair.org/forums/ssjf/pivot.html. 
B-4

Methodology for emissions estimates

Emissions estimates for both point sources and area sources were obtained from the WRAP emissions 

inventory discussed above. For each source category, emissions associated with oil and natural gas 

production first were identified and then divided between oil production and natural gas production.

Emissions data for area sources

The area source emissions database obtained from WRAP presents emissions at the State level, organized 

by Standard Classification Code (SCC). To identify emissions associated with oil and/or natural gas 

production, all emissions from the SCCs for “Industrial Processes-Oil and Gas Production” were selected. 

The full list of SCCs used to identify area-source emissions from oil and natural gas production can be 

found in Table 2. 
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Table 2. Area Source SCCs.

SCC CODE SCC DESCRIPTION OIL OR GAS

2310000220 Industrial Processes_Oil and Gas Production_All Processes_Drill rigs Unable to be determined

2310000330 Industrial Processes_Oil and Gas Production_All Processes_Artificial lift Unable to be determined

2310000440 Industrial Processes_Oil and Gas Production_All Processes_Saltwater disposal engines Unable to be determined

2310010000 Industrial Processes_Oil and Gas Production: SIC 13_Crude Petroleum_Total: All Processes Oil

2310010100 Industrial Processes_Oil and Gas Production_Crude Petroleum_Heaters Oil

2310010200 Industrial Processes_Oil and Gas Production_Crude Petroleum_Tanks - Flashing & Standing/Working/Breathing Oil

2310010300 Industrial Processes_Oil and Gas Production_Crude Petroleum_Pneumatic Devices Oil

2310010700 Industrial Processes_Oil and Gas Production_Crude Petroleum_Well Fugitives Oil

2310010800 Industrial Processes_Oil and Gas Production_Crude Petroleum_Well Truck Loading Oil

2310020000 Industrial Processes_Oil and Gas Production: SIC 13_Natural Gas_Total: All Processes Gas

2310020600 Industrial Processes_Oil and Gas Production_Natural Gas_Compressor Engines Gas

2310020700 Industrial Processes_Oil and Gas Production_Natural Gas_Gas Well Fugitives Gas

2310020800 Industrial Processes_Oil and Gas Production_Natural Gas_Gas Well Truck Loading Gas

2310021100 Industrial Processes_Oil and Gas Production_Natural Gas_Heaters Gas

2310021300 Industrial Processes_Oil and Gas Production_Natural Gas_Pneumatic Devices Gas

2310021400 Industrial Processes_Oil and Gas Production_Natural Gas_Dehydrators Gas

2310021500 Industrial Processes_Oil and Gas Production_Natural Gas_Completion - Flaring and venting Gas

2310021600 Industrial Processes_Oil and Gas Production_Natural Gas_Gas Well Venting Gas

2310023000 Industrial Processes_Oil and Gas Production_Natural Gas_CBM - Dewatering pump engines Gas

31000299 Industrial Processes_Oil and Gas Production_Natural Gas Production_Other Not Classified Gas

2310030000 Industrial Processes_Oil and Gas Production: SIC 13_Natural Gas Liquids_Total: All Processes Gas

2310030210 Industrial Processes_Oil and Gas Production_Natural Gas Liquids_Tanks - Flashing & Standing/Working/Breathing, Uncontrolled Gas

2310030220 Industrial Processes_Oil and Gas Production_Natural Gas Liquids_Tanks - Flashing & Standing/Working/Breathing, Controlled Gas
B-6

SCC descriptions also were used to determine which emissions were attributable to oil production and 

which to natural gas production. As Table 2 shows, most SCC descriptions indicate whether the 

emissions source is associated with either crude petroleum or natural gas. Using this process, about 85 

percent of total NOx emissions, 99 percent of VOC emissions, and 92 percent of CO emissions, but less 

than one percent of SO2 emissions, were allocated to area sources specific to oil or natural gas production. 

The primary emissions source that could not be attributed to either natural gas or crude oil production 

based on the SCC description was the operation of drill rigs (SCC: 2310000220). Because drill rigs are 

associated with production of both natural gas and crude oil, it would not be appropriate to fully allocate

emissions from drill rigs to either category. Instead, these emissions were divided between oil and natural 

gas production according to the division of emissions from all other SCCs. In the case of SO2 emissions, 

some states did not have emissions from any sources other than drill rigs. In those states, SO2 emissions 

from drill rigs were divided in proportion to the division of NOx emissions from other sources. 

Emissions data for point sources

As with emissions from area sources, all emissions from SCCs for “SCCs for Industrial Processes_Oil 

and Gas Production” first were selected. Based on communications with the WRAP Air Quality Project 

Manager, however, it was determined that sources with those SCCs did not account for all emissions from 

activities associated with oil and natural gas production.53 As one example, emissions from well

compression, pump engines, and electric generators are coded with SCCs for “Internal 

Combustions_Industrial.” Accordingly, the WRAP emissions inventory includes emissions from diesel 

and natural gas-fueled industrial internal combustion engines. However, SCCs for industrial internal 

combustion engines also include sources not associated with oil and natural gas production, such as 

internal combustion engines used as generators in other industrial processes. The same problem also 

arises with other SCCs.

To address this limitation of the WRAP point-source inventory, information from the 2002 NEI was used 

to determine what portion of emissions in the WRAP database, by SCC, can reliably be attributed to oil 

and natural gas production. Each individual emissions source included in the 2002 NEI is classified by 

SCC and by North American Industry Classification System (NAICS) code. Whereas SCCs specify the 

kind of equipment or facility that is the source of emissions, NAICS codes provide more information on 

the industry associated with that source. Using the industry codes in the 2002 NEI, scaling factors were 

developed by state, pollutant, and SCC, which allowed for estimation of the emissions in the WRAP 

database attributable to oil and natural gas production. These scaling factors were estimated using the 

following three steps.

1. Four NAICS codes were identified and assumed to be associated with oil and natural gas 

production, presented in Table 3.

2. Then emissions sources in the WRAP database were matched to sources in the NEI database in 

order to assign NAICS codes to each source. Of the 53,062 individual sources in the WRAP 

point-source emissions database, matching sources with NAICS codes were identified for 39,620, 

or about 75 percent.

3. For this sample of point sources from the WRAP database, determinations were made by state, 

SCC, and pollutant, of the percent of total emissions that come from facilities with oil and natural 

gas production NAICS codes (listed in Table 3). As an example, for the facilities from the 

 

53 Personal communication with Lee Gribovicz, WRAP Air Quality Project Manager, February 24, 2010.
B-7

WRAP database with matches in the NEI database, it is estimated that 71.1 percent of NOx

emissions from natural gas-fired industrial internal combustion engines in Colorado were 

associated with oil and natural gas production.

These state-, SCC-, and pollutant-specific scaling factors were used to estimate emissions associated with 

oil and natural gas production. The full list of SCCs for which point source emissions were estimated 

related to onshore oil and natural gas production is presented in Table 4. 

Table 3. NAICS Codes Associated with Crude Oil and Natural Gas Production.

NAICS 

CODE NAICS DESCRIPTION

211111 U.S. national industry classification for crude petroleum and natural gas extraction

211112 U.S. national industry classification for natural gas liquid extraction

213111 U.S. national industry classification for drilling oil and natural gas wells

213112 U.S. national industry classification for support activities for oil and natural gas operations
B-8

Table 4. Point-Source SCCs.

SCC CODE SCC DESCRIPTION OIL OR GAS

102006 External Combustion Boilers_Industrial_Natural Gas Unable to be determined

102007 External Combustion Boilers_Industrial_Process Gas Unable to be determined

201001 Internal Combustion Engines_Electric Generation_Distillate Oil (Diesel) Unable to be determined

201002 Internal Combustion Engines_Electric Generation_Natural Gas Unable to be determined

202001 Internal Combustion Engines_Industrial_Distillate Oil (Diesel) Unable to be determined

202002 Internal Combustion Engines_Industrial_Natural Gas Gas

202004 Internal Combustion Engines_Industrial_Large Bore Engine Unable to be determined

203002 Internal Combustion Engines_Commercial/Institutional_Natural Gas Unable to be determined

280002 Internal Combustion Engines_Diesel Marine Vessels_Commercial Unable to be determined

288888 Internal Combustion Engines_Fugitive Emissions_Other Not Classified Unable to be determined

301032 Industrial Processes_Chemical Manufacturing_Elemental Sulfur Production Unable to be determined

306001 Industrial Processes_Petroleum Industry_Process Heaters Oil

306008 Industrial Processes_Petroleum Industry_Fugitive Emissions Oil

306009 Industrial Processes_Petroleum Industry_Flares Oil

306099 Industrial Processes_Petroleum Industry_Incinerators Oil

306888 Industrial Processes_Petroleum Industry_Fugitive Emissions Oil

310001 Industrial Processes_Oil and Gas Production_Crude Oil Production Oil

310002 Industrial Processes_Oil and Gas Production_Natural Gas Production Gas

310003 Industrial Processes_Oil and Gas Production_Natural Gas Processing Facilities Gas

310004 Industrial Processes_Oil and Gas Production_Process Heaters Unable to be determined

310005 Industrial Processes_Oil and Gas Production_Liquid Waste Treatment Unable to be determined

310888 Industrial Processes_Oil and Gas Production_Fugitive Emissions Unable to be determined

399900 Industrial Processes_Miscellaneous Manufacturing Industries_Miscellaneous Manufacturing Industries Unable to be determined

399999 Industrial Processes_Miscellaneous Manufacturing Industries_Miscellaneous Industrial Processes Unable to be determined

402009 Petroleum and Solvent Evaporation_Surface Coating Operations_Thinning Solvents - General Unable to be determined

403010 Petroleum and Solvent Evaporation_Petroleum Product Storage at Refineries_Fixed Roof Tanks (Varying Sizes) Oil
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Table 4. Point-Source SCCs.

SCC CODE SCC DESCRIPTION OIL OR GAS

403011 Petroleum and Solvent Evaporation_Petroleum Product Storage at Refineries_Floating Roof Tanks (Varying Sizes) Oil

404001 Petroleum and Solvent Evaporation_Petroleum Liquids Storage (non-Refinery)_Bulk Terminals Unable to be determined

404002 Petroleum and Solvent Evaporation_Petroleum Liquids Storage (non-Refinery)_Bulk Plants Unable to be determined

404003 Petroleum and Solvent Evaporation_Petroleum Liquids Storage (non-Refinery)_Oil and Gas Field Storage and Working Tanks Unable to be determined
B-10

In order to apportion point-source emissions between crude oil and natural gas production, the same 

approach was followed initially as described above for area sources. However, a large portion of 

emissions – particularly VOC emissions – remained uncategorized. For these remaining emissions, it was 

assumed that all emissions from facilities in the 211112 NAICS – the classification for natural gas liquid 

extraction – were from processes related to natural gas production. This assumption allowed assignment 

of all but 3.5 percent of VOC emissions to either natural gas production or crude oil production. Finally, 

all remaining uncategorized emissions were divided between oil and natural gas production according to 

the division of emissions from all other SCCs, as with area source emissions

Results

In this section, estimates of state-level NOx, SO2, VOC, CO, PM10, and PM2.5 emissions from point and 

nonpoint sources associated with onshore oil production in 2002 are presented. Table 5 presents

emissions from area sources associated with oil production, while Table 6 presents emissions from point 

sources. Total emissions from both point and area sources are presented in Table 7. Finally, Table 8

presents total production of crude oil for the 10 states for which emissions were estimated, together with 

emissions rates for each of the seven pollutants. The totals in Table 8 represent estimates of the 

nationwide emissions rates associated with onshore oil production. 

Table 5. 2002 Area Source Emissions from Onshore Oil Production.

STATE NAME

EMISSIONS (TONS)

NOX SO2 VOC CO PM10 PM2.5

Arizona <1 0 15 <1 0 0

Colorado 10 <1 927 2 0 0

Montana 49 1 4,079 8 0 0

North Dakota 112 9 6,901 15 0 0

New Mexico 369 7 12,890 78 0 0

Nevada 2 <1 128 <1 0 0

Oregon 0 0 0 0 0 0

South Dakota 3 <1 258 <1 0 0

Utah 35 <1 2,930 7 0 0

Wyoming 169 2 10,788 57 0 0

Total 750 19 38,916 168 0 0

Source: WRAP 2002 stationary source inventory for area sources.

Note: Totals may not sum due to rounding.
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Table 6. 2002 Point Source Emissions from Onshore Oil Production.

STATE NAME

EMISSIONS (TONS)

NOX SO2 VOC CO PM10 PM2.5

Arizona 0 0 0 0 0 0

Colorado 21 2 1,399 45 <1 0

Montana 0 0 0 0 0 0

North Dakota 0 0 0 0 0 0

New Mexico 5 463 1,028 4 <1 <1

Nevada 0 0 0 0 0 0

Oregon 0 0 0 0 0 0

South Dakota 0 0 0 0 0 0

Utah 221 2,423 525 561 0 0

Wyoming 10 140 639 129 0 0

Total 256 3,028 3,591 739 <1 <1

Source: WRAP 2002 stationary source inventory for point sources.

Note: Totals may not sum due to rounding.

Table 7. Total 2002 Emissions from Onshore Oil Production.

STATE NAME

EMISSIONS (TONS)

NOX SO2 VOC CO PM10 PM2.5

Arizona <1 0 15 <1 0 0

Colorado 31 2 2,326 47 <1 0

Montana 49 1 4,079 8 0 0

North Dakota 112 9 6,901 15 0 0

New Mexico 374 470 13,918 82 <1 <1

Nevada 2 <1 128 <1 0 0

Oregon 0 0 0 0 0 0

South Dakota 3 <1 258 <1 0 0

Utah 255 2,423 3,454 568 0 0

Wyoming 179 141 11,427 186 0 0

Total 1,006 3,047 42,507 907 <1 <1

Source: WRAP 2002 stationary source inventory for area and point sources.

Note: Totals may not sum due to rounding.
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Table 8. Tons of Emissions Per Thousand Barrels of Onshore Oil Production.

STATE NAME

PRODUCTION 

(THOUSAND 

BARRELS)

EMISSIONS PER UNIT (TONS PER THOUSAND BARRELS)

NOX SO2 VOC CO PM10 PM2.5

Arizona 63 0.00236 0.0000 0.240 0.00047 0 0

Colorado 17,734 0.00173 0.0001 0.131 0.00268 2.57E-05 0

Montana 16,855 0.00292 0.0001 0.242 0.00050 0 0

North Dakota 30,993 0.00362 0.0003 0.223 0.00048 0 0

New Mexico 67,041 0.00557 0.0070 0.208 0.00122 2.94E-06 2.67E-06

Nevada 553 0.00411 0.0001 0.232 0.00050 0 0

Oregon 0 0.00000 0.0000 0.000 0.00000 0 0

South Dakota 1,214 0.00252 0.0000 0.213 0.00052 0 0

Utah 13,676 0.01868 0.1772 0.253 0.04155 0 0

Wyoming 54,717 0.00327 0.0026 0.209 0.00339 0 0

Total 202,846 0.00496 0.0150 0.210 0.00447 3.22E-06 8.81E-07
C-1

APPENDIX C: MODELING THE IMPACTS OF OFFSHORE EMISSIONS 

ON ONSHORE AIR QUALITY

Introduction and Overview

This appendix documents the methods employed to estimate the impact of offshore criteria pollutant 

emissions on air quality in the contiguous United States. As described in the main body of this document, 

the APEEP analysis model (Muller and Mendelsohn, 2007 and 2009; Muller et al., 2011) was used to 

assess the onshore air quality effects of emissions from nearly 1,500 offshore source locations in the 

Atlantic and Pacific Oceans and the Gulf of Mexico. These particular source locations reflect possible 

locations of offshore oil and natural gas exploration and extraction sites. In past applications, the APEEP

model’s domain included both sources and receptors in the contiguous United States. In the current 

application, the source domain is extended to include offshore emission sites. This is accomplished using 

regression analysis. 

The transfer coefficients in the APEEP model (denoted as Tijs), which characterize the impact on air 

pollution levels in receptor location (j) due to an emission of pollutant species (s) from source location (i), 

Tijs, are derived from the Gaussian Plume Model (Turner, 1994). Two critical determinants of Tijs are the 

distance between source and receptor and the compass bearing (direction) between source and receptor.

Hence, the Tijs are regressed on distance and bearing to characterize this relationship. Using the fitted 

regression model, the distance and bearing between each offshore source location and each onshore U.S. 

county are inserted into the regression model to estimate Tijs.

The results of this exercise indicate that the impact of a source’s emissions on air pollution levels at a 

receptor is inversely related to distance. That is, the further a receptor is from a source, the smaller the 

impact on air quality. Second, the impact of compass direction on the link between emissions and 

pollution levels is non-linear. The nature of this non-linearity suggests that sources located nearly due 

west of a receptor have the greatest impact on its pollution levels, while sources located due east of a 

receptor have the smallest impact. This is intuitive in the sense that prevailing winds tend to be from the 

west, on average directing emitted pollutants from west to east. 

Methods 

An integrated assessment model, APEEP, is used to connect offshore emissions to their onshore 

consequences in terms of air pollution levels. The APEEP model has been used in prior analyses to 

connect emissions from onshore sources to onshore consequences (Muller and Mendelsohn, 2007 and

2009; Muller et al., 2010). Hence, the APEEP model currently is equipped to this modeling task with one 

exception – connecting emissions generated offshore to air pollution levels in each county in the 

coterminous United States. Since running an air quality model nearly 1,500 times to quantify the sourcereceptor relationships between each offshore source and all onshore counties would be prohibitively 

expensive and time-consuming, a reduced-form approach is employed.

The air quality model in the APEEP model is derived from the Gaussian Plume model (Turner, 1994). As 

such, the APEEP model contains a series of source-receptor matrices that are comprised of transfer 

coefficients that depict the relationship between emissions in source location (i) and receptor location (j), 

denoted Tijs. Note that (s) corresponds to pollution species. Hence, the APEEP model contains distinct 

source-receptor matrices for each emitted pollutant. The (i,j) entry in matrix (s) characterizes the impact 
C-2

of one ton of emissions from source (i) on annual average concentrations in receptor location (j). Tijs

values are used as the basis for characterizing the impact of offshore emissions on county receptors. 

The extension to modeling the impact of offshore emissions on onshore counties relies on developing a 

regression model that describes the Gaussian transfer coefficients Tijs in the APEEP model as a function 

of the distance and compass direction between source and receptor locations. As such, the distance 

between each modeled offshore source and each onshore county is determined using Formulas (1) and (2)

below.

The distance in miles (Dij) between offshore source (i) and receptor county (j) is computed using Formula 

(1).

Formula (1):

Dij = (((Lati – Latj)

2

x 69) + ((Loni – Lonj)

2

x 53))0.5 x (cos(Latj/57.3))

where: Lati = latitude in source grid cell (i)

Loni = longitude in source grid cell (i)

Since prevailing wind direction also impacts the emission-concentration relationship, compass bearing is 

determined. The bearing expressed in radians is determined using Formula (2)

Formula (2):

θij = (atan2(sin(Lonj -Loni)*cos(Latj), 

cos(Lati)*sin(Latj)-sin(Lati)*cos(Latj)*cos(Lonj –Loni)))

In order to convert the resultant θij to degrees, θij is multiplied by 180/Π, or 57.3. Finally, since (2) 

produces values on the interval -180◦, 180◦, 360◦is added to θij and the modulus function applied to derive 

the bearing (Bij) between each source (i) and receptor (j). This is shown in Formula (3).

Formula (3):

Bij = mod(360◦+ θij).

The next step toward modeling the impact of criteria pollutant emissions from grid cell to county involves 

the estimation of transfer coefficients that describe the impact on ambient concentrations of pollutant 

species (s) in county location (j) due to emissions in source (i), denoted Tijs in Formula (4). Note that Tijs

is constructed as a function of distance and bearing between (i) and (j).

Formula (4):

Tijs = β0s + β1sDij + β2sBij + β3sDijBij + εijs
C-3

Empirically, this procedure employs the transfer coefficients in the source-receptor matrices in the 

APEEP model, as the Tjis in (4). Transfer coefficients are specific to each pollutant species (s) and for 

particular emission heights. This analysis employs the Tijs corresponding to ground-level emissions since 

it is unlikely that offshore emissions would be produced by a facility with a tall smokestack similar to 

what is observed in large industrial facilities or power plants. 

The model in Formula (4) forms the basis of a fitted regression model in which transfer coefficients are 

regressed on distance and compass bearing for all of the ground-level county-to-county transfer 

coefficients in the APEEP model. This estimation procedure results in a set of parameter estimates (βks) 

for each pollutant species (s), which describe Tijs as a function of distance and bearing. The estimated 

parameters from Formula (4) reflect the impact of distance and bearing on the emission-to-concentration 

relationships among counties in the coterminous United States. 

In order to generate transfer coefficients that capture the impact of emissions from offshore sources on 

U.S. counties, the coordinates (latitude, longitude) for each offshore source and each county are used to 

calculate both distance and bearing for each source-county pair denoted (Dij, Bij). The distance and 

bearing values are inserted into the fitted model for pollutant species (s). The resulting, predicted Tijs

reflects the impact of an emission of pollution species (s) from offshore source (i) on ambient county (j).

There is one additional step in the air quality modeling phase of the APEEP model before concentrations 

are linked to exposure and damages. The ambient PM2.5 level predicted in each county is calculated in a 

manner that reflects the interactions among ambient NOx, SO2, and NH4 (ammonium). Specifically, a 

reduced form representation of the processes that link ambient levels of these pollutants to particulate 

sulfate and particulate nitrate, important constituents of ambient PM2.5, is embedded in the APEEP model 

and calculated in each onshore receptor location. Hence, when modeling an emission of SO2, for 

example, from an offshore source, the estimated (Tijs) predicts the resulting incremental increase in 

ambient SO2 in each receptor county (j). This level of SO2 is then fed into the existing ammonium submodule to determine resulting concentrations of particulate sulfate and total PM2.5. 

In order to compute ambient ozone (O3) levels, offshore emissions of NOx and VOCs are linked to 

ambient concentrations of NOx and VOCs through the (Tijs) fitted using the approach described above.

Then the resulting NOx and VOC levels onshore are processed in the O3 sub-module in the APEEP model 

(Muller and Mendelsohn, 2007). Specifically, a reduced-form model translates ambient levels of NOx

and VOCs into O3 levels in each receptor county. (Note that the model also incorporates the effects of a 

multitude of other factors on ambient O3.) Connecting the (Tijs) to the O3 sub-module links offshore 

emissions of O3 precursors to onshore ambient levels of O3.

Results

Tables 1 and 2 display the results from the estimation procedure for Formula (4), by pollutant species (s).

Although there is not a necessarily preferred functional form for Formula (4), a third-order approximating 

polynomial is used for the explanatory variables and the natural log form of the dependent variables (Tijs). 

Table 1 focuses on the impact of emissions of NOx, SO2, PM2.5, and VOCs on ambient concentrations of 

PM2.5. The results shown are ordinary least squares (OLS) estimates. First, the large number of 

observations (>8 million) results in hypothesis tests with high statistical power; note that most of the OLS

coefficients are significant at α = 0.01. 
C-4

For each emitted pollutant, the impact of distance on Tijs is quite similar. Both the linear and the cubic 

forms have a negative impact on Tijs while the quadratic term figures a positive impact. The resulting 

functional form is shown in Figure 1. The magnitude of Tijs is at first steeply declining as distance 

between source and receptor increases. At approximately 750 miles, the effect of distance mitigates and 

Tijs is no longer declining dramatically as distance increases up to 3,500 miles. 

The fitted coefficients for compass direction (bearing) are less uniform across pollutants. For NOx, PM2.5, 

and VOCs, the linear terms are positive, while the quadratic terms are negative. For NOx, the cubic term 

also is positive. In contrast, for PM2.5 and VOCs the cubic term is negative. Bearing appears to have a 

somewhat different impact on the Tijs corresponding to SO2. Specifically, the linear and cubic terms are 

negative while the quadratic term is positive. The nature of the functional forms for the relationship 

between bearing and the Tijs for PM2.5, NOx, VOCs, and SO2 is shown in Figure 2. This figure indicates 

that for NOx, VOCs, and PM2.5, Tijs maximizes at between approximately 45o and 90o. That is, if the 

receptor is located from the northeast to due east of the source, the Tijs is at the largest magnitude, holding 

the effect of distance constant. The intuition is that, in North America, prevailing winds tend to be 

oriented west-to-east. For SO2, there is not a clear maximum before 90o. Rather, the Tijs gradually 

declines from 0oto 90o. 

Conversely, Figure 2 indicates that for each pollutant, Tijs minimizes between approximately 250oand 

270o. That is, if the receptor is located approximately due west of the source, the Tijs is at the smallest 

magnitude, again holding the effect of distance constant. The intuition for this result is the same. For an 

emission to travel east to west, it would be moving counter to prevailing winds.

Figure 3 maps (Tijs) for emissions of primary PM2.5 corresponding to four different offshore source 

locations. The top left panel maps the consequences of emissions from a source just offshore of southern 

California. Intuitively, the largest impact of emissions is concentrated in southern California. This figure 

clearly displays the impact of wind direction on emissions; the plume spreads from the source in a 

generally northeasterly direction. Recall that the effect is what Figure 2 implies since the transfer 

coefficients for primary PM2.5 emissions are greatest between 45° and 90° (northeast and due east).

The top right panel of Figure 3 shows (Tijs) for a source located off the southeastern United States in the 

Atlantic Ocean. Again, the importance of bearing is clear in this example. Because the nearest land is 

located upwind from this source, the impact of emissions extends over a much smaller land area than the 

source off the coast of California. That is, the greatest impact of the emission in the Atlantic Ocean is 

likely to be over the ocean since prevailing winds send the emission to the northeast. 

The bottom left panel of Figure 3 displays the effect of emissions from a source in the western Gulf of

Mexico. This emission has the greatest effect on air quality in Texas and Louisiana. The figure shows 

that the plume is distorted towards the northeast (again, the impact of prevailing wind direction through 

bearing), and it also clearly shows the effect that distance between source and receptor has on the 

magnitude of (Tijs). Specifically, county receptors that are impacted most by emissions from the western 

Gulf are located relatively near to the source. The impact on air quality declines in nearly concentric 

distance bands from the source location.
C-5

Finally, the bottom right panel of Figure 3 shows the impact of an emission from a source in the eastern 

Gulf of Mexico. The greatest impact of discharges from this location are in Florida, and the impact 

spreads northeast over other receptors in the southeastern United States. This panel, like the others in 

Figure 3, shows the influence of both bearing and distance on (Tijs).

Table 2 reports the results of the regression model applied to estimate the Tijs corresponding to emissions 

of SO2 and the resulting impact on concentrations of SO2, and emissions of NOx and the resulting impact 

on concentrations of NOx. Note the distinction with Table 1 where transfer coefficients reflect the impact 

of emissions on resulting concentrations of PM2.5. 

Table 2 indicates that the impact of distance on (Tijs) is similar for both NOx and SO2. Namely, the linear 

and cubic distance terms have a negative impact on (Tijs), whereas the quadratic term increases (Tijs). 

Also, the fitted coefficients in the NOx are roughly an order of magnitude larger than the fitted coefficients 

for SO2. Bearing has an increasing effect on (Tijs) for NOx through the linear and cubic forms and a 

negative impact through the quadratic term. The orientation of this relationship is reversed for SO2. The 

linear and cubic terms are negative, while the quadratic term is positive. Hence, the impact of bearing for 

both SO2 and NOx on (Tijs) is quite similar to the relationship reported in Table 1. 

Table 1. Regression Analysis Results for Primary and Secondary Particulate Matter (PM2.5). Dependent 

Variable: Log Tjis.

(1) (2) (3) (4)

VARIABLES NOx PM2.5 SO2 VOC

Distance -4.76e-03*** -5.87e-03*** -4.65e-03*** -5.87e-03***

(7.34e-06) (8.07e-06) (7.00e-06) (8.07e-06)

Distance23.22e-06*** 3.93e-06*** 3.08e-06*** 3.93e-06***

(6.96e-09) (7.65e-09) (6.64e-09) (7.65e-09)

Distance3-7.37e-10***

(1.88e-12)

-8.85e-10***

(2.07e-12)

-6.92e-10***

(1.80e-12)

-8.85e-10***

(2.07e-12)

Bearing 8.55e-03*** 5.60e-03*** -6.08e-04*** 5.60e-03***

(4.82e-05) (5.30e-05) (4.60e-05) (5.30e-05)

Bearing2-9.17e-05*** -7.11e-05*** 2.77e-05*** -7.11e-05***

(3.07e-07) (3.37e-07) (2.92e-07) (3.37e-07)

Bearing31.080e-07*** -1.45e-07*** -7.15e-08*** -1.45e-07***

(5.62e-10) (6.17e-10) (5.36e-10) (6.17e-10)

Constant -14.79*** -14.04*** -14.80*** -14.04***

(2.97e-03) (3.26e-03) (2.83e-03) (3.26e-03)

Observations 8,512,743 8,511,952 8,511,952 8,511,952

R

2

0.183 0.189 0.162 0.189

Ordinary Least Squares

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1
C-6

Table 2. Regression Analysis Results for NOx to NOx and SO2 to SO2. Dependent Variable: Log Tjis.

(1) (2)

VARIABLES NOx – NOx SO2 – SO2

Distance -1.14e-02*** -6.00e-03***

(1.83e-05) (1.00e-05)

Distance21.17e-05*** 3.91e-06***

(2.22e-08) (1.08e-08)

Distance3-3.31e-09***

(7.42e-12)

-7.65e-10***

(3.27e-12)

Bearing 1.38e-02*** -1.23e-02***

(9.65e-05) (5.70e-05)

Bearing2-9.72e-05*** 1.01e-04***

(6.23e-07) (3.60e-07)

Bearing31.71e-07*** -1.99e-07***

(1.14e-09) (6.53e-10)

Constant -13.06*** -13.84***

(5.56e-03) (3.57e-03)

Observations 2,194,075 6,500,105

R

2

0.191 0.172

Ordinary Least Squares

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1
C-7

Figure 1. The Effect of Distance on Tjis.

0

20 40 60 80

TC

0 1000 2000 3000 4000

Distance (Miles)
C-8

Figure 2. The Effect of Direction (Bearing) on Tjis.

2 4 6 8

10

TC

0 100 200 300 400

Bearing (Degrees)
C-9

Figure 3. Transfer Coefficients: Tijs for Primary PM2.5.*

* Locations of offshore emission sources are for illustrative purposes only.

)

")

)

Gulf of Mexico

Atlantic Ocean

Pacific Ocean

Canada

Mexico

Forest Recreation Damages by County ($ x 1,000)

<Double-click here to enter text>

Change PM2.5 (ug/m^3)

0 - 0.50

0.50 - 0.75

0.75 - 1.00

1.00 - 1.50

1.50 - 1.80

")

")

)

Gulf of Mexico

Atlantic Ocean

Pacific Ocean

Canada

Mexico

Forest Recreation Damages by County ($ x 1,000)

<Double-click here to enter text>

Change PM2.5 (ug/m^3) 0 - 0.50

0.50 - 0.75

0.75 - 1.00

1.00 - 1.50

1.50 - 4.20

Gulf of Mexico

Atlantic Ocean

Pacific Ocean

Canada

Mexico

Forest Recreation Damages by County ($ x 1,000)

<Double-click here to enter text>

Change PM2.5 (ug/m^3) 0 - 0.50

0.50 - 0.75

0.75 - 1.00

1.00 - 1.50

1.50 - 4.20

Gulf of Mexico

Atlantic Ocean

Pacific Ocean

Canada

Mexico

Forest Recreation Damages by County ($ x 1,000)

<Double-click here to enter text>

Change PM2.5 (ug/m^3) 0 - 0.50

0.50 - 0.75

0.75 - 1.00

1.00 - 1.50

1.50 - 4.20
C-10

References

Muller, N.Z. and R.O. Mendelsohn. 2007. Measuring the Damages Due to Air Pollution in the United 

States. Journal of Environmental Economics and Management 54(1): 1-14.

Muller, N.Z. and R.O. Mendelsohn. 2009. Efficient Pollution Regulation: Getting the Prices Right. The 

American Economic Review 99(5): 1714-1739.

Muller, N.Z., W.D. Nordhaus, and R.O. Mendelsohn. 2011. Environmental Accounting: Methods with an 

Application to the U.S. Economy. The American Economic Review 101(5): 1649-1675.

Turner, D.B. 1994. Workbook of Atmospheric Dispersion Estimates - An Introduction to Dispersion 

Modeling, 2nd ed. Ann Arbor, MI: Lewis Publishers.
D-1

APPENDIX D: PHYSICAL EFFECTS AND VALUATION ESTIMATES 

FOR THE OECM AIR QUALITY MODULE

As described in the main body of this document, the assessment of air quality impacts in the OECM relies 

upon estimates of monetized impacts per ton of offshore emissions. Estimated by the APEEP model, 

these dollar-per-ton estimates reflect human health effects, changes in agricultural productivity, and 

damage to manmade materials. Building upon the methods discussion presented in the main body of this 

document, this appendix provides additional detail on the methods used to quantify and monetize these 

effects on a per-ton basis. Specifically, the data used to assess exposure to increased pollutant 

concentrations, the dose-response functions employed for each impact category, and valuation 

information are described.

Exposure Assessment

To estimate air pollution exposure, the APEEP model relies upon county-level estimates of receptor 

populations for the contiguous United States. These receptors vary by impact category based on the 

exposure metrics used in the dose-response literature. Because the magnitude of damages associated with 

air pollution changes over time as receptor populations increase or decrease in size, the APEEP modelruns for the OECM include receptor projections for those receptor categories for which projections are 

available. The receptor information included in the APEEP model for each major impact category is as 

follows.

Human Health: To assess exposures for human health effects, the APEEP model uses population 

projections from the U.S. EPA’s BenMAP model by county and age group.54 The U.S. EPA has 

used BenMAP to assess the human health impacts of air pollution for several regulatory impact 

analyses, all of which have undergone extensive review with the Office of Management and 

Budget. Based on the BenMAP population data, the APEEP model estimates health effects for 

individuals of different ages. This is critical for correctly assessing the incidence of those health 

endpoints where the epidemiological literature shows differing levels of vulnerability to pollution 

across age groups. To allow for the estimation of dollar-per-ton values that vary over time, the 

APEEP model includes population projections for every fifth year (e.g., 2010, 2015, etc.) in the 

75-year time horizon of the OECM. 

Note that BenMAP’s county- and age-specific population projections cover 2010 through 2030. 

Population projections by county and age group beyond 2030 are not available from the Census 

or other sources. In the absence of such projections, post-2030 population and age demographics 

are held constant at 2030 levels.55 To the extent that the U.S. population grows significantly after 

2030, this leads to underestimation of impacts.

 

54 BenMAP is available for download at http://www.epa.gov/air/benmap/.

55 As an alternative to holding population and demographics constant at 2030 levels, different approaches were 

explored for extrapolating population by age group and county beyond 2030 based on projected population growth 

for the 2020s. Over several decades, however, the extrapolation of the trends projected for the 2020s by age group 

and county yields population projections that do not appear credible. For example, in cases where the county-level 

population projections for 2029 and 2030 show a significant increase in population for the 25-29 age group in a 

given county, extrapolation of this trend through 2075 might suggest that most of the county’s 2075 population is 
D-2

Agriculture: The APEEP model estimates exposure for agriculture based on county-level yield 

estimates for corn, cotton, peanuts, dry edible beans, grain sorghum, soybeans, spring wheat, and 

tobacco from USDA’s 2002 Census of Agriculture.56 The APEEP model uses these yield 

estimates for each year in the OECM’s analytic time horizon.

Materials Damage: For materials damage, the APEEP model assesses exposure based on 

inventories of infrastructure, commercial buildings, and residential buildings constructed from 

materials susceptible to pollution-related damage, as indicated in the dose-response literature 

(i.e., galvanized steel, painted wood surfaces, and carbonate stone). For infrastructure materials, 

the APEEP model uses inventories developed from methods outlined in the National Acid 

Precipitation Assessment Program (NAPAP) (1991). The NAPAP reports the estimated surface 

area of galvanized and carbon steel focusing on bridges, transmission towers, railroads, and 

guardrails for select areas of the country. An inventory was developed for other areas by 

applying the ratios of exposed surface area to land area from the NAPAP study to states and 

regions not covered by the original NAPAP surveys.

To develop inventories for commercial and residential buildings, an inventory previously 

developed from DOE’s Commercial Buildings Energy Consumption Survey and Residential 

Energy Consumption Survey was used, as well as the Census Bureau’s Annual Housing Survey.57 

These surveys report the number of buildings by region (for the DOE sources) or by state (for the 

Census Bureau survey). To develop county-level estimates, the inventory distributes the 

regional/state values to the counties within each region/state in proportion to population. The 

extent of pollution-related materials-damage to these buildings depends on the surface area of 

their exterior walls. While the DOE data provide regional estimates of the average square 

footage per building, they do not provide the average exterior wall area or the average number of 

floors per building. Thus, to estimate exterior area, the inventory employs the simplifying 

assumption that each building is cubic in shape with two stories of living/working space. Under 

this assumption, the exterior wall space of a building is twice its floor space.

After estimating the number and size of buildings by county, the inventory calculates the amount 

of painted wood, etc., used on exterior walls based on data from the DOE commercial and 

residential surveys. Based on the DOE data, it is possible to directly estimate the percentage of 

buildings with exterior walls constructed from each material. The inventory applied these 

percentages to the estimated exterior wall area of each county to generate county-level estimates 

of vulnerable material, by material type. 

Dose-Response Functions

To estimate the physical effects of air pollution for exposed populations, the APEEP model will use a 

series of impact-specific dose-response (D-R) functions that relate changes in ambient pollutant 

concentrations to changes in the risk or probability of a given effect. As detailed below, these D-R 

relationships are derived from several analyses in the peer-reviewed literature. 

 

in the 25-29 age group, even though the individuals in this group in 2029 and 2030 are no longer between the ages 

of 25 and 29 in 2075.

56 Data from the 2002 Census of Agriculture are available at 

http://www.agcensus.usda.gov/Publications/2002/index.asp. 

57 This inventory was developed for EPA’s ongoing benefit-cost analysis of the Clean Air Act Amendments of 1990. 

See U.S. EPA (2010b).
D-3

Human Health

The epidemiological literature includes several studies that examine the relationship between air pollutant 

exposure and the risk of various adverse health effects. Based upon reviews of this literature conducted 

by the National Research Council (2002) and EPA’s Science Advisory Board (SAB), the U.S. EPA has 

relied upon many studies from this literature to develop regulatory impact analyses (RIAs) for proposed 

and final air rules. To ensure that the OECM reflects the advice of the SAB and NAS expert reviewers, 

the APEEP model-runs conducted for the development of the OECM used the same D-R functions used 

by the U.S. EPA in recent RIAs. 

For many health endpoints, the peer-reviewed literature includes multiple studies that estimate the 

statistical relationship between exposure and risk. To incorporate the results of multiple studies into a 

single estimate of the D-R relationship, a pooled estimate of this relationship was developed based on the 

pooling procedures outlined in Abt Associates (2008). In effect, a pooled estimate of multiple D-R 

coefficients (β’s) is a weighted average of the β values estimated in different studies. Weights may be 

assigned subjectively (e.g., assigning equal weight to all studies) or through more formal methods, such 

as fixed effects weighting and random effects weighting. Under fixed effects weighting, it is assumed 

that one true parameter value for β exists and that differences in β estimates across studies reflect 

sampling error. Given that the variance of each β value is an indicator of the certainty of the estimate, 

fixed effects pooling weights each β value based on the inverse of its variance.58 Under this approach, 

estimates with small variance (with relatively low uncertainty) receive large weights and estimates with 

large variance receive small weights.

An alternative to fixed effects pooling is random effects pooling. Unlike fixed effects pooling, which 

assumes one true value of β, random effects pooling allows for the possibility that multiple values for β 

may exist. For example, the β value for PM mortality may vary geographically due to differences in the 

characteristics of PM in different locations. To account for the possibility of multiple β values, random 

effects pooling weights each β based on both within-study variance (like fixed effects weighting) and the 

variance between studies.59 

To pool the β values for a given health endpoint, the functional form used in the underlying studies must 

be consistent. For example, if one study uses a log-linear functional form to estimate the relationship 

between pollutant concentrations and risk and another uses a logistic functional form, it would be 

inappropriate to pool the β values from these studies because they do not represent parameter estimates 

for the same health impact function. Due to such differences in functional form, the pooled β estimates 

for some health endpoints exclude a limited number of studies from the epidemiological literature.

Table 1 summarizes the human health dose-response information incorporated into the APEEP model to 

develop dollar-per-ton values for the OECM. For each health endpoint, the figure identifies the study(s) 

used and describes how the studies for a given endpoint were pooled (where applicable). 

Note that the health effects incorporated into the APEEP model, as detailed in Table 1, include most, but 

not all, of the health impacts typically included in EPA’s RIAs. For some health effects, the 

 

58 If the variance of the β estimate in a study is represented by vi and the weight assigned to that study is wi

, then





i

i

i

v

v

w

1/

1/

59 Under random effects pooling, the weights assigned to a given β are based on the sum of within-study variance 

(vi), as specified as in the previous footnote, and between study variance (η2), as specified in Abt Associates (2008). 

More specifically, the random effects weights are based on

*

i

v

, which is the sum of vi and η2. Given 

*

i

v

, the weight 

(

*

wi

) assigned to a given β is specified as follows:





*

*

*

1/

1/

i

i

i

v

v

w
D-4

epidemiological literature specifies impacts using a metric of air quality that is inconsistent with the 

metric used by the APEEP model. In particular, the health impact functions for many PM-related 

endpoints are based on the 24-hour average PM2.5 concentration, rather than the annual average 

concentration estimated by the APEEP model. These endpoints include nonfatal myocardial infarction, 

respiratory hospital admissions, cardiovascular hospital admissions, asthma-related emergency room 

visits, acute bronchitis, lower respiratory symptoms, upper respiratory symptoms, asthma exacerbation, 

minor restricted activity days60, and work-loss days. The exclusion of these health endpoints from the 

dollar-per-ton impact values estimated by the APEEP model has minimal impact on the magnitude of 

these estimates, as the mortality effects of PM and ozone make up the vast majority of the monetized 

health impacts associated with air pollution.61 Both PM- and ozone-related mortality are reflected in the 

dollar-per-ton values generated by the APEEP model.

The D-R functions employed in the studies listed in Figure 1 use the baseline incidence rate of each 

respective health endpoint as a variable in estimating changes in health impacts associated with air 

pollution. Table 2 presents the baseline incidence rates assumed in developing the dollar-per-ton values 

for the OECM.

Agriculture

To estimate changes in crop yield associated with changes in ozone concentrations, the APEEP model uses 

D-R functions from the National Crop Loss Assessment Network (Lesser et al., 1990). These functions are 

specified as follows.

(1)

where: CY* = crop yield following emissions perturbation

CYb= baseline crop yield (1996)

O3 = 7- or 12-hour daily mean ozone concentrations (parts per million by volume)

γ = statistically estimated shape parameter

σ = statistically estimated parameter

Based on this equation, the APEEP model derives the change in crop yield associated with a given emissions 

scenario. 

 

60 Minor restricted activity days associated with ozone exposure were included in the APEEP model for this 

analysis. Exposure to PM2.5 also increases the risk of experiencing minor restricted activity days, but this change in 

risk related to PM2.5 was not incorporated into the APEEP model for this analysis.

61 As indicated in many RIAs published by EPA, PM- and ozone-related mortality dominates the estimates of 

monetized human health impacts. See U.S. EPA (2010c), U.S. EPA (2008), U.S. EPA (2006), and U.S. EPA 

(1999). 

b

O

CY e CY 















 



















3

1

*
D-5

Table 1. Summary of Human Health Dose-Response Information Reflected in Dollar-Per-Ton Estimates for Air Quality Impacts.

POLLUTANT

HEALTH 

EFFECT

LITERATURE SOURCES FOR 

D-R FUNCTIONS

D-R 

COEFFICIENT 

INCORPORATED 

INTO APEEP

FUNCTIONAL 

FORM NOTES

PM2.5

Premature 

mortality (age 

29 and older)

Mean of a Weibull distribution 

of dose-response coefficients 

under which the dose-response 

coefficient from Pope et al.

(2002) is the 25th percentile and 

the dose-response coefficient 

from Laden et al. (2006) is the 

75th percentile.

0.0106 Log-linear  Recent EPA regulatory impact analyses present separate estimates of the 

mortality impacts of PM2.5 based upon both the Pope et al. (2002) and 

Laden et al. (2006) D-R functions.1 

 This approach for the OECM is based upon input from the Health Effects 

Subcommittee of the SAB Advisory Council for Clean Air Compliance 

Analysis, which recommended that EPA define a distribution of possible

D-R coefficients with the 25th percentile equal to the Pope et al. (2002) 

estimate, the 75th percentile equal to the Laden et al. (2006) estimate, and 

the mean approximately equal to the mean of these two values (EPA 

2010d).

Infant mortality 

(age <1 year)

Woodruff et al. (1997) 0.003922 Logistic  EPA has estimated infant mortality based on the Woodruff et al. (1997) 

study in several RIAs.2

Chronic 

bronchitis (ages 

27 and older)

Abbey et al. (1995) 0.013185 Logistic  Few studies have examined the impact of air pollution on new cases of 

chronic bronchitis. Abbey et al. (1995) provides evidence that long-term 

PM2.5 exposure gives rise to the development of chronic bronchitis among 

U.S. populations. 
D-6

POLLUTANT

HEALTH 

EFFECT

LITERATURE SOURCES FOR 

D-R FUNCTIONS

D-R 

COEFFICIENT 

INCORPORATED 

INTO APEEP

FUNCTIONAL 

FORM NOTES

Ozone

Premature 

mortality (all 

ages)3

Pooled estimate of D-R 

coefficients, using equal 

weighting, from the following 

studies:

 Ito et al. (2005)

 Bell et al. (2004)

0.000717 Log-linear  EPA’s regulatory impact analyses for the reconsideration of the ozone 

NAAQS (U.S. EPA, 2010c) estimates changes in ozone-related mortality 

based on both of these studies and based on Schwartz (2005), Bell et al.

(2005), Levy et al. (2005), and Huang et al. (2005). Three of these studies 

(Bell et al., 2004; Huang et al., 2005; and Schwartz, 2005) are based upon 

the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) 

data set, and the other three (Bell et al., 2005; Ito et al., 2005; and Levy et 

al., 2005) are meta-analyses of data from other studies. Because of 

differences in functional specification across studies (i.e., log-linear versus 

logistic functional form) and differences in the type of mortality estimated 

in each study (i.e., all-cause, non-accidental, and cardiopulmonary 

mortality), it would not be appropriate to pool the D-R coefficients across 

all six of these studies. The Ito et al. (2005) and Bell et al. (2004) D-R

coefficients were pooled because (1) they have the same functional 

specification, (2) they reflect the same type of mortality (i.e., nonaccidental), and (3) they reflect both the NMMAPS data set and the metaanalyses referenced above.

 Because EPA regulatory impact analyses have presented separate estimates 

of mortality impacts based on each mortality study, equal weighting is 

used rather than fixed effects or random effects weighting for ozone 

mortality.
D-7

POLLUTANT

HEALTH 

EFFECT

LITERATURE SOURCES FOR 

D-R FUNCTIONS

D-R 

COEFFICIENT 

INCORPORATED 

INTO APEEP

FUNCTIONAL 

FORM NOTES

Ozone 

(continued)

Respiratory 

hospital 

admissions 

(adults aged 65 

and older)

Pooled estimate of city-specific 

D-R coefficients from the 

following studies, using the 

random effects pooling 

procedure described in Abt 

Associates (2008):

 Schwartz (1995): New Haven

 Schwartz (1995): Tacoma

0.002994 Log-linear  The Schwartz (1995) assessments for New Haven and Tacoma examined 

respiratory hospital admissions associated with all respiratory disease.

 EPA regulatory impact analyses also have used results from the 

Moolgavkar et al. (1997) Minneapolis and Schwartz (1994b) Detroit 

studies. Because each of these studies estimate separate D-R coefficients 

for multiple respiratory conditions that may lead to hospitalization (i.e.,

they estimate D-R functions for multiple types of respiratory hospital 

admissions), it was not possible to pool the D-R coefficients from these 

studies with those from the New Haven, Tacoma, and Minneapolis studies, 

each of which estimates just one D-R coefficient. 

 EPA regulatory impact analyses also have estimated changes in respiratory 

hospital admissions based on Schwartz (1994a). This study estimates 

pneumonia-related hospital admissions rather than admissions associated 

with all respiratory disease. Therefore, it would not be appropriate to pool 

the D-R coefficient from this study with those from Schwartz (1995).

Respiratory 

hospital 

admissions (age 

<2 years)

Burnett et al. (2001) 0.008177 Log-linear  Several recent EPA regulatory impact analyses relied upon the Burnett et 

al. study to estimate ozone-related changes in respiratory hospital 

admissions among children less than two years old.4

Asthma-related 

emergency 

room visits (all 

ages)

Pooled estimate of D-R

coefficients from Peel et al.

(2005) and Wilson et al. (2005), 

using random effects pooling 

procedure described in Abt 

Associates (2008)

0.001320 Log-linear  This is consistent with the D-R functions employed in the U.S. EPA 

(2010c). 

Ozone 

(continued)

Minor restricted 

activity days 

(ages 18-64)

Pooled estimate (using fixed 

effects weighting) of yearspecific D-R coefficients 

estimated in Ostro and 

Rothschild (1989)

0.002596 Log-linear  Ostro and Rothschild (1989) estimate separate D-R parameter values for 

the years 1976 through 1981. Consistent with several U.S. EPA regulatory 

impact analyses, the weighted average of these values was used, using the 

inverse of the variance of each parameter estimate as weights.4
D-8

POLLUTANT

HEALTH 

EFFECT

LITERATURE SOURCES FOR 

D-R FUNCTIONS

D-R 

COEFFICIENT 

INCORPORATED 

INTO APEEP

FUNCTIONAL 

FORM NOTES

School loss days 

(children age 5 

to 17)

Chen et al. (2000) 0.015763 Linear  Chen et al. (2000) focused on children between the ages of 6 and 11. 

Based upon recommendations issued by the National Research Council 

(2002) and the Health Effects Subcommittee of the U.S. EPA Science 

Advisory Board’s Advisory Council on Clean Air Compliance Analysis 

(2004), the APEEP model estimates changes in school absences for all 

school-aged children given the biological similarities between children 

aged 5 to 17.

 Recent U.S. EPA regulatory impact analyses have developed pooled 

estimates of school loss days based on both Chen et al. (2000) and Gilliland 

et al. (2001). It was not possible to pool the D-R coefficients from these 

two studies because Chen et al. (2001) uses a linear specification while 

Gilliland et al. (2001) uses a log-linear specification. 

Notes:

1. Examples of EPA regulatory impact analyses that have used both the Pope et al. (2002) and Laden et al. (2006) D-R functions include U.S. EPA (2010a), U.S. EPA (2010c), U.S. 

EPA (2008), and U.S. EPA (2006).

2. For example, see U.S. EPA (2008) and U.S. EPA (2006).

3. The ozone mortality studies referenced here use the 24-hour or 1-hour maximum ozone levels as metrics of exposure. Neither of these metrics, however, is the most relevant for 

characterizing population-level exposure. Because most people tend to be outdoors only during daylight hours, which is when ozone concentrations are highest, the 24-hour metric 

is not appropriate. In addition, the 1-hour maximum ozone metric is inconsistent with that used for the current ozone NAAQS. The most biologically relevant metric is the 8-hour 

maximum standard, which has been used in the ozone NAAQS since 1997. For this analysis, the ozone health impact functions that use a 24-hour average or 1-hour maximum 

ozone metric to maximum 8-hour average ozone concentrations were therefore converted using the procedure described in Abt Associates (2008).

4. For example, see U.S. EPA (2010c) and U.S. EPA (2008).
D-9

Table 2. Summary of Baseline Incidence Rates for Human Health Effects.

Endpoint Notes/Source

Rate Per 100 People Per Year by Age Group

<1 <2 <18 18-24 25-29 30-34 35-44 45-54 55-64 65-74 75-84 85+

Mortality (all causes) CDC compressed 

mortality file, 

accessed via CDC 

Wonder (1996-1998)

0.045 0.093 0.119 0.119 0.211 0.437 1.056 2.518 5.765 15.16

Mortality (non-accidental)

0.025 0.022 0.057 0.057 0.15 0.383 1.006 2.453 5.637 14.859

Infant Mortality (all causes) Derived from 2002 

mortality data, CDC 

multiple cause-ofdeath public-use data 

files

0.7037

Chronic Bronchitis Abbey et al. (1993), 

for ages 27+ 0.378

Respiratory Hospital 

Admissions (all respiratory, 

ages 65 and older)

1999 NHDS publicuse data files 5.2

Respiratory 

Hospital 

Admissions 

(all 

respiratory, 

ages 0 and 1)

West 1999 NHDS publicuse data files

6.059

South 5.709

Northeast 4.785

Midwest 4.938

Asthma Emergency Room

Visits

2000 NHAMCS 

public-use data files; 

1999 NHDS publicuse data files

1.011 1.087 0.751 0.438 0.352 0.425 0.232

Minor Restricted Activity 

Days

Ostro and Rothschild 

(1989) 780

School Loss Days National Center for 

Education Statistics 

(1996) 

990

Notes:

NHDS – National Hospital Discharge Survey, see ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/.

NHAMCS – National Hospital Ambulatory Medical Care Survey, see ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/.

Northeast - Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York, New Jersey, Pennsylvania

Midwest - Ohio, Indiana, Illinois, Michigan, Wisconsin, Minnesota, Iowa, Missouri, North Dakota, South Dakota, Nebraska, Kansas

South - Delaware, Maryland, District of Columbia, Virginia, West Virginia, North Carolina, South Carolina, Georgia, Florida, Kentucky, Tennessee, Alabama, 

Mississippi, Arkansas, Louisiana, Oklahoma, Texas

West - Montana, Idaho, Wyoming, Colorado, New Mexico, Arizona, Utah, Nevada, Washington, Oregon, California, Alaska, Hawaii

*Blank cells indicate that no value was necessary, as the D-R function for that health endpoint applies only to a limited number of age groups.
D-10

Man-made Materials

The APEEP model uses D-R functions for man-made materials from two sources, the NAPAP studies 

(Atteraas and Haagenrud, 1982; Haynie, 1986) and the International Cooperative Programme on Effects on 

Materials (ICP, 1998). These studies specify separate D-R functions for (1) galvanized steel, (2) painted 

surfaces, and (3) carbonate stone surfaces.

Atteraas and Haagenrud (1982) estimate a linear D-R function relating ambient concentrations of SO2 to the 

corrosion of galvanized steel. This function is based on an analysis of mass loss data from 22 field sites in 

Norway and is specified as follows.

(1)

Mb M ( SO )   0 2  1

where: ΔM = mass loss of material,

β0, β1 = statistically estimated parameters (6.05 and 0.22, respectively, as estimated in Atteraas and 

Haagenrud (1982),

SO2 = ambient concentration of SO2, and

Mb = existing material quantity

For painted surfaces, the APEEP model relies upon the D-R relationship estimated by Haynie (1986), which 

was developed from erosion data for painted specimens exposed to both SO2 and moisture. This function is 

specified as follows.

(2)

  C C

p H MC RC 1SO2 F5.2    0 10 10   

where: ΔMC = mass loss of material,

β0, β1 = statistically estimated parameters 

SO2C = ambient concentration of SO2,

pH = average pH by region, as measured by the National Atmospheric Deposition Program 

(NADP),

FC = frequency exposed surface area is wet by county (C), and

RC = annual rainfall

The model predicts the increase in erosion relative to a baseline under which pH is 5.2 and the SO2

concentration is zero or representative of a clean environment. 

The APEEP model uses the D-R function from ICP (1998) to estimate the effect of ambient SO2 on 

carbonate stone surfaces. ICP’s dose-response function is based upon an extensive field exposure program 

in which data on materials corrosion, gaseous pollutants, precipitation, and climate parameters were 

collected at 39 exposure sites in 12 European countries, the United States, and Canada. The D-R function 

estimated from these data is as follows.

(3)

   

 S  SO  RC H

TC

0 2 1  exp 

 

where: ΔS = surface recession of material,
D-11

β0, β1,γ,κ = statistically estimated parameters,

SO2 = ambient concentration of SO2,

TC = ambient temperature,

RC = annual rainfall, and

H

+

= hydrogen concentration of precipitation.

In using the ICP dose-response function, the APEEP model holds ambient temperature, annual rainfall, and 

the hydrogen concentration of precipitation constant. 

Valuation

To estimate the value of the health, agricultural, and materials impacts outlined above, the APEEP model 

uses a combination of market price data, WTP values estimated in the peer-reviewed literature, and for 

certain health impacts, cost-of-illness (COI) estimates derived from studies of treatment costs. These 

values are described below by major impact category.

Health Effects

To assess the value of the adverse health effects associated with increased pollutant concentrations, the 

APEEP model relied upon two types of valuation estimates: WTP values and COI estimates. In economic 

terms, the value of avoiding an adverse health effect is the dollar amount necessary such that a person 

would be indifferent between avoiding the effect and receiving the compensation. In most cases, the 

dollar amount required to compensate a person for exposure to an adverse effect is roughly the same as 

the dollar amount a person is willing to pay to avoid the effect. Therefore, in economic terms, WTP is the 

appropriate measure of the value of avoiding an adverse effect. Where possible, the APEEP model used 

WTP values derived from the peer-reviewed literature to estimate the value of the adverse health effects 

associated with changes in ambient pollutant concentrations.

For some health effects (e.g., hospital admissions), WTP estimates are not available from the peerreviewed literature. In these cases, the APEEP model used the cost of treating or mitigating the effect as 

a primary estimate. These COI estimates generally understate the true value of reducing the risk of a 

health effect because they reflect the direct expenditures related to treatment, but not the value of avoided 

pain and suffering (Harrington and Portney, 1987; Berger et al., 1987).

For both WTP and COI estimates, the APEEP model relies upon valuation studies employed by the U.S. 

EPA for numerous RIAs of air pollution policy. These studies have undergone extensive peer review and 

are widely accepted as the state of the science. 

Economic theory maintains that individuals’ WTP for goods, including the avoidance of an adverse health 

effect, increases as real income increases. Given that incomes are likely to increase during the 75-year 

analytic time horizon of the OECM, the APEEP model (where possible) uses income-adjusted valuation 

estimates to assess the value of the adverse health effects associated with changes in ambient pollutant

concentrations. The model made these adjustments only for those health effects for which WTP valuation 

estimates were used. Adjusted COI estimates were not used because the cost of treating an illness is not 

dependent upon income. 
D-12

To develop income-adjusted estimates, income elasticities were used from EPA’s BenMAP model that 

represents the percentage change in WTP associated with a 1 percent change in real income (Abt 

Associates, 2008). These elasticity values were then applied to GDP per capita, as projected in DOE’s 

Annual Energy Outlook (2010). The DOE data cover the years 2010 through 2035. To extend the 

income adjustments through the end of the OECM’s analytic time horizon, DOE’s projected growth rates 

for GDP and population in 2035 were presumed to apply to later years as well. The main body of this 

document presents the valuation estimates for each health endpoint for 2015 and 2065, with information 

on the source for each value. Tables 3a and 3b contain estimates for the intervening years.
D-13

Table 3a. Income Adjusted Values Per Statistical Case, by Health Endpoint: 2010-2040 (2006$).

HEALTH ENDPOINT 2010 2015 2020 2025 2030 2035 2040

Premature mortality $8,200,000 $8,600,000 $8,900,000 $9,200,000 $9,500,000 $9,800,000 $10,000,000 

Chronic bronchitis $450,000 $470,000 $490,000 $510,000 $530,000 $550,000 $570,000 

Respiratory hospital admissions 

(65+) $25,000 $25,000 $25,000 $25,000 $25,000 $25,000 $25,000 

Respiratory hospital admissions 

(<2) $10,000 $10,000 $10,000 $10,000 $10,000 $10,000 $10,000 

Asthma-related emergency 

room visits $370 $370 $370 $370 $370 $370 $370 

Minor restricted activity days $61 $62 $63 $64 $65 $66 $66 

School loss days $89 $89 $89 $89 $89 $89 $89 
D-14

Table 3b. Income Adjusted Values Per Statistical Case, by Health Endpoint: 2045-2075 (2006$).

HEALTH ENDPOINT 2045 2050 2055 2060 2065 2070 2075

Premature mortality $10,000,000 $11,000,000 $11,000,000 $12,000,000 $12,000,000 $12,000,000 $13,000,000 

Chronic bronchitis $590,000 $610,000 $630,000 $660,000 $680,000 $700,000 $730,000 

Respiratory hospital admissions 

(65+) $25,000 $25,000 $25,000 $25,000 $25,000 $25,000 $25,000 

Respiratory hospital admissions 

(<2) $10,000 $10,000 $10,000 $10,000 $10,000 $10,000 $10,000 

Asthma-related emergency 

room visits $370 $370 $370 $370 $370 $370 $370 

Minor restricted activity days $67 $68 $69 $70 $70 $71 $72 

School loss days $89 $89 $89 $89 $89 $89 $89 
D-15

Agriculture

To estimate the economic value of changes in crop yield, the APEEP model will use crop pricing data 

from the U.S. Department of Agriculture, as summarized in Table 4.

Table 4. Summary of Crop Prices (2006$).

CROP PRICE

Corn $4.08 per bushel

Cotton $0.59 per pound

Peanut $0.20 per pound

Grain Sorghum $7.08 per hundredweight

Soybeans $9.82 per bushel

Spring Wheat $7.31 per bushel

Tobacco $1.65 per pound

Source: USDA/NASS (2009)

Materials Damage

The APEEP values materials-damage as the change in the present value of future maintenance costs. 

Under baseline emission conditions, the APEEP model assumes a 5-year maintenance schedule for manmade materials. Based on this maintenance schedule, the model calculates the present value of materialsmaintenance costs using the following formula.

(4) Mrb = δ x (RCrb(e-rt)/(1 - e

-rt)

where: Mrb = annual maintenance costs in county (r), baseline SO2,

δ = market interest rate

RCrb = replacement costs in receptor county (r), baseline SO2, and

t = time of repairs (5,10,15,...,.)

As materials decay due to increased air pollution, regularly scheduled maintenance will occur more 

frequently. The APEEP model calculates the increased frequency of maintenance activities based on the 

ratio of the materials inventory after the emission change (Ip) to the materials inventory before the change 

(Ib). This ratio characterizes the extent to which a change in emissions enhances or mitigates materials 

decay rates. If the emission change increases pollution, then Ip<Ib, and the optimal maintenance schedule 

will occur earlier than every 5 years. To estimate the amended maintenance schedule, the APEEP model

multiplies the ratio of Ip to Ib by the baseline five-year maintenance schedule, as shown in Equation 6.

(6) t

*

= 5 x (Ip/Ib)

To estimate the present value of maintenance costs under this new maintenance schedule, the APEEP

model incorporates the modified maintenance schedule into the materials maintenance cost equation 

(Equation 5). The change in the present value of the maintenance schedules extending into the future 

constitutes the monetary impact of an emission change on materials damage. 
D-16

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E-1

APPENDIX E: DERIVATION OF ARCTIC AND NON-ARCTIC ALASKA 

EMISSION FACTORS 

Introduction and Overview

To provide additional documentation of the emission factors used in the OECM for planning areas off the 

coast of Alaska, this appendix provides a detailed description of the data sources and methods used to 

derive these values. Before presenting this information, it is important to note three challenges in

estimating emissions associated with offshore oil and natural gas production in Alaska’s offshore 

environment:

1. First, sea ice variability necessitates the presence of ice breaker vessels for OCS oil and gas 

activity in the Alaskan Arctic that must be captured in the emission factors for this area. For the 

purposes of estimating emission factors, it is assumed that ice breakers would be necessary in the 

Chukchi Sea and Beaufort Sea Planning Areas.

2. Under Section 432 of the omnibus spending bill enacted by Congress in December 2011, 

jurisdiction over air permitting on the OCS in the Arctic was transferred from the U.S. EPA to the 

U.S. Department of the Interior (DOI). Because the U.S. DOI is less likely to require best 

available control technology (BACT) than EPA, emission factors for Arctic Alaska are likely to 

be higher than those for the rest of Alaska’s offshore environment, all else being equal. 

3. Lastly, the limited availability of Alaska-specific emissions data complicates the derivation of 

emission factors for E&D activity in Alaska. Due to the limited availability of such data, a 

combination of Alaska-specific emissions data and emissions data for the Gulf of Mexico that the 

OECM applies to all planning areas along the contiguous 48 states is relied upon. 

As noted above, this analysis used Alaska-specific emission factors where available. The following 

sources represent the most recently available data: 

 U.S. EPA Region 10, Statement of Basis for Proposed Outer Continental Shelf Prevention of 

Significant Deterioration Permit No. R10OCS/PSD-AK-09-01, Shell Gulf of Mexico, Inc.,

Frontier Discoverer Drillship Chukchi Sea Exploration Drilling Program, January 8, 2010, and

 Shell Gulf of Mexico, Inc., The Revised Outer Continental Shelf Lease Exploration Plan, 

Chukchi Sea, Alaska. Appendix A: Outer Continental Shelf Information Form 137, May 2011.

In addition to these sources, this analysis also relied upon data from the following U.S. EPA sources to 

develop Alaska-specific emissions factors for CO2:

 U.S. EPA, AP-42, 3.4 Large Stationary Diesel and All Stationary Dual-fuel Engines, October 

1996; 

 U.S. EPA, AP-42, 3.3 Gasoline and Diesel Industrial Engines, October 1996; and

 U.S. EPA, AP-42, 1.4 Natural Gas Combustion, July 1998.

Emission factors for exploration wells

Criteria pollutant emission factors for exploration wells drilled on the Alaska OCS were derived from the 

Statement of Basis for Shell’s Frontier Discoverer exploration program, as referenced above. Appendix 
E-2

A of this document (page A-1) provides a detailed accounting of the annual emissions associated with the 

Frontier Discover’s exploration activity, including emissions for the Frontier Discover itself as well as 

associated fleets (ice breakers, resupply ship, and the oil-spill response fleet). The emissions data 

reported in the Statement of Basis are presented as tons of emissions per year and assume a drilling 

season of 168 days per year. The OECM, however, requires emission factors expressed as tons per well. 

To convert the Statement of Basis emissions data from tons per year to tons per well, the data were scaled 

under the assumption of 32 drilling days per well (i.e., the estimates were multiplied by 32/168).62 Table 

1 presents the resulting estimates. 

The emissions data in Table 1 (and on page A-1 of the Statement of Basis) reflect the application of 

BACT to each Discoverer and associated fleets emissions source. As such, they are applicable to nonArctic Alaska, given the December 2011 transfer of air permitting jurisdiction from U.S EPA to U.S. 

DOI.63 Under U.S. DOI authority, BACT will not always be required in Arctic Alaska. For the purposes 

of developing emission factors for the OECM, it is assumed that U.S. DOI would not require BACT 

controls for offshore emissions sources operating in the Arctic. 

To estimate uncontrolled emissions per well, the values in Table 1 were scaled upward based on the 

control efficiency for each source/pollutant combination, as indicated in Appendix A of the Statement of 

Basis for Shell’s Chukchi Sea exploration drilling program (referenced above). For example, the 

Statement of Basis indicates that the PM2.5 control efficiency for BACT installed on generator engines is 

50 percent. Uncontrolled PM2.5 emissions from generator engines are therefore double the value for 

controlled engines. Based on this approach, Table 2 presents uncontrolled emissions per well for each of 

the vessels in the Statement of Basis document.

 

62 The estimate of 32 days per well was obtained from Shell Gulf of Mexico, Inc., The Revised Outer Continental 

Shelf Lease Exploration Plan, Chukchi Sea, Alaska. Appendix A: Outer Continental Shelf Information Form 137, 

May 2011.

63 Although the Frontier Discoverer will operate in the Arctic, the Statement of Basis for the Frontier Discoverer 

permit includes estimates of controlled emissions because the document was completed prior to the December 2011 

transfer of jurisdiction from the U.S. EPA to the U.S. DOI.
E-3

Table 1. Controlled Criteria Pollutant Emissions per Exploration Well in Alaska.

VESSEL COMPONENT UNITS

CONTROLLED EMISSIONS PER UNIT (TONS PER WELL)

NOX SO2 PM10 PM2.5 CO VOC 

Frontier 

Discoverer

Generator Engine16

0.2952 0.0038 0.0762 0.0762 0.1067 0.0152

Emergency Generator 1

0.0149 0.0000 0.0029 0.0029 0.0082 0.0016

MLC Compressor 1

1.0229 0.0016 0.0248 0.0248 0.4762 1.0229

HPU Engine 1

1.5581 0.0009 0.0305 0.0305 0.0476 0.0229

Deck Crane 1

1.8095 0.0013 0.0133 0.0133 0.0381 0.0114

Cementing Units and 

Logging Winches

1

2.2552 0.0011 0.0552 0.0552 0.1257 0.5733

Heat Boiler12

0.6152 0.0049 0.0724 0.0724 0.2381 0.0038

Incinerator 1

0.0114 0.0057 0.0190 0.0171 0.0743 0.0076

Fuel Tanks 1

0.0000 0.0000 0.0000 0.0000 0.0000 0.0019

Supply Ship 1

0.0819 0.0000 0.0057 0.0057 0.0171 0.0057

Drilling Mud System 1

0.0000 0.0000 0.0000 0.0000 0.0000 0.0114

Sub-total 9.7558 0.0433 0.7534 0.7515 1.9034 1.7577

Associated 

Fleets

Ice Breaker #1 161.8819 0.1238 7.3200 6.4000 30.5714 6.8324

Ice Breaker #2 13.5600 0.1295 2.2457 2.1238 45.1752 5.2743

Resupply Ship 0.8076 0.0002 0.0610 0.0495 0.1067 0.0190

OSR Ship -Nanuq 32.8286 0.0743 0.4781 0.3543 7.4552 2.5886

OSR Ships Kvichak 1 & 2 w/ 3 

work boats

7.5029 0.0076 0.1486 0.1486 0.3276 0.1524

Total 226.3368 0.3787 11.0067 9.8277 85.5396 16.6244

Notes:

1. For those components where the number of units exceeds 1, the emission factors provided in this table reflect emissions 

for just one unit. For example, 0.2952 tons of NOx are emitted per well by each of the six generator engine units on the 

Frontier Discoverer. The emissions total, therefore, reflects 1.77 tons of NOx emissions from generator engines (6 × 

0.2952).
E-4

Table 2. Uncontrolled Criteria Pollutant Emissions per Exploration Well in Alaska.

VESSEL(S) COMPONENT UNITS

UNCONTROLLED EMISSIONS PER UNIT (TONS PER WELL)

NOX SO2 PM10 PM2.5 CO VOC 

Frontier 

Discoverer

Generator Engine16

2.9524 0.0038 0.1524 0.1524 0.5333 0.0508

Emergency Generator 1

0.0149 0.0000 0.0029 0.0029 0.0082 0.0016

MLC Compressor 1

1.0229 0.0016 0.0495 0.0495 0.8985 1.9299

HPU Engine 1

1.5581 0.0009 0.2032 0.2032 0.4762 0.2286

Deck Cranes 1

1.8095 0.0013 0.0889 0.0889 0.3810 0.1143

Cementing Units and 

Logging Winches 1

2.2552 0.0011 0.3683 0.3683 0.9678 0.6624

Heat Boiler12

0.6152 0.0049 0.0724 0.0724 0.2381 0.0038

Incinerator 1

0.0114 0.0057 0.0190 0.0171 0.0743 0.0076

Fuel Tanks 1

0.0000 0.0000 0.0000 0.0000 0.0000 0.0019

Supply Ship 1

0.0819 0.0000 0.0057 0.0057 0.0171 0.0057

Drilling Mud System 1

0.0000 0.0000 0.0000 0.0000 0.0000 0.0114

Sub-total 25.6987 0.0433 1.7966 1.7947 6.4992 3.2758

Associated 

Fleets

Ice Breaker #1 161.8819 0.1238 7.3200 6.4000 30.5714 6.8324

Ice Breaker #2 13.5600 0.1295 2.2457 2.1238 45.1752 5.2743

Resupply Ship 0.8076 0.0002 0.0610 0.0495 0.1067 0.0190

OSR Ship -Nanuq 32.8286 0.0743 0.6645 0.5233 7.6542 3.1263

OSR Ships Kvichak 1 & 2 w/ 3 

work boats

7.5029 0.0076 0.1486 0.1486 0.3276 0.1524

Total 242.2797 0.3787 12.2363 11.0399 90.3344 18.6802

Notes:

1. For those components where the number of units exceeds 1, the emission factors provided in this table reflect emissions 

for just one unit. For example, 2.952 tons of NOx are emitted per well by each of the six generator engine units on the 

Frontier Discoverer. The emissions total therefore reflects 17.7 tons of NOx emissions from generator engines (6 × 

2.952).

Using the data in Tables 1 and 2, separate exploration well emission factors for Arctic Alaska (the 

Chukchi and Beaufort Sea Planning Areas) and non-Arctic Alaska (all other planning areas in the Alaska 

OCS region) were estimated. These emission factors are specified as follows:

 Arctic Alaska: For Arctic Alaska, the exploration well emission factors reflect uncontrolled

emissions for all vessels and components reflected in Table 2. Thus, the “Total” row at the 

bottom of Table 2 represents the per-well emission factors incorporated into the OECM.

 Non-Arctic Alaska: Emission factors for Alaska planning areas outside the Arctic reflect 

controlled emissions, as presented in Table 1, for all vessels and components listed in the table 

with the exception of ice breakers. These vessels are assumed not to be necessary for exploration 

activity in non-Arctic planning areas.
E-5

Table 3 presents the estimated criteria pollutant emission factors for Artic and non-Arctic Alaska based 

on the assumptions outlined above.

Table 3. Criteria Pollutant Emission Factors for Exploration Wells Drilled on the OCS in Alaska (Tons Per Well).

GEOGRAPHIC 

AREA NOX SO2 PM10 PM2.5 CO VOC 

Arctic Alaska 242.3 0.3787 12.24 11.04 90.33 18.68

Non-Arctic Alaska 50.89 0.1254 1.441 1.304 9.793 4.518

As suggested above, the data in the Statement of Basis document are limited to criteria pollutants and do 

not reflect emissions of greenhouse gases. To estimate CO2 emissions per well, emission factors were 

obtained from EPA’s AP-42 emissions documentation and these values were matched to the emissions 

sources included in Tables 1 and 2. The data that were used from AP-42 are as follows:

 Large Stationary Diesel Engines: AP-42 reports an emission factor of 165 pounds per million 

British thermal units (lbs/MMBtu) for these sources.64 This value is applied to generator engines, 

the Frontier Discoverer supply ship, and the associated fleets’ resupply ship.

 Diesel Industrial Engines: The CO2 emission factor for these sources is an estimated 164 

lb/MMBtu in AP-42.65 This value is applied to the emergency generator, MLC compressor, HPU 

engine, deck cranes on the Discoverer, and the cementing units and logging winches.

 Natural Gas Boilers: AP-42 estimates CO2 emissions of 120,000 pounds per million cubic feet 

for these sources.66 To convert to lbs/MMBtu (making it consistent with the units used for the 

other two AP-42 values), it is assumed that natural gas has a heating value of 1,028 Btu per cubic 

foot. This converts the AP-42 value to 116 lbs/MMBtu. This value was applied to the heat 

boilers on the Frontier Discoverer.

Insufficient data were available to match the other emissions sources in Tables 1 and 2 with CO2 emission 

factors in AP-42.

As noted above, the AP-42 emission factors are expressed as lbs/MMBtu. Equations 1 and 2 summarize 

the procedure that was followed for converting these values to tons per well. Equation 1 was applied to 

those sources for which the Statement of Basis provides a power rating expressed in terms of horsepower 

(i.e., all of the sources listed in the three bullets above except for heat boilers); equation 2 was applied to 

sources for which the Statement of Basis provides a power rating in terms of MMBtu per hour (i.e., heat 

boilers).

(1) 

where: E = emission factor expressed as tons per well drilled;

e = emission factor expressed as pounds per MMBtu;

h = diesel heat rate, assumed to be 0.007 MMBtu/hp-hr, as presented in the Statement of 

Basis document;

 

64 U.S. EPA, AP-42, 3.4 Large Stationary Diesel and All Stationary Dual-fuel Engines, October 1996.

65 U.S. EPA, AP-42, 3.3 Gasoline and Diesel Industrial Engines, October 1996.

66 U.S. EPA, AP-42, 1.4 Natural Gas Combustion, July 1998.
E-6

p = power rating expressed in terms of horsepower;

24 = number of hours per day;

f = operational factor indicating percent of hours during the day that the equipment is 

operational; 

d = number of days to drill a well (32 days); and 

2000 = pounds per ton

(2) 

Where pt = power rating expressed as MMBtu per hour; and

E, e, f, and d are defined as above.

As indicated in Equations 1 and 2, estimation of the CO2 emissions factor for each source requires 

information on its power rating and the percentage of the time that it operates. Both were obtained from 

the Statement of Basis document. Power ratings were obtained directly from the document, and f was 

estimated by dividing the maximum hours of daily operation by 24 hours per day. Table 4 summarizes 

these values.

Table 4. Power Rating and Operational Information for Select Sources.

EMISSIONS SOURCE

POWER RATING

PERCENT OF TIME 

OPERATIONAL

Generator Engine 1325 hp 100%

Emergency Generator 131 hp 8%

MLC Compressor 540 hp 100%

HPU Engine 250 hp 100%

Deck Cranes 365 hp 100%

Cementing Units and Logging Winches1220 hp121%2

Heat Boiler 7.97 MMBtu/hr 100%

Supply Ship at Discoverer 292 hp 50%

Associated Fleets Resupply Ship 6,344 hp 17%

Source: Values derived from U.S. EPA Region 10, Statement of Basis for Proposed Outer Continental Shelf Prevention 

of Significant Deterioration Permit No. R10OCS/PSD-AK-09-01, Shell Gulf of Mexico, Inc., Frontier 

Discoverer Drillship Chukchi Sea Exploration Drilling Program, January 8, 2010.

Notes:

1. The power rating for cementing units and logging winches represents the average of the values reported in 

Appendix A of U.S. EPA Region 10, Statement of Basis for Proposed Outer Continental Shelf Prevention of 

Significant Deterioration Permit No. R10OCS/PSD-AK-09-01, Shell Gulf of Mexico, Inc., Frontier Discoverer 

Drillship Chukchi Sea Exploration Drilling Program, January 8, 2010.

2. Operational time value for cement units and logging winches based on the maximum hourly fuel use (in pounds) 

for each equipment unit reflected in this category and the maximum daily fuel use for the components combined 

(320 gallons per day), both of which are presented in the Statement of Basis document (see note 1 for full 

citation). 
E-7

Applying the values in Table 4 to Equations 1 and 2, the CO2 emission factors presented in Table 5 are 

estimated. Summing across the sources listed, it is estimated that there are 5,309 tons of CO2 emissions 

per well. This value is applied to exploration wells in both Arctic and non-Arctic Alaska.

Table 5. CO2 Emission Factors for Exploration Wells in Alaska.

EMISSIONS SOURCE

NUMBER OF 

UNITS CO2 EMISSIONS PER WELL (TONS)

Generator Engine 6 587.7

Emergency Generator 1 4.81

MLC Compressor 1 238.1

HPU Engine 1 110.2

Deck Cranes 1 160.9

Cementing Units and Logging Winches11 20.6

Heat Boiler 2 357.25

Supply Ship at Discoverer 1 64.75

Associated Fleets Resupply Ship 1 469.0

TOTAL 5308.8

It is noted that the approaches for estimating criteria pollutant and CO2 emissions factors presented here 

for exploration wells implicitly assume that engines operate at full load (i.e., the load factor equals 1). In 

practice, engines typically would run below their capacity. The Statement of Basis, however, does not 

include load factors for the individual emissions sources used during exploration, as the intent of the 

document is to estimate the potential emissions of these sources. The emission factors presented here 

may therefore overestimate the emissions associated with exploration activity in the Alaska offshore 

environment.

Emissions factors for development wells

Emissions data specific to development wells in the Alaska OCS region are not readily available. In the 

absence of such data, the emission factors for exploration wells presented above are used as an 

approximation of the emission factors for development wells. This is consistent with the approach used 

for planning areas not in the Alaska OCS region, as indicated by the emission factors presented in Chapter 

4.

Emissions factors for platform construction and removal

Similar to development wells, emissions data for platform construction and removal are not readily 

available for the Alaska OCS Region. Absent such information, emission factors for platform 

construction and removal in Alaska are developed based on the corresponding emission factors for 

planning areas offshore from the contiguous 48 states67 and for the associated fleets (i.e., ice breakers, 

 

67 While the OECM uses the same emission factors for all planning areas along the contiguous U.S., we note that 

these emission factors are based on emissions data for the Gulf of Mexico.
E-8

resupply ship, and oil spill response vessels) emission factors for exploration activity in Alaska. These 

emission factors are developed separately for Arctic Alaska and non-Arctic Alaska as follows:

 Arctic Alaska: For each pollutant, the emission factor for platform construction and demolition in 

Arctic Alaska is estimated as the sum of (1) the corresponding emission factor for planning areas 

offshore from the contiguous U.S., and (2) modified versions of the associated fleet emissions 

factors presented above for uncontrolled emissions (i.e., without BACT). As indicated in Chapter 

4, the OECM uses platform construction/demolition emission factors for three depth categories in 

planning areas along the contiguous U.S. To minimize the risk of underestimating emissions in 

Alaska, it is assumed that the emissions factors for the deepest depth category (i.e., the highest 

emission factors of the three categories) are applicable to Alaska. 

Emissions associated with ice breakers, a resupply ship, and oil-spill response vessels are added 

under the assumption that all would be necessary during platform construction.68 However, the 

estimates presented in Table 2 above are based on 32 days of activity, consistent with the average 

time to drill an exploration well. For platform construction, these estimates are scaled to reflect 

the average time required to construct a platform. Based on input from BOEM, this is estimated

to be 90 days.69

 Non-Arctic Alaska: To develop emission factors for platform construction/demolition in nonArctic Alaska, this analysis follows the same approach as in Arctic Alaska with two exceptions. 

First, it is assumed that ice breakers are not required outside the Arctic. The associated fleet 

emissions for non-Arctic Alaska, therefore, reflect resupply ships and oil-spill response vessels 

only. Second, instead of using the uncontrolled emissions for these vessels, controlled emissions

are used. Outside of the Arctic planning areas, the U.S. EPA has jurisdiction over air permitting 

and would be much more likely to require BACT. 

Following this approach, this analysis estimates the emission factors presented in Table 6.

Table 6. Emission Factors for Platform Construction and Demolition in Alaska (Tons Per Platform)1.

NOX SO2 PM10 PM2.5 CO VOC CO2 CH4 N2O

Arctic Alaska 800.9 32.1 34.1 30.8 263.3 48.1 13,910 0.103 0.576

Non-Arctic 

Alaska

307.5 31.4 6.7 6.3 49.7 12.5 13,910 0.103 0.576

Notes:

1. The emission factors presented here are applied separately to platform construction and demolition.

2. Although ice breakers are not required in the non-Arctic planning areas, the CO2, CH4, and N2O emission factors for 

non-Arctic Alaska are the same as the values for Arctic Alaska because insufficient data were available to estimate ice 

breaker emission factors for these gases. 

 

68 Based on personal communication with Virginia Raps, BOEM, January 22, 2013. 

69 Personal communication with James Craig, BOEM, January 28, 2013.
E-9

Emissions factors for support vessels

Comprehensive data on support vessel emissions for the Alaska OCS Region are not readily available. In 

the absence of these data, the support vessel emission factors (expressed as tons per platform) for the 

planning areas along the contiguous U.S.70 are applied to both Arctic and non-Artic Alaska. For the 

Arctic, however, the support vessel fleet will likely include ice breakers, which are not reflected in the 

emission factors for the contiguous U.S. To incorporate emissions from these vessels into the emission 

factor for Arctic Alaska, this analysis estimates ice breaker emissions per platform (without BACT for 

Arctic Alaska) by scaling the emission factors presented in Table 2. As noted above, the values in Table 

2 reflect 32 days of operation per year. To estimate ice breaker emissions per platform, this analysis

assumes 365 days of operation per year andscales the estimates in Table 2 (i.e., it is assumed that they 

must always be available). For example, to estimate annual NOx emissions per platform for ice breaker 1, 

the estimate of 161.9 tons in Table 2 is multiplied by 365/32, which yields 1,847 tons per year per 

platform. Following this approach, Table 7 presents the emission factors for support vessels in Alaska.

Table 7. Emission Factors for Support Vessels in Alaska (Tons Per Platform Per Year).

NOX SO2 PM10 PM2.5 CO VOC CO2 CH4 N2O

Arctic Alaska 2,096.8 15.8 110.8 98.9 873.1 139.7 5,247 0.0332 0.2489

Non-Arctic 

Alaska

95.6 12.9 1.7 1.7 9.1 1.7 5,247 0.0332 0.2489

Note: CO2, CH4, and N2O values are the same for Arctic and non-Arctic Alaska because insufficient data were available to 

estimate emission factors specific to these gases for the ice breakers reflected in the Arctic Alaska emission factors for 

other pollutants.

Emission factors for other sources

The OECM estimates emissions for several other sources in Alaska, including, but not limited to, pipelaying vessels, helicopters, survey vessels, and FPSOs. For these sources, the OECM uses the emission 

factors for sources in planning areas along the contiguous U.S.71

 

70 Although the OECM uses the same emission factors for all planning areas along the contiguous U.S., note that 

these emission factors are based on emissions data for the Gulf of Mexico.

71 As indicated in previous footnotes, the emission factors for all planning areas along the contiguous U.S. are based 

on emissions data from the Gulf of Mexico OCS Region.
F-1

APPENDIX F: SPATIAL DISTRIBUTION OF OIL AND NATURAL GAS 

SUBSTITUTES UNDER THE NO ACTION ALTERNATIVE

Within the OECM, the No Action Alternative (NAA) represents the absence of Five Year Program 

activity (i.e., no lease sales are scheduled for the five year period).

72 To estimate the benefits and costs 

associated with this scenario, the model must account for the response of energy markets to forgone OCS 

production. The Market Simulation Model (MarketSim) provides the first step in this analysis. MarketSim 

calculates three primary impacts of the No Action Alternative on energy markets: (1) the increase in the 

quantity of crude oil shipped to the U.S. via tanker; (2) the increase in the quantity of natural gas shipped 

to the U.S. via tanker; and (3) the increase in domestic onshore production of oil, natural gas, and coal. 

The environmental consequences associated with these shifts in energy markets include: (1) oil spills 

from incoming tankers; (2) emissions from incoming tankers; and (3) emissions from increased onshore 

production.73

To quantify these impacts, the OECM must make assumptions about the geographic distribution of 

increased tanker imports and incremental onshore production. The version of the OECM used for the 

2012-2017 Program assumed that incremental tanker imports of crude oil were distributed in proportion 

to the average annual fraction of crude tanker trips that arrived at each planning area and that incremental 

shipments of natural gas were also distributed in proportion to current patterns. Additionally, the model

assumed that the geographic distribution of increased onshore production was consistent with the current 

distribution of onshore production. The costs of these onshore emissions were quantified using generic 

onshore emission factors and monetized using generic dollar per ton values that reflected the current 

distribution of onshore oil, gas, and coal production across the U.S.

The current version of the OECM described in this document improves upon the previous version by 

refining the model assumptions concerning the geographic distribution of impacts under the No Action 

Alternative. This appendix describes the methods used to develop the improved geographic data. Various 

data sets available from the U.S. Energy Information Administration (EIA) on production, refinement, 

imports, and transportation of oil and natural gas serve as the basis for this analysis. All EIA data on the 

transport of crude oil are reported at the level of Petroleum Administration for Defense Districts 

(PADDs).74 The United States is divided into five PADDs: PADD 1 (East Coast), PADD 2 (Midwest), 

PADD 3 (Gulf of Mexico area), PADD 4 (Rocky Mountains), and PADD 5 (West Coast, AK, HI) (See 

Figure 1). PADDs 1, 3, and 5 all contain coastline with OCS planning areas located offshore. The 

Atlantic OCS planning areas are in PADD 1, the Gulf of Mexico OCS planning areas are in PADD 3, and 

the Pacific and Alaska planning areas are in PADD 5. Throughout this appendix, all regional production 

and transport data for oil are presented by PADD. In addition, although natural gas production and 

 

72 In practice, Five Year Program decisions are made by planning area, and a “No Sale Option” is always available 

for each area. The models described in this report can be used for a full NAA (no lease sales for any areas) or to 

estimate the effects of selecting the No Sale Option for one or more areas. The models work the same way in either 

case.

73 The OECM does not estimate other environmental and social costs that might result from increased onshore oil 

and gas (or coal) production, such as contamination of groundwater and rail car derailment. Similarly, it does not 

estimate costs that might result from pipeline transportation of oil and gas. 

74 While it is impossible to determine the ultimate destination of crude oil and gas, the availability of aggregate data 

on imports and flows between PADDs does allow for improvements to the OECM’s geographical allocation of 

incremental oil and gas production, importation, and transportation under the NAA. 
F-2

transport data are not reported by PADD, these data are presented at the PADD level for consistency with 

the information presented on oil production and transportation. 

Figure 1: Map of Petroleum Administration for Defense Districts

Geographic distribution of incremental crude oil imports

The OECM’s assumptions regarding the geographic distribution of incremental imports under the NAA 

reflect the following two-step analysis described in greater detail below:

1. Determine which regions of the U.S. are served by oil production in a given OCS planning 

area: For a given PADD, the OECM makes this determination based on the portion of domestic 

crude production that is shipped to refineries in that PADD versus transported to other PADDs. 

For example, if 30 percent of production in PADD 3 is estimated to remain in PADD 3, the 

OECM assumes that 30 percent of the OCS production in the Central Gulf of Mexico is sent to 

refineries in the Gulf of Mexico region and that the remainder is sent to other PADDs (regions).

2. For each area served by OCS production, identify the regions that supply crude oil to that area. 

The different areas served by a given PADD have their own unique mix of crude oil imports. To 

varying degrees, each PADD relies in part on imports it receives directly from foreign sources 

and imports that arrive at ports in other PADDs but are then transported to that PADD. 

3. Integrate the information from Steps 1 and 2: The information from Steps 1 and 2, when 

combined, provide the distribution of oil imports associated with the NAA. For example, for Step 

1, assume that an E&D scenario includes OCS production in the Central Gulf of Mexico and that 

30 percent of OCS oil in the planning area is assumed to be shipped to refineries in that planning 
F-3

area and that the remaining 70 percent is shipped to refineries in the Atlantic region. Assume 

further that 50 percent of the oil imports sent to refineries in the Gulf of Mexico arrived at ports 

in that region and that 90 percent of imports processed in refineries in the Atlantic first arrive in 

the U.S. at Gulf of Mexico ports. Based on these assumptions, 78 percent (0.3×0.5 + 0.7×0.9) of 

the oil imports that displace OCS production in the Central Gulf of Mexico would be replaced by 

imports to the Gulf of Mexico region 

EIA data on crude oil movements between PADDs via pipeline, tanker, and barge serve as the basis for 

the estimates of inter-regional transport of crude.75 These numbers were adjusted to account for two 

primary shortcomings in the EIA data: (1) the EIA does not track transport of crude by rail, and (2) the 

EIA does not differentiate between the transport of domestic and foreign crude. The approach for 

addressing these shortcomings is presented in the following sections. This appendix then turns to 

implementation of Steps 1 and 2 above.

Crude by rail

The first issue with the data on inter-PADD movements is that, at the time of this writing, the EIA does 

not track transport of crude via railroad, a method of growing importance in recent years. However, the 

EIA does compile information on refinery receipts of crude oil by rail for each PADD. Since the majority 

of shipments by rail originate from North Dakota’s Bakken formation76 (located in PADD 2), it is 

reasonable to infer that all refinery receipts of crude oil by rail represent deliveries from PADD 2. 77

Foreign versus domestic crude

The second issue with EIA data on inter-PADD movements is that the data do not differentiate between 

transport of domestic and foreign crude. It is likely that domestic crude oil, including OCS production, 

follows a considerably different transport pattern than foreign oil.78 As a result, this analysis parses out 

the foreign portion of inter-PADD deliveries to provide a more accurate estimate of the distribution of oil 

produced domestically.

EIA data on crude oil imports provide some insight into the movements of foreign crude. The EIA reports 

crude imports two different ways: (1) quantity of imports that enter each PADD and (2) quantity of 

 

75 U.S. EIA. Movements by Pipeline between PAD Districts. Released September 29, 2014. Accessed October 20, 

2014 at: http://www.eia.gov/dnav/pet/pet_move_pipe_dc_R20-R10_mbbl_m.htm

U.S. EIA. Movements by Tanker and Barge between PAD Districts. Released September 29, 2014. Accessed 

October 20, 2014 at: http://www.eia.gov/dnav/pet/pet_move_tb_dc_R20-R10_mbbl_m.htm

76 U.S. EIA. Crude-by-rail transportation provides Bakken Shale production access to major markets. June 10, 2014. 

Accessed December 2014 at: http://www.eia.gov/todayinenergy/detail.cfm?id=16631#

77 Following the completion of the analysis presented in this appendix, the EIA released a dataset which tracks 

movements of crude oil by rail. While this analysis correctly inferred that the majority of crude oil moved by rail 

originates in PADD 2, the new EIA dataset indicates that this analysis underestimates total movements of crude by 

rail. Regardless, incorporation of the newly released data has only a minor impact on the estimated geographical 

allocation of incremental imports and onshore production presented in this appendix. The new dataset on 

movements of crude oil by rail can be accessed at: 

http://www.eia.gov/dnav/pet/pet_move_rail_a_EPC0_RAIL_mbbl_a.htm

78 The quality of crude oil ranges from light sweet oil that is volatile and easily refined into gasoline and other such 

high-value fuels to heavy sour crude, which is viscous and must be extensively refined. The equipment in each 

refinery is designed to most efficiently handle a specific range of crude quality, and refineries will blend the least 

expensive crudes to obtain that quality. Accordingly, the recent abundance of domestically produced light sweet 

crude has caused dramatic changes in the quality of crude imported. This likely has led to even greater differences 

in the transportation and use of domestic and imported crude oil by refineries.
F-4

imports that are processed in each PADD. Together these numbers indicate whether a PADD is, on net, 

receiving or delivering foreign crude to other PADDs (see Table 1).

Table 1: Imports of Crude Oil by PADD (Thousand Barrels, 2013).

PADD

IMPORTS 

INTO PADD

IMPORTS 

PROCESSED IN 

PADD

NET RECEIPTS OF 

FOREIGN CRUDE FROM 

OTHER PADDS

PADD 1 (East Coast) 283,040 287,076 4,036 

PADD 2 (Midwest) 683,916 671,163 (12,753)

PADD 3 (Gulf Coast) 1,347,373 1,373,682 26,309 

PADD 4 (Rocky Mountains) 107,879 90,009 (17,870)

PADD 5 (West Coast) 399,272 399,550 278 

Total 2,821,480 2,821,480 - 

Sources: 

1. U.S. EIA. Crude Oil Imports by Area of Entry. Released September 29, 2014. Accessed October 22, 

2014 at: http://www.eia.gov/dnav/pet/pet_move_imp_a_epc0_im0_mbbl_a.htm

2. U.S. EIA. Crude Oil Imports by Processing Area. Released October 30, 2014. Accessed November 3, 

2014 at: http://www.eia.gov/dnav/pet/pet_move_imp2_a_epc0_ip0_mbbl_a.htm

Further inferences can be drawn from EIA data on U.S. imports by country of origin. In particular, the 

breakdown of imports from Canada versus other countries provides useful information (see Table 2). 

Because the landlocked PADDs (2 and 4) can only receive imports directly from Canada, any nonCanadian imports that are processed within PADD 2 or 4 must have been delivered from a coastal PADD. 

Similarly, PADD 3 cannot receive any crude oil directly from Canada by land, without the crude first 

passing through another PADD. Though PADD 3 receives a small amount of Canadian crude via oil 

tanker, the majority of all Canadian crude processed in PADD 3 must be delivered from PADD 2 or 

PADD 4. Data from the U.S. Army Corps of Engineers indicate that PADD 3 received approximately 

890,000 barrels of crude from Canada via waterborne methods of transportation in 2012. 79 This 

represents less than two percent of all Canadian imports processed in PADD 3 in that year.

 

79 U.S. Army Corps of Engineers, Navigation Data Center, Waterborne Commerce Statistics Center. “Foreign Cargo 

Inbound and Outbound.” Accessed November 4, 2014 at: http://www.navigationdatacenter.us/data/dataimex.htm
F-5

Table 2: Imports of Crude Oil by Country of Origin (Thousand Barrels, 2013)

PADD

IMPORTS 

PROCESSED FROM 

CANADA

IMPORTS PROCESSED FROM 

ALL OTHER COUNTRIES

PADD 1 (East Coast) 78,254 208,822

PADD 2 (Midwest) 655,581 15,582

PADD 3 (Gulf Coast) 46,877 1,326,805

PADD 4 (Rocky Mountains) 90,009 -

PADD 5 (West Coast) 70,515 329,035

Total 941,236 1,880,244

Source: 

1. U.S. EIA. Crude Oil Imports from Canada. Released November 26, 2014. Accessed December 9, 2014 

at: http://www.eia.gov/dnav/pet/pet_move_impcp_a1_nca_epc0_ip0_mbbl_a.htm

2. U.S. EIA. Crude Oil Imports by Processing Area. Released October 30, 2014. Accessed November 3, 

2014 at: http://www.eia.gov/dnav/pet/pet_move_imp2_a_epc0_ip0_mbbl_a.htm

Data on crude oil imports by country of origin therefore provide two primary insights:

1. PADD 2 processed 15,582 thousand barrels of crude oil from foreign countries other than 

Canada. Since PADD 2 is landlocked, this crude must have been transported from PADD 1 or 

PADD 3. Since PADD 1 only transported 2,729 thousand barrels of oil to PADD 2 overall 

(domestic or foreign; see Table 3), it is assumed that these deliveries are from PADD 3. 

2. PADD 3 processed 46,877 thousand barrels of crude from Canada. Aside from a small amount 

imported via tanker, this crude must have first passed through PADD 2 or PADD 4. Since PADD 

4 only transported 5,385 thousand barrels of oil to PADD 3 overall, it is assumed that all 

deliveries of Canadian crude to PADD 3 not sent by tanker pass through PADD 2.

The various sources of data on crude oil imports described above, combined with data on the movement 

of all crude oil, allow for the estimation of the movement of foreign crude between PADDs. The transport 

of domestic crude can then be estimated by subtracting foreign movements of crude oil from total 

movements. Table 3 displays the transport of all crude oil (both foreign and domestic) between PADDs. 

Table 3: Inter-PADD Transport of All Crude Oil (Thousand Barrels, 2013)

TRANSPORT 

TO PADD 1

TRANSPORT 

TO PADD 2

TRANSPORT 

TO PADD 3

TRANSPORT 

TO PADD 4

TRANSPORT 

TO PADD 5

PADD 1 (East Coast) - 2,729 9,369 - - 

PADD 2 (Midwest) 43,052 - 194,911 25,727 - 

PADD 3 (Gulf Coast) 8,426 332,053 - - - 

PADD 4 (Rocky Mountains) - 86,565 5,835 - 25,683 

PADD 5 (West Coast) - - - - - 

Note: Table derived from the baseline EIA data on inter-PADD transport of crude by pipeline, tanker, and barge, adjusted to 

include all domestic receipts of crude by rail as deliveries from PADD 2.
F-6

Table 4 displays an estimate of the foreign portion of inter-PADD transports. These estimates incorporate 

the information derived from the examination of imports by country, while ensuring that net receipts of 

foreign crude for each PADD match the values indicated in Table 1. Specifically, the following 

assumptions and calculations were made:

 On net, PADD 5 receives 278,000 barrels of foreign crude from other PADDs. PADD 5 receives 

no crude from PADD 3 (see Table 3), so all of these barrels must be delivered from PADD 4.

 On net, PADD 4 delivers 17,870 thousand barrels of foreign crude to other PADDs. As discussed 

above, 278 thousand barrels are delivered to PADD 5. The remaining 17,592 thousand barrels are 

assumed to be delivered to PADD 2. While it is possible that PADD 4 could also deliver foreign 

crude to PADD 3, this seems much less likely. PADD 4 delivers 86,565 thousand barrels of crude 

(domestic or foreign) to PADD 2, versus only 5,835 to PADD 3. Additionally, a map of crude oil 

pipelines produced by the American Petroleum Institute (API) indicates that there are no major 

pipelines transporting imported crude oil directly between PADD 4 and PADD 3 (see Figure 2). 

Table 4: Inter-PADD Transport of Foreign Crude Oil (Thousand Barrels, 2013)

TRANSPORT 

TO PADD 1

TRANSPORT 

TO PADD 2

TRANSPORT 

TO PADD 3

TRANSPORT 

TO PADD 4

TRANSPORT 

TO PADD 5

PADD 1 (East Coast) - - - - 

PADD 2 (Midwest) - - 45,927 - - 

PADD 3 (Gulf Coast) 4,036 15,582 - - - 

PADD 4 (Rocky Mountains) - 17,592 - - 278 

PADD 5 (West Coast) - - - - - 

 PADD 2 processes 15,582 thousand barrels of crude from foreign countries other than Canada. 

This crude is assumed to be delivered from PADD 3. While it is possible that PADD 1 could also 

deliver foreign crude to PADD 2, this seems much less likely. PADD 2 receives 332,053 

thousand barrels of crude (domestic or foreign) from PADD 3, and only 2,729 from PADD 1. 

Additionally, there are no major pipelines transporting non-Canadian foreign crude directly 

between PADD 1 and PADD 2 (see Figure 2).

 PADD 3 processes 46,877 thousand barrels of Canadian crude. Only a small portion of this 

amount is imported by tanker directly from Canada, so the majority must travel by pipeline 

through PADD 2. For the net receipts of foreign crude from PADD 2 and PADD 3 to match the 

constraints in Table 1, 45,927 thousand barrels of Canadian crude must be transported from 

PADD 2 to PADD 3. This leaves 950 thousand barrels of Canadian imports unaccounted for 

which must have been shipped directly from Canada (which is comparable to the estimate of 890 

thousand barrels discussed earlier).80

 

80 The estimate of 890,000 barrels of crude shipped directly from Canada was derived from USACE data on imports 

of foreign cargo. The USACE data are presented in tons of crude oil instead of barrels. The USACE estimate of tons 

of crude imports was converted to barrels using EIA data on barrels of crude oil per metric ton for Canada. Because 

this conversion may lead to some imprecision in estimating the number of barrels of Canadian crude oil supplies to 

the U.S. via tanker, this analysis assumes that the 950,000 barrels of Canadian imports left unaccounted for in the 

EIA data represents the amount of Canadian imports delivered to PADD 3 via tanker. For information on the barrels 

of metric ton of Canadian crude oil, see U.S. EIA, International Energy Statistics. “Barrels of Crude Oil per Metric 

Ton,” accessed December 2014 at: http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=94&pid=57&aid=32
F-7

 On net, PADD 1 receives 4,036 thousand barrels of foreign crude from other PADDs. This crude 

is assumed to be delivered from PADD 3 (instead of PADD 2) to ensure consistency with the net 

receipts for each PADD presented in Table 1 and the other assumptions stated above.81

Inter-PADD transport of domestic crude can now be estimated by subtracting the estimate of inter-PADD 

transport of foreign crude from the data on inter-PADD transport of all crude. Table 5 provides a 

summary of domestic inter-PADD transfers.

Table 5: Inter-PADD Transport of Domestic Crude Oil (Thousand Barrels, 2013)

PADD

TRANSPORT 

TO PADD 1

TRANSPORT 

TO PADD 2

TRANSPORT 

TO PADD 3

TRANSPORT 

TO PADD 4

TRANSPORT 

TO PADD 5

PADD 1 (East Coast) - 2,729 9,369 - - 

PADD 2 (Midwest) 43,052 - 148,984 25,727 - 

PADD 3 (Gulf Coast) 4,390 316,471 - - - 

PADD 4 (Rocky Mountains) - 68,973 5,835 - 25,405 

PADD 5 (West Coast) - - - - - 

 

81 As established above, PADD 3 likely receives 45,927 thousand barrels from PADD 2, and likely sends 15,582 

thousand barrels to PADD 2. Together these transfers leave PADD 3 receiving 30,345 thousand barrels on net. 

Table 1 shows that on net, PADD 3 receives 26,309 thousand barrels of imported crude from other PADDs. Thus 

PADD 3 is assumed to deliver 4,036 thousand barrels of crude to PADD 1 to bring its net receipts down to 26,309 

thousand barrels.
F-8

Figure 2: Map of U.S. Crude Oil Pipelines

Source: American Petroleum Institute. “Where are the oil pipelines?” Accessed at: http://www.api.org/oil-and-naturalgas-overview/transporting-oil-and-natural-gas/pipeline/where-are-the-oil-pipelines

Distribution of imports between PADDs

To determine the proportion of OCS production that would serve each PADD, the data on inter-PADD 

transfers must be combined with data on the quantity of crude that remains in its PADD of production. 

This analysis defines the amount of domestic crude produced and refined within the same PADD as 

production in a given PADD minus transfers of domestic crude to other PADDs. In addition, because the 

OECM estimates impacts related to an increase in crude imports via tanker, any incremental imports are 

distributed between the three coastal PADDs (PADD 1, PADD 3, and PADD 5). 

Taking these factors into consideration, the distribution of tanker imports under the NAA that would 

displace a planning area’s production is a function of (1) the percentage of crude produced in a given 

PADD that is also refined there and (2) the percentage of crude oil produced in that PADD that is sent to 

other PADDs. For example, PADD 1 produced 14,353 thousand barrels of crude in 2013, and delivered 

9,369 thousand barrels of domestic crude to PADD 3 (and no barrels to PADD 5; see Table 5). 

Consequently, 65.3% of incremental imports resulting from forgone production in the OCS planning 

areas within PADD 1 (9,369 thousand barrels / 14,353 thousand barrels = 0.653), will be delivered to 
F-9

PADD 3, and the remaining 34.7% will be delivered to PADD 1. Table 6 presents the allocation of 

incremental imports resulting from forgone production in each coastal PADD.

Table 6: Allocation of Incremental Crude Oil Imports by PADD

PADD/OCS REGION 

WITH FORGONE 

PRODUCTION

REPLACED BY 

IMPORTS INTO PADD 

1 (ATLANTIC OCS 

REGION)

REPLACED BY 

IMPORTS INTO PADD 3 

(GULF OF MEXICO OCS 

REGION)

REPLACED BY 

IMPORTS INTO PADD 5 

(PACIFIC AND ALASKA 

OCS REGIONS) TOTAL

PADD 1 (Atlantic OCS 

Region)

34.7% 65.3% 0% 100%

PADD 3 (GOM OCS 

Region)

0.3% 99.7% 0% 100%

PADD 5 (Pacific and AK 

OCS Regions)

0% 0% 100% 100%

Distribution of imports within PADDs

Table 6 above presents an estimate of which coastal PADDs are most likely to receive incremental 

imports in the absence of program activity. This section specifies how those imports are assumed to be 

distributed across the OCS planning areas within each PADD.

The U.S. Army Corps of Engineers (USACE) Navigation Data Center maintains information on foreign 

deliveries of cargo to U.S. ports. For each port receiving foreign cargo, this dataset contains information 

on the tonnage of cargo received for each type of commodity. Each port that imports crude oil was 

located on a map to determine the associated PADD and OCS planning area. This enabled the calculation 

of the distribution of crude oil imports across the planning areas located in each PADD. For oil imported 

into a given PADD, the OECM allocates across planning areas according to the distribution within the 

USACE data. This distribution of impacts is representative as long as the average size of a crude oil 

delivery does not differ greatly between planning areas.

Table 7: Crude Oil Imports to U.S. Ports, Organized by Planning Area and PADD

PLANNING AREA CRUDE OIL TONNAGE PERCENT OF PADD IMPORTS

PADD 1 (East Coast, corresponds to the Atlantic OCS Region)

North Atlantic 40,042,470 83.9%

Mid Atlantic 7,274,415 15.3%

South Atlantic 383,815 0.8%

PADD 3 (Gulf Coast, corresponds to the Gulf of Mexico OCS Region)

Central Gulf of Mexico 103,701,134 40.7%

Western Gulf of Mexico 151,294,769 59.3%

PADD 5 (West Coast, corresponds to the Pacific OCS Region)

Central California 16,874,529 32.4%

Cook Inlet 273,230 0.5%

Southern California 29,081,637 55.8%

Washington Oregon 5,889,823 11.3%
F-10

Table 8 combines the information in Tables 6 and 7 to estimate the allocation of incremental imports by 

planning area. The values in Table 8 were derived by multiplying the proportion of incremental imports 

allocated to each PADD by the proportion of imports delivered to each planning area within a PADD. For 

example, Table 6 shows that 65.3% of incremental imports resulting from forgone production in PADD 1 

will be delivered to PADD 3, and Table 7 shows that 40.7% of imports into PADD 3 arrive in the Central 

Gulf of Mexico planning area. As a result, 26.5% (65.3% x 40.7%) of incremental imports resulting from 

forgone production in PADD 1 are assumed to be delivered to the Central Gulf of Mexico. 

The values in Table 8 are incorporated directly into the OECM (under Manage Scenario InputsNo 

Action Alternative) and are combined with data on the E&D scenario and MarketSim results to estimate 

the allocation of crude oil tanker imports across planning areas. For example, assume that MarketSim 

projects a one million barrel increase in tanker imports under the No Action alternative and that 30 

percent of new OCS oil production under the E&D scenario occurs in the Atlantic, 60 percent is in the 

Gulf of Mexico, and 10 percent is in the Pacific and Alaska OCS regions. Under this scenario, the 

increase in crude oil tanker imports under the NAA is assumed to be distributed as follows: 300,000 

barrels for the Atlantic, 600,000 barrels for the Gulf of Mexico, and 100,000 barrels for the 

Pacific/Alaska. Using these data in conjunction with the values in Table 8, the estimated increase in 

imports to the Western Gulf of Mexico planning area is 471,000 barrels (300,000 × 0.387 + 600,000 × 

0.592 + 100,000 × 0).
F-11

Table 8: Allocation of Incremental Crude Oil Imports by Planning Area

PADD 1 (ATLANTIC OCS REGION)

PADD 3 (GULF OF 

MEXICO OCS REGION) PADD 5 (PACIFIC AND ALASKA OCS REGIONS)

TOTAL

PADD/OCS REGION 

WITH FORGONE 

PRODUCTION

NORTH 

ATLANTIC

MID 

ATLANTIC

SOUTH 

ATLANTIC

CENTRAL 

GULF OF 

MEXICO

WESTERN 

GULF OF 

MEXICO

CENTRAL 

CALIFORNIA

COOK 

INLET

SOUTHERN 

CALIFORNIA

WASHINGTON 

OREGON

PADD 1 (Atlantic OCS 

Region)

29.1% 5.3% 0.3% 26.5% 38.7% 0.0% 0.0% 0.0% 0.0% 100%

PADD 3 (Gulf of 

Mexico OCS Region)

0.2% 0.0% 0.0% 40.6% 59.2% 0.0% 0.0% 0.0% 0.0% 100%

PADD 5 (Pacific and 

Alaska OCS Regions)

0.0% 0.0% 0.0% 0.0% 0.0% 32.4% 0.5% 55.8% 11.3% 100%
F-12

Geographic distribution of onshore oil production

In the absence of OCS program activity, onshore oil production in the U.S. may increase. The OECM 

allocates such increases to the regions of the country that would be most likely to substitute for OCS 

production in the absence of program activity. The OECM makes this determination based where OCS oil 

is sent in the U.S. (i.e., OCS oil destinations) and the domestic sources of oil for each OCS oil destination. 

More specifically, the steps in making this determination are as follows: 

1. Identify which regions of the country are most likely to be served by OCS production. This is

defined as the percentage of the production in each coastal region (PADD) that is refined in that 

region (PADD) and the percentage that is transported to other regions (PADDs).

2. For each region identified in Step 1 (i.e., the OCS oil destinations), identify the sources of 

domestic crude oil refined in each PADD. This is defined as the percentage of domestic crude 

refined in each PADD that is produced in that same PADD and the percentage that is received 

from other PADDs.

Incremental onshore oil production under the No Action Alternative is distributed to each region (PADD)

based on the percentage of domestic crude they supply to the regions served by OCS production.

PADDs served by OCS oil production

The first step in this analysis is to identify which regions of the country are served by OCS production in 

a given planning area. This step requires the same analysis used to allocate the distribution of incremental 

imports, the only difference being that this part of the analysis is concerned with movements to all regions 

of the country, not just coastal regions. This is because incremental domestic production could occur in 

any PADD, whereas incremental imports can only be delivered to coastal PADDs. Table 9 presents the

regions of the country served by OCS production. For example, the data in Table 9 show that nearly 20 

percent of the production in the Gulf Coast region (PADD 3) is sent to the Midwest (PADD 2). While the 

data in Table 9 are for the entirety of a given PADD and are not specific to a given OCS planning area, 

this analysis assumes that the values for each PADD apply to the corresponding OCS planning areas 

within each PADD.
F-13

Table 9: PADDs Served by Domestic Crude Oil Production in Coastal PADDs 

SERVING PADD 1 

(EAST COAST)

SERVING PADD 2

(MIDWEST)

SERVING PADD 3 

(GULF COAST)

SERVING PADD 4 

(ROCKY MOUNTAINS)

SERVING PADD 5 

(WEST COAST) TOTAL

PADD 1 (Atlantic OCS 

Region) production 15.7% 19.0% 65.3% 0.0% 0.0% 100%

PADD 3 (Gulf of Mexico 

OCS Region) production 0.28% 19.8% 79.9% 0.0% 0.0% 100%

PADD 5 (Pacific and AK 

OCS Regions) production 0.0% 0.0% 0.0% 0.0% 100.0% 100%

Source: U.S. EIA. Movements by Pipeline, Tanker, and Barge between PAD Districts. Released September 29, 2014. Accessed October 20, 2014 at: 

http://www.eia.gov/dnav/pet/pet_move_pipe_dc_R20-R10_mbbl_m.htm and http://www.eia.gov/dnav/pet/pet_move_tb_dc_R20-R10_mbbl_m.htm
F-14

Geographic distribution of domestic production for areas served by OCS oil

After identifying the areas served by OCS production, the next step in determining the distribution of 

onshore oil production is examining where the areas served by OCS oil get their oil. For instance, as 

shown in Table 9, 19.8 percent of domestic production in PADD 3 is transported to PADD 2. Absent 

program activity in PADD 3, PADD 2 would likely receive substitute sources of domestic crude. As 

shown in Table 10, PADD 2 receives 0.4 percent of its domestic crude from PADD 1, 46.7 percent from 

PADD 3, 10.2 percent from PADD 4, and 42.7 percent from production within PADD 2. These PADDs 

would therefore be assigned incremental onshore production in these proportions to make up for the 

portion of forgone OCS production in PADD 3 that would have been delivered to PADD 2. Table 10 

shows the sources of domestic crude for each region (PADD) of the U.S. 

Table 10: Sources of Domestic Crude for Each Region (PADD)

PRODUCED IN 

PADD 1 

(EAST COAST)

PRODUCED 

IN PADD 2

(MIDWEST)

PRODUCED IN 

PADD 3

(GULF COAST)

PRODUCED IN 

PADD 4

(ROCKY MTNS.)

PRODUCED IN 

PADD 5 

(WEST COAST)

Sources of crude for 

PADD 1 (East Coast)

4.5% 86.6% 8.8% 0.0% 0.0%

Sources of crude for 

PADD 2 (Midwest)

0.4% 42.7% 46.7% 10.2% 0.00%

Sources of crude for 

PADD 3 (Gulf Coast)

0.7% 10.3% 88.6% 0.4% 0.0%

Sources of crude for 

PADD 4 (Rocky 

Mountains)

0.0% 21.8% 0.0% 78.2% 0.0%

Sources of crude for 

PADD 5 (West Coast)

0.0% 0.0% 0.0% 5.9% 94.1%

Source: U.S. EIA. Movements by Pipeline, Tanker, and Barge between PAD Districts. Released September 29, 2014. Accessed 

October 20, 2014 at: http://www.eia.gov/dnav/pet/pet_move_pipe_dc_R20-R10_mbbl_m.htm and 

http://www.eia.gov/dnav/pet/pet_move_tb_dc_R20-R10_mbbl_m.htm

Combining the information presented in Tables 9 and 10 yields the percentage of incremental onshore 

production that would be expected in each region (PADD) absent program activity (see Table 11). For 

instance, Table 11 indicates that absent program activity in the Gulf of Mexico OCS Region (part of 

PADD 3), 17% of incremental production is expected to occur in PADD 2. This was calculated as the 

proportion of PADD 3 production that serves each PADD, multiplied by the proportion of crude supplied 

to each of these PADDs by PADD 2. Specifically: 

 0.28% of PADD 3 production serves PADD 1 (Table 9), and PADD 2 produces 86.6% of the 

crude supplied to PADD 1 (Table 10) (0.28% x 86.6% = 0.2%)

 19.8% of PADD 3 production serves PADD 2, and PADD 2 produces 42.7% of the crude 

supplied to PADD 2 (19.8% x 42.7% = 8.5%).

 79.9% of PADD 3 production serves PADD 3, and PADD 2 produces 10.3% of the crude 

supplied to PADD 3 (79.9% x 10.3% = 8.3%).
F-15

 So overall, PADD 2 will supply 0.2% + 8.5% + 8.3% = 17% of incremental production resulting 

from forgone program activity in PADD 3.

Table 11: Incremental Onshore Crude Oil Production in Each PADD Resulting From Forgone OCS Production 

PADD/OCS REGION 

WITH FORGONE 

PRODUCTION

INCREMENTAL 

PRODUCTION 

IN PADD 1

INCREMENTAL 

PRODUCTION 

IN PADD 2

INCREMENTAL 

PRODUCTION 

IN PADD 3

INCREMENTAL 

PRODUCTION 

IN PADD 4

INCREMENTAL 

PRODUCTION 

IN PADD 5 TOTAL

PADD 1 (Atlantic OCS 

Region)

1.2% 28.5% 68.1% 2.2% 0.0% 100%

PADD 3 (Gulf of 

Mexico OCS Region)

0.6% 17.0% 80.1% 2.3% 0.0% 100%

PADD 5 (Pacific and 

Alaska OCS Regions)

0.0% 0.0% 0.0% 5.9% 94.1% 100%

The values in Table 11 were used to inform the development of air pollutant-specific dollar-per-ton 

values for onshore oil production under the NAA. For example, the dollar-per-ton values used to 

monetize the damages associated with onshore oil production that substitutes for OCS production in the 

Atlantic Region is a weighted average of the dollar-per-ton values in PADDs 1 through 5, with weights of 

0.012 for PADD 1, 0.285 for PADD 2, 0.681 for PADD 3, 0.022 for PADD 4, and 0 for PADD 5. 

Geographic distribution of incremental natural gas imports

In the absence of program activity, the United States may import a greater quantity of natural gas. There 

are only 11 natural gas import terminals in the United States, so any increase in imports must be divided 

between these terminals.82 Six of the import terminals are located along the Gulf Coast (PADD 3) and 

five are located along the East Coast (PADD 1).83 Of these, only five import terminals received any 

natural gas deliveries in 2014. This likely reflects the significant reduction in U.S. natural gas imports in 

recent years due to the rapidly increasing shale gas production in the U.S. (see Figure 3). The quantity of 

LNG imports, by terminal, is presented in Table 12.

 

82 While incremental imports may also arrive via pipeline, the OECM does not estimate costs associated with 

pipeline imports due to the small risk of environmental impacts relative to tankers. Consequently, the MarketSim 

output is exclusive to tankers and this appendix considers only the distribution of tanker imports.

83 As noted at the beginning of this appendix, we aggregate the available data on natural gas production and 

shipments to the PADD level for consistency with the data for crude oil presented earlier in this appendix.
F-16

Figure 3: U.S. LNG Imports 2009 – 2014

Table 12: Distribution of LNG Imports (Mmcf, 2009-2013)

PADD PLANNING AREA

IMPORT 

TERMINAL

IMPORTS 

(2009)

IMPORTS 

(2010)

IMPORTS 

(2011)

IMPORTS 

(2012)

IMPORTS 

(2013)

IMPORTS 

(2014)

PADD 1 North Atlantic Everett, MA

 155,817 148,954 

 

135,278 86,609 63,987 28,825

North Atlantic Neptune Deepwater 

Port, MA - 1,332 - - - 

North Atlantic Northeast Gateway, 

MA 5,669 14,698 - - - 

Mid-Atlantic Cove Point, MD 72,339 43,431 13,981 2,790 5,366 11,585

South Atlantic Elba Island, GA 142,244 106,454 75,641 59,266 15,575 7,155

PADD 3 Central Gulf of 

Mexico

Cameron, LA

 9,654 7,011 12,662 5,716 - 

Central Gulf of 

Mexico

Pascagoula, MS

 - - 5,774 - - 

Central Gulf of 

Mexico

Lake Charles, LA

 31,348 39,037 2,282 2,514 - 

Central Gulf of 

Mexico

Sabine, LA

 29,097 44,819 45,610 11,902 5,750 5,880

Western Gulf of 

Mexico

Freeport, TX

 5,789 12,236 21,427 5,851 5,627 5,698

Western Gulf of 

Mexico

Golden Pass, TX

 - 13,037 36,284 - - 

Total

 451,957 431,009 

 

348,939 

 

174,648 96,305 59,143

*All values presented in million cubic feet (MMcf)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

2009 2010 2011 2012 2013 2014

LNG Imports (MMcf)
F-17

Since only PADD 1 (East Coast – Atlantic OCS Region) and PADD 3 (Gulf Coast – Gulf of Mexico OCS 

Region) receive imports of LNG, any incremental imports of LNG resulting from forgone OCS 

production are distributed between these two PADDs. Incremental imports are assigned to different 

PADDs (regions) based on the available information regarding the transportation patterns of gas produced 

in individual regions. Specifically:

 All natural gas produced in PADD 1 (East Coast – Atlantic OCS Region) is either consumed 

within the PADD, or transported to PADD 2 (Midwest). As a result, any incremental imports of 

LNG resulting from forgone OCS production in PADD 1 are assumed to be delivered to PADD 1.

 Natural Gas produced in PADD 3 (Gulf Coast – Gulf of Mexico OCS Region) is consumed in 

PADDs 1, 2, 3, and 5. As a result, this analysis assumes that incremental imports resulting from 

forgone OCS production in the Gulf of Mexico are delivered to PADD 3 and PADD 1, based on 

the proportion of PADD 3 production that remains in the PADD versus transported to PADD 1.84

 All natural gas produced in the Pacific and Alaska OCS Regions (PADD 5) is either consumed 

within the PADD, or transported to PADD 4 (Rocky Mountains). However, PADD 5 can not 

directly accept imports because it does not have any LNG ports. As a result, we assign all 

incremental imports associated with forgone OCS production in the Pacific and Alaska OCS 

Regions to PADD 3 (Gulf Coast), since PADD 3 borders PADD 5 (unlike PADD 1).

Table 13 summarizes the assumed distribution of incremental LNG imports resulting from forgone OCS 

activity in each PADD, based on natural gas production and interstate transfer data from the EIA (datasets 

and calculations are described at greater length in the following section).85

Table 13: Distribution of Incremental LNG Imports Between PADDs (Mmcf, 2012)

PADD/OCS REGION WITH

FORGONE PRODUCTION

REPLACED BY IMPORTS 

TO PADD 1 (ATLANTIC 

OCS REGION)

REPLACED BY IMPORTS TO 

PADD 3 (GULF OF MEXICO 

OCS REGION) TOTAL

PADD 1 (Atlantic OCS 

Region) 100.0% 0.0% 100%

PADD 3 (Gulf of Mexico 

OCS Region) 25.70% 74.30% 100%

PADD 5 (Pacific and Alaska 

OCS Regions) 0% 100% 100%

The allocation of incremental LNG imports can be further disaggregated by combining the information in 

Table 13 with the information on the distribution of imports by planning area from Table 12. More 

specifically, each value in Table 13 can be disaggregated to the individual planning areas within each 

 

84 No incremental imports are assumed to be distributed to PADD 5 because there are no LNG import terminals in 

PADD 5. No tanker imports are assumed to be transported to PADD 2 because it is landlocked.

85 U.S. EIA. International & Interstate Movements of Natural Gas by State. Released September 30, 2014. Accessed 

October 24, 2014 at: http://www.eia.gov/dnav/ng/ng_move_ist_a2dcu_nus_a.htm

U.S EIA. Natural Gas Dry Production (Annual Supply & Disposition). Released September 30, 2014. Accessed 

October 24, 2014 at: http://www.eia.gov/dnav/ng/ng_sum_snd_a_epg0_fpd_mmcf_a.htm
F-18

PADD in proportion to current import patterns. Table 14 summarizes the distribution of incremental 

LNG imports resulting from these calculations. For example, based on the data in Table 12, 61 percent of 

the imports to PADD 1 are shipped to the North Atlantic planning area. Applying this to the 25.7 percent 

of Gulf of Mexico OCS production replaced by imports to the Atlantic OCS Region, we estimate that 

approximately 16 percent (.257 × .61 =.157) of the imports that displace forgone OCS production in the 

Gulf of Mexico would be shipped to ports in the North Atlantic planning area. 

Table 14: Distribution of Incremental LNG Imports Between Planning Areas (Mmcf, 2012)

PADD 1 (ATLANTIC OCS REGION)

PADD 3 (GULF OF MEXICO OCS 

REGION)

TOTAL

PADD/OCS REGION 

WITH FORGONE 

PRODUCTION

NORTH 

ATLANTIC

MID 

ATLANTIC

SOUTH 

ATLANTIC

CENTRAL 

GULF OF 

MEXICO

WESTERN 

GULF OF 

MEXICO

PADD 1 (Atlantic OCS 

Region)

61% 24% 15% 0% 0% 100%

PADD 3 (Gulf of Mexico 

OCS Region)

16% 6% 4% 38% 37% 100%

PADD 5 (Pacific and 

Alaska OCS Regions)

0% 0% 0% 51% 49% 100%

The values in Table 14 are incorporated directly into the OECM and are combined with data on the E&D 

scenario and MarketSim results to estimate the allocation of natural gastanker imports across planning 

areas. For example, assume that MarketSim projects a 10 trillion cubic feet increase in tanker imports 

under the No Action alternative and that 20 percent of new OCS gas production under the E&D scenario 

occurs in the Atlantic, 70 percent is in the Gulf of Mexico, and 10 percent is in the Pacific and Alaska 

OCS regions. Under this scenario, the increase in natural gas tanker imports under the NAA is assumed 

to be distributed as follows: 2 trillion cubic feet for the Atlantic, 7 trillion cubic feet for the Gulf of 

Mexico, and 1 trillion cubic feet for the Pacific/Alaska. Using these data in conjunction with the values in 

Table 14, the estimated increase in natural gas tanker imports to the North Atlantic planning area is 2.34 

trillion cubic feet (tcf) (2 tcf × 0.61 + 7 tcf × 0.16 + 1 tcf × 0).

Geographic distribution of onshore natural gas production

In the absence of OCS program activity, onshore natural gas production may increase. The OECM 

spatially allocates such increases in production to those regions of the U.S. most likely to substitute for 

OCS production. The approach for determining this allocation follows the same basic methods used to 

determine the geographic distribution of incremental onshore oil production: (1) determine which regions 

of the country are most likely to be served by OCS production, and (2) determine which regions (PADDs)

provide domestic natural gas to the regions served by OCS production.
F-19

Regions served by OCS natural gas production

Data on the transport of natural gas within the United States is available only at the level of interstate 

transfers. To determine regional flows of natural gas, interstate data was summed to the PADD level. This 

was accomplished by summing all transfers between states on the borders between PADDs. Production of 

natural gas was also summed to the PADD-level. Table 15 presents PADD-level production and transfers

of natural gas.

Table 15: PADD Level Natural Gas Production and Transfers of Natural Gas 

(Mmcf, 2012)

PRODUCTION

TRANSFERS 

TO PADD 1

TRANSFERS 

TO PADD 2

TRANSFERS 

TO PADD 3

TRANSFERS 

TO PADD 4

TRANSFERS 

TO PADD 5

PADD 1 (East Coast) 2,965,220 - 284,260 - - -

PADD 2 (Midwest) 2,654,532 903,383 - 1,130,868 841,622 -

PADD 3 (Gulf Coast) 13,785,715 2,960,757 3,611,945 - - 1,256,916

PADD 4 (Rocky Mnts.) 4,087,396 - 2,790,462 462,284 - 1,838,894

PADD 5 (West Coast) 564,747 - - - 9,031 - 

Sources: 

1. U.S. EIA. International & Interstate Movements of Natural Gas by State. Released September 30, 2014. Accessed 

October 24, 2014 at: http://www.eia.gov/dnav/ng/ng_move_ist_a2dcu_nus_a.htm

2. U.S EIA. Natural Gas Dry Production (Annual Supply & Disposition). Released September 30, 2014. Accessed 

October 24, 2014 at: http://www.eia.gov/dnav/ng/ng_sum_snd_a_epg0_fpd_mmcf_a.htm

Interstate transfer data as presented by the EIA includes transfers of all natural gas, domestically sourced 

or imported. For this analysis we are only interested in transfers of domestically produced natural gas. 

The transfer numbers were therefore adjusted to subtract any movement of imported natural gas.

Based on the directional flows of major natural gas transportation corridors (see Figure 4), it is likely that 

natural gas imports into PADDs 1, 2, and 5 (the East Coast, Midwest, and West Coast, respectively) are 

primarily consumed in their PADD of entry. While it is less clear if PADD 3 is transporting any imported 

natural gas to other PADDs, this should have little impact on the broader analysis as PADD 3 accounts 

for less than one percent of all natural gas imports. In PADD 4 however, it is clear that natural gas 

imports are delivered to other PADDs. As illustrated in Figure 4, natural gas imports into Idaho only pass 

through the northern portion of the state on the way to Washington and the rest of the West Coast. 

Similarly, natural gas imports into Montana only pass through the northeast corner of the state on the way 

to North Dakota and the rest of the Midwest. As a result:

 All interstate transfers from Idaho (PADD 4) to Washington (PADD 5) are assumed to contain 

imported Canadian natural gas and are subtracted from total transfers from PADD 4 to PADD 5.

 All interstate transfers from Montana (PADD 4) to North Dakota (PADD 2) are assumed to 

contain imported Canadian natural gas and are subtracted from total transfers from PADD 4 to 

PADD 2.
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Figure 4: Major U.S. Natural Gas Transportation Corridors (2008)86

One final adjustment is made to the inter-PADD transfer data to account for the fact that some natural gas 

transfers from PADD 3 to PADD 2 are ultimately destined for PADD 1. This intermediary transfer step is

parsed out so that all natural gas that passes through PADD 2 on the way to PADD 1 is recorded as a 

delivery from PADD 3. While transfers from PADD 3 to PADD 2 that are destined for PADD 1 are not 

explicitly noted in EIA data, it is possible to parse out a reasonable estimate from the interstate transfer 

data. Greater than 99 percent of all natural gas deliveries to Tennessee (PADD 2) come from Alabama or 

Mississippi (PADD 3). The majority of this natural gas is then delivered to Kentucky (PADD 2), where it 

is dispersed to various states in PADD 1 and PADD 2. The portion of natural gas delivered from 

Kentucky to PADD 1 is therefore assumed to represent a transfer from PADD 3, not PADD 2. 

Additionally, Kentucky delivers natural gas to Ohio (PADD 2). Thirty-four percent of natural gas 

deliveries from Ohio are sent to West Virginia (PADD 1). This analysis will therefore assume that 34

percent of the natural gas delivered from Kentucky to Ohio is passed on to West Virginia. This portion is

considered a delivery from PADD 3 to PADD 1.

Based on the assumptions outlined above, Table 16 presents Inter-PADD transfers of domestic natural 

gas, after subtracting transfers of imported natural gas and adjusting for intermediary transfers.

 

86 The numbers on the map indicate 11 distinct corridors of natural gas transport identified by the EIA. Descriptions 

of these corridors can be accessed at: 

http://www.eia.gov/pub/oil_gas/natural_gas/analysis_publications/ngpipeline/transcorr.html
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Table 16: Inter-PADD Transfers of Domestic Natural Gas (Mmcf, 2012)

PADD OF ORIGIN

TRANSFERS 

TO PADD 1

TRANSFERS 

TO PADD 2

TRANSFERS 

TO PADD 3

TRANSFERS 

TO PADD 4

TRANSFERS 

TO PADD 5

PADD 1 (East Coast) - 284,260 - - -

PADD 2 (Midwest) 321,550 - 1,130,868 841,622 -

PADD 3 (Gulf Coast) 3,542,590 3,611,945 - - 1,256,916 

PADD 4 (Rocky Mountains) - 2,036,404 462,284 - 1,220,070

PADD 5 (West Coast) - - - 9,031 -

Source: U.S. EIA. International & Interstate Movements of Natural Gas by State. Released September 30, 2014. 

Accessed October 24, 2014 at: http://www.eia.gov/dnav/ng/ng_move_ist_a2dcu_nus_a.htm

Combining the data on inter-PADD transfers of domestic natural gas (Table 16) with PADD level 

production data (from Table 15) indicates the percentage of production produced in each PADD that 

serves that same PADD as well as the percentage that serves other PADDs (see Table 17). For instance, 

PADD 1 produces 2,965,220 MMcf of natural gas and transfers 284,260 MMcf of natural gas to PADD 2. 

Natural gas that is not transported out of PADD 1 is assumed to be consumed in PADD 1. Thus, 9.6

percent of PADD 1 production is assumed to serve PADD 2, while the other 90.4 percent is assumed to

serve PADD 1. Table 17 presents these data for the three coastal PADDs. The data in the table are 

assumed to apply to the OCS regions that correspond with each PADD.

Table 17: PADDs Served by OCS Production (Mmcf, 2012)

PADD/OCS REGION WITH

FORGONE PRODUCTION

SERVING 

PADD 1

SERVING 

PADD 2

SERVING 

PADD 3

SERVING 

PADD 4

SERVING 

PADD 5 TOTAL

PADD 1 (Atlantic OCS 

Region) 90.4% 9.6% 0.0% 0.0% 0.0% 100%

PADD 3 (Gulf of Mexico 

OCS Region) 25.7% 26.2% 39.0% 0.0% 9.1% 100%

PADD 5 (Pacific and Alaska 

OCS Regions) 0.0% 0.0% 0.0% 1.6% 98.4% 100%

Geographic distribution of domestic production for areas served by OCS gas

As with incremental onshore oil production, increased onshore natural gas production is distributed 

geographically in proportion to the percent of domestic natural gas each PADD supplies to the regions 

served by OCS production. Table 18 presents the sources of domestic natural gas for each PADD. This 

information is then combined with the data on PADDs served by OCS production to produce estimates of 

the geographic location of incremental onshore natural gas production (see Table 19).

For instance, Table 19 suggests that absent program activity in PADD 1, 54 percent of incremental 

onshore production will occur in PADD 3. This was calculated by multiplying the proportion of PADD 1 

production that serves each PADD by the proportion of natural gas supplied to each of those PADDs by 

PADD 3. Specifically:
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 90.4 percent of PADD 1 production serves PADD 1, and PADD 3 produces 54.1 percent of the 

natural gas supplied to PADD 1 (90.4% x 54.1% = 48.9%).

 9.6 percent of PADD 1 production serves PADD 2, and PADD 3 produces 57.4 percent of the 

natural gas supplied to PADD 2 (9.6% x 57.4% = 5.5%).

 Thus, PADD 3 is likely to supply 48.9 percent + 5.5 percent = 54.4 percent of incremental 

natural gas production resulting from the absence of program activity in PADD 1.

Table 18: Sources of Domestic Natural Gas for Each PADD

SUPPLY TO 

PADD 1

SUPPLY TO 

PADD 2

SUPPLY 

TO PADD 3

SUPPLY TO 

PADD 4

SUPPLY TO 

PADD 5

PADD 1 (East Coast) 41.0% 4.5% 0.0% 0.0% 0.0%

PADD 2 (Midwest) 4.9% 5.7% 16.2% 69.0% 0.0%

PADD 3 (Gulf Coast) 54.1% 57.4% 77.1% 0.0% 41.4%

PADD 4 (Rocky Mountains) 0.0% 32.4% 6.6% 30.2% 40.2%

PADD 5 (West Coast) 0.0% 0.0% 0.0% 0.7% 18.3%

TOTAL 100% 100% 100% 100% 100%

Table 19: Incremental Onshore Natural Gas Production From Forgone OCS Production (Mmcf, 2012)

PADD/OCS REGION 

WITH FORGONE 

PRODUCTION

REPLACED 

BY 

PRODUCTION 

IN PADD 1

REPLACED 

BY 

PRODUCTION 

IN PADD 2

REPLACED 

BY 

PRODUCTION 

IN PADD 3

REPLACED 

BY 

PRODUCTION 

IN PADD 4

REPLACED 

BY 

PRODUCTION 

IN PADD 5

PADD 1 (Atlantic OCS 

Region)

37.5% 5.0% 54.4% 3.1% 0.0%

PADD 3 (Gulf of 

Mexico OCS Region)

11.7% 9.1% 62.8% 14.7% 1.7%

PADD 5 (Pacific and 

Alaska OCS Regions)

0.0% 1.1% 40.8% 40.1% 18.0%

The values in Table 19 were used to inform the development of air pollutant-specific dollar-per-ton 

values for onshore natural gas production under the NAA. For example, the dollar-per-ton values used to 

monetize the damages associated with onshore gas production that substitutes for OCS production in the 

Gulf of Mexico Region is a weighted average of the dollar-per-ton values in PADDs 1 through 5, with 

weights of 0.117 for PADD 1, 0.091 for PADD 2, 0.628 for PADD 3, 0.147 for PADD 4, and 0.017 for 

PADD 5. 
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APPENDIX G: A COMPARISON OF ECOSYSTEM SERVICE 

VALUATION AND HABITAT EQUIVALENCY ANALYSIS 

FRAMEWORKS 

Introduction

The purpose of the Bureau of Ocean Energy Management’s (BOEM’s) Offshore Environmental Cost 

Model (OECM) is to provide information on the anticipated environmental and social costs attributable to 

given oil and gas exploration and development scenarios on the Outer Continental Shelf (OCS), net of 

impacts that would be realized absent a given scenario. The current OECM evaluates six cost categories, 

as follows:

1) Recreation: spill-related consumer surplus losses associated with offshore fishing and beach 

visitation;

2) Air quality: emissions-related human health and environmental damage;

3) Property values: infrastructure-related impacts of visual disamenities and spill-related losses in 

economic rent;

4) Subsistence harvest: replacement costs for spill-related loss of select species harvested for 

subsistence;

5) Commercial fishing: increased operational costs due to infrastructure-related accessibility 

limitations; and

6) Ecological: restoration costs for spill-related habitat and biota injuries.

The focus of this appendix is assessing the methods applied in the OECM to evaluate ecological damages 

from oil spill events (category 6 above). Specifically, we consider the strengths and limitations of the 

habitat equivalency framework applied, and contemplate whether an ecosystem services approach – an 

alternative valuation framework – may affect the utility of BOEM’s model.

HEA and ecosystem service valuation framework comparison

Ecosystems provide an array of goods and services of value to people. We refer to these goods and 

services collectively as “ecosystem services.” More specifically, the Science Advisory Board of the U.S. 

Environmental Protection Agency (USEPA) defines ecosystem services as, “...the direct or indirect 

contributions that ecosystems make to the well-being of human populations” (USEPA SAB, 2009). 

Accordingly, the distinction between ecosystem characteristics and ecological functions (e.g., nutrient 

cycling, carbon sequestration, and providing wildlife habitat), and ecosystem services (e.g., water 

purification, climate stabilization, and recreational opportunities) is grounded in the explicit connection 

between services and their value to people (USEPA SAB, 2009; NRC, 2005).87 Measuring values of 

ecosystem services is a focus of environmental and resource economics, and is a central element of 

benefit-cost analyses and regulatory analyses of natural resource management alternatives.

 

87 The National Research Council (NRC, 2005) distinguishes between ecosystem functions and services, as follows 

(pg. 1): “Ecosystem function describes a process that takes place in an ecosystem as a result of the interactions of 

the plants, animals, and other organisms in the ecosystem with each other or their environment... Ecosystem 

structure and function provide various ecosystem goods and services of value to humans such as fish for 

recreational or commercial use, clean water to swim in or drink, and various esthetic qualities...”
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How do Habitat and Resource Equivalency Analyses measure ecological damages?

A focus of Natural Resource Damage Assessment (NRDA) is determining appropriate compensation 

owed to the public for injuries to natural resources. For example, the Oil Pollution Act of 1990 (OPA) 

requires that the environment and the public be made whole for injuries to natural resources resulting 

from an oil discharge (15 CFR 990). Over the past two decades, Habitat Equivalency Analysis (HEA) 

and, more generically, Resource Equivalency Analyses (REA), have become the predominant frameworks 

for determining compensation for natural resource injuries, including those associated with oil spills.88

The basic concept of HEA/REA analyses is that the public can be compensated for injuries to natural 

resources through replacement projects that are designed to provide habitats or resources of the same 

type, quality, and value (NOAA, 2006). In the context of NRDA, the costs of the restoration projects 

constitute the damage claim, and therefore reflect the costs of making the public whole from the pollution 

event. The OECM currently employs a HEA/REA framework to monetize the ecological damages from 

oil spills in terms of the costs of restoration. 

How is this different from Ecosystem Service Valuation approaches measuring service losses?

While the notion of ecosystem services and their value to people is inherent in HEA/REA frameworks 

(i.e., through the concept of making the public whole from the ecological injury), the focus is on 

restoration costs and not on the values of the ecosystem service losses. From an economic perspective, the 

restoration cost is an accounting measure whereas the value of a good or service is a measure of its 

contribution to human well-being (and may therefore be greater or less than the cost). An ecosystem 

services approach to evaluating the costs and benefits of an oil spill focuses on quantifying the values 

associated with the ecosystem service losses due to ecological damages. The objective of an ecosystem 

services approach is to provide a holistic accounting of the gains and losses to society associated with 

changes in the quality or quantity of ecosystem services due to a pollution event or resource management 

decision. Simply stated, a HEA framework answers the question of the amount of restoration that would 

be sufficient to leave the public indifferent to the injury whereas an ecosystem services framework 

answers the question of how much the public valued what was lost (Zafonte and Hampton, 2007).

Will HEA and Ecosystem Service Valuation approaches to measuring losses result in similar 

estimates?

In effect, HEA represents a replacement cost approach to approximating the values lost due to the injury. 

Under certain circumstances, replacement costs are considered valid measures of economic value. 

Specifically, replacement costs, such as those measured through HEA, may be representative of the 

ecosystem service value losses if three conditions are met:

1) The replacement actions/projects provide services of equivalent quality and magnitude to those 

that were lost;

2) The replacement projects selected must be the least costly alternatives; and 

3) The public is willing to incur these replacement costs (Freeman et al., 2014).

Accordingly, HEA approaches and ecosystem service valuation approaches to valuing ecological 

damages would theoretically provide a similar monetized estimate where these three conditions are met. 

When the conditions are met, the HEA measure of restoration costs may be considered a reasonable 

 

88 Whereas HEA specifically references habitat-based restoration, REA more broadly applies to restoration of any 

given natural resource(s), such as fish or birds (Zafonte and Hampton, 2007).
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approximation of the value of ecosystem service losses. Regulatory requirements and best practices 

regarding the conduct of HEA for a given damage case mirror these three replacement cost conditions. 

First, HEA best practices specify processes for scaling the restoration actions so that they reflect a 

reasonable estimate of the value of the losses. For example, the National Oceanic and Atmospheric 

Administration’s (NOAA’s) Damage Assessment and Restoration Program specifies conditions for HEA 

focused on scaling the restoration actions to ensure that the project gains are equal to the value of the 

losses (incorporating appropriate discounting to account for the interim loss in services). These conditions 

are:

1) A common metric can be defined for natural resource services that captures the level of services 

provided by the habitats and captures any significant differences in the quantities and qualities of 

services provided by injury and replacement habitats; and

2) The changes in resources and services (due to the injury and the replacement project) are 

sufficiently small that the value per unit of service is independent of the change in service levels 

(NOAA, 2006).

These two HEA conditions are focused on ensuring that the quantity, quality, and value of services 

injured are equivalent to those replaced. This concept mirrors the first condition for replacement costs to 

serve as an adequate proxy for values, as described above. 

Regarding the second replacement cost approach condition (i.e., that restoration is the least cost 

alternative), OPA requires that natural resource trustees select the most cost-effective restoration 

alternative (15 CFR 990.54). This ensures that, of the restoration projects identified that restore equivalent 

services, the least cost option is selected.

Finally, HEA involves two key assumptions regarding the value of the lost ecosystem services such that 

the third replacement cost approach condition described above would also be met (i.e., the public is 

willing to pay for the restoration). These assumptions are: a) a fixed proportion of habitat services to 

habitat value (e.g., a 40 percent loss in provision of a given service equates to a 40 percent loss in the 

value of the service); and b) equal unit value of the services lost due to injury and the services gained 

through the restoration activities (Dunford et al., 2004).89 Where these assumptions hold true for a given 

site, the value the public holds for the restoration services would be reflective of the value lost due to 

damage. By definition, value is a measure of an individual’s or population’s willingness-to-pay for a 

service (as described further in Section III). Extending this concept, the values of services lost due to 

injury measure a loss in services in terms of what the public was willing to pay for them. If the HEA 

assumption holds that the value of what is being restored is equivalent to the value of what was lost (on a 

per unit basis), the public would likewise be willing to pay for the restored services. In other words, if the 

public was willing to pay for the service before the injury, they would be willing to pay for the equivalent 

quantity and quality of the same service as restored following the injury. These HEA assumptions replace 

the need to explicitly calculate the value the public holds for the given service because the service is being 

replaced in-kind through the restoration activities.

The HEA/REA framework applied in the OECM is a generalized version of this approach. The spatial 

scale of the model and the associated uncertainty regarding specific locations of potential spills under any 

 

89 A number of studies define and explore HEA methods and assumptions in more detail in the context of their use 

in estimating damage claims. These studies highlight specific circumstances under which HEA/REA assumptions 

are most likely to hold. For example, additional information is included in: Unsworth and Bishop, 1995; Dunford et 

al., 2006; Zafonte and Hampton, 2007 and ; Shaw and Wlodarz, 2013.
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given OCS development scenario make it infeasible to verify the analysis meets the conditions and 

assumptions regarding equivalency between what was lost and what is being replaced. Of note, these

uncertainties are not specific to the HEA approach; the need to generalize the nature of injuries in the 

OECM would likewise require a simplified ecosystem service valuation approach. As emphasized in 

Freeman et al. (2014), when the three conditions for a valid replacement cost approach are not met, “there 

is no presumption that replacement cost is either an overestimate or an underestimate of true economic 

value—all that can be said is that the two numbers are measures of different things.” 

Analytic methods comparison

HEA/REA analyses all generally follow a similar analytic method. On the other hand ecosystem service 

valuation methods are various. In the context of welfare economics, value is most frequently measured in 

terms of people’s “willingness-to-pay” (WTP) for a good or service, where WTP is the maximum amount 

(typically in monetary terms) that an individual would be willing to pay rather than do without a 

particular benefit (Freeman et al., 2014). The White House Office of Management and Budget (OMB) 

recognizes WTP as the appropriate measure for valuing costs and benefits in the context of regulatory 

analysis (OMB, 2003). The analytic methods characterized below accordingly describe the various means 

by which economists may estimate WTP.

What ecosystem services are measured in Ecosystem Service Approaches?

The foundation of ecosystem service frameworks is to attempt to capture the full range of contributions 

ecosystems make to people’s well-being. This includes ecosystem services that are consumed (production 

of food or fiber), used but not consumed (e.g., wildlife viewing), as well as non-use values (e.g.. existence 

values for threatened and endangered species). For example, the U.S. Environmental Protection Agency 

(USEPA) convened a subcommittee of its Science Advisory Board (SAB), the Committee on Valuing the 

Protection of Ecological Systems and Services (CVPESS), charged with assessing the Agency’s needs, 

identifying the state-of-the-art and science, and identifying key areas for research with respect to 

ecosystem service valuation. The CVPESS report provided the following three key recommendations, as 

well as specific advice on how to implement them:

1. Identify early in the valuation process the ecological responses that are likely to be of 

greatest importance to people and focus the valuation effort on these responses.

2. Predict ecological responses in terms that are relevant to valuation by focusing on the 

effects of decisions on ecosystem services that are of direct concern to people.

3. Consider the use of a wide range of possible valuation methods to better capture the full 

range of contributions stemming from ecosystem protection (USEPA SAB, 2009). 

The suite of ecosystem services relevant to a resource management question or ecological injury event 

depends on the nature of the landscape in providing the services, as well as the population benefitting 

from them. The services that may be affected by oil spills in marine and coastal areas (including for 

example, beaches, wetlands, and forests) may include:

 Recreational opportunities (e.g., fishing, hunting, wildlife viewing, beach visitation, swimming, 

boating, hiking, etc.)

 Commercial fishing and aquaculture

 Flood protection (e.g., due to wetland water storage capacity)
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 Coastal storm surge protection (e.g., due to wave attenuation)

 Climate stabilization (e.g., through carbon storage and sequestration)

 Aesthetic benefits (e.g., viewscapes)

 Biodiversity and habitat provision (people may hold “existence values” for wildlife species even 

absent any direct or indirect use of them)

 Cultural or historical values (these may be use or non-use values)

How does the HEA/REA analysis in the OECM currently account for these types of services?

As discussed previously, the HEA/REA methods employed in the OECM to evaluate ecological losses 

avoid the need to measure lost social welfare benefits. The model estimates habitat impacts (in terms of 

the extent of intertidal oiling) and wildlife impacts (in terms of the biomass loss) and estimates what it 

would cost to replace those resource losses, assuming the services associated with those losses would also 

be replaced. As a result, the ecological injury module (the HEA/REA analysis) of the OECM does not 

provide insight on the particular ecosystem services affected.

Of note, however, three other modules of the OECM do attempt to estimate ecosystem service value 

losses associated with spill events, as mentioned in the introduction to this appendix. Specifically,

 Recreation: spill-related consumer surplus losses associated with offshore fishing and beach 

visitation;

 Property values: spill-related losses in economic rent; and

 Subsistence harvest: replacement costs for spill-related loss of marine life of cultural and 

subsistence value.

These functions of the OECM, while not a comprehensive accounting of the ecosystem services affected, 

provide information on the types and magnitude of ecosystem service losses using valid measures of 

value. The benefit of including these separate ecosystem service value analyses in the model is the 

additional information provided on why and to what extent the public may be worse off under a given 

development scenario. On the other hand, including these value losses in addition to estimates of 

ecological injury expressed in terms of restoration costs generates the potential for some double-counting 

across cost categories. That is, to the extent that the OECM’s HEA/REA measures of restoration costs are 

a reflection of the value losses associated with spill-related ecological injury (which is uncertain), the 

restoration cost estimates would reflect, at least in part, recreational, property value, and subsistence 

harvest losses. 

What are the different analytic methods for HEA versus Ecosystem Service Approaches?

HEA/REA methods and ecosystem service valuation methods both begin with quantifying a change in 

ecological function, though this may be measured differently. Exhibit 1 provides a comparison of the 

general methodological approaches, highlighting how they diverge after this first step.

While the HEA method is more linear from the outset, requiring only a single metric of ecological 

change, this simplification can introduce uncertainty into the model results. The ecological metric used to 

determine the amount of interim losses in HEA can impact the subsequent damages amount. According to 

Shaw and Wlodarzz (2013), "Some metrics lead to suggestions of full recovery within a relatively short 

amount of time, while others do not. The fact that different metrics give rise to different restoration
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conclusions underscores the difficulty in measuring the impact of environmental change on human wellbeing in general." 

The significant data requirements for an ecosystem services valuation approach is evident at each stage. 

In particular, estimating non-use values via stated preference surveys, such as the contingent valuation 

method (as described below), requires significant time and resources, and has been subject to scrutiny 

regarding the validity of results due to their hypothetical nature (i.e., survey respondents express values 

but are not required to actually pay) (Roach and Wade, 2006). While best practices have improved the 

implementation of these methods over time through integration of validity and scope tests (Shaw and 

Wlodarz, 2013), these methods remains resource-intensive processes.

As noted previously, a variety of methods with significant precedent in the economics literature are 

available to value ecosystem service losses. While some ecosystem services are amenable to valuation via 

market prices (e.g., contributions to commercial fisheries), others require use of non-market methods. 

Non-market valuation methods are divided into two types: revealed preference and stated-preference:

 Revealed preference methods infer values for natural resources and associated services from 

people’s behavior. For example, the value of a day of beach recreation can be estimated using 

information on the costs one incurs to travel to that beach (travel cost methods) and the value of 

particular beach attributes can be estimated by examining how people make beach visit choices 

across a number of available sites (random utility maximization models). Similarly, the value of 

an environmental amenity (e.g., a viewscape) may be revealed through land and housing price 

premiums (hedonic property value methods). The advantage of revealed preference methods is 

that they are grounded in actual consumer choices. However, their applicability is limited to those 

ecosystem services for which a link to observable behavioral changes exists. 

Table 1. Comparison of General HEA and Ecosystem Service Valuation Methods.

HEA/REAa ECOSYSTEM SERVICE VALUATION

1) Quantify the changes in ecological functions or 

processes associated with the injury.

HEA/REA methods typically rely on a single metric of 

change (e.g., in the OECM this is the extent of 

intertidal zone oiling (HEA) and numbers of wildlife 

organisms killed translated into biomass loss (REA)).

1) Quantify the changes in ecological functions or processes 

associated with the injury.

For ecosystem service valuation, as much information on the 

ecological changes as possible is required to ensure as full an 

accounting as achievable of the related ecosystem services. 

For example, an oiling event could reduce fish and bird 

populations, decrease carbon sequestration potential, and 

reduce flood protection. Measures of all of these changes 

would be needed.

2) Identify appropriate restoration project and 

evaluate it in terms of the degree and duration of 

ecological benefits that it is likely to provide. 

Given the need to generalize for the purposes of the 

OECM, this HEA/REA step is not included.

2) Identify the full suite of ecosystem services associated with 

the injured resources (inclusive of use and non-use values).

A comprehensive list requires understanding of the 

accessibility of the site (for on-site use values), whether the 

public likely holds non-use, or existence, values for damaged 

resources, etc.

3) Scale the project in size so that the total value of 

ecological service benefits offsets the value of 

ecological service losses resulting from the injury, 

including discounting over time. 

The OECM estimates the amount of salt marsh 

restoration required based on habitat and resource 

scaling factors taking into account the timeframe of 

injury and recovery, and discounting over time.

3) Quantify the ecosystem service losses for each type of 

service.

Beyond quantifying the injury, the analyst needs to relate the 

injury change to a change in the production of an ecosystem 

service at the site. For example, reductions in fish populations 

may reduce the quality or quantity of recreational fishing 

(reduced fishing trips). Similarly, reductions in water retention 

may increase flood risk; this increased risk would need to be 

quantified.
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HEA/REAa ECOSYSTEM SERVICE VALUATION

4) Quantify costs of implementing the restoration 

project. 

The OECM applies average, area-specific, per-acre 

coastal marsh restoration costs.

4) Monetize the quantified ecosystem service losses and 

discount over time.

There are many valuation methods that may be used to 

quantify ecosystem services. Generally an injury that affects 

multiple ecosystem services will require studies (or primary 

research) that rely on multiple different methods. In some 

cases, a single stated preference study may estimate the total 

value of all ecosystem service losses. The information in steps 

1 through 3 are still required for such a survey, however, to 

ensure the public has sufficient information on service losses 

to express a value.

a HEA/REA method adapted from Zafonte and Hampton, 2007 to be descriptive of the steps followed in the current OECM 

model.

 Stated-preference methods involve the creation of hypothetical markets that allow individuals 

to explicitly state their value for a resource. This is accomplished through carefully designed 

surveys (known as contingent valuation and attribute-based or choice modeling methods). 

Numerous stated preference studies related to ecosystem services have been conducted. The 

advantage of stated preference methods is that they can be applied to any actual or potential 

change in all categories of ecosystem services. Absent a related market or change in behavior, 

stated preference methods are the only way to measure non-use values for ecosystem services, 

such as existence values for wildlife species. However, defensible stated preference studies can 

be costly and time-consuming to conduct. 

Circumstances often do not justify the time and resources required to implement a primary study using 

one of the above methods. In this case, existing valuation information can be adapted to new applications 

or policy questions, a process referred to as “benefit transfer.” Owing to schedule and budget constraints, 

the majority of policy and resource management decisions are informed by benefit-transfer analyses, and 

best-practice guidelines exist for the conduct and evaluation of such studies (USEPA, 2000; OMB, 2003). 

Benefit transfer analyses are convenient in this manner, and there exists a broad literature to draw upon. 

The OECM currently relies on benefit transfer, for example, in valuing recreational fishing and beach 

visitation losses.

When conducted properly, benefit transfer is an appropriate and less-resource intensive method to value 

ecosystem services. OMB has written guidelines for conducting credible benefit transfers. The important 

steps in the OMB guidance are: (1) specify the value to be estimated; and (2) identify appropriate studies 

to conduct benefits transfer based on the following criteria (OMB, 2003):

 The selected studies should be based on adequate data as well as sound and defensible empirical 

methods and techniques.

 The selected studies should document parameter estimates of the valuation function.

 The study and policy contexts should have similar populations (e.g., demographic 

characteristics). The market size (e.g., target population) between the study site and the policy site 

should be similar.

 The good, and the magnitude of change in that good, should be similar in the study and policy 

contexts.

 The relevant characteristics of the study and policy contexts should be similar.
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 The distribution of property rights should be similar so that the analysis uses the same welfare 

measure (i.e., if the property rights in the study context support the use of willingness-to-accept 

measures while the rights in the rulemaking context support the use of willingness-to-pay 

measures, benefits transfer is not appropriate).

 The availability of substitutes across study and policy contexts should be similar.

A number of comments submitted on BOEM’s Net Benefits Analysis of the Proposed Outer Continental 

Shelf Oil & Gas Leasing Program, highlighted existing studies valuing ecosystem services, and 

suggesting BOEM could use them in evaluating the effects of oil and gas exploration and development 

scenarios on ecosystem services. Specifically, two comments cited a study of Gulf Coast wetlands that 

estimated values of wetland ecosystem services of between $2,760 and $12,630 per acre per year (Batker 

et al., 2010). This study is a benefit transfer study that relies on literature regarding the value of particular 

wetland ecosystem services to develop a low and high end estimate of value per service per acre per year. 

These estimates are then summed across services and acres, and discounted over time. This is sometimes 

characterized as a “rapid ecosystem service valuation.” The comments on the Net Benefits analysis 

suggested that BOEM could apply similar methods to evaluate ecosystem service losses (to wetlands and 

other affected ecosystems) due to oil spill events.

A rapid assessment of ecosystem services, such as that described in the comments, is unlikely to meet the 

criteria specified by OMB. Multiple responses to similar studies have highlighted the theoretical and 

practical problems associated with estimating and extrapolating per acre estimates of values taken from 

other studies of ecosystem services (e.g., Bockstael et al., 2000). Of particular relevance to the OECM, 

rapid assessment ecosystem service values do not provide information on the effects of changes in the 

condition or quality of an ecosystem on the associated service values. These studies assign an equal per 

unit (acre) value to a given land or habitat type (e.g., coastal wetland) and therefore do not provide any 

information to support an analysis of the ecosystem service losses of changes in quality of a service, as 

opposed to quantity of a land cover type (e.g., wetlands).

The USEPA, in its September 2000 guidance document on cost-benefit analysis (USEPA, 2000), also 

broadly rejects this approach, stating: 

“In estimating ecological benefits, one is generally forced to value individual ecological service flows 

separately and then sum these estimates rather than constructing prices for changes in the structure and 

function of entire ecosystems. Alternative approaches that estimate the total value of ecosystems based on 

the replacement cost of the entire ecosystem or its embodied energy (e.g., Costanza et al., 1997; Ehrlich 

and Ehrlich, 1997; Pearce, 1998; Pimentel et al., 1997) have received considerable attention as of late. 

However, the results of these studies should not be incorporated into benefit assessments. The methods 

adopted in these studies are not well grounded in economic theory nor are they typically applicable to 

policy analysis. Pearce (1998) contains a critical review of the total value approach, as does Bockstael et 

al. (2000).”90

Implications for the OECM

Overall, while ecosystem service approaches would provide more information on what the public values 

with respect to an ecosystem, information limitations in the context of the OECM would preclude a 

comprehensive accounting of ecosystem services. 

 

90 USEPA. 2000. Guidelines for Preparing Economic Analyses. 
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Would ecosystem service approaches provide better information?

The OECM’s current approach is to value the oil spill-related ecosystem services for which data are 

available and reasonably generalizable to do so, including for recreational fishing and beach visitation, 

property value losses, and subsistence values. These services, however, reflect only a subset of the 

ecosystem services potentially affected, and exclude non-use values entirely. Non-use, such as existence 

values, can represent a significant portion of the lost ecosystem service values. 

In their book, Economic Analysis for Ecosystem-Based Management, Holland et al. (2010) explain that 

values associated with preserving the north Atlantic whale mostly comprise non-use values, and if the 

species’ value to society were only measured using recreation-demand models (i.e. the value of whale 

watching), a significant portion of the total economic value would be ignored. Along the same lines, in a 

literature survey on passive-use values of public forestlands, Vincent et al. (1995) find that most studies 

on the economic value of forest preservation focus on the recreational value of the forest. They argue that 

this methodology understates the benefits associated with the preservation of wilderness areas because it 

does not account for the relatively more dominant, passive-use value. For example, from one study on the 

total valuation of wildlife and fishery resources in the Northern Rockies, Vincent et al. find that in four 

out of the five cases, existence value was at least 62 percent and as high as 83 percent of the total value. 

As a result, it is appropriate to include a separate measure of the ecological injuries that attempts to 

capture these types of values.

Ecosystem service approaches would more explicitly account for service-by-service values than HEAbased approaches, thereby providing additional information on what is being lost due to a spill event in 

the OECM and avoiding double counting.91 The OECM mirrors an ecosystem services approach in 

valuing a subset of services for which information is available and reasonably generalizable. For services 

not currently in the OECM, including non-use values, the existing stated preference literature is limited 

and specific, and unlikely to meet the conditions of a benefit transfer for use in the OECM. At the very 

least, traditional methods to value non-use ecosystem services would be subject to similar levels of 

uncertainty as the HEA/REA approach applied to measure ecological injury costs.

What are the key limitations of integrating ecosystem service valuation methods into the OECM?

The data requirements to integrate a holistic accounting of ecosystem services would likely be 

prohibitive. Whereas the ecological injury and cost estimates in HEA are boiled down to a single metric 

for each (one injury metric and one restoration cost), ecosystem service approaches would require 

additional specificity, linking the injury to the production of ecosystem services and then again to their 

associated economic value. A key benefit of HEA is the relative ease of estimating restoration costs as 

compared to conducting a series of linked ecological and economic assessments of lost goods and 

services.

Furthermore, the HEA/REA restoration costs are more readily transferable across geographies and 

scalable at various geographic scopes. There is not a great deal of variation in restoration costs for a given 

habitat type across geographies. On the other hand, ecosystem service values may vary greatly by site 

regardless of the scope of the injury. Use values depend on accessibility to a site and the size of the 

 

91 In some instances, stated preference studies may be focused on eliciting information on the public’s WTP to avoid 

a particular type of ecological injury, thus providing information on the “total value” of the ecosystem. Depending 

on how the survey is designed, these studies may provide information on values of particular ecosystem services 

(e.g., recreation versus non-use values for a particular resource). Under an ecosystem services framework, more 

information on values for particular services, and therefore on tradeoffs across services under alternative resource 

management scenarios, is preferable. 
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population benefitting from a given service. Non-use values may not be as readily scalable, as it is often 

unclear the extent to which marginal changes in population size for a given species affect the existence 

values held by the public.

Summary conclusion

HEA and ecosystem service valuation methods are both subject to uncertainty. As noted above, at any 

given site, it is unclear whether the HEA approach overestimates or underestimates the true value of the 

ecosystem service losses. The HEA approach requires simplifying assumptions – in particular that people 

derive utility from natural resources in proportion to the ecological services they provide (Roach and 

Wade, 2006). In addition, the restoration costs measured do not provide information on what aspects of 

the natural resource are valued by people. 

Despite these limitations, the data requirements for developing ecosystem service-specific valuation 

models in the OECM may make it impractical. In addition, simplified approaches to valuing ecosystem 

services that would be required to accommodate a national scale model like the OECM would introduce 

their own uncertainties, requiring additional assumptions not only about the ecological changes, but also 

the number of people affected and their WTP for the foregone benefit. 

Overall, ecosystem service valuation methods would be more difficult to implement and would not 

necessarily provide better information to BOEM. These methods would likely result in different monetary 

estimates, and the relative differences across regions and development scenarios are uncertain. 
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