OCS Study

MMS - 2001 - 018

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

April 2001

MMS U.S. Department of the Interior

Minerals Management Service

Environmental Division
OCS Study

MMS - 2001 - 018

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

Authors

Brian Roach

William W. Wade

Jason R. Plater

Marvin Feldman

Technical Appendix

Debbie French

Technical Advisors

Theodore Tomasi

David Hughes

Foster Associates, Inc.

810 Walker St.

Columbia, TN 38401

for

Minerals Management Service

Environmental Studies Branch

Prepared under MMS Contract

1435-01-99-CT-30996

Published by

U.S. Department of the Interior

Minerals Management Service

Herndon, Virginia April 2001

Volume 2: Determinants of Environmental and Social Costs iii

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DISCLAIMER

This report was prepared under contract between the Minerals Management Service (MMS) and

Foster Associates, Inc. This report has been technically reviewed by the MMS and approved for

publication. Approval does not signify that the contents necessarily reflect the views and

policies of the MMS, nor does mention of trade names or commercial products constitute

endorsement or recommendation for use. It is, however, exempt from review and compliance

with MMS editorial standards.

REPORT AVAILABILITY

Extra copies of the report may be obtained from:

Environmental Studies Branch

U.S. Department of the Interior

Minerals Management Service

381 Elden Street (MS 4041)

Herndon, VA 20170-4817

Telephone: 703-787-1717

CITATION

Suggested citation:

Roach, Brian, William W. Wade and Jason 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, MMS OCS Study

2001-018. U.S. Dept. of the Interior, Minerals Management Service, Environmental Studies

Branch, Herndon, VA. 254 pp.

Volume 2: Determinants of Environmental and Social Costs v

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ABSTRACT

The Minerals Management Service (MMS) is required by Congress to balance benefits

associated with offshore oil and gas development with corresponding environmental and social

costs. In order to estimate these costs for the 2002 - 2007 5-Year Leasing Program, MMS

commissioned an update to its existing General Purpose Environmental Cost Model (GPECM)

developed in 1991. The revised model, called the Offshore Environmental Cost Model (OECM),

makes significant improvements in usability and flexibility over the 1991 GPECM model. Core

model parameters such as production, installation and removal of offshore facilities, and per-unit

environmental and social costs may all be specified by the user. The model, which runs on any

PC running Microsoft Excel 97 or later, is capable of forecasting environmental and social costs

associated with OCS exploration and development for a period of 55 years. Environmental

benefits associated with decreases in imported oil due to OCS production are also estimated.

The net social and environmental costs from the OECM are combined with estimates of private

benefits calculated in MMS' Net Economic Value (NEV) model and estimates of consumer

surplus benefits from the Market Simulation Model to arrive at net benefits from OCS

production.

The model’s documentation is divided into two volumes. The first describes the structure and

use of the spreadsheet model. The second volume documents the research on environmental and

social costs that led to the cost coefficients used in the model including a description of all

assumptions, data sources, methodology, and economic damage drivers.

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 ACKNOWLEDGEMENTS

Foster Associates received substantial support for this project. Many people provided data and

factual information. The following people and organizations were especially helpful in supplying

information and resources, and in some cases, reviewing subsections. Without their contributions,

this report could not have been written. Errors that remain are the authors'.

Cheryl Anderson, Minerals Management Service

Dr. Rodney Cluck, MMS, Environmental Studies Branch

James Corbett, University of Deleware

Deborah French, Applied Science Associates, Inc.

Steve Hampton, California Department of Fish and Game

Douglas Helton, National Oceanic and Atmospheric Administration

Brian Julius, National Oceanic and Atmospheric Administration

Vernon Leeworthy, National Oceanic and Atmospheric Administration

Dr. Larry Leistritz, U. of North Dakota Dept of Agricultural Economics

Tony Penn, National Oceanic and Atmospheric Administration

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TABLE OF CONTENTS

1. Introduction.............................................................................................................................. 1

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

1.2 Definition of Social Costs and Benefits.........................................................................................1

1.3 Scope of Analysis............................................................................................................................2

1.4 Comparison with the General Purpose Environmental Cost Model.........................................4

1.5 Cost Drivers ....................................................................................................................................4

1.6 Cost Elements .................................................................................................................................5

1.6.1 Spill-Related Damage Methodology (Section 2)..................................................................................... 5

1.6.2 Oil Spill Modeling (Section 3) ................................................................................................................ 5

1.6.3 Beach Recreation (Section 4) .................................................................................................................. 6

1.6.4 Recreational Fishing (Section 5) ............................................................................................................. 6

1.6.5 Ecological Service Losses (Section 6)..................................................................................................... 6

1.6.6 Commercial Fishing (Section 7).............................................................................................................. 7

1.6.7 Subsistence Resources (Section 8) .......................................................................................................... 7

1.6.8 Air Quality Externalities (Section 9) ....................................................................................................... 7

1.6.9 Onshore Government Revenue Collections and Infrastructure Costs (Section 10) ................................. 7

1.6.10 Private Property Impacts (Section 11)..................................................................................................... 8

1.6.11 Water Quality Externalities (Section 12)................................................................................................. 8

1.7 Summary of Results .......................................................................................................................8

1.8 Validation of Results ......................................................................................................................8

References..............................................................................................................................................10

2. Spill-Related Damage Estimation Methodology ................................................................... 21

2.1 Definition of Damages under Natural Resource Damage Assessment ....................................21

2.2 Valuation-Based Compensation..................................................................................................22

2.2.1 Use Values............................................................................................................................................. 22

2.2.2 Nonuse Values....................................................................................................................................... 23

2.3 Service-Based Measures of Compensation.................................................................................24

2.4 Synthesis of Valuation-Based and Service-Based Compensation ............................................25

2.5 Identification of Coastal Recreation Sites..................................................................................26

2.6 Primary Restoration Costs ..........................................................................................................26

2.7 Damage Assessment and Administrative Costs.........................................................................27

References..............................................................................................................................................28

3. Oil Spill Modeling .................................................................................................................. 33

3.1 Introduction..................................................................................................................................33

3.2 NRDAM/CME User Inputs.........................................................................................................34

3.2.1 Spill Locations....................................................................................................................................... 35

3.2.2 Wind Data.............................................................................................................................................. 35

3.2.3 Spill Size................................................................................................................................................ 35
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3.2.4 Oil Types ............................................................................................................................................... 37

3.3 NRDAM/CME Scenario Results.................................................................................................37

3.4 Model Output Adjustments.........................................................................................................38

3.4.1 Rationale for Adjustment ............................................................................................................38

3.4.2 Supplemental SIMAP Analysis...................................................................................................38

3.4.2.1 Introduction to Supplemental SIMAP Analysis.......................................................................... 38

3.4.2.2 SIMAP Scenarios........................................................................................................................ 39

3.4.2.3 Scenario Outputs......................................................................................................................... 39

3.4.3 Case Study Scenarios............................................................................................................................. 40

3.4.4 Closure Periods...................................................................................................................................... 41

3.4.5 Adjustment Summary............................................................................................................................ 42

3.5 Economic Damage Quantification ..............................................................................................43

3.5.1 Introduction ........................................................................................................................................... 43

3.5.2 Beach Recreation Economic Damages.................................................................................................. 43

3.5.3 Recreational Fishing Economic Damages ............................................................................................. 43

3.5.4 Ecological Economic Damages ............................................................................................................. 45

3.5.5 Commercial Fishing Economic Damages.............................................................................................. 46

3.5.6 Subsistence Harvest Economic Damages .............................................................................................. 47

3.5.7 Damage Assessment and Administrative Costs..................................................................................... 47

3.5.8 Property Value Economic Damages...................................................................................................... 48

3.5.9 Economic Damages Per Barrel Produced and Spilled........................................................................... 48

References..............................................................................................................................................49

4. Beach Recreation ................................................................................................................... 67

4.1 Introduction..................................................................................................................................67

4.2 Previous Methodologies to Estimate Beach Recreation Consumer Surplus Losses as a Result

of an Oil Spill ................................................................................................................................67

4.2.1 Summary of GPECM Methodology ...................................................................................................... 67

4.2.2 GPECM Methodology Assessment ....................................................................................................... 68

4.2.3 Summary of NRDA/CME Methodology............................................................................................... 69

4.2.4 NRDA/CME Methodology Assessment................................................................................................ 69

4.3 Summary of Current Methodology ............................................................................................70

4.4 Identification of Beach Sites........................................................................................................71

4.5 Extent and Duration of Beach Closure ......................................................................................71

4.6 Baseline Beach Recreational Visitation......................................................................................72

4.6.1 Baseline Beach Recreational Visitation – Alabama .............................................................................. 72

4.6.2 Baseline Beach Recreational Visitation – California............................................................................. 72

4.6.3 Baseline Beach Recreational Visitation – Connecticut.......................................................................... 72

4.6.4 Baseline Beach Recreational Visitation – Delaware.............................................................................. 73

4.6.5 Baseline Beach Recreational Visitation – Florida ................................................................................. 73

4.6.6 Baseline Beach Recreational Visitation – Georgia................................................................................ 73

4.6.7 Baseline Beach Recreational Visitation – Louisiana ............................................................................. 73

4.6.8 Baseline Beach Recreational Visitation – Maine................................................................................... 73

4.6.9 Baseline Beach Recreational Visitation – Maryland ............................................................................. 74

4.6.10 Baseline Beach Recreational Visitation – Massachusetts...................................................................... 74

4.6.11 Baseline Beach Recreational Visitation – Mississippi........................................................................... 74
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4.6.12 Baseline Beach Recreational Visitation – New Hampshire................................................................... 74

4.6.13 Baseline Beach Recreational Visitation – New Jersey .......................................................................... 75

4.6.14 Baseline Beach Recreational Visitation – New York ............................................................................ 75

4.6.15 Baseline Beach Recreational Visitation – North Carolina..................................................................... 75

4.6.16 Baseline Beach Recreational Visitation – Oregon................................................................................. 75

4.6.17 Baseline Beach Recreational Visitation – Rhode Island........................................................................ 75

4.6.18 Baseline Beach Recreational Visitation – South Carolina..................................................................... 75

4.6.19 Baseline Beach Recreational Visitation – Texas ................................................................................... 76

4.6.20 Baseline Beach Recreational Visitation – Virginia ............................................................................... 76

4.6.21 Baseline Beach Recreational Visitation – Washington.......................................................................... 76

4.6.22 Comparison to Other Beach Visitation Estimates ................................................................................. 76

4.6.23 Beach Visitation by OCS Planning Area............................................................................................... 77

4.7 Beach Visitation Conversions......................................................................................................77

4.8 Literature Review for Consumer Surplus Estimates................................................................78

4.8.1 Bell (1986)............................................................................................................................................. 78

4.8.2 Bell and Leeworthy (1990).................................................................................................................... 78

4.8.3 Dunford (1999)...................................................................................................................................... 78

4.8.4 Dunford and Fowler (1996)................................................................................................................... 78

4.8.5 Dunford, et al. (1999) ............................................................................................................................ 79

4.8.6 Edwards and Gable (1991) .................................................................................................................... 79

4.8.7 Environmental Economics Research Group (1998) .............................................................................. 79

4.8.8 Leeworth and Bowker (1997)................................................................................................................ 79

4.8.9 Leeworthy, et al (1991) ......................................................................................................................... 79

4.8.10 Leeworthy, et al. (1990) ........................................................................................................................ 80

4.8.11 Leeworthy, et al. (1989a)....................................................................................................................... 80

4.8.12 Leeworthy, et al. (1989b) ...................................................................................................................... 80

4.8.13 Leeworthy and Meade (1989)................................................................................................................ 81

4.8.14 Leeworthy and Schruefer (1990)........................................................................................................... 81

4.8.15 Leeworthy and Wiley (1991)................................................................................................................. 81

4.8.16 Leeworthy and Wiley (1993)................................................................................................................. 81

4.8.17 Leeworthy and Wiley (1994)................................................................................................................. 82

4.8.18 Silberman and Klock (1988).................................................................................................................. 82

4.8.19 Stronge and Schultz (1997) ................................................................................................................... 82

4.9 Representative Beach Recreation Values...................................................................................82

4.10 Impacts of Oil Spill Scenarios .....................................................................................................83

References..............................................................................................................................................84

5. Recreational Fishing.............................................................................................................. 94

5.1 Introduction..................................................................................................................................94

5.2 Previous Methodologies Estimating Recreational Saltwater Angling Consumer Surplus

Losses as a Result of an Oil Spill.................................................................................................94

5.2.1 Summary of GPECM Methodology ...................................................................................................... 94

5.2.2 GPECM Methodology Assessment ....................................................................................................... 95

5.2.3 Summary of NRDA/CME Methodology............................................................................................... 96

5.2.4 NRDA/CME Methodology Assessment................................................................................................ 97

5.3 Summary of Current Methodology ............................................................................................97

5.4 Recreational Fishing Visitation...................................................................................................98
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5.5 NRDAM/CME Outputs and Linkages .....................................................................................100

5.6 Literature Review for Consumer Surplus Estimates..............................................................101

5.6.1 Agnello and Han (1993) ...................................................................................................................... 101

5.6.2. Bockstael, et al. (1989a) ...................................................................................................................... 101

5.6.3 Bockstael, et al. (1989b)...................................................................................................................... 101

5.6.4 Cameron (1992)................................................................................................................................... 102

5.6.5 Cameron (1988)................................................................................................................................... 102

5.6.6 Cameron and James (1987) ................................................................................................................. 102

5.6.7 Downing and Ozuna (1996) ................................................................................................................ 102

5.6.8 Greene, et al. (1997) ............................................................................................................................ 102

5.6.9 Hausman, et al. (1995)......................................................................................................................... 103

5.6.10 Hicks, et al. (1999) .............................................................................................................................. 103

5.6.11 Huppert (1989) .................................................................................................................................... 103

5.6.12 Jones and Stokes (1987) ...................................................................................................................... 103

5.6.13 Kaoru (1995) ....................................................................................................................................... 103

5.6.14 Kaoru, et al. (1995).............................................................................................................................. 104

5.6.15 Leeworthy and Bowker (1997)............................................................................................................ 104

5.6.16 McConnell and Strand (1994) ............................................................................................................. 104

5.6.17 Milon (1988)........................................................................................................................................ 104

5.6.18 Morey , et al. (1991)............................................................................................................................ 104

5.6.19 NOAA, et al. (1999) ............................................................................................................................ 105

5.7 Representative Fishing Values ..................................................................................................105

5.8 Impacts of Oil Spill Scenarios ...................................................................................................106

References............................................................................................................................................108

6. Ecological Service Losses .................................................................................................... 123

6.1 Introduction................................................................................................................................123

6.2 Habitat Equivalency Analysis Applications.............................................................................123

6.2.1 Lake Barre Oil Discharge .................................................................................................................... 123

6.2.2 North Cape Oil Spill............................................................................................................................ 124

6.2.3 Apex Houston Oil Spill ....................................................................................................................... 125

6.2.4 U.S. vs. Melvin A. Fisher, et al. .......................................................................................................... 125

6.2.5 M/V Miss Beholden Grounding .......................................................................................................... 125

6.2.6 Presidente Rivera Oil Spill .................................................................................................................. 126

6.2.7 Dixon Bay Wellhead Failure ............................................................................................................... 126

6.2.8 M/V Jacquelyn L. Grounding.............................................................................................................. 126

6.2.9 M/V Formosa Six Discharge ............................................................................................................... 127

6.2.10 Chevron Pipeline Oil Spill................................................................................................................... 127

6.2.11 Tampa Bay Oil Spill............................................................................................................................ 128

6.2.12 M/V World Prodigy Oil Spill .............................................................................................................. 128

6.2.13 Apex Barges Oil Spill.......................................................................................................................... 128

6.2.14 Tenyo Maru Oil Spill........................................................................................................................... 129

6.2.15 Greenhill-Timbalier Well Blowout...................................................................................................... 129

6.2.16 Summary of Applications.................................................................................................................... 129

6.3 Feasibility and Costs of Restoration Actions ...................................................................... 130

6.3.1 Rocky Shore ........................................................................................................................................ 130

6.3.2 Gravel Beach ....................................................................................................................................... 131

6.3.3 Sand Beach .......................................................................................................................................... 131

6.3.4 Mud Flats............................................................................................................................................. 132
TABLE OF CONTENTS (CONT.)

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6.3.5 Saltmarsh ............................................................................................................................................. 132

6.3.6 Mangrove............................................................................................................................................. 133

6.3.7 Macrophyte (macroalgal and seagrass) beds ....................................................................................... 134

6.3.8 Coral Reef............................................................................................................................................ 135

6.3.9 Mollusk (Oyster) Reef......................................................................................................................... 135

6.3.10 Birds .................................................................................................................................................... 135

6.3.11 Marine Mammals................................................................................................................................. 136

6.3.12 Reptiles................................................................................................................................................ 136

6.3.13 Summary of Compensatory Restoration Costs.................................................................................... 136

6.4 Credit and Debit Calculations...................................................................................................138

6.4.1 Conceptual Issues ................................................................................................................................ 138

6.4.2 Debit and Credit Calculations.............................................................................................................. 139

References............................................................................................................................................141

7. Commercial Fishing Damages ............................................................................................ 155

7.1 Introduction................................................................................................................................155

7.2 Fishing Ground Preemption Catch Loss..................................................................................155

7.2.1 Fishing Ground Preemption Damage Methodology............................................................................ 155

7.2.2 Fishing Ground Preemption Data Sources........................................................................................... 156

7.2.3 Fishing Ground Preemption Damage Estimates.................................................................................. 157

7.3 Gear Loss Damage Estimates....................................................................................................157

7.3.1 Gear Loss Damage Methodology ........................................................................................................ 157

7.3.2 Gear Loss Data Sources....................................................................................................................... 158

7.3.3 Gear Loss Damage Estimates .............................................................................................................. 158

7.4 Oil Spill Damages .......................................................................................................................158

7.4.1 Oil Spill Damage Methodology........................................................................................................... 158

7.4.2 Oil Spill Damage Data Sources ........................................................................................................... 159

7.4.3 Oil Spill Damage Estimates................................................................................................................. 160

References............................................................................................................................................161

8. Subsistence Resources.......................................................................................................... 171

8.1 Economic Value of Alaskan Subsistence Harvest ...................................................................171

8.1.1 Subsistence Harvest by OCS Planning Area ....................................................................................... 171

8.1.2 Economic Values................................................................................................................................. 172

8.2 Losses Due to Pipeline or Facility Landfall..............................................................................173

8.3 Oil Spill Losses............................................................................................................................174

References............................................................................................................................................176

9 Air Quality Social Damages................................................................................................. 185

9.1 Introduction and Caveats ..........................................................................................................185

9.2 Emission Estimates.....................................................................................................................187

9.2.1 Location of Emissions ......................................................................................................................... 187

9.2.2 Supply and Crew Boat Emissions........................................................................................................ 187

9.2.3 Platform Operations Emissions ........................................................................................................... 188

9.2.4 Tanker Loading and Transport Emissions ........................................................................................... 188

9.2.5 Well Drilling Emissions ...................................................................................................................... 189
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9.2.6 Platform Construction Emissions ........................................................................................................ 189

9.2.7 Pipeline Construction Emissions ......................................................................................................... 190

9.2.8 Oil Spill Emissions .............................................................................................................................. 190

9.3 Pollutant Transport ...................................................................................................................190

9.3.1 Literature Review ................................................................................................................................ 190

9.3.2 Effect of Winds.................................................................................................................................... 191

9.3.3 Transport Assumptions........................................................................................................................ 192

9.4 Economic Damages of Emissions..............................................................................................193

9.4.1 Literature Review ................................................................................................................................ 193

9.4.2 A Priori Assumptions .......................................................................................................................... 194

9.4.3 Damage Estimates ............................................................................................................................... 195

9.4.3.1 Per-ton NOx Damage Estimates ............................................................................................... 195

9.4.3.2 Per-ton CO Damage Estimates ................................................................................................. 196

9.4.3.3 Per-ton SOx Damage Estimates................................................................................................ 196

9.4.3.4 Per-ton VOC Damage Estimates............................................................................................... 196

9.4.3.5 Per-ton PM Damage Estimates ................................................................................................. 196

9.4.3.6 Per-ton CO2 Damage Estimates ............................................................................................... 197

9.5 Air Quality Damage Estimates from OCS Activities ..............................................................197

9.5.1 Methodology........................................................................................................................................ 197

9.5.2 Supply and Crew Boat Damages ......................................................................................................... 197

9.5.3 Platform Operations Damages............................................................................................................. 198

9.5.4 Tanker Loading and Transport Damages............................................................................................. 198

9.5.5 Well Drilling Damages........................................................................................................................ 199

9.5.6 Platform Construction Damages.......................................................................................................... 199

9.5.7 Pipeline Construction Emissions and Damages................................................................................... 200

9.5.8 Oil Spill Emissions and Damages........................................................................................................ 200

9.5.9 Summary of Damage Estimates........................................................................................................... 200

References............................................................................................................................................201

10. Onshore Government Revenue Collections and Infrastructure Costs............................... 235

10.1 General Onshore Public Service and Infrastructure Effects .................................................235

10.1.1 Population Induced Effects.................................................................................................................. 235

10.1.2 Direct Industry Effects......................................................................................................................... 236

10.1.3 Fixed and Variable Costs..................................................................................................................... 236

10.2 Public Revenues..........................................................................................................................237

10.3 Net Fiscal Impacts ......................................................................................................................237

10.4 Findings from the Recent Literature........................................................................................238

10.4.1 Central and Western Gulf FEISs ......................................................................................................... 238

10.4.2 COOGER, 1999................................................................................................................................... 238

10.4.3 Hughes, et al, 1999 .............................................................................................................................. 239

10.4.4 Foster, 1996; 1999 ............................................................................................................................... 239

10.4.5 RPC, 1999 ........................................................................................................................................... 240

10.5 Comments on A. T. Kearney, 1991, Reliance on Santa Barbara Studies..............................240

10.5.1 Lagged Receipts Create A. T. Kearney OCS Impacts ......................................................................... 241

10.5.2 Lag Effects on Fixed and Variable Costs ............................................................................................ 241

10.5.3 Lags and Revenue Generation............................................................................................................. 242

10.6 Lessons Learned for Modeling..................................................................................................243
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10.6.1 Modeling Issues Per Se........................................................................................................................ 243

References............................................................................................................................................245

11. Private Property Losses........................................................................................................ 247

11.1 Private Property Oil Spill Damages .........................................................................................247

11.2 Private Property Values Affected by OCS Visual Intrusions ................................................248

12. Water Quality Externalities ................................................................................................. 253

12.1 Water in the 1991 GPECM: ......................................................................................................253

12.2 Water Quality Externalities Rationale.....................................................................................253

Appendix A: SIMAP Spill Modeling Results ................................................................................ 1

A.1 Introduction............................................................................................................................... A-1

A.2 Modeling Design........................................................................................................................ A-1

A.3 Model Inputs.............................................................................................................................. A-2

A.4 Geographical Data .................................................................................................................... A-3

A.5 Results ........................................................................................................................................ A-4

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LIST OF TABLES

Table 1.1. Summary of OCS Externality Damages .......................................................................................... 11

Table 1.1. Summary of OCS Externality Damages (Continued)..................................................................... 12

Table 1.1. Summary of OCS Externality Damages (Continued)..................................................................... 13

Table 1.1. Summary of OCS Externality Damages (Continued)..................................................................... 14

Table 1.1. Summary of OCS Externality Damages (Continued)..................................................................... 15

Table 1.1. Summary of OCS Externality Damages (Continued)..................................................................... 16

Table 1.2. Summary of Damages by Cost Drivers............................................................................................ 17

Table 1.2. Summary of Damages by Cost Drivers (Continued)....................................................................... 18

Table 1.2. Summary of Damages by Cost Drivers (Continued)....................................................................... 19

Table 1.3. Oil Spill Natural Resource Damage Costs, in 1997 Dollars (from Helton and Penn, 1999) ........ 20

Table 2.1. Damage Assessment, Administrative, and Monitoring Costs ........................................................ 31

Table 3.1. Oil Spill Locations.............................................................................................................................. 50

Table 3.1. Oil Spill Locations (Continued) ........................................................................................................ 51

Table 3.2. Wind Stations..................................................................................................................................... 52

Table 3.2. Wind Stations (Continued) ............................................................................................................... 53

Table 3.3. Oil Spill Magnitudes and Rates ........................................................................................................ 53

Table 3.4. Oil Types............................................................................................................................................. 54

Table 3.5 NRDAM/CME Model Results: Oil Slick Output, Wildlife Impacts, and Economic Damages ... 55

Table 3.5 NRDAM/CME Model Results: Oil Slick Output, Wildlife Impacts, and Economic Damages ... 56

Table 3.6. NRDAM/CME Model Results: Coastal Oiling Output .................................................................. 59

Table 3.6. NRDAM/CME Model Results: Coastal Oiling Output (Continued)............................................. 60

Table 3.7. SIMAP Scenario Inputs..................................................................................................................... 63

Table 3.8. Comparison of Surface Spills Modeled Using SIMAP and the NRDAM/CME: Wildlife Impacts

............................................................................................................................................................. 64

Table 3.9. Comparison of Surface Spills Modeled Using SIMAP and the NRDAM/CME: Oil Slick and

Coastal Oiling Output ....................................................................................................................... 64

Table 3.10. Comparison of Subsurface and Surface Spills Modeled using SIMAP......................................... 65

Table 3.11. Comparison of NRDAM/CME Output with Field Observations .................................................. 65

Table 3.12. Oil Spill Closure Periods (beach or fishery closures)...................................................................... 66

Table 3.13. Adjustment Factors to be Applied to the Estimates in Tables 3.5 and 3.6.................................... 66

Table 4.1. Miles of Beach, by Coastal State....................................................................................................... 87

Table 4.2. Proportion of Beach Coastline by OCS Planning Area.................................................................. 88

Table 4.3. Beach Visitation (Number of Visits)................................................................................................. 89

Table 4.4. Beach Visitation Data, by OCS Planning Area ............................................................................... 90

Table 4.5. Studies with Total Consumer Surplus Estimates for Saltwater Beach Visitation........................ 91

Table 4.6. Summary of Per-Trip Beach Recreation Consumer Surplus Estimates (1999 dollars)............... 92

Table 5.1. Average Number of Saltwater (Ocean and Inland Saltwater) Fishing Trips by State (1996-1998

data) .................................................................................................................................................. 112

Table 5.2. Average Number of Saltwater (Ocean and Inland Saltwater) Fishing Trips by OCS Planning

Area (1996-1998 data) ..................................................................................................................... 113

Table 5.3. Coastal Fishing Visitation Rates..................................................................................................... 114

Table 5.4. Offshore Fishing Visitation Rates Linked to Coastlines............................................................... 115

Table 5.5. Location of Offshore Fishing Trips by State (1996-1998 data).................................................... 116

Table 5.6. Average Number of Offshore Fishing Trips by OCS Planning Area and Location (1996-1998

data) .................................................................................................................................................. 117

Table 5.7. Area of Offshore Recreational Fisheries by OCS Planning Area................................................ 118

Table 5.8. Average Number of Offshore Anglers per Square Meter-Day by OCS Planning Area and

Location (1996-1998 data)............................................................................................................... 119

Table 5.9. Studies with TotalError! No index entries found. Consumer Surplus Estimates for Saltwater

Recreational Fishing........................................................................................................................ 120

Table 5.10. Summary of Per-Trip Saltwater Fishing Consumer Surplus Estimates (1999 dollars)............. 121

Table 6.1. Lake Barre Estimates of Marsh Service Losses ............................................................................ 145
LIST OF TABLES (CONT.)

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Table 6.2. North Cape Oil Spill Injuries and Compensatory Restoration.................................................... 146

Table 6.3. Damages and Preferred Restoration Alternatives, Tampa Bay Oil Spill.................................... 147

Table 6.4. Wetland Construction Restoration Alternatives for the Apex Barges Oil Spill (1997 dollars). 148

Table 6.5. Saltmarsh Restoration Project Costs (1999 dollars)..................................................................... 149

Table 6.6. Natural Resource Damages for Bird Kills ..................................................................................... 150

Table 6.7. Final Compensatory Restoration Costs ......................................................................................... 151

Table 6.8. Credit/Debit Example...................................................................................................................... 152

Table 7.1. Fishing Ground Preemption Catch Loss Damage Estimates ....................................................... 162

Table 7.1. Fishing Ground Preemption Catch Loss Damage Estimates (Continued).................................. 163

Table 7.2. Gear Loss Damage Estimates ......................................................................................................... 164

Table 7.2. Gear Loss Damage Estimates (Continued).................................................................................... 165

Table 7.3. Summary of Fishing Ground Preemption and Gear Loss Damage Estimates ........................... 166

Table 7.4. Shore-Based Commercial Fishery Values of Landings ................................................................ 167

Table 7.5. Offshore Commercial Fishery Values of Landings....................................................................... 168

Table 7.6. Final Commercial Fishing Spill-Related Damage Estimates ....................................................... 169

Table 8.1. Mapping State Management Units into OCS Planning Areas..................................................... 177

Table 8.2. State Game Management Unit and Sub-Unit Subsistence Harvest Data.................................... 178

Table 8.3. OCS Planning Area Subsistence Harvest Data ............................................................................. 179

Table 8.4. Subsistence Values by OCS Planning Area (1999 dollars)........................................................... 180

Table 8.5. Annual Subsistence Damage Estimates Per Facility or Pipeline Landfall (1999 dollars)......... 181

Table 8.6. Comparison Between Subsistence Harvest and Species Populations .......................................... 182

Table 9.1. Assumed Distance of Platforms from Shore, by OCS Planning Area......................................... 203

Table 9.2. Emissions Rates for Supply and Crew Boats................................................................................. 204

Table 9.3. Estimated Annual Supply and Crew Boat Emissions per Platform, by OCS Planning Area

(tons) ................................................................................................................................................. 205

Table 9.4. Predominant Wind Direction, by OCS Planning Area................................................................. 206

Table 9.5. Energy Information Administration per-ton Damage Estimates (1999 dollars)........................ 207

Table 9.6. Western Area Power Administration per-ton Damage Estimates (1999 dollars) ...................... 208

Table 9.7. Percent of Coastline Classified as Nonattainment by the EPA.................................................... 209

Table 9.8. Damages per Ton of Pollutant that Reaches Shore (1999 dollars) .............................................. 210

Table 9.9. Percent of Boat Emissions that Reach Shore, by OCS Planning Area........................................ 211

Table 9.10. Annual Supply and Crew Boat Emissions that Reach Land, by OCS Planning Area (tons) .... 212

Table 9.11. Annual Boat Emissions Damage Estimates per Active Platform, Low Estimates (in 1999 dollars)

........................................................................................................................................................... 213

Table 9.12. Annual Boat Emissions Damage Estimates per Active Platform, High Estimates (in 1999

dollars) .............................................................................................................................................. 214

Table 9.13. Percent of Platform Emissions that Reach Land, by OCS Planning Area ................................. 215

Table 9.14. Amount of Annual Platform Emissions per Platform that Reach Land, by OCS Planning Area

(tons) ................................................................................................................................................. 216

Table 9.15. Annual Platform Emissions Damage Estimates per Active Platform, Low Estimates (in 1999

dollars) .............................................................................................................................................. 217

Table 9.16. Annual Platform Emissions Damage Estimates per Active Platform, High Estimates (in 1999

dollars) .............................................................................................................................................. 218

Table 9.17. Damages from Tanker Loading and Transport per Thousand Barrels Loaded/Transported,

Low Damage Estimates (1999 dollars)........................................................................................... 219

Table 9.18. Damages from Tanker Loading and Transport per Thousand Barrels Loaded/Transported,

High Damage Estimates (1999 dollars) .......................................................................................... 220

Table 9.19. Boat Emissions Damages per Well Drilled, Low Estimates (1999 dollars)................................. 221

Table 9.20. Boat Emissions Damages per Well Drilled, High Estimates (1999 dollars) ................................ 222

Table 9.21. Annual Emissions Damages per Development Well, Low Estimates (1999 dollars).................. 223

Table 9.22. Annual Emissions Damages per Development Well, High Estimates (1999 dollars) ................. 224

Table 9.23. Damages per Platform Constructed, Low Estimates (1999 dollars)............................................ 225

Table 9.24. Damages per Platform Constructed, High Estimates (1999 dollars)........................................... 226
LIST OF TABLES (CONT.)

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Table 9.25. Damages per Mile of Pipeline Constructed, Low Estimates (1999 dollars) ................................ 227

Table 9.26. Damages per Mile of Pipeline Constructed, High Estimates (1999 dollars) ............................... 228

Table 9.27. Oil Spill Air Quality Damages, Low and High Estimates (1999 dollars) .................................... 229

Table 9.28. Summary of Air Quality Damages from Continuous Operations, Low Estimates (1999 dollars)

........................................................................................................................................................... 230

Table 9.29. Summary of Air Quality Damages from Continuous Operations, High Estimates (1999 dollars)

........................................................................................................................................................... 231

Table 9.30. Summary of Air Quality Damages from One-time Events, Low Estimates (1999 dollars) ....... 232

Table 9.31. Summary of Air Quality Damages from One-time Events, High Estimates (1999 dollars) ...... 233

Table 11.1. Property Value Losses per Meter of Coastal Oiling, by OCS Planning Area ............................ 249

Table 11.2. Property Value Damage Estimates Due to Spills (1999 dollars).................................................. 250

Table 11.3. Property Value Damage Estimates Due to Visual Impacts (1999 dollars).................................. 251

Table A.1. Model Scenarios Run with SIMAP ................................................................................................ A-2

Table A.2. Time When Oil First Reaches Shoreline, By Location and Wind File........................................ A-4

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LIST OF FIGURES

Figure 4.1. OECM Impact Drivers for Beach Recreation................................................................................. 93

Figure 5.1. OECM Impact Drivers for Recreational Fishing.......................................................................... 122

Figure 6.1. Ecological Service Losses OECM Impact Drivers........................................................................ 153

Figure 7.1. Commercial Fisheries OECM Impact Drivers.............................................................................. 170

Figure 8.1. Subsistence Resources OECM Impact Drivers............................................................................. 183

Figure 9.1. Air Quality OECM Impact Drivers ............................................................................................... 234

Figure 10.1. Onshore Government Revenue Collections and Infrastructure Costs OECM Impact Drivers 246

Figure 11.1. Property Value OECM Impact Drivers......................................................................................... 252

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1. Introduction

1.1 Background

The Minerals Management Service (MMS) manages the exploration and development of mineral

resources on the Federal Outer Continental Shelf (OCS). The mission of the MMS is:

• to manage the mineral resources on the Nation's OCS in an environmentally sound and safe

manner; and

• to collect, verify, and distribute, in a timely fashion, mineral revenues generated from Federal

and Indian lands.

The MMS collects an average of $3.5 billion annually as bonuses, rents, and royalties from OCS

leases. The production of oil and natural gas from OCS lands is an important source of energy

for the United States. OCS production accounts for about 27% of the natural gas and about 20%

of the oil produced domestically. The MMS divides the OCS lands into 26 Planning Areas;

fifteen in Alaska, four off the Pacific Coast, and seven off the Atlantic and Gulf of Mexico

Coasts. The vast majority of current OCS production occurs in the Gulf of Mexico.

Under the OCS Lands Act Amendments of 1978, MMS is directed to develop a series of 5-year

leasing programs that consider the energy, economic, and environmental goals of the nation. As

part of these programs, the costs and benefits of OCS activities must be considered in assessing

the net economic benefits of proposed OCS leases. The net economic impact of OCS activity

depends on many factors. The benefits include oil and gas production, profits for oil and gas

companies, job creation, energy security, and federal revenues. These benefits must be

compared to the costs of OCS activity in a complete benefit-cost analysis. These costs include

the exploration and development costs incurred by industry as well as the environmental

damages and social impacts imposed on society as a whole. The analysis detailed in this report

provides estimates of the external social costs of OCS activities. While the analysis is not a

complete benefit-cost analysis of OCS activity, the social and environmental cost estimates

derived in this report are an integral component of a complete benefit-cost analysis.

This report provides estimates of the social and environmental costs associated with OCS

activity for the 2002-2007 5-year leasing plan. It updates and revises the General Purpose

Environmental Cost Model GPECM) developed by A.T. Kearney (1991). We refer to our model

as the Offshore Environmental Cost Model (OECM).

1.2 Definition of Social Costs and Benefits

Economic activity generates costs and benefits. In private markets, buyers and sellers engage in

voluntary transactions because of perceived net benefits. Buyers and sellers will agree to

transactions if they perceive that the benefits exceed the costs and no better transactions are

available. For example, a consumer purchases gasoline only if the anticipated benefits of the

gasoline exceed the selling price.
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Economists define the benefits consumers obtain in market transactions as consumer surplus.

Consumer surplus is the dollar amount consumers are willing to pay for a good or service in

excess of the price. For example, if a consumer is willing to pay $2.00 for a gallon of gasoline

and the selling price is $1.50, he or she will obtain a consumer surplus of $0.50 by making the

purchase. Note that consumer surplus is measured in consideration of the available substitutes.

As the price of a good or service increases, consumer surplus decreases until the price becomes

high enough so that the consumer switches to an alternative. This alternative does not

necessarily have to be a purchase of a similar good or service. For example, an increase in the

price of gasoline may cause a consumer to prefer a day of relaxation at home to a trip to a beach.

Sellers and producers also receive a benefit from market transactions. Economists use the term

producer surplus to measure these benefits. Assuming that sellers receive no non-financial

benefits, producer surplus is equal to profits.

In a restrictive sense, every market transaction generates net benefits to both the buyer and seller

because the transaction would not occur otherwise. However, market transactions often result in

external costs that are not captured in markets. For example, the use of gasoline results in air

pollution that imposes a cost on society. These costs are termed externalities. The impacts of

externalities may be observed in changes in related markets. For example, air pollution from the

use of gasoline may lead to increases in private health care costs. In other cases, externalities

may be observed through increases in public expenditures. Air quality impacts lead to

government regulations, increases in public health care costs, and litigation costs. Other

externalities may be more difficult to identify and measure. For example, reductions in visibility

due to air pollution may not produce observable changes in private markets.

1.3 Scope of Analysis

This report estimates the social externalities associated with OCS activity. Costs and benefits

that can be observed in oil and gas markets are not analyzed. Also, the administrative costs to

MMS of managing the OCS program are not estimated. The benefits generated by the public

expenditure of OCS rents, bonuses, and royalties are also not included. The net social and

environmental costs from the OECM are combined with estimates of social benefits calculated in

MMS' Net Economic Value (NEV) model and estimates of consumer surplus from the Market

Simulation Model to arrive at the net benefits from OCS production.

This report is divided into sections. Each section estimates the social costs of a specific category

of externalities. The externalities considered are:

1. beach recreation,

2. recreational fishing,

3. ecological services,

4. commercial fishing,

5. subsistence resources,

6. air quality,
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7. onshore government revenue collections and infrastructure,

8. private property values, and

9. water quality.

OCS activity generally imposes externalities on the above cost elements. Some damages are

caused by routine OCS operations. For example, the routine operation of an oil platform results

in air pollution that may lead to human health impacts. The construction of a platform or

pipeline will preempt a certain marine area from commercial fishing. Other externalities arise

from oil spills including the closure of recreational beaches and ecological damages.

We consider only the direct externalities of OCS activity in this report and do not address

"downstream" or "upstream" externalities. Upstream externalities include those externalities

associated with inputs into OCS activity. For example, the externalities associated with

producing the steel used for offshore platforms are upstream externalities. Downstream

externalities include those associated with the production of goods or services that use OCS oil

and gas as a production input. Downstream externalities include the air pollution generated

when people drive vehicles powered by OCS oil or when companies use OCS products to make

other goods. We consider these upstream and downstream externalities to be primarily

associated with the markets for these upstream or downstream goods. We focus only on the

direct externalities associated with the exploration, development, and production of OCS oil and

gas.

We also limit our analysis to the externalities that impact the United States. Some OCS

externalities, such as air pollution and commercial fishing impacts, may damage other nations.

We do not estimate those externalities in this report and consider such effects to be minor.

The demand for oil and gas in the United States can be met from OCS production, land-based

production within the United States, and foreign sources. Increasing the production of oil and

gas from OCS lands reduces national dependence on foreign oil sources. One benefit of this is a

reduction in the importation of foreign oil by tankers and an associated reduction in the

probability of oil spills. Of course, this benefit must be weighed against the potential for oil

spills from increased OCS activity. We do address these two factors in our analysis. However,

we do not estimate the additional social benefits of reduced foreign dependence such as an

increase in the reliability of supplies, price stability, and an increase in national security.

We also do not estimate the regional economic effects of OCS activity. Economists consider

regional effects to be the jobs and income generated from an economic sector. While large, these

effects are generally considered to be regional transfers rather than net benefits. Assuming

relatively full employment, new jobs created in a particular region will be generated at the

expense of jobs elsewhere. Expenditures that consumers make in one region occur at the

expense of similar expenditures in another region. Of course, there are cases when regional

impacts do generate net social benefits. If an unemployed person is employed as a direct result

of OCS activity, this represents a net social gain. Whether regional impacts represent net

benefits is a complex field of study and dependent on broader economic activity such as

employment and consumer spending. As is the convention in social welfare research, we assume
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that regional effects represent transfers and do not have a significant impact on social costs and

benefits.

1.4 Comparison with the General Purpose Environmental Cost Model

Our analysis differs from the 1991 GPECM in a number of ways. For some cost elements, we

basically update the GPECM analysis using more current data. For other cost elements, our

analysis differs conceptually from the GPECM. These differences are due to recent research

findings, legislative or judicial action, or the authors' experience. The sections that follow

present the methodology used to estimate the externalities for each cost element. Differences

between our approach and that taken by the GPECM are discussed and justified.

The GPECM considers four types of costs associated with OCS activity:

1. production costs, including the costs of regulatory compliance;

2. costs associated with mitigating adverse impacts;

3. social costs; and

4. administrative costs.

As discussed above, we limit our analysis to the social costs of OCS activity. Production costs,

including regulatory expenses, are considered part of doing business for companies that lease

OCS lands. Assuming these companies generate a profit, production costs are an input that

ultimately produces a net benefit. Regulatory controls, such as equipment to reduce air

pollution, generate costs and benefits. Assuming economic efficiency, the social benefits of

these controls will exceed the costs to the companies. Thus, we do not consider regulatory costs

as external social costs. The same principle applies to mitigation costs. Assuming economic

efficiency, mitigation actions generate social benefits that exceed the associated costs.

Agency administrative costs are ruled out of the analysis, which keys on externalities. Clearly,

the MMS requires the use of public funds to administer the OCS program. The rents, bonuses,

and royalties collected by MMS represent a social benefit. However, this benefit is not properly

measured as the dollar amount collected. Instead, the true social benefit depends on the use of

these funds. For example, the use of OCS revenues to purchase a wildlife reserve results in

benefits that accrue to visitors of the reserve. The benefits generated by OCS revenues are

dependent on the use of the funds and would be very difficult to measure accurately; we do not

attempt to estimate these social impacts.

1.5 Cost Drivers

The extent of the externalities associated with OCS activity depends on the level of activity. An

increase in OCS activity will generally result in an increase in the externalities for the nine cost

elements we consider in this analysis. We link externality estimates to OCS activity through

"drivers," which are measures of the level of activity. The drivers considered in this analysis

include:
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1. the number of oil platforms operating/constructed

2. the miles of pipeline operating/constructed

3. the number of wells installed

4. the barrels of oil produced/transported

5. the barrels of oil spilled

The first four drivers are determined external to this report. The last driver, the number of

barrels of oil spilled, is linked to oil production and does not need to be determined by the user of

the OECM. A complete discussion of oil spill probability and magnitudes is presented in Section

3.

1.6 Cost Elements

As mentioned above, this report includes a separate section that details the methodology,

assumptions, data sources, and damage estimates for each of nine cost elements. Below is a brief

summary of each section.

1.6.1 Spill-Related Damage Methodology (Section 2)

The impacts of oil spills can be classified as either damages to resource users or damages to nonusers. Resource users include beach visitors, marine anglers, and subsistence harvesters.

Consumer surplus losses to resource users as a result of a natural resource damage are referred to

as lost use values. Oil spills may impact non-users, who may never visit the site of a spill,

because of effects on ecological habitat and wildlife impacts. Economists typically have

estimated these so-called passive use impacts using non-market valuation techniques such as

contingent valuation. However, several recent natural resource damage cases involving oil spills

have quantified injuries in terms of ecological service losses instead of economic welfare losses.

This ecological approach, commonly referred to as habitat equivalency analysis (HEA), seeks

compensation for natural resource damages directly through ecological restoration projects. In

Section 2, we present our methodology for estimating spill damages using both economic and

ecological measures of injury. We conclude that a HEA approach is preferred to an economic

approach in assessing passive use losses based on practical and conceptual issues. However,

economic approaches for estimating use losses are well-developed and accepted in natural

resource damage cases.

1.6.2 Oil Spill Modeling (Section 3)

One of the main differences between our analysis and the GPECM model is that we model

specific spill scenarios. Most of the spill-related damage estimates in the GPECM are derived

from a spill size of about 25,000 barrels based on a database of spill sizes. An average value is

also used for the proportion of oil that comes ashore. The GPECM analysis then estimates perbarrel spilled damage estimates using these average values.

Recent research suggests that the per-barrel spilled damages differ by spill size (Sirkar, et al.,

1997). We define three spill size scenarios based on data from MMS and the U.S. Coast Guard.
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Unlike the GPECM model, we consider small spills (less than 1,000 barrels) since these spills

account for the vast majority of spills related to OCS activities.

Our spill-related damage estimates are derived from spill scenarios defined for each OCS

Planning Area. From one to six likely spill locations were identified for each Planning Area.

For each location, three spill sizes were modeled using the Natural Resource Damage

Assessment Model for Coastal and Marine Environments (NRDAM/CME), version 2.4. Using

input data on winds, temperature, and other variables, the NRDAM/CME estimates the

magnitude of coastal oiling, the oil slick area, and the number of wildlife deaths for specific

species. Oiling estimates are given for specific ecological habitat types. The use of the

NRDAM/CME increases the specificity of our analysis since we do not have to rely on average

values that may not apply to individual OCS Planning Areas.

In Section 3 we explain how we modeled the spill scenarios using the NRDAM/CME. We

present the model outputs and indicate how these outputs are used to determine the drivers for

individual spill-related cost elements. We also describe the adjustments that were made to the

NRDAM/CME output to increase the accuracy of the damage estimates.

1.6.3 Beach Recreation (Section 4)

In this section we estimate beach recreation damages from oil spills. We present our

methodology and contrast it to the methodologies used in the GPECM and the NRDAM/CME.

Our driver for beach recreation damages is the amount of coastal oiling. We derive baseline

beach visitation estimates based on state and federal data. Then, we review the literature to

obtain estimates of consumer surplus for beach recreation and choose representative consumer

surplus values.

1.6.4 Recreational Fishing (Section 5)

In Section 5 we estimate the recreational fishing damages from oil spills. We present our

methodology and contrast it to the methodology used in the GPECM and the NRDAM/CME.

We use coastal oiling as our driver for damages to shore-based recreational fishing. We use oil

slick magnitude as our driver for damage to offshore recreational fishing. We obtain estimates of

the number of annual baseline recreational fishing trips using data from the National Marine

Fisheries Service and individual states. We then review the literature to obtain estimates of

consumer surplus for marine recreational fishing and choose representative values.

1.6.5 Ecological Service Losses (Section 6)

In Section 6 we estimate the damages for injuries to coastal habitats and wildlife from oil spills.

No previous applications of a generalized HEA exist; we review natural resource damage cases

that apply the HEA methodology or provide estimates of restoration costs. We then review the

literature to determine the feasibility of compensatory restoration projects for each coastal habitat

along with estimates of restoration costs. We also consider restoration projects for injuries to

wildlife. We choose representative values for restoration projects and describe how to link these

estimates to the NRDAM/CME outputs. The NRDAM/CME drivers include the amount of
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coastal oiling of each habitat and the number of wildlife deaths. The injury estimates from the

NRDAM/CME are considered "debits" that are balanced against the "credits" of compensatory

restoration such that the present value of each are equal.

1.6.6 Commercial Fishing (Section 7)

OCS activities impact commercial fishing through both spill and non-spill effects. The non-spill

effects include fishing ground preemption and gear loss and damage and are driven by the

number of platforms and miles of pipeline. Oil spills affect both coastal and offshore

commercial fishing. We obtain baseline estimates of the value of commercial fisheries. We then

estimate the reduction in commercial catch using NRDAM/CME drivers for the amount of

coastal oiling and oil slick magnitude.

1.6.7 Subsistence Resources (Section 8)

We estimate the impacts of OCS activity on subsistence harvesting in Alaska. Subsistence

impacts occur through both spill and non-spill drivers. We derive estimates of the value of

subsistence harvest using state harvest data and economic values. We present our methodology

for estimating the reduction in subsistence harvest from pipeline landfalls and oil spills.

1.6.8 Air Quality Externalities (Section 9)

OCS activities result in the emission of pollutants that may reduce air quality over populated

regions. In Section 9 we review the literature to estimate the impacts that offshore emissions

have on air quality over land. We determine transport factors for individual pollutants since all

pollutants are not transported equally. We present data on the amount of emissions from

different OCS activities including routine operations, construction events, and oil spills. We then

review the literature to determine the per-unit damages for each pollutant. The drivers for air

quality damages include the number of platforms, amount of platform/pipeline construction,

barrels of oil spilled, and barrels of oil transported by tankers.

1.6.9 Onshore Government Revenue Collections and Infrastructure Costs (Section 10)

OCS activities affect nearby coastal communities in several ways. For instance, an increase in

OCS activities may cause worker migration into local communities, which in turn can add to the

burden of expected government services. OCS activities place demands on local infrastructure

including public roads, emergency services, harbors, and airports. While minor changes in

population and industry demand may simply represent a transfer of resources, large changes in

OCS activities may generate net social impacts. In Section 10 we estimate the magnitude of the

demands that OCS activities place on local communities and weigh these against the additional

revenues that communities receive as a consequence of OCS activities. Note that the net effect

can be a benefit or loss. We review the literature on the net impact of OCS activities on the

demand for public services and the level of public revenues. We compare our methodology with

that of the GPECM. Our drivers for this section include the amount of oil production and the

number of wells.
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1.6.10 Private Property Impacts (Section 11)

OCS activities can affect private property values in two ways. First, oil spills will temporarily

reduce the value of coastal property. Any property that is sold during the period after an oil spill

will sell at a reduced price. In Section 11 we estimate the reduction in selling prices as a result

of expected oil spills from OCS activity. The other way that OCS activities can impact private

property values is because of the visual effect of offshore activity. We also estimate the

reduction in property values due to the presence of OCS structures.

1.6.11 Water Quality Externalities (Section 12)

We conclude that the externality impacts of OCS activities on water quality are negligible

beyond those damages caused by oil spills. In Section 11, we present our rationale for assuming

that water quality externalities do not present significant damages from OCS activities.

1.7 Summary of Results

For most cost elements we present our damage estimates as a range based on "low" and "high"

assumptions. In reviewing the literature, we normally found that damage estimates can vary

significantly based on the methodology and the circumstances behind the estimates. One should

not simply assume that a damage estimate in the center of our range is most probable. Both our

"low" and "high" values are chosen to reflect reasonable values that have been obtained in

certain circumstances. Either the "low" or "high" estimate will likely be more appropriate than a

middle (or mean) value. Thus, our estimates must be applied with consideration for the range of

values rather than a tendency to choose a specific value.

A summary of our damage estimates for each cost element is presented in Table 1.1. Note that

the oil spill scenarios being considered differ across OCS Planning Area in terms of distance to

shore and meteorological conditions. Thus, differences in values across Planning Areas are not

simply due to differences in baseline values such as the number of beach visitors or the value of

commercial fishery catch. All values in Table 1.1 are presented in 1999 dollars. In most cases,

both low and high estimates are presented. Table 1.2 presents an aggregation of all damages for

each cost driver.

1.8 Validation of Results

A broad environmental cost model is not complete without some assessment of the validity of

the estimates. The damages per barrel spilled presented in Table 1.2 can be compared to other

information on economic damages from oil spills. Sirkar, et al. (1997) estimate the NRDA

damages associated with tanker spills. Their Figure 8 indicates NRDA costs for tanker spills

around 25,000 barrels (our large tanker spill size) to be approximately $5,000 per m3

, equivalent

to about $800 per barrel. This value is similar to our average low damage estimate per barrel

spilled of $987 (the average weighing each OCS Planning Area equally).

Helton and Penn (1999) present NRDA costs for over 25 spills since 1984. Their data set

includes spills into freshwater environments as well as spills of chemicals other than crude oil.

Table 1.3 presents their data on spill size, NRDA costs, and NRDA costs per barrel spilled. The
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NRDA damages per barrel spilled range from $3 up to $15,759 with an average of $1,745 per

barrel spilled. These values are comparable to our values in Table 1.2, which range from $27 to

$14,418 and average $2,916 (considering both low and high estimates). Note that the crude oil

spill damages per barrel spilled in Table 1.3 tend to be larger than the damages for non-crude

spills. The average damage per barrel spilled for the crude oil spills in Table 1.3 is $2,714,

which is similar to our average of $2,916 per barrel spilled.

The final data source for damage estimates per barrel spilled is recent NRDA reports not

considered by Helton and Penn (1999). A review of NRDA reports and the NOAA Damage

Assessment and Restoration Program Internet site located three additional spills for which

NRDA damages per barrel spilled could be calculated. For the 1990 Apex Barges spill, damages

were $76 per barrel spilled. For the 1990 Apex Galveston spill, damages were $102 per barrel

spilled. For the 1996 North Cape spill, damages were $1,400 per barrel spilled. These values

fall within the range of our low per-barrel-spilled damage estimates.

Overall, our damage estimates per barrel spilled presented in Table 1.2 must be considered

reasonable based on the data presented in this section. Our estimates span the range of observed

NRDA costs. Our low per-barrel-spilled damage estimates are similar to those of Sirkar, et al.

(1997). Our combined low and high damage estimates are very similar to those presented in

Helton and Penn (1999) for crude oil spills. We conclude that both the low and high damage

estimates present reasonable estimates of the NRDA damages associated with oil spills. Note

that our per-barrel-spilled estimates include a couple of cost elements that would not normally be

included in a natural resource damage claim (air quality and private property damages).

However, these two cost elements typically contribute less than 3% to the total damages per

barrel spilled so the values in Table 1.2 can be directly compared with NRDA values.

Note that our results indicate high per-barrel-spilled damages for the Alaska OCS Planning

Areas relative to the OCS Planning Areas off the contiguous United States. For the contiguous

U.S., the low damage estimates average $471 per barrel spilled while the high damage estimates

average $2,153. For the Alaska OCS Planning Areas the low damage estimates average $1,364

per barrel spilled and the high damage estimates average $6,821. The main reason for the larger

damage estimates in Alaska is the large damages for wildlife deaths in Table 1.1.
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References

A.T. Kearney, Inc. 1991. Estimating the Environmental Costs of OCS Oil and Gas Development

and Marine Oil Spills: A General Purpose Model. Report submitted to Minerals Management

Service under contract no. 14-35-0001-30500.

Helton, Douglas, and Tony Penn. 1999. Putting Natural Resource Damage Costs in Perspective.

Paper presented at the 1999 International Oil Spill Conference, paper ID #114.

Sirkar, Jaideep, Paul Ameer, Alan Brown, Patrick Gross, Keith Michel, and Wayne Willis. 1997.

A Framework for Assessing the Environmental Performance of Tankers in Accidental

Groundings and Collisions. Summary final report of the Society of Naval Architects and

Marine Engineers Ad Hoc Panel on the Environmental Performance of Tankers.
Section 1: Introduction 11

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Table 1.1. Summary of OCS Externality Damages

Beach Recreation

Damages per

Barrel Spilled

Recreational

Fishing Damages

per Barrel Spilled

Ecological Coastal

Habitat Damages per

OCS Planning Area Barrel Spilled

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

North Atlantic $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Mid-Atlantic $0.00 $0.00 $7.41 $38.39 $0.00 $0.00

South Atlantic $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Straits of Florida $18.92 $89.85 $137.59 $712.95 $106.13 $530.87

Eastern Gulf of

Mexico

$3.89 $18.48 $69.94 $362.43 $0.52 $2.82

Central Gulf of

Mexico

$0.59 $2.81 $54.54 $282.59 $37.11 $185.61

Western Gulf of

Mexico

$5.57 $26.47 $19.20 $99.47 $38.70 $195.30

Southern California $4.03 $19.14 $21.52 $111.49 $7.11 $40.64

Central California $40.54 $192.56 $52.83 $273.76 $61.54 $351.65

Northern California $0.29 $1.39 $2.89 $14.95 $4.27 $24.42

Washington/Oregon $1.30 $6.17 $78.78 $408.22 $48.52 $277.28

Gulf of Alaska $0.00 $0.00 $1.02 $5.27 $35.50 $179.99

Kodiak $0.00 $0.00 $0.14 $0.71 $2.12 $12.75

Cook Inlet $0.00 $0.00 $6.79 $35.18 $31.33 $164.30

Shumagin $0.00 $0.00 $0.07 $0.34 $19.24 $110.44

North Aleutian Basin $0.00 $0.00 $0.01 $0.05 $41.57 $237.55

Saint George Basin $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Aleutian Arc $0.00 $0.00 $0.00 $0.01 $28.82 $172.92

Bowers Basin $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Aleutian Basin $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Navarin Basin $0.00 $0.00 $0.00 $0.00 $175.28 $1,051.67

Saint Matthew Hall $0.00 $0.00 $0.03 $0.16 $0.00 $0.00

Norton Basin $0.00 $0.00 $0.00 $0.03 $0.00 $0.00

Hope Basin $0.00 $0.00 $0.00 $0.01 $0.00 $0.00

Chukchi Sea $0.00 $0.00 $0.00 $0.00 $2.87 $16.42

Beaufort Sea $0.00 $0.00 $0.00 $0.01 $242.05 $1,266.67
12 Section 1: Introduction

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Table 1.1. Summary of OCS Externality Damages (Continued)

Wildlife Death

Damages per Barrel

Spilled

Annual

Commercial

Fishing Gear

Loss Damage

per Platform OCS Planning Area 1

Low

Estimate

High

Estimate

Annual

Commercial

Fishing

Damages

per Barrel

Spilled 1

Annual

Commercial

Fishing

Ground

Preemption

Damage per

Pipeline

Mile1

Annual

Commercial

Fishing

Ground

Preemption

Damage per

Platform 1

North Atlantic $801 $4,005 $231 $838 $4,453 $15,233

Mid-Atlantic $420 $2,099 $32 $818 $290 $1,016

South Atlantic $4 $20 $17 $46 $64 $785

Straits of Florida $240 $1,202 $6 $4 $6 $89

Eastern Gulf of

Mexico

$43 $221 $27 $3 $6 $103

Central Gulf of

Mexico

$122 $620 $59 $14 $26 $421

Western Gulf of

Mexico

$55 $278 $39 $15 $25 $394

Southern California $339 $1,704 $9 $72 $90 $188

Central California $949 $4,797 $13 $461 $659 $816

Northern California $64 $319 $5 $280 $98 $443

Washington/Oregon $239 $1,199 $25 $117 $210 $2,513

Gulf of Alaska $394 $1,985 $6 $11 $20 $2,557

Kodiak $342 $1,720 $25 $10 $18 $394

Cook Inlet $631 $3,165 $5 $1 $2 $84

Shumagin $1,200 $6,030 $10 $13 $25 $3,634

North Aleutian Basin $181 $936 $21 $1 $2 $2,910

Saint George Basin $981 $4,906 $16 $2 $4 $2,185

Aleutian Arc $2,607 $13,042 $11 $1 $2 $2,053

Bowers Basin $1,645 $8,223 $7 $1 $2 $2,052

Aleutian Basin $1,465 $7,340 $6 $3 $4 $2,115

Navarin Basin $1,665 $8,335 $7 $3 $4 $2,115

Saint Matthew Hall $2,275 $11,377 $2 $0 $0 $272

Norton Basin $1,116 $5,581 $0 $0 $0 $0

Hope Basin $2,010 $10,069 $0 $0 $0 $0

Chukchi Sea $960 $4,807 $0 $0 $0 $0

Beaufort Sea $576 $2,890 $0 $0 $0 $0

1 - Low and high estimates were not derived for the commercial fishing damages. There is

accurate data on the value of commercial landings so no range of values was necessary.
Section 1: Introduction 13

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Table 1.1. Summary of OCS Externality Damages (Continued)

Subsistence

Damages per

Barrel Spilled

Subsistence

Damages per

Facility or Pipeline

Landfall

NRDA

Administrative

Costs per Barrel OCS Planning Area Spilled

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

North Atlantic $0.00 $0.00 $0 $0 $93 $381

Mid-Atlantic $0.00 $0.00 $0 $0 $41 $195

South Atlantic $0.00 $0.00 $0 $0 $2 $3

Straits of Florida $0.00 $0.00 $0 $0 $46 $229

Eastern Gulf of

Mexico

$0.00 $0.00 $0 $0 $13 $57

Central Gulf of

Mexico

$0.00 $0.00 $0 $0 $25 $103

Western Gulf of

Mexico

$0.00 $0.00 $0 $0 $14 $57

Southern California $0.00 $0.00 $0 $0 $34 $170

Central California $0.00 $0.00 $0 $0 $101 $507

Northern California $0.00 $0.00 $0 $0 $7 $33

Washington/Oregon $0.00 $0.00 $0 $0 $35 $172

Gulf of Alaska $0.15 $0.57 $11,242 $43,319 $39 $196

Kodiak $0.49 $1.90 $1,178 $4,540 $33 $158

Cook Inlet $0.71 $2.74 $5,978 $23,035 $61 $303

Shumagin $2.57 $9.90 $515 $1,984 $111 $554

North Aleutian Basin $1.66 $6.40 $230 $888 $22 $108

Saint George Basin $0.00 $0.00 $2,986 $11,505 $90 $443

Aleutian Arc $0.02 $0.09 $230 $888 $238 $1,190

Bowers Basin $0.00 $0.00 $0 $0 $149 $741

Aleutian Basin $0.00 $0.00 $0 $0 $132 $661

Navarin Basin $0.00 $0.00 $3,117 $12,010 $166 $845

Saint Matthew Hall $0.02 $0.09 $0 $0 $205 $1,024

Norton Basin $0.01 $0.03 $810 $3,122 $100 $502

Hope Basin $0.17 $0.66 $5,429 $20,918 $181 $906

Chukchi Sea $0.20 $0.76 $1,253 $4,830 $87 $434

Beaufort Sea $0.23 $0.88 $1,432 $5,517 $74 $374
14 Section 1: Introduction

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Table 1.1. Summary of OCS Externality Damages (Continued)

Annual Air Quality

Damages per

Operating Platform

Air Quality Damages

per 1,000 Barrels

Transported by

Tanker

Air Quality Damages

per Barrel Spilled OCS Planning Area

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

North Atlantic $20,135 $65,123 $4.95 $20.81 $4.22 $17.65

Mid-Atlantic $22,292 $70,193 $5.95 $27.48 $4.96 $23.36

South Atlantic $20,135 $65,123 $4.95 $20.81 $4.22 $17.65

Straits of Florida $20,135 $65,123 $4.95 $20.81 $4.22 $17.65

Eastern Gulf of

Mexico

$21,443 $68,604 $2.63 $10.92 $1.87 $7.84

Central Gulf of

Mexico

$41,814 $123,574 $10.06 $42.11 $7.49 $31.37

Western Gulf of

Mexico

$51,192 $143,830 $12.84 $55.49 $8.82 $41.52

Southern California $85,402 $436,490 $33.16 $162.04 $18.74 $92.86

Central California $85,402 $436,490 $33.16 $162.04 $18.74 $92.86

Northern California $59,980 $274,044 $27.61 $115.97 $15.93 $66.67

Washington/Oregon $38,713 $115,086 $17.49 $73.64 $14.99 $62.75

Gulf of Alaska $44,960 $126,826 $8.32 $34.00 $5.15 $21.57

Kodiak $44,905 $132,691 $8.31 $34.45 $5.15 $21.57

Cook Inlet $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

Shumagin $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

North Aleutian Basin $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

Saint George Basin $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

Aleutian Arc $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

Bowers Basin $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

Aleutian Basin $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

Navarin Basin $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

Saint Matthew Hall $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

Norton Basin $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

Hope Basin $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

Chukchi Sea $18,333 $60,700 $1.02 $4.10 $0.51 $2.16

Beaufort Sea $18,333 $60,700 $1.02 $4.10 $0.51 $2.16
Section 1: Introduction 15

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Table 1.1. Summary of OCS Externality Damages (Continued)

Air Quality

Damages per Well

Installed

Air Quality Damages

per Platform

Constructed

Air Quality

Damages per Mile of

Pipeline OCS Planning Area Constructed

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

North Atlantic $13,137 $41,281 $30,312 $78,287 $2,121 $8,698

Mid-Atlantic $13,633 $41,899 $33,578 $83,408 $2,578 $11,052

South Atlantic $13,137 $41,281 $30,312 $78,287 $2,121 $8,698

Straits of Florida $13,137 $41,281 $30,312 $78,287 $2,121 $8,698

Eastern Gulf of

Mexico

$13,137 $41,281 $30,312 $78,287 $1,943 $6,431

Central Gulf of

Mexico

$19,814 $52,861 $76,646 $159,653 $5,275 $17,135

Western Gulf of

Mexico

$21,766 $55,284 $89,423 $179,582 $6,580 $22,007

Southern California $30,870 $147,508 $173,051 $1,063,054 $8,842 $42,949

Central California $30,870 $147,508 $173,051 $1,063,054 $8,842 $42,949

Northern California $23,341 $97,675 $107,875 $585,426 $5,916 $25,293

Washington/Oregon $19,814 $52,861 $76,646 $159,653 $5,845 $24,387

Gulf of Alaska $19,814 $52,739 $76,646 $157,053 $5,098 $13,533

Kodiak $19,802 $52,861 $76,399 $159,653 $5,086 $14,869

Cook Inlet $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

Shumagin $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

North Aleutian Basin $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

Saint George Basin $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

Aleutian Arc $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

Bowers Basin $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

Aleutian Basin $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

Navarin Basin $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

Saint Matthew Hall $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

Norton Basin $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

Hope Basin $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

Chukchi Sea $11,792 $38,921 $20,969 $61,585 $1,254 $4,039

Beaufort Sea $11,792 $38,921 $20,969 $61,585 $1,254 $4,039
16 Section 1: Introduction

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Table 1.1. Summary of OCS Externality Damages (Continued)

Private Property Damages

from Visual Impacts per

Platform Constructed

OCS Planning Area

Net Revenue

Collections and

Infrastructure

Damages per

Well Installed 1

Net Revenue

Collections

and

Infrastructure

Damages per

1,000 Barrels

Produced 1

Private

Property

Damages from

Reduced Sale

Prices per

Barrel Spilled

Low

Estimate

High

Estimate

North Atlantic -$2,882 -$1.00 $0.00 $0 $0

Mid-Atlantic -$2,882 -$1.00 $0.00 $0 $0

South Atlantic -$2,882 -$1.00 $0.00 $0 $0

Straits of Florida -$2,882 -$1.00 $11.22 $0 $0

Eastern Gulf of

Mexico

-$2,882 -$1.00 $0.33 $0 $0

Central Gulf of

Mexico

-$2,882 -$1.00 $0.24 $1,267 $3,802

Western Gulf of

Mexico

-$2,882 -$1.00 $1.22 $0 $0

Southern California -$2,882 -$1.00 $10.61 $279,529 $838,588

Central California -$2,882 -$1.00 $83.43 $168,251 $504,752

Northern California -$2,882 -$1.00 $0.95 $7,713 $23,140

Washington/Oregon -$2,882 -$1.00 $0.36 $2,138 $6,415

Gulf of Alaska -$2,882 -$1.00 $0.01 $0 $0

Kodiak -$2,882 -$1.00 $0.41 $0 $0

Cook Inlet -$2,882 -$1.00 $0.26 $0 $0

Shumagin -$2,882 -$1.00 $0.00 $0 $0

North Aleutian Basin -$2,882 -$1.00 $0.11 $0 $0

Saint George Basin -$2,882 -$1.00 $0.00 $0 $0

Aleutian Arc -$2,882 -$1.00 $0.20 $0 $0

Bowers Basin -$2,882 -$1.00 $0.00 $0 $0

Aleutian Basin -$2,882 -$1.00 $0.00 $0 $0

Navarin Basin -$2,882 -$1.00 $0.00 $0 $0

Saint Matthew Hall -$2,882 -$1.00 $0.00 $0 $0

Norton Basin -$2,882 -$1.00 $0.00 $0 $0

Hope Basin -$2,882 -$1.00 $0.00 $0 $0

Chukchi Sea -$2,882 -$1.00 $0.01 $0 $0

Beaufort Sea -$2,882 -$1.00 $0.01 $0 $0

1 - Negative values indicate a net benefit rather than a cost.
Section 1: Introduction 17

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Table 1.2. Summary of Damages by Cost Drivers

Damages per Barrel

Spilled

Damages per

Platform

Constructed

Damages per Facility

or Pipeline Landfall OCS Planning Area

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

North Atlantic $1,129 $4,635 $30,312 $78,287 $0 $0

Mid-Atlantic $505 $2,388 $33,578 $83,408 $0 $0

South Atlantic $27 $58 $30,312 $78,287 $0 $0

Straits of Florida $570 $2,800 $30,312 $78,287 $0 $0

Eastern Gulf of

Mexico

$160 $697 $30,312 $78,287 $0 $0

Central Gulf of

Mexico

$306 $1,285 $77,913 $163,455 $0 $0

Western Gulf of

Mexico

$182 $738 $89,423 $179,582 $0 $0

Southern California $444 $2,158 $452,580 $1,901,64

2

$0 $0

Central California $1,320 $6,311 $341,302 $1,567,80

6

$0 $0

Northern California $100 $465 $115,588 $608,566 $0 $0

Washington/Oregon $443 $2,151 $78,784 $166,068 $0 $0

Gulf of Alaska $481 $2,394 $76,646 $157,053 $11,242 $43,319

Kodiak $408 $1,940 $76,399 $159,653 $1,178 $4,540

Cook Inlet $737 $3,678 $20,969 $61,585 $5,978 $23,035

Shumagin $1,343 $6,717 $20,969 $61,585 $515 $1,984

North Aleutian Basin $268 $1,311 $20,969 $61,585 $230 $888

Saint George Basin $1,088 $5,367 $20,969 $61,585 $2,986 $11,505

Aleutian Arc $2,886 $14,418 $20,969 $61,585 $230 $888

Bowers Basin $1,802 $8,973 $20,969 $61,585 $0 $0

Aleutian Basin $1,604 $8,009 $20,969 $61,585 $0 $0

Navarin Basin $2,014 $10,241 $20,969 $61,585 $3,117 $12,010

Saint Matthew Hall $2,483 $12,405 $20,969 $61,585 $0 $0

Norton Basin $1,217 $6,085 $20,969 $61,585 $810 $3,122

Hope Basin $2,192 $10,978 $20,969 $61,585 $5,429 $20,918

Chukchi Sea $1,051 $5,260 $20,969 $61,585 $1,253 $4,830

Beaufort Sea $893 $4,534 $20,969 $61,585 $1,432 $5,517
18 Section 1: Introduction

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Table 1.2. Summary of Damages by Cost Drivers (Continued)

Damages per Mile

of Pipeline

Constructed

Damages per Well

Installed

Damages per 1,000

Barrels Produced

and Transported by OCS Planning Area Tanker

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

North Atlantic $2,121 $8,698 $10,255 $38,399 $3.95 $19.81

Mid-Atlantic $2,578 $11,052 $10,751 $39,017 $4.95 $26.48

South Atlantic $2,121 $8,698 $10,255 $38,399 $3.95 $19.81

Straits of Florida $2,121 $8,698 $10,255 $38,399 $3.95 $19.81

Eastern Gulf of

Mexico

$1,943 $6,431 $10,255 $38,399 $1.63 $9.92

Central Gulf of

Mexico

$5,275 $17,135 $16,932 $49,979 $9.06 $41.11

Western Gulf of

Mexico

$6,580 $22,007 $18,884 $52,402 $11.84 $54.49

Southern California $8,842 $42,949 $27,988 $144,626 $32.16 $161.04

Central California $8,842 $42,949 $27,988 $144,626 $32.16 $161.04

Northern California $5,916 $25,293 $20,459 $94,793 $26.61 $114.97

Washington/Oregon $5,845 $24,387 $16,932 $49,979 $16.49 $72.64

Gulf of Alaska $5,098 $13,533 $16,932 $49,857 $7.32 $33.00

Kodiak $5,086 $14,869 $16,920 $49,979 $7.31 $33.45

Cook Inlet $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

Shumagin $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

North Aleutian Basin $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

Saint George Basin $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

Aleutian Arc $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

Bowers Basin $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

Aleutian Basin $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

Navarin Basin $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

Saint Matthew Hall $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

Norton Basin $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

Hope Basin $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

Chukchi Sea $1,254 $4,039 $8,910 $36,039 $0.02 $3.10

Beaufort Sea $1,254 $4,039 $8,910 $36,039 $0.02 $3.10
Section 1: Introduction 19

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Table 1.2. Summary of Damages by Cost Drivers (Continued)

Annual Damages

per Operating

Platform

Annual Damages

per Mile of Pipeline OCS Planning Area

Low

Estimate

High

Estimate

Low

Estimate

High

Estimate

North Atlantic $39,821 $84,809 $838 $838

Mid-Atlantic $23,598 $71,499 $818 $818

South Atlantic $20,984 $65,972 $46 $46

Straits of Florida $20,230 $65,218 $4 $4

Eastern Gulf of

Mexico

$21,552 $68,713 $3 $3

Central Gulf of

Mexico

$42,261 $124,021 $14 $14

Western Gulf of

Mexico

$51,611 $144,249 $15 $15

Southern California $85,680 $436,768 $72 $72

Central California $86,877 $437,965 $461 $461

Northern California $60,521 $274,585 $280 $280

Washington/Oregon $41,436 $117,809 $117 $117

Gulf of Alaska $47,537 $129,403 $11 $11

Kodiak $45,317 $133,103 $10 $10

Cook Inlet $18,419 $60,786 $1 $1

Shumagin $21,992 $64,359 $13 $13

North Aleutian Basin $21,245 $63,612 $1 $1

Saint George Basin $20,522 $62,889 $2 $2

Aleutian Arc $20,388 $62,755 $1 $1

Bowers Basin $20,387 $62,754 $1 $1

Aleutian Basin $20,452 $62,819 $3 $3

Navarin Basin $20,452 $62,819 $3 $3

Saint Matthew Hall $18,605 $60,972 $0 $0

Norton Basin $18,333 $60,700 $0 $0

Hope Basin $18,333 $60,700 $0 $0

Chukchi Sea $18,333 $60,700 $0 $0

Beaufort Sea $18,333 $60,700 $0 $0
20 Section 1: Introduction

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Table 1.3. Oil Spill Natural Resource Damage Costs, in 1997 Dollars (from Helton and

Penn, 1999)

Case

Amount

Spilled

(barrels)

NRDA Costs NRDA Cost per

Barrel Spilled

Amazon Venture (1986) 11,905 $1,909,000 $160

American Trader (1990) 1 9,476 $28,257,000 $2,982

An Ping (1994) 619 $25,000 $40

Apex Houston (1986) 1 595 $9,376,000 $15,759

Apex Towing (1990) 16,524 $1,979,000 $120

Arco Pipeline (1994) 1 4,523 $7,793,000 $1,723

Arco Anchorage (1985) 1 5,690 $471,000 $83

Berry Petroleum (1994) 1 2,071 $1,471,000 $710

BT Nautilus (1990) 6,019 $4,915,000 $817

Burlington Asphalt (1994) 238 $55,000 $229

Colonial Pipeline (1993) 9,690 $2,775,000 $286

Exxon Bayway (1990) 13,500 $12,214,000 $905

Exxon Valdez (1989) 1 261,905 $1,150,404,000 $4,392

Greenhill (1992) 1 2,905 $2,283,000 $786

Jahre Spray (1995) 1 1,333 $150,000 $113

Jupiter (1990) 20,000 $786,000 $39

Mega Borg (1990) 1 121,429 $335,000 $3

Mobil (1984) 4,048 $336,000 $83

Nestucca (1988) 550 $5,928,000 $10,778

Nosac Forest (1993) 149 $136,000 $914

Presidente Rivera (1989) 7,310 $3,287,000 $450

Quinnipac River (1995) 119 $40,000 $334

RTC-380 (1992) 643 $264,000 $411

Tenyo Maru (1991) 4,119 $6,483,000 $1,574

Texaco Anacortes (1991) 1 5,000 $590,000 $118

Unocal Tank Farm (1992) 1 500 $1,594,000 $3,189

World Prodigy (1989) 6,873 $836,000 $122

1 – Indicates a crude oil spill
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2. Spill-Related Damage Estimation Methodology

2.1 Definition of Damages under Natural Resource Damage Assessment

Three cost components comprise the damages associated with a natural resource injury caused

by an oil spill or other accidental chemical release (NOAA, 1999):

1. The cost of restoring the injured resource to baseline conditions, also called primary

restoration;

2. The cost of compensatory restoration, or, alternatively, the value of interim damages that

accrue to the public related to lost services until the resource recovers to baseline conditions;

3. The costs of performing a damage assessment.

Under the Comprehensive Environmental Response, Compensation, and Liability Act

(CERCLA) and the Oil Pollution Act (OPA), the objective of natural resource damage cases is to

make the public “whole.” The public can be made whole through monetary compensation for

natural resource injuries such that the gain in lifetime utility from compensation equals the utility

loss from the injury (Jones and Pease, 1997).

Utility losses include use and nonuse values. Use values are those associated with the direct use

of a natural resource, such as beach visitation and recreational fishing. Nonuse, passive use, or

passive enjoyment values are those values that are not associated with the direct use of a natural

resource. Both types of values are estimated based on the demand curves for natural resources

estimated using stated and/or revealed preference methods. An economically efficient

compensation scheme would compensate each individual who suffers a utility loss based on that

individual’s loss. The practical difficulties of individual compensation are prohibitive, leading to

the second-best scheme of compensation at the aggregate level through public trustees. Money

collected for compensatory restitution is used to fund natural resource projects.

As defined by NOAA’s rulemaking under CERCLA and OPA, money from natural resource

damage settlements must be used for the purpose of “restoring, rehabilitating, replacing and/or

acquiring the equivalent of the affected natural resources” (NOAA, 1994; NOAA, 1996).

Primary restoration activities are undertaken if they are cost-effective compared to natural

recovery. Primary restoration may be prohibitively expensive and result in further ecological

injury.

Analysts have considered several theoretical approaches to calculating compensatory restoration.

Under the “value-to-cost” approach to compensatory restoration, the costs of the restoration

projects are scaled to the dollar estimate of utility losses (Chapman, et al., 1998). If the utility

gain provided to the public from these projects equals the lost interim utility, then the public is

fully compensated at the aggregate level. However, there is no assurance that the benefits of

compensatory projects will equate to the lost utility. For example, consider a NRDA example

where compensatory damages, including use and nonuse values, are estimated to be $1 million.

If a $1 million settlement is used to fund natural resource projects, there is no assurance that

these projects will provide a utility gain equal to $1 million. The public may be over- or undercompensated depending on the benefits derived from the natural resource projects.
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An alternative to the “value-to-cost” approach is the “value-to-value” method, which scales

restoration actions so that the discounted utility from the natural resource projects equals the

utility loss from the natural resource injury. Both values are estimated in dollar terms. The

“value-to-value” method requires the estimation of the expected utility of future natural resource

projects, including both use and nonuse values. As discussed in more detail below, estimating

lost utility (in dollar terms) as a result of a natural resource injury is a difficult conceptual and

practical task. These difficulties are compounded when analyzing a non-existent project.

The disjunction between monetary measurement of damages and compensation via resource

enhancement projects, along with practical difficulties in demand estimation, has led policy

makers to prefer compensation scaling methods based on ecological services. These “service-toservice” techniques, including habitat equivalency analysis (HEA), have been operationalized in

recent natural resource damage cases.

This section presents a summary of damage estimation methods. The advantages and

disadvantages of both valuation and HEA compensation methodologies are discussed. The

methodology used in the model to estimate natural resource damages is then detailed. Finally,

the assumptions and limitations of the model are discussed.

2.2 Valuation-Based Compensation

2.2.1 Use Values

Valuation-based approaches to measure natural resource damages estimate the necessary

monetary compensation to make the public whole. Valuation includes the lost use and non-use

values. The lost values of users include both lost economic rent and lost consumer surplus.

Economic rents are the loss in profits from commercial activities, such as commercial fishing.

This analysis assumes that the benefits of commercial angling are equal to profits. In other

words, commercial anglers receive no welfare benefits from the activity other than financial

benefits. The methodology for estimating the spill-related costs to commercial fishing is

discussed in Section 7. Non-commercial users of marine natural resources include beach

visitors, recreational saltwater anglers, marine boaters, and those observing or photographing

marine wildlife. These visitors receive an economic benefit, called consumer surplus, from using

the marine resource. The consumer surplus associated with the use of a marine natural resource

is the maximum amount an individual is willing to pay for the activity less actual costs. For

example, an individual may be willing to pay up to $30 for a day visit to a specific beach. If

actual costs, such as gas, marginal vehicle costs, and entrance fees, amount to $20, then the

individual’s consumer surplus is $10.

Consumer surplus is measured relative to the next best alternative. As such, consumer surplus

estimates must properly account for the availability of substitutes including other sites that

provide similar activities and different activities altogether. Failure to account for substitutes in

benefit estimates can lead to a significant overstatement of benefits (Rosenthal, 1987; Smith and

Kaoru, 1990).

The two most common methodologies for estimating lost use values are travel cost model and

contingent valuation. Travel cost models estimate use values through preferences revealed in
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markets. A demand curve for a recreation site is estimated based on the relationship between

travel costs and participation levels and/or site choices, controlling for other independent

variables. Different types of travel cost models include zonal, individual, varying parameters,

and random utility models. The current state-of-the-art travel cost models are random utility

models. These models predict the probability an individual will choose a particular recreation

site as a function of travel costs and site characteristics. Unlike other travel cost models, random

utility models are explicitly derived from a utility function with a specific functional form.

Also, random utility models incorporate substitute sites in a manner consistent with economic

theory. Other travel cost models typically incorporate substitute sites in an ad hoc fashion.

Another advantage of random utility models is these allow one to value the loss of a particular

recreation site due to closure. For a summary of travel cost methodologies, see Milon (1988).

As noted in the Point Comfort/Lavaca Bay damage assessment (Texas General Land Office, et

al., 1999), random utility models "are well-established in NRDA as an appropriate tool for

measuring recreation losses" (p. 17). Random utility models were also used to estimate

recreation losses for the recent North Cape and Tampa Bay oil spills.

Contingent valuation methods rely on the stated preferences of a sample of respondents

(Cameron, 1988; Downing and Ozuna, 1996). There are several formats for contingent valuation

questions. Using an open-ended format, respondents are asked to directly indicate their

maximum willingness to pay for an activity. With a dichotomous-choice format, respondents are

asked whether they would be willing to pay a given dollar amount for an activity. Other question

formats include double-bounded, multiple-bounded, and payment card questions. Due to the

hypothetical nature of contingent valuation questions, considerable debate exists over the validity

of contingent valuation estimates. Specifically, estimates of consumer surplus from contingent

valuation surveys can be invalid due to various biases, including embedding effects, strategic

bias, “yea-saying”, and “warm glow” effects. For an overview of contingent valuation, see

Mitchell and Carson (1989). Recent research has also explored combining stated and revealed

preference methods (Adamowicz, et al., 1994).

2.2.2 Nonuse Values

Under CERCLA and OPA, natural resource trustees can recover passive use losses so long as the

values can be reliably measured. Contingent valuation is the only methodology widely used to

estimate total (passive use plus direct use) values. NOAA convened an expert panel to explore

the reliability of contingent valuation to assist in the final rulemaking process under OPA

(NOAA, 1993). The panel concluded that contingent valuation estimates could provide useful

information in the judicial determination of damages so long as the survey met certain

requirements. The panel provided recommendations for contingent valuation surveys including a

preference for in-person interviews, the referendum question format, a reminder of the

availability of substitutes, and multiple survey versions over time. The panel concluded that

contingent valuation estimates tended to overstate actual losses and subsequent NOAA

guidelines suggest that contingent valuation estimates be scaled downward by a factor of two in

the absence of more reliable scaling information (NOAA, 1994).

The use of contingent valuation for estimating passive use values in natural resource damage

cases remains a controversial issue (Binger, et al., 1995; Portney, 1994; Renner, 1998). While
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some researchers have presented theoretical and empirical evidence supporting the reliability of

contingent valuation estimates (Hanemann, 1994; Randall, 1993; Smith, 1996), others argue that

contingent valuation estimates are not reliable (Diamond and Hausman, 1994, Stevens, et al.,

1991).

Current rules by NOAA and the Department of the Interior limit the inclusion of nonuse values

in natural resource damage assessments. DOI regulations limit the use of contingent valuation

nonuse values to cases where “the authorized official determines that no use values can be

determined” (Robinson, 1996).

2.3 Service-Based Measures of Compensation

Instead of measuring damages based on the demand curves of users and nonusers of a natural

resource, service-based damage measures recognize that, as an approximation, the values

humans place on natural resources are proportional to the ecological services of the resources.

Ecological habitats provide natural services including water quality benefits, wildlife habitat, and

flood control. If the natural services lost as a result of an injury can be compared to the service

gain from compensatory restoration projects, then compensation can be scaled using changes in

ecological services alone. Service-based approaches use an ecological metric, rather than

dollars, to estimate damages. The metric can be biological, such as the number of birds killed or

the lost primary productivity, or based on habitat characteristics, such as the loss of acre-years of

wetland habitat.

The objective of service-based compensation is to scale compensatory restoration projects to

equal the interim service loss from the natural resource injury. In-kind compensatory restoration

projects are those that are similar in type and quality to the injured resource. Out-of-kind

projects are those in habitats different from the injured resource. For example, compensatory

restoration projects on a sandy beach (e.g. dune stabilization) would be in-kind restoration for

oiling of a beach, but out-of-kind restoration for injuries to an intertidal wetland.

Habitat equivalency analysis (HEA) has been used to determine the scale of compensatory

restoration in several natural resource damage cases. Service-based compensation using HEA

requires the following steps:

1. Choose one or more service-based ecological metric(s) as indicators of services;

2. Estimate the ecological service loss from the natural resource injury relative to a reference or

baseline condition;

3. Identify a range of compensatory restoration projects;

4. Choose one or more compensatory restoration projects that provide a present value of service

gains equal to the present value of service losses from the natural resource injury.

HEA is most straightforward when the compensatory projects provide services of the same type

and quality as the injured resource. This allows a one-to-one scaling between the injury and the

compensation using the same metric (NOAA, 1999). For example, the creation of 1,000 acreyears of wetland services can be used to compensate for an interim service loss of 1,000 acre-
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years of wetland services so long as the created and injured wetlands are of similar type and

quality (not accounting for timing of effects through discounting). If identical compensatory

projects are not available, then scaling ratios need to be determined. For example, created

wetlands are often of lower quality than natural wetlands. A.T. Kearney, Inc. (1991) concluded

that a compensation ratio of 2:1 is an appropriate scaling factor in wetland compensation cases.

HEA is used to determine these ratios when the projects provide the same type of service, but of

different quality or timing. Using HEA when out-of-kind projects are chosen for compensatory

restoration requires scaling factors across services.

2.4 Synthesis of Valuation-Based and Service-Based Compensation

Both compensation approaches, valuation-based and service-based, have advantages. The main

advantage of valuation-based compensation is that an extensive literature exists on the use

benefits of natural resources. Freeman (1995) reviewed the literature on the benefits of marine

recreation activities including fishing, beach visits, and saltwater boating. Industrial Economics

(1999) has assembled a database of studies on the benefits of sport fishing. Smith and Kaoru

(1990) conducted a meta-analysis of travel cost recreation demand models. Review of existing

studies on recreation use benefits has been used to estimate natural resource damages in several

injury cases, such as NOAA, et al. (1999). In other cases, original analyses have been conducted

to estimate the recreational losses associated with natural resource damages (e.g., Texas General

Land Office, et al., 1999). Value-to-value or value-to-cost scaling using demand methods is

especially useful when compensation projects are out-of-kind, and is required for large events

where the assumptions of HEA (constant marginal resource values) are not valid. The main

disadvantage of valuation-based compensation is that damages are difficult to assess when nonuse losses are a significant component of the natural resource injury. Few studies exist on the

non-use benefits of marine resources. The only natural resource damage case where non-use

values were a component of the settlement was the Exxon Valdez oil spill. The magnitude of the

Exxon Valdez spill and its extensive ecological impacts led to the inclusion of non-use values in

the damage estimate. For recent spills of smaller magnitude, ecological impacts are typically

assessed using a service-based approach. Other research that estimates the non-use values of

marine resources includes Le Goffe (1995), Silberman (1992), and Stevens, et al. (1991).

Due to the lack of existing research and the small likelihood that non-use values would be used

as a damage component in an actual injury, we conclude that a reliable valuation based

methodology for integrating non-use losses cannot be utilized in this project. We conclude that

using a valuation-based approach to estimate the damages from loss of on-site human use is

appropriate due to the availability of significant previous research and a reasonable probability

that use losses will be a component of an actual injury.

HEA is an appropriate methodology for assessing damages when there is little on-site human use

in the vicinity of the injured area as noted by Julius, et al. (1995). HEA is a preferred

methodology when “the on-site uses are primarily ecological/biological and the off-site human

uses are difficult to quantify” (Julius, et al., 1995, p. 3). The Natural Resource Trustees for Pearl

Harbor (1999) state that “HEA should be used in situations involving primarily the loss of

ecological services with relatively little or no loss of direct human use” (p. 107). Data on
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specific HEA applications are available from completed natural resource damage assessment

reports.

We base our decision to use HEA or valuation approaches to estimate natural resource damages

depending on the primary on-site use of the injured area. For sites where the primary on-site

uses are ecological/biological, we estimate damages using a generalized HEA approach. For

sites where the primary on-site use is human recreation, we estimate damages using a

generalized valuation approach. Thus, a key component of our research is to identify the

primary on-site use along the coast of the U.S. This issue is discussed in detail below.

2.5 Identification of Coastal Recreation Sites

Our analysis is based on hypothetical releases within each OCS Planning Area; we do not have

specific information on where a spill will come ashore. A deep-water spill may not even come

ashore. Using a generalized approach based on spill scenarios, it is necessary to estimate the

probability that a spill will come ashore where the primary on-site use is recreation. Without

specific information on where a spill will come ashore, we assume that the probability that a spill

will affect a recreation area is proportional to the relative amount of recreation area in an OCS

Planning Area. The first step in this procedure is to obtain data on the coastal recreation area in

each OCS Planning Area.

A current national database of coastal recreation sites does not exist (Leeworthy, 1999). The

most recent effort to collect national data on coastal recreation sites was conducted by the

National Oceanic and Atmospheric Administration (1988). As part of the National Estuarine

Inventory, NOAA contacted national, state, and local agencies in the mid-1980’s to identify all

coastal recreation sites. While a more recent inventory would be preferred, significant changes

in the number and magnitude of coastal recreation areas are likely to be few. The methodology

used to identify coastal recreation sites is described in Section 4.4.

2.6 Primary Restoration Costs

Our analysis of natural resource damages due to oil spills is concerned primarily with

compensatory restoration costs. However, the other two components of a natural resource

damage claim, primary restoration and damage assessment costs, need to be addressed.

The Ohio vs. United States Department of the Interior decision in 1989 indicated a strong

preference for resource restoration in natural resource damage cases. Primary restoration actions

should be undertaken unless they are technically infeasible or if the costs of primary restoration

are “grossly disproportionate” to the benefits, namely the reduction in interim losses (Mazzotta,

et al., 1994). While the court did not define “grossly disproportionate”, the Department of the

Interior operationalized the ruling using a 10:1 cost-benefit ratio in the NRDAM/CME (French,

et al., 1997). The NRDAM/CME includes primary restoration costs in its damage assessment if

such costs are less than 10 times the reduction in compensatory damage. Otherwise, natural

recovery is assumed. For the oil spill scenarios analyzed in this project, we use the

NRDAM/CME to determine if primary restoration is undertaken. We rely on the NRDAM/CME

to estimate primary restoration costs for each spill scenario. However, the model indicated that
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primary restoration was not cost-effective in every one of our oil spill scenarios. Thus, primary

restoration costs were always zero.

2.7 Damage Assessment and Administrative Costs

The other component of natural resource damages to consider is the costs of damage assessment,

monitoring, administration, and oversight by the trustees. We reviewed recent damage

assessment reports to determine the magnitude of damage assessment and other administrative

costs. Table 2.1. presents the cost data from six natural resource damage cases. Assessment and

administrative costs could not be obtained for other cases because detailed cost categories were

not presented or assessment and administrative costs were not discussed in the reports. Based on

the data in Table 2.1. and the possibility that assessment and administrative costs may not be

included in a damage claim, we assume that a representative value for assessment and

administrative costs is a 9% addition to natural resource restoration costs.
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 References

Adamowicz, W., J. Louviere, and M. Williams. 1994. “Combining Revealed and Stated

Preference Methods for Valuing Environmental Amenities”. Journal of Environmental

Economics and Management, vol. 26(3): 271- 292.

Binger, Brian R., Robert Copple, and Elizabeth Hoffman. 1995. “Contingent Valuation

Methodology in the Natural Resource Damage Regulatory Process: Choice Theory and the

Embedding Problem”, Natural Resources Journal, vol. 35(3): 443-459.

Cameron, Trudy Ann, 1988, “A New Paradigm for Valuing Non-market Goods Using

Referendum Data: Maximum Likelihood Estimation by Censored Logistic Regression”,

Journal of Environmental Economics and Management, vol. 15(3): 355-379.

Diamond, Peter A., and Jerry A. Hausman. 1994. “Contingent Valuation: Is Some Number

Better than No Number?” Journal of Economic Perspectives, vol. 8(4):45-64.

Downing, M. and T. Ozuna, 1996, “Testing the Reliability of the Benefit Function Transfer

Approach”, Journal of Environmental Economics and Management, vol. 30(3): 316-322.

Freeman, A. Myrick, III. 1995. “The Benefits of Water Quality Improvements for Marine

Recreation: A Review of the Empirical Evidence”, Marine Resource Economics, vol. 10(4):

385-406.

French, Deborah P., Mark Reed, Kathy Jayko, Shih-shing Feng, Henry Rines, Sharon Pavignano,

Tatsu Isaji, Suzanna Puckett, Aimee Keller, Fred W. French III, Dian Gifford, John McCue,

Thomas Opishinski, Garry Brown, Emily MacDonald, Jacqueline Quirk, Stefan Natzke,

Bradley S. Ingram, Richard Bishop, Michael Welsh, and Marty Phillips. 1997. “The

CERCLA Type A Natural Resource Damage Assessment Model for Coastal and Marine

Environments (NRDAM/CME)”. Prepared for the Office of Environmental Policy and

Compliance, U.S. Department of the Interior, contract no. 14-01-0001-91-C-11.

Hanemann, W. Michael. 1994. “Valuing the Environment Through Contingent Valuation”,

Journal of Economic Perspectives, vol. 8(4): 19-43.

Industrial Economics, Inc. 1999. A Meta Analysis of Sport Fishing Values. Report prepared for

Economics Division, U.S. Dept. of the Interior, Fish and Wildlife Service.

Julius, Brian E., John W. Iliff, Charles M. Wahle, J. Harold Hudson, and Erik C. Zobrist. 1995.

“Natural Resource Damage Assessment M/V Miss Beholden Grounding Site, Western

Sambo Reef, FKNMS, March 13, 1993”.

Leeworthy, Vernon R. 1999. Personal communication. National Oceanic and Atmospheric

Administration, Silver Spring, MD.

Louisiana Oil Spill Coordinators Office, Louisiana Department of Environmental Quality,

Louisiana Department of Natural Resources, Louisiana Department of Wildlife and Fisheries,
Section 2: Spill-Related Damage Estimation Methodology 29

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National Oceanic and Atmospheric Administration, and U.S. Fish and Wildlife Service.

1999. “Damage Assessment and Restoration Plan, Texaco Pipeline, Inc. Crude Oil

Discharge, Lake Barre, Louisiana, May 16, 1997”, Final Version.

Mazzotta, Maris J., James J. Opaluch, and Thomas A. Grinalunas, 1994, “Natural Resource

Damage Assessment: The Role of Resource Restoration”, Natural Resources Journal, vol.

34(1): 153-178.

Milon, J. Walter. 1988. “Travel Cost Methods for Estimating the Recreational Use Benefits of

Artificial Marine Habitat”, Southern Journal of Agricultural Economics, vol. 19(1): 87-101.

Mitchell, Robert Cameron, and Richard T. Carson. 1989. Using Surveys to Value Public Goods,

The Contingent Valuation Method. Resources for the Future, Washington D.C.

National Oceanic and Atmospheric Administration. 1999. “Habitat Equivalency: An Overview”.

National Oceanic and Atmospheric Administration. 1996. “Natural Resource Damage

Assessments; Final Rule”, Federal Register, 15 CFR Part 990.

National Oceanic and Atmospheric Administration. 1994. “Natural Resource Damage

Assessments; Final Rule”, Federal Register, 43 CFR Part 11.

National Oceanic and Atmospheric Administration. 1993. “Natural Resource Damage

Assessments Under the Oil Pollution Act of 1990; Proposed Rules”, Federal Register, 15

CFR Part 9.

National Oceanic and Atmospheric Administration. 1988. “National Estuarine Inventory Data

Atlas, Volume 4: Public Recreation Facilities in Coastal Areas”.

National Oceanic and Atmospheric Administration, Rhode Island Department of Environmental

Management, U.S. Department of the Interior, and U.S. Fish and Wildlife Service. 1999.

“Restoration Plan and Environmental Assessment for the January 19, 1996 North Cape Oil

Spill”, Revised Draft.

Natural Resource Trustees for Pearl Harbor, Oahu, Hawaii. 1999. “Final Restoration Plan and

Environmental Assessment for the May 14, 1996 Chevron Pipeline Oil Spill into Waiau

Stream and Pearl Harbor”, Oahu, Hawaii.

Portney, Paul R. 1994. “The Contingent Valuation Debate: Why Economists Should Care”,

Journal of Economic Perspectives, vol. 8(4):3-17.

Randall, Alan. 1993. “Passive-use Values and Contingent Valuation – Valid for Damage

Assessment”. Choices, vol. 8(2): 12-15.

Renner, Rebecca. 1998. “Calculating the Cost of Natural Resource Damage”, Environmental

Science and Technology, vol. 32(3): 86-90, February.
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Robinson, Judith, 1996, “The Role of Nonuse Values in Natural Resource Damages: Past,

Present, and Future”, Texas Law Review, vol. 75(1): 189-214.

Rosenthal, D.H. 1987. “The Necessity for Substitute Prices in Recreation Demand Analyses”,

American Journal of Agricultural Economics, vol. 69(4): 828-837.

Smith, V. Kerry, and Yoshiaki Kaoru. 1990. “Signals or Noise? Explaining the Variation in

Recreation Benefit Estimates”, American Journal of Agricultural Economics, vol. 72(2):

419-433.

Stevens, Thomas H., Jamie Echeverria, Ronald J. Glass, Tim Hager, and Thomas A. More. 1991.

“Measuring the Existence Value of Wildlife: What do CVM Estimates Really Show?” Land

Economics, vol. 67(4): 390-400.

Texas General Land Office, Texas Parks and Wildlife Department, Texas Natural Resource

Conservation Commission, National Oceanic and Atmospheric Administration, and U.S. Fish

and Wildlife Service, U.S. Department of the Interior. 1999. “Damage Assessment and

Restoration Plan and Environmental Assessment for the Point Comfort/Lavaca Bay NPL Site

Recreational Fishing Service Losses”.
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Table 2.1. Damage Assessment, Administrative, and Monitoring Costs

Case Total Damages

Assessment,

Administrative, and

Monitoring Costs

Assessment,

Administrative, and

Monitoring Cost

Percentage

Apex Houston, 1986 $6,400,000 $983,570 15%

Jahre Spray, 1995 $171,674 $54,674 32%

Melvin Fisher, et al., 1992 $351,648 $32,345 9%

Miss Beholden, 1993 $1,873,741 $202,405 11%

North Cape, 1996 $27,597,198 $1,314,152 5%

World Prodigy, 1989 $567,2991 $42,299 7%

1 – Includes only the damages paid to NOAA.

Source: Oil spill damage assessment reports.

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3. Oil Spill Modeling

3.1 Introduction

Damage estimates derived in this report are based on the physical modeling of hypothetical oil

spills. This section presents the oil spill modeling methodology and assumptions. The oil spill

scenarios modeled were chosen to be representative of the location and magnitude of spills that

would likely result from MMS oil and gas lease activity. The spills represent three potential spill

sources: tankers, platforms, and pipelines. At least one spill location is modeled in each of the

26 OCS Planning Areas. This section first describes the assumptions made to produce the inputs

into the Natural Resource Damage Assessment Model for Coastal and Marine Environments

(NRDAM/CME). Then, the model outputs for the oil spill scenarios are presented. Finally, the

physical results are linked to the economic damage approaches, which underlie the estimates

made in following chapters.

Two modeling software programs are used, NRDAM/CME and SIMAP. The Natural Resource

Damage Assessment Model for Coastal and Marine Environments (NRDAM/CME, version 2.4)

is a publicly available program that models the physical, biological, and economic impacts of

chemical releases into marine environments. The model requires input data on such variables as

spill location, spill magnitude, wind direction and speed, and type of chemical. The model

produces a three-dimensional trajectory for the chemical over time. The data base for the model

includes information pertaining to the abundance of various wildlife species and the location of

different habitat types (marsh, mud flats, sand beach, etc.). Using data on toxicity, the model

estimates the biological impact of the chemical’s trajectory. We use the NRDAM/CME model’s

estimate of the biological injury as the basis for economic damage estimates made in following

chapters.

The other computer program used to model oil spills is SIMAP, a proprietary program of

Applied Science Associates, Inc. SIMAP is a refined version of the NRDAM/CME that includes

a more detailed ecological database and allows one to model a subsurface chemical release.

Several oil spills were modeled using SIMAP to estimate adjustment factors for the

NRDAM/CME results. Thus, the NRDAM/CME is used to model all oil spill scenarios and then

the results are adjusted based on selected comparisons between the NRDAM/CME and SIMAP.

These comparisons are described in Section 3.4. It is reasonable to ask why not use SIMAP for

all oil spill analysis. The answer is that NRDAM/CME is in the public domain and, thus,

scenario runs cost only the computer and operator time they take. SIMAP, being proprietary, has

a cost associated with every run. To save financial resources this project commissioned the

minimum number of SIMAP runs needed to refine the analysis.

Both SIMAP and the NRDAM/CME produce estimates of oiling based on lethal concentrations.

For example, the models estimate an oil slick area and a linear estimate of coastal oiling based on

the lethal threshold criterion. An important issue is how the model estimates of oiling compare

with injury estimates that would result in an actual damage assessment. For example, the linear

estimate of coastal oiling (based on the lethal threshold criterion) may not accurately reflect a

coastal oiling injury assessment based on field observations. The oiling estimates produced by

the models do not consider sub-lethal impacts or minor habitat service losses. Actual injury
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assessments may include service losses that are relatively minor. About 55% of the lost marsh

services in the 1997 Lake Barre oil spill in Louisiana were a result of light oiling with an initial

service loss of only 10% (Louisiana Oil Spill Coordinators Office, et al., 1999). In the 1996

Chevron pipeline spill in Hawaii, the responsible party proposed scaling initial marsh service

losses as either 95%, 50%, or 10% based on the degree of oiling (The Natural Resource Trustees

for Pearl Harbor, Oahu, Hawaii, 1999). The relationship between the NRDAM/CME and

SIMAP outputs and field observations of oiling impacts is uncertain. This issue is addressed by

comparing the outputs of the NRDAM/CME with field observations in actual damage

assessments. This comparison is described in Section 3.4.3.

3.2 NRDAM/CME User Inputs

The NRDAM/CME requires the user to input data on several variables. These inputs include:

1. Spill location

2. Date and time of spill

3. Chemical spilled

4. Volume and duration of spill

5. Wind speed and direction time series

6. Tidal and background currents

7. Air and water temperature

8. Suspended sediment concentration and settling rate

Additional inputs are not relevant to our analysis. For example, the NRDAM/CME requires a

price index to adjust for inflation but we estimate economic damages using a different approach.

Several inputs are held constant for all scenarios. We assume no cleanup efforts following the

spill and that the spill release occurs over a 24-hour period. Most spills begin on May 1 at 0:00

hours. This date was chosen to maximize the biological injury of the spill (French, 2000). While

colder weather reduces oil evaporation and mixing causing more oil to come ashore, the

abundance of most wildlife species are lowest during colder weather. Thus, modeling all spills

during May represents a compromise that should result in more damages than modeling spills in

winter or summer months.

Spills in several Alaska Planning Areas (St. Matthew Hall, Navarin Basin, Norton Basin, Hope

Basin, Chukchi Sea, and Beaufort Sea) begin on August 1 at 0:00 hours. Preliminary analysis

using the NRDAM/CME indicated that this spill date resulted in the most significant biological

impacts.

We assume no background currents because complete background current data are not available.

All spills involve crude oil specific to the location. For example, all spills in the Gulf of Mexico

are of South Louisiana crude oil. The air and water temperatures, sediment concentrations, and

settling rate reflect the default values in the NRDAM/CME database for each spill location and

time of year. The input data for spill location, spill size, winds, and oil types are detailed below.
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3.2.1 Spill Locations

The spill locations were chosen to represent areas with relatively high spill probabilities in the

future. Spill locations are chosen for all OCS Planning Areas. For those areas with no current

MMS oil and gas activity, only one spill location was chosen. Up to six locations were chosen

for selected OCS Planning Areas. The spill locations vary significantly in distance to shore.

Some are within a few miles of the coast while others are over 100 miles from shore. Table 3.1.

presents the locations of all oil spill scenarios.

3.2.2 Wind Data

The wind speed and direction following an oil spill significantly influences the trajectory of the

spill. Several options were considered for obtaining input wind data. The use of a constant wind

speed and direction is considered to be unrealistic. A constant wind speed and direction results

in an oil spill trajectory that is basically a straight line without significant “spreading.” Thus, a

spill reaching the shore would impact a small segment of the coast. Another option is to

randomly draw actual observations on wind speed and direction using NOAA wind data from

offshore buoys and coastal meteorological stations. This approach was not used because random

draws could result in unrealistic sudden shifts in wind speed and direction.

The approach chosen is to draw an actual wind time series from historical data. This approach

results in a wind time series that is realistic but the time series can still be drawn to meet certain

criteria (French, 2000). The wind time series used for each spill location is the series that results

in the most coastal oiling. For each spill location, the nearest location for wind data is located

(either offshore buoys or coastal stations). Table 3.2. indicates the wind station used for each

spill location. The most recent five years of hourly observations on wind speed and direction are

obtained for this station. This produced about 44,000 continuous hourly wind observations.

The NRDAM/CME normally requires about a month of wind data before chemical

concentrations become sufficiently low not to cause significant injury. The wind observations

were broken into about 44 continuous subsets of 1,000 hourly observations (about 42 days of

data). Then, the 360 degree compass range was divided into eight segments, each 45 degrees in

range. For each data subset, the number of observations in each of the eight compass segments

is obtained. Maps are used to identify the onshore wind direction for each spill location. For

each spill location the wind time series used in the analysis is the data subset where the winds

were blowing onshore most frequently to maximize coastal impacts.

3.2.3 Spill Size

Ecological injuries and natural resource damages vary by the size of a spill. Previous research

indicates that the relationship between spill magnitude and damages is nonlinear (Sirkar, et al,

1997). Thus, it is necessary to analyze spills of different magnitudes to properly account for a

range of damages. Analyzing many spills of various magnitudes allows a greater representation

of likely scenarios but requires more detailed input data and more computing resources. Based

on these tradeoffs, we analyze both large and small spills for each spill location. This requires

information on the average size of “small” and “large” spills from each of three sources (tankers,

pipelines, and platforms).
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We define a “small” spill, regardless of source, as ranging from 1,000 gallons (about 24 barrels)

up to 1,000 barrels. NRDAM/CME produces very low damage estimates (generally less than

$100) for spills of less than 1,000 gallons (Sirkar, et al., 1997). U.S. Coast Guard data from

1973-1998 indicates that spills of less than 1,000 gallons account for only 4.2% of the total

volume of oil spilled into U.S. navigable waters (USCG, 2000). We exclude spills of less than

1,000 gallons from the analysis, which does not create a significant omission of spill volume or

damages. We define a “large” spill as any spill greater than 1,000 barrels. This is the value

chosen by MMS in their “OCS Oil Spill Facts” (MMS, 1999).

The average spill magnitudes within the “small” and “large” ranges need to be determined.

MMS maintains a database on spills from pipelines and platforms. Between 1971 and mid-1999,

there were a total of 173 pipeline or platform small (1,000 gallons-1,000 barrels) spills from

OCS activity. Based on these data, the average small spill size is 111 barrels. To reduce the

number of scenarios that need to be run through the NRDAM/CME, we assume that all small

spills, regardless of source, are 111 barrels. Oil spill data indicates that an average large (greater

than 1,000 barrels) tanker spill differs in magnitude from an average large platform or pipeline

spill (MMS, 1999). From 1980-1998, the average spill size for large (greater than 1,000 barrels)

platform and pipeline spills associated with OCS activity was 7,000 barrels. For 1980-1997, the

average large tanker spill in U.S. waters was 25,500 barrels.

The probability of the spill scenarios also needs to be determined. Spill rates for large platform

and pipeline spills are provided by MMS (1999). The spill rate for platforms is 0.45 large spills

per billion barrels handled and the spill rate for pipelines is 1.32 large spills per billion barrels

handled. MMS does not publish small spill rates. During the period between 1971 and mid1999, total offshore production was 10.387 billion barrels. Thus, the small spill occurrence rate

was 16.66 spills per billion barrels handled (173/10.387 billion). Ideally, separate spill rates

would be developed for pipelines and platforms. However, the database often fails to indicate

the source of the spill (it is missing for 68 of the 173 spills). Without more complete data, the

small spills can be allocated only based on the allocation of large spills. The MMS database

indicates that large platform spills are only about one-third as likely as large pipeline spills.

(25.4% of large OCS spills are from platforms and 74.6% are from pipelines.) We assume the

same relationship for small spills – that 25.4% of all OCS small spills are also from platforms.

Thus, the spill rate for small platform spills is 4.23 spills per billion barrels handled and the spill

rate for small pipeline spills is 12.43 spills per billion barrels handled.

The worldwide spill rate for tankers is 1.3 large spills per billion barrels handled based on data

from 1980-1997 (MMS, 1999). We use a slightly lower large spill rate, 1.21 spills per billion

barrels handled, based on spill data for U.S. waters (Anderson, 2000). Finally, the small spill

rate for tankers needs to be determined. Data on all reported chemical releases into U.S.

navigable waterways are available from the U.S. Coast Guard’s Marine Casualty and Pollution

Database. This database was queried to obtain information on all tanker spills since 1992 (a

different reporting system was used prior to 1992). The data indicate a total of 126,059 chemical

spills since 1992, with 4,939 from tanker ships. Data on spill size were not available for most of

these spills. A total of 416 tanker spills of a petroleum product greater than 1,000 gallons were

identified. Of these, 202 spills were between 1,000 gallons and 1,000 barrels and 214 spills were

larger than 1,000 barrels. Thus, the small spill rate is 0.944 times the large spill rate. Using the
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large spill rate of 1.21 large spills per billion barrels handled, we estimate the small spill rate for

tankers in U.S. waters to be 1.14 small spills per billion barrels handled.

A summary of spill magnitudes and rates is presented in Table 3.3. The most likely spill is a

small pipeline spill while the least likely spill is a large platform spill.

3.2.4 Oil Types

The type of oil spilled influences ecological injuries and economic damages. Oil types vary in

physical characteristics and toxicity. Regional variations exist in crude oil properties. The

NRDAM/CME database includes several crude oil types. The most appropriate oil type for each

OCS Planning Area was determined. The oil types for platform and pipeline spills are based on

the “local” oil type. The oil type for tanker spills represent the type of crude oil that would be

imported into the area. The oil types, by OCS Planning Area and spill type, are presented in

Table 3.4.

3.3 NRDAM/CME Scenario Results

This section reports on the direct outputs from the NRDAM/CME model runs. Data are

presented on the following variables for each spill scenario:

1. The magnitude of the oil slick

2. The location of the oil slick (distance from shore)

3. The number of wildlife killed, by species category

4. The NRDAM/CME estimate of economic damages

5. The amount of linear coastal oiling

6. The amount of area coastal oiling

7. The oiled coastal habitats

8. The percent of the oil coming ashore

These model output variables provide “drivers” used to estimate the economic damages

associated with each hypothetical spill. To provide a comparison for the results obtained in this

project, the estimated economic damages from the NRDAM/CME are also provided. Note that

these estimates assume no beach closures and, thus, likely underestimate actual damages for

those spills that cause coastal impacts.

Table 3.5 presents the model output for each scenario for the oil slick magnitude and location,

the estimated wildlife killed, and the NRDAM/CME estimate of economic damages. Table 3.6

presents the model output for the linear and area measures of coastal oiling, the coastal habitats

oiled, and the percent of oil reaching shore.
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3.4 Model Output Adjustments

3.4.1 Rationale for Adjustment

The outputs in Table 3.5 and 3.6 generally cannot be used directly as estimates of injury for this

project for four reasons:

1. The NRDAM/CME was not designed to model sub-surface chemical releases;

2. The level of resolution in the NRDAM/CME is relatively large;

3. The “lethal threshold” criterion used in the NRDAM/CME may not correspond to field

estimates of coastal oiling;

4. The NRDAM/CME does not estimate closure periods.

The first two issues are addressed based on a supplemental analysis comparing output from the

NRDAM/CME with output from SIMAP, an improved version of the NRDAM/CME. The third

issue is addressed using a supplemental analysis comparing the output from the NRDAM/CME

with field observation of coastal impacts. The fourth issue is addressed by comparing the

NRDAM/CME output with existing data on closure periods. These analyses are discussed

below.

3.4.2 Supplemental SIMAP Analysis

3.4.2.1 Introduction to Supplemental SIMAP Analysis

SIMAP is an improved version of the NRDAM/CME that allows additional flexibility and

incorporates more detailed ecological data. Several scenarios were analyzed using SIMAP to

address limitations of the NRDAM/CME. The purpose of analyzing some oil spill scenarios

using SIMAP is to provide adjustment factors to apply to the NRDAM/CME outputs listed in

Tables 3.5 and 3.6. Two types of adjustments are considered:

1. Adjustments based on differences between surface spills modeled using SIMAP and the

NRDAM/CME;

2. Adjustments based on the differences between surface and subsurface spills modeled using

SIMAP.

To adjust the NRDAM/CME output in Tables 3.5 and 3.6 to account for the improvements

incorporated into SIMAP, similar surface spill scenarios were analyzed using both SIMAP and

the NRDAM/CME. To provide a direct comparison, all input data (spill date, wind time series,

etc.) were the same for both models. Thus, all output differences are attributed to the differences

in the two modeling procedures. To compare surface and subsurface spills, the paired scenarios

were analyzed using SIMAP with the only difference being the depth of the spill. Thus, all

differences are attributed to the depth of the spill.

The output data are compared on the basis of the output variables in Tables 3.5 and 3.6. Dr.

Deborah French of Applied Science Associates, Inc performed the SIMAP analysis. A copy of

the ASA report is presented as the appendix to this document.
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3.4.2.2 SIMAP Scenarios

A total of 16 oil spill scenarios were analyzed using SIMAP. The input data are presented in

Table 3.7. The spill locations are approximately 40 miles off the Texas coast and 4 miles off the

coast of the Florida Panhandle. All spills occur on May 1 at 0:00 hours and involve South

Louisiana crude oil. Other input data are as described in Section 3.2. for the NRDAM/CME

scenarios. Two wind time series were chosen for each spill location to produce impacts in

different ecological habitats. Spill depths were chosen based on the likely depth of spills for the

spill locations. The eight surface spill scenarios in Table 3.7 were also modeled using the

NRDAM/CME with the same inputs.

3.4.2.3 Scenario Outputs

Tables 3.8 and 3.9 compare the output data for the surface spills modeled using SIMAP and the

NRDAM/CME. The estimated number of bird deaths tends to be significantly higher for the

SIMAP output as compared to the NRDAM/CME output (Table 3.8). Based on these results, we

conclude that the NRDAM/CME estimated bird deaths in Table 3.5 need to be adjusted upwards

to account for the improvements incorporated in the SIMAP model. The ratios for the two small

Texas spills (54 and 374) appear to be outliers as compared to the other six outputs. Thus, a

simple average of the eight ratios may not produce a valid adjustment factor. We choose an

adjustment factor of 3.15 for bird deaths, which is the average of the six comparisons excluding

the small Texas spills. For both marine mammals and reptiles, we conclude that an adjustment

factor for the NRDAM/CME output is not necessary because NRDAM/CME is just as likely as

SIMAP to result in higher mammal and reptile deaths.

The comparisons in Table 3.9 indicate that SIMAP estimates a larger oil slick for all Texas

scenarios and two Florida scenarios. We choose an adjustment factor of 1.61 for the

NRDAM/CME oil slick estimates as the average of the eight ratios in Table 3.9. While SIMAP

tends to produce a larger oil slick than the NRDAM/CME, it tends to produce lower estimates of

coastal oiling. For the NRDAM/CME linear estimate of coastal oiling, we choose an adjustment

factor of 0.54 as the average of the eight linear oiling ratios in Table 3.9. For the NRDAM/CME

area estimate of coastal oiling, we choose an adjustment factor of 0.60 as the average of the eight

area oiling ratios in Table 3.9.

The final issue to address with the SIMAP output is the difference between surface and

subsurface spills. As shown in Table 3.10, the output estimate for the similar surface and

subsurface spills tend to be verycomparable. The average ratios in Table 3.10 are 0.97 for bird

deaths, 1.22 for oil slick size, and 1.05 for coastal oiling area. Based on these data, we conclude

that no adjustment factor is necessary to use the NRDAM/CME output in Tables 3.5 and 3.6 to

represent the impacts of a subsurface pipeline release.

In summary, the supplemental SIMAP analysis indicates that adjustment factors are necessary

for some output variables. Specifically, the NRDAM/CME output in Tables 3.5 and 3.6 should

be adjusted as follows:

1. Bird deaths should be increased by a factor of 3.15;

2. Oil slick area should be adjusted upwards by a factor of 1.61;
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3. Linear oiling should be adjusted downward by a factor of 0.54;

4. Area oiling should be adjusted downward by a factor of 0.60.

Marine mammal and reptile deaths should not be adjusted. Note that the SIMAP analysis does

not provide insight on whether the area-time and linear distance-time measures in Tables 3.5 and

3.6 should be adjusted. The SIMAP model was run for a period of 14 days while the

NRDAM/CME ran until oil concentrations reached model-defined minimum levels. Thus, the

NRDAM/CME area-time and linear distance-time measures were significantly higher than the

comparable SIMAP estimates. A separate supplemental analysis was necessary to address this

issue because the duration of an impact is an important component of damage assessment.

3.4.3 Case Study Scenarios

The oil slick and coastal oiling estimates produced by either the NRDAM/CME or SIMAP are

based on a lethal threshold concentration criterion. This section addresses the relationship

between the model outputs and field observations of oiling that would be used in a damage

assessment. A particular concern is that the output from the models may fail to account for sublethal impacts and low-percentage service losses.

We explore the relationship between the models’ outputs and observed injuries by comparing

model outputs to field data from actual damage assessments. The damage assessment reports for

several oil spills were identified that provide estimates of observed oiling impacts using either

linear, area, or area-time metrics. We determined whether these cases could be modeled with the

NRDAM/CME considering the availability of data on spill date, magnitude of spill, chemical

spilled, and location. We found five cases that met these criteria:

1. Lake Barre, 1997 (Louisiana)

2. Waiau Bay and Pearl Harbor, 1996 (Hawaii)

3. North Cape, 1996 (Rhode Island)

4. Tampa Bay, 1993 (Florida)

5. Greenhill-Timbalier Bay, 1992 (Louisiana)

Each of these cases was modeled using the NRDAM/CME. Wind data beginning the date of the

spill were obtained from NOAA offshore buoy historical records. Data on air and water

temperature were also obtained from historical records. All cases were run without clean-up

efforts. Default water current and tide data were used.

Table 3.11 compares the oiling estimates based on the NRDAM/CME output and field

observations from the damage assessment reports. The NRDAM/CME results are presented both

unadjusted and adjusted as described in Section 3.4.2.3. For the Lake Barre spill, the

NRDAM/CME area estimate of coastal oiling is greater than the observed “heavy” oiling but less

than the observed total oiling (including “light” and “heavy”). Note that the marsh service loss is

adjusted by 0.60, the same factor as the area estimates. The adjusted marsh service loss estimate

from the model is about 2.8 times higher than the observed service loss. For the Pearl Harbor

spill, the NRDAM/CME estimates of linear and area oiling are closer to the observed values
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prior to adjustment. For the North Cape spill, the NRDAM/CME estimate of linear oiling, after

adjustment, is within 20% of the observed value. For the Tampa Bay spill, the adjusted

NRDAM/CME estimates are greater than the observed linear and area values by factors of 2.2

and 11.1 respectively. Finally, for the Greenhill-Timbalier Bay spill, the adjusted

NRDAM/CME estimate of area oiling is less than the observed value by a factor of 13.6.

Considering these comparisons, we conclude that the adjusted NRDAM/CME outputs provide

reasonable estimates of injuries that would be observed in the field. No adjustment is considered

necessary to account for sub-lethal impacts. However, we do apply an adjustment factor of 0.60

to the area-time measures in Table 3.6 considering that the comparison in Section 3.4.2.3

suggests this adjustment factor for area coastal oiling estimates and the Lake Barre example

(Table 3.11) indicates that adjustment improves the accuracy of the estimate.

The time path of the ecological service loss is important in relation to discounting. If all

ecological damages occur within one year, a discount factor does not need to be applied.

However, if ecological impacts occur several years into the future, then the appropriate discount

factor should be applied. The approximate duration of ecological service loss was calculated by

dividing the area-time coastal oiling estimate in Table 3.6 by the matching area estimate. Only

two of the 87 scenarios with coastal oiling indicated impacts beyond one year. The average

duration of the coastal area oiling was about 180 days. Thus, we assume that all ecological

service losses occur within one year and no discounting of the service loss is required.

3.4.4 Closure Periods

The NRDAM/CME estimates injuries using both a physical (linear or area) metric and a time

metric. For human use impacts (beach recreation, fishing), estimation of economic damages

requires “drivers” with both a physical and time component. For example, estimation of beach

recreation damages requires an estimate of the length of beach closed and the duration of the

closure. Two options to obtain drivers for human use impacts are:

1. Use the distance-time and area-time outputs from the model to estimate both the physical and

time components for the drivers;

2. Use only the distance and area outputs from the model along with an independent estimate of

the closure period.

The first option would rely on the model to imply a time component. Consider beach recreation.

One could take the output from Table 3.6 to provide an estimate of both the amount of beach

affected and the closure period. Section 3.4.3. indicates that the linear estimate of coastal oiling,

adjusted downward by a factor of 0.54, provides a reasonable estimate for what would be

observed in a damage assessment. The model estimate of coastal oiling measured in meter-days

divided by the estimate in terms of meters provides an estimate of the duration of the impact (in

days). This calculation was performed for all oil spill scenarios in Table 3.6 for which coastal

impacts are indicated. The resulting average duration of impact is 170 days, ranging from 0.8 to

378 days. Most of the values (70%) are between 50 and 250 days. The same calculations were

performed on the outputs for area coastal oiling. The average duration is 178 days, ranging from

0.8 to 675 days. Again, most (66%) were between 50 and 250 days. Finally, the duration of the
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oil slick was explored by dividing the area-time oil slick output in Table 3.5 by the area output.

The average duration is only 0.15 days with a range from 0.01 days to 2.20 days.

The next issue is whether these implied duration estimates are consistent with closure periods for

actual oil spill cases. Damage assessment reports were explored to obtain data on the closure

period as a result of oil spills. Table 3.12 present closure period data for six oil spills. The

closure periods in Table 3.12 are all less than the 170 or 178 day periods implied by the data in

Table 3.6 but greater than the oil slick duration implied in Table 3.5. Thus, we conclude that the

closure periods should not be defined by the NRDAM/CME output. While the NRDAM/CME

provides a reasonable estimate of the physical impact (linear distance or area), the implied

duration estimates are not valid as closure period estimates. Thus, we choose to use the second

option above and base our drivers on an independent estimate of closure periods.

Table 3.12 provides several estimates of beach or coastal recreation closures. At the minimum,

closures of several days are indicated. The longest beach closure is two months in the World

Prodigy case. Based on the data in Table 3.12, we choose a closure period of three weeks (21

days) for beach recreation. This value is the closure period for the Tampa Bay spill, similar to

the coastal closure period for the Pearl Harbor, and within the bounds of the closure periods for

the World Prodigy and American Trader spills. The fishing closure periods in Table 3.12 tend to

be longer than the beach closure periods. Three of the fishing closure periods are longer than

one month while one is only a week. Based on these data, we choose a fishing closure period of

60 days. This value is slightly less than the Lake Barre and North Cape closure periods, within

the Tampa Bay closure periods, but greater than the World Prodigy closure period.

The 60-day fishery closure period applies to oiling of shore-based fisheries and offshore fisheries

closed by the presence of an oil slick. A separate closure period is considered for the closure of

an oiled harbor that restricts navigation. As discussed in Section 5.5., harbor closures and

navigation restrictions can reduce the number of offshore fishing trips. The period of

navigational restrictions in a harbor is likely to correspond to the period of clean-up operations.

The 1990 Apex Barges oil spill resulted in navigational restrictions for nine to twelve days

(Texas General Land Office, et al., 1997). We assume a harbor closure period of ten days.

During this harbor closure period, we assume that fishing boats will not be able to leave port.

3.4.5 Adjustment Summary

Table 3.13 presents the final adjustment factors for the data in Tables 3.5 and 3.6 based on the

supplementary analyses. No further adjustment is considered necessary for sub-surface spills.

Note that no adjustment factor is applied to the variables not listed in Table 3.13 (percent of oil

coming ashore, location of oil slick, etc.) because no information was available to address these

issues. Based on the results in Table 3.11, we believe that the adjusted model outputs will

produce accurate injury estimates within an order of magnitude of what would be observed in the

field and reported in an actual damage assessment.
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3.5 Economic Damage Quantification

3.5.1 Introduction

The adjusted physical model outputs have to be linked to the economic estimates presented in

following chapters. This section considers three categories of economic damages:

1. Beach recreation

2. Recreational fishing

3. Ecological damages

The procedures for estimating the economic damages in each of these categories for all oil spill

scenarios are discussed below.

3.5.2 Beach Recreation Economic Damages

The injury metric for beach recreation is the number of days of lost beach recreation. Estimation

of the economic damages for beach recreation for each spill scenario proceeds as follows:

1. Adjust the linear meter estimates in Table 3.6 by a factor of 0.54 as described in Sections

3.4.2.3. This produces an estimate of the linear amount of coastal oiling for each scenario

measured in meters. Note that if the value in the Table is zero, there is no coastal oiling and

no damage to beach recreation.

2. Adjust the estimate obtained in Step 1 by the appropriate ratio in Table 4.2. This provides an

estimate of how much of this coastal oiling occurs on public beaches.

3. Multiply the value obtained in Step 2 by the beach closure period of 21 days as described in

Section 3.4.4. This produces an estimate of the natural resource injury, measured in meterdays.

4. Multiply the value obtained in Step 3 by the beach visitation rate for the OCS Planning Area

from Table 4.4. This provides an estimate of the number of lost days of beach recreation due

to beach closure.

5. Multiply the value obtained in Step 4 by the low and high estimates for the consumer surplus

of a day of beach recreation; $4 and $19 per day. (See Section 4.9.) This produces the low

and high estimates for the beach recreation economic damages.

3.5.3 Recreational Fishing Economic Damages

The injury metric for recreational fishing is the number of lost days of fishing. As described in

Section 5.4, recreational fishing impacts occur in three ways:

1. The loss of coastal fishing days (shore fishing and shellfishing) due to coastal oiling;

2. The loss of off-shore fishing days due to coastal oiling that prevents boats from leaving the

harbor;

3. The loss of offshore fishing days due to the presence of an offshore oil slick.
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The economic damage to recreational fishing is the sum of these three damage estimates. The

steps to estimate the economic damage to coastal fishing for each spill scenario are:

1. Adjust the linear (meter) estimate of coastal oiling for each spill scenario from Table 3.6. by

a factor of 0.54 as described in Section 3.4.2.3. This produces an adjusted estimate of linear

coastal oiling. Note that if this value is zero there is no damage to coastal fishing.

2. Multiply the value obtained in Step 1 by the fishery closure period of 60 days. This produces

an estimate of the natural resource injury to recreational fishing, measured in meter-days.

3. Multiply the value obtained in Step 2 by the appropriate visitation rates for coastal fishing

and shellfishing from Table 5.3. For each activity, this step produces an estimate of the lost

days of fishing.

4. Multiply the two values from Step 3 by the low and high estimates of the consumer surplus

of a day of recreational fishing; $11 and $57 per day. (See Section 5.6.) This produces a low

and high estimate for the economic damages to coastal fishing for each oil spill scenario.

The economic damages to offshore fishing due to coastal oiling is calculated with the following

steps:

1. Adjust the linear (meter) estimate of coastal oiling from Table 3.6. for each spill scenario by

a factor of 0.54 as described in Section 3.4.2.3. This produces an adjusted estimate of linear

coastal oiling. Note that if this value is zero, there is no damage to offshore fishing from

coastal oiling.

2. Multiply the value obtained in Step 1 by the harbor closure period of 10 days. This produces

an estimate of the natural resource injury to recreational fishing, measured in meter-days.

3. Multiply the value obtained in Step 2 by the appropriate visitation rates for offshore

private/rental boat and private/party boat fishing from Table 5.4. For each activity, this step

produces an estimate of the lost days of fishing.

4. Multiply the two values from Step 3 by the low and high estimates of the consumer surplus

of a day of recreational fishing; $11 and $57 per day. (See 5.6.) This produces a low and

high estimate for the economic damages to offshore fishing from coastal oiling for each oil

spill scenario.

The economic damage to offshore fishing due to the presence of oil slicks in calculated with the

following steps:

1. Adjust the area (square meter) estimate of coastal oiling from Table 3.5. for each spill

scenario by a factor of 1.61 as described in Section 3.4.2.3. This produces an adjusted

estimate of the area covered by the oil slick.

2. Refer to Table 3.5. for an estimate of the percentage of the oil slick that occurs in the

Territorial Seas and EEZ. Use these percentages to calculate the area of the oil slick in each

of these two zones.

3. Multiply the two values obtained in Step 2 (one for the Territorial Seas and one for the EEZ)

by the 60 day fishery closure period. Note that some of these values may be zero if the oil
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slick does not enter one or both of these zones. Values of zero imply no offshore recreational

fishing damage in those zones.

4. Multiply the two values obtained in Step 3 by the appropriate visitation rates from Table 5.8.

Note that the two visitation rates for the Territorial Seas should be multiplied by the square

meter-day value from Step 3 for the Territorial Seas and the two visitation rates for the EEZ

should be multiplied by the square meter-day value from Step 3 for the EEZ. This step will

produce four estimates of lost offshore fishing days.

5. Multiply each of the four values obtained in Step 4 by the low and high estimates of the

consumer surplus of a day of recreational fishing; $11 and $57 per day. (See Section 5.6.)

Sum the four low estimates to obtain a low estimate of the economic damage to offshore

fishing due to the oil slick. Sum the four high estimates to obtain a high estimate of the

economic damage to offshore fishing due to the oil slick.

This will produce a total of eight low and eight high estimates (coastal fishing, shellfishing,

offshore private/rental boat from coastal oiling, offshore party/charter boat from coastal oiling,

offshore private/rental boat from oil slicks in Territorial Seas, offshore party/charter boat from

oil slicks in Territorial Seas, offshore private/rental boat from oil slicks in the EEZ, offshore

party/charter boat from oil slicks in the EEZ). Simply sum these eight low values to obtain the

total low estimate of the economic damage to recreational fishing for each oil spill scenario.

Sum the eight high values to obtain the total high estimate of the economic damage to

recreational fishing for each oil spill scenario.

3.5.4 Ecological Economic Damages

The ecological damages can be divided into two categories: oiling of coastal habitats and injury

to wildlife. The following steps estimate the coastal habitat damages:

1. Adjust the area-time (square meter-days) estimates in Table 3.6. downward by a factor of

0.60 as described in Sections 3.4.2.3. and 3.4.3. Note that the data for the amount of coastal

oiling for each habitat type are not presented in Table 3.6. Separate estimates for each

habitat were used in the actual calculations. This step produces an adjusted estimate of the

natural resource injury to each coastal habitat for each oil spill scenario.

2. Convert the square meter-day values from Step 1 to acre-years by dividing by a factor of

0.00000068. This will produce an estimate of the service loss to each coastal habitat for each

oil spill scenario.

3. Divide each value obtained in Step 2 by a factor of 4.23 as described in Section 6.4.2. This

produces an estimate of the needed acres of compensatory restoration for each coastal

habitat.

4. Multiply the values obtained in Step 3 by the low and high restoration costs from Table 6.7.

Sum the values across all habitats affected by each spill scenario. This produces a low and

high estimate for the economic damage of coastal oiling of ecological habitats for each oil

spill scenario.

The damages due to wildlife impacts are estimated with the following steps:
46 Section 3: Oil Spill Modeling

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1. Adjust the number of bird deaths from Table 3.5. upwards by a factor of 2.15. Do not adjust

the number of marine mammal and reptile deaths. (See Section 3.4.2.3.)

2. Multiply the number of wildlife killed, by category, by the appropriate low and high damage

estimate from Table 6.7. Sum the value for each category to obtain low and high estimates

for wildlife damages.

3.5.5 Commercial Fishing Economic Damages

Commercial fishing economic damages are calculated using procedures similar to recreational

fishing. The baseline value of commercial fishing is based on the value of commercial landings

rather than a consumer surplus measure. The value of commercial fisheries is divided into shorebased and offshore fisheries (see Section 7.4.2). These values are converted to a metric to allow

linkage to the NRDAM/CME outputs. Shore-based fishery values are converted to a value per

meter-day of coast. Offshore fishery values are converted to a value per meter-day of coast and

per square meter-day of fishery area.

As described in Section 7.4.1, commercial fishing impacts occur in three ways:

1. The loss of shore-based fishing values due to coastal oiling;

2. The loss of offshore fishing values due to coastal oiling that prevents boats from leaving the

harbor;

3. The loss of offshore fishing values due to the presence of offshore oil slicks.

The economic damage to commercial fishing is the sum of these three damage estimates. The

steps to estimate the economic damage to coastal commercial fishing for each spill scenario are:

1. Adjust the linear (meter) estimate of coastal oiling for each spill scenario from Table 3.6. by

a factor of 0.54 as described in Section 3.4.2.3. This produces an adjusted estimate of linear

coastal oiling. Note that if this value is zero, there is no damage to shore-based commercial

fishing.

2. Multiply the value obtained in Step 1 by the fishery closure period of 60 days. This produces

an estimate of the natural resource injury to commercial fishing, measured in meter-days.

3. Multiply the value obtained in Step 2 by the shore-based landings value per meter-day from

Table 7.4. This produces an estimate of the damage to shore-based commercial fisheries due

to coastal oiling.

The economic damages to offshore commercial fishing due to coastal oiling is calculated with

the following steps:

1. Adjust the linear (meter) estimate of coastal oiling from Table 3.6. for each spill scenario by

a factor of 0.54 as described in Section 3.4.2.3. This produces an adjusted estimate of linear

coastal oiling. Note that if this value is zero, there is no damage to offshore commercial

fishing from coastal oiling.

2. Multiply the value obtained in Step 1 by the harbor closure period of 10 days. This produces

an estimate of the natural resource injury to commercial fishing, measured in meter-days.
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3. Multiply the value obtained in Step 2 by the offshore landings value per meter-day from

Table 7.5. This produces an estimate of the damage to offshore commercial fisheries due to

coastal oiling.

The economic damage to offshore commercial fishing due to the presence of oil slicks is

calculated with the following steps:

1. Adjust the area (square meter) estimate of coastal oiling from Table 3.5. for each spill

scenario by a factor of 1.61 as described in Section 3.4.2.3. This produces an adjusted

estimate of the area covered by the oil slick.

2. Multiply the value obtained in Step 1 by the 60-day fishery closure period. This provides an

estimate of the natural resource injury measured in square meter-days.

3. Multiply the value obtained in Step 2 by the value of offshore commercial landings per

square meter-day from Table 7.5

These steps will produce three commercial fishing damage estimates. Sum these three values to

obtain the total damage estimate to commercial fishing.

3.5.6 Subsistence Harvest Economic Damages

Subsistence harvest economic damages are driven by the estimate of marine mammal deaths

produced by the NRDAM/CME (see Section 8.3). Subsistence damages only occur in the

Alaska OCS Planning Areas. The economic damages to subsistence harvest are estimated with

the following steps:

1. Multiply the NRDAM/CME estimate of marine mammal deaths by 0.02 as described in

Section 8.3. This produces an estimate of the reduction in the marine mammal subsistence

harvest.

2. Divide the value obtained in Step 1 by the OCS Planning Area marine mammal subsistence

harvest from Table 8.3. This produces an estimate of the proportional decrease in the marine

mammal subsistence harvest.

3. Multiply the value obtained in Step 2 by the low and high total values of the subsistence

harvest for marine, bird, and fish species from Table 8.4. This produces a low and high

estimate of the subsistence harvest economic damages.

3.5.7 Damage Assessment and Administrative Costs

As discussed in Section 2.7, natural resource trustees are permitted to collect damages for the

costs of damage assessment, monitoring, administration, and oversight. Based on a review of

natural resource damage cases, we assume that damage assessment and administrative costs are

9% of the total damages to beach recreation, recreational fishing, ecological resources,

commercial fishing, and subsistence harvest (see Section 2.7). Thus, the sum of these natural

resource damage cost elements is multiplied by 0.09 to obtain an estimate of the damage

assessment and administrative costs.
48 Section 3: Oil Spill Modeling

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3.5.8 Property Value Economic Damages

The final spill-related cost element is damages to coastal property values. Property value

damages are not included in natural resource damage suits; thus, damage assessment and

administrative costs do not apply to property value damages. The injury driver for estimating

property value damages is the amount of coastal oiling. This injury estimate needs to be linked

to the decrease in property sales values per meter of coast (see Section 11.1). The steps for

estimating property value damages are:

1. Adjust the linear (meter) estimate of coastal oiling from Table 3.6. for each spill scenario by

a factor of 0.54 as described in Section 3.4.2.3. This produces an adjusted estimate of linear

coastal oiling. Note that if this value is zero, there is no damage to coastal property values.

2. Multiply the value obtained in Step 1 by the decrease in property sales value per coastal

meter from Table 11.2.

3.5.9 Economic Damages Per Barrel Produced and Spilled

The calculations described in Sections 3.5.2, 3.5.3, and 3.5.4 produce an estimate of the

economic damages associated with each oil spill scenario. These economic damages need to be

linked to oil production and oil spill rates.

While Tables 3.5 and 3.6 list three spill sizes for each location, this analysis considers six types

of spills as listed in Table 3.3 (small pipeline, small platform, small tanker, large pipeline, large

platform, and large tanker spills). No adjustment is considered necessary for subsurface spills.

(See Section 3.4.2.3.) Pipeline spills are not differentiated from platform spills. For those OCS

Planning Areas with multiple spill locations, each spill location is weighted equally. Thus,

within an OCS Planning Area the average economic damage for a particular spill type (e.g., a

small pipeline spill) is calculated as the average of damages for that spill type over all spill

locations in that area. For example, the Eastern Gulf of Mexico OCS Planning Area has four

spill locations. To calculate the average economic damages of a large tanker spill, simply take

the average of all damages for the four large tanker spill scenarios (EGM1-LT, EGM2-LT,

EGM3-LT, and EGM4-LT).

Once the average economic damages for each of the six spills types is obtained for each OCS

Planning Area, these values need to be weighed according to the spill probabilities in Table 3.3.

We multiply each of the six spill damages by the spill rates in Table 3.3. This produces an

estimate of the total economic damages associated with the production and transport of a billion

barrels of oil for each OCS Planning Area.

Spill damages can also be estimated per barrel spilled. For each billion barrels produced and

transported, the data in Table 3.3 indicates that there will be a total of 17.8 small spills, 1.77

large platform/pipeline spills, and 1.21 large tanker spills. Using the spill sizes in Table 3.3, this

converts to spills totaling 45,221 barrels. Dividing the value obtained above for the total

economic damages associated with the production and transport of a billion barrels of oil by

45,221 barrels produces estimate of the damage per barrel spilled for each OCS Planning Area.
Section 3: Oil Spill Modeling 49

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References

Anderson, Cheryl. 2000. Personal communication. Minerals Management Service.

Florida Department of Environmental Protection, National Oceanic and Atmospheric

Administration, and U.S. Department of the Interior. 1997. “Damage Assessment and

Restoration Plan/Environmental Assessment for the August 10, 1993 Tampa Bay Oil Spill,

Volume 1 – Ecological Injuries”. Final Version.

French, Deborah. 2000. Personal communication. Applied Science Associates, Inc.

Louisiana Oil Spill Coordinators Office, Louisiana Department of Environmental Quality,

Louisiana Department of Natural Resources, Louisiana Department of Wildlife and Fisheries,

National Oceanic and Atmospheric Administration, and U.S. Fish and Wildlife Service.

1999. “Damage Assessment and Restoration Plan, Texaco Pipeline, Inc. Crude Oil

Discharge, Lake Barre, Louisiana, May 16, 1997”, Final Version.

Miller, Gregory B. 1994. “Coastal Habitat Restoration Planning in Louisiana: Lessons from the

Greenhill-Tmibalier Bay Oil Spill Case”. Coastal Management, vol. 22:413-420.

Minerals Management Service. 1999. Oil Spill Facts. Web address:

www.mms.gov/stats/PDFs/Spills.pdf

The Natural Resource Trustees for Pearl Harbor, Oahu, Hawaii. 1999. “Final Restoration Plan

and Environmental Assessment for the May 14, 1996 Chevron Oil Spill into Waiau Stream

and Pearl Harbor”, Oahu, Hawaii.

Sirkar, Jaideep, Paul Ameer, Alan Brown, Patrick Gross, Keith Michel, and Wayne Willis. 1997.

“A Framework for Assessing the Environmental Performance of Tankers in Accidental

Groundings and Collisions”. Summary final report of the Society of Naval Architects and

Marine Engineers Ad Hoc Panel on the Environmental Performance of Tankers.

Texas General Land Office, Texas Natural Resource Conservation Commission, Texas Parks and

Wildlife Department, U.S. Fish and Wildlife Service, and National Oceanic and Atmospheric

Administration. 1997. “Restoration Plan/Environmental Assessment for the Apex Barges Oil

Spill, July 28, 1990”.

U.S. Coast Guard. 2000. Oil Spill Compendium Data Table, Volume of Spills by Spill Size. Web

address: www.uscg.mil/hq/g-m/nmc/response/stats/C2Data.htm.
50 Section 3: Oil Spill Modeling

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Table 3.1. Oil Spill Locations

OCS Planning Area Location

Name Latitude Longitude Distance toShore (km) 1

North Atlantic NA1 41 ̊15’N 68 ̊45’W 110

Mid-Atlantic MA1 35 ̊25’N 74 ̊40’W 72

South Atlantic SA1 32 ̊00’N 78 ̊00’W 170

Straits of Florida SF1 25 ̊00’N 80 ̊00’W 40

EGM1 30 ̊12’N 87 ̊37’W 6

EGM2 29 ̊54’N 87 ̊28’W 42

EGM3 28 ̊30’N 87 ̊48’W 140

Eastern Gulf of Mexico

EGM4 29 ̊20’N 85 ̊10’W 32

CGM1 29 ̊42’N 88 ̊35’W 28

CGM2 29 ̊00’N 90 ̊00’W 18

CGM3 29 ̊25’N 92 ̊30’W 17

CGM4 28 ̊31’N 88 ̊28’W 83

CGM5 27 ̊00’N 90 ̊00’W 225

Central Gulf of Mexico

CGM6 27 ̊30’N 91 ̊35’W 183

WGM1 29 ̊12’N 94 ̊33’W 25

WGM2 28 ̊14’N 96 ̊28’W 7

WGM3 26 ̊17’N 96 ̊50’W 133

WGM4 27 ̊14’N 93 ̊00’W 265

WGM5 28 ̊20’N 95 ̊10’W 63

Western Gulf of Mexico

WGM6 26 ̊14’N 94 ̊00’W 323

Southern California SC1 34 ̊14’N 119 ̊48’W 20

SC2 34 ̊00’N 120 ̊56’W 67

Central California CC1 38 ̊40’N 123 ̊33’W 12

Northern California NC1 41 ̊00’N 124 ̊15’W 12

Washington/Oregon WO1 45 ̊00’N 124 ̊06’W 8

Gulf of Alaska GA1 59 ̊57’N 142 ̊40’W 17

GA2 58 ̊55’N 139 ̊10’W 39

Kodiak KO1 57 ̊00’N 153 ̊00’W 11

CI1 60 ̊00’N 152 ̊15’W 19

CI2 59 ̊31’N 152 ̊30’W 36

CI3 58 ̊50’N 152 ̊50’W 24

Cook Inlet

CI4 58 ̊10’N 153 ̊55’W 15

Shumagin SH1 55 ̊00’N 161 ̊00’W 17

Aleutian Arc AA1 52 ̊00’N 174 ̊00’W 8

North Aleutian Basin NAB1 56 ̊00’N 162 ̊00’W 17

Saint George Basin SGB1 56 ̊10’N 167 ̊45’W 117

Bowers Basin BB1 53 ̊10’N 174 ̊00’W 84

Aleutian Basin AB1 57 ̊00’N 174 ̊10’W 231
Section 3: Oil Spill Modeling 51

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Table 3.1. Oil Spill Locations (Continued)

OCS Planning Area Location

Name Latitude Longitude Distance toShore (km) 1

St. Matthew-Hall SMH1 61 ̊10’N 166 ̊00’W 18

Navarin Basin NVB1 60 ̊30’N 174 ̊10’W 62

Norton Basin NOB1 65 ̊10’N 167 ̊30’W 26

Hope Basin HB1 68 ̊15’N 167 ̊00’W 14

Chukchi Sea CS1 72 ̊00’N 156 ̊10’W 68

CS2 69 ̊50’N 164 ̊30’W 54

BS1 70 ̊17’N 143 ̊35’W 14

BS2 70 ̊31’N 148 ̊40’W 11

BS3 70 ̊35’N 149 ̊30’W 3

BS4 70 ̊30’N 147 ̊00’W 21

BS5 70 ̊45’N 150 ̊50’W 28

Beaufort Sea

BS6 70 ̊50’N 149 ̊00’W 34

1 – Based on the nearest straight-line distance to a coastline, including islands.
52 Section 3: Oil Spill Modeling

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Table 3.2. Wind Stations

OCS Planning Area Location

Name

Wind Station

Name Latitude Longitude

North Atlantic NA1 44008 40.50 ̊N 69.43 ̊W

Mid-Atlantic MA1 DSLN7 35.15 ̊N 75.30 ̊W

South Atlantic SA1 41004 32.51 ̊N 79.10 ̊W

Straits of Florida SF1 MLRF1 25.01 ̊N 80.38 ̊W

EGM1 DPIA1 40.50 ̊N 69.43 ̊W

EGM2 DPIA1 40.50 ̊N 69.43 ̊W

EGM3 42039 28.78 ̊N 86.04 ̊W

Eastern Gulf of Mexico

EGM4 CSBF1 29.67 ̊N 85.36 ̊W

CGM1 42040 29.18 ̊N 88.30 ̊W

CGM2 BURL1 28.90 ̊N 89.43 ̊W

CGM3 SRST2 29.67 ̊N 94.05 ̊W

CGM4 42040 29.18 ̊N 88.30 ̊W

CGM5 42001 25.93 ̊N 89.65 ̊W

Central Gulf of Mexico

CGM6 42001 27.22 ̊N 90.42 ̊W

WGM1 42035 29.25 ̊N 94.41 ̊W

WGM2 PTAT2 27.83 ̊N 97.05 ̊W

WGM3 42020 26.92 ̊N 96.70 ̊W

WGM4 42002 25.89 ̊N 93.57 ̊W

WGM5 42019 27.92 ̊N 95.35 ̊W

Western Gulf of Mexico

WGM6 42002 25.89 ̊N 93.57 ̊W

Southern California SC1 46053 34.24 ̊N 119.90 ̊W

SC2 46054 34.27 ̊N 120.45 ̊W

Central California CC1 PTAC1 38.96 ̊N 123.74 ̊W

Northern California NC1 46022 40.74 ̊N 124.51 ̊W

Washington/Oregon WO1 46050 44.62 ̊N 124.53 ̊W

Gulf of Alaska GA1 46001 56.30 ̊N 148.17 ̊W

GA2 46001 56.30 ̊N 148.17 ̊W

Kodiak KO1 46001 56.30 ̊N 148.17 ̊W

CI1 DRFA2 59.38 ̊N 153.35 ̊W

CI2 DRFA2 59.38 ̊N 153.35 ̊W

CI3 DRFA2 59.38 ̊N 153.35 ̊W

Cook Inlet

CI4 DRFA2 59.38 ̊N 153.35 ̊W

Shumagin SH1 46034 55.10 ̊N 163.10 ̊W

Aleutian Arc AA1 46035 56.91 ̊N 177.81 ̊W

North Aleutian Basin NAB1 46035 56.91 ̊N 177.81 ̊W

Saint George Basin SGB1 46035 56.91 ̊N 177.81 ̊W

Bowers Basin BB1 46035 56.91 ̊N 177.81 ̊W

Aleutian Basin AB1 46035 56.91 ̊N 177.81 ̊W
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Table 3.2. Wind Stations (Continued)

OCS Planning Area Location

Name

Wind Station

Name Latitude Longitude

St. Matthew-Hall SMH1 46017 60.30 ̊N 172.30 ̊W

Navarin Basin NVB1 46035 56.91 ̊N 177.81 ̊W

Norton Basin NOB1 46016 63.30 ̊N 170.30 ̊W

Hope Basin HB1 46016 63.30 ̊N 170.30 ̊W

CS1 460351 Chukchi Sea 56.91 ̊N 177.81 ̊W

CS2 460351 56.91 ̊N 177.81 ̊W

BS1 460351 56.91 ̊N 177.81 ̊W

BS2 460351 56.91 ̊N 177.81 ̊W

BS3 460351 56.91 ̊N 177.81 ̊W

BS4 460351 56.91 ̊N 177.81 ̊W

BS5 460351 56.91 ̊N 177.81 ̊W

Beaufort Sea

BS6 460351 56.91 ̊N 177.81 ̊W

1- No offshore buoys are in operation in these areas and data from onshore stations could not be

obtained. Station 46035 is the nearest station with several years of recent data.

Table 3.3. Oil Spill Magnitudes and Rates

Spill Source Variable

Platforms Pipelines Tankers

Spill Rate 1 4.23 12.43 1.14

Small Spill Average Size 2 111 111 111

Spill Rate 1 0.45 1.32 1.21

Large Spill Average Size 2 7,000 7,000 25,500

1 – Numbers of spills of a given size per billion barrels produced/handled.

2 – Measured in barrels

Sources: Minerals Management Service, 1999; U.S. Coast Guard, 2000.
54 Section 3: Oil Spill Modeling

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Table 3.4. Oil Types

OCS Planning Areas Oil Type – Platforms

and Pipelines Oil Type – Tankers

North Atlantic, Mid-Atlantic,

South Atlantic

Arabian Crude (Light) Arabian Crude (Light)

Straits of Florida, all Gulf of

Mexico Areas

South Louisiana Crude Arabian Crude (Light)

Washington/Oregon, all

California Areas

Alaska North Slope

Crude

Alaska North Slope

Crude

Hope Basin, Chukchi Sea, and

Beaufort Sea

Alaska North Slope

Crude

Alaska North Slope

Crude

All other Alaska Areas Endicott Crude Endicott Crude
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Table 3.5 NRDAM/CME Model Results: Oil Slick Output, Wildlife Impacts, and

Economic Damages

Oil Slick Magnitude Oil Slick

Location Wildlife Killed

Scenario

sq. meters sq. mdays

% Terr.

Seas

% in

EEZ Birds Marine

Mammals Reptiles

Econ.

Damages

$1999

NA1-S 1 6.78*108 7.85*106 0% 0% 212 0.2 0 $1,063

NA1-LPP 1 1.60*109 3.03*108 0% 0% 3,532 0.2 0 $31,405

NA1-LT 1 4.00*109 9.78*108 0% 0% 10,690 0.2 0 $101,665

MA1-S 8.87*107 9.20*106 10% 0% 0 0 0 $0

MA1-LPP 5.61*108 6.15*107 20% 0% 3,469 0 0 $21,133

MA1-LT 1.16*109 1.31*108 30% 0% 4,886 0 0 $66,561

SA1-S 1.82*108 2.23*107 0% 0% 0 0 0 $0

SA1-LPP 1.50*109 2.44*108 0% 0% 24 0 0 $7,593

SA1-LT 2.81*109 4.83*108 0% 0% 61 0 0 $24,100

SF1-S 2.23*108 3.08*107 80% 20% 0 0 0 $5

SF1-LPP 1.38*109 1.95*108 70% 30% 1,819 0 0.7 $253,918

SF1-LT 2.60*109 3.83*108 70% 30% 3,035 0.6 2 $451,802

EGM1-S 5.31*107 9.61*106 20% 80% 2 0 0 $56

EGM1-LPP 5.88*108 1.07*108 20% 80% 41 0.1 0.2 $1,488

EGM1-LT 3.31*109 5.48*108 30% 70% 1,338 3 2 $51,179

EGM2-S 6.85*107 8.60*106 80% 20% 3 0 0 $75

EGM2-LPP 5.91*109 7.05*107 80% 20% 173 0.4 1 $3,913

EGM2-LT 2.41*109 3.36*108 80% 20% 340 3 2 $12,720

EGM3-S 6.29*107 7.89*106 0% 0% 1 3 0 $99

EGM3-LPP 4.66*108 5.52*107 0% 0% 2 3 0 $4,604

EGM3-LT 1.05*109 1.34*108 0% 0% 5 4 0 $12,965

EGM4-S 2.68*108 4.77*107 30% 0% 11 1 0.3 $146

EGM4-LPP 1.87*109 4.26*108 50% 0% 273 2 2 $6,264

EGM4-LT 4.08*109 1.27*109 50% 0% 766 3 5 $18,090

CGM1-S 1.15*108 1.59*107 90% 20% 23 3 1 $274

CGM1-LPP 6.36*108 8.68*107 80% 20% 66 3 1 $6,642

CGM1-LT 1.52*109 1.91*108 80% 20% 239 3 2 $20,137

CGM2-S 1.33*108 1.65*107 50% 50% 0.3 0 0 $107

CGM2-LPP 9.18*108 1.28*108 50% 50% 2,731 11 0 $53,799

CGM2-LT 2.17*109 2.58*108 50% 50% 3,693 14 0.4 $78,705

CGM3-S 9.58*107 1.01*107 30% 70% 8 0 0 $98

CGM3-LPP 4.55*108 4.36*107 30% 70% 2,250 11 0 $46,836

CGM3-LT 9.36*108 8.58*107 30% 70% 2,066 10 0 $42,391

CGM4-S 6.46*107 7.10*106 0% 0% 1 3 0 $120

CGM4-LPP 3.63*108 4.18*107 0% 0% 2 3 0 $4,930

CGM4-LT 1.10*109 1.26*108 0% 0% 6 4 0 $25,090
56 Section 3: Oil Spill Modeling

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Table 3.5 NRDAM/CME Model Results: Oil Slick Output, Wildlife Impacts, and

Economic Damages

Oil Slick Magnitude Oil Slick

Location Wildlife Killed

Scenario

sq. meters sq. mdays

% Terr.

Seas

% in

EEZ Birds MarineMammals Reptiles

Econ.

Damages

$1999

CGM5-S 1.11*108 1.45*107 0% 0% 1 3 0 $87

CGM5-LPP 1.15*109 1.61*108 0% 0% 7 3 0 $3,243

CGM5-LT 5.47*109 1.74*109 0% 0% 33 3 0 $3,097

CGM6-S 1.14*108 1.49*107 0% 0% 0 0 0 $0

CGM6-LPP 1.19*109 1.66*108 0% 0% 5 0 0 $3,485

CGM6-LT 5.34*109 1.67*109 0% 0% 1,699 0 0.6 $11,329

WGM1-S 1.33*108 1.54*107 70% 30% 7 0 0 $63

WGM1-LPP 8.41*108 9.53*107 70% 30% 419 0 0.2 $4,496

WGM1-LT 1.84*109 2.30*108 70% 30% 1,361 0 0.2 $58,231

WGM2-S 1.91*107 1.58*106 20% 80% 29 0.3 0 $119

WGM2-LPP 4.23*108 4.14*107 20% 80% 88 0.4 0 $7,363

WGM2-LT 2.43*109 2.81*108 20% 80% 88 0 0 $1,979,93

WGM3-S 6.52*107 1.02*107 80% 20% 18 0.4 0.2 $320

WGM3-LPP 4.89*108 4.90*107 80% 20% 159 0.4 0.4 $6,995

WGM3-LT 1.44*109 1.52*108 80% 20% 369 0.5 1 $6,108

WGM4-S 4.91*107 6.18*106 0% 0% 0 0 0 $0

WGM4-LPP 3.99*108 5.06*107 0% 0% 0 0 0 $4,296

WGM4-LT 3.55*108 7.80*108 20% 0% 796 8 0.2 $15,301

WGM5-S 9.23*107 9.64*106 60% 20% 5 0 0 $64

WGM5-LPP 9.41*108 1.03*108 60% 20% 718 0 0 $6,367

WGM5-LT 1.68*109 2.05*108 60% 20% 1,119 0 0.4 $47,988

WGM6-S 5.69*107 6.75*106 0% 0% 2 6 0 $119

WGM6-LPP 8.36*108 1.04*108 0% 0% 8 8 0 $4,036

WGM6-LT 4.56*109 9.42*108 0% 0% 26 10 0 $12,621

SC1-S 1.69*108 1.96*107 80% 20% 142 0.4 0 $3,116

SC1-LPP 9.72*108 1.19*108 80% 20% 3,675 5 0 $120,328

SC1-LT 1.47*109 1.86*109 80% 20% 4,122 6 0 $133,379

SC2-S 9.10*107 1.03*107 0% 0% 62 1 0 $320

SC2-LPP 8.45*108 8.89*107 0% 0% 899 17 0 $12,410

SC2-LT 1.69*109 1.74*108 0% 0% 1,540 28 0 $40,154

CC1-S 3.45*107 3.64*106 40% 60% 926 13 0 $19,555

CC1-LPP 4.20*108 3.41*107 30% 70% 2,692 6 0 $64,264

CC1-LT 6.34*108 5.04*107 30% 70% 3,251 8 0 $86,226

NC1-S 3.92*107 4.63*106 70% 30% 36 0 0 $120

NC1-LPP 3.68*108 4.58*107 70% 30% 383 1 0 $5,428

NC1-LT 6.87*108 9.16*107 80% 20% 400 1 0 $16,260

WO1-S 9.06*107 1.24*107 20% 80% 77 0.1 0 $226

WO1-LPP 4.74*108 4.50*107 20% 80% 1,061 3 0 $3,343
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Table 3.5 NRDAM/CME Model Results: Oil Slick Output, Wildlife Impacts, and

Economic Damages

Oil Slick Magnitude Oil Slick

Location Wildlife Killed

Scenario

sq. meters sq. mdays

% Terr.

Seas

% in

EEZ Birds MarineMammals Reptiles

Econ.

Damages

$1999

WO1-LT 7.37*108 6.61*107 20% 80% 2,924 3 0 $61,982

GA1-S 1.17*108 1.08*107 100% 28 0.4 0 % $254

GA1-LPP 8.34*108 8.82*107 100% 2,968 17 0 $2,940

GA1-LT 1.43*109 1.53*108 100% 4,170 24 0 $4,149

GA2-S 1.23*108 1.26*107 70% 41 0.4 0 $231

GA2-LPP 7.38*108 7.87*107 80% 3,112 17 0 $12,711

GA2-LT 1.55*109 1.83*108 80% 3,761 21 0 $3,818

KO1-S 5.84*107 7.75*106 100% 186 1 0 $47

KO1-LPP 3.93*108 5.06*107 100% 1,827 9 0 $3,059

KO1-LT 2.97*108 2.47*107 100% 2,369 13 0 $67,427

CI1-S 2.04*108 2.89*107 100% 14 0 0 $239

CI1-LPP 1.87*109 2.79*108 100% 3,223 8 0 $17,834

CI1-LT 3.03*109 4.64*108 100% 8,056 22 0 $58,427

CI2-S 3.86*108 5.84*107 100% 76 0.2 0 $375

CI2-LPP 3.17*109 4.95*108 100% 4,581 13 0 $22,715

CI2-LT 5.09*109 7.89*108 100% 8,755 25 0 $71,223

CI3-S 2.77*108 4.07*107 100% 86 0.3 0 $413

CI3-LPP 2.35*109 3.60*108 100% 4,125 14 0 $6,179

CI3-LT 3.27*109 4.89*108 100% 4,647 15 0 $8,933

CI4-S 1.56*108 2.21*107 100% 47 0.4 0 $18

CI4-LPP 1.38*109 2.47*108 100% 5,903 28 0 $6,309

CI4-LT 2.14*109 3.07*108 100% 7,367 34 0 $12,113

SH1-S 6.15*107 5.74*106 100% 847 4 0 $354

SH1-LPP 7.07*108 8.37*107 100% 3,685 18 0 $499

SH1-LT 1.30*109 1.59*108 100% 9,543 45 0 $3,300

AA1-S 9.35*107 1.01*107 100% 710 0.5 0 $56

AA1-LPP 3.67*108 3.02*107 100% 18,718 12 0 $1,882

AA1-LT 1.21*109 1.43*108 100% 23,694 15 0 $3,048

NAB1-S 9.98*107 1.33*107 100% 73 3 0 $58

NAB1-LPP 6.67*109 9.79*107 100% 747 33 0 $644

NAB1-LT 9.84*108 1.44*108 100% 995 44 0 $1,650

SGB1-LPP 7.65*108 9.38*107 0% 4,824 0 0 $10,535

SGB1-LT 2.18*109 3.50*108 0% 16,225 0 0 $21,842

BB1-S 1.20*108 1.49*107 10% 1,116 0 0 $161

BB1-LPP 9.07*108 1.14*108 10% 7,453 0 0 $9,802

BB1-LT 1.80*109 2.37*108 20% 11,706 0 0 $37,535

AB1-S 7.78*107 9.27*106 0% 943 0.1 0 $183

AB1-LPP 6.16*108 7.20*107 0% 6,233 18 0 $10,129

AB1-LT 1.76*109 2.22*108 0% 11,356 24 0 $36,573
58 Section 3: Oil Spill Modeling

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Table 3.5 NRDAM/CME Model Results: Oil Slick Output, Wildlife Impacts, and

Economic Damages

Oil Slick Magnitude Oil Slick

Location Wildlife Killed

Scenario

sq. meters sq. mdays

% Terr.

Seas

% in

EEZ Birds MarineMammals Reptiles

Econ.

Damages

$1999

SMH1-S 3.26*108 3.98*107 90% 476 0 0 $503

SMH1-LPP 2.92*109 4.13*108 80% 14,951 3 0 $19,930

SMH1-LT 4.71*109 6.88*108 80% 25,051 3 0 $49,424

NVB1-S 1.09*108 1.08*107 70% 828 2 0 $619

NVB1-LPP 9.31*108 1.00*108 70% 8,548 3 0 $6,517

NVB1-LT 1.47*109 1.55*108 70% 14,524 3 0 $11,115

NOB1-S 1.19*108 1.28*107 90% 480 0 0 $522

NOB1-LPP 1.02*109 1.13*108 70% 6,689 1 0 $15,391

NOB1-LT 1.60*109 1.81*108 60% 9,618 1 0 $44,132

HB1-S 8.39*107 9.78*106 90% 710 4 0 $1,221

HB1-LPP 8.40*108 1.01*108 80% 13,029 9 0 $42,924

HB1-LT 1.97*109 2.48*108 80% 17,499 11 0 $124,422

CS1-S 7.43*107 9.41*106 40% 68 0.1 0 $864

CS1-LPP 7.89*108 1.04*108 70% 3,788 8 0 $39,816

CS1-LT 1.87*109 2.62*108 70% 8,394 20 0 $52,353

CS2-S 8.20*107 8.64*106 80% 216 0.6 0 $419

CS2-LPP 7.36*108 7.40*107 90% 9,384 3 0 $39,485

CS2-LT 1.50*109 1.66*108 90% 13,306 5 0 $35,748

BS1-S 1.42*108 2.11*107 100% 134 0.3 0 $778

BS1-LPP 9.04*108 1.27*108 100% 1,094 2 0 $7,052

BS1-LT 1.29*109 1.90*108 100% 1,616 4 0 $9,966

BS2-S 8.96*107 1.38*107 100% 305 1 0 $1,739

BS2-LPP 3.82*108 8.38*107 100% 8,460 14 0 $72,850

BS2-LT 4.37*108 3.95*107 100% 10,088 18 0 $82,474

BS3-S 2.41*107 3.75*106 100% 37 1 0 $218

BS3-LPP 1.36*108 2.12*107 100% 71 1 0 $1,156

BS3-LT 3.23*108 4.85*107 100% 212 2 0 $4,162

BS4-S 1.64*108 2.93*107 100% 362 1 0 $2,081

BS4-LPP 9.55*108 2.17*108 100% 4,293 11 0 $25,615

BS4-LT 1.59*109 3.74*108 100% 5,349 14 0 $34,594

BS5-S 2.61*108 3.63*107 100% 137 2 0 $780

BS5-LPP 1.69*109 2.29*108 100% 2,255 7 0 $13,290

BS5-LT 3.03*109 4.14*108 100% 4,532 12 0 $27,197

BS6-S 1.30*108 1.42*107 100% 381 2 0 $2,117

BS6-LPP 9.57*108 1.41*108 100% 5,806 16 0 $33,556

BS6-LT 1.44*109 1.56*108 100% 6,069 16 0 $37,416

1 – “S” indicates the small spill size (111 barrels), “LPP” indicates the large platform/pipeline

spill size (7,000 bbl), and “LT” indicates the large tanker spill size (25,500 bbl).
Section 3: Oil Spill Modeling 59

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Table 3.6. NRDAM/CME Model Results: Coastal Oiling Output

Linear coastal oiling Area coastal oiling Scenario

Meters m-days sq. meters sq. m-days

Oiled

Habitats 2

Percent of

Oil Coming

Ashore

NA1-S 1 0 0 0 0 NA 0%

NA1-LPP 1 0 0 0 0 NA 0%

NA1-LT 1 0 0 0 0 NA 0%

MA1-S 0 0 0 0 NA 0%

MA1-LPP 0 0 0 0 NA 0%

MA1-LT 0 0 0 0 NA 0%

SA1-S 0 0 0 0 NA 0%

SA1-LPP 0 0 0 0 NA 0%

SA1-LT 0 0 0 0 NA 0%

SF1-S 2.20*103 5.55*104 6.35*104 3.06*106 IW,SB 0.8%

SF1-LPP 5.72*104 1.13*107 3.74*106 8.63*108 IW,SB 7.8%

SF1-LT 6.78*104 1.52*107 4.61*106 1.17*109 IW,SB 4.6%

EGM1-S 8.28*103 3.81*105 1.66*104 7.61*105 SB 28.0%

EGM1-LPP 1.24*104 6.99*105 2.49*104 1.40*106 SB 25.9%

EGM1-LT 1.20*105 7.10*106 3.21*105 3.37*107 SB,IW 29.7%

EGM2-S 6.21*103 1.70*105 1.24*104 3.40*105 SB 28.0%

EGM2-LPP 1.60*104 8.03*105 3.19*104 1.61*106 SB 16.3%

EGM2-LT 5.62*104 3.21*106 1.12*105 6.43*106 SB 15.5%

EGM3-S 0 0 0 0 NA 0%

EGM3-LPP 0 0 0 0 NA 0%

EGM3-LT 0 0 0 0 NA 0%

EGM4-S 0 0 0 0 NA 0%

EGM4-LPP 0 0 0 0 NA 0%

EGM4-LT 0 0 0 0 NA 0%

CGM1-S 3.52*103 4.63*104 7.05*103 9.25*104 SB 0.2%

CGM1-LPP 1.23*104 6.43*105 2.47*104 1.29*106 SB 1.4%

CGM1-LT 4.25*104 2.69*106 6.14*104 1.27*!07 SB,IW 11.0%

CGM2-S 4.29*103 1.30*105 8.58*103 2.61*104 SB <0.1%

CGM2-LPP 2.43*104 1.21*106 4.86*104 2.41*106 SB 0.4%

CGM2-LT 3.00*104 1.50*106 6.01*104 3.00*106 SB <0.1%

CGM3-S 2.61*104 5.30*106 7.83*105 1.59*108 IW 25.7%

CGM3-LPP 9.33*104 3.16*107 2.80*106 9.45*108 IW 26.1%

CGM3-LT 1.29*105 4.87*107 3.86*106 1.46*109 IW 42.7%

CGM4-S 0 0 0 0 NA 0%

CGM4-LPP 0 0 0 0 NA 0%

CGM4-LT 0 0 0 0 NA 0%

CGM5-S 0 0 0 0 NA 0%

CGM5-LPP 0 0 0 0 NA 0%
60 Section 3: Oil Spill Modeling

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Table 3.6. NRDAM/CME Model Results: Coastal Oiling Output (Continued)

Linear coastal oiling Area coastal oiling

Scenario Meters m-days sq. meters sq. m-days Oiled

Habitats

Percent of

Oil Coming

Ashore

CGM5-LT 0 0 0 0 NA 0%

CGM6-S 0 0 0 0 NA 0%

CGM6-LPP 0 0 0 0 NA 0%

CGM6-LT 0 0 0 0 NA 0%

WGM1-S 1.86*104 3.56*106 6.35*104 7.49*106 SB,IW 14.2%

WGM1-LPP 9.69*104 2.30*107 4.11*105 9.50*107 IW,SB 16.0%

WGM1-LT 1.13*105 2.21*107 1.84*106 5.72*108 IW,SB 23.8%

WGM2-S 2.70*104 6.09*106 5.41*104 1.22*107 SB 27.9%

WGM2-LPP 4.64*104 9.94*106 9.27*104 1.99*107 SB 25.4%

WGM2-LT 6.26*104 4.03*106 2.33*105 2.44*107 SB,IW 28.8%

WGM3-S 2.11*103 2.58*105 8.45*103 1.20*105 SB 4.2%

WGM3-LPP 1.90*104 4.33*105 7.60*104 1.73*106 SB 0.7%

WGM3-LT 2.40*105 5.16*107 1.37*107 3.86*109 IM,SB 19.0%

WGM4-S 0 0 0 0 NA 0%

WGM4-LPP 0 0 0 0 NA 0%

WGM4-LT 0 0 0 0 NA 0%

WGM5-S 1.42*104 2.71*106 2.85*104 5.43*106 SB 4.7%

WGM5-LPP 3.96*104 8.75*106 3.89*105 8.92*107 IW,SB 9.8%

WGM5-LT 6.17*104 1.55*107 1.41*106 4.41*108 IW,SB 31.2%

WGM6-S 0 0 0 0 NA 0%

WGM6-LPP 0 0 0 0 NA 0%

WGM6-LT 0 0 0 0 NA 0%

SC1-S 6.60*103 1.16*106 8.58*104 1.51*107 SB 16.4%

SC1-LPP 2.64*104 7.02*106 3.43*105 9.12*107 SB 36.1%

SC1-LT 3.83*104 9.15*106 4.97*105 1.19*108 SB 24.5%

SC2-S 0 0 0 0 NA 0%

SC2-LPP 0 0 0 0 NA 0%

SC2-LT 0 0 0 0 NA 0%

CC1-S 5.04*104 5.39*106 6.55*105 7.01*107 SB 69.3%

CC1-LPP 1.23*105 2.66*107 1.59*106 3.45*108 SB 64.2%

CC1-LT 1.62*105 3.97*107 2.10*106 5.17*108 SB 65.4%

NC1-S 1.41*103 8.16*104 3.10*104 1.80*106 SB 0.6%

NC1-LPP 2.33*104 3.25*106 5.12*105 7.15*107 SB 1.0%

NC1-LT 3.52*103 5.25*105 7.75*104 1.15*107 SB 0.6%

WO1-S 2.05*104 2.37*106 4.51*105 5.21*107 SB 55.6%

WO1-LPP 6.05*104 1.43*107 1.33*106 3.14*108 SB 71.3%

WO1-LT 7.45*104 1.78*107 1.64*106 3.93*108 SB 72.0%
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Table 3.6. NRDAM/CME Model Results: Coastal Oiling Output (Continued)

GA1-S 0 0 0 0 NA 0%

GA1-LPP 4.43*103 1.08*106 1.33*106 3.24*108 IW 1.1%

GA1-LT 1.18*104 3.42*106 3.56*106 1.03*109 IW 3.4%

GA2-S 0 0 0 0 NA 0%

GA2-LPP 9.26*102 1.07*103 2.87*104 3.33*104 SB <0.1%

GA2-LT 3.24*104 6.29*106 1.26*106 2.32*108 SB,IM 2.0%

KO1-S 6.66*103 4.23*105 3.33*104 2.51*106 GB,RC 1.5%

KO1-LPP 3.88*104 6.95*106 2.19*105 4.09*107 GB,RC 4.4%

KO1-LT 4.80*103 7.28*105 2.56*104 4.17*106 GB,RC 2.2%

CI1-S 0 0 0 0 NA 0%

CI1-LPP 0 0 0 0 NA 0%

CI1-LT 0 0 0 0 NA 0%

CI2-S 0 0 0 0 NA 0%

CI2-LPP 0 0 0 0 NA 0%

CI2-LT 3.11*103 2.56*103 1.87*104 1.54*104 GB <0.1%

CI3-S 2.58*104 4.55*106 1.55*105 2.73*107 GB 27.4%

CI3-LPP 6.25*104 1.74*107 3.75*105 1.05*108 GB 20.1%

CI3-LT 1.02*105 2.64*107 6.15*105 1.58*108 GB 20.1%

CI4-S 1.57*104 2.48*106 3.62*105 2.03*107 IW,GB,RC 11.0%

CI4-LPP 1.23*105 3.32*107 2.92*106 8.45*108 IW,GB,RC 19.7%

CI4-LT 1.49*105 4.50*107 4.71*106 1.40*109 IW,GB,RC 25.1%

SH1-S 0 0 0 0 NA 0%

SH1-LPP 2.54*104 3.58*106 2.42*105 3.62*107 SB,GB,RC 2.1%

SH1-LT 1.32*105 2.72*107 2.91*106 6.13*108 SB,GB,RC 16.8%

AA1-S 4.64*104 9.54*106 2.78*105 5.72*107 GB 51.3%

AA1-LPP 5.03*104 1.63*107 3.02*105 9.81*107 GB 53.8%

AA1-LT 2.11*105 6.01*107 1.26*106 3.61*108 GB 53.5%

NAB1-S 5.85*103 3.13*105 2.40*105 1.28*107 SB 6.5%

NAB1-LPP 5.56*104 9.50*106 2.28*106 3.90*108 SB 33.6%

NAB1-LT 7.61*104 1.53*107 3.12*106 6.28*108 SB 38.4%

SGB1-S 0 0 0 0 NA 0%

SGB1-LPP 0 0 0 0 NA 0%

SGB1-LT 0 0 0 0 NA 0%

BB1-S 0 0 0 0 NA 0%

BB1-LPP 0 0 0 0 NA 0%

BB1-LT 0 0 0 0 NA 0%

AB1-S 0 0 0 0 NA 0%

AB1-LPP 0 0 0 0 NA 0%

AB1-LT 0 0 0 0 NA 0%

SMH1-S 0 0 0 0 NA 0%

SMH1-LPP 0 0 0 0 NA 0%
62 Section 3: Oil Spill Modeling

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Table 3.6. NRDAM/CME Model Results: Coastal Oiling Output (Continued)

SMH1-LT 0 0 0 0 NA 0%

NVB1-S 0 0 0 0 NA 0.4%

NVB1-LPP 2.48*104 5.97*106 9.93*106 2.39*109 IM 5.7%

NVB1-LT 4.54*104 1.17*107 1.82*107 4.69*109 IM 5.8%

NOB1-S 0 0 0 0 NA 0%

NOB1-LPP 0 0 0 0 NA 0%

NOB1-LT 0 0 0 0 NA 0%

HB1-S 0 0 0 0 NA 0%

HB1-LPP 0 0 0 0 NA 0%

HB1-LT 0 0 0 0 NA 0%

CS1-S 0 0 0 0 NA 0%

CS1-LPP 3.11*103 1.40*104 2.17*104 9.78*104 GB <0.1%

CS1-LT 8.06*103 6.74*105 1.73*105 1.17*107 SB,GB 0.2%

CS2-S 0 0 0 0 NA 0.3%

CS2-LPP 0 0 0 0 NA 0%

CS2-LT 2.49*104 4.40*106 1.02*106 1.81*108 SB 0.9%

BS1-S 1.30*104 1.48*106 5.34*105 6.05*107 SB 45.1%

BS1-LPP 8.14*104 1.68*107 9.95*105 6.72*108 SB,IM,GB 61.9%

BS1-LT 1.29*105 3.10*107 1.00*107 2.53*109 IM,SB,GB 65.4%

BS2-S 1.38*104 1.26*106 3.87*106 3.82*108 IW,SB,GB 69.2%

BS2-LPP 7.35*104 1.92*107 2.13*107 6.19*109 IW,IM,SB,

GB

75.9%

BS2-LT 8.44*104 2.67*107 2.63*107 9.41*109 IW,SB,IM 74.9%

BS3-S 5.89*103 7.15*105 2.42*105 2.93*107 SB 71.7%

BS3-LPP 3.09*104 6.02*106 1.27*106 2.47*108 SB 75.4%

BS3-LT 6.19*104 1.36*107 6.25*106 1.12*109 IW,SB 75.7%

BS4-S 2.18*104 2.03*106 8.92*105 8.31*107 SB 56.3%

BS4-LPP 7.62*104 1.58*107 3.68*106 7.37*108 SB.IM 67.6%

BS4-LT 1.26*105 2.68*107 6.18*106 1.17*109 SB,IM,GB 68.9%

BS5-S 7.37*103 1.65*106 5.16*104 1.16*107 GB 7.0%

BS5-LPP 3.68*104 1.12*107 2.58*105 7.87*107 GB 30.0%

BS5-LT 8.99*104 2.83*107 1.21*106 2.00*108 GB,IW 35.3%

BS6-S 1.92*104 1.86*106 7.85*105 7.61*107 SB 33.2%

BS6-LPP 9.72*104 1.71*107 9.29*106 1.94*109 IM,SB 48.6%

BS6-LT 1.28*105 3.03*107 6.32*106 1.35*109 SB,IW 51.8%

1 – “S” indicates the small spill size (111 barrels), “LPP” indicates the large platform/pipeline

spill size (7,000 bbl), and “LT” indicates the large tanker spill size (25,500 bbl).

2 – Oiled coastal habitats are presented in order of area oiled. Abbreviations are sand beach

(SB), intertidal wetland (IW), intertidal mudflat (IM), rocky shore(RS), and gravel beach (GB).

NA = not applicable.
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Table 3.7. SIMAP Scenario Inputs

Scenario Name Location Spill Size

(barrels)

Release

Depth (m)

TX1-S-S 28 ̊20’N, 95 ̊10’W 111 0

TX1-S-U 28 ̊20’N, 95 ̊10’W 111 49

TX1-L-S 28 ̊20’N, 95 ̊10’W 25,500 0

TX1-L-U 28 ̊20’N, 95 ̊10’W 25,500 49

TX2-S-S 28 ̊20’N, 95 ̊10’W 111 0

TX2-S-U 28 ̊20’N, 95 ̊10’W 111 49

TX2-L-S 28 ̊20’N, 95 ̊10’W 25,500 0

TX2-L-U 28 ̊20’N, 95 ̊10’W 25,500 49

FL1-S-S 29 ̊50’N, 84 ̊20’W 111 0

FL1-S-U 29 ̊50’N, 84 ̊20’W 111 6

FL1-L-S 29 ̊50’N, 84 ̊20’W 25,500 0

FL1-L-U 29 ̊50’N, 84 ̊20’W 25,500 6

FL2-S-S 29 ̊50’N, 84 ̊20’W 111 0

FL2-S-U 29 ̊50’N, 84 ̊20’W 111 6

FL2-L-S 29 ̊50’N, 84 ̊20’W 25,500 0

FL2-L-U 29 ̊50’N, 84 ̊20’W 25,500 6
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Table 3.8. Comparison of Surface Spills Modeled Using SIMAP and the NRDAM/CME:

Wildlife Impacts

Bird Deaths Marine Mammal Deaths Reptile Deaths Scenario

SIMAP NRDAM/

CME Ratio1 SIMAP NRDAM/CME Ratio1 SIMAP NRDAM/CME Ratio1

TX1-S-S 270 5 54.0 0.1 0 NA 0.2 0 NA

TX1-L-S 2,367 1,138 2.1 1 0 NA 1 0.4 2.50

TX2-S-S 374 1 374.0 0.2 0 NA 0.2 0 NA

TX2-L-S 2,900 1,210 2.4 1 0 NA 2 0.4 5.00

FL1-S-S 179 24 7.5 0.4 1 0.40 0.1 0.4 0.25

FL1-L-S 2,469 1,731 1.4 0.6 2 0.30 1 0.7 1.42

FL2-S-S 71 15 4.7 0 1 0.00 0.1 0.4 0.25

FL2-L-S 501 613 0.8 0.2 2 0.10 0.3 0.6 0.50

1 – Ratio of SIMAP output to NRDAM/CME output

Table 3.9. Comparison of Surface Spills Modeled Using SIMAP and the NRDAM/CME:

Oil Slick and Coastal Oiling Output

Oil Slick

(square meters)

Linear Oiling

(meters) 1

Area Oiling

(square meters) 1

Scenario

SIMAP NRDAM/

CME

Ratio2 SIMAP NRDAM/

CME

Ratio2 SIMAP NRDAM/

CME

Ratio2

TX1-S-S 2.10*108 9.23*107 2.28 1.69*103 1.42*104 0.12 3.38*103 2.85*104 0.12

TX1-L-S 3.13*109 1.55*109 2.02 1.81*104 9.96*104 0.18 3.62*104 1.09*106 0.03

TX2-S-S 4.12*108 1.90*108 2.17 3.39*103 2.38*104 0.14 3.39*104 9.18*104 0.37

TX2-L-S 4.41*109 1.73*109 2.55 2.49*104 3.96*104 0.63 2.44*105 3.45*105 0.71

FL1-S-S 4.66*107 3.23*107 1.44 3.11*103 1.23*103 2.53 3.11*104 3.68*104 0.85

FL1-L-S 7.07*108 5.09*108 1.39 1.86*104 9.33*104 0.20 5.18*105 2.76*106 0.19

FL2-S-S 3.16*107 3.87*107 0.82 3.11*103 1.47*104 0.21 3.11*104 2.95*104 1.05

FL2-L-S 3.06*108 1.64*109 0.19 1.35*104 4.54*104 0.30 1.35*105 9.08*104 1.49

1 – Oiling of all coastal habitats combined

2 – Ratio of SIMAP output to NRDAM/CME output
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Table 3.10. Comparison of Subsurface and Surface Spills Modeled using SIMAP

Bird Deaths Oil Slick

(square meters)

Coastal Oiling

(square m)1

Scenario

Subsurface Surface Ratio2 Subsurface Surface Ratio2 Subsurface Surface Ratio2

TX1-S-S 221 270 0.82 2.18*108 2.10*108 1.04 5.64*103 3.38*103 1.67

TX1-L-S 2,021 2,367 0.85 2.30*109 3.13*109 0.73 3.72*104 3.62*104 1.03

TX2-S-S 331 374 0.89 3.80*108 4.12*108 0.92 2.82*104 3.39*104 0.83

TX2-L-S 2,207 2,900 0.76 2.75*109 4.41*109 0.62 2.56*105 2.44*105 1.05

FL1-S-S 263 179 1.47 7.46*107 4.66*107 1.60 2.07*103 3.11*103 0.67

FL1-L-S 1,988 2,469 0.81 7.79*108 7.07*108 1.10 1.86*104 1.86*104 1.00

FL2-S-S 84 71 1.18 5.27*107 3.16*107 1.67 3.11*103 3.11*103 1.00

FL2-L-S 501 501 1.00 6.28*108 3.06*108 2.05 1.55*104 1.35*104 1.15

1 – Coastal oiling of all ecological habitats combined

2 – Ratio of subsurface spill output to surface spill output

Table 3.11. Comparison of NRDAM/CME Output with Field Observations

Case Observed Coastal

Impacts

Unadjusted

NRDAM/CME

Coastal Impacts

Adjusted

NRDAM/CME

Coastal Impacts

4,165 acres (light oiling)

162 acres (heavy oiling)

Lake Barre 435 acres 239 acres

75.6 acre-years marsh

service

359 acre-years

marsh service

215 acre-years of

marsh service 1

Pearl Harbor 77,965 linear feet 66,256 linear feet 36,441 linear feet

25 acres 10 acres 5.5 acres

North Cape 35 miles 50 miles 28 miles

Tampa Bay 13 miles of beach 53 miles of beach 29 miles of beach

9 acres 182 acres 100 acres

Geenhill-Timbalier Bay 122 acres 16 acres 9 acres

1 – Measure is adjusted by a factor of 0.54 similar to the linear and area measures.
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Table 3.12. Oil Spill Closure Periods (beach or fishery closures)

Oil Spill Closure Type Closure Period

American Trader beaches Up to 35 days

Lake Barre shellfishing 74 days

North Cape ocean fishery 79 days

Waiau Stream/Pearl Harbor bicycle/jogging path 19 days

USS Arizona Memorial 4 days

Tampa Bay beaches 3 weeks

shellfishing 42-109 days

World Prodigy beaches Several days to 2 months

shellfishing One week

Sources: Oil spill damage assessment reports.

Table 3.13. Adjustment Factors to be Applied to the Estimates in Tables 3.5 and 3.6.

Variable Adjustment Factor

Oil Slick area 1.61

Birds killed 3.15

Marine mammals killed None

Reptiles killed None

Linear coastal oiling 0.54

Area coastal oiling 0.60

Area-time coastal oiling 0.60

Beach closure 21 days

Fishery closure 60 days
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4. Beach Recreation

4.1 Introduction

Oil spills can reduce the consumer surplus accruing to beach visitors in two ways. A spill may

result in the closure of a beach area. Those planning to visit affected sites will substitute to

lesser-valued activities, including visiting other beach sites or participating in alternate activities.

The other possibility is that affected sites will remain open but the quality of the recreation

experience will be decrease by the impacts of the spill. Consumer surplus will decrease for visits

made to sites with decreased environmental quality.

Section 3 described the linkage between the NRDAM/CME model outputs and the beach

recreation estimates in this section. This section describes the procedures used to estimate the

beach recreation-related consumer surplus losses associated with a hypothetical oil spill. First,

the methodologies used in the GPECM and the NRDAM/CME are reviewed. Then, the

methodology employed in the present application is detailed. Baseline estimates of beach

visitation are given. The literature review conducted to obtain information on the consumer

surplus of beach trips is discussed.

4.2 Previous Methodologies to Estimate Beach Recreation Consumer Surplus Losses as a

Result of an Oil Spill

4.2.1 Summary of GPECM Methodology

The GPECM estimates the economic losses of beach recreation as part of the “Spill-Related

Recreation and Tourism Losses.” Saltwater windsurfing/sailing is considered a form of

“specialized” recreation. Saltwater swimming and saltwater beach activities are classified as

“general” recreation.

Baseline visitation data for the beach visitation activities were obtained from the State

Comprehensive Outdoor Recreation Plan (SCORP) for each coastal state. Several SCORP’s did

not provide the necessary data so various assumptions were required to estimate complete

visitation records. Data on the miles of coastline in each OCS Planning Area were then used to

estimate visitation per mile of shoreline.

Unit day values for general and specialized recreation were obtained from a literature review.

The average consumer surplus for general recreation was obtained from nine studies of beach

visitation. The average consumer surplus for specialized recreation was determined from studies

measuring the benefits of angling, both freshwater and saltwater. The estimates from these

studies varied considerably; one study was chosen to represent a median estimate. It was

assumed that most individuals planning a trip to a site affected by an oil spill would take a trip to

a substitute site instead. The consumer surplus loss for trips taken elsewhere was assumed to be

25% of the unit day values. All consumer surplus was counted as a loss only for trips not taken

as a result of the spill.
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A typical spill was considered to be 24,793 barrels based on the mean of 53 actual spill cases. A

regression model, based on data from 39 spills worldwide, was used to estimate the relationship

between spill size and the length of shoreline oiled. Using the regression coefficients, it was

estimated that a typical spill would oil 30.231 miles of coastline. The analysis assumed that a

typical spill would result in the loss or diversion of coastal tourism for a 35-day period based on

the closure period for the American Trader spill.

4.2.2 GPECM Methodology Assessment

Due to limited data availability on coastal visitation and consumer surplus, the GPECM

methodology is based on several assumptions. This section discusses the validity of several of

these assumptions for consideration in the current application. Several assumptions made in the

GPECM application are not considered valid for the current application due to differences in

available resources or advances in research.

The GPECM divides baseline tourism visitation statistics into six categories (coastal

visitation/saltwater beach activities, on-shore saltwater fishing, saltwater swimming, offshore

saltwater fishing, saltwater waterskiing/motorboating, and saltwater windsurfing/sailing). Three

potential sources of error arise in these estimates. First, there may be considerable overlap of the

activities, especially when data are collected from different sources. The on-shore saltwater

angling data from the National Survey of Fishing, Hunting, and Wildlife-Associated Recreation

are likely to overlap with the beach visitation statistics presented in the SCORP’s. Also, the

offshore saltwater fishing data are likely to overlap with saltwater boating visitation.

A second source of error is that visitation records vary considerably across states. While some

SCORPS, such as those compiled by Oregon and Mississippi, present detailed visitation

estimates that correspond to the categories defined in the GPECM, most states do not provide

such data. As noted in the GPECM report, only a few states make the distinction between

freshwater and saltwater in visitation statistics for water-based activities. Thus, the visitation

estimates used by the GPECM are based on extrapolations of data across states. The validity of

such extrapolations was not discussed in the GPECM.

A third source of error is that the GPECM used estimates from the most recent year. Statistics

from the most recent year may not be representative of typical visitation due to annual variations

in weather, prices, and site quality. Using visitation data averaged across several recent years

minimizes the influence of annual visitation fluctuations.

The GPECM methodology assumes that beach visitors who take trips to substitute sites when

their primary site choice is affected by an oil spill obtain a consumer surplus that is 75% of the

unit day value. This assumption contradicts economic theory. The consumer surplus associated

with a site visit is defined as the net willingness-to-pay relative to the next best alternative. The

next best alternative to a beach trip may be a trip to a different beach or a different activity

altogether. Whether the next best alternative is a substitute beach trip is not relevant to the

consumer surplus estimate. While the substitute assumption made in the GPECM analysis

contradicts economic theory, it was not necessarily inconsistent with the empirical methods of

the time. Substitute choices were often poorly modeled in recreation demand studies during the

1970’s and 1980’s.
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Many empirical recreation studies conducted in the 1970’s and 1980’s failed to properly account

for the existence of substitutes. Failure to include substitute prices in recreation demand models

produces a significant upward bias in consumer surplus estimates (Rosenthal, 1987). The rise in

popularity of random utility models, starting in the late 1980’s, reflects an improved framework

for modeling recreation behavior in the presence of substitutes (Milon, 1988). So, in theory one

should count the entire consumer surplus estimate as a social loss when substitution occurs

regardless of whether the alternative is a substitute trip or a substitute activity.

More recent recreation demand models, particularly random utility models, include substitutes in

a manner consistent with economic theory. The studies used to estimate unit day values in the

present application explicitly model substitute choices. Thus, the total consumer surplus

estimate is counted as a social loss when a trip is not taken to a preferred location as a result of

an oil spill.

4.2.3 Summary of NRDA/CME Methodology

The NRDA/CME Type A Model estimates the lost value of beach recreation due to closures that

are specified by the model user. The model does not estimate the extent or duration of the

closure period; these must be specified. The model does not estimate the losses associated with

trips still taken to beaches with reduced environmental quality. Thus, unlike the marginal

analysis used by the NRDAM/CME to estimate recreational fishing losses, beach recreation

losses are based only on the loss of trips to the affected site. Beach recreation is divided into

National Seashore visitation and visitation to state and local beaches. The analysis was based on

information on average monthly visitation, the duration of visits, the length of usable recreational

beach, and the average value of beach visitation.

The NRDAM/CME analysis uses beach visitation data for all National Seashores obtained from

the National Park Service. Average visits per linear meter of usable recreational beach are

calculated for each month using data from several years. Data on other beaches were obtained

from the Public Area Recreation Visitors Survey (PARVS). Based on a sample of 28 public

beaches, the average visitation per linear meter of beach is calculated. The model applies this

visitation rate to all beach visitation in each economic province.

The NRDAM/CME identifies seven studies that estimate the consumer surplus associated with

beach visitation. An average value of $11.65 per day (in 1991 dollars) is used to represent the

consumer surplus of a day at the beach. The welfare losses estimated by the model are

seasonally adjusted based on both the number of visits and length of visits during different

months.

4.2.4 NRDA/CME Methodology Assessment

In consideration of the limited data pertaining to beach visitation and consumer surplus, the

NRDAM/CME methodology attempts to present reasonable estimates of consumer surplus

losses. Two potential sources of error in the NRDAM/CME estimates are discussed below.

First, the NRDAM/CME is designed to analyze the welfare losses of a relatively minor chemical

spill. A minor spill would be less likely to result in beach closure compared to a major spill. If a
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minor spill results in no official beach closures, then the model would estimate no welfare losses

associated with trips that continue to be taken to the affected area. The authors note that a

marginal analysis could not be performed due to the limited number of studies that address the

marginal values of beach recreation. Thus, the methodology of the NRDAM/CME appears

better suited to analyzing a major spill resulting in beach closure rather than a minor spill that

does not result in closures.

While the NRDAM/CME uses visitation data for all National Seashores, the visitation estimates

provided for other public beaches are based on a small sample of sites, six or fewer for each

economic province. If these sites are not representative of all public beaches in the province,

then the estimates provided may be biased. Leeworthy (1999) indicated that the included sites

may not be representative, resulting in an upward visitation bias. This suggests that a more

representative sample of public beaches would produce more valid visitation estimates.

4.3 Summary of Current Methodology

The current methodology estimates the beach recreation welfare loss from a hypothetical oil spill

as a function of four factors:

1. The probability that the spill will come ashore at a public beach site;

2. The duration and extent of the beach closure;

3. The baseline beach visitation;

4. The consumer surplus associated with a visit to a beach.

First, the amount of coastal public beach, measured in linear miles, for each OCS Planning Area

is estimated as a proportion of the total coastal miles. The duration and extent of beach closure

is determined by the analysis of the hypothetical oil spill scenarios using the NRDAM/CME

model. We use all available data sources to derive beach visitation estimates for each OCS

Planning Area. A literature review is conducted to obtain information on the benefits of beach

visitation. Finally, the output from the NRDAM/CME scenarios is used to derive estimates of

beach visitation welfare losses.

The NRDAM/CME estimates beach injury in terms of linear meter-days of beach shoreline oiled

above lethal thresholds. An important issue is whether this estimate approximates the extent and

period of beach closure. This is discussed in more detail in Section 4.5 below. During the

closure period, all economic benefits associated with the site are lost. We develop visitation

estimates using the same metric (visits per linear meter-day of beach shoreline) for each OCS

Planning Area. Thus, the visitation loss from a hypothetical oil spill is linked to the

NRDAM/CME output.

The NRDAM/CME does not estimate any beach recreation loss for Alaska. The GPECM

includes only a small amount of saltwater boating in estimating coastal tourism losses. The

Alaska SCORP does not discuss the issue of beach recreation (Alaska Department of Natural

Resources, 1999). Thus, this analysis does not estimate the impacts of oil spills on beach

recreation for the Alaska OCS Planning Areas.
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 4.4 Identification of Beach Sites

As part of the Public Area Recreation Visitors Survey, the National Oceanic and Atmospheric

Administration compiled an inventory of coastal recreation sites. Data are available on the miles

of public beach in each coastal state (NOAA, 1988). These data are presented in Table 4.1. Data

are also available on the tidal shoreline of all coastal states (NOAA, 1975).

The state-specific data from Table 4.1 are organized into OCS Planning Areas in Table 4.2. The

percentages in Table 4.2 represent the proportion of coastal oiling assumed to affect public

beaches. For example, if a hypothetical spill in the North Atlantic Planning Area oils 29,000

meters of coast, we assume that oiling of public beaches is 2,059 meters.

4.5 Extent and Duration of Beach Closure

The NRDAM/CME estimates coastal oiling as the amount above “lethal thresholds” using

several metrics:

1. Meters;

2. Square meters;

3. Meter-days;

4. Square meter-days.

Similar to the GPECM and the NRDAM/CME, we use a linear measure as the basis for

estimating both visitation and injury. Thus, we are limited to either a linear (meter) or lineartime (meter-days) metric. We considered two options for measuring coastal oiling in relation to

beach recreation. One option is to use the linear (meter) output from the NRDAM/CME and use

an independent estimate of the closure period. Data on the period of beach closure from actual

oil spills could be used to estimate closure periods. However, such an approach still must

address the relationship between the amount of beach that would be closed and the linear output

from the NRDAM/CME. The linear oiling above lethal thresholds estimated by the

NRDAM/CME may differ from the linear coastline that would be closed to recreation. Thus,

this option requires two important pieces of data: the closure period and the relationship between

the NRDAM/CME linear output and the likely magnitude of linear closure.

The other approach is to use the linear-time estimate from the NRDAM/CME as a basis for

estimating injury. This approach would not require an independent estimate for the duration of

closure. However, the relationship between the injury estimate from the model and the likely

beach closure still must be addressed. For example, if the NRDAM/CME estimates that coastal

beach oiling is X meter-days, does this imply that the extent of beach closure is also X meterdays?

To explore this issue, as well as several other issues in this project, we conducted an empirical

analysis comparing the NRDAM/CME model outputs to injury data from actual oil spills. See

Section 3.4.3.) for a description of this analysis. As discussed in Section 3.4.4, the duration

estimates for beach impacts implied by the NRDAM/CME are not consistent with observed

beach closure periods. Thus, we use the linear coastal oiling estimate from the NRDAM/CME
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for the physical impact component of injury but use an independent beach closure period of 21

days for the time component. (See Section 3.4.4.)

4.6 Baseline Beach Recreational Visitation

Prior analyses estimating the welfare losses of oil spills have obtained beach visitation data from

State Comprehensive Outdoor Recreation Plans (SCORP’s) or from the Public Area Recreation

Visitors Survey (PARVS) data. Both of these data sources are used for the current application to

obtain data on beach visitation. Each coastal state was contacted in an attempt to locate

additional information on beach visitation. Data were obtained from most states regarding state

park visitation. Some states provided data on total beach visitation, including locally managed

and private beaches. The availability of data differed significantly across states. A summary of

the data collection effort for each state is discussed below. One should note that the data

provided by each state are based on different methodologies and sources. Some data were

derived from visitor counts or entrance fee receipts while other data were based on general

population surveys. The data collection effort for this project attempted to obtain the most

relevant and complete data for each state. A summary of annual beach visitation for each coastal

state is presented in Table 4.3. Note that the values in Table 4.3 are all measured in terms of

visits (trips). The distinction between visits and days is discussed later in this chapter.

4.6.1 Baseline Beach Recreational Visitation – Alabama

A SCORP is currently being prepared for Alabama and was not available for this study. The

state has two coastal state parks: Gulf State Park and Meather State Park. Alabama State Parks

provided data on total guest count at these two parks for fiscal years 1998 and 1999. The data

were averaged over these two years. Gulf State Park is a full-service resort with a hotel, golf

course, and freshwater lake, as well as a sandy beach. Only a portion of total visitation should be

considered “beach” visitation and closure of the beach would not eliminate visitation. We

assume that beach visitation accounts for 25% of total visitation due to the availability of other

recreation activities at the site.

4.6.2 Baseline Beach Recreational Visitation – California

The California SCORP did not provide data on visitation (CA Department of Parks and

Recreation, 1994). The California Department of Parks and Recreation provided visitation data

for all state-managed recreation sites. All visitation at state beaches is included in Table 4.3. A

detailed map of California was used to identify other public coastal recreation areas. For these

locations, 25% of visitation is included in Table 4.3. The data are based on visitation for the

1998-1999 fiscal year. Point Reyes National Seashore is located in California and the average

annual visitation from 1996-1998 is included in Table 4.3.

4.6.3 Baseline Beach Recreational Visitation – Connecticut

A SCORP was not available for Connecticut. The Connecticut Department of Environmental

Protection was contacted to obtain data on visitation to state-managed coastal recreation areas.

The data in Table 4.3 include visitation to all state-managed coastal areas for 1998.
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4.6.4 Baseline Beach Recreational Visitation – Delaware

A SCORP was not available for Delaware. Delaware has three coastal state parks: Cape

Henlopen, Delaware Seashore, and Fenwick Island. Delaware State Parks provided annual

attendance data for each park. The average annual visitation to these sites in 1994, 1995, and

1997 (data for 1996 were not available) was 2,007,900 visits. Data were also available on

visitation to Sussex County, Delaware (Delaware Economic Development Office, 1999). Of an

estimated 3.3 million visitors to Sussex County in 1998, about 42% (1,386,000) participated in

beach recreation. The value in Table 4.3 is the sum of 2,007,900 and 1,386,000 trips.

 4.6.5 Baseline Beach Recreational Visitation – Florida

A SCORP for Florida was not available. The Florida Department of Environmental Protection

provided data on beach visitation. Based on 1997 data, the total number of user occasions for

saltwater beach activities was 143,387,200. This value includes all beach visitation, including

visitation to Gulf Islands National Seashore.

4.6.6 Baseline Beach Recreational Visitation – Georgia

The Georgia SCORP did not provide any data on visitation (GA Dept. of Natural Resources,

1995). The Georgia Department of Natural Resources was contacted to obtain information on

state park visitation but there is only one state-operated beach site. Due to the lack of data, a

beach visitation total was estimated for Georgia. We base our estimate on the beach visitation

rate for South Carolina (117,419 annual visits per linear mile of beach). South Carolina is

adjacent to Georgia and is likely to have similar beach visitation rates. Applying this visitation

to Georgia, with 69 miles of beach, results in a visitation estimate of 8,101,900 visits.

4.6.7 Baseline Beach Recreational Visitation – Louisiana

A SCORP was not available for Louisiana and no other data on beach visitation could be located.

Similar to Georgia, beach visitation was estimated based on visitation rates from other states.

We calculated the average beach visitation rate per linear mile of beach for Texas, Alabama, and

Mississippi. The average rate for these three states is 71,070 visits per linear mile. We apply

this rate to Louisiana, with 108 miles of beaches, to obtain a visitation estimate of 7,675,500

visits per year.

4.6.8 Baseline Beach Recreational Visitation – Maine

The Maine SCORP estimates the number of resident ocean swimming days to be 4,372,069 for

the 1991-1992 season. This does not include visitation from non-residents. A.T. Kearney, Inc.

(1991) assumes that residents account for 60% of visitation for northern states. Applying this

same assumption to Maine results in an estimate for non-resident visitation of 2,914,700. While

more recent data on beach recreation are not available, it is estimated that tourist expenditures

increased 16% in Maine between 1991 and 1996 (Roach, et al, 1999). The 1991 visitation

estimates are increased by 16% to reflect more recent tourism trends.
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4.6.9 Baseline Beach Recreational Visitation – Maryland

A SCORP was not available for Maryland. Data on beach visitation were obtained from the

Maryland Office of Tourism Development (1999). A total of 18,386,000 tourist trips were taken

to Maryland sites in 1997, including visits by Maryland residents. Of these, 13.3% were trips to

visit beaches, or 2,445,300 trips. Similar data for 1998 indicate 2,087,500 trips. The average of

these values (2,266,400) is presented in Table 4.3. Note that Assateague National Seashore is

partially located in Maryland. It is assumed that the visitation estimates provided by the

Maryland Office of Tourism Development capture these visits.

4.6.10 Baseline Beach Recreational Visitation – Massachusetts

A SCORP was not available for Massachusetts. Data were provided by the Massachusetts

Department of Environmental Management on the attendance at five state-managed beaches over

several recent fiscal years. Using an average over fiscal years 1997, 1998, and 1999, annual total

attendance equals 3,349,500 visits. Cape Cod National Seashore is located in Massachusetts.

Averaged over 1996-1998, annual visitation equaled 4,781,300. Finally, data were obtained for

the nonresident parking at two private beaches (Crane Beach and Duxbury Beach). Averaged

over 1996-1998, about 77,000 cars were parked at these sites annually. We assume 2.5 visitors

per car to obtain a visitation estimate of 192,500 trips. The visitation number in Table 4.3. is the

sum of these three values.

4.6.11 Baseline Beach Recreational Visitation – Mississippi

The Mississippi SCORP provides visitation estimates for swimming but does not differentiate

between freshwater and saltwater (MS Department of Wildlife, Fisheries, and Parks, 1995). No

state parks are located along the Mississippi coast. Gulf Islands National Seashore is located in

Mississippi and Florida. Data were available from the Gulf Coast Economic Research Center

(1998) on the number of visitors to the Mississippi segment. There are several county and local

beaches along the Mississippi coast. Data on Mississippi coastal visitation was obtained from

Longwoods (1997). This report indicated 19.3 million trips to the Mississippi coastal area in

1996. While an estimated 40% of these visitors go to the beaches (Pitts, 2000), a smaller

percentage go for the primary purpose of beach visitation. We assume that 15% of the 19.3

million trips are for the primary purpose of visiting the beach.

4.6.12 Baseline Beach Recreational Visitation – New Hampshire

A SCORP was not available for New Hampshire. Data from the Institute for New Hampshire

Studies (1999) indicated that there were 6.63 million visitor trips to the “Seacoast Region” of

New Hampshire in fiscal year 1998. Also, 49% of all New Hampshire visitors indicated that

beach recreation was one of their activities during their trip. We assume that 25% of all trips to

the Seacoast Region were for the primary purpose of beach recreation.
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4.6.13 Baseline Beach Recreational Visitation – New Jersey

A SCORP was not available for New Jersey. The New Jersey Commerce and Economic Growth

Commission (1999) provided data on the number of beach trips. The value in Table 4.3 reflects

the annual average number of day and overnight beach trips for 1996-1998.

4.6.14 Baseline Beach Recreational Visitation – New York

The New York SCORP (NY State Parks, Recreation, and Historic Preservation, 1994) provides

data on swimming participation but did not differentiate between freshwater and saltwater. The

New York Office of Parks, Recreation, and Historic Preservation provided data on state park

visitation for all sites with coastal beaches for fiscal year 1997-1999. Visitors to these sites

comprise the majority of all coastal beach visitors (Sawyer, 2000). Fire Island National Seashore

is located in New York. The average annual visitation for 1996-1998 is included in Table 4.3.

4.6.15 Baseline Beach Recreational Visitation – North Carolina

The North Carolina SCORP includes participation levels for beach and swimming activities but

does not differentiate between freshwater and saltwater (NC Division of Parks and Recreation,

1995). North Carolina has two National Seashores: Cape Lookout and Cape Hatteras. While

visitation data were available for these sites, no other data on beach visitation could be obtained.

Thus, we estimated the beach visitation for North Carolina based on data from adjacent states.

Visitation per linear mile of beach was calculated for Virginia and South Carolina. The average

of these two visitation rates is 134,348 visits per linear beach mile. Applying this rate to North

Carolina, with 160 miles of beaches, results in a visitation estimate of 21,495,700 visits.

4.6.16 Baseline Beach Recreational Visitation – Oregon

The Oregon SCORP does provide estimates of annual beach use by region of the state for 1987

and also presents a projection for 2000. Considering only the coastal regions, the projected

activity occasions for beach recreation in 2000 are 3,913,700. The SCORP does not differentiate

between saltwater and freshwater beach visitation. The coastal regions do not extend far inland.

We assume that 70% of these trips are visits to saltwater beaches.

4.6.17 Baseline Beach Recreational Visitation – Rhode Island

A SCORP was not available for Rhode Island. The Rhode Island Department of Environmental

Management provided 1998 data on the number of visitors to each state-managed beach.

4.6.18 Baseline Beach Recreational Visitation – South Carolina

The South Carolina SCORP provides information on beach visitation but no differentiation is

made between freshwater and saltwater (SC Department of Parks, Recreation, and Tourism).

Data were provided by the South Carolina Department of Parks, Recreation, and Tourism on the

number of visitors who indicated “beach visitation” as an activity (Flowers, 1999).
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4.6.19 Baseline Beach Recreational Visitation – Texas

The Texas SCORP provides a data projection for the year 2000 for the number of saltwater

swimming user occasions. The total projected visitation estimate is provided in Table 4.3. No

additional information was available to determine the accuracy of the projected visitation value.

4.6.20 Baseline Beach Recreational Visitation – Virginia

The Virginia SCORP presents data on the number of activity days in 1992 for different activities

for each region of the state (VA Department of Conservation and Recreation, 1996). The

visitation total for “beach use and swimming” summed across all coastal regions was

11,597,900. This includes freshwater activity but this portion is likely to be small because maps

of the regions indicate few freshwater beaches and separate tallies are kept for freshwater fishing

and boating. We assume that 90% of the visitation total pertains to saltwater sites.

4.6.21 Baseline Beach Recreational Visitation – Washington

The Washington SCORP provides data on the 2000 projected participation in

“swimming/wading at a beach” by regions of the state. Only one region in the state includes

saltwater coast. The beach visitation estimate includes both freshwater and saltwater. The

SCORP indicates that 37% of the power boating and 32% of the non-motorized boating in the

coastal region occurs in saltwater. Thus, we assume that 35% of the beach activity in the region

occurs at saltwater sites. The data are measured in household trips. We assume that the average

household trip includes 2.5 visitors.

4.6.22 Comparison to Other Beach Visitation Estimates

Beach visitation estimates in Table 4.3 were compared to the beach visitation estimates from the

GPECM analysis. Exhibit 10-6 of the GPECM report estimates the number of resident and

visitor trips for “coastal visitation/saltwater beach activities” and “saltwater swimming” as about

1.2 billion.

Another source for data on beach visitation is the Travel Industry of America (TIA). Several

states contract with TIA to estimate tourist numbers, visitor activities, and economic impacts.

TIA derives estimates based on monthly telephone interviews of 20,000 American households

regarding their travel behavior. TIA estimates the total number of person-trips to beaches in

1998 to be about 144 million (Travel Industry of America, 2000). This only includes trips

involving travel over 100 miles. Thus, the TIA data should be adjusted upwards to account for

trips by those within 100 miles. The GPECM report indicates that state residents make 47% of

all saltwater beach/swimming trips. Many state residents likely travel more than 100 miles; this

suggests that the TIA value be adjusted upwards by a factor up to 1.89. In other words, the total

estimate of beach visitation using the TIA data would be a maximum of 272 million, which

would imply that the visitation estimates in the GPECM report are too high. The national beach

visitation estimates in Table 4.3 or Table 4.4 (about 333 million) is slightly higher than the

national estimate based on the TIA data but significantly lower than the GPECM data. The
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similarity between the TIA estimate and our data suggests that the GPECM beach visitation is

too large.

4.6.23 Beach Visitation by OCS Planning Area

The state-level data in Table 4.3 need to be allocated according to OCS Planning Area. For the

continental United States, most OCS Planning Area boundaries correspond to state boundaries.

The differences that need to be resolved are:

1. North Carolina includes both the Mid-Atlantic and South Atlantic OCS Planning Areas;

2. Florida includes the South Atlantic, Straits of Florida, and Eastern Gulf of Mexico OCS

Planning Areas;

3. California is divided into three OCS Planning Areas.

The California visitation data are site specific. These data were allocated into the three

California OCS Planning Areas by identifying the location of each beach area on a map. For

Florida, data were provided on the distribution of beach visitation by region of the state

(Trimble, 2000). No data were available to allocate the North Carolina beach visitation data so

we assumed that 50% of the North Carolina beach visitation total occurred in both the MidAtlantic and South Atlantic OCS Planning Areas. Annual representative beach visitation, by

OCS Planning Area, is presented in Table 4.4.

4.7 Beach Visitation Conversions

The beach visitation data need to be linked to the NRDAM/CME output of coastal oiling. The

NRDAM/CME estimates coastal oiling in terms of linear meter-days. Thus, the beach visitation

data need to be converted to visits per linear meter-day. The number of linear miles of public

beach in each OCS Planning Area is presented in Table 4.4. Multiplying this by 1,609 meters

per mile produces an estimate of the meters of beach in each OCS Planning Area.

An important distinction is between visitation on a “per-day” or “per-trip” basis. The visitation

estimates in Table 4.3 and 4.4 are measured in terms of numbers of trips. Beach closure is

measured in terms of days. The appropriate metric to link the beach closure period with a loss in

beach recreation is days. Thus, data are needed to convert beach recreation trips to days. We

used PARVS survey data that collected information on the number of annual trips and days to

over 50 beach sites. Based on these data, the average length of stay for a beach trip is 1.36 days.

Thus, the visit data in Tables 4.3 and 4.4 are converted to days of beach recreation by

multiplying by 1.36.

Beach recreation can now be estimated in terms of visitors per meter-day of beach. The beach

visit estimate for each OCS Planning Area in Table 4.4 is first multiplied by 1.36 to convert to

days. Then, this value is divided by the meters of beach in each OCS Planning Area. Finally,

division by 365 converts visitation to a per-day value. The final column in Table 4.4 presents the

estimated visitation per linear meter-day of beach in each OCS Planning Area.
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The data in Table 4.4 are combined with the data in Table 4.2 to estimate the impact of a

hypothetical oil spill on beach recreation. For example, assume a spill in the North Atlantic

Planning Area oils 29,000 meters of coast. Based on data in Table 4.2, we estimate that this spill

oils 2,059 meters of beach. Assuming a closure period of 21 days, the spill would result in a loss

of 43,239 meter-days of beach recreation. Beach visitation in the North Atlantic Planning Area

is estimated at 0.118 visitors per meter-day (Table 4.4). This hypothetical spill would result in

the loss of an estimated 5,102 days of beach recreation.

4.8 Literature Review for Consumer Surplus Estimates

A literature review was conducted to obtain consumer surplus estimates for beach visitation.

Freeman (1995) referenced four studies of beach recreation in a review paper on marine

recreation. The GPECM references nine studies of beach recreation. The NRDAM/CME

references seven studies of beach recreation. Several of the studies referenced in the GPECM

and NRDAM/CME analyses were studies conducted prior to recent advances in the discipline of

resource economics. Similar to the case with studies estimating the consumer surplus of

recreation fishing, the present application only considers studies conducted since the mid-1980’s.

A listing of all relevant studies is presented in Table 4.5. Each study is briefly discussed below.

4.8.1 Bell (1986)

This article asked a sample of Florida residents their willingness to pay for an annual pass for

beach use. The analysis estimated willingness to pay per day of beach recreation as a function of

beach crowding density. Under current crowding, the benefits per day for Pompano Beach

recreation were estimated to be $1.41 (in 1985 dollars).

4.8.2 Bell and Leeworthy (1990)

This study of beach recreation in Florida develops individual-level travel cost models. The

authors present a linear OLS model based on statistical performance. They estimate consumer

surplus for a day of beach recreation in Florida to be $33.91 (in 1984 dollars).

4.8.3 Dunford (1999)

This article discusses the valuation of lost beach recreation associated with the American Trader

tanker spill. The PARVS data were used to estimate the value of beach recreation by both the

trustees and the responsible parties. The trustees’ value, based on contingent valuation, was $15

per visit (in 1990 dollars). In research supported by the responsible parties, a travel cost analysis

of the same survey produced estimates of $5.19 to $5.53 per visit for Santa Monica beaches and

$4.88 to $8.96 for all southern California beaches.

4.8.4 Dunford and Fowler (1996)

This paper uses the PARVS contingent valuation data to develop benefit estimates for beach

recreation in Florida and California. The authors estimate binomial logit models to analyze the

responses to in-person dichotomous choice questions. For the California sites, the median WTP
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ranged from $3.37 to $3.51 per day (in 1990 dollars) depending on the independent variables

included in the model. For the Florida sites, the median WTP ranged from $3.31 to $3.44.

4.8.5 Dunford, et al. (1999)

This paper used random utility models to obtain benefit estimates for beach recreation in central

Florida. The researchers estimated the marginal value per trip of having an average site in the

average party’s choice set. Based on model specification, the marginal value was $0.084 or

$0.092 (in 1994 dollars) per trip.

4.8.6 Edwards and Gable (1991)

This paper uses a hedonic model to estimate the use value of beaches in Rhode Island to local

residents. Based on the average household income, the authors estimate that the annual

willingness to pay for all forms of beach recreation is $469 (in 1980 dollars). However, this

consumer surplus estimate could not be converted to a per-trip or a per-day measure with the

reported data.

4.8.7 Environmental Economics Research Group (1998)

This report used a random utility approach to estimate the value of lost beach recreation

associated with the Tampa Bay oil spill in 1993. Based on a survey of Florida residents within a

16-county region, average benefits per visit are estimated to be $22.75 (in 1994 dollars).

4.8.8 Leeworth and Bowker (1997)

This paper estimates the consumer surplus associated with different recreation activities in the

Florida Keys. The authors estimate truncated negative binomial models using individual-level

travel cost models. The resulting benefit per day for all beach activities is $86.64 (in 1995

dollars) based on surveys from June 1995 to May 1996.

4.8.9 Leeworthy, et al (1991)

This paper estimates the consumer surplus associated with beach recreation using contingent

valuation. Eleven study sites were sampled including four in California, two in Florida and one

each in Connecticut, New York, Oregon, Virginia, and Washington. Using open-ended

questions, the mean willingness to pay for a site-specific annual vehicle pass was $10.18 (in

1990 dollars). The values ranged from a low of $4.78 to a high of $22.94. The average number

of trips to the site was 11.39 and average group size was 3.93 people. Assuming one vehicle per

group, this suggests a value of $0.23 per person per trip.

The paper provides data on the responses to each dollar amount in referendum questions. Using

the approach described in Haab and McConnell (1997), these data were analyzed to provide a

non-parametric lower-bound estimate to willingness to pay. Based on an on-site survey, the

willingness to pay estimate is $3.32 per person per day. Based on a mailback survey, the

estimate is $19.94 for an annual vehicle pass (suggesting a value of $0.45 per person per trip).
80 Section 4: Beach Recreation

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4.8.10 Leeworthy, et al. (1990)

This paper uses contingent valuation questions to estimate the benefits of beach recreation at 10

sites along the Pacific coast (California, Oregon, and Washington). Based on open-ended

questions, the mean willingness to pay for an annual vehicle pass to a site was $7.91 (in 1989

dollars). The mean number of trips per year was 7.16 and average group size was 3.95.

Assuming one vehicle per group, this implies a benefit estimate of $0.28 per person per trip.

The paper also presents the responses (percent yes) to bid values using referendum questions.

Using the methods of Haab and McConnell (1997), it was possible to estimate a lower bound

willingness to pay from the responses. Based on an on-site survey, the lower bound willingness

to pay across all sites was $4.42 per person per day. Based on a mailback survey, the nonparametric willingness to pay estimate was $11.95 for an annual vehicle pass. This implies a

willingness to pay of $0.42 per person per trip.

4.8.11 Leeworthy, et al. (1989a)

This paper used open-ended contingent valuation questions to estimate the willingness to pay for

beach recreation in the northeastern United States, from Maine to Maryland. The valuation

question asked respondents their maximum willingness to pay for an annual pass to visit the site.

The mean value across all sites was $12.78 with a range from $4.28 to $32.88 (in 1988 dollars).

The mean number of trips by respondents was 7.8 per year with an average group size of 3.71

people, suggesting a mean willingness to pay of $0.44 per trip per person (assuming one vehicle

per group).

Referendum contingent valuation questions were also presented to respondents but the authors

do not present a consumer surplus estimate. The responses (percent yes) to each dollar value

were given, allowing a non-parametric lower-bound estimate to willingness to pay using the

approach described in Haab and McConnell (1997). The implied willingness to pay from the

data in the paper is $5.63 per person per day based on on-site surveys. The responses based on a

mailback survey produce a willingness to pay estimate of $16.78 per year for an annual vehicle

pass, implying a value of $0.58 per person per trip.

4.8.12 Leeworthy, et al. (1989b)

This paper uses contingent valuation questions to estimate the consumer surplus associated with

visits to five beach sites in Florida, Alabama, Mississippi, and Louisiana. The average maximum

willingness to pay for an annual vehicle pass was $7.27 (in 1988 dollars) with a range from

$4.07 to $11.09. The mean number of trips per year was 4.68 and the average group size was

6.96 people. It is difficult to convert the benefit estimate to a per person per trip metric because

the large average group size suggests that many groups use multiple vehicles to travel to the

sites.

The authors also present data on the responses to referendum contingent valuation questions.

Using the non-parametric methods described in Haab and McConnell (1997), the lower bound

willingness to pay for beach recreation per person per day is $5.36. For an annual vehicle pass
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the lower bound estimate is $18.65. Again, converting this to a per person per trip basis does not

seem to be valid based on the available data.

4.8.13 Leeworthy and Meade (1989)

This paper uses contingent valuation questions to value beach recreation at sites in North

Carolina, South Carolina, Georgia, Mississippi, and Florida. Using open-ended questions, the

average willingness to pay for an annual vehicle pass for the study site was $8.30 with a range

from $2.94 to $17.52 (in 1987 dollars). Average group size was 4.84 people and the average

respondent took 3.52 trips to the site per year. This implies an average willingness to pay of

$0.49 per person per trip.

The approach of Haab and McConnell (1997) was used to derive a willingness to pay estimate

from the data provided on the responses to the referendum questions. The willingness to pay

estimate from an on-site survey was $7.34 per person per day. The estimate from a mailback

survey was $18.11 for an annual vehicle pass (suggesting a value of $1.06 per person per trip.

4.8.14 Leeworthy and Schruefer (1990)

This paper relies on contingent valuation to estimate the benefits of beach recreation at six sites

on the Gulf of Mexico. Using open-ended questions, the mean willingness to pay for an annual

vehicle pass was $9.50 (in 1989 dollars) with a range from $5.74 to $17.75. With an average

group size of 3.04 people and an average of 9.94 trips per year, this suggests a mean value per

person per trip of $0.31.

The authors provide data on the responses to referendum questions. These data were analyzed

using the approach of Haab and McConnell (1997) to derive a non-parametric lower-bound

estimate for willingness to pay. Based on an on-site survey, the lower-bound willingness to pay

for beach recreation was $4.16 per person per day. Based on a mailback survey, the lowerbound willingness to pay for an annual vehicle pass was $14.87 (suggesting a value of $0.49 per

person per trip).

4.8.15 Leeworthy and Wiley (1991)

This paper describes an individual-level travel cost model for Island Beach State Park in New

Jersey. Using a linear specification, the preferred model produces a benefit estimate of $15.44

per person per day (in 1988 dollars).

4.8.16 Leeworthy and Wiley (1993)

This paper estimates individual-level travel cost models for three beach sites in Southern

California. Using a linear model that eliminates outliers and excludes the value of travel time,

the benefit estimates for the three sites are $8.16, $18.36, and $51.94 per person per day (in 1989

dollars).
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4.8.17 Leeworthy and Wiley (1994)

This paper analyzes individual-level travel cost data from two beach sites in Florida. The

authors indicate a preference for a linear model that excludes any value of travel time. The

benefits for Clearwater Beaches range from $55.96 to $62.02 per person per day (in 1990

dollars) depending on the included independent variables and the sample composition. The

benefits for Honeymoon Island ranged from $14.91 to $16.95 per person per day

4.8.18 Silberman and Klock (1988)

This paper describes a contingent valuation survey asking respondents their willingness to pay

for a daily admission pass to use a stretch of beach in New Jersey. Additional questions elicited

data on the willingness to pay for a beach re-nourishment project. The mean willingness to pay

for daily use of the beach under existing conditions was $3.60 (in 1987 dollars).

4.8.19 Stronge and Schultz (1997)

This report used open-ended contingent valuation questions to obtain benefit estimates for beach

recreation in Broward County, Florida. The value per visit ranged from $3.78 (in 1996 dollars)

for Broward County residents to $5.00 for international visitors with an average of $4.14.

Values for other Florida beach locations were ranged from $3.96 to $6.36 per visit.

4.9 Representative Beach Recreation Values

A listing of all per-trip consumer surplus estimates for beach recreation is presented in Table 4.6.

All estimates are updated to 1999 dollars. A distinction is made between per-trip and per-day

benefit estimates. The data in Table 4.4 are based on a “per-day” metric; benefit estimates per

person per day are preferred to per-trip estimates. If all trips are one day in duration, then no

distinction exists. If a significant portion of trips are multiple-day trips, then applying per-day

benefit estimate to trip data will understate total benefits unless an adjustment is made for

average trip duration.

The estimated benefits per trip or per day in Table 4.6 range significantly from $0.09 to $94.71

($1999). Six studies tend to present larger benefit estimates – above $20 per trip or per day (Bell

and Leeworthy, 1990; EERG, 1998; Leeworthy and Bowker, 1997; and the three Leeworthy and

Wiley studies). Of these studies, we consider the EERG study to be the most appropriate for

providing representative beach recreation values for two reasons.

First, the study used a state-of-the-art nested random utility model based on actual behavior.

This model handles site substitution in a theoretically correct manner. The other studies use

either contingent valuation or travel cost models that do not incorporate recent theoretical or

statistical advances.

Second, the study was conducted as part of a natural resource damage case (the Tampa Bay oil

spill). The survey data used to derive the EERG benefit estimate include multiple-day trips. No

data were presented on average trip duration in the report. We assume an average trip duration

of 1.36 days based on the PARVS data. Dividing the EERG benefit estimate of $26 per trip by
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1.36 produces a benefit estimate of $19 per day. We use $19 per day as our high representative

value for a day of beach recreation.

The other twelve benefit estimates in Table 4.6 are all below $20 per trip or per day. Only one of

these, the Dunford, et al. (1999) paper, is a random utility model. However, we consider the

benefit estimates obtained in this analysis to be too low to use as representative values. The

estimates are very low compared to all other estimates of beach values and the analysis was

performed to explore a specific hypothesis, the impact of party composition on benefit estimates,

rather than to obtain valid benefit estimates. In the absence of a study using state-of-the-art

methods to provide a low representative value, we take the average benefit estimate from the

twelve studies that provide low estimates. Weighing all twelve studies equally, the average

benefit estimate is $3.85. Thus, we choose a value of $4 per day as the representative low

estimate of beach recreation.

4.10 Impacts of Oil Spill Scenarios

The analysis in this chapter is combined with the NRDAM/CME scenario outputs to provide an

estimate of beach recreation damages. Consider the example used in Section 4.7. where a

hypothetical oil spill results in the loss of an estimated 5,102 days of beach recreation. Using a

low benefit estimate of $4 per day, the beach recreation damage from this spill would be

$20,408. Using a high benefit estimate of $19 per day, the beach recreation damage would be

$96,938. The calculation of beach recreation damages for specific oil spill scenarios is explained

in more detail in Section 3.5.2.
84 Section 4: Beach Recreation

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References

A.T. Kearney, Inc. 1991. “Estimating the Environmental Costs of OCS Oil and Gas

Development and Marine Oil Spills: A General Purpose Model, Volume I – Economic

Analysis of Environmental Costs”. Report prepared for the Minerals Management Service

under contract number 14-35-0001-30500.

Bell, Frederick W. 1986. “Economic Policy Issues Associated with Beach Renourishment”,

Policy Studies Review, vol. 6(2):374-381.

Bell, Frederick W. and Vernon R. Leeworthy, 1990, “Recreational Demand by Tourists for

Saltwater Beach Days”, Journal of Environmental Economics and Management, vol.

18(3):189-205.

California Department of Parks and Recreation. 1994. “California Outdoor Recreation Plan,

1993”. Sacramento, CA.

Dunford, Richard W. 1999. “The American Trader Oil Spill: An Alternative View of Recreation

Use Damages”. AERE Newsletter, vol. 19(1):12-20.

Dunford, Richard W., and Robert B. Fowler. 1996. "An Analysis of Intrastate and Interstate

Differences in Beach-Use Values”. Paper presented at the AAEA Meeting, San Antonio, TX,

July 28-31, 1996.

Dunford, Richard W., Tori H. Knight, and Jennifer A. Murdock. 1999. “Random Utility Models

and Household Decisionmaking: The Effect of Party Composition on Beach Recreation

Values”. Paper presented at the 1999 AAEA Meeting, Nashville, TN, August 8-11, 1999.

Edwards, Steven F. and Frank J. Gable, 1991, “Estimating the Value of Beach Recreation from

Property Values: An Exploration with Comparisons to Nourishment Costs”, Ocean and

Coastal Management, vol. 15(1):37-55.

Environmental Economics Research Group. 1998. “Natural Resource Damage Assessment for

the Tampa Bay Oil Spill: Recreation Use Losses for Florida Residents”. Report submitted to

the Florida Department of Environmental Protection and National Oceanic and Atmospheric

Administration under contract number LE022, Lansing, MI.

Flowers, Julie. 1999. Personal communication. South Carolina Department of Parks, Recreation,

and Tourism.

Georgia Department of Natural Resources. 1995. “Georgia SCORP, 1995 Statewide

Comprehensive Outdoor Recreation Plan”.

Haab, Timothy C., and Kenneth E. McConnell. 1997. “Referendum Models and Negative

Willingness to Pay: Alternative Solutions”, Journal of Environmental Economics and

Management, vol. 32:251-270.
Section 4: Beach Recreation 85

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Leeworthy, Vernon R., and J.M. Bowker. 1997. “Nonmarket Economic User Values of the

Florida Keys/Key West”. National Oceanic and Atmospheric Administration, Monroe

County Tourist Development Council, The Nature Conservancy, The University of Georgia,

and the U.S. Forest Service.

Leeworthy, Vernon R., Daniel S. Schruefer, and Peter C. Wiley. 1991. “A Socioeconomic

Profile of Recreationists at Public Outdoor Recreation Sites in Coastal Areas: Volume 6”.

U.S. Department of Commerce, National Oceanic and Atmospheric Administration.

Leeworthy, Vernon R., Daniel S. Schruefer, and Peter C. Wiley. 1990. “A Socioeconomic

Profile of Recreationists at Public Outdoor Recreation Sites in Coastal Areas: Volume 5”.

U.S. Department of Commerce, National Oceanic and Atmospheric Administration.

Leeworthy, Vernon R., Norman F. Meade, Kathleen Drazek, and Daniel S. Schruefer. 1989a. “A

Socioeconomic Profile of Recreationists at Public Outdoor Recreation Sites in Coastal Areas:

Volume 2”. U.S. Department of Commerce, National Oceanic and Atmospheric

Administration.

Leeworthy, Vernon R., Norman F. Meade, Kathleen Drazek, and Daniel S. Schruefer. 1989b. “A

Socioeconomic Profile of Recreationists at Public Outdoor Recreation Sites in Coastal Areas:

Volume 3”. U.S. Department of Commerce, National Oceanic and Atmospheric

Administration.

Leeworthy, Vernon R., and Norman F. Meade. 1989. “A Socioeconomic Profile of

Recreationists at Public Outdoor Recreation Sites in Coastal Areas: Volume 1”. U.S.

Department of Commerce, National Oceanic and Atmospheric Administration.

Leeworthy, Vernon R., and Dan Schruefer. 1990. “A Socioeconomic Profile of Recreationists at

Public Outdoor Recreation Sites in Coastal Areas: Volume 4”. U.S. Department of

Commerce, National Oceanic and Atmospheric Administration.

Leeworthy, Vernon R., and Peter C. Wiley. 1991. “Recreational Use Value for Island Beach

State Park”. National Oceanic and Atmospheric Administration.

Leeworthy, Vernon R., and Peter C. Wiley. 1993. “Recreational Use Value for Three Southern

California Beaches”. National Oceanic and Atmospheric Administration.

Leeworthy, Vernon R., and Peter C. Wiley. 1994. “Recreational Use Value for Clearwater Beach

and Honeymoon Island State Park, Florida”. National Oceanic and Atmospheric

Administration.

Longwoods. 1997. “Mississippi Gulf Coast, 1996 Travel Year”.

Maryland Office of Tourism Development. 1999. “Annual Report FY 1999”.

Mississippi Department of Wildlife, Fisheries, and Parks. 1995. “Mississippi State

Comprehensive Outdoor Recreation Plan – 1995”.
86 Section 4: Beach Recreation

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National Oceanic and Atmospheric Administration. 1988. “National Estuarine Inventory Data

Atlas, Volume 4: Public Recreation Facilities in Coastal Areas”.

National Oceanic and Atmospheric Administration. 1975. “The Coastline of the United States”.

NOAA/PA 71046.

New Jersey Commerce and Economic Growth Commission. 1999. “1998 New Jersey Travel

Research Program, Travel and Tourism in New Jersey”.

New York State Parks, Recreation, and Historic Preservation. 1994. “Statewide Comprehensive

Outdoor Recreation Plan”.

North Carolina Division of Parks and Recreation. 1995. “North Carolina Outdoor Recreation

Plan, 1995-2000”.

Pitts, Jim. 2000. Personal communication. Mississippi Gulf Coast Convention and Visitors

Bureau, Gulfport, MS.

Roach, Brian, Jonathan Rubin, and Charles Morris. 1999. “Measuring Maine’s Marine

Economy”, Maine Policy Review, vol. 8(2):56-62.

Sawyer, Randall. 2000. Personal communication. New York State Office of Parks, Recreation,

and Historic Preservation, Albany, NY.

Silberman, Jonathan and Mark Klock. 1988, “The Recreation Benefits of Beach

Renourishment”, Ocean and Shoreline Management, vol. 11:73-90.

South Carolina Department of Parks, Recreation, and Tourism. 1995. “1995 South Carolina State

Comprehensive Outdoor Recreation Plan”.

Stronge, William B., and Ronald R. Schultz. 1997. “Broward County Beaches: An Economic

Study, 1995-1996”. Report prepared for Broward County Department of Natural Resource

Protection.

Travel Industry of America. 2000. Fast Facts – Domestic Travel.

www.tia.org/press/fastfacts3.stm

Trimble, David. 2000. Personal communication. Florida Department of Environmental

Protection, Tallahassee, FL.

Virginia Department of Conservation and Recreation. 1996. “Virginia Outdoors Plan 1996”.
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Table 4.1. Miles of Beach, by Coastal State

State Miles of Beach Total Coastal Miles

Alabama 13 607

California 868 3,427

Connecticut 64 618

Delaware 3 381

Florida 452 8,426

Georgia 69 2,344

Louisiana 108 7,721

Maine 32 3,478

Maryland 62 3,190

Massachusetts 321 1,519

Mississippi 75 359

New Hampshire 9 131

New Jersey 69 1,792

New York 130 1,850

North Carolina 160 3,375

Oregon 107 1,410

Rhode Island 21 384

South Carolina 62 2,876

Texas 200 3,359

Virginia 69 3,315

Washington 174 3,026

TOTAL 3068 53,588
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Table 4.2. Proportion of Beach Coastline by OCS Planning Area

OCS Planning Area

Total

Miles of

Beach

Total Miles

of Coast

Proportion of

Beach

Coastline

North Atlantic 362 5,128 7.1%

Mid-Atlantic 501 13,780 3.6%

South Atlantic 303 8,344 3.6%

Straits of Florida 79 1,332 5.9%

Eastern Gulf of Mexico 278 5,095 5.5%

Central Gulf of Mexico 196 8,687 2.3%

Western Gulf of Mexico 200 3,359 6.0%

Southern California 447 1,419 31.5%

Central California 301 1,254 24.0%

Northern California 120 754 15.9%

Washington/Oregon 281 4,436 6.3%

Gulf of Alaska NA NA NA

Kodiak NA NA NA

Cook Inlet NA NA NA

Shumagin NA NA NA

North Aleutian Basin NA NA NA

Saint George Basin NA NA NA

Aleutian Arc NA NA NA

Bowers Basin NA NA NA

Aleutian Basin NA NA NA

Navarin Basin NA NA NA

Saint Matthew Hall NA NA NA

Norton Basin NA NA NA

Hope Basin NA NA NA

Chukchi Sea NA NA NA

Beaufort Sea NA NA NA

TOTAL 3,068 53,588 --
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Table 4.3. Beach Visitation (Number of Visits)

State Beach Visitation

Alabama 514,700

California 41,422,900

Connecticut 2,743,700

Delaware 3,393,900

Florida 143,387,200

Georgia 8,101,900

Louisiana 7,675,500

Maine 8,452,700

Maryland 2,266,400

Massachusetts 8,323,300

Mississippi 4,118,400

New Hampshire 1,657,500

New Jersey 17,733,300

New York 13,868,000

North Carolina 21,495,700

Oregon 2,739,600

Rhode Island 2,037,800

South Carolina 7,280,000

Texas 23,741,000

Virginia 10,438,100

Washington 1,674,800

TOTAL 333,066,400
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Table 4.4. Beach Visitation Data, by OCS Planning Area

OCS Planning Area Annual

Beach Visits

Total

Miles of

Beach

Total Meters

of Beach

Visitors per

Meter-Day of

Beach

North Atlantic 18,433,500 362 582,458 0.118

Mid-Atlantic 63,229,000 501 806,109 0.292

South Atlantic 55,297,800 303 487,527 0.423

Straits of Florida 48,764,600 79 127,111 1.429

Eastern Gulf of Mexico 65,454,600 278 447,302 0.545

Central Gulf of Mexico 12,308,600 196 315,364 0.146

Western Gulf of Mexico 23,741,000 200 321,800 0.275

Southern California 23,385,100 447 719,223 0.121

Central California 16,695,300 301 484,309 0.128

Northern California 1,342,500 120 193,080 0.026

Washington/Oregon 4,414,400 281 452,129 0.037

Gulf of Alaska 0 NA NA NA

Kodiak 0 NA NA NA

Cook Inlet 0 NA NA NA

Shumagin 0 NA NA NA

North Aleutian Basin 0 NA NA NA

Saint George Basin 0 NA NA NA

Aleutian Arc 0 NA NA NA

Bowers Basin 0 NA NA NA

Aleutian Basin 0 NA NA NA

Navarin Basin 0 NA NA NA

Saint Matthew Hall 0 NA NA NA

Norton Basin 0 NA NA NA

Hope Basin 0 NA NA NA

Chukchi Sea 0 NA NA NA

Beaufort Sea 0 NA NA NA

TOTAL 333,066,400 3068 4,936,412 --
Section 4: Beach Recreation 91

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Table 4.5. Studies with Total Consumer Surplus Estimates for Saltwater Beach Visitation

Author(s) Date 1 Location Methodology

Bell 1986 Florida CVM

Bell & Leeworthy 1990 Florida TCM

Dunford 1999 California TCM

Dunford & Fowler 1996 Florida, California CVM

Dunford, et al. 1999 Florida TCM

Edwards & Gable 1991 Rhode Island Hedonic

EERG 1998 Florida TCM

Leeworthy & Bowker 1997 Florida TCM

Leeworthy, et al. 1991 CA,FL,NY,OR,VA,WA CVM

Leeworthy, et al. 1990 CA,OR,WA CVM

Leeworthy, et al. 1989a Northeast CVM

Leeworthy, et al. 1989b AL,FL,LA,MS CVM

Leeworthy & Meade 1989 FL,GA,MS,NC,SC CVM

Leeworthy & Schruefer 1990 Florida, Texas CVM

Leeworthy & Wiley 1994 Florida TCM

Leeworthy & Wiley 1993 California TCM

Leeworthy & Wiley 1991 New Jersey TCM

Silberman & Klock 1988 New Jersey CVM

Stronge & Schultz 1997 Florida CVM

1 – Date given is the date of publication, not the date that data were collected
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Table 4.6. Summary of Per-Trip Beach Recreation Consumer Surplus Estimates (1999

dollars)

Source Estimate(s) in 1999 dollars

Per-Day or

Per-Trip

Metric

Bell, 1986 $2.18 Per-day

Bell and Leeworthy, 1990 $54.37 Per-day

Dunford, 1999 $6.22 - $11.42 (avg. = $7.98) Per-trip

Dunford and Fowler, 1996 $4.22 - $4.47 (avg. = $4.32) Per-day

Dunford, et al., 1999 $0.09 and $0.10 Per-trip

EERG, 1998 $25.57 Per-trip

Leeworthy and Bowker, 1997 $94.71 Per-day

Leeworthy, et al., 1991 $0.29, $0.57, and $4.23 Both

Leeworthy, et al., 1990 $0.38, $0.56, and $5.94 Both

Leeworthy, et al., 1989a $0.62, $0.82, and $7.93 Both

Leeworthy, et al., 1989b $7.55 Both

Leeworth and Meade, 1989 $0.72, $1.55, and $10.76 Both

Leeworthy & Schruefer, 1990 $0.42, $0.66, and $5.59 Both

Leeworthy and Wiley, 1994 $19.00 - $79.06 (avg. = $47.48) Per-day

Leeworthy and Wiley, 1993 $10.96, $24.67, and $69.78 Per-day

Leeworthy and Wiley, 1991 $21.74 Per-day

Silberman and Klock, 1988 $5.28 Per-day

Stronge & Schultz, 1997 $4.20, $4.40, $5.04, and $6.75 Per-trip
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1. Name Cost Categories and Assign Associated Impact Driver Variables

Cost Name

Impact Driver

BBL Spilled 

(plat/pipe)

BBL Spilled 

(tanker)

None BBL Spilled 

(plat/pipe)

BBL Spilled 

(tanker)

None

2. Specify Cost Coefficients $ / BBL $ / BBL N/A $ / BBL $ / BBL N/A

North Atlantic - - --

Mid-Atlantic - - $ 10 $ 6

South Atlantic - - --

Straits of Florida $ 37 $ 10 $ 268 $ 77

G Eastern Gulf $ 7 $ 3 $ 128 $ 43

o Central Gulf $ 1 $ 0 $ 107 $ 31

M Western Gulf $ 12 $ 3 $ 31 $ 14

Southern California $ 10 $ 2 $ 48 $ 9

Central California $ 104 $ 11 $ 120 $ 22

Northern California $ 1 $ 0 $ 6 $ 1

Washington/Oregon $ 3 $ 0 $ 177 $ 33

Gulf of Alaska - - $ 2 $ 1

Cook Inlet - - $ 0 $ 0

A Kodiak - - $ 14 $ 3

Shuimagin - - $ 0 $ 0

L North Aleutian Basin - - $ 0 $ 0

Aleutian Arc - - --

A St. George Basin - - $ 0 $ 0

St. Matthew Hall - - --

S Bowers Basin - - --

Aleutian Basin - - --

K Navarin Basin - - $ 0 $ 0

Norton Basin - - $ 0 $ 0

A Hope Basin - - $ 0 $ 0

Chukchi Sea - - $ 0 $ 0

Beaufort Sea - - $ 0 $ 0

18 17 21 18 17 21

3. Specify the dollar-year of cost coefficients 1999

A

T

L

P

A

C

Beach Recreation Recreational Fishing

Figure 4.1. OECM Impact Drivers for Beach Recreation

Beach recreation and Recreational Fishing are combined on the Recreation cost

element worksheet.
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5. Recreational Fishing

5.1 Introduction

Oil spilled into marine environments can reduce the economic benefits of recreational saltwater

fisheries. The economic benefit of a saltwater angling trip is defined as the angler’s maximum

willingness to pay less actual costs. The total economic benefit of saltwater fishing in an OCS

Planning Area is the number of fishing trips multiplied by the average benefit per trip or the

number of fishing days multiplied by the average benefit per day.

An oil spill may decrease the economic benefits of saltwater angling in two ways. First, the

number of trips to an affected site may decrease due to closure of the site. Anglers will then

switch to lower-valued activities such as trips to substitute sites or alternate activities (freshwater

fishing, hiking, etc.). For each trip not taken to a preferred site, all associated consumer surplus

is lost, assuming consumer surplus is properly measured as the maximum willingness to pay

considering the availability of substitutes. The other pathway for a decrease in saltwater angling

consumer surplus is if the quality of trips still taken to an affected site decreases after an oil spill.

A decrease in average catch rates, for example, would be expected to decrease the consumer

surplus per trip.

This chapter describes the methodology used to estimate the saltwater recreational fishery

consumer surplus losses as a result of hypothetical oil spills. The approaches used to estimate

recreational saltwater fishing losses by the GPECM and the NRDA/CME Type A Model are first

discussed. The approach used in this application is then detailed. Additional sections present

recreational fishing visitation statistics and data from previous research on the economic benefits

of saltwater recreational fishing. Finally, the data are used to indicate how estimates of

consumer surplus losses as a result of hypothetical oil spills are calculated.

5.2 Previous Methodologies Estimating Recreational Saltwater Angling Consumer Surplus

Losses as a Result of an Oil Spill

5.2.1 Summary of GPECM Methodology

The GPECM estimates the economic losses of recreational saltwater anglers as part of the “SpillRelated Recreation and Tourism Losses.” Offshore recreational saltwater fishing is considered a

form of “specialized” recreation along with marine boating, water-skiing, windsurfing, sailing,

and kayaking. On-shore recreational fishing is considered a form of “general” recreation along

with saltwater beach activities and swimming.

Baseline visitation data for saltwater angling were mostly obtained from the National Survey of

Fishing, Hunting, and Wildlife-Associated Recreation, which makes a distinction between onshore and off-shore angling trips. Data on the miles of coastline in each OCS Planning Area

were then used to estimate visitation per mile of shoreline.

Unit day values for general and specialized recreation were obtained from a literature review.

The average consumer surplus for general recreation was obtained from nine studies of beach
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visitation. The average consumer surplus for specialized recreation was determined from studies

measuring the benefits of angling, both freshwater and saltwater. The estimates from these

studies varied considerably. One study was chosen to represent a median estimate. GPECM

assumed that most anglers planning a trip to a site affected by an oil spill would take a trip to a

substitute site instead. The consumer surplus loss for trips taken elsewhere is assumed to be 25%

of the unit day values. All consumer surplus is counted as a loss only for trips not taken as a

result of the spill.

A typical spill is considered to be 24,793 barrels based on the mean of 53 actual spill cases. A

regression model, based on data from 39 spills worldwide, is used to estimate the relationship

between spill size and the length of shoreline oiled. Using the regression coefficients, GPECM

estimated that a typical spill would oil 30.231 miles of coastline. The analysis assumes that a

typical spill will result in the loss or diversion of coastal tourism for a 35-day period based on the

closure period for the American Trader spill.

5.2.2 GPECM Methodology Assessment

The GPECM methodology is based on several assumptions due to limited data availability on

coastal visitation and consumer surplus. This section discusses the validity of several of these

assumptions for consideration in the current application. Several assumptions made in the

GPECM application are not considered valid for the current application due to differences in

available resources or advances in research.

The GPECM divides baseline tourism visitation statistics into six categories (coastal

visitation/saltwater beach activities, on-shore saltwater fishing, saltwater swimming, offshore

saltwater fishing, saltwater water-skiing/motorboating, and saltwater windsurfing/sailing). Three

potential sources of error arise in these estimates. First, there may be considerable overlap of the

activities, especially when data are collected from different sources. The on-shore saltwater

angling data from the National Survey of Fishing, Hunting, and Wildlife-Associated Recreation

is likely to overlap with the beach visitation statistics presented in the State Comprehensive

Outdoor Recreation Plans (SCORP’s). Also, the offshore saltwater fishing data likely overlaps

with saltwater boating visitation.

Second, visitation records vary considerably across states. While some SCORPS, such as those

compiled by Oregon and Mississippi, present detailed visitation estimates that correspond to the

categories defined in the GPECM, most states do not provide such data. As noted in the

GPECM report, only a few states make the distinction between freshwater and saltwater in

visitation statistics for water-based activities. Thus, the visitation estimates used by the GPECM

are based on extrapolations of data across states. The validity of such extrapolations was not

discussed in the GPECM.

Third, GPECM used visitation estimates from the most recent year. Statistics from the most

recent year may not be representative of typical visitation due to annual variations in weather,

prices, and site quality. Using visitation data averaged across several recent years minimizes the

influence of annual visitation fluctuations.
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The GPECM methodology assumes that anglers who take trips to substitute sites when their

primary site choice is affected by an oil spill obtain a consumer surplus that is 25% of the unit

day value. This assumption contradicts economic theory. The consumer surplus associated with

a site visit is defined as the net willingness-to-pay relative to the next best alternative. The next

best alternative to a fishing trip may be a trip to a different fishing location or a different activity

altogether. Whether the next best alternative is a substitute fishing trip is not relevant to the

consumer surplus estimate if the researcher has properly performed the analysis. While the

substitute assumption made in the GPECM analysis contradicts economic theory, it does not

necessarily contradict the empirical methods of the time.

Many empirical recreation studies conducted in the 1970’s and 1980’s failed to properly account

for the existence of substitutes. Failure to include substitute prices in recreation demand models

produces a significant upward bias in consumer surplus estimates (Rosenthal, 1987). The rise in

popularity of random utility models, starting the late 1980’s, reflects an improved framework for

modeling recreation behavior in the presence of substitutes (Milon, 1988). So, in theory one

should count the entire consumer surplus estimate as a social loss when substitution occurs

regardless of whether the alternative is a substitute trip or a substitute activity. The methodology

of the GPECM has a reasonable empirical basis because substitute choices were often poorly

modeled in recreation demand studies during the 1970’s and 1980’s. More recent recreation

demand models, particularly random utility models, model substitutes in a manner consistent

with economic theory. The studies used to estimate unit day values in the present application

explicitly model substitute choices. Thus, the total consumer surplus estimate is counted as a

social loss when a trip is not taken to a preferred location as a result of an oil spill.

5.2.3 Summary of NRDA/CME Methodology

The NRDA/CME Type A Model is designed to estimate the natural resource damages associated

with a relatively minor chemical release. Economic losses are defined as the marginal loss in

consumer surplus associated with a decrease in harvest. A key assumption of the Type A Model

is that the level of recreational fishing effort is not affected by the spill. (The number of trips

remains constant.)

Data on recreational harvest, in kilograms, were obtained for each of eight economic provinces

from the National Recreational Fishery Statistics Survey, conducted annually by the National

Marine Fisheries Service. The average catch over the most recent five years for which data were

available was considered representative. The harvest data were separated by species for over 80

marine species, including shellfish.

Four modes of fishing were considered including shore-based, coastal boat, offshore and game

fish, and shellfish collection. For each species and economic province, the primary mode of

fishing was determined. Average consumer surplus for each fishing mode was obtained from a

review of 33 studies of saltwater fishing. Per-trip consumer surplus estimates were converted to

benefits per kilogram of catch.

The Type A Model methodology emphasizes that recreational fishing losses must be evaluated at

the margin. A data set was developed from the literature to estimate the relationship between

willingness to pay, the number of trips, and fish catch. The results were used to estimate the
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marginal value of a kilogram of catch for each of the four fishing modes. The analysis assumes

that changes in catch rates represent only 20% of total trip values.

5.2.4 NRDA/CME Methodology Assessment

The methodology of the Type A Model made use of the best available information in a manner

consistent with economic theory. Sufficient data were not available on the marginal change in

consumer surplus with a change in catch rate so the researchers estimated a relationship from the

available data. At present, several studies directly estimate the relationship between catch rate

and consumer surplus for saltwater fishing. Thus, the need for estimating a relationship using

studies of total consumer surplus is no longer necessary.

The assumption in the Type A Model that fishing level remains constant is likely appropriate for

a small spill. This assumption is not valid for a larger spill, especially when a recreational

fishery is closed for a period after an oil spill. The methodology of the Type A Model is not

directly applicable because the current model considers a range of spill magnitudes, including

large spills.

5.3 Summary of Current Methodology

Two potential sources of nationwide data on saltwater fishing exist: the National Survey of

Fishing, Hunting and Wildlife-Associated Recreation (NSFHWR) and the Marine Recreational

Fishery Statistics Survey (MRFSS). The MRFSS is preferred for two reasons. First, the survey

is done annually while the NSFHWR is done about every five years. Thus, the MRFSS can be

used to obtain a running average for several variables over a number of years. Second, the

MRFSS has a much larger sample size. While the 1996 NSFHWR included a screening phase of

nearly 80,000 households nationwide, the number of detailed interviews of active participants

was fewer than 900 per state (U.S. Department of the Interior and U.S. Department of

Commerce, 1997). Further, most of the individuals interviewed were not saltwater anglers. The

annual MRFSS is based on more than 76,000 intercept interviews and over 265,000 telephone

interviews. The intercept surveys specifically target saltwater anglers and the telephone

interviews over-sample residents of coastal counties. The MRFSS also provides detailed data on

recreational harvest and fishing mode while the NSFHWR does not. Similar to the

NRDAM/CME methodology, we base recreational fishing visitation statistics on the MRFSS.

The MRFSS divides visitation into four modes: beach/bank, party/charter boat, private boat, and

man-made structures. The actual estimates are presented in the next section.

Similar to the GPECM and the NRDAM/CME, a literature review was conducted to obtain

consumer surplus estimates for recreational fishing. Several sources were identified to provide

guidance on locating relevant studies. Freeman (1995) reviewed the empirical data on the

benefits of marine recreation. Smith and Kaoru (1990) performed a meta-analysis of travel cost

recreation demand models. The U.S. Fish and Wildlife Service has sponsored meta-analysis

research that collects and analyzes consumer surplus estimates of sport fishing (Boyle, et al.,

1999).

Numerous studies provide consumer surplus estimates for recreational fishing. For example, the

U.S. Fish and Wildlife Service database includes 161 studies. Most of the estimates are
98 Section 5: Recreational Fishing

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associated with freshwater fishing. The GPECM analysis included studies of both freshwater

and saltwater fishing. While the benefit estimates used in the NRDAM/CME were based mostly

on saltwater studies, some freshwater analyses also were included. An important issue that needs

to be addressed is the validity of including freshwater fishing benefit estimates to provide

information on the benefits of saltwater fishing. A meta-analysis of sport fishing valuation

studies by Boyle, et al. (1999) indicates that consumer surplus estimates of marine sport fishing

tend to be significantly greater than estimates based on freshwater fishing. Based on this finding,

we limit our literature review to studies that analyze saltwater fishing.

The GPECM damage estimates were based on consumer surplus losses per trip. The Type A

Model uses lost catch (measured in kg) as the metric for assessing damages. We use angling

trips as a metric because the vast majority of economic analyses produce per trip (or per day)

consumer surplus estimates. Also, NRDA cases that have included recreational fishing damages

have used the number of lost trips as a basis for calculating loses (NOAA, et al., 1999; Texas

General Land Office, et al., 1999). Ideally, a methodology that considers both the value of lost

trips as well as the marginal losses of trips with decreased quality would be preferred. However,

we conclude that sufficient data are not available on how a hypothetical oil spill would reduce

the quality of trips.

5.4 Recreational Fishing Visitation

Data are available on recreational saltwater fishing participation from several sources. The

National Marine Fisheries Service conducts the Marine Recreational Fisheries Statistics Survey

(MRFSS) on an annual basis. The MRFSS collects data on the number of fishing trips by mode

occurring in each coastal state except Texas and Alaska. These states conduct their own

recreational fishery surveys.

The NRDAM/CME estimated annual recreational fishing visitation and harvest by calculating an

average over the most recent five-year period. The GPECM used visitation data from the most

recent year for which data were available. To avoid irregularities due to annual fluctuations, our

visitation and harvest estimates are calculated as an average over the most recent three years for

which data were available.

The MRFSS database is available over the Internet (National Marine Fisheries Service, 2000).

The database provides information on saltwater sport fishing trips and harvest categorized by

state and fishing mode. Three fishing modes are considered:

1. Shore – includes beaches, banks, and man-made structures such as breakwaters, piers, and

bridges;

2. Party or charter boat;

3. Private or rental boat.

The data are divided into ocean and saltwater inland fishing. Saltwater inland fishing includes

estuaries, bays, inlets, and tidal portions of rivers. We include both ocean and saltwater fishing

in our visitation estimates because both activities may be impacted by an oil spill. The

methodology used in the Type A Model uses slightly different recreational fishing categories
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than those listed above. Based on the availability of the MRFSS data, we use the fishing

categories listed above.

The MRFSS does not cover Texas and Alaska. Texas Parks and Wildlife provided estimates of

saltwater fishing for 1996-98 (McEachron, 2000). The Alaska Department of Fish and Game

(Howe, et al., 1998; Howe, et al., 1997; Howe, et al., 1996), provide detailed saltwater fishing

visitation estimates. The saltwater fishing visitation data for Alaska are divided into boat and

shore fishing. Only one area, the Kenai Peninsula, further divides boat fishing into private and

charter boat categories. Further data on charter boat fishing was obtained from Dean and Howe

(1999) and used to divide boat visitation estimates into private and charter boat categories. The

Dean and Howe report only covered South-central and Southeast Alaska. We assume that 90%

of fishing trips in other regions of the state were on private boats because no data were available

for other regions of the state. This percentage is based on data from the Dean and Howe report

for areas of South-central and Southeast Alaska where few charter boats operate.

Visitation data for shellfishing were obtained from the 1985 National Survey of Fishing,

Hunting, and Wildlife-Associated Recreation. Contact with the U.S. Fish and Wildlife Service

revealed that more recent national data on recreational shellfishing are not available. Thus, the

1985 estimates were used in this analysis (NOAA and USFWS, 1991). The data presented the

number of days of shellfishing. We assume that the average shellfishing trip lasts 1.1 days.

Based on data from the NSFHWR (U.S. Department of the Interior and U.S. Department of

Commerce, 1997), the average saltwater fishing trip lasts 1.19 days. We assume that shellfishing

trips are less likely to be multiple-day trips as compared to other saltwater fishing activities,

particularly off-shore boat fishing.

Table 5.1 presents estimates of averaged fishing trips by coastal state and fishing mode. The

data in Table 5.1 need to be organized by OCS Planning Area. For the continental United States,

most OCS Planning Area boundaries correspond to state boundaries. The differences that need to

be resolved are:

1. North Carolina includes both the Mid-Atlantic and South Atlantic OCS Planning Areas;

2. Eastern Florida includes both the South Atlantic and Straits of Florida OCS Planning Areas;

3. California is divided into three OCS Planning Areas while the MRFSS data divides

California into two regions.

The North Carolina data are distributed 90% into the Mid-Atlantic OCS Planning Area for shore

and charter boat fishing and shellfishing and 50% for private boat fishing (Mumford, 2000).

Data from the Florida Fish and Wildlife Conservation Commission are used to divide the Florida

fishing visitation (Norris, 2000). County-level data from California indicate the number of

charter boat trips in 1998 (Aseltine-Neilson, 2000). No detailed county-level data are available

on private boating and shore fishing for California. The charter boat proportions are used to

divide all fishing modes.

The Alaska Department of Fish and Game keeps sport fishing visitation estimates for 25 areas

and nearly all areas include some saltwater fishing locations (Howe, et al., 1998). Data on the

number of saltwater fishing trips in each region are used to obtain estimates for each OCS
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Planning Area in Alaska. In some cases the regions defined by the Alaska Department of Fish

and Game do not match the boundaries of the OCS Planning Areas. Allocations of visitation in

such cases were made using the data from A.T. Kearney, Inc. (1991). No regional data were

available for Alaska shellfishing. Alaska shellfishing is allocated among the OCS Planning

Areas using the same proportions as shore fishing. The final estimate of annual recreational

saltwater fishing trips in each OCS Planning Area is presented in Table 5.2.

5.5 NRDAM/CME Outputs and Linkages

The NRDAM/CME measures coastal oiling in terms of linear-meter days. To link the visitation

data in Table 5.2 to the model output, it needs to be converted to visitation per linear meter-day

of shoreline for the coastal fishing modes (shore/bank and shellfishing). Table 5.3. presents data

on the length of shoreline in each OCS Planning Area. Oiling impacts are measured in terms of

days, rather than trips; the trip data need to be converted to days of fishing. The NSFHWR

estimates that average trip duration for all saltwater trips is 1.19 days (U.S. Department of the

Interior and U.S. Department of Commerce, 1997). We assume an average trip duration of 1.3

days for offshore fishing and 1.1 days for on-shore fishing. Applying these values to the trip

statistics in Table 5.1. produces an average trip duration for all saltwater fishing of 1.19 days to

match the NSFHWR data.

For the offshore fishing modes, the visitation data are linked to the NRDAM/CME model output

in two ways. First, coastal oiling may preclude some offshore fishing trips because an oil spill

may close marinas and harbors or lead to navigation restrictions during a period of clean-up

activities. We link the NRDAM/CME estimate of coastal oiling, measured in linear meter-days,

to offshore trips, measured as the number of anglers per linear meter-day of coastline. In other

words, we assume that the percentage reduction in offshore fishing days is equal to the

percentage of coastal oiling. Data for the number of offshore anglers per linear meter-day of

coastline are presented for each OCS Planning Area in Table 5.4.

The second way that an oil spill is assumed to affect offshore fishing is that the presence of an

offshore oil slick may close offshore fisheries. We link the NRDAM/CME estimate of oil slick

magnitude, measured in square meter-days, to an estimate of offshore recreational fishing,

measured in terms of anglers per square meter day. The presence of an oil slick is assumed to

preclude fishing in that area for the duration of the slick. Offshore fishing visitation statistics

from the NMFS are divided into trips in the Territorial Seas (normally less than 3 miles from the

coast) and trips in the Exclusive Economic Zone (greater than 3 miles from the coast). Further,

contacts with several state fish and wildlife agencies indicated that the vast majority of offshore

trips occur within 30 miles from the coast. We assume all offshore trips occur either within the

Territorial Seas or within the Exclusive Economic Zone within 30 miles from the coast.

The NMFS data set for each state was used to determine the percentage of offshore trips that

occur within the Territorial Seas (Table 5.5). We assume that fishing days are allocated using

the same percentages. Normally, most party/charter boat offshore fishing trips occur outside of

the Territorial Seas while most private/rental boat trips occur within the Territorial Seas. The

data in Table 5.5 are used to estimate the average number of annual fishing days occurring

within the Territorial Seas and the EEZ within 30 miles, by OCS Planning Area (Table 5.6).
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Data are not available to divide saltwater fishing visitation for Alaska into Territorial Seas and

EEZ. However, the assumption that virtually all recreation fishing occurs within 30 miles of the

coast is applicable to Alaska (Howe, 2000).

To link the offshore visitation data to the NRDAM/CME output, it needs to be in terms of

anglers per square meter-day. Table 5.7 presents data on the area of the Territorial Sea and EEZ

in each OCS Planning Area. By combining the data in Tables 5.6. and 5.7, we obtain an estimate

for the number of offshore anglers per square meter-day within the Territorial Sea and EEZ for

each OCS Planning Area by offshore fishing mode (Table 5.8).

5.6 Literature Review for Consumer Surplus Estimates

A literature review was conducted to obtain average per trip or per day consumer surplus

estimates for saltwater fishing. In consideration of the recent advances in resource economics

discussed above, particularly with respect to the inclusion of substitutes, the literature review

was limited to studies since the mid-1980’s. Also, the review was limited to estimates of the

economic benefits of saltwater fishing. (Freshwater estimates were excluded.) Nineteen

pertinent analyses were identified. Those studies that provide estimates of the total consumer

surplus associated with saltwater fishing are listed in Table 5.9. A brief description of each study

is given below along with the benefit estimates.

5.6.1 Agnello and Han (1993)

This paper estimated a varying-parameters travel cost model based on MRFSS data on Long

Island, NY saltwater fishing. Their models are based on individual data on the number of trips

taken to the study sites, but a random utility analysis is not conducted. They present models with

and without substitution effects. The resulting consumer surplus estimates, based on the

preferred models with site-substitution effects, were $18.20 (without substitute-site catch

variables) and $19.32 per trip (with substitute-site catch variables) measured in 1984 dollars.

5.6.2. Bockstael, et al. (1989a)

This paper uses several statistical models, including Tobit, Heckman, Cragg, and OLS models, to

estimate the value of striped bass fishing trips in Maryland. The authors estimate participation

and trip frequency models based on individual data. Aggregate benefits over the entire sample

are presented for each model using both predicted trips and actual trips as a basis for the benefit

calculations. The aggregate benefits using actual trips can be converted to per-day benefits using

the visitation data in Table 2 of the paper. The authors indicate a preference for the Tobit and

Cragg models. The implied per-day estimate for these models are $32.55 and $21.74

respectively (in 1980 dollars).

5.6.3 Bockstael, et al. (1989b)

This paper presents a random utility analysis of sport fishing in Florida. Estimates are given for

access per choice occasion (or trip) for nine coastal counties in eastern Florida. Separate

estimates are given for private boating and for private boating combined with shore fishing. The
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estimates for private boating range from $0.73 to $6.81 per trip with an average of $2.87 (in

1988 dollars). The estimates for both modes combined range from $0.81 to $7.94 per trip with

an average of $3.32.

5.6.4 Cameron (1992)

This paper combines contingent valuation data with travel cost data to estimate the value of a

reduction in coastal fishing access in Texas. They found that the equivalent variation for a

complete loss of coastal fishing access in Texas was $3,451 per year (in 1987 dollars). The

implied value per trip is $198 because the sample averaged 17.4 fishing trips per year. For a

10% reduction in fishing trips, the mean equivalent variation was $35, implying a value of $20

per trip. Values between $20 and $198 are implied for access restrictions between 10% and

100%. Thus, the value per trip is relative to the number of trips lost. The authors note that the

terms “trips” and “fishing days” are used synonymously because the majority of sampled trips

were one day in duration.

5.6.5 Cameron (1988)

This paper uses contingent valuation to estimate the benefits of salmon fishing in British

Columbia. The median WTP estimate, based on logistic errors, is $19.69 per fishing day while

the mean is $26.27 (in 1984 dollars).

5.6.6 Cameron and James (1987)

This paper uses contingent valuation to estimate the benefits of a saltwater fishing day in British

Columbia. The mean WTP is estimated to be $48.83 per day (in 1984 dollars).

5.6.7 Downing and Ozuna (1996)

This paper uses referendum contingent valuation to estimate the benefits of saltwater fishing in

Texas. Values were presented for each of eight saltwater bays for 1987, 1988, and 1989. The

median per trip benefits for each bay ranged from $38.12 to $258.20 (in 1989 dollars). The

“average” median value was $109.20 per trip.

5.6.8 Greene, et al. (1997)

This paper uses a nested random utility model to estimate the benefits of saltwater fishing in

Tampa Bay, Florida. Two scenarios were considered: the loss of fishing access to Tampa Bay

and the loss of all fishing access in Pinellas County (Tampa Bay fishing includes the eastern

shore of Pinellas County). The estimated welfare loss associated with the loss of fishing access

to Tampa Bay was estimated to be $1.68 per trip (in 1992 dollars). The welfare associated with a

loss of all fishing access in Pinellas County was estimated to be $3.66 per trip.
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5.6.9 Hausman, et al. (1995)

This paper uses random utility analysis to estimate the recreational welfare loss associated with

the Exxon Valdez oil spill. The estimated consumer surplus for sport fishing was $119 per trip

(in 1989 dollars) using a non-nested model and $148 per trip using a nested model.

5.6.10 Hicks, et al. (1999)

This technical memorandum uses a nested random utility model to estimate the economic value

of New England and Mid-Atlantic marine sport fishing. Estimates were provided for the welfare

loss associated with the closure of all marine fishing sites in a state during a two-month period.

The estimates ranged from $0.00 to $86.24 per trip (in 1994 dollars) depending on the state and

time of year, with a mean of $11.45.

5.6.11 Huppert (1989)

This paper studies saltwater fishing for chinook salmon and striped bass in central California.

Using the same survey data, the authors estimated welfare effects with both contingent valuation

and travel cost models. Consumer surplus per trip was estimated using different statistical travel

cost models. The dependent variable for all models was the number of trips taken. Using an

OLS model, consumer surplus per trip ranged from $288 to $296 (in 1986 dollars) depending on

the independent variables included in the model. Using nonlinear least squares, consumer

surplus ranged from $128 to $134 per trip. Using a truncated maximum likelihood model,

consumer surplus ranged from $60 to $61. The author uses “trips” and “days” interchangeably

in the paper.

5.6.12 Jones and Stokes (1987)

This report uses random utility models to obtain many estimates of the net willingness to pay for

sport fishing in South-central Alaska. Considering only the saltwater fishing sites, net

willingness to pay per choice occasion for summer fishing for Alaska residents ranged from

$1.27 to $7.56 (in 1986 dollars) with a mean of $3.48, depending on the location and species.

For nonresidents, the net WTP per choice occasion for marine fishing ranged from $2.70 to

$27.20 with a mean of $9.93. A value of $2.70 per choice occasion for razor clams was also

presented for nonresidents. While the estimates are measured in terms of benefits per choice

occasion, the authors imply that these can be used as benefit estimates per trip (Tables 8-10 and

8-15 of the report). The average number of days fishing per trip for residents was 2.12 days.

Trip duration data were not presented for nonresidents.

5.6.13 Kaoru (1995)

This paper uses nested random utility models to analyze the benefits of saltwater fishing in the

Albemarle-Pamlico Estuary of North Carolina. The models were used to estimate the welfare

loss associated with the closure of different fishing sites, ranging from $0.37 to $2.19 per party

per trip (in 1982 dollars) with an average of $5.67. The benefit estimates pertain to the fishing

party and no data were presented on average party size.
104 Section 5: Recreational Fishing

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5.6.14 Kaoru, et al. (1995)

Using the same data as the above paper, this paper uses random utility analysis to study saltwater

fishing in North Carolina. Several different models are estimated with variation in the number of

included sites and the valuation in travel time. For a model with 35 sites, the closure of a 10-site

subset results in a welfare loss of $9.80 or $16.86 per party per trip (in 1982 dollars), depending

on the value of travel time. For a model with 23 sites, the closure of a 7-site subset resulted in a

welfare loss of $4.27 or $7.36 per party per trip. For a model with 11 sites, the closure of one

site resulted in a welfare loss of $4.46 or $7.70 per party per trip. Again, no data were available

to convert benefits per party to benefits per angler.

5.6.15 Leeworthy and Bowker (1997)

This study estimates the benefits of various recreation activities for visitors to the Florida Keys.

The authors use truncated negative binomial regression models to analyze individual-level travel

cost data. Estimated benefits per day for fishing are $96.58 in 1995 dollars.

5.6.16 McConnell and Strand (1994)

This report presents many different estimates of the welfare of saltwater fishing from Long

Island, New York to Key Biscayne, Florida. Both contingent valuation and random utility

models were used. The willingness to sell one year of access to marine sport fishing was valued

using contingent valuation. The estimates for the loss of access to fishing in one state ranged

from $538 to $604 per year (in 1988 dollars) with an average of $575. The willingness to sell

two months of fishing access to a state ranged from $23 to $336 depending on the state and the

period of the year access was lost. Considering all states and time periods, the mean value was

$173.

Random utility models were used to estimate the value of fishing access. Using expected catch

rate models, the willingness to pay for a one-day fishing trip ranged from $8.66 to $85.04

depending on the state and time of year. Across all states and time periods, the mean willingness

to pay was $48.13.

5.6.17 Milon (1988)

This paper presents a nested random utility model of saltwater fishing in southeast Florida. The

model was used to estimate the compensating variation associated with a new artificial habitat

fishing site based on three site locations. The mean values were $1.60, $1.75, and $1.80 per trip

(in 1985 dollars).

5.6.18 Morey , et al. (1991)

This paper uses a random utility analysis that models both participation and site choice. Welfare

estimates are presented for the loss of shore, boat, and all fishing access in Clatsop County,

Oregon as a function of an angler’s county of origin. The welfare loss for the loss of shore

fishing access ranged from $5.88 to $111.62 per year depending on the angler’s county of origin
Section 5: Recreational Fishing 105

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with a mean of $41.21 (in 1981 dollars). The welfare loss for the elimination of boat fishing

access ranged from $3.22 to $60.47 per year with a mean of $22.41. The welfare loss for the

elimination of all fishing access ranged from $9.10 to $175.02 with a mean of $64.27. The paper

also estimated the compensating variation to prevent elimination of salmon fishing in Clatsop

County. The estimated values ranged from $0.31 to $2.76 per year with a mean of $1.07. Note

that all values are per-year and data were not provided to convert the estimates to per-trip values.

5.6.19 NOAA, et al. (1999)

In the damage assessment for the North Cape oil spill, the trustees reviewed more than 100

papers dealing with recreational values. The trustees chose to use a consumer surplus estimate of

$58.52 per trip (in 1988 dollars) from the McConnell and Strand (1994) report to be

representative of New York charter boat angler benefits. This benefit estimate pertains to New

York fishing across all modes and species. This value was chosen based on the random utility

methodology of the McConnell and Strand report and the geographic similarity between the

report and the spill.

5.7 Representative Fishing Values

All estimates of the consumer surplus associated with a recreational fishing trip are updated to

1999 dollars (Table 5.10). As seen in the table, the estimates vary significantly from a dollar or

less per trip or per day to over $300 per trip or per day. Based on the limited number of studies,

we feel that we can not provide valid representative per-trip consumer surplus estimates for

different fishing modes. Similar to other economic damage values presented in this report, we

present “low” and “high” representative angling values.

Similar to NOAA, et al. (1999), we indicate a preference for studies based on the random utility

model due to its specific incorporation of substitutes, its theoretical basis, and its reliance on

observed behavior. Estimates based on a random utility analysis are noted in Table 5.10. Note

that the random utility analyses tend to produce lower benefit estimates compared to the other

studies based on different travel cost models or contingent valuation. A total of seven original

random utility analyses are identified. (The NOAA, et al., 1999 report presents an estimate from

the McConnell and Strand, 1994 study.) Of these, three provide estimates that are generally less

than $10 per trip (Bockstael, et al, 1989b; Greene, et al., 1997; and Milon, 1988). Two studies

present a broader range of benefit estimates but suggest typical values around $10-$15 per trip

(Hicks, et al., 1999 and Jones and Stokes Associates, Inc., 1987). McConnell and Strand (1994)

also presents a wide range of benefit estimates but suggest a typical trip value of around $40-

$80. The only random utility study that presents only high benefit estimates is Hausman, et al.

(1995). So, the majority of the fishing benefit estimates based on random utility analysis suggest

a typical per-trip value of less than $15. As a representative “low” estimate, we choose a value

of $13 per trip based on the average value from Hicks, et al. (1999). This study uses a state-ofthe-art methodology (a nested random utility model supplemented with an expected catch model)

and was sponsored by NOAA. Assuming an average trip duration of 1.2 days, a value of $13 per

trip converts to $11 per day.
106 Section 5: Recreational Fishing

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For a “high” representation value, we choose a value of $68 per trip based on the analysis of

McConnell and Strand (1994). This study also relies on a nested random utility model

supplemented with an expected catch model. Further, an estimate from McConnell and Strand

(1994) was used in the North Cape oil spill to value lost fishing trips (NOAA, et al., 1999).

Again assuming an average trip duration of 1.2 days, a value of $68 per trip converts to $57 per

day.

While the difference between our low per-day recreational fishing value of $11 and the high

value of $57 per day may seem large, one must recognize the wide range of estimates presented

in Table 5.10. We feel that the chosen high and low values provide a reasonable bounds for the

per-trip values that would be used to value recreational fishing trips lost as a result of an oil spill.

5.8 Impacts of Oil Spill Scenarios

The oil spill scenarios analyzed by the NRDAM/CME are assumed to cause damage to shorebased recreational fishing by oiling the coastline. Coastal oiling or the presence of an offshore

oil slick can damage offshore fishing. This section describes how the NRDAM/CME outputs are

linked to a measure of economic damages.

Assume a hypothetical oil spill in the North Atlantic OCS Planning Area results in 4.00*104

meters of coastal oiling and an oil slick of 2.00*109 square meters with 30% of the slick

occurring in the EEZ and 20% in the Territorial Seas. As discussed in Section 3.4.4 we assume

that the impact period is 60 days for fishery oiling and 10 days for harbor closure. The coastal

oiling impact is converted to 2.40*106 meter-days for fishery closure and 4.00*105 for harbor

closure. The oil slick impacts are 3.60*1010 within the EEZ and 2.40*1010 with the Territorial

Seas.

Combining these impacts with data from Table 5.3, the number of lost shore fishing days is

estimated as 2,081 ((2.40*106)*(8.670*10-4)) and the number of lost shellfishing days is 933

((2.40*106)*(3.889*10-4)). The total value of these 3,014 lost fishing days is a low estimate of

$33,154 and a high estimate of $171,798.

For offshore fishing, the effect due to coastal oiling is calculated by multiplying the impact

(4.00*105

 meter-days) by the fishing densities in Table 3.4. (9.136*10-4 for private/rental boat

fishing and 9.652*10-5 for party/charter boats). Thus, the estimated lost number of private/rental

boat fishing days is 365 and the number of lost party/charter boat fishing days is 39. The value

of these lost 404 days is $4,444 based on the low per-day value of $11 and $23,028 based on the

high value of $57 per day.

The effect of the oil slick would be calculated by multiplying the oil slick impact in the EEZ and

Territorial Seas by the fishing densities in Table 5.8. The estimated lost days of private/rental

boat fishing is 37,920 within the Territorial Seas ((1.58*10-6)* (2.40*1010)) and 2,639 within the

EEZ ((7.33*10-8)*(3.60*1010)). The estimated lost days of party/charter boat fishing is 1,135

within the Territorial Seas ((4.73*10-8)* (2.40*1010)) and 760 within the EEZ ((2.11*10-

8

)*(3.60*1010)). The total value of these lost 42,454 days is $466,994 using the low benefit

estimate of $11 per day and $2,419,878 using the high benefit estimate of $57 per day.
Section 5: Recreational Fishing 107

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All these values are added to estimate the total damage to recreational fishing. Summing all the

low estimates results in a total damage estimate of $504,592. Summing all the high estimates

results in a total damage estimate of $2,614,704.
108 Section 5: Recreational Fishing

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Section 5: Recreational Fishing 111

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Stronge, William B., and Ronald R. Schultz. 1997. “Broward County Beaches: An Economic

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112 Section 5: Recreational Fishing

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Table 5.1. Average Number of Saltwater (Ocean and Inland Saltwater) Fishing Trips by

State (1996-1998 data)

Fishing mode

State Shore/bank/

Structure

Private/rental

boat

Party/charter

boat Shellfishing

Alabama 397,800 505,600 71,400 167,000

Alaska 1 121,700 372,300 187,600 122,700

California (northern) 1,078,700 744,100 120,600 1,002,500

California (southern) 1,181,300 1,359,500 783,700 1,099,100

Connecticut 478,100 787,100 32,300 224,500

Delaware 388,600 421,600 51,700 237,500

Florida (eastern) 4,984,800 5,189,800 463,500 1,577,500

Florida (western) 5,510,300 6,414,700 725,200 1,743,600

Georgia 227,100 331,200 30,100 449,600

Louisiana 753,200 2,067,100 73,800 503,000

Maine 421,700 316,100 12,700 168,600

Maryland 1,044,300 1,673,400 171,400 4,655,900

Massachusetts 1,825,100 1,673,400 171,400 830,800

Mississippi 367,100 507,500 49,400 252,000

New Hampshire 126,800 126,900 39,500 65,200

New Jersey 1,500,400 2,887,500 635,000 1,186,000

New York 1,549,000 2,063,900 306,200 1,542,300

North Carolina 2,906,100 1,532,900 259,300 584,500

Oregon 187,800 329,000 64,000 421,300

Rhode Island 606,400 425,500 65,000 419,500

South Carolina 769,900 680,600 134,400 981,900

Texas 2 1,136,200 718,300 57,000 1,963,000

Virginia 1,019,800 2,020,000 97,700 1,627,800

Washington 454,700 999,700 12,500 1,542,600

TOTAL 29,036,900 34,147,700 4,615,400 23,368,400

1 – Alaska estimate based on data from 1995-1997.

2 – Texas data originally estimated in man-hours. Data were converted to trips using information

on average hours per trip.

Sources: Marine Recreational Fishery Statistics Survey, Alaska Department of Fish and Game,

Texas Parks and Wildlife.
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Table 5.2. Average Number of Saltwater (Ocean and Inland Saltwater) Fishing Trips by

OCS Planning Area (1996-1998 data)

Fishing mode

OCS Planning Area Shore/bank/

Structure

Private/rental

boat

Party/charter

boat Shellfishing

North Atlantic 2,373,600 2,116,400 223,600 1,064,600

Mid-Atlantic 9,202,100 11,045,500 1,592,700 10,419,600

South Atlantic 3,381,200 3,958,000 385,100 2,152,500

Straits of Florida 2,891,200 3,010,100 268,800 915,000

Eastern Gulf of Mexico 5,510,300 6,414,700 725,200 1,743,600

Central Gulf of Mexico 1,518,100 3,080,200 194,600 922,000

Western Gulf of Mexico 1,136,200 718,300 57,000 1,963,000

Southern California 1,246,000 1,404,100 790,900 1,159,300

Central California 863,000 595,300 96,500 802,000

Northern California 151,000 104,200 16,900 140,400

Washington/Oregon 642,500 1,328,700 76,500 1,963,900

Gulf of Alaska 60,900 169,800 100,100 61,400

Kodiak 7,300 14,800 8,000 7,400

Cook Inlet 50,200 182,300 78,700 50,600

Shumagin 2,113 4,468 681 2,130

North Aleutian Basin 172 304 56 173

Saint George Basin 54 0 0 54

Aleutian Arc 112 0 0 113

Bowers Basin 0 0 0 0

Aleutian Basin 0 0 0 0

Navarin Basin 0 0 0 0

Saint Matthew Hall 160 113 12 161

Norton Basin 509 431 48 513

Hope Basin 15 109 12 15

Chukchi Sea 48 0 0 48

Beaufort Sea 128 0 0 129

TOTAL 29,036,911 34,147,825 4,615,409 23,368,636
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Table 5.3. Coastal Fishing Visitation Rates

Visitation per linear meter-day

of coast (angling days) OCS Planning Area Miles of

coast

Meters of

coast

(miles*1609) Shore/bank/

Structure Shellfishing

North Atlantic 5,128 8,250,952 8.670*10-4 3.889*10-4

Mid-Atlantic 13,780 22,172,020 1.251*10-3 1.416*10-3

South Atlantic 8,344 13,425,496 7.590*10-4 4.832*10-4

Straits of Florida 1,332 2,143,188 4.066*10-3 1.287*10-3

Eastern Gulf of Mexico 5,095 8,197,855 2.026*10-3 6.410*10-4

Central Gulf of Mexico 8,687 13,977,383 3.273*10-4 1.988*10-4

Western Gulf of Mexico 3,359 5,404,631 6.336*10-4 1.095*10-3

Southern California 1,419 2,283,171 1.645*10-3 1.530*10-3

Central California 1,254 2,017,686 1.289*10-3 1.198*10-3

Northern California 754 1,213,186 3.751*10-4 3.488*10-4

Washington/Oregon 4,436 7,137,524 2.713*10-4 8.292*10-4

Gulf of Alaska 11,121 17,893,689 1.026*10-5 1.034*10-5

Kodiak 983 1,581,647 1.391*10-5 1.410*10-5

Cook Inlet 2,916 4,691,844 3.224*10-5 3.250*10-5

Shumagin 2,882 4,637,138 1.373*10-6 1.384*10-6

North Aleutian Basin 2,305 3,708,745 1.398*10-7 1.406*10-7

Saint George Basin 848 1,364,432 1.193*10-7 1.193*10-7

Aleutian Arc 3,187 5,127,883 6.582*10-8 6.641*10-8

Bowers Basin 0 0 0 0

Aleutian Basin 0 0 0 0

Navarin Basin 0 0 0 0

Saint Matthew Hall 1,797 2,891,373 1.668*10-7 1.678*10-7

Norton Basin 2,407 3,872,863 3.961*10-7 3.992*10-7

Hope Basin 2,339 3,763,451 1.201*10-8 1.201*10-8

Chukchi Sea 1,391 2,238,119 6.463*10-8 6.463*10-8

Beaufort Sea 1,729 2,781,961 1.387*10-7 1.397*10-7

TOTAL 87,493 140,776,237 -- --
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Table 5.4. Offshore Fishing Visitation Rates Linked to Coastlines

Visitation per linear meter-day

of coast (angling days) OCS Planning Area Miles of

coast

Meters of

coast

(miles*1609) Private/rental

boat

Party/charter

boat

North Atlantic 5,128 8,250,952 9.136*10-4 9.652*10-5

Mid-Atlantic 13,780 22,172,020 1.774*10-3 2.558*10-4

South Atlantic 8,344 13,425,496 1.050*10-3 1.022*10-4

Straits of Florida 1,332 2,143,188 5.002*10-3 4.467*10-4

Eastern Gulf of Mexico 5,095 8,197,855 2.787*10-3 3.151*10-4

Central Gulf of Mexico 8,687 13,977,383 7.849*10-4 4.959*10-5

Western Gulf of Mexico 3,359 5,404,631 4.734*10-4 3.756*10-5

Southern California 1,419 2,283,171 2.190*10-3 1.234*10-3

Central California 1,254 2,017,686 1.051*10-3 1.703*10-4

Northern California 754 1,213,186 3.059*10-4 4.961*10-5

Washington/Oregon 4,436 7,137,524 6.630*10-4 3.817*10-5

Gulf of Alaska 11,121 17,893,689 3.380*10-5 1.992*10-5

Kodiak 983 1,581,647 3.333*10-5 1.801*10-5

Cook Inlet 2,916 4,691,844 1.384*10-4 5.974*10-5

Shumagin 2,882 4,637,138 3.432*10-6 5.231*10-7

North Aleutian Basin 2,305 3,708,745 2.919*10-7 5.378*10-8

Saint George Basin 848 1,364,432 0 0

Aleutian Arc 3,187 5,127,883 0 0

Bowers Basin 0 0 0 0

Aleutian Basin 0 0 0 0

Navarin Basin 0 0 0 0

Saint Matthew Hall 1,797 2,891,373 1.392*10-7 1.478*10-8

Norton Basin 2,407 3,872,863 3.964*10-7 4.414*10-8

Hope Basin 2,339 3,763,451 1.032*10-7 1.136*10-8

Chukchi Sea 1,391 2,238,119 0 0

Beaufort Sea 1,729 2,781,961 0 0

TOTAL 87,493 140,776,237 -- --
116 Section 5: Recreational Fishing

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Table 5.5. Location of Offshore Fishing Trips by State (1996-1998 data)

State Percent of private/rental

boat trips occurring

within the Territorial

Seas 1

Percent of party/charter

boat trips occurring

within the Territorial

Seas 1

Alabama 42.7 7.6

Alaska NA NA

California (northern) 76.5 42.2

California (southern) 73.1 73.1

Connecticut 80.8 18.5

Delaware 54.7 28.0

Florida (eastern) 49.4 31.8

Florida (western) 75.7 38.1

Georgia 46.4 20.1

Louisiana 76.6 53.3

Maine 84.2 14.4

Maryland 34.2 4.7

Massachusetts 69.1 19.0

Mississippi 22.6 14.3

New Hampshire 56.0 26.1

New Jersey 77.3 41.0

New York 64.9 64.7

North Carolina 67.5 13.3

Oregon 90.5 68.8

Rhode Island 86.9 30.8

South Carolina 82.2 9.5

Texas 45.3 39.8

Virginia 62.9 14.2

Washington 80.4 5.3

1 – Territorial Sea area extends out to three miles except for the west coast of Florida, which

extends to 10 miles.

Sources: Marine Recreational Fishery Statistics Survey, Texas Parks and Wildlife
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Table 5.6. Average Number of Offshore Fishing Trips by OCS Planning Area and

Location (1996-1998 data)

Private/rental boat trips Party/charter boat trips

OCS Planning Area Territorial

Seas

Exclusive

Economic

Zone

Territorial

Seas

Exclusive

Economic

Zone

North Atlantic 1,493,500 622,900 44,700 178,900

Mid-Atlantic 7,168,100 3,877,400 551,900 1,040,800

South Atlantic 2,287,300 1,670,700 84,200 300,900

Straits of Florida 1,487,000 1,523,100 85,500 183,300

Eastern Gulf of Mexico 4,855,900 1,558,800 276,300 448,900

Central Gulf of Mexico 1,914,000 1,166,200 51,800 142,800

Western Gulf of Mexico 325,400 392,900 22,700 34,300

Southern California 1,027,900 376,200 575,900 215,000

Central California 455,400 139,900 40,700 55,800

Northern California 79,700 24,500 7,100 9,800

Washington/Oregon 1,101,500 227,200 44,700 31,800

TOTAL 22,195,700 11,579,800 1,785,500 2,642,300
118 Section 5: Recreational Fishing

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Table 5.7. Area of Offshore Recreational Fisheries by OCS Planning Area

OCS Planning Area

Facing

coastline

(miles)

Facing

coastline

(meters)

Territorial

Sea area

(square

meters)

Exclusive

Economic Zone

area (square

meters)

North Atlantic 433 696,697 3.36*109 3.03*1010

Mid-Atlantic 618 994,362 4.80*109 4.32*1010

South Atlantic 727 1,169,743 5.65*109 5.08*1010

Straits of Florida 445 716,005 3.46*109 3.11*1010

Eastern Gulf of Mexico 684 1,100,556 1.77*1010 3.54*1010

Central Gulf of Mexico 494 794,846 3.84*109 3.45*1010

Western Gulf of Mexico 367 590,503 2.85*109 2.57*1010

Southern California 711 1,143,999 5.52*109 4.97*1010

Central California 311 500,399 2.42*109 2.17*1010

Northern California 245 394,205 1.90*109 1.71*1010

Washington/Oregon 453 728,877 3.52*109 3.17*1010

Gulf of Alaska 1,500 2,413,500 1.16*1010 1.05*1011

Kodiak 700 1,126,300 5.44*109 4.89*1010

Cook Inlet 600 965,400 4.66*109 4.19*1010

Shumagin 400 643,600 3.11*109 2.80*1010

North Aleutian Basin 800 1,287,200 6.21*109 5.59*1010

Saint George Basin 240 386,160 1.86*109 1.68*1010

Aleutian Arc 500 804,500 3.88*109 3.49*1010

Bowers Basin 0 0 0 0

Aleutian Basin 0 0 0 0

Navarin Basin 0 0 0 0

Saint Matthew Hall 600 965,400 4.66*109 4.19*1010

Norton Basin 500 804,500 3.88*109 3.49*1010

Hope Basin 350 563,150 2.72*109 2.45*1010

Chukchi Sea 300 482,700 2.33*109 2.10*1010

Beaufort Sea 600 965,400 4.66*109 4.19*1010

Source: A.T. Kearney, Inc. (1991)
Section 5: Recreational Fishing 119

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Table 5.8. Average Number of Offshore Anglers per Square Meter-Day by OCS Planning

Area and Location (1996-1998 data)

Private/rental boat anglers Party/charter boat anglers

OCS Planning Area Territorial

Seas

Exclusive

Economic

Zone

Territorial

Seas

Exclusive

Economic

Zone

North Atlantic 1.58*10-6 7.33*10-8 4.73*10-8 2.11*10-8

Mid-Atlantic 5.32*10-6 3.20*10-7 4.10*10-7 8.58*10-8

South Atlantic 1.44*10-6 1.17*10-7 5.31*10-8 2.11*10-8

Straits of Florida 1.53*10-6 1.74*10-7 8.81*10-8 2.10*10-8

Eastern Gulf of Mexico 9.77*10-7 1.57*10-7 5.56*10-8 4.51*10-8

Central Gulf of Mexico 1.78*10-6 1.20*10-7 4.81*10-8 1.47*10-8

Western Gulf of Mexico 4.07*10-7 5.45*10-8 2.84*10-8 4.76*10-9

Southern California 6.63*10-7 2.70*10-8 3.71*10-7 1.54*10-8

Central California 6.72*10-7 2.29*10-8 6.00*10-8 9.14*10-9

Northern California 1.49*10-7 5.10*10-9 1.33*10-8 2.04*10-9

Washington/Oregon 1.12*10-6 2.56*10-8 4.53*10-8 3.58*10-9

Gulf of Alaska 5.77*10-9 3.40*109

Kodiak 1.08*10-9 5.82*10-10

Cook Inlet 1.55*10-8 6.68*10-9

Shumagin 5.69*10-10 8.67*10-11

North Aleutian Basin 1.94*10-11 3.57*10-12

Saint George Basin 0 0

Aleutian Arc 0 0

Bowers Basin 0 0

Aleutian Basin 0 0

Navarin Basin 0 0

Saint Matthew Hall 9.60*10-12 1.02*10-12

Norton Basin 4.39*10-11 4.89*10-12

Hope Basin 1.59*10-11 1.75*10-12

Chukchi Sea 0 0

Beaufort Sea 0 0
120 Section 5: Recreational Fishing

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Table 5.9. Studies with Total Consumer Surplus Estimates for Saltwater Recreational

Fishing

Author(s) Date 1 Mode Species Location Methodology

Agnello and Han 1993 All All New York TCM

Bockstael, et al. 1989a Not Specified Striped bass Maryland TCM

Bockstael, et al. 1989b Shore All Florida TCM

Cameron 1992 Not specified All Texas CVM/TCM

Cameron 1988 Not specified Salmon British Columbia CVM

Cameron and James 1987 Not specified Salmon British Columbia CVM

Downing and Ozuna 1996 Boat All Texas CVM

Greene, et al. 1997 Not specified All Florida TCM

Hausman, et al. 1995 All All Alaska TCM

Hicks, et al 1999 All All Northeast TCM

Huppert 1989 All Salmon/bass California CVM/TCM

Jones & Stokes

Assoc., Inc.

1987 All All Alaska TCM

Kaoru 1995 Boat All North Carolina TCM

Kaoru, et al. 1995 Boat All North Carolina TCM

Leeworthy & Bowker 1997 Shore All Florida TCM

McConnell and

Strand

1994 All All East Coast TCM

Milon 1988 Boat All Florida TCM

Morey, et al. 1991 Not specified All Oregon TCM

NOAA, et al. 1999 Charter boat All New York TCM

1 – Date given is the date of publication, not the date that data were collected
Section 5: Recreational Fishing 121

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Table 5.10. Summary of Per-Trip Saltwater Fishing Consumer Surplus Estimates (1999

dollars)

Source Fishing Mode(s) Estimate(s) in 1999 dollars Estimate

Metric

Agnello and Han, 1993 All $29.18 and $30.98 Per-trip

Bockstael, et al., 1989a Not specified $41.63 and $62.33 Per-day

Bockstael, et al., 1989b Private boating $1.02 - $9.59 (avg. = $4.04) Per-trip 1

All $1.14 - $11.18 (avg. = $4.68) Per-trip

Cameron, 1992 Not specified $29.33 - $290.38 Per-day

Cameron, 1988 Not specified $31.57 (median), $42.12 (mean) Per-day

Cameron and James, 1987 Not specified $78.30 Per-day

Downing and Ozuna, 1996 Boat $51.22 - $346.90 Per-trip

Greene, et al., 1997 1 Not specified $1.99 and $4.35 Per-trip

Hausman, et al., 1995 1 All $159.88 and $198.85 Per-trip

Hicks, et al., 1999 1 All $0.00 - $96.95 (avg. = $12.87) Per-trip

Huppert, 1989 All $91.20 and $92.72 (preferred

model)

Per-day

All, residents $1.93 - $11.49 (avg. = $5.29) Per-trip

All, nonresidents $4.10 - $41.35 (avg. = $15.09) Per-trip

Jones & Stokes Assoc., Inc.,

1987 1

Clams $4.10 Per-trip

Leeworth and Bowker, 1997 Shore $105.58 Per-day

McConnell and Strand, 1994 1 All $12.19 - $119.76 (avg. = $67.78) Per-day

Milon, 1988 1 Boat $2.48, $2.71, and $2.79 Per-trip

NOAA, et al., 1999 1 Charter boat $85.23 Per-trip

1 – Estimate(s) based on a random utility analysis.
122 Section 5: Recreational Fishing

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1. Name Cost Categories and Assign Associated Impact Driver Variables

Cost Name

Impact Driver

BBL Spilled 

(plat/pipe)

BBL Spilled 

(tanker)

None BBL Spilled 

(plat/pipe)

BBL Spilled 

(tanker)

None

2. Specify Cost Coefficients $ / BBL $ / BBL N/A $ / BBL $ / BBL N/A

North Atlantic - - --

Mid-Atlantic - - $ 10 $ 6

South Atlantic - - --

Straits of Florida $ 37 $ 10 $ 268 $ 77

G Eastern Gulf $ 7 $ 3 $ 128 $ 43

o Central Gulf $ 1 $ 0 $ 107 $ 31

M Western Gulf $ 12 $ 3 $ 31 $ 14

Southern California $ 10 $ 2 $ 48 $ 9

Central California $ 104 $ 11 $ 120 $ 22

Northern California $ 1 $ 0 $ 6 $ 1

Washington/Oregon $ 3 $ 0 $ 177 $ 33

Gulf of Alaska - - $ 2 $ 1

Cook Inlet - - $ 0 $ 0

A Kodiak - - $ 14 $ 3

Shuimagin - - $ 0 $ 0

L North Aleutian Basin - - $ 0 $ 0

Aleutian Arc - - --

A St. George Basin - - $ 0 $ 0

St. Matthew Hall - - --

S Bowers Basin - - --

Aleutian Basin - - --

K Navarin Basin - - $ 0 $ 0

Norton Basin - - $ 0 $ 0

A Hope Basin - - $ 0 $ 0

Chukchi Sea - - $ 0 $ 0

Beaufort Sea - - $ 0 $ 0

18 17 21 18 17 21

3. Specify the dollar-year of cost coefficients 1999

A

T

L

P

A

C

Beach Recreation Recreational Fishing

Figure 5.1. OECM Impact Drivers for Recreational Fishing

Beach recreation and Recreational Fishing are combined on the Recreation cost

element worksheet.
Section 6: Ecological Service Losses 123

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6. Ecological Service Losses

6.1 Introduction

This section details the estimation of the ecological service damages associated with hypothetical

oil spills. Ecological service losses are estimated using a generalized habitat equivalency

analysis (HEA). This section describes the steps taken to operationalize the HEA approach for a

hypothetical spill.

An issue of concern for this analysis is to properly account for compensatory damages to natural

habitats. The GPECM analysis addressed wildlife damages using an early version of the

NRDAM/CME. Both the GPECM and the current version of the NRDAM/CME provide

estimates for primary restoration of natural habitats to baseline conditions through restoration

actions and replacement/restocking costs. Neither analysis addressed the compensatory

component of lost ecological services.

One option to estimate the damages from ecological injuries is to measure passive use losses. As

discussed in Section 2, estimating the passive use damages associated with hypothetical oil spills

is not considered appropriate for this analysis. We use a generalized HEA approach to estimate

ecological damages because it reflects the current preference towards restoration and avoids the

controversy regarding contingent valuation. HEA is a service-based approach to damage

estimation particularly appropriate for damages to resources that receive little human use (Julius,

et al., 1995). Research is on going to revise the NRDAM/CME to include a compensatory

restoration module but the analysis is not currently complete (French, 2000). Thus, the OECM is

the first attempt to generalize the HEA approach for hypothetical oil spills.

Our analysis relies on the NRDAM/CME to provide estimates of ecological service losses due to

hypothetical oil spills. Section 6.2 presents relevant summaries of HEA applications. The

potential compensatory restoration actions for each habitat type included in the NRDAM/CME,

along with cost estimates, are discussed in Section 6.3. Generalized damage estimates are

provided in Section 6.3.13. Finally, Section 6.4. discusses a methodology for linking ecological

injuries to compensatory restoration through a balancing of credits and debits.

6.2 Habitat Equivalency Analysis Applications

This section describes recent natural resources damage assessment cases that have relied on

habitat restoration for compensatory damages. An attempt was made to obtain publicly available

damage assessment and restoration plan (DARP) reports for cases involving marine oil spills and

other marine injuries. Several of these reports use HEA to scale compensatory restoration

projects. Additional reports do not apply the HEA methodology but are relevant because cost

estimates of habitat restoration activities are provided. These applications are described below.

6.2.1 Lake Barre Oil Discharge

This 1997 case involved a release of crude oil from a pipeline buried underneath the sediment in

Lake Barre, Louisiana (Louisiana Oil Spill Coordinators Office, et al., 1999). Approximately
124 Section 6: Ecological Service Losses

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6,561 barrels (275,562 gallons) of oil were released into estuarine and marsh habitat. Four

categories of injuries were considered: aquatic fauna, birds, marsh function, and human use. The

human use component was judged to be minor and no damages were assessed. Injuries to

aquatic fauna were measured in terms of lost biomass. Injuries to birds were quantified by

estimating the number killed. The metric for marsh damages was acre-years of marsh service.

The aquatic fauna and bird injuries were converted to marsh service acre-year equivalents based

on the efficiency of biomass transfer across levels of the food chain (Penn, 1999).

Aquatic fauna and bird injuries were estimated using a site-specific version of the

NRDAM/CME. Based on the trustee’s analysis, an estimated 333 birds were killed and the lost

biomass of aquatic fauna was approximately 7,465 kg. Marsh injuries were quantified using

field studies. As indicated in Table 6.1, four classifications of marsh injury were defined based

on oiling densities. The initial service loss ranged from 10% for lightly oiled areas with rapid

recovery to near 100% for heavy oiling with slow recovery. Based on estimates of recovery

time, the total service loss was estimated as 75.6 lost discounted acre-years of marsh service.

Natural recovery was preferred over primary restoration for all injuries. Compensatory

restoration was deemed necessary for aquatic faunal, bird, and marsh service damages. The

trustees indicated a preference for in-kind restoration through marsh restoration. For a spill of

5,000 barrels, the trustees estimated that the injuries to aquatic fauna were equivalent to 3.21

acres-years of lost marsh service and the injuries to birds were equivalent to 26.99 acre-years of

lost marsh service. For a spill of 7,000 barrels, the trustees estimated that the aquatic faunal

losses were equivalent to 4.17 acre-years of marsh service and the bird losses were equivalent to

27.65 acre-years of marsh service.

A HEA approach was taken to measure debits (service losses due to ecological injuries) and

credits (service gains due to compensatory restoration.) The preferred restoration alternative was

to plant salt marsh vegetation (Spartina alterniflora) on East Timbalier Island. The project was

considered marsh enhancement rather than marsh restoration. The trustees estimated that a

hypothetical created marsh generates 8.46 discounted service acre-years per acre based only on

faunal services. The trustees estimated that 3.7 acres would need to be planted to compensate for

the faunal and bird damages and that 14.9 acres would need to be planted to compensate for the

marsh injuries. The probability of success was considered to be high and performance criteria

(such as percent plant cover and percent plant survival) were set for the restoration project.

6.2.2 North Cape Oil Spill

The barge North Cape struck ground off Moonstone Beach in Rhode Island in January 1997 and

released approximately 828,000 gallons of fuel oil in coastal and offshore environments

(National Oceanic and Atmospheric Administration, et al., 1999). Damages were classified into

four categories: offshore, salt ponds, birds, and human use.

For offshore and salt pond injuries, the injuries were quantified based on estimates of the

numbers of different species killed, which were then converted to biomass killed. Additional

biomass losses were classified as “production forgone,” or the lost biomass growth that would

have occurred in the absence of the spill. Production forgone generally included reproductive

losses when recovery times exceeded one year.
Section 6: Ecological Service Losses 125

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For bird injuries, quantification was in terms of lost bird-years. Recovery times varied from one

year for pond birds to six years for loons. The total number of birds killed was estimated to be

2,292 birds.

Table 6.2 summarizes the injuries. Note that all ecological injuries are faunal. Injuries to

vegetation, including intertidal wetlands, were considered but determined to be insignificant.

Natural recovery was preferred over primary restoration for all injuries except for injuries to

piping plovers, a species listed as threatened under the Endangered Species Act. Several

compensatory restoration projects were chosen as shown in Table 6.2. The total cost of

compensatory restoration actions was approximately $28 million. Note that the damages per lost

bird-year are $194 per bird-year for marine birds other than loons and $2,056 per bird-year for

loons. Lobster damages are $1.10 per lobster killed. It is difficult to allocate the costs of land

acquisition and shellfish restoration by specific injury. A salt pond dredging project was among

the non-preferred alternatives. The project would have created 56 acres of new eelgrass for a

total cost of $7.94 million ($142,000 per acre).

6.2.3 Apex Houston Oil Spill

Between January 28 and February 4, 1986 the Apex Houston discharged an unknown amount of

crude oil along the California coast (U.S. Fish and Wildlife Service, no date given).

Approximately 9,000 seabirds were killed as a result of the spill, including 6,000 common

murres, 1,293 rhinoceros auklets, and 12 marbled murrelets (a federally listed species). As part

of the damage settlement, $4,916,430 was allocated for a restoration project for common murres

and $500,000 was allocated for habitat acquisition for marbled murrelets. On a per-bird-killed

basis, damages were $41,667 per marbled murrelet and $546 per other seabird. Additional

damages of approximately $1 million were collected for penalties and other spill-related costs.

6.2.4 U.S. vs. Melvin A. Fisher, et al.

This case involved the destruction of seagrass by treasure hunters in the Florida Keys National

Marine Sanctuary (Julius, 1997). While the case did not involve a chemical release, it is relevant

for our analysis because a marine habitat was injured and a HEA was employed to scale

compensatory restoration. The responsible parties destroyed 1.63 acres of seagrass. The initial

service loss of the effected area was 100%. Full recovery was expected to take 75 years with a

logistic recovery curve. The total injury was expected to be 44.08 acre-years of seagrass

services. After discounting, restoration of 1.55 acres of seagrass habitat was estimated to fully

compensate for the injury. The total cost to implement the restoration project, including

monitoring costs, was estimated to be $351,648 in 1998 dollars ($226,870 per acre).

Assessment costs were estimated to be $211,130. Including interest on the assessment costs,

total damages were $589,331.

6.2.5 M/V Miss Beholden Grounding

This case involved the grounding of the M/V Miss Beholden in 1993 in a shallow coral reef in

the Florida Keys National Marine Sanctuary (Julius, et al., 1995). While the case did not involve

a chemical release, it is relevant to our analysis because the case concerns damage to a marine
126 Section 6: Ecological Service Losses

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habitat, compensatory restoration information is provided in the damage assessment report, and a

HEA was employed to scale compensatory restoration. The grounding resulted in near total

destruction of 1,025.6 square meters of coral habitat. This habitat was divided into spur tops,

spur framework, and groove areas. For spur tops and spur framework, recovery times were

estimated at 30-50 years based on active restoration efforts with a linear recovery curve.

Recovery time for the groove areas was expected to be 15-25 years given active restoration with

a linear recovery curve. For all cases, the productivity of created habitat was assumed to be

equal to the productivity of like natural habitat. Based on the HEA, it was determined that 518.6

square meters (0.128 acres) of compensatory habitat would need to be created in addition to

primary restoration. The total cost of habitat restoration was estimated to be $1,671,337

including $112,849 for rubble removal, $623,000 for reef replacement, $887,988 for

transplantation, and $47,500 for environmental review, permitting, and supervision. Monitoring

costs were estimated to be $126,547 and assessment costs were $71,963 plus interest.

6.2.6 Presidente Rivera Oil Spill

The oil tanker M/V Presidente Rivera ran aground near Marcus Hook, PA in June 1989 spilling

approximately 200,000 – 255,000 gallons (about 5,000-6,000 barrels) of fuel oil into the

Delaware River (New Jersey Office of Natural Resource Damages, 1996). The natural resource

injuries included impacts to blue crab and bird populations, and diminished recreational use.

Part of the restoration plan is a project to restore a 450-acre wetland. The project involved the

use of a herbicide to kill exotic pest plant species, the refurbishment of water control structures,

and water level management. The project was expected to restore approximately 400 acres to a

diverse, brackish system dominated by native submerged and emergent plant species. The total

cost of the project was estimated at $396,500, including $100,000 for land acquisition ($991 per

acre).

6.2.7 Dixon Bay Wellhead Failure

A 1995 wellhead failure located in Dixon Bay, Louisiana released an undetermined amount of

crude oil and produced water into the estuarine environment (Finley, et al, 1995). Injury

categories included: marsh, birds/wildlife, benthos, and water column. No primary restoration

actions were considered necessary. Compensatory restoration was deemed necessary for marsh,

water column, and bird/wildlife damages. Approximately 200 acres of marsh were oiled to some

degree by the release. Seven oiled birds were found dead and 24 other birds, mostly pelicans,

were rehabilitated. A HEA was conducted to estimate the required compensatory restoration.

Approximately five acres of marsh creation was required to provide compensatory restoration.

The preferred restoration alternative for all injuries was a freshwater diversion project to create

marsh habitat. Marsh creation would provide similar services to those lost and provide both

water column and wildlife habitat benefits. The proposed freshwater diversion was anticipated

to provide up to 100 acres of restored wetlands. No cost estimates were provided in the report.

6.2.8 M/V Jacquelyn L. Grounding

This case involved the grounding of a sport-fishing vessel in 1991 in the Florida Keys National

Marine Sanctuary (Julius, et al., 1997). While the grounding did not produce a chemical release,
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it involves restoration of a marine environment, which is insightful for our analysis. The

grounding caused 123.3 square meters (0.03 acres) of total injury to coral habitat, 0.5 square

meters of partial injury, and fractured 73.0 square meters of the reef framework. The selected

restoration plan involved coral transplanting, rubble stabilization, and reef framework repair.

The total cost of the restoration plan was $125,127.

6.2.9 M/V Formosa Six Discharge

A collision in 1997 led to the release of 1,500 to 1,800 metric tons of ethylene dichloride (EDC)

off the coast of Louisiana (Kern, et al., 1999). Most of the EDC rapidly settled on the sea floor

approximately three miles offshore of the Southwest Pass of the Mississippi River. An area of

approximately 12 acres of bottom sediments had EDC concentrations above 100 ppm. The EPA

water quality standard is 113 ppm. The only damage category was a reduction in benthic habitat

services. No primary restoration activities were considered necessary. The preferred

compensatory restoration project was to create marsh habitat. A HEA was conducted to

determine the scale of compensatory restoration. At least one to two acres of marsh creation was

considered necessary. The chosen restoration project involved the removal of levees to restore

estuarine water flow. No cost estimates were provided.

6.2.10 Chevron Pipeline Oil Spill

In 1996 a pipeline rupture led to the release of an estimated 982 barrels (41,244 gallons) of fuel

oil into Waiau Stream and Pearl Harbor, Hawaii (The Natural Resource Trustees for Pearl

Harbor, Oahu, Hawaii, 1999). Oiling of shorelines and intertidal areas affected freshwater and

saltwater wetlands, mangroves, mudflats, rocky shorelines, sandy beaches, riprap, seawalls, and

piers. The shoreline of the USS Arizona Memorial Visitor Center was also oiled.

The natural resource damage claim included losses to intertidal habitat, water column habitat,

subtidal habitat, freshwater marsh habitat, and human use. The trustees estimated that 25 acres

of intertidal habitat were affected including industrial shoreline, mangrove forest, rocky

shoreline, and mixed sediment. The initial service loss of the affected area was estimated at 80%

with a recovery period of up to 10 years. The water column injury covered 2,300 acres with an

initial service loss of 10%. A linear recovery period over 10 years was assumed. Subtidal

injuries occurred over 5 acres with an initial service loss of 30%. Tourism losses were estimated

at 16,200 lost visits to the Arizona Memorial with an additional 24,220 visits of reduced quality.

The trustees used a HEA to scale compensatory restoration actions. Several preferred

compensatory restoration projects were chosen. A coastal marsh restoration project involved the

clearing of vegetation, the removal of fill, fencing, and the creation of a hydrologic link.

Another project involved the removal of red mangrove, an exotic plant species, to improve

habitat for waterbirds and shorebirds. The cost of these two projects was estimated at $1 million.

For lost human use, the trustees scaled compensatory restoration projects to equate with the

value of lost visitor services. The projects included shoreline protection and the replacement of a

boat dock. The total cost of these two projects was estimated at $1.55 million.
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6.2.11 Tampa Bay Oil Spill

A marine collision in 1993 resulted in a spill of approximately 32,000 gallons of jet fuel, diesel,

and gasoline and approximately 330,000 gallons of fuel oil near the entrance to Tampa Bay,

Florida (Florida Department of Environmental Protection, et al., 1997). Ecological injury

categories included mangroves, seagrass beds, water column, birds, sea turtles, salt marshes,

shellfish beds, bottom sediments, and sand loss. A summary of resource injuries and preferred

restoration alternatives is presented in Table 6.3. Cost estimates for the compensatory

restoration projects were not available.

6.2.12 M/V World Prodigy Oil Spill

The tanker World Prodigy ran aground in Narragansett Bay, Rhode Island in 1989 releasing

approximately 290,000 gallons (about 7,000 barrels) of fuel oil (NOAA, 1996). Injured

resources included crustaceans, fish, shellfish, and benthic organisms. Human use was also

adversely affected due to closure of beaches and shellfishing areas. Over 800 dead lobsters and

crabs were collected but the total killed was considered to be more.

Four restoration projects were proposed. To compensate for lobster injuries, a lobster

enhancement project was proposed consisting of reef construction, stocking, and monitoring.

The total cost of the project was estimated at $270,000. To compensate for shellfishing injuries,

$75,000 was made available to expand a quahog spawner program and transplant quahogs to

harvesting areas. To enhance fish habitat, an eelgrass bed restoration project was proposed. A

total of 540 square meters (0.13 acres) would be transplanted with eelgrass at a cost of

approximately $100,000. Finally, a salt marsh restoration project was chosen to compensate for

damage to fish habitat. The project involved the restoration of tidal flushing to approximately 13

acres of degraded marsh through channel modification and the placement of culverts. The

estimated cost was $80,000. Note that habitat acquisition was not proposed due to the

prohibitive cost of approximately $500,000 per acre. The total cost of the restoration projects,

including oversight and administration, was $567,299.

6.2.13 Apex Barges Oil Spill

As a result of a collision, two T/B Apex Barges released approximately 694,000 gallons (over

16,000 barrels) of catalytic feedstock oil into Galveston Bay in 1990 (Texas General Land

Office, et al., 1997). The oil spill damaged salt marsh habitat and led to the closure of portions

of Galveston Bay to finfishing for two days, shrimping for eight days, and crabbing for twelve

days. Navigation in the Houston Ship Channel was also affected.

Eleven restoration alternatives were considered (one was a “no action” alternative). Cost

estimates were provided for all alternatives. Six of the alternatives involved the construction of

wetland habitat. A summary of these six restoration projects, along with cost estimates, is

presented in Table 6.4. The cost per acre of wetland construction range from $3,478 up to

$55,000. One other alternative was the restoration of waterbird nesting habitat through raising

the elevation of a 5-acre island and reducing future erosion. The estimated cost of this project

was $529,650. The preferred alternatives were the wetland construction projects at Pierce
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Marsh, Galveston Island State Park, and the Interstate 45 highway corridor. The settlement

amount was $1,312,962.

6.2.14 Tenyo Maru Oil Spill

As a result of a collision with another ship, the Tenyo Maru sank in 1991 approximately 20 miles

northwest of Cape Flattery in Canadian waters (Malah Indian Tribe, et al., 1999). The ship was

carrying 354,800 gallons of intermediate fuel oil, 97,800 gallons of diesel fuel, and 22,500

gallons of fish oil. An unknown amount of oil was released. The spill impacted American

coastline from the Canadian border to northern Oregon. The most significant impacts occurred

along the Makah Indian Reservation and Olympic National Park. The injuries primarily included

marine bird deaths and damage to kelp beds. A total of 4,300 oiled bird carcasses were

recovered including 3,157 common murres, 281 rhinoceros auklets, and 45 marbled murrelets (a

federally threatened species). Actual bird deaths may have been significantly higher. For

example, the trustees estimated the total number of common murre deaths at 3,740 to 19,559 and

the number of marbled murrelet deaths at 161 to 273.

An integrative restoration approach that combines attributes of several restoration alternatives

was preferred that encompassed both primary and compensatory restoration. Several projects

were included in this alternative. One project sought to restore common murre colonies in the

Copalis National Wildlife Refuge using social attraction at a cost of $1.8 million. Another

project to reduce the seabird by-catch, particularly of common murres, from coastal net fishing at

a cost around $600,000. A third project involved the protection of marbled murrelet nesting

habitat and the reduction of silt deposits of kelp habitat at a total cost of $2.5 million. Additional

projects included a contribution for an oiled wildlife rehabilitation center and the funding of

public education signs and brochures. The total settlement was $5.2 million.

6.2.15 Greenhill-Timbalier Well Blowout

A well blowout in September 1992 in Timbalier Bay, Louisiana released between 72,000 and

122,000 gallons (1,700-2,900 barrels) of Louisiana crude oil into the marine environment

(Miller, 1994). A total of 122.37 acres of intertidal marsh were impacted by the spill with an

estimated service loss of 37%. The trustees determined that 21.7 acres of created wetlands was

required to compensate for the natural resource damages. The estimated cost of the restoration

project, including dredging, planting, and monitoring, was estimated to be less than $1 million.

6.2.16 Summary of Applications

The applications above provide information on possible habitat restoration projects and costs.

The applications indicate a strong preference for in-kind restoration. Compensatory damages

have been assessed for injury to habitats (e.g., Miss Beholden grounding, Chevron pipeline oil

spill), injury to species (e.g., Apex Houston and World Prodigy oil spills), or injury to both

species and habitats (e.g., Lake Barre and Tampa Bay oil spills).

Based on the injury quantification performed in the above natural resource damage cases and the

output produced by the NRDAM/CME, we include injuries, measured in number of animals
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killed, for birds, reptiles, and mammals. These species categories were also included by EG&G

(1996) in their analysis of restoration activities. Fish injuries are not assessed as ecological

impacts because injuries to fish are considered in the recreational and commercial fishing

damages.

In addition, we estimate compensatory restoration costs within damages for coastal marine

environments. We define habitat categories based on the output of the model because the

analysis of oil spill scenarios is conducted using the NRDAM/CME model. The NRDAM/CME

includes injuries to the following nine coastal habitats:

• Rocky shore

• Gravel beach

• Sand beach

• Mud flats

• Saltmarsh

• Mangrove

• Macrophyte (macroalgal and seagrass) beds

• Coral reef

• Mollusk (oyster) reef

The NRDAM/CME estimates the amount of oiling, in terms of square-meter days, for each of

these habitats. The next section considers the potential for compensatory restoration actions,

along with costs, for each of the above coastal habitats and for the three species categories (birds,

mammals, and reptiles). An attempt is made to obtain a high and low cost estimate for

restoration costs for each habitat.

6.3 Feasibility and Costs of Restoration Actions

The OPA guidance document on primary restoration (EG&G, 1996) is the most comprehensive

assembly of data regarding restoration activities, effectiveness, and cost. While the report

focuses on primary restoration, it also discusses many possible compensatory restoration

activities such as stocking, planting, and habitat construction. The NRDAM/CME report

(French, et al., 1996) provides additional information on restoration options. The U.S. Army

Corps of Engineers has complied a review of both Corps (Muncy, et al., 1996) and non-Corps

(Shreffler, et al., 1995) restoration projects. We conducted a literature review to identify further

restoration options and costs for coastal habitats. The sections below discuss compensatory

restoration options and costs by habitat type. All costs are updated to 1999 dollars.

6.3.1 Rocky Shore

EG&G (1996) present five potential restoration actions for rocky shore: natural recovery, sand

blasting, steam cleaning, flushing, and bioremediation. All of these actions pertain to primary,
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but not compensatory, restoration by dealing with the recovery of the oiled habitat to baseline

conditions. Of the cases summarized in Section 6.2., only the Chevron Pipeline oil spill

specifically mentions oiling of rocky coastline. Approximately 7.3 acres of rocky shoreline was

oiled in the Chevron Pipeline spill. None of the preferred compensatory restoration projects

involved actions on rocky shoreline, although one alternative involves the construction of a

permanent riprap system to provide shoreline protection.

Rocky shore habitat may be degraded due to causes other than oil spills, such as shellfish

collection, point source pollution, and seaweed harvesting (Hawkins, et al., 1999). Rocky

shorelines degraded by the collection of food and bait have recovered when people were

excluded from the area. However, this scenario is probably not applicable to the issue of

compensatory restoration except perhaps with the reintroduction or restocking of species.

Restoration work in California has focused on sub-tidal zones of rocky shores that have been

degraded due to the commercial harvest of kelp (Schiel and Foster, 1992). These efforts have

focused on re-seeding with kelp, along with either increasing or restoring hard substrate or

reducing the impacts of marine grazers such as sea urchins. The costs of restoration efforts that

have been successful have been over $1,400/acre (in 1999 dollars).

6.3.2 Gravel Beach

EG&G (1996) present five restoration options for cobble-gravel beach: natural recovery,

flushing, sediment washing, sediment agitation, and bioremediation. All of these alternatives

pertain to primary restoration of an oiled area and not compensatory restoration activities.

Injuries to gravel beach are not specifically discussed in any of the oil spill cases in Section 6.2.

6.3.3 Sand Beach

EG&G (1996) present six restoration options for sand beach: natural recovery, flushing,

sediment washing, sediment agitation, bioremediation, and incineration. None of these

alternatives pertain to compensatory restoration.

Beach renourishment is a feasible compensatory restoration project (Charlier, et al., 1989). The

number of beach renourishment projects in the United States has significantly increased due to

coastal erosion (World Dredging, Mining, and Construction, 1997). The Eastern Isle Dernieres

Barrier Island restoration project involves the use of overwash sediments to restore eroded dunes

and a re-seeding effort. This project is expected to restore at least 105 acres of wetlands at a cost

of about $60,000/acre (Muncy, et al., 1996).

Pickart, et al. (1998) experimented with different methods of removing yellow bush lupine, an

invasive species, from coastal dunes in California. The study found that the restoration costs per

acre were similar using manual removal and removal by heavy equipment. The total cost of

restoration using both methods was around $31,000 per hectare (about $12,500 per acre).
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6.3.4 Mud Flats

EG&G (1996) includes three restoration options for mud flats: natural recovery, sediment

removal and replacement, and bioremediation. Restoration of mudflats using sediments may be

a feasible compensatory restoration action but no data were located on restoration costs.

Cordgrass invasion has reduced the extent of tidal mud flats by reducing the abundance of

macrobenthic fauna and the populations of shorebirds (Frid, et al., 1999). The population density

of cordgrass can be reduced through the use of herbicides or physical disruption. However, no

cost estimates were provided.

The Sonoma Bay Tidal Wetlands restoration project in California was designed to restore 31

acres of intertidal mudflat and wetland habitat through the disposal of dredge material and the

construction of a levee (Muncy, et al., 1996). The cost of this project was approximately

$30,700/acre.

6.3.5 Saltmarsh

EG&G (1996) considers eight potential restoration actions for saltmarsh. The most applicable to

compensatory restoration activities is the creation of new saltmarsh. Saltmarsh creation may

involve the restoration of a degraded marsh habitat, enhancement of existing habitat, or the

creation of new marsh habitat. Compensatory restoration of saltmarsh habitat was included in

eight of the oil spill cases described in Section 6.2 (Lake Barre, Presidente Rivera, Dixon Bay,

Formosa Six, Chevron Pipeline, World Prodigy, Apex Barges, and Greenhill-Timbalier).

The costs of saltmarsh restoration are highly variable. Cost differences arise due to many factors

including the method of vegetation, the amount of excavation work required, the costs of

acquiring the site, tidal characteristics, topography, and the degree of degradation (EG&G,

1996). Costs of saltmarsh restoration projects are available from several sources. Table 6.5.

presents data on several saltmarsh restoration projects where information was available on both

the cost and the size of the project. Based on the 42 estimates in Tables 6.4. and 6.5., restoration

costs range from $233/acre up to nearly $450,000/acre. Only two of the estimates are above

$70,000/acre and five are below $1,000/acre. For those projects related to natural resource

damage cases, the cost range is about $3,600 to $57,000 with most between $7,000 and $30,000.

As part of the 1991 GPECM, a literature review was conducted on wetland restoration costs

(A.T. Kearney, 1991). This review included data on both freshwater and saltwater marshes. The

report used a lower value restoration cost of $19,120/acre and an upper value of $50,987/acre (in

1999 dollars).

King and Bohlen (1994) collected data on approximately 1,000 wetland restoration projects from

agricultural conversion projects and the U.S. Army Corps of Engineer’s section 404 projects.

The average per-acre cost of restoration was presented for nine categories of wetland types,

including saltmarsh. Based on nine saltmarsh restoration projects, the average restoration cost

was $20,900 per acre (in 1999 dollars) with a median of $11,800 per acre.
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Another source of data on compensatory wetland costs is the use of mitigation banks. Mitigation

banks allow parties to mitigate for wetland impacts by purchasing credits in restoration projects

(Liebesman and Plott, 1996). These restoration projects are completed for the purpose of

providing mitigation alternatives for future wetland impacts. These projects tend to be large with

a higher chance of success than smaller, non-contiguous projects that are case-specific. Credits

are normally sold on a per-acre basis to compensate for impacts to wetlands also measured on a

per-acre basis. Compensation ratios may require that credits be purchased at a ratio higher than

on-to-one. Costs for wetland credits vary. Florida Wetlandsbank, a for-profit company, initially

offered credits in the mid-1990’s for $40,000 to $45,000 per acre (Liebesman and Plott, 1996).

The Army Corps of Engineers reports on credit costs of around $2,400 per acre for a wetlands

bank in Florida, $2,000/acre in Louisiana, and ranging from around $14,000 to $70,000 in

Maryland (Institute for Water Resources, 1994).

Based on the data in Tables 6.4 and 6.5 and A.T. Kearney (1991), an upper value for wetland

restoration of $50,000/acre was selected. This value is basically the same as the value chosen by

A.T. Kearney and similar to several of the values presented in Tables 6.4. and 6.5. The value is

also similar to some of the wetland mitigation banking credit costs. The lower value of about

$19,000/acre chosen by A.T. Kearney is higher than most of the estimates presented in Table 6.4.

and 6.5. Instead, a lower value of $10,000/acre is chosen because many of the estimates in Table

6.4. and 6.5. are approximately $10,000/acre including several estimates based on natural

resource damage cases. This value is also similar to the median value presented in King and

Bohlen (1994). While this cost range for saltmarsh wetland is somewhat larger than the value

used by A.T. Kearney, it more accurately reflects the broad range of saltmarsh restoration costs

that may be appropriate for compensatory restoration.

6.3.6 Mangrove

Among the restoration actions discussed in EG&G (1996), mangrove replanting is a feasible

compensatory restoration action. The costs of mangrove planting vary significantly depending

on whether the effort uses seeds, seedlings, or 3-year-old trees. Assuming replanting with

seedlings at a density of 0.91-1.23 seedlings per meter, the restoration costs presented are

$2,534/acre, $9,582/acre, and $20,341/acre (in 1999 dollars).

Bann (1998) presents planting costs for mangroves in Vietnam to be only about $25/acre.

However, such costs are not applicable to the United States. Thorhaug (1990) also presents data

on mangrove restoration primarily from developing countries. The labor costs for mangrove

restoration using seeds ranges from 180 to 730 person-hours. The labor costs for mangrove

restoration by planting small trees range from 1,000 to 1,300 person-hours per acre. While the

restoration methods may not be applicable to the United States, the labor costs would range from

$2,700 to $19,500 per acre assuming a labor cost of $15 per hour.

King and Bohlen (1994) present summary data on four mangrove restoration projects. The mean

restoration costs were $20,800 per acre (in 1999 dollars) with a median of $15,700 per acre.

Based on the above data, a low restoration cost of $3,000 per acre and a high restoration cost of

$20,000 per acre is chosen. These values reflect the range of the cost data above.
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6.3.7 Macrophyte (macroalgal and seagrass) beds

EG&G (1996) presents data on restoration actions for kelp and seagrass beds. Primary

restoration through transplanting is also a possible compensatory restoration action.

Three transplanting/restoration options are considered for kelp beds:

• The placement of artificial growth centers to increase natural colonization

• The placement of artificial growth centers along with transplants

• The stapling of kelp to the habitat bottom

Based on a kelp restoration project in California, the use of artificial growth centers (without

transplants) costs approximately $750 per acre (in 1999 dollars). Combining artificial growth

centers with transplants is more expensive. Based on the same California restoration project,

costs were approximately $1,500/acre. For restoration through the stapling of kelp to the habitat

bottom, costs were approximately $900 per acre.

Replanting is a restoration alternative for seagrass, including eelgrass. Seedlings, shoots or plugs

can be used to replant seagrass. The costs of temperate seagrass (eelgrass) replanting using plugs

ranged from $4,000 to $18,000 per acre with an average of $11,000/acre (in 1999 dollars). The

use of shoots to replant eelgrass costs between $15,000 and $30,000/acre. Costs are higher for

the restoration of subtropical and tropical seagrass. One estimate of replanting using plugs was

provided (about $96,000/acre). The cost for the use of seedlings ranged from $11,000 up to

$300,000 per acre with an average of $113,000/acre.

A seagrass restoration project in Texas is described in Muncy, et al. (1996). This project

involved the transplanting of seagrass into dredged material. A total of 3.7 acres of seagrass was

transplanted at a cost of $323,000 (in 1999 dollars), or $87,000/acre. This cost is within the

range presented in EG&G (1996).

Thorhaug (1990) mentions that the costs of seagrass planting range from $1,900 to $25,500 per

hectare ($800-$10,300 per acre). This summary includes data from other countries including

Australia and the Philippines though most of the estimates were based on projects from the

southeast United States.

Fonseca, et al. (1994) discuss seagrass planting using three methods: cores, stapled bare roots,

and peat-pot plugs. They present planting costs in terms of work-minutes per planting unit based

on experiments in North Carolina and Florida. Their cost estimates range from 1.21 to 3.53

work-minutes per planting unit. The planting density in the experiments was 0.5 meter spacing,

implying 40,000 plants per hectare or 16,200 plants per acre. Thus, it would take an estimated

327 to 953 hours to plant one acre of seagrass. At a labor cost of $15 per hour, the planting cost

per acre would range from $4,900 to $14,300. This cost is comparable to the data presented in

EG&G (1996). Note that this cost estimate excludes the cost of the transplants.
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Finally, King and Bohlen (1994) present summary data on three aquatic bed restoration projects.

The mean restoration cost per acre was $11,000 (in 1999 dollars) with a median value of $21,400

per acre.

Based on the above data, a low restoration cost of $4,000 per acre and a high restoration cost of

$30,000 per acre is chosen for macrophyte beds. Note that the extremely high costs of $100,000

per acre or more are not considered representative.

6.3.8 Coral Reef

Reef restoration can be accomplished using coral transplants (EG&G, 1996). Based on the

restoration project as a result of the M/V Elpis grounding that occurred in the Key Largo National

Marine Sanctuary, the cost of reef restoration was approximately $1.1 million per acre (in 1999

dollars). The cost of reef restoration in the U.S. vs. Melvin A. Fisher, et al. case described in

Section 6.2.4. was approximately $230,000 per acre (in 1999 dollars) (Julius, 1997). The coral

reef restoration project in response to the M/V Miss Beholden grounding described in Section

6.2.5. was approximately $14 million per acre (in 1999 dollars) (Julius, et al., 1995). Restoration

costs per acre could not be determined from the M/V Jacquelyn L grounding described in

Section 6.2.8.

A wide range of coral reef restoration activities, including replanting, establishing buffer zones,

removing competing species, and habitat creation, are discussed in Maragos (1992). Detailed

cost estimates are not provided but any coral reef restoration is said to be expensive – probably

exceeding one million dollars.

6.3.9 Mollusk (Oyster) Reef

Mollusk reefs have been restored through either the reconstruction of the reef substrate or the

reestablishment of the habitat with seed mollusks (EG&G, 1996). Costs for reconstruction of the

reef substrate vary depending on the materials used (concrete, shells, limestone, or gravel). The

range of reconstruction costs is from $900 to $6,700 per acre (in 1999 dollars) with an average

around $3,200/acre. Based on data from Maryland, the cost of reestablishment using seed

oysters is approximately $600/acre. However, this estimate may understate actual costs in a

compensatory restoration project because the oysters were provided to the state at a discount.

6.3.10 Birds

The two restoration actions discussed in EG&G (1996) that are relevant for compensatory

restoration are habitat restoration and enhancement and habitat protection and acquisition. Costs

were not presented for either alternative. Information was obtained from the California

Department of Fish and Game on the natural resource damages associated with bird deaths from

oil spills (Hampton, 1999) as well as from the North Cape oil spill (National Oceanic and

Atmospheric Administration, et al., 1999). As seen in Table 6.6. the natural resource damages

associated with bird deaths can range from about $200 per bird up to near $20,000 per bird.

Based on the data in Table 6.6. a low damage value per bird death of $500 was chosen

(approximately the average value of the lowest five estimates in the table). A high damage value
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of $2,500 per bird killed was chosen. A higher damage value, such as the value of loon

restoration from the North Cape spill, may be applicable in cases where rare or endangered

species are affected.

6.3.11 Marine Mammals

The two restoration actions mentioned in EG&G (1996) that are most relevant as compensatory

restoration actions are habitat restoration and enhancement and habitat protection and

acquisition. Unfortunately, cost estimates for these actions were not provided. Examples of

restoration actions pertaining specifically to marine mammals could not be located. The only

data available on restoration actions pertaining to marine mammals was data on

restocking/replacement from EG&G (1996). While these data pertain to primary restoration,

they provide a reference point for comparison to the bird data in section 6.3.11. Bird restocking

costs were also provided in EG&G (1996). Over 100 restocking costs were provided and over

90% were in the range of $500-$3,000 per bird. Thus, bird restocking costs are very similar to

the per-bird compensatory damages. We assume that marine mammal restocking costs are

similar to the compensatory natural resource damages associated with marine mammal deaths.

Restocking costs were provided for several species of whales, dolphins, and seals, as well as

several other marine mammals (EG&G, 1996). A total of 34 estimates were provided. Based on

the average value from the lowest 17 of these estimates, a low compensatory damage estimate

per marine mammal killed of $13,000 is obtained. Based on the highest 17 estimates, a high

compensatory damage estimate is obtained of $74,000 per marine mammal killed.

6.3.12 Reptiles

Three restoration actions pertaining to reptiles are discussed in EG&G (1996): natural recovery,

restocking/replacement, and protection of nesting sites. The mostly likely compensatory

restoration action would be protection of nesting sites; but no cost estimates are provided for this

option. Thus, the cost of restocking/replacement is used as a proxy for compensatory damages.

As discussed in Section 6.3.12., per-unit species restocking/replacement costs have historically

been similar in scale to compensatory damages. EG&G provides data on restocking costs for

American alligators and several species of turtles. The highest restocking cost presented in

$9,300 per individual (in 1999 dollars) and the lowest cost is $2,800 per individual. These

values are used as the high and low representative compensatory damages for each reptile killed.

6.3.13 Summary of Compensatory Restoration Costs

As evident from the sections above, a significant amount of data exists for compensatory

restoration costs for some habitats but little available data exist for other habitats. For example,

many examples of wetland restoration projects provide cost estimates but no data were located

on the cost of restoration projects on gravel beaches. The cost estimates provided above for

some habitats, particularly rocky shores and coral reefs, are probably not applicable to the

situation of compensatory restoration as a result of an oil spill.
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A sufficient amount of data was considered available to estimate a high and low restoration cost

for saltmarsh, mangrove, and macrophyte bed habitats. For the remaining coastal habitats, a lack

of restoration cost data makes choosing a high and low estimate difficult. One alternative is to

convert damages to these habitats to “wetland equivalents.” Compensatory restoration actions for

marine oil spills often include activities that restore, create, or enhance saltmarshes. Thus, if inkind restoration is not feasible, saltmarsh restoration is the likely second-best option. Habitat

conversions have been conducted in the past in natural resource damage cases (Penn, 1999;

Hampton, 1999). However, very little data are available on the appropriate methods for

conducting habitat conversions. The approach of Penn (1999) develops wetland equivalents for

damage to species by estimating the marsh production that would support an equivalent biomass

of based on the efficiency of transfer across trophic levels. An approach used in California

develops an index on the basis of productivity, species diversity, and the presence of any

endangered species (Hampton, 1999). Sufficient ecological data for conducting such an index on

a national level is not available.

We assume that saltmarshes would produce the highest value using an ecological habitat index.

Saltmarshes are the most ecologically productive coastal habitats. (See Volume IV of French, et

al., 1997.) Freshwater and saltwater wetlands provide habitat to 43 percent of all federally listed

endangered and threatened species (National Wildlife Federation, 2000). Further, biological

diversity is often quite high in wetland habitats and much of the historical wetlands in the United

States have been lost to development (Zedler, 1992).

Assuming that out-of-kind compensatory restoration for a coastal habitat could be converted to

saltmarsh restoration with a ratio greater than 1:1 implies that the compensatory restoration costs

for other habitats should not exceed the costs of wetland restoration. Thus, the maximum low

cost estimate for habitat restoration is $10,000 per acre and the maximum high cost estimate is

$50,000 per acre.

The one restoration project that provides a cost estimate related to mud flat restoration (Muncy,

et al., 1996) indicates a cost of about $30,000 per acre. This figure is used as the high cost

estimate for mud flat restoration. The mollusk habitat restoration costs provided by (EG&G,

1996) average around $3,000 per acre. In light of the habitat restoration costs for other habitats

(particularly mangroves and macrophyte beds), this appears to be a low estimate. Thus, $3,000

is chosen as the low restoration estimate for mollusk restoration. Muncy, et al. (1996) provide an

estimate of sand beach restoration of $60,000 per acre but the actual amount of beach habitat

affected by the project may be higher (implying a lower cost per acre). Considering this

uncertainty and the limit assumed by the costs of wetland restoration, a high cost estimate for

sand beach restoration of $40,000 per acre is chosen. The cost range for beach renourishment of

$66,000 to $115,000. Restoration from an oil spill ought to run less than half that range. This

implies that beach restoration costs should be less than $33,000-$57,500. Table 6.7. indicates

that beach restoration costs are $7,000 (low estimate) to $40,000 (high estimate) per acre. The

coastal dune restoration costs of Pickart, et al. (1998) are lower (around $12,500 per acre) but do

not necessarily reflect the lowest possible sand beach restoration costs. Schiel and Foster (1992)

report cost estimates for rocky shore restoration of over $1,400/acre. Considering the value

range for other habitats, this likely represents a low cost estimate. A low value of $3,000 per

acre is chosen as the low estimate for rocky shore restoration. The very high coral reef
138 Section 6: Ecological Service Losses

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restoration estimates presented in Section 6.3.9. represent the cost of construction after a ship

grounding. Our analysis only considers the effects of an oil spill, not a ship grounding. As noted

by Maragos (1992) the costs of coral reef restoration are likely to be high. French, et al. (1997)

note that the recovery period for coral reefs is approximately 30 years longer than for any other

habitat type. Rinkevich (1995) also notes that coral reef recovery is a long process with recovery

periods from less than 10 years for minor damage up to 50 years or more for more serious

damage. Based on this evidence, we assume that the restoration costs of coral reefs are similar to

those of saltmarshes. Both habitat types are highly diverse, ecologically sensitive, and slow to

recover after an impact.

For the remaining restoration estimates, no cost data are available. Thus, values were assumed

based on the range of costs for other habitats. For saltmarsh, mangrove, and macrophyte beds,

the high estimates are about 5-7 times the low estimates. Assuming an approximate 6:1 ratio

between the high and low cost estimates, high estimates were obtained for rocky coast and

mollusk reefs and low estimates were obtained for sand beach and mud flat restoration. No data

were available for gravel beach restoration. We assume that gravel beach restoration is

ecologically most similar to rocky shore and sand beach restoration. The chosen values for

gravel beach restoration represent the median value of rocky shore and sand beach restoration.

6.4 Credit and Debit Calculations

6.4.1 Conceptual Issues

The concept of compensating for interim (until recovery to baseline) natural resource damages

with ecological restoration projects is discussed in NOAA (1999) and Chapman, et al. (1998).

There are four steps to performing a habitat equivalency analysis (HEA):

1. Quantifying the losses from the injury;

2. Quantifying the gains from the habitat replacement project;

3. Determining the size of the replacement project;

4. Calculating the cost of the replacement project.

The ecological losses and gains must be measured using the same metric. In our analysis, acreyears of service loss is used as the common metric. As discussed elsewhere in this report, the

NRDAM/CME estimate of coastal oiling, measured in terms of square-meter days, is used to

estimate the ecological service loss of the oil spill scenarios. The conversion between squaremeter days and acre-years is a straightforward calculation. Ecological service losses are not

discounted as discussed in Section 3.4.3.

The benefits of compensatory restoration projects occur in the future and thus need to be

discounted to present values. A rate of 3% is common in natural resource damage cases (NOAA,

1999) and is used in this project.

Compensatory restoration projects typically take some time to reach maturity and are designed to

last a specified period of time. The actual ecological services provided by a compensatory

restoration project vary over time. The service value of a project will increase until it reaches
Section 6: Ecological Service Losses 139

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maturity and then decrease over time. Little data are available on the ecological service values

of restored habitats over time. We use data presented in Tomasi and Penn (1999) for information

on the service value of restoration projects. Their analysis was used to estimate the HEA

saltmarsh credit in the Lake Barre oil discharge. They base their calculations on a 25-year

restoration project that matures in three years with a linear functional form. After maturity, the

project is assumed to linearly decrease in service such that the service value is zero at the end of

the project life span. They assume a 3% rate of discount. Their calculations indicated that each

acre of created marsh would provide 8.46 discounted acre-years of ecological service. We base

our HEA credit calculations on this value. No estimates were available for other ecological

habitats; we assume this value applies to all habitats.

Perhaps the most difficult procedural decision in a generalized HEA is the compensation ratio

between the damaged habitat and the compensatory restoration. In-kind compensatory

restoration projects normally do not provide a level of ecological services equal to those

provided by the damaged habitat prior to damage. For example, restored wetlands normally do

not achieve the same level of ecological function as natural wetlands (A.T. Kearney, Inc., 1991).

Experiments by Zedler and Callaway (1999) indicate that constructed wetlands do not compare

with natural wetlands in terms of soil organic matter, soil nitrogen, plant density, and other

characteristics. There is also uncertainty concerning whether the restored habitat projects will be

successful. Another factor is that the habitat to be restored rarely starts at 0% functionality.

Compensation ratios for wetland mitigation banks range for 1.5:1 up to 10:1 (Environmental

Law Institute, 1994). Similar to A.T. Kearney, Inc. (1991), we assume that compensatory

restoration projects will, at maturity, provide half the ecological services of the damaged

habitats. In other words, we assume a compensation ratio of 2:1. This reflects the assumptions

that the initial ecological services of the habitat are not zero and the functionality of the mature

restored habitat will not equal the baseline functionality of the damaged habitat. Applying this

ratio to the value of 8.46 discounted acre-years of service for each acre of restored habitat means

that each acre of restored habitat provides the equivalent of 4.23 acre-years of ecological service.

Bird, mammal, and reptile losses are based on individuals killed. Discounted debit/credit

calculations are not required for species impacts. One simply obtains the output from the

NRDAM/CME for the number of individuals killed for each of the three species categories and

multiplies these values by the compensation cost estimates in Table 6.7.

6.4.2 Debit and Credit Calculations

This section describes the steps taken to calculate the ecological damages as a result of a

hypothetical oil spill. These damages are measured using the ecological metric of acre-years of

service losses to specific habitats. These damages represent an ecological debit that will be

equated to ecological credit through compensatory restoration actions. The NRDAM/CME

produces the amount of ecological service loss (measured in square meter-days) due to lethal

concentrations of oiling. Square meter-days are converted to acre-years by multiplying by

0.00000068 to estimate the ecological injury.

Table 6.8 illustrates a compensatory restoration debit/credit example. Assume that a

hypothetical oil spill injuries both saltmarsh and sand beach habitats by the amounts indicated in
140 Section 6: Ecological Service Losses

OECM Volume 2.doc 03/24/01 11:15 AM

Table 6.8. The injury produced by the NRDAM/CME in terms of square meter-days is easily

converted to acre-years of service loss. As described above, each acre of restored habitat

provides 4.23 acre-years of service value relative to baseline conditions. Thus, dividing the

service loss (debit) measured in acre-years by 4.23 produces the number of acres of habitat that

must be restored to cancel the debit. Once the required compensation is determined, the costs of

restoration are obtained from Table 6.7. In the example in Table 6.8, compensation requires the

restoration of 0.40 acres of saltmarsh and 8.64 acres of sand beach. The total restoration cost for

both habitats ranges from $64,480 to $365,600.
Section 6: Ecological Service Losses 141

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Development and Marine Oil Spills: A General Purpose Model”. Report submitted to the

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Chapman, David, Nicholas Iadanza, and Tony Penn. 1998. “Calculating Resource

Compensation: An Application of the Service-to-Service Approach to the Blackbird Mine,

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Charlier, R.H., Chr. P. De Meyer, and D. Decroo. 1989. “Beach Protection and Restoration, Part

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Finley, Heather, Jim Hanifen, Chris Piehler, Linda Pace, John Kern, Mike Devany, Tim Osborn,

Cheryl Scannell, Brian Julius, and Eli Reinharz. 1995. “Damage Assessment and Restoration

Plan and Environmental Assessment – Wellhead Failure and Release, Dixon Bay, Louisiana,

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Administration, and U.S. Department of the Interior. 1997. “Damage Assessment and

Restoration Plan/Environmental Assessment for the August 10, 1993 Tampa Bay Oil Spill,

Volume 1 – Ecological Injuries”. Final Version.

Fonseca, Mark S., W.J. Kenworthy, F.X. Courtney, and M. O. Hall. 1994. “Seagrass Planting in

the Southeastern United States: Methods for Accelerating Habitat Development”. Restoration

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French, Deborah. 2000. Personal communication. Applied Science Associates, Narragansett,

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Tatsu Isaji, Suzanna Puckett, Aimee Keller, Fred W. French III, Dian Gifford, John McCue,

Thomas Opishinski, Garry Brown, Emily MacDonald, Jacqueline Quirk, Stefan Natzke,
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Bradley S. Ingram, Richard Bishop, Michael Welsh, and Marty Phillips. 1997. “The

CERCLA Type A Natural Resource Damage Assessment Model for Coastal and Marine

Environments (NRDAM/CME)”. Prepared for the Office of Environmental Policy and

Compliance, U.S. Department of the Interior, contract no. 14-01-0001-91-C-11.

Frid, C.L.J., W.U. Chandrasekara, and P. Davey. 1999. “The Restoration of Mud Flats Invaded

by Common Cord-grass (Spartina anglica, CE Hubbard) Using Mechanical Disturbance and

Its Effects on the Macrobenthic Fauna”. Aquatic Conservation: Marine and Freshwater

Ecosystems, vol. 9(1):47-61.

Hampton, Steve. 1999. Personal communication. California Department of Fish and Game,

Sacramento, CA.

Hawkins, S.J., J.R. Allen, and S. Bray. 1999. “Restoration of Temperate Marine and Coastal

Ecosystems: Nudging Nature”, Aquatic Conservation: Marine and Freshwater Ecosystems,

vol. 9(1):23-26.

Institute for Water Resources, U.S. Army Corps of Engineers. 1994. “National Wetland

Mitigation Banking Study, Wetland Mitigation Banking: Resource Document”. IWR Report

94-WMB-2.

Julius, Brian E. 1997. “U.S. vs. Melvin Fisher, et al”. National Oceanic and Atmospheric

Administration, Silver Spring, MD.

Julius, Brian E., Erik Zobrist, J. Harold Hudson, and Walter Jaap. 1997. “Restoration and

Monitoring Plan M/V Jacquelyn L Grounding Site, Western Sambo Reef, FKNMS, July 7,

1991”.

Julius, Brian E., John W. Iliff, Charles M. Wahle, J. Harold Hudson, and Erik C. Zobrist. 1995.

“Natural Resource Damage Assessment M/V Miss Beholden Grounding Site, Western

Sambo Reef, FKNMS, March 13, 1993”.

Kern, John, Brian Julius, John Iliff, Stephanie Fluke, LisaMarie Frietas, Heather Finley, Jim

Hanifen, Chris Piehler, and Linda Pace. 1999. “Damage Assessment and Restoration Plan

and Environmental Assessment, M/V Formosa Six Ethylene Dichloride Discharge, Gulf of

Mexico, Louisiana, April 11, 1997’. Final Version.

King, Dennis, and Curtis Bohlen. 1994. “Estimating the Cost of Restoration”. National Wetlands

Newsletter, vol. 16(3):3-8.

Liebsman, Lawrence R., and David M. Plott. 1996. “Assessing Private Wetlands Mitigation

Banking”, Urban Land, vol. 55(6):31-35,62.

Louisiana Oil Spill Coordinators Office, Louisiana Department of Environmental Quality,

Louisiana Department of Natural Resources, Louisiana Department of Wildlife and Fisheries,

National Oceanic and Atmospheric Administration, and U.S. Fish and Wildlife Service.
Section 6: Ecological Service Losses 143

OECM Volume 2.doc 03/24/01 11:15 AM

1999. “Damage Assessment and Restoration Plan, Texaco Pipeline, Inc. Crude Oil

Discharge, Lake Barre, Louisiana, May 16, 1997”, Final Version.

Makah Indian Tribe, State of Washington, U.S. Department of Commerce, and U.S. Department

of the Interior. 1999. “Draft Restoration Plan and Environmental Assessment for the Tenyo

Maru Oil Spill”. Public Review Draft.

Maragos, James E. 1992. “Restoring Coal Reefs with an Emphasis on Pacific Reefs” in Thayer,

G.W. (ed.), Restoring the Nations Marine Environment, Maryland Sea Grant College,

University Park, MD.

Miller, Gregory B. 1994. “Coastal Habitat Restoration Planning in Louisiana: Lessons from the

Greenhill-Tmibalier Bay Oil Spill Case”. Coastal Management, vol. 22:413-420.

Muncy, Joy D., J. Craig Fischenich, and E.A. Dardeau. 1996. “National Review of Corps

Environmental Restoration Projects”. IWR Report 96-R-27.

National Oceanic and Atmospheric Administration, Damage Assessment and Restoration

Program. 1999. Habitat Equivalency Analysis: An Overview,

http://www.darp.noaa.gov/pdf/heaoverv.pdf

National Oceanic and Atmospheric Administration, Rhode Island Department of Environmental

Management, U.S. Department of the Interior, and U.S. Fish and Wildlife Service. 1999.

“Restoration Plan and Environmental Assessment for the January 19, 1996 North Cape Oil

Spill”, Revised Draft.

National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Office of

Habitat Protection Restoration Center. 1996. “Final M/V World Prodigy Oil Spill Restoration

Plan and Environmental Assessment, Narragansett Bay, Rhode Island”.

National Wildlife Federation. 2000. Wetland Benefits.

http://www.nwf.org/wetlands/facts/benefits.html

The Natural Resource Trustees for Pearl Harbor, Oahu, Hawaii. 1999. “Final Restoration Plan

and Environmental Assessment for the May 14, 1996 Chevron Oil Spill into Waiau Stream

and Pearl Harbor, Oahu, Hawaii”.

New Jersey Office of Natural Resource Damages. 1996. “Natural Resources Restoration Plan for

Damages Associated with the Presidente Rivera Oil Spill of June 1989’.

Penn, Tony. 1999. “Summary of Faunal Injury and Restoration Requirements, Memorandum to

Administrative Record for the Lake Barre Incident”. National Oceanic and Atmospheric

Administration, Silver Spring, MD.

Pickart, Andrea J., Karen C. Theiss, Howard B. Stauffer, and Garret T. Olsen. 1998. “Yellow

Bush Lupine Invasion in Northern California Coastal Dunes, II. Mechanical Restoration

Techniques”, Restoration Ecology, vol. 6(1):69-74.
144 Section 6: Ecological Service Losses

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Rinkevich, Baruch. 1995. “Restoration Strategies for Coral Reefs Damaged by Recreational

Activities: The Use of Sexual and Asexual Recruits”, Restoration Ecology, vol. 3(4):241-

251.

Schiel, D.R. and M. Foster. 1992. “Restoring Kelp Forests” in Thayer, G.W. (ed.), Restoring the

Nations Marine Environment, Maryland Sea Grant College, University Park, MD.

Shreffler, David K., Michael J. Scott, Katharine F. Wellman, and Mark Curran. 1995. “National

Review of Non-Corps Environmental Restoration Projects”. IWR Report 95-R-12.

Texas General Land Office, Texas natural Resource Conservation Commission, Texas Parks and

Wildlife Department, U.S. Fish and Wildlife Service, and National Oceanic and Atmospheric

Administration. 1997. “Restoration Plan/Environmental Assessment for the Apex Barges Oil

Spill, July 28, 1990”.

Thorhaug, Anitra. 1990. “Restoration of Mangroves and Seagrasses – Economic Benefits for

Fisheries and Mariculture” in Environmental Restoration: Science and Strategies for

Restoring the Earth, John J. Berger, editor. Island Press, Washington, D.C.

U.S. Fish and Wildlife Service. No date given. “Restoration of Nearshore Breeding Seabird

Colonies on the Central California Coast: Final Plan”.

World Dredging, Mining, and Construction (no author given). 1997. “East Coast Beach

Restoration Projects for T. L. James”, World Dredging, Mining, and Construction, vol.

33(4):6-7,20.

Zedler, Joy B. 1992. “Restoring Cordgrass Marshes in Southern California” in Thayer, G.W.

(ed.), Restoring the Nations Marine Environment, Maryland Sea Grant College, University

Park, MD.

Zedler, Joy B., and John C. Calloway. 1999. “Tracking Wetland Restoration: Do Mitigation Sites

Follow Desired Trajectories?”, Restoration Ecology, vol. 7(1):69-73.
Section 6: Ecological Service Losses 145

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Table 6.1. Lake Barre Estimates of Marsh Service Losses

 Degree of Oiling

Initial Service

Loss

Percentage

Acres

Affected

Discounted

Total Loss

 Recovery

Time

Light w/rapid recovery 10% 4,165 41.9 acreyearsFour months

Heavy w/moderate

recovery

40% 154 26.5 acreyearsTwo years

Heavy w/slow to

moderate recovery

75% 8 4.6 acreyearsTwo years

Heavy w/slow recovery Near 100% 0.28 2.6 acreyearsTwenty years

Source: Louisiana Oil Spill Coordinators Office, et al., 1997
146 Section 6: Ecological Service Losses

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Table 6.2. North Cape Oil Spill Injuries and Compensatory Restoration

Injured

Resource/Service Damage RestorationAction(s) Cost

Offshore

Lobsters 9,039,200 killed Restocking and

protection $9,915,625

Surf clams 19,402,300 killed Shellfish

restoration $5,954,110

Other marine benthic

organisms

535,00 kg biomass

killed

Fish 4,200,000 killed

Land acquisition

and shellfish

restoration

Included in shellfish

restoration and land

acquisition

Salt Ponds

Worms/amphipods 66,000 kg biomass

killed

Crabs and shrimp 642,000 killed

Forage fish 533,400 killed

Winter flounder 1,600 killed

Land acquisition $1,782,500

Softshell clams and

oysters

648,500 killed Shellfish

restoration

Included in shellfish

restoration

Birds

Piping plovers Lost production of

5-10 fledged chicks

Plover protection $232,706

Loons 402 killed, 3,641

loon-years

Loon habitat

protection

$7,485,170

Marine birds other

than loons

1,680 killed, 3,254

bird-years

Marine bird

habitat

protection

$631,250

Pond birds 198 killed, 476 kg

biomass killed

Land acquisition Included in land

acquisition

Human Use

Party and charter boat

fishing

3,305 lost trips, lost

value of $281,685

Anadromous fish

runs and shore

access

$281,685

Project Oversight $1,314,152

Total $27,597,198

Source: National Oceanic and Atmospheric Administration, et al., 1999
Section 6: Ecological Service Losses 147

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Table 6.3. Damages and Preferred Restoration Alternatives, Tampa Bay Oil Spill

Habitat Damage Primary

Restoration

Compensatory

Restoration

Mangroves 5.5 acres of moderate or

heavy oiling

Stabilization Create or enhance

nearby mangrove

habitat

Seagrass beds 2.5 acres of moderate or

heavy oiling

Natural recovery Water quality

improvement projects

Water column Oil slick of 327 square

miles

Natural recovery Installation of artificial

reefs and/or water

quality improvement

Birds 732 injured birds Rehabilitation None

Sea turtles 2,177 injured and 212

killed

Nest monitoring

and protection

and/or recovery

projects

None

Salt marshes 0.85 acres oiled Natural recovery Enhancement of salt

marsh vegetation

Shellfish beds 0.22 acres of heavy oiling Substrate

replacement

Creation of oyster

beds or water quality

improvement

Bottom

sediments

1.34 acres oiled Natural recovery Water quality

improvement projects

Sand loss 13 miles oiled Sand replacement None

Source: Florida Department of Environmental Protection, et al., 1997
148 Section 6: Ecological Service Losses

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Table 6.4. Wetland Construction Restoration Alternatives for the Apex Barges Oil Spill

(1997 dollars)

Alternative Description Total Cost Cost/Acre of

Wetland

Swan Lake Construct a rock barrier

and establish a 20-acre

wetland

$1.1 million $55,000

Galveston Bay Construct wave barriers,

plant wetland species, and

follow-up monitoring

Not given $29,900

Pierce Marsh Build levees and plant

wetland species

$207,000 $6,100

Galveston Island

State Park

Construction of waveprotection berms$537,000 $17,900

Interstate 45

highway corridor

Dredging to enhance

habitat

$350,000 $7,000

San Jacinto State

Park

Filling with dredge

material and planting

wetland species

$139,100 $3,478

Source: Texas General Land Office, et al., 1997
Section 6: Ecological Service Losses 149

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Table 6.5. Saltmarsh Restoration Project Costs (1999 dollars)

Restoration Method Size

(Acres)

Cost

($/ac.) Source

Dredge disposal with minimal planting 581 405 EG&G, 1996

Dredge disposal & excavation w/ min.

planting

672 3,118 EG&G, 1996

Dredge disposal & excavation w/ min.

planting

764 3,114 EG&G, 1996

Dredge disposal with minimal planting 99 4,529 EG&G, 1996

Tidal gates and regrading 917 1,167 EG&G, 1996

Excavation and dike breaching 225 2,504 EG&G, 1996

Dredge disposal 610 8,368 EG&G, 1996

Excavation and dike breaching 1,374 1,286 EG&G, 1996

Excavation and tidal gate 79 10,383 EG&G, 1996

Excavation and dike breaching 37 8,763 EG&G, 1996

Excavation; flooding problems encountered 91 5,771 EG&G, 1996

Excavation and water control structures 946 828 EG&G, 1996

Minimal excavation 1,342 233 EG&G, 1996

Dredge disposal and protective structures 20 33,908 EG&G, 1996

Dredge disposal 20 19,779 EG&G, 1996

Dredge disposal 12 8,579 EG&G, 1996

Excavation and tidal gate 6,103 1,079 EG&G, 1996

Excavation and tidal gate 6,103 6,047 EG&G, 1996

Excavation and tidal gate 709 13,032 EG&G, 1996

Single-channel restoration 34 53,733 Muncy, et al., 1996

Double-channel restoration 34 65,110 Muncy, et al., 1996

Dike creation and barrier island restoration 40 28,969 Muncy, et al., 1996

Levee construction 31 30,722 Muncy, et al., 1996

Dredging 90 12,579 Muncy, et al., 1996

Barrier island restoration 105 59,749 Muncy, et al., 1996

Dredge disposal 445 680 Muncy, et al., 1996

Dredge disposal 415 2,348 Muncy, et al., 1996

Dredge disposal and dam construction 184 27,105 Muncy, et al., 1996

Excavation and grading 10 449,839 Shreffler, et al., 1995

Excavation and planting 5 110,329 Shreffler, et al., 1995

Dike removal 208 429 Shreffler, et al., 1995

Culvert installation and excavation 7 24,176 Shreffler, et al., 1995

Tree disposal and brush fence installation 16 13,362 Shreffler, et al., 1995

Dredge disposal and planting 22 51,800 Miller, 1994

Culvert installation and channel

modification

11 7,723 NOAA, 1996

Excavation 20 21,236 NJONRD, 1996
150 Section 6: Ecological Service Losses

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Table 6.6. Natural Resource Damages for Bird Kills

 Spill

 Dead

Birds

Recovered

Estimated

Total Bird

Kill

Natural Resource

Damages for

Birds (1999 $’s)

Natural

Resource

Damage per

Bird Killed

Puerto Rican

(1984)

1,368 4,815 $1,100,000 $228

Apex Houston

(1986)

4,198 10,577 $8,100,000 $766

Exxon Valdez

(1989)

Unknown 250,000 $900,000,000 $3,600

American Trader

(1990)

914 5,000 1 $3,400,000 $680

Cape Mohican

(1996)

257 4,000 $820,000 $205

North Cape (1996)

– Loons

67 402 $7,485,170 $18,620

North Cape (1996)

– Other birds

338 1,680 $631,250 $376

 Command (1998) 177 3,000 $4,000,000 $1,333

1 – Estimated based on an average expansion factor (total birds killed/birds recovered) of about

five.

Sources: Hampton, 1999; National Oceanic and Atmospheric Administration, et al., 1999
Section 6: Ecological Service Losses 151

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Table 6.7. Final Compensatory Restoration Costs

Habitat/Species

Type Low Estimate High Estimate

 Rocky shore $3,000/acre $20,000/acre

 Gravel beach $5,000/acre $30,000/acre

 Sand beach $7,000/acre $40,000/acre

 Mud flats $5,000/acre $30,000/acre

 Saltmarsh $10,000/acre $50,000/acre

 Mangrove $3,000/acre $20,000/acre

 Macrophyte beds $4,000/acre $30,000/acre

 Coral reef $10,000/acre $50,000/acre

 Mollusk reef $3,000/acre $20,000/acre

 Birds $500/individual $2,500/individual

 Mammals $13,000/individual $74,000/individual

 Reptiles $2,800/individual $9,300/individual
152 Section 6: Ecological Service Losses

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Table 6.8. Credit/Debit Example

Variable Saltmarsh

Habitat

Sand Beach

Habitat

Debit Calculations

NRDAM/CME service loss (square meterdays)2.50*106 5.40*107

NRDAM/CME service loss (acre-years) 1.69 36.56

Credit Calculations

Credit per restored acre (acre-years) 4.23 4.23

Required restoration (acres) 0.40 8.64

Cost (low estimate) $4,000 $60,480

Cost (high estimate) $20,000 $345,600
Section 6: Ecological Service Losses 153

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1. Name Cost Categories and Assign Associated Impact Driver Variables

Cost Name

Impact Driver

BBL Spilled 

(plat/pipe)

BBL Spilled 

(tanker)

BBL Spilled 

(tanker)

BBL Spilled 

(plat/pipe)

BBL Spilled 

(tanker)

None

2. Specify Cost Coefficients $ / BBL $ / BBL $ / BBL $ / BBL $ / BBL N/A

North Atlantic - - $ 1,086 $ 670

Mid-Atlantic - - $ 679 $ 301

South Atlantic - - $ 5 $ 4

Straits of Florida $ 177 $ 73 $ 356 $ 187

G Eastern Gulf $ 0 $ 1 $ 50 $ 40

o Central Gulf $ 81 $ 17 $ 207 $ 83

M Western Gulf $ 12 $ 51 $ 84 $ 41

Southern California $ 16 $ 3 $ 664 $ 189

Central California $ 140 $ 26 $ 2,441 $ 264

Northern California $ 12 $ 1 $ 143 $ 27

Washington/Oregon $ 112 $ 19 $ 356 $ 186

Gulf of Alaska $ 32 $ 37 $ 692 $ 258

Cook Inlet $ 6 $ 0 $ 730 $ 164

A Kodiak $ 53 $ 22 $ 1,004 $ 459

Shuimagin $ 4 $ 26 $ 2,372 $ 661

L North Aleutian Basin $ 71 $ 28 $ 380 $ 89

Aleutian Arc - - $ 944 $ 998

A St. George Basin $ 63 $ 13 $ 5,000 $ 1,506

St. Matthew Hall - - $ 3,516 $ 785

S Bowers Basin - - $ 2,989 $ 765

Aleutian Basin $ 238 $ 147 $ 3,235 $ 944

K Navarin Basin - - $ 3,809 $ 1,570

Norton Basin - - $ 2,196 $ 620

A Hope Basin - - $ 3,935 $ 1,125

Chukchi Sea $ 0 $ 4 $ 1,565 $ 682

Beaufort Sea $ 448 $ 147 $ 1,166 $ 305

18 17 17 18 17 21

3. Specify the dollar-year of cost coefficients 1999

A

T

L

P

A

C

Coastal Habitat Wildlife Deaths

Figure 6.1. Ecological Service Losses OECM Impact Drivers

These are found on the Ecology cost element worksheet.

Section 7: Commercial Fisheries 155

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7. Commercial Fishing Damages

7.1 Introduction

OCS activities can cause economic damages to commercial fishing in three ways. First, routine

development and production can cause commercial fishing ground preemption. Second, OCS

facilities can damage commercial fishing gear. Third, oil spills can reduce the value of

commercial fishing landings. In this section, we estimate the economic damages for each of

these injuries.

The methodology for estimating these impacts is essentially identical to that described in A.T

Kearny (1991). The methods are summarized briefly in the following sections; more detail is

available in A.T. Kearny (1991) – Section 6.0 for damages due to routine development and

production, and Section 11 for oil spill damages to commercial fishing. The results are updated

to reflect more recent data on the value of commercial fishing landings.

7.2 Fishing Ground Preemption Catch Loss

7.2.1 Fishing Ground Preemption Damage Methodology

Commercial fishing for sea floor (benthic) fish is preempted in the vicinity of oil platforms and

pipelines. Similar to A.T. Kearney (1991), we assume that a half-mile radius no-fishing zone

surrounds each platform, equal to 0.785 square miles. Also, for bottom trawls (the most

common method used to fish for benthic species), we assume a 0.25-mile preempted zone on

either side of a pipeline. Thus, each mile of pipeline preempts 0.5 square miles of fishery area.

By dividing the amount of fishing ground preempted by the total area of a fishery, we obtain the

proportion of the fishery affected. However, the proportion reduction in the value of commercial

fishing landings is not equal to the proportion of the fishery area preempted. The reason is that

fish are mobile. Fish move in and out of the preempted area. Thus, the proportional reduction in

the value of commercial fishing landings is expected to be less than the proportion of the fishery

area preempted.

A.T. Kearney (1991) used a mathematical model to develop catch loss parameters, by species

and OCS Planning Area, to account for the mobility of fishery species. These parameters

estimate a scaling factor to link the proportion reduction in the value of landings to the

proportion of area preempted. The catch loss parameters vary from 0.002 to 0.1. We use these

catch parameters in our analysis. The catch loss parameters are presented in Table 7.1.

Not all OCS platforms and pipelines will be located within commercial fishery areas. A.T.

Kearney (1991) estimated the probability that a platform or pipeline will be within a commercial

fishery, by species and OCS Planning Area. These probabilities range from zero to one. We use

these probabilities in our analysis (Table 7.1).

For each platform constructed in an OCS Planning Area, the catch loss damages are estimated as:

Σs [(Vs*0.785*Cs*PFs)/As] for all s species in the OCS Planning Area
156 Section 7: Commercial Fisheries

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For each mile of pipeline constructed in an OCS Planning Area, the catch loss damages are

estimated as:

=

S

s s

s s s

A

V C PL

1

* *0.5*

for all s species in the OCS Planning Area

Where:

Vs = Landed value of species s in the fishery

As = Area of the fishery, by species

Cs = Catch loss parameter, by species

PFs = Probability that platform is located in fishery, by species

PLs = Probability that pipeline is located in fishery, by species

Catch losses are estimated for benthic species. Free-swing species (pelagic fishes) are not

included because the presence of OCS facilities is not likely to reduce their catch.

The ex-vessel price rather than the higher retail price is used to value catch loss. This is

consistent with regional economic theory. Counting the retail value would assume that the

additional production input costs needed for retail production were expended and that the value

added by these inputs was lost. In reality, the market adjusts to the reduced local catch by

obtaining fish from other areas or by redirecting retail inputs to other goods. Thus neither the

retail costs nor the retail profits from the sales of lost catch are likely to change due to catch loss

from OCS activities.

7.2.2 Fishing Ground Preemption Data Sources

Values of commercial fishing landings were obtained from the National Marine Fisheries

Service. For the contiguous United States, landings data are available over their web site

(NMFS, 2000). Queries were conducted by species, year, and state. State-level data were

organized into OCS Planning Areas.

The values of landings are based on an average over several years to obtain representative data.

The value of landings is averaged for the period 1994 to 1998 except for the Alaska OCS

Planning Areas. For Alaska OCS Planning Areas, the landing values are averaged over 1995 to

1998 because the 1994 data were not available. All values of landings were updated to 1999

dollars using the Consumer Price Index.

Values for Alaska landings are obtained by species from NMFS form 88-50. The NMFS divides

Alaska commercial fishing data into two regions, the Gulf of Alaska and the Bering Sea/Aleutian

Islands. Because the landings are reported for only two Alaska areas, we needed to divide the

value of landings among the 15 Alaska OCS Planning Areas. Additional data were obtained

from the Alaska Office of the NMFS that presents values of landings for 20 Alaska ports but not

by species. We divide the port-specific total landings value into species categories based on the
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species-specific data for either the Gulf of Alaska or the Bering Sea/Aleutian Islands regions. In

other words, we calculated the value proportion for each species in the Gulf of Alaska and the

Bering Sea/Aleutian regions and used these proportions to divide the port-specific total values of

landings. The Alaska ports were mapped into OCS Planning Areas. Several ports service more

than one OCS Planning Area. In these cases, the port-specific data were divided equally

amongst each nearby OCS Planning Area.

7.2.3 Fishing Ground Preemption Damage Estimates

The catch loss calculation and results are presented in Table 7.1. For platforms, the proportional

reduction in landings value is calculated as the area preempted around each platform (0.785

square miles) multiplied by the catch loss parameter and the probability a platform will be

located in the fishery divided by the area of the fishery. For pipelines, the proportional reduction

in landings value is calculated as the area preempted around each mile of pipeline (0.5 square

miles) multiplied by the catch loss parameter and the probability a pipeline will be located in the

fishery divided by the area of the fishery.

The catch loss damages per platform range from $2 per platform in several Alaska OSC Planning

Areas to $4,453 in the North Atlantic Planning Area (Table 7.1). The catch loss damages per

mile of pipeline range from $1 in several Alaska OCS Planning Areas to $838 in the North

Atlantic Planning Area.

7.3 Gear Loss Damage Estimates

7.3.1 Gear Loss Damage Methodology

Gear loss is estimated using a methodology similar to the catch loss methodology. Gear loss

damages are estimated on a per-platform basis using the methodology described in A.T. Kearney

(1991). A mathematical model was presented that obtains a "gear damage parameter" as a

function of fishing density, repair costs, and several other factors. This parameter divided by the

area of the fishery provides the proportional reduction in landings value due to gear loss. This

proportion is then multiplied by the portion of OCS activity that occurs in a fishery. Similar to

the catch loss methodology, the analysis is divided by species. The affected species are

described in A.T. Kearney (1991).

The gear loss formula, by OCS Planning Area, is the sum of damages to all affected speciesspecific fisheries:

Σs[(Vs*GDs*Ps)/As] for all s species in the OCS Planning Area

Where:

Vs = Landed value of species s in the fishery

As = Area of the fishery, by species

GDs = Gear damage parameter, by species

Ps = Probability that OCS activity is located in fishery, by species
158 Section 7: Commercial Fisheries

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7.3.2 Gear Loss Data Sources

Data from A.T. Kearney were used for As, GDs, and Ps. These parameters are considered

applicable because fishery locations and commercial fishing technology has not changed

appreciably in recent years. The parameters are presented in Table 7.2.

As described in Section 7.2.2. above, more recent data on values of landings were obtained from

the National Marine Fisheries Service. See Section 7.2.2. for a description of the steps taken to

obtain values of landings by species and by OCS Planning Area. Values of landings are

presented in Table 7.2.

7.3.3 Gear Loss Damage Estimates

Gear loss damages per platform by OCS Planning Area are presented in Table 7.2. Damages per

platform range from $84 in the Cook Inlet Planning Area to $15,233 in the North Atlantic

Planning Area.

Table 7.3. presents a summary for the fishing ground preemption and gear loss damage

estimates. The total commercial fishing damage per platform is presented in the last column as

the sum of the fishing ground preemption and gear loss damage estimates.

7.4 Oil Spill Damages

7.4.1 Oil Spill Damage Methodology

The methodology for estimating oil spill damages to commercial fishing closely parallels the

methodology used to estimate recreational fishing spill-related damages. Coastal oiling is

assumed to injure shore-based commercial fisheries. Both coastal oiling and offshore oil slicks

are assumed to injure offshore commercial fisheries. Coastal oiling impacts offshore coastal

fisheries because of harbor closure. Unlike the fishing ground preemption and gear loss

damages, oil spills may affect all commercial fishing species.

The NRDAM/CME output provides data on coastal oiling (measured in meters) and the oil slick

area (measured in square meters). As discussed in Section 3.4.2.3, these values are adjusted on

the basis of the supplementary SIMAP analysis. We assume similar closure periods for

commercial fishing and recreational fishing. Thus, the harbor closure period is assumed to be 10

days and the fishery closure period for coastal oiling or oil slicks is assumed to be 60 days (see

Section 3.4.4.).

While recreational fishing is assumed to occur within 30 miles of the coast, commercial fishing

occurs further offshore. Virtually all of the NMFS commercial fishing landings data is based on

fishing activities within 200 miles of the coast. This distance corresponds to the furthestoffshore oil spill scenario (see Table 3.1). Thus, we assume that the commercial fishing area

within each OCS Planning Area extends out to 200 miles and that all oil spill scenarios affect

commercial fishing either through coastal oiling or the presence of oil slicks. The commercial

fishing area in each OCS Planning Area (in square miles) is calculated as the facing miles of
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coast multiplied by 200 miles. This value is converted to square meters by multiplying by 1,609

squared.

For shore-based fisheries, the injury driver for each spill scenario is the meters of coastal oiling

obtained from the NRDAM/CME output. This is multiplied by the SIMAP adjustment factor of

0.54 and multiplied by the 60-day fishery closure period. This produces an estimate of the lost

meter-days of shore-based commercial fishing, which needs to be multiplied by the value of the

shore-based commercial fishery per meter-day. The value of the shore-based commercial fishery

per meter-day is the annual value of shore-based landings in the OCS Planning Area divided by

the meters of coastline and 365 days.

For offshore fisheries, two NRDAM/CME injury drivers produce damage estimates. First,

coastal oiling may cause harbor closure. Harbor closure is calculated in terms of meter-days as

the NRDAM/CME estimate of coastal oiling multiplied by the 0.54 SIMAP adjustment factor

multiplied by the 10-day harbor closure period. This is multiplied by the value of offshore

commercial fishing per meter-day equal to the annual value of offshore landings in the OCS

Planning Area divided by the meters of coastline and 365 days.

The second offshore fishing injury driver is the oil slick magnitude. The oil slick injury driver is

the NRDAM/CME oil slick estimate measured in square meters multiplied by the 1.61 SIMAP

adjustment factor multiplied by the 60-day fishery closure period. This produces an estimate of

oil slick magnitude measured in terms of square meter-days. The value of the offshore fishery

per square meter-day is calculated as the landings value for offshore fisheries in the OCS

Planning Area divided by the offshore fishery area (facing miles of coast multiplied by 200 miles

and converted to meter units). Multiplying the oil slick magnitude by the value of the offshore

fishery per square meter-day produces the damage estimate.

7.4.2 Oil Spill Damage Data Sources

For the contiguous United States, species-specific commercial fishing data were obtained from

the NMFS web site for 1994-1998. Data were averaged across years to produce representative

estimates. The NMFS landings value data are available at the state level. State-specific data

were mapped into OCS Planning Areas. Where state boundaries do not correspond to OCS

Planning Area boundaries, the appropriate states (North Carolina, Florida, and California) were

contacted to obtain county-level or port-level data. Thus, the state-level data were accurately

mapped into OCS Planning Areas.

Commercial fishing species were designated as either shore-based or offshore as indicated in

A.T. Kearney (1991). For the lower 48 States, clams, oysters, and scallops (except for sea

scallops) were considered shore-based fisheries. For the Northern California and

Washington/Oregon Planning Areas, salmon were also considered shore-based species.

For Alaska, values of commercial fishery landings were obtained as described in Section 7.2.2

above. Shore-based species in Alaska include salmon and herring. The landings value of other

shore-based species in Alaska, such as clams and oysters, is insignificant.
160 Section 7: Commercial Fisheries

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7.4.3 Oil Spill Damage Estimates

Table 7.4 presents data on the value of shore-based commercial fisheries. Table 7.5 presents

data on the value of offshore commercial fisheries. The tables present the data in the appropriate

metrics for linkage to the NRDAM/CME injury outputs. For shore-based commercial fisheries,

the landings data are divided by the coastal meters and 365 days. For offshore fisheries, a value

per square meter-day is obtained by dividing the landings value by the fishery area and 365 days

and a value per coastal meter-day is obtained by dividing the landings value by the coastal

meters and 365 days.

These values are multiplied by the appropriate NRDAM/CME injury estimates to obtain damage

estimates. Table 7.6 presents final commercial fishing spill-related damage estimates by OCS

Planning Area and spill size. For cases where multiple spill scenario locations exist in an OCS

Planning Area, the value in Table 7.6 is an average across all spill locations.
Section 7: Commercial Fisheries 161

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References

A.T. Kearney, Inc.1991. “Estimating the Environmental Costs of OCS Oil and Gas Development

and Marine Oil Spills: A General Purpose Model”. Report submitted to Minerals

Management Service under contract no. 14-35-0001-30500.

National Marine Fisheries Service. 2000. Commercial Fisheries Annual Commercial Landing

Statistics. Internet site

http://www.st.nmfs.gov/st1/commercial/landings/annual_landings.html.
162 Section 7: Commercial Fisheries

OECM Volume 2.doc 03/24/01 11:15 AM

Table 7.1. Fishing Ground Preemption Catch Loss Damage Estimates

OCS

Planning

Area

Fishery

Annual

Landings

Value

(1000 $'s)

Area of

Fishery

(sq. mi)

Catch

Loss

Parameter

Proportion of

Platforms

in Fishery

Proportion of

Pipelines

in Fishery

Catch

Loss per

Platform

Catch

Loss per

Mile of

Pipeline

Flounder $38,764 18,000 0.050 0.50 1.00 $42 $54

Other

groundfish

$28,211 8,000 0.002 0.50 1.00 $3 $4

Ocean Quahog/

surf clam

$106,941 2,000 0.100 0.30 0.10 $1,260 $267

Sea scallop $47,561 8,500 0.120 0.05 0.05 $26 $17

Lobster $238,504 3,600 0.300 0.20 0.05 $3,122 $497

North

Atlantic

TOTAL $459,981 $4,453 $838

Groundfish $7,161 23,000 0.003 0.65 1.00 <$1 <$1

Ocean quahog $7,172 21,600 0.070 0.05 0.65 $1 $8

Surf clam $27,388 25,000 0.100 0.00 0.50 $0 $27

Sea scallop $10,533 2,600 0.100 0.05 1.00 $16 $203

Lobster $34,802 6,000 0.200 0.30 1.00 $273 $580

Mid-Atlantic

TOTAL $87,056 $290 $818

Groundfish $13,456 5,000 0.050 0.60 0.60 $63 $40

Shrimp $62,911 15,000 0.005 0.05 0.50 $1 $5

South

Atlantic

TOTAL $76,367 $64 $46

Straits of

Florida

Shrimp $53,164 60,000 0.010 0.80 0.80 $6 $4

Eastern

GOM

Shrimp $55,046 60,000 0.010 0.90 0.70 $6 $3

Central

GOM

Shrimp $212,609 60,000 0.010 0.95 0.80 $26 $14

Western

GOM

Shrimp $198,951 60,000 0.010 0.95 0.90 $25 $15

Rockfish $2,318 1,200 0.150 0.30 0.40 $68 $58

Other

groundfish

$692 300 0.030 0.40 0.40 $22 $14

Southern

California

TOTAL $3,010 $90 $72
Section 7: Commercial Fisheries 163

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Table 7.1. Fishing Ground Preemption Catch Loss Damage Estimates (Continued)

OCS

Planning

Area

Fishery

Annual

Landings

Value

(1000 $'s)

Area of

Fishery

(sq. mi)

Catch

Loss

Parameter

Proportion of

Platforms

in Fishery

Proportion of

Pipelines

in Fishery

Catch

Loss per

Platform

Catch

Loss per

Mile of

Pipeline

Rockfish $7,146

600

0.150 0.45 0.50 $631 $447

Other

groundfish

$6,227

3,200

0.030 0.60 0.50 $28 $15

Central

California

TOTAL $13,373 $659 $461

Rockfish $3,283

700

0.150 0.10 0.70 $55 $246

Other

groundfish

$5,227

2,300

0.030 0.65 0.85 $35 $29

Shrimp $9,592

600

0.006 0.10 0.10 $8 $5

Northern

California

TOTAL $18,102 $98 $280

Washington/

Oregon

Groundfish $40,100

6,000

0.050 0.80 0.70 $210 $117

Gulf of

Alaska

Groundfish $12,382

10,000

0.026 0.80 0.70 $20 $11

Kodiak Groundfish $11,001

10,000

0.026 0.80 0.70 $18 $10

Cook Inlet Groundfish $1,276

10,000

0.026 0.60 0.50 $2 $1

Shumagin Groundfish $40,540

10,000

0.026 0.30 0.25 $25 $13

North

Aleutian

Basin

Groundfish $32,896

100,000

0.026 0.30 0.15 $2 $1

St. George

Basin

Groundfish $24,986

100,000

0.026 0.80 0.70 $4 $2

Aleutian Arc Groundfish $23,063

100,000

0.026 0.50 0.30 $2 $1

Bowers

Basin

Groundfish $23,063

100,000

0.026 0.40 0.30 $2 $1

Aleutian

Basin

Groundfish $23,063

100,000

0.026 0.80 0.90 $4 $3

Navarin

Basin

Groundfish $23,063

100,000

0.026 0.80 0.85 $4 $3

St. Matthew

Hall

No fisheries

affected

$0 $0

Norton Basin No fisheries

affected

$0 $0

Hope Basin No fisheries

affected

$0 $0

Chukchi Sea No fisheries

affected

$0 $0

Beaufort Sea No fisheries

affected

$0 $0
164 Section 7: Commercial Fisheries

OECM Volume 2.doc 03/24/01 11:15 AM

Table 7.2. Gear Loss Damage Estimates

OCS Planning

Area Fishery

Annual

Landings

Value

(1000 $'s)

Area of

Fishery

(sq. miles)

Gear

Damage

Parameter

Proportio

n of OCS

Activity in

Fishery

Gear Loss

Damage

per

Platform

Flounder $38,764 18,000 0.470 0.50 $506

Other groundfish $28,211 8,000 0.470 0.50 $829

Ocean Quahog/surf

clam

$106,941 2,000 0.470 0.30 $7,539

Sea scallop $47,561 8,500 0.470 0.05 $131

Lobster $238,504 3,600 0.470 0.20 $6,228

North Atlantic

TOTAL $459,981 $15,233

Groundfish $7,161 23,000 0.470 0.65 $95

Ocean quahog $7,172 21,600 0.470 0.05 $8

Surf clam $27,388 25,000 0.470 0.00 $0

Sea scallop $10,533 2,600 0.470 0.05 $95

Lobster $34,802 6,000 0.470 0.30 $818

Mid-Atlantic

TOTAL $87,056 $1,016

Groundfish $13,456 5,000 0.470 0.60 $759

Shrimp $62,911 15,000 0.125 0.05 $26

South Atlantic

TOTAL $76,367 $785

Straits of Florida Shrimp $53,164 60,000 0.125 0.80 $89

Eastern GOM Shrimp $55,046 60,000 0.125 0.90 $103

Central GOM Shrimp $212,601 60,000 0.125 0.95 $421

Western GOM Shrimp $198,951 60,000 0.125 0.95 $394

Rockfish $2,318 1,200 0.125 0.3 $72

Other groundfish $692 300 0.125 0.4 $115

Southern

California

TOTAL $3,010 $188

Rockfish $7,146 600 0.125 0.45 $670

Other groundfish $6,227 3,200 0.125 0.60 $146

Central

California

TOTAL $13,373 $816

Rockfish $3,283 700 0.125 0.10 $59

Other groundfish $5,227 2,300 0.125 0.65 $185

Shrimp $9,592 600 0.125 0.10 $200

Northern

California

TOTAL $18,102 $443

Washington/Ore

gon

Groundfish $40,100 6,000 0.470 0.80 $2,513
Section 7: Commercial Fisheries 165

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Table 7.2. Gear Loss Damage Estimates (Continued)

OCS Planning

Area Fishery

Annual

Landings

Value

(1000 $'s)

Area of

Fishery

(sq. miles)

Gear

Damage

Parameter

Proportio

n of OCS

Activity in

Fishery

Gear Loss

Damage

per

Platform

Groundfish $12,382 10,000 0.470 0.80 $466

Crab $4,979 1,200 0.630 0.80 $2,091

Gulf of Alaska

TOTAL $17,361 $2,557

Groundfish $11,001 10,000 0.026 0.80 $23

Crab $4,424 6,000 0.630 0.80 $372

Kodiak

TOTAL $15,425 $394

Groundfish $1,276 10,000 0.470 0.60 $36

Crab $513 2,000 0.630 0.30 $49

Cook Inlet

TOTAL $1,789 $84

Groundfish $40,540 10,000 0.026 0.30 $32

Crab $42,889 6,000 0.630 0.80 $3,603

Shumagin

TOTAL $83,429 $3,634

Groundfish $32,896 100,000 0.47 0.60 $93

Crab $33,541 6,000 0.63 0.80 $2,817

North Aleutian

Basin

TOTAL $66,437 $2,910

Groundfish $24,986 100,000 0.47 0.60 $70

Crab $25,173 6,000 0.63 0.80 $2,114

St. George Basin

TOTAL $50,159 $2,185

Groundfish $23,063 100,000 0.026 0.50 $3

Crab $24,400 6,000 0.630 0.80 $2,050

Aleutian Arc

TOTAL $47,463 $2,053

Groundfish $23,063 100,000 0.026 0.40 $2

Crab $24,400 6,000 0.630 0.80 $2,050

Bowers Basin

TOTAL $47,463 $2,052

Groundfish $23,063 100,000 0.470 0.60 $65

Crab $24,400 6,000 0.630 0.80 $2,050

Aleutian Basin

TOTAL $47,463 $2,115

Groundfish $23,063 100,000 0.470 0.60 $65

Crab $24,400 6,000 0.630 0.80 $2,050

Navarin Basin

TOTAL $47,463 $2,115

Groundfish $2,967 100,000 0.470 0.60 $8

Crab $3,139 6,000 0.630 0.80 $264

St. Matthew

Hall

TOTAL $6,106 $272

Norton Basin No fisheries

affected

$0

Hope Basin No fisheries

affected

$0

Chukchi Sea No fisheries

affected

$0

Beaufort Sea No fisheries

affected

$0
166 Section 7: Commercial Fisheries

OECM Volume 2.doc 03/24/01 11:15 AM

Table 7.3. Summary of Fishing Ground Preemption and Gear Loss Damage Estimates

OCS Planning Area

Fishing

Ground

Preemption

Damage per

Pipeline Mile

Fishing

Ground

Preemption

Damage per

Platform

Gear Loss

Damage per

Platform

Total

Damage per

Platform

(Preemption

+ Gear Loss)

North Atlantic $838 $4,453 $15,233 $19,686

Mid-Atlantic $818 $290 $1,016 $1,306

South Atlantic $46 $64 $785 $849

Straits of Florida $4 $6 $89 $95

Eastern Gulf of Mexico $3 $6 $103 $109

Central Gulf of Mexico $14 $26 $421 $447

Western Gulf of Mexico $15 $25 $394 $419

Southern California $72 $90 $188 $278

Central California $461 $659 $816 $1,475

Northern California $280 $98 $443 $541

Washington/Oregon $117 $210 $2,513 $2,723

Gulf of Alaska $11 $20 $2,557 $2,577

Kodiak $10 $18 $394 $412

Cook Inlet $1 $2 $84 $86

Shumagin $13 $25 $3,634 $3,659

North Aleutian Basin $1 $2 $2,910 $2,912

Saint George Basin $2 $4 $2,185 $2,189

Aleutian Arc $1 $2 $2,053 $2,055

Bowers Basin $1 $2 $2,052 $2,054

Aleutian Basin $3 $4 $2,115 $2,119

Navarin Basin $3 $4 $2,115 $2,119

Saint Matthew Hall $0 $0 $272 $272

Norton Basin $0 $0 $0 $0

Hope Basin $0 $0 $0 $0

Chukchi Sea $0 $0 $0 $0

Beaufort Sea $0 $0 $0 $0
Section 7: Commercial Fisheries 167

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Table 7.4. Shore-Based Commercial Fishery Values of Landings

OCS Planning Area

Value of Shore-Based

Landings

(1999 dollars)

Meters of

Coast

Value of ShoreBased Landings

per Meter-day

North Atlantic $19,231,500 8,250,952 $0.0064

Mid-Atlantic $111,555,100 22,172,020 $0.0138

South Atlantic $11,619,300 13,425,496 $0.0024

Straits of Florida $3,932,100 2,143,188 $0.0050

Eastern Gulf of Mexico $9,967,400 8,197,855 $0.0033

Central Gulf of Mexico $34,046,000 13,977,383 $0.0067

Western Gulf of Mexico $8,873,600 5,404,631 $0.0045

Southern California $4,780,800 2,283,171 $0.0057

Central California $2,135,000 2,017,686 $0.0029

Northern California $1,456,800 1,213,186 $0.0033

Washington/Oregon $57,901,600 7,137,524 $0.0222

Gulf of Alaska $74,722,600 17,893,689 $0.0114

Kodiak $66,387,500 1,581,647 $0.1150

Cook Inlet $7,702,200 4,691,844 $0.0045

Shumagin $6,139,500 4,637,138 $0.0036

North Aleutian Basin $16,290,400 3,708,745 $0.0120

Saint George Basin $20,331,500 1,364,432 $0.0408

Aleutian Arc $8,732,000 5,127,883 $0.0047

Bowers Basin $0 0 $0.0000

Aleutian Basin $0 0 $0.0000

Navarin Basin $0 0 $0.0000

Saint Matthew Hall $449,300 2,891,373 $0.0004

Norton Basin $0 3,872,863 $0.0000

Hope Basin $0 3,763,451 $0.0000

Chukchi Sea $0 2,238,119 $0.0000

Beaufort Sea $0 2,781,961 $0.0000
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Table 7.5. Offshore Commercial Fishery Values of Landings

OCS Planning Area

Value of

Offshore

Landings

(1999 dollars)

Area of

Commercial

Offshore

Fishery

(sq. meters)

Value of

Offshore

Landings per

sq. meter-day

Value of

Offshore

Landings per

coastal meterday

North Atlantic $447,394,200 2.242*1011 $0.00000547 $0.1486

Mid-Atlantic $446,968,800 3.200*1011 $0.00000383 $0.0552

South Atlantic $114,967,000 3.764*1011 $0.00000084 $0.0235

Straits of Florida $17,748,700 2.304*1011 $0.00000021 $0.0227

Eastern Gulf of Mexico $167,811,200 3.542*1011 $0.00000130 $0.0561

Central Gulf of Mexico $373,563,600 2.558*1011 $0.00000400 $0.0732

Western Gulf of Mexico $216,492,300 1.900*1011 $0.00000312 $0.1097

Southern California $79,471,600 3.681*1011 $0.00000059 $0.0954

Central California $34,836,900 1.610*1011 $0.00000059 $0.0473

Northern California $41,213,300 1.269*1011 $0.00000089 $0.0931

Washington/Oregon $144,043,000 2.346*1011 $0.00000168 $0.0553

Gulf of Alaska $139,490,100 7.767*1011 $0.00000049 $0.0214

Kodiak $123,930,400 3.624*1011 $0.00000094 $0.2147

Cook Inlet $14,378,300 3.107*1011 $0.00000013 $0.0084

Shumagin $75,826,600 2.071*1011 $0.00000100 $0.0448

North Aleutian Basin $79,587,900 4.142*1011 $0.00000053 $0.0588

Saint George Basin $64,791,900 1.243*1011 $0.00000143 $0.1301

Aleutian Arc 1 $43,138,200 2.589*1011 $0.00000046 $0.0230

Bowers Basin 1 $43,138,200 2.000*1011 $0.00000059 $0.0000

Aleutian Basin 1 $43,138,200 2.000*1011 $0.00000059 $0.0000

Navarin Basin $43,138,200 2.000*1011 $0.00000059 $0.0000

Saint Matthew Hall $5,549,600 3.107*1011 $0.00000005 $0.0053

Norton Basin $0 2.589*1011 $0.00000000 $0.0000

Hope Basin $0 1.812*1011 $0.00000000 $0.0000

Chukchi Sea $0 1.553*1011 $0.00000000 $0.0000

Beaufort Sea $0 3.107*1011 $0.00000000 $0.0000

1 - Planning Areas have no coast. The area of the commercial fishery (2.000*1011 square meters)

is an assumed value.
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Table 7.6. Final Commercial Fishing Spill-Related Damage Estimates

OCS Planning Area

Damage of a

Small Spill

(any source)

Damage of a

Large Pipeline or

Platform Spill

Damage of a

Large Tanker

Spill

North Atlantic $358,075 $845,015 $2,112,538

Mid-Atlantic $32,791 $207,393 $428,834

South Atlantic $14,711 $121,247 $227,137

Straits of Florida $5,174 $44,458 $72,354

Eastern Gulf of Mexico $15,674 $279,868 $358,250

Central Gulf of Mexico $44,261 $316,794 $1,085,809

Western Gulf of Mexico $30,388 $222,292 $677,108

Southern California $9,740 $61,156 $103,691

Central California $19,583 $67,020 $92,898

Northern California $4,229 $45,836 $61,215

Washington/Oregon $35,608 $138,669 $195,676

Gulf of Alaska $5,704 $38,662 $81,565

Kodiak $37,820 $225,107 $50,325

Cook Inlet $5,115 $35,716 $53,570

Shumagin $5,959 $77,635 $173,411

North Aleutian Basin $9,213 $378,507 $103,869

Saint George Basin $10,294 $105,563 $300,821

Aleutian Arc $16,912 $30,048 $111,515

Bowers Basin $6,850 $51,775 $102,752

Aleutian Basin $4,441 $35,164 $100,468

Navarin Basin $6,222 $53,145 $83,914

Saint Matthew Hall $1,541 $13,805 $22,268

Norton Basin $0 $0 $0

Hope Basin $0 $0 $0

Chukchi Sea $0 $0 $0

Beaufort Sea $0 $0 $0
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Commercial Fisheries

Environmental Cost Parameters

1.

2.

1. Name Cost Categories and Assign Associated Impact Driver Variables

Cost Name

Impact Driver

Pipelines 

(Cum)

Platforms 

(Cum)

None Platforms 

(Cum)

BBL Spilled 

(tanker)

None BBL Spilled 

(plat/pipe)

BBL Spilled 

(tanker)

None

2. Specify Cost Coefficients $ / Mile $ / platform N/A $ / platform $ / BBL N/A $ / BBL $ / BBL N/A

North Atlantic $ 838 $ 4,453 $ 15,233 $ 524 $ 96

Mid-Atlantic $ 818 $ 290 $ 1,016 $ 64 $ 18

South Atlantic $ 46 $ 64 $ 785 $ 32 $ 9

Straits of Florida $ 4 $ 6 $ 89 $ 12 $ 3

G Eastern Gulf $ 3 $ 6 $ 103 $ 53 $ 15

o Central Gulf $ 14 $ 26 $ 421 $ 91 $ 44

M W estern Gulf $ 15 $ 25 $ 394 $ 63 $ 28

Southern California $ 72 $ 90 $ 188 $ 19 $ 4

Central California $ 461 $ 659 $ 816 $ 31 $ 4

Northern California $ 280 $ 98 $ 443 $ 11 $ 3

W ashington/Oregon $ 117 $ 210 $ 2,513 $ 59 $ 9

Gulf of Alaska $ 11 $ 20 $ 2,557 $ 11 $ 3

Cook Inlet $ 10 $ 18 $ 394 $ 72 $ 3

A Kodiak $ 1 $ 2 $ 84 $ 10 $ 2

Shuimagin $ 13 $ 25 $ 3,634 $ 17 $ 7

L North Aleutian Basin $ 1 $ 2 $ 2,910 $ 58 $ 4

Aleutian Arc $ 2 $ 4 $ 2,185 $ 25 $ 12

A St. George Basin $ 1 $ 2 $ 2,053 $ 24 $ 5

St. Matthew Hall $ 1 $ 2 $ 2,052 $ 14 $ 4

S Bowers Basin $ 3 $ 4 $ 2,115 $ 10 $ 4

Aleutian Basin $ 3 $ 4 $ 2,115 $ 14 $ 4

K Navarin Basin - - $ 272 $ 4 $ 1

Norton Basin - - - --

A Hope Basin - - - --

Chukchi Sea - - - --

Beaufort Sea - - - --

10 8 21 8 17 21 18 17 21

3. Specify the dollar-year of cost coefficients 1999

The coefficients reflect dollars of what year?

A

T

L

P

A

C

Three separate cost categories can be entered, each with up to three impact drivers. Enter the cost category name and choose 

the associated impact driver. If a cost category does not use three drivers, set unused columns to "None."

Enter the environmental cost coefficient for each impact driver variable. The proper units to express damage coefficients are shown 

below the impact variable name.

Fishing Ground Preemption Gear Loss Spill Effects

Print 

Annual Results

Regional Costs

National Costs

Regional Costs

National Costs

Print Preview Print

Present Values

Figure 7.1. Commercial Fisheries OECM Impact Drivers

These are found on the CommFish cost element worksheet.
Section 7: Commercial Fisheries 171

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8. Subsistence Resources

The 1991 GPECM estimated subsistence losses associated with oil spills, costs to oil companies

to minimize spill risk to subsistence resources, and non-spill subsistence losses due to the

presence of oil exploration and production facilities. Similar to the GPECM, subsistence losses

are assumed to only occur in the Alaskan OCS Planning Areas.

Section 8.1 presents estimates for the total value of subsistence harvest in Alaska. Section 8.2

discusses subsistence losses due to routine operations and development. Spill-related subsistence

losses are treated in Section 8.3.

8.1 Economic Value of Alaskan Subsistence Harvest

8.1.1 Subsistence Harvest by OCS Planning Area

Data for the annual subsistence harvest in Alaska were obtained from the Alaska Community

Profile Database (CPDB). The CPDB is considered the "the sole automated repository for

information on subsistence fish and wildlife harvests in Alaska" (Alaska Fish and Game, 2000).

The CPDB database is downloadable as a Microsoft Access file from the CPDB web site.

The CPDB presents regional subsistence harvest data. Regions are delineated several ways:

individual communities, state subsistence regions, federal subsistence regions, and state game

management units. We choose to use state game management units as the basis for estimate

subsistence harvest for each OCS Planning Area for two reasons. First, state game management

units permit the most accurate mapping into OCS Planning Areas. None of the state or federal

regional definitions corresponds to OCS Planning Areas. There are six state subsistence regions,

ten federal subsistence regions, and 26 state game management units. Further, most state game

management units are subdivided into several sub-units. The relatively small size of these subunits allows us to accurately group harvest data into OCS Planning Area boundaries. Because we

assume that interior subsistence activities are not impacted by MMS operations or oil spills, the

state game management regions allows us to exclude the most interior area.

Table 8.1 presents the mapping of state game management units and sub-units into OCS

Planning Areas. In most instances, the state game management unit or sub-unit boundaries

correspond to OCS Planning Area boundaries. However, four cases were identified where no

state unit or sub-unit boundaries correspond to OCS Planning Area boundaries. To fully allocate

the state subsistence harvest data, we made the following assumptions:

1. Half of state game management unit 8 is allotted to the Kodiak OCS Planning Area while the

other half is allotted to the Cook Inlet OCS Planning Area.

2. Half of state game management units 9D and 9E is allotted to the Schumagin OCS Planning

Area while the other half is allotted to the North Aleutian Basin OCS Planning Area.

3. Half of state game management unit 10 is allotted to the Aleutian Arc OCS Planning Area

while the other half is allotted to the St. George Basin OCS Planning Area.
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4. Half of state game management unit 26A is allotted to the Chukchi Sea OCS Planning Area

while the other half is allotted to the Beaufort Sea OCS Planning Area.

The CPDB database presents annual subsistence harvest data for each community in each state

game management unit and sub-unit. Harvest data are given for the most representative year.

Thus, unlike our methodology for other cost elements, we do not need to average harvest data

over several years to reduce potential problems with outlier data. The CPDB categorizes the

harvest data into six groupings: fish (salmon and non-salmon), land mammals (small and large),

marine mammals, birds and eggs, marine invertebrates, and vegetation. For each food group and

community, data are given for the total pounds of harvest, average household harvest, and per

capita harvest. For mammals and salmon, the number harvested may also be given.

We separate the harvest data into two food types: those food types that would be impacted by

non-spill coastal development and those types impacted by oil spills. For non-spill impacts we

include only the terrestrial subsistence food types (land mammals, birds and eggs, and

vegetation). For spill-related damages, we include only marine food types (fish, marine

mammals, and marine invertebrates).

Harvest data, measured in pounds, are obtained from the CPDB for state game management units

and sub-units by querying the database. Tables are produced that list harvest data by food type

and community within each unit or sub-unit. Summing harvest data across all communities

produces an estimate of total harvest for each unit or sub-unit. Table 8.2 presents subsistence

harvest data for each state game management unit or sub-unit necessary for mapping the data

into OCS Planning Areas. Total harvest data are separated into terrestrial and marine species.

Table 8.3 presents the harvest data from Table 1 mapped into OCS Planning Areas.

8.1.2 Economic Values

Since the subsistence harvest is measured in terms of pounds, economic values using a perpound metric are preferred. Two sources were identified that present subsistence values for

Alaska. Duffield (1997) summarizes the subsistence harvest damage claim resulting from the

Alaska Valdez oil spill. The defendants proposed a settlement based on a per-pound replacement

value of $10.03 (in 1994 dollars). The plaintiffs proposed two claims, one based on a

subsistence harvest replacement value of $12.55 per pound and another based on a value of

$14.01 per pound. The Alaska Department of Fish and Game presents estimates of the total

value of subsistence harvest based on replacement values of $3 and $5 per pound (Alaska

Department of Fish and Game, 1998).

Similar to the approach of other cost elements, we derive economic values based on high and

low assumptions. For our high values, we use Duffield's value of $14.01/lb. updated to

$15.75/lb. in 1999 dollars. For our low values, we use a value of $4 based on the Alaska

Department of Fish and Game report, updated to $4.09 in 1999 dollars.

An additional factor that may increase the value of subsistence harvest is the cultural and

experiential benefit of subsistence activities. Although we would prefer to use the joint

production function, which includes replacement food values, cultural values and experiential

values, estimates of this function are not yet available. The Anchorage Office of MMS is
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presently funding a 3-year Coastal Marine Institute study on this very issue. Pending the results

of this study we therefore continue to follow the 1991 GPECM procedure of adding 25 percent to

the food replacement value to reflect cultural and experiential values, pending results of the

University of Alaska Marine Institute study. Thus, our low value estimate is increased to

$5.11/lb. and our high value is increased to $19.69/lb.

Table 8.4 presents the high and low estimates of the value of subsistence harvest in Alaska by

OCS Planning Area. Our low estimate of the total value of Alaskan subsistence harvest activities

in OCS Planning Areas is about $120 million. Our high estimate of total subsistence value in

OCS Planning Areas is $462 million. About 76% of this value is attributable to marine, bird, or

fish species while 24% is due to harvest of land mammals and vegetation.

8.2 Losses Due to Pipeline or Facility Landfall

Non-spill subsistence losses were estimated in the 1991 GPECM using the following

methodology:

1. Express the disturbed land area as a percent of the terrestrial habitat.

2. Estimate the value of the subsistence harvest on that habitat.

3. The economic loss equals the product of steps 1 and 2.

We continue to use this general methodology but increase the amount of land assumed disturbed

by each facility or pipeline landfall. The 1991 GPECM assumes that the disturbed area for a

facility or pipeline landfall is 5.5 square miles. However, the 1991 GPECM implicitly assumes

that this disturbed area is typical of the entire subsistence gathering area. This assumption is not

sufficiently conservative because of two reasons. First, facilities will be located at the shoreline,

which is an inherently more productive than average habitat and thus a more likely harvesting

area. Second, facilities are likely to be located in close proximity to population centers and thus

cause more disturbance of harvest activities than a randomly selected area. To reflect these two

factors, we increase the disturbed area by a factor of 3, yielding a weighted disturbed area of

15.75 square miles per facility (pipeline landfall or shore base).

Similar to the GPECM, we derive an estimate of terrestrial habitat based on caribou habitat in the

Arctic region. Game management unit area is not used since terrestrial species, especially large

land mammals, may range over a large area. Arctic caribou habitat area equals 12,800 square

miles (GPECM, 1991, 13-36). Therefore, 15.75 square miles represents a habitat loss of 0.12%

per facility. For other regions, we assume the same proportion of habitat loss, consistent with the

1991 GPECM assumption.

We assume that pipeline or facility landfalls only affect terrestrial subsistence food types. We

also assume that the loss in terrestrial subsistence harvest is proportional to the habitat loss.

Thus, each pipeline or facility landfall is assumed to reduce terrestrial subsistence harvest values

by 0.12% per year. High and low damage estimates per pipeline or facility landfall are provided

in Table 8.5. Damages are zero for OCS Planning Areas that do not have any coast. Damages

are highest in the Gulf of Alaska Planning Area.
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8.3 Oil Spill Losses

The losses due to oil spills are limited to the sum of fish, marine mammal, and marine

invertebrate food sources. The economic values in Table 8.4 need to be linked to the injury

outputs from the NRDAM/CME. Three potential metrics could be used to determine spillrelated subsistence injuries:

1. amount of coastal oiling (measured in meters)

2. size of oil slick (measured in square-meters)

3. number of marine mammals killed

While there are limitations with each metric, we choose the third metric to derive estimates of

injury. There is no necessary biological link between coastal oiling or oil slick size and

reduction in subsistence harvest. Using the coastal oiling metric implies that any spill that does

not come ashore results in no subsistence damage. This is contradicted by some NRDAM/CME

scenarios that indicate no coastal oiling but positive marine mammal deaths. The link between

oil slick size and subsistence harvest is also weak. Marine mammal deaths can be linked to the

marine mammal subsistence harvest data presented in Table 8.3.

While the NRDAM/CME estimate the number of marine mammals killed by each oil spill

scenario, we still need to estimate the reduction in subsistence harvest. Assuming that all marine

mammals killed in an oil spill scenario would have been caught as subsistence harvest may

dramatically overstate the reduction in subsistence harvest. To further explore this issue, data

were collected on the population and subsistence harvest of particular species. Species-specific

statewide subsistence harvest data are available from the CPDB. Data on statewide populations

are available from Alaska Department of Fish and Game (1997) and the Alaska Department of

Fish and Game Wildlife Notebook Series available on their web site. Data were available for

statewide subsistence harvest and statewide population for several marine mammal species. The

data in Table 8.6 indicate that annual subsistence harvest for marine mammals is commonly

between 1% and 2% of the total population. To be conservative (higher damage estimates), we

assume that 2% of the marine mammals killed in an oil spill scenario would have been harvested

for subsistence food.

Since we could not locate similar data for other marine subsistence food types (fish and marine

invertebrates), we apply our marine mammal assumptions to all marine food types. The resulting

methodology for estimate subsistence losses due to oil spills is:

1. Determine the percentage of marine mammals killed by each oil spill scenario from the

NRDAM/CME output.

2. Estimate the reduction in marine mammal subsistence harvest by assuming that subsistence

harvest decreases by 2% of the NRDAM/CME marine mammal deaths. For example,

assume that the NRDAM/CME output indicates that an oil spill in the Beaufort Sea Planning

Area kills 200 marine mammals. We estimate that the marine mammal subsistence harvest

would decrease by 4 animals, or 1.1% (4/366).
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3. Assume that this percentage reduction applies to all other subsistence marine species. In the

above example, we would assume that total marine subsistence value in the Beaufort Sea

Planning Area would decrease by 1.1%. Using the low value estimate in Table 8.4,

economic damages would be $41,200. Using the high value estimates, economic damages

would be $158,600.

Note that this methodology assumes that subsistence harvest returns to normal in the next year.

Based on the relatively small number of marine mammal deaths in all oil spill scenarios, no longterm population reductions would be likely. The methodology also assumes that all marine

subsistence species are injured in similar proportions to marine mammals. This assumption

could not be fully explored because the NRDAM/CME does not provide injury output data for

all species and statewide population estimates were not available for species other than marine

mammals.
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References

Alaska Department of Fish and Game. 2000. Community Profile Database web site,

www.state.ak.us/local/akpages/FISH.GAME/subsist/geninfo/publctns/cpdb.htm.

Alaska Department of Fish and Game. 1998. Subsistence in Alaska: 1998 Update. ADFG

subsistence articles and presentations web site,

www.state.ak.us/local/akpages/FISH.GAME/subsist/download/subupd98.pdf.

Alaska Department of Fish and Game. 1997. Overview Information about Subsistence Uses of

Marine Mammals in Aleutian/Pribilof Islands Communities. ADFG web site,

www.state.ak.us/local/akpages/FISH.GAME/subsist/download/mmap.pdf.

Duffield, John. 1997. "Nonmarket Valuation and the Courts: The Case of the Exxon Valdez",

Contemporary Economic Policy, vol. 15(4):98-110.
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Table 8.1. Mapping State Management Units into OCS Planning Areas

OCS Planning Area State Management Unit(s) or Sub-units

Gulf of Alaska 1,2,3,4,5,6,7

Kodiak 8

Cook Inlet 8,9A,9B,9C,15,16

Shumagin 9D,9E

Aleutian Arc 10

North Aleutian Basin 9D,9E,17

Saint George Basin 10

Bowers Basin NA1

Aleutian Basin NA1

St. Matthew-Hall 18

Navarin Basin NA1

Norton Basin 22A,22B,22C,22D

Hope Basin 22E,23

Chukchi Sea 26A

Beaufort Sea 26A,26B,26C

1 - Planning Areas include no coastline.
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Table 8.2. State Game Management Unit and Sub-Unit Subsistence Harvest Data

State Game

Management Unit or

Sub-unit1

Total

Estimated

Annual

Subsistence

Harvest (lbs.)2

Harvest of

Marine, Bird,

& Fish Species

(lbs.)3

Harvest of

Land Mammal

and Vegetation

Species (lbs.) 4

Percent of

Harvest

Marine,

Bird, or Fish

Number of

Marine

Mammals

Harvested2

1 726,395 574,152 152,243 79% 61

2 1,156,174 869,707 286,467 75% 762

3 1,334,537 934,528 400,009 70% 328

4 2,414,026 1,712,349 701,677 71% 1,465

5 234,204 215,290 18,914 92% 218

6 833,957 574,202 259,755 69% 2,026

7 40,342 26,039 14,303 65% 0

8 1,971,086 1,586,836 384,250 81% 360

9A-9C 1,082,751 757,161 325,590 70% 89

9D-9E 601,199 433,223 167,976 72% 60

10 648,458 573,286 75,172 88% 2,699

15 1,577,081 1,143,669 433,412 73% 266

16 93,507 69,748 23,759 75% 1

17 1,174,753 771,832 402,921 66% 192

18 3,834,288 3,325,975 508,313 87% 2,736

22A-22D 1,228,669 1,096,524 132,145 89% 1,104

22E 5,485 4,740 745 86% 1,958

23 2,732,778 1,848,201 884,577 68% 500

26A 1,598,810 1,189,991 408,819 74% 425

26B-26C 170,940 141,846 29,094 83% 153

TOTAL 23,459,442 17,849,302 5,610,141 76% 15,403

1 - Include only the game management units with coastal lands.

2 - From the Alaska CPDB, harvest based on data from representative years.

3 - Includes salmon, non-salmon fish, marine mammals, marine invertebrates, and birds and

eggs.

4 - Includes feral animals, small and large land mammals, and vegetation.
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Table 8.3. OCS Planning Area Subsistence Harvest Data

OCS Planning

Area

Total

Estimated

Annual

Subsistence

Harvest (lbs.)1

Harvest of

Marine, Bird,

& Fish

Species (lbs.) 2

Harvest of

Land Mammal

and Vegetation

Species (lbs.)3

Percent of

Harvest

Marine,

Bird, or

Fish

Number of

Marine

Mammals

Harvested1

Gulf of Alaska 6,739,635 4,906,267 1,833,368 73% 4,860

Kodiak 985,543 793,418 192,125 81% 180

Cook Inlet 3,738,882 2,763,996 974,886 74% 536

Shumagin 300,600 216,612 83,988 72% 30

Aleutian Arc 324,229 286,643 37,586 88% 1,350

North Aleutian

Basin

1,475,353 88,444 486,909 67% 222

Saint George

Basin

324,229 286,643 37,586 88% 1,350

Bowers Basin 0 0 0 NA 0

Aleutian Basin 0 0 0 NA 0

St. MatthewHall3,834,288 3,325,975 508,313 87% 2,736

Navarin Basin 0 0 0 NA 0

Norton Basin 1,228,669 1,096,524 132,145 89% 1,104

Hope Basin 2,738,263 1,852,941 885,322 68% 2,458

Chukchi Sea 799,405 594,996 204,410 74% 213

Beaufort Sea 970,345 736,842 233,504 76% 366

TOTAL 23,459,442 16,949,303 5,610,145 76% 15,405

1 - From the Alaska CPDB, harvest based on data from representative years.

2 - Includes salmon, non-salmon fish, marine mammals, marine invertebrates, and birds and

eggs.

3 - Includes feral animals, small and large land mammals, and vegetation.
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Table 8.4. Subsistence Values by OCS Planning Area (1999 dollars)

Total Value of

Subsistence Harvest

Total Value of Marine,

Bird, and Fish Species

Total Value of Land

Mammal and

Vegetation Species

OCS

Planning

Area Low Value High Value LowValue High Value Low Value High Value

Gulf of

Alaska

$34,439,535 $132,703,413 $25,071,024 $96,604,397 $9,368,510 $36,099,016

Kodiak $5,036,125 $19,405,342 $4,054,366 $15,622,400 $981,759 $3,782,941

Cook Inlet $19,105,687 $73,618,587 $14,124,020 $54,423,081 $4,981,667 $19,195,505

Shumagin $1,536,063 $5,918,804 $1,106,885 $4,265,080 $429,179 $1,653,724

Aleutian Arc $1,656,810 $6,384,069 $1,464,746 $5,644,001 $192,064 $740,068

N. Aleutian

Basin

$7,539,051 $29,049,691 $5,050,946 $19,462,453 $2,488,105 $9,587,238

St. George

Basin

$1,656,810 $6,384,069 $1,464,746 $5,644,001 $192,064 $740,068

Bowers

Basin

$0 $0 $0 $0 $0 $0

Aleutian

Basin

$0 $0 $0 $0 $0 $0

St. Mat. Hall $19,593,212 $75,497,131 $16,995,732 $65,488,448 $2,597,479 $10,008,683

Navarin

Basin

$0 $0 $0 $0 $0 $0

Norton Basin $6,278,499 $24,192,493 $5,603,238 $21,590,558 $675,261 $2,601,935

Hope Basin $13,992,524 $53,916,398 $9,468,529 $36,484,408 $4,523,995 $17,431,990

Chukchi Sea $4,084,960 $15,740,284 $3,040,427 $11,715,461 $1,044,533 $4,024,823

Beaufort Sea $4,958,463 $19,106,093 $3,765,260 $14,508,409 $1,193,203 $4,597,684

TOTAL $119,877,739 $461,916,374 $91,209,919 $351,452,697 $28,667,819 $110,463,675
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Table 8.5. Annual Subsistence Damage Estimates Per Facility or Pipeline Landfall

(1999 dollars)

OCS Planning Area Low Damage Estimate Per

Facility or Pipeline Landfall

High Damage Estimate Per

Facility or Pipeline Landfall

Gulf of Alaska $11,242 $43,319

Kodiak $1,178 $4,540

Cook Inlet $5,978 $23,035

Shumagin $515 $1,984

Aleutian Arc $230 $888

North Aleutian Basin $2,986 $11,505

Saint George Basin $230 $888

Bowers Basin $0 $0

Aleutian Basin $0 $0

St. Matthew-Hall $3,117 $12,010

Navarin Basin $0 $0

Norton Basin $810 $3,122

Hope Basin $5,429 $20,918

Chukchi Sea $1,253 $4,830

Beaufort Sea $1,432 $5,517
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Table 8.6. Comparison Between Subsistence Harvest and Species Populations

Species

Annual Statewide

Subsistence

Harvest1

Estimated

Statewide

Population2

Percent of

Population

Harvested

Annually

Beluga Whales 218 13,500-21,000 1.0%-1.6%

Harbor Seals 3,779 200,000-300,000 1.3%-1.9%

Northern Fur Seals 2,129 900,000 0.2%

Sea Lions 462 44,000 1.1%

Sea Otters 2,167 110,000-160,000 1.4%-2.0%

Spotted Seals 2,098 200,000-250,000 0.8%-1.0%

Walrus 647 201,000 0.3%

1 - Harvest data from CPDB, most representative years' data.

2 - Population data from AK Dept. of Fish and Game.
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Cost Name

Impact Driver

Pipeline 

Landfalls

None None BBL Spilled 

(plat/pipe)

None None

2. Specify Cost Coefficients $ / Landfall N/A N/A $ / BBL N/A N/A

North Atlantic - -

Mid-Atlantic - -

South Atlantic - -

Straits of Florida - -

G Eastern Gulf - -

o Central Gulf - -

M Western Gulf - -

Southern California - -

Central California - -

Northern California - -

Washington/Oregon - -

Gulf of Alaska $ 11,242 $ 0

Cook Inlet $ 1,178 $ 1

A Kodiak $ 5,978 $ 1

Shuimagin $ 515 $ 5

L North Aleutian Basin $ 230 $ 3

Aleutian Arc $ 2,986 -

A St. George Basin $ 230 $ 0

St. Matthew Hall - -

S Bowers Basin - -

Aleutian Basin $ 3,117 -

K Navarin Basin - $ 0

Norton Basin $ 810 $ 0

A Hope Basin $ 5,429 $ 0

Chukchi Sea $ 1,253 $ 0

Beaufort Sea $ 1,432 $ 1

11 21 21 18 21 21

3. Specify the dollar-year of cost coefficients 1999

The coefficients reflect dollars of what year?

A

T

L

P

A

C

Routine Operations Oil Spills

Figure 8.1. Subsistence Resources OECM Impact Drivers

These are found on the Subsistence cost element worksheet.

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9 Air Quality Social Damages

9.1 Introduction and Caveats

Several categories of offshore oil and gas activities result in the emission of air pollutants. Air

pollutants emitted offshore may be transported over landmasses by winds and atmospheric

mixing. Air pollution over landmasses may cause several types of social damages such as

human health impacts, reduced visibility, reduction in agricultural yields, and ecological losses.

We focus on human health impacts in this research because of the limited data on other damage

elements. For example, we could locate no research that links offshore air emissions to marine

ecology damages. The omission of impacts other than human health damages is probably minor.

Consider that Delucchi (1997) estimates that human health damages account for about 95% of

the total social damages (including human health, visibility, and agricultural impacts) from air

pollutants emitted by motor vehicles.

Air quality damages from oil and gas activities include the impact of exploration, platform and

pipeline construction, well drilling, boat trips, platform operations, and oil spills. We consider

three categories of air quality damages that are one-time damages:

1. Damages from boat trips associated with the drilling of exploratory and development wells

2. Damages from platform construction

3. Damages from the construction of pipelines

We consider four categories of damages from continuous OCS operations. These damages are

annual:

1. Damages from platform emissions

2. Damages from supply and crew boat trips

3. Damages from tanker loading and transport

4. Damages from the operation of development wells

We also consider the air quality impacts of oil spills measured as the damage per barrel spilled.

We exclude emissions from helicopters, tugboats, and other miscellaneous sources. Preliminary

analysis suggested that damages from these minor sources of emissions are insignificant.

The link between these types of offshore air emissions and onshore pollution damages has not

been well established. National estimates of the emissions from marine vessels have been

developed. Marine diesel engines are responsible for 0.2% of VOC emissions, 4.4% of NOx

emissions, 1.0% of PM emissions, and 0.2% of CO emissions nationwide (U.S. Environmental

Protection Agency, 1999). However, no research has estimated the dollar-denominated damages

caused by these emissions. Similarly, research is not currently available on the economic

damages caused by emissions from oil platforms and other offshore MMS activities. Thus, the

estimates presented in this section must be considered approximations based on limited physical

and economic research. While we provide estimates using the currently available literature,
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future research may allow more accurate estimates of the physical and economic impacts of

offshore air pollutants.

We consider air quality impacts from six primary pollutants:

1. Carbon monoxide (CO)

2. Nitrogen oxides (NOx)

3. Sulfur oxides (SOx)

4. Particulate matter (PM)

5. Volatile organic compounds (VOC's)

6. Carbon dioxide (CO2 )

Note that carbon dioxide is generally not considered an air pollutant but CO2 causes economic

damages through its effect on global warming. Primary pollutants interact with other pollutants

and atmospheric compounds to form secondary pollutants such as ozone and sulfuric acid.

Ozone and its precursors (mainly NOx and VOC's) are typically the focus of air pollution control

efforts. Several coastal areas exceed the national air quality standards for ozone including

regions of coastal Texas, California, and the Mid-Atlantic States. Few coastal areas, however,

exceed the national standards for other criteria pollutants. Thus, we conclude that the greatest

potential threat of offshore emissions is the contribution to coastal ozone levels, particularly in

areas that already exceed ozone standards.

The method used for estimating air quality externalities from OCS operations is described in this

section including the data sources and assumptions. The methodology is discussed in the

following order of the analytic steps:

1. Determine the emission rates for all relevant OCS activities;

2. Determine the potential for pollutant transport to land from offshore areas;

3. Obtain estimates of the social costs of each pollutant listed above;

4. Combine the estimates from the above steps to estimate the social cost of air emissions

associated with each activity in each OCS Planning Area.

Much of the input data used to estimate air pollution damages, such as boat trip frequencies and

emissions rates, are based on MMS operations in the Gulf of Mexico. While we do adjust our

analysis for each OCS Planning Area based on assumed development distances from shore, input

data specific to each OCS Planning Area were not available. However, the Gulf of Mexico input

data is considered to be representative and applicable to other OCS Planning Areas.
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9.2 Emission Estimates

9.2.1 Location of Emissions

The location of likely OCS activity is important for two reasons. First, the distance between an

OCS platform and the shore influences the quantity of emissions from boats that supply materials

and crew. Platforms that are farther from shore require longer boat trips and, consequently,

result in more emissions. Second, the distance to shore influences the percentage of emissions

that reach shore. As discussed in more detail below, the percentage of emissions that reach shore

and cause social damages generally decreases as the distance to shore increases.

The distance between likely OCS development areas and the shore differs across OCS Planning

Areas. King (2001) provided estimates of the typical one-way offshore distance for platforms in

each OCS Planning Area (Table 9.1). Platforms tend to be farthest from the shore in the Western

Gulf Planning Area (130 miles) and closest to the shore in the Gulf of Alaska and Beaufort Sea

Planning Areas (7 miles).

Emission damages are normally assumed to occur in the coastal regions of the OCS Planning

Area in which the emissions are emitted. While some transport of emissions across OCS

Planning Areas is likely, detailed modeling of air emissions transport is beyond the scope of this

analysis. The only damages detailed in this section that involve transport across OCS Planning

Areas are the tanker transport damages. All oil obtained from Alaska Planning Areas is initially

transported to shore via pipelines. The vast majority of this oil is eventually loaded onto tankers

at Valdez for transport to refineries in Washington and California. Thus, oil originating in

Alaska leads to air pollution damages in other OCS Planning Areas. The damages are included

in the damages for the Alaska OCS Planning Area in which the oil is obtained, not the

Washington/Oregon and California OCS Planning Areas. However, information is provided

below to allow one to assign these damages to the OCS Planning Area where the damage

actually occurs.

9.2.2 Supply and Crew Boat Emissions

Boats travel to platforms with supplies and personnel. Supply boats are larger and emit more

pollutants than crew boats. Supply boats make an average of 71 trips per year to each active

platform while crew boats make an average of 33 trips per year to each active platform (Minerals

Management Service, 1997.) Note that these trips are round trips.

Data on fuel usage rates for boat trips were obtained from Sholten (2000). Based on a typical

platform in the Western Gulf of Mexico Planning Area located 130 miles (about 209 km)

offshore, a supply boat with a speed of 13 miles per hour will make a round trip in 20 hours.

With a fuel usage rate of 110 gallons per hour, a supply boat will burn 2,192 gallons of fuel on a

round trip. A crew boat travels at about 24 miles per hour and will make the same trip in about

11 hours. With a fuel usage rate of 67 gallons per hour, it will burn 741 gallons of fuel per trip.

Data on boat emissions rate were obtained from Jacobs Engineering Group (1989) and MMS

(1998). Table 9.2 presents the emissions of supply and crew boats per 1,000 gallons of fuel

burned. Assuming a supply boat will burn 2,192 gallons of fuel per trip and a crew boat will
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burn 741 gallons, Table 9.2 also provides estimates of the per-trip emissions from supply and

crew boats for the Western Gulf of Mexico Planning Area.

The per-trip emissions estimates in Table 9.2 are based on the assumed one-way trip distance of

130 miles for the Western Gulf of Mexico Planning Area. These values need to be adjusted for

the distances presented in Table 9.1 for other OCS Planning Areas. Table 9.3 presents the

annual emissions for supply and crew boat emissions per platform by OCS Planning Area.

These calculations assume 71 supply and 33 crew boat trips per year. For example, in the

Western? Gulf of Mexico Planning Area, the NOx emissions per platform include 27 supply

boats trips with 0.6028 tons emitted per trip for a total of 16.275 tons and 27 crew boat trips with

0.1914 tons emitted per trip for a total of 5.168 tons. Emissions for OCS Planning Areas other

than the Gulf of Mexico Areas are calculated by multiplying the Gulf of Mexico emissions rates

by the distance factors from Table 9.1. While boats will not be able to operate year-round in

many Alaska Planning Areas, we assume that supply and crew trips will still be required and the

emissions from ice-based vehicles will be similar to those of boats.

9.2.3 Platform Operations Emissions

The operation of platforms creates emissions through the use of generators and other equipment

and emissions from routine operations. The annual emissions from a platform are estimated to

be 50.88 tons of NOx, 11.84 tons of CO, 8.55 tons of SOx, 38.58 tons of VOC's, 0.93 tons of PM,

and 2,198 tons of CO2 (Jacobs, 1989; MMS, 1998).

9.2.4 Tanker Loading and Transport Emissions

For most OCS Planning Areas, all transport of oil to shore is assumed to occur via pipelines. For

the Western Gulf of Mexico and the Central Gulf of Mexico Planning Areas, we assume that

10% of oil is transported to shore via tanker from Floating Production, Storage, and Off-loading

(FPSO) facilities. In Alaska, all oil is originally transported to shore via pipelines. For the Cook

Inlet Planning Area, 95% of the oil is used locally. For all other oil produced in Alaska, the oil is

loaded onto tankers at Valdez and transported to one of three refinery locations: Puget Sound,

San Francisco, or Los Angeles. We assume that 31% of the oil transported via tanker from

Valdez is delivered to Puget Sound, 36% is delivered to San Francisco, and 33% is delivered to

Los Angeles. As tankers travel from Valdez to these ports, air pollutants are emitted from the

ships and from the volatilization of oil as described below.

Data on tanker loading and transport emissions were obtained from Sholten (2000). Tanker

loading causes the emissions of VOC's only. Tanker loading causes 0.0227 tons of VOC

emissions per 1,000 barrels loaded. Assuming a typical tanker load of 233,000 barrels, loading a

tanker will produce 5.289 tons of VOC emissions. Note that all tanker loading in Alaska occurs

at Valdez (located in the Gulf of Alaska Planning Area). However, damages are attributed to the

OCS Planning Area where production initially occurs. In the Western and Central Gulf of

Mexico Planning Areas, tanker loading occurs far offshore and is transported to shore.

Additional VOC emissions will escape from crude oil as it is transported. The emissions rate is

0.0431 tons of VOC's per 1,000 barrels transported per week (Sholten, 2000). For tanker

transportation in the Western and Central Gulf of Mexico Planning Areas, a transport time of one
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day is assumed. For tanker transport from Alaska, a travel time of five days to Puget Sound, six

days to San Francisco, and seven days to Los Angeles is assumed.

Tankers emissions rates are obtained from Sholten (2000) as 1.4166 lbs. of NOx, 0.1549 lbs. of

CO, 1.7597 lbs. of SOx, 0.0021 lbs. of VOC's, 0.5091 lbs. of PM, and 26.6 lbs. of CO2 per km.

We assume that tankers travelling from Valdez to Pacific refineries maintain a distance of about

100 miles from shore except when docking. The assumed distance from Valdez to the three

refineries are: 2,700 km to Puget Sound, 3,700 km to San Francisco, and 4,300 km to Los

Angeles. However, some of this travel (1,100 km) is along the Canadian coast of British

Columbia. Damages that occur outside of United States territory are not included in our

analysis. A tanker travelling from Valdez to the Pacific refineries may pass through up to five

OCS Planning Areas (Gulf of Alaska, Washington/Oregon, and the three California areas). A

tanker travelling from Valdez to Puget Sound is assumed to travel 1,300 km in the Gulf of

Alaska Planning Area and 300 km in the Washington/Oregon Planning Area. A tanker travelling

from Valdez to San Francisco is assumed to travel 1,300 km in the Gulf of Alaska Planning

Area, 700 km in the Washington/Oregon Planning Area, 300 km in the Northern California

Planning Area, and 300 km in the Central California Planning Area. A tanker travelling from

Valdez to Los Angeles is assumed to travel 1,300 km in the Gulf of Alaska Planning Area, 700

km in the Washington/Oregon Planning Area, 300 km in the Northern California Planning Area,

400 km in the Central California Planning Area, and 500 km in the Southern California Planning

Area. Finally, tankers in the Western and Central Gulf of Mexico Planning Areas are assumed to

travel directly from floating platforms to shore over a distance of 250 km.

9.2.5 Well Drilling Emissions

Annual emissions rates for an operating development well are 21.49 tons of NOx, 5.73 tons of

CO, 2.52 tons of SOx, 2.07 tons of VOC's, 0.61 tons of PM, and 1,832 tons of CO2 (Sholten,

2000). We assume that well drilling in each OCS Planning Area occurs at a distance from shore

as given in Section 9.2.1 above.

Supply and crew boat trips are necessary to support exploration activities. We assume that

drilling a development or exploratory well will require 18 supply boat trips and 8 crew boat trips

(Minerals Management Service, 1997). Each of these boat trips is a round trip. Note that these

boat trips are in addition to those trips that are associated with the operation of a platform

(described in Section 9.2.2 above).

9.2.6 Platform Construction Emissions

Platform construction emissions per platform constructed as given in Sholten (2000) as 211 tons

of NOx, 42 tons of CO, 12 tons of SOx, 12 tons of VOC's, 13 tons of PM, and 2,485 tons of CO2.

We assume that platform construction occurs at a distance from shore in each OCS Planning

Area equal to the one-way distances presented in Section 9.2.1.
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9.2.7 Pipeline Construction Emissions

Pipeline construction emissions rates are given in Sholten (2000) as 7.4 tons of NOx, 2.5 tons of

CO, 1.0 tons of SOx, 0.7 tons of VOC's, 0.6 tons of PM, and 139 tons of CO2 per mile of pipeline

constructed. We use the values in Section 9.2.1 as the estimates for pipeline distance to shore for

each OCS Planning Area.

9.2.8 Oil Spill Emissions

Oil spills result in VOC emissions as the spilled material volatizes into the air. The VOC

emissions rate is estimated as 0.039 tons (78 pounds) of VOC's per barrel of oil spilled (MMS,

1998). We assume that spills occur along a line between a platform and the shore as described in

Section 9.2.1

9.3 Pollutant Transport

9.3.1 Literature Review

We assume that pollutants emitted offshore cause economic damages primarily because of

reductions in air quality over populated land areas. While offshore emissions may cause some

economic damages to oil platform workers, boaters, and the marine ecology, we could not find

sufficient data to estimate these impacts. We conducted a literature review for data on the

transport of offshore pollutants to coastal areas. Unfortunately, few data are available. The

impact of offshore pollutants on air quality over populated regions is a problem that would

benefit from additional research. Seinfeld and Pandis (1998) discuss the need for improved data

on pollution transport and source-receptor relations. While we make explicit assumptions below

regarding the transport of offshore pollutants using the available data, we emphasize that these

assumptions should be considered preliminary. Future research on pollution transport and

impacts should allow refinement of our assumptions to increase the validity of the economic

damage estimates.

Research by the California Air Resources Board and the South Coast Air Quality Management

District (2000) used tracer gases and model simulations to estimate the flux of NOx emissions

from offshore marine vessels into the South Coast air basin. The research focused on emissions

from ships in the shipping lanes into Southern California. These shipping lanes are generally

within 10-60km (6-37 miles) of the coast. Based on daily emissions from ships of 67.35 tons of

NOx, the daily net mass flux of NOx into the South Coast air basin ranged from 8 to 37 tons

depending on the weather. Average net flux into the basin was about 23 tons of NOx. Thus, the

study suggests that an average of about 34% of the mass of NOx emissions from ships is

transported into the basin. Note that the winds in the area blow predominately onshore, which

would tend to maximize the transport of offshore emissions to the air basin.

Galloway, et al. (1984) studied the transport of nitrogen and sulfur across the Atlantic Ocean

from North America to Europe. They estimated that 34% of the sulfur and 24-71% of the

nitrogen was transported across the Atlantic Ocean. While the nitrogen transport range is quite

broad, they note that it is generally believed that nitrogen compounds are removed from the
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atmosphere more quickly than sulfur compounds. This is supported by evidence from Galloway

and Whelpdale (1987) that 25 to 35% of the SOx emitted in North America is transported off the

continent while only 15 to 25% of the NOx is transported off the continent.

Seinfeld and Pandis (1998) summarize the available research on pollutant transport distances.

They note that the typical daily transport distance for pollutants is about 400 km (around 250

miles). Average atmospheric lifetimes for SO2 and NOx are one to three days, implying mean

transport distances of 400 to 1,200 km. Seinfled and Pandis note that the development of

detailed source-receptor relationships is a difficult task that is subject to significant uncertainties.

Current mathematical models have uncertainty factors of two or more.

Benkovitz, et al, (1994) also studied the long-range transport of sulfur compounds. They found

that about 50% of sulfate is transported 900 km (560 miles) on average. The 90% confidence

interval estimates typical transport ranging from 400 km to 1,700 km, similar to the values from

Seinfeld and Pandis (1998).

While the behavior of pollutants over oceans may differ from transport over land, research on the

transport of pollutants from land-based sources provides additional insights. Clark (1993)

summarizes research on the potential for NOx emissions to increase ozone levels downwind.

Three separate studies are discussed that conclude no increase in ozone concentrations in

distances from 45 km to 93 km downwind from large NOx sources. Several other studies are

mentioned that identified ozone formation 30-50 km downwind from large NOx sources. Two

other studies are mentioned that identified some increase in ozone concentrations 100 km

downwind from a NOx source. The Committee on Tropospheric Ozone Formation and

Measurement (1992) notes that ozone plumes can cover an area of 150 km by 50 km and

maintain integrity for 12 hours.

Lyons and Scott (1990) provide data on the residence time of various pollutants in the

atmosphere. They estimate that NO2 particles have a residence time of approximately 10 hours

with an associated horizontal transport of about 50 km (31 miles). The residence time of SO2 is

highly variable but is typically around 50 hours with a horizontal transport of around several

hundred km. The residence time and transport of particulate matter is highly dependent on the

size of the particles. Particles smaller than 1.0 micrometers (μm) will reside longer and be

transported further than SO2 emissions. However, larger particles around 10 μm may display

properties more similar to NO2. Note that the EPA air quality standards for particulate matter

focus on particles smaller than 10 μm. The tropospheric lifetimes of VOC's can also vary

significantly from several years to less than an hour depending on the compound (Committee on

Tropospheric Ozone Formation and Measurement, 1992). Lyons and Scott (1990) note that CO

is remarkably stable in the atmosphere.

9.3.2 Effect of Winds

Pollutants generally move downwind. The California Air Resources Board and the South Coast

Air Quality Management District (2000) notes that a pollutant plume will have a width of

approximately 10 km after 100 km of travel downwind. We could not find data indicating

whether the transport of pollutants upwind is significant. Some pollutant transport seems likely

in directions other than the direction of the predominant winds because winds are variable.
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We use historical NOAA data from offshore buoys to determine the prevailing wind direction in

each OCS Planning Area (NOAA, 2000). Prevailing wind direction for offshore buoys was

determined either directly from data on average wind direction or analysis of daily wind data

from 1998. As a simplification, we categorized prevailing wind direction as either "onshore" or

"offshore" by comparing the prevailing wind data with maps of each OCS Planning Area. Table

9.4 indicates whether the prevailing winds are onshore or offshore for each OCS Planning Area.

The assumptions of pollutant transport if winds are blowing predominantly onshore are discussed

below. If winds are predominantly offshore, we assume that the amount of pollutant transport is

25% of the transport with onshore winds. This considers the possibility that winds will

occasionally shift and some pollutants will be transported against the predominant winds. Note

that we do not consider the effect of different average wind speeds.

9.3.3 Transport Assumptions

Based on the references in Section 9.3.1 and data on prevailing winds, we make the following

assumptions about pollutant transport:

1. The primary concern with NOx emissions is the formation of ozone. Available research

suggests that ozone concentrations do not generally increase 100 km downwind from a NOx

source (Clark, 1993). Thus, we assume that NOx emissions will not increase ozone

concentrations at distances greater than 100 km downwind from the source. Further, typical

horizontal transport of NOx emissions is around 50 km (Lyons and Scott, 1990). If winds are

predominately blowing onshore, we assume that 34% of the NOx emissions within 100 km

(62 miles) are transported to land based on the research by the California Air Resources

Board and the South Coast Air Quality Management District (2000). If winds are

predominately offshore, then we assume that one-quarter of 34%, or 9%, of the NOx

emissions emitted within 100 km of the shore are transported to land. Any NOx emissions

greater than 100 km from shore are assumed to cause no significant economic damages.

2. No data could be located on the potential for VOC emissions to affect ozone levels

downwind. In the absence of applicable data, we apply the same assumptions for NOx to

VOC emissions. We assume that VOC's emitted greater than 100 km from the coast will not

impact onshore ozone concentrations and will not cause measurable economic damages.

However, some VOC's emitted within 100 km of the coast will cause economic damages

depending on the predominant wind direction as described above for NOx emissions.

3. SOx emissions appear to be transported over greater distances than NOx emissions. We do

not assume a transport limit for SOx emissions because 50% of sulfate is transported 900 km

(Benkovitz, et al., 1994) and the typical horizontal transport of SOx is several hundred km

(Lyons and Scott, 1990). Nearly all MMS oil and gas exploration and development activities

occur within 200 km of the shore and SOx emissions clearly have the potential to be

transported distances greater than 200 km. We assume that 75% of SOx emissions are

transported to land if winds are predominantly onshore. If winds are blowing predominantly

offshore, we assume that one-quarter of this value, or 19%, of SOx emissions are transported

to shore.
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4. CO is very stable in the atmosphere (Lyons and Scott, 1990). Seinfeld and Pandis (1998)

note that the chemical lifetime of CO is typically 30 to 90 days. We assume that all CO will

be transported to land if the winds are predominantly onshore. If the winds are

predominately offshore, we assume that 25% of CO will be transported to land.

5. PM transport is highly dependent on the size of the particles (Lyons and Scott, 1990).

Seinfeld and Pandis (1998) note that the residence times of particulate matter are relatively

short, typically from a few days to a few weeks. We assume that some settling of PM will

occur with transport. In the absence of better information, we assume that 50% of PM will

be transported to land if the winds are blowing predominantly onshore regardless of the

distance of the source from the shore. We assume that one-quarter of this value, or 13%, of

the PM will be transported to land if the winds are blowing predominately offshore.

6. CO2 mainly causes economic damages through its contribution to global warming. Some

portion of the offshore emissions of CO2 will be absorbed into the oceans. Miller (1998)

indicates that the oceans remove 29% of global CO2 emissions. Seinfeld and Pandis (1998)

note that 6% of CO2 particles emitted into the atmosphere will dissolve within one year while

29% will dissolve within 10 years. Particularly if winds are offshore, marine-based CO2

emissions may be removed at higher rates as compared to land-based emissions. However,

we could not locate any research on the issue. Without better data, we assume that 85% of

the offshore CO2 emissions cause economic damages due to a contribution to global

warming. This value is independent of winds or source location.

9.4 Economic Damages of Emissions

9.4.1 Literature Review

Emissions from OCS activities are measured in terms of tons of pollutant emitted. Thus, we

require economic damages measured in terms of dollars per ton of pollutant. While other data on

the economic damages of air pollution exist, such as the value of a statistical life or the value of a

symptom day, we could not link such estimates to the mass of pollutant emitted.

Perhaps the most comprehensive report on the social costs of air pollutants is an overview by the

Energy Information Administration (1995). The report summarizes several case studies that

estimate pollution externalities. Table 9.5 presents State-specific externality values updated to

1999 dollars. As expected, damage values for California tend to be the highest estimates.

However, some of the estimates are surprising. The per-ton values tend to be relatively low for

New York but high for Nevada. While the effects of CO2 emissions tend to be global, the

damage estimates vary significantly from $1 to $30 per ton.

As part of an Environmental Impact Statement, the Western Area Power Administration (1995)

provides a summary of per-ton damage estimates for different pollutants from various studies.

Table 9.6 updates these estimates to 1999 dollars. Again, the estimates vary significantly.

Consider the large difference (a factor of over 40:1) in the SO2 damage estimates from the CA

Energy Commission and the CA South Coast AQMD. Also note that Table 9.6 shows variability

in CO2 damages similar to Table 9.5.
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Krupnick, et al. (2000) estimate the benefits of NOx reductions leading to a decrease in ozone

concentrations. They estimated "mid" and "high" benefits estimates per ton of NOx emitted from

six different source regions in the Eastern U.S. from Georgia to New England. The "mid"

benefit estimates per ton of NOx reduction ranged from $255 (in 1999 dollars) to $481 with an

average of $367. The "high" estimates ranged from $3,937 to $7,410 per ton with an average of

$5,649.

Burtraw, et al. (1999) estimate the human mortality and morbidity benefits of NOx reductions

related to reductions in greenhouse gases. They develop estimates for four different scenarios

depending on the level of carbon taxes and the cost of capital. Their results imply per-ton

benefits for NOx reductions ranging from $603 to $804 per ton (in 1999 dollars) with an average

of $690 per ton.

Burtraw and Mansur (1999) estimate the benefits of SO2 reduction through trading and banking.

Under different market scenarios, they estimate the national benefits of SO2 reductions to be

$2,558, $4,070, $5,084, or $5,169 per ton (in 1999 dollars).

9.4.2 A Priori Assumptions

There are two a priori expectations about the economic damages of a ton of pollutant emitted in

different areas. Assuming increasing marginal damages of air pollutants, we expect that a ton of

a pollutant emitted in a region with poor air quality would cause more damage than a ton of the

same pollutant emitted in a region with good air quality, all other factors held constant. The U.S.

Environmental Protection Agency designates regions of the country as either "attainment" or

"nonattainment" for criteria pollutants based on the National Ambient Air Quality Standards

(NAAQS). Table 9.7 provides estimates of the percentage of the coastline that exceeds these

NAAQS for each OCS Planning Area (U.S. Environmental Protection Agency, 2000). The OCS

Planning Areas with the most serious air quality problems, measured as percent of coastal area

classified as nonattainment, are the Southern and Central California Planning Areas (70%-80%

nonattainment). Next, about 40%-50% of the coast of the Mid-Atlantic and Western Gulf of

Mexico Planning Areas are classified as nonattainment. Small coastal nonattainment areas exist

in the Central Gulf of Mexico, Gulf of Alaska, and Cook Inlet Planning Areas. The EPA

classifies all coastal areas of the remaining OCS Planning Areas as attainment.

The other expectation about air quality damages is that a ton of pollutant should cause greater

economic damages in a region with a high population density compared to a region with a low

population density, all other factors held constant. There is generally a strong correlation

between the population density of a region and whether it is classified as nonattainment. The

issue of population density is perhaps most significant in estimating the damages of pollutants

emitted in the Alaska Planning Areas. Consider that most of coastal Alaska is classified as

attainment and all of the Washington/Oregon Planning Area is classified as attainment. As the

population densities in Alaska outside of Anchorage are significantly lower than in the

Washington/Oregon Planning Area, we would expect lower per-unit damages in the Alaska

Planning Areas.
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9.4.3 Damage Estimates

Similar to other cost elements, we estimate per-unit air quality damages using both "low" and

"high" values. A range of values is used to reflect the range of values presented in the literature.

The differences in values are due to such factors as uncertainties in the estimates, temporal

differences, and differences in methodology.

If possible, we would prefer to choose air quality damage estimates for each OCS Planning Area

based on research that it is geographically proximate to the Planning Area. For example, in

choosing a damage value for the South Atlantic Planning Area, we would prefer to use a value

from research conducted in North Carolina, South Carolina, Georgia, or Florida. Our chosen

values should also agree with our a priori expectations described above. For example, if a

damage estimate based on research in North Carolina indicates a higher value than a damage

estimate from Southern California, this violates our a priori assumptions.

In weighing the tradeoffs between geographic proximity and correspondence with our a priori

assumptions, we generally place more weight on correspondence with our a priori assumptions.

Thus, we assume that damage estimates should be highest in the Southern and Central California

Planning Areas because these regions have the largest percentage of coastal area classified as

nonattainment (Table 9.7). The next highest values should apply to the Mid-Atlantic and

Western Gulf of Mexico Planning Areas. The Central Gulf of Mexico also contains coastal nonattainment regions. While the Gulf of Alaska and Cook Inlet Planning Areas do have coastal

nonattainment regions, these areas (particularly the Gulf of Alaska) have relatively low

population densities. Even though the Gulf of Alaska Planning Area has coastal nonattainment

regions while several OCS Planning Areas in the contiguous U.S. do not have nonattainment

regions (North Atlantic, Straits of Florida), we do not assume that the Gulf of Alaska should

have higher per-unit damage estimates. We could not locate any air quality damage estimates

derived from research in Alaska. Except for the Gulf of Alaska and Cook Inlet Planning Areas,

we assume that per-unit damage estimates in Alaska Planning Areas are 10% of the values for

the Washington/Oregon Planning Area based on the low population densities and good air

quality.

9.4.3.1 Per-ton NOx Damage Estimates

Our literature review located 12 per-ton NOx damage estimates in Tables 9.5 and 9.6 as well as

the estimates by Krupnick, et al. (2000) and Burtraw, et al. (1999). Considering all our NOx

damage estimates, only the $349,261 per-ton estimate in Table 9.6 seems to be an outlier. The

low damage estimates seem to be around $2,000 or less. The higher damage estimates are

generally between $4,000 and $12,000. We choose a high value of $11,854 per ton for the

Southern and Central California Planning Areas because this is the highest NOx value (except for

the $349,261 estimate) and it is based on research in California. For the Mid-Atlantic and

Western Gulf of Mexico Planning Areas, we choose a high value of $8,546 per ton from Table

9.5. Note that this value is based on research in MA, which borders the Mid-Atlantic Planning

Area. For all other OCS Planning Areas in the contiguous U.S., we choose a high value of

$5,649 per ton from the Krupnick, et al. (2000) research from the Eastern U.S. We also use this

as our high damage estimate for the Cook Inlet Planning Area. We use a slightly lower high

damage estimate of $4,155 for the Gulf of Alaska Planning Area (from Table 9.5). For all
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remaining Alaska Planning Areas, we assume a high value of $565 per ton (10% of the WA/OR

Planning Area estimate).

We choose $1,009 as our low NOx estimate for the Southern and Central CA Planning Areas

(from Table 9.5). Even though this value is from research in MN, it is one of the larger "low"

estimates. For the Western Gulf of Mexico and Mid-Atlantic Planning Areas, we choose a low

value of $690 per ton (from Burtraw, et al., 1999). We use the Krupnick, et al. (2000) average

estimate of $367 per ton for the other non-Alaska Planning Areas. We apply the $367 value to

the Cook Inlet and Gulf of Alaska Planning Areas. Finally, we apply a value of $37 per ton for

the remaining Alaska Planning Areas.

Table 9.8 includes a summary of the NOx per-ton damage estimates. Note that the values in

Table 9.8 apply to those emissions that reach shore. Emissions that do not reach shore are

assumed to cause no measurable damages.

9.4.3.2 Per-ton CO Damage Estimates

We located only two per ton damage estimates for CO (Table 9.5.). Based on these limited data,

we use the average of these two values ($1,171) as our low and high damage estimates for all

OCS Planning Areas except the remote Alaska Planning Areas.

9.4.3.3 Per-ton SOx Damage Estimates

The only SOx estimate that appears to be an upper outlier is the $99,935 per-ton value from

Table 9.6 while the lower outliers appear to be the $178 and $0 values from Table 9.5. There are

several estimates in the $1,000 to $3,000 range and several in the $4,000 to $6,000 range. We

assume these two ranges represent low and high estimates. Thus, we choose $5,405 per-ton as

our high damage estimate for the Southern and Central California Planning Areas (from Table

9.6. For the Mid-Atlantic and Western Gulf of Mexico Planning Areas, we choose a high value

of $5,084 per ton (from Burtraw and Mansur, 1999). For the remaining OCS Planning Areas

except for the remote Alaska Planning Areas, we choose a high SOx damage estimate of $4,070

per ton.

We choose a low SOx damage estimate of $2,345 for the Southern and Central California

Planning Areas. For most Planning Areas outside of Alaska, we choose a low SOx damage

estimate of $1,096 from Table 9.6. A summary of all SOx values is presented in Table 9.8.

9.4.3.4 Per-ton VOC Damage Estimates

We located six damage estimates of per ton VOC emissions. Of these, the $38,750 estimate in

Table 9.6 appears to be an outlier. Of the remaining estimates, the high values are $5,028,

$6,654, and $7,003 per ton. The low values are $1,201 and $1,413. We apply these values to

different OCS Planning Areas as shown in Table 9.8.

9.4.3.5 Per-ton PM Damage Estimates

We identified 11 per-ton PM damage estimates. We consider the $15,576 estimate in Table 9.6

to be an outlier. The high estimates range between $3,000 and $6,000 while the low estimates
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range between $300 and $1,600. These values are applied to each OCS Planning Area as shown

in Table 9.8.

9.4.3.6 Per-ton CO2 Damage Estimates

As described in Section 9.3.3, CO2 damages are independent of source location and winds. The

CO2 damage estimates in Table 9.5 and 9.6 range from $1 to $30 per ton. We choose a low CO2

damage estimate of $7 per ton as the average of the six lowest damage estimates in Tables 9.5

and 9.6. We choose a high CO2 damage estimate of $24 per ton as average of the five highest

values in Tables 9.5 and 9.6. This range of values is similar to the carbon tax scenarios of $10

and $25 per ton used in the analysis of Burtraw, et al. (1999).

We should emphasize that estimating CO2 damages is more difficult than estimating the damages

of other pollutants for two reasons. First, the damages from current CO2 emissions occur many

years in the future. Thus, damage estimates are dependent on how one addresses the issue of

discounting. Second, there is considerable uncertainty regarding the relationship between CO2

emissions, global climate change, and economic damages. While our damage bounds include the

majority of current CO2 damage estimates, future research will likely improve the validity of

CO2 damage estimates.

9.5 Air Quality Damage Estimates from OCS Activities

9.5.1 Methodology

Based on the above analytic steps, we combine the data on emissions rates with the assumptions

about pollutant transport and economic damages to estimate OCS economic damages. For each

OCS activity below, we first estimate the actual amount of emissions (in tons) that reach shore in

each OCS Planning Area. We then multiply these values by the per-ton damage estimates from

Section 9.4 to obtain our low and high damage estimates.

9.5.2 Supply and Crew Boat Damages

We need to estimate the amount of the emissions in Table 9.8 that reach shore and cause

economic damages. Using the assumptions from Section 9.3.3, the wind data from Table 9.4,

and the distance data in Table 9.1, we estimate the percentage of emissions that reach shore for

each pollutant and OCS Planning Area in Table 9.9. For example, in the Eastern Gulf of Mexico

the average development distance is 115 miles from shore. Only those boat emissions of NOx

and VOC's emitted within 62 miles, 54% of the emissions, reach shore based on our transport

assumptions (See Section 9.3.3.). We assume that only 9% of this 54% will reach shore if the

winds are predominately offshore. Thus, the final estimate for the percentage of NOx and VOC

emissions that reach shore in the Eastern Gulf of Mexico Planning Area is (9%*54%), or 5%.

Table 9.10 combines the data in Tables 9.8 and 9.9 to estimate the annual amount of each

pollutant that reaches shore per platform from boat emissions in each OCS Planning Area. The

final step to estimate boat emissions damages is to multiply the values in Table 9.10 by the

damage estimates in Table 9.5. Tables 9.11 and 9.12 provide the annual low and high damage

estimates per active platform due to supply and crew boat emissions.
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9.5.3 Platform Operations Damages

The percent of platform emissions reaching shore is a function of the assumed distance to shore

(from Table 9.1) and the assumptions in Section 9.3.3. The percent of emissions that reach shore

due to routine platform operations is presented in Table 9.13. Note that these percentages are

different from Table 9.9 for NOx due to the location of new platforms beyond 100 km (62 miles).

Table 9.14 presents the amount of annual emissions that reach shore and cause economic

damages due to the operation of one active platform. Finally, Tables 9.15 and 9.16 present the

low and high estimates of economic damages from routine platform operations.

9.5.4 Tanker Loading and Transport Damages

As described in Section 9.3.3, we assume that VOC emissions reach shore only if the emission

source is within 100 km (62 miles). Tanker loading in the Western and Central Gulf of Mexico

Planning Areas is assumed to occur farther offshore than 100 km and, thus, causes no economic

damages. However, tanker loading in Alaska occurs at Valdez and causes damage in the Gulf of

Alaska Planning Area. We assume that all VOC's emitted during tanker loading at Valdez cause

economic damages because loading occurs at port. Each ton of VOC emissions in the Gulf of

Alaska Planning Area causes a low damage estimate of $1,201 and a high damage estimate of

$5,028 (Table 9.8). Each 1,000 barrels of oil loaded onto a tanker will result in 0.0227 tons of

VOC emissions (Section 9.2.4). Thus, each 1,000 barrels of oil loaded at Valdez will result in a

low estimate of $27.26 in VOC emissions damages and a high estimate of $114.14 in damages

(Tables 9.17 and 9.18). Note again that all oil produced in Alaska, except for oil from the Cook

Inlet Planning Area, is loaded onto tankers at Valdez. For oil from the Cook Inlet Planning Area,

only 5% is loaded onto tankers at Valdez.

During tanker transport, oil volatilization results in VOC emissions (Section 9.2.4). However,

because VOC emissions reach shore and cause damages only if they are emitted within 100 km,

we assume that most VOC emissions from volatilization do not cause economic damages.

However, volatile VOC's emitted within 100 km will cause economic damages. In the Western

and Central Gulf of Mexico Planning Areas, a one-day trip will result in 0.0062 tons of VOC

emissions per 1,000 barrels transported (based on 0.0431 tons of VOC emissions per 1,000

barrels transported for a one-week trip). We assume that 40% of these emissions occur within

100 km, or 0.0025 tons per 1,000 barrels transported. With onshore winds, 34% of this amount

(0.0009 tons) will be transported to shore and cause damages (Section 9.3.3). Based on the perton damages in Table 9.8, the VOC damages per 1,000 barrels transported by tanker in the

Western Gulf of Mexico Planning Area are a low estimate of $1.23 and a high estimate of $5.78.

The VOC damages per 1,000 barrels transported by tanker in the Central Gulf of Mexico

Planning Area are a low estimate of $1.04 and a high estimate of $4.36. These values are given

in Tables 9.17 and 9.18.

Tankers travelling from Valdez to the Pacific refineries are outside of the 100 km VOC transport

limit for all but a brief period during exit from Valdez and entry into the refinery port. Thus, we

assume volatilization damages for tankers leaving Alaska for the Pacific refineries are negligible.

The final tanker-related air pollution damage is tanker emissions during transport. Tankers will

emit pollutants as give in Section 9.2.4 above. For tankers travelling in the Western and Central
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Gulf of Mexico Planning Areas, a 250 km round trip will result in the emissions of 708 lbs. of

NOX, 78 lbs. of CO, 880 lbs. of SOx, 1 lb. of VOC's, 254 lbs. of PM, and 13,300 lbs. of CO2. We

use the assumptions in Section 9.3.3 to determine transport percentages. For NOx and VOC's, we

assume that 40% of the omissions occur within 100 km. Thus, the amount of pollutants that

cause damages are: 96 lbs. of NOX, 78 lbs. of CO, 660 lbs. of SOx, 0.14 lbs. of VOC's, 127 lbs.

of PM, and 11,300 lbs. of CO2. Assuming a tanker size of 233,000 barrels, the economic

damage estimates from tanker emissions in the Western and Central Gulf of Mexico Planning

Areas are presented in Tables 9.17 and 9.18.

We assume that NOx and VOC's emitted by tankers travelling from Valdez to the Pacific ports

almost exclusively originate outside of 100 km from shore and cause negligible air pollution

economic damages. However, economic damages from CO, SOx, PM, and CO2 emissions do

occur. Note that all winds in the Alaska and Pacific Planning Areas are predominately onshore

(Table 9.4). Thus, we assume that 100% of CO, 75% of SOx, 50% of PM, and 85% of CO2

emissions cause economic damages. For each potential refinery location, we estimated the low

and high damages for each pollutant to each potential OCS Planning Area using the assumed

travel distances in Section 9.2.4. We then weighed these damages based on the proportion of oil

transported to each refinery port. These values are present in Tables 9.17 and 9.18. Note that

these damages are attributed to the Alaska OCS Planning Area where production originally

occurs. However, one can also distribute these damages to the OCS Planning Areas where

damages actually occur. Using the weighted average of distribution by refinery port, the low

damages are distributed 45% to the Gulf of Alaska, 20% to the WA/OR, 7% to Northern CA,

17% to Central CA, and 11% to Southern CA. The high damages are distributed 49% to the Gulf

of Alaska, 22% to the WA/OR, 8% to Northern CA, 12% to Central CA, and 8% to Southern

CA.

9.5.5 Well Drilling Damages

Drilling an exploratory or development well causes one-time air pollution damages from supply

and crew boat trips. Development wells also cause emissions on an on-going basis as described

in Section 9.2.5. For each OCS Planning Area, we assume that well drilling occurs the same

distance from shore as the location of platforms in Table 9.1. Thus, the percentages of boat

emissions that reach shore from well drilling are the same as in Table 9.9. Tables 9.19 and 9.20

present the present the low and high boat emissions damages per well drilled. All of these are

one-time damages that occur when the well is drilled.

Each operating development well also emits pollutants as described in Section 9.2.5. The

percentages of these pollutants that reach shore and cause economic damages are the same as in

Table 9.13. The low and high estimates of the annual damages per development well are given

in Tables 9.21 and 9.22.

9.5.6 Platform Construction Damages

We assume the proportion of emissions reaching shore for platform construction is the same as

in Table 9.13 for platform operations emissions. The low and high damage estimates per

platform constructed are given in Tables 9.23 and 9.24.
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9.5.7 Pipeline Construction Emissions and Damages

We assume the proportion of pipeline emissions reaching shore is equal to the boat emissions

proportions in Table 9.9. The low and high pipeline emissions damage estimates are presented

in Tables 9.25 and 9.26.

9.5.8 Oil Spill Emissions and Damages

The proportion of VOC's from oil spills reaching shore is assumed to be the same as the

proportions for boat emissions (Table 9.9). The low and high air quality damage estimates per

barrel spilled are presented in Table 9.27.

9.5.9 Summary of Damage Estimates

Tables 9.28 and 9.29 present a summary of all air quality damages from continuous operations.

Tables 9.30 and 9.31 present a summary of all air quality damages from one-time events (well

drilling, platform construction, and pipeline installation). Tables 9.30 and 9.31 also present the

air quality damages from oil spills.
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References

Benkovitz, Carmen L., Carl M. Berkowitz, Richard C. Easter, Seth Nemesure, Richard Wagener,

and Stephen E. Schwartz. 1994. "Sulfate Over the North Atlantic and Adjacent Continental

Regions: Evaluation for October and November 1986 Using a Three-Dimensional Model

Driven by Observation-Driven Meteorology", Journal of Geophysical Research, vol.

99(D10): 20725-20756.

Burtraw, Dallas, and Erin Mansur. 1999. “The Effects of Trading and Banking in the SO2

Allowance Market.” Resources for the Future Discussion Paper 99-25, Washington, D.C.

Burtraw, Dallas, Alan Krupnick, Karen Palmer, Anthony Paul, Michael Toman, and Cary Bloyd.

1999. “Ancillary Benefits of Reduced Air Pollution in the U.S. from Moderate Greenhouse

Gas Mitigation Policies in the Electricity Sector. “ Resources for the Future Discussion Paper

99-51, Washington, D.C.

California Air Resources Board and the South Coast Air Quality Management District. 2000.

“Air Quality Impacts from NOx Emissions of Two Potential Marine Vessel Control

Strategies in the South Coast Air Basin, Final Draft.”

Clark, P.A. 1993. "Photochemical Oxidants from Large Combustion Sources" in The Chemistry

and Deposition of Nitrogen Species in the Troposphere, A.T. Cocks (editor), The Royal

Society of Chemistry, Cambridge, England.

Committee on Tropospheric Ozone Formation and Measurement. 1992. Rethinking the Ozone

Problem in Urban and Regional Air Pollution. National Academy Press, Washington, D.C.

Delucchi, Mark A. 1997. "The Annualized Social Cost of Motor-Vehicle Use in the U.S., 1990-

1991: Summary of Theory, Data, Methods, and Results". Report UCD-ITS-RR-96-3(1),

Institute of Transportation Studies, University of California, Davis.

Energy Information Administration, U.S. Department of Energy. 1995. “Electricity Generation

and Environmental Externalities: Case Studies.” Report DOE/EIA-0598.

Galloway, James N., Douglas M. Whelpdale, and George T. Wolff. 1984. "The Flux of S and

N Eastward from North America". Atmospheric Environment, vol. 18(12):2595-2607.

Galloway, James N., and Douglas M. Whelpdale. 1987. "WATOX-86 Overview and Western

North Atlantic Ocean S and N Atmospheric Budgets". Global Biogeochemical Cycles, vol.

89:1447-1458.

Jacobs Engineering Group. 1989. “Air Quality Impact of Proposed OCS Lease Sale #95, Final

Report.” Pasadena CA.

King, Bill. 2001. Personal communication. Minerals Management Service.
202 Section 9: Air Quality

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Krupnick, Alan, Virginia McConnell, Matt Cannon, Terrell Stoessell, and Michael Batz. 2000.

“Cost-Effective NOx Control in the Eastern United States.” Resources for the Future

Discussion Paper 00-18, Washington, D.C.

Lyons, T.J., and W.D. Scott. 1990. Principles of Air Pollution Meteorology. CRC Press, Boca

Raton, FL.

Miller, G. Tyler. 1998. Living in the Environment, 10th edition. Wadsworth Publishing

Company, Belmont, CA.

Minerals Management Service. 1998. “Gulf of Mexico OCS Oil and Gas Lease Sales 171,174,

177 and 180. Western Planning Area FEIS.”

Minerals Management Service. 1997. “Gulf of Mexico OCS Oil and Gas Lease Sales 169,172,

175, 178, and 182.” Central Planning Area FEIS.

National Oceanic and Atmospheric Administration. 2000. National Data Buoy Center,

http://seaboard.ndbc.noaa.gov/Maps/

Scholten, Terry. 2000. Spreadsheet on Western Gulf of Mexico Planning Area emissions,

Minerals Management Service.

Seinfeld, John H. and Spyros N. Pandis. 1998. Atmospheric Chemistry and Physics. John

Wiley & Sons, Inc., New York.

U.S. Environmental Protection Agency. 1999. “Final Regulatory Impact Analysis: Control of

Emissions from Marine Diesel Engines.” Report EPA420-R-99-026.

U.S. Environmental Protection Agency. 2000. “The EPA Green Book,”

www.epa.gov/oar/oaqps/greenbk/.

Western Area Power Administration. 1995. “WAPA EIS: Energy Planning and Management

Program EIS,” DOE/EIS-0182.
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Table 9.1. Assumed Distance of Platforms from Shore, by OCS Planning Area

OCS Planning Area

One-Way Distance

of Platform to Shore

(miles)

North Atlantic 65

Mid-Atlantic 65

South Atlantic 65

Straits of Florida 15

Eastern Gulf 115

Central Gulf 88

Western Gulf 130

Southern California 15

Central California 15

Northern California 15

Washington/Oregon 15

Gulf of Alaska 7

Cook Inlet 15

Chukchi Sea 15

Beaufort Sea 7

All other Alaska

Planning Areas

65

Source: see text.
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Table 9.2. Emissions Rates for Supply and Crew Boats

Supply Boats Crew Boats

Pollutant Emissions per

1,000 gal. fuel

(pounds)

Emissions per

260-mile

round trip

(tons)

Emissions per

1,000 gal. Fuel

(pounds)

Emissions per

260-mile

round trip

(tons)

NOx 550.0 0.6028 516.7 0.1914

CO 54.8 0.0601 54.8 0.0203

SOx 56.8 0.0623 56.8 0.0210

VOC 23.8 0.0261 23.8 0.0088

PM 33.0 0.0362 33.0 0.0122

CO2 6,490 7.1130 6,490 2.4045

Source: Sholten, 2000
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Table 9.3. Estimated Annual Supply and Crew Boat Emissions per Platform, by OCS

Planning Area (tons)

OCS Planning Areas

Gulf of

AK,

Beaufort

Sea

Straits of

FL, CA,

WA/OR,

Cook Inlet,

Chukchi

Sea

Atlantic

Areas, All

Other AK

Areas

Central

Gulf of

Mexico

Eastern

Gulf of

Mexico

Western

Gulf of

Mexico

One-way Distance

to Shore (miles) 7 15 65 88 115 130

Supply Boats

NOx 2.30 4.94 21.40 28.97 37.86 42.80

CO 0.23 0.49 2.13 2.89 3.77 4.26

SOx 0.24 0.51 2.21 2.99 3.91 4.42

VOC 0.10 0.21 0.93 1.25 1.64 1.85

PM 0.14 0.30 1.28 1.74 2.27 2.57

CO2 27.19 58.27 252.51 341.86 446.75 505.03

Crew Boats

NOx 0.34 0.73 3.16 4.28 5.59 6.32

CO 0.04 0.08 0.34 0.45 0.59 0.67

SOx 0.04 0.08 0.35 0.47 0.61 0.69

VOC 0.02 0.03 0.15 0.20 0.26 0.29

PM 0.02 0.05 0.20 0.27 0.36 0.40

CO2 4.27 9.16 39.67 53.71 70.19 79.35

Source: see text.
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Table 9.4. Predominant Wind Direction, by OCS Planning Area

OCS Planning Area(s) Predominant Wind Direction

North Atlantic, Mid-Atlantic,

South Atlantic, Straits of

Florida, Eastern Gulf of

Mexico

Offshore

Central and Western Gulf of

Mexico Onshore

All California Planning

Areas Onshore

All Alaska Planning Areas Onshore

Source: National Oceanic and Atmospheric Administration, 2000.
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Table 9.5. Energy Information Administration per-ton Damage Estimates (1999 dollars)

Pollutant

State

SO2 NOx PM VOC's CO2 CO

CA 5,325 10,825 5,470 5,028 11 NA1

MA 2,018 8,546 5,223 7,003 28 1,140

MN 178 1,009 1,512 1,413 12 NA1

NV 2,037 8,879 5,458 1,201 28 1,201

NY 1,706 2,252 395 NA1 1 NA1

OR 0 4,155 3,561 NA1 30 NA1

WI NA1 NA1 NA1 NA1 18 NA1

1 - No value specified in the report.

Source: Energy Information Administration, U.S. Department of Energy, 1995.
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Table 9.6. Western Area Power Administration per-ton Damage Estimates (1999 dollars)

Pollutant

Source

SO2 NOx PM VOC's CO2

Tellus Institute 1,988 8,668 5,328 7,062 31

CA Energy

Commission 2,345 11,854 15,576 6,654 10

New York DPS 1,096 2,447 433 NA1 1

CA South Coast

AQMD 99,935 349,261 5,864 38,750 NA1

PACE 5,405 2,192 3,161 NA1 18

Bonneville 2,549 548 1,122 NA1 8

1 - No value specified in the report.

Source: Western Area Power Administration, 1995.
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Table 9.7. Percent of Coastline Classified as Nonattainment by the EPA

OCS Planning Area Percent of Coast Classified

as Nonattainment

North Atlantic 0%

Mid-Atlantic 50%

South Atlantic 0%

Straits of Florida 0%

Eastern Gulf of Mexico 0%

Central Gulf of Mexico 8%

Western Gulf of Mexico 40%

Southern California 80%

Central California 70%

Northern California 0%

Washington/Oregon 0%

Gulf of Alaska 1%

Kodiak 0%

Cook Inlet 2%

Shumagin 0%

North Aleutian Basin 0%

Saint George Basin 0%

Aleutian Arc 0%

Bowers Basin 0%

Aleutian Basin 0%

Navarin Basin 0%

Saint Matthew Hall 0%

Norton Basin 0%

Hope Basin 0%

Chukchi Sea 0%

Beaufort Sea 0%

Source: U.S. Environmental Protection Agency, 2000.
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Table 9.8. Damages per Ton of Pollutant that Reaches Shore (1999 dollars)

OCS NOx CO SOx VOC PM

Planning

Area Low High Low High Low High Low High Low High

North Atlantic 367 5649 1171 1171 1096 4070 1201 5028 433 3561

Mid-Atlantic 690 8546 1171 1171 2018 5084 1413 6654 1122 5223

South Atlantic 367 5649 1171 1171 1096 4070 1201 5028 433 3561

Straits of FL 367 5649 1171 1171 1096 4070 1201 5028 433 3561

Eastern GOM 367 5649 1171 1171 1096 4070 1201 5028 433 3561

Central GOM 367 5649 1171 1171 1090 4070 1201 5028 433 3561

Western GOM 690 8546 1171 1171 2018 5084 1413 6654 1122 5223

S. California 1009 11854 1171 1171 2345 5405 1413 7003 1512 5470

C. California 1009 11854 1171 1171 2345 5405 1413 7003 1512 5470

N. California 367 5649 1171 1171 1096 4070 1201 5028 433 3561

WA/OR 367 5649 1171 1171 1096 4070 1201 5028 433 3561

Gulf of Alaska 367 4155 1171 1171 1096 4070 1201 5028 395 3161

Cook Inlet 367 5649 1171 1171 1096 4070 1201 5028 433 3561

Kodiak 37 565 117 117 110 407 120 503 43 356

Shumagin 37 565 117 117 110 407 120 503 43 356

N. Aleutian

Basin 37 565 117 117 110 407 120 503 43 356

Aleutian Arc 37 565 117 117 110 407 120 503 43 356

St. George

Basin 37 565 117 117 110 407 120 503 43 356

St. Matt. Hall 37 565 117 117 110 407 120 503 43 356

Bowers Basin 37 565 117 117 110 407 120 503 43 356

Aleutian Basin 37 565 117 117 110 407 120 503 43 356

Navarin Basin 37 565 117 117 110 407 120 503 43 356

Norton Basin 37 565 117 117 110 407 120 503 43 356

Hope Basin 37 565 117 117 110 407 120 503 43 356

Chukchi Sea 37 565 117 117 110 407 120 503 43 356

Beaufort Sea 37 565 117 117 110 407 120 503 43 356

Source: see text.
Section 9: Air Quality 211

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Table 9.9. Percent of Boat Emissions that Reach Shore, by OCS Planning Area

OCS Planning Areas

Gulf of AK,

Beaufort

Sea, Cook

Inlet,

Chukchi

Sea, CA,

WA/OR,

Straits

of FL

All

Other

AK

Areas

All

Atlantic

Areas

Central

Gulf of

Mexico

Eastern

Gulf of

Mexico

Western

Gulf of

Mexico

Predominant

Wind Direction Onshore Offshore

OnshoreOffshoreOnshoreOffshore On-shore

Pollutant

NOx 34% 9% 32% 8% 25% 5% 17%

CO 100% 25% 100% 25% 100% 25% 100%

SOx 75% 19% 75% 19% 75% 19% 75%

VOC 34% 9% 32% 8% 25% 5% 17%

PM 50% 13% 50% 13% 50% 13% 50%

CO2 85% 85% 85% 85% 85% 85% 85%

Source: see text.
212 Section 9: Air Quality

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Table 9.10. Annual Supply and Crew Boat Emissions that Reach Land, by OCS Planning

Area (tons)

OCS Planning Areas

Gulf of

AK,

Beaufort

Sea

Cook

Inlet,

Chukchi

Sea, CA,

WA/ OR

Straits

of FL

All

Other

AK

Areas

All

Atlantic

Areas

Central

Gulf of

Mexico

Eastern

Gulf of

Mexico

Western

Gulf of

Mexico

Supply Boats

NOx 0.78 1.68 0.44 6.85 1.71 7.24 1.89 7.28

CO 0.23 0.49 0.12 2.13 0.53 2.89 0.94 4.26

SOx 0.18 0.38 0.10 1.66 0.42 2.24 0.74 3.31

VOC 0.03 0.07 0.02 0.30 0.07 0.31 0.08 0.31

PM 0.07 0.15 0.04 0.64 0.17 0.87 0.30 1.28

CO2 23.11 49.53 49.53 214.64 214.64 290.58 379.74 429.27

CREW BOATS

NOx 0.12 0.25 0.07 1.01 0.25 1.07 0.28 1.07

CO 0.04 0.08 0.02 0.34 0.08 0.45 0.15 0.67

SOx 0.03 0.06 0.02 0.26 0.07 0.35 0.12 0.52

VOC 0.01 0.01 0.00 0.05 0.01 0.05 0.01 0.05

PM 0.01 0.02 0.01 0.10 0.03 0.14 0.05 0.20

CO2 3.63 7.78 7.78 33.72 33.72 45.66 59.67 67.45

SUPPLY AND CREW BOATS COMBINED

NOx 0.90 1.93 0.51 7.86 1.96 8.31 2.17 8.35

CO 0.27 0.57 0.14 2.47 0.62 3.34 1.09 4.93

SOx 0.21 0.44 0.11 1.92 0.49 2.60 0.86 3.84

VOC 0.04 0.08 0.02 0.34 0.09 0.36 0.09 0.36

PM 0.08 0.17 0.04 0.74 0.19 1.01 0.34 1.49

CO2 26.75 57.31 57.31 248.36 248.36 336.24 439.41 496.72

Source: see text.
Section 9: Air Quality 213

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Table 9.11. Annual Boat Emissions Damage Estimates per Active Platform, Low Estimates

(in 1999 dollars)

OCS Planning

Area

NOx

Damages

CO

Damages

SOx

Damages

VOC

Damages

PM

Damages

CO2

Damages

Total

Damages

North Atlantic $721 $722 $533 $103 $84 $1,739 $3,901

Mid-Atlantic $1,356 $722 $980 $121 $217 $1,739 $5,135

South Atlantic $721 $722 $533 $103 $84 $1,739 $3,901

Straits of FL $187 $167 $123 $27 $19 $401 $924

Eastern Gulf $797 $1,278 $942 $114 $148 $3,076 $6,355

Central Gulf $3,050 $3,911 $2,846 $436 $435 $2,354 $13,032

Western Gulf $5,761 $5,778 $7,741 $515 $1,667 $3,477 $24,939

Southern CA $1,944 $667 $1,038 $119 $259 $401 $4,428

Central CA $1,944 $667 $1,038 $119 $259 $401 $4,428

Northern CA $707 $667 $485 $101 $74 $401 $2,435

WA/OR $707 $667 $485 $101 $74 $401 $2,435

Gulf of AK $330 $311 $226 $47 $35 $187 $1,136

Cook Inlet $707 $667 $485 $101 $68 $401 $2,429

Kodiak $291 $289 $211 $41 $32 $1,739 $2,602

Shumagin $291 $289 $211 $41 $32 $1,739 $2,602

N. Aleutian B. $291 $289 $211 $41 $32 $1,739 $2,602

Aleutian Arc $291 $289 $211 $41 $32 $1,739 $2,602

St. George B. $291 $289 $211 $41 $32 $1,739 $2,602

St. Matt. Hall $291 $289 $211 $41 $32 $1,739 $2,602

Bowers Basin $291 $289 $211 $41 $32 $1,739 $2,602

Aleutian Bas. $291 $289 $211 $41 $32 $1,739 $2,602

Navarin Bas. $291 $289 $211 $41 $32 $1,739 $2,602

Norton Basin $291 $289 $211 $41 $32 $1,739 $2,602

Hope Basin $291 $289 $211 $41 $32 $1,739 $2,602

Chukchi Sea $71 $67 $49 $10 $7 $401 $605

Beaufort Sea $33 $31 $23 $5 $3 $187 $282

Source: see text.
214 Section 9: Air Quality

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Table 9.12. Annual Boat Emissions Damage Estimates per Active Platform, High

Estimates (in 1999 dollars)

OCS Planning

Area

NOx

Damages

CO

Damages

SOx

Damages

VOC

Damages

PM

Damages

CO2

Damages

Total

Damages

North Atlantic $11,098 $722 $1,977 $431 $688 $5,961 $20,877

Mid-Atlantic $16,790 $722 $2,470 $570 $1,009 $5,961 $27,522

South Atlantic $11,098 $722 $1,977 $431 $688 $5,961 $20,877

Straits of FL $2,881 $167 $456 $112 $159 $1,376 $5,150

Eastern Gulf $12,272 $1,278 $3,499 $477 $1,217 $10,546 $29,288

Central Gulf $46,954 $3,911 $10,568 $1,823 $3,581 $8,070 $74,908

Western Gulf $71,357 $5,778 $19,501 $2,424 $7,760 $11,921 $118,742

Southern CA $22,841 $667 $2,392 $589 $938 $1,376 $28,802

Central CA $22,841 $667 $2,392 $589 $938 $1,376 $28,802

Northern CA $10,885 $667 $1,801 $423 $610 $1,376 $15,762

WA/OR $10,885 $667 $1,801 $423 $610 $1,376 $15,762

Gulf of AK $3,736 $311 $841 $197 $253 $642 $5,980

Cook Inlet $10,885 $667 $1,801 $423 $610 $1,376 $15,762

Kodiak $4,440 $289 $781 $172 $264 $5,961 $11,907

Shumagin $4,440 $289 $781 $172 $264 $5,961 $11,907

N. Aleutian B. $4,440 $289 $781 $172 $264 $5,961 $11,907

Aleutian Arc $4,440 $289 $781 $172 $264 $5,961 $11,907

St. George B. $4,440 $289 $781 $172 $264 $5,961 $11,907

St. Matt. Hall $4,440 $289 $781 $172 $264 $5,961 $11,907

Bowers Basin $4,440 $289 $781 $172 $264 $5,961 $11,907

Aleutian Bas. $4,440 $289 $781 $172 $264 $5,961 $11,907

Navarin Bas. $4,440 $289 $781 $172 $264 $5,961 $11,907

Norton Basin $4,440 $289 $781 $172 $264 $5,961 $11,907

Hope Basin $4,440 $289 $781 $172 $264 $5,961 $11,907

Chukchi Sea $1,089 $67 $180 $42 $61 $1,376 $2,814

Beaufort Sea $508 $31 $84 $20 $28 $642 $1,313

Source: see text.
Section 9: Air Quality 215

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Table 9.13. Percent of Platform Emissions that Reach Land, by OCS Planning Area

OCS Planning Areas

Gulf of AK,

Beaufort

Sea, CA,

WA/OR,

Cook Inlet,

Chukchi

Sea

Straits

of FL

All

Other

AK

Areas

All

Atlantic

Areas

Central

Gulf of

Mexico

Eastern

Gulf of

Mexico

Western

Gulf of

Mexico

Predominant

Wind

Direction

Onshore Offshore

Onshore Off-shore On-shore Off-shore Onshore

Pollutant

NOx 34% 9% 0% 0% 0% 0% 0%

CO 100% 25% 100% 25% 100% 25% 100%

SOx 75% 19% 75% 19% 75% 19% 75%

VOC 34% 9% 0% 0% 0% 0% 0%

PM 50% 13% 50% 13% 50% 13% 50%

CO2 85% 85% 85% 85% 85% 85% 85%

Source: see text.
216 Section 9: Air Quality

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Table 9.14. Amount of Annual Platform Emissions per Platform that Reach Land, by OCS

Planning Area (tons)

OCS Planning Areas

Gulf of AK,

Beaufort

Sea, CA,

WA/OR,

Cook Inlet,

Chukchi

Sea

Straits

of FL

All

Other

AK

Areas

All

Atlantic

Areas

Central

Gulf of

Mexico

Eastern

Gulf of

Mexico

Western

Gulf of

Mexico

Pollutant

NOx 17.30 4.58 0.00 0.00 0.00 0.00 0.00

CO 11.84 2.96 11.84 2.96 11.84 2.96 11.84

SOx 6.41 1.62 6.41 1.62 6.41 1.62 6.41

VOC 13.12 3.47 0.00 0.00 0.00 0.00 0.00

PM 0.47 0.12 0.47 0.12 0.47 0.12 0.47

CO2 1868.30 1868.30 1868.30 1868.30 1868.30 1868.30 1868.30

Source: see text.
Section 9: Air Quality 217

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Table 9.15. Annual Platform Emissions Damage Estimates per Active Platform, Low

Estimates (in 1999 dollars)

OCS Planning

Area

NOx

Damages

CO

Damages

SOx

Damages

VOC

Damages

PM

Damages

CO2

Damages

Total

Damages

North Atlantic $0 $3,466 $1,780 $0 $52 $13,078 $18,377

Mid-Atlantic $0 $3,466 $3,278 $0 $136 $13,078 $19,958

South Atlantic $0 $3,466 $1,780 $0 $52 $13,078 $18,377

Straits of FL $1,681 $3,466 $1,780 $4,170 $52 $13,078 $24,228

Eastern Gulf $0 $3,466 $1,780 $0 $52 $13,078 $18,377

Central Gulf $0 $13,865 $7,028 $0 $201 $13,078 $34,172

Western Gulf $0 $13,865 $12,940 $0 $522 $13,078 $40,405

Southern CA $17,455 $13,865 $15,037 $18,535 $703 $13,078 $78,673

Central CA $17,455 $13,865 $15,037 $18,535 $703 $13,078 $78,673

Northern CA $6,349 $13,865 $7,028 $15,754 $201 $13,078 $56,275

WA/OR $6,349 $13,865 $7,028 $15,754 $201 $13,078 $56,275

Gulf of AK $6,349 $13,865 $7,028 $15,754 $201 $13,078 $56,275

Cook Inlet $6,349 $13,865 $7,028 $15,754 $184 $13,078 $56,257

Kodiak $0 $1,385 $705 $0 $20 $13,078 $15,189

Shumagin $0 $1,385 $705 $0 $20 $13,078 $15,189

N. Aleutian B. $0 $1,385 $705 $0 $20 $13,078 $15,189

Aleutian Arc $0 $1,385 $705 $0 $20 $13,078 $15,189

St. George B. $0 $1,385 $705 $0 $20 $13,078 $15,189

St. Matt. Hall $0 $1,385 $705 $0 $20 $13,078 $15,189

Bowers Basin $0 $1,385 $705 $0 $20 $13,078 $15,189

Aleutian Bas. $0 $1,385 $705 $0 $20 $13,078 $15,189

Navarin Bas. $0 $1,385 $705 $0 $20 $13,078 $15,189

Norton Basin $0 $1,385 $705 $0 $20 $13,078 $15,189

Hope Basin $0 $1,385 $705 $0 $20 $13,078 $15,189

Chukchi Sea $640 $1,385 $705 $1,574 $20 $13,078 $17,403

Beaufort Sea $640 $1,385 $705 $1,574 $20 $13,078 $17,403

Source: see text.
218 Section 9: Air Quality

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Table 9.16. Annual Platform Emissions Damage Estimates per Active Platform, High

Estimates (in 1999 dollars)

OCS Planning

Area

NOx

Damages

CO

Damages

SOx

Damages

VOC

Damages

PM

Damages

CO2

Damages

Total

Damages

North Atlantic $0 $3,466 $6,612 $0 $431 $44,839 $55,348

Mid-Atlantic $0 $3,466 $8,259 $0 $631 $44,839 $57,196

South Atlantic $0 $3,466 $6,612 $0 $431 $44,839 $55,348

Straits of FL $25,868 $3,466 $6,612 $17,458 $431 $44,839 $98,674

Eastern Gulf $0 $3,466 $6,612 $0 $431 $44,839 $55,348

Central Gulf $0 $13,865 $26,099 $0 $1,656 $44,839 $86,459

Western Gulf $0 $13,865 $32,601 $0 $2,429 $44,839 $93,734

Southern CA $205,065 $13,865 $34,660 $91,860 $2,544 $44,839 $392,831

Central CA $205,065 $13,865 $34,660 $91,860 $2,544 $44,839 $392,831

Northern CA $97,723 $13,865 $26,099 $65,953 $1,656 $44,839 $250,135

WA/OR $97,723 $13,865 $26,099 $65,953 $1,656 $44,839 $250,135

Gulf of AK $71,878 $13,865 $26,099 $65,953 $1,470 $44,839 $224,104

Cook Inlet $97,723 $13,865 $26,099 $65,953 $1,656 $44,839 $250,135

Kodiak $0 $1,385 $2,610 $0 $166 $44,839 $49,000

Shumagin $0 $1,385 $2,610 $0 $166 $44,839 $49,000

N. Aleutian B. $0 $1,385 $2,610 $0 $166 $44,839 $49,000

Aleutian Arc $0 $1,385 $2,610 $0 $166 $44,839 $49,000

St. George B. $0 $1,385 $2,610 $0 $166 $44,839 $49,000

St. Matt. Hall $0 $1,385 $2,610 $0 $166 $44,839 $49,000

Bowers Basin $0 $1,385 $2,610 $0 $166 $44,839 $49,000

Aleutian Bas. $0 $1,385 $2,610 $0 $166 $44,839 $49,000

Navarin Bas. $0 $1,385 $2,610 $0 $166 $44,839 $49,000

Norton Basin $0 $1,385 $2,610 $0 $166 $44,839 $49,000

Hope Basin $0 $1,385 $2,610 $0 $166 $44,839 $49,000

Chukchi Sea $9,774 $1,385 $2,610 $6,598 $166 $44,839 $65,372

Beaufort Sea $9,774 $1,385 $2,610 $6,598 $166 $44,839 $65,372

Source: see text.
Section 9: Air Quality 219

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Table 9.17. Damages from Tanker Loading and Transport per Thousand Barrels

Loaded/Transported, Low Damage Estimates (1999 dollars)

Tanker Emissions Damages

OCS Planning

Area

Loading

Damages

(VOC

only)

Crude Oil

VOC

Emissions

during

Transport

NOx CO SOx VOC PM CO2

Total

Tanker

North Atlantic $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Mid-Atlantic $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

South Atlantic $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Straits of FL $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Eastern Gulf $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Central Gulf $0.00 $1.04 $0.08 $0.20 $1.54 $0.00 $0.12 $0.17 $2.11

Western Gulf $0.00 $1.23 $0.14 $0.20 $2.86 $0.00 $0.31 $0.17 $3.68

Southern CA $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Central CA $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Northern CA $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

WA/OR $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Gulf of AK $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Cook Inlet $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Kodiak $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Shumagin $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

N. Aleutian B. $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Aleutian Arc $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

St. George B. $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

St. Matt. Hall $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Bowers Basin $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Aleutian Bas. $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Navarin Bas. $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Norton Basin $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Hope Basin $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Chukchi Sea $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Beaufort Sea $27.26 $0.00 $0.00 $0.97 $9.16 $0.00 $0.80 $0.85 $11.78

Source: see text.
220 Section 9: Air Quality

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Table 9.18. Damages from Tanker Loading and Transport per Thousand Barrels

Loaded/Transported, High Damage Estimates (1999 dollars)

Tanker Emissions Damages

 OCS Planning

 Area

Loading

Damages

(VOC

only)

Crude Oil

VOC

Emissions

during

Transport

NOx CO SOx VOC PM CO2

Total

Tanker

North Atlantic $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Mid-Atlantic $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

South Atlantic $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Straits of FL $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Eastern Gulf $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Central Gulf $0.00 $4.36 $1.16 $0.20 $5.76 $0.00 $0.97 $0.58 $8.67

Western Gulf $0.00 $5.78 $1.76 $0.20 $7.20 $0.00 $1.42 $0.58 $11.16

Southern CA $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Central CA $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Northern CA $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

WA/OR $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

Gulf of AK $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Cook Inlet $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Kodiak $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Shumagin $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

N. Aleutian B. $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Aleutian Arc $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

St. George B. $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

St. Matt. Hall $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Bowers Basin $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Aleutian Bas. $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Navarin Bas. $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Norton Basin $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Hope Basin $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Chukchi Sea $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Beaufort Sea $114.14 $0.00 $0.00 $0.97 $30.21 $0.00 $4.98 $2.90 $39.06

Source: see text.
Section 9: Air Quality 221

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Table 9.19. Boat Emissions Damages per Well Drilled, Low Estimates (1999 dollars)

OCS Planning

Area

NOx

Damages

CO

Damages

SOx

Damages

VOC

Damages

PM

Damages

CO2

Damages

Total

Damages

North Atlantic $182 $182 $134 $26 $21 $438 $983

Mid-Atlantic $342 $182 $247 $31 $55 $438 $1,294

South Atlantic $182 $182 $134 $26 $21 $438 $983

Straits of FL $47 $42 $31 $7 $5 $101 $233

Eastern Gulf $201 $322 $237 $29 $37 $775 $1,602

Central Gulf $769 $986 $717 $110 $110 $593 $3,285

Western Gulf $1,452 $1,456 $1,951 $130 $420 $876 $6,285

Southern CA $490 $168 $262 $30 $65 $101 $1,116

Central CA $490 $168 $262 $30 $65 $101 $1,116

Northern CA $178 $168 $122 $25 $19 $101 $614

WA/OR $178 $168 $122 $25 $19 $101 $614

Gulf of AK $83 $78 $57 $12 $9 $47 $286

Cook Inlet $178 $168 $122 $25 $17 $101 $612

Kodiak $73 $73 $53 $10 $8 $438 $656

Shumagin $73 $73 $53 $10 $8 $438 $656

N. Aleutian B. $73 $73 $53 $10 $8 $438 $656

Aleutian Arc $73 $73 $53 $10 $8 $438 $656

St. George B. $73 $73 $53 $10 $8 $438 $656

St. Matt. Hall $73 $73 $53 $10 $8 $438 $656

Bowers Basin $73 $73 $53 $10 $8 $438 $656

Aleutian Bas. $73 $73 $53 $10 $8 $438 $656

Navarin Bas. $73 $73 $53 $10 $8 $438 $656

Norton Basin $73 $73 $53 $10 $8 $438 $656

Hope Basin $73 $73 $53 $10 $8 $438 $656

Chukchi Sea $18 $17 $12 $3 $2 $101 $153

Beaufort Sea $8 $8 $6 $1 $1 $47 $71

Source: see text.
222 Section 9: Air Quality

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Table 9.20. Boat Emissions Damages per Well Drilled, High Estimates (1999 dollars)

OCS Planning

Area

 NOx

Damages

 CO

Damages

 SOx

Damages

 VOC

Damages

 PM

Damages

 CO2

Damages

 Total

Damages

North Atlantic $2,798 $182 $498 $109 $173 $1,502 $5,262

Mid-Atlantic $4,233 $182 $623 $144 $254 $1,502 $6,937

South Atlantic $2,798 $182 $498 $109 $173 $1,502 $5,262

Straits of FL $726 $42 $115 $28 $40 $347 $1,298

Eastern Gulf $3,094 $322 $882 $120 $307 $2,658 $7,382

Central Gulf $11,837 $986 $2,663 $460 $903 $2,034 $18,882

Western Gulf $17,989 $1,456 $4,915 $611 $1,956 $3,004 $29,930

Southern CA $5,758 $168 $603 $148 $236 $347 $7,260

Central CA $5,758 $168 $603 $148 $236 $347 $7,260

Northern CA $2,744 $168 $454 $107 $154 $347 $3,973

WA/OR $2,744 $168 $454 $107 $154 $347 $3,973

Gulf of AK $942 $78 $212 $50 $64 $162 $1,507

Cook Inlet $2,744 $168 $454 $107 $154 $347 $3,973

Kodiak $1,119 $73 $197 $43 $67 $1,502 $3,001

Shumagin $1,119 $73 $197 $43 $67 $1,502 $3,001

N. Aleutian B. $1,119 $73 $197 $43 $67 $1,502 $3,001

Aleutian Arc $1,119 $73 $197 $43 $67 $1,502 $3,001

St. George B. $1,119 $73 $197 $43 $67 $1,502 $3,001

St. Matt. Hall $1,119 $73 $197 $43 $67 $1,502 $3,001

Bowers Basin $1,119 $73 $197 $43 $67 $1,502 $3,001

Aleutian Bas. $1,119 $73 $197 $43 $67 $1,502 $3,001

Navarin Bas. $1,119 $73 $197 $43 $67 $1,502 $3,001

Norton Basin $1,119 $73 $197 $43 $67 $1,502 $3,001

Hope Basin $1,119 $73 $197 $43 $67 $1,502 $3,001

Chukchi Sea $274 $17 $45 $11 $15 $347 $709

Beaufort Sea $128 $8 $21 $5 $7 $162 $331

Source: see text.
Section 9: Air Quality 223

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Table 9.21. Annual Emissions Damages per Development Well, Low Estimates (1999

dollars)

OCS Planning

Area

 NOx

Damages

 CO

Damages

 SOx

Damages

 VOC

Damages

 PM

Damages

 CO2

Damages

 Total

Damages

North Atlantic $0 $1,677 $525 $0 $34 $10,900 $13,137

Mid-Atlantic $0 $1,677 $966 $0 $89 $10,900 $13,633

South Atlantic $0 $1,677 $525 $0 $34 $10,900 $13,137

Straits of FL $710 $1,677 $525 $224 $34 $10,900 $14,071

Eastern Gulf $0 $1,677 $525 $0 $34 $10,900 $13,137

Central Gulf $0 $6,710 $2,071 $0 $132 $10,900 $19,814

Western Gulf $0 $6,710 $3,814 $0 $342 $10,900 $21,766

Southern CA $7,372 $6,710 $4,432 $994 $461 $10,900 $30,870

Central CA $7,372 $6,710 $4,432 $994 $461 $10,900 $30,870

Northern CA $2,682 $6,710 $2,071 $845 $132 $10,900 $23,341

WA/OR $2,682 $6,710 $2,071 $845 $132 $10,900 $23,341

Gulf of AK $2,682 $6,710 $2,071 $845 $132 $10,900 $23,341

Cook Inlet $2,682 $6,710 $2,071 $845 $120 $10,900 $23,329

Kodiak $0 $670 $208 $0 $13 $10,900 $11,792

Shumagin $0 $670 $208 $0 $13 $10,900 $11,792

N. Aleutian B. $0 $670 $208 $0 $13 $10,900 $11,792

Aleutian Arc $0 $670 $208 $0 $13 $10,900 $11,792

St. George B. $0 $670 $208 $0 $13 $10,900 $11,792

St. Matt. Hall $0 $670 $208 $0 $13 $10,900 $11,792

Bowers Basin $0 $670 $208 $0 $13 $10,900 $11,792

Aleutian Bas. $0 $670 $208 $0 $13 $10,900 $11,792

Navarin Bas. $0 $670 $208 $0 $13 $10,900 $11,792

Norton Basin $0 $670 $208 $0 $13 $10,900 $11,792

Hope Basin $0 $670 $208 $0 $13 $10,900 $11,792

Chukchi Sea $270 $670 $208 $84 $13 $10,900 $12,147

Beaufort Sea $270 $670 $208 $84 $13 $10,900 $12,147

Source: see text.
224 Section 9: Air Quality

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Table 9.22. Annual Emissions Damages per Development Well, High Estimates (1999

dollars)

OCS Planning

Area

 NOx

Damages

 CO

Damages

 SOx

Damages

 VOC

Damages

 PM

Damages

 CO2

Damages

 Total

Damages

North Atlantic $0 $1,677 $1,949 $0 $282 $37,373 $41,281

Mid-Atlantic $0 $1,677 $2,434 $0 $414 $37,373 $41,899

South Atlantic $0 $1,677 $1,949 $0 $282 $37,373 $41,281

Straits of FL $10,926 $1,677 $1,949 $937 $282 $37,373 $53,144

Eastern Gulf $0 $1,677 $1,949 $0 $282 $37,373 $41,281

Central Gulf $0 $6,710 $7,692 $0 $1,086 $37,373 $52,861

Western Gulf $0 $6,710 $9,609 $0 $1,593 $37,373 $55,284

Southern CA $86,612 $6,710 $10,215 $4,929 $1,668 $37,373 $147,508

Central CA $86,612 $6,710 $10,215 $4,929 $1,668 $37,373 $147,508

Northern CA $41,275 $6,710 $7,692 $3,539 $1,086 $37,373 $97,675

WA/OR $41,275 $6,710 $7,692 $3,539 $1,086 $37,373 $97,675

Gulf of AK $30,359 $6,710 $7,692 $3,539 $964 $37,373 $86,637

Cook Inlet $41,275 $6,710 $7,692 $3,539 $1,086 $37,373 $97,675

Kodiak $0 $670 $769 $0 $109 $37,373 $38,921

Shumagin $0 $670 $769 $0 $109 $37,373 $38,921

N. Aleutian B. $0 $670 $769 $0 $109 $37,373 $38,921

Aleutian Arc $0 $670 $769 $0 $109 $37,373 $38,921

St. George B. $0 $670 $769 $0 $109 $37,373 $38,921

St. Matt. Hall $0 $670 $769 $0 $109 $37,373 $38,921

Bowers Basin $0 $670 $769 $0 $109 $37,373 $38,921

Aleutian Bas. $0 $670 $769 $0 $109 $37,373 $38,921

Navarin Bas. $0 $670 $769 $0 $109 $37,373 $38,921

Norton Basin $0 $670 $769 $0 $109 $37,373 $38,921

Hope Basin $0 $670 $769 $0 $109 $37,373 $38,921

Chukchi Sea $4,128 $670 $769 $354 $109 $37,373 $43,403

Beaufort Sea $4,128 $670 $769 $354 $109 $37,373 $43,403

Source: see text.
Section 9: Air Quality 225

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Table 9.23. Damages per Platform Constructed, Low Estimates (1999 dollars)

OCS Planning

Area

 NOx

Damages

 CO

Damages

 SOx

Damages

 VOC

Damages

 PM

Damages

 CO2

Damages

 Total

Damages

North Atlantic $0 $12,296 $2,499 $0 $732 $14,786 $30,312

Mid-Atlantic $0 $12,296 $4,601 $0 $1,896 $14,786 $33,578

South Atlantic $0 $12,296 $2,499 $0 $732 $14,786 $30,312

Straits of FL $6,969 $12,296 $2,499 $1,297 $732 $14,786 $38,578

Eastern Gulf $0 $12,296 $2,499 $0 $732 $14,786 $30,312

Central Gulf $0 $49,182 $9,864 $0 $2,815 $14,786 $76,646

Western Gulf $0 $49,182 $18,162 $0 $7,293 $14,786 $89,423

Southern CA $72,386 $49,182 $21,105 $5,765 $9,828 $14,786 $173,051

Central CA $72,386 $49,182 $21,105 $5,765 $9,828 $14,786 $173,051

Northern CA $26,329 $49,182 $9,864 $4,900 $2,815 $14,786 $107,875

WA/OR $26,329 $49,182 $9,864 $4,900 $2,815 $14,786 $107,875

Gulf of AK $26,329 $49,182 $9,864 $4,900 $2,815 $14,786 $107,875

Cook Inlet $26,329 $49,182 $9,864 $4,900 $2,568 $14,786 $107,628

Kodiak $0 $4,914 $990 $0 $280 $14,786 $20,969

Shumagin $0 $4,914 $990 $0 $280 $14,786 $20,969

N. Aleutian B. $0 $4,914 $990 $0 $280 $14,786 $20,969

Aleutian Arc $0 $4,914 $990 $0 $280 $14,786 $20,969

St. George B. $0 $4,914 $990 $0 $280 $14,786 $20,969

St. Matt. Hall $0 $4,914 $990 $0 $280 $14,786 $20,969

Bowers Basin $0 $4,914 $990 $0 $280 $14,786 $20,969

Aleutian Bas. $0 $4,914 $990 $0 $280 $14,786 $20,969

Navarin Bas. $0 $4,914 $990 $0 $280 $14,786 $20,969

Norton Basin $0 $4,914 $990 $0 $280 $14,786 $20,969

Hope Basin $0 $4,914 $990 $0 $280 $14,786 $20,969

Chukchi Sea $2,654 $4,914 $990 $490 $280 $14,786 $24,113

Beaufort Sea $2,654 $4,914 $990 $490 $280 $14,786 $24,113

Source: see text.
226 Section 9: Air Quality

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Table 9.24. Damages per Platform Constructed, High Estimates (1999 dollars)

OCS Planning

Area

 NOx

Damages

 CO

Damages

 SOx

Damages

 VOC

Damages

 PM

Damages

 CO2

Damages

 Total

Damages

North Atlantic $0 $12,296 $9,280 $0 $6,018 $50,694 $78,287

Mid-Atlantic $0 $12,296 $11,592 $0 $8,827 $50,694 $83,408

South Atlantic $0 $12,296 $9,280 $0 $6,018 $50,694 $78,287

Straits of FL $107,275 $12,296 $9,280 $5,430 $6,018 $50,694 $190,992

Eastern Gulf $0 $12,296 $9,280 $0 $6,018 $50,694 $78,287

Central Gulf $0 $49,182 $36,630 $0 $23,147 $50,694 $159,653

Western Gulf $0 $49,182 $45,756 $0 $33,950 $50,694 $179,582

Southern CA $850,406 $49,182 $48,645 $28,572 $35,555 $50,694 $1,063,054

Central CA $850,406 $49,182 $48,645 $28,572 $35,555 $50,694 $1,063,054

Northern CA $405,259 $49,182 $36,630 $20,514 $23,147 $50,694 $585,426

WA/OR $405,259 $49,182 $36,630 $20,514 $23,147 $50,694 $585,426

Gulf of AK $298,080 $49,182 $36,630 $20,514 $20,547 $50,694 $475,646

Cook Inlet $405,259 $49,182 $36,630 $20,514 $23,147 $50,694 $585,426

Kodiak $0 $4,914 $3,663 $0 $2,314 $50,694 $61,585

Shumagin $0 $4,914 $3,663 $0 $2,314 $50,694 $61,585

N. Aleutian B. $0 $4,914 $3,663 $0 $2,314 $50,694 $61,585

Aleutian Arc $0 $4,914 $3,663 $0 $2,314 $50,694 $61,585

St. George B. $0 $4,914 $3,663 $0 $2,314 $50,694 $61,585

St. Matt. Hall $0 $4,914 $3,663 $0 $2,314 $50,694 $61,585

Bowers Basin $0 $4,914 $3,663 $0 $2,314 $50,694 $61,585

Aleutian Bas. $0 $4,914 $3,663 $0 $2,314 $50,694 $61,585

Navarin Bas. $0 $4,914 $3,663 $0 $2,314 $50,694 $61,585

Norton Basin $0 $4,914 $3,663 $0 $2,314 $50,694 $61,585

Hope Basin $0 $4,914 $3,663 $0 $2,314 $50,694 $61,585

Chukchi Sea $40,533 $4,914 $3,663 $2,052 $2,314 $50,694 $104,170

Beaufort Sea $40,533 $4,914 $3,663 $2,052 $2,314 $50,694 $104,170

Source: see text.
Section 9: Air Quality 227

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Table 9.25. Damages per Mile of Pipeline Constructed, Low Estimates (1999 dollars)

OCS Planning

Area

 NOx

Damages

 CO

Damages

 SOx

Damages

 VOC

Damages

 PM

Damages

 CO2

Damages

 Total

Damages

North Atlantic $217 $732 $208 $67 $34 $827 $2,085

Mid-Atlantic $408 $732 $383 $79 $88 $827 $2,517

South Atlantic $217 $732 $208 $67 $34 $827 $2,085

Straits of FL $244 $732 $208 $76 $34 $827 $2,121

Eastern Gulf $136 $732 $208 $42 $34 $827 $1,979

Central Gulf $679 $2,928 $822 $210 $130 $827 $5,596

Western Gulf $868 $2,928 $1,514 $168 $337 $827 $6,641

Southern CA $2,539 $2,928 $1,759 $336 $454 $827 $8,842

Central CA $2,539 $2,928 $1,759 $336 $454 $827 $8,842

Northern CA $923 $2,928 $822 $286 $130 $827 $5,916

WA/OR $923 $2,928 $822 $286 $130 $827 $5,916

Gulf of AK $923 $2,928 $822 $286 $130 $827 $5,916

Cook Inlet $923 $2,928 $822 $286 $119 $827 $5,904

Kodiak $88 $293 $83 $27 $13 $827 $1,329

Shumagin $88 $293 $83 $27 $13 $827 $1,329

N. Aleutian B. $88 $293 $83 $27 $13 $827 $1,329

Aleutian Arc $88 $293 $83 $27 $13 $827 $1,329

St. George B. $88 $293 $83 $27 $13 $827 $1,329

St. Matt. Hall $88 $293 $83 $27 $13 $827 $1,329

Bowers Basin $88 $293 $83 $27 $13 $827 $1,329

Aleutian Bas. $88 $293 $83 $27 $13 $827 $1,329

Navarin Bas. $88 $293 $83 $27 $13 $827 $1,329

Norton Basin $88 $293 $83 $27 $13 $827 $1,329

Hope Basin $88 $293 $83 $27 $13 $827 $1,329

Chukchi Sea $93 $293 $83 $29 $13 $827 $1,337

Beaufort Sea $93 $293 $83 $29 $13 $827 $1,337

Source: see text.
228 Section 9: Air Quality

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Table 9.26. Damages per Mile of Pipeline Constructed, High Estimates (1999 dollars)

OCS Planning

Area

 NOx

Damages

 CO

Damages

 SOx

Damages

 VOC

Damages

 PM

Damages

 CO2

Damages

 Total

Damages

North Atlantic $3,344 $732 $773 $282 $278 $2,836 $8,244

Mid-Atlantic $5,059 $732 $966 $373 $407 $2,836 $10,373

South Atlantic $3,344 $732 $773 $282 $278 $2,836 $8,244

Straits of FL $3,762 $732 $773 $317 $278 $2,836 $8,698

Eastern Gulf $2,090 $732 $773 $176 $278 $2,836 $6,885

Central Gulf $10,451 $2,928 $3,053 $880 $1,068 $2,836 $21,214

Western Gulf $10,751 $2,928 $3,813 $792 $1,567 $2,836 $22,686

Southern CA $29,825 $2,928 $4,054 $1,667 $1,641 $2,836 $42,949

Central CA $29,825 $2,928 $4,054 $1,667 $1,641 $2,836 $42,949

Northern CA $14,213 $2,928 $3,053 $1,197 $1,068 $2,836 $25,293

WA/OR $14,213 $2,928 $3,053 $1,197 $1,068 $2,836 $25,293

Gulf of AK $10,454 $2,928 $3,053 $1,197 $948 $2,836 $21,415

Cook Inlet $14,213 $2,928 $3,053 $1,197 $1,068 $2,836 $25,293

Kodiak $1,338 $293 $305 $113 $107 $2,836 $4,991

Shumagin $1,338 $293 $305 $113 $107 $2,836 $4,991

N. Aleutian B. $1,338 $293 $305 $113 $107 $2,836 $4,991

Aleutian Arc $1,338 $293 $305 $113 $107 $2,836 $4,991

St. George B. $1,338 $293 $305 $113 $107 $2,836 $4,991

St. Matt. Hall $1,338 $293 $305 $113 $107 $2,836 $4,991

Bowers Basin $1,338 $293 $305 $113 $107 $2,836 $4,991

Aleutian Bas. $1,338 $293 $305 $113 $107 $2,836 $4,991

Navarin Bas. $1,338 $293 $305 $113 $107 $2,836 $4,991

Norton Basin $1,338 $293 $305 $113 $107 $2,836 $4,991

Hope Basin $1,338 $293 $305 $113 $107 $2,836 $4,991

Chukchi Sea $1,422 $293 $305 $120 $107 $2,836 $5,081

Beaufort Sea $1,422 $293 $305 $120 $107 $2,836 $5,081

Source: see text.
Section 9: Air Quality 229

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Table 9.27. Oil Spill Air Quality Damages, Low and High Estimates (1999 dollars)

OCS Planning Area Low Damage Estimate per

Barrel Spilled

High Damage Estimate per

Barrel Spilled

North Atlantic $3.75 $15.69

Mid-Atlantic $4.41 $20.76

South Atlantic $3.75 $15.69

Straits of Florida $4.22 $17.65

Eastern Gulf $2.34 $9.80

Central Gulf $11.71 $49.02

Western Gulf $9.37 $44.12

Southern California $18.74 $92.86

Central California $18.74 $92.86

Northern California $15.93 $66.67

Washington/Oregon $15.93 $66.67

Gulf of Alaska $15.93 $66.67

Cook Inlet $15.93 $66.67

Kodiak $1.50 $6.28

Shumagin $1.50 $6.28

North Aleutian Basin $1.50 $6.28

Aleutian Arc $1.50 $6.28

St. George Basin $1.50 $6.28

St. Matthew Hall $1.50 $6.28

Bowers Basin $1.50 $6.28

Aleutian Basin $1.50 $6.28

Navarin Basin $1.50 $6.28

Norton Basin $1.50 $6.28

Hope Basin $1.50 $6.28

Chukchi Sea $1.59 $6.67

Beaufort Sea $1.59 $6.67

Source: see text.
230 Section 9: Air Quality

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Table 9.28. Summary of Air Quality Damages from Continuous Operations, Low

Estimates (1999 dollars)

Per Active Platform

Per Year

Per 1,000 Barrels

OCS Planning Loaded/Transported

Area Boat

Emissions

Platform

Emissions

Tanker

Loading

Crude

Transport

Tanker

Emissions

Per

Operating

Development Well

North Atlantic $3,901 $18,377 $0.00 $0.00 $0.00 $13,137

Mid-Atlantic $5,135 $19,958 $0.00 $0.00 $0.00 $13,633

South Atlantic $3,901 $18,377 $0.00 $0.00 $0.00 $13,137

Straits of FL $924 $24,228 $0.00 $0.00 $0.00 $14,071

Eastern Gulf $6,355 $18,377 $0.00 $0.00 $0.00 $13,137

Central Gulf $13,032 $34,172 $0.00 $1.04 $2.11 $19,814

Western Gulf $24,939 $40,405 $0.00 $1.23 $3.68 $21,766

Southern CA $4,428 $78,673 $0.00 $0.00 $0.00 $30,870

Central CA $4,428 $78,673 $0.00 $0.00 $0.00 $30,870

Northern CA $2,435 $56,275 $0.00 $0.00 $0.00 $23,341

WA/OR $2,435 $56,275 $0.00 $0.00 $0.00 $23,341

Gulf of AK $1,136 $56,275 $27.26 $0.00 $11.78 $23,341

Cook Inlet $2,429 $56,257 $27.26 $0.00 $11.78 $23,329

Kodiak $2,602 $15,189 $27.26 $0.00 $11.78 $11,792

Shumagin $2,602 $15,189 $27.26 $0.00 $11.78 $11,792

N. Aleutian B. $2,602 $15,189 $27.26 $0.00 $11.78 $11,792

Aleutian Arc $2,602 $15,189 $27.26 $0.00 $11.78 $11,792

St. George B. $2,602 $15,189 $27.26 $0.00 $11.78 $11,792

St. Matt. Hall $2,602 $15,189 $27.26 $0.00 $11.78 $11,792

Bowers Basin $2,602 $15,189 $27.26 $0.00 $11.78 $11,792

Aleutian Bas. $2,602 $15,189 $27.26 $0.00 $11.78 $11,792

Navarin Bas. $2,602 $15,189 $27.26 $0.00 $11.78 $11,792

Norton Basin $2,602 $15,189 $27.26 $0.00 $11.78 $11,792

Hope Basin $2,602 $15,189 $27.26 $0.00 $11.78 $11,792

Chukchi Sea $605 $17,403 $27.26 $0.00 $11.78 $12,147

Beaufort Sea $282 $17,403 $27.26 $0.00 $11.78 $12,147

Source: see text.
Section 9: Air Quality 231

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Table 9.29. Summary of Air Quality Damages from Continuous Operations, High

Estimates (1999 dollars)

Per Active Platform

Per Year

Per 1,000 Barrels

Loaded/Transported

OCS Planning

Area

Boat

Emissions

Platform

Emissions

Tanker

Loading

Crude

Transport

Tanker

Emissions

Per

Operating

Development Well

North Atlantic $20,877 $55,348 $0.00 $0.00 $0.00 $41,281

Mid-Atlantic $27,522 $57,196 $0.00 $0.00 $0.00 $41,899

South Atlantic $20,877 $55,348 $0.00 $0.00 $0.00 $41,281

Straits of FL $5,150 $98,674 $0.00 $0.00 $0.00 $53,144

Eastern Gulf $29,288 $55,348 $0.00 $0.00 $0.00 $41,281

Central Gulf $74,908 $86,459 $0.00 $4.36 $8.67 $52,861

Western Gulf $118,742 $93,734 $0.00 $5.78 $11.16 $55,284

Southern CA $28,802 $392,831 $0.00 $0.00 $0.00 $147,508

Central CA $28,802 $392,831 $0.00 $0.00 $0.00 $147,508

Northern CA $15,762 $250,135 $0.00 $0.00 $0.00 $97,675

WA/OR $15,762 $250,135 $0.00 $0.00 $0.00 $97,675

Gulf of AK $5,980 $224,104 $114.14 $0.00 $39.06 $86,637

Cook Inlet $15,762 $250,135 $114.14 $0.00 $39.06 $97,675

Kodiak $11,907 $49,000 $114.14 $0.00 $39.06 $38,921

Shumagin $11,907 $49,000 $114.14 $0.00 $39.06 $38,921

N. Aleutian B. $11,907 $49,000 $114.14 $0.00 $39.06 $38,921

Aleutian Arc $11,907 $49,000 $114.14 $0.00 $39.06 $38,921

St. George B. $11,907 $49,000 $114.14 $0.00 $39.06 $38,921

St. Matt. Hall $11,907 $49,000 $114.14 $0.00 $39.06 $38,921

Bowers Basin $11,907 $49,000 $114.14 $0.00 $39.06 $38,921

Aleutian Bas. $11,907 $49,000 $114.14 $0.00 $39.06 $38,921

Navarin Bas. $11,907 $49,000 $114.14 $0.00 $39.06 $38,921

Norton Basin $11,907 $49,000 $114.14 $0.00 $39.06 $38,921

Hope Basin $11,907 $49,000 $114.14 $0.00 $39.06 $38,921

Chukchi Sea $2,814 $65,372 $114.14 $0.00 $39.06 $43,403

Beaufort Sea $1,313 $65,372 $114.14 $0.00 $39.06 $43,403

Source: see text.
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Table 9.30. Summary of Air Quality Damages from One-time Events, Low Estimates

(1999 dollars)

OCS Planning Area

Well

Drilling

(per well)

Platform

Construction

(per platform)

Pipeline

Construction

(per mile of

pipeline)

Oil Spills

(per barrel

spilled)

North Atlantic $983 $30,312 $2,085 $3.75

Mid-Atlantic $1,294 $33,578 $2,517 $4.41

South Atlantic $983 $30,312 $2,085 $3.75

Straits of FL $233 $38,578 $2,121 $4.22

Eastern Gulf $1,602 $30,312 $1,979 $2.34

Central Gulf $3,285 $76,646 $5,596 $11.71

Western Gulf $6,285 $89,423 $6,641 $9.37

Southern CA $1,116 $173,051 $8,842 $18.74

Central CA $1,116 $173,051 $8,842 $18.74

Northern CA $614 $107,875 $5,916 $15.93

WA/OR $614 $107,875 $5,916 $15.93

Gulf of AK $286 $107,875 $5,916 $15.93

Cook Inlet $612 $107,628 $5,904 $15.93

Kodiak $656 $20,969 $1,329 $1.50

Shumagin $656 $20,969 $1,329 $1.50

N. Aleutian B. $656 $20,969 $1,329 $1.50

Aleutian Arc $656 $20,969 $1,329 $1.50

St. George B. $656 $20,969 $1,329 $1.50

St. Matt. Hall $656 $20,969 $1,329 $1.50

Bowers Basin $656 $20,969 $1,329 $1.50

Aleutian Basin $656 $20,969 $1,329 $1.50

Navarin Basin $656 $20,969 $1,329 $1.50

Norton Basin $656 $20,969 $1,329 $1.50

Hope Basin $656 $20,969 $1,329 $1.50

Chukchi Sea $153 $24,113 $1,337 $1.59

Beaufort Sea $71 $24,113 $1,337 $1.59

Source: see text.
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Table 9.31. Summary of Air Quality Damages from One-time Events, High Estimates

(1999 dollars)

OCS Planning Area

Well

Drilling

(per well)

Platform

Construction

(per platform)

Pipeline

Construction

(per mile of

pipeline)

Oil Spills

(per barrel

spilled)

North Atlantic $5,262 $78,287 $8,244 $15.69

Mid-Atlantic $6,937 $83,408 $10,373 $20.76

South Atlantic $5,262 $78,287 $8,244 $15.69

Straits of FL $1,298 $190,992 $8,698 $17.65

Eastern Gulf $7,382 $78,287 $6,885 $9.80

Central Gulf $18,882 $159,653 $21,214 $49.02

Western Gulf $29,930 $179,582 $22,686 $44.12

Southern CA $7,260 $1,063,054 $42,949 $92.86

Central CA $7,260 $1,063,054 $42,949 $92.86

Northern CA $3,973 $585,426 $25,293 $66.67

WA/OR $3,973 $585,426 $25,293 $66.67

Gulf of AK $1,507 $475,646 $21,415 $66.67

Cook Inlet $3,973 $585,426 $25,293 $66.67

Kodiak $3,001 $61,585 $4,991 $6.28

Shumagin $3,001 $61,585 $4,991 $6.28

N. Aleutian B. $3,001 $61,585 $4,991 $6.28

Aleutian Arc $3,001 $61,585 $4,991 $6.28

St. George B. $3,001 $61,585 $4,991 $6.28

St. Matt. Hall $3,001 $61,585 $4,991 $6.28

Bowers Basin $3,001 $61,585 $4,991 $6.28

Aleutian Basin $3,001 $61,585 $4,991 $6.28

Navarin Basin $3,001 $61,585 $4,991 $6.28

Norton Basin $3,001 $61,585 $4,991 $6.28

Hope Basin $3,001 $61,585 $4,991 $6.28

Chukchi Sea $709 $104,170 $5,081 $6.67

Beaufort Sea $331 $104,170 $5,081 $6.67

Source: see text.
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Figure 9.1. Air Quality OECM Impact Drivers

These are found on the Air cost element worksheet
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10. Onshore Government Revenue Collections and Infrastructure Costs

Counties, cities and special service districts provide a wide range of public services for which no

individual user charges are collected. Public services include commonly provided public,

semipublic and private services and facilities, such as education, police and fire protection,

sewage treatment, water supply, recreation facilities, transportation, health care, and other

utilities. OCS onshore support operations, typically situated at select and limited port facilities,

may cause changes in demand for public services that vary with the level of offshore marine

operations.

Public infrastructure, used to support public services, includes public roads and highways, ports

and harbors, and airports. Changing levels of onshore development associated with offshore oil

and gas activity may cause changes in direct usage of public infrastructure.

10.1 General Onshore Public Service and Infrastructure Effects

OCS activity may affect locally provided public services and infrastructure in the following two

ways:

• Population-induced effects;

• Direct industry effects.

These effects are associated with variable costs, such as the need for more teachers, and with

physical infrastructure capacity limitations, such as new schools buildings, which tend to require

new municipal project investments. The following three subsections discuss these.

10.1.1 Population Induced Effects

OCS activity can cause changes in local economic activity, including in-migration of OCS

workers and support industry businesses. These changes can lead to changes in the demand for

locally provided public services, or so-called population-induced effects.

Dramatic or rapid changes in population can affect the adequacy of the public infrastructure and

the availability or cost of essential public services. Changes in demands for and usage of public

services resulting from OCS activities may cause adverse public service and infrastructure

effects if the amount or rate of increase or decrease in the usage significantly exceeds or falls far

below the capability of a local area to provide a satisfactory level of service.

OCS-related, impact-producing factors to public services and community infrastructure include

workforce fluctuations, migration (in or out), and the effect of relative income.

Fluctuations in the workforce due to changing levels of OCS-related oil and gas activity can

affect public services and community infrastructure in several ways. As economic activity

increases, current local residents may increase income and spending due to increased

employment of previously idle (voluntary or involuntary) workers or by upgrading to better

paying jobs.
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Changes in employment due to in-commuters or in-migrants may increase the demand for public

services. However, specialized labor associated with deepwater drilling will tend to be

commuters, especially during the drilling and development phases. Longer rotations, which are

increasingly found in the OCS, will tend to mitigate in-migration during the production phase

because workers commute from their existing homes to ports of embarkation. Changing rotation

patterns reduces the potential for public service and infrastructure impacts due to offshore

employment.

10.1.2 Direct Industry Effects

OCS activity may place a direct industry demand on locally provided infrastructure. Public

infrastructure expected to experience the greatest direct demand associated with onshore

development due to OCS activity include public roads and highways, ports and harbors, airports,

and police and fire emergency services. Marine supply operations typically are situated at select

and limited port facilities. Personnel transport generally raises few public infrastructure

concerns because private industry piers accommodate substantial crew vessel activity and local

airports easily handle the industry-related helo activity.

Port Fourchon, Louisiana, provides an example. The rapid expansion of OCS activity led to a

large increase in demand for city water services. OCS activity requires large amounts of clean

water for use in drilling muds and cleaning equipment. The increased load on the South

Lafourche Water District has been a function of direct industry requirements, not general growth

in population.

The change in OCS activity also may place a direct industry demand on public infrastructure that

state, not local, government is responsible for. Port Fourchon provides yet another example.

The rapid expansion of deepwater OCS activity has led to heavy use of and increased

deterioration of the state highway LA 1. (Guo, Hughes, et al, 1997.) LA 1 is the only highway

to the port, and water transportation is the only alternative mode of transportation. Thus, the

road receives very heavy truck use that has not been a function of population increases, but of

direct industry activity. Highway construction and maintenance is a responsibility of state

government, not the local government entity.

Onshore support industry employment may have an effect on in-migration, which does have the

potential to increase the demand for public services.

10.1.3 Fixed and Variable Costs

Both population-induced costs and direct industry costs fall into the categories of either variable

or fixed costs.

Variable costs: For population-induced costs, variable costs change with the level of population

and include items such as number of school teachers, number of police, and so forth. Variable

direct industry costs include, for example, additional water system deliveries and maintenance

due to increased use.
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Fixed costs: These costs refer to the physical infrastructure necessary to support locally provided

public services such as school buildings and roads. As population grows, the physical

infrastructure to support a growing school system may have to be increased. Special taxing

districts and new bond sales generally fund such fixed cost increases. A long run commitment to

provide services for a growing population is based on the assumption that the economic activity

will remain to generate the payback (taxes and fees) of such a long-term investment. Similar

logic applies to direct industry activity. For example, industry may be a heavy user of a water

system; thus, the industry must be there in the future to generate tax and fee revenues to support

the long run payoff of expanded pumping and treating capacity.

10.2 Public Revenues

Offshore oil and gas production and related activities generate local government revenues from

four sources:

1. property taxes;

2. sales and business taxes;

3. direct use fees;

4. intergovernmental transfers.

Property taxes are paid by industry facilities located in the local area and by employees who live

in the area. Sales and business taxes are paid directly by commercial and industrial support

industry, but are also associated with retail purchases by employees, plus the ripple effect of respending. Direct fees payments include mitigation and licensing fees. The level of

intergovernmental transfer may be affected positively or negatively. For example, as wealth in a

school system (most are parish-wide in Louisiana) increases, transfers from state government to

the local school system are reduced. Hence, the net revenue yield (the local revenue increase

minus the loss in intergovernmental transfer dollars) is the proper measure of impacts.

10.3 Net Fiscal Impacts

The difference between the increase in revenue collections and the increase in variable costs is

one net effect of OCS onshore fiscal impacts. A surplus represents funds that can be devoted to

other local government needs, such as the need to expand capacity of various public services as

demand grows. A shortfall represents a negative externality related to the OCS activity.

COOGER, 1999, and Hughes et al, 1999 studies both show positive net fiscal effects related to

offshore activity.

The reason that Central California in the 80s and 90s and Lafourche Parish, which is the major

staging point for 1990s deepwater Gulf of Mexico support, experienced positive fiscal impacts

appears to be related to changing migration patterns. Dr. Joachim Singlemann reports that

commuting to coastal areas for employment is more likely than commuting for employment in

non-coastal areas. (Personal communication, June 20, 2000.) The 7/7 (7 days on/7 days off) and

14/14 work rotations, common on current offshore facilitates, facilitate these long commutes. In

both Central California and Port Fourchon, a large offshore industry presence creates substantial

new government revenues, while small in-migration places minimal added burden on local
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public services. While in-migration due to the offshore oil industry is minimal, especially with

people working 14 days on and 14 days off, oil exploration and development can lead to growth

in the ancillary support sectors of the local economy. People will move to take these jobs.

10.4 Findings from the Recent Literature

This section summarizes recent research on the impacts of OCS activity on local communities.

10.4.1 Central and Western Gulf FEISs

Two recent MMS FEIS documents for Gulf of Mexico lease sales related to Gulf of Mexico

(Western Planning Area (MMS 98-0008)) and (Central Planning Area (MMS 97-0033)). Both

predicted no dramatic increases or decreases of population-induced effects on public services

resulting from OCS activities. Local areas were predicted to experience less than one-percent

fluctuation in populations, as locally present labor forces would meet most employment needs.

The FEIS (MMS 97-0033) for the Gulf of Mexico Central Planning Area lease sale found that

direct industry impacts to Port Fourchon and related highway infrastructure will likely continue.

"The limited number of service bases capable of servicing deepwater activities suggests that

stresses placed on local infrastructure at these bases will continue as a result of ” leasing and

exploration of deepwater tracts in the Gulf. (p. IV-169.)

10.4.2 COOGER, 1999

The recent FEIS findings are consistent with the findings from the 1999 California Offshore Oil

and Gas Energy Resources (COOGER) study. The COOGER study found that relatively small

population changes associated with California OCS activity, compared to baseline conditions,

created negligible changes in demand for public services. (p. 2-418.) The finding of virtually no

measurable change in the level of public services associated with California OCS activity

suggests that the previous California Socioeconomic Monitoring Program (SEMP)-derived

methodology overstates the actual impacts based on faulty assumptions about in-migrants. (p. 2-

422.) The SEMP monitored local activities in the tri-county area (Ventura, Santa Barbara, and

San Luis Obispo) affected by California OCS activity. The SEMP methodology postulated that

increased demand for public services was approximately proportional to increased local

population.

A second explanation for the lack of public service and infrastructure impacts postulated by the

COOGER study is that the significance of any change caused by OCS activity depends on the

state of the baseline economy and other sectors in the area. Declines elsewhere in the economy

may act to offset increases due to OCS activity.

Increased revenue collections (taxes and fees) by local government are used to pay for increased

public services. The COOGER study (Section 2.6.4) shows that facilities directly related to the

offshore oil and gas industry provide a small percentage share of local county property tax

collections. (COOGER, nonetheless, calculated no other revenue effects of offshore California

oil and gas activity.) These facilities include obvious shore base locations and processing plants,

as well as pipeline rights of ways. In Ventura and Santa Barbara counties, industry facilities
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provided 2.2 percent and 4.6 percent of county property tax collections, respectively, not

including any residential property taxes paid by employees. While a small part of total revenue

collections, industry direct tax payments totaled nearly $13 million in Santa Barbara County and

$30.1 million in Ventura, which is the location for the major shore-based facility to service

offshore California activity.

10.4.3 Hughes, et al, 1999

Dr. David Hughes, et al, developed an IMPLAN based model of Lafourche Parish, (referred to as

Louisiana Community Impact Model (LCIM)) in which Port Fourchon is located, under MMS

Cooperative Agreement 14-35-0001-30660 to examine the effects of the deepwater OCS activity

on the local economy and public services provision. A key element of the assignment was to

estimate the ability of local government to deliver publicly provided goods and services. Within

this model, Dr. Hughes developed ten expenditure equations to examine changes in spending for

public services related to changes in OCS activity. Schools are responsible for around 35% of

the projected change in per capita expenditures. Nine revenue equations predict the changes in

local government revenues.

Predicted revenues and expenditures are shown on Tables 10 and 11 of the Hughes et al report.

Table 10.1 of this section shows the predicted year-to-year changes in revenues and expenditures

disentangled to show the incremental effects of deepwater activity occurring in Lafourche Parish.

Consistent with the COOGER report findings reported above, revenues are predicted to grow

more than expenditures related to provision of public services.

Onshore infrastructure externalities may not apply to the Gulf Coast Production Regions because

of extensive commuting (instead of in-migration) and surplus capacity. The Hughes, et al., study

of Port Fourchon shows that development of the Central Gulf caused growth in the Lafourche

Parish economy (especially the southern half of the parish), but the increase in jobs has not yet

led to a big increase in the local population.

The recent growth of Port Fourchon activity has primarily resulted in a decline in local

unemployment rather than an influx of in-migrant households. Property taxes paid by residents

(homeowners) remained constant; but sales taxes increased as people had more money to spend.

One also will expect to find a business property tax effect. Retail businesses have expanded and

located to the area because of more money circulating in the local economy. The property tax

yield from businesses has grown. In Louisiana, this effect tends to be very important because

businesses are a larger source of property tax revenues than in most other states. (The first

$75,000 for a primary residence for homeowners is exempt from all property taxes.)

10.4.4 Foster, 1996; 1999

Foster’s Mobile County economic impact studies related to Coastal Alabama reveals little impact

on local public service requirements because excess capacity existed and little in-migration

occurred. (See Section 6.3, Foster 1996.) Demand for population-induced public services might

be expected to lag the development cycle based on Foster’s research in Mobile County.

Commuters supported exploration and development. People have been slow to move into the

Mobile area to eliminate the commute. Now that the Norphlet gas fields are mature, and the
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activity has moved into routine O&M, commuters still dominate. Many out of state vehicles can

still be observed in the parking lots at shore based facilities on Dauphin Island and Theodore.

Foster’s 1999 MMS research unraveled the many sources of revenues to the local counties and

communities adjoining Mobile Bay showing substantial tax and royalty revenue collections to

Mobile County.

10.4.5 RPC, 1999

The December, 1999, RPC Technical Summary of “Socioeconomic Baseline and Projections on

the Impact of an OCS Onshore Base for Selected Florida Panhandle Communities” documents

the assumptions in the RPC Panhandle model. RPC modeled the cost of public service impacts

on local governments based on the average per capita expenditures for local government in

Florida Panhandle counties and municipalities. The average annual per capita expenditures for

all government services (e.g., annual budget divided by population) was multiplied by the

predicted change in county population due associated with OCS activity. (See page 68 and 75 of

Volume II of the RPC report.)

Predicting in-migrants versus commuters is a difficult, but standard, starting point. More recent

experience (COOGER, 1999; Hughes, et al, 1999; Foster Associates, 1996) than RPC relied on

suggests that linking public service impacts to in-migrants may lead to misleading conclusions

because of changing crew rotations and migration patterns. Moreover, translating that inmigration prediction to a linear reliance on the average per capita cost of providing services,

instead of a more appropriate incremental cost, while standard in years past, ignores

considerations of fixed versus variable service costs. Certainly, the average cost of government

services includes the average cost of maintaining existing capacity. Existing capacity may be

more than adequate to meet the small incremental demands implied by the few hundred inmigrants predicted by the RPC model. If so, average per capita costs greatly overstates the

incremental public service costs. Incremental or average depends, of course, on capacity.

RPC estimated added tax revenues in relation to residential and commercial and industrial tax

streams in ways that cannot be discerned from section 8 of their report. Page 68 of Volume II

states that “the [Fiscal Impact] module uses the change in the number of households in some of

the tax revenue impacts.” If so, this would ignore the larger tax revenue sources associated with

direct industry payments. This would be another reason that RPC’s revenue estimates for the

scenarios fall below infrastructure expenditures.

10.5 Comments on A. T. Kearney, 1991, Reliance on Santa Barbara Studies

A. T. Kearney, 1991, relied on the community impact estimates developed in relation to the

offshore activity in the Santa Barbara Channel and the Santa Maria Basin in Southern California

and explicitly assumed that these are reasonable surrogates of community impact costs that can

be expected elsewhere. A number of problems exist within the A.T. Kearney treatment of

onshore infrastructure impacts. These problems are listed as follows.

1. A.T.Kearney reflects the relationship between OCS activity and local infrastructure impacts

in terms of literature of 1970s and 1980s, and relies on West Coast Santa Barbara data.
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Notably, this literature postulates much in-migration and infrastructure capacity limits with

the introduction of expanded OCS activity.

2. A. T. Kearney makes no distinction between variable costs and fixed costs in the provision of

locally provided public services. Again, this is a capacity issue.

3. A. T. Kearney is unclear about the difference between general impacts due to population

growth and specific industry impacts.

4. A. T. Kearney assumes a questionable lag between the cost of population-based public

service requirements and tax and fee revenue collections related to in-migration population

growth. In fact, while A.T. Kearney refers to their estimates as “non-recovered,” to mean net

of new taxes and fees, revenues are not estimated.

10.5.1 Lagged Receipts Create A. T. Kearney OCS Impacts

The assumption of the lag between costs for local public services and local tax revenues due to

population growth or direct industry impacts is questionable for the following reasons:

1. Local sales tax collections increase almost immediately (a lag of a month at most) due to

population growth assuming, of course, that in-migrants or local formerly unemployed

workers now working have a positive marginal propensity to spend locally. (If they do not,

for example, if they migrate for 7on / 7off from a permanent residence elsewhere,

presumably their marginal infrastructure impacts remain at home.)

2. Direct use fees go up immediately as permit fees are collected from industry before the

activity begins or while it is in progress.

3. Intergovernmental transfers tend to decline with population growth, but the fall tends to lag

the increase in population growth. Hence, the lag effect would be positive; i.e., local

government hangs-on to the transfers longer for a net beneficial effect.

4. Only property tax revenues significantly lag the demand placed on publicly provided

services. The structure of the local tax base (relative size of sales versus property tax

revenues, for example) is important to the effect of this lag.

The bottom line is that revenue collections may lag services, but the lag is probably small.

Reliance upon it as the causation of onshore infrastructure costs is questionable.

10.5.2 Lag Effects on Fixed and Variable Costs

The A. T. Kearney assumption of the lag ignores the difference between variable and fixed costs

of publicly provided services. As the demand for publicly provided services increases, some

variable costs will increase, but these will not be uniformly instantaneous. For example, with

sufficient population growth, the need for public school teachers will increase; but some

classroom crowding will occur first. (In other words, the reactions by local government will not

be instantaneous.) Moreover, some underutilized capacity may exist. Before the population

growth occurs, all teachers may not have been working at full capacity.
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The lag between the demand for local public services and required fixed cost expansions would

be expected to be longer in certain cases. For example, crowding would force a school system to

expand its physical capital; but temporary (and cheaper) measures (mobile classrooms for

example) are often taken before the actual expansion occurs. More importantly, the income flow

from a bond issue does not lag expenditures. The main point is that the question of lags is fairly

meaningless with respect to commitment of fixed costs.

One critical issue in infrastructure costs is how the demand for publicly provided services

translates into increases in fixed cost items versus the revenues necessary to generate the support

for such expansion. This has two components:

The increase in direct industry demand for publicly provided services. The pressures on

publicly provided services can be expected to be highest in the development phase and to lessen

once platforms and equipment have passed over the docks and been installed offshore. For

example, the Central Gulf of Mexico OCS Planning Area is currently experiencing extensive

exploration and platform construction and installation. This activity requires much shipping of

materials and equipment over the Port Fourchon road system. Once the production infrastructure

is largely in-place in the Gulf, road traffic (and Port Fourchon activity) will be reduced to crew

and platform support. If local government were paying part of the cost of a major upgrade in LA

1 based on some variable fee, the end of the “boom” could lead to a very nice highway that is no

longer heavily used but still must be paid for.

The timing of impacts from new leasing will be a function of future expected field development

schedules. If activity in the Gulf follows the timing of the development reported in recent

literature, lease exploration and development drilling will occur over a ten-year period. MMS

scenario projections can be used to arrive at an estimate of the timing of future wells and

platform installations, and related onshore direct industry impacts. Wells drilled and offshore

production can be tied to local economic activity and infrastructure use to reflect both the E&D

phase and the O&M phase.

The same situation exists in terms of population growth. Infrastructure capacity increases

caused by population growth during a “boom” may not be needed when the “boom” ends. If not,

then local government could have excess capacity in public services, but still need to pay off the

bonds. However, changes in rotations for offshore workers implies that more commuting and

less in-migration is occurring within the various segments of the industry. This mitigates local

population build-ups. Shore based support firms and shore based operation personnel tend to

reside locally and employ mostly local workers. This group, however, is smaller than the

offshore employment sector.

10.5.3 Lags and Revenue Generation

The literature generated in the late 1960s through the early 1980s looked at the difference in

local economic impacts between in-migrants and current local residents because of differences in

spending patterns between the two groups. A finding of this literature is that in-migrants

generate greater levels of initial spending in the local economy (housing mainly) than current

residents. This result has implications for any lag between revenue collections and the cost for

local public services. That is, because the level of local spending by in-migrants may be initially
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greater, the resulting level of local government revenues also may be initially greater. Thus, this

initial in-migrant spending effect may be another reason for ignoring the lag in revenue

collections that A. T. Kearney emphasized.

10.6 Lessons Learned for Modeling

Lessons learned from Hughes, et al, Lafourche Parish study, Foster’s Mobile County studies;

RPC’s modeling effort, and the COOGER study guide our fiscal impacts modeling. A variety of

differences, including differences in tax structure, relative size of the coastal economy, and,

perhaps most importantly, slack in the public sector capacity, will cause important variations

between places and situations that cannot be captured in a general model. If the five-year lease

sale plan caused activity in the Gulf to rapidly accelerate, then some in-migration might occur to

support continued major offshore expansion because the Coastal economy is reasonably robust

currently; this may not be true elsewhere. Port Fourchon did not have a tight labor market when

offshore activity began to pick up. Further activity in Lafourche Parish, or elsewhere, could

induce in-migration sooner to take onshore non-specialized jobs. We assume that in-migration

will follow with a lag. Direct industry impacts will follow the E&D drilling cycle. Government

revenues follow the E&D drilling cycle and remain stable as production matures.

Population effects will follow the production cycle; following the experience of Coastal

Alabama; i. e., people slowly migrate closer to work as production matures.

10.6.1 Modeling Issues Per Se

The following steps represent an ideal approach to modeling onshore infrastructure effects

related to OCS activities based on the recent literature reported above.

1. Establish a relationship between direct industry use of public services/infrastructure and OCS

activity. Annual wells drilled supported from the onshore support base will serve this

purpose.

2. Establish the relationship between growth in population (and the economy in general) and the

demand for variable public services and fixed cost infrastructure expansion. Sorting out

infrastructure capacity indicators requires local research appropriate at the EIS level of

investigation; we will assume away fixed capacity limits and relate population effects to

offshore oil production. Foster’s research in Mobile County and MMS FEIS’s cited above

both show that population effects are very small incrementally.

3. Estimate government revenues consistent with COOGER, but more inclusive of all income

streams based on Hughes, et al. Compare to infrastructure cost estimates to determine net

onshore infrastructure impacts.

A perfect model would have to track these logical relationships:

• Oil Field explore and develop => local employment impacts => change in population =>

population based fiscal impacts;

• Oil Field explore and develop => Industry fiscal impacts;
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• Oil Field production => local employment impacts => change in population => population

based fiscal impacts;

• Oil Field production => Industry fiscal impacts.

Sufficient data do not exist in the recent literature to track all of these steps in relation to

forthcoming activity associated with the 5-Year Leasing Plan. The best alternative is to rely on

the work contained in the Hughes, et al, study of Lafourche Parish. Their work can be related to

deepwater wells drilled in the Gulf of Mexico and incremental deepwater production. This

subsumes the intermediate population effects. Hughes, et al, estimates of incremental local

revenues and public service costs are shown on Table 10.1 along with deepwater wells and

production, which are supported from Port Fourchon. We can relate deepwater wells and

production to onshore fiscal impacts (revenues and expenditures).

Calculations that show government revenues and expenditures per BOE and per new well are

shown on Table 10.1. In the early years of a new lease sale, before production starts,

government revenues and expenditures will be related to new wells. After production starts,

revenues and expenditures will be related half to production and half to new wells based on the

1999 ratios shown on the table. While industry activity may impose specific costs in terms of

publicly provided infrastructure such as roads, such costs are not quantified in any of the cited

studies. We assume that the surplus of revenues over variable costs will be sufficient to pay for

necessary capacity infrastructure changes.

The model must run on fixed relationships. Consequently, Table 10.2 shows that we selected

the 1999 values for costs and revenues per MMBOE and per new well to reflect fiscal and

infrastructure costs. Because we drive the model with both wells and production, the values

shown are the 1999 values divided in half. The column labeled Use contains the data used in the

OECM.
Section 10: Fiscal and Infrastructure 245

OECM Volume 2.doc 03/24/01 11:15 AM

References

Dames & Moore. “California Offshore Oil and Gas Energy Resources Baseline Conditions and

Future Development Scenarios, Draft”, January 30, 1999.

Foster Associates. “Social and Economic Consequences of Onshore OCS-Related Activities in

Coastal Alabama: Final Baseline Report”. OCS Study MMS 98-0046. Contract No. 1435-01-

97-CT-30829.

Foster Associates. “Impact of Destin Dome on Public Services, Community Infrastructure and

Public Finance”. Volume IV. A, Destin Dome 56 Unit Economic, Fiscal and Infrastructure

Impacts. Chevron USA DPP, November 1996.)

Guo, Jimine, David Hughes, Walter R. Keithly. “An Analysis of Louisiana Highway 1 in

Relation to Expanding Oil and Gas Activities in the Central Gulf of Mexico”. Draft, October

13, 1997.

Hughes, David W., J. Matthew Fannin, Walter Keithly, Williams Olatubi and Jimine Guo.

“Impact of Gulf of Mexico OCS Petroleum Mining Industry on the Economy of and Public

Service Provision in Lafourche Parish, Louisiana”. MMS draft report, under cooperative

agreement 14-35-001-30660. 2000 Forthcoming.

MMS. “Deepwater Production Summary by Year”.

MMS. “Deepwater Gulf of Mexico: America’s Emerging Frontier”, MMS 2000-022.

RPC. “Socioeconomic Baseline and Projections on the Impact of an OCS Onshore Base for

Selected Florida Panhandle Communities”. Final Report, Vol. II, December 23, 1999.
246 Section 10: Fiscal and Infrastructure

OECM Volume 2.doc 03/24/01 11:15 AM

Infrastructure and Fiscal

Environmental Cost Parameters

1.

2.

1. Name Cost Categories and Assign Associated Impact Driver Variables

Cost Name

Impact Driver

W ells Inst. BBOE 

produced

None W ells Inst. BBOE 

produced

None

2. Specify Cost Coefficients $ / Well $ / BBOE N/A $ / Well $ / BBOE N/A

North Atlantic $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Mid-Atlantic $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

South Atlantic $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Straits of Florida $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

G Eastern Gulf $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

o Central Gulf $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

M W estern Gulf $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Southern California $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Central California $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Northern California $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

W ashington/Oregon $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Gulf of Alaska $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Cook Inlet $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

A Kodiak $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Shuimagin $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

L North Aleutian Basin $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Aleutian Arc $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

A St. George Basin $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

St. Matthew Hall $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

S Bowers Basin $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Aleutian Basin $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

K Navarin Basin $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Norton Basin $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

A Hope Basin $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Chukchi Sea $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

Beaufort Sea $ (5,029) $ (0.0024) $ 2,147 $ 0.0014

3 16 21 3 16 21

3. Specify the dollar-year of cost coefficients 1995

A

T

L

P

A

C

Three separate cost categories can be entered, each with up to three impact drivers. Enter the cost category name and

the associated impact driver. If a cost category does not use three drivers, set unused columns to "None."

Enter the environmental cost coefficient for each impact driver variable. The proper units to express damage coefficients

below the impact variable name.

Revenue Increases Cost Increases

Annual Results

Regional Costs

National Costs

Regional Costs

National Costs

Print Preview Print

Present Values

Figure 10.1. Onshore Government Revenue Collections and Infrastructure Costs OECM

Impact Drivers

These are found on the Infrastructure cost element worksheet
Section 11: Property Values 247

OECM Volume 2.doc 03/24/01 11:15 AM

11. Private Property Losses

Proximity to OCS facilities may degrade coastal property values. The sight of offshore

platforms may cause a degradation of property values if the existing stock of coastal property

existed prior to offshore development. Oil spills may come ashore that affect private properties

in addition to public resources discussed in other sections of this report. A. T. Kearney (1991)

describes the basis for cost elements included within the OECM, which are updated in this

section.

The specific cost elements estimated in the new model are:

4. The costs associated with oil spills, which result in a motivational discount—an amount

beyond the cleanup and repair costs required to sell a property;

5. The costs associated with visual degradation, which result when aesthetically displeasing

features of OCS development reduce the view premium attached to coastal property.

Oil spill private property damages are described in Section 11.1. Aesthetic damages are

described in Section 11.2. This section relies on Section 8 of A. T. Kearney (1991) because that

report developed substantial information as the basis for these damages.

11.1 Private Property Oil Spill Damages

Table 11.1 calculates the property value losses per meter of coastal oiling for each OCS Planning

Area. The first data column presents the value of vulnerable coastal property taken from the

A.T. Kearney report. This is the amount of property that is assumed sold during the one-year

impact period. This value is updated to 1999 dollars using a planning area-specific price

adjustment. The decrease in sales value due to oil spills is calculated based on an assumed

reduction in the selling prices of 2.74%. (A. T. Kearney (1991), p. 8-13.) The dollar loss is

converted to a per-meter basis by dividing by the number of non-public beach coastal meters

based on the assumption that impacts to public beaches do not affect private property values.

The per-meter values in Table 11.1 are used to estimate the property value losses for each oil

spill scenario. The NRDAM/CME output provides data on the meters of coastal oiling. As

discussed in Section 3, the NRDAM/CME output for coastal oiling is adjusted based on the

supplementary SIMAP analysis. By multiplying this adjusted estimate of coastal oiling by the

per-meter values in Table 11.1, we obtain an estimate of the property value loss for each spill

scenario.

Table 11.2 presents the final property value damage estimate for each spill magnitude (small

spill, large platform/pipeline spill, and large tanker spill). For planning areas with multiple spill

locations, the value in Table II.2 is an average over all locations. Values of zero indicate that the

spill scenario(s) did not result in coastal oiling.
248 Section 11: Property Values

OECM Volume 2.doc 03/24/01 11:15 AM

11.2 Private Property Values Affected by OCS Visual Intrusions

The vast majority of offshore federal unleased oil and gas resources are well beyond the distance

visible from shore. Nonetheless, substantial research went into this issue in the 1970s and 1980s

and A.T. Kearney (1991) summarized this literature. The key variable affecting coastal property

values is the distance to shore of likely OCS platforms—unchanged since A. T. Kearney (1991).

Ten miles remains a cut-off. Beyond this distance sight lines are deemed non-existent.

Unleased resources in each OCS Planning Area within 10 miles of shore are shown in A. T.

Kearney (1991). Only the Central Gulf of Mexico, California and the Pacific Northwest show

unleased resources within 10 miles of coastline.

MMS showed that eight percent of Central Gulf resources were within 10 miles of shore in 1991.

Table 11.2 shows the percentage of undiscovered resources for California and the Pacific

Northwest.

We calibrate the OECM for only these five areas. Table 11.1 shows the coastal private property

at risk of diminution of property values due to the unexpected arrival of offshore platforms

within ten miles. A. T. Kearney (1991) established six percent as the upper limit of diminution

of property value due to a change in the viewscape due to the addition of an OCS platform. Six

percent was benchmarked to an estimate of 20 percent as the amount of premium coastal

properties might carry. Not all of the premium would be eliminated by the presence of OCS

activity. The maximum loss was scaled from six percent at three miles to zero at ten miles. We

assume two percent to represent a positive minimum case.

To illustrate with the Central Gulf, $672.5 million dollars is the stock value of Central Gulf

coastal property based on the assumption discussed in 11.1 that 8.3 percent is sold annually.

This is equivalent to an average of $79,213 per mile, excluding public beaches, over the 8,687

miles of coastline. A. T. Kearney (1991) assumed at max that property within five miles on

either side of the perpendicular line to a platform could see a platform, with declining vision as a

function of horizontal and perpendicular distance. So, on average over the coastal area affected,

ten miles of coastal properties with a value of $792,130 may be affected by any single platform.

If the platform causes at max a six percent decline in property value, this amounts to $47,527 per

platform within ten miles of shore. The OECM must calibrate this visual impact over all

platforms. Eight percent of OCS resources in the Central Gulf are within the ten miles of shore.

Consequently, we assume that eight percent of platforms are likely to occur within ten miles of

shore. The expected value of the platform impact is $3802 for all platforms. The low estimate is

$1,267. The highest visual impacts would occur off Southern California, where the range is

$279,500-$838,600 per each platform.
Section 11: Property Values 249

OECM Volume 2.doc 03/24/01 11:15 AM

Table 11.1. Property Value Losses per Meter of Coastal Oiling, by OCS Planning Area

Planning Area

Value of

Vulnerable

Property

Sold ($1989)

Price

Index

Adj.

($1989 to

$1999)

Value of

Vulnerable

Property

Sold ($1999)

Decrease in

Sales Value

Due to Oil

Spills

Coastal

Meters

(Excl. Public

Beaches)

Decrease in

Value Per

Coastal m

Due to

Spills

North Atlantic $4,067,000 1.149 $4,672,097 $128,015 7,668,494 $0.017

Mid-Atlantic $27,945,000 1.186 $33,155,562 $908,462 21,365,911 $0.043

South Atlantic $7,946,000 1.342 $10,665,124 $292,224 12,937,969 $0.023

S. of Florida $233,235,000 1.332 $310,721,357 $8,513,765 2,016,077 $4.223

Eastern GOM $39,782,000 1.507 $59,953,974 $1,642,739 7,750,553 $0.212

Central GOM $39,782,000 1.409 $56,049,758 $1,535,763 13,662,019 $0.112

Western GOM $39,782,000 1.409 $56,049,758 $1,535,763 5,082,831 $0.302

Southern CA $425,815,000 1.131 $481,742,320 $13,199,740 1,563,948 $8.440

Central CA $263,631,000 1.131 $298,256,777 $8,172,236 1,533,377 $5.330

Northern CA $37,135,000 1.131 $42,012,379 $1,151,139 1,020,106 $1.128

WA/OR $10,499,000 1.237 $12,989,198 $355,904 6,685,395 $0.053

Gulf of Alaska $7,837,000 1.652 $12,944,868 $354,689 17,893,689 $0.020

Kodiak $6,171,000 1.652 $10,193,031 $279,289 1,581,647 $0.177

Cook Inlet $6,475,000 1.652 $10,695,167 $293,048 4,691,844 $0.062

Shumagin $103,000 1.652 $170,132 $4,662 4,637,138 $0.001

N. Aleutian B. $2,542,000 1.652 $4,198,782 $115,047 3,708,745 $0.031

St. George B. $700,000 1.652 $1,156,234 $31,681 1,364,432 $0.023

Aleutian Arc $1,622,000 1.652 $2,679,160 $73,409 5,127,883 $0.014

Bowers Basin $0 1.652 $0 $0 0 $0.000

Aleutian Basin $0 1.652 $0 $0 0 $0.000

Navarin Basin $0 1.652 $0 $0 0 $0.000

St. Mat. Hall $4,348,000 1.652 $7,181,866 $196,783 2,891,373 $0.068

Norton Basin $3,189,000 1.652 $5,267,473 $144,329 3,872,863 $0.037

Hope Basin $3,050,000 1.652 $5,037,878 $138,038 3,763,451 $0.037

Chukchi Sea $2,479,000 1.652 $4,094,721 $112,195 2,238,119 $0.050

Beaufort Sea $84,000 1.652 $138,748 $3,802 2,781,961 $0.001
250 Section 11: Property Values

OECM Volume 2.doc 03/24/01 11:15 AM

Table 11.2. Property Value Damage Estimates Due to Spills (1999 dollars)

Planning Area

Property

Value Losses,

Small Spill

(any source)

Property Value

Losses, Large

Platform/Pipeline

Spill

Property Value

Losses, Large

Tanker Spill

North Atlantic $0 $0 $0

Mid-Atlantic $0 $0 $0

South Atlantic $0 $0 $0

S. of Florida $5,017 $130,438 $154,610

Eastern GOM $415 $813 $5,042

Central GOM $343 $1,314 $2,039

Western GOM $1,684 $5,490 $12,979

Southern CA $15,040 $60,160 $87,278

Central CA $145,049 $353,990 $466,231

Northern CA $859 $14,198 $2,145

WA/OR $589 $1,739 $2,142

Gulf of Alaska $0 $29 $237

Kodiak $635 $3,700 $458

Cook Inlet $350 $1,564 $2,143

Shumagin $0 $14 $72

N. Aleutian B. $98 $931 $1,275

St. George B. $0 $0 $0

Aleutian Arc $359 $389 $1,631

Bowers Basin $0 $0 $0

Aleutian Basin $0 $0 $0

Navarin Basin $0 $0 $0

St. Mat. Hall $0 $0 $0

Norton Basin $0 $0 $0

Hope Basin $0 $0 $0

Chukchi Sea $0 $42 $446

Beaufort Sea $10 $49 $76
Section 11: Property Values 251

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Table 11.3. Property Value Damage Estimates Due to Visual Impacts (1999 dollars)

Maximum Loss in

Value Due to

Visual Impact

Low Estimate of Loss

in Value Due to

Visual Impact Planning Area

Value of

Property

Per

Coastal

Mile

Value of

Coastal

Property at

Risk from

OCS

Platforms

% of OCS

Resource

Within 10

Miles of

Coastline

Percen

tage

$ Per

Platform

Percentage$ Per

Platform

Central GOM

(CGM)

$79,213 $792,129 8 6 $3,802 2 $1,267

Southern CA (SC) $5,947,436 $59,474,360 23.5 6 $838,588 2 $279,529

Central CA (CC) $3,755,594 $37,555,943 22.4 6 $504,752 2 $168,251

Northern CA (NC) $795,187 $7,951,870 4.85 6 $23,140 2 $7,713

WA/OR (WO) $37,514 $375,139 28.5 6 $6,415 2 $2,138
252 Section 11: Property Values

OECM Volume 2.doc 03/24/01 11:15 AM

Property Values

Environmental Cost Parameters

1.

2.

1. Name Cost Categories and Assign Associated Impact Driver Variables

Cost Name

Impact Driver

Platforms Inst. None None BBL Spilled 

(plat/pipe)

BBL Spilled 

(tanker)

None

2. Specify Cost Coefficients $ / platform N/A N/A $ / BBL $ / BBL N/A

North Atlantic - --

Mid-Atlantic - --

South Atlantic - --

Straits of Florida - $ 22 $ 6

G Eastern Gulf - $ 1 $ 0

o Central Gulf $ 1,267 $ 1 $ 0

M Western Gulf - $ 3 $ 1

Southern California $ 279,529 $ 25 $ 4

Central California $ 168,251 $ 214 $ 24

Northern California $ 7,713 $ 3 $ 0

Washington/Oregon $ 2,138 $ 1 $ 0

Gulf of Alaska - $ 0 $ 0

Cook Inlet - $ 1 $ 0

A Kodiak - $ 1 $ 0

Shuimagin - $ 0 $ 0

L North Aleutian Basin - $ 0 $ 0

Aleutian Arc - --

A St. George Basin - $ 0 $ 0

St. Matthew Hall - --

S Bowers Basin - --

Aleutian Basin - --

K Navarin Basin - --

Norton Basin - --

A Hope Basin - --

Chukchi Sea - $ 0 $ 0

Beaufort Sea - $ 0 $ 0

7 21 21 18 17 21

3. Specify the dollar-year of cost coefficients 2000

The coefficients reflect dollars of what year?

A

T

L

P

A

C

Three separate cost categories can be entered, each with up to three impact drivers. Enter the cost category name and

the associated impact driver. If a cost category does not use three drivers, set unused columns to "None."

Enter the environmental cost coefficient for each impact driver variable. The proper units to express damage coefficients

below the impact variable name.

Visual Impact Spill-Related

Annual Results

Regional Costs

National Costs

Regional Costs

National Costs

Print Preview Print

Present Values

Figure 11.1. Property Value OECM Impact Drivers

These are found on the Property cost element worksheet
Section 12: Water Quality 253

OECM Volume 2.doc 03/24/01 11:15 AM

12. Water Quality Externalities

12.1 Water in the 1991 GPECM:

The 1991 GPECM included water pollution costs associated with water quality control costs and

oil spills. Water quality costs associated with normal operations, such as the discharge of

cuttings and drilling muds from exploration and development wells and from workovers of

production wells, were assumed to be more than offset by the additional habitat created by the

artificial reef conditions, which are created by platforms.

12.2 Water Quality Externalities Rationale

The current OECM includes only externalities. Under this framework, the first cost category

mentioned above, control costs, is not included because it is an internal cost of the OCS

developer. The second category, oil spills, is treated in another section. This leaves only the

effects of routine discharges of cuttings, drilling fluids, sanitary sewage and non-point runoff

from drilling rigs and offshore platforms. We concur with the 1991 GPECM that externalities

associated with this cost category are negligible.

Discharges of drill cuttings, mud and produced water from drill ships or other drilling platforms

and from production platforms require a NPDES (National Pollution Discharge Elimination

System) permit from the EPA or from the nearest onshore regional water quality board. Sanitary

waste discharges are subject to NPDES permit requirements also1. In issuing NPDES permits,

agencies apply rigorous standards to ensure that water quality is not unreasonably degraded and

that discharges comply with state requirements adopted under Clean Water Act authority.2 In

many lease sale areas, cuttings and mud as well as sanitary sewage are returned to shore for

disposal. Produced waters are either discharged (again subject to NPDES discharge

requirements) or reinjected down injection wells. In the later stages of production, water

discharges are considerable in “water drive” formations. The reinjection is usually used as a

means of managing the yield of the field.

In all cases where water is injected or brought to shore for disposal, it is reasonable to assume

that there will be no negative externality costs. Where cuttings, drilling muds and produced

waters are discharged, there could conceivably be external costs. Seven out of 21 actively

producing platforms in the federal California OCS discharged muds and cuttings in 1990.3

 Only

two platforms in the California OCS discharged sanitary wastes. A study by the Southern

California Coastal Water Research Project (quoted in Resource Agency of California, 1990)

indicates that the total wastes discharged in the California OCS is less than 1 percent of the mass

discharged by the four largest municipal waste systems in California. This would suggest that

the discharges are negligible from a mass standpoint. The constituents of drilling muds and

cuttings can contain small amounts of trace metals, notably zinc and lead. Barium, a fairly

benign substance is the main metallic ingredient in drilling muds. Most drilling muds in current

 

1

CWA Section 301(a), 33 USC 1311(a) and Section 402 33 USC 1342.

2

CWA Section 510, 33 USC 1370.

3

Resource Agency of California, 1999. California’s Ocean Resources: An Agenda for the Future, Chapter 5E,

Appendix 1, page 2.
254 Section 12: Water Quality

OECM Volume 2.doc 03/24/01 11:15 AM

use are non-toxic (see Baroid web site: <www.baroid.com>). Because of the high cost of land

disposal, synthetic, non oil-based drilling muds are rapidly gain favor in the industry. A costbenefit analysis of effluent limitations for these fluids indicates that under most regulatory and

disposal conditions, they are cost-effective4

. Microdrilling technology, a technique for drilling

exploratory wells with much smaller than normal borehole diameters, offers promise for further

reducing exploratory drill emissions. Microdrilling, uses only one-fortieth of the drilling fluid

needed for a conventional well5.

The conclusion that water quality externalities are negligible is supported by several EIS’s. The

recent Western Gulf EIS6 concludes:

Current and future limits on the levels of contaminants in drilling muds and cuttings and

produced-water discharges, discharge-rate restrictions, and monitoring and toxicity testing

requirements are expected to eliminate significant biological or ecological effects that were

documented in the past. For shallow developments, elevated levels of some contaminants found

in the plumes of produced-water discharges and drilling mud discharges are expected to be

detected out to a few thousand meters down current from the discharge point; however, no

ecological effects to water-column organisms are expected. For deepwater facilities, although

levels of discharges per deepwater facility would be higher than shallower water facilities, there

would be fewer locations where discharges would take place. Drilling discharges from facilities

located in waters deeper than 400 m could reach the seafloor but would result in extremely low

levels of sediment contamination, if any at all, and any cuttings would be distributed in very thin

accumulations distributed somewhere within a broader area than they would be in shallower

waters. The plume from produced-water discharges is not expected to reach the seafloor in water

depths greater than 100 m. Despite any possible sediment accumulation of discharged chemicals,

biological responses in soft-tissue benthic organisms are not expected to be detectable beyond a

couple of hundred meters. Toxic effects to the benthos from OCS discharges are expected to be

very localized, limited to within 100 m from the discharge, and will be of a relatively small

magnitude. Given that the contaminants are exhibiting localized, low level sediment retention

and biological perturbation, regional water quality is not expected to be compromised by

discharges related to the proposed action.

Given these trends and conclusions, it is reasonable to assume that the environmental

externalities associated with water pollution from OCS activities will be negligible.

 

4

 USEPA, 1999. Economic Analysis of Proposed Effluent Limitations Guidelines and Standards for Synthetic-Based

Drilling Fluids and other Non-Aqueous Drilling Fluids in the Oil and Gas Point Source Category. EPA-821-98-020,

Office of Water, February, 1999.

5

 See PTTC Technology Connections website <www.pttc.org/ts/ts_171.htm>.

6

 MMS, 1998. Gulf of Mexico OCS Oil and Gas Lease Sales 171, 174, 177, and 180. Western Planning Area FEIS.

MMS-98-0008, pages IV-112-113.
Appendix A: SIMAP Spill Modeling Results A-1

OECM Volume 2.doc 03/24/01 11:15 AM

Appendix A. SIMAP Spill Modeling Results

A.1 Introduction

This document describes the results of modeling expected impacts to fauna and service losses of

oiled habitats and shorelines. This information will be input to the economic model, being

developed by the Foster team. Spill modeling using SIMAP provides measures of oil-impacted

areas and fauna to be used in these calculations.

The SIMAP modification of NRDAM/CME model (developed by ASA for use by the

Department of the Interior in CERCLA NRDA type A regulations) was used. This model is an

upgrade of the type A model in that (1) thresholds of concern (toxic endpoints) may be varied to

evaluate degree of impacts to different resources; (2) subsurface (pipeline or blowout) releases of

oil may be simulated; (3) higher resolution grids of habitat and water depth may be input to the

model; (4) additional outputs of exposure and impact may be provided; and (5) it may be run in

stochastic mode to effectively produce results and statistics for multiple model runs. The ability

to model a subsurface release was the new feature used in this study. Also, higher resolution and

more accurate habitat mapping were included to provide a better estimate of shoreline habitat

impacts, by shore type.

A.2 Modeling Design

In order to perform the modeling, detailed geographical (habitat and depth mapping) must be set

up for each location. Existing location data for two study areas were used. ASA had previously

developed these data sets for other projects, including one for MMS. These locations are where

pipelines are in most significant use, and the principle objective in the use of SIMAP in this

study is to evaluate subsurface releases.Texas-Louisiana shelf (west of the Mississippi Delta)NW

Florida shelf (Big Bend region north and west of Cedar Key)

In discussions with Brian Roach, the following approach to the modeling was developed. Dr.

Roach has run cases using the publicly available type A model. SIMAP was run for selected

spills already run in the type A model. Dr. Roach will use the SIMAP results to scale the type A

model results to quantify sublethal impacts and percent service losses.

Each SIMAP model scenario consists of the following:

• A single spill site (per location)

• A single oil type (South Louisiana crude oil)

• Two oil spill volumes, to represent a small and large spill

• Two wind time series, provided by Dr. Roach. One wind times series is a representative

scenario while the other wind time series is a “worst case” scenario for oiling shoreline.

• A surface spill and a subsurface spill (the depth of the subsurface spill is at the sea floor).

The surface spill represents a tanker/platform spill and the subsurface spill represents a

pipeline spill.
A-2 Appendix A: SIMAP Spill Modeling Results

OECM Volume 2.doc 03/24/01 11:15 AM

Thus, each spill location involves eight model runs with the SIMAP model (two spill sizes,

surface/subsurface, and two wind time series). These scenarios are named as in Table A.1.

Table A.1. Model Scenarios Run with SIMAP

Scenario Location Volume

(bbl) Wind File Release Depth(m)

TEXV1W1D0 Texas 111 TEXAS1.ASW 0

TEXV1W1DB Texas 111 TEXAS1.ASW 49

TEXV1W2D0 Texas 111 TEXAS2.ASW 0

TEXV1W2DB Texas 111 TEXAS2.ASW 49

TEXV2W1D0 Texas 25,500 TEXAS1.ASW 0

TEXV2W1DB Texas 25,500 TEXAS1.ASW 49

TEXV2W2D0 Texas 25,500 TEXAS2.ASW 0

TEXV2W2DB Texas 25,500 TEXAS2.ASW 49

FLV1W1D0 Florida 111 FLORDIA1.ASW 0

FLV1W1DB Florida 111 FLORDIA1.ASW 6

FLV1W2D0 Florida 111 FLORIDA2.ASW 0

FLV1W2DB Florida 111 FLORIDA2.ASW 6

FLV2W1D0 Florida 25,500 FLORDIA1.ASW 0

FLV2W1DB Florida 25,500 FLORDIA1.ASW 6

FLV2W2D0 Florida 25,500 FLORIDA2.ASW 0

FLV2W2DB Florida 25,500 FLORIDA2.ASW 6

A.3 Model Inputs

Dr. Brian Roach provided the model-input assumptions. These are summarized below.

Both Locations

1. All spills begin on May 1, 1999 at 0:00 hours;

2. All spills are in one stage with a duration of 24 hours;

3. All wind time series begin on May1, 1999 at 0:00 hours and end on June 11, 1999 at 15:00

hours;

4. All spills use no current data;

5. Tides are set at defaults (high tide = 0, tide range = 1);

6. No clean-up efforts are undertaken;
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7. No closures;

8. The chemical spilled is South Louisiana crude oil;

For the Texas location:

1. 28o

20’N, 95 o 10’W (off the Texas coast);

2. Air temperature = 24 o C;

3. Water temperature = 23 o C;

4. Suspended sediment concentration = 10 mg/l;

5. Settling velocity = 3 m/day;

For the Florida location:

1. 29 o 50’N, 84 o 20’W (off the Florida panhandle coast);

2. Air temperature = 25 o C;

3. Water temperature = 27 o C;

4. Suspended sediment concentration = 10 mg/l;

5. Settling velocity = 3 m/day;

A.4 Geographical Data

The location setup involves obtaining geographical information system (GIS) data sets of shore

and habitat type for each location. These data are available in the form of Environmental

Sensitivity Index (ESI) maps, available from the states and from Research Planning Inc (RPI),

the company that has mapped such data for government response organizations for all these

locations.

The model uses gridded maps to determine location of the shoreline, habitat (or shore) type, and

water depth. These gridded maps are prepared for each location using the GIS data and Arc

View (ESRI) GIS software. A depth data grid of the same dimensions and area coverage as the

habitat grid contains bathymetry from the NOS soundings database (National Ocean Service,

Hydrographic Survey Data, Version 3.2). Appendix Attachment A shows the gridded habitat

and depth data.

Environmental Sensitivity Index (ESI) and coastline data for Texas were obtained from the

Texas General Land Office (TGLO) web site. The TGLO shoreline features were extracted from

National Wetland Inventory data and other hydrographic data sets and assigned ESI values by

the University of Texas Bureau of Economic Geology. Oyster reefs in the Galveston Bay system

(except for part of Trinity Bay) were mapped by Dr. Eric Powell formerly of Texas A&M

University and downloaded from the TGLO web site http://www.glo.state.tx.us/oilspill/osdata.html.

Coastline and wetland data for Florida were obtained from RPI in the form of digital ESI mapsK

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A-4 Appendix A: SIMAP Spill Modeling Results

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A.5 Results

Appendix Attachment B contains plots of the oil trajectories and concentrations of dissolved

aromatics (the toxic components of the oil). All four of the wind time series were for winds from

a steady direction, such that the trajectories run straight toward shore. For each spill site and

wind file, each of the four trajectories looks the same, following the same path down wind. The

plots in Appendix Attachment B show one trajectory snapshot for each location and wind file,

taken when the oil reaches the shoreline. The trajectory plot shows the oil’s current location (at

the time of the snapshot) and its previous path (in gray). The trajectory plot also shows the

locations of mass as surface oil (black), subsurface entrained oil (blue), and dissolved aromatics

(green). Table A.2 indicates when the oil reaches shore for each scenario.

Table A.2. Time When Oil First Reaches Shoreline, By Location and Wind File.

Location Wind Date Shore

Reached

Time Shore

Reached

Hours after

Spill Start

Texas TEXAS1.ASW May 4 11:00 83

Texas TEXAS2.ASW May 4 23:00 95

Florida FLORDIA1.ASW May 2 06:00 36

Florida FLORIDA2.ASW May 1 18:00 18

Appendix Attachment B also includes snapshots of (1) concentrations of total hydrocarbons

(both on the surface as slicks and subsurface) and (2) dissolved low molecular weight aromatics

(the toxic fraction). The snapshots are for the larger of the two volumes and for the subsurface

releases. These represent the largest water column impact scenarios for each spill location and

wind file. The threshold for toxic effects is 1% of the lowest LC50, which is 0.14 ppb for fish

eggs and larvae (from the type A model database). The toxic plumes are significant in size, even

for the Texas site. This will be born out by the biological impact results (below).

Table A.3 summarizes the biological impact results. The text summary files, equivalent to those

produced by the type A model, are in Appendix Attachment C. The detailed outputs (equivalent

to those produced by the type A model) are available in electronic form.

In comparing the SIMAP model results to those produced by the NRDAM/CME, the following

should be kept in mind:

• Both models use the same thresholds for impacts:

− Slick thickness causing impact to wildlife on the water = 10 microns

− Shoreline oil thickness causing impact to wildlife = 100 microns

− Water area swept by slicks: > 1 micron

− Shoreline area oiled: > 1 micron
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• The resolution of the habitat grid is much finer, and more accurate, for the SIMAP runs than

in the NRDAM/CME. While habitat types are mapped in both, they are more up-to-date and

accurate in the SIMAP maps.

• The NRDAM/CME automatically runs for weeks to months, until all oil disappears form the

water surface and falls below 1 micron on shorelines. SIMAP was run for exactly 14 days.

By this time in the SIMAP runs, the slicks had come ashore and disappeared from the water.

Thus, both the area swept (square meters) and area swept times time (m2-days) for SIMAP

and the NRDAM/CME are comparable. However, while the shoreline areas oiled may be

compared, the shore area oiled times time (m2-days) data for the 2 model runs are not

comparable. A better measure of shoreline impact is area oiled. In reality, in the US the

duration of shoreline oiling is more a function of how soon it is cleaned up than the natural

cleansing rate assumed in the model.

• The two models use different algorithms for the evaporation rate. In the NRDAM/CME,

evaporation is modeled using vapor pressure for individual components in the mixture, and

assuming each component behaves as if it were alone. In SIMAP, evaporation rate is based

on empirical observations for whole oil in tank studies. Because the components are in the

oil mixture, and there is some masking and slowing of evaporation, the evaporation of the

whole is less than the sum of evaporation of each component, taken alone. Thus, the SIMAP

evaporation rate is likely more accurate. Wildlife impacts on the water occur during the brief

period when oil slicks exceed 10 microns. Thus, the subtle differences in evaporation rate

influence the computed impact to wildlife. The influence is larger for smaller spill volumes,

where the models are more sensitive to the assumed evaporation rate.

• The impact to wildlife on the shoreline is assumed to occur only while oil is fresh (contains

light aromatics and is sticky, i.e., for the first week). Thus, the shoreline impacts to wildlife

are calculated in the same manner in the two models, and the results are not affected by the

run time of the models beyond the 14 days used in the SIMAP runs. Differences in shoreline

wildlife impacts are due to differences in shore area oiled.

In all cases, the subsurface release produced much higher impacts to fish and invertebrates than

the equivalent surface release. This is because the subsurface release injects the oil with the

toxic aromatics into the water, where the aromatics more readily dissolve. Impacts to YOY are

for the number of young-of-the-year that would have otherwise survived to 365 days of age.

These impacts are higher than for adult stock (animals larger than the recruitment size to the

fishery) because (1) the 96-hour LC50 for YOY is 14 ppb dissolved aromatics as opposed to 119

ppb for older animals and (2) the YOY move with the currents and the plume, such that their

exposure is longer than for older animals that swim through the plume.

Oil released from the bottom (49m at the Texas site or 6m at the Florida site) rapidly surfaces

and forms slicks. Thus, the wildlife and shoreline impacts are similar for surface and subsurface

releases of the same volume and wind conditions. The subsurface release does disperse the oil

somewhat, so the slick area that would cause impact is often (but not in all cases) less than for

the surface release.