Final EIS Appendix N

APPENDIX N—TECHNICAL REPORT: SOCIAL AND 

ECONOMIC IMPACT ANALYSIS METHODOLOGY 

TABLE OF CONTENTS

CHAPTER 1 —GENERAL ASPECTS OF THE METHODOLOGIES................................................. N-1

1.1 ASSUMPTIONS........................................................................................................................ N-1

1.2 QUANTITATIVE ECONOMIC IMPACT ANALYSIS USING IMPLAN .......................................... N-3

1.3 QUALITATIVE ECONOMIC IMPACT ANALYSIS........................................................................ N-6

1.4 SOCIAL IMPACT ANALYSIS .................................................................................................... N-6

1.5 ENVIRONMENTAL JUSTICE IMPACT ANALYSIS ...................................................................... N-7

CHAPTER 2 —METHODOLOGIES BY RESOURCE USE ................................................................ N-7

2.1 OIL AND GAS ......................................................................................................................... N-7

2.2 WIND ENERGY ..................................................................................................................... N-12

2.3 LIVESTOCK GRAZING........................................................................................................... N-16

2.4 RECREATION........................................................................................................................ N-19

2.5 NONMARKET VALUES.......................................................................................................... N-25

LIST OF TABLES

Table 1. Wyoming Counties Included in the BLM Field Office Study Areas......................................... N-4

Table 2. Wyoming Counties Included in the Forest Service Planning Unit Study Areas........................ N-5

Table 3. Oil and Gas Well Cost and Completion Rates by Field Office/Planning Unit .......................... N-9

Table 4. Prices and Tax Revenue Estimates for Oil and Gas Production .............................................. N-12

Table 5. Projected Wind Energy Development Scenarios, in Megawatts ............................................. N-14

Table 6. Value of an AUM for Cattle Production, Basin & Range Region ........................................... N-18

Table 7. Value of an AUM for Cattle Production, Northern Great Plains Region................................. N-18

Table 8. Value of an AUM for Sheep Production ................................................................................. N-19

Table 9. Total Non-Skiing Recreation Visits........................................................................................ N-20

Table 10. Trip Segment Data and Expenditures by Trip Segment ........................................................ N-23

Table 11. Direct Recreation Expenditures Calculation Example: Rock Springs Field Office ............... N-24

Table 12. Calculation of Visitor Days, Bridger-Teton National Forest................................................. N-26

Table 13. Calculation of Visitor Days, Medicine Bow National Forest and Thunder Basin 

National Grassland................................................................................................................... N-26

Table 14. Total Visits, Visitor Days, and Consumer Surplus Value...................................................... N-27

CHAPTER 1—GENERAL ASPECTS OF THE 

METHODOLOGIES

1.1 Assumptions

The following basic assumptions underlie all of the social and economic analyses: 

• Economic benefits to the socioeconomic study area (defined in Chapter 3 and the Socioeconomic 

Baseline Report), in terms of labor earnings and employment, would accrue from U.S. Department 

of the Interior, Bureau of Land Management (BLM) and U.S. Department of Agriculture, Forest 

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-1 
Appendix N Final EIS

Service (Forest Service)-influenced activities such as oil and gas development, wind energy 

development, livestock grazing, and recreation.

• Employment and income (especially labor earnings) would continue to be a driver of economic and 

population growth in the socioeconomic study area.

• Housing supply and costs, and community infrastructure and services, may be constraints on 

population growth in some locations within the planning area.

• Tax and royalty revenues derived from activities on BLM and Forest Service-administered lands 

would continue to have fiscal implications for communities within the socioeconomic study area, 

the state, and the Federal Government.

• Activities and resources available in and around the planning area would continue to be important 

to the quality of life of current and future residents.

• The pace and timing of mineral development activities is dependent on a variety of factors outside 

the management decisions of BLM and Forest Service. These include national and international 

energy demand and prices, production factors within the planning area, and business strategies of 

operators. The Reasonable Foreseeable Development (RFD) scenario projects expected rates of 

well drilling, and the BLM Reservoir Management Group (RMG) has also estimated completion 

rates and production decline curves. Together these parameters allow for projection of future oil 

and gas production volumes for use in the economic impact analysis. Actual economic impacts 

could vary if actual development or production varies from the projections, or if prices change. 

• The pace and timing of wind energy development activities is also dependent on a variety of factors 

outside the management decisions of BLM and Forest Service. These include demand for 

electricity, availability of transmission infrastructure capacity, prices for other energy sources such 

as coal for electricity generation, costs of wind energy generation technologies, production factors 

within the planning area, and business strategies of operators. The impacts analysis uses a wind 

deployment scenario developed from multiple sources. Actual impacts could vary if the rate of 

development over the study period is different.

• Demand for use of BLM and Forest Service-administered land for livestock grazing will continue 

through the study period.

• Demand for use of BLM and Forest Service-administered land for recreational activities, including 

off-highway vehicle (OHV) use, throughout the planning area will remain steady or increase 

through the study period. 

• To the extent that management alternatives require linear construction projects to avoid Greater 

Sage-Grouse habitat or require increased construction costs for projects crossing Greater SageGrouse habitat, some impacts could occur. A 2012 Western Electricity Coordinating Council study 

cites transmission line costs per mile, ranging from $927 thousand to $2,967 thousand depending 

on voltage and type of lines. This study provides cost multipliers for difficult terrains, reaching up 

to 2.25 in the case of forested lands. According to the Energy Information Administration (2013), 

transmission costs form typically around 11 percent of the cost of energy bills, with the remaining 

being formed by power generation and distribution. Restrictions on federal lands which surround 

state and private lands could adversely impact the current use of those lands, including ability to 

lease for mineral or energy development.

The discussions below of the specific methodologies for each resource use provide additional assumptions 

used in the analyses.

N-2 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

1.2 Quantitative Economic Impact Analysis Using IMPLAN

The economic impact analysis uses two general approaches. These are quantitative analysis and qualitative 

analysis. The quantitative analysis approach is used when given adequate available information and 

resources. In this study, adequate data was available for four resource uses: 

• Oil and gas development and production

• Wind energy development and production

• Livestock grazing

• Recreation

The basic strategy used in quantitative economic impact analysis is to first identify the primary impacts of 

an economic activity affected by management decisions. For instance, primary impacts include 

expenditures made by oil and gas companies to drill a well, and to complete the well for production. Primary 

impacts also include the value of the oil and gas that is produced and sold. Next, where primary impacts 

can be quantified, they can generally also be run through an economic model to estimate the economic 

activity that is generated as the primary impact ripples through the economy, “upstream” to providers of 

goods and services necessary for production, and “downstream” as income generated from production is 

spent by the households that receive the income. 

The upstream, downstream, and total effects are estimated in this study through use of the IMPLAN (IMPact 

analysis for PLANning) model. The IMPLAN model was originally developed by the Forest Service and 

is commonly used by the BLM and many other government and private sector organizations to estimate the 

total economic impacts of various activities, actions, and policies. The model tracks inter-industry and 

consumer spending in a local (or regional) economy, allowing estimation of indirect and induced economic 

impacts in the local economy that result from the original economic activity or a change in economic 

activity. Indirect impacts results from local inter-industry purchases caused by the direct impact, and

induced impacts results from re-spending of labor income (i.e., local purchases by households of employees 

and proprietors of the affected industries). The re-spending represented by indirect and induced impacts is 

often referred to as the “multiplier effect.” 

Outputs of the IMPLAN model include economic output, labor income, and employment. These are defined 

as follows:1

Employment (jobs) – A job in IMPLAN equals the annual average of monthly jobs in that 

industry.2 Thus, 1 job lasting 12 months equals 2 jobs lasting 6 months each, equals 3 jobs lasting 

4 months each. It is important to note that IMPLAN, based on some of its data sources, does not 

distinguish between full-time and part-time jobs. Sectors with higher labor earnings per job are 

likely to reflect a high proportion of full-time jobs, while sectors with low labor earnings per job 

often reflect a significant number of part-time jobs.

Labor Income (earnings) – All forms of employment income, including Employee Compensation 

(wages and benefits) and Proprietor Income.

Economic Output (gross regional economic output) – Output represents the value of industry 

production. In IMPLAN these are annual production estimates for the year of the data set and are 

in producer prices. For manufacturers, output issales plus or minus change in inventory. For service 

1 Based on the glossary available at the website of the Minnesota IMPLAN Group, the publisher of the IMPLAN model/software, 

http://implan.com/V4/index.php?option=com_glossary&Itemid=57. 

2 This is the same definition used nationally by the Quarterly Census of Employment and Wages, United States Bureau of Labor 

Statistics, and United States Bureau of Economic Analysis.

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-3

 
Appendix N Final EIS

sectors production, output equals sales. For retail and wholesale trade, output equals gross margin, 

not gross sales.

By constructing “social accounts” that describe the structure and function of a specific economy, IMPLAN 

creates a localized model to investigate the consequences of projected economic activity in a geographic 

region – each of the selected BLM field offices and Forest Service planning units, or the state as a whole. 

The IMPLAN model uses data specific to the local economy wherever possible, but also uses some data 

based on national-level economic relationships. Therefore, the model benefits from “calibration” of some 

of its data to better reflect the local economy. For this study, IMPLAN was calibrated based on work the 

University of Wyoming has done with the model in Wyoming over many years and with data specific to 

this study. The specific IMPLAN impact analysis methodologies and assumptions for each resource use are 

described below.

The analyses used Version 3.0 of the IMPLAN modeling system. The IMPLAN model is managed by and 

available from the Minnesota IMPLAN Group (MIG, Inc. http://implan.com/V4/Index.php). 

1.2.1 Study Areas

The economic impact analyses were conducted for federal lands administered by six BLM field offices and 

three Forest Service planning units in the state. The six BLM field offices included: 

• Casper

• Kemmerer

• Newcastle

• Pinedale

• Rawlins

• Rock Springs

The three Forest Service planning units included: 

• Bridger-Teton National Forest (BTNF)

• Medicine Bow National Forest (MBNF)

• Thunder Basin National Grassland (TBNG)

For the analyses of oil and gas, grazing, and recreation impacts, IMPLAN analyses were prepared for each 

of the six field offices and three planning units. The analysis for wind was conducted at the state level only.

Each field office or planning unit IMPLAN analysis was based on a study area consisting of the counties 

in and around the field office or planning unit that could potentially be most directly impacted by the 

alternatives. Table 1 and Table 2 provide lists of the individual counties included in each IMPLAN model. 

Table 1. Wyoming Counties Included in the BLM Field Office Study Areas

Casper Field 

Office

Kemmerer

Field Office

Newcastle

Field Office

Pinedale

Field Office

Rawlins Field 

Office

Rock Springs

Field Office

Converse

Goshen

Natrona

Platte

Lincoln

Sweetwater

Uinta

Crook

Weston

Niobrara

Campbell

Lincoln

Sublette

Sweetwater

Carbon

Albany

Laramie

Sweetwater

Sweetwater

Uinta

Lincoln

Sublette

Fremont

N-4 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

Table 2. Wyoming Counties Included in the Forest Service Planning Unit Study Areas

Bridger-Teton 

National Forest

Medicine Bow 

National Forest

Thunder Basin 

National 

Grassland

Fremont

Lincoln

Sublette

Sweetwater

Teton

Albany

Carbon

Converse

Platte

Campbell

Converse

Crook

Niobrara

Weston

Economic impacts for the entire state were also calculated for each resource use. The methodology used to 

appropriately address regional (study area) and state-level impacts is as follows: For each field office or 

planning unit, two IMPLAN models are constructed. The first is a primary model which includes the 

counties in the field office or planning unit study area. The second is a secondary model for the rest of the 

counties in the state not included in the specific study area. The secondary model is linked to the primary 

model through IMPLAN’s Multi-Region Input-Output (MRIO) capabilities. For the field office or planning 

unit analysis, we report the primary model results. For the state-level analysis we report the primary model 

results for each field office or planning unit, plus the secondary model results for each office or unit and 

sum across all nine offices and units, to get the state totals. This accounts for the fact that the direct impacts 

may not be the same for all units which is what one would have to assume for one state model. This is the 

preferred approach recommended by MIG, Inc.

1.2.2 Timeframe of the Analyses

Economic impacts were estimated across an 8-year time period (2013-2020). This is the only period within 

the larger planning period for which the necessary quantitative input data was available for all four resource 

uses. Specifically, the oil and gas development projections were only available for all BLM field offices 

and Forest Service planning units for this period. All other analyses were scoped to the same period.

1.2.3 Base Year Dollars and Discounting

All dollar figures throughout the economic analysis are in constant 2011 dollars. This is the base year used 

in the IMPLAN model.

All dollar figures in the summary results tables of Chapter 4 represent the total value across the period 2013 

to 2020. Values for future years are discounted to adjust for the “time value of money.” This is an economic 

concept that refers to the value of a given amount of money being less in the future. Most people, presented 

with a choice, would rather have a dollar now than a dollar 10 years from now, or even one year from now 

because the dollar can be put to productive use now. When monetary costs and benefits of an action vary 

over time (e.g., for a capital project the costs are up-front but the benefits occur over many years), 

economists adjust for the time value of money by applying an annual discount rate to the amounts in future 

years. This is different than adjusting for inflation, which is a loss in money’s value in the future due to a 

rise over time in prices for given products and services across the economy. The result of adjusting for the 

time value of money is known as the “present value.” Providing present values for 2013-2000 for all the 

economic impact analyses allows for comparison – based on a reasonably lengthy period, and subject to 

some differences in approach noted in each resource use summary section – of the relative economic 

impacts of each resource use for each of the field offices and planning units.

The choice of a discount rate is a key analytical decision, because as the discount rate increases, the value 

of future dollars when “brought back to the present” decreases. Often economists use the discount rates 

recommended by the federal Office of Management and Budget (OMB) in OMB Circular A-94, 

“Guidelines and Discount Rates for Benefit-Cost Analysis of Federal Programs.” OMB periodically 

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-5
Appendix N Final EIS

updates the discount rates for the Circular in “Appendix C: Discount Rates for Cost-Effectiveness, LeasePurchase, and Related Analyses for OMB Circular No. A-94.” OMB pegs discount rates to interest rates on 

Treasury notes and bonds of specific maturities corresponding to the planning period for a particular 

economic analysis. As of the December 2012 edition of Circular A-94 Appendix C, the recommended 

annual real discount rate (a rate from which any inflation premium has been removed) for a 10-year 

planning period is 0.1 percent. This is a very low discount rate compared to discount rates economists have 

typically used over recent decades. It reflects the fact that interest rates on Treasury notes and bonds are at 

present extremely low, historically speaking. Therefore, BLM has chosen to use a more historically typical 

annual discount rate for this economic impact analysis. The rate used is 3.0 percent.

1.3 Qualitative Economic Impact Analysis

In the other approach, where primary impacts cannot be readily quantified, often the economic impacts can 

still be described qualitatively. In such cases, the focus of the analysis is to describe the type of impact in a 

base scenario (in this planning effort, Alternative A) and then assess the relative changes (qualitative 

indications of increases or decreases in costs or the value of production) that would be likely under other 

alternatives. This approach may be used with impacts to market values and is often used with impacts to 

nonmarket values. The term nonmarket values refers to the benefits individuals attribute to experiences of 

the environment or uses of natural and cultural resources that do not involve market transactions and 

therefore lack prices. Because these values are not priced, they are difficult to estimate but nonetheless 

BLM guidance calls for efforts to be made to identify and assess impacts to nonmarket values in the 

planning process (BLM Instruction Memorandum No. 2013-131, Guidance on Estimating Nonmarket 

Environmental Values, May 31, 2013).

Some of the management decisions under this planning action would result in increased costs to operators 

– the firms or individuals who undertake the activities – or to project proponents. The economic impacts of 

decisions that increase costs for operators and/or project proponents are many and can be complex. Cost 

increases may cut into profitability and drive delays to, reductions in, or cessation of operations or projects. 

However, where operations or projects are not delayed, reduced, or terminated, increased costs also 

represent increased economic activity. For instance, if restrictions under an Alternative result in a new 

power line having to take a longer route, additional expenditures for materials, equipment, and labor would 

be made. These increased expenditures would support some amount of additional income and employment. 

However, increased costs may also represent opportunity costs; that is, the project proponent or society 

may have benefited more if the additional funds were used in another way. In the socioeconomic analysis 

in Chapter 4, where management actions would potentially increase costs to operators or project

proponents, these increased costs are pointed out and discussed qualitatively. Readers should keep in mind 

that these increased costs may negatively impact operators, may benefit others in society, and may incur 

opportunity costs.

1.4 Social Impact Analysis

Social impacts may be driven by economic impacts, such as when changes in employment due to 

management decisions lead to impacts on population, housing, and community services. Other impacts may 

be more purely social and cultural in nature and can include impacts on quality of life, recreation and 

amenity values, and traditional land uses and associated cultural values. Social impacts may be marginal or 

substantial, depending on the degree to which new and revised management actions alter the course set in 

previous BLM and Forest Service decisions.

Sometimes social impacts can be quantified; however, in this analysis social impacts are described 

qualitatively. This is because social impacts of BLM and Forest Service management decisions may vary 

considerably depending on the nature of the community, or communities, involved. For a planning effort 

N-6 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

that covers as large a geographic area as this effort, analysis of social impacts must necessarily use a broad 

brush.

A key aspect of the social impacts analysis approach is to address impacts based on the varying points of 

view of key types of stakeholders. The Socioeconomic Baseline Report identifies several broad categories 

of stakeholders to sage-grouse management decisions in Wyoming. These categories reflect different 

linkages people have to public lands. They also reflect distinct sets of attitudes, beliefs, values, opinions, 

and perceptions about public resources and the effects of various management policies and actions.

Categorization of stakeholders is not meant to imply that all individuals and social groups fit neatly into a 

single category; many specific individuals or organizations may have multiple interests and would see 

themselves reflected in more than one stakeholder category. The point of categorization is to allow 

differentiation of social impacts based on broad differences in points of view. The social impacts analysis 

in Chapter 4 of the environmental impact statement (EIS) assesses the alternatives against the different 

points of view in the broad stakeholder categories.

1.5 Environmental Justice Impact Analysis

Definitions and methods for analysis of potential environmental justice (EJ) issues are described in the 

Socioeconomic Baseline Report. In short, the socioeconomic study area was screened to identify counties 

with minority and low-income populations that qualify as potential EJ populations based on guidance for 

EJ analysis from the Council on Environmental Quality. These counties and their potential EJ populations 

are noted in Chapter 3 of the EIS, as well as the Socioeconomic Baseline Report. Further assessment of the 

likelihood of impacts to these populations was conducted as described in Chapter 4 of the EIS.

CHAPTER 2—METHODOLOGIES BY RESOURCE USE

2.1 Oil and Gas

2.1.1 Introduction

The analysis for oil and gas economic impacts was divided into two phases of oil and gas economic activity: 

• Development (Drilling and Completion)

• Production

The economic impacts were evaluated at both the study area and state level for each field office and 

planning unit. The MRIO analysis feature of IMPLAN was used to evaluate the economic impacts for each 

field office and planning unit at the state level.

It is very important to note that the analysis focuses only on new oil and gas wells on federal mineral estate 

within the sage-grouse core/priority habitat, general habitat, and connectivity habitat areas. This is because 

the management decisions under consideration in the Resource Management Plan (RMP) and the Land and 

Resource Management Plan (LRMP) (hereafter, land use plans [LUP]) amendments essentially only apply 

to new oil/gas leasing and not to the existing leases. The economic impact figures for the new oil and gas 

wells are a subset of the economic impacts of all oil and gas wells (new and existing) on federal mineral 

estate in each field office and planning unit, which in turn are a subset of the economic impacts of all oil 

and gas wells on all federal and non-federal mineral estate in each field office and planning unit (i.e., 

including wells on privately and state-owned mineral estate). Put another way, the impact estimates do not

include the economic impacts of any wells (new and existing) on federal mineral estate outside of sageWyoming Greater Sage-Grouse Land Use Plan Amendment N-7
Appendix N Final EIS

grouse habitat areas,3 nor of any wells (new and existing) on non-federal mineral estate. Nor do the figures 

include the economic impacts of production from existing wells on federal mineral estate within sagegrouse habitat areas; this existing production is not affected by the management decisions of this planning 

action.4

Likewise, the percentage differences for Alternatives B, C, and D and the Proposed Land Use Plan (LUP) 

Amendments in comparison to Alternative A only represent changes for new wells on federal mineral estate 

in habitat areas; they do not represent the percentage change to total economic activity resulting from all 

oil and gas development and production. The percentage change to total oil and gas related economic 

activity would be smaller, because while the absolute difference in dollars or jobs would be as shown in the 

tables in Chapter 4, the base for comparison, all oil and gas related economic activity, would be larger 

because it would include the contributions of existing wells on federal mineral estate within sage-grouse 

habitat areas, of wells on federal mineral estate outside of sage-grouse habitat areas, and of wells on nonfederal mineral estate. 

2.1.2 IMPLAN Model Modifications

The IMPLAN modeling system utilizes national production coefficients. To better reflect local production 

practices, the oil and gas sectors of each model were modified. In IMPLAN, oil and gas development and 

production is divided into three sectors: 

Number Sector Name

20 Oil and Gas Extraction

28 Drilling Oil and Gas Wells

29 Support Activities for Oil and Gas Operations

The following protocol was used to modify the individual sectors. Total output for the Oil and Gas 

Extraction Sector was based on county level production quantities reported by the Wyoming Oil and Gas 

Commission and state price forecasts from the State of Wyoming’s Consensus Revenue Estimating Group 

(CREG 2012). Total output for the other two sectors was estimated from output per employee ratios derived 

from the United States Census Bureau’s Economic Census. Employment estimates were based on United 

States Bureau of Labor Statistics (BLS) covered employment data. These estimates were adjusted to 

account for self-employment using United States Bureau of Economic Analysis (BEA) data. Earnings were 

also based on BLS data. These estimates were adjusted to account for benefits using BEA Survey of Current 

Business data. Intermediate payments for oil and gas production were scaled based on estimated cost of 

production for oil and gas production in the Rocky Mountain region.

2.1.3 Development (Drilling and Completion) Impacts 

Information on the number of wells to be drilled by BLM field office or Forest Service planning unit for 

each alternative was obtained from the RFD estimates provided by the BLM’s RMG. The RFD estimates 

were broken down between conventional and coal bed natural gas (CBNG) wells and by wells on federal 

minerals and non-federal minerals. Only the wells on federal minerals were considered in this analysis. The 

RFD also provided a breakdown of the number of wells drilled each year for each alternative for each field 

3 In some of the field offices and planning units, sage-grouse habitat covers most of the office/unit. In these cases, the economic 

analysis encompasses most if not all of the new wells on federal mineral estate projected for each alternative. 

4 Any restrictions resulting from this planning action would only be applied to new leases (i.e., currently unleased areas). 

Production rates from existing wells in already leased areas would not be affected. With respect to new wells within already 

leased areas, these wells may be affected by this planning action. Action # 57 provides that BLM will consider inclusion of 

stipulations to protect sage-grouse or their habitats as permit Conditions of Approval (COAs), where adequately protective 

stipulations are not already in place. Whether COAs will be placed on new wells in already leased areas, and to what extent 

COAs would impact economic activity, cannot be known or reasonably estimated; therefore, the economic impacts of Action 

# 57, if any, are not included in the quantitative economic impact analysis.

N-8 Wyoming Greater Sage-Grouse Land Use Plan Amendment

 
Final EIS Appendix N

office or planning unit. The number of wells completed each year was based on completion rates developed 

from the BLM surface disturbance assumptions.

Table 3 summarizes the oil and gas well drilling and completion costs used in the analysis. Estimates of per 

well drilling and completion costs for conventional wells and CBNG wells were provided by each BLM 

field office or district office. Many of the estimates were based on data from industry collected for this 

planning effort. Cost estimates for Forest Service planning units were provided by nearby BLM staff. For 

the Forest Service planning units, it was assumed that no drilling would occur on the Medicine Bow 

National Forest during the period of the analysis (per the RFD) and that drilling costs on the Bridger-Teton 

National Forest would be comparable to those for the Pinedale Field Office. The figures in Table 3 represent 

“composite well” costs. That is, the differences between the costs of vertically drilled and directionally 

drilled conventional (non-CBNG) wells were accounted for, based on estimated proportions of each type 

of well. The percentage of horizontal wells assumed for each field office or planning unit was provided by 

RMG and was derived from Wyoming Oil and Gas Conservation Commission data. The data accounted for 

all wells spudded during a particular time period, after any CBNG were removed. The time period was 

2000 to present in most cases. The exceptions were: 2008 to present for the Casper Field Office distribution 

area, and 2003 to present for the distribution areas for the Pinedale Field Office, Rawlins Field Office, and 

Rock Springs Field Office. The percentage of horizontal wells ranged from 0.6% in the Rawlins Field 

Office to 34.8% in the Thunder Basin National Grassland. In a few cases, cost differences between shallow 

and deeper vertical wells were also accounted for.

All cost estimates and assumptions were reviewed by the BLM’s RMG. The final ratio between the cost of 

drilling and completion varied based on what was reported for each well category by the BLM field or 

district staff. Completion rates were estimated from BLM surface disturbance assumptions that were 

prepared by the RMG. Completion rates were multiplied by the number of wells drilled per year from the 

RFD to yield the number of wells completed per year. 

The percent of total well costs that were spent within each field office/planning unit study area was 

estimated to be 89.0 percent for conventional drilling, 61.8 percent for conventional completion, 83.4 

percent for CBNG drilling, and 54.4 percent for CBNG completion for all units. These percentages 

represent composite estimates developed from previous analysis of the LUPs for four of the six field offices 

included in the analysis. These estimates were applied to all field offices and planning units in this analysis.

The percentage of local spending for completion was lower than the percentage for drilling because 

completion involves the purchase of more tangible inputs that are not readily available in the study areas.

Overall, if a well was completed, 73.2 to 79.9 percent of the total costs of a conventional well were 

estimated to be spent locally depending on the ratio of drilling costs to completion costs and 64.1 to 77.6 

percent of the total costs for CBNG wells were estimated to be spent locally (within the study area for field 

office) depending on the ratio of drilling costs to completion costs.

Table 3. Oil and Gas Well Cost and Completion Rates by Field Office/Planning Unit

Convent.

Drilling

Convent.

Completion

Convent.

Total

CBNG

Drilling

CBNG

Completion

CBNG

Total

Total Costs Per Well

Casper $2,140,000 $1,430,000 $3,570,000 $100,000 $200,000 $300,000

Kemmerer $1,260,000 $1,000,000 $2,260,000 $740,000 $185,000 $925,000

Newcastle $550,000 $340,000 $890,000 $100,000 $200,000 $300,000

Pinedale $2,652,000 $1,851,000 $4,503,000 $740,000 $185,000 $925,000

Rawlins $1,130,000 $570,000 $1,700,000 $740,000 $185,000 $925,000

Rock Springs $1,115,000 $1,426,000 $2,541,000 $740,000 $185,000 $925,000

BTNF $2,652,000 $1,851,000 $4,503,000 $740,000 $185,000 $925,000

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-9 
Appendix N Final EIS

Convent. 

Drilling

Convent. 

Completion

Convent. 

Total

CBNG 

Drilling

CBNG

Completion

CBNG

Total

MBNF N.A. N.A. N.A. N.A. N.A. N.A.

TBNG $1,430,000 $1,990,000 $3,420,000 $100,000 $200,000 $300,000

Local Spending Per Well - Percent

Casper 89.0% 61.8% 78.1% 83.4% 54.4% 64.1%

Kemmerer 89.0% 61.8% 77.0% 83.4% 54.4% 77.6%

Newcastle 89.0% 61.8% 78.6% 83.4% 54.4% 64.1%

Pinedale 89.0% 61.8% 77.8% 83.4% 54.4% 77.6%

Rawlins 89.0% 61.8% 79.9% 83.4% 54.4% 77.6%

Rock Springs 89.0% 61.8% 73.7% 83.4% 54.4% 77.6%

BTNF 89.0% 61.8% 77.8% 83.4% 54.4% 77.6%

MBNF N.A. N.A. N.A. N.A. N.A. N.A.

TBNG 89.0% 61.8% 73.2% 83.4% 54.4% 64.1%

Local Spending Per Well - Percent

Casper $1,904,346 $883,541 $2,787,887 $83,442 $108,794 $192,236

Kemmerer $1,121,250 $617,861 $1,739,111 $617,474 $100,634 $718,108

Newcastle $489,435 $210,073 $699,507 $83,442 $108,794 $192,236

Pinedale $2,359,965 $1,143,661 $3,503,626 $614,474 $100,634 $718,108

Rawlins $1,005,556 $352,181 $1,357,747 $614,474 $100,634 $718,108

Rock Springs $992,218 $881,070 $1,873,287 $617,474 $100,634 $718,108

BTNF $2,359,965 $1,143,661 $3,503,626 $614,474 $100,634 $718,108

MBNF N.A. N.A. N.A. N.A. N.A. N.A.

TBNG $1,272,530 1,229,543 $2,502,073 $83,442 $108,794 $192,236

Completion Rates

Casper 80.0% 90.0%

Kemmerer 86.4% 90.1%

Newcastle 71.3% 87.0%

Pinedale 90.6% 89.8%

Rawlins 95.1% 90.1%

Rock Springs 62.4% 80.4%

BTNF 90.6% 89.8%

MBNF N.A. N.A.

TBNG 77.4% 90.1%

The local spending (direct impact) figure per well for each field office or planning unit was parsed into 

various industrial sectors of the IMPLAN model based on breakdowns of the different types of costs for 

drilling and completion (each addressed separately) taken from various sources – mainly Authorizations 

for Expenditures (AFEs) provided by industry. The expenditure data was disaggregated across the 

following 11 IMPLAN sectors:

Number Sector Name

28 Drilling Oil and Gas Wells

29 Support Activities for Oil and Gas Operations

36 Construction of Other New Nonresidential Structures

N-10 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

Number Sector Name

319 Wholesale Trade

335 Truck Transportation

351 Telecommunications

65 Commercial & Industrial Machinery & Equipment Rental & Leasing

367 Legal Services

369 Architectural, Engineering, & Related Services

389 Other Support Services

HH Households

The IMPLAN model provided estimates of direct, indirect and induced output, employment, and labor 

earnings. Induced impacts were reduced by 60 percent for sectors 28 and 29 to account for non-local 

workers involved with drilling and completion of oil and gas wells in Wyoming. This estimate was based 

on information provided by industry for previous oil and gas economic impact analyses in Wyoming and 

is consistent with Wyoming Department of Employment data.

Once the economic impacts per well were estimated for drilling and for completion, those figures were 

multiplied by the total number of wells drilled or completed. The resulting figures were then summed to 

yield the total impacts of the development stage, by year and by field office or planning unit.

2.1.4 Production Impacts

Information on production of oil and gas was provided by the BLM’s RMG and BLM field and district 

office staff. This information included the number of wells drilled each year by alternative for each field 

office or planning unit (from the RFD), the percent of wells that were oil versus gas, the percent of wells 

completed, production decline curves for oil and gas wells, and estimates of cross production from both oil 

and gas wells. This information was used to develop total oil and gas production estimates by year for each 

alternative and each field office or planning unit. 

The procedure to determine total production was as follows. For each year, the number of wells completed 

was broken down into oil and gas wells based on the breakdown assumptions per field office and planning 

unit provided by BLM staff. For each well type, the average first year production rate (volume) from the 

annual decline curves for each field office and planning unit (as provided by RMG) was then applied to 

determine the total production from first-year wells. In subsequent years, the appropriate average 

production rates from the decline curves were applied to the number of second year wells, third year wells, 

and so on. Total production was then summed across all the well age cohorts for each year within the 

analysis period. Co-production volume was calculated based on the numbers of wells of each type and the 

co-production rates from the RMG, and added to the total production volume.

The production volume data was then multiplied by price estimates to estimate total annual sales value 

revenue streams for oil and gas production. The market prices for oil and gas were based on the State of 

Wyoming CREG’s October 2012 forecasts for 2012–2018 oil and gas prices in Wyoming, expressed in 

2011 dollars. These revenue streams were then entered into the IMPLAN models to estimate the total 

economic impacts from production.

Per unit ad valorem and severance tax revenues estimates were developed from per unit tax revenue rates 

from the Wyoming Department of Revenue’s 2012 Annual Report. These estimated rates were applied to 

the forecasted market sales values, with the assumption that the Wyoming tax structure will remain constant 

over the analysis period. The estimates for Federal Mineral Royalties represents Wyoming’s 48 percent 

share of the Federal Government’s 12.5 percent royalty rate, which amounts to 6 percent of the market 

value. Table 4 summarizes the prices and tax revenue rate estimates used in the analysis. 

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-11
Appendix N Final EIS

Table 4. Prices and Tax Revenue Estimates for Oil and Gas Production

Oil

($/BBL)

Gas

($/MCF)

Market Price (1) $85.00 $4.10

Ad Valorem Tax (2) $4.81 $0.19

Severance Tax (2) $4.46 $0.18

Federal Mineral Royalties (3) $5.10 $0.25

BBL: Barrel; MCF: Million Cubic Feet

(1) Average 2012-2018 CREG Forecast (October 2012) 

(2) Wyoming Department of Revenue 2010 Annual Report adjusted to market 

prices

(3) Assumed Wyoming's share is 6 percent of market value

2.2 Wind Energy

2.2.1 Introduction

The wind energy economic analysis involved two major steps:

• Projecting the amount of wind energy development on BLM and Forest Service-administered land 

within the study period.

• Estimating the economic impacts of the projected levels of wind energy development. This effort 

required use of two economic models.

Each of these steps is described in detail below.

The analysis for wind energy economic impacts was divided into two phases of wind energy economic 

activity: 

• Development (construction)

• Production (operation / generation of electricity) 

The economic impact analysis for wind energy was conducted at the planning area level only. The level of 

uncertainty regarding amounts and locations of wind energy development in the planning area was 

considered too large to allow estimation of economic impacts by BLM field office or Forest Service

planning unit. While BLM has site specific project information for the Chokecherry Sierra Madre (CCSM) 

project (BLM 2012), it is inappropriate to extrapolate the specifics of the CCSM project across the entire 

planning area due to difference, including but not limited to, wind potential, land ownership status, 

proximity to transmission lines and other infrastructure. Of particular importance is the fact that wind 

energy development typically takes place as large projects involving hundreds of megawatts (MW) of 

capacity. Thus the amount of wind energy projected to be built on BLM and Forest Service-administered 

land in the planning area during the analysis period would mostly be built in a small number of large 

projects. It is difficult to foresee where these projects would be built (some in the current pipeline might 

not ultimately be built; others will come forward), and it is possible that they will not be distributed 

throughout the planning area in proportion to available wind resources due to locations of transmission 

lines, business strategies, etc. 

The impact analysis did not address how much wind energy development would occur on sage-grouse 

habitat versus non-habitat. However, to the extent that specific management decisions prohibit or preclude 

wind energy development on sage-grouse habitat, this was encompassed in the analysis through reduction 

of the acreage considered available for wind energy development. These reductions impacted the projected 

amounts of wind energy development, given the assumptions described below.

N-12 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

2.2.2 Wind Energy Development Projections

Projections of the amount of wind energy to be developed over the planning period were based on an 

analysis by the National Renewable Energy Laboratory (NREL) of the United States Department of Energy. 

This analysis estimated wind resource availability under the different alternatives. NREL took the 

constraints of each alternative and overlaid them on wind resource data generated during the BLM wind 

energy Programmatic EIS analysis. The result of the NREL analysis was an estimate of total potential 

installed capacity (MW), on BLM and Forest Service-administered land in each field office and planning 

unit under each alternative.

The NREL analysis was sometimes referred to as a “Reasonable Foreseeable Development” (RFD) 

analysis, but was in fact a resource potential analysis. It indicated how much wind energy might ultimately 

be developable on BLM and Forest Service-administered land if all land available for wind energy 

development were developed. To convert resource potential by alternative to a reasonable estimate of wind 

energy development over a limited time period, additional analysis and assumptions were needed. The time 

period chosen for the analysis was 2013 to 2020, to match the analysis period used for the other resource 

uses.

Estimates of likely near-term wind energy development levels in Wyoming vary. One well-regarded study 

is a 2011 NREL report on Wyoming wind energy development commissioned by the Wyoming 

Infrastructure Authority (WIA). This study prepared a likely “infrastructure deployment scenario” in 

Wyoming over the period 2012–2021, and projected total development of 9,000 MW of generating capacity 

during that period (Lantz and Tegen 2011). The scenario was developed by WIA in conjunction with 

industry stakeholders, and reflected expected (as of 2011) market-driven development of wind energy 

generation and transmission to serve out-of-state loads. No major studies of wind energy development in 

Wyoming have been prepared since. Therefore, several industry experts were consulted in February–March

2013 for their current opinions on likely near-term development. The director of the WIA (Drain 2013) 

indicated that the 2011 projection is still a reasonable estimate of likely development over the next 10 years 

(2013–2022). The director of the Wind Energy Research Center (WERC) at the University of Wyoming 

(Naughton 2013) indicated that wind energy development will depend upon what happens with several 

major proposed electricity transmission projects. Development of new transmission capacity is essential to 

getting Wyoming wind energy to markets in other states, and thus is a prerequisite to high levels of wind 

energy development in the state. The WERC contact suggested that the minimum likely level of Wyoming 

wind development from 2013 to 2022 is 3,000 MW, and the maximum likely level is 10,000 MW. The 

wind energy lead in the BLM Wyoming State Office generally concurred with these opinions, but suggested 

that 4,000 MW is a better assumption for the minimum level of development. Thus, the 4,000 MW and 

10,000 MW figures were used as “bookend” projections (low and high development scenarios) for the 

economic impact analysis. 

The next step was to estimate how much of each bookend projection should be assumed to be developed 

on BLM and Forest Service-administered lands. First, it was assumed that all wind development in the state 

would occur within the planning area, which covers most of the state and almost all of the areas of Wyoming 

with high wind resource values. Second, it was assumed that wind energy development on BLM and Forest 

Service-administered lands would match the proportion of BLM/Forest Service “wind feasible” lands 

relative to all “wind feasible” lands (including private land and other government land) in the planning area. 

The definition of wind feasible lands excluded lands where wind development is administratively prohibited 

(e.g., wilderness areas, National Park Service and Fish and Wildlife Service managed lands, and various 

other protected or specially designated areas) and where it is physically unlikely (e.g., areas with slopes 

greater than 20%, wetland and water areas). The definition also reduced the amount of land considered 

available, if it was under certain management; for instance, only 50% of non-ridge crest Forest Service and 

Department of Defense lands remaining after application of the other constraints was considered available. 

Based on the various exclusions, BLM and Forest Service-administered wind feasible lands make up 

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-13
Appendix N Final EIS

approximately 25% of all wind feasible lands in the planning area. Therefore, in the no action alternative 

(Alternative A) the amount of wind energy development on BLM and Forest Service-administered lands 

over the 10-year period from 2013 to 2022 was assumed to be 1,003 MW under the low bookend scenario 

and 2,507 MW under the high bookend scenario.

The amount of wind developed on BLM and Forest Service-administered lands in the action alternatives 

was based on the ratio between the resource potential from the NREL analysis for each action alternative 

and the resource potential for the no action alternative. For instance, under Alternative B, the resource 

potential from the NREL analysis was 10.1% of the resource potential for Alternative A. Therefore, it was 

assumed that Alternative B would see 10.1% of the wind energy development of Alternative A. Table 5

presents the different levels of wind energy projected for 2013–2022 for each alternative under the low and 

high development scenarios.

Table 5. Projected Wind Energy Development Scenarios, in Megawatts

Alternative

Low 

Development 

Scenario

High 

Development 

Scenario

Alternative A 1,003 MW 2,507 MW

Alternative B 101 MW 253 MW

Alternative C 101 MW 253 MW

Alternative D 784 MW 1,960 MW

Proposed 

LUP 

Amendments

101 MW 253 MW

MW: Megawatts

The final step was to develop the annual deployment projections. First, because no projects are currently 

approved on BLM and Forest Service-administered lands, it was assumed that development would start in 

2015, wind energy generation would begin in 2016, and all projected capacity would be developed and 

generating electricity by 2022. The total development figures in Table 5 were then straight-line interpolated 

from 2015–2021 for development, and 2016–2022 for generation. Because the economic impact analysis 

study period ends in 2020, only the annual values through 2020 were used to develop the direct economic 

impacts that were entered into the IMPLAN model. 

2.2.3 Economic Impact Analysis Approach

The analysis used two economic models to estimate the potential economic and fiscal impacts of BLM LUP

sage-grouse management amendment decisions related to wind energy development and generation. First, 

in order to obtain the detailed per unit (MW) construction and operations expenditure information needed 

to conduct the economic impact analysis, industry standard information was accessed through a wind 

generation cost model – the Jobs and Economic Development Impact (JEDI) model from NREL. This 

information was adjusted to reflect the expenditure patterns for a typical wind project in Wyoming.

Secondly, in order to estimate the economic impacts of the project, the IMPLAN economic input-output 

model was used. Per unit (MW) construction and operations expenditure rates from the JEDI model were 

multiplied by the number of MWs of capacity constructed or in production in each year for each alternative 

in order to estimate direct expenditure impacts. The total direct expenditure estimates based on the JEDI 

model were entered into the IMPLAN model to estimate the indirect, induced, and total economic impacts 

of the project. As a part of the analysis, estimates of state and local tax revenues resulting from the project 

were developed based on current tax revenue rates. The following sections summarize the data points and 

assumptions used in the IMPLAN analysis of wind energy economic impacts. All dollar amounts are 

expressed in 2011 dollars.

N-14 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

2.2.4 Wind Energy Development (Construction) Inputs

The following economic data and assumptions and assumptions were used in the economic impact analysis:

• Construction expenditures are based on NREL’s Wyoming JEDI model for a 100 MW wind project, 

with some adjustments for expenditure patterns for a typical wind project in Wyoming.

• 30 percent of construction workers are local (Wyoming Industrial Siting Department).

• Non-local constructions workers spend $59.54 per day for lodging and $47.06 per day for meals 

and incidental expenses (adjusted Federal Daily Per Diem Rates) during their stay in the area.

• Total cost of wind project development is $2,047,600 per MW with $326,328 per MW spent in 

Wyoming (per the adjusted NREL Wyoming JEDI model).

• 5.5 percent sales and use tax rate for installed equipment (Wyoming Department of Revenue).

The JEDI-based estimates of direct economic impacts for development were disaggregated across seven 

IMPLAN sectors to estimate the indirect, induced, and total economic impacts of the project. The seven 

IMPLAN sectors were:

Number Sector Name

26 Sand, Gravel, and Clay Mineral Mining and Quarrying

161 Ready-Mix Concrete Manufacturing

323 Retail – Building Materials and Garden Supply

411 Hotels and Motels

413 Food Services and Drinking Places

N.A. Employee Compensation

N.A. State/Local Government – Non-Educational

2.2.5 Wind Energy Production (Generation) Inputs

The following economic data and assumptions and assumptions were used in the economic impact analysis:

• Operational expenditures are based on NREL’s Wyoming JEDI model for a 100 MW wind project, 

with some adjustments for expenditure patterns for a typical wind project in Wyoming.

• 100 percent of operation workers are local.

• Total spending for operations is $24,409 per MW (adjusted NREL’s Wyoming JEDI model).

• Local spending for operations is $17,935 per MW (adjusted NREL’s Wyoming JEDI model).

• Generation tax based on $1.00 per MWh beginning after three years of production with a 35.0 

percent capacity factor and a 98.0 percent availability factor.

• Property tax 62.9 mill levy assuming 20 year straight line depreciation for equipment.

• 5.5 percent sales and use tax rate on operation expenditures (Wyoming Department of Revenue).

The JEDI-based estimates of direct economic impacts for operations were disaggregated across nine 

IMPLAN sectors to estimate the indirect, induced, and total economic impacts of the project. The nine 

IMPLAN sectors were:

Number Sector Name

31 Electrical Power Generation, Transmission, & Distribution

39 Maintenance & Repair of Nonresidential Structures

319 Wholesale Trade

320 Retail – Motor Vehicles and Parts

323 Retail – Building Supplies and Garden Supply

326 Retail – Gasoline Stations

N.A. Employee Compensation

N.A. State/Local Government – Non-Educational

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-15
Appendix N Final EIS

Number Sector Name

N.A. State/Local Government – Educational

2.3 Livestock Grazing

2.3.1 Introduction

The livestock grazing economic analysis involved three major steps:

• Estimating the amounts of forage utilized on BLM and Forest Service-administered lands in the 

planning area under each alternative.

• Estimating the economic value of forage use.

• Estimating the economic impacts based on the value of production.

Each of these steps is described in detail below. There is only one “phase” of economic activity for livestock 

grazing – livestock production. There is no “development” phase equivalent to the construction activities 

in the oil/gas and wind energy industries.

The analysis was based around cattle and sheep grazing, which were analyzed separately. Forage utilization 

for buffalo was combined with cattle forage utilization. Forage utilization for goats was combined with 

sheep forage utilization. Forage utilization for horses was excluded from the analysis. It was assumed that 

most forage utilization for horses occurs as support for a ranching operation and thus is a cost of production. 

Therefore, forage utilization for horses is accounted for in the livestock operations budgets used in 

developing the value of production for marketable livestock.

The economic impacts were evaluated at both the study area and state level for each field office and 

planning unit. The MRIO analysis feature of IMPLAN was used to evaluate the economic impacts for each 

field office and planning unit at the state level.

It is also very important to note that the forage utilization estimates, and thus the economic impact estimates, 

only represent livestock grazing in sage-grouse habitat. They do not represent the total impact of livestock 

grazing in each BLM field office or Forest Service planning unit. Because of this, the percentage decrease 

between the action alternatives and Alternative A would be less on a total impact basis than in the figures 

in Chapter 4, which are for sage-grouse habitat only.

2.3.2 Estimation of Forage Utilization

The economic impact estimates for livestock grazing were based on the 10-year average (2003–2012) of 

billed animal unit months (AUMs) of forage use for cattle, sheep, and other livestock for each BLM field 

office and Forest Service planning unit. One AUM is equal to the amount of forage consumed by a cow 

and calf during a 1-month grazing period. Billed forage use is the closest available proxy for actual forage 

use. Because billed use may exceed actual grazing use, the economic analyses may overstate the actual 

economic impacts of grazing to some degree. 

Two procedures were used to estimate forage utilization specific to sage-grouse habitat. For Forest Service

planning units, the procedure was as follows:

• Grazing in sage-grouse habitat was evaluated on an allotment basis, based on available geographic 

information system (GIS) data layers for the total Forest Service acres capable/suitable for grazing 

in each allotment, the Forest Service capable/suitable acres in each allotment that are in sage-grouse 

core/priority habitat, and the Forest Service capable/suitable acres in each allotment that are in 

sage-grouse general habitat.

• For each allotment, the percentage of land in each type of habitat was calculated relative to the total 

Forest Service acres capable/suitable for grazing.

N-16 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

• This percentage was then multiplied by the total AUMs in each allotment to determine the number 

of AUMs in each type of sage-grouse habitat in each allotment.

• The number of AUMs in each type of sage-grouse habitat were then summed for each Forest 

Service planning unit.

For BLM field offices, the GIS data necessary for an allotment-level analysis were not available. Therefore, 

grazing in sage-grouse habitat was evaluated on a field-office basis. This was done by determining the 

proportion of each field office’s BLM-administered surface land5 that is within each habitat type, and 

applying that same percentage breakdown to the total AUMs in the field office. This approach makes two 

assumptions: (1) all portions of the grazing allotments are suitable for grazing, and (2) all BLMadministered lands within the BLM field offices are within grazing allotments. While it is clear that these 

assumptions are not entirely accurate, BLM staff considered the methodology to be reasonable given the 

nature of a planning-level analysis and the lack of data that would permit a more precise methodology.

The forage utilization analyses were conducted separately for cattle and sheep. As noted earlier, bison 

AUMs were grouped with cattle AUMs, and goat AUMs were grouped with sheep AUMs. Horse AUMs 

were excluded from the analysis on the grounds that horses are factor of production rather than a marketable 

commodity.

The forage utilization estimates did not vary between Alternatives A, C, and D and the Proposed LUP 

Amendments. While forage utilization could vary somewhat under these alternatives (e.g., due to 

differences in treatment of voluntary relinquishment of permits or grazing preference), the differences 

between the alternatives could not be quantified. In the case of Alternative C, the management decision to 

prohibit livestock grazing within sage-grouse priority habitat was quantifiable, and the economic impact 

analysis therefore reflects a lower level of forage utilization and economic production under this alternative.

2.3.3 Estimation of the Economic Value of Forage Use

The value of grazing in a specific area can be estimated based on the grazing use of the area in AUMs as 

describe above, and the value of an AUM. The direct value of production per AUM was estimated based 

on regional livestock production value data and ratios in the livestock economics literature. According to 

Workman (1986), it takes 16 AUMs to produce a marketable cow. Thus, the average value of an AUM can 

be estimated using data on the value of cattle production per bred cow and dividing by 16. A similar 

procedure can be used to estimate the value of an AUM used for sheep production, using 3.2 AUMs per 

ewe.

The value per AUM for cattle was based on a 10-year average (2002–2011) of the annual value of 

production per bred cow estimates from the United States Department of Agriculture (USDA) Economic 

Research Service’s Commodity Cow-Calf Costs & Returns estimates for the Basin and Range and Northern 

Great Plains portions of the United States. The Casper Field Office, Newcastle Field Office, and Thunder 

Basin National Grassland are located in the Northern Great Plains region. All other field offices and 

planning units are located in the Basin and Range region. The methodology and data for calculation of the 

average value of cattle production from one AUM of forage are show in Table 6 and Table 7.

The direct value of production for sheep was based on the annual value of production per ewe from the 

most recent (2010) University of Idaho Cooperative Extension Service’s Idaho Livestock Costs and Returns 

Estimates for range sheep (Gray and Painter 2010). This is the most analogous currently available data for 

Wyoming sheep production. The methodology and data for this calculation are shown in Table 8.

The figures for the value per AUM for cattle or sheep grazing were multiplied by the number of AUMs in 

sage-grouse habitat under each alternative and for each field office and planning unit. The result was the 

5 Other federal land for which grazing is managed by BLM was also included.

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-17

 
Appendix N Final EIS

total economic value of livestock production, which was used as the direct impact input to the IMPLAN 

model.

Table 6. Value of an AUM for Cattle Production, Basin & Range Region 

Year

Value of 

Production 

Per Bred 

Cow1

AUMs 

Per 

Cow2

Value of 

Production 

Per AUM

IMPLAN 

Inflator

Inflated 

Value of 

Production 

Per AUM*

Cow-Calf 

Adjustment3

Adjusted 

Value of 

Production 

per AUM

2002 $533.64 16 $33.35 1.230 $41.01 1.2 $49.21

2003 $609.07 16 $38.07 1.204 $45.84 1.2 $55.01

2004 $706.24 16 $44.14 1.171 $51.70 1.2 $62.04

2005 $752.44 16 $47.03 1.134 $53.31 1.2 $63.97

2006 $720.09 16 $45.01 1.098 $49.42 1.2 $59.31

2007 $681.50 16 $42.59 1.067 $45.45 1.2 $54.54

2008 $496.02 16 $31.00 1.044 $32.37 1.2 $38.84

2009 $472.00 16 $29.50 1.035 $30.53 1.2 $36.64

2010 $570.50 16 $35.66 1.021 $36.42 1.2 $43.70

2011 $648.59 16 $40.54 1.000 $40.54 1.2 $48.64

10-year Average $51.19

1 USDA Economic Research Service, Commodity Costs & Returns, data for Basin & Range region, cow-calf pair. 2 Workman 1986. 3 National Agricultural Statistical Service.

*Value times inflator.

Table 7. Value of an AUM for Cattle Production, Northern Great Plains Region

Year

Value of 

Production 

Per Bred 

Cow1

AUMs 

Per 

Cow2

Value of 

Production 

Per AUM

IMPLAN 

Deflator

Inflated 

Value of 

Production 

Per AUM*

Cow-Calf 

Adjustment3

Adjusted 

Value of 

Production 

per AUM

2002 $481.53 16 $30.10 0.831 $36.22 1.2 $43.46

2003 $561.31 16 $35.08 0.848 $41.37 1.2 $49.64

2004 $644.37 16 $40.27 0.872 $46.18 1.2 $55.42

2005 $692.18 16 $43.26 0.901 $48.01 1.2 $57.62

2006 $653.97 16 $40.87 0.930 $43.95 1.2 $52.74

2007 $628.65 16 $39.29 0.957 $41.06 1.2 $49.27

2008 $541.86 16 $33.87 0.978 $34.63 1.2 $41.55

2009 $519.55 16 $32.47 0.989 $32.83 1.2 $39.40

2010 $608.95 16 $38.06 1.000 $38.06 1.2 $45.67

2011 $703.61 16 $43.98 1.020 $43.11 1.2 $51.74

10-year Average $48.65

1 USDA Economic Research Service, Commodity Costs & Returns, data for Basin & Range region, cow-calf pair. 2 Workman 1986. 3 National Agricultural Statistical Service.

*Value divided by deflator. Note: 2010 was used as the base year here. The 10-year average, if 2011 were used as the base 

year, would be about 2% higher. Thus, the figures in Chapter 4 underestimate the economic impacts by about 2%. However, this 

difference is well within the margin of error of the economic impact analysis methodology. 

N-18 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

Table 8. Value of an AUM for Sheep Production

Year

Value of

Production

Per Ewe1

AUMs Per 

Ewe (1/5)2

Value of

Production

Per AUM

IMPLAN

Inflator

Inflated

Value of

Production

Per AUM*

2010 $185.65 3.2 $58.02 1.000 $58.02

1 University of Idaho Extension, Idaho Livestock Costs and Returns Estimates, Sheep—Range, 

gross receipts per ewe.

2 Workman 1986. 

* The value should be inflated to 2011 dollars, but the inflator used was for 2010. The value of 

production if 2011 dollars were used would be about 0.6 percent higher. Thus, the figures in 

Chapter 4 underestimate the economic impacts by about 0.6%. However, this difference is well 

within the margin of error of the economic impact analysis methodology. 

2.3.4 IMPLAN Model Modifications

The value-added components of the All Other Crop Farming sector (sector 10) in IMPLAN were modified 

to better reflect hay production in the study area. The modifications were based on a grass hay operation 

budget from the University of Idaho (Painter 2011).

2.3.5 Livestock Production Impacts

The economic impacts of livestock grazing were estimated in IMPLAN using analysis-by-parts 

methodology. The total value of production from the steps described above was allocated to different 

IMPLAN sectors based on 2010 livestock production budgets from the University of Idaho (Gray et al.

2010; Gray and Painter 2010).

The costs of production were disaggregated across 14 IMPLAN sectors:

Number Sector Name

2 Grain Farming

10 All Other Crop Farming

11 Cattle Ranching and Farming

14 Animal Production, Except Cattle, Poultry, and Eggs

19 Support Activities for Agriculture and Forestry

40 Maintenance & Repair Construction of Residential Structures

319 Wholesale Trade

335 Truck Transportation

354 Monetary Authorities and Depository Credit Intermediation

360 Real Estate

368 Accounting, Tax Preparation, Bookkeeping

379 Veterinary Services

417 Commercial and Industrial Machinery and Equipment Repair

N.A. Employee Compensation

2.4 Recreation

2.4.1 Introduction

The tables in this subsection present two views of the economic effects of recreation. Economic impact 

measures only the effects of “new” income in the study area; in the case of recreation, economic impact is 

based on all spending of non-local residents on local recreation, and the spending by local residents that 

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-19
Appendix N Final EIS

would be lost to other regions if the local BLM or Forest Service recreational opportunity did not exist 

(some spending by local residents would continue, using local substitute recreation opportunities). 

Economic contribution includes the effects of all expenditures made by local residents (roughly, individuals 

who live within the socioeconomic study area), as well as the role of spending from recreators from outside 

the study area. In other words, economic contribution is based on all spending of local residents on local 

recreation and all spending of non-local residents on local recreation. Economic impact is the measure used 

in the analyses above of oil and gas development and production, wind energy development and production, 

and livestock grazing. Local residents buy only a very small proportion of the total output of those 

industries, so a measure of economic contribution would be only slightly greater than the measure of 

economic impact. In the case of recreation, however, local residents make considerable recreation-related 

expenditures (gas, food, and so on while on local trips), so it is fair to include those expenditures in an 

analysis of the economic role of recreation. Put another way, expenditures by local and non-local 

recreationists alike help keep local businesses going.

The quantitative economic analyses in Chapter 4 for recreation consist of one set of figures for all 

alternatives for each geographic area. The estimates do not address differences between the alternatives. 

This is because the differences in management actions affecting recreation cannot be quantified. 

Differences in impacts between the alternatives are discussed qualitatively. It is also important to note that 

the analyses assume future recreation use levels (from 2013 to 2020) remain constant at recent actual use 

levels. This assumption may mean that the figures in Chapter 4 underestimate the economic effects of 

recreation over this period. Population trends in the Rocky Mountains and the U.S. generally would tend to 

indicate that travel and recreation will increase in the future; however, it is not entirely clear that outdoor 

recreation is increasing. Finally, readers should understand that the estimates of economic effects presented 

in Chapter 4 are for all recreation use in each field office or planning unit; insufficient information exists 

to allow estimation of recreation use on habitat areas only.

2.4.2 Estimation of Recreation Usage

For BLM field offices, recreation estimates were taken from the Recreation Management Information

System (RMIS). Figures from the most recent year (fiscal year 2012) were used for all field offices except 

the Pinedale Field Office. For Pinedale, figures from fiscal year 2010 were used, due to 2011/2012 fire 

closures of certain recreation areas in that field office. 

For Forest Service planning units, recreation estimates were taken from the most recent recreation survey 

report from the Forest Service National Visitor Use Monitoring (NVUM) program. The most recent 

available NVUM surveys were from fiscal year 2008 for the Bridger-Teton National Forest and for the 

Medicine Bow National Forest. The Thunder Basin National Grassland was included in the Medicine Bow 

National Forest NVUM survey, thus one economic impact analysis was completed that included both of 

these planning units together.

Recreation usage data is expressed in “visits.” A visit is defined as one individual who enters and recreates 

on a national forest unit (or BLM unit) for an indeterminate period of time. A visit ends when that individual 

spends a night off the national forest or BLM unit. The fact that some visits are of a single day or less, and 

some are for multiple days, is accounted for in the approach to estimating the direct impacts (expenditures) 

of visitors, as discussed below. Table 9 shows the total visits in each field office or planning unit and the 

year and source of the data.

Table 9. Total Non-Skiing Recreation Visits

Unit Total Visits* Year Source

Casper Field Office 322,431 2012 BLM RMIS

Kemmerer Field Office 223,083 2012 BLM RMIS

Newcastle Field Office 8,220 2012 BLM RMIS

N-20 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

Unit Total Visits* Year Source

Pinedale Field Office 372,518 2010 BLM RMIS

Rawlins Field Office 226,251 2012 BLM RMIS

Rock Springs Field Office 426,439 2012 BLM RMIS

Bridger-Teton National Forest 1,522,797 2008 Forest Service NVUM

Medicine Bow National Forest and Thunder Basin 

National Grassland 955,669 2008 Forest Service NVUM

*Excluding downhill skiing visits, based on segmentation data from White et al. 2012.

2.4.3 Estimation of the Direct Economic Impacts of Recreation

In the case of the Forest Service planning units included in this planning action, the direct economic impacts 

or contributions were developed by multiplying the available NVUM recreation use data for the planning 

unit by the NVUM expenditure data applicable to the planning unit. In other words, the impacts or 

contributions are very closely based on the actual NVUM survey findings. The dollar values were updated 

from 2008 to 2011.

Due to the lack of recreation expenditure data for BLM field offices, Forest Service NVUM data were used 

to estimate the economic impact of recreation for all field offices and Forest Service planning units. The 

NVUM program provides a robust data source that is widely used for recreation economic impact analysis 

for areas besides Forest Service-managed lands. This is done by identifying national forest units that are 

reasonably analogous to another recreation management area, and applying the recreational expenditure 

data from NVUM to other area-specific recreation use data or estimates. 

In order to match field offices with national forests, the recreation specialists at each office was asked to 

select a comparator forest from neighboring forests in the region. The following is a list of the comparator 

forest for each field office: 

Field Office Comparator Forest

Casper Field Office Medicine Bow National Forest

Kemmerer Field Office Wasatch-Cache National Forest

Newcastle Field Office Dakota Prairie National Grasslands

Pinedale Field Office Bridger-Teton National Forest

Rawlins Field Office Medicine Bow National Forest

Rock Springs Field Office Ashley National Forest

The NVUM recreation segment / expenditure data were applied to the BLM field offices as described 

below. All expenditures from NVUM as applied to the BLM field offices were assumed to be local (within 

the socioeconomic study area for each study area), based on how the NVUM data was collected (surveys 

asked interviewees for their expenses within 50 miles of the recreation site).

• Trip segment estimates from the comparator forest were used to allocate, by trip type, total field 

office recreation visit estimates from the BLM’s RMIS. 

• The estimates of number of visits by trip type were then converted to party visits and multiplied by 

averages days/nights per visit to estimate total party days/nights.

• Total party days/nights were multiplied by party spending per day/night to estimate direct spending 

by visitors, by segment. The party spending figures from NVUM are averages for high, medium, 

and low-expenditure forests (as applicable) and incorporate adjustments for different survey years 

across the forest units that are included. The NVUM researchers determined that expenditure 

figures based on averages across multiple forests are more reliable than individual forest 

expenditures.

• Party spending per visit was adjusted to 2011 dollars using IMPLAN inflators. 

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-21
Appendix N Final EIS

• Total direct spending across all segments was summed to yield the total direct economic 

contribution estimates.

• Total direct spending was adjusted (reduced) for the local trip segments to reflect estimated out-ofarea substitution (based on national data). The remaining direct spending across all segments was 

then summed to yield the total direct economic impact estimates.

Table 10 shows the NVUM trip segment data and expenditures by trip segment used in the analysis. Table 

11 shows an example of the calculation of total direct economic contribution or impact.

N-22 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

Table 10. Trip Segment Data and Expenditures by Trip Segment

Unit Analog National Forest NVUM 

Year

Non-Local Recreators Local Recreators Non-Primary

Day OVN on NF OVN off 

NF

Day OVN on NF OVN off 

NF

Distribution of National Forest Visits by Market Segment – excluding downhill skiing

Casper FO Medicine Bow NF FY2008 14.1% 14.5% 9.9% 44.4% 4.1% 1.0% 12.0%

Kemmerer FO Wasatch-Cache NF FY2007 1.0% 4.6% 3.9% 78.4% 5.6% 0.0% 6.6%

Newcastle FO Dakota Prairies NG1 FY2008 7.0% 0.0% 16.0% 50.0% 10.0% 5.0% 12.0%

Pinedale FO Bridger-Teton NF FY2008 5.0% 9.0% 21.0% 51.0% 1.0% 1.0% 12.0%

Rawlins FO Medicine Bow NF FY2008 14.1% 14.5% 9.9% 44.4% 4.1% 1.0% 12.0%

Rock Springs FO Ashley NF FY2007 10.0% 33.0% 2.0% 37.0% 9.0% 0.0% 9.0%

Bridger-Teton NF Bridger-Teton NF FY2008 5.0% 9.0% 21.0% 51.0% 1.0% 1.0% 12.0%

Medicine Bow NF & 

Thunder Basin NG

Medicine Bow NF (includes 

Thunder Basin NG) FY2008 14.1% 14.5% 9.9% 44.4% 4.1% 1.0% 12.0%

Average Spending by Market Segment – excluding downhill skiing (2009$)

Casper FO Medicine Bow NF* $63 $73 $117 $33 $68 $58 $33

Kemmerer FO Wasatch-Cache NF** $56 $60 $75 $32 $66 $48 $32

Pinedale FO Bridger-Teton NF*** $88 $95 $160 $28 $87 $81 $28

Newcastle FO Dakota Prairies NG* $63 $73 $117 $33 $68 $58 $33

Rawlins FO Medicine Bow NF* $63 $73 $117 $33 $68 $58 $33

Rock Springs FO Ashley NF* $63 $73 $117 $33 $68 $58 $33

Bridger-Teton NF Bridger-Teton NF*** $88 $95 $160 $28 $87 $81 $28

Medicine Bow NF & 

Thunder Basin NG

Medicine Bow NF (includes 

Thunder Basin NG)* $63 $73 $117 $33 $68 $58 $33

1 Trip segmentation for the Dakota Prairies NG was adjusted (final figures shown here) for application to the Newcastle Field Office based on input from the Outdoor Recreation 

Planner for the Buffalo and Newcastle Field Offices. 

* Average spending forest

** Low spending forest

*** High spending forest

Acronyms: FY – Fiscal Year; NF – National Forest; NG – National Grassland; NVUM – National Visitor Use Monitoring program; OVN – Overnight

Primary Source: White et al. 2012

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-23
Appendix N Final EIS

Table 11. Direct Recreation Expenditures Calculation Example: Rock Springs Field Office

Trip Type

Forest % of Non-Skier 

Visits

FO Individual Visits

Forest Average Party

Size

FO Party Visits

Forest Average Days/

Nights

Party Days/ Nights

Forest Party Spending

Per D/N (2009$)

IMPLAN Inflator

Forest Party Spending

Per D/N (2011$)

Estimated Field Office

Direct Economic

Contribution

Forest Out of Area

Substitution* 

Estimated Field Office

Direct Economic

Impact

Non-Local

Day Trips 10.0% 42,644 3.1 13,756 1.0 13,756 $62.65 1.035 $64.84 $891,983 100.0% $891,983

OVN on 

Forest 33.0% 140,725 2.8 50,259 4.9 246,269 $72.95 1.035 $75.50 $18,594,074 100.0% $18,594,074

OVN off 

Forest 2.0% 8,529 3.0 2,843 2.8 7,960 $116.86 1.035 $120.95 $962,786 100.0% $962,786

Local

Day Trips 37.0% 157,782 2.7 58,438 1.0 58,438 $33.02 1.035 $34.18 $1,997,157 17.0% $339,517

OVN on 

Forest 9.0% 38,380 3.1 12,380 1.6 19,809 $67.70 1.035 $70.07 $1,387,991 36.0% $499,677

OVN off 

Forest 0.0% 0 0.0 0 0 0 $57.56 1.035 $59.57 $0 46.0% $0

Non Primary Visits

Non 

Primary 

Visits

9.0% 38,380 2.4 15,991 1.0 15,991 $33.02 1.035 $34.18 $546,519 100.0% $546,519

Total 100.0% 426,439 2.8 153,668 362,223 $24,380,511 $21,834,556

The NVUM national forest analog is the Ashley National Forest. 

*Out-of-Area Substitution: The portion of spending of local recreation visitors that would be lost to the region in the absence of the local recreation opportunities. If local visitors were 

to go outside the region because of the absence of the local recreation opportunities (rather than spending their money on something else locally), their spending would constitute a 

loss to the local economy and should therefore be included in an impact analysis.

Acronyms: D/N – Day/Night; FO – Field Office; OVN: Overnight

Sources: Rock Springs Field Office total visits from BLM RMIS data, 2012. National Forest Out-of-Area Substitution: White and Stynes 2010. All other National Forest data: White et 

al. 2012, adjusted to 2011 dollars using IMPLAN inflators.

N-24 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

2.4.4 IMPLAN Model Modifications

No modifications were made to the IMPLAN model for recreation-related sectors. The coefficients used by 

the model for these sectors are generally considered reliable for Wyoming.

2.4.5 Recreation Impacts

As noted earlier, the economic importance of recreation in the BLM field offices and Forest Service

planning units was considered both in terms of “economic contribution” which is a descriptive analysis that 

simply tracks the gross economic activity as the dollars cycle through the region’s economy and “economic 

impact” which estimates the net economic activity that would be lost from the local economy without the 

resource. The total direct economic contributions or impacts were used in the IMPLAN model to estimate 

the indirect, induced, and total economic effects of recreation. Also, the recreation analysis differed from 

the other analyses in that it considered all recreation use (except for downhill skiing) for each field office 

or planning unit, not just the recreation use in areas affected by sage-grouse management. 

The total direct economic contributions or impacts were entered into the IMPLAN model using expenditure 

distributions by trip types that were developed by the Forest Service from the NVUM surveys. The direct 

recreation spending was disaggregated across the following 10 IMPLAN sectors based on the spending 

distributions from NVUM:

Number Sector Name

324 Retail – Food and Beverages

326 Retail – Gasoline Stations

328 Retail – Sporting Goods, Hobby, Book, Music

330 Retail – Miscellaneous

362 Automotive Equipment Rental and Leasing

410 Other Amusement and Recreation Industries

411 Hotels and Motels

412 Other Accommodations

413 Food Services and Drinking Places

429 Other Federal Government Enterprises

2.5 Nonmarket Values

The analysis of nonmarket values was partly quantitative and partly qualitative. Nonmarket values 

(consumer surplus value) associated with recreation were estimated quantitatively at a very high level using

a methodology described below. Other nonmarket values were discussed qualitatively in Chapter 4.

2.5.1 Recreation Consumer Surplus Value Methodology

As discussed in the Socioeconomic Baseline Report, consumer surplus is the maximum dollar amount 

above any actual payments made that a consumer would be willing to pay to enjoy a good or service. It is 

a measure of the value recreationists receive that is not priced in markets. Consumer surplus values have 

been researched and defined in many studies in the economics literature for many recreation activities and 

many locations (see for example, Loomis 2005 and Rosenberger 2012). Values from this literature can be 

applied to new locations using a methodology known as “benefits transfer.” Studies representing sites and 

recreation activities that are analogous to the site and activity under consideration are selected, and 

consumer surplus values are then applied to the new location. The values must be expressed in or converted 

to the same units (e.g., per visitor day) and generally multiple analogous studies are selected and their values 

averaged.

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-25
Appendix N Final EIS

The benefits transfer approach has been applied in an analysis for Chapter 4 of the Northwest Colorado 

Greater Sage-Grouse Draft Land Use Plan Amendment / Environmental Impact Statement (BLM 2013). 

The authors of that analysis reviewed and selected from a large number of studies across in the Rocky 

Mountain and Great Basin area (Colorado, Utah, Wyoming, 31 Arizona, New Mexico, Idaho, Montana, 

and Nevada). The analysis selected studies relevant to the types of recreation activities taking place on 

BLM and Forest Service-administered lands, multiplied average values for each activity by the number of 

visitor days per activity, and then summed the results. The total estimated consumer surplus value based on

the selected studies and recreation levels in recent years was $193.8 million in 2012 dollars, based on just 

over 4 million estimated recreation visitor days. Thus the overall average consumer surplus value was 

$48.36 per visitor day.

For the analysis of consumer surplus values associated with recreation in the Wyoming planning area, this 

EIS used the average consumer surplus value per day from the results of the Colorado Sage-grouse EIS. 

This figure was applied to estimated visitor days on BLM and Forest Service-administered lands in the 

planning area in Wyoming. If a more detailed approach (activity by activity, as in the Colorado Sage-grouse 

EIS) were used for this analysis for Wyoming, it is likely that many of the same nonmarket studies would 

be used. Thus the average consumer surplus value per day from the results of the Colorado Sage-grouse 

EIS is a reasonable proxy for this Wyoming EIS. In addition, the types of activities taking place on BLM 

and Forest Service-administered lands in the Wyoming planning area is likely to be very similar to that 

taking place on such lands in Colorado. The proportions of visitor days by activity probably differ across 

the two planning areas, but the differences would be unlikely to yield vastly different results in terms of 

total consumer surplus value. 

Total visitor days for BLM field offices were taken directly from RMIS. Visitor days for the Forest Service

planning units were calculated from NVUM data in order to remove downhill skiing visits. Table 12 and 

Table 13 show these calculations. National Forest visits were used as the basis for the calculations because 

the activity data showing hours by activity in the individual forest NVUM master reports is based on 

National Forest Visits rather than site visits. 

Table 12. Calculation of Visitor Days, Bridger-Teton National Forest 

Recreation 

Type

Total 

National 

Forest Visits*

Average 

Hours/Visit**

Total Hours (by 

multiplication)

Visitor 

Days 

(Hours/12)

All Visits 2,181,657 24.0 52,359,768 4,363,314 

Downhill Skiing 

Visits 658,860 5.1 3,360,188 280,016 

Non-Downhill 

Skiing Visits (by 

subtraction)

1,522,797 -- 48,999,580 4,083,298 

*White et al. 2012.

**Forest Service 2012a.

Table 13. Calculation of Visitor Days, Medicine Bow National Forest and Thunder Basin 

National Grassland

Recreation 

Type

Total 

National 

Forest Visits*

Average 

Hours/Visit**

Total Hours (by 

multiplication)

Visitor 

Days 

(Hours/12)

All Visits 990,331 28.3 28,026,367 2,335,531

Downhill Skiing 

Visits 34,662 5.1 176,774 14,731

N-26 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

Recreation 

Type

Total 

National 

Forest Visits*

Average 

Hours/Visit**

Total Hours (by 

multiplication)

Visitor 

Days 

(Hours/12)

Non-Downhill 

Skiing Visits (by 

subtraction)

955,669 -- 27,849,593 2,320,799

*White et al. 2012.

**Forest Service 2012b.

Table 14 shows the total visitor days across the planning area, and the calculation of total consumer surplus.

In round numbers, appropriate to the uncertainties inherent in the estimation of nonmarket values, the 

estimated total consumer surplus value is $362 million on an annual basis, and $2,543 million in net present 

value over the period 2013–2020. The calculation of consumer surplus is only provided at the planning area 

level, in order to even out any variations in the breakdown of different activity types by individual office 

or unit. The use of the average consumer surplus value per visitor day from the Colorado sage-grouse 

amendments EIS is likely to be more accurate at the planning area level than at the individual office or unit 

level. In addition, the figure was also only applied at the planning area level in the Colorado Sage-grouse 

EIS.

Table 14. Total Visits, Visitor Days, and Consumer Surplus Value

Field Office or Planning Unit Year Source Total Visits Visitor Days 

Casper Field Office 2012 BLM RMIS 322,431 251,016 

Kemmerer Field Office 2012 BLM RMIS 223,083 90,383 

Newcastle Field Office 2012 BLM RMIS 8,220 9,289 

Pinedale Field Office 2010 BLM RMIS 372,518 217,384 

Rawlins Field Office 2012 BLM RMIS 226,251 238,303 

Rock Springs Field Office 2012 BLM RMIS 426,439 280,842 

Bridger-Teton National Forest 2008 Forest Service

NVUM 1,522,797 4,083,298 

Medicine Bow National Forest and 

Thunder Basin National Grassland 2008 Forest Service

NVUM 955,669 2,320,799 

Totals 4,057,408 7,491,315

Average Consumer Surplus Value per Visitor Day from Colorado Sage-grouse 

Amendment EIS $48.36

Total Estimated Annual Consumer Surplus Value $362,273,127

Total Estimated Consumer Surplus Value through 2020* $2,543,045,837 

*Based on a 3% discount rate applied to the annual consumer surplus value from 2013 to 2020

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-27
Appendix N Final EIS

ACRONYMS LIST

AFE Authorization for Expenditures

AUM Animal Unit Month

BBL Barrel

BEA Bureau of Economic Analysis

BLM Bureau Land Management

BLS Bureau of Labor Statistics

BTNF Bridger-Teton National Forest

CBNG Coal Bed Natural Gas

CREG Consensus Revenue Estimating Group

D/N Day/Night

EIS Environmental Impact Statement

EJ Environmental Justice

EO Executive Order

FO Field Office

FY Fiscal Year

GIS Geographic Information System

IMPLAN IMPact analysis for PLANning

JEDI Jobs and Economic Development Impact

LQ Location Quotient

LRMP Land and Resource Management Plan

MBNF Medicine Bow National Forest

MCF Million Cubic Feet

MIG Minnesota IMPLAN Group

MRIO IMPLAN’s Multi-Region Input-Output

MW Megawatt

NF National Forest

NG National Grassland

NREL National Renewable Energy Laboratory

NVUM Forest Service National Visitor Use Monitoring

OHV Off-Highway Vehicle

OMB Office of Management and Budget

OVN Overnight

RFD Reasonable Foreseeable Development

N-28 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

RMG Reservoir Management Group

RMIS Recreation Management Information System

RMP Resource Management Plan

TBNG Thunder Basin National Grassland

USDA United States Department of Agriculture

Forest Service United States Forest Service

WERC Wind Energy Research Center

WIA Wyoming Infrastructure Authority

Wyoming Greater Sage-Grouse Land Use Plan Amendment N-29
Appendix N Final EIS

REFERENCES

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N-30 Wyoming Greater Sage-Grouse Land Use Plan Amendment
Final EIS Appendix N

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