Arnold Schwarzenegger 

Governor

BIOMASS TO ENERGY:

FOREST MANAGEMENT FOR WILDFIRE 

REDUCTION, ENERGY PRODUCTION,

AND OTHER BENEFITS

PIER FINAL PROJECT REPORT 

Prepared For: 

California Energy Commission 

Public Interest Energy Research Program 

Prepared By: 

USDA Forest Service 

Pacific Southwest Research Station 

January 2010 

CEC-500-2009-080 

Prepared By: 

Mark Nechodom, PhD, Principal Investigator 

USDA Forest Service, Pacific Southwest Research Station 

Albany, CA 94702 

Commission Contract No. 500-03-019 

Prepared For:

Public Interest Energy Research (PIER) 

California Energy Commission 

Linda Spiegel 

Contract Manager 

Linda Spiegel 

Program Area Lead 

Energy-Related Environmental Research 

Kenneth Koyama 

Office Manager 

Energy Generation Research Office 

Thom Kelly, Ph.D. 

Deputy Director 

ENERGY RESEARCH & DEVELOPMENT DIVISION 

Melissa Jones

Executive Director 

DISCLAIMER 

This report was prepared as the result of work sponsored by the California Energy Commission. It does not necessarily represent the views of the 

Energy Commission, its employees or the State of California. The Energy Commission, the State of California, its employees, contractors and 

subcontractors make no warrant, express or implied, and assume no legal liability for the information in this report; nor does any party represent 

that the uses of this information will not infringe upon privately owned rights. This report has not been approved or disapproved by the California 

Energy Commission nor has the California Energy Commission passed upon the accuracy or adequacy of the information in this report. 

Acknowledgements 

This report is the result of four years’ work of a team with more than 65 participants from more

than a dozen institutions.  Nearly a third of the total resources required for this complex project

were contributed on an in‐kind basis, and several scientists and consultants dedicated

additional volunteer time.   Much credit belongs to the Technical Advisory Committee and the

Policy Advisory Committee that were assembled for the project.  Without their consultation and

guidance, this complex work would have been diminished.   

A full list of team members, research cooperators, and technical and policy advisors is available

in Appendix 12.

Recommended Citation:

USDA Forest Service, Pacific Southwest Research Station. 2009. Biomass to Energy: Forest

Management for Wildfire Reduction, Energy Production, and Other Benefits. California Energy

Commission, Public Interest Energy Research (PIER) Program. CEC‐500‐2009‐080.

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Table of Contents 

Executive Summary...................................................................................................................... 1

1.0 Introduction and Background ........................................................................................... 7

1.1. Project Objectives ........................................................................................................... 9

2.0 Project Approach................................................................................................................. 11

2.1. B2E Modeling Domains and Processes ....................................................................... 14

2.2. Selection of Landscape Archetype............................................................................... 16

2.2.1. The B2E Beta Landscape .......................................................................................... 17

2.3. Temporal Scope of B2E Modeling Domains............................................................... 19

2.4. B2E Scenario Development........................................................................................... 20

2.5. Vegetation Treatments .................................................................................................. 21

2.5.1. Locating Treatment Areas and Assigning Forest Management Regimes......... 21

2.5.2   Sequencing Treatment Activities Over Time....................................................... 23

2.6. Vegetation Dynamics Modeling................................................................................... 23

2.6.1. Initial Vegetation Inventory .................................................................................... 24

2.6.2. Modeling the Effects of Forest Treatments on Vegetation.................................. 25

2.6.3. Modeling the Effects of Wildland Fires on Vegetation........................................ 25

2.6.4. Modeling Vegetation Growth ................................................................................. 26

2.7. Wildfire Behavior and Severity.................................................................................... 26

2.8. Fire Emissions Model .................................................................................................... 29

2.8.1. Fuel Consumption .................................................................................................... 30

2.8.2. Calculating Fire Emissions ...................................................................................... 31

2.9. Forest Operations and Equipment Configuration..................................................... 34

2.10. Biomass Energy Conversion Technology Characterization..................................... 38

2.11. Life Cycle Assessment................................................................................................... 39

2.11.1. Goal and Scope of LCA............................................................................................ 42

2.11.2. Gross vs. Net Inventory and Assessment.............................................................. 44

2.11.3. Forest Treatment and Chip Transport Models and Data .................................... 44

2.11.4. Fuel, Oils, and Electricity Production Models and Data ..................................... 47

Biomass Power Plant Operation Models and Data ....................................................... 47

2.11.5. LCA Environmental Impact Assessment .............................................................. 47

2.11.6. B2E LCA Interpretation............................................................................................ 48

2.12. Landscape Greenhouse Gas Model ............................................................................. 49

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2.13. Wildlife Habitat Assessment ........................................................................................ 50

2.13.1. Habitat Element Specialist Guilds .......................................................................... 50

2.13.2. Service Providing Units ........................................................................................... 51

2.13.3. Species of Special Interest........................................................................................ 52

2.13.4. Aquatic Species.......................................................................................................... 52

2.14. Ecosystem Services and Ecological Endpoints Analysis .......................................... 52

2.14.1. Ecological Production Functions ............................................................................ 54

2.14.2. Scarcity and Substitution ......................................................................................... 55

2.15. Cumulative Watershed Effects Analysis .................................................................... 57

2.15.1. Cumulative Watershed Effects Model ................................................................... 57

2.15.2. Watershed Disturbances .......................................................................................... 58

2.15.3. ERA Coefficients and Recovery Curves ................................................................ 58

2.15.4. Watershed Thresholds.............................................................................................. 61

2.16. Economic Analysis ......................................................................................................... 62

2.16.1. Valuation of Assets at Risk to Fire.......................................................................... 64

2.16.2. Sawlog Valuation...................................................................................................... 65

2.16.3. Fuels Treatment Costs .............................................................................................. 65

2.16.4. Power Plant Costs and Revenues ........................................................................... 66

2.16.5. Fire Suppression and Rehabilitation Costs ........................................................... 66

3.0 Project Results ...................................................................................................................... 69

3.1. Vegetation dynamics and change ................................................................................ 71

3.1.1. Reference Case and Test Scenario........................................................................... 71

3.2. Wildfire Behavior........................................................................................................... 72

3.3. Life Cycle Assessment Results ..................................................................................... 75

3.3.1. Gross Inventory Results ........................................................................................... 75

3.3.2. Gross Impact Characterization................................................................................ 76

3.3.3. LCA Interpretation ................................................................................................... 77

3.3.4. Comparison of biomass electricity generation systems....................................... 79

3.4. Landscape Greenhouse Gas Model ............................................................................. 82

3.5. Wildlife Habitat Effects ................................................................................................. 83

3.5.1. Forest Structure ......................................................................................................... 83

3.5.2. Forest Structure Associated Species ....................................................................... 89

3.5.3. Service Providing Units ........................................................................................... 93

3.5.4. American Marten ...................................................................................................... 96

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3.6. Cumulative Watershed Effects..................................................................................... 99

3.6.1. Total Equivalent Roaded Acres (ERAs)................................................................. 99

3.6.2. Cumulative Watershed Effects Analysis ............................................................... 100

3.7. Economic Results ........................................................................................................... 103

3.7.1. Conclusions and Key Findings ............................................................................... 106

4.0 Conclusions, Observations and Recommendations ....................................................... 109

4.1.1. Scenario development recommendations ............................................................. 109

4.1.2. Life Cycle Assessment Recommendations ............................................................ 110

4.1.3. Wildlife habitat modeling recommendations ....................................................... 111

4.1.4. Economic analysis limitations and recommendations ........................................ 112

Effects of Population Growth and Future Land Use Development............................ 113

4.2. Benefits to California ..................................................................................................... 113

5.0 Glossary ................................................................................................................................ 117

6.0 References............................................................................................................................. 119

7.0 Appendices........................................................................................................................... 123

7.1. Appendix 1:  Landscape Vegetation Changes (Barber, Perrot, et al.)..................... 123

7.2. Appendix 2:  B2E Fire Behavior Domain (Ganz, Saah, Barber, et al.)..................... 123

7.3. Appendix 3:  Forest Operations and Equipment Configuration (Mason, Hartsough,

et al.)................................................................................................................................. 123

7.4. Appendix 4:  Life Cycle Assessment of Producing Electricity from California Forest

Wildfire Fuels Treatments (Cooper)............................................................................ 123

7.5. Appendix 5:  Wildlife Habitat Evaluation (Manley, et al.)....................................... 124

7.6. Appendix 6:  Cumulative Watershed Effects Analysis (Wright, Perrot, et al.) ..... 124

7.7. Appendix 7:    Counting Ecosystem Services:    Ecological Endpoints and their

Application (Boyd)......................................................................................................... 124

7.8. Appendix 8:  Project Economic Analysis (Wegge, Trott and Barnett).................... 124

7.9. Appendix 9:  Landscape Carbon Model (Morris)...................................................... 124

7.10. Appendix 10:  Power Plant Analysis for Conversion of Forest Remediation Biomass

to Renewable Fuels and Electricity (Schuetzle, Tamblyn, Tornatore, et al.).......... 124

7.11. Appendix 11:    Synthesis of Economic Valuation Studies of Forest Landscape

Disturbances (Berkenklau)............................................................................................ 124

7.12. Appendix 12:   Biomass to Energy Project Team, Committee Members, and Project

Advisors........................................................................................................................... 125
List of Tables 

Table 1 ‐ B2E project modeling and analysis domains .......................................................................13

Table 2.  Sequence of B2E model analytical processes........................................................................14

Table 3.  Land ownership categories in the B2E Beta landscape....................................................... 18

Table 4. Major vegetation types in the B2E Beta landscape ............................................................... 19

Table 5. B2E Beta model scenarios.........................................................................................................20

Table 6. CONSUME Multiplier Factors (MFs) for non‐lethal, mixed lethal, and lethal fires........ 30

Table 7. Inventory assumptions for the brush and grass vegetation types ..................................... 31

Table 8. Combustion efficiencies (combustion factors, CF) for various vegetative components by

fire severity class ..............................................................................................................................31

Table 9. Example Crosswalk between NFRDS Codes and CWHR Types (partial table) .............. 33

Table 10. Example WRAP Emission Factors (abbreviated and partial table).................................. 34

Table 11 ‐ Equipment configuration code by slope class and treatment prescription ................... 36

Table 12. B2E Project power plant characterization............................................................................ 39

Table 13. B2E Project LCA decision and impact categories ............................................................... 43

Table 14. Landscape treatment scenarios .............................................................................................43

Table 15. Treatments, equipment configuration, and production Rates.......................................... 45

Table 16. Harvest and chip transport emissions models.................................................................... 46

Table 17. Equivalency factors used (equivalent mass/mass emitted)............................................... 47

Table 18. Normalization factors (estimated 40‐year California values)........................................... 48

Table 19. Examples of relationships between benefits and endpoints ............................................. 54

Table 20. Examples of endpoints and relationships to metrics of scarcity and substitution......... 56

Table 21. Mean sediment by treatment (1999‐2000) and proportioned ERA coefficient ............... 59

Table 22. Beta landscape Equivalent Roaded Acre (ERA) coefficients, recovery years, and

recovery curves for different types of disturbances.................................................................... 60

Table 23. Analytical frameworks for economic analyses ................................................................... 62

Table 24. Geographic specification and valuation parameters for the assets at risk analysis....... 64

Table 25 ‐ Format for presentation of B2E model results ...................................................................69

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Table 26.  Land ownership categories in the B2E Beta landscape..................................................... 70

Table 27 ‐ Acres and products from the Beta landscape under the Test Scenario.......................... 72

Table 28 ‐ Acres treated by decade per scenario under the Test Scenario ....................................... 72

Table 29 ‐ B2E burned areas by scenario and by year......................................................................... 73

Table 30 ‐ Fire Severity for Modeled Wildfires Under the Reference Case and Test Scenario ..... 74

Table 31. Fire severity results comparing reference to test ................................................................74

Table 32. Gross assessment system products.......................................................................................75

Table 33 ‐ Gross life cycle inventory results for Test Scenario........................................................... 76

Table 34.  Gross assessment of climate impact categories from LCA............................................... 77

Table 35. Gross assessment impact normalization (as percent of California impact).................... 77

Table 36.  Net assessment system characteristics (electricity only)................................................... 78

Table 37. Power plant characterizations for net assessment comparison ........................................ 81

Table 38. Life cycle impacts of the three modeled B2E power plant technologies ......................... 82

Table 39. Risk ratios for watersheds over threshold by model decade .......................................... 101

Table 40 ‐ Study Area Market Asset Value Changes by Treatment Scenario (in millions of 2006

dollars)............................................................................................................................................. 104

Table 41  ‐  Annualized Costs and Revenues for No Treatment (Reference Case) and With

Treatment (Test Scenario), in millions of 2006 dollars..............................................................105

Table 42. Probability that any given acre will support a particular forest condition or suite of

species as defined by ecological groupings, and how that probability changes with each

scenario............................................................................................................................................ 112
List of Figures 

Figure 1. B2E project model....................................................................................................................16

Figure 2. Randomized ignition locations to locate most logical representative ignition points on

the B2E Beta landscape....................................................................................................................27

Figure 3. B2E Beta landscape fire history and ignition placement by modeled decade ................ 28

Figure 4. Classes of fire severity used in B2E fire modeling: (N) = non‐lethal, (X) = mixed lethal,

(L) = lethal.......................................................................................................................................... 29

Figure 5.  LCA process flows..................................................................................................................41

Figure 6. Example of new equipment purchase dates for emissions degradation estimation...... 46

Figure 7. Recovery curves used for B2E Beta landscape cumulative impacts analysis ................. 61

Figure 8 ‐ Location of B2E Beta landscape............................................................................................71

Figure 9 ‐ Life cycle energy consumption net assessment ................................................................. 80

Figure 10 ‐ Changes in Diameter Class, Reference Case Define key. ............................................... 84

Figure 11 ‐ Changes in Diameter Class, Test Scenario Define Key................................................... 85

Figure 12 ‐ CWHR Types, Reference Case Define Key.......................................................................86

Figure 13 – California Wildlife Habitat Relationships habitat types, Test Scenario....................... 86

Figure 14 ‐ Mid‐seral (diameter class 4) and old forests (diameter class 5/6) with moderate (M;

40‐60%) to dense (D, >60%) canopy closure, Reference Case .................................................... 88

Figure 15 ‐ Mid‐seral (diameter class 4) and old forests (diameter class 5/6) with moderate (M;

40‐60%) to dense (D, >60%) canopy closure, Test Scenario........................................................ 88

Figure 16 – Richness of old forest associated species (n = 59) supported across the landscape,

Reference Case.................................................................................................................................. 89

Figure 17 – Richness of old forest associated species (n = 59) supported across the landscape,

Test Scenario ..................................................................................................................................... 90

Figure 18 – Richness of old forest dependent species (n = 16) supported across the landscape,

Reference Case.................................................................................................................................. 91

Figure 19 – Richness of old forest dependent species (n = 16) supported across the landscape,

Test Scenario ..................................................................................................................................... 91

Figure 20 – Richness of early‐seral associated species (n = 118) supported across the landscape,

Reference Case.................................................................................................................................. 92

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Figure 21 – Richness of oak and acorn‐associated species (n = 44) supported across the

landscape, Reference Case ..............................................................................................................93

Figure 22 – Richness of seed dispersing species (n = 22) supported across the landscape,

Reference Case.................................................................................................................................. 94

Figure 23 – Richness of seed dispersing species (n = 22) supported across the landscape in the

Test Scenario ..................................................................................................................................... 94

Figure 24 – Richness of herbivore regulating species (n = 9) supported across the landscape in

the Reference Case ...........................................................................................................................95

Figure 25 – Richness of herbivore regulating species (n = 9) supported across the landscape in

the Test Scenario...............................................................................................................................96

Figure 26 – Probability of occupancy within suitable reproductive habitat for marten (Martes

americana) in the B2E landscape based on predictive models.................................................... 98

Figure 27 ‐ Total Equivalent Roaded Acres by Year........................................................................... 99

Figure 28 ‐ ERAs by year and disturbance type ................................................................................100

Figure 29. CWE risk assessment by watershed in 2006 model year............................................... 102

Figure 30.  CWE risk assessment by watershed in 2016 model year.............................................. 103

Figure 31 ‐ Review of B2E Process Models and Results ................................................................... 109
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Abstract 

The Biomass to Energy project models the costs and benefits of generating electricity from forest

thinnings1 over a 40‐year period beginning in 2006.  The study demonstrates that economic

valuation is possible for many, but not all, valued assets2 on the landscape, and that the impacts

and costs of forest disturbance (including thinning operations) can be accurately modeled.  The

study includes a life cycle assessment3 of forest operations4 and energy conversion, measuring

three biomass conversion technologies.  A test of the model structure was developed on a

Northern California forest landscape comprising approximately 2.7 million acres spanning the

crest of the Sierra Nevada range and encompassing the Feather River basin.  A Reference Case

and Test Scenario were developed to test the structure and accuracy of the model using real‐life

data from Mt. Lassen Power (an existing biomass conversion plant), public and private forestry

operations, and historic wildfire ignition patterns.  Wildlife habitat impacts and cumulative

watershed effects were also modeled.  Results of the Test Scenario show that thinning reduces

wildfire size and severity — therefore reducing fire‐generated greenhouse gas emissions —

while producing renewable energy.  With appropriate caveats about data resolution and model

sensitivity, impacts to wildlife habitat and watershed appeared minimal. The Biomass to Energy

project benefits California by contributing to the state’s capacity to analyze forest biomass

utilization opportunities at the landscape scale.   

Keywords:  Biomass, renewable energy, forest biomass, forest thinnings, biomass energy

conversion, wildfire, greenhouse gas, life cycle assessment, Northern California, Mt. Lassen

Power

 

1 Forest thinnings are the material that is generated during forest thinning operations. Forest thinning is

the selective removal of trees from a forest or portion of a forest.

2 Examples of valued assets include structures, infrastructure, recreation resources, agricultural

production, water quality, air quality, and biological diversity.

3 A life cycle assessment is an evaluation performed to compare the full range of environmental and

social impacts assignable to a product or service.

4 Forest operations are the physical actions which change the forest, altering structure, composition,

condition, or value in order to meet society’s needs for clean air and water, forest products, wildlife,

recreation, and other benefits.  

xi 
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Executive Summary 

Introduction

The Biomass to Energy (B2E) project breaks new ground by offering a framework for deciding

whether biomass energy generation is a suitable investment for a given forest.  This study offers

a credible way to establish the relative values of converting forest thinnings into energy, as well

as the costs (especially wildfires and air pollution) of not doing so.  Such a framework updates

the debate about structuring financial incentives that correspond to avoided costs.

Biomass has been used as a source of renewable energy for approximately 50 years and

currently generates about 1 percent of California’s total power.  From an engineering

perspective, the technology is not the most efficient way to make electricity.  Yet biomass

conversion offers a unique paired benefit:  a way to recycle forest waste into renewable energy

while simultaneously decreasing potential fuel for wildfires.

Modern management practices designed to protect forests (for example, restricting thinning or

excessive harvesting) have produced increasingly dense vegetation.  In recent years, wildfires

occurring in overstocked forests consumed brush and smaller trees (ladder fuels), growing into

larger, more intense fires involving hundreds to thousands of acres.  Such catastrophic wildfires

produce more air‐polluting emissions and cause more devastation to forests, including wildlife

habitat and watersheds, than less intense fires.

The B2E project integrates existing U.S. Forest Service models of fire planning and forest

ecology with life cycle assessment models of energy use, emissions, and cost.  The life cycle

assessment portion of the project assesses environmental impacts associated with treating,

disposing of, and using forest biomass and producing electricity or biofuels.

In the short time between beginning this project and completing this Final Report, energy costs

have jumped beyond anyone’s expectations.  The research team has not attempted to update

cost/revenue numbers to their present dollar worth, as energy price volatility makes such

computation a moving target.  All cost and revenue numbers reported in these pages are in 2006

dollars.

Purpose and Project Objectives

The B2E project demonstrates the capabilities of a robust and complex modeling structure using

real‐world data to identify and analyze costs and benefits associated with removing biomass

(thinnings and other waste) from the forest and using it to generate electrical power.

Environmental benefits associated with removing biomass from forest ecosystems are

quantified by the model, including decreased size and severity of wildfires and reductions in

life cycle greenhouse gases and other air pollutants.  In addition, this project demonstrates that

it is possible to build a set of interconnected and interdisciplinary models to represent

quantifiable relationships between economic investment, forest vegetation, and wildfire, on the

one hand, and impacts on air quality, energy production, wildlife habitat, and other ecosystem

effects on the other.

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At the core of this study is a life cycle assessment, which examined approximately 2.7 million

contiguous acres of Northern California forest (referred to in this study as the beta landscape.)  

This beta landscape contains public and private lands located at the northern end of the Sierra

Nevada range straddling both the Central Valley (to the west) and the Great Basin (to the east.)  

Forestry operations and biomass power facilities have been part of the economic fabric of this

rural region — including parts of five counties and the Feather River basin — for nearly 30

years.  The beta landscape was selected to represent high infrastructure and other asset values,

high fire risk, and a broad range of economic, social, and ecological diversity.  

A decade is the shortest meaningful period for modeling forest vegetation growth. This study

projects data over four decades, beginning in 2006, and accommodates the typical life of a

power plant, which is 35‐40 years.  In each decade, the model measures existing forest inventory

and allows for vegetation growth over time.  For modeling and analysis, the beta landscape was

divided into units called grid cells, each measuring 100 square meters.

For this project, the life cycle assessment models two possible conditions in the beta landscape.  

First, the Reference Case models only the interaction of vegetation growth and wildfires to

establish an ecosystem baseline.  To establish the fire model, the research team used historical

data.  Historically, approximately 65,000 acres within the beta landscape burn over a typical

decade.  Modeled fires were also burned on the landscape consistent with historic ignition

patterns.

This no‐treatment Reference Case is compared to a Test Scenario in which a complete menu of

forest management treatments such as thinning, clear‐cutting, and selective harvesting are

modeled.  Sawlog and biomass removal occurs during all of these treatments.  In the Test

Scenario, an average of 492,863 acres per decade receive treatments across the beta landscape.

The energy use, emissions, costs, and revenues related to these treatments are quantified.

This B2E study used actual data from the Mount Lassen Power plant in Westwood, California,

built around 1980.  Mount Lassen Power, which has a long history of continuous operations,

receives most of its feedstocks from forest thinning operations within a 30‐50 mile radius.  

Mount Lassen Power provided empirical data representative of operating and maintaining a

typical biomass power plant.  In addition, a technical working group collected empirical data

about equipment used in forest harvest operations from forestry experts.  This was used to

model fuel and lubricating oil consumption, machine‐specific emissions, and productive

machine hours.  

Most landscape models lack a time dynamic.  Such models make a statement about a single

event or condition.  The reality of forest management at a landscape level is that neither

treatments nor fires take place all at once; impacts are distributed over space and time.  Also,

forests are dynamic systems that change and recover from impacts over time.  For this reason,

the team explored the need for building a time dimension into the study and concluded that

sequential treatments and disturbances should be accommodated.

Developing such a conceptual framework to model landscape level changes over 40 years

required a kind of daisy‐chaining of various scientific models.  For example, vegetation models

2 
feed into fuel models, which feed into fire models.  Looking at the forest in this way presented

major computational and database management challenges.  Forest inventory datasets (called

tree lists) often reached tens of gigabytes while calculating time‐dynamic changes.

The total biomass available for energy conversion is calculated by measuring the amount of

vegetation (small trees, branches, brush and litter) removed during a treatment.  The next

modeling steps include processing, transportation (assuming an average haul distance of 30

miles), and energy production.  It should be noted that many biomass plants operating in

forests use additional fuels (such as agricultural and urban waste) to achieve economic

efficiencies.  For this study, the model assumes that only forest waste was used to generate

energy.   

After treatments are modeled, a reduced level of wildfires occur over the four decades in the

Test Scenario, where they are fed by post‐treatment fuels conditions.  Post‐fire treatments (such

as salvage operations) are also modeled, as are the growth of treated and untreated forest

stands as well as burned stands.  The model finally reports on vegetation conditions at the end

of each decade resulting from treatments or fires as well as the interactions of treatments and

fires in places where the two events overlapped.

Project Outcomes

An initial scenario was built to test the model.  This Test Scenario — modeling thinning,

transporting, and converting biomass into electrical power — yielded the following results

when compared to the no‐treatment Reference Case:

• $1.58 billion in power revenues, assuming an 8.3‐cent per kilowatt‐hour price on the

wholesale power market.  A negligible amount of fossil fuels (approximately 1.3 percent

of total energy consumed) is required to produce this power.

• Clear life cycle climate change benefits, including a 65 percent net reduction in

greenhouse gas emissions (from 17 million tons of carbon dioxide (CO2) equivalents to

5.9 million tons of CO2 equivalents.)  

• A 22 percent reduction in the number of acres burned by wildfires.  Even greater

reductions can be anticipated by strategically locating thinning projects in areas of high

fire hazard.

• A significant economic gap between the cost of biomass fuel delivered to power plants,

estimated at $68 per bone dry ton, and the 2006 financial analysis of greenfield power

plant development under which maximum fuel costs would have to be less than $8.20

per bone‐dry ton in order to build the project.   

• A dramatic drop in fire severity.  Again, strategic location of thinning treatments would

likely enhance this result.

• Savings of $246 million in avoided wildfire damage to assets, including timber,

buildings, and infrastructure, as well as $18 million savings in avoided fire suppression

costs.

3 
• A substantial offset of fossil fuel consumed to generate the same amount of electricity

over the same period (estimated at a life cycle savings of approximately 120 terawatt‐

hours.)   

• Impacts on habitat suitability over the 40‐year period from treatments and fires could

not be accurately determined.

• Minimal cumulative watershed effects over the 40‐year period.  

This final report contains detailed explanations of these findings.  Further information,

including highly detailed and specific analyses, model specifications and model run results can

be found in the appendices.

Conclusions and Recommendations

This study offers insights into potential economic, energy, and environmental trade‐offs

associated with managing forest biomass.  By modeling the effects of biomass removal at the

landscape level, this forest‐based life cycle assessment provides a credible method of measuring

the relative values of converting forest biomass to energy as well as the costs of not doing so.  

Such a framework can inform the debate about structuring financial incentives that correspond

to avoided costs.  In addition, this study invites future examination of biomass to energy

applications stretching beyond the forest landscape.

Instead, the study supports development of scenarios to demonstrate the interactions of

multiple benefits and impacts associated with treatments from which biomass is used for

energy production.  When quantifying the economics of converting forest waste into renewable

energy, the net benefits to the environment extend far beyond energy production, as this study

demonstrates.

The vegetation data used in the model were well‐suited for estimating wildfire behavior and

emissions, the economic value of harvested wood products, and power plant operation and

emissions. However, these data were not as well‐suited to the characterization of disturbance

impacts on habitat.  The study’s conclusion that treatments have minimal impacts on habitat

quality for the nearly 120 habitat types modeled must be viewed with caution and may change

with additional research and improvements in habitat modeling.

Cumulative effects on watershed appear to be minimal.  Watershed effects are highly localized

depending on severity of wildfire.  High severity wildfire impacts watersheds more than the

impacts associated with treatments.  The Test Scenario shows reduced wildfire severity, and

therefore reduced soil erosion, as a result of forest thinning.

Some items cannot be modeled, such as social choices that impute value to forest ecosystems.

The costs of quantifying and modeling social preferences were found to be beyond the scope

and capacity of the B2E project.  For example, quantification of “healthy forest conditions”

proved elusive and subjective.  Further research on this topic would be needed to meet the

stringent quantification requirements of the life cycle assessment and economics models used in

this study.

4 
5 

Benefits to California

The Biomass‐to‐Energy project has contributed to California’s capacity to analyze forest

biomass usage opportunities at the landscape scale.  Even in draft form, the Secretary of the

United States Department of Agriculture has identified the project as a “highly influential

scientific assessment,” with implications for how the USDA Forest Service would use life cycle

assessment to evaluate the benefits of biomass power.

California has approximately 40 million acres of forest lands, nearly half of which are managed

by private landowners.  The economics of private forest land management historically have

constrained opportunities for effective and sustainable management.  The B2E project’s

approach is likely to assist policy makers and landowners in evaluating comprehensive and

long‐term benefits to the environment, as well as enhancing economic opportunities in forest‐

dependent communities.

Realization of the benefits of thinning forests and using the waste products for energy

production are largely a matter of public choice and policy making.  Many of the benefits of

managing California’s forests — such as reducing wildfire effects, saving fire suppression costs,

providing clean air and water and other climate benefits — may be better reflected in future

markets and public policy as a result of this project.  Biomass power is a rare form of renewable

energy in that it provides a broad range of benefits at relatively low cost to the consumer and

substantial ancillary benefits to the environment.  Further quantification and analysis, building

on the work presented by the project, will help California’s policy makers and legislators

evaluate how forest biomass will contribute to larger societal and environmental goals.
6 
1.0 Introduction and Background 

California’s forests represent a significant potential resource for generating biofuels and

bioenergy.  More recently, policy makers and land managers have begun considering the

potential for forested landscapes to substantially alter carbon cycling as a mitigation strategy for

greenhouse gas reduction.  In addition, most of the state’s developed water resources depend

heavily on California’s forests being maintained in healthy and resilient conditions, especially in

the light of probable climate changes over the coming century.  In short, forests represent a

complex and critical resource, providing a broad array of public and private goods and services

(Nechodom et al. 2008).   

Nearly 40 million acres, or 40 percent of the state’s land area, are covered by some kind of forest

(USDA Forest Service 2007).  The rich variety of forest ecosystems is almost unequalled in the

world, with vegetation types ranging from dense coastal redwood to foothill oak woodland to

mid‐elevation mixed conifer to high alpine fir.  California’s Sierra Nevada Range hosts some of

the world’s most productive temperate forests, growing an impressive range of species from

giant sequoia to ponderosa pine and Douglas fir to high‐elevation white bark pine.  

These forests are prone to wildfire.  In fact, fire has been such an integral part of their evolution

that foresters refer to them as fire adapted forests.   In the absence of fire, most Western temperate

forest ecosystems become vulnerable to drought, disease and catastrophic 5 wildfires.  A

management policy of excluding wildfires from forest ecosystems over the past century has

contributed to an increasing risk of large‐scale wildfires that leave forests in worse shape than

after fires that burn under less intense conditions.  In sum, some of the forest must burn to be

healthy, but high‐severity fires over large land areas can leave landscapes and ecosystems in

unhealthy conditions for a very long time.  

Managing California’s forests toward a more stable relationship with fire has become a major

focus of forest policy and management over the last several years.  How to do this has been

controversial.  Some would prefer to “let nature take its course,” leaving fires to burn largely

unabated, protecting only relatively small buffer areas around valuable assets and communities,

eventually “resetting” the equilibrium of forested landscapes.  Others contend that only

intensive forest management over very large areas will create forest structures that will be

resilient to fires, allow fire suppression resources to deploy safely around communities, and

ensure the long‐term sustainability of forests that have been allowed to grow in unsustainable

ways.  Yet another perspective holds that strategically thinning patches in a pattern across the

landscape reduces the rapid spread of intense wildfires, encourages fires to burn undergrowth

and to thin trees naturally, and reduces the amount of resources required to manage wildfires in

the future.

 

5. Catastrophic fire refers to stand replacement or high intensity fires that cause damage to ecological

and/or economic assets and values.  The B2E project also refers to these types of fires as

uncharacteristically severe wildfires.

7 
Each of these options has both ecological and economic consequences.  Some of these outcomes

are quantifiable, while other impacts are more elusive and difficult to measure.  For example,

the costs of thinning are easily measured by accounting for the expenses associated with

moving machinery into the forests, removing trees and brush, and moving products with any

value to their respective processing facilities and markets.  However, the “costs” associated with

impacts to streams, air quality, wildlife habitat or other non‐market values are far more difficult

to quantify.  The “benefits” associated with safer forests, beauty and amenity values, or the

social value of sustainably managed forests can be even more elusive.

Nonetheless, society decides how to manage public and private forests based on more than

economics and cost, or on the measurable and tradable goods that flow from forest

management actions.   The values that inhere in forests and their management are a complex

bundle of market and non‐market, measurable and immeasurable, quantitative and qualitative

goods and services.  In an ideal universe, we would be able to hold all of these competing

values against the same measuring stick, and we would be able to compare them and make

choices based on a single metric.  This is, of course, simply not possible.  There are goods and

services in our forests that are highly valued by some – and are often at the center of extreme

contention – which cannot be compared to tangible goods and services, or impacts or damages

to those goods and services.   

In this context, a team of researchers, engineers, and forestry professionals undertook a study,

funded by the California Energy Commission, to quantify where possible the multiple costs,

benefits, and impacts associated with thinning forests in order to reduce wildfire risks.  In

addition, since management nearly always produces a flow of products, co‐products and waste,

the project focused on the potential to produce energy from the non‐commercial portion of the

wood produced.  This product stream, which the project calls wildland biomass, or simply

biomass, is the focus of a life cycle analysis, in which various environmental impacts associated

with removal, transportation, processing and conversion of biomass to electricity are compared

to the impacts associated with other ways of generating electricity.   

The research team also took on another dimension of the problem that has never been modeled

before in quite the same way.  Could a life cycle assessment be developed to analyze the flow of

biomass, energy and costs associated with the production of a healthy forest?  To answer this

question, two parallel life cycle assessments were attempted, one focused on the impacts, costs,

benefits, and co‐products associated with the production of electricity, and the other focused on

the impacts, costs, benefits, and co‐products associated with the production of healthy and

sustainable forests.  There was a risk inherent in this strategy.  Defining the end‐product of

“electricity delivered to the grid” is fairly straightforward.  However, clearly defining the end‐

product of a “healthy and sustainable forest” is fraught with difficulty.  The former can be

measured against a simple metric, i.e., megawatt‐hours generated.  The team attempted to

measure the latter in acres that have reached a quantifiable state of “health” (which may be

defined differently, based on the management objectives of different landowners) without

substantially diminished capacity to maintain qualitatively measured multiple ecological

benefits.  While not entirely successful, this attempt rendered some very useful quantification

and analytical techniques as well as modeling insights.

8 
The team took on this challenge because of the persistent difficulties that face decision makers

and stakeholders in managing public and private forests.  The team tried to respond to a fairly

consistent call for an honest accounting of trade‐offs, costs and benefits in managing forests for

multiple benefits and outcomes.  At the most simple level, the driving question is:  Can forests

be managed sustainably to produce energy and other products while meeting objectives for

maintaining healthy forest conditions (which include reducing wildfire risk and severity) as

well as deriving other important forest benefits and values?   

To address this question, the team set two primary objectives for the project, with three secondary

objectives.   

1.1. Project Objectives 

The first primary objective of the research was to produce the structural framework for a life

cycle assessment (LCA) approach to identify a range of environmental trade‐offs and impacts

involved in removal of wildland biomass to produce electricity.  A second primary objective

was to create and analyze a test scenario, using the LCA developed by the team.  A Test

Scenario was designed to test the structure of the model, to ensure that logical relationships

among processes and sub‐models had been established, and to allow sensitivity analyses of key

modeling parameters.   

Three secondary objectives were required in order to build the modeling platform.  Each

involved reviews and syntheses of key literature and science.  First, the team synthesized

scientific knowledge in key environmental areas potentially affected by wildland biomass

removal, and evaluated data sources and quality for model development.  This step involved a

thorough review of the ecological production functions and critical ecological processes that

were key to the model, and synthesis of the science about those functions and processes.   

The next secondary objective was to investigate, synthesize and report the status of knowledge

in environmental and resource economics pertaining to the market and non‐market valuation of

key indicators to be developed for the model.   

Finally, since the model represents an attempt to bring disparate disciplines in the natural

sciences and economics into quantified relationships with each other, a number of gaps in

research were expected to become far more evident as the model was developed.  Therefore a

final objective was to identify critical research gaps that would allow further development of

the prototype model after the first phase of the project.   

9 
10 
2.0 Project Approach 

The B2E Project was proposed to the Energy Commission in the context of a growing need to

consider environmental effects across large landscapes, an approach that has been manifest in

federal land management planning over the past several years.  Several notable federal and

state regional planning efforts have received tremendous investment and public attention in

recent years, including the Northwest Forest Plan, the Sierra Nevada Framework, the Southern

California Conservation Plan, the Natural Communities Conservation Plan, the San Diego

County Multi‐species Habitat Conservation Plan, and the Western Riverside County Habitat

Conservation Plan.  While many of these plans started out as a means to address one or more

threatened or endangered species, in each case both scientific analysis and public pressure have

resulted in an increasing focus on multiple ecological and economic processes at different

scales.  Challenges and appeals to federal planning processes have often focused on the “failure

to analyze” landscape level processes that interact with one another to produce unintended

consequences.   

Federal and state public agencies have begun to put greater emphasis on complex scalar and

landscape interactions.  Recent discussions to improve National Environmental Policy Act

(NEPA) and National Forest Management Act (NFMA) disclosure requirements have focused

on how to integrate multiple processes at different scales so that the public can evaluate the

impacts and trade‐offs of management actions.  None of this is simple, and all of it requires

dramatic increases in public agency personnel, public and private analytical processes,

stakeholder involvement, and complex modeling of ecological and economic impacts.

Because of this increasing emphasis on complexity and scale, the team recognized a need to

“push the envelope” and attempt to meet what appears to be more frequent demand for

accounting for complex interactions at landscape scales. The team recognized early in its project

scoping efforts that the architecture of the final product needed to reflect the actual complexity

of public and private land management decision making, taking into account the kinds of

fragmented decision processes that often happen on the same landscape, with synthetic

consequences for resources and values beyond the technical scope of a particular management

decision.  Hence, for example, the team modeled patterns of private commercial forestry

harvesting alongside the landscape level thinning operations of the Forest Service on national

forest lands.  And these management patterns were subsequently analyzed in the context of

protected and reserved areas that have an effect on the total quality of habitat and watershed

resources at the landscape scale.

The research team and the technical advisory committee generally agreed that, while the effort

could prove burdensome and fraught with errors and disconnections, it was nonetheless

important to attempt to capture complex and highly interrelated ecological and economic

processes at a scale that continues to confound land managers and decision makers.  Trade‐offs

at a small watershed scale may be relatively easy to identify, especially when only one or two

landowners are involved.  But the public cares more about how the entire landscape responds,

including impacts on key terrestrial and aquatic species, water quality, air quality and other

public goods and services.  In this spirit, the team developed a comprehensive forest biomass to

11 
energy (B2E) model to achieve the project’s primary objective of identifying a range of

environmental trade‐offs and impacts as well as key cost and benefit relationships associated

with managing forests and using the biomass generated from managing those forests to

produce electrical energy.  

The comprehensive B2E Model is actually comprised of a series of interconnected sub‐models,

which together identify and analyze economic and environmental costs and benefits of using

forest biomass to generate electrical power while meeting an array of landowner forest

management objectives at a landscape scale. The sub‐models were built to analyze the

environmental effects of different forest management strategies conducted over a specified time

period through two arenas: the wildland landscape and the biomass power production plant.

The landscape and power plant arenas are linked by the transport of biomass material from the

wildlands to power plant. The landscape provides the surface for exploring how forest

management treatments (which generate biomass material for electricity production) affect

vegetation and fire behavior. Landscape‐scale changes in vegetation and fire behavior

ultimately determine many of the benefits associated with forest‐based biomass power

(including reductions in wildfire impacts on communities, forests, wildlife habitats, and

watersheds; improvements in air quality and water quality; protection of human health and

welfare; and renewable energy production) and the costs associated with achieving these

benefits.

Three existing biomass power plant technologies and two emerging biomass conversion

technologies were analyzed by the engineering and life cycle assessment teams for this project.  

The results of these analyses have been published by Nechodom et al. (2008), and include very

early results from a next‐generation thermochemical conversion technology that produces both

electricity and ethanol.  For the purposes of the LCA portion of this study, three of the

technologies specific to electricity generation were analyzed and compared in the LCA model,

per the scope of the original Energy Commission contract.  The comparison of three biomass‐to‐

electricity technologies allowed the LCA team to reveal differences in efficiencies of electricity

production, energy use, and emissions impacts associated with different conversion

technologies.

Data for three types of biomass power plants (a current generation combustion plant, a current

generation integrated gasification/ combustion plant, and a next generation thermochemical

conversion plant) were provided by the LCA and engineering teams.  Nameplate and net

capacity, efficiencies, and stack emissions are presented below.  The emissions are

supplemented to include CH4 and N2O emissions as described by the U.S. EPA (2003).  The use

of supporting equipment used at the power plant (i.e., a bulldozer, two loaders, a bobcat, a tub

grinder, and a natural gas emergency generator) and ancillary grid electricity use were also

included.  Although the fuel use and emissions of the supporting equipment were deteriorated

over time, based on the U.S. EPA’s NONROAD2004 Model, the stack emissions and efficiency

were held constant throughout the plant life cycle.   

Ultimately, the spatial and temporal analytical methods used in the study required separation

of the project’s modeling processes into different domains in order to conduct discrete modeling

12 
and analyses.  These domains are listed in Table 1 below and described in detail in subsequent

sections of this report.  Each of the major domain teams has completed a comprehensive report

that describes in detail the modeling approaches and findings within that domain.  The domain

reports are included here as appendices.

Table 1 - B2E project modeling and analysis domains 

Domain Purpose and Modeling Processes 

Vegetation Determine the amount, area, and structure of vegetation across a landscape over time 

under different scenarios. Interact with the fire modeling domain to reflect changes in 

vegetation condition before and after modeled fire events. Use Forest Inventory 

Analysis (FIA) and other vegetation datasets to establish the initial vegetation condition 

on the landscape. Then use the Forest Vegetation Simulator (FVS) with the Fire and 

Fuels Extension (FFE) to model changes in the initial vegetation inventory in each time 

period (decade) due to forest management treatments and/or fires. 

Fire Use inputs from the vegetation domain for each model decade as the initial inventory 

and condition. Apply a series of representative ignition points and model 

representative forest fires and report fire effects (fire size and severity) under treated 

and untreated scenarios. 

Equipment 

Configuration 

Establish forest operation equipment options under each scenario. Design a 

representative configuration of equipment used for each forest management treatment 

prescription, and scale the equipment configuration to the size, location and distance of 

the forest treatment operation, including transportation to the conversion facility 

(sawmill or biomass power plant). 

Life Cycle 

Assessment 

Analyze all energy and material inputs and outputs by unit process, beginning with 

forest management treatments on the landscape and following all operations to 

terminate with interconnection with the California power grid. Assess environmental 

impacts, comparing them with those required to produce an equivalent amount of 

electricity from natural gas. 

Economics Determine economic values associated with changes in natural resource conditions 

across the landscape. Analyze costs and revenues of forest management and 

biomass conversion, and integrate these costs and revenues into market transaction 

and other measurable costs and benefits at the landscape level over time. 

Ecosystem 

Services 

Develop a framework for analyzing the non-market values of ecosystem services 

associated with forest conditions in the landscape. Specify discrete ecological 

endpoints that could be measured in economic terms to determine total system costs 

and benefits. (This domain remains incomplete due to cost limitations.) 

Wildlife Habitat Evaluate vegetation conditions and other environmental variables to determine effects 

of forest management treatments and wildfire on wildlife habitat. Assess impacts of 

treatments and fire on native biological diversity indicators. Integrate key ecosystem 

services into the habitat suitability indicators matrix for a comprehensive model of 

sustainable habitat conditions. Use California Wildlife Habitat Relationships (CWHR) 

as initial model to determine change; develop improved, empirically driven models for 

specific species to refine initial modeling assumptions. 

Cumulative 

Watershed 

Evaluate effects on soil erosion in aquatic systems due to forest management 

treatments and fire disturbances. Use the Forest Service’s Water Erosion Prediction 

13 
Domain Purpose and Modeling Processes 

Effects Project (WEPP) model, a standardized tool for measuring watershed effects, and 

integrate the Fuel Management Erosion (FuME) extension to determine specific effects 

of forest treatments on key watershed indicators. Normalize all impacts to a 

standardized Equivalent Roaded Acre (ERA), used in the Forest Service’s Pacific 

Southwest Region cumulative watershed effects methodology, to compare projected 

watershed impacts. 

2.1. B2E Modeling Domains and Processes 

The following sections describe the analytical processes used in the project domains, with the

general sequence of process steps outlined in Table 2  below.  While the first four process steps

were conducted in a sequential manner, subsequent steps were either iterative, interacting with

other process steps, such as vegetation and fire modeling, or initiated separately from other

processes once treatment plans were in place (for example, equipment configuration).  All of the

process steps were connected with other process steps, albeit to varying degrees.  For example,

the wildlife habitat analysis was inherently connected to the vegetation and fire analyses;

however, it was independent of the LCA.  Both the LCA and economic analyses relied heavily

on pre‐processing steps in the other domains to provide input to their respective models. Figure

1 graphically depicts the relationships and data flows between the project’s various modeling

and analysis domains.

Table 2. Sequence of B2E model analytical processes 

Process Domain Outputs 

Establish landscape analysis area. General: consultation with 

team and stakeholders 

Geographic area of analysis 

Select temporal scope. General: consultation with 

team and stakeholders 

Time frame of analysis 

Determine scenarios to be tested. General: consultation with 

team and stakeholders 

Hypotheses to test; independent 

variables; landscape goals and 

objectives identified 

Develop vegetation treatment 

plans. 

Vegetation, in consultation 

with team and stakeholders 

Locations, sizes and types of 

treatments 

Track changes in vegetation 

resulting from the interactions of 

treatments, fire, and growth over 

time. 

Vegetation Dynamics Acres treated by prescription and 

scenario; amount of vegetation 

removed by treatment; amount of 

vegetation removed by fire; amount 

of vegetation retained on site 

following treatment, fire, and growth

Analyze wildfire behavior. Fire Behavior Representative ignition points 

(following probability and risk 

analysis); acres burned by severity 

class in each decade 

Calculate emissions from each Fire Emissions Emission factors for each type of 

14 
Process Domain Outputs 

type of burn, i.e. wildfire, 

underburning, or pile burning. 

burning; emission quantities for 

each decade, scenario and type of 

burn (severity and type of burn) 

Determine equipment used for 

forest treatment operations. 

Equipment Configuration List of all machinery used, including 

horsepower ratings, operation 

hours, distances traveled, 

personnel required; calculation of 

sides6

 required scaled to treatment 

areas 

Characterize power plants or other 

biomass conversion technologies. 

Life Cycle Assessment Selected technologies to compare; 

fossil fuel use and other operational 

materials; energy use, production 

and waste heat; emissions 

Conduct life cycle assessment of 

in-forest operations and biomass 

energy conversion facilities. 

Life Cycle Assessment; 

Equipment Configuration 

Energy use; emissions; 

environmental impacts including 

contribution to global warming 

potential (GWP), acidification, and 

smog production 

Assess changes in wildlife habitat 

over time. 

Wildlife Habitat Impacts to key species (requires 

selection of species matrix) 

Conduct ecosystem services 

assessment. 

Ecosystem Services Impacts to key ecosystem services 

and ecological endpoints 

Analyze cumulative watershed 

effects. 

Cumulative Watershed 

Effects 

Impacts to soil; soil movement 

Analyze economic costs and 

benefits. 

Economics Damage to assets at risk from 

wildfire; treatment costs and 

revenues; power plant costs and 

revenues; fire suppression and 

rehabilitation costs (on a per acre 

basis) 

 

6 Side is a common term used by harvest contractors to denote a separate and distinct blend of harvest

equipment conducting harvest activities as a separate operation. This is discussed at length in the

Equipment Configuration domain section.

15 
Figure 1. B2E project model 

The sections below present the approaches used by each modeling domain. Section 3.0 of this

report presents the modeling results in the same logical sequence.

2.2. Selection of Landscape Archetype 

In this step, the team worked from an initial Alpha test landscape (1.1 million acres located in

the northern Sierra Nevada), expanding to a final landscape archetype, or the Beta landscape, to

include more logical regional boundaries.  Criteria used to select the Beta landscape archetype

included:

• Hazardous Fuels Conditions: A significant portion of the landscape has forest

conditions that contribute to high risk and hazard for catastrophic wildfire.   

• Ownership Mix:  Given the range of values being measured as a function of changes in

resource conditions, the landscape contains multiple ownerships and a range of values.   

• Human Settlement and Capital Assets:  The landscape contains a diversity of

infrastructure, structures, and other assets at risk of loss to wildfire.   

• Habitat:  The landscape has a diversity of threatened and endangered species habitats

that are at risk of loss from wildfire. (This allows testing the variation in higher‐value

habitats in the face of disturbance.)

16 
• Data Quality:  Sufficient data about the attributes to be assessed in each domain exist.  

• Landowner/Agency Interest:  Landowners who have significant data holdings or who

are in a position to offer critical review of the data and conclusions regarding the study

area are willing to cooperate.

• Current and Past Management:  The landscape has been actively managed and active

management strategies are in place.  (This allows testing of the differences between

treated and untreated areas.  If no management regimes were in place, the project would

have to make several broad assumptions about management potential on the

landscape.)  

• Geographic Scope and Representative Ecoregion:  The landscape is sufficiently large to

capture a diversity of landscape‐level ecological processes and to measure changes in

ecological endpoints (see ecosystem services model).

2.2.1. The B2E Beta Landscape 

The Beta landscape encompasses approximately 2.7 million acres in the northern Sierra Nevada

mountain range.  The region included parts or all of five counties, a river basin (Feather River)

significant for its hydropower resources, as well as other developed water assets, and nearly 180

different vegetation types, ranging from brush and shrub to dense mixed conifer forests.

Population in the region was typical of a predominantly rural forested region, and forestry

operations and biomass power facilities had been common parts of the economy and landscape

for several decades.  A growing recreation and amenity industry in the region made it ideal for

testing likely changes in value of property, recreation, and other aesthetic changes due to

wildfire losses as well as the impacts of fuels treatments.   

The study used the following five simplified land ownership categories, stratified in large part

by the degree to which lands may be managed to modify wildfire behavior and differences in

administrative or policy conditions:

• Public Multiple Use (PMU):  lands administered and managed by public agencies,

specifically the Forest Service (FS) and USDI Bureau of Land Management (BLM). PMU

lands do not include areas that are reserved or withdrawn from management (such as

wilderness areas, wild and scenic rivers, special management areas, and so forth): such

lands are included within the Public Conservation and Recreation category described

below.

• Public Conservation and Recreation (PCR):  lands administered by public agencies that

are typically not managed at any significant scale, and therefore assumed to have little

or no impacts on wildfire behavior through management.

• Industrial Private Forests (IPF):  private lands managed primarily for commercial

timber production.

• Non‐Industrial Private Forests (NIPF):  private lands managed occasionally for timber

values.  The project used the California Board of Forestry’s definition for NIPF owners: 

17 
those who typically own less than 5,000 acres of forest land and do not own a sawmill or 

other forest products processing facility. 

• Urban and Other (U):  land that is divided into smaller parcels, usually developed with

assets, structures or other infrastructure (such as dams, penstocks, landfills, and so

forth).  These lands are assumed to have little or no management potential in changing

wildfire behavior.   

Table 3 indicates the distribution of ownerships in the Beta landscape and shows that public

lands comprise a significant portion of the landscape.   

Table 3. Land ownership categories in the B2E Beta landscape 

Land Category Acres % of Total 

Public Multiple Use 1,374,783 50% 

Public Conservation & Recreation 407,776 15% 

Industrial Private Forests 457,427 17% 

Non Industrial Private Forests 383,008 14% 

Urban & Other 112,816 4% 

Total 2,735,809 100% 

The 2.7‐million acre Beta landscape’s location at the northern terminus of the Sierra Nevada

Range and its straddling of the Central Valley (to the west) and the Great Basin (to the east)

results in representation of a wide range of vegetation types, from hardwood to Jeffrey pine

forest types, mixed conifer to pinyon juniper forest types. Table 4 displays the major vegetation

types and their respective acreages within the Beta landscape.    

18 
Table 4. Major vegetation types in the B2E Beta landscape 

Vegetation Type Acres % of Total

Alpine 5 nominal 

Douglas-fir 4,777 0.2% 

Eastside pine 398,401 14.6% 

Eastside mixed conifer 579,683 21.2% 

Hardwoods (productive) 34,758 1.3% 

Hardwoods (non-productive) 67,640 2.5% 

Jeffrey pine 10,872 0.4% 

Knobcone pine 395 nominal 

Lodgepole pine 27,550 1.0% 

Westside mixed conifer 519,507 19.0% 

Pinion-Juniper 12,209 0.4% 

Ponderosa pine 219,552 8.0% 

Red fir 101,499 3.7% 

Non-forest types 301,392 11.0% 

White fir 167,792 6.1% 

Shrub types (non-productive) 289,163 10.6% 

Total 2,735,1957 100% 

2.3. Temporal Scope of B2E Modeling Domains 

Most landscape models are static.  That is, there is no time dynamic in the modeling, which

limits the landscape model to statements about a single event or condition.  The team explored

the need for building a time dimension into the study, and concluded that sequential treatments

and disturbances should be accommodated.  The reality of forest management at a landscape

scale is that neither treatments nor fires take place all at once.  Compressing treatments or fire

events spatially and temporally onto a landscape renders a distorted view of impacts that are in

fact distributed over space and time.  This distribution is meaningful in dynamic systems that

recover and change over time.   

The team analyzed the computational challenges of modeling vegetation changes over time,

including the addition of fire and treatment events.  In each decade (which is the shortest

meaningful time period for modeling forest vegetation growth), the model would need to

measure existing forest conditions (forest inventory); apply treatments across the landscape,  

providing post‐treatment fuels conditions for fire modeling; then apply wildfires across the

 

7 The slight difference in acreage (614 ac.) between the ownership and vegetation classification,

in Tables 3 and 4, is due to the methods used to measure vegetation types.  Ownership is drawn

from actual polygons while vegetation types are measured in 100 square meter grid cells, and a

grid cell is counted under a classification only when a majority of the cell is occupied by the

vegetation type.

19 
landscape; apply post‐fire treatments, under specified conditions; allow for the growth of

treated and untreated forest stands as well as burned stands; and finally report on the

vegetation conditions at the end of the decade that resulted from treatments and fires alone as

well as the interactions of treatments and fires in places where the two events overlapped.  For

the Beta landscape, a typical scenario resulted in treating approximately 300,000 acres per

decade and burning approximately 65,000 acres per decade, across hundreds of discrete

polygons, presenting major computational and data management challenges.  Forest inventory

datasets (called tree lists) often reached tens of gigabytes while calculating time‐dynamic

changes.   

Given these computational challenges, the team determined that a 40‐year time frame – or four

decadal modeling periods – was an appropriate time scale for the B2E Model to show

statistically meaningful variation. In addition, this timeframe fits well with the economics that

drive timber harvest (scheduled treatments are typically conducted at 20‐year intervals) as well

as the life cycle of the technology being evaluated (biomass conversion plants typically have a

30 to 40 year life cycle before replacement).  

2.4. B2E Scenario Development 

Landscape scenarios were developed to test key parameters, assumptions, and ecosystem

effects.  The key independent variables that could be manipulated to change outcomes and

effects were primarily related to the forest management treatments: their size, spatial

distribution, and intensity or type of treatment.  Secondary independent variables included

power plant technology and location.  Dependent variables included the kinds of equipment

and their configuration chosen to accomplish the treatments; economic values associated with

changes in treatment and fire; and other environmental indicators of habitat, watershed, and

carbon storage conditions.   

The team developed a Reference Case (which modeled no vegetation treatments) and a Test

Scenario (which modeled vegetation management regimes on public multiple use (PMU),

industrial private (IPF) and non‐industrial private (NIPF) forest lands).  Table 5 below shows

the basic structure and logic of the scenarios used to test the B2E Model.  

Table 5. B2E Beta model scenarios 

Scenario Assumptions related to 

Wildland Fires and Forest 

Management Treatments 

Average acres per decade 

treated 

Reference Case Baseline wildland fires burn; No 

treatments. 

IPF/NIPF: 0 

PMU: 0 

Total: 0 

Test Scenario (IPF, NIPF and 

PMU) 

Baseline wildland fires burn; 

Treatments on IPF, NIPF, and 

PMU lands. 

IPF and NIPF lands: 313,416 

PMU lands: 179,447 

Total: 492,863 

20 
The total number of acres treated does not reflect the variation in types, sizes or locations of

treatments.  These are described in greater detail in the section below on vegetation treatments.  

It is also important to emphasize that the Reference Case is technically not a scenario, but a

hypothetical baseline case that allows comparisons with actual management scenarios.   

Scenario development is importantly a collaborative process.  The team used wide experience

and previous analyses to develop the Test Scenario.  The main purpose of the Test Scenario was

to test the functioning of the B2E Model, particularly the linkages between the series of the

Model’s interconnected domains.  However, it was clear to the team that scenarios are deeply

embedded in social and policy preferences.  Additional scenarios would need to engage

stakeholders or specific clients in extensive discussions about landscape level goals and

objectives before designs for size, type, and location of treatments could be developed.

2.5. Vegetation Treatments 

As depicted in Figure 1, all of the B2E Project’s models require inputs related to assumptions

about vegetation treatments. For the Test Scenario, the team used experience and previous

analyses to spatially arrange different types of treatments across the Beta landscape over the 40‐

year time period. Under future model applications, each scenario must have  a clearly‐defined

set of assumptions relative to treatment area designs for each land ownership category and site

condition (for example, slope steepness and proximity to streams). The final treatment plan for

each scenario should consist of a map displaying the location of each treatment area and a

crosswalk assigning a treatment prescription (a series of discrete treatment activities applied

over a specified timeframe to a specific piece of ground) to each treatment area. Hence, the key

steps in formulating forest treatment plans include: (a) spatially locating treatment areas across

the landscape and (b) applying a series of treatment activities over time (i.e. a treatment

prescription) to each treatment area.

2.5.1. Locating Treatment Areas and Assigning Forest Management Regimes 

Each treatment area’s size, location, and forest management regime in the Beta landscape was

determined based on land ownership category. On IPF lands, treatments were designed to meet

California Forest Practices Act requirements, and the modeled treatments were developed

through a collaborative effort involving scientists, forestry professionals, and the team (see the

B2E Project Forest Operations and Equipment Configuration Report, Appendix 3).  IPF

management regimes were based on a roughly evenly distributed representation of the kinds of

commercial operations typically found in California.  Of the approximately four million acres of

commercial forest lands in California, and among the half‐dozen or so major commercial

operators in fire‐prone forested areas, treatment prescriptions range from clear‐cutting to

selective harvest.  In each commercial forestry operation, throughout the cycle of growing trees

to maturity and harvest, thinning operations are typically used to enhance growth and reduce

impacts from fire, insects, and disease.  These are referred to as pre‐commercial thinning and

commercial thinning, the former removing smaller stems to reduce competition and enhance

the growth, and the latter removing merchantable sawlogs to further enhance the growth of the

remaining trees.   

21 
Similarly, non‐industrial private NIPF lands are modeled under different management

objectives than many of the IPF lands.  Therefore a set of prescriptions must be developed that

reflect the average common practices of smaller, non‐industrial land management regimes.  For

the Test Scenario, the team assumed that these lands would be treated using selective harvests

intended to continually maintain trees on site, thereby conserving future options, either for

timber production or other purposes.

To locate treatments on national forest lands (which comprised most of the PMU lands in the

Beta landscape), the team needed an approach that could be used to validate the modeling

assumptions and verify the modeling results. Toward this end, the team relied on existing

management direction from the Sierra Nevada Forest Plan Amendment Record of Decision (USDA

Forest Service 2004), which the national forests in the Beta landscape (Plumas, Lassen, and

Tahoe National Forests) are currently following to design fuels treatments.  Hence, treatment

area locations on national forest lands in the Beta landscape were based on a combination of

defensible fuels profile zones (DFPZs), or shaded fuelbreaks, and strategically placed area

treatments (SPLATs). While these two specific treatment designs were used for the Beta test, the

B2E Model is designed to accommodate and test an endless array of prescriptions for

management regimes and different treatment designs.  

DFPZs were generally located along ridge tops and roads: these are areas where firefighters

would make a stand to contain a wildland fire. A DFPZ’s width is determined by potential fire

behavior based on available fuels, weather, and topography.  DFPZs are not designed to stop an

oncoming fire, but rather to provide a safe location to facilitate fire suppression efforts. The

DFPZs on the Beta landscape were located as mapped for the Final Environmental Impact

Statement for the Herger‐Feinstein Quincy Library Group Forest Recovery Act Pilot Project (USDA

Forest Service 1999).

The SPLATs were located using a conceptual herringbone pattern of area treatments distributed

across the Beta landscape, based on the premise that disconnected fuel treatment areas

overlapping across the general direction of fire spread are theoretically effective in changing fire

spread (Finney 2001).  For purposes of modeling, each SPLAT was a 150‐acre rectangle, oriented

according to the prevailing wind direction in order to intercept a spreading fire. The Beta

landscape’s highly‐stylized herringbone pattern of treatment areas was designed to statistically

mimic what is often referred to as “the Finney Effect.”  The pattern has been used as a starting

point for landscape‐level fuels reduction planning on national forest lands in California. This

pattern of treatments starts with the assumption that forest thinning treatments on

approximately 30% of the total land area in a given landscape can have a “speed bump” effect

by interrupting and slowing the spread of an oncoming wildland fire, ultimately resulting in

smaller wildland fires with less severe effects.  As the planning process proceeds, more detailed

analyses of actual fuel characteristics and likely wildfire behavior allows planners and

stakeholders to work collaboratively to adjust the size, location, shape and treatment

prescriptions among SPLATS.  The full planning process, which the Forest Service refers to as

its Stewardship and Fireshed Assessment (SFA) process, involves several hundred person‐hours of

preparatory work, including a series of planning meetings for agency and public stakeholders,

before the final pattern and timing of treatments is ready to be applied.  The final step for the

22 
Forest Service involves following National Environmental Policy Act (NEPA) environmental

analysis before the SPLAT strategy can be implemented.  It is a labor‐ and data‐intensive

process, resulting ultimately in a highly‐refined, landscape‐level plan for modifying the

behavior, and hence the effects, of large wildland fires, balanced with other values and

concerns.   

The B2E project did not engage the complete SFA process as used by the Forest Service, largely

due to time and resource constraints, but also because the purpose of this study was a more

comprehensive landscape analysis, well beyond the boundaries of a national forest.  The

research team, working with the SFA cadre, did however use most of the modeling approaches

used in the SFA process (described in other parts of this “Approach” section), in which

vegetation and fire modeling are used to assess likely fire behaviors and outcomes on a

landscape.   

Approximately 30% of the PMU lands in the Beta landscape were overlaid by SPLATs; the

remaining 70% of PMU lands were not assigned treatments over the 40‐year time period. This

should be kept in mind as further scenario development is considered in later phases of the

study or uses of the model.  For example, a slight increase in extent or intensivity of

management on PMU lands could result in substantial increases in biomass produced,

emissions avoided, electricity produced, etc.   

2.5.2 Sequencing Treatment Activities over Time

As described above, types of treatment activities on lands in the Beta landscape ranged from

thinning, selective harvest, and clearcut harvest on private lands to thinning on national forest

lands. Regardless of ownership or type of treatment activity, all treatments were assumed to be

followed by prescribed burning (broadcast burning, understory burning, or slash pile burning)

to remove the slash (woody residues that are generated in the forest from harvesting activities).  

It is important to emphasize the distinction between treatment prescriptions and activities in the

modeling.  Treatment activities are discrete management actions or events, such as thinning or

understory burning.  A treatment prescription is a series of management activities applied over

the 40‐year timeframe to a specific piece of ground. Hence, each treatment area was assigned a

prescription sequence consisting of a series of treatment activities for each 10‐year time period.

In certain time periods, no treatments were assigned and the growth of the vegetation was

simply tracked.  For example, a regeneration prescription on IPF land would assign an even‐

aged harvest (clearcut) on a 20‐acre treatment area in the first decade (2006), followed by pre‐

commercial thinning in the second period (2016), and then a commercial thinning 20 years later

(2036).  The entire 40‐year series of management activities is captured in specific coding in the

B2E model databases (see the B2E Project Vegetation Dynamics Domain Report, Appendix 1).  

2.6. Vegetation Dynamics Modeling 

The vegetation domain tracked changes in vegetation and fuels resulting from modeled growth,

vegetation management treatments, and fire across the Beta landscape over the 40‐year period.

The vegetation domain was closely integrated with the Project’s fire domain: the condition of

the vegetation and surface fuels (combined with topological and weather variables) was used to

23 
model fire behavior (as described in the following section), with the resulting fire effects

subsequently fed back and used to modify the vegetation.  

The analysis assumed a dynamic landscape where vegetation was constantly undergoing

change through the processes of growth, treatment, and wildland fire. The vegetation domain

spatially tracked these vegetation changes at multiple scales (from a per‐acre scale to an entire

landscape scale) over time. The outputs from this domain supplied the raw data used by the

B2E Project’s other domains, which explored how these landscape‐scale vegetation changes

ultimately affected wildland fire behavior, electricity production, habitat conditions, emissions,

carbon cycling, hydrologic conditions, economics, and ecosystem services.

Geographic Information System (GIS) spatial layers for vegetation, ownership, elevation, and

slope in the beta landscape provided the foundation for the vegetation domain. This GIS

coverage overlaid the entire landscape with 100‐meter grid cells, with each cell assigned to a

specific vegetation, ownership, elevation, and slope class.

2.6.1. Initial Vegetation Inventory 

The starting point for vegetation modeling was the vegetation inventory, which described the

existing condition of the vegetation across the Beta landscape in 2006.  Over time, this inventory

was modified by modeled growth, treatments, and wildland fire. The initial (2006) vegetation

inventory consisted of two components: (1) a vegetation map and (2) Forest Inventory Analysis

(FIA) plot data (which describe numbers of trees by species and size class) linked to the

vegetation polygons delineated on the vegetation map.  

The Forest Service periodically collects vegetation inventory data (known as Forest Inventory

Analysis (FIA) data) for national forest lands as well as non‐national forest lands. FIA data are

gathered over a series of plots located across lands within the Beta landscape. The FIA plot data

consist of (a) site reference information (plot location, inventory date, slope, aspect, elevation)

and (b) the characteristics of each tree sampled (species, size, canopy position, and so forth,

collectively referred to as the tree list for each sample plot).  The Forest Service Pacific

Southwest Region’s Remote Sensing Laboratory provided the FIA data for the Beta landscape.

The vegetation domain team linked the FIA plot data to the vegetation map to provide for

statistically valid estimates of vegetation change. Vegetation strata labels served as the bridge

between the mapped vegetation polygons and the FIA plot data. A strata label can be thought

of as each mapped vegetation polygon’s address: the strata label is based on the polygon’s

vegetation type, tree size, and canopy cover in 2006. Each FIA plot was assigned to a specific

vegetation stratum, thereby making it possible to aggregate the tree lists from all the plots

assigned to that vegetation stratum. Hence, the vegetation inventory for a particular stratum

was represented by an aggregated tree list, which was comprised of the individual tree lists

from each of its plots added together. Embedded within each mapped vegetation polygon was a

set of 100 square meter grid cells. Every grid cell with that stratum label address started

through the vegetation modeling with the same aggregated tree list. Over time, treatment,

wildland fire, and growth changed each cell’s tree list, depending on the prescription sequence

assigned to it.

24 
2.6.2. Modeling the Effects of Forest Treatments on Vegetation 

As previously described, management prescriptions were comprised of a series of discrete

treatment activities applied over the 40‐year beta‐test timeframe to a specific grid cell. Hence,

each grid cell had a prescription sequence consisting of a series of treatment activities assigned

by time period. The strata label served as each grid cell’s address while the prescription

sequence described the treatment activities that occurred at that address over time (for example,

thinning followed‐up by underburning in the first decade, no treatment in the second decade, a

second thinning in the third decade, and no treatment in the fourth decade).  

Sawlog and Biomass Material Removed. For each treatment activity, the vegetation dynamics

domain team developed specifications for removing trees from the tree lists. (As previously

described, the tree lists were linked to the strata labels (addresses) assigned to each grid cell.)  

These specifications determined the quantities of sawlogs and biomass material removed from

the treated stands.

Residual Stand Conditions following Treatments. The specifications for removing trees from

the tree lists had a direct impact on residual stand conditions.  For example, specifications for

pre‐commercial thinning were to remove trees from the tree list such that approximately 225

trees per acre were retained following treatment. The specifications also included favoring

retention of commercial conifer species, in other words, targeting non‐commercial conifers and

other tree species for removal from the tree list. The specifications developed for the Beta test

are but a small sample of the possible specifications that could be developed in further scenario

testing: the B2E Model was designed to accommodate any possible specifications for removing

trees from the tree lists (as well as adding seedlings).  

2.6.3. Modeling the Effects of Wildland Fires on Vegetation 

The fire domain team modeled wildfire behavior and resulting severity as described in the next

section. The vegetation domain team then used the fire severity data for each decade to

determine the numbers of trees killed and the amount of biomass consumed by fire. The

vegetation team used the First Order Fire Effects Model (FOFEM) (Reinhardt and Keane 2003)

to estimate tree mortality (in other words, which trees to remove from the tree lists) based on

flame lengths associated with different fire severity classes (described in the fire behavior

section below). FOFEM predicts fire‐caused tree mortality using bark thickness (based on tree

species and diameter) and crown volume scorched (based on scorch height, tree height, and

canopy base height).  

In addition to estimating trees killed and trees consumed by wildfire, the vegetation team also

used the wildfire severity data (combined with ownership classification and management

regime) to adjust the prescription sequence for treatment areas that burned in wildfire during

any of the four time periods.  For example, a cell with an initial prescription sequence of

thinnings applied in the first and third decades that intersected with a lethal fire in the third

decade would have the following prescription sequence assigned: (1) thinning followed‐up by

underburning in the first decade, (2) no treatment in the second decade, (3) a lethal fire followed

by salvage and tree planting in the third decade, and (4) pre‐commercial thinning in the fourth

decade).  

25 
2.6.4. Modeling Vegetation Growth 

Vegetation growth was modeled on all grid cells in each decade. To account for growth, the

vegetation domain team used the Forest Vegetation Simulator (FVS) Model (Stage 1973), a

computer program used to project the development of forest stands. FVS is an individual‐tree,

distance‐independent growth and yield model. It has its structural roots in the Stand Prognosis

Model developed by Albert Stage from the Intermountain Research Station. Staff at the Forest

Service’s Forest Management Service Center in Fort Collins have calibrated many variants of

the model to specific geographic areas throughout the United States. For the Beta landscape,

growth equations were derived directly from the Inland California Southern Cascades

(ICASCA) variant of FVS.  

In any given decade, it was possible for a single grid cell to be assigned a treatment, wildfire

disturbance (with effects on the vegetation), and subsequent salvage harvest.  In these instances,

the following sequence of activities (and effects on the vegetation inventory) was tracked in

order: treatment, wildfire, and salvage, all of which were assumed to occur in the first year of

the decade. Vegetation growth for the grid cell was then modeled for the remainder of the

decade.

2.7. Wildfire Behavior and Severity 

This section briefly describes how the team modeled the interaction between wildfire and

vegetation under treated and untreated conditions.  While Table 2 above separates these steps

to clarify the overall project’s sequencing of processes, in fact the vegetation dynamics and

wildfire behavior domains interacted iteratively to produce the final results for vegetation

change for the 40‐year span of the study.   

Wildfire modeling is a complex process, and models are difficult to calibrate.  When scientists

model wildfire, they are usually modeling what is called fire line intensity.  That is, rather than

modeling the behavior of an entire fire, with all its complex dynamics and internal weather

patterns, most fire models attempt to mimic the behavior of flames at the perimeter of the fire as

it moves through vegetation.  Two major forms of data are used to model fire line intensity:  

vegetation and weather.  The vegetation data tells the model about the structure and condition

of fuels that are being burned.  The weather data fundamentally tells the model about the

amount of oxygen available by applying models of wind behavior and moisture in the air.  

Between these two factors, scientists are able to predict how high the flames will get (flame

length); whether the flames will reach into the crowns of the trees (active or passive crowning),

thereby increasing the speed at which the fire will spread; and the number of trees of a

particular size that will be killed by the fire (fire severity).   

Locating the places where modeled wildland fires would start (ignition points) presented the

team with a fundamental challenge.  Since the team determined that the B2E modeling effort

would be spatially explicit where possible, the locations of wildfire ignition points had to be

specified.  The fire domain team ran a randomized ignition experiment across the entire

landscape, using risk ratings for ignitions and fuel hazard ratings for vegetation fuel conditions

(Figure 2).   

26 
Figure 2. Randomized ignition locations to locate most logical representative ignition points on 

the B2E Beta landscape. 

The randomized ignition map was compared with maps of historical ignition locations from 60

years of fire history.  This mapping exercise allowed the team to select discrete ignition points at

locations across the Beta landscape, recognizing that demographics, human activities, and

climatic conditions would vary with time (Figure 3).  

27 
Figure 3. B2E Beta landscape fire history and ignition placement by modeled decade 

After the representative ignition points were selected, the wildfire modeling team used the

vegetation GIS layers provided by the vegetation dynamics domain team for each decade

within the Reference Case and Test Scenario, as inputs to the wildfire model.  The wildfire

modeling team then tested for change in wildfire severity and the number of acres burned

through each decade for the Reference Case and Test Scenario.  Modeling outputs included the

number of acres burned in three classes of fire severity (lethal, mixed‐lethal, and non‐lethal fire

effects).  

Fire behavior was summarized into three classes of severity to distinguish and report changes

in wildfire effects across the Beta landscape (Figure 4).  Burned areas were classified based on

spatially explicit FlamMap Model (Finney, Britten et al. 2006) results of fireline intensity and the

crowning behavior of the fires. The severity of wildfire burned on each 100 meter2 grid cell was

assigned to one of three classes (non‐lethal, mixed lethal, or lethal effects) depending on its

flame length and fire type (ground fire, passive crowning fire, or active crowning fire).  

28 
Fire severity was used to determine the numbers of trees killed and the amount of biomass

consumed by fire, and these effects were tracked in the vegetation assessment domain.

Simulations were performed on a 10‐year temporal sequence for 40 years with a series of fires

taking place immediately at the beginning of each decade in each management scenario. Note

that if a particular grid cell was treated and burned in the same decade, the treatment effects on

vegetation were assumed prior to wildfire being modeled on that grid cell. Both treatment and

fire (and, in specific instances, salvage) were assumed to occur in the first year of the decade

with growth of the modified vegetation modeled for the remaining years of the decade.  

Figure 4. Classes of fire severity used in B2E fire modeling: (N) = non-lethal, (X) = mixed lethal, (L) 

= lethal 

The interaction of vegetation and fire is a critical component of any landscape level analysis.  In

the B2E Project, these two domains constituted the core from which all further modeling

domains operated.  Once the spatial and temporal scope had been selected and the interaction

of vegetation, treatments and wildfire had been modeled, the team was ready to investigate

secondary or “downstream” activities.  The following sections contribute to analysis of the

impacts and effects that follow from the interactions of vegetation and wildfire on a landscape

level.  

2.8. Fire Emissions Model 

After modeling interactions of vegetation and fire on the landscape, the fire emissions domain

team was able to characterize emissions from three types of fire:  wildfire, underburning, and

pile burning.  Wildfire burning has been described in detail above.  The other two classes of fire

are built into the treatment prescriptions analyzed by the vegetation domain team.  Under most

prescriptions, biomass that is too costly to collect and process is generally piled and burned at

the treatment site.  Useable biomass was assumed to be removed to the power plant.  The

vegetation domain report describes each prescription in detail while the equipment

configuration domain report describes equipment used to pile slash and burn the piles.   

Underburning, the final of the three classes of fire modeled, is used as a treatment follow‐up

procedure, and was included in the “package” for each treatment prescription, where

appropriate.  Typically follow‐up underburning is conducted within the first one to three years

after a treatment is implemented.  Since the B2E model has compressed all treatments and fire

events to the beginning of each decade for computational efficiency, underburning was

assumed to be part of the treatment.   

29 
The starting point for calculating fire emissions was determining the amount of woody material

the fire (either wildfire or prescribed fire) consumed.  Once the amount of material consumed

by fire was determined, the corresponding emissions were calculated using vetted relationships

between types of forest fuels combustion and corresponding emissions, typically referred to as

emission factors.

2.8.1. Fuel Consumption 

Wildfire. The vegetation domain team supplied data on amounts of vegetation consumed by

wildfire in each decade, based on the CONSUME Model (Ottmar et al. 2001). The CONSUME

Model calculates the amount of woody material (in the form of duff, litter, twigs, and foliage)

that would be consumed during a low intensity fire (approximating the level of consumption

expected in a prescribed underburn).  Hence, several adjustments were needed to account for

(1) the range of possible wildfire severities, from non‐lethal to lethal fires and (2) the full range

of vegetation types (including tree stems as well as brush and grass) that could be consumed by

modeled wildfires in the Beta landscape.

CONSUME Model vegetation consumption data supplied by the vegetation dynamics domain

team was used for all areas affected by modeled non‐lethal wildfire.  To account for

consumption during mixed lethal and lethal fire events, consumption multiplier factors (Table

6) were applied to the vegetation domain consumption data, based on consumption efficiencies

measured from actual crown fires in similar forest types (Environment Canada 2007, Taylor and

Sherman 1996).

Table 6. CONSUME Multiplier Factors (MFs) for non-lethal, mixed lethal, and lethal fires. 

Fire Severity

Non‐Lethal Mixed Lethal Lethal

Multiplier Factor (MF)   1 1.8285 2.657

The vegetation domain data do not include inventory or consumption values for the grass

vegetation type (strata code UGR) or the brush/shrub vegetation type (strata code ZBR) because

there are no growth models for these vegetation types in the Forest Vegetation Simulator (FVS).  

In addition, the vegetation domain’s consumption data do not include consumption values for

the boles of the trees. The fire emissions domain team applied the following assumptions to first

calculate the inventory of grass and shrub types (in bone dry tons, BDTs) (Table 7) and then to

calculate wildfire consumption of tree stems and fire events in the grass and brush vegetation

types (Table 8).

30 
Table 7. Inventory assumptions for the brush and grass vegetation types 

Strata Label Vegetation Type Amount of Vegetation (BDT/Acre)

UGR Grass 4

ZBR Brush/Shrub 12

Table 8. Combustion efficiencies (combustion factors, CF) for various vegetative components by 

fire severity class 

Fire Severity

Non‐lethal Mixed Lethal Lethal

Tree Stems 0.02 0.02 0.02

Brush 0.3 0.6 1

Grass 1 1 1

Piling and Burning, Underburning.  As previously described, smaller woody material that is

left on site following tree removal operations is treated either through piling and burning the

material (typically on flat terrain or gentle slopes where mechanized equipment can be used to

pile the material) or underburning.  The vegetation domain supplied information on the

quantities of this material that would be consumed in prescribed fire treatments as follows:

small woody material between 0 and 3 inches diameter (reported in cubic feet, converted into

BDTs); tops and limbs of all trees removed (BDTs); and brush (BDTs) remaining in treated

stands.   

2.8.2. Calculating Fire Emissions 

Once the quantities of material consumed by fire were determined, the emissions associated

with the amount of material burned under specified burning conditions were calculated using

emission factors. Emission factors (EF) are defined as “the mass of pollutant produced per mass

of fuel consumed” (Ottmar 2001).  EFs for each of the three fire severities (non‐lethal, mixed

lethal, and lethal) were provided by the Western Regional Air Partnership (WRAP) Program’s

Fire Emissions Joint Forum (FEJF) and the Inter‐RPO National Wildfire Emission Inventory

Project (Air Sciences Inc. 2005; WRAP‐FEJF 2006; Randall 2006; Adelman 2004; EPA 2002). The

EFs are the result of collaborative research between WRAP and the FEJF, and rely on a point

source approach to represent wildland fires, their combustion and emissions within typical

western forest types.   

Determining the appropriate EF for a particular type of burn first required identification of each

burned grid cell’s applicable NFDRS (National Fire Danger Rating System) Fuel Model.  NFDRS

Fuel Models are typically linked to vegetation strata labels. Since the strata labels in the B2E

vegetation modeling process served as each grid cell’s “permanent address,” they did not

31 
change from one time period to another. Hence, the emissions domain team used an alternative

approach to accommodate changes in the vegetation over time (i.e. succession and growth

between time periods).  The team constructed a crosswalk based upon expert opinion of both

systems (Table 9). This crosswalk links emission factors developed for the NFDRS fuel models

(applicable to California vegetation types) to California Wildlife Habitat Relationships (CWHR)

types across the Beta landscape. Hence, each CWHR type was linked to a specific NFDRS Fuel

Model8, as shown in Table 9 below.  

The first step in developing the crosswalk was to disregard NFDRS fuel models not applicable

to California vegetation communities. For example, Fuel Model N was eliminated from

consideration because it was constructed specifically for the sawgrass prairies of south Florida.

Then the expert reviewed the text accounts and available habitat stages for each CWHR habitat

type (California Department of Fish and Game 2002). For each possible CWHR habitat and

habitat stage, the NFDRS fuel model that most closely portrayed the typical fuels conditions

present in the given habitat and stage was identified.  This determination was made through

information presented in the CWHR habitat text accounts, and expert knowledge of the various

habitats.  In some cases, assigning a fuel model code to a given CWHR habitat and stage was

difficult due to the relatively few number of models, and the inability of any model to

accurately describe the vegetative conditions present in the given habitat and stage.  In these

cases, expert knowledge was used to select the fuel model that most closely approximated the

fuel conditions (rather than vegetative conditions) that would be present within the habitat type

and stage.  

 

8 http://www.fs.fed.us/fire/planning/nist/nfdr.htm

32 
Table 9. Example Crosswalk between NFRDS Codes and CWHR Types (partial table) 

NFDRS Fuel

Model4 Vegetation Type

CWHR

Vegetation

Type

CWHR

Tree Size

and

Canopy

Cover

CWHR Tree Size and Canopy

Cover

L Ponderosa Pine P 1S Seedling, sparse cover

F Ponderosa pine P 2D Sapling, dense cover

L Ponderosa pine P 2M Sapling, moderate cover

L Ponderosa pine P 2P Sapling, open cover

L Ponderosa pine P 2S Sapling, sparse cover

U Ponderosa pine P 3D Pole‐sized tree, dense cover

C Ponderosa pine P 3M Pole‐sized tree, moderate cover

F Ponderosa pine P 3P Pole‐sized tree, open cover

U Ponderosa pine P 4D Small tree, dense cover

C Ponderosa pine P 4M Small tree, moderate cover

C Ponderosa pine P 4P Small tree, open cover

C Ponderosa pine P 4S Small tree, sparse cover

U Ponderosa pine P 5D Med./large tree, dense cover

C Ponderosa pine P 5M Med./large tree, moderate cover

C Ponderosa pine P 5P Med./large tree, open cover

C Ponderosa pine P 5S Med./large tree, sparse cover

U Ponderosa pine P 6 Multilayered canopy, dense cover

Using the NFDRS Code (crosswalked from CWHR type) and the moisture condition for lethal,

mixed lethal, or non‐lethal fire, the WRAP Emissions Factor Table (Table 10) provided the

applicable emissions factors. For all wildfires, a moisture condition of dry was assumed. For

underburning a moisture condition of moist was assumed and for pile burning, wet conditions

were assumed.  

33 
Table 10. Example WRAP Emission Factors (abbreviated and partial table) 

NFDRS

Code

Moisture

Condition (MC)

NFDRS

Code,

MC

Fire

Severity

PM 2.5 EF (lbs.

emitted per ton of

material consumed)

PM 10 EF (lbs.

emitted per ton of

material consumed)

U 2 ‐ Dry U2 lethal 24.5594 28.98

U 4 ‐ Moist U4

non‐

lethal 28.6554 33.8133

U 5 ‐ Wet U5

non‐

lethal 27.9954 33.0345

For each fire, the tons of emissions for each pollutant (TP) were calculated using the following

formula:

P = ( *PBDTT effire ) ÷ 2000

Where:

TP = Tons of Pollutant

BDT fire = Bone‐Dry Tons of biomass consumed by fire

Pef= emission factor (ef) expressed as pounds of pollutant produced per ton consumed by fire

2.9. Forest Operations and Equipment Configuration 

From a modeling perspective, forest management treatments are deterministic events.  The

activities associated with management of vegetation constitute a realm of actions that,

collectively, have energy use, emissions, and costs and revenues that can be quantified.  In order

to quantify these elements, assumptions must be made about the nature of management

activities, including the prescriptions applied and the machinery used, that result in movement

of products and co‐products from the forest to the processing facility (power plant or sawmill).

The equipment configuration team identified the combinations of equipment that would be

representative of an “average” forest treatment operation.  Equipment configuration modeling

was a collaborative effort, involving members of the team, representatives from the logging

industry, forestry academics, and biomass power plant fuel procurement officers.  Several

iterations of the equipment configuration model were developed, beginning with the Alpha

model phase and based on actual field data and experience from the Westwood, California, area

and the Mt. Lassen Power biomass plant.  The types of equipment used, and how it would be

deployed on a given site to implement treatment prescriptions, provided the basis for the next

step in the B2E modeling sequence, the life cycle assessment.   

34 
The equipment configuration domain team’s approach used the expert opinion of harvesting

contractors and supervisors currently conducting forest management activities in Northern

California.  Experts were interviewed regarding specific types and quantities of equipment for

each harvest treatment prescription and range of slope conditions.  Different equipment

configurations were developed that were representative of the kinds of side9 that would be

deployed for each modeling condition.  Table 11 provides a summary of slope class, treatment

prescription, and equipment configuration code.

 

9.  Side is a common term used by harvest contractors to denote a separate and distinct blend of harvest

equipment conducting harvest activities as a separate operation. For example, a large thinning operation

(several hundred acres in different locations) on national forest land might use two or three sides

deployed separately in order to complete the work within the 120 day operating season.  Each side is a

complete set of all equipment needed to complete harvest, collection, processing and transportation

operations.

35 
Table 11 ‐ Equipment configuration code by slope class and treatment prescription

TREATMENT PRESCRIPTION 

PER OWNERSHIP TYPE SLOPE CLASS 

EQUIPMENT CONFIGURATION 

CODE 

Clearcut (CC) - Even-aged management. Only occurs on industrial forest lands. 

IPF Less than 35% CC <35 

IPF 35 to 50% CC 35-50 

Pre-Commercial Thinning (PCT) - No sawlogs removed. Only biomass fuel removed. Typically in 

plantations. 

IPF Less than 35% PCT <35 

IPF 35 to 50% PCT 35-50 

Commercial Thinning (CT) - Sawlogs and biomass fuel removed. Typically in plantations. 

IPF Less than 35% CT <35 

IPF 35 to 50% CT 35-50 

Salvage (SAL) - Assumes that no biomass fuel (3.0 to 9.9 inches diameter at breast height (dbh) or 

limbs/tops) is recovered. 

IPF Less than 35% SAL <35 

PMU 35 to 50% SAL <35 Public 

IPF Less than 35% SAL 35-50 

PMU 35 to 50% SAL 35-50 Public 

Select Harvest (SH) - Uneven-aged management harvest removing high-risk trees in mature 

stands. 

IPF/PMU Less than 35% SH <35 

IPF/PMU 35 to 50% SH 35-50 

IPF/PMU Greater than 50% SH 50+ 

Restrictive Thinning (RT) - A light thin, but retain 40% canopy. Public lands only. 

PMU Less than 35% RT <35 Public 

PMU 35 to 50% RT 35-50 Public 

The blend of equipment is labeled with the equipment configuration code as shown in Table 11,

based on slope class (topography) and treatment prescription.  The equipment configuration

36 
team’s analysis also synthesized estimates provided by interviewed experts of average

production rates for each side.  This approach contrasts with those that use empirical or

mechanistic models to calculate production rates as a function of site and stand conditions (such

as average tree size and skidding distance).  The Project’s equipment configuration model can

easily generate estimates for an almost unlimited number of scenarios.  The B2E approach is

considered more realistic and provides more precise values for overall costs and production

rates because of the level of detail included.  However, this approach suggests that further

model development must take into account the need to review equipment configurations that

comport with regional and local forestry practices.  

For each type of equipment, the equipment configuration team selected one or more

representative models used in California or currently available equivalents.  The team then

collected data on purchase prices, fuel consumption rates, and other parameters.  The team’s

equipment choices do not indicate recommendations or preferences for any particular models.  

It was not practical, nor did the team consider it necessary, to include the full range of

equipment model options in the analysis.

Equipment used for prescribed burning was not included in this analysis.  Prescribed burning

equipment configurations would be more appropriately modeled in another iteration of the B2E

Model, as they tend to be more similar to the fire suppression equipment configuration.  Fire

suppression was not modeled as part of the LCA because of the highly diverse configurations of

equipment deployed during fire suppression operations on any given fire.   

With the equipment configuration analysis completed, the team was able to proceed to build

the LCA model.  The LCA required detailed analysis of at least two major categories of

activities:  in‐forest (described here) and power plant operations (described in the next section).  

In‐forest operations included all operational steps from harvest to delivery of biomass to the

power plant.  The modeling did not include any of the resources or impacts associated with

scoping, planning or monitoring of in‐forest operations.  As with fire suppression, these

equipment configurations tend to be highly variable for any given operation, and it was

deemed impractical by the team to include even a highly‐abstracted version of planning and

administrative infrastructure.   

To summarize clearly the areas of forest management that are not included in the LCA, but

which may have measurable impacts on emissions and forest operations:

1.  Equipment configurations appropriate to prescribed fire operations (producing the emissions

that are generated by underburning, as is seen in the LCA model);

2.  Equipment configurations and impacts associated with fire suppression activities;   

3.  Administrative operations, such as planning, monitoring or research and analysis.

Given the high degree of variability associated with each of these activities, it would not have

been realistic to attempt to build representative operational models for them.

37 
2.10. Biomass Energy Conversion Technology Characterization 

As mentioned above, two areas of operation affect total system emissions and energy

consumption.  The first is in‐forest operations, captured by the equipment configuration

domain, and the second the types of technologies used to convert biomass to energy.  At the

outset of this research study, the scope of modeling was restricted to electricity production.  

However, as the research project progressed, it became clear that other forms of energy

production would become important to planners and decision makers.  The research team

therefore developed the conversion facility modules as independent modeling components.  

This allows future iterations of B2E Model development to include other bioenergy conversion

systems, such as thermochemical conversion to ethanol or hydrogen.   

In fact, the full analysis undertaken by the LCA team included engineering studies of five

existing or emerging biomass conversion technologies.  The results of this analysis have been

published in a separate publication (Nechodom et al. 2008), and include very early results from

next‐generation thermochemical conversion technology that produces both electricity and

ethanol.  For the purposes of this study, the LCA team included three of the technologies

specific to electricity generation, per the scope of the original Energy Commission contract.  The

comparison of three biomass‐to‐electricity technologies allowed the LCA team to compare

differences in electricity production, energy use, and emissions impacts associated with

different conversion technologies.

Data for three types of biomass power plants (a current generation combustion plant, a current

generation integrated gasification/ combustion plant, and a next generation thermochemical

conversion plant) were provided by the LCA team.  Nameplate and net capacity, efficiencies,

and stack emissions are presented in Table 12 below, as described in Nechodom et al. (2008).  

The emissions are supplemented to include CH4 and N2O emissions as described by the U.S.

EPA (2003).  The use of supporting equipment (a dozer, two loaders, a bobcat, a tub grinder,

and a natural gas emergency generator) and ancillary grid electricity use were also included.  

Although the fuel use and emissions of the supporting equipment were deteriorated over time,

based on the U.S. EPA’s NONROAD2004 Model, the stack emissions and efficiency were held

constant throughout the plant life cycle.  Data were not found to support a time‐scaled

deterioration rate for the three technologies reviewed.

The LCA Report (Appendix 4) includes the inventory data used for the biomass power plants.

These data take into account the proportions of each major species provided by the forest

feedstocks and their relative heating values.  Since power plant efficiency is a function of Btus

per MWh, it is important to note the significant range of heating values per ton among tree

species.  For example, ponderosa pine contains a total of 17.2 mmBtus per bone dry ton.  In

contrast, hardwoods have 16.7 mmBtus per bone dry ton.  These differences matter, particularly

at larger scales of fuel use where biomass plant managers make daily and hourly decisions

about fuel blends in order to optimize among Btus per ton and moisture content of fuels in the

fuel yard.  Nearly all biomass power plants seek out a diversified portfolio of wood fuels, and

then blend what goes into the power plant on an hourly basis in order to achieve optimal

balances between heating values, moisture content, specific fuels behaviors, and so forth.   

38 
For this study, analyzing the heating value by species allows estimation of the net electricity

generated and delivered to the grid adjustments for each power plant’s efficiency.   

Table 12. B2E Project power plant characterization 

Current 

Generation 

Biomass 

Combustion 

Power Plant 

Current 

Generation 

Integrated 

Gasification/ 

Combustion 

Power Plant 

Next Generation 

Thermochemical 

Conversion Power 

Plant 

Plant Size (dry tons per day) 500 500 500

Electricity (kWh/dry ton) 1,000 1,200 1,400

Net Energy Efficiency 20% 22% 28% 

Plant Emissions (lbs./mmBtu output) 

NOX 0.329 0.067 0.008 

SOX 0.125 0.010 0.002 

PM 0.269 0.030 0.032 

CO 0.897 0.070 0.042 

VOC 0.085 0.018 0.003 

CO2 972.000 886.000 694.000 

CH4 0.329 0.067 0.008 

N2O 0.125 0.010 0.002 

2.11. Life Cycle Assessment 

The environmental life cycle assessment (LCA) was a core element of the B2E study.  The LCA

tracked energy use, air emissions, and environmental impacts (in terms of climate change,

acidification, and smog formation) associated with removing forest biomass to generate

electricity.  However, this approach accounted only for the energy use, emissions, and impacts

associated with producing a megawatt‐hour of electricity.  The team was also interested to see if

an LCA could provide a way to quantify energy use, emissions, and impacts associated with

producing a healthy, resilient forest landscape.  

The team spent considerable time and effort attempting to develop a landscape system LCA, at

the same time realizing the inherent risk in this strategy.  Defining the end‐product of

“electricity to grid” is fairly straightforward, and LCAs are clearly designed to deal with these

types of engineered systems, where flows of materials can be tracked and controlled. Clearly

defining and quantifying the end‐product of a “healthy and sustainable forest” however is

fraught with difficulty.  Ideally, the end‐product of a “healthy and sustainable forest” could be

measured in acres that have reached a quantifiable state of “health” (which may be defined

differently, based on the management objectives of different landowners) without substantially

diminished capacity to maintain qualitatively measured multiple “ecological benefits.”  

39 
Differing forest management objectives on different ownerships added complexity, particularly

since some objectives operate at the stand scale (for example, sawlog production on private

lands, which allows a per acre accounting) while others operate at much larger scales (for

example, landscape‐scale fire behavior modification on national forest lands, which a simple

per‐acre accounting will not capture).  The multitude of management objectives and desired

outcomes at a variety of spatial scales made it difficult to develop a specific, single metric that

could be used to indicate that the Beta landscape had “arrived” at a “healthy” condition.

Extensive work on this problem yielded the following approach: the LCA could track the

energy, emissions, and environmental impacts associated with a 2.7 million‐acre landscape of

differing management outcomes, as measured by extent and severity of wildfire..

The LCA tracked energy use, emissions, and environmental impacts associated with harvesting,

chipping, and transporting woody biomass and converting it into electricity.  The LCA’s

environmental impacts were assessed using the outputs of the following B2E Project sub‐

models: (1) landscape characterization and scenario design, (2) characterization of forest

operations and equipment configurations, (3) vegetation dynamics assessment, (4) fire behavior

assessment, and (5) power plant analysis. As depicted previously in Figure 2, the outputs from

these five sub‐models fed data into the LCA.  Development of the LCA portion of the B2E study

focused the research team on the structural framework of the LCA itself, as well as on the

interconnections between the LCA and the sub‐models that provided its inputs.  

The LCA estimated the life cycle impacts of harvest, biomass chip transport, and electricity

generation.  The life cycle begins at resource acquisition (in other words, at the well for mobile

fuels and grid electricity generation and in the forest for biomass electricity generation) and

extends through fuel combustion or point‐of‐use.  Figure 5 presents the main process flows for

the LCA.

40 
Figure 5. LCA process flows 

The LCA included four phases based on the protocol standardized by the International

Standards Organization (ISO) 10 and based on the computational structure described in

Heijungs and Suh (2002).  The four phases, briefly summarized here, are described in detail in

the LCA Report for the B2E Project (Appendix 4). The first phase, goal and scope definition,

described the reasons for carrying out the study, the intended audience, geographic and

temporal considerations, system functions and boundaries, impact assessment and

interpretation methods11.  Next, the inventory assessment quantified life cycle energy use (total,

fossil, and petroleum) and eight air emissions (carbon dioxide (CO2), carbon monoxide (CO),

methane (CH4) nitrous oxide (N2O), nitrogen oxides (NOx), non‐methane volatile organic

compounds (NMVOCs), particulate matter less than 10 microns in diameter (PM10), and sulfur

oxides (SOx)) for acquisition and processing of residual biomass (harvest and chipping

 

10 ISO 14040:2006 and ISO 14044:2006 replace the previous standards (ISO 14040:1997, ISO 14041:1999,

ISO 14042:2000 and ISO 14043:2000). The new editions have been updated since the development of the

goal and scope (Heijungs and Suh 2002) to improve the readability, while leaving the requirements and

technical content unaffected, except for errors and inconsistencies (from

http://elsmar.com/Forums/showthread.php?t=17459)  

11 An extensive literature review of forest product LCAs formed the basis for the development of the

project’s goal and scope definition document (Cooper et al. 2006).   

41 
operations within the forest); transport of chips to a biomass power plant; and conversion of

chips into electricity. Third, the impact assessment estimated air emissions contributing to global

climate change (from CO2, N2O, and CH4) and acidification (from SOx and NOx), and

photochemical smog (from CH4, NOx, CO, and NMVOCs). The final phase, the interpretation

step, formulated the results in different ways, including an evaluation of alternative biomass

electricity generation technologies.   

Model interconnection was tested through the development of an LCA that compared the

Reference Case (no treatment in the Beta landscape) with a Test Scenario that included a variety

of vegetation and fuels treatments in the Beta landscape, designed to meet objectives

emphasizing production of wood products on private lands and strategic fuels treatments to

enhance suppression capabilities and modify landscape fire behavior on national forest lands.  

The LCA results in Section 3.0 are presented in net form, depending on the selected

production function, allowing comparisons between systems that have different levels of

sawlog production and electricity generation.  The Test Scenario allows us to present different

views of energy use, outputs and impacts by placing the focus on which part of the system the

user may care about.  For example, if one is mostly interested in the use, outputs and impacts

associated with the biomass fuel chip production only, the net analysis allows a full assessment

based on that primary interest.  Examples of how this works are presented below.   

2.11.1. Goal and Scope of LCA 

In keeping with international standards for life cycle assessment, a complete discussion of the

goal and scope of the B2E Project LCA is provided in the LCA Report (Appendix 4). The goal of

the LCA was to analyze utilization of forest biomass to generate electricity.  The scope of the

LCA was based on selected decision categories as defined by the B2E Project’s Technical

Advisory Committee in June 2005. Table 13 below displays the decision categories and

associated impact categories investigated in the LCA for the B2E Project.   

42 
Table 13. B2E Project LCA decision and impact categories 

Decision 

Category Impact Category Impact Category Description 

Infrastructure 

and Human 

Use Impacts 

Total energy 

consumption* 

Sum of the total energy consumption for the life cycle (as 

mmBtu***) 

Fossil energy 

consumption* 

Sum of the fossil energy consumption for the life cycle (as 

mmBtu) 

Petroleum energy 

consumption* 

Sum of the petroleum energy consumption for the life cycle (as 

mmBtu) 

Air 

Resources 

Impacts 

Contribution to 

climate change** 

Total carbon dioxide equivalents from life cycle air emissions of 

CO2, N2O, & CH4 (as tons CO2 equiv) 

Contribution to 

acidification** 

Total hydrogen ion equivalents from life cycle air emissions of 

SOx & NOx (as tons H+ equiv) 

Contribution to 

photochemical 

smog** 

Total nitrogen oxides equivalents from life cycle air emissions of 

CH4, NOx, CO, & NMVOCs (as tons NOX equiv) 

PM10 emissions* Sum of particulate matter emissions (as tons PM10) 

* The contribution of the inventory flows was measured by the amount of the inventory flows  

** The contribution of the inventory flows was measured using impact equivalency factors

*** mmBtu is  1 million Btu

The LCA evaluated the Reference Case as well as a Test Scenario. Table 14 summarizes the

assumptions regarding treatments for the Reference Case and the Test Scenario.  The total

biomass loading on the landscape ranged from approximately 4 bone dry tons per acre

(BDTs/acre) for grasslands to 60 to 80 BDTs/acre for fully stocked forested areas.  Treatments

were assumed to occur over a 120‐day period each year (during the summer months) on both

private and public lands.  Biomass removed from the landscape not destined for use as sawlogs

was assumed to be chipped in the forest and used to generate electricity.  

Table 14. Landscape treatment scenarios 

Reference Case – No 

Treatment 

No treatment is performed on public or private lands. 

Test Scenario - Treatment of 

Private and Public Multiple Use 

(PMU) forest lands 

Treatments on private forest lands are designed to meet objectives for 

producing wood products. On industrial private forest (IPF) lands, 

treatments include regeneration harvest (clearcutting), precommercial 

thinning, commercial thinning, and underburning. Treatments on nonindustrial private forest (NIPF) lands include selective harvesting. 

Treatments on public multiple use (PMU) lands are aimed at 

strategically managing fuels and include thinning to create defensible 

fuels profile zones (DFPZs) as well as strategically placed area 

treatments (SPLATs). An average of 20 BDT/acre of forest biomass 

was assumed to be removed after saw timber was harvested. 

43 
2.11.2. Gross vs. Net Inventory and Assessment 

The Approach and Results sections for the LCA rely heavily on an understanding of the

difference between the “gross inventory assessment” and the “net LCA assessment.”  In the

gross assessment, life cycle energy use and emissions for wildfires and all treatment processes

are included, with only the Test Scenario producing sawlogs and electricity.  The net assessment

gives the Test Scenario “credit” (i.e., a subtraction of mass or energy) for producing sawlogs

and electricity, so that the Reference Case, the Test Scenario, and conventional electricity

generation can be compared.

A gross inventory assessment was undertaken to estimate the life cycle energy use and

emissions for the Reference Case and the Test Scenario, representing the management regime as

it would occur, including using a current‐generation biomass combustion power plant,

following the main process flows for the LCA depicted in Figure 5 above. The function of the

gross LCA assessment was simply to track management of the Beta landscape over a 40‐year

period while producing sawlogs and electricity (from the biomass chips).  Note that in the LCA,

the term “chips” and “chipped forest biomass” are used. This includes tops, limbs, and waste

material from the harvested trees, which are processed through a chipper to create wood chips,

which can be converted to electricity at a power plant or used for other purposes, such as

mulch.

2.11.3. Forest Treatment and Chip Transport Models and Data 

The B2E Project’s vegetation dynamics modeling (described in Section 2.1.5 of this Report)

provided data on the amount of acres treated and quantities of sawlogs and chips generated

from the treatments.  The forest operations and equipment characterization model (described in

Section 2.1.8) provided the data related to the type and quantities of equipment used in

harvesting operations and to transport the harvested material to the processing facility (sawmill

or biomass power plant). Forest treatment and chip transport included off‐road equipment use

for biomass harvest, such as the use of feller bunchers, skidders, and chippers, on‐road

equipment use for harvest equipment mobilization, the use of a water truck for forest road dust

control, crew transport, and chip transport at an average distance of 30 miles from the treatment

site.  The Test Scenario was assumed to be executed based on combinations of six harvest

methods (clearcut, pre‐commercial thinning, commercial thinning, salvage, selective harvest,

and restrictive thinning) for three slope ranges as displayed in Table 15.  For each prescription,

per‐acre production rates for sawlogs and chips were determined via the forest operations and

equipment characterization model.  

44 
Table 15. Treatments, equipment configuration, and production Rates 

Treatment Description Slope % Equipment 

Configuration

Chips (dry 

tons/ acre) 

Sawlogs 

(dry tons/ 

acre) 

Clearcut 

Even-aged management. Only 

occurs on Industrial Forest 

Lands. 

<35% CC <35 30 47 

35 to 50% CC 35-50 33 48 

PreCommercial 

Thinning 

No sawlogs removed. Only 

biomass fuel removed. 

Typically in plantations. 

<35% PCT <35 5.0 0 

35 to 50% PCT 35-50 8.3 0 

Commercial 

Thinning 

Sawlogs and biomass fuel 

removed. Typically in 

plantations. 

<35% CT <35 14 7.5 

35 to 50% CT 35-50 21 6.7 

Salvage 

Assumes that no biomass fuel 

(3"-9.9" diameter at breast 

height or limbs/tops) was 

recovered (burned up in 

wildfire). 

<35% SAL <35 0 11 

35 to 50% SAL 35-50 0 13 

Select 

Harvest 

Uneven-aged management 

harvest removing individual or 

small groups of trees. 

<35% SH <35 12 16 

35 to 50% SH 35-50 13 16 

>50% 

SH 50+ (only 

on Industrial 

Forest Lands) 

14 17 

Restrictive 

Thinning 

Thinning aimed at ladder fuel 

reduction, constrained canopy 

thinning, and retaining 40% 

canopy cover. 

<35% RT <35 6.6 12 

35 to 50% RT 35-50 6.8 17 

Data on equipment power, life, and fuel and oil use were combined with the productive

machine hours per dry tons of woody material leaving the forest as chips or sawlogs to estimate

fuel and oil use. Given the fuel and oil use on a dry‐ton basis, total fuel and oil use for the Test

Scenario was estimated based on the chips and sawlogs generated per decade (as derived from

the vegetation dynamics assessment).

Based on the equipment fuel use, the equipment emissions were estimated based on the U.S.

Environmental Protection Agency’s NONROAD2004 and MOBILE6 emission inventory models

as listed in Table 16.  For the application of these models, estimation began with zero‐hour or

zero‐mile emissions and break‐specific fuel consumption (BSFC) and was followed by

adjustments (where applicable) to account for transient operation, changes in emission factors

over time, and technology distributions (for tiered regulatory compliance).  For example,

emission factors account for changes in regulations on diesel sulfur content.  Beginning June 1,

2007, non‐road diesel was required to have maximum of 500 ppm sulfur, and beginning June 1,

2012, the sulfur content must be reduced to 15 ppm (US EPA 2004).

45 
Table 16. Harvest and chip transport emissions models 

Off-road 

equipment 

except 

chainsaws 

NONROAD2004 emission inventory model (technology distributions, zero hour 

emissions, deterioration factors, transient adjustment factors) [12]; California 

Statewide Off-Road Fuel Correction Factors [13] 

Chainsaws NONROAD2004 emission inventory model (zero hour emissions, deterioration factors, 

transient adjustment factors) [14]; Deterioration rates [15] 

On-road 

equipment 

MOBILE6 emission inventory model (zero hour emissions, correction factors) [16, 17]; 

Native road PM emissions [18] 

Equipment was assumed to be dedicated to the project, to be new in the first year of the project,

and subsequently replaced at the end of its operating life, thus returning the accounting to zero‐

hour or zero‐mile performance (for example, Figure 6 shows that a Morbark Model 30/36

chipper is used for 8,000 hours and then replaced).  Machine hours were estimated per decade

for the Test Scenario based on the “productive” machine hours and the amounts of sawlogs and

chips generated per decade. In addition, average cumulative operating hours per decade were

estimated for each piece of equipment. Although emissions were adjusted for transient

operation and degraded until the equipment was replaced, fuel and oils use was assumed to be

constant over the life of the equipment (to match assumptions made in the project’s economic

analysis).  Finally, PM emissions for on‐road equipment traveling on native roads have been

calculated and regulated adjustments in fuel sulfur levels have been considered in appropriate

years as described in U.S.EPA (1995). The resulting “effective” emission factors are presented in

sub‐appendix A of the LCA Report (Appendix 4).  

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Cumulative Operating Hours

Chipper ‐ Morbark Model 30/36

New equipment purchased

Figure 6. Example of new equipment purchase dates for emissions degradation estimation 

46 
2.11.4. Fuel, Oils, and Electricity Production Models and Data 

The U.S. Department of Energy (DOE) Argonne National Lab’s Greenhouse Gases, Regulated

Emissions, and Energy Use in Transportation (GREET) Model (Version 1.7) was used to

estimate the life cycle of diesel and oils production, grid electricity, and natural gas power

plants in California.  Since GREET Version 1.7 estimates emissions only to the year 2020, energy

use and emissions beyond 2020 have been assumed to be at 2020 levels.  The data used are

presented in sub‐appendix B of the LCA Domain Report, essentially representing well‐to‐point

of use values for all fuels, oils, and electricity production processes.

Biomass Power Plant Operation Models and Data 

The “Biomass Energy Technology Conversion Characterization” section above describes in

detail the biomass power plant operation models and data used in the LCA.

2.11.5. LCA Environmental Impact Assessment 

In all assessments, environmental impact was measured in two ways.  The first metrics of

environmental impacts were based on the amount of inventory flows, such as the amount of

energy or the mass of particulate matter emissions, which applied to four of the impact

categories specified by the project’s Technical Advisory Committee.  The second metric used

impact equivalency factors (i.e., scoring factors based on fate, transport, and effects models)

from the 1996 Intergovernmental Panel on Climate Change values (IPCC 2007) or as compiled

in the U.S. Environmental Protection Agency’s Tool for the Reduction and Assessment of

Chemical and Other Environmental Impacts or TRACI model (US EPA 2008).  When

equivalency factors were used, impacts were measured relative to one of the emissions

contributing to the impact.  For example, contribution to climate change was measured in “CO2

equivalents” such that each species of emissions was assumed to have some multiple of the

impact of CO2 (for example, an emission of 1 kg of CH4 contributes 21 times that of an emission

of 1 kg of CO2). Table 17 lists the equivalency factors used, consistent with the IPCC and the

EPA standards.

Table 17. Equivalency factors used (equivalent mass/mass emitted) 

Impacts Considered12 CH4 CO CO2 N2O NMV

OC NOx PM SOx 

Contribution to Climate Change (CO2 

equivalents) 21 0 1 310 0 0 0 0 

Contribution to Acidification (H+ 

equivalents) 0 0 0 0 0 40 0 50.8 

Contribution to photochemical smog 

(NOx equivalents) 0.0030 0.01

3 0 0 0.78 1 0 0 

 

12 Climate change equivalency factors were for 100‐year time horizons and chosen to match the data

used in the U.S. EPA’s values in the Draft 2007 Inventory of Greenhouse Gas Emissions and Sinks (US EPA

2007a).    Data were from the most recent version of TRACI (developed in 2006) for the US average

condition and available at http://www.epa.gov/nrmrl/std/sab/traci/  

47 
Finally, energy consumption, PM10 emissions, and the impact characterization results were

normalized by the commensurate California estimates for the 40‐year period, presented in Table

18.  Energy use projections represented the 40‐year sum of annual values forecasted based on

linear regressions of 1996‐2006 data from the U.S. DOE Annual Energy Review (U.S.

Department of Energy 2006) and multiplied by the ratio of the population of California to that

of the U.S. from the U.S. Census Bureau (US Census Bureau 2004).  Next, and again multiplied

by the same population ratio, California PM10 emissions and emissions contributing to

acidification and smog formation were estimated from the 40‐year sum of 2005 emission

estimates from the U.S. Environmental Protection Agency (US EPA 2007b) 13.  Contribution to

climate change was estimated as a 40‐year sum of the 2004 level for California (CARB 2008).

When combined, the normalization factors are intended to allow the life cycle environmental

impacts to be placed within the context of their contribution to the overall California condition.

Further model development and application would require the same normalization procedure

for any given state in which the model was applied.  If actual state‐level inventory and analysis

exist, it is recommended that those data be used for normalization of test landscape

contributions.  

Table 18. Normalization factors (estimated 40-year California values) 

Factor Result Units 

Total energy consumption 260,000 tera Btu 

Fossil fuel consumption 190,000 tera Btu 

Petroleum consumption 190,000 tera Btu 

Climate change 21,000 million tons CO2 

equivalents 

Smog formation 150 million tons NOx 

equivalents 

Acidification 6,800 million tons H+ 

equivalents 

PM10 emissions 8.9 million tons 

Note that the assessments in this study did not include the construction, maintenance, and

decommissioning of facilities and capital equipment (in other words, harvest equipment,

distribution/ transport equipment, power plant buildings and equipment) and the life cycle of

other feedstocks needed to ensure continuous power plant operation.

2.11.6. B2E LCA Interpretation 

The B2E Project’s approach modeled one Test Scenario; however, the Test Scenario could be

compared to other future scenarios, each of which would produce a variation in:

 

13 Although the 40‐year data could be forecasted from the U.S. Environmental Protection Agency data

this method results in negative U.S. emission values as early as 2023.   Thus, using the 2005 per capita

value was chosen for emissions normalization.    Note also that the non‐greenhouse gas U.S. emissions

data do not include fire and dust.

48 
• the amount of sawlogs produced, and  

• the amount of chips used in the production of electricity (and thus the generation of a

different amount of electricity).  

The B2E Project included developing an LCA framework that would facilitate the comparison

of different management scenarios.  While the LCA Domain Report (Appendix 4) presents the

gross LCA results (which represent the main LCA flows depicted in Figure 5 for a current

generation biomass combustion power plant), the gross assessment results do not allow for the

comparison between scenarios because they do not account for the variation in sawlogs and

chips produced under different scenarios.  Hence, the focus of this section is on interpreting the

gross LCA results to provide the means for future scenario comparisons.  Interpretation of the

gross results is done in two steps.  First processes, inputs and emissions are allocated to sawlog

production and electricity generation.  Next variations in the conversion technology (i.e., type of

power plant) used to generate electricity are calculated. These steps are described in the Results

Section (3.0) to provide context for, and explanation of, the LCA modeling results.

2.12. Landscape Greenhouse Gas Model 

The Biomass to Energy project attempted to build an atmospheric carbon flux model based on

an interpretation of the data generated by the tree lists and the fire modeling.  The Landscape

GHG model (LGHG) was developed in order to test assumptions about total fates of carbon

dioxide (CO) and methane (CH4) in the atmosphere, and to relate those greenhouse gases to

forest growth and biomass removals over the 40 year modeling period.   

While the LGHG had promising results, the team determined that the model would need

further development in order to fully account for the greenhouse gases associated with post‐fire

decay and decomposition and the net sequestration from forest growth.  The initial version of

the model used generalized growth curves for forest mensuration after disturbance, and used

assumptions about the sequestration capabilities of broad forest types.  Additional assumptions

were made about the fate of wood products and biofuels used for energy generation that were

consistent with the assumptions used in the life cycle assessment.  The LGHG model does not

attempt to track all landscape‐related carbon flows.  Rather, the model is constrained to

analyzing the flow of carbon in above‐ground live tree biomass into biofuels and sawlogs, and

into the atmosphere as a result of wildfire and decomposition.  

The LGHG model was tested against a one‐year GHG fate model (Morris 1999), developed in

order to estimate total CO2 and CH4 burdens and fates in the atmosphere.  This original model

accommodates a wider variety of biomass fuels (such as sawmill waste, forest treatment

residues, agricultural residues, recovered municipal waste and landfill gas).  The modules

pertaining to in‐forest waste and tree growth were compared to the LGHG model in order to

test the accuracy of LGHG accounting.  The equations used in the B2E application of the

original model to account for fire probability and losses, post‐fire mortality and decomposition

and post‐fire growth and sequestration were derived from the tree lists generated through the

vegetation and fire analyses presented in Appendices 1 and 2.

49 
2.13. Wildlife Habitat Assessment 

Once the analysis of vegetation growth, fire disturbances, and treatments were completed, the

habitat domain team subsequently modeled their effects on wildlife habitat and associated

values.  As most analyses of wildlife habitat depend on data reflecting the structure and

condition of vegetation, the habitat domain team used tree list data produced by the vegetation

domain team, adapted to allow for habitat quality analysis.  

A key reference used for the wildlife habitat analysis was the California Wildlife Habitat

Relations (CWHR) model (CDFG 2002).  The habitat domain team evaluated vegetation

conditions, along with environmental variables (for example, elevation, slope, and

precipitation) to evaluate how habitat conditions for wildlife species would be expected to

change under different forest management scenarios.  The team evaluated the potential effects

of the Reference Case and the Test Scenario on biological diversity by evaluating changes in

habitat suitability from five perspectives: habitat element specialists, aquatic species, service‐

providing units, indicator species, and individual species of special interest.  Each of the five

perspectives addresses concerns regarding the direct and indirect effects of management on

biological diversity and the services it provides.   

Habitat element specialist guilds represent the effects of forest management on vegetation

structure, and in turn, habitat conditions for wildlife species.   

Service providing units represent the effects of forest management on ecosystem services by

affecting the diversity of service providers and biological diversity as a whole.   

Species of special interest are those that are of particular interest or concern based on their

current population status or their vulnerability to forest management practices.   

Aquatic species use upland habitats to meet a variety of life history needs, including foraging,

cover, estivation, and dispersal.  Upland conditions and activities can also affect aquatic

habitats.   

Current vegetation conditions across the Beta landscape were generated from existing Forest

Inventory and Analysis (FIA) data.  Future vegetation conditions were modeled based on

current FIA‐based vegetation, Forest Vegetation Simulator (FVS) growth models, and a

predetermined array of disturbance events (see vegetation dynamics modeling domain).  The

Reference Case and Test Scenario each had eight time steps (pre‐ and post‐treatment for each of

four decades), with one shared starting condition, providing a total of 17 landscape condition

snapshots.  Vegetation conditions for each landscape were provided by the vegetation dynamics

team.   

2.13.1. Habitat Element Specialist Guilds 

Species associations with primary habitat features that are likely to change as a result of

biomass harvesting were identified to create five habitat specialist element guilds: old forests,

early seral conditions, snags, logs, and oaks.  Membership in each of the five guilds developed

for this analysis was determined based on multiple sources.  The old growth associates were

derived from Graber’s (1996) old growth conifer dependent species for the Sierra Nevada, and

50 
included old growth dependent and associated species.  Early seral species were those for

which early seral stages (open, seedling, or sapling stages) were considered high quality habitat

for reproduction, feeding, and cover in the CWHR database.  Oak guild membership reflected a

combination of species dependent upon oak foothill habitats (Graber 1996), and five additional

species for which the CWHR database identified acorns as secondarily essential or essential.  

Snag and log associates were identified using the CWHR database: species for which snags or

logs (large and medium diameter) were considered an essential or secondarily essential were

included in these guilds.   

Each of the habitat element specialist guilds consisted of representatives from multiple

vertebrate classes.  The team identified a total of 63 old growth associates, including 2

amphibians, 3 reptiles, 36 birds, and 22 mammals (two of which are currently extirpated).  

Seventy‐nine species were identified as early seral associates, including 9 reptiles, 37 birds, and

33 mammals.  Forty‐four species were associated with oaks or acorns, including 1 amphibian, 3

reptiles, 30 birds, and 10 mammals.  Thirty‐four species were associated with snags, including

24 birds and 10 mammals (one extirpated); and 16 species were associated with logs, including 1

amphibian, 3 reptiles, 2 birds, and 10 mammals (one extirpated).  Modeling snag and log guilds,

particularly the patterns of post‐fire mortality and snag and log recruitment, has a degree of

uncertainty that should be viewed with caution. The team found that the Test Scenario effects

on snag and log dependent species were difficult to quantify, and point to a larger finding that

habitat modeling requires more refined vegetation data.  

2.13.2. Service Providing Units 

Following (Luck et al. 2003), seven service providing units can be identified in similar ecological

systems. The habitat domain team identified four categories of service providing units (insect

regulators, seed dispersers, decomposition aides, and herbivore regulators) to evaluate the

effects of forest management on ecosystem services provided by vertebrate species.  

Insectivorous animals serve to keep populations of herbivorous insects in check, limiting a

variety of undesirable damages associated with outbreaks of these insects, such as stress to

native plant species including trees (for example, bark beetles).  With an emphasis on aerial

insects, the team identified 93 members of the insectivorous service‐providing unit, including

birds and bats.  Seed dispersal is a key service in any ecosystem that is provided by vertebrates,

as well as invertebrates (for example, ants).  Many species eat and transport seeds; the habitat

domain team targeted two groups of species (22 species total): conifer seed dispersers (small

mammals), and fruit‐bearing plant seed dispersers (frugivorous birds and mammalian

omnivores).  Snags and logs contribute significantly to soil nutrient availability and nutrient

cycling in forested ecosystems.  Although other biota, such as bacteria, fungi, and ants, serve the

primary role in decomposition, the 13 species of woodpeckers and secondary cavity nesters in

the Beta landscape contribute to decomposition in an ecologically significant manner by

exposing trees to disease through sapsucker feeding holes, and speeding the breakdown of

snags and logs through the creation of cavities for feeding and nesting.   Herbivore regulators

are carnivores, which serve a regulatory function in ecosystems, keeping populations of lower

trophic level species (primarily herbivorous mammals) in check.  Finally, the team identified

two tiers of herbivore regulators: primary (top carnivores; n = 21) and secondary (n = 9).   

51 
2.13.3. Species of Special Interest 

The habitat domain team considered species with special status, exotic species, and aquatic

species in identifying species of special interest.  There were five exotic vertebrate species in the

Beta landscape: three birds, one amphibian, and one mammal.  The team analyzed the richness

of exotic species as a group.   The team identified Forest Service Sensitive Species and existing

Management Indicator Species for the Plumas and Lassen National Forests as species of special

interest, and these included 23 species: 8 aquatic species (4 amphibians, 3 birds, 1 reptile), and

16 terrestrial associates (6 birds, 9 mammals).   

To test the sensitivity of the data used, the team evaluated the individual responses of one

species, the American marten, as a demonstration of the type of analysis that can be conducted

in any landscape where systematic surveys have been conducted.  For the American marten, the

team developed a predictive model for probability of occurrence based on GIS‐based

environmental data associated with survey data (detection, non‐detection) collected in the study

area and adjacent landscapes.  In 1999 to 2002, the Forest Service’s Pacific Southwest Research

Station conducted a survey for mammalian carnivores in the Greater Southern Cascades Region

of northeastern California using baited trackplate and camera stations (Barrett 1983; Kirk 2007).  

The surveyed landscape overlaps the Beta landscape, extending far to the north but not as far

east as the Beta landscape.  Nonetheless, the surveyed landscape is comprised of vegetation and

other environmental conditions typical of the Beta landscape; hence, the predictive models

developed for the surveyed landscape could be reliably applied to the Beta landscape.   

2.13.4. Aquatic Species 

The team then identified 79 non‐fish aquatic species that were primarily dependent on aquatic

habitats, including 15 amphibians, 58 birds, 4 mammals, and 2 reptiles (Appendix 5).  In

addition, 38 species of fish were identified as confirmed or likely to occur in the Beta landscape

(Appendix 5).  Aquatic species were only included in the aquatic guild, and were excluded from

the habitat element specialist guilds because of their unique considerations relative to changes

in upland conditions.  Specific aquatic species were not included in this analysis.     

2.14. Ecosystem Services and Ecological Endpoints Analysis 

This section describes a framework for analyzing ecosystem services pertinent to the Beta

landscape.  The habitat team analyzed impacts on key ecosystem functions that were classified

as service‐providing units.  However, the B2E research team determined that a broader analysis

would be appropriate in a further development of the study beyond Phase 1.   

This broader analysis would include quantification of the ecological endpoints identified in this

section of the study. Ecological endpoints are characterized as ecological functions that have a

directly measurable human welfare function, and that can be quantified in an accounting

system that makes them fungible. Forest ecosystems can purify water, reduce flood and fire

risks, support recreation, provide beauty, improve nearby agricultural output, sequester carbon,

and enhance air quality (Daily 1997).  However, the services that connect directly to human

welfare functions substantially narrow the field of indicators that needs to be measured to

52 
understand changes due to disturbances such as wildfire or management such as fuels

treatments.

The methods used in the ecosystem services assessment are innovative in the area of resource

economics.  While the initial scope of this study had hoped to establish a series of values for non

market resources, the team found through its parsing of economic values on the Beta landscape

that further analysis would be required before those values could be quantified.

Given that the overall goal of this study was to explore trade‐offs among all values associated

with disturbance and treatment at the landscape level, the fundamental requirement in meeting

that goal would be to quantify or at least normalize values in order to make them comparable.  

The B2E economic model took into account all values that could be measured with market

signals, or at least with reasonable proxies for market signals. However, several other values

that in fact drive human choice and behavior on the Beta landscape are clearly importantly

affected by treatments and fire.  Assigning them fungible values proved frustratingly elusive to

the research team.

This section illuminates the problem, and even provides a framework for a potential solution.

The longer report (see Appendix 7) on ecological endpoints is an analysis of the historically

difficult issues of benefits transfer.  And, it offers a recommended strategy for distilling

ecological endpoints that can be quantified and measured against other values

The methods employed in the analysis if ecosystems services are still under development.  The

recommended strategy for development is an additional effort to quantify the values identified

in the framework in Appendix 7, and summarized in Table 19.

53 
Table 19. Examples of relationships between benefits and endpoints 

Benefits Endpoints 

Scenic, aesthetic enjoyment Undeveloped landcover, untreated landcover (if visible), 

burned landcover 

Residential water provision Water quality and availability at intake (wells, POTW 

sources) 

Commercial water provision Water quality and availability at intake (wells, POTW 

sources) 

Irrigation water provision Water quality and availability at source 

Commercially important soils Soil availability & quality 

Recreational open space, aquatic Boatable waters area, depth, flow 

Recreational open space, terrestrial Parks and public lands 

Active hunting and angling Target species populations (deer, adult steelhead, ducks) 

Passive species observation Target species (songbirds, elk, deer) 

Stewardship Endangered and threatened species not included in active 

and passive categories, wilderness 

Pollutant reductions – Air Air quality, particulates 

Property damage avoidance – Water Flood events and flood map 

Property damage avoidance – Fire Fire events and fire map 

Property damage avoidance – Pests 

invasives, pollinator losses 

Pest, invasive, and pollinator species populations. 

2.14.1. Ecological Production Functions 

A production function describes the relationships between inputs and outputs in a system.  One

of the key limitations of this kind of analysis is the ability to state ecological production

functions in ways that can be analytically observed and measured.  As Boyd points out in

Appendix 7, the production functions on the B2E landscape are well on their way to clear

definition, but further analysis and modeling would need to be completed before the ecological

economic analysis could be completed.   

The current analysis for this study concluded with the following evaluation of the existing and

needed tools to support identification and quantification of ecological endpoints:

• Better species models that incorporate the spatial configuration of habitat.  Models of

reproduction, forage, predation, and migration to better predict the location and timing

of populations.  Of interest are not just valued populations, but pest and invasive

populations as well.

• Better hydrological models to link land cover to aquifer and downstream surface water

availability.  Forests can prevent ‘flashy’ runoff and thus protect against flood surges.  

Dense growth is also thought to reduce groundwater delivery.  

• Better water quality modeling to link land cover and land management practices to

downstream water quality.  Forest‐related impacts on nutrient cycling and nutrient

loads is an example.  

54 
• Better understanding of soil quality effects arising from treatment and hydrological

processes.  

• Better air quality models to allow for the analysis of human health, ecological, and

aesthetic impacts.

2.14.2. Scarcity and Substitution 

A common principle of resource economics holds that non‐market valuation is fundamentally

dependent upon some kind of stated preference method14 being applied at the appropriate scale,

surveying an appropriate sample population.  However, welfare‐significant conclusions can be

drawn without knowledge of underlying preferences.  This is because economic production

obeys certain fundamental properties, or principles. For example, all else equal, the following

statements are typical of economic logic, and apply to human evaluation of ecological scarcity

and value:

• The scarcer an ecological feature, the greater its value.

• The scarcer are substitutes for an ecological feature, the greater its value.  

• The more abundant are complements to an ecological feature, the greater its value.15  

Note that scarcity can be measured, as well as the abundance of substitutes and complements,

without detailed knowledge of underlying preferences.  For any of the endpoints found to vary

as a result of management, policy, or protection useful things can be said about the social value

of the change by exploring the scarcity of what is gained or lost.   While stated‐preference

methods are highly recommended to refine the scarcity analysis approach, for a gross

assessment employed at the scale of this study, it is sufficient to detect analytically significant

differences among policy scenarios.

For example, a waterbody whose quality is enhanced will – all else equal – be more valuable if it

is scarce.  Is it the only swimmable lake in the county, or one of many?   The same holds true of

parks, open space, and wilderness.  Are these land uses scarce or plentiful?  Knowing the

answer to these questions may help decision‐makers make more informed choices about

impacts and priorities.   

 

14 Stated preference methods fall into three primary categories:   a) contingent valuation, in which the

respondent is required to make a comparison of value between the resource value in question and known

trade‐off values; b) travel‐cost analysis, in which travel effort and investment constitutes a proxy for the

value of the resource; and c) hedonic pricing, which uses property values as a proxy for the value of the

resource as compared with comparable purchase prices.  Each has strengths and weaknesses, depending

on application.  For this study, the team recognized that each method represented fruitful areas of future

research to enhance and refine the substitution and “rarity” methods used to determine ranges of values

and change due to disturbance and management.   

15 Though note that not all ecological inputs require complements to yield a benefit.

55 
Substitutes are also important to analyze.  If water flows in a stream are reduced, but there are

alternative groundwater sources for irrigation or drinking, the social costs of reduced flows will

– all else equal – be lower than if there are no substitutes. The benefits of fire and flood damage

are likewise influenced by the availability of averting actions which are a substitute for fire or

flood risk reductions.  Flood pulse attenuation is less valuable in watersheds where there are

built flood controls such as levees, dams, and reservoirs.  It is more valuable when those built

substitutes are absent.   

The scarcity of, substitutes for, and complements to many ecosystem goods and services are

relatively easy to assess.  In many cases, metrics can be derived from social and biophysical GIS

data. (Boyd and Wainger 2002; Boyd and Wainger 2003).      

Table 20. Examples of endpoints and relationships to metrics of scarcity and substitution 

Endpoint (w/benefit) Scarcity Metric Substitute Metric 

Undeveloped land in viewshed 

(Aesthetic enjoyment) 

% landcover undeveloped in 

service zone 

% landcover lightly developed 

land 

Water quality (Drinking, 

Irrigation) 

Degree to which consumption 

constrained by availability 

Other water sources Wells, 

POTWs, 

Water availability (Irrigation, 

Commercial) 

Degree to which consumption 

constrained by availability 

Other water sources Wells, 

POTWs 

Boatable waters, depth, flow 

(Recreation) 

Number, size of waters in 

service zone 

n/a 

Parks & public lands 

(Recreation) 

Number, size of lands n/a 

Species (Hunting, 

Subsistence) 

Population density species (bass for trout) 

Species (Observation) Population density in service 

zone 

Substitutable target species 

Species (Stewardship) Global or regional population 

viability 

n/a 

Wilderness (Stewardship) Global or regional wilderness 

availability 

n/a 

Fire events (Damage 

avoidance) 

n/a Protective actions (fire breaks, 

water) 

Flood events (Damage 

avoidance) 

n/a Protective actions (levees, 

dams) 

Each of the three subsections above are areas for further development.  Given the limitations of

resources and expertise for this aspect of the project, the B2E team determined that recognition

of the issues and a recommendation for further development should be sufficient during this

proof‐of‐concept phase of the project.   

56 
2.15. Cumulative Watershed Effects Analysis 

Cumulative watershed effects analysis evaluates the impacts of multiple disturbance activities

across a landscape over time.  The underlying premise for this approach is that watersheds

recover over time, and the length and progression of the recovery depends on the type of

disturbance involved.

In the northern Sierra Nevada, impacts from vegetation management activities are primarily

increased erosion and stream sedimentation resulting from decreased soil infiltration, decreased

soil cover, bank and fill failures along roads, and altered runoff patterns.  While mass wasting

can be a potential problem in localized areas, this process was not analyzed for the Beta

landscape.

Watershed impacts are most effectively mitigated using Best Management Practices in

designing and locating roads and skid trails, maintaining protective vegetation cover, and

limiting the extent and intensity of disturbance (Rice and Berg 1987).   MacDonald (1994) states

that the effects of present day management activities on water quality are usually transient and

rarely severe enough to cause significant damage to fish populations.  Exceptions to this

conclusion include:  

• unstable areas or areas with highly erodible soils,  

• the combination of management activities with extreme storm events, and  

• downstream deposition areas where there is potential for cumulative effects.

The B2E cumulative effects analysis assumed implementation of Best Management Practices in

concert with the modeled treatment prescriptions.    

Cumulative watershed effects in the Beta landscape were assessed using the Forest Service

Pacific Southwest Region’s cumulative watershed effects (CWE) model and verified using

WEPP FuME. The CWE model is a disturbance‐based model that normalizes all disturbances

(treatments, wildfires, and so forth) to an acre of road. WEPP FuME is a web based interface

(http://forest.moscowfsl.wsu.edu/cgi‐bin/fswepp/fume/fume.pl) that predicts soil erosion

associated with vegetation and fuels management practices (prescribed fire, thinning, and a

road network) and compares the result with erosion from wildfire.   

2.15.1. Cumulative Watershed Effects Model 

The CWE model is a non‐specific model for modeling disturbance and recovery.  It can be

designed to model disturbance and recovery related to sediment and soil erosion, or can be

modified for other disturbance related processes, such as mass wasting. The model uses

equivalent roaded acres (ERAs) to equate all disturbances to one acre of road, and estimates the

recovery of the disturbed areas over a specified period of time, based on a recovery curve for

the disturbance being modeled.  The CWE model can be modified for different disturbance

processes by changing the model’s ERA coefficients and recovery curves.   

For the B2E Project, GIS coverages displaying watershed disturbances were analyzed using a

CWE model written in visual basic.  The program read each disturbance and disturbance year

57 
and, based on the ERA coefficient, recovery curve, and years to recover, computed the ERAs by

year for each watershed, as described below.   

2.15.2. Watershed Disturbances 

The Beta landscape was divided into 122 watersheds, ranging from 2,500 to 46,000 acres

(averaging 23,000 acres). Disturbances were quantified using Forest Service corporate GIS layers

displaying: (1) past vegetation management activities on national forest lands (from the Forest

Service’s Activity Tracking System), (2) forest roads, (3) fire history, including burn severity,

and (4) the Beta landscape treatments and wildfires modeled for the three time periods of 2006,

2016, and 2026. Past and ongoing timber harvest activities on private lands were modeled using

the state’s Timber Harvest Plan GIS coverages, which covered only two of the four counties

(Lassen and Sierra Counties) in the Beta landscape at the time of the analysis.   

2.15.3. ERA Coefficients and Recovery Curves 

ERA coefficients were based on the likely effects of management activities on erosion and

sedimentation, these two processes being the most likely mechanisms that would result in a

cumulative watershed effects in the Beta landscape.  ERA values recover over time. To run the

cumulative watershed effects model, the watershed team defined a recovery period (years) and

assigned a recovery curve (Figure 7) for each type of disturbance. At this broad scale of analysis,

sensitive areas, such as highly erodible soils or areas adjacent to streams, were not separated out

for customized ERA coefficients and recovery curves.   

ERA coefficients and recovery curves were based on erosion and sedimentation studies from

the American River Study on the Eldorado National Forest. (MacDonald et al. 2004) The study

measured erosion rates from wildfire, logging roads, timber harvest, and prescribed fire, using

sediment fences on the American, Cosumnes and Yuba River Basins. Sediment delivery was

also examined. The study results are presented in Table 21.

58 
Table 21. Mean sediment by treatment (1999-2000) and proportioned ERA coefficient 

Disturbance Type

Mean Sediment

Rates\2

Sample

Size

(n)

     

Tons/ha Kg/M2 ERA Coef\3

Est % Skid

trails/landings

Roads Dirt 8.80 0.798 17‐55* 1.000 N/A

Roads Gravel or Paved 0.90 0.082 10 0.102 N/A

OHV Trails   3.90 0.354 7 0.443 N/A

Minimally disturbed sites 0.01 0.001 3 0.001 N/A

High Severity Wild Fire 11.00 0.998 3 1.250 N/A

Holland soil skid trail 8.20 0.744 2 0.932 N/A

Other skid trails 0.40 0.036 34 0.045 N/A

Skid Trails (Mean) 0.83 0.076 36 0.095 N/A

Prescribed Fire 0.01 0.001 15 0.001 N/A

Thin Unit (estimated\1) 0.13 0.012 0.014 15%

Regeneration Harvest

(estimated\1) 0.23 0.021    0.026 27%

\1 Harvest Units estimated from skid trail erosion rates and minimally disturbed rates.  15

percent of the area in thinnings was assumed to be in skid trails and landings, while 26 percent

of regeneration harvest areas were assumed to be in skid trails and landings.

\2 Mean sediment rates taken from the American River Sediment Fence study.

\3 ERA Coefficients:  Dirt roads are set to 1 ERA per acre and all other ERAs are calculated

based on the amount of sediment per acre relative to roads.  For example, the mean skid trail

produces 0.83 Tons/ha or about 1/10 that of dirt roads.

In the American River study, sediment rates were highly concentrated, with a relative few sites

producing the majority of the sediment from each land use (Table 22).  For roads, a few

segments, with inadequate road drainage, and road segments that were recently graded,

produced a majority of the road sediment.  On skid trails most of the erosion came from 2 of 36

segments on the Holland soil type. These outliers were not excluded from the B2E analysis but

averaged into the ERA coefficients.   

For this analysis, dirt roads were set to 1 ERA per acre.  All other ERA values were calculated

based on the average sediment yield relative to dirt roads.  For example, the average sediment

59 
yield on high severity wildfire areas is 11 tons/ha and dirt roads are 8.8 tons/ha.  High severity

wildfire therefore received an ERA coefficient of 11/8.8 or 1.25.     

All disturbances were assigned an ERA coefficient, a recovery curve, and recovery years based

on the American River study, other studies, and local expertise. Sample ERA coefficients used

in the analysis are listed in Table 21. The complete ERA coefficient tables are listed in the

cumulative watershed effects domain report.

Table 22. Beta landscape Equivalent Roaded Acre (ERA) coefficients, recovery years, and 

recovery curves for different types of disturbances. 

Activity Group Activity Method ERA’s /

Acre

Recovery

Years

Recovery Curve

Harvest

Activity Fuels

Broadcast Burn 0.001 3 Concave

Harvest

Activity Fuels

Machine Pile 0.021 8 Concave

Fuel Treat Thin From Below, Tractor 0.014 8 Concave

Harvest Clear Cut Tractor 0.026 10 Concave

Harvest Thin Tractor 0.014 8 Concave

Road Gravel   0.22 60 Flat

Road Dirt 1 60 Flat

Wildfire High Severity with Salvage 1.4 8 Concave

Wildfire Moderate Severity 0.3 4 Concave

Recovery curves are used in this kind of damage analysis as a variable that can help set

management priorities.  For example, a convex recovery curve can give a particular area a

higher priority for restoration or rehabilitation because of the risks of long‐term damage

without immediate intervention.  Recovery curves are assigned based on the type and severity

of disturbance being modeled.  For example, since an active road does not recover over time, a

flat recovery curve would be assigned.  Most sediment recovery can be plotted as a concave

curve. Each recovery curve plots a unique pace at which watershed recovery from a specified

disturbance is modeled.  For example, a concave curve plots a more rapid recovery during the

earlier part of the recovery period compared to a linear recovery curve, which plots a constant

rate of recovery over the recovery period, as illustrated in Figure 7 below. Recovery curves used

for the Beta landscape analysis are shown in Figure 7.  Note that Figure 7 shows a 100‐year

60 
recovery period; however, this period varies depending on the type of disturbance being

modeled, as shown in Table 22 above.

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

0 20 40 60 80 100 120

Year

Percent Recovery

Concave

Convex

Linear

Flat

Sigmoid

FlatLinear

Figure 7. Recovery curves used for B2E Beta landscape cumulative impacts analysis 

2.15.4. Watershed Thresholds 

The basic area for cumulative watershed effects analysis is a watershed. ERAs are calculated for

a watershed over various time periods, and these values are compared to a threshold of concern

(TOC) established for the specific watershed. The TOC is expressed as the ERA level that

indicates the upper limits of the watershed’s tolerance to disturbance. The established TOC is

based on the watershed’s sensitivity to disturbance and beneficial uses of the water. The TOC

does not represent the point at which cumulative watershed effects will occur. Rather, it serves

as a “yellow flag” indicator of a particular watershed’s increasing susceptibility to potential

significant adverse cumulative effects.  

The ERA or disturbance threshold for any given watershed is unknown. Thresholds can vary

within a watershed based on the intensity and duration of storm events. A very large 100‐ to

200‐year storm event can exceed the threshold of an undisturbed watershed. A relatively small

1 or 2 year storm event may cause debris slides after a wildfire.  The amount and severity of

disturbances increase the likelihood of a storm event exceeding the watershed threshold.   

Thresholds of concern can be estimated based on local knowledge of past events. It is typically a

conservative estimate, based on 30‐ to 100‐year storm events.  The threshold values used for the

B2E analysis were from the Tahoe and Plumas National Forests.  Specific thresholds of concern

are listed in appendices to the Cumulative Watershed Effects Domain Report (Appendix 6).    

61 
2.16. Economic Analysis 

The economic analysis focused on determining economic values of changes in resource

conditions in the Beta landscape and at the power plant. As such, the analysis necessarily

depended on modeling results from most of the Project’s other domains, as well as financial

data for constructing and operating power plants and the valuation of assets (structures,

infrastructure, recreation resources, and agricultural production) at risk to wildfire in the Beta

landscape.  The analysis, however, was not a comprehensive economic assessment, which

would require estimating monetary values for the ecological public goods and services, such as

water quality, air quality and biological diversity, supported by the Beta landscape.  As

discussed in the section on ecosystem services above, prices and quantities were not readily

observable for most public goods and services affected by changes in the Beta landscape.  

(Recreation resources are an exception.) The analysis instead focused on valuating private

goods and services (marketable assets, such as timber, structures and power from biomass

plants) affected by changes in resource conditions within the Beta landscape and at the power

plant.  For the most part, data on prices and quantities consumed were available from market

transactions to place economic value on these goods.   

Different analytical frameworks (Table 23) can be used to present the outputs from economic

analyses.  Deciding on the appropriate framework often largely depends on the policy

questions that need to be answered and the availability of data to conduct the analysis.  For

example, cost‐effectiveness analysis is typically used when a specific project outcome is

predetermined and the analytical objective is to determine the least cost way to achieve the

objective.  Economic costs are derived in terms of the opportunity costs of foregone uses of

resources, including any direct costs incurred by implementing agencies.  Benefit‐cost analysis

is a more comprehensive approach to economic analysis, involving equal consideration of

economic costs and benefits.  Benefits reflect the increased value of market and non‐market

goods and services (such as recreational, aesthetic, and cultural values).  

Table 23. Analytical frameworks for economic analyses 

ANALYTICAL FRAMEWORK PRIMARY OBJECTIVE 

Private Investment/Financial Analysis Identify rate of return on investment 

Cost-effectiveness Analysis Identify least cost program or project 

Fiscal Analysis Identify effect on local government budgets 

Local/Regional Economic Impact Analysis Identify effects on the local economy (jobs, 

income) 

Social/Community Analysis Identify effects on community well-being 

Benefit-Cost Analysis Identify the net economic value to society 

Successfully applying either cost‐effectiveness or benefit‐cost analysis depends on scientific

understanding of the underlying physical and biological processes.  The physical changes in

environmental and resource conditions are often described by response functions that relate

changes in the physical and biological environment to policy actions. The economic analysis

62 
attempts to characterize these physical and biological changes in monetary terms.  However, if

key relationships among the physical and biological processes are not well understood, the

economic analysis will mirror (and often compound) the level of uncertainty.     

The overarching research question for the B2E Project’s economic analysis was as follows: “Do

the benefits of removing biomass (both timber and other biomass) from the forest and using it

as a fuel source to generate electricity exceed the costs associated with the forest management

activities?” The economic analysis focused on estimating the net present value (NPV) of

changes in resource conditions associated with the vegetation management treatment scenarios

(s1...s3) over a 40‐year analysis period (t1...t40). Notationally, the analysis can be expressed as

follows:

NPV 1...1 = ∑ CR EB EC ttttttttns 40...140...140...140...1 Δ+−+− MAV tt 40...1

where:

• R = Revenues:  discounted annual revenues for power generation, biomass co‐products

(sawlogs), and salvage logs 

• C = Costs:  discounted annual operations, maintenance, and capital costs for forest fuels

management, power plant operations, fire suppression, and forest rehabilitation  

• EB = Environmental Benefits: discounted annual positive changes in the provision of 

ecosystem services 

• EC = Environmental Costs: discounted annual negative changes in the provision of

ecosystem services  

• ∆MAC = Change in Market Asset Values: discounted change in market asset values

attributable to wildfires, including structures, infrastructure, agricultural lands

production, and recreation resources.  

• s = scenario:  comprehensive array of treatments, vegetation changes and wildfire

interactions

• t = time:  annual time period for each change in value

Although the analytical notation includes consideration of environmental benefits and costs as

part of a comprehensive economic analysis, research limitations did not allow for monetizing

the environmental effects of the treatment scenarios evaluated for the B2E Project.  These

limitations and ways to address them are discussed above in the section on ecosystem services.

Consistent with requirements for economic efficiency analysis, the economics domain team

used a benefit‐cost analytical framework to evaluate changes in resource costs and benefits

associated with the Reference Case and the Test Scenario.  The analysis also included

calibrations against the Private Land scenario, developed to test model sensitivity and to show

likely marginal impacts and benefits from private land management.  Resource costs were

measured in terms of opportunity costs, and benefits were evaluated in terms of willingness‐to‐

pay.  

63 
The team developed a spreadsheet model to calculate the annual costs and benefits of the no‐

treatment Reference Case and the Test Scenario over the 40‐year analysis period.  As a practical

matter, costs and benefits that occur beyond 40 years in the future have little or no present

value.  Costs and benefits are discounted to present value using a 3 percent real discount rate.   

2.16.1. Valuation of Assets at Risk to Fire 

A primary focus of the economic analysis was on evaluating changes in the economic value of

assets at risk to wildland fire.  Based on previous research (California Department of Forestry

and Fire Protection 2005 and Baerenklau 2006), key assets with established market values that

are at risk to fire include agricultural land resources, timberland resources, recreation resources,

structures, infrastructure, and mineral resources.

A geographic information system (GIS)‐supported approach that allows for considering

spatially‐explicit relationships was used to conduct the analysis of assets.  GIS was used to

develop baseline values of assets at risk and to assess how fire affected these values over time.   

The geographic mapping levels, units of valuation, and basis of valuation for the assets

evaluated in the analysis are shown in Table 24.  

Table 24. Geographic specification and valuation parameters for the assets at risk analysis 

Applicable 

Assets 

Geographic 

Mapping/Level of Disaggregation Units for Valuation Basis of Valuation 

Agricultural 

resources 

Region-wide, based on 

designated rangelands 

(CDF) and irrigated 

croplands (ag covertype) Acres 

Average rangeland and 

irrigated cropland values per 

acre; crop composite values 

used for specific areas (e.g., 

Sierra Valley) 

Timberland 

resources 

100 square meter grids 

used for SFA output, by 

county 

Green and salvage volume 

(thousand board feet) 

classifications defined by 

BOE 

Average stumpage values 

from BOE for harvest, 

treatment (co-products) and 

salvage 

Recreation 

resources 

Region wide, based on 

CDF designated recreation 

areas and other important 

recreation areas Visitor days 

Average net economic value 

per visitor day 

Structures Parcels, by county 

Improvements to real and 

personal property, by 

parcel 

Assessment value of 

improvements and other 

personal property 

Infrastructure 

Region wide, based on 

updated CDF data on 

powerline and facility siting 

Improvements to real 

property and easements Replacement value 

Mining 

Region wide, based on 

designated mining areas Not valued Not valued 

64 
GIS layers were created for each of the assets considered in this analysis.  Baseline values for

each asset were calculated by assigning a known or estimated dollar amount to each cell

containing an asset.  For example, one mile of transmission line was determined to have an

average replacement value of $300,000. This value was converted to cost per meter giving each

100‐meter cell containing a transmission line a baseline value for that asset.  Layers for

recreation, agriculture, structures, and infrastructure (power transmission lines) were then

compared to modeled fire location and intensity for each 10‐year time period in the Reference

Case.  Fire intensity (flame length) was translated into a damage function appropriate for each

asset type.  Burned cells containing assets were assigned a percent loss based on the fire

intensity in that cell. Asset damages were aggregated within each asset type to determine total

asset loss.

2.16.2. Sawlog Valuation 

The economic analysis estimated the net revenues from harvesting of conifer sawtimber by

species.  The economics domain team reviewed potential sources for objective and consistent

valuation data and found that the best available source was the California State Board of

Equalization (BOE).  The BOE sets timber harvest values as the basis for property taxes paid by

California forest landowners and purchasers of public timber, per the California Timber Yield

Tax Law of 1976. These values reflect net revenues to operators, thereby accounting for

production costs.

The BOE values are derived from market analysis conducted by BOE foresters using actual

sawtimber transaction data for each of the 11 timber value areas in California. The market

analysis provides approximate stumpage16 values for the timber before it is harvested,

processed and transported to the market (i.e. sawmill, paper mill, composite panel facility).  

Valuation is expressed in dollars per thousand board feet ($/mbf)17. The BOE provides timber

harvest values for miscellaneous, green and salvage timber respectively. In addition, BOE

timber valuation assumes that value for some conifer species, such as ponderosa pine and

Douglas fir, is also a function of size.  For these tree species, the BOE assumes that the larger the

diameter of the sawlog, the more value it has in the marketplace.   

2.16.3. Fuels Treatment Costs 

Forest management costs for the Test Scenario were estimated using a spreadsheet model

developed by the Project’s equipment configuration domain team.  Costs were estimated by

using the equipment configuration team’s characterization of the type and blend of forest

harvest and removal equipment used to perform forest management activities in the Beta

landscape.   Cost estimation was based on information provided by experts (harvesting

contractors and supervisors) currently conducting forest management activities in Northern

California.   

 

16 Stumpage values represent the value of timber as it stands prior to harvesting. 

17 Thousand board feet (mbf) represents the volume of a log based upon board foot measure. One 

board foot represents the amount of wood contained in an unfinished board measuring one inch thick, 

one foot long, and one foot wide. 

65 
Total costs were allocated to the two different products, chips and sawlogs, using the following

logic: because a primary intent of the operations is to remove fuel and remediate forest stands,

the biomass and sawlogs should share equally, on a per‐ton basis, the costs of all activities that

handle or process both products.  Thus, costs were partitioned by weight over the biomass and

sawlogs for activities directly associated with each distinct configuration of harvest equipment.  

The costs of ancillary activities were also shared on the same basis.  Costs allocated solely to

biomass included chipping and transport of chips to the power plant. (The analysis did not

consider loading and hauling activities associated only with sawlogs.) Finally, capital and

operations and maintenance costs were calculated separately on a per‐ton unit basis for each

product (sawlogs and biomass).   

2.16.4. Power Plant Costs and Revenues 

Power plant cost estimates developed for the B2E Project were primarily comprised of the

following three components:

• initial capital and development costs for the permitting and building of the project,

• cost of financing these up‐front costs during construction as well as the operating phases

of the project, and

• actual operating and maintenance of the project during its operating life.

Initial capital costs were estimated on the basis of similar‐sized plants that are being built on the

West Coast, with costs based on the assumption that a new facility would be constructed on

land that is rural in nature, and built where permitting for a biomass power plant would be a

reasonable financial undertaking.  Financing cost was based on the assumption that the project

will be a stand‐alone entity, and that the equity investors and debt lenders would only have

recourse to the project itself.  Operating and maintenance costs of the project were based strictly

on the experience of the Mt. Lassen Power biomass plant in Westwood, California. Mt. Lassen

Power has had a long history of continuous operations, sourcing a majority of its feedstocks

from thinning operations within a 30 to 50 mile radius of the power plant, providing empirical

data representative of the costs associated with the operations of a typical biomass power plant.  

Power plant revenue estimates were developed by multiplying the electricity output of the

power plants constructed under each scenario by a price per kWh.  The prices incorporated into

the revenue estimates were based on information provided by the California Renewable

Portfolio Standard Program.  This program calls for the California Public Utilities Commission

to establish a methodology to determine the market price of electricity for terms corresponding

to the length of contracts with renewable generators. The market price must reflect the long‐

term market price of electricity a utility would need to purchase to meet its capacity and energy

needs from conventional fossil fuel resources instead of the renewable resources proposed

under the RPS bidding process.

2.16.5. Fire Suppression and Rehabilitation Costs 

Fire suppression and rehabilitation costs can vary considerably due to differences in location,

terrain, fuel type, proximity to populated areas, weather, fire intensity, and so forth.  The team’s

review of literature and data concerning fire costs revealed little fire suppression and

66 
rehabilitation information specific to the Beta landscape.  Therefore, the team used national data

and information available for fires in other areas to develop costs for modeling purposes,

implicitly acknowledging that costs may differ for fires in the Beta landscape.   

To estimate suppression costs on a per acre basis, the economics domain team relied on cost

data from the Forest Service (Strategic Issues Panel on Fire Suppression 2004) as well as a study

of Colorado fires (Lynch 2004).  The Colorado suppression cost data were generally consistent

with the Forest Service cost data, indicating that the national suppression cost of $403 per acre

was reasonable for estimating suppression costs in the Beta landscape.  This cost was adjusted

to 2006 dollars using the Employment Cost Index for state and local government workers,

resulting in an average fire suppression cost of $465 per acre.

Expenditures on post‐fire rehabilitation vary considerably because this spending is more

discretionary than is spending on fire suppression.  Additionally, variations in locations of fires

can play a larger role in costs.  For example, a fire that results in erosion that threatens urban

water supplies or that increases the chances for major flooding in urban areas may spur

significant emergency and long‐term rehabilitation spending, whereas a fire in a remote area

that does little damage to major watersheds may generate little or no rehabilitation spending.  

Activities funded by rehabilitation spending can vary from emergency erosion control to multi‐

year programs that include watershed seeding and tree plantings. The team used data from a

Forest Service study that evaluated emergency rehabilitation treatments following 480 fires,

primarily on National Forest System lands, from 1973 through 1998 to develop assumptions for

average rehabilitation costs for the Beta landscape.  

67 
68 
3.0 Project Results 

Project results are presented in the following sections in a sequence following the format in the

project approach section.  Detailed reports for each domain reported are available in the

Appendices, along with complete metadata reports for each model and dataset used.  The

presentation of results in each domain follows the following format (Table 25):

Table 25 - Format for presentation of B2E model results 

Domain Types of results

Vegetation Dynamics Types, size and location of treatments; general

characteristics of the Beta landscape; removal

of sawlogs and biomass under each scenario

Wildfire Behavior   Size, severity and locations of fires under each

scenario  

Life Cycle Assessment Smoke emissions from each class of fire;

energy use and emissions for each equipment

configuration; energy use and emissions for

each type of biomass powerplant; comparison

of biomass energy with California grid and

natural gas power plant; total system

emissions; environmental impact analysis for

climate change, smog formation, acidification

and particulate matter

Landscape Greenhouse Gas

Habitat Changes in habitat quality due to fire,

treatments or both

Cumulative Watershed Effects Impacts to soil, soil movement, sedimentation

in aquatic systems

Economics Changes in asset values due to fire and

treatment; treatment costs and revenues;

power plant costs and revenues; fire

suppression costs; rehabilitation costs

Note that the results table above does not address several of the other domains and processes

identified in the approach section (2.0).  The team considered many of the approach processes

and domains to be intermediary steps required only to produce model outputs for the domains

reported in the results section (3.0).   

69 
It is worth emphasizing that the actual numerical and marginal results presented here are the

outcomes of the B2E modeling assumptions.  The differences in wildfire severity or size, for

example, may not be as dramatic in the Test Scenario as one might have hoped.  Or the

reduction in wildfire greenhouse emissions may not seem significant, given the effort and

expense seen in the equipment deployments or economic returns.  This is purely an artifact of

the modeled scenario assumptions.  Future evolutions or applications of the B2E model would

include development of additional scenarios that would test the landscape’s ability to produce

significant changes, perhaps in greenhouse gases, biomass power produced, or other

production functions that might be socially preferable.  The Test Scenario assumptions were

designed to be as close to current practice and experience as possible so as to allow the

modeling teams to calibrate model functions and assumptions.  In other words, the team chose

to test the models as closely to reality as possible, using actual data where possible, in order to

assure proper model functioning.   

The Test Scenario applied different types of vegetation treatments to account for different

landowner objectives, for example, modeled treatments on private industrial forest lands clearly

had a sawlog production management objective. PMU modeled treatments accounted for a

variety of objectives.  For example, most of the treatments on national forest lands in the first

decade were focused on the construction of defensible fuels profile zones (DFPZs), which are

designed with the objective of providing a place to deploy firefighters in the event of a wildland

fire.  Firefighters would use DFPZs to make a stand to hold or contain a fire.  Treatments on

national forest lands during the second model decade focused more on strategically placed area

treatments (SPLATs).  SPLATs are designed to interrupt the spread of a wildland fire, thereby

slowing its spread.  DFPZs are a fire suppression enhancement strategy while the objective of

SPLATs is landscape‐scale fire behavior modification.  

The landscape selected for analysis covers approximately 2.7 million acres of both east and west

side forest in the northern Sierra Nevada mountains.  Figure 8 shows the location of the Beta

landscape.  The area spans three national forests, five counties and contains parts of Lassen

National Park, several state parks, a state game reserve and several thousand acres of

commercial forest lands.  Table 26 (duplicated from the Approach section) shows a breakout of

the types of land ownerships on the Beta landscape.  

Table 26. Land ownership categories in the B2E Beta landscape 

Land Category Acres % of Total 

Public Multiple Use 1,374,783 50%

Public Conservation & Recreation 407,776 15%

Industrial Private Forests 457,427 17%

Non Industrial Private Forests 383,008 14%

Urban & Other 112,816 4%

Total 2,735,809 100%

70 
Figure 8 - Location of B2E Beta landscape 

The B2E model tracks the changes in vegetation type and condition through four modeling

decades for both a Reference Case and Test Scenario.  The Beta test of the model assumes a

beginning year of 2006, and ends with final conditions stated for 2046.  Therefore, changes from

fire and treatment are displayed for the years 2006, 2016, 2026 and 2036.   

3.1. Vegetation dynamics and change 

Vegetation conditions are reported in this section under both the Reference Case and the Test

Scenario.  Treatments are reported by ownership class and slope condition, so that the reader

can appreciate the extent of treatments under each scenario.  Scenarios will be compared at the

end of the section.

3.1.1. Reference Case and Test Scenario 

A Reference Case was developed to compare changes under other scenarios, and is typically

characterized as the “no treatment” scenario, in which the model grows and burns trees without

any treatments being applied.  The primary purpose of this scenario is to establish both the

71 
extent of growth on the landscape without intervention and the extent and severity of fires

without treatment.  It is a totally hypothetical case for reference purposes only.

Vegetation changes in the Reference Case are due exclusively to growth and wildfire, and are

reported in the next section on wildfire behavior.   

Table 27 - Acres and products from the Beta landscape under the Test Scenario 

Acres Treated 1,971,451

Biomass Chips Produced (BDT) 20,804,604

Sawlogs Produced (mbf) 15,682,776

Table 28 - Acres treated by decade per scenario under the Test Scenario 

2006 2016 2026 2036

Test Scenario 525,825 447,478 538,485 459,663

3.2. Wildfire Behavior 

Wildfire is one of the most critical variables being tested through the use of the B2E model.  

Since the key objective of the model was to test the effect of thinning on fire extent and severity,

the model needed to show sensitivity to the effects of thinning operations.  Table 27 above

shows the average BDTs removed per acre during the entire 40‐year modeling period.  This

shows a gross level of change in the fine fuels present, available for burning in wildfire.   

The fire behavior domain team modeled wildfire behavior under the Reference Case (no

treatment) and the Test Scenario. Under the Reference Case, weighted average biomass levels

were 79 bone‐dry tons (BDTs) per acre; under the Test Scenario, private land treatments

removed an average of 31 BDTs/acre, while SPLATs and DFPZs removed an average of 24

BDTs/acre (Appendix 8, Table 1).  

While the ownerships, forest type, density and slope dictated the type of treatment

prescriptions, the wildfire modeling found that the spatial arrangement of treatments has a

greater impact on their ability to change fire intensity and extent than the prescription applied

(see Appendix 8).   The Test Scenario fires were modeled with spatial adjustments of treatments

to protect sensitive wildlife habitat, reduce negative watershed effects, shape recreational

opportunities, and capture timber volume under industrial private forest ownerships. The

assumptions used in the spatial distribution of treatments are shown in the treatment allocation

rule sets and logic described in the vegetation domain appendix.

The wildfire behavior modeling showed a 22% reduction in extent of wildfire compared to the

Reference Case (Table 29). The second decade shows the greatest impact on reducing overall

wildfire perimeters (Table 29).  One might expect to see a similar trend for reducing fire

72 
perimeters across all four decades.  However, differences due to modeling assumptions and fire

ignition locations may explain the variance from the downward trend in the third and fourth

decades under the Test Scenario. The  substantial changes in decade two are most likely

attributable to the location of the ignitions, higher proportion of private industrial ownership,

and the topography within the fire perimeters.

Table 29 - B2E burned areas by scenario and by year 

Year Reference Case Test Scenario

2006           92,684           80,487

2016           60,153           39,846

2026           69,953           44,385

2036           76,543           67,796

Total Acreage         299,334         232,514

  

% Change from

Reference Case 0% ‐22%

As would be expected, the Reference Case generated more acres burned compared to the Test

Scenario, with an average of 74,833 acres burned per decade.  Ignoring the small fires, the B2E

Beta landscape’s fire history on record (last 80 years) averaged approximately 65,000 burned

acres per decade. Wildfire behavior modeling for the B2E Beta test attempted to mimic the fire

history on record, burning 65,000 acres in a variety of fire sizes and intensities.

Evaluations of fire hazard mitigation programs tend to focus primarily on changes in the

number of acres burned (since those are easiest to monitor).  However, the B2E fire modeling

also captured changes in the severity of fires.   Across the Reference Case and the Test Scenario,

approximately 32 % of the acres burned were characterized as non‐lethal, that is, surface fires

with flame lengths between one and four feet (Table 30).  This acreage corresponds well with

the Forest Service’s wildfire severity monitoring for the Sierra Nevada (Miller and Fites 2006),  

in which the authors found that approximately one third of the area of large fires burns in the

“non‐lethal” severity class.

73 
Table 30 - Fire Severity for Modeled Wildfires Under the Reference Case and Test Scenario 

Fire Severity

Class Reference Case

% of acres

burned Test Scenario

% of acres

burned

N ‐ nonlethal    81,471 27%    86,586   37%

X ‐ mixed lethal 136,887 46%    98,560   42%

L ‐ lethal    80,976 27%    47,368   20%

Grand Total 299,334 100% 232,514   100%

Fire severity classes are important to the B2E modeling because many of the downstream

models evaluate the effects of fuel treatment scenarios based upon the three severity classes. For

instance, consumption rates for canopy fuels and resultant wildfire emissions for green house

gases are all modeled and calibrated based on fire severity classes.   

The percentages of acres with lethal and mixed‐lethal fire severity classes were highest in

decade two (Table 31). Only decade three showed a decrease in the number of acres in the non‐

lethal severity class (3,880 acres) but that is due to the dramatic drop in total acres burned from

implementing both public and private treatments in this particular decade with a combined

reduction of 25,568 acres or a 36.5% reduction from the Reference Case (Table 32).  

Table 31. Fire severity results comparing reference to test 

      Fire Severity Classes

   Year Non‐Lethal Mixed Lethal Lethal

Reference Case 2006 36,579 33,176 22,929

   2016 19,447 20,947 19,759

   2026 19,296 31,691 18,965

   2036    6,148 51,072 19,324

Test Case 2006 37,889 24,740 17,858

   2016 19,914 15,452    4,480

   2026 15,417 18,496 10,472

   2036 13,366 39,873 14,557

Overall, the results of the vegetation and fire modeling show a reduction in acres burned and

the severity with which those acres burned.  This demonstrates successful implementation of

the interactive vegetation and fire modeling.  In terms of treatment efficacy, the modeling

74 
confirms increasing experience in the management community as well as empirical

measurements in the literature:  treatments can have a positive effect on reducing the extent and

severity of wildfire at the landscape scale.

3.3. Life Cycle Assessment Results 

3.3.1. Gross Inventory Results 

The gross inventory assessment estimated the life cycle energy use and emissions for the

Reference Case and the Test Scenario assuming use of the current generation biomass

combustion power plant and as the treatments would be performed (as described in Section

2.0).  The Test Scenario in the gross assessment was assumed to produce different amounts of

sawlogs and biomass electricity over the 40 study years (Table 32).  

Table 32. Gross assessment system products 

Reference Case: No

treatment

Test Scenario: Treatments

on IPF , NIPF, and PMU

lands

Landscape managed (acres) 2,700,000 2,700,000

Area treated (acres) 0 1,971,451

Sawlogs produced (dry

tons) 0 31,000,000

Biomass electricity

generation: current

generation biomass

combustion power plant

(MWh)

0 19,000,000

Installed biomass

electricity generation

capacity (MW)

0 61

Table 33 presents the gross inventory analysis results for the Test Scenario, including the life

cycle energy use and emissions for harvest equipment operation (including forest treatment and

chip transport), underburning, and power plant operations. Total life cycle energy includes the

life cycle for fuels used by harvest equipment and during chip transport and for energy use by

the power plant (for example, diesel use for forklifts and use of propane for building heat and

plant start up), as well as the energy contained in the chips, minus the energy generated by the

power plant.  Because the power plant was assumed to operate at a20 percent conversion

efficiency the energy in the chips dominates total energy consumption.

75 
Table 33 ‐ Gross life cycle inventory results for Test Scenario

Life Cycle

for Harvest

Equipment

Operation

and Chip

Transport  

In‐Forest

Underburning

Life Cycle of

Power Plant

Operation  

Total Test

Scenario  

Total energy consumed   mmBtu   2,924,894 ‐ 291,970,023 294,894,918

Fossil energy consumed   mmBtu   2,861,358 ‐ 809,596 3,670,954

Petroleum energy consumed   mmBtu   1,354,024 ‐ 154,141 1,508,165

NMVOC emissions to air   tons   884 27,789 3,601 32,275

CO emissions to air   tons   3,112 120,892 33,153 157,157

NOx emissions to air   tons   1,750 1,125 14,322 17,197

PM10 emissions to air   tons   356 11,599 9,765 21,719

SOx emissions to air   tons   248 736 1,660 2,644

CH4 emissions to air   tons   1,649 5,690 1,689 9,027

N2O emissions to air   tons   29 150 4,377 4,556

CO2 emissions to air   tons   1,182,172 1,576,269 40,834,701 43,593,142

Beyond the total life cycle energy, life cycle fossil and petroleum consumption for the gross

assessment was dominated by fuel use for harvest and chip transport (as expected).  For

emissions, in‐forest underburning becomes important in the Test Scenario for NMVOC and CO

emissions.  Finally, power plant operations account for  approximately 94% to total system

emissions in raw tons.    

3.3.2. Gross Impact Characterization 

Table 34 presents the estimated life cycle contribution to climate change, acidification, and smog

formation for the gross assessment of the Test Scenario.  These results were based on the

inventory results presented in Table 33 above and the equivalency factors described in Section

2.0.  

76 
Table 34. Gross assessment of climate impact categories from LCA 

Impact Category Units Life Cycle for

Harvest

Equipment

Operation

and Chip

Transport  

In‐Forest

Underburning

Life Cycle of

Power Plant

Operation  

Climate Change tCO2e       1,200,000         1,700,000         42,000,000  

Acidification tH+e           83,000             82,000             660,000  

Smog formation tNOxe             2,500         2,040,000               18,000  

Finally, Table 35 presents the gross assessment energy consumption, PM10 emissions, and

impact characterization results normalized by the 40‐year California contribution to each flow

or impact using the normalization factors presented in Section 2.0.  As shown, the life cycle total

energy is estimated to approach a 0.1 percent increase in the California total for the Test

Scenario. Normalized values are less than 0.1 percent of the California totals for fossil and

petroleum consumption and contribution to acidification.  

Table 35. Gross assessment impact normalization (as percent of California impact) 

Total

energy

Fossil

energy

Petroleum

energy

Climate

change Acidification

Smog

formation

PM10

emissions

Test

Scenario   0.12% 0.0019% 0.00080% ‐0.18% 0.023% 0.18% 1.4%

3.3.3. LCA Interpretation 

The gross assessment results for the Test Scenario above are not technically comparable to any

other possible scenarios for three reasons: (1) different scenarios would produce different

amounts of sawlogs, (2) different scenarios would produce different amounts of electricity, and

(3) different scenarios result in a 2.7 million‐acre landscape characterized by differing

management outcomes, as measured by extent and severity of wildfire.  In LCA terminology,

the systems represented by different scenarios would be multifunctional, and the LCA

standards followed in this study provide computational remedies for managing multifunctional

systems.  In LCA terms, co‐products are products produced but not used within the system

boundaries. (In the Test Scenario, sawlogs are a co‐product.) There are three computational

options for the management of co‐products in life cycle assessments:  

1.  system expansion (subtracting from the inventory analysis the life cycle energy use and

emissions for an alternative means to produce the co‐product),  

77 
2.  allocation (dividing the energy use and emissions among process products and co‐products

on the basis of the equipment applied, stoichiometry, or co‐product mass, energy, or economic

value), or  

3.  ignoring the co‐products (which is essentially what has been done in the gross assessment

described in this Section).    

System expansion is the preferred method when an alternative means to produce the co‐

product exists (such as another way to produce electricity or sawlogs), with most LCA

practitioners using allocation when system expansion is not feasible.

The importance of these computational remedies cannot be overstated.  In order to facilitate a

net or comparative assessment (for example, the comparison of ways to produce electricity),

credit (or subtraction) must be used for system co‐products as needed to model systems that

produce only one product.  For example, consider the net assessment defined in Table 36, in

which the electricity generation system was designed to produce only electricity.   

Table 36. Net assessment system characteristics (electricity only) 

Electricity generation systems

Final product Electricity  

Magnitude of

service

per MWh

Duration of service 40 years

Expected level of

performance Continuous electricity generation

For comparison to Electricity generation by conventional means

such as using the California grid or a natural

gas power plant

What needs to be

done

computationally

Remove energy use, emissions, and impacts

for sawlog production

The assessment of the electricity generation system defined in Table 36 follows below for the

comparison of different electricity generation systems.  In this net assessment, energy use,

emissions, and impacts for sawlog production were removed from the gross assessment results

using allocation, because system expansion was deemed infeasible in the absence of a viable

process to generate sawlogs only.  To remove sawlogs from the assessment, the harvest and

chip transport equipment were first grouped as that dedicated to chip production, to sawlog

production, or to both.  Next, fuel and oil use and emissions for chips were estimated to include

that for equipment dedicated to chips and that for equipment dedicated to both chips and

78 
sawlogs, with the latter mass allocated to the amount of sawlogs produced.  Thus, a

combination of process‐based and mass allocation was used in the net assessment.    

3.3.4. Comparison of biomass electricity generation systems 

A comparison of different electricity generation systems follows, using the assessment of the

electricity generation system defined in Table 36.  In this net assessment, energy use, emissions,

and impacts for sawlog production were removed from the gross inventory assessment using

an allocation method.  A system‐expansion method was deemed infeasible in the absence of a

viable process model that would show the inputs, processes and emissions for sawlog

production only.  To remove sawlog‐related processes from the assessment, the harvest and

chip transport equipment were first grouped as those processes dedicated to chip production,

those processes dedicated to sawlog production, or those processes dedicated to both.  Next,

fuel consumption, oil use and emissions for chip production were estimated for equipment

dedicated to chip production and for equipment dedicated to both chips and sawlogs.  The

latter were mass‐allocated to the amount of sawlogs produced.  Thus, a combination of process‐

based and mass‐based allocation methods were used in the net assessment.    

The comparison of electricity generation systems, detailed in the LCA Domain Report

(Appendix 4), offers at least two important insights into the system.  First, the effect of the low

B2E power plant efficiency on the total life cycle energy consumption is revealed, reflecting the

differences in the source‐to‐point‐of‐use efficiency of conversion of fuel type to energy output

for each system:  35 percent efficiency for the natural gas power plant; 45 percent efficiency for

the California grid; and 18 percent efficiency for the Test Scenario.  Second, the life cycle

consumption of fossil and petroleum fuel and the contribution to acidification for the Test

Scenario are estimated to be less than that needed to produce equivalent amounts of electricity

using conventional means (i.e., NGPP or the California grid).   

79 
Alternative Power Plant Technologies 

In Figure 9, life cycle energy is dominated by the difference between the energy embodied in

the chips and the biomass electricity generated, again ultimately reflecting the power plant

efficiency.  Table 37 provides characterizations of a current generation integrated gasification/

combustion power plant and a next generation thermochemical conversion power plant as

described in Nechodom et al. (2008). In all cases, the supporting equipment energy use and

emissions were assumed to be consistent with that of the current generation combustion plant

characterized in Section 2.0 , with the exception of the use of grate grease, which was assumed

only to be applicable in operations at the current‐generation combustion power plant.  The LCA

Domain Report in Appendix 4 presents the inventory data used for the biomass power plant,

considering the species of trees used and the heating values described in Section 2.0.  An 8

percent transmission loss is also assumed when calculating total power plant efficiencies.   

Figure 9 - Life cycle energy consumption net assessment 

80 
In order to show the effects of power plant efficiencies, Table 37 presents the two other biomass

conversion systems modeled for the final report of the B2E project.  These characterizations are

important to understand in the context of the net assessment, which compares the assessment

impacts depending on which technology is selected.  Power plant efficiencies range from 18%

for the current generation combustion plant used in the gross assessment to 28% for the

thermochemical conversion technology.  A complete characterization of all technologies used in

the B2E project is available in the LCA Domain Report (Appendix 4) and in Nechodom, et al.

(2008).   

Table 37. Power plant characterizations for net assessment comparison 

Current

Generation

Integrated

Gasification/

Combustion

Power Plant

Next Generation

Thermochemical

Conversion

Power Plant

Electricity (kWh/dry ton) 1,200 1,400

Plant Energy Efficiency 22% 28%

Plant Emissions (lbs/mmBtu output)

     NOX 0.067 0.0084

     SOX 0.010 0.0016

     PM 0.030 0.032

     CO 0.070 0.042

     NMVOC 0.018 0.0031

     CO2 890 690

     CH4 19 19

     N2O 0.065 0.065

81 
Table 38 presents the LCA results for all three power plant technologies based on the forest

management net assessment, in which the managed acres are held constant in order to compare

life cycle impacts from electricity generation.

Table 38. Life cycle impacts of the three modeled B2E power plant technologies 

Current

Generation

Biomass

Combustion

Power Plant

Current

Generation

Integrated

Gasification/

Combustion

Power Plant

Next Generation

Thermochemical

Conversion

Power Plant

Landscape managed Acres 2,700,000 2,700,000   2,700,000

Area treated Acres 1,971,451 1,971,451 1,971,451

Total energy consumed tera Btu 170 150   99

Fossil energy

consumed

tera Btu (120) (130) (160)

Petroleum energy

consumed

tera Btu (0.78) (0.96) (1.50)

Climate Change million tons CO2

equiv

5.90 4.60   0.37

Acidification million tons H+

equiv

0.47 (0.02) (0.43)

Smog formation million tons NOx

equiv

0.24 0.23   0.22

PM10 emissions to air million tons 0.12 0.11   0.11

3.4. Landscape Greenhouse Gas Model 

The results from the LGHG model were found to be divergent from the results of the LCA

model.  However, as a generalized model of GHG fluxes between above ground biomass and

the atmosphere, it provides some useful insights.  According to the LGHG model, both the

Reference Case and the Test Scenario show net increases in carbon in above‐ground live

biomass.  Over the 40‐year timeframe of the B2E model, the Reference Case sequesters

approximately 100,000 tons of CO2 from the atmosphere, while the Test Scenario removes more

than 125,000 tons.  Even calculating the release of CO2 from powerplant operations, the net CO2

sequestered in the forest remains positive when compared to the amount of CO2 released by

fossil‐fuel generation.   

82 
The consultant report in Appendix 9 shows a positive relationship between thinning and

reduction of CO2 and CH4 emissions.  When projected over a 100‐year timeframe (the

commonly accepted time period during which CO2 “clears” from the atmosphere), Morris

shows that the “net biogenic greenhouse gas reduction associated with treatments remains

greater than 1 ton of CO2 equivalents per bdt of treatment removal...”.

3.5. Wildlife Habitat Effects 

Wildlife habitat effects are measured through changes in vegetative structure.  The underlying

data for analyzing habitat effects associated with any kind of disturbance, whether wildfire or

treatments, are derived from the vegetation dynamics analysis.  Data extraction and

assumptions are described above in the Approach section, and results are reported here in the

following three categories:  (1) changes in forest structure; (2) impacts on forest‐structure

associated species; and (3) changes in service providing units (SPUs).  A case study focused on

American marten habitat was developed as a complement to the B2E project to test the

sensitivity of the B2E habitat modeling against observed marten occupancy data in the Sierra

Nevada.  Detailed results of effects on American marten habitat from disturbance regimes

modeled in the B2E project are reported in Appendix 5.

3.5.1. Forest Structure 

Changes in forest structure are measured by two variables: canopy closure and average tree

diameter.  Prior to treatment, the B2E landscape was predominantly in a high canopy closure

condition, with approximately 45% of the landscape characterized by dense canopy closure

(greater than 60%  or D closure class), and 25% of the landscape characterized by moderate

canopy closure (40 to 60% or M closure class).  The Reference Case, without treatment, resulted

in an increase in the proportion of the landscape with dense canopy closure (D) from 45% to

nearly 70% and a decrease in moderate canopy closure (M) to approximately 5%.  In contrast,

the Test Scenario remained largely unchanged over the duration of the 40 year treatment

period.  There was a significant difference in the three higher canopy closure classes in the

Reference Case, with open (25‐40% orP) and moderate (M) canopy closure classes declining and

the dense (D) canopy cover class increasing relative to the Reference Case.  

Prior to treatment, approximately 50% of the landscape (over 60% of the forested area) was

occupied by small diameter forests (average diameter of 12 to 24 inches dbh; diameter class 4),

with an additional 20% of the landscape (approximately 35% of the forested area) occupied by

medium/large diameter forests (mature forest; average diameter >24 inch dbh; CWHR diameter

classes 5 and 6).  Pole stands (diameter class 3) occupied  less than 3% of the landscape, with the

remaining landscape occupied by non‐forested habitat conditions.   

The Reference Case and Test Scenario differed greatly in the amount of the landscape in small

diameter (class 4) and medium/large diameter (class 5/6) forests.  In the Reference Case, the

proportion of the landscape in small and medium/large diameter forests switched in dominance

over the 40‐year period, such that at the end of the treatment period, 60% of the forested area

was occupied by medium/large diameter trees and 30% occupied by small diameter forest

(Figure 10).  In the Test Scenario, the proportion of the landscape in small diameter and

83 
medium/large diameter forests converged over the course of the modeling period to where each

diameter class occupied approximately 35% of the landscape (Figure 11).  No change occurred

in the number of acres of pole forests (diameter class 3) in the Reference Case or Test Scenario.   

0%

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WHR Size 4

WHR Size 5 & 6

Figure 10 - Changes in Diameter Class, Reference Case Define key. 

84 
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60%

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WHR Size 3

WHR Size 4

WHR Size 5 & 6

Figure 11 - Changes in Diameter Class, Test Scenario Define Key 

CWHR forested habitat types are defined by a combination of vegetation type, canopy closure

and average diameter.  Prior to treatment, nearly 50% of the landscape was occupied by small

diameter forest with higher canopy closure (4MD), typically considered mid‐seral conditions,

and most of the remaining forested area (20% of the landscape) was medium/large diameter

with higher canopy closure (5/6MD), typically considered mature or old forest conditions

(Figure 12).  Changes in average tree diameter made the greatest difference between the

Reference Case and the Test Scenario.  In the Reference Case, the landscape was dominated by

mid‐seral forests (4MD)  in the first decades, and changed to an old forest dominated landscape

(5/6MD) toward the end of the modeling period (Figure 12).  Old forests increased from about

25% of the landscape to about 50% of the landscape, whereas mid‐seral forests declined from

nearly 50% of the landscape to 20% of the landscape by the end of the 40‐year cycle.  In the Test

Scenario, mid‐seral and old forests equalized by the end of the treatment period, indicating that

both treatment and fire resulted in a decline in old forest conditions. In addition, open‐canopied

small diameter forest (4SP) increased slightly to 5 to 10% in the Test scenario.  

85 
0%

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60%

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pre

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3SP

3MD

4SP

4MD

5SP

5MD,6

Figure 12 - CWHR Types, Reference Case Define Key 

0%

5%

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pre

3SP

3MD

4SP

4MD

5SP

5MD,6

Figure 13 – California Wildlife Habitat Relationships habitat types, Test Scenario 

86 
Patterns of change within old forest conditions (5MD,6), revealed that mid‐seral forests had

approximately half moderate (M) and half dense (D) canopy closure, whereas almost all of old

forests had dense (D) canopy closure (Figure 14).  Thus, prior to treatment, the landscape was

two‐thirds mid‐seral and one‐third old forest conditions (one‐third each of 4M, 4D, and 5/6D).  

In the Reference Case, these three conditions diverged over the course of the modeling period,

resulting in old forests with dense canopy closure increasing from 25% to 50% of the landscape,

mid‐seral with dense closure declining slightly, and mid‐seral with a moderate closure nearly

declining to zero.  In the Test Scenario, the three conditions diverged only slightly, with a slight

increase in old forest characteristics with moderate closure.   

Within old forests alone, prior to treatment, none of the landscape fell into the medium to large

diameter forest classification (diameter class 5) and only a limited amount of medium to large

diameter forest remained on the landscape in both the Reference Case and Test Scenario by the

end of the modeling period (Figure 15).  This reflects the modeling assumption that, as small

diameter forests grow, they become multi‐layered old forests.  In the Test Scenario, it appears

treatments in old forests on private lands result in mid‐seral conditions, whereas on public

lands some remain old forests. This pattern reflects the differences in the types of treatments

modeled on public and private lands.   

Stand diameter was based on the quadratic mean diameter of the largest 75% of all trees.  The

use of this approach to determine average diameter is the reason so little of the landscape was

classified as pole forests (diameter class 3).  It is also likely that this diameter calculation

reduced the magnitude of changes observed as a result of biomass removal, which primarily

consists of removing smaller diameter understory trees.  For example, if primarily smaller

diameter trees are removed, then 75% of the remaining trees will consist of fewer small trees

with a larger average diameter.  Since canopy closure, or the density of tree crowns relative to

openings in the forest canopy, is a strong proxy for old‐growth forest habitat quality, this

method is considered a legitimate means to measure habitat condition.  However, it is relatively

insensitive to changes in the understory, that is, the conditions created by the smaller trees

growing beneath the canopy.  Calculation of the quadratic mean diameter of all trees (not just

75% of trees) would likely render a more accurate picture of the effects of fuels treatments on

both forest structure and wildfire behavior.  

 

87 
0

10

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60

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pre

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2046

pre

Time step

Percent of the landscape

4M

4D

5M

5D6

Figure 14 - Mid-seral (diameter class 4) and old forests (diameter class 5/6) with moderate (M; 40-

60%) to dense (D, >60%) canopy closure, Reference Case 

0

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4M

4D

5M

5D6

Figure 15 - Mid-seral (diameter class 4) and old forests (diameter class 5/6) with moderate (M; 40-

60%) to dense (D, >60%) canopy closure, Test Scenario 

88 
3.5.2. Forest Structure Associated Species 

Habitat guilds showed varied responses to changes in forest structure.  Prior to treatment,

nearly 70% of the landscape was occupied by 21 to 40 old forest associated species (n = 59

species total), followed by approximately 30% of the landscape occupied by 1 to 20 old forest

species (Figure 16).   No major changes were observed over time or between the Reference Case

and the Test Scenario – in both, the proportion of the landscape with 21 to 40 old forest species

declined by around 10%.  However, in the Test Scenario the proportion of the landscape with

greater than 40 species increased slightly (Figure 17).  The team found that, according to

CWHR, multi‐layered old forests (diameter class 6) were considered to have lower suitability

for a number of old forest associated species than single‐layered old forests (class 5MD).  Thus,

as multi‐layered forests were replaced by other conditions (e.g., small and medium/large

diameter forests), CWHR indicated that habitat conditions improved for some old forest

associated species.     

Old forest associated species richenss

0

10

20

30

40

50

60

70

2006

pre

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post

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Time step

Percent of the landscape

0 sp.

1 to 20

21 to 40

>40

Figure 16 – Richness of old forest associated species (n = 59) supported across the landscape, 

Reference Case 

89 
Old forest associated species richness

0

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20

30

40

50

60

70

2006

pre

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post

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pre

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Percent of the landscape

0 sp.

1 to 20

21 to 40

>40

Figure 17 – Richness of old forest associated species (n = 59) supported across the landscape, 

Test Scenario 

Patterns for old forest dependent species (which are a subset of 16 old forest associated species)

were evaluated to see if they provided any additional information about the potential effects of

harvest treatments on old forest associated species. Prior to treatment, nearly 40% of the

landscape was occupied by the highest species richness class (>10 species), with the remaining

landscape occupied equivalently (~20% each) by the other three richness classes (i.e., 0, 1‐5, 6‐10;

Figure 18).  In the Reference Case, an increasing proportion of the landscape supported high

numbers of old forest dependent species, following the pattern seen in the kinds of multi‐

layered forests mentioned above.  In the Test Scenario, the proportion of the landscape occupied

by each of the richness classes did not change.  However, the treatments in the Test Scenario

reduced the species richness index from the  highest richness class (>10 species) to the next

lower richness class (6‐10 species) immediately following treatment, then appearing to recover

at the end of the 10‐year growth period (Figure 19).   

90 
Od forest dependent species richness

0

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20

30

40

50

60

2006

pre

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post

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Time step

Percent of the landscape

0 sp.

1 to 5

6 to 10

>10

Figure 18 – Richness of old forest dependent species (n = 16) supported across the landscape, 

Reference Case 

Old forest dependent species richness

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5

10

15

20

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30

35

40

2006

pre

2006

post

2016

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Percent of the landscape

0 sp.

1 to 5

6 to 10

>10

Figure 19 – Richness of old forest dependent species (n = 16) supported across the landscape, 

Test Scenario 

The richness of early‐seral associated species, that is, species that are best associated with the

early stages of forest growth, (n = 118) followed the same pattern as changes in the prevalence

91 
of mid‐seral and old forest conditions.  Prior to treatment, approximately half the landscape

supported 1 to 20 early‐seral species, and the other half supported 20 to 40 early‐seral species.  

In the Reference Case, approximately half of the sites supporting 20 to 40 species were reduced

to supporting only 1 to 20 species.  Little change was observed in the highest (>40 species) and

the lowest (0 species) richness classes for early‐seral associated species (Figure 20).  In the Test

Scenario, the richness classes remained relatively constant.  

Early-seral associated species richness

0

10

20

30

40

50

60

70

2006

pre

2006

post

2016

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Time step

Percent of the landscape

0 sp.

1 to 20

21 to 40

>40

Figure 20 – Richness of early-seral associated species (n = 118) supported across the landscape, 

Reference Case 

The final habitat guild evaluated was the oak and acorn associated species (n = 44).  Prior to

treatment, nearly 50% of the landscape supported 1 to 5 species, and nearly 30% of the

landscape supported 5 to 10 species (Figure 21).  In the Reference Case, an increasing proportion

of the landscape (approximately 10% more) supported fewer oak associated species (1 to 5

species).  However a roughly equal proportion supported the highest species richness class (>10

species) as seen in Figure 21.  In contrast, the Test Scenario appeared to slightly improve

conditions for oak associated species.  Increases in oak associated species were significant with

each increment of increasing treatment.  The indications of these results would be strengthened

if the presence of oaks in conifer forests could have been considered.  However, data on the

density of oaks was not available at the time of this analysis.  

92 
Oak and acorn associated species richness

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

2006

pre

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post

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Time step

Percent of the landscape

0 sp

1 to 5

5 to 10

> 10

Figure 21 – Richness of oak and acorn-associated species (n = 44) supported across the 

landscape, Reference Case 

The analysis of exotic species richness was not very informative.  It simply showed that that

proportion of the landscape with one or more exotic species started low (~10%) and did not

change over time under the Reference Case or Test Scenario.   

3.5.3. Service Providing Units 

Seed dispersers (n = 22) and bioturbators (n = 15) had similar patterns of response within and

among scenarios as early‐seral associated species – as illustrated by the seed dispersers, which

declined over time in the Reference Case (Figure 22), but remained at similar or higher levels

over time in the Test Scenario (Figure 23).   

93 
Seed disperser species richness

0

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20

30

40

50

60

70

80

2006

pre

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Percent of the landscape

0 sp

1 to 5

6 to 10

> 10

Figure 22 – Richness of seed dispersing species (n = 22) supported across the landscape, 

Reference Case 

Seed disperser species richness

0

10

20

30

40

50

60

S3 06

pre

S3 06

post

S3 16

pre

S3 16

post

S3 26

pre

S3 26

post

S3 36

pre

S3 36

post

Time step

Percent of the landscape

0 sp

1 to 5

6 to 10

> 10

Figure 23 – Richness of seed dispersing species (n = 22) supported across the landscape in the 

Test Scenario 

Pollinators (n = 3) and decomposers (n = 13) showed no substantive change either over the time

of the study or between the Reference Case and the Test Scenario.  Prior to treatment, less than

10% of the landscape had one or more pollinator species.  No change was observed over time in

the Reference Case.  However the Test Scenario did show a 5% fluctuation in the proportion of

the landscape supporting one or more pollinators, varying between 5% and 10%.  Prior to

treatment, 75% of the landscape supported one or more decomposer species.  Both the

94 
Reference Case and the Test Scenario showed about a 5% decline in the proportion of the

landscape supporting one or more decomposer species.    

Prior to treatment in the Test Scenario, approximately 15% of the landscape supported the

highest richness (>40 species) of insect regulators (n = 93) and nearly 60% of the landscape

supported 21 to 40 insect regulators, the second to the highest richness class for this guild. In the

Reference Case, there was a 10% decline in the proportion of the landscape supporting the

greatest richness of insect regulators, with a concomitant increase in areas that supported only 1

to 20 species, indicating a modest decline in the ability of the landscape to support a diversity of

insect regulators.  In the Test Scenario, the 20‐to‐40 species richness class declined and the 1‐to‐

20 species richness class increased by about 10% each, indicating a minor decline in the

landscape’s ability to support a diversity of insect regulators as compared to the Reference Case.   

Over 70% of the landscape supported habitat suitable for one or more herbivore regulators (top

carnivores; n = 9), with 50% of the landscape providing habitat for 3 or more species (Figure 24).  

In the Reference Case, the proportion of the landscape supporting the highest species richness

(>4 species) declined nearly 20% to 3 to 4 species (Figure 24).  In the Test Scenario, only minor

changes in species richness were observed and they appeared to balance out to minimal overall

change in capacity to support herbivore regulators (Figure 25).  No substantive response in the

richness of secondary herbivore regulators (n = 21) was discernable over time for either

scenario.       

Herbivore regulator species richness

0

5

10

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20

25

30

35

40

45

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2006

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Percent of the landscap

e

0 sp.

1 to 2

3 to 4

>4

Figure 24 – Richness of herbivore regulating species (n = 9) supported across the landscape in 

the Reference Case 

95 
Herbivore regulator species richness

0

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35

40

2006

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Percent of the landscape

0 sp.

1 to 2

3 to 4

>4

Figure 25 – Richness of herbivore regulating species (n = 9) supported across the landscape in 

the Test Scenario 

3.5.4. American Marten 

Surveys of the B2E landscape and a new predictive model were used to evaluate whether the

landscape could support reproductive populations of American marten.   

This element of the study was developed experimentally as an improvement in habitat

modeling methods, comparing the results with the CWHR modeling used for the larger

landscape.  Field data from the landscape supported the assumption that no suitable habitat for

marten occurred below 5,000 feet.  Above 5,000 feet, modeling showed that the amount of land

in old forest conditions (CWHR diameter classes 5 and 6 and canopy cover classes M and D)

was the best predictor of American marten occurrence. Based on these data, the predictive

model was used to determine the percent of the landscape in each of four categories of

probability of occupancy:  none (< 10%), low (10 to 30%), moderate (30‐60%) and high (>60%).   

The predicted values from vegetation mapping for the B2E project area showed only a small

amount (3%) of highly suitable habitat (Figure 26).  The majority of the B2E project area (89.9%)

was characterized by habitat with low or no probability of occupancy (< 30%). The predictive

model indicated that the western portion of the B2E project area (south of Lake Almanor)

contained relatively little habitat that could support marten reproduction.  This finding is

consistent with the lack of marten detections by surveys in this region (Tom Kirk, personal

communication).  The west side was the most likely region to be used by dispersing martens as

a corridor between known population centers located to the north and south.   

96 
Management scenarios had no appreciable effect on the amount of habitat suitable for marten.  

The only variable in the model that could be affected by forest management was the amount of

old forest conditions (e.g., in CWHR 5M, 5D, and 6 habitat).  Old forest habitat (a characteristic

of suitable marten habitat) increased more over time in the Reference Case than in the Test

Scenario.  However, those increases occurred below 5000 feet, the typical lower elevation limit

for habitat suitable for the American marten.  

97 
Figure 26 – Probability of occupancy within suitable reproductive habitat for marten (Martes 

americana) in the B2E landscape based on predictive models. 

98 
3.6. Cumulative Watershed Effects 

Cumulative watershed effects analysis assesses the potential movement of soil and water to

approximate synergistic effects at a watershed scale.  A watershed may be defined differently,

depending on the purpose and scale of the analysis: For the Beta landscape, watershed

boundaries were determined by standardized Hydrologic Unit Codes (HUCs) at the 6th order of

coding (U.S. Geological Survey 2009), which resulted in analytical units ranging in size from

2,500 to 46,000 acres.  As described in the Approach section of this report, the CWE model is a

disturbance‐based model that normalizes all disturbances (treatments, wildfires, and so forth)

to an acre of road. The resulting metric of equivalent roaded acres (ERAs) is compared with its

established watershed threshold of concern (TOC) to assess the potential for cumulative

watershed effects.

3.6.1. Total Equivalent Roaded Acres (ERAs) 

Figure 27 shows the total ERAs for all watersheds in the Beta landscape by year for both the

Reference Case and Test Scenario.  The Test Scenario produced lower ERAs than the Reference

Case, explained by the fact that treatments were effective in reducing the size and intensity of

wildfires, as shown in the sections on wildfire and vegetation treatments in this report.  Soil

erosion, as modeled through ERAs, was reduced accordingly by wildfire reductions. Industrial

Private Forestry (IPF) lands are included in the figure as an reference point of some interest,

given the ongoing concerns expressed by the public about potential watershed impacts of

commercial harvesting.  This study shows that the impacts of no treatment under the Reference

Case are slightly higher, as measured by ERAs, than IPF commercial treatments.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

2004 2009 2014 2019 2024 2029 2034

Year

Total ERAs

Reference Case

IPF Only

Test Scenario

Figure 27 - Total Equivalent Roaded Acres by Year 

99 
Figure 28 shows ERAs by scenario, disturbance type and year.  The Reference Case scenario has

the highest wildfire sediment ERAs and the Test Scenario has the lowest.  In the Test Scenario,

the reduced ERAs from wildfire still exceed the increase in ERAs from treatment, which

constitutes a net reduction.  Roads were held constant in both the Reference Case and Test

Scenario. IPF treatments and IPF wildfire effects are also shown, as in Figure 28, to give an

additional reference point for analysis.

0

5000

10000

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30000

35000

40000

45000

50000

2004 2009 2014 2019 2024 2029 2034

Year

ERAs

Reference Case

IPF Only

Test Scenario

Wildfire Ref

Wildfire IPF

Wildfire Test

Roads

Figure 28 - ERAs by year and disturbance type 

3.6.2. Cumulative Watershed Effects Analysis 

The findings of the cumulative watershed effects analysis strongly parallel the findings in the

wildfire analysis.  The study shows that fuels treatments reduce the overall potential for

cumulative watershed effects, as measured by ERAs, when compared to the effects of wildfire.   

Table 39 shows the risk ratios for the Reference Case and Test Scenario for those watersheds

over the threshold of concern (TOC) for year 2006, 2016 and 2026.  (As described in the

“Approach” section, the TOC is based on a watershed’s sensitivity to disturbance and the

beneficial uses of its water. A watershed exceeds the TOC when the risk ratio exceeds 100.)  The

Reference Case shows a higher number of HUC6 watersheds over TOC, and by a greater

percent, compared to the Test Scenario.  

100 
Table 39. Risk ratios for watersheds over threshold by model decade 

 Reference Case Test Scenario 

HUC_NAME 2006 2016 2026 2006 2016 2026

Stoney Creek 490 434 

Lower Last Chance 

Creek 488 413 

Clarks Creek 275 142 

McClellan Canyon 295 218 

Mc Dermott Creek 270 190 

Middle Last Chance 

Creek 258 177 

Pineleaf Creek 184 87 

Otis Canyon 122 35 

Lower Pine Creek 114 107 

Upper Red Clover Creek 572 216 

Dixie Creek 297 49 

Big Grizzly Creek 200 74 

Bald Rock Canyon 138 38 

Adams Neck 180 75 

Wild Yankee Creek 112 48 

Last Chance Creek 101 42 

Carman Creek 419 251

Antelope Creek 267 123

Clairville Flat 154 69

Seneca 172 143

In the Test Scenario, the treatments by themselves did not push any of the HUC6 watersheds

over TOC.  In all cases, the TOC was exceeded due to modeled wildfires.  In watersheds that

did not have fires modeled in them, there were more disturbances due to treatments and small

increases in sediment, but not enough to exceed TOC.  The slight increase in sediment runoff by

treatments was entirely compensated by the reductions in fire intensity and fire size in

watersheds where wildfire was modeled.  Recovery from the effects of wildfire commonly

happens in a relatively short time period (2 to 4 years). However, the initial adverse watershed

effect can be severe if a high intensity rainfall event occurs shortly after the fire.  In the Test

Scenario, the number of watersheds over TOC dropped from seven to one as a result of the fuel

treatments.  An example, as seen in Table 39, is the change in the risk ratio of Upper Red Clover

Creek, which decreased from 572 to 216 because the fuel treatments reduced modeled wildfire

intensity.   

The following two Figures (28 and 29) compare watersheds in the B2E Beta landscape by

threshold class for the Reference Case and the Test Scenario in model years 2006 and 2016. The

fuel treatments moved a few watersheds from one threshold class to the next higher class, but

none of the treatments exceeded the watershed threshold.  The B2E modeled wildfires moved

the watershed well over TOC in most cases.  Treatments did not shift any of the study’s

101 
watersheds over TOC.  Eight watersheds were shifted by treatments from over TOC in the

Reference Case to under TOC in the Test Scenario in years 2006 and 2016.

Figure 29. CWE risk assessment by watershed in 2006 model year 

102 
Figure 30. CWE risk assessment by watershed in 2016 model year 

3.7. Economic Results 

The economic analysis of the B2E project was intended to span all domains wherein costs could

be measured.  Pricing of non‐market values or ecosystem services was used where studies in

the peer‐reviewed literature were deemed robust enough to warrant reporting in this study.  

Additional studies to prince non‐market ecosystem services were beyond the scope of this

analysis. Reasonable attempts were made to capture values, such as recreation damage

functions, where possible.  The analysis is intended to show benefit‐cost relationships, and does  

not attempt to resolve for economic efficiency.   

As with the other domains, the economic analyses were conducted on a Reference Case (with

no treatments, but including impacts of wildfire) and a Test Scenario.  The detailed economic

analysis can be found in Appendix 8.  Additionally, a spreadsheet model is available for

download, and may be accessed through the Energy Commission’s web services.

Table 40 shows estimates of the initial value of assets in the study area at risk to fire damage

and fire‐related losses (in present value) to these assets over the 40‐year analysis period.  Of the

$20.8 billion in initial value, timberland resources comprise more than $18.1 billion in asset

103 
value, followed by structures ($2.4 billion), recreation resources ($117.8 million), infrastructure

($102.3 million), and agricultural lands ($41.3 million).    

Table 40 - Study Area Market Asset Value Changes by Treatment Scenario (in millions of 2006 

dollars) 

Asset Type

Initial

Value

Loss in Value Due to Fires

Reference Case Test Scenario

  Agricultural lands $41.3 $3.6 $2.8

  Recreation resources $117.8 $1.7 $0.95

  Infrastructure $102.3 $3.0 $1.9

  Structures $2,364.5 $43.0 $27.2

  Timber $18,144.2 $612.7 $385.6

Total $20,770.2 $664.1 $418.5

Note: The loss in value for the treatment scenarios represents the accrued present value of

the losses from fires over the 40‐year project period.   

The present value of losses due to fire over the 40‐year period is $664.1 million under the

Reference Case and $418.5 million under the Test Scenario. The reduction in asset value losses,

relative to the Reference Case, is attributable to timber harvest and fuels treatment activities

associated with the vegetation management treatments under this scenario.    

Table 41 shows the annualized costs and revenues associated with the Reference Case and the

Test Scenario.   The costs include capital and operations and maintenance (O&M) costs for fuels

treatment and power production, and the revenues are those generated from the sale of power

and saw logs, both from timber harvest and salvage activities.  (Note that the costs of timber

harvesting are accounted for in saw log revenues in Table 41, which are revenues net of

production costs.) The volume of chips generated by thinning activities are sufficient to fuel

seven power plants at an average capacity of 9.8 MW per plant under the Test Scenario.   

104 
Table 41 - Annualized Costs and Revenues for No Treatment (Reference Case) and With Treatment 

(Test Scenario), in millions of 2006 dollars. 

Value Category

Reference Case Test Scenario

Project costs:

  Fuels treatment

    ‐‐ Capital costs N/A $4.2

    ‐‐ Operations & maintenance costs N/A $15.0

  Power Plant Operations

    ‐‐ Capital costs N/A $15.6

    ‐‐ Operations & maintenance costs N/A $31.9

  Fire suppression $2.05 $1.6

  Rehabilitation N/A $0.03

Total costs $2.05 $68.3

Project revenues:

  Power generation N/A $27.9

  Saw logs from timber harvest N/A $72.3

  Saw logs from salvage N/A $4.2

Total revenues N/A $104.4

Fuels treatments under the Test Scenario also produce biomass that would be available for

power plant operations.  Based on treatment and transportation requirements under this

treatment scenario, biomass fuel delivered to power plants would cost an estimated $68 per

BDT.  Based on the modeling of power plant financials (see Appendix 10) power plant operators

can pay up to $8.20 per BDT in order to achieve an acceptable rate of return on investment

under the Test Scenario.  (The financial model assumed that power plant project investors

would require a long‐term after‐tax return to equity of 14.5 percent to attract investment for a

project.)  Barring some other source of revenue, such as revenue from steam sales or

government grants, constructing and operating biomass power plants would not be feasible at a

fuel cost of $68 per BDT.   

This last finding, that biomass plant operators may only be able to afford $8.20 per BDT for

feedstock, is counterintuitive, and contradicted by the existing evidence of the biomass power

industry in California (which typically pays between $25 and $45 per BDT for forest fuels).  

105 
However, it must be recognized that the current biomass power industry in California is in

large measure dependent upon the subsidies for capacity payments under the federal Public

Utility Regulatory Policy Act (PURPA), passed in 1978 in response to the 1973 energy crisis.  

Among its provisions was to require purchase of renewable power by investor owned utilities.  

In addition, biomass power plants negotiated prices for power that were typically at least 1.5 to

2 cents above the wholesale price of electricity, under long‐term contracts.  In California, many

of the biomass power facilities built in the late 1980s and early 1990s had retired their debt by

2006, the year in which this analysis begins.  The combination of capacity payments and debt‐

free power sales accounts for the difference between the current (2006) market, and a greenfield

power plant developed in 2006 without PURPA subsidies.  

From a broader societal perspective, subsidies based on the value of avoided asset losses and

avoided fire suppression and rehabilitation costs could be offered to power plant operators to

offset the relatively high cost of biomass as a feedstock.  Under the Test Scenario, the avoided

fire damage to assets and reduced fire suppression and rehabilitation costs in the B2E landscape

would total about $4.6 million annually.  When this asset benefit is incorporated into the power

plant financials (by lowering annual O&M costs by $4.6 million), the analysis indicates  that a

power plant operator could pay up to $54.80 per BDT for biomass fuel, while still achieving the

targeted return on investment.  Fuel subsidies for biomass power plant operations of up to

$46.60 per BDT ($54.80 minus $8.20) would be required in order to achieve break‐even based on

total costs and benefits.

Although environmental costs and benefits are not monetized and included in the economic

analysis, results from evaluating effects of the treatment scenarios on habitat indicate that

implementation of the treatment scenarios would likely have an overall beneficial effect that

would positively contribute to the net present value of these scenarios.  On the other hand, the

evaluation of carbon sequestration effects of the treatment scenarios indicate that, in the short

term, carbon sequestration of the treated forest would be reduced and greenhouse gases would

increase.  In the long term, however, the increased productivity and fire resiliency of the treated

forest would result in a substantial and prolonged net decrease in the level of atmospheric

greenhouse gases.  Effects on air quality would vary under the treatment scenarios, with CO

and NOx emissions increasing and particulate matter, VOC, and SOx emissions decreasing over

the four‐decade study period.  Overall, it appears that consideration of habitat, carbon, and air

quality effects would likely contribute positively to the net economic value of the treatment

scenarios.

3.7.1. Conclusions and Key Findings 

The Test Scenario, which includes treating public and private lands, generates annualized

benefits that exceed estimated costs, indicating that implementing the Test Scenario would

incrementally contribute to net economic value.   

Vegetation management treatments on public lands in the Beta landscape cost an estimated $5.3

million annually and  generate about $4.6 million annually in benefits from avoided asset

damage (due to fire) and reduced fire suppression and rehabilitation costs, in addition to $22

106 
million in saw log net revenues.  The benefits from vegetation management treatments (i.e.,

avoided asset damages and reduced fire suppression and rehabilitation costs) are relatively

small in the context of total economic benefits of the Test Scenario, which are generated

primarily by revenues related to the sale of sawlogs from vegetation treatments.  The relatively

small effect on avoided fire‐related damages to agricultural, recreation, structural, and

infrastructure assets from vegetation management treatments reflects the undeveloped and

generally rural characteristics of the Beta landscape.  Only avoided fire‐related damages to

timberland assets are significant.   

The estimated net operating deficit of power plants that use chips produced from forest biomass

in the study area reflects the relatively high cost of producing and delivering chips.  Break‐even

analysis indicates that, under the Test Scenario, the cost of chips for fuel would need to decrease

from about $68 per BDT to about $8.20 per BDT for the power plants to be economically viable.  

Subsidies based on avoided asset damage and reduced fire suppression and rehabilitation costs

would need to contribute an estimated $46 per BDT.    

Although environmental costs and benefits were not monetized and included in the economic

analysis, results from evaluating effects of the treatment scenarios on habitat, carbon

sequestration, and air quality suggest that consideration of these effects would likely contribute

positively to the net present value of the scenarios

107 
108 
4.0 Conclusions, Observations and Recommendations 

Overall conclusions and recommendations of the B2E project can be summarized in four key

categories, as briefly described below.  The following figure is repeated from the introductory

section, as a reminder to the reader of the complex interactions of the processes modeled by the

project.    Detailed recommendations for further model improvements or development are

included in many of the appendices, pertaining to specific components of the B2E project.

Figure 31 - Review of B2E Process Models and Results 

4.1.1. Scenario development recommendations 

Further development of the B2E model would include varying the size, spatial distribution, and

types of treatments to determine the impacts of pursuing other goals and objectives on the same

land base.  For example, the research team determined that further model development would

include the following scenarios on PMU lands (with private forest land treatments being held

constant):

• Natural Disturbance Regime:  PMU lands managed to maximize long‐term biological

diversity and integrity in the face of anticipated climate change dynamics

109 
• Maximum Fire Resilience Regime:  PMU lands managed to maximize resilience to

natural wildfire regimes, with aggressive reduction of fire severity; heavy thinning in

most forest areas not specifically reserved for sensitive species habitat

• Carbon Sequestration Regime:  PMU lands managed to maximize in‐forest carbon

sequestration (i.e., harvested wood products not counted in C sequestration accounting

scheme), while reducing risk to wildfire (as threat to Carbon assets)

While these scenarios were not developed fully in the B2E Project analysis, the analytical basis

for them has been recorded in treatment prescription specifications on PMU lands.  These

specifications can be achieved either by application of algorithms to the existing tree lists (i.e.,

proportional changes in treated strata), or by altogether new runs of altered prescriptions on the

original 2006 tree lists, generating new tree lists for each out‐year, as was done for the Test

Scenario.   

4.1.2. Life Cycle Assessment Recommendations 

The life cycle assessment for the B2E project has provided important insights on at least two

levels:  1) what has been learned about application of LCA principles to complex and multi‐

objective systems; and 2)  the quantifiable life cycle results of the Test Scenario compared with

the Reference Case, and what it suggests for further research and development.   

On the first level, the team has concluded the following:

1. It is possible to construct a set of interconnected forest operations and equipment

characterization, fire behavior assessment, and the power plant analysis models in

support of LCA.   

2. Data and models are available to represent the life cycle of a range of technologies for

developing U.S. forest bioproduct systems.   Reliance on the discipline‐specific B2E

project models (the forest operations and equipment characterization, the vegetative

dynamics assessment, the fire behavior assessment, and the power plant analysis)

combined with the U.S. EPA NONROAD and MOBILE models and the U.S. DOE’s

GREET model provided a wealth of data for systems assessments.  Similar data

availability is expected for technology alternatives, as demonstrated in the assessment of

power plant alternatives.  However, shortcomings herein included the limited scope

(e.g., the small number of impacts assessed, the omission of infrastructure construction

models, and the limited number of possible treatment scenarios investigated), lack of

uncertainty data for all assessments, and the need to project estimates for all models into

the future.  These shortcomings do not appear to be insurmountable in the short term,

and are recommended for future research.

3. In addition to addressing scope and modeling shortcomings, there are a number of

remaining forest bioproduct questions that can be explored with currently available

data.  For example, investigations of power plant and transfer station citing and

optimization of regional utilization of forest residuals, agricultural residuals, and bio‐

based municipal solid waste could be built around the data presented here.

110 
4. Presentation of results in gross and followed by a variety of computational

interpretations provide insights for decision making and a starting place for future

assessments.   

On the second level, the LCA modeling and assessment work suggestions at least the following:

1. The Test Scenario provides a net benefit for total energy consumption and reduces fossil

and petroleum consumption when compared to the Reference Case.  Also, whereas the

B2E power plant efficiency is critical to the overall energy balance, the consumption of

fossil and petroleum fuels during harvest, chip transport, or power plant operation play

a less important role.

2. The Test Scenario results show an improvement for NMVOCs, CO, and SOx when

compared to the Reference Case.  Alternatively, little difference is seen in NOx and

PM10 emissions.  

3. Forest processes related to photosynthesis, plant respiration, decomposition of litter and

soils, despite the uncertainty in estimates, are the most important to understanding

whether or not the Reference Case and the Test Scenario contribute to climate change.   

Recommendations for future work include the addition of sensitivity analysis to the

assessments, comparison of the results to related LCAs, consideration of additional process

alternatives throughout the life cycle, and other aspects needed to complete the study as

described in the goal and scope document.   

4.1.3. Wildlife habitat modeling recommendations 

The wildlife habitat modeling team calculated the probability of any given acre having certain

structural conditions and being suitable for co‐occurring species in a given ecological grouping.  

Changes in the seral condition of forests were expected and observed to change as a result of

forest management.  As expected, the starting probability of 23% of any given acre having a

large average diameter increased under all scenarios, but increased twice as much in the

Reference Case compared to the Test Scenario.  Canopy closure was expected to decline in

harvested areas, and indeed the probability of a given acre having high canopy cover started at

44% and went from a 50% increase in the Reference Case to a slight decrease in the Test

Scenario.   The combination of large diameter and high canopy cover represents optimal old

forest conditions, and we see the probability of this condition starting at near 20%, more than

doubling in the Reference Case, and still increasing a modest 23% in the Test Scenario.

The probability of a given acre supporting a high number (>20; maximum observed = 46) of co‐

occurring old forest associated species started high (66%), and experienced a minor decline for

all the scenarios (see Table 42).  This response is attributable to the fact that many species

associated with old forests are also associated with earlier seral conditions (e.g. American

robin), so some will respond positively to a shift to more early seral conditions, while others

will respond negatively.  The probability of conditions supporting a high number (>10;

maximum observed = 14) of old forest dependent species closely followed that of high canopy

cover conditions, with an over 30% increase in probability occurring in the Reference Case, and

111 
a decline occurring in the Test Scenario.  Approximately half of the existing landscape is

estimated to support a high number (>20; maximum observed = 81) of early seral associates.  

Habitat for early seral associates declined by nearly half in the Reference Case and showed a

slight increase in the Test Scenario.   

The habitat analysis team examined a number of species groups that have been demonstrated to

perform important ecosystem services.  Insect regulators consist of invertivores, and the

probability of suitability for this group was high (nearly 75%) declined between 10 and 20%

across all scenarios.  Seed dispersers is one group, and since it is comprised primarily of species

associated with early seral conditions, suitability for the majority of these species was nearly

identical to early seral associates.      

Table 42. Probability that any given acre will support a particular forest condition or suite of 

species as defined by ecological groupings, and how that probability changes with each scenario 

Reference Case Test Scenario 

Attribute 

Starting 

point 

End point % change End point % change 

Large average 

diameter 

(>24 in) 

0.23 0.53 +134.4 0.37 +63.8

High canopy 

closure (>60%) 

0.44 0.68 +52.5 0.43 -1.4

High canopy 

closure and 

large average 

diameter 

0.22 0.49 +123.5 0.27 +23.3

Old forest 

associates 

(>20) 

0.66 0.57 -13.6 0.62 -7.5

Old forest 

dependents 

(>10) 

0.38 0.50 +34.1 0.37 -1.3

Early seral 

associates 

(>20) 

0.48 0.28 -41.7 0.50 +5.5

Insect 

regulators 

(>20) 

0.73 0.59 -19.7 0.65 -12.1

Seed dispersers 

(>5) 

0.42 0.21 -50.4 0.42 -1.5

4.1.4. Economic analysis limitations and recommendations 

Although benefit‐cost analysis is widely used in the analysis of regulations and public policy,

the approach is based on a number of underlying assumptions that have been challenged over

the years.  These assumptions include equating changes in income with social well‐being,

112 
assuming that willingness‐to‐accept compensation and willingness‐to‐pay measures are

essentially equal and substitutable, and using straight‐line discount rates.  According to Gowdy

(2007), these and other basic assumptions underlying benefit‐cost analysis are coming under

increasing scrutiny because the assumptions are at odds with observed human behavior.     

The successful application of benefit‐cost analysis to natural resource policy issues depends on

a scientific understanding of underlying physical and biological processes that shape the

valuation of environmental costs and benefits.  If these processes are not well understood,

deriving valid estimates of monetary values is difficult.  Boyd (2007) addresses the

measurement challenges inherent to valuation of ecosystem services in the B2E study area. The

lack of observable data from market transactions greatly increases the challenge to monetizing

most of the environmental costs and benefits from the B2E Project.    

Although sensitivity analysis was used to test the validity of certain conclusions drawn from the

benefit‐cost analysis, a more rigorous application is needed to thoroughly evaluate the

sensitively of the results to the omission of monetized environmental costs and benefits and to

data uncertainties.  Conducting a comprehensive economic assessment at the B2E landscape

would require a research effort that is an order‐of‐magnitude greater than this one.   

Effects of Population Growth and Future Land Use Development 

Although the economic analysis considered costs and benefits over a 40‐year analysis period,

changes in baseline conditions due to external forces such as population growth, recreation

growth, and urban development were not considered.  With the exception of tree growth in the

supporting vegetation analysis, the economic analysis is considered static and does not account

for important dynamic effects that would affect the value of assets at risk to wildfire.  

More research is needed to refine the damage functions and asset recovery rates that were

incorporated into the benefit‐cost model.  This is partly responsible for the fact that

rehabilitation costs are difficult to capture adequately in the economics model.  Rehabilitation

costs associated with wildland fires are highly variable.  The per acreage rehabilitation cost

estimate incorporated into the benefit‐cost model likely does not accurately capture probable

rehabilitation costs within the study area.  Similarly, assumptions built into the benefit‐cost

model concerning the number of acres that would be rehabilitated under each scenario may

have considerable error.   

4.2. Benefits to California 

The Biomass to Energy project has contributed to California’s capacity to analyze forest biomass

utilization opportunities at the landscape scale.  Even in draft form, the Secretary of the United

States Department of Agriculture has identified the project as a “highly influential scientific

assessment,” with implications for how the USDA Forest Service would use life cycle

assessment to evaluate the benefits of biomass power.

California has approximately 40 million acres of forest lands, nearly half of which are managed

by private landowners.  The economics of private forest land management historically have

constrained opportunities for effective and sustainable management.  The Biomass to Energy

113 
114 

project’s approach is likely to assist policy makers and landowners in evaluating

comprehensive and long‐term benefits to the environment, as well as enhancing economic

opportunities in forest‐dependent communities.

The benefits of thinning forests, and using the waste products for energy production, are largely

a matter of public choice and policy making.  Many of the benefits of managing California’s

forests – such as reducing wildfire effects, saving fire suppression costs, providing clean air and

water and other climate benefits – may be better reflected in future markets and public policy as

a result of this project.  Biomass power is a rare form of renewable energy in that it provides a

broad range of benefits at relatively low cost to the consumer and substantial ancillary benefits

to the environment.  Further quantification and analysis, building on the work presented by the

project, will help California’s policy makers and legislators evaluate how forest biomass will

contribute to larger societal and environmental goals.   
Acronym Key

Original Term Acronym/Abbreviation 

Biomass to Energy B2E

bone dry tons BDTs

USDI Bureau of Land Management BLM

California State Board of Equalization BOE

break‐specific fuel consumption BSFC

British thermal unit, one million Btu, one

trillion Btu

Btu, mmBtu, tera Btu

Clearcut CC

CALFIRE (formerly California Department

of Forestry and Fire Protection)

CDF

California Department of Fish and Game CDFG

methane CH4

carbon monoxide, carbon dioxide CO, CO2

Commercial Thinning CT

Cumulative Watershed Effects CWE

California Wildlife Habitat Relationships CWHR

diameter at breast height dbh

defensible fuels profile zone DFPZ

emission factors EF

Equivalent Roaded Acre ERA

Fire Emissions Joint Forum FEJF

Fire and Fuels Extension FFE

Forest Inventory Analysis FIA

First Order Fire Effects Model FOFEM

USDA Forest Service   Forest Service/FS

Fuel Management Erosion FuME

Forest Vegetation Simulator (Model) FVS

Geographic Information System GIS

global warming potential GWP  

hydrogen ions H+

Inland California Southern Cascades ICASCA

Industrial Private Forests IPF

International Standards Association ISO  

kilogram kg

kilowatt hour   kWh  

life cycle assessment LCA

115 
thousand board feet mbf

moisture condition MC

mean diameter MD

Megawatt/megawatt hour MW/MWh

nitrous oxide N2O

NFDRS National Fire Danger Rating System

National Environmental Policy Act NEPA

National Forest Management Act NFMA

Non‐Industrial Private Forests NIPF

non‐methane volatile organic compounds NMVOC

non‐greenhouse gas Non‐GHG

nitrogen oxides NOx

Public Conservation and Recreation PCR

Pre‐Commercial Thinning PCT

particulate matter, particulate matter less

than 2.5 microns in diameter, particulate

matter less than 10 microns in diameter

PM, PM 2.5, PM10

Public Multiple Use PMU

Restrictive Thinning RT

Salvage SAL

Stewardship and Fireshed Assessment SFA

Selective Harvest   SH

sulfur dioxide SOx

strategically placed area treatment SPLAT

threshold of concern TOC

Tool for the Reduction and Assessment of

Chemicals and Other Environmental

Impacts

TRACI

Urban and Other U

United States Department of Energy U.S. DOE

United States Environmental Protection

Agency

U.S. EPA

volatile organic compounds VOC

Watershed Erosion Prediction Project WEPP

Western Regional Air Partnership WRAP

116 
5.0 Glossary 

biomass non-commercial component of the wood produced in 

forest harvesting operations 

California Wildlife Habitat 

Relationships (CWHR) 

a state-of-the-art information system for California's 

wildlife. CWHR contains life history, geographic 

range, habitat relationships, and management 

information on 692 species of amphibians, reptiles, 

birds, and mammals known to occur in the state. 

catastrophic fire stand replacement or high intensity fires that cause 

damage to ecological and/or economic assets and 

values. The B2E Project also refers to these types of 

fires as uncharacteristically severe wildfires. 

defensible fuels profile zones 

(DFPZs) 

shaded fuelbreaks which are designed with the 

objective of providing a place to deploy firefighters 

in the event of a wildland fire. Firefighters use 

DFPZs to make a stand to hold or contain a fire. 

domains discrete segments of modeling and analysis in the 

B2E Project 

ecological endpoints ecological functions that have a directly measurable 

human welfare function, and that can be quantified 

in an accounting system that makes them fungible 

equivalent roaded acre (ERA) equates all disturbances to one acre of road 

fire adapted forests forests that have evolved with wildfire 

fire line intensity behavior of the flames at the perimeter of the fire as 

it moves through vegetation 

First Order Fire Effects Model 

(FOFEM) 

a computer program developed to predict and plan 

for fire effects. First order fire effects are those that 

concern the direct or indirect or immediate 

consequences of fire. FOFEM provides quantitative 

fire effects information for tree mortality, fuel 

consumption mineral soil exposure, smoke and soil 

heating. 

national fire danger rating system 

(NFDRS) codes 

a Forest Service rating system which defines fuel 

models based on the primary carriers of fire 

service providing units categories of species including insect regulators, 

seed dispersers, decomposition aides, and herbivore 

regulators which provide ecological services to 

forest management 

side common term used by harvest contractors to denote 

a separate and distinct blend of harvest equipment 

conducting harvest activities as a separate operation. 

slash woody residues that are generated in the forest from 

harvesting activities 

speciose relative term for species richness 

117 
stated preference method a classification system for economic analysis. Stated 

preference methods fall into three primary 

categories: a) contingent valuation, in which the 

respondent is required to make a comparison of 

value between the resource value in question and 

known trade-off values; b) travel-cost analysis, in 

which travel effort and investment constitutes a 

proxy for the value of the resource; and c) hedonic 

pricing, which uses property values as a proxy for 

the value of the resource as compared with 

comparable purchase prices. 

strategically placed area 

treatments (SPLATs) 

pattern of treatment areas distributed across a 

landscape oriented according to the prevailing wind 

direction in order to intercept a spreading wildfire 

thousand board feet (mbf) volume of the log based upon board foot measure. 

One board foot represents the amount of wood 

contained in an unfinished board measuring one inch 

thick, one foot long, and one foot wide. 

treatment activities discrete management actions or events, such as 

thinning or understory burning. 

treatment prescription a series of management activities applied over the 

40-year timeframe to a specific piece of ground 

tree lists, including The Larch forest inventory datasets which consist of (a) site 

reference information (plot location, inventory date, 

slope, aspect, elevation) and (b) the characteristics 

(species, size, canopy position, and so forth) of the 

trees sampled, including The Larch. 

118 
6.0 References 

Adelman, Z. 2004. Quality Assurance Protocol – WRAP RMC Emissions Modeling with

SMOKE, Prepared for the WRAP Modeling Forum by the WRAP Regional Modeling Center,

Riverside, CA.

Air Sciences, Inc., 2005. 2002 Fire Emission Inventory for the WRAP Region – Phase II, Prepared

for the Western Governors Association/WRAP by Air Sciences, Inc., Denver, CO.

Baerenklau, Kenneth.  2006.  Synthesis of economic valuation studies of forest landscape

disturbances.  Department of Environmental Sciences, University of California, Riverside.  June

2006.

Barrett, R.H.  1983.  Smoked aluminum track plates for determining furbearer distribution and

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122 
7.0 Appendices 

General note on appendices:  key terminology evolved throughout the course of this project.  

Most importantly, what became the Reference Case and the Test Scenario had early iterations

that were based on separating Industrial Private Forest (IPF) from Public Multiple Use (PMU),

creating a “third” interim scenario in order to analyze the impacts of IPF management apart

from the broader landscape.  Thus, the following notice has been inserted at the beginning of

each Appendix.

Notice of Change in Scenario Naming Conventions  

Key assumptions, modeling structures and terminology were altered and refined to accommodate new thinking

during the course of this study.  The reader will observe in the appendices that the scenarios are referred to as

“Scenarios 1, 2 and 3” or “S1, S2 and S3.”   

In both the main text of the Final Report and in the Life Cycle Assessment appendix (Appendix 4), the former

Scenario 1 (S1) was renamed to the “Reference Case.”  Scenario 3 (S3) has been renamed the “Test Scenario.”  

Scenario 2 (S2), focused on the relative contributions and impact of Industrial Private Forestry (IPF) has been

eliminated from most of the analyses that make up the entire study.  These changes better reflect the focus of the

study, which is fundamentally about the landscape level changes in wildfire, habitat, and other dynamics.  The

modification of terminology do not substantively affect the findings or recommendations of the study.

7.1. Appendix 1: Landscape Vegetation Changes (Barber, Perrot, et 

al.) 

Describes the sources of data and the modeling processes used to establish the inventory and

changes in vegetation on the B2E Beta landscape.  Approximately 65 pp.

7.2. Appendix 2: B2E Fire Behavior Domain (Ganz, Saah, Barber, et 

al.) 

Explains processes of using vegetation modeling outputs and applying fire behavior models to

each of the scenarios during each modeling time period.  Approximately 10 pp.

7.3. Appendix 3: Forest Operations and Equipment Configuration 

(Mason, Hartsough, et al.) 

Describes all equipment used in the life cycle assessment and analysis, including variations

under different treatment prescriptions and land management regimes.  Approximately 10 pp.

7.4. Appendix 4: Life Cycle Assessment of Producing Electricity 

from California Forest Wildfire Fuels Treatments (Cooper) 

Presents a detailed report on the life cycle assessment model developed to integrate life cycle

inventory information, calculate impacts and support LCA interpretations.  Approximately 78

pp.

123 
7.5. Appendix 5: Wildlife Habitat Evaluation (Manley, et al.) 

Reports the methods, data sources and analyses used to assess wildlife habitat conditions under

all scenarios. Includes a case study on American Marten, which demonstrates alternatives to

using California Wildlife Habitat Relations (CWHR) data and modeling.  Approximately 68 pp.

7.6. Appendix 6: Cumulative Watershed Effects Analysis (Wright, 

Perrot, et al.) 

Uses results of vegetation and fire dynamics modeling to model cumulative watershed effects

on the B2E beta landscape.  Approximately 20 pp.

7.7. Appendix 7: Counting Ecosystem Services: Ecological 

Endpoints and their Application (Boyd) 

Presents an independent consultant report on new methodologies developed for the B2E

landscape using “ecological endpoints” as a means to focus and narrow the description and

valuation of ecosystem services.  This appendix is not technically a part of the Energy

Commission contract; the research and writing was funded separately by USDA Forest Service,

Pacific Southwest and Pacific Northwest Research Stations.  Approximately 55 pp.

7.8. Appendix 8: Project Economic Analysis (Wegge, Trott and 

Barnett) 

Presents methods, applications and results of the economic analysis, including data derived

from the Excel spreadsheet model developed for the B2E project.  Approximately 57 pp.

7.9. Appendix 9: Landscape Carbon Model (Morris) 

Describes in an independent consultant report forest landscape level greenhouse gas changes

through wildfire, treatment and forest decay over the 40 year modeling period of the B2E

project.  Approximately 23 pp.

7.10. Appendix 10: Power Plant Analysis for Conversion of Forest 

Remediation Biomass to Renewable Fuels and Electricity (Schuetzle, 

Tamblyn, Tornatore, et al.) 

Analyzes five powerplant technologies for the B2E project, three of which are included in the

life cycle assessment domain.  Two additional technologies included options for ethanol or

other liquid fuel production, and were not directly used in the life cycle assessment because of

the differences in outputs.  The additional technologies were analyzed in anticipation of further

work stemming from the B2E project that would include a life cycle assessment for

transportation fuels. Approximately 6 pp.

7.11. Appendix 11: Synthesis of Economic Valuation Studies of 

Forest Landscape Disturbances (Berkenklau) 

Synthesizes in an independent consultant report the broad range of research on the economic

valuation of disturbance on forested landscapes.  Approximately 12 pp.

124 
125 

7.12. Appendix 12: Biomass to Energy Project Team, Committee 

Members, and Project Advisors 

Lists the members of the research team, the Technical Advisory Committee and the Policy

Advisory Committee.