    Wildland Fire Management: Better Information and a Systematic
    Process Could Improve Agencies' Approach to Allocating Fuel
    Reduction Funds and Selecting Projects (28-SEP-07, GAO-07-1168).

    Recognizing that millions of acres are at risk from wildland
    fire, the federal government expends substantial resources on
    thinning brush, trees, and other potentially hazardous fuels to
    reduce the fire risk to communities and the environment. However,
    questions have been raised about how the agencies responsible for
    wildland fire management--the Department of Agriculture's Forest
    Service and the Department of the Interior's (Interior) Bureau of
    Indian Affairs (BIA), Bureau of Land Management (BLM), Fish and
    Wildlife Service (FWS), and National Park Service (NPS)--allocate
    their fuel reduction budgets and select projects. GAO was asked
    to report on the agencies' processes for allocating funds and
    selecting projects, and on how, if at all, these processes could
    be improved to better ensure that they contribute to the
    agencies' overall goal of reducing risk. To obtain this
    information, GAO visited headquarters and field offices of all
    five agencies; obtained data on fuel reduction funding and
    accomplishments; and reviewed previous evaluations of the fuel
    reduction program.
    -------------------------Indexing Terms-------------------------
    REPORTNUM:   GAO-07-1168
        ACCNO:   A76890
      TITLE:     Wildland Fire Management: Better Information and a
    Systematic Process Could Improve Agencies' Approach to Allocating
    Fuel Reduction Funds and Selecting Projects
         DATE:   09/28/2007
      SUBJECT:   Allocation (Government accounting)
    	     Budgeting
    	     Budgets
    	     Environmental protection
    	     Federal agencies
    	     Federal funds
    	     Forest conservation
    	     Forest fires
    	     Forest management
    	     Funds management
    	     Land management
    	     Policy evaluation
    	     Risk assessment
    	     Risk management
    	     Wildfires
    	     Policies and procedures

    ******************************************************************
    ** This file contains an ASCII representation of the text of a  **
    ** GAO Product.                                                 **
    **                                                              **
    ** No attempt has been made to display graphic images, although **
    ** figure captions are reproduced.  Tables are included, but    **
    ** may not resemble those in the printed version.               **
    **                                                              **
    ** Please see the PDF (Portable Document Format) file, when     **
    ** available, for a complete electronic file of the printed     **
    ** document's contents.                                         **
    **                                                              **
    ******************************************************************
    GAO-07-1168

         * [1]WILDLAND FIRE MANAGEMENT
         * [2]Better Information and a Systematic Process Could Improve Agencies'
           Approach to Allocating Fuel Reduction Funds and Selecting Projects

              * [3]Contents

                   * [4]Results in Brief
                   * [5]Background

                        * [6]Wildland Fire Is a Natural Process
                        * [7]Five Agencies Are Responsible for Wildland Fire
                          Management
                        * [8]Expansion into the Wildland-Urban Interface Has
                          Increased, as Has the Federal Focus on Wildland Fire
                          Management
                        * [9]GAO Has Reviewed Agencies' Fuel Treatment Programs

                   * [10]The Forest Service Uses a Mix of Quantitative and
                     Judgmental Processes and Considers a Range of Factors in
                     Allocating Funds and Selecting Projects

                        * [11]At the National Level, the Forest Service's
                          Allocation Process Increasingly Relies on a
                          Quantitative Approach
                        * [12]Some Forest Service Regions Use Quantitative
                          Allocation Processes, While Others Rely More on
                          Professional Judgment
                        * [13]National Forests Select Projects Using Quantitative
                          and Judgmental Processes

                   * [14]Interior and Its Agencies Use a Mix of Quantitative and
                     Judgmental Processes and Consider a Range of Factors in
                     Allocating Funds and Selecting Projects

                        * [15]Interior Allocates Funds to Its Agencies Primarily
                          on the Basis of Historical Funding Levels
                        * [16]BLM Increasingly Uses Quantitative Processes in
                          Allocating Funds and Selecting Projects
                        * [17]BLM Allocates Funds to Its State Offices Primarily
                          on the Basis of Historical Funding Levels but Plans to
                          Use a More Quantitative Approach in 2008
                        * [18]The Majority of BLM State Offices Incorporate
                          Quantitative Approaches in Their Allocation Processes
                        * [19]The Majority of BLM Field Units Incorporate
                          Quantitative Approaches into Their Project Selection
                          Processes
                        * [20]BIA Allocates Funds Largely on the Basis of Units'
                          Performance History, while FWS and NPS Use Quantitative
                          and Judgmental Processes

                             * [21]BIA and FWS Headquarters Allocate Funds Using
                               Quantitative Processes, While NPS Headquarters
                               Allocates Funds Primarily on the Basis of
                               Historical Funding Levels
                             * [22]BIA, FWS, and NPS Regional Offices Allocate
                               Funds to Field Units Using Quantitative and
                               Judgmental Processes
                             * [23]Local BIA, FWS, and NPS Units Select Projects
                               Using Quantitative and Judgmental Processes

                   * [24]Several Improvements Could Help Better Ensure That Fuel
                     Reduction Funds Are Allocated to Effectively Reduce Risk

                        * [25]The Agencies Do Not Consistently Assess All
                          Elements of Risk When Allocating Funds
                        * [26]The Agencies Do Not Consider Treatment
                          Effectiveness in Their Allocation Processes Because
                          They Have No Measure for Effectiveness
                        * [27]The Agencies Often Consider Costs, but Not
                          Cost-Effectiveness, When Allocating Funds
                        * [28]The Agencies Have Not Established Clear Guidance on
                          the Relative Importance of Factors Used in Setting
                          Priorities
                        * [29]Agencies' Allocation Processes Are Not Always
                          Systematic

                   * [30]Conclusions
                   * [31]Recommendations for Executive Action
                   * [32]Agency Comments and Our Evaluation

              * [33]Appendix I: Objectives, Scope, and Methodology

                   * [34]Fuel Reduction Funding Allocation and Project Selection
                     Processes
                   * [35]Potential Improvements to Agency Processes to Better
                     Ensure They Contribute to Reducing Risk

              * [36]Appendix II: Forest Service and Interior Fuel Reduction
                Funding Allocations, Fiscal Years 2005, 2006, and 2007
              * [37]Appendix III: Summary of Fuel Treatment Accomplishments for
                the Forest Service and Interior, Fiscal Years 2005 and 2006
              * [38]Appendix IV: Comments from the Department of the Interior and
                the Forest Service
              * [39]Appendix V: GAO Contact and Staff Acknowledgments
              * [40]Related GAO Products

                   * [41]Order by Mail or Phone

                     United States Government Accountability Office

    Report to Congressional Requesters

    GAO

        September 2007

                                     WILDLAND FIRE
                                       MANAGEMENT

    Better Information and a Systematic Process Could Improve Agencies' Approach to
                 Allocating Fuel Reduction Funds and Selecting Projects

    GAO-07-1168

                                        Contents

    Letter Appendix I    Results in Brief Background The Forest        [42]1 6
                            Service Uses a Mix of Quantitative and          10 19
                            Judgmental Processes and Considers a Range of   29 44
                            Factors in Allocating Funds and Selecting       64 66
                            Projects Interior and Its Agencies Use a Mix    67 69
                            of Quantitative and Judgmental Processes and
                            Consider a Range of Factors in Allocating
                            Funds and Selecting Projects Several
                            Improvements Could Help Better Ensure That
                            Fuel Reduction Funds Are Allocated to
                            Effectively Reduce Risk Conclusions
                            Recommendations for Executive Action Agency
                            Comments and Our Evaluation Objectives,
                            Scope, and Methodology
    Appendix II          Forest Service and Interior Fuel Reduction
                            Funding Allocations, Fiscal Years 2005, 2006,  [43]77
                            and 2007
    Appendix III         Summary of Fuel Treatment Accomplishments for
                            the Forest Service and Interior, Fiscal Years  [44]91
                            2005 and 2006
    Appendix IV          Comments from the Department of the Interior  [45]100
                            and the Forest Service
    Appendix V           GAO Contact and Staff Acknowledgments         [46]101
    Related GAO Products                                               [47]102

    Tables
                 Table 1: Factors Considered in Forest Service Fuel Reduction
                                Funding Allocation Model                   [48]21
                 Table 2: Forest Service Regions' Fiscal Year 2007 Fuel
                                       Reduction
                        Priority Scores and Funding Allocations            [49]23
              Table 3: BLM Allocations to State Offices, Fiscal Year 2007  [50]35
              Table 4: Factors and Factor Categories BLM Considers in BLM
                        Fuel Reduction Funding Allocation Model            [51]37
                         Table 5: Regional Offices GAO Visited             [52]71
                            Table 6: Field Units GAO Visited               [53]73
                  Table 7: Total Appropriations to Forest Service, and
                                     Allocations to
                  Interior Agencies, Fiscal Years 2005, 2006, and 2007     [54]78
                   Table 8: Forest Service Allocations to Regions and
                                     Headquarters,
                           Fiscal Years 2005, 2006, and 2007               [55]80
                Table 9: Interior Allocations to BLM, BIA, FWS, and NPS,
                                       Including
                 WUI and Non-WUI Allocations, Fiscal Years 2005, 2006,
                                        and 2007                           [56]82
              Table 10: BLM Allocations to State Offices and Headquarters,
                           Fiscal Years 2005, 2006, and 2007               [57]83
                 Table 11: BIA Allocations to Regions and the National
                                      Interagency
                     Fire Center, Fiscal Years 2005, 2006, and 2007        [58]85
                Table 12: NPS Allocations to Regions and the Washington
                                        Office,
                           Fiscal Years 2005, 2006, and 2007               [59]87
                 Table 13: FWS Allocations to Regions and Headquarters,
                                         Fiscal
                               Years 2005, 2006, and 2007                  [60]89
                  Table 14: Summary of Fiscal Years 2005 and 2006 Fuel
                                       Reduction
                    Accomplishments for Interior and Forest Service        [61]92
                  Table 15: Summary of Fiscal Years 2005 and 2006 Fuel
                                       Reduction
                       Accomplishments for Forest Service Regions          [62]93
                  Table 16: Summary of Fiscal Years 2005 and 2006 Fuel
                                       Reduction
                         Accomplishments for BLM State Offices             [63]94
                  Table 17: Summary of Fiscal Years 2005 and 2006 Fuel
                                       Reduction
                            Accomplishments for BIA Regions                [64]96
                  Table 18: Summary of Fiscal Years 2005 and 2006 Fuel
                                       Reduction
                            Accomplishments for NPS Regions                [65]98
                  Table 19: Summary of Fiscal Years 2005 and 2006 Fuel
                                       Reduction
                            Accomplishments for FWS Regions                [66]99

    Figures
                 Figure 1: Annual Appropriation and Allocation Process for Fuel
                                     Reduction Funds                        [67]4
               Figure 2: Distribution of Total Land Managed by the Forest
                                        Service,
                                 BIA, BLM, FWS, and NPS                    [68]11
               Figure 3: A Mechanical Thinning Project for Fuel Reduction
                                           on
                                 BLM Land in California                    [69]13
                 Figure 4: Prescribed Fire for Fuel Reduction on Forest
                                         Service
                                 Land in South Carolina                    [70]13
                Figure 5: Forest Service Regions' Fuel Reduction Priority
                                        Scores as
                   a Percentage of Total, Compared to Regions' Funding
                Allocations as a Percentage of Total Allocations, Fiscal
                                        Year 2007                          [71]24
                 Figure 6: Percentage of Interior's Total Fuel Reduction
                                          Funds
                  Allocated to the Interior Agencies, Fiscal Years 2001
                                      through 2007                         [72]30
                Figure 7: Density of Wildland-Urban Interface Treatments
                                           and
                             Population Density, by ZIP Code               [73]50
               Figure 8: Location of Federal Lands and Populated Areas in
                                           the
                                Continental United States                  [74]52
                   Figure 9: Map of Los Angeles County Wildland-Urban
                                        Interface
                  Fuel Reduction Treatments Completed in 2005 and 2006,
                                 and Population Density                    [75]54
                   Figure 10: Map of Rio Blanco County Wildland-Urban
                                        Interface
                  Fuel Reduction Treatments Completed in 2005 and 2006,
                                 and Population Density                    [76]56
                Figure 11: Agency Funding Levels as a Percentage of Total
                                          Fuel
                           Reduction Funding, Fiscal Year 2007             [77]79

    Abbreviations

    BIA        Bureau of Indian Affairs
    BLM        Bureau of Land Management
    FPA        Fire Program Analysis
    FWS        Fish and Wildlife Service
    GAO        Government Accountability Office
    GIS        Geographic information system
    HFI        Healthy Forests Initiative
    HFRA       Healthy Forests Restoration Act
    NEPA       National Environmental Policy Act
    NPS        National Park Service
    USDA       United States Department of Agriculture
    WUI        Wildland-urban interface

    This is a work of the U.S. government and is not subject to copyright
    protection in the United States. The published product may be reproduced
    and distributed in its entirety without further permission from GAO.
    However, because this work may contain copyrighted images or other
    material, permission from the copyright holder may be necessary if you
    wish to reproduce this material separately.

    United States Government Accountability Office
    Washington, DC 20548

    September 28, 2007

    The Honorable Norman D. Dicks
    Chairman
    The Honorable Todd Tiahrt
    Ranking Member
    Subcommittee on Interior, Environment, and Related Agencies
    Committee on Appropriations
    House of Representatives

    The Honorable Raul M. Grijalva
    Chairman
    The Honorable Rob Bishop
    Ranking Member
    Subcommittee on National Parks, Forests, and Public Lands
    Committee on Natural Resources
    House of Representatives

    The Honorable Greg Walden
    House of Representatives

    Decades of fire suppression in the nation's forests, together with such
    practices as logging followed by dense tree planting, have resulted in the
    accumulation of brush, small trees, and other vegetation that can fuel
    wildland fires. Similarly, the nation's rangelands have suffered from
    decades of fire suppression and livestock overgrazing, which have degraded
    ecosystems and made the rangelands vulnerable to the invasion of
    flammable, nonnative species, such as cheat grass. This accumulation and
    alteration of vegetation, as well as drought and other stresses related to
    climate change, have fueled wildland fires. Collectively, these fires have
    cost billions of dollars to suppress, forced thousands from their homes,
    and damaged cultural and natural resources. The impacts of these fires
    have intensified as more and more communities develop in areas that are
    adjacent to fire-prone lands--the wildland-urban interface.

    In response to the increasing threat of wildland fires, the federal
    agencies responsible for wildland fire management developed the National
    Fire Plan. ^[78]1 These agencies are the Department of Agriculture's
    Forest Service and the Department of the Interior's (Interior) Bureau of
    Indian Affairs (BIA), Bureau of Land Management (BLM), Fish and Wildlife
    Service (FWS), and National Park Service (NPS). Two components of the
    National Fire Plan are a 10-year strategy and an implementation plan for
    protecting communities and the environment that were developed in 2001 and
    2002, respectively, by the Secretaries of Agriculture and of the Interior,
    along with governors of western states and other interested parties, and
    updated in December 2006. The 2002 plan emphasized reducing hazardous fuel
    in forests and rangelands to mitigate the risk from wildland fire. In
    2003, Congress passed the Healthy Forests Restoration Act (HFRA), ^[79]2
    with the stated purpose of reducing wildland fire risk to communities,
    municipal water supplies, and other at-risk federal land through a
    collaborative process of planning, setting priorities, and implementing
    fuel reduction projects. ^[80]3 HFRA also authorized grants to commercial
    facilities that use biomass--that is, small-diameter trees and
    branches--to offset the costs incurred to purchase biomass. Fuel reduction
    projects can generate substantial amounts of biomass.

    According to the updated 10-Year Strategy Implementation Plan, the goal of
    the fuel reduction program is to reduce the risk of wildland fire to
    communities and the environment. Fuel reduction projects--using prescribed
    fire, mechanical thinning, herbicides, grazing, or combinations of these
    methods--are intended to remove or modify wildland fuel to reduce the
    potential for severe wildland fires, lessen the damage caused by fires,
    limit the spread of flammable invasive species, and restore and maintain
    healthy ecosystems. Local land management units, such as
    national forests and parks, are typically responsible for selecting and
    implementing fuel reduction projects.

    ^1The National Fire Plan comprises multiple documents, including (1) a
    September 2000 report from the Secretaries of Agriculture and of the
    Interior to the President in response to the wildland fires of 2000, (2)
    congressional direction accompanying substantial new appropriations in
    fiscal year 2001, and (3) several strategies to implement all or parts of
    the plan. For a description of these documents and their contents, goals,
    and relationships to one another, see Severe Wildland Fires: Leadership
    and Accountability Needed to Reduce Risks to Communities and Resources,
    GAO-02-259 (Washington, D.C.: Jan. 31, 2002).

    ^2Pub. L. No. 108-148 (2003).

    ^3HFRA defines "federal land" to include land administered by the Forest
    Service and BLM. Consequently, HFRA fuel reduction project authorities are
    available only to the Forest Service and BLM, and its fuel reduction
    project requirements apply only to these agencies as well. In some cases,
    BIA, FWS, and NPS have chosen to comply with some of the requirements.

    Since 2001, Congress has appropriated more than $3.2 billion in fuel
    reduction funds to the Forest Service and Interior. For 2007, the Forest
    Service received about $300 million, and Interior received about $200
    million. ^[81]4 After receiving its annual appropriation, the Forest
    Service allocates funds to its nine regional offices which in turn
    allocate funds to individual national forests and grasslands. Interior,
    upon receiving its annual appropriation, allocates funds to BIA, BLM, FWS,
    and NPS. BLM generally receives about 50 percent of Interior's funding,
    BIA about 20 percent, and FWS and NPS about 15 percent each. These
    agencies then allocate funds to their regional or state offices, which, in
    turn, allocate funds to individual field units, such as national parks or
    wildlife refuges. (BIA, FWS, and NPS have regional offices, while BLM has
    state offices. For the purposes of this report, we refer to all of these
    as regional offices when we discuss the Interior agencies collectively.)
    Figure 1 shows the annual appropriation and allocation process.

    ^4Years cited in this report refer to fiscal years except where  otherwise
    specified.

     Figure 1: Annual Appropriation and Allocation Process for Fuel Reduction Funds

    Recognizing that treating all of the land in need of fuel reduction may
    take decades, the agencies have acknowledged the importance of setting
    priorities for which lands are to receive treatment so that they can
    select those treatments that will be the most effective at reducing the
    risks from wildland fire. However, we have found a long-standing pattern
    of shortcomings in the processes the Forest Service and Interior agencies
    use to identify and set priorities for lands needing fuel reduction.
    Between 1999 and 2003, we reported that the Forest Service and Interior
    had made it a priority to treat lands at the highest risk from wildland
    fire, but they had not identified the amount or location of such lands and
    had not issued guidance specific enough for field staff to set priorities
    for individual projects. We concluded that the agencies needed a cohesive
    strategy outlining long-term options and associated costs for reducing
    fuel. ^[82]5 In subsequent reports, we noted, among other things, the
    progress the agencies had made in improving their data, but reiterated
    that they needed to complete ongoing efforts to identify lands at risk
    from wildland fire--by collecting information on the hazards, the
    likelihood of fire occurring, and the values at risk--so funds could be
    targeted to such lands. We also reiterated the need to develop a cohesive
    strategy that included long-term options and associated costs so that
    Congress could make informed decisions about cost-effective approaches to
    fuel reduction. ^[83]6

    In this context, you asked us to report on the agencies' current processes
    for identifying and setting priorities for fuel reduction. Specifically,
    you asked us to (1) identify the processes the Forest Service, Interior,
    and the four Interior agencies use to allocate fuel reduction funds and
    select projects for implementation, including the factors that influence
    these processes, and (2) determine how, if at all, these processes could
    be improved to better ensure that they contribute to the agencies' goal of
    effectively reducing the risk of wildland fire to communities and the
    environment.

    To address these objectives, we met with national, regional, state, and
    local officials of the Forest Service, Interior, and Interior agencies. At
    the national level, we met with agency officials at their Washington,
    D.C., headquarters, as well as at the National Interagency Fire Center in
    Boise, Idaho. At the regional and state levels, we used a structured
    interview guide to speak, in person or by telephone, with officials in all
    Forest Service regional and BLM state offices, as well as with officials
    in selected BIA, FWS, and NPS regional offices that collectively received
    a substantial portion of each agency's fuel reduction funds. At the local
    level, we visited
    20 local units, such as national forests and BLM field offices, in eight
    states to gain a better understanding of their processes for selecting
    fuel reduction projects for implementation. We selected local units that
    are diverse in geographic location, predominant vegetation type, and
    proximity to communities and development. We also obtained and reviewed
    applicable laws, regulations, and agencywide and regional policies; agency
    data on funding allocations; and electronic data on the extent of agency
    fuel treatment activities. We tested these data and found that they were
    sufficiently reliable for the purposes of this review. Finally, we
    interviewed several nonfederal parties, including representatives from
    environmental groups and the Western Governors' Association. ^[84]7 We
    conducted our work from August 2006 to September 2007 in accordance with
    generally accepted government auditing standards. See appendix I for a
    detailed description of our methodology.

    ^5GAO, Western National Forests: A Cohesive Strategy Is Needed to Address
    Catastrophic Wildfire Threats, GAO/RCED-99-65 (Washington, D.C.: Apr. 2,
    1999); Reducing Wildfire Threats: Funds Should Be Targeted to the Highest
    Risk Areas, GAO/T-RCED-00-296 (Washington, D.C.: Sept. 13, 2000);
    GAO-02-259; Wildland Fire Management: Additional Actions Required to
    Better Identify and Prioritize Lands Needing Fuels Reduction, GAO-03-805
    (Washington, D.C.: Aug. 15, 2003).

    ^6GAO, Wildland Fires: Forest Service and BLM Need Better Information and
    a Systematic Approach for Assessing the Risks of Environmental Effects,
    GAO-04-705 (Washington, D.C.: June 24, 2004); Wildland Fire Management:
    Important Progress Has Been Made, but Challenges Remain to Completing a
    Cohesive Strategy, GAO-05-147 (Washington, D.C.: Jan. 14, 2005); Wildland
    Fire Management: Update on Federal Agency Efforts to Develop a Cohesive
    Strategy to Address Wildland Fire Threats, GAO-06-671R (Washington, D.C.:
    May 1, 2006).

                                    Results in Brief

    In allocating fuel reduction funds and selecting projects, the Forest
    Service--at the national, regional, and local levels--uses both
    quantitative processes (such as computer models or scoring systems) and
    professional judgment and, in doing so, considers multiple factors, such
    as risk assessments, treatment cost per acre, and collaboration with
    communities or other entities. Specifically, for 2007, we found the
    following:

    o At headquarters, the Forest Service began using a computer model to
    influence funding allocations to regions. To set priorities for each
    region's fuel reduction funding, the model considers multiple factors,
    including some intended to assess risk, such as the potential for fires
    occurring in each region and their expected severity, as well as other
    factors, such as regional use of biomass removed in fuel treatments and
    treatment cost per acre. However, the Forest Service's funding allocations
    to its regions were not consistent in all cases with the priority scores
    resulting from the model, with some high-scoring regions receiving less
    funding than some lower-scoring regions. These disparities occurred for a
    number of reasons, such as the higher costs of fuel reduction in some
    areas. However, the model did not substantially influence the agency's
    2007 allocations; instead, the Service relied largely on prior year
    funding levels and used results from the model only to make minor
    adjustments. Officials said they expect
    the model's results to have more influence on future allocation decisions,
    but curbed this influence initially because they were still refining the
    model and wanted to maintain relatively stable regional funding levels.

    ^7The Western Governors' Association is an independent, nonpartisan
    organization of governors representing 19 western states. The governors
    use the association to develop and advocate policies that reflect regional
    interests.

         o In the regions, each region determined how to allocate funds to
           national forests and what factors to consider, as long as they were
           consistent with the factors used in the national allocation model.
           Four of the Service's nine regions relied primarily on quantitative
           data in their allocation processes, while five relied primarily on
           professional judgment. For example, the Rocky Mountain region used a
           computer model that evaluated data on multiple factors, such as
           vegetative conditions and areas of insect-killed trees, while the
           Southern region convened a group of officials who used professional
           judgment and considered historical funding levels, the capabilities of
           the forests, per-acre treatment cost, local priorities, and acreage
           targets when allocating funds. Beginning with the 2008 allocations,
           the Forest Service plans to require regions to use the headquarters
           model to inform allocation decisions.

              o Locally, national forests had discretion in determining how to
                select projects. Some used quantitative, data-driven processes,
                while others relied primarily on professional judgment or
                collaborative processes involving other agencies and local
                communities. Forests considered a range of factors--similar to
                those used at the national and regional levels--when selecting
                projects.
              o Like the Forest Service, Interior and its agencies' national,
                regional, and local offices used both quantitative and judgmental
                processes for allocating fuel reduction funds and selecting
                projects and considered multiple factors that are similar to
                those the Forest Service uses. More specifically, for fiscal year
                2007, we found the following:

         o Interior allocated funds to its four agencies primarily on the basis
           of historical funding levels; however, Interior is developing a
           computer model similar to the Forest Service's, and it used the model
           to allocate 5 percent of its funds in 2007. Interior agencies, in
           turn, had the flexibility to determine how to allocate funds, within
           the parameters of departmental guidance.
         o BLM headquarters allocated funds primarily on the basis of historical
           funding levels, but officials told us that, starting in 2008, they
           plan to use a quantitative process incorporating multiple factors,
           such as the potential for fires to occur, treatment cost, and local
           risk ratings.
         o BIA headquarters allocated funds using quantitative processes, but
           these processes generally emphasized a single factor--BIA units' past
           performance (measured in acres treated) in carrying out fuel reduction
           activities.
         o FWS headquarters allocated funds to regional offices using a
           quantitative process--a model that considers a range of factors, such
           as the history of fires, fuel conditions, and communities at risk.
         o NPS headquarters allocated funds to regions largely on the basis of
           historical funding levels. These funding levels were originally
           determined using a model that assessed the risk from wildland fire,
           among other factors.

    At the regional and local levels, regional offices and field units in all
    four agencies used a variety of processes to allocate funds and select
    projects for implementation. Some processes emphasized quantitative data,
    while others emphasized professional judgment. The regional and local
    offices also considered a range of factors, consistent with departmental
    direction.

    Although the Forest Service and Interior have begun taking action to
    enhance their funding allocation processes, there are additional steps
    they could take to improve these processes to better ensure they advance
    the agencies' goal of effectively reducing the risk of wildland fire to
    communities and the environment. Specifically, the agencies could improve
    their processes by taking the following five steps:

    o Consistently using risk assessments. The agencies did not consistently
    use risk assessments in their 2007 allocation processes at the national,
    regional, and local levels, in some cases because national or regional
    offices expected local units to do so. However, agency officials cannot be
    sure that projects identified as high risk locally would likewise be the
    highest risk from a regional or national perspective. Even when the
    agencies did conduct risk assessments, they found it difficult to
    meaningfully distinguish between higher- and lower-priority locations
    because one key value at risk--the wildland-urban interface--is broadly
    defined and many different areas are classified as interface. Although the
    agencies' guidance sets a priority on projects in the interface, it does
    not specify whether some of the areas classified as interface ought to be
    higher priority than others. As a result, projects as diverse as those
    protecting remote power lines, individual ranch houses, or large suburban
    subdivisions can all fall within the wildland-urban interface category
    and, thus, be designated high priority--complicating agency officials'
    attempts to identify and
    direct their limited resources toward the highest-priority areas.

         o Developing information on treatment effectiveness. The agencies did
           not consider treatment effectiveness--that is, how much risk reduction
           can be achieved, and for how long--when making allocation decisions
           because they currently have no measure for effectiveness, although
           they are working to develop such a measure. Without information on
           treatment effectiveness, the agencies could be funding treatments that
           have little effect on reducing risk.
         o Developing information on cost effectiveness. The agencies often
           considered costs when allocating funds, but not cost effectiveness--
           primarily because they lack information on treatment effectiveness.
           Without such information, it is difficult to know whether a
           treatment's cost is warranted or to compare the cost effectiveness of
           different potential treatments to decide how to optimally allocate
           funds.
         o Clarifying the importance of factors unrelated to risk or
           effectiveness. The agencies often considered factors other than risk,
           treatment effectiveness, and cost effectiveness when allocating funds
           and selecting projects. When these external factors--such as funding
           stability and the use of biomass resulting from fuel reduction
           treatments--have considerable influence, it is difficult for the
           agencies to ensure that they are allocating funds so that treatments
           will most effectively reduce risk.
         o Applying more systematic processes. The agencies sometimes relied
           exclusively on professional judgment when allocating funds or
           selecting projects. Although judgmental processes might result in
           allocations that maximize risk reduction, the agencies cannot be
           assured that they routinely do because such processes are not
           necessarily systematic--that is, methodical, based on criteria, and
           applied consistently.

    To improve the agencies' ability to ensure that fuel reduction funds are
    directed to most effectively reduce the risk from wildland fire, we are
    recommending that the Secretaries of Agriculture and of the Interior take
    actions to implement a more systematic allocation process; develop
    additional information on risk, treatment effectiveness, and
    cost-effectiveness to support the process; and clarify the relative
    importance of multiple criteria for setting priorities in allocation and
    project selection decisions. We provided a draft of this report to the
    Secretaries of Agriculture and of the Interior for review and comment. The
    Forest Service and the Department of the Interior generally agreed with
    our report; their joint comment letter is presented in appendix IV.

                                       Background

    Wildland Fire Is a Natural Process

    Although its effect on communities can be devastating, wildland fire is a
    natural and necessary process that provides many benefits to ecosystems,
    such as maintaining habitat diversity, recycling soil nutrients, limiting
    the spread of insects and disease, and promoting new growth by causing the
    seeds of fire-dependent species to germinate. Wildland fire also
    periodically removes brush, small trees, and other vegetation that can
    otherwise accumulate and increase the size, intensity, and duration of
    subsequent fires. However, human uses and land management practices--
    including decades of wildland fire suppression--have excluded fire from
    ecosystems, reducing the normal frequency of wildland fire and
    subsequently causing an accumulation of vegetation. Federal researchers
    have estimated that unnaturally dense fuel accumulations on 90 million to
    200 million acres of federal lands in the contiguous United States place
    these lands at an elevated risk of severe wildland fire and that these
    conditions also hold true for many nonfederal lands.

    Most lands in the United States evolved with fire, and each ecosystem has
    a characteristic fire regime that describes the role fire plays in the
    ecosystem, including typical fire frequency, scale, intensity, and
    duration. These regimes are numbered I through V, with fire regime I
    characterized by low-severity fires that historically occurred every 35
    years or less, fire regime II characterized by high-severity fires that
    historically occurred every 35 years or less, fire regime III
    characterized by mixed-severity fires that historically occurred every 35
    to 100 or more years, fire regime IV characterized by high-severity fires
    that historically occurred every 35 to 100 or more years, and fire regime
    V characterized by high-severity fires that historically occurred every
    200 or more years. Many ecosystems-- particularly those in fire regimes I
    and II--have now missed numerous fire cycles as a result of past
    suppression policies and other land management practices. This departure
    from the natural fire regime is categorized by a measure called condition
    class, which the agencies have used as a generalized rating for the risk
    of uncharacteristic wildland fires that may cause undesirable ecological
    consequences. Ecosystems in condition class 1 are generally within their
    historical fire return interval, so fires in these areas pose little risk
    to natural processes--although fires in such ecosystems may still pose a
    high risk to communities. Areas in condition classes 2 and 3 have moderate
    to significant departures from historical fire experiences. In such areas,
    fuel that would typically have burned periodically has instead
    accumulated, posing a higher risk that uncharacteristically large amounts
    of vegetation and other natural resources would be lost from wildland
    fire; fires in these areas may also pose a high risk to communities.

    Five Agencies Are Responsible for Wildland Fire Management

    The Forest Service, BIA, BLM, FWS, and NPS are responsible for wildland
    fire management, including fuel reduction. These five agencies manage
    about 700 million acres of land in the United States, including national
    forests, national grasslands, Indian reservations, national parks, and
    national wildlife refuges. The Forest Service and BLM manage the majority
    of these lands, with the Forest Service managing about 190 million acres
    and BLM managing about 260 million acres; BIA, FWS, and NPS each manage
    less than 100 million acres. Figure 2 shows the distribution of land among
    the five agencies. Each agency has between 7 and 12 regional offices that
    oversee field units.

    Figure 2: Distribution of Total Land  Managed by the Forest Service,  BIA,
    BLM, FWS, and NPS

    BLM BIA

    NPS

    FWS

    Forest Service

    Forest Service Interior agencies

               Source: GAO analysis of Forest Service and Interior data.

    Each year, the Forest Service, Interior, and Interior agencies set
    performance targets for region- and state-level fuel reduction by
    establishing the number of acres the agencies expect to be treated--both
    within and outside of the wildland-urban interface--using the funds
    allocated. For example, for fiscal year 2007, BLM assigned a target of
    almost 90,000 acres to its Oregon/Washington state office and specified
    that two-thirds of the acres should be in the wildland-urban interface.
    Between 2001 and August 2007, land managers treated more than 18 million
    acres under the fuel reduction program, including about

    8.5 million acres near communities. ^[85]8 These acres include federal,
    state, and private land, because, in addition to conducting fuel
    treatments on federal lands, the agencies work with and grant funds to
    local communities to conduct fuel reduction treatments on state and
    private lands. These acres also include those that have been treated more
    than once.

    The agencies generally reduce fuel using either mechanical treatments, in
    which equipment--such as chainsaws, chippers, bulldozers, or mowers-- is
    used to cut vegetation, or prescribed burning, in which fires are
    deliberately set by land managers to restore or maintain desired
    vegetation conditions. ^[86]9 Figure 3 depicts a mechanical thinning
    project, and figure 4 depicts a prescribed burn.

    ^8The agencies treated an additional 1.8 million acres from 2004 to August
    2007 through other land management activities.

    ^9The agencies also conduct some  treatments using other methods, such  as
    applying herbicides and allowing animals to graze on the land.

    Figure 3: A Mechanical Thinning Project for Fuel Reduction on BLM Land  in
    California

    Source: BLM.

    Figure 4: Prescribed  Fire for Fuel  Reduction on Forest  Service Land  in
    South Carolina

                                Source: Forest Service.

    Although prescribed burning can be risky, burning under specified fuel and
    weather conditions enables fire to be controlled at a relatively low
    intensity level within a confined area. Prescribed burning is very
    effective in removing smaller vegetation, such as grasses, leaves, pine
    needles, and twigs, but is not as effective in removing larger fuel, such
    as trees, or in thinning stands to desired densities. In contrast,
    mechanical treatment methods are effective in thinning stands and removing
    larger vegetation but may increase the amount of smaller fuel on the
    ground, including tree tops and limbs (referred to as slash) and other
    debris from thinning. As a result, some fuel reduction projects use
    multiple treatment methods and may span several years. For example, a
    field unit may first treat an area mechanically to thin accumulated
    vegetation and then follow with a prescribed burn to remove remaining
    slash and litter on the ground.

    In addition to reducing the risk of fire to communities and the
    environment, one of the long-term goals of the fuel reduction program is
    to allow fire to resume its natural role. By conducting treatments,
    including creating fire breaks to help contain the spread of fire, the
    agencies increase the amount of land where naturally ignited fires can
    safely be allowed to burn. Under wildland fire use policies, land managers
    may allow wildland fires that are naturally ignited to continue to burn,
    as long as fuel and weather conditions are appropriate and the fire is
    located within an area designated for wildland fire use. ^[87]10 Managers
    are thus able to use natural fire to meet resource objectives, such as
    removing excess vegetation.

    Although the five agencies all reduce fuel in order to reduce risk to
    communities and the environment, their fuel reduction programs reflect
    differences in their missions, predominant vegetation types, and allowable
    land uses. For example, FWS's mission is focused on the conservation of
    wildlife habitat, and the agency generally conducts more prescribed burns
    than mechanical treatments because such burns frequently improve habitat
    as well as reduce risk; the agency has been conducting prescribed burns
    since the 1930s. Similarly, prescribed burns, as well as wildland fire
    use, are the preferred fuel treatment methods at NPS and have been used by
    the agency for decades. NPS prefers these treatment methods over
    mechanical treatments because its mission emphasizes preservation of
    natural and cultural resources, and fire is a natural process that better
    aligns with this mission. Regarding predominant vegetation types, BLM's
    lands are largely rangelands, while lands managed by other agencies, such
    as the Forest Service and FWS, include more forests. As a result of this
    difference, BLM not only conducts mechanical treatments and prescribed
    burns, as do the other agencies, but also uses herbicides to reduce fuel,
    especially where rangelands have been invaded by exotic plants such as
    cheat grass. Agency differences in allowable land use also affect their
    fuel reduction programs. For example, the Forest Service, BIA, and BLM
    have active commercial timber programs, and field units may therefore
    conduct fuel treatments that benefit both the timber and fuel reduction
    programs. BIA and NPS also manage lands with numerous archaeological
    sites, which must be considered when conducting treatments. In contrast,
    the majority of BLM's land is used for grazing, and, as a result, BLM
    coordinates fuel treatments with potentially affected ranchers.

    ^10Interagency policy directs land managers to select firefighting
    strategies in accordance with local federal units' land and fire
    management plans. If a plan has not been developed and approved, the
    policy directs land managers to suppress the fire. Thus, under the policy,
    the areas where wildland fire use is allowed must be defined in a fire
    management plan, along with prescribed weather and other conditions. The
    fires are monitored, and if weather conditions change in a way that would
    potentially allow the fires to escape from the designated areas, the fires
    are suppressed.

    Expansion into the Wildland-Urban Interface Has Increased, as Has the
    Federal Focus on Wildland Fire Management

    Urban and suburban expansion into the wildland-urban interface has
    increased the number of communities and structures at risk of wildland
    fire near federal lands that the five agencies manage. Experts estimate
    that almost 60 percent of all new housing units built in the 1990s were
    located in the wildland-urban interface and that this growth trend
    continues. They also estimate that more than 30 percent of housing units
    overall are located in the wildland-urban interface and that the interface
    covers about 9 percent of the nation's land. In addition to housing units,
    other types of infrastructure are located in the wildland-urban interface,
    including power lines, campgrounds and other recreation facilities, oil
    and gas wells, communications towers, and roads.

    After the National Fire Plan was developed, the agencies began receiving
    sharp increases in funding for fuel reduction and, since 2001, Congress
    has appropriated between about $400 million and $500 million annually for
    fuel reduction under the plan. (App. II shows agency fuel reduction
    funding appropriations and allocations for 2005 through 2007; app. III
    shows the agencies' fuel treatment accomplishments.) In 2002, the
    President announced the Healthy Forests Initiative (HFI), directing the
    departments of Agriculture and of the Interior and the Council on
    Environmental Quality to provide regulations to ensure more timely
    decisions, increase efficiency, and improve results in reducing the risk
    of catastrophic wildland fires.

    In 2003, Congress passed HFRA to reduce wildland fire risk to communities,
    municipal water supplies, and other at-risk federal lands through a
    collaborative process of planning, setting priorities for, and
    implementing fuel reduction projects. In funding authorized fuel reduction
    projects on federal land, HFRA requires the agencies to use at least 50
    percent of these funds in the wildland-urban interface. ^[88]11 The act
    also established separate environmental analysis and administrative review
    procedures for fuel reduction projects authorized under HFRA. In providing
    assistance for fuel reduction activities on nonfederal lands, HFRA
    requires the agencies, to the maximum extent practicable, to give priority
    to communities that have adopted a community wildfire protection plan
    (community plan) or have taken proactive measures to encourage willing
    property owners to reduce fire risk on private property. A community plan
    identifies and sets priorities for fuel reduction treatments and
    recommends the types and methods of treatment on federal and nonfederal
    land that will protect at-risk communities and essential infrastructure;
    community plans also recommend measures to reduce structural ignitability
    throughout the at-risk community. These plans are to be agreed upon by the
    applicable local government, local fire department, and state forest
    management agency, in consultation with other interested parties and the
    federal land management agencies. As of February 2007, there were at least
    1,100 completed community plans covering almost 3,300 communities
    throughout the United States, and approximately 450 additional plans in
    progress, according to the National Association of State Foresters. A
    community plan may cover one or more communities, and some cover entire
    counties.

    According to the 10-Year Strategy Implementation Plan, the goal of the
    fuel reduction program is to reduce the risk of wildland fire to
    communities and the environment. However, some fuel treatments provide
    other benefits in addition to this overall program goal; for example,
    agency staff sometimes conduct prescribed burns to both reduce fuel and
    enhance wildlife habitat, or conduct mechanical thinning projects before a
    commercial timber sale. Similarly, in addition to the approximately $400
    to $500 million appropriated for fuel reduction each year, funds from
    other agency programs, such as wildlife management or timber, often are
    used to conduct vegetation treatment projects that reduce fuels as a
    secondary
    benefit. In addition, the  agencies sometimes receive partnership  funding
    from outside organizations, such as  the Rocky Mountain Elk Foundation  or
    The Nature Conservancy, to conduct collaborative treatments. ^[89]12

    ^11This requirement applies only to projects conducted using HFRA
    authorities. Agency officials told us they do not have reliable data on
    the portion of their fuel reduction projects that used HFRA authorities.

    GAO Has Reviewed Agencies' Fuel Treatment Programs

    While the federal agencies acknowledge the importance of setting
    priorities for lands needing fuel treatments, we have identified a
    long-standing pattern of shortcomings in the processes the Forest Service
    and Interior use to identify and set priorities for these lands. Between
    1999 and 2007, we conducted several reviews of the agencies' wildland fire
    management efforts, including the fuel reduction program. We found that,
    while the agencies aimed to target fuel reduction efforts to the highest
    risk areas, they could not ensure that they were doing so. For example, in
    1999, we found that the Forest Service intended to give priority to
    treatments in the wildland-urban interface but was hampered in doing so
    because it had not fully defined and mapped such areas. ^[90]13 We
    concluded that the Forest Service needed a cohesive strategy outlining
    options and associated costs for reducing fuel. We reiterated the
    agencies' need for a cohesive strategy in several additional reports and
    testimonies issued between 2002 and 2007. ^[91]14 In 2000 and 2002, we
    reported that the Forest Service and Interior did not know how many
    communities were at high risk of severe wildland fire or their locations
    and the cost to treat them and, therefore, could not set treatment
    priorities. ^[92]15 We further reported in 2002 and 2003 that the agencies
    did not have quantifiable long-term and annual performance measures to
    assess progress in reducing the risks of wildland fire and that they
    measured the performance of the fuel reduction program by number of acres
    treated, which does not necessarily correlate to risk reduction. ^[93]16

    Similarly, in 2004, we reported that the agencies did not systematically
    assess the risks to environmental resources and ecosystems and,
    therefore, could not target fuel reduction efforts to the resources and
    ecosystems at highest risk. To set priorities for fuel reduction
    activities, the agencies must first identify areas at risk from wildland
    fire by considering three elements: hazard, risk, and value. ^[94]17 A
    hazard is a potential event, such as a wildland fire, and the conditions
    that cause it; in the case of wildland fire, both the fuel conditions and
    the fire itself are the hazard. Risk is the probability that an event such
    as a wildland fire will occur. Values are the resources and property that
    could be lost or damaged because of a hazard; in the case of wildland
    fire, values might include social, economic, or environmental values.
    ^[95]18 Without considering all three elements, the agencies may not be
    appropriately setting priorities for areas needing fuel reduction. For
    example, an area with high vegetation hazard may not be in an area where
    fires are likely to occur, making it a lower priority for treatment;
    likewise, a high hazard area might not be near something of value that
    could be lost or damaged in a fire, also making it a lower priority for
    treatment.

    ^12Our review was limited to fuel reduction work activities using federal
    funds appropriated specifically for this purpose. As a result, fuel
    reduction work funded by other agency programs or outside organizations is
    beyond the scope of this review.

    ^13GAO/RCED-99-65.

    ^14GAO, Wildland Fire Management: Reducing the Threat of Wildland Fires
    Requires Sustained and Coordinated Effort, GAO-02-843T (Washington, D.C.:
    June 13, 2002); GAO05-147; GAO-06-671R; Wildland Fire Management: Lack of
    a Cohesive Strategy Hinders Agencies' Cost-Containment Efforts,
    GAO-07-427T (Washington, D.C.: Jan. 30, 2007).

    ^15GAO/T-RCED-00-296; GAO-02-259.

    ^16GAO-02-259; GAO-03-805.

    We also found, through multiple reviews, that the agencies could benefit
    from coordinating their efforts to manage wildland fires because wildland
    fire is a shared problem that transcends administrative boundaries. For
    example, in 2001 we reported that federal policy for managing wildland
    fire required coordination, consistency, and agreement among the Forest
    Service, Interior, and Interior agencies, but we found that the agencies
    planned and managed wildland fire management activities largely on
    agency-by-agency and unit-by-unit bases, and could not ensure, among other
    things, that they were allocating funds to the highest-risk communities
    and ecosystems. ^[96]19 In a 2002 report, we noted that the Forest Service
    and Interior had either developed or were in the process of developing
    numerous strategies that had different goals and objectives and that were
    not linked, primarily because the agencies had been managing their lands
    on an agency-by-agency basis for decades. ^[97]20 In a subsequent
    testimony, which emphasized the agencies' need for a cohesive strategy as
    well as clearly defined and effective leadership, we concluded that
    effectively addressing wildland fire would require a sustained and
    coordinated effort between departments. ^[98]21 (See Related GAO
    Products.)

    ^17This approach, outlined by the National Academy of Public
    Administration, uses risk as a specific term referring to the probability
    of an event, as well as an umbrella term that encompasses all three of
    these elements. See National Academy of Public Administration,
    Managing Wildland Fire: Enhancing Capacity to Implement the Federal
    Interagency Policy (Washington, D.C.: December 2001).

    ^18For more information on the hazard-risk-value framework, see
    GAO-04-705.

    ^19GAO, The National Fire Plan: Federal Agencies Are Not Organized to
    Effectively and Efficiently Implement the Plan, GAO-01-1022T (Washington,
    D. C.: July 31, 2001).

    ^20GAO-02-259.

    The Forest Service Uses a Mix of Quantitative and Judgmental Processes and
    Considers a Range of Factors in Allocating Funds and Selecting Projects

    The Forest Service uses both quantitative and judgmental processes in
    deciding how to allocate fuel reduction funds. At headquarters, the agency
    increasingly relies on a quantitative process--reflected in a computer
    model--to determine the relative need for fuel reduction funds in each
    region. At the regional level, some offices primarily use quantitative
    processes to allocate resources while others rely on professional
    judgment. Similarly, the national forests use a mix of quantitative and
    judgmental processes to select projects.

    At the National Level, the Forest Service's Allocation Process
    Increasingly Relies on a Quantitative Approach

    At the headquarters level, the Forest Service has developed a computer
    model that assesses regions on various factors and assigns a score to each
    region reflecting its relative priority for fuel reduction funds. ^[99]22
    According to Forest Service officials, they developed the model to address
    shortcomings that were highlighted by Congress and that were previously
    identified by GAO, the Department of Agriculture's Office of Inspector
    General, and the Office of Management and Budget. These shortcomings
    included inadequate assessment of the risk of wildland fires to
    communities, failure to clearly identify fuel reduction priorities, and
    little
    assurance that funding is targeted to these priorities. ^[100]23 In
    addition, agency officials said they developed the model to provide
    transparency, so that agency officials at all levels, as well as Congress
    and others, can understand the rationale behind allocation decisions.

    ^21GAO-02-843T.

    ^22The Forest Service's system for setting priorities for reducing
    hazardous fuels and allocating resources excluded the Alaska region (and
    did not give it a priority score) because the Alaska region's program
    accounts for less than 1 percent of the agency's fuel reduction funds.

    In developing the model, the Forest Service brought together an
    interdisciplinary group of senior leaders to determine the final list of
    factors, which was based on an initial list developed by regional fuel
    program managers. To determine the factor weightings, the group followed a
    multistep process in which they determined the relative importance of each
    factor by comparing it separately to every other factor, and then
    synthesized the results to determine overall weightings. ^[101]24 The
    model includes 18 weighted factors, as shown in table 1.

    ^23See GAO-03-805; U.S. Department of Agriculture, Office of Inspector
    General, Audit Report: Implementation of the Healthy Forests Initiative,
    08601-6-AT (Washington, D.C.: September 2006); Office of Management and
    Budget, Program Assessment Rating Tool: Review of U.S. Department of
    Agriculture's Wildland Fire Management Program
    (Washington, D.C.: 2006); U.S. House of Representatives, Committee on
    Appropriations,
    Department of the Interior, Environment, and Related Agencies
    Appropriations Bill of 2007, House Report 109-465, 109th Cong., 2nd Sess.
    (Washington, D.C.: May 15, 2006); U.S. Senate, Committee on
    Appropriations, Department of the Interior, Environment, and Related
    Agencies Appropriation Bill of 2007, Senate Report 109-275, 109th Cong.,
    2nd Sess. (Washington, D.C.: June 29, 2006).

    ^24The process the officials used to weight the factors is called the
    analytical hierarchy process, which is a systematic process often used in
    private industry to make complex decisions involving multiple criteria,
    such as investment decisions. We did not assess the appropriateness of the
    factors selected or the weights assigned, nor did we evaluate the model's
    accuracy in applying these factors to determine priority scores.

    Table 1:  Factors  Considered in  Forest  Service Fuel  Reduction  Funding
    Allocation Model

                                Factors Weight (percent)

                             Treatment effectiveness^a 16.7

                                Wildfire potential 12.5

    Wildland-urban interface

    Treatment method availability

    Wildlife habitat objectives

    Municipal water supply

    Ecosystem vulnerability^b

    Associated benefits^a,c

    Vegetative maintenance

    Biomass opportunity

    Insects and disease

    Invasive species

    Vegetation departure^b

    Watershed condition

    Life cycle cost^a,d

    Commercial timber

    Smoke emissions^e

    Use of legislative tools^f

                                       Total 100

    Source: GAO analysis of Forest Service data.

    Notes: Totals do not add to 100 percent due to rounding.

    ^aNo data were available for the 2007 allocation process.

    ^b"Ecosystem vulnerability" and "vegetation departure" are measures of
    fire regime condition class.

    ^c"Associated benefits" is a measure of acres treated with fuel reduction
    funds that achieve benefits for other programs, such as wildlife or
    watershed.

    ^d"Life cycle cost" is intended to measure the cost of treatments per
    year.

    ^e"Smoke emissions" is a measure of the acres of vegetation that produces
    high levels of smoke during a wildland fire.

    ^f"Use of legislative tools" is a measure of acres treated in projects
    authorized in HFRA or HFI, identified in community wildfire protection
    plans, or implemented using stewardship contracts. Stewardship contracting
    involves the use of any of several contracting authorities that were first
    authorized for use by the Forest Service on a pilot basis in 1998, and
    were subsequently extended to BLM. In practice, stewardship contracts
    generally involve the exchange of goods, such as timber, for contract
    services, such as thinning of brush.

    Several of the factors--such as fire potential, ecosystem vulnerability,
    and wildland-urban interface--are designed to assess the potential for
    severe wildland fires in each region and the likelihood of damage
    resulting from such fires. For example, to determine the potential for
    severe fires, the model analyzes data such as the number and size of large
    wildland fires in each region. To determine the likelihood of damage
    resulting from wildland fires, the model includes data on values at risk,
    such as the locations of municipal water supplies and wildland-urban
    interface.

    Other factors are intended to encourage efficiency and effectiveness
    within the fuel reduction program and across multiple Forest Service
    programs, such as the forest products or wildlife management program, to
    take advantage of opportunities to achieve objectives in other programs.
    Regarding effectiveness, the model included a factor intended to assess
    effectiveness in the regions--method availability. However, in practice,
    this factor used data on the total number of acres treated in each
    region-- in effect rewarding regions for treating a large number of acres
    regardless of how well the treatments reduced risk or of the risk level of
    the areas treated. In addition, the model was designed to include a factor
    to assess how effective individual fuel reduction treatments are likely to
    be in reducing risk. However, the Forest Service does not currently have
    data to make such an assessment; consequently, for 2007, this factor did
    not influence allocations to the regions.

    In 2007, officials used the model's results to inform their decisions
    about funding allocations to the regions, although they relied mainly on
    the prior year's funding levels along with their professional judgment.
    Officials used the model's results only to make minor adjustments to
    allocations because the model was still being refined and because they
    wanted to phase in funding changes gradually in order to minimize
    budget-related disruptions. Headquarters officials said they expect the
    model to have a stronger influence on future allocation decisions.

    The model assigned a numerical score to each region that indicated the
    region's relative priority for fuel reduction funds, with higher scores
    indicating higher priority. However, as shown in table 2 and figure 5, the
    Forest Service's funding allocations to its regions are often at odds with
    the priority scores resulting from the model, with some high-scoring
    regions--such as the Northern and Eastern regions--receiving less funding
    than some lower scoring regions such as the Pacific Southwest and the
    Southwest regions.

    Table 2: Forest Service Regions' Fiscal Year 2007 Fuel Reduction  Priority
    Scores and Funding Allocations

                                  Dollars in thousands

                Forest Service region Priority score Funding allocation

                                  Southern 574 $29,092

                                  Northern 455 15,782

                                   Eastern 416 9,718

                                Intermountain 408 16,165

                               Rocky Mountain 399 25,445

                              Pacific Northwest 389 25,794

                              Pacific Southwest 388 43,737

                                Southwestern 367 37,341

                                     Alaska^a a 805

                                     Total $203,879

    Source: Forest Service.

    Notes: The Forest Service also allocated $2.265 million to its research
    stations and $ 95.109 million to its headquarters office and to cost
    pools, which are used for expenses that cannot reasonably be charged to a
    single program, including indirect, support, and common services charges.

    ^aThe Forest Service excluded the Alaska region from its model because the
    region has a small fuel reduction program relative to the other regions
    and receives less than 1 percent of the agency's fuel reduction funds.

    Figure 5: Forest Service Regions' Fuel Reduction Priority Scores as a
    Percentage of Total, Compared to Regions' Funding Allocations as a
    Percentage of Total Allocations, Fiscal Year 2007

    Percentage

    25

    Region

    Regional priority score as a percent of total Regional portion of national
    allocations

    Source: GAO analysis of Forest Service data.

    According to Forest Service officials, the allocation amounts were not
    more closely correlated with the priority scores for the following
    reasons:

    o As noted, the officials wanted to temper changes to regions' budget
    allocations until they completed revisions to the model and developed more
    confidence in its output in order to minimize funding shifts that might
    prove inappropriate once the model is refined. ^[102]25 Agency officials
    told us that even when they become confident in the model's output,
    they will likely implement changes incrementally in order to minimize
    disruption to regions and national forests.

    ^25GAO has previously noted the importance of the integrity, credibility,
    and quality of data that underlie budget decisions and, thus, the value in
    implementing changes gradually when the quality of such data is in
    question. See, for example, GAO, Performance Budgeting: Opportunities and
    Challenges, GAO-02-1106T (Washington, D.C.: Sept. 19, 2002).

         o Until the revisions have been completed, the model's results will be
           tentative. An important focus of the revisions will be those 3 of the
           model's 18 factors for which the Forest Service had no data sources in
           2007. Because of the lack of data, these elements had no effect on the
           regions' 2007 priority scores, ^[103]26 but agency officials hope to
           have data to inform these elements for future allocations.
         o The relatively high priority score assigned to the Eastern region was
           not consistent with agency officials' knowledge of the area--that is,
           they believed that, relative to the other regions, there were fewer
           destructive wildland fires in the Eastern region and, consequently,
           they expected the region's priority score to be lower than it was.
           When the officials consulted data on the number of structures burned
           in wildland fires, their belief was confirmed. Consequently, agency
           officials are reexamining the measures they used to assess risk and
           exploring options for refining them.
         o Fuel reduction costs vary widely from region to region, and when
           making final allocations, the officials made adjustments to
           accommodate this variation. For example, the Pacific Southwest region
           received the largest allocation of any region, despite its relatively
           low priority score, in part because treatment costs in the region are
           very high (averaging about $535 per acre in 2006). Therefore, a
           relatively large allocation is needed to fund even a moderate amount
           of work. At the other end of the spectrum, treatment costs in the
           Southern region are low (averaging about $32 per acre in 2006),
           meaning that needed work can be accomplished with a smaller
           allocation. In addition, agency officials said that, although the
           Southern region's priority score might point toward a larger
           allocation for the region, nonmonetary constraints--such as the size
           of the workforce--limit the amount of work the region can accomplish
           and, therefore, the amount of funds that can prudently be invested
           there. Further, in order to maintain overall funding stability to the
           regions, officials coordinated regional fuel reduction funding
           allocations with those of other Forest Service resource programs, such
           as watershed management or forest products. This coordination
           sometimes resulted in officials adjusting fuel reduction funding
    		 allocations in order to compensate for adjustments in these other
    		 programs' funding levels.

    ^26To ensure that  the elements  without data  had no  effect on  priority
    scores, all of the regions were assigned the same score for each of  these
    elements.

    o The Forest Service allocated a portion of its fuel reduction funds
    according to congressional direction. Specifically, congressional
    committee reports accompanying relevant appropriations acts directed the
    Forest Service to spend about $34 million (12 percent) of its 2006 fuel
    reduction funds in certain areas or on certain projects. The Forest
    Service accommodated this congressional direction, regardless of whether
    doing so was consistent with priority scores.

    Some Forest Service Regions Use Quantitative Allocation Processes, While
    Others Rely More on Professional Judgment

    For 2007, the Forest Service allowed each region to determine how to
    allocate funds to its national forests and what factors to consider in the
    process, as long as the factors were consistent with those considered in
    the national allocation process. Four of the Service's nine regions relied
    primarily on quantitative data in their allocation processes to national
    forests, while five relied primarily on professional judgment. In applying
    these processes, all nine regions considered a combination of factors,
    many of which were similar to those used at the national level. Beginning
    with the 2008 allocations, the Forest Service plans to require regions to
    use the headquarters model to inform allocation decisions.

    Of the four regions that relied primarily on quantitative processes in
    2007, one--the Rocky Mountain region--used a computer model that analyzed
    geospatial data on vegetative condition and areas of insect-killed trees
    to help assess relative wildland fire risk among the national forests in
    the region. Regional officials then used their judgment to consider other
    factors, such as lands in the wildland-urban interface and acreage
    targets, to refine allocation amounts. Through the risk assessment
    process, the region identified 6 emphasis forests out of the 11 forests in
    the region and allocated over 70 percent of the region's fuel reduction
    funds to these 6 forests. The Pacific Northwest region also used a model,
    but its model incorporated regional data on a number of factors, including
    the number of acres in fire regimes I, II, and III; the number of acres
    identified in community plans as being in the wildland-urban interface;
    and per-acre treatment costs. Using these data, regional officials
    identified five forests in the region where an extremely wet climate made
    the risk of damaging wildland fires so low that they decided not to
    allocate any fuel reduction funds to these forests and excluded them from
    the model. Another region that relied on a quantitative process--the
    Pacific Southwest region--used a scoring system that ranked forests
    primarily on the basis of a risk assessment; the assessment incorporated
    multiple factors, such as the number of acres in condition classes 2 and 3
    and in the wildland-urban interface. The region also used other factors,
    such as the forests' capacity to conduct fuel treatment work, to make
    smaller adjustments. The Intermountain region allocated about 80 percent
    of its fuel reduction funds in accordance with forests' historical funding
    levels. For the remaining funds, the region delegated priority decisions
    to collaborative interagency groups in each of the region's states. These
    groups scored and ranked proposed projects against a set of standard
    criteria and made funding recommendations to the regional office.

    The remaining five Forest Service regions relied primarily on professional
    judgment and negotiation among agency officials when determining funding
    allocations to national forests. Although these regions did not use
    quantitative processes to assign priorities among forests, they
    incorporated some of the same information included in other regions'
    quantitative processes. For example, the Northern region conducted a risk
    assessment for the region, but instead of using the risk assessment to
    guide its allocations to the forests, the region directed forests to use
    it to identify potential treatments. The region then allocated funds to
    forests primarily on the basis of the forests' proposed annual workloads.
    In the Southern region, officials used their professional judgment to
    decide on allocations largely on the basis of forests' reported
    capabilities, per-acre treatment costs, and local priorities, and how they
    fit with expected regional targets and budgets.

    Factors outside of the formal process influenced allocations, according to
    Forest Service officials, but they did not always formally incorporate
    these factors into the allocation process. For example, in several
    regions, fuel reduction officials said they coordinated with officials
    from other resource programs, such as the wildlife management and
    vegetation management programs, when deciding on final allocations. In
    doing so, they sometimes adjusted fuel reduction allocations to, for
    example, prevent multiple programs from reducing allocations to a given
    forest in the same year or to take advantage of efficiencies when
    different programs' priorities overlapped in a given forest. In addition,
    nearly every region reported considering acreage targets when making
    allocation decisions--even those that did not report it as an official
    part of their allocation processes. Regional officials noted that pressure
    to meet the acreage targets established by Forest Service headquarters
    sometimes trumped all other factors in allocation decisions, especially in
    2007 when targets increased at a faster rate than funding levels. Another
    factor, according to agency officials in several locations, was direction
    contained in congressional committee reports accompanying relevant
    appropriations acts that a certain amount of funding be allocated to
    specific forests or specific districts within forests. As with
    headquarters, regional offices allocated funds according to this
    direction, apart from any priority-setting process. For example, in 2006,
    the Pacific Southwest region allocated nearly $21 million (about 52
    percent of the region's budget) on the basis of congressional committee
    report direction, in part to treat areas of insect-killed trees in
    southern California. Finally, regions reported shifting funds among
    forests, after initial allocation decisions had been made, to accommodate
    unexpected circumstances during the year, such as large wildland fires
    that prevented fuel reduction treatments from being implemented as
    planned.

    National Forests Select Projects Using Quantitative and Judgmental
    Processes

    Like regional offices, national forests are allowed to determine what
    processes to use and which factors to consider in selecting fuel reduction
    projects to fund and implement, within the parameters of national and
    regional direction. In practice, some forests rely more on quantitative,
    data-driven processes, while others rely more on professional judgment.
    Both consider a mix of factors, as the following examples, based on our
    site visits to national forests, illustrate:

         o Quantitatively based selection. The Arapaho-Roosevelt National Forest
           in Colorado collaborated with another national forest, a national
           park, Forest Service research scientists, and the Colorado State
           Forest Service to develop a risk assessment that used quantitative
           data to map the highest priority locations for fuel reduction
           treatments in the area. Forest officials then used the risk assessment
           to prepare a 10-year strategy with proposed annual treatments. Each
           year, forest officials first consult the strategy and the risk
           assessment to identify a list of projects to fund, and then adjust the
           list to meet acreage targets within budget constraints. Similarly,
           officials of the Angeles National Forest in Southern California
           convened a diverse group of stakeholders and followed a step-by-step
           process to identify priorities for fuel reduction treatments. During
           the process, Forest Service officials provided information, such as
           the locations of historical wildland fires and developed areas, as
           well as places where fuel reduction was not feasible because, for
           example, the topography was too steep to operate needed equipment.
           They then used fire behavior models to show where fires could
           potentially burn and how various proposed fuel reduction treatments
           might affect such fires. The end result was a multiyear list of
           proposed projects that forest officials used to select projects each
           year.
         o Judgmental based selection. At the Medicine Bow-Routt National Forest
           in Wyoming and the Chattahoochee-Oconee National Forest in Georgia,
           officials relied largely on their knowledge and experience about the
           area to select fuel reduction projects. Some of these officials had
           worked at the same forest for decades. At the Ocala National Forest in
           Florida, officials use their professional judgment to select projects,
           which are almost all prescribed burns. However, because the forest's
           fuel reduction program is so large and the vegetation grows so
           quickly, the project selection process is founded on a rotational
           schedule. Under this schedule, the forest aims to treat nearly all of
           its approximately 130,000 burnable acres over a 4-year period.
           Consequently, officials try to treat about a quarter of the
           acreage--or slightly over 30,000 acres--each year. Forest officials
           also said they consider other factors, such as wind direction,
           humidity, and human activity (for example, popular areas for weekend
           recreation), when determining the specific timing of a prescribed
           burn.

    In addition to factors that national forests considered, unanticipated
    factors influenced project selection decisions at the local level,
    sometimes preventing planned projects from being implemented. In such
    cases, agency staff frequently carried out lower priority projects in
    place of the originally planned projects. For example, wildland fires
    sometimes burned in locations planned for fuel reduction treatments,
    making the treatments unnecessary; in other cases, litigation prevented
    planned treatments from being implemented as scheduled.

    Interior and Its Agencies Use a Mix of Quantitative and Judgmental Processes and
    Consider a Range of Factors in Allocating Funds and Selecting Projects

    Interior and its agencies--BLM, BIA, FWS, and NPS--use both quantitative
    and judgmental processes for allocating fuel reduction funds and selecting
    projects, and consider multiple factors, many of which are similar to
    those used by the Forest Service. In 2007, Interior allocated funds to its
    four agencies primarily on the basis of historical funding levels, but it
    is currently developing a computer model similar to the Forest Service's.
    Like Interior, the BLM national office allocated funds to its state
    offices primarily on the basis of historical funding levels in 2007 but is
    expecting to implement a new funding allocation model in 2008. The
    majority of BLM state and local offices allocated funds and selected
    projects using quantitative processes, many of which use scoring systems.
    The other three Interior agencies' national, state, and local offices used
    both quantitative and judgmental processes to allocate funds and select
    projects, considering a range of factors.

    Interior Allocates Funds to Its Agencies Primarily on the Basis of
    Historical Funding Levels

    Interior's allocations to BLM, BIA, FWS, and NPS have remained fairly
    constant from year to year, measured on a percentage basis, because the
    department primarily allocates fuel reduction funds on the basis of past
    funding levels--what one departmental official called "allocation by
    tradition." This funding pattern dates back to 2001, when the Interior
    agencies began receiving a sharply increased amount of fuel reduction
    funds as a result of the National Fire Plan. Since then, the percentage of
    Interior's fuel reduction funding that is allocated to each of the
    agencies has remained consistent, with BLM receiving about 50 percent of
    the funding, BIA receiving about 20 percent, and FWS and NPS each
    receiving about 15 percent. Figure 6 shows the percentage of Interior's
    total fuel reduction appropriation that was distributed to each agency
    from 2001 through 2007.

    Figure 6: Percentage of Interior's Total Fuel Reduction Funds Allocated to
    the Interior Agencies, Fiscal Years 2001 through 2007

    Percentage 100

    80

    60

                                           40

                                           20

    0

    2001 2002 2003 2004 2005 2006 2007 Fiscal year

    BLM
    BIA
    NPS
    FWS

    Source: GAO analysis of Interior data.

    In 2001, Interior established initial funding allocations on the basis of
    estimates of each agency's infrastructure and capacity (i.e., the amount
    of work each could accomplish), which it determined by compiling field
    requests from the four agencies. However, at the time, most of the
    agencies and their field units had little infrastructure related to the
    fuel reduction program--including limited staff--so many units did not
    have the resources to collect extensive information on fuel reduction
    needs, according to agency officials. As a result, agency officials had to
    make allocation decisions based on limited information. Some agency and
    departmental officials have stated that the allocations need to be
    revisited now that the fuel reduction program has been in place for
    several years.

    Each year, the department tells the four agencies how much funding the
    department has requested for the fuel reduction program and what its
    acreage targets are for treatments within and outside of the
    wildland-urban interface. The agencies' fuel program leads--the
    headquarters officials in charge of each agency's fuel reduction
    program--then meet to determine how to divide the funds and set targets
    for each agency. However, the fuel program leads do not have the authority
    to significantly adjust the funding allocations from previous levels;
    rather, such changes would have to be determined at the department level,
    according to headquarters officials. The agencies' field units submit
    proposed project lists to the regions, which review these lists before
    forwarding them to headquarters; these lists provide the fuel program
    leads with an idea of each agency's needs and capabilities when
    determining funding allocations. After the fuel program leads decide upon
    initial allocations, they may shuffle funds within or between their
    agencies throughout the year to adapt to uncontrollable circumstances,
    such as weather conditions. The majority of fuel treatments that the
    Interior agencies conduct depend on the weather, and sometimes weather
    conditions prevent work from being completed. For example, if a drought in
    the Southeast makes vegetation too dry for safe prescribed burns, Interior
    may shift funds to units in the western United States. In practice, these
    considerations may result in Interior's shifting funds from FWS and NPS,
    which conduct a large number of fuel treatments in the Southeast, to BLM
    or BIA, which conduct most of their fuel treatments in the West.

    In 2007, Interior allocated 5 percent of its funds to the agencies using a
    model similar to the Forest Service model, and it plans to use the model
    to influence a greater portion of allocations in future years. ^[104]27
    The department developed the model after Interior and the Forest Service
    received congressional committee direction in 2005 to develop a common
    method for setting project priorities. Interior's 2007 model included a
    range of factors, such as the amount of land each agency manages with
    certain fuel conditions and the degree to which each agency used biomass,
    but included fewer factors than the Forest Service's model because some
    data were not yet available. Because Interior does not currently have a
    good method for measuring efficiency or effectiveness, its 2007 model used
    the legislative tools factor, which measures the extent of use of HFI and
    HFRA planning authorities, to measure efficiency, and the number of acres
    treated to measure effectiveness. The following provides the complete list
    of factors used in Interior's 2007 model: ^[105]28

         o number of fire starts,
         o number of large fires (defined as 500 acres or more),
         o fuel conditions,
         o biomass utilization,
         o number of threatened and endangered species,
         o fire regime condition class improvement,
         o use of legislative tools (HFI/HFRA),
         o number of acres treated, and
         o wildland-urban interface.

    ^27The funds Interior allocated using the model represented 5 percent of
    project funds--that is, funds expected to be spent on individual
    projects--rather than 5 percent of the total allocation, which would
    include program management expenses such as salaries, facility costs, and
    so forth. NPS did not receive any of the 5 percent of 2007 funds that were
    allocated using the new model because Interior allocated the funds late in
    the fiscal year and NPS had already met its 2007 acreage targets.

    ^28Weights are not included in the list because Interior had not yet
    finalized them at the time of our review.

    Results from the 2007 model were generally consistent with Interior's
    allocations to the agencies in previous years. However, Interior is still
    making changes to the model, including determining how to weight the
    factors, so this may not be the case in future years. According to
    departmental officials, Interior intends to be cautious in applying the
    new model and making significant changes to current allocations because
    Interior and the Forest Service are currently developing the Fire Program
    Analysis (FPA) system--an interagency fire management planning and
    budgeting model--and they expect information from that system to inform
    future allocation decisions. ^[106]29 By proceeding slowly, the department
    hopes to avoid potentially disruptive fluctuations in regional and field
    unit allocations.

    Once they have received their allocations from the department, Interior
    agencies determine how to allocate fuel reduction funds to the regions
    within the parameters of departmental and congressional direction.
    Interior officials have stated that they would like the agencies to use
    more rigorous allocation processes in the future, though one departmental
    official noted that he does not want the agencies to invest substantial
    funding or time and effort to develop new allocation processes pending the
    expected completion of the FPA. Interior guidance lists the following
    priorities for selecting projects:

         o All projects must result from a collaborative process.
         o Funding will be targeted to the wildland-urban interface.
         o Within the wildland-urban interface, focus should be on projects near
           wildland-urban interface communities at greatest risk of fire;
           communities that have completed a community plan or its equivalent;
           and communities where there is an active partnership with volunteer
           efforts, in-kind services, or partners who contribute funding.
         o Outside of the wildland-urban interface, focus should be on areas in
           condition class 2 or 3 in fire regimes I, II, or III, or those in
           condition class 1 where landscape conditions could quickly deteriorate
           to condition 2 or 3.

    ^29While agency officials told us the  new model will be coordinated  with
    FPA, they did not provide details on how this coordination will occur.

         o Priority should also be given to projects using mechanical treatments,
           with special emphasis on projects yielding biomass that can be sold or
           traded to companies or the local community; and projects using
           contractors, particularly those projects conducted under contracts
           that support rural communities' stability.
         o Prescribed burning is to be used when weather and resource conditions
           permit, where mechanical treatments are not appropriate, and as
           maintenance treatments following mechanical work.
         o Managers must make maximum practical use of tools provided by HFRA and
           HFI.

    BLM Increasingly Uses Quantitative Processes in Allocating Funds and
    Selecting Projects

    In 2007, BLM headquarters allocated funds to its state offices primarily
    on the basis of historical funding levels; however, agency officials told
    us that, starting in 2008, BLM plans to use a quantitative process
    incorporating factors similar to those used in Interior's new model, with
    a greater emphasis on collaboration and local priorities. BLM headquarters
    provides flexibility to state offices and local units when allocating
    funds and selecting projects but directs these offices to consider
    Interior and agency guidance. The majority of BLM state offices and local
    units used quantitative processes to allocate funds and select projects in
    2007, frequently scoring projects against a set of weighted factors.

    BLM Allocates Funds to Its State Offices Primarily on the Basis of
    Historical Funding Levels but Plans to Use a More Quantitative Approach in
    2008

    In 2007, BLM headquarters allocated funds to its state offices largely on
    the basis of past funding levels--as in previous years--as a way to ensure
    that funding levels remain relatively stable, but it also considered
    proposed projects, national priorities, and the extent to which state
    offices met past acreage targets established by BLM. While the project
    lists do not largely influence allocations to state offices, state offices
    use these lists to allocate funds to field units, and field units use them
    to select projects for implementation. Table 3 shows the 2007 allocations
    to the BLM state offices. (App. II also shows 2005 and 2006 allocations.)

    Table 3: BLM Allocations to State Offices, Fiscal Year 2007
    Percent of BLM total
    State office                    Allocation         state office allocation
    Oregon/Washington              $24,878,000                            27.1
    Idaho                           14,598,000                            15.9
    Utah                            10,078,000                            11.0
    California                      7,322,000                              8.0
    Colorado                        6,843,000                              7.5
    Nevada                          6,414,000                              7.0
    New Mexico                      6,412,000                              7.0
    Montana                         5,461,000                              6.0
    Arizona                         4,355,000                              4.7
    Wyoming                         3,684,000                              4.0
    Alaska                          1,556,000                              1.7
    Eastern States                   126,000                               0.1
    Total                          91,727,000a                           100.0

    Source: GAO analysis of BLM data.

    Notes: Total allocation includes the allocation for the current year plus
    carryover from the previous fiscal year.

    ^aBLM allocated an additional $8,473,000 for BLM headquarters, science
    centers, training costs, and other support costs.

    As shown in table 3, the Oregon/Washington, Idaho, and Utah state offices
    got substantially more funding than the other states--more than half of
    BLM's total funding. The Oregon/Washington state office alone received
    more than $24 million--27 percent of BLM's state office funding; one BLM
    field unit in Oregon, the Medford district office, received over $9
    million in 2007--more than nine state offices each received in total
    funding. According to some agency officials, the relatively high level of
    fuel reduction funding directed toward the Oregon/Washington state and
    Medford district offices is, in part, the result of BLM's emphasis on
    providing stable levels of funding to states and field units. According to
    these officials, when BLM (along with other federal agencies) received a
    sharp increase in fuel reduction funding in 2001, agency officials sought
    to identify units that could implement fuel reduction projects quickly.
    Because the Oregon/Washington and Medford offices were identified as
    having the capacity to undertake a large number of fuel reduction
    projects, they received a substantial portion of the new funding. However,
    another agency official told us these large amounts are justified because
    there is substantial wildland fire risk in Oregon and, therefore, a great
    need for fuel treatments because vegetation grows very quickly in the
    western part of the state, there is considerable wildland-urban interface,
    and wildland fire suppression costs are high.

    Starting in 2008, BLM plans to use a model to influence funding
    allocations to state offices for fuel reduction. Use of the model is
    intended to ensure that the highest priority work is funded and that BLM's
    fuel reduction treatments are integrated with other vegetation treatments,
    such as range improvement projects, to effectively achieve fire and
    resource management goals and objectives. According to a headquarters
    official, the new model is intended to facilitate comparison of risk and
    needed work at the national and state levels in order to set priorities
    for funding among states and communities. Headquarters officials will use
    the model results to make allocation decisions but will shift no more than
    20 percent of the previous year's allocations to each state in 2008 and
    2009.

    The model has three components: (1) treatment characteristics; (2) a
    measure of the degree of threat; and (3) an efficiency measure. For the
    first component--treatment characteristics--the model will score every
    proposed project on a set of weighted factors, such as local priority
    ratings, the availability of joint funding, and condition class; there are
    separate factors and weights for projects within and outside of the
    wildland-urban interface. The second component--the measure of the degree
    of threat--currently combines three elements: the number of fire starts,
    the number of large fires (i.e., fires greater than 300 acres), and local
    risk ratings. The third component--efficiency--is currently measured by
    past performance on acreage targets, past performance on estimating
    treatment costs, and treatment cost per acre. According to agency
    officials, they intend to eventually include a measure of effectiveness in
    the model, which would indicate how well a treatment reduces risk or
    achieves other objectives. However, because BLM does not currently have a
    good way to measure effectiveness, it is using measures of efficiency
    until it develops a better approach. Table 4 shows the complete list of
    factors used in the model and their weights.

    Table 4: Factors and Factor Categories BLM Considers in BLM Fuel Reduction
    Funding Allocation Model
    Weights for
                                            wildland-urban Weights for treatments
    Funding allocation                        interface    outside the Overall
                                                                wildland-
    model components   Factors evaluated     treatments          urban  weight
                                                                interface
                          Community plan or           0.14           0.01
                          equivalent
                          High local                  0.14           0.11
                          prioritya
                          Mechanical                  0.12           0.04
                          treatment
                          Joint funding               0.10           0.08
                          available
                          HFRA/HFI NEPA               0.10           0.08
                          typeb
                          Stewardship                 0.10           0.08
                          projectc
                          Multiple land               0.08           0.03
                          ownership
    Treatment          Moderate local              0.08           0.06    0.45
                          prioritya
    characteristics    Biomass utilizedd           0.05           0.04
                          Large-scale                 0.05           0.10
                          treatmente
                          Low local                   0.02           0.02
                          prioritya
                          Condition class 2           0.01           0.11
                          or 3
                          Impacted species            0.01           0.10
                          Fire regime I,                 f           0.06
                          II, or III
                          Fire or other                  f           0.08
                          treatment methodg
                          Number of large
                          fires (greater              0.50           0.50
                          than 300 acres)h
    Degree of threat   Number of fire              0.25           0.25    0.35
                          startsh
                          Local risk                  0.25           0.25
                          ratingi
                          Past performance
                          on acreage                  0.50           0.50
                          targetsj
                          Past performance
    Efficiency         on treatment cost           0.40           0.40    0.20
                          estimatesk
                          Cost per acre               0.10           0.10

    Source: GAO analysis of BLM data.

    ^aThe local priority rating is assessed at the local level and is a way
    for the field to communicate project priorities that may not be
    well-represented by other factors.

    ^bThe "HFRA/HFI NEPA-type" factor weights projects that use National
    Environmental Policy Act (NEPA) planning tools authorized by HFRA or HFI.

    ^cStewardship projects are accomplished through the use of stewardship
    contracting, which involves the use of any of several contracting
    authorities that were first authorized for use by the Forest Service on a
    pilot basis in 1998, and were subsequently extended to BLM. In practice,
    stewardship contracts generally involve the exchange of goods, such as
    timber, for contract services, such as thinning of brush.

    ^dThe "biomass utilized" factor weights projects that make use of
    biomass--small-diameter trees, branches, and other organic
    material--removed through fuel reduction.

    ^eLarge-scale treatments are treatments that are at least 150 percent
    larger than the average treatment.

    ^fThis factor was not used to determine the treatment scores for
    wildland-urban interface treatments.

    ^gThis factor weights projects treated with prescribed fire or other
    treatment methods, such as grazing or herbicides.

    ^hThe "number of large fires" and "number of fire starts" factors are
    determined at the field office or district level and applied to all
    treatments within that field office or district.

    ^iThe risk rating is assessed at the local level and is to be determined
    from community plans or risk assessment programs.

    ^jThe "past performance on acreage targets" factor is calculated at the
    state level and applied to all treatments within the state.

    ^kThe "past performance on treatment cost estimates" factor is calculated
    at the state level and applied to all treatments within the state.

    The BLM national office also directs state offices and local units to
    consider Interior and BLM priorities when allocating funds and selecting
    projects. BLM-specific guidance directs state offices and local units to
    coordinate fuel treatments with other resource management activities, such
    as timber and wildlife habitat; target funds to wildland-urban interface
    areas identified through a collaborative process; target nonwildland-urban
    interface funds to ecosystems that have the highest risk-reduction
    potential; and use HFI and HFRA planning tools.

    The Majority of BLM State Offices Incorporate Quantitative Approaches in
    Their Allocation Processes

    The BLM national office allows state offices to choose the approach they
    use in allocating funding to field units, as long as they take into
    account departmental and BLM priorities, and state offices will continue
    to have this flexibility with the implementation of the new national
    allocation process, according to headquarters officials. In 2007, 6 of the
    11 BLM state offices primarily used quantitative approaches to inform
    their allocation processes, and 5 primarily used a judgmental approach.
    ^[107]30 Nine of 11 state offices considered targets or past performance,
    and 10 considered at least one factor related to collaboration, such as
    community plans. Eight of 11 state offices considered at least one factor
    to estimate wildland fire risk, such as local- or state-level risk
    assessments or fire regime condition class.

    The six state offices that allocated funds using quantitative processes in
    2007 primarily used weighted scoring systems--similar to the state scoring
    component of BLM's new model--to set priorities for projects. While the
    specific factors and their weights varied by state, many factors were
    commonly used and were similar to those used in BLM's headquarters system;
    each of the states had separate lists of factors for projects within and
    outside of the wildland-urban interface. For wildland-urban interface
    projects, five of the six state offices emphasized factors such as local
    risk ratings or community hazard assessments to estimate risk from
    wildland fire, and all six offices considered a variety of other factors,
    including community support and joint funding, to measure the extent of
    collaboration. For projects outside of the wildland-urban interface, all
    six offices gave priority to projects in condition classes 2 or 3, jointly
    funded or collaborative projects, and projects that improved threatened
    and endangered species habitat, as well as a variety of other factors.
    Once the state offices had the field offices' project lists, state and
    field offices generally negotiated to determine final funding allocations.

    ^30While there are 12 BLM state offices--11 in the West and 1 in the
    East--the vast majority of BLM-managed land is in the West, and the
    Eastern States office receives only about
    0.1 percent of BLM's total fuel reduction funding. Further, this funding
    is allocated to just one field unit. As a result, the Eastern States
    office is not included in our description of BLM state office allocation
    processes.

    The remaining five state offices primarily used judgmental processes to
    allocate funding to field units. For example, in 2007, the Oregon/
    Washington state office allocated funding using professional judgment and
    negotiation, which included numerous discussions with field units' fuel
    program staff to assess the units' priorities and capabilities. The state
    office primarily considered capability and past performance of field
    offices and BLM's national priorities when making the final allocations.
    Starting in 2008, the office plans to use a model to allocate base funding
    for fuel reduction, which covers salaries and other fixed costs, but will
    continue to allocate project funding using the current approach, which
    relies primarily on professional judgment and negotiation.

    BLM officials told us that factors outside of the formal process
    influenced allocations. For example, in several states, agency officials
    said they coordinated with officials from other resource programs, such as
    the range or weeds programs, at the state or local level when deciding on
    final allocations or selecting projects. As a result, they sometimes
    selected projects that used funding from multiple resource areas, or
    benefited these areas, over other projects in order to take advantage of
    efficiencies. Many state offices also reported that they considered
    acreage targets when making allocation decisions. According to one state
    official, acreage targets were the most influential factor in allocation
    decisions, and several agency officials said that lower priority projects
    were sometimes funded to meet acreage targets. Finally, state offices
    reported shuffling funds among or within field units after allocation
    decisions had been made to accommodate uncontrollable circumstances
    throughout the year, such as weather conditions that prevented prescribed
    burns from being implemented as planned.

    BLM state offices also devote substantial effort and funding to assist in
    the development of community plans, and allocate a significant portion of
    fuel reduction funding to projects on private land. For example, the
    Montana state office funded the development of 49 out of 54 completed
    community plans throughout the state, according to an agency official.
    Also, when the Montana state office and its field units allocate funding
    to, and select projects in, the wildland-urban interface, proposed
    projects on private land--which are submitted to BLM by counties--are
    ranked using the same system as BLM projects. Consequently, BLM projects
    on federal land essentially compete for the same funding as projects on
    private land. In California, the BLM state office allocates more than half
    of its wildland-urban interface funding to a community assistance program,
    through which fuel treatments on private, state, or tribal lands adjacent
    to or in the vicinity of federal lands are funded through an interagency
    grant process.

    The Majority of BLM Field Units Incorporate Quantitative Approaches into
    Their Project Selection Processes

    As with the BLM state offices, in 2007, the majority of BLM field units
    used quantitative approaches that incorporated a range of factors--many of
    which were similar or identical to the ones used by state offices--to
    select and rank projects. For example, the Twin Falls district office in
    Idaho scored all projects using a weighted scoring system developed by the
    BLM Idaho state office. It then ranked the projects, considering factors
    such as project scores and areas identified in community plans. The
    Billings field office in Montana also used a weighted scoring system to
    rank projects. Field staff initially identified projects using community
    plans or the field office's risk assessment--which analyzed fuel type,
    fire regime condition class, and fire occurrence to identify high-risk
    fire areas--and then scored the projects using the Montana state office's
    weighted scoring system to identify high-, medium-, and low-priority
    projects.

    Also, like national forests, BLM field units were sometimes influenced by
    unanticipated factors when selecting projects. For example, agency
    officials sometimes deferred planned projects because newly proposed
    projects suddenly became a high priority. They pointed to situations in
    which nonprofit organizations donated funds to pay for projects and agency
    officials gave those projects a higher priority. In Colorado, recent oil
    and gas development, as well as construction of new subdivisions in
    high-risk areas, have caused field units to shift priorities to conduct
    treatments near these developments, according to a BLM official.

    BIA Allocates Funds Largely on the Basis of Units' Performance History,
    while FWS and NPS Use Quantitative and Judgmental Processes

    In 2007, the three remaining Interior agencies--BIA, FWS, and NPS--
    allocated fuel reduction funds using quantitative and judgmental processes
    and considering a variety of factors. Like BLM, these agencies provide
    flexibility to regional offices and local units in determining how to
    allocate funds and select projects and direct them to consider
    departmental priorities. (See app. II for these agencies' 2005 through
    2007 allocations to their regional offices.)

    BIA and FWS Headquarters Allocate Funds Using Quantitative Processes,
    While NPS Headquarters Allocates Funds Primarily on the Basis of
    Historical Funding Levels

    In 2007, BIA headquarters allocated fuel reduction funds to its regions
    using a formula that considered past performance and proposed work and
    that essentially rewarded regions for their accomplishments. The formula
    allocated to each region a percentage of the region's total budget
    request, based on the percentage of the prior 3 years' acreage targets
    that the region met. For example, if a region had met 95 percent of its
    total acreage target since 2004, the region would receive about 95 percent
    of its requested budget for 2007. BIA placed a cap on the amount of
    funding that regions could request, based on their previous year's
    accomplishments. ^[108]31 According to a headquarters official, BIA
    rewards those regions and units that achieve acreage targets because, in
    many instances, units do not meet targets.

    FWS headquarters allocated 2007 funds to regional offices using a
    quantitative model that considers multiple factors, including historical
    fire occurrence, fuel conditions, community assessments of risk, and field
    unit past performance. The model has separate modules for projects within
    and outside of the wildland-urban interface, and produces a weighted score
    for each FWS field unit. In the wildland-urban interface module, the most
    influential factors are communities at risk, local hazard rankings, and
    fire conditions. For the non-wildland-urban interface module, the most
    influential factors are past performance and proposed work.

    NPS headquarters allocated 2007 funding to regional offices primarily on
    the basis of historical funding levels. These levels were originally set
    by a model that determined funding allocations through a risk assessment,
    which considered vegetation types, fuel types, fire return intervals, and
    other data, and through an effectiveness measure that examined treatment
    success for different vegetation types. According to an NPS official, the
    agency maintained funding proportions at the model's 2005 level after
    Interior directed it to work on the FPA; NPS decided that it would have
    been too much work for field staff to maintain the model while also
    preparing data for the FPA. Furthermore, they believed that the model,
    initially developed more than 20 years ago, was outdated and did not merit
    additional financial investment while the FPA was being developed.

    ^31Regions that accomplished 90 percent or more of the previous year's
    acreage target could request up to 120 percent of the prior year's funding
    amount, while regions that accomplished less than 90 percent could request
    only up to 105 percent of the previous year's amount.

    BIA, FWS, and NPS Regional Offices Allocate Funds to Field Units Using
    Quantitative and Judgmental Processes

    BIA, FWS, and NPS allow their regions the flexibility to determine how to
    allocate funding to field units, provided the processes and factors are
    consistent with departmental and agency guidance. The BIA national office
    encourages regions to adopt allocation strategies similar to the one used
    at headquarters--which rewards past performance--and some of BIA's
    regional offices have done so, such as the Rocky Mountain and Northwest
    regions. The Rocky Mountain region, for example, used a quantitative
    process to allocate fuel reduction funds, using an allocation formula
    similar to the one used by BIA headquarters but using only the previous
    year's accomplishment rate, rather than the 3-year average headquarters
    used. Likewise, one FWS regional office that we visited used a
    quantitative process to allocate fuel reduction funds in 2007: FWS's
    Mountain-Prairie region allocated funds to local units using FWS's
    national model, but regional officials adjusted the model's allocations on
    the basis of their knowledge about local factors, such as community
    support for projects and field unit staffing levels.

    Other BIA and FWS regional offices, and all of the NPS regional offices
    that we visited, allocated fuel reduction funds in 2007 using judgmental
    processes that incorporated a range of factors. For example, BIA's
    Southwest region allocated funds primarily on the basis of project
    rankings (as determined at the local level) and cost efficiency, according
    to a regional official. Likewise, FWS's Southeast region allocated 2007
    funds according to a regional official's assessment of a variety of
    factors, such as field units' programs of work and wildland fire activity;
    this official has many years of experience managing the region's fuel
    reduction program. In NPS's Pacific West region, a group of local and
    regional fire and fuel program staff determined funding allocations on the
    basis of park priorities, past performance, and conformance with NPS
    policy, balanced against regional funding levels and acreage targets.

    As in other agencies, officials told us that factors outside of the formal
    process also influenced allocations. Several BIA and NPS officials told us
    that staffing constraints at field units may affect allocations. For
    example, many park units have very small fuel treatment programs and no
    staff dedicated solely to the program; therefore, the fuel reduction
    programs at
    such units may be eliminated if staff, who have numerous collateral
    duties, no longer have the time to plan or implement treatments.
    Furthermore, the location of some field units makes it difficult to
    recruit and retain qualified staff; the field units are located either in
    areas with high costs of living or in remote areas. Without dedicated
    staff to manage fuel reduction programs at such field units, their
    capacity to plan and implement projects and spend any funding allocation
    is limited, so capacity becomes the determining factor regardless of other
    factors considered in the allocation process, according to agency
    officials. Some BIA officials told us that self-determination limits BIA's
    influence over the tribes; self-determination provides tribes with the
    authority to manage federal programs when they choose to do so, as well as
    the authority to choose not to emphasize a given program. In some regions,
    acreage targets also affected allocation and project selection processes,
    and one agency official told us that projects were sometimes developed and
    implemented specifically to meet targets. However, other BIA, FWS, and NPS
    regional officials told us that they did not assign acreage targets to
    field units or that there was little pressure to meet targets. Finally,
    regions reported shifting funds among field units after allocation
    decisions had been made to adapt to uncontrollable circumstances, such as
    weather conditions that prevented planned projects from being implemented.

    Local BIA, FWS, and NPS Units Select Projects Using Quantitative and
    Judgmental Processes

    Some BIA, FWS, and NPS local units selected projects in 2007 using
    quantitative processes. For example, in NPS's Sequoia and Kings Canyon
    National Parks in California, agency officials identified projects using a
    model that determined high-risk areas on the basis of several factors,
    such as the risk of a fire starting and the location of the wildland-urban
    interface. Park officials used the model information, as well as
    additional factors, such as values at risk, sequencing of treatments, and
    project accessibility, to select projects. BIA's Zuni Agency in New Mexico
    also used a quantitative process to select projects. The fuels specialist
    analyzed geographic information--for example, on housing density and
    existing vegetation--to identify and rank projects.

    Other BIA, FWS, and NPS units primarily used judgmental processes when
    selecting projects for 2007. For example, at FWS's Merritt Island National
    Wildlife Refuge in central Florida, field staff selected projects
    primarily on the basis of the rotational schedule for prescribed burns.
    Refuge officials also considered other factors, such as wildlife habitat,
    to select which projects to complete that year. According to agency
    officials, the refuge has habitat for the scrub jay, a threatened species,
    and while prescribed burns generally improve this habitat, too much
    prescribed burning can be disruptive. NPS's Cape Canaveral National
    Seashore, which neighbors Merritt Island National Wildlife Refuge, also
    selected projects judgmentally, and in coordination with refuge staff. The
    process was primarily influenced by the location of the wildland-urban
    interface and threatened and endangered species habitat.

    BIA, FWS, and NPS field units, like national forests and BLM field
    offices, also adapted to unanticipated events when selecting projects. In
    some cases, field units were forced to accommodate unique circumstances.
    For example, the Merritt Island National Wildlife Refuge is adjacent to a
    National Aeronautics and Space Administration facility, and, during the
    days immediately before and during scheduled rocket or shuttle launches,
    the refuge must put all prescribed burns on hold.

    Several Improvements Could Help Better Ensure That Fuel Reduction Funds Are
    Allocated to Effectively Reduce Risk

    Although the Forest Service and Interior are taking steps to enhance their
    funding allocation and project selection processes--for example, by
    developing models to assist in making allocation decisions--there are
    several improvements they could make to better ensure that they allocate
    fuel reduction funds to effectively reduce risk. Specifically, when
    allocating funds and selecting projects, the agencies could improve their
    processes by (1) consistently assessing all elements of wildland fire
    risk, including hazard, risk, and values; (2) developing and using
    measures of the effectiveness of fuel reduction treatments; (3) using this
    information on effectiveness, once developed, to assess the
    cost-effectiveness of potential treatments; (4) clarifying the relative
    importance of the numerous factors they use in allocating funds, including
    factors unrelated to risk or effectiveness; and (5) following a more
    systematic process in allocating funds. While the agencies have recognized
    the importance of these elements--particularly risk, treatment
    effectiveness, and cost effectiveness--in several strategy documents, they
    have not effectively incorporated them into their allocation processes.

    The Agencies Do Not Consistently Assess All Elements of Risk When
    Allocating Funds

    The agencies have repeatedly stressed the importance of identifying
    high-risk areas in setting priorities and allocating funds for fuel
    reduction; for example, in their 2006 document Protecting People and
    Natural Resources: A Cohesive Fuels Treatment Strategy (Cohesive
    Strategy), ^[109]32 the Forest Service and Interior declared that they
    "expect to ensure that fuel project investments are cost-effectively
    allocated to achieve risk reductions." Similarly, in its 2007 budget
    justification, the Forest Service declared that the fuel reduction program
    focuses on reducing the risk of wildland fire and long-term damage to
    resources and property; likewise, Interior's 2007 budget justification
    declared that the department intended to reduce fuels in order to "provide
    better risk reduction to communities and resources."

    At the national level, the Forest Service and FWS headquarters
    incorporated nationwide risk assessments into their 2007 allocation
    processes; Interior did so for only 5 percent of the funds it allocated to
    the four Interior agencies; and BIA, BLM, and NPS did not include risk
    assessments in their national allocation processes at all, although BLM
    officials said they are taking steps to do so in the future. According to
    Forest Service and Interior agency officials, it has been difficult to
    develop national risk assessments because they require nationally
    consistent data, which have not always been available. ^[110]33
    Furthermore, some of the available national data on vegetation type and
    condition were designed for forests and, consequently, are not as accurate
    for shrublands and grasslands.

    At the regional and local levels, some agency offices used risk
    assessments when allocating funds and selecting projects, while others did
    not. One of the Forest Service's 9 regions and 2 of BLM's 11 state offices
    considered all three elements required for a risk assessment in their 2007
    allocation processes, and several other Forest Service regions considered
    two of the three elements--hazard and values--but did not consider risk.
    Some, but
    not all, of the other Interior agencies' regional offices we visited
    considered elements of risk assessments in their allocation processes as
    well. Regional officials offered several reasons for not always
    systematically considering risk assessments when allocating funds, such as
    not having the necessary data for a regionwide risk assessment or only
    informally considering risk.

    ^32U.S. Department of the Interior and USDA Forest Service, "Protecting
    People and Natural Resources: A Cohesive Fuels Treatment Strategy,"
    February 2006. Note that, although the document is referred to as a
    cohesive strategy, previous GAO reports concluded that it does not contain
    all the elements GAO called for in its earlier recommendations for such a
    strategy. See, for example, GAO-06-671R.

    ^33The agencies expect that nationally consistent data will be available
    through LANDFIRE, a geospatial data and modeling system currently being
    implemented. LANDFIRE data are complete for some of the country, with data
    for the remainder of the country expected to be completed by 2009.

    Several agency officials told us that they do not consider the lack of a
    formal national or regional risk assessment to be a significant problem
    because they rely on field units to assess risk when selecting projects.
    However, as with regions, not all local units used risk assessments when
    selecting projects; some local units used only partial assessments or did
    not use risk assessments at all. Even when field units do use risk
    assessments to help select projects in high-risk areas at the local level,
    agency officials cannot be confident that areas designated as high risk
    locally would still be designated as high risk at the regional or national
    level. For example, one BLM field office in Colorado oversees a rural area
    with only two communities, neither of which is at risk from wildland fire,
    according to BLM officials. For officials at this office, the most
    important values at risk are rural power lines and oil and gas
    infrastructure; therefore, they give the highest priority to projects that
    protect these features. From a regional or national perspective, however,
    other projects may be a higher priority for funding because the values at
    risk are more important, the area is at higher risk from fire, the level
    of hazard is greater because of fuel conditions, or some combination of
    these reasons. Without using national, regional, and local-level risk
    assessments that systematically assess hazards, risks, and values, it is
    difficult to ensure that allocation decisions are grounded in a clear
    understanding of which areas are at the highest risk.

    Even when the agencies conduct risk assessments that include hazards,
    risks, and values, they may find it difficult to distinguish between high-
    and low-priority locations because one key value at risk--the
    wildland-urban interface--has multiple definitions that leave considerable
    room for interpretation on the part of agency officials. As a result, many
    different areas can be classified as wildland-urban interface, and the
    term's usefulness in helping agency officials identify, and direct funds
    toward, the highest-priority lands is diminished. In 2001, the
    agencies--in cooperation with tribes and states--defined the interface as
    including three categories:

    (1) dense populations (250 or more people per square mile) abutting
    wildlands; (2) scattered populations (28 to 250 people per square mile)
    intermixed with wildlands, and (3) development surrounding an island of
    wildland fuel, such as a park or open space. Agency officials told us that
    they developed this definition very quickly, in response to legislative
    direction, but later came to believe that it overemphasized population
    density and was not flexible enough to accommodate differences in
    landscape features such as vegetation, terrain, and prevailing weather
    patterns, which can affect the size and shape of areas in the
    wildland-urban interface.

    In 2003, HFRA defined the wildland-urban interface to include an area
    within or adjacent to an at-risk community, that is identified in project
    recommendations to a federal agency in a community wildfire protection
    plan. For areas not in community plans, HFRA specified that areas within
    one-half mile of an at-risk community were to be considered wildland-urban
    interface, ^[111]34 as were areas within 1-1/2-miles of an at-risk
    community under certain conditions, and areas adjacent to evacuation
    routes for at-risk communities. According to agency officials, this
    definition offered more flexibility by moving away from the focus on
    population density, but it applies only to projects conducted using HFRA
    authorities.

    Most recently, the 2006 10-Year Strategy Implementation Plan developed by
    the agencies, western governors, and others, defined the wildland-urban
    interface as the "the zone where structures and other human development
    meet at-risk forest and rangelands." While this definition provided broad
    flexibility, agency officials told us it did not replace the 2001
    definition (which focused on population densities), and both the 2001 and
    2006 definitions apply to projects other than those conducted using HFRA
    authorities. The end result is multiple definitions that--individually and
    collectively--allow many different areas to be classified as
    wildland-urban interface without specifying whether some ought to be given
    higher priority than others.

    ^34In 2001, a Federal Register notice was published with a list of
    wildland-urban interface communities identified by states as being "in the
    vicinity of federal lands" and "at high risk from wildfire." However, the
    states and tribes used inconsistent approaches to identify these
    communities at risk. To standardize these approaches, the National
    Association of State Foresters was tasked, in the 10-Year Implementation
    Plan, with developing a definition for community at risk, and a process
    for states and tribes to follow to identify and prioritize the
    communities. Accordingly, in 2003, the National Association of State
    Foresters finalized its guidance, defining community as "a group of people
    living in the same locality and under the same government," and specifying
    that a community was to be considered at risk from wildland fire if it was
    located within the wildland-urban interface as defined in the 2001 Federal
    Register, which stated that, "the urban-wildland interface community
    exists where humans and their development meet or intermix with wildland
    fuel."

    In part because of this lack of clarity, agency officials we spoke with
    reported including several types of locations under the category of
    wildland-urban interface. Some units interpreted the interface to mean
    only the area surrounding houses, while others also included roads, power
    lines, oil and gas development, communications infrastructure, campgrounds
    and recreation areas, and other features. For example, the BLM Colorado
    state office defined industrial interface as a subcategory of the
    wildland-urban interface, including features such as power lines or oil
    and gas development, which are common features in or near some of BLM's
    rural field units. In contrast, officials for two national forests near
    urban areas (Atlanta and Los Angeles) determined that most or all of their
    forests were in the wildland-urban interface because, they estimated, a
    wildland fire could move into nearby urban and suburban areas within a
    single day. In yet another interpretation, a BIA agency in New Mexico
    tailored its definition of wildland-urban interface to accommodate
    cultural differences between tribes, as the differences were reflected in
    the arrangement of their homes: one tribe built its homes in clusters
    while another built its homes in a scattered pattern.

    Although each of these interpretations of wildland-urban interface may
    have merit given the situations the field units face, the lack of clear
    definition effectively allows a wide range of areas to be defined as
    wildland-urban interface. The fluid nature of the wildland-urban interface
    definition is illustrated by guidance that one FWS region issued to its
    local units in 2006, when it notified them that it was expanding the
    relatively strict definition of wildland-urban interface the region had
    previously used to reflect interagency guidance. According to this region,
    "this expanded definition may enhance our ability to fund a project with
    [wildland-urban interface] ... funding and will help us meet the
    [wildland-urban interface] treatment targets mandated by the Department."

    Given the range of definitions available for wildland-urban interface, it
    is not surprising to find that in 2005 and 2006 many of the fuel reduction
    treatments the agencies identified as being in the wildland-urban
    interface were in ZIP code areas with fewer than 28 people per square
    mile, on average. ^[112]35 (See fig. 7.) Specifically, about 2.2 million
    acres, or 65 percent of all acres treated in areas identified as the
    wildland-urban interface during
    that period, were in ZIP code areas with fewer than 28 people per square
    mile. While the agencies may have had legitimate reasons for some of these
    treatments--for example, to protect a critical evacuation route for a
    larger community--it is not clear why, as a whole, so many acres treated
    are far from more densely populated areas. Expressing its concern about
    this situation in 2006, the Office of Management and Budget noted, "As the
    agencies increase their emphasis on [wildland-urban interface] treatments
    over time, field staff and/or project proponents may simply be defining
    more projects as [wildland-urban interface] projects in order to increase
    the likelihood of having their projects funded."

    ^35We conducted our analysis using  census data on the average  population
    per square mile across areas defined by ZIP codes. However, especially  in
    larger ZIP  codes,  there may  be  smaller pockets  where  the  population
    density is higher or lower than the average used in our analysis.

    Figure 7: Density of Wildland-Urban Interface Treatments and Population
    Density, by ZIP Code

    Density of 2005 and 2006 Fuel Reduction Treatments Identified as Being in
    the Wildland-Urban Interface, by ZIP Code

    Less than 5,000 acres per zip code 5,000 or more acres per zip code

    Population Density in 2000 in the Continental United States, by ZIP Code

    Fewer than 28 people per square mile 28 to 249 people per square mile 250
    or more people per square mile

        Source: GAO analysis of Forest Service, Interior, and U.S. Census data.

    Note: We conducted our analysis using census data on the average
    population per square mile across areas defined by ZIP codes. However,
    especially in larger ZIP codes, there may be smaller pockets where the
    population density is higher or lower than the average used in our
    analysis. When mapping the data, we included a 1.5-mile buffer around the
    ZIP code areas with 28 or more people per square mile to account for the
    1.5-mile buffer specified in the HFRA definition for wildland-urban
    interface.

    Conversely, population density alone may not be sufficient justification
    for selecting locations for fuel reduction. One Forest Service official
    cautioned against "prioritization by census," because more densely
    populated areas are not necessarily at greater risk than less populated
    areas. For example, although Chicago is a densely populated urban area,
    the Forest Service has not conducted more treatments in the nearby
    grassland because the risk of a fire threatening the urban area is very
    low, according to agency officials. In addition, highly populated urban
    areas are often not as close to federal lands as are communities with
    smaller populations, and the agencies conduct the majority of their fuel
    reduction work on federal lands. Figure 8 shows the location of federal
    lands relative to more densely populated areas in the continental United
    States. Even if a dense urban area is near federal lands, the entire area
    is not typically at risk from a fire originating on federal lands; only
    the portion of structures closest to federal lands is at risk, according
    to Forest Service officials. ^[113]36 Finally, vegetation and other
    conditions on some federal lands make it unlikely that a fire would burn
    or that a fire would threaten a nearby population.

    ^36However, in a fire behavior assessment of the June 2007 Angora Fire in
    California, Forest Service officials stated that a large number of houses
    ignited because of embers from other burning houses, rather than from
    wildland fuel--suggesting that even homes that are not immediately
    adjacent to federal lands could be at risk from wildland fire.

    Figure 8: Location of Federal Lands and Populated Areas in the Continental
    United States

    Federal Lands Managed by the Forest Service, BLM, BIA, FWS, and NPS in the
    Continental United States

        Population Density in 2000 in the Continental United States, by ZIP Code

    Fewer than 28 people per square mile 28 to 249 people per square mile 250
    or more people per square mile

    Source: GAO analysis of U.S. Censusand U.S. Geological Survey's National
    Atlas Website data.

    Note: We conducted our analysis using census data on the average
    population per square mile across areas defined by ZIP codes. However,
    especially in larger ZIP codes, there may be smaller pockets where the
    population density is higher or lower than the average used in our
    analysis. When mapping the data, we included a 1.5-mile buffer around the
    ZIP code areas with 28 or more people per square mile to account for the
    1.5-mile buffer specified in the HFRA definition for wildland-urban
    interface.

    While many important contextual details are not visible on a national map,
    some can be seen at the county level. For example, in Los Angeles
    County--the most populous U.S. county--many of the fuel reduction
    treatments completed in 2005 and 2006 were adjacent to densely populated
    areas, as shown in figure 9, but some were miles away and in ZIP code
    areas with relatively low population.

    Figure  9:  Map  of  Los  Angeles  County  Wildland-Urban  Interface  Fuel
    Reduction Treatments Completed in 2005 and 2006, and Population Density

                                        Angeles
                                    National Forest

    Note: We conducted our analysis using census data on the average
    population per square mile across areas defined by ZIP codes. However,
    especially in larger ZIP codes, there may be smaller pockets where the
    population density is higher or lower than the average used in our
    analysis.

    Treatments occurred in these low-density areas for several reasons. First,
    many of the treatments conducted in the county during that period, while
    not immediately adjacent to the city of Los Angeles, were on the federal
    land closest to the city, the Angeles National Forest, which of course is
    not highly populated. Also, while the average population density for the
    general area is low, individual communities with populations ranging from
    about 1,000 to 3,000 are located inside the boundaries of the forest, and
    the Forest Service conducted some treatments to protect them. Second,
    developed sites--such as campgrounds, roads, and recreation areas-- where
    people temporarily congregate may not be reflected on a census map of
    population density. According to officials at the Angeles National Forest,
    human-caused wildland fires generally coincide with such areas, making it
    important to conduct fuel treatments around these sites. Finally,
    low-density areas within the forest were more feasible to treat than some
    areas closer to population centers because steep terrain across much of
    the forest--including along its southern boundary adjacent to heavily
    populated Los Angeles--makes it difficult and expensive to conduct fuel
    treatments, and, in some cases, would make treatments ineffective,
    according to agency officials.

    In contrast to Los Angeles County, Rio Blanco County, Colorado, is a rural
    county with a total population of about 6,000 and an average population
    density throughout the county of less than 28 people per square mile.
    Nevertheless, BLM classified some of its fuel reduction treatments in this
    county as wildland-urban interface treatments. As figure 10 shows, these
    treatments in 2005 and 2006 were generally located far from the largest
    towns in the county--Meeker and Rangely--which each has a population of
    about 2,000.

    Population Density

    Source: GAO analysis of Forest Service, Interior, U.S. Census, and U.S.
    Geological Survey's National Atlas Web site data.

    Note: We conducted our analysis using census data on the average
    population per square mile across areas defined by ZIP codes. However,
    especially in larger ZIP codes, there may be smaller pockets where the
    population density is higher or lower than the average used in our
    analysis. In Rio Blanco County, there were no ZIP code areas with an
    average population of 28 or more people per square mile.

    According to BLM officials, they did not conduct wildland-urban interface
    treatments closer to these towns because the towns are not at significant
    risk from wildland fire; they are surrounded in large part by rocky
    outcroppings and irrigated agricultural fields, where fires would not
    likely start, and area roads serve as fire breaks. In addition, the county
    had prepared a community plan identifying its highest priorities, and the
    federal lands surrounding the towns were not among them. Instead, many of
    the fuel reduction treatments the agencies did implement--including the
    five located southwest of Meeker--were conducted to protect energy
    development facilities, such as a coal mine and oil and gas wells, or
    power lines that service such facilities, according to BLM officials.
    According to the officials, they selected these projects because they were
    higher priority than other potential projects in the county, and because
    the county's community plan had identified the protection of energy
    development and power lines as its priorities, defining the wildland-urban
    interface to include the areas around such infrastructure. While these
    decisions may be reasonable given local priorities, it is not clear from a
    national perspective whether the values at risk in this case are of higher
    priority than the values at risk in other locations--in part because the
    definitions of the wildland-urban interface do not distinguish the
    relative importance of different values at risk, such as homes, power
    lines, or oil and gas wells, among others.

    The Agencies Do Not Consider Treatment Effectiveness in Their Allocation
    Processes Because They Have No Measure for Effectiveness

    Although the agencies recognize the importance of measuring the
    effectiveness of fuel reduction treatments--that is, how much risk
    reduction is achieved through a given treatment and for how long--none of
    the agencies considered effectiveness when allocating funds in 2007
    because they have not yet developed a method for measuring it. Without
    understanding the potential effectiveness of fuel reduction treatments,
    the agencies cannot ensure that funds are allocated appropriately, because
    not all areas that rank high in a risk assessment can be treated with the
    same degree of success. For example, parts of southern California are
    dominated by chaparral ecosystems, which feature plants with
    fire-resistant roots, enabling the plants to re-sprout quickly. Some of
    the plants also encourage fire because their leaves are coated with a
    flammable resin. Although these areas of chaparral ecosystems would score
    high on a risk assessment--because there is a high vegetation hazard near
    populated areas with considerable values at risk--agency officials told us
    that fuel reduction treatments in chaparral may be effective for only a
    short time because the vegetation often grows back quickly. In addition,
    many of the damaging fires in southern California chaparral have been
    fanned by the warm, dry, and extremely powerful Santa Ana winds, making it
    difficult for fuel treatments to affect fire severity, according to some
    Forest Service officials. As a result, some of these areas, though at high
    risk from fire, might not be designated as high priority for fuel
    treatments. In general, understanding the expected effectiveness of fuel
    reduction treatments under different conditions can help the agencies
    target their funds toward treatments that will achieve the most risk
    reduction for a given cost. The agencies have, on multiple occasions,
    recognized the significance of treatment effectiveness; for example, in
    the 2006 10-Year Strategy Implementation Plan, the agencies identified the
    need to "explore the feasibility of developing measures that determine the
    degree and longevity of fire hazard reduction achieved by hazardous fuels
    treatments."

    Although the agencies have not yet developed a measure of effectiveness,
    they have designed their allocation models to accommodate data on
    effectiveness in the expectation that such data will eventually become
    available. The Forest Service's model includes two elements intended to
    assess effectiveness, but, because the agency does not have data on
    effectiveness, one of the elements serves as a placeholder--by assigning
    each region an identical score--and thus does not influence priority
    scores, while the other uses data on the total number of acres treated in
    each region instead. Forest Service officials acknowledged that the number
    of acres treated does not reveal how effective the treatments are in
    reducing risk, but told us they used this information because they wanted
    a measure that would reflect the variation in accomplishment levels from
    one region to the next. Interior and BLM also plan to include a measure of
    effectiveness in their allocation models, but Interior--like the Forest
    Service--currently uses total acres treated, and BLM uses data on
    efficiency, including total acres treated and average cost per acre,
    because these are the only data available. According to agency officials,
    it is difficult to develop a single measure of effectiveness for different
    geographic locations and vegetation types, because, for example, a
    treatment in grass might be effective for 1 year, while a treatment in
    some forests might be effective for 30 years. Nevertheless, as long as the
    agencies continue to allocate funds without knowing how effective
    treatments are likely to be, they cannot be sure that funds are being
    spent on projects that substantially reduce overall risk.

    According to Forest Service research scientists, developing a measure of
    treatment effectiveness would require that the agencies first determine
    how to estimate the level of risk in a given location so they could track
    any changes in risk resulting from fuel treatments. For example, they
    could use data on fire intensity, severity, or occurrence, or some
    combination of these and other factors, to evaluate risk. Once agency
    officials determined how to estimate risk, they could use the information
    to measure treatment effectiveness. However, there is no consensus on how
    best to do so and any method would likely require considerable effort. For
    example, under one approach described by the researchers, available
    scientific studies about fuel reduction treatments in various vegetation
    types would be analyzed to ascertain where fuel treatments are more or
    less effective, and effectiveness ratings would be calculated for each
    vegetation type on the basis of this information. After establishing the
    ratings, they would collect field data to verify their initial conclusions
    and ratings--a costly and time-consuming exercise, according to some
    researchers. Such an approach would have drawbacks, however; the
    researchers told us that it would be difficult to establish a single
    rating that would apply to vegetation types under all circumstances
    because fuel conditions within a given vegetation type vary widely,
    depending, for example, on geographic location and previous fuel reduction
    activity. In addition, factors other than vegetation--such as terrain,
    weather, and soil--also influence treatment effectiveness. Consequently,
    some researchers have proposed alternative approaches, such as one that
    would consider many factors, in addition to vegetation type, to assign
    effectiveness ratings to individual treatment areas rather than general
    vegetation types. However, developing an effectiveness rating scheme using
    this approach--or others that incorporate numerous factors--would require
    significant research and analysis over a long time period, according to
    one researcher.

    A less expensive, quicker approach outlined by another Forest Service
    researcher would rely on expert opinion rather than field data. Under this
    simplified approach, a panel of experts with knowledge about and
    experience in fuel reduction treatments and their effectiveness would use
    their professional judgment to collectively estimate the extent to which
    fuel treatments would be effective in each of several vegetation or fire
    regime condition class categories. The experts' estimates could then be
    used to inform decisions on allocating funds.

    The Agencies Often Consider Costs, but Not Cost-Effectiveness, When
    Allocating Funds

    The agencies also do not consider the cost-effectiveness of treatments
    when allocating funds, primarily because they do not have data on
    treatment effectiveness. Treatment costs can vary widely in different
    areas, from as little as $10 per acre to well over $1,000 per acre, even
    ranging as high as $30,000 per acre under unusual circumstances, and
    allocating funds wisely involves not simply targeting those acres that can
    be treated most cheaply, but those acres where treatments yield the most
    cost-effective result. While considering costs is an important step in
    making allocation decisions, it is equally important to consider
    effectiveness in conjunction with costs to avoid funding ineffective
    projects simply because they are cheap. However, until the agencies have
    data on treatment effectiveness, they will find it difficult to do so. In
    support of these considerations, the 2006 Cohesive Strategy emphasized the
    importance of reducing fuel in the most cost effective manner possible,
    because federal funds can support only a finite number of fuels treatments
    each year covering a fraction of the acres at high risk from unusually
    severe fires.

    In practice, the agencies frequently consider costs when allocating funds
    and selecting projects. They sometimes give priority to projects with low
    per-acre costs in order to leave more funds available for other projects
    or to treat more acres within their budgets--an important factor for
    agencies trying to meet increasing acreage targets. Also, agencies
    sometimes give priority to low-cost treatments in areas that have
    previously been treated and are consequently of relatively low risk, in
    order to prevent them from becoming higher risk. According to agency
    officials, these treatments are a priority because they are a
    cost-effective way to maintain low-risk conditions once achieved; it is
    generally much cheaper to reduce fuel in areas that have recently been
    treated than to do so in areas that have never been treated or have not
    been treated for a long time. However, without knowing the effectiveness
    of treatments in reducing risk, agency officials may not be able to
    compare the relative benefits of potential projects when deciding where to
    invest fuel reduction funds--and, thus, may not know which projects are
    likely to be the most cost-effective.

    In some cases, the agencies also give lower priority to treatments with
    very high per-acre costs--even in high-risk areas--because the expected
    benefit does not justify the expense. For example, the Desert National
    Wildlife Refuge in southern Nevada identified a mechanical thinning
    treatment to remove palm trees as its highest-priority fuel reduction
    project in 2006. The proposed project was in the wildland-urban interface
    and would also improve the habitat of an endangered fish, according to
    agency officials. However, it would have cost hundreds of thousands of
    dollars--nearly the entire budget for the region--and, therefore, FWS
    regional officials did not fund the project.

    The Agencies Have Not Established Clear Guidance on the Relative
    Importance of Factors Used in Setting Priorities

    In addition to more consistently using information on risk and developing
    measures of treatment- and cost-effectiveness, the agencies could improve
    their allocation process by clarifying the relative importance of the
    different factors they use in setting priorities. Without such
    clarification, it is not clear how agency officials are to resolve
    conflicts that arise between competing factors. In addition, when factors
    other than risk, treatment effectiveness, and cost effectiveness have
    considerable influence on allocation decisions, it is difficult for the
    agencies to ensure that funds are allocated to areas where they will most
    effectively reduce risk.

    The agencies consider such factors in part because they are directed to do
    so; many of the factors they consider are tied to federal laws or
    congressional direction. For example, fuel reduction projects authorized
    under HFRA include, among others, projects on federal land in the
    wildland-urban interface and certain projects in areas where ecological
    restoration is needed because vegetation has departed significantly from
    its historical regime. The act requires the agencies to develop annual
    programs of work for federal land that give priority to authorized
    hazardous fuel reduction projects that provide for the protection of
    at-risk communities or watersheds or that implement community wildfire
    protection plans. Congressional committee direction has also called for
    the agencies to put a priority on fuel reduction work completed through
    mechanical treatments and projects that use biomass.

    Some of the factors the agencies consider are also intended to encourage
    efficiency in the fuel reduction program, as well as more broadly in their
    land management missions. Specifically, the agencies give priority to
    projects that achieve benefits not only for the fuel reduction program but
    also for other programs such as wildlife management and watershed
    improvement--an approach referred to as integration among programs. Agency
    officials said implementing such projects is a way to leverage funds and
    coordinate resources. The Forest Service also emphasizes these projects
    because its interpretation of the President's HFI calls for a focus on
    integrated management, according to agency officials.

    In the face of multiple directives and competing agency priorities, agency
    officials must balance numerous factors when allocating funds and
    selecting projects, as the following examples illustrate:

    o Priorities in community plans may not always align with
    agency-identified priorities, forcing agencies to choose between them.

           According to Montana BLM officials, one community proposed a fuel

         reduction project in an area the officials believed was relatively low

         risk because it had vegetation that does not burn easily. However, the

    officials agreed to implement the project because they are directed to

    give priority to locally identified projects and because they did not

    want to damage their relationship with the community. Several agency

    officials told us that community plans did not always include federal

    lands or propose projects in locations where the agencies could

    feasibly implement a treatment. In such cases, agency officials

          sometimes worked with the communities to identify project locations

    agreeable to all, while other times they implemented agency-identified

    projects instead of those identified in the plans.

         o Direction to give priority to high-risk areas may also conflict with
           the agencies' commitment to meet acreage targets. Several agency
           officials told us that they sometimes implemented lower-priority
           projects with low unit costs because they felt pressure to meet
           acreage targets. In some cases, these projects, although low priority
           for fuel reduction purposes, were a high priority for other resource
           programs or achieved other management objectives.
         o Direction to give priority to areas in the wildland-urban interface
           may conflict with other agency priorities. For example, NPS officials
           told us that giving priority to fuel reduction treatments at the
           interface conflicted with the agency's mission to preserve natural
           ecosystems and processes, which would call for giving priority to
           treatments in undeveloped areas.
         o Desire for stable funding and staff levels may make officials
           reluctant to shift funds on the basis of risk assessments. When
           allocating funds, the agencies frequently emphasized the importance of
           maintaining stable funding levels and minimizing disruptions to staff,
           which can conflict with the direction to emphasize high-risk areas.
           According to agency officials, stable allocations to regions and field
           units are needed to ensure predictability and enable regional and
           field staff to plan ahead. In addition, a minimum level of funding is
           needed to maintain the workforce and infrastructure required to
           support viable fuel reduction programs in regions and field units.
           Several agency officials told us they were reluctant to shift funding
           on the basis of risk assessments because doing so could require staff
           to relocate-- potentially multiple times--and the officials wanted to
           avoid uprooting staff.

    Agency guidance offers little in the way of clarification for staff
    confronted with numerous, conflicting priorities, as the multiplicity of
    priorities in the Forest Service and Interior's 2006 Cohesive Strategy
    illustrates. In this strategy, the Forest Service and Interior outline a
    set of national fuel treatment priorities but do not establish a hierarchy
    of their relative importance. Among the treatment priorities are areas in
    the wildland-urban interface as well as some areas outside the interface
    in condition classes 2 or 3. In addition, some areas in condition class
    1--the only remaining condition class--are to be given equal priority,
    according to the strategy. Similarly, the strategy calls for priority to
    be given to mechanical treatments where appropriate, but also to
    prescribed burns where appropriate. After providing a list of priority
    criteria, the strategy declares that the more criteria a fuel reduction
    project meets, the higher its priority should be for funding. However, it
    also acknowledges that, in exercising management discretion, the agencies
    may need to make exceptions to the process described for ranking and
    selecting projects.

    Agencies' Allocation Processes Are Not Always Systematic

    Although the agencies are working to develop and implement models that
    will allow them to allocate funds more systematically, such systematic
    approaches are not used by all agencies or at all levels within the
    agencies. By allocating funds using a systematic process--one that is
    methodical, based on established criteria, and applied consistently--the
    agencies can better ensure that they uniformly consider all relevant
    criteria and appropriately apply these criteria in all decisions.

    In particular, when agency officials rely primarily on professional
    judgment and negotiation to allocate funds, they do not always follow a
    step-by-step approach or consistently apply a predetermined set of
    criteria. We recognize that agency decision makers--particularly those who
    have served in the same location for many years--often have detailed
    knowledge about on-the-ground conditions and a thorough understanding of
    fuel reduction needs. Nevertheless, without using a systematic approach,
    even knowledgeable and well-meaning decision makers may be more
    susceptible to influences that are not intended to be part of the
    decisions, as illustrated by the following examples:

         o According to several agency officials, they face considerable pressure
           to meet acreage targets. Under these circumstances, and with no
           predetermined set of criteria in an allocation process, targets could
           have more influence than intended. That is, agency officials might
           fund lower priority projects in order to treat more acres.
         o In NPS's Southeast region, agency officials told us that the location
           of full-time fuel reduction staff has considerable influence on
           allocations, even though it is not officially a factor in the
           allocation process. Few parks in this region have full-time staff
           devoted to fuel reduction, and parks without such staff request and
           receive much less fuel reduction funding than do the parks with
           dedicated staff--potentially because there are fewer staff to perform
           the work necessary to identify fuel reduction needs and request funds.
           Consequently, according to agency officials, it is difficult to ensure
           that all of the highest-priority areas for fuel reduction across the
           region are identified and targeted for funding

    because some high-priority areas may not be identified if they are
    located in parks with fewer staff. NPS officials in another region
    expressed a similar concern, stating that the agency needs to shift fuel
    reduction funds within the region to direct them to high-priority
    locations and acknowledging that it cannot do so without also shifting
    personnel to high-priority locations.

    Moving toward more systematic allocation processes also enhances
    transparency and accountability. In many of the locations we visited, the
    agency offices that relied primarily on professional judgment to allocate
    fuel reduction funds and select projects did not document the rationale
    for their decisions. As a result, the processes were not transparent, and
    neither agency officials nor others--including Congress and the public--
    could understand the rationale behind the decisions or have confidence
    that the resulting allocations were directed to the highest-priority areas
    for reducing risk to communities and the environment. For example,
    officials in BLM's Oregon/Washington state office used their professional
    judgment to determine allocations to its 10 district offices. Under this
    process in 2007, BLM's Medford district office received an allocation of
    about $9 million--over 7 times the average allocation received by the
    other nine district offices that year. While this disparity may be
    appropriate, without a transparent process it is difficult to determine
    the extent to which the allocation reflects agency priorities for reducing
    risk to communities and the environment, rather than other factors. The
    agencies themselves have emphasized the importance of transparency and
    accountability; for example, the 10-Year Strategy Implementation Plan
    states that the agencies should "strive for maximum transparency in the
    decision-making process."

                                      Conclusions

    Our nation's wildland fire problem has been decades in the making and will
    not be solved quickly. Nevertheless, with careful choices about where to
    spend their limited fuel reduction dollars, federal agencies can
    meaningfully, if incrementally, reduce the risks faced by communities and
    the environment. Doing so will require the agencies to continue moving
    away from allocation by tradition to allocation by priority. Toward this
    end, the agencies could improve their current approaches in three key
    areas.

    First, the agencies would benefit from routinely using an allocation
    process that is systematic, and that is common to all the agencies. A
    systematic process can help ensure that the agencies apply their
    allocation and project selection criteria consistently, and can help
    interested parties outside of the process--Congress, local communities,
    and other entities-- understand the rationale for the funding and project
    selection decisions that are made. While the models that some of the
    agencies are developing represent substantial steps forward in this regard
    and will affect larger portions of funding allocations over time, not all
    of the agencies have models, and none consistently uses models at the
    national, regional, and local levels. Further, the models, even where
    used, often exert only a small influence on allocation decisions, partly
    because the agencies do not yet have full confidence in the models' data.
    As a result, the agencies often base decisions mainly on historical
    funding patterns and professional judgment. We recognize that professional
    judgment will always have a role in the allocation process to account for
    difficult-to-quantify factors, such as local priorities or political
    considerations. However, the agencies and the public are best served if a
    systematic process, such as a model, serves as the foundation for
    allocation decisions, and professional judgment plays a supporting, rather
    than a lead, role. Also, given that wildland fire is a nationwide problem
    that does not respect administrative boundaries, the agencies would do
    well to develop and use a common process for allocating fuel reduction
    funds--as Congress has called for--that can be customized to accommodate
    differences in scale, type of ecosystem, agency mission, and other
    criteria.

    Second, the agencies could improve the information they use to make
    allocation decisions. Because the agencies do not always use risk
    assessments and currently lack data on treatment effectiveness, they often
    make allocation decisions without knowing, on a broad scale, where the
    acres at highest risk are located, which treatments are most effective at
    reducing risk, and which areas respond best to treatment. To improve their
    allocation decisions, they should continue, over the long term, to develop
    and use information on risk and treatment effectiveness. The agencies can
    then use this information, in concert with cost information, to
    effectively assess tradeoffs among potential treatments and identify the
    most cost-effective investments.

    Finally, the agencies could strengthen their allocation processes by
    sorting through the numerous prioritization factors that have accumulated
    over the years and establishing a hierarchy for considering them. Without
    such a hierarchy, the exercise of setting priorities can be
    frustrating--or even meaningless--because virtually any project can
    qualify as high priority. While we recognize that, in some cases, the
    agencies are bound by law or congressional direction to give priority to
    certain factors, we believe there may remain enough room within those
    constraints not only to establish a hierarchy of factors, but also to
    clarify the relative importance of categories within some factors--in
    particular, various categories of wildland-urban interface. We do not
    advocate prioritization by census-- simply directing fuel reduction funds
    to areas with the highest populations--but neither do we believe that the
    agencies or the public are well-served by the broad definitions of the
    interface currently used. However, if the agencies determine, through
    further analysis, that laws or congressional direction create conflicts
    prohibiting them from implementing a consistent, systematic approach that
    distinguishes the relative importance of various priorities, they should
    so inform Congress and seek additional clarification.

    It will not be easy to carry out these tasks. As they work to improve
    their processes, the agencies will need to devote considerable effort to
    developing measures and collecting data on risk and effectiveness and
    considerable thought to balancing this information against the many goals
    of the fuel reduction program--all in a way that yields transparent
    results. And once these steps are carried out, the agencies face perhaps
    an even more difficult decision: how best to redirect fuel reduction funds
    in a way that improves the agencies' effective use of their limited funds
    despite the potentially disruptive consequences for individual field units
    or nearby communities. Our findings suggest that the agencies are
    increasingly mindful of the merits of such an approach and that their
    recent actions have begun to lay the necessary groundwork. Nevertheless,
    many challenges remain, and a difficult road lies ahead.

    Recommendations for Executive Action

    We are recommending that the Secretaries of Agriculture and of the
    Interior take the following five actions to improve their ability to
    allocate fuel reduction funds so that these funds contribute most
    effectively to risk reduction.

    First, we recommend that the Secretaries of Agriculture and of the
    Interior direct the agencies to develop a common, systematic funding
    allocation process in order to enhance the transparency and accountability
    of their allocation decisions and to ensure a common federal approach to
    allocating funds. Such a systematic process should serve as the foundation
    of each agency's allocation process and should be applied at all levels
    within the agencies. Existing models or those under development may serve
    as useful prototypes; for example, while we have not assessed its accuracy
    or technical soundness, the Forest Service's model for allocating funds
    shows promise as the foundation of a systematic process.

    In addition, we recommend that the Secretaries of Agriculture and of the
    Interior direct their agencies to develop information to support this
    systematic process. Development of the information should include the
    following actions:

         o Develop and implement a common approach to risk assessment, to provide
           for a broad, national assessment of hazard, risk, and values, as in
           the Forest Service's allocation model, as well as more refined
           regional and local assessments.
         o Devote resources to developing a measure of, and subsequently
           collecting data on, fuel reduction effectiveness, so that the agencies
           can usefully estimate the extent and duration of risk reduction from
           potential fuel treatments. Because developing the measure and
           collecting data are likely to be difficult and time-consuming
           endeavors, the agencies might find it useful to proceed with convening
           a panel of experts to devise a rudimentary framework for estimating
           treatment effectiveness.
         o Use information on risk and fuel treatment effectiveness, once
           available, in concert with information on the cost of treatments, to
           assess the cost-effectiveness of various potential fuel reduction
           treatments.

    Finally, the Secretaries of Agriculture and of the Interior should provide
    guidance that clearly distinguishes the relative importance of the various
    factors used in allocating funds and selecting projects, including the
    importance of risk, effectiveness, and cost in comparison with other
    factors. This guidance should also distinguish the relative priority of
    different values at risk, especially different elements within the
    wildland-urban interface, such as homes, power lines, and municipal
    watersheds.

                                    Agency Comments
                                   and Our Evaluation

    We provided the Secretaries of Agriculture and of the Interior with a
    draft of this report for review and comment. The Forest Service and the
    Department of the Interior generally agreed with the findings and
    recommendations in the report, noting their ongoing efforts to develop and
    implement a risk-informed allocation process, and reiterating the
    importance of including state, tribal, and local concerns in the
    prioritization process. Their joint comment letter is reproduced in
    appendix IV.

    We are sending copies of this report to interested congressional
    committees, the Secretaries of Agriculture and the Interior, the Chief of
    the Forest Service, and other interested parties. We will also make copies
    available to others upon request. In addition, the report will be
    available at no charge on the [114]GAO Web site at http://www.gao.gov.

    If you or your staffs have any questions about this report, please contact
    me at (202) 512-3841 or nazzaror@gao.gov. Contact points for our Offices
    of Public Affairs and Congressional Relations may be found on the last
    page of this report. GAO staff who made major contributions to this report
    are listed in appendix V.

    Robin M. Nazzaro
    Director, Natural Resources and Environment

    Appendix I: Objectives, Scope, and Methodology

    We were asked to (1) identify the processes the Forest Service, the
    Department of the Interior (Interior), and Interior's agencies--the Bureau
    of Indian Affairs (BIA), Bureau of Land Management (BLM), Fish and
    Wildlife Service (FWS), and National Park Service (NPS)--use to allocate
    fuel reduction funds and select projects for implementation, including the
    factors that influence these processes; and (2) determine how, if at all,
    the agencies could improve these processes to better ensure they
    contribute to their goal of effectively reducing the risk of wildland fire
    to communities and the environment. We focused our review primarily on the
    Forest Service and BLM because these two agencies accounted for about 80
    percent of the fuel reduction funds appropriated by Congress for 2005,
    2006, and 2007, although we collected information on the other three
    agencies as well. ^[115]1 We focused our review on fuel reduction work
    funded through congressional fuel reduction appropriations; therefore,
    fuel reduction work funded by other agency programs or outside
    organizations is outside the scope of this review. To gain an
    understanding of outside perspectives on the agencies' fuel reduction
    efforts, we met with several nonfederal parties, including representatives
    from the National Association of State Foresters, The Nature Conservancy,
    the Western Governors' Association, and the Wilderness Society.

    Fuel Reduction Funding Allocation and Project Selection Processes

    To learn how the agencies allocate fuel reduction funds and select
    projects, and to identify the factors that influence these processes, we
    first obtained and reviewed documents on policies and procedures governing
    the fuel reduction program. These included applicable laws, administrative
    initiatives, congressional committee reports, and interagency agreements,
    as well as guidance for fuel reduction from the departments, agency
    headquarters, and regional offices. ^[116]2 We also obtained and analyzed
    agency data on funding allocations.

    To learn about the processes used to allocate fuel reduction funds at the
    national level, we met with agency officials from the Forest Service and
    Interior at their Washington, D.C., headquarters, and with officials from
    all five agencies at the National Interagency Fire Center in Boise, Idaho.
    We also met with agency researchers and modeling experts to better
    understand the data used in the national models currently under
    development by the Forest Service, Interior, and BLM. We did not, however,
    assess the accuracy or technical soundness of these models.

    ^1Years cited in this appendix refer to fiscal years except where
    otherwise specified.

    ^2BIA, FWS, and NPS have regional offices, while BLM has state offices.
    For the purposes of this appendix, we refer to all of these as regional
    offices when we discuss the Interior agencies collectively.

    At the regional and state levels, we used a structured interview guide to
    speak, in person or by telephone, with officials in all Forest Service
    regional and BLM state offices, as well as with officials in selected BIA,
    FWS, and NPS regional offices. The structured interview guide included
    questions about the processes used to allocate fuel reduction funds, the
    factors that influence those processes, the extent and nature of regional
    guidance provided to field units, and the amount of oversight on the part
    of the regional offices. Because developing and administering a structured
    interview guide may introduce errors--caused by the way a particular
    question is interpreted, for example--we included steps in the development
    and administration of the interview guide to minimize such errors. We
    pretested the guide at several locations and modified it to reflect
    questions and comments we received. We also visited a number of the
    agencies' regional offices to obtain a greater understanding of the
    funding allocation processes in those regions. We selected regional
    offices that collectively received a substantial portion of their agency's
    fuel reduction funds and represented diversity with respect to fuel
    reduction funding levels, fuel reduction acreage accomplishments,
    predominant vegetation type, and geographic location. These selection
    criteria are shown in table 5.

    : Objectives, Scope, and
    Methodology
    : Regional Offices GAO Visited
    Northern x x x x
    Pacific Northwest x x x x
    Forest
    Pacific Southwest x x x x
    Service
    Rocky Mountain x x x x
    Southern x x x x
    Northwest x x x x x
    Pacific (by phone) x x x x
    BIA
    Rocky Mountain x x x x x
    Southwest x x x x
    California x x x x x
    Colorado x x x x
    BLM Idaho x x x x
    Montana x x x x
    Oregon/Washington x x x x x
    California-Nevada^c x x x x
    FWS Mountain-Prairie x x x x
    Southeast x x x x
    d d dIntermountain x x x
    d d dNPS Pacific West x x x
    Southeast x x x x
    : Objectives, Scope, and
    Methodology
    : Field Units GAO Visited
    x
    National Grassland
    Bitterroot National Forest^a Montana and Idaho x
    Boise National Forest Idaho x
    Chattahoochee-Oconee National Forests Georgia x
    Medicine Bow-Routt National Forests and Thunder Basin Wyoming and Colorado
    x
    National Grassland
    National Forests in Florida (Ocala National Forest) Florida x
    San Bernardino National Forest California x x
    BIA
    Crow Agency Montana x
    Zuni Agency New Mexico x
    BLM
    Albuquerque District Office New Mexico x
    Billings Field Office Montana x
    Little Snake Field Office Colorado x x
    Twin Falls District Office Idaho x
    White River Field Office Colorado x x
    Rocky Mountain Arsenal National Wildlife Refuge Colorado x
    NPS
    Cape Canaveral National Seashore Florida x
    Rocky Mountain National Park Colorado x
    Sequoia and Kings Canyon National Parks^a California x

    Fuel reduction Predominant funding level^a Acres treated^b vegetation type
    Geographic location

    Greater Less Greater Less Region/ than than than than Agency state office
    average average average average Forest Grass Shrub West Central East

    Source: GAO analysis of Forest Service and Interior data.

    ^a"Greater than average" refers to regions that received more than the
    average funding amount received by that agency's regions in 2007, and
    "less than average" refers to regions that received less than the average
    amount in 2007.

    ^b"Greater than average" refers to regions that treated more than the
    average acres treated by that agency's regions in 2006, and "less than
    average" refers to regions that treated less than the average acres
    treated in 2006.

    ^cFWS's California-Nevada Operations office is officially part of the
    Pacific region, but manages its own fuel reduction program.

    ^dThese regions each cover several states and have a large variety of
    vegetation; therefore, no one vegetation type is predominant, according to
    agency officials.

    To learn about the project selection processes used by local units, we
    selected a nonprobability sample of 20 local units in eight states to
    interview. ^[117]3 The sample included 8 national forests, 5 BLM district
    or field offices, 2 BIA agencies, 2 national wildlife refuges, and 3
    national parks. Table 6 lists the units we visited. The local units
    selected for interviews represented diversity with respect to geographic
    location and predominant vegetation type. In addition, we selected units
    that represented diversity with respect to their proximity to communities
    and development, including units that were located in counties that were
    predominantly rural or urban.

    ^3Results from nonprobability samples cannot be used to make inferences
    about a population, because in a nonprobability sample, some elements of
    the population being studied have no chance or an unknown chance of being
    selected as part of the sample.

    Table 6: Field Units GAO Visited

    Predominant vegetation type Agency and unit State Forest Grass Shrub

    Forest Service

    Angeles National Forest California

    Arapaho-Roosevelt National Forests and Pawnee Colorado

    FWS

    Merritt Island National Wildlife Refuge Florida

    Source: GAO analysis of Forest Service and Interior data.

    ^aWe met with officials from these field units at off-site locations, in
    order to facilitate cost-effective travel logistics.

    During all of these visits, we collected documents and interviewed staff;
    during some of these visits, we also observed fuel reduction treatments.
    Because we conducted in-depth analyses of only a few selected units, we
    cannot generalize our findings beyond the local units and officials we
    contacted.

    Potential Improvements to Agency Processes to Better Ensure They
    Contribute to Reducing Risk

    To identify potential improvements to the agencies' processes for
    allocating fuel reduction funds and selecting treatments, we analyzed the
    information we collected through our site visits, structured interviews,
    agency documentation, and discussions with other agency officials. To
    identify the overall goals of the fuel reduction program, and the extent
    to which earlier assessments of the program identified shortcomings in the
    agencies' ability to meet these goals, we also evaluated (1) agency policy
    documents, including strategy documents, program guidance, and related
    documents discussing the program's objectives; (2) legislative direction
    associated with the fuel reduction program, including laws, congressional
    committee report language, and other direction; and (3) previous reviews
    of the fuel reduction program by GAO, the Inspectors General, and others.

    In our interviews with agency officials, we asked about the factors they
    considered when allocating funds and selecting projects--including the
    influence of specific factors, such as acreage targets and risk
    assessments--as well as factors that prevented high-priority work from
    being accomplished. We also asked about regional and local definitions of
    the wildland-urban interface. We assigned the allocation processes used by
    the agencies' headquarters, regional offices, and local units to one of
    two categories: quantitative or judgmental. We also verified the factors
    used in allocation processes with agency officials.

    To determine the extent to which the locations of wildland-urban interface
    treatments, population centers, and federal lands coincided, we analyzed
    fuel reduction data from a Forest Service and Interior database--the
    National Fire Plan Operations and Reporting System (NFPORS)--as well as
    population data from the U.S. Census Bureau, and federal lands data from
    the U.S. Geological Survey's National Atlas Web site (NationalAtlas.gov).
    Using the agency data on fuel reduction treatments, we used geographic
    information system (GIS) tools to map the location and size of
    wildland-urban interface treatments completed in 2005 and 2006. We also
    applied GIS tools to Census data to map population density in three
    categories: (1) fewer than 28 people per square mile, on average;
    (2) 28 to 249 people per square mile, on average; and (3) 250 or more
    people per square mile, on average. ^[118]4 We used these categories
    because they reflect the definition of wildland-urban interface published
    in the
    January 4, 2001 Federal Register and used by the agencies. ^[119]5 We also
    mapped the location of federal lands using the data from the U.S.
    Geological Survey. In addition, we created maps of two counties--one urban
    and one rural--showing the locations of wildland-urban interface
    treatments completed in 2005 and 2006, population density, and federal
    lands. We also contacted officials in those two counties to discuss the
    location of specific wildland-urban interface projects and their rationale
    for selecting those projects.

    ^4We conducted our analysis using U.S. Census data on the average
    population per square mile across areas defined by ZIP codes. However,
    especially in larger ZIP codes, there may be small pockets where the
    population density is higher or lower than the average used in our
    analysis.

    To determine the reliability of the agencies' fuel reduction data, we
    reviewed related documentation, such as the NFPORS database users' manual;
    interviewed knowledgeable agency officials, including database
    administrators; discussed data input and verification procedures with
    regional and field staff; and conducted electronic data testing. We found
    that these fuel reduction data were sufficiently reliable for the purposes
    of this review. We obtained the federal lands data prepared by
    NationalAtlas.gov and reviewed the documentation provided on the
    limitations of the file. From this review, we determined that the federal
    lands data were sufficiently reliable for our purposes. To measure
    population density, we used Census ZIP Code Tabulation Area data from the
    2000 U.S. Census and the geographic boundary for those areas. We reviewed
    documentation provided on the limitations of these files and compared
    their consistency with other Census sources. From this review,
    we determined that the population density data were sufficiently reliable
    for our purposes.

    ^5In the 2001 Federal Register, the agencies provide three categories of
    wildland-urban interface communities. The first is "interface community,"
    which exists where structures directly abut wildland fuel; an alternative
    definition of the interface community specifies a population density of
    250 or more people per square mile. The second is "intermix community,"
    which exists where structures are scattered throughout a wildland area; an
    alternative definition of intermix community specifies a population
    density of between 28-250 people per square mile. The third is "occluded
    community," where structures, often within a city, abut an island of
    wildland fuel (e.g., park or open space).

    We conducted our work from August 2006 to September 2007 in accordance
    with generally accepted government auditing standards.

      Appendix II: Forest Service and Interior Fuel Reduction Funding Allocations,
                           Fiscal Years 2005, 2006, and 2007

    This appendix provides information on fuel reduction funding
    appropriations and allocations to the Forest Service, the Department of
    the Interior (Interior) and its agencies, and their regions for 2005,
    2006, and 2007. ^[120]1 Interior allocates separate fuel reduction funds
    to its four agencies for treatments within and outside of the
    wildland-urban interface (WUI), while the Forest Service allocates one
    single source of fuel funding to its regions. Therefore, information on
    allocation amounts to WUI and non-WUI areas are included for Interior but
    not for the Forest Service. Table 7 provides total appropriations and
    allocations to the Forest Service and Interior agencies--Bureau of Indian
    Affairs (BIA), Bureau of Land Management (BLM), National Park Service
    (NPS), and Fish and Wildlife Service (FWS)--for 2005, 2006, and 2007. As
    shown in table 7 and figure 11, of the approximately $500 million
    appropriated to the Forest Service and Interior for fuel reduction in
    2007, the Forest Service received about 61 percent of the total; BLM
    received about 19 percent of the total; and the remaining 20 percent was
    allocated to BIA, NPS, and FWS.

    ^1Years cited in this appendix refer to fiscal years except where
    otherwise specified. BIA, FWS, and NPS have regional offices, while BLM
    has state offices. For the purposes of this appendix, we refer to all of
    these as regional offices when we discuss the Interior agencies
    collectively.

    Table 7:  Total  Appropriations  to Forest  Service,  and  Allocations  to
    Interior Agencies, Fiscal Years 2005, 2006, and 2007

    Agency BLM 2005    Total allocation $91,386,000        Percentage of total
                                                                  allocation 18.8
    2006                                 96,299,000                       19.9
    2007                                 93,389,000                       18.8
    BIA
    2005                                 42,488,000                        8.7
    2006                                 43,237,000                        8.9
    2007                                 40,664,000                        8.2
    NPS
    2005                                 33,040,000                        6.8
    2006                                 33,299,000                        6.9
    2007                                 31,396,000                        6.3
    FWS
    2005                                 27,527,000                        5.7
    2006                                 32,162,000                        6.6
    2007                                 30,666,000                        6.2
    Subtotal--Interior
    agencies
    2005                                194,441,000                       39.9
    2006                                204,997,000                       42.3
    2007                                196,115,000                       39.4
    Forest Servicea
    2005                                292,389,000                       60.1
    2006                                280,119,000                       57.7
    2007                                301,258,000                       60.6
    Total
    2005                                486,830,000                        100
    2006                                485,116,000                        100
    2007                                497,373,000                        100

    Source: GAO analysis of Forest Service and Interior data.

    Notes: Interior allocated additional amounts of $6,968,000 in 2005;
    $5,115,000 in 2006; and
    $3,672,000 in 2007 to the Office of Wildland Fire Coordination, which is
    responsible for the
    coordination, integration, and oversight of wildland fire management
    programs within Interior.
    Total allocations do not include carryover from the previous fiscal year.
    Numbers may not total due to rounding.

    ^aForest Service figures represent appropriations.

    Figure 11: Agency Funding Levels as a Percentage of Total Fuel Reduction
    Funding, Fiscal Year 2007

    Forest Service FWS NPS BIA

    BLM

    Forest Service

                                   Interior agencies

    Source: GAO analysis of Forest Service and Interior data.

    Notes: Interior allocated an additional $3,672,000 to the Office of
    Wildland Fire Coordination. Total allocations do not include carryover
    from the previous fiscal year.

    Table 8 shows the Forest Service's total allocations to its nine regions
    and its headquarters for 2005, 2006, and 2007. In 2007, of the total
    funding allocated to the Forest Service for fuel reduction, about 68
    percent was allocated to the regions. Approximately 32 percent was
    allocated to the Forest Service's headquarters, research stations, and
    general cost pools that are used for expenses not charged to a single
    program, including indirect, support, and common services charges. The
    Pacific Southwest region received the most funding for 2005, 2006, and
    2007; the Southwestern region received the second-most funding during that
    time period.

    Table 8: Forest  Service Allocations to  Regions and Headquarters,  Fiscal
    Years 2005, 2006, and 2007

                                                                    Percentage of
                                                                 Forest Service's
    Region                                  Total allocation  total allocation
    Pacific Southwest
    2005                                         $66,656,000              22.8
    2006                                          41,944,000              15.0
    2007                                          43,737,000              14.5
    Southwestern
    2005                                          30,638,000              10.5
    2006                                          36,891,000              13.2
    2007                                          37,341,000              12.4
    Southern
    2005                                          25,478,000               8.7
    2006                                          26,368,000               9.4
    2007                                          29,092,000               9.7
    Pacific Northwest
    2005                                          24,622,000               8.4
    2006                                          23,179,000               8.3
    2007                                          25,794,000               8.6
    Rocky Mountain
    2005                                          21,032,000               7.2
    2006                                          23,706,000               8.5
    2007                                          25,445,000               8.4
    Intermountain
    2005                                          13,673,000               4.7
    2006                                          15,881,000               5.7
    2007                                          16,165,000               5.4
    Northern
    2005                                          11,875,000               4.1
    2006                                          12,006,000               4.3
    2007                                          15,782,000               5.2
    Eastern
    2005                                           8,633,000               3.0
    2006                                           8,631,000               3.1
    2007                                           9,718,000               3.2

                                                                    Percentage of
                                                                 Forest Service's
    Region                                  Total allocation  total allocation
    Alaska
    2005                                           1,834,000               0.6

    2006                                                   853,000         0.3
    2007                                                   805,000         0.3
    Subtotal, regions
    2005                                               204,441,000        69.9
    2006                                               189,459,000        67.6
    2007                                               203,879,000        67.7
    Headquarters, Research stations, and cost pools
    2005                                                87,948,000        30.1
    2006                                                90,659,000        32.4
    2007                                                97,379,000        32.3
    Total
    2005                                               292,389,000         100
    2006                                               280,119,000         100
    2007                                               301,258,000         100

    Source: GAO analysis of Forest Service data.

    Notes: Total allocations do not include carryover from the previous fiscal
    year. Numbers may not total due to rounding.

    Table 9 shows Interior's allocations to BLM, BIA, NPS, and FWS-- including
    WUI and non-WUI allocations--for 2005, 2006, and 2007. In 2007, about 65
    percent of Interior's total allocation was to WUI areas and 35 percent was
    to non-WUI areas. In 2007, BLM received the largest percentage of
    Interior's fuel reduction funding allocation--almost 48 percent.

    Table 9: Interior Allocations to BLM, BIA, FWS, and NPS, Including WUI and
    Non-WUI Allocations, Fiscal Years 2005, 2006, and 2007

                                        Percentage of
                               Total Interior's total     Total WUI Total non-WUI
                          allocation    allocation to    allocation    allocation
    Agency BLM                        the agencies
    2005              $91,386,000             47.0   $64,437,000   $26,949,000
    2006               96,299,000             47.0    66,245,000    30,054,000
    2007               93,389,000             47.6    66,590,000    26,799,000
    BIA
    2005               42,488,000             21.9    27,299,000    15,189,000
    2006               43,237,000             21.1    27,494,000    15,743,000
    2007               40,664,000             20.7    26,681,000    13,983,000
    NPS
    2005               33,040,000             17.0    15,320,000    17,720,000
    2006               33,299,000             16.2    14,948,000    18,351,000
    2007               31,396,000             16.0    14,583,000    16,813,000
    FWS
    2005               27,527,000             14.2    15,583,000    11,944,000
    2006               32,162,000             15.7    19,772,000    12,390,000
    2007               30,666,000             15.6    18,922,000    11,744,000
    Total--Interior
    agencies
    2005             $194,441,000              100  $122,639,000   $71,802,000
    2006             $204,997,000              100  $128,459,000   $76,538,000
    2007             $196,115,000              100  $126,776,000   $69,339,000

    Source: GAO analysis of Interior data.

    Notes: Interior allocated an additional $6,968,000 in 2005, $5,115,000 in
    2006, and $3,672,000 in 2007 to the Office of Wildland Fire Coordination.

    Total allocations do not include carryover from the previous fiscal year.

    Table 10 shows BLM's allocations to its 12 state offices and headquarters
    for 2005, 2006, and 2007. In 2007, about 71 percent of BLM's total fuel
    reduction funding was allocated to WUI areas, while about 29 percent was
    allocated to non-WUI areas. In 2005, 2006, and 2007, the Oregon/
    Washington state office received the most funding, followed by the Idaho
    and Utah state offices. These three offices accounted for about 50 percent
    of BLM's total annual fuel reduction allocation.

    Table 10: BLM Allocations to State Offices and Headquarters, Fiscal Years
                                  2005, 2006, and 2007

    State office             Total      Percentage of    Total WUI       Total
    Oregon/Washington   allocation        BLM's total   allocation     non-WUI
    2005               $26,177,000    allocation 27.6  $19,027,000  allocation
                                                                       $7,150,000
    2006                24,596,000               24.1   17,966,000   6,630,000
    2007                24,878,000               24.8   18,542,000   6,336,000
    Idaho
    2005                14,536,000               15.3   10,130,000   4,406,000
    2006                14,787,000               14.5   10,033,000   4,754,000
    2007                14,598,000               14.6   10,338,000   4,260,000
    Utah
    2005                 8,557,000                9.0    5,479,000   3,078,000
    2006                 7,968,000                7.8    5,225,000   2,743,000
    2007                10,078,000               10.1    6,164,000   3,914,000
    California
    2005                 7,257,000                7.6    6,096,000   1,161,000
    2006                 6,364,000                6.2    5,382,000     982,000
    2007                 7,322,000                7.3    6,294,000   1,028,000
    Nevada
    2005                 6,663,000                7.0    4,572,000   2,091,000
    2006                 5,794,000                5.7    3,881,000   1,913,000
    2007                 6,414,000                6.4    4,317,000   2,097,000
    Colorado
    2005                 6,480,000                6.8    4,891,000   1,589,000
    2006                 6,068,000                5.9    4,589,000   1,479,000
    2007                 6,843,000                6.8    5,285,000   1,558,000
    New Mexico
    2005                 5,676,000                6.0    2,930,000   2,746,000
    2006                 6,167,000                6.0    3,347,000   2,820,000
    2007                 6,412,000                6.4    3,630,000   2,782,000
    Montana
    2005                 5,338,000                5.6    4,248,000   1,090,000
    2006                 4,871,000                4.8    4,000,000     871,000
    2007                 5,461,000                5.5    4,366,000   1,095,000

          Table 11 shows BIA's allocations for 2005, 2006, and 2007 to its 12
          regions and the National Interagency Fire Center. In 2007, about 67
    percent of BIA's total fuel reduction funding was allocated to WUI areas,
    while about 33 percent was allocated to non-WUI areas. In 2005, 2006, and
                                       2007, the

    State office           Total Percentage of BLM's   Total WUI Total non-WUI
    Arizona 2005      allocation    total allocation  allocation    allocation
                          4,219,000                 4.4   2,509,000     1,710,000
    2006               3,787,000                 3.7   2,396,000     1,391,000
    2007               4,355,000                 4.3   2,608,000     1,747,000
    Wyoming
    2005               3,143,000                 3.3   1,830,000     1,313,000
    2006               2,898,000                 2.8   1,786,000     1,112,000
    2007               3,684,000                 3.7   2,185,000     1,499,000
    Alaska
    2005                 785,000                 0.8     365,000       420,000
    2006               1,044,000                 1.0     502,000       542,000
    2007               1,556,000                 1.6     786,000       770,000
    Eastern states
    2005                  98,000                 0.1      78,000        20,000
    2006                  83,000                 0.1      58,000        25,000
    2007                 126,000                 0.1      90,000        36,000
    Subtotal, state
    offices
    2005              88,929,000                93.7  62,155,000    26,774,000
    2006              84,427,000                82.6  59,165,000    25,262,000
    2007              91,727,000                91.5  64,605,000    27,122,000
    Headquarters
    2005               5,979,000                 6.3   4,527,000     1,452,000
    2006              17,822,000                17.4  12,029,000     5,793,000
    2007               8,473,000                 8.5   6,315,000     2,158,000
    Total
    2005              94,908,000                 100  66,682,000    28,226,000
    2006             102,249,000                 100  71,194,000    31,055,000
    2007             100,200,000                 100  70,920,000    29,280,000

    Source: GAO analysis of BLM data.

    Notes: Total allocations include the allocation for the current year plus
    carryover from the previous fiscal year.

    Numbers may not total due to rounding.

    Northwest region received the most funding of the BIA regions, followed by
    the Southwest region. These two regions accounted for about 50 percent of
    BIA's total fuel reduction allocation in 2007.

         Table 11: BIA Allocations to Regions and the National Interagency Fire
                       Center, Fiscal Years 2005, 2006, and 2007

    Region               Total   Percentage of BIA's   Total WUI Total non-WUI
    Northwest       allocation      total allocation  allocation    allocation
    2005           $12,204,855                  28.1  $7,823,617    $4,381,238
    2006            11,387,925                  26.4   8,012,751     3,375,174
    2007            11,835,643                  29.6   8,465,745     3,369,898
    Southwest
    2005             7,651,861                  17.6   3,518,105     4,133,756
    2006             9,175,694                  21.3   4,115,843     5,059,851
    2007             8,366,522                  21.0   5,128,157     3,238,365
    Western
    2005             4,497,336                  10.4   2,912,773     1,584,563
    2006             4,020,742                   9.3   2,218,682     1,802,060
    2007             3,366,120                   8.4   2,287,498     1,078,622
    Pacific
    2005             3,846,125                   8.9   2,863,206       982,919
    2006             3,096,619                   7.2   2,546,681       549,938
    2007             2,401,976                   6.0   1,453,604       948,372
    Great Plains
    2005             2,464,878                   5.7   1,461,855     1,003,023
    2006             3,196,997                   7.4   1,742,168     1,454,829
    2007             2,281,299                   5.7   1,061,894     1,219,405
    Alaska
    2005             2,417,117                   5.6   2,247,117       170,000
    2006             2,256,079                   5.2   2,163,458        92,621
    2007             1,780,638                   4.5   1,538,479       242,159
    Midwest
    2005             1,876,013                   4.3   1,280,813       595,200
    2006             2,736,830                   6.3   1,988,630       748,200
    2007             2,913,975                   7.3   2,497,657       416,318
    Rocky
    Mountain
    2005             1,699,576                   3.9   1,004,728       694,848
    2006             1,769,815                   4.1   1,015,550       754,265

    Percentage of                                                        Total
    Region                           Total BIA's total   Total WUI     non-WUI
                                   allocation  allocation  allocation  allocation
    2007                         1,795,609         4.5   1,066,157     729,452
    Navajo
    2005                         1,258,919         2.9     342,421     916,498
    2006                         1,335,559         3.1     443,588     891,971
    2007                           933,183         2.3     357,126     576,057
    Southern Plains
    2005                           572,247         1.3      27,552     544,695
    2006                           568,471         1.3      30,174     538,297
    2007                           489,663         1.2      21,867     467,796
    Eastern
    2005                           503,175         1.2     425,375      77,800
    2006                           215,825         0.5     115,732     100,093
    2007                           433,609         1.1     272,952     160,657
    Eastern Oklahoma
    2005                           284,557         0.7     216,900      67,657
    2006                           224,465         0.5     189,709      34,756
    2007                           240,074         0.6     164,172      75,902
    Subtotal, regions
    2005                        39,276,659        90.4  24,124,462  15,152,197
    2006                        39,985,021        92.7  24,582,966  15,402,055
    2007                        36,838,311        92.3  24,315,308  12,523,003
    National Interagency Fire
    Center
    2005                         4,172,185         9.6   3,621,793     550,392
    2006                         3,156,566         7.3   2,720,552     436,014
    2007                         3,092,337         7.7   2,608,684     483,653
    Total
    2005                        43,448,844         100  27,746,255  15,702,589
    2006                        43,141,587         100  27,303,518  15,838,069
    2007                        39,930,648         100  26,923,992  13,006,656

    Source: GAO analysis of BIA data.

    Notes: Total allocations include the allocation for the current year plus
    carryover from the previous fiscal year.

    Numbers may not total due to rounding.

        Table 12 shows NPS's allocations to its seven regions and the Washington
    Office for 2005, 2006, and 2007. In 2007, about 46 percent of NPS's total
    fuel reduction funding was allocated to WUI areas, while about 54 percent
    was allocated to non-WUI areas. NPS was the only Interior agency that
    allocated more funds to non-WUI areas than to WUI areas. In 2005, 2006,
    and 2007, the Pacific West region received the most funding; followed by
    the Intermountain region. These two regions accounted for about 60 percent
    of NPS's total annual fuel reduction allocation in 2007.

         Table 12: NPS Allocations to Regions and the Washington Office, Fiscal
                               Years 2005, 2006, and 2007

    Region Pacific           Total     Percentage of   Total WUI Total non-WUI
    West                allocation       NPS's total  allocation    allocation
                                             allocation
    2005               $12,550,296              35.5  $7,317,240    $5,233,056
    2006                10,478,930              32.4   6,231,822     4,247,108
    2007                10,693,592              31.6   6,401,068     4,292,524
    Intermountain
    2005                10,070,518              28.5   4,268,238     5,802,280
    2006                 8,578,937              26.5   3,138,962     5,439,975
    2007                 9,398,600              27.8   4,006,559     5,392,041
    Southeast
    2005                 4,279,340              12.1   1,973,600     2,305,740
    2006                 4,047,002              12.5   1,648,782     2,398,220
    2007                 4,604,308              13.6   1,843,531     2,760,777
    Midwest
    2005                 3,104,724               8.8     627,690     2,477,034
    2006                 3,341,288              10.3     634,129     2,707,159
    2007                 3,469,731              10.3     644,967     2,824,764
    Northeast
    2005                   898,084               2.5     521,020       377,064
    2006                   879,070               2.7     406,936       472,134
    2007                 1,201,497               3.6     757,338       444,159
    Alaska
    2005                   560,582               1.6           0       560,582
    2006                   813,140               2.5           0       813,140
    2007                   739,037               2.2           0       739,037
    National Capital
    2005                   142,043               0.4     122,320        19,723
    2006                    99,851               0.3      99,851             0
    2007                   103,631               0.3     103,631             0

    Region           Total Percentage of NPS's total   Total WUI Total non-WUI
    Subtotal,   allocation                allocation  allocation    allocation
    regions
    2005        31,605,587                      89.5  14,830,108    16,775,479
    2006        28,238,218                      87.4  12,160,482    16,077,736
    2007        30,210,396                      89.3  13,757,094    16,453,302
    Washington
    Office
    2005         3,705,107                      10.5   1,572,455     2,132,652
    2006         4,080,417                      12.6   1,904,137     2,176,280
    2007         3,605,604                      10.7   1,804,906     1,800,698
    Total
    2005        35,310,694                       100 $16,402,563    18,908,131
    2006        32,318,635                       100  14,064,619    18,254,016
    2007        33,816,000                       100  15,562,000    18,254,000

    Source: GAO analysis of NPS data.

    Notes: Total allocations include the allocation for the current year plus
    carryover from the previous fiscal year.

    Numbers may not total due to rounding.

    Table 13 shows FWS's allocations in 2005, 2006, and 2007 to its seven
    regions, the California-Nevada Operations office, and headquarters.
    ^[121]2 In 2007, about 61 percent of FWS's total fuel reduction funding
    was allocated to WUI areas, while about 39 percent was allocated to
    non-WUI areas. In 2005, 2006, and 2007, the Southeast region received the
    most funding, followed by the Great Lakes-Big Rivers region.

    ^2While FWS has seven regions, it has an eighth office--the
    California-Nevada Operations Office--that, although officially part of the
    Pacific region, manages its own fuel reduction program.

    Table 13: FWS Allocations to Regions and Headquarters, Fiscal Years 2005,
                                     2006, and 2007

    Region Southeast         Total     Percentage of   Total WUI Total non-WUI
    2005                allocation       FWS's total  allocation    allocation
                           $7,005,484   allocation 24.7  $3,715,115    $3,290,369
    2006                 7,966,857              23.8   4,780,616     3,186,241
    2007                 7,543,624              24.2   4,481,330     3,062,294
    Great Lakes-Big
    Rivers
    2005                 4,867,717              17.1   2,111,460     2,756,257
    2006                 5,438,168              16.2   2,658,862     2,779,306
    2007                 5,336,376              17.1   2,604,636     2,731,740
    Mountain-Prairie
    2005                 3,376,546              11.9   1,281,699     2,094,847
    2006                 3,776,901              11.3   1,658,254     2,118,647
    2007                 3,690,940              11.8   1,566,284     2,124,656
    Southwest
    2005                 3,363,824              11.9   2,139,589     1,224,235
    2006                 3,903,921              11.7   2,499,122     1,404,799
    2007                 3,721,205              11.9   2,434,533     1,286,672
    Pacific
    2005                 2,556,707               9.0   1,504,681     1,052,026
    2006                 2,853,522               8.5   1,864,739       988,783
    2007                 2,566,156               8.2   1,736,153       830,003
    Northeast
    2005                 2,201,297               7.8   1,751,683       449,614
    2006                 2,597,811               7.8   2,106,088       491,723
    2007                 2,416,798               7.7   1,977,669       439,129
    California-Nevadaa
    2005                 1,712,138               6.0     986,919       725,219
    2006                 2,542,027               7.6   1,960,241       581,786
    2007                 2,254,492               7.2   1,460,866       793,626
    Alaska
    2005                 1,000,439               3.5     815,607       184,832
    2006                 1,271,882               3.8   1,050,646       221,236
    2007                 1,224,552               3.9   1,074,688       149,864

    Region              Total    Percentage of FWS's   Total WUI Total non-WUI
    Subtotal,      allocation  total allocation 91.9  allocation    allocation
    regions 2005   26,084,152                         14,306,753    11,777,399
    2006           30,351,089                   90.6  18,578,568    11,772,521
    2007           28,754,143                   92.1  17,336,159    11,417,984
    Washington
    Office
    2005            2,302,509                    8.1   1,571,629       730,880
    2006            3,153,074                    9.4   2,037,897     1,115,177
    2007            2,450,015                    7.9   1,723,281       726,734
    Total
    2005           28,386,661                    100  15,878,382    12,508,279
    2006           33,504,163                    100  20,616,465    12,887,698
    2007           31,204,158                    100  19,059,440    12,144,718

    Source: GAO analysis of FWS data.

    Notes: Total allocations include the allocation for the current year  plus
    carryover from the previous fiscal year.

    Numbers may not total due to rounding.

    ^aWhile  FWS   has   seven  regions,   it   has  an   eighth   office--the
    California-Nevada Operations office--that, although  officially part  of
    the Pacific region, manages its own fuel reduction program.

     Appendix III: Summary of Fuel Treatment Accomplishments for the Forest Service
                        and Interior, Fiscal Years 2005 and 2006

    The tables in this appendix summarize the fuel reduction accomplishments
    of the Forest Service and the four Interior agencies we reviewed--Bureau
    of Land Management (BLM), Bureau of Indian Affairs (BIA), National Park
    Service (NPS), and Fish and Wildlife Service (FWS)-- for 2005 and 2006,
    ^[122]1 the most recent years for which complete data were available.
    National and regional office data are presented for each agency. ^[123]2

    Table 14 provides nationwide information for each of the five agencies,
    including total acres treated; acres treated in the wildland-urban
    interface (WUI) and in non-WUI areas; and acres treated with prescribed
    fire, mechanical methods, and other treatment methods such as herbicides
    and grazing. The Forest Service treated more acres than the four Interior
    agencies combined--almost 1.7 million acres in 2005 and more than
    1.5 million acres in 2006. Within Interior, BLM and FWS treated the most
    acres, with BLM treating more than 500,000 acres in 2005 and almost
    430,000 acres in 2006, and FWS treating almost 420,000 acres in 2005 and
    more than 370,000 acres in 2006. In each year, about 60 percent of the
    total acres treated by the five agencies were in the WUI, and about 40
    percent of total acres treated were outside of the WUI. The majority of
    acres were treated with prescribed fire--almost 75 percent in 2005 and
    almost 65 percent in 2006.

    ^1Years cited in this appendix refer to fiscal years except where
    otherwise specified.

    ^2BIA, FWS, and NPS have regional offices, while BLM has state offices.
    For the purposes of this appendix, we refer to all of these as regional
    offices when we discuss the Interior agencies collectively.

             Table 14: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
                    Accomplishments for Interior and Forest Service

                                                                  Acres
                                             Acres              treated     Acres
                                                                  using
                                             treated using   mechanical   treated
                                                                            using
    Agency    Treated      WUI Non-WUI    prescribed           means     other
                   acres    acres acres      fire                          meansa
    BLM

    2005                 506,168  253,001   253,167   194,553  211,852  99,763
    2006                 427,912  230,932   196,980   107,443  206,123 114,346
    BIA
    2005                 193,617  71,983    121,634   96,881   94,168    2,568
    2006                 187,653  89,961    97,692    78,304   106,204   3,145
    NPS
    2005                 153,972  58,873    95,099    139,455  13,036    1,481
    2006                 116,635  38,558    78,077    102,765  11,532    2,338
    FWS
    2005                 415,646  158,711   256,935   389,686  21,734    4,226
    2006                 373,933  173,113   200,820   333,038  32,118    8,777
    Interior subtotal
    2005               1,269,403  542,568   726,835   820,575  340,790 108,038
    2006               1,106,133  532,564   573,569   621,550  355,977 128,606
    Forest Service
    2005               1,672,909 1,198,663  474,246  1,366,988 303,002   2,919
    2006               1,503,475 1,090,721  412,754  1,061,277 433,077   9,121
    Total Forest Service and
    Interior
    2005               2,942,312 1,741,231 1,201,081 2,187,563 643,792 110,957
    2006               2,609,608 1,623,285  986,323  1,682,827 789,054 137,727

    Source: GAO analysis of Interior and Forest Service data.

    ^a"Other" category includes treatments such as herbicides and grazing.

    Table 15 summarizes 2005 and 2006 fuel treatment information for the
    Forest Service regions. In both years, the Southern region treated
    substantially more acres than the other regions, treating more than half
    of the Forest Service's total treated acres. The Forest Service treated
    more than twice as many acres in the WUI as in non-WUI areas, and treated
    most acres with prescribed fire.

             Table 15: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
                       Accomplishments for Forest Service Regions

                                                         Acres      Acres   Acres
                                                       treated    treated treated
                                                         using      using   using
                         Treated            Non-WUI prescribed mechanical   other
    Region Pacific      acres  WUI acres   acres       fire      means  meansa
    Southwest 2005    100,540     59,194  41,346     41,565     58,785     190
    2006               95,729     62,229  33,500     36,779     50,423   8,527
    Southwestern
    2005              164,506     69,929  94,577    118,326     46,180       0
    2006              180,616     84,973  95,643    134,289     46,327       0
    Southern
    2005              976,176    803,654 172,522    969,528      6,616      32
    2006              776,145    674,189 101,956    625,605    150,540       0
    Pacific
    Northwest
    2005              139,470     79,975  59,495     65,955     73,515       0
    2006              133,528     60,904  72,624     73,068     60,432      28
    Rocky Mountain
    2005               93,969     65,862  28,107     51,353     39,919   2,697
    2006              102,953     77,650  25,303     49,313     53,394     246
    Intermountain
    2005               74,676     34,163  40,513     47,077     27,599       0
    2006               87,957     33,995  53,962     58,075     29,562     320
    Northern
    2005               70,594     43,824  26,770     39,726     30,868       0
    2006               68,639     46,892  21,747     43,480     25,159       0
    Eastern
    2005               51,472     40,556  10,916     33,089     18,383       0
    2006               57,221     49,202   8,019     40,242     16,979       0
    Alaska
    2005                1,506      1,506       0        369      1,137       0
    2006                  687        687       0        426        261       0
    Total
    2005            1,672,909  1,198,663 474,246  1,366,988    303,002   2,919
    2006            1,503,475  1,090,721 412,754  1,061,277    433,077   9,121

    Source: GAO analysis of Forest Service data.

    ^a"Other" category includes treatments such as herbicides and grazing.

    Table 16 summarizes 2005 and 2006 fuel treatment information for the BLM
    state offices. In both years, the Oregon/Washington and Idaho state
    offices treated the most acres, followed by the New Mexico state office.
    BLM treated about the same number of acres in the WUI and in non-WUI areas
    in 2005, and treated about 34,000 more acres in the WUI than in non-WUI
    areas in 2006. Unlike the Forest Service, BLM treated more acres
    mechanically than with prescribed fire, and also treated a substantial
    number of acres using other treatment methods, such as herbicides or
    grazing.

             Table 16: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
                         Accomplishments for BLM State Offices

                                                                    Acres
                                                        Acres     treated   Acres
                                                                    using
                                                      treated  mechanical treated
                                                        using               using
    State office        Treated     WUI Non-WUI prescribed       means   other
                             acres   acres   acres       fire              meansa
    Oregon/ Washington
    2005                108,909  71,218  37,691     47,273      61,636       0
    2006                 92,918  69,521  23,397     30,138      62,762      18
    Idaho
    2005                112,254  54,460  57,794     13,321      46,231  52,702
    2006                113,778  69,648  44,130     12,307      47,614  53,857
    Utah
    2005                 40,706  26,616  14,090      6,140      33,966     600
    2006                 40,535  28,906  11,629      3,900      36,601      34
    California
    2005                 24,191  21,439   2,752      2,342      19,349   2,500
    2006                 19,389  16,231   3,158      4,187      11,422   3,780
    Nevada
    2005                 28,427  15,190  13,237     10,391      16,272   1,764
    2006                 35,465   9,655  25,810     15,242      15,014   5,209
    Colorado
    2005                 20,417  13,616   6,801      6,950      13,012     455
    2006                 17,870  10,132   7,738      4,906      12,774     190
    New Mexico
    2005                 48,107   3,397  44,710     21,297       7,704  19,106
    2006                 53,329   4,390  48,939     11,682       5,643  36,004
    Montana
    2005                 10,867   6,124   4,743      4,577       5,340     950
    2006                 12,446   7,530   4,916      5,910       6,426     110

                                                 Acres     Acres treated    Acres
                 Treated            Non-WUI    treated  using mechanical  treated
    State       acres WUI acres    acres      using             means    using
    office                               prescribed                      other
    Arizona                                    fire                     meansa
    2005       35,424    17,078   18,346     17,297             4,391   13,736
    2006       19,557     7,424   12,133      5,845             3,625   10,087
    Wyoming
    2005       30,839     1,976   28,863     19,885             3,004    7,950
    2006       18,662     4,507   14,155      9,816             3,789    5,057
    Alaska
    2005       45,707    21,847   23,860     45,080               627        0
    2006        3,963     2,988      975      3,510               453        0
    Eastern
    states
    2005          320        40      280          0               320        0
    2006            0         0        0          0                 0        0
    Total
    2005      506,168   253,001  253,167    194,553           211,852   99,763
    2006      427,912   230,932  196,980    107,443           206,123  114,346

    Source: GAO analysis of Interior data.

    ^a"Other" category includes treatments such as herbicides and grazing.

    Table 17 summarizes 2005 and 2006 fuel treatment information for the BIA
    regions. The Northwest and Western regions treated the most acres in 2005,
    with each region treating more than 38,000 acres. In 2006, the Northwest
    and Southwest regions treated the most acres, with each region treating
    more than 45,000 acres. The agency treated more acres in non-WUI areas
    than in WUI areas in 2005 and 2006.

             Table 17: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
                            Accomplishments for BIA Regions

                                               Acres  Acres treated using   Acres
                  Treated     WUI Non-WUI    treated     mechanical means treated
                    acres   acres   acres      using                        using
    Region                             prescribed                        other
    Northwest                                fire                       meansa
    2005        38,284  17,297  20,987     15,589               21,488   1,207
    2006        48,733  25,354  23,379     14,687               32,590   1,456
    Southwest
    2005        28,212  11,839  16,373      2,678               24,433   1,101
    2006        45,132  20,558  24,574      8,096               36,049     987
    Western
    2005        38,753  16,210  22,543     13,813               24,880      60
    2006        22,167  10,360  11,807      7,892               14,275       0
    Pacific
    2005         2,584   1,817     767        180                2,404       0
    2006         4,431   3,179   1,252        331                4,100       0
    Great
    Plains
    2005        14,386   6,986   7,400      7,595                6,591     200
    2006        13,234   4,828   8,406      6,217                6,508     509
    Alaska
    2005         1,253   1,253       0        167                1,086       0
    2006         2,222   1,497     725        563                1,659       0
    Midwest
    2005        21,356   6,478  14,878     17,792                3,564       0
    2006        18,559  16,401   2,158     15,585                2,974       0
    Rocky
    Mountain
    2005        11,347   2,856   8,491      6,616                4,731       0
    2006         7,400   3,634   3,766      4,177                3,223       0
    Navajo
    2005        14,274     956  13,318     13,318                  956       0
    2006        11,065     470  10,595     10,595                  470       0
    Southern
    Plains
    2005        12,322     434  11,888      8,401                3,921       0
    2006         8,796     672   8,124      4,770                3,833     193
    Eastern
    2005         7,788   5,616   2,172      7,718                   70       0
    2006         4,607   2,547   2,060      4,099                  508       0

                                                Acres Acres treated using   Acres
                  Treated     WUI  Non-WUI    treated    mechanical means treated
    Region       acres   acres    acres      using                       using
    Eastern                             prescribed                       other
    Oklahoma                                  fire                      meansa
    2005         3,058     241    2,817      3,014                  44       0
    2006         1,307     461      846      1,292                  15       0
    Total
    2005       193,617  71,983  121,634     96,881              94,168   2,568
    2006       187,653  89,961   97,692     78,304             106,204   3,145

    Source: GAO analysis of Interior data.

    ^a"Other" category includes treatments such as herbicides and grazing.

    Table 18 summarizes 2005 and 2006 fuel treatment information for the NPS
    regions. In 2005 and 2006, the Southeast region treated the most acres,
    followed by the Intermountain region. NPS treated more acres in non-WUI
    areas than the WUI, and treated the vast majority of acres (more than 90
    percent in 2005 and about 88 percent in 2006) using prescribed fire.

             Table 18: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
                            Accomplishments for NPS Regions

                                                Acres treated       Acres   Acres
                                                        using     treated treated
                                              prescribed fire       using   using
    Region         Treated     WUI Non-WUI          18,922  mechanical   other
    Pacific West     acres   acres   acres                 means 5,681  meansa
    2005            25,949   9,432  16,517                               1,346
    2006            22,433   9,042  13,391          16,220       4,101   2,112
    Intermountain
    2005            43,823  28,585  15,238          38,874       4,844     105
    2006            25,350  14,447  10,903          19,397       5,727     226
    Southeast
    2005            63,602  17,963  45,639          62,491       1,081      30
    2006            45,471   9,413  36,058          44,641         830       0
    Midwest
    2005            20,082   2,433  17,649          18,971       1,111       0
    2006            22,872   5,432  17,440          22,150         722       0
    Northeast
    2005               453     417      36             188         265       0
    2006               486     224     262             348         138       0
    Alaska
    2005                29       9      20               0          29       0
    2006                23       0      23               9          14       0
    National
    Capital
    2005                34      34       0               9          25       0
    2006                 0       0       0               0           0       0
    Total
    2005           153,972  58,873  95,099         139,455      13,036   1,481
    2006           116,635  38,558  78,077         102,765      11,532   2,338

    Source: GAO analysis of Interior data.

    ^a"Other" category includes treatments such as herbicides and grazing.

    Table 19 summarizes 2005 and 2006 fuel treatment information for the FWS
    regions. In both years, the Southeast region treated substantially more
    acres than the other regions--about 35 percent of total acres treated in
    2005 and about 30 percent in 2006--followed by the Southwest and Great
    Lakes-Big Rivers regions. Like NPS, FWS treated most acres outside of the
    WUI, and treated the vast majority of acres (about 94 percent in 2005 and
    about 89 percent in 2006) using prescribed fire.

             Table 19: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
                            Accomplishments for FWS Regions

                                                                    Acres
                                                         Acres    treated   Acres
                                                                    using
                                                       treated mechanical treated
                                                         using              using
    Region              Treated      WUI Non-WUI prescribed      means   other
                             acres    acres   acres       fire             meansa
    Southeast
    2005                144,902   83,218  61,684    141,616      3,202      84
    2006                114,212   75,024  39,188    106,864      7,348       0
    Great Lakes-Big      Rivers
    2005                 73,550   27,719  45,831     70,880      2,204     466
    2006                 70,756   27,499  43,257     68,854      1,548     354
    Mountain-Prairie
    2005                 42,252    7,994  34,258     42,032        220       0
    2006                 39,095   10,100  28,995     38,862        233       0
    Southwest
    2005                 76,495   21,786  54,709     71,820      4,424     251
    2006                 56,607   17,036  39,571     54,792      1,800      15
    Pacific
    2005                 13,865    6,039   7,826      7,703      6,063      99
    2006                 23,996   15,704   8,292     11,225     11,683   1,088
    Northeast
    2005                 18,596    6,609  11,987     13,166      2,104   3,326
    2006                 16,515    8,791   7,724     13,007      1,794   1,714
    California-Nevadab
    2005                 45,216    4,595  40,621     42,176      3,040       0
    2006                 43,023   18,864  24,159     29,771      7,646   5,606
    Alaska
    2005                    770      751      19        293        477       0
    2006                  9,729       95   9,634      9,663         66       0
    Total
    2005                415,646  158,711 256,935    389,686     21,734   4,226
    2006                373,933  173,113 200,820    333,038     32,118   8,777

    Source: GAO analysis of Interior data.

    ^a"Other" category includes treatments such as herbicides and grazing.

    ^bWhile FWS has only seven regions, it has an eighth office--the
    California-Nevada Operations office--that, although technically part of
    the Pacific region, manages its own fuel reduction program.

                   Appendix V: GAO Contact and Staff Acknowledgments

    GAO Contact

    Robin Nazzaro, (202) 512-3841 or [124]nazzaror@gao.gov

    Staff Acknowledgments

    In addition to the individual named above, Steve Gaty, Assistant Director;
    Christy Feehan; Rich Johnson; Ches Joy; Amanda Miller; John Mingus;
    Lesley Rinner; and Carol Shulman made key contributions to
    this report.  Elizabeth Curda,  Mehrzad Nadji,  Jackie Nowicki,  and  Jena
    Sinkfield also made important contributions to the report.

                                  Related GAO Products

    Wildland Fire Management: A Cohesive Strategy and Clear Cost-
    Containment Goals Are Needed for Federal Agencies to Manage Wildland
    Fire Activities Effectively. [125]GAO-07-1017T. Washington, D.C.:
    June 19, 2007.

    Wildland Fire Management: Lack of a Cohesive Strategy Hinders
    Agencies' Cost-Containment Efforts. [126]GAO-07-427T. Washin gton, D.C.:
    January 30, 2007.

    Wildland Fire Management: Update on Federal Agency Efforts to Develop
    a Cohesive Strategy to Address Wildland Fire Threats. [127]GAO-06-671R .
    Washington, D.C.: May 1, 2006.

    Wildland Fire Management: Timely Identification of Long-Term Options
    and Funding Needs Is Critical. [128]GAO-05-923T . Washington, D.C.:
    July 14, 2005.

    Wildland Fire Management: Forest Service and Interior Need to Specify
    Steps and a Schedule for Identifying Long-Term Options and Their
    Costs. [129]GAO-05-353T . Washington, D.C.: February 17, 2005.

    Wildland Fire Management: Important Progress Has Been Made, but
    Challenges Remain to Completing a Cohesive Strategy. [130]GAO-05-147 .
    Washington, D.C.: January 14, 2005.

    Wildland Fires: Forest Service and BLM Need Better Information and a
    Systematic Approach for Assessing the Risks of Environmental Effects.

    [131]GAO-04-705 . Washington, D.C.: June 24, 2004.

    Wildland Fire Management: Additional Actions Required to Better Identify
    and Prioritize Lands Needing Fuels Reduction. [132]GAO-03-805. Washington,
    D.C.: August 15, 2003.

    Wildland Fire Management: Reducing the Threat of Wildland Fires Requires
    Sustained and Coordinated Effort. [133]GAO-02-843T. Washington, D.C.: June
    13, 2002.

    Severe Wildland Fires: Leadership and Accountability Needed to Reduce
    Risks to Communities and Resources. [134]GAO-02-259. Washington, D.C.:
    January 31, 2002.

    Related GAO Products

    The National Fire Plan: Federal Agencies Are Not Organized to Effectively
    and Efficiently Implement the Plan. [135]GAO-01-1022T . Washington, D.C.:
    July 31, 2001.

    Reducing Wildfire Threats: Funds Should Be Targeted to the Highest Risk
    Areas. [136]GAO/T-RCED-00-296. Washington, D.C.: September 13, 2000.

    Western National Forests: A Cohesive Strategy Is Needed to Address
    Catastrophic Wildfire Threats. [137]GAO/RCED-99-65 . Washington, D.C.:
    April 2, 1999.

    Western National Forests: Catastrophic Wildfires Threaten Resources and
    Communities. [138]GAO/T-RCED-98-273 . Washington, D.C.: September 28,
    1998.

    (360745)

    GAO's Mission

    The Government Accountability Office, the audit, evaluation and
    investigative arm of Congress, exists to support Congress in meeting its
    constitutional responsibilities and to help improve the performance and
    accountability of the federal government for the American people. GAO
    examines the use of public funds; evaluates federal programs and policies;
    and provides analyses, recommendations, and other assistance to help
    Congress make informed oversight, policy, and funding decisions. GAO's
    commitment to good government is reflected in its core values of
    accountability, integrity, and reliability.

    Obtaining Copies of Reports and Testimony

    The fastest and easiest way to obtain copies of GAO documents at no cost
    [139]is through GAO's Web site (www.gao.gov ). Each weekday, GAO posts GAO
    newly released reports, testimony, and correspondence on its
    Web site. To have GAO e-mail you a list of newly posted products every
    afternoon, go to [140]www.gao.gov and select "Subscribe to Updates."

                                 Order by Mail or Phone

    The first copy of each printed report is free. Additional copies are $2
    each. A check or money order should be made out to the Superintendent of
    Documents. GAO also accepts VISA and Mastercard. Orders for 100 or more
    copies mailed to a single address are discounted 25 percent. Orders should
    be sent to:

    U.S. Government Accountability Office 441 G Street NW, Room LM Washington,
    D.C. 20548

    To order by Phone: Voice: (202) 512-6000
    TDD: (202) 512-2537
    Fax: (202) 512-6061

    Contact:

    To Report Fraud, Waste, and Abuse in Federal Programs

    Web site: [141]www.gao.gov/fraudnet/fraudnet.htm
    E-mail: [142]fraudnet@gao.gov
    Automated answering system: (800) 424-5454 or (202) 512-7470

    Congressional

    Gloria Jarmon, Managing Director, [143]JarmonG@gao.gov (202) 512-4400
    U.S.  Government  Accountability  Office,  441  G  Street  NW,  Room  7125
    Relations Washington, D.C. 20548

    Public Affairs

    Susan Becker, Acting Manager, [144]Beckers@gao.gov (202) 512-4800
    U.S.  Government  Accountability  Office,  441  G  Street  NW,  Room  7149
    Washington, D.C. 20548

    References

    Visible links
     124. mailto:nazzaror@gao.gov
     125. http://www.gao.gov/cgi-bin/getrpt?GAO-07-1017T
     126. http://www.gao.gov/cgi-bin/getrpt?GAO-07-427T
     127. http://www.gao.gov/cgi-bin/getrpt?GAO-06-671R
     128. http://www.gao.gov/cgi-bin/getrpt?GAO-05-923T
     129. http://www.gao.gov/cgi-bin/getrpt?GAO-05-353T
     130. http://www.gao.gov/cgi-bin/getrpt?GAO-05-147
     131. http://www.gao.gov/cgi-bin/getrpt?GAO-04-705
     132. http://www.gao.gov/cgi-bin/getrpt?GAO-03-805
     133. http://www.gao.gov/cgi-bin/getrpt?GAO-02-843T
     134. http://www.gao.gov/cgi-bin/getrpt?GAO-02-259
     135. http://www.gao.gov/cgi-bin/getrpt?GAO-01-1022T
     136. http://www.gao.gov/cgi-bin/getrpt?GAO/T-RCED-00-296
     137. http://www.gao.gov/cgi-bin/getrpt?GAO/RCED-99-65
     138. http://www.gao.gov/cgi-bin/getrpt?GAO/T-RCED-98-273
     139. http://www.gao.gov/
     140. http://www.gao.gov/
     141. http://www.gao.gov/fraudnet/fraudnet.htm
     142. mailto:fraudnet@gao.gov
     143. mailto:JarmonG@gao.gov
     144. mailto:Beckers@gao.gov
    *** End of document. ***