Estimating rates of substitution for protecting values at risk for initial attack planning and budgeting

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Forest Policy and Economics 10 (2008) 205 – 219 www.elsevier.com/locate/forpol

Estimating rates of substitution for protecting values at risk for initial attack planning and budgeting Douglas B. Rideout a,⁎, Pamela S. Ziesler a , Robert Kling b , John B. Loomis c , Stephen J. Botti d a

Fire Economics and Management Laboratory, Department of Forest, Rangeland and Watershed Stewardship, Colorado State University, Fort Collins, CO 80523, United States b Department of Economics, Colorado State University, Fort Collins, CO 80523, United States c Department of Agricultural and Resource Economics, Colorado State University, Fort Collins, CO 80523, United States d National Park Service, U.S. Department of the Interior, U.S. National Interagency Fire Center, Boise, Idaho, United States Received 27 February 2007; received in revised form 27 September 2007; accepted 17 October 2007

Abstract With changes in land management planning and a new federal fire policy, increased emphasis has been placed on protecting a broader set of resource values such as those associated with sensitive species habitat or cultural resources. Fire managers have long needed a system for assessing values at risk across the landscape that can be implemented in accordance with the budgeting and appropriation process and that can be updated annually or every several years. A viable system has to be operational at a reasonable cost and it must support strategic planning and budgeting. Currently available valuation methods, in their entirety, can be costly and time consuming making them problematic for these purposes. Consequently, managers have become accustomed to assessing values at risk without the direct support of structured economic analysis. This paper discusses an approach (Marginal Attribute Rate of Substitution) to assessing values at risk for initial attack planning and budgeting. MARS is an attribute based method for estimating rates of substitution among fire protection attributes in a spatial context. It consists of and builds upon specific elements from well known and peer-reviewed valuation methods for resource valuation. As such, MARS relies upon stated preference, expert opinion, the hedonic price equation and other familiar procedures. The paper concludes with an empirical example of the application of MARS to a forested area in California. As the first construction of this approach it has the potential for further modification and refinement for those that may find it of interest. Published by Elsevier B.V. Keywords: Wildland fire; Initial attack; Fire planning; Values at risk; Valuation; Rates of substitution

1. The need for and criteria of a new approach to estimating values at risk By the mid 1990s each of the major U.S. federal land management agencies had begun the transition to ecosystem management. This shift reflected an important change in land management philosophy that became a driving force behind policy reformation and a reconsideration of management and planning systems including those associated with federal ⁎ Corresponding author. E-mail address: [email protected] (D.B. Rideout). 1389-9341/$ - see front matter. Published by Elsevier B.V. doi:10.1016/j.forpol.2007.10.003

wildland fire preparedness planning and budgeting. The transition to an interagency ecosystem-based wildland fire policy was accelerated by the review of the 1988 Yellowstone fires (Mills, 1989) and the review of the 1994 wildfire season, which included the South Canyon fire. The 1995 Federal Wildland Fire Management Policy and Program Review with its 2001 update (U.S. Department of Agriculture et al., 2001) emphasized the tenets of ecosystem management by stressing broad scale management, interagency cooperation, and the natural role that fire plays in ecosystem functioning while reducing emphasis on traditional monetized measures of performance. With increasing reliance on the management of

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non-market resources and with an interagency perspective, the new federal fire policy elevates the role of non-market resource considerations in initial attack fire planning. Public officials and researchers (Rideout and Botti, 2002; U.S. Department of Agriculture and U.S. Department of the Interior 2001; U.S. Department of Agriculture et al., 2001) recognized the need to develop fire management planning and budgeting systems that would better reflect the broad environmental values (market and non-market) in fire planning. Federal bureaus recognized that budgeting and planning systems could benefit from improved ways to address the challenges inherent in nonmarket valuation that are often associated with values that the federal bureaus are mandated to protect and enhance, such as healthy ecosystems and historic properties. Valuation techniques used in annual preparedness planning1 would also have to be nimble enough to inform the annual budgeting process as applied to a local planning unit. As stated by Gregory and Slovic (1997) in their commentary on environmental valuation: “…the environmental goods being evaluated for public policy decisions are often complex, unfamiliar, and richly multidimensional, involving a broad range of… values. … People need a method that first helps to articulate their values for the many parts or aspects of the good and then helps them to combine these parts.” Consequently, this paper explains a new method for assessing protection values for initial attack planning and budgeting at the planning unit level. The method builds on the strengths and procedures of existing well-established, peer-reviewed techniques while compensating for difficulties in information, timing and cost that would occur if any of the standard techniques were used in their entirety. The objectives of this research are stated as a set of design criteria that were used to guide the development of the valuation process. These included: 1. provide a non-monetized expression of values to be protected that includes non-market natural services while also including the values of traditional multiple use commodities, 2. be potentially applicable across the range of federal agencies, each with diverse missions and some of whose resource values are expressed in non-monetized language,2 3. address landscape scale planning at the planning unit level, 4. be largely executable by local planning and budgeting personnel, 5. be applied frequently enough to support annual budgeting and planning at reasonable cost, 6. reflect local fire suppression decision processes including unit level goals and objectives as reflected in the land management planning documents and reflect detailed 1 Preparedness planning refers to preparing for the upcoming fire year including the organization of initial attack resources. 2 For example, the National Park Service preserves unimpaired the natural and cultural resources and values of the national park system for the enjoyment, education, and inspiration of this and future generations. The Park Service cooperates with partners to extend the benefits of natural and cultural resource conservation and outdoor recreation throughout this country and the world. (http://www.nps.gov/legacy/mission.html).

knowledge of fire effects and the influence that deployment of initial attack resources can have on these effects, and 7. produce relative pricing and valuation information for potential use in initial attack resource allocation decisions. Some of these criteria, for example criterion (1), can be addressed with traditional valuation methods such as the contingent valuation method (CVM) or conjoint analysis. However, current applications of attribute-based methods (ABMs) and CVM would be problematic for the initial attack problem because elicitation of relative values for a moderate number of fire protection attributes would require numerous versions of the fire protection scenario, even for a single planning unit. When compounded by different fire intensities and times of year, the costs and time required to elicit value protected would be excessive. Specifically CVM, among others, requires extensive use of general population surveys that are costly and time consuming putting them beyond the reach of most planning units. For example, a CVM or conjoint analysis application to a particular resource valuation problem will require up to a year to complete and cost upwards of $100,000. The costs of CVM and conjoint surveys can be quite expensive when sampling the general public. To make technical information understandable, repeated focus groups and pretests are often required. In-person interviews are recommended as the preferred survey administration mode for accuracy (Arrow et al., 1993; Mitchell and Carson, 1989). However, in-person interviews are quite expensive, costing upwards of $500,000 if a high response rate of a nationwide sample of the general public is required (Harrison and Lesley, 1996). The total cost of the most thorough in-person CVM surveys can be as high as $1 million (Harrison and Lesley, 1996). While mail surveys are a fraction of this cost, they may not be suitable if the survey must convey a large amount of background information and there are numerous stated preference choice questions. Thus, there are some economies to be obtained by surveying experts assembled in one location. The complexity of the fire problem often requires estimates of fire effects on resources across a range of fire intensities and across seasons thus compounding the number of valuation estimates required for a particular site. For example, some species may only be susceptible to moderate or high intensity fire as is often the case with sequoia groves and some wildlife. In particular, some ground nesting birds are sensitive to wildfire only during certain seasons, such as the endangered Mississippi sandhill crane which benefits from fire adapted ecosystems but is particularly susceptible to fire during the spring nesting season.3 While criteria (1) through (3) may be addressed with conventional resource valuation techniques, issues of knowledge, time and cost make the criteria in (4) through (6) problematic with such methods. It is unlikely that conventional methods, which are analytically intensive, could overcome the time and cost constraints required for estimating a diverse set of use and non-use values at risk for annual budgeting. Criterion (7), the estimation of relative values, is a customary and pragmatic feature of initial attack modeling and has been used in the past in 3

http://www.fws.gov/mississippisandhillcrane/.

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systems such as the U.S. preparedness planning model known as the National Fire Management Analysis System (NFMAS). 2. Current valuation techniques and the context of fire management Identifying a suitable valuation technique for a landscape scale initial attack planning and budgeting problem which addresses the criteria listed above poses unique and difficult challenges. There are, however, important advantages to the stated preference methods and to the attribute based methods (ABMs) that are essential to retain in a new method. For example, stated preference methods including the CVM and conjoint analysis have important advantages in their ability to address both use and non-use values consistent with the broad scope of ecosystem management. Also, contingent valuation has been successfully applied to resources affected by wildland fire (Hesseln et al., 2004; Loomis et al., 2001) such as specific outdoor recreation sites or experiences. Recent advances in attribute based valuation theory (Holmes and Adamowicz, 2003) and the wider availability of geographic information systems (GIS) suggest that attribute based methods (ABMs) can have strengths when valuation across a landscape is an important consideration. Advances in ABMs include improving the connection between choice theory and welfare economics (Roe et al., 1996), relaxing restrictive assumptions of the conditional logit model to allow for heterogeneity of preferences with mixed logit models (Holmes and Adamowicz, 2003), and tying econometric estimation to widely available software packages such as LIMDEP (Hensher et al., 2005). ABMs have the potential to enhance landscape level fire planning systems by defining values at risk as a function of spatially explicit valuation attributes while conforming to the criteria listed above regarding initial attack modeling4 that are beyond the reach of traditional methods. Therefore, we adapt a stated preference ABM approach to the problem of initial attack planning and budgeting based upon expert elicitation of rates of attribute substitution. Economic valuation theory and valuation methods based on marginal analysis have been used only in recent decades to estimate wildland resource values and the potential for resource value degradation from disturbances such as fire. Early valuesat-risk assessments often used physical proxies such as acres burned for the value of individual resources. Recognizing the limitations in such approaches Crosby (1977) made two separate observations. First, he expanded the application of valuation theory in fire by developing the concept of “value protected” which is identified as the portion of total resource value that is subject to fire damage, and he related “value protected” to fire intensity. Net value change (NVC) was developed as a monetized and practical extension of Crosby's 4 Several other techniques have been used in natural resource management decision making, most notably the Analytic Hierarchy Process (AHP) and variants of the Simple Multi-Attribute Rating Technique (SMART). These techniques were not intended to produce estimates of prices or of resource values. These techniques do not typically appear in the resource economics literature on non-market valuation and do not directly address rates of substitution. Therefore, they are not considered here.

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work. Mills and Bratten (1982) used a discrete accounting approach to define NVC as the value of the resource(s) after the fire minus the value of the resource(s) before the fire. Traditional applications of NVC were developed under a multiple use paradigm consistent with Resource Planning Act direction (NFMAS, 2002). Separately, Crosby recognized that monetization of protection values was unnecessary for fire planning as evidenced by his statements that “…strict adherence to dollars is not a necessity to obtain values for planning purposes…” Monetization is unnecessary where relative prices (ratio of prices or values) are used to guide resource allocation decisions because the currency metric “divides out” effectively making the choice of currency mathematically irrelevant. Also, researchers of environmental valuation increasingly recognize that expressions of environmental values can be facilitated by non-monetized expressions of the valuation problem. For example, Gregory and Slovic (1997) state that: “…research has suggested that although people do hold strongly felt values for nonmonetary aspects of goods, such values often are not cognitively represented in monetary terms (Gregory et al., 1995). Requiring a monetary response for these aspects places an additional burden on the respondent. In addition, an emphasis on monetary valuation can imply that the ecological, cultural, health or other aspects of a project are considered to be less important. In the extreme, people may consider the monetization of a valued cultural or ecological impact to be immoral and refuse to participate in a CV (contingent valuation) survey.” Because non-monetization can improve the ability of respondents to relate to and participate in the analysis of tradeoffs, and because the choice of currency (monetization) is mathematically irrelevant, we develop an approach based on physical comparisons of fire protection attributes. 3. Developing the theory of fire protection and rates of attribute substitution Because fire protection involves protecting resource values that might fall within a fire's perimeter, it is useful to consider the values to be protected as physical attributes of a land area where acreage on the landscape might contain one or more fire protection attributes. In land management and in wildfire protection, where public officials are often charged with managing specific acreages, the values to be protected can be expressed as a function of the composition of the physical attributes on a particular site. Examples of wildland fire protection attributes may include items such as threatened and endangered species habitat or commercial timber areas. This parallels the more customary recreation site value expressed as a function of its physical attributes such as proximity to water, showers and picnic tables (Holmes and Adamowicz, 2003). Moreover, the importance to protect valuable attributes on the landscape from unwanted wildland fire can be summarized as depending upon fire intensity, the season in which the fire might occur and the level of the initial attack budget. While fire managers are concerned with fuels and weather conditions, these affect the importance of protecting any

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particular resource attribute through their influence on intensity at a given season or time of year. Therefore we can generally express a unit of fire protection attribute (A) as a function of season (S), fire intensity (I) and of the budget level (B). For a particular attribute, Aj, the expression is as in Eq. (1): Aj ¼ Aj ðS; I; BÞ

ð1Þ

For initial attack applications involving small fires we focus on the marginal benefits of the set of fire protection attributes, and therefore we need to estimate the rate of substitution between attributes. For ease of development we establish the mathematical equivalence between the rate of attribute substitution (RAS) for threatened attribute losses due to fire and the RAS between overall attribute levels for the planning unit. The key to this equivalency is that they both involve marginal changes from the status quo. Consider a given initial attribute endowment (A1,A2) that generates a social utility level U(A1,A2). The RAS in utility between A1 and A2, as reflected in the slope of the indifference curve between attribute levels, is (∂U/∂A 1 )/(∂U/∂A 2 ). For small, discreet changes the RAS would be (ΔU/ΔA1)/ (ΔU/ΔA2), or − ΔA2/ΔA1 because the ΔU values are assumed equal and opposite in sign. Alternatively, if we consider the indifference between loss increments ΔA1 and ΔA2, from a starting point of (0,0) if the two losses are of equal value, the rate of loss substitution will again be − ΔA2/ΔA1. Therefore, the RAS between losses, which is measured in this study, is equivalent to the RAS between attribute levels in a utility function U(A1,A2). This discrete approximation to a RAS is summarized in Eq. (2) where the RAS is defined as a function of the fire protection attributes. RASi−

DA2 ð I; S; BÞ AU =AA1 IAP1 ð I; S; BÞ i i DA1 ð I; S; BÞ AU =AA2 IAP2 ð I; S; BÞ

ð2Þ

Eq. (2) states that the RAS between the attributes can be approximated by the ratio at which the decision (planning) unit is willing to protect incremental changes in the attributes and this is equivalent to the ratio of the attributes' marginal utility. For purposes of our application, we define this RAS as the ratio of the “implicit attribute prices” or IAPs. If we know how the planning unit would be willing to trade off between different levels of the two attributes to achieve the same level of fire protection utility as in Eq. (2), we also know the relative values or contributions of each attribute to utility. Because the RAS is defined as the ratio of the IAPs, the unit or currency in which the IAP is measured cancels, making it unimportant relative to the general theoretical development. This is consistent with Crosby's observation regarding monetization. For pragmatic purposes related to attribute elicitation that are addressed later, we introduce a currency defined by the “numeraire attribute” which in principle could be monetized or non-monetized. From Eq. (2) we generalize to the case of j attributes and denote the numeraire attribute as “N” in Eq. (3):
 IAPj ¼ IAPN TRAS AN ð I; S; BÞ; Aj ð I; S; BÞ ð3Þ Eq. (3) states that an implicit attribute price is defined as a function of the numeraire attribute, the rate of substitution with the

numeraire attribute, fire intensity, seasonality and the appropriation or budget level. Seasonality, intensity and the numeraire attribute are further developed in the following sections. The relationship between net value change (NVC) for a particular protection attribute (NVCj) and its implicit attribute price (IAPj) is defined by a rate of conversion between the two currencies: for example, between U.S. dollars and the unit of the numeraire attribute as defined in Eq. (4), remembering that NVC is traditionally defined as a cost for damaging fires:   K 1 0 NVCj ¼ Vj  Vj ¼  IAPj T ð4Þ IAPN In Eq. (4), Vj1 denotes the unit value of the jth attribute after burning and Vjo denotes the unit value before burning. The conversion rate is expressed as: K/IAPN where K denotes the constant of conversion. Use of the non-monetized approach developed here for a set of fire protection attributes occupying a unit of land of interest is developed next. 4. Discrete application to initial attack Estimation of the marginal value of fire protection for initial attack planning as a function of the underlying protection attributes suggests developing a discrete application in a spatial context. Because the initial attack problem is, by definition, focused on small fires that occur in a single operational period (24 h or less) (NWCG, 2004), we continue with the customary assumption of a linear application. That is, for small changes in the quantity of attributes, the marginal values or the IAPs are assumed to be constant. The total fire protection value (FPV) is composed of the quantity of each attribute times its implicit price. This approach is the reverse of the traditional hedonic price method that decomposes the total value into the implicit prices, but the logic is the same. We therefore begin with a form of the hedonic price function for an initial attack fire, F, in Eq. (5) tailored for a GIS application. FPVF ¼

Q X J X q¼1

Aj TIAPj ð I; S; BÞ

ð5Þ

j¼1

In Eq. (5), FPVF denotes the fire protection value for fire F as a function of the sum of attributes (Aj) found on a particular cell (acre, pixel, etc.) times their respective price IAPj. If Aj denotes the percentage of an acre (cell) containing attribute j then IAPj is a rate of substitution per unit acre (cell size). The expression “Aj ⁎ IAPj” (quantity times price) is summed over all attributes (1 to J) on the cell and then all cells are summed (1 to Q) to estimate the FPVF.5 The procedures for defining fire protection attributes and estimating their implicit prices to support the estimation of Eq. (5) is the focus of the remainder of our paper. Because the heart of the technique relies upon estimating marginal rates of attribute substitution, for ease, we refer to it as MARS.6 5 The relationship to NVC for a particular cell is directly denoted as: FPVF = −NVCF ⁎ (IAPN / K). 6 Marginal attribute rate of substitution.

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5. Identifying fire protection attributes and their implicit prices To identify the fire protection attributes important to preparedness planning in a strategic context and to estimate their implicit prices (denoted in Eq. (5)), we apply a set of customary procedures carefully selected from well-established peerreviewed non-market valuation techniques. Table 1 provides an overview of the distinguishing elements of several nonmarket valuation techniques and it compares MARS to five other methods commonly used to estimate natural resource values. At the broadest level, MARS is an attribute-based method based upon the recognition that the initial attack problem has important and potentially unique considerations. The first point of comparison is whether the method can capture passive use values such as existence values. The ability to capture such values is an advantage of the stated preference (SP) methods. To the extent that managers consider passive use values to non-visitors in their trade-offs between fire protection attributes, the passive use values are reflected in MARS. Like the commonly used non-market valuation methods, MARS is capable of estimating relative prices of the different resource attributes. MARS also shares with conjoint analysis, CVM and the Delphi method its reliance on peoples' statements of their values (SP). This is often an advantage for ex ante analysis of future events. MARS uses a consensus based approach to arrive at a single set of relative prices rather than using averaging. In sum, MARS draws appropriate features from standard methods such as CVM and conjoint analysis to create a new valuation method better suited to evaluate initial attack planning and budgeting for wildfire management. Fire planning and management is a specialized application that has relied on expert opinion for a variety of purposes including the elicitation of fireline production rates (Hirsch et al., 1998; Hirsch et al., 2004) and for subjective probability assessments to predict forest fire occurrence (Cunningham and Martell, 1976). Our problem of identifying fire protection attributes and their prices is particularly specialized in that it

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requires detailed knowledge of many resources across the landscape and how they would be affected by wildfire intensity and season. Recognizing that “…many members of the general public are not well informed about the issues of the good to be valued, and it is impossible to convey the facts involved with complex natural resource decisions or goods adequately in the time available…” Brown et al., (1995) considered the approach of a “values jury.” Gregory and Slovic (1997) also recognized the challenge of public surveys when “…environmental goods being evaluated… are complex, unfamiliar, and richly multidimensional…” in their development of a multiattribute approach to value construction. Both of these approaches require informing and educating public participants for purposes of valuation in natural resources problems and stress the importance of a well-informed and knowledgeable response. Our problem can be characterized as assessing the importance of protecting a set of resources from fire damage across a discrete area as opposed to valuation of a particular resource or of a particular resource decision. This effort requires an understanding of both the resources that would be engaged and the complex natural disturbance mechanism of wildland fire. For the implicit prices to be an accurate guide to the values at risk, we would want respondents to be fully informed to reflect the impractical ideal of perfect knowledge often assumed in economic analysis (Varian, 1992). Fire managers and resource specialists, collectively, best approximate a group with such information and they are charged the responsibility for such knowledge. In the planning process, valuation information is part of the NEPA and consequent land and resource management plans. The responsibility of the officials at the fire planning unit is to use the valuation information from the planning process in context with an understanding of fire effects, for which they are extensively trained. Given the superior information of a group of managers, the implicit prices derived from this group would have a higher “signal to noise” ratio than from the public who cannot reach the level of understanding of managers, especially regarding an often wide range of fire effects, from just reading a short survey booklet. Therefore, we address the identification of

Table 1 Comparison of valuation method characteristics indicating their relationship to MARS (Marginal Attribute Rate of Substitution) Characteristic

Valuation method

Valuation method characteristic

Travel Cost Hedonic Conjoint analysis method methods

Estimates passive use values Estimates relative prices Expert opinion Revealed preference technique (RP) or stated preference (SP) Requires monetary payment vehicle Restricted to marginal or price estimate Elicitation method

Contingent MARS valuation methods

Delphi method ?

X

X

X X

X X

RP

RP

SP

SP

Population sampled (S) or uses a consensus (C) S Compositional (C) or decompositional (D) C Attribute based method (ABM) x

X

S D x

X X X hypothetical bundles of attributes Dichotomous (hypothetical goods) choice S S D C/D x

Note: only key distinguishing characteristics are shown recognizing that there are many special situations and “hybrids.”

X X X SP

X SP

X Series of substitution ? comparisons C S C C X

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fire protection attributes and the estimation of their implicit prices by way of expert elicitation with careful definition of the “expert”. Using expert opinion in this context requires assembling fire planning and management officials who are familiar with the key land and resource management planning documents and who are knowledgeable of fire effects and of their importance at a range of intensity levels and seasons. Assembling resource management teams is a common practice in federal land management planning and teams similar to these are often used to develop fire suppression response documents such as fire management plans and handbooks. By virtue of their responsibilities as public fire management and planning officials, this group has the collective responsibility for intimate knowledge of the unit resources and of the effects of fire on those resources under different intensities and seasonality. This group, the “Expert”, will also have an incentive for meaningful participation in that the results of their estimations are used in the annual budgeting and planning process; and, to some extent, they may have responsibility for results related to the estimation of IAPs. In this sense using affected managers makes the value elicitation exercise less hypothetical and more consequential to them. The more consequential the elicitation exercise the more likely estimations will be demand revealing (Carson et al., 2000). While the process benefits from using potentially affected fire management and planning officials this may introduce a potential tradeoff to the extent that it might encourage gaming or strategic behavior. This is addressed in a following section. Traditional valuation methods would provide respondents with a set of attributes to evaluate. Because MARS is new and there is extensive variation in natural systems and potential fire effects across the U.S., we initially relied upon the knowledge and experience of planners and managers for knowing why fire protection is important on their unit. Therefore, for this study, we elicited a custom set of fire protection attributes from the “Expert.” Additional applications and data collection could provide the information necessary to support future development of a more general set of fire protection attributes that might lend themselves to tailoring to a particular fire planning unit. For example, an established or pre-defined set of attributes could be established nationally or by eco-region for specific tailoring to the planning unit. 6. Example elicitation and application: the Southern Sierra prototype Our elicitation process for estimating rates of substitution for fire protection planning is best explained by example. A portion of the Southern Sierra prototype fire planning unit (FPU) was selected as a test site early in the development of the FPA process because it has several attractive features for evaluating new planning techniques. This FPU contains a wide range of physical and social considerations for which a diverse array of fire protection attributes and rates of substitution may be elicited. Also, the planning team working on this FPU is long established and has considerable experience with planning applications and GIS that facilitates data transfer and collabora-

tion. We selected two management units within the FPU known as the Grant Hume and Redwood Case fire management units to best illustrate the application of MARS. Fig. 1 indicates the overall location of the study site. This location was chosen because it is rich in a range of fire protection attributes that can illustrate variation in the attributes and fire protection values across the landscape. 6.1. Selection and definition of the “Expert” and defining the problem We began the elicitation process by cooperating with public officials on the Southern Sierra FPU to organize a collection of approximately 20 fire and resource professionals to form the “Expert” from which the set of fire protection attributes and implicit attribute prices would be elicited. It is important to include the relevant range of valuation perspectives and knowledge such that a reasonably complete set of values will be reflected, but to not have so many perspectives that the process becomes unwieldy (Gregory and Slovic, 1997). Our respondents included fire planners and fire management officers, wildlife biologists, soils scientists, social scientists, timber management specialists, and GIS specialists. These individuals have diverse skills and experience in land management planning, in evaluating fire effects at different seasonality and intensity, and in the management of the full range of resources to be protected from wildfire. For data collection we facilitated a meeting in which all members of the planning team were present. We presented a detailed description of the valuation process including the fire management planning context, theoretical foundation of value protected, and guidelines and pertinent definitions related to the elicitation exercise.7,8 Respondents were encouraged to ask questions and discuss the theory and definitions to foster a deeper understanding of the choice task in which they were to participate. This aided with the development of fire protection attributes and the estimation of their implicit prices. This process shares important features with the Gregory et al. (1993) and McDaniels and Rossler (1998) “constructive” approach to value elicitation by taking the steps outlined for guiding an analyst's interaction with the affected stakeholders. Specifically: to construct the best informed “Expert” regarding fire protection, we gather individuals knowledgeable of the full range of fire protection issues. A list of protection attributes is then elicited directly from the Expert. For instance, instead of attempting to assess the importance of protecting a particular acre from wildfire, this procedure defines the valuation process as a set of discrete physical protection attributes, such as sequoia groves, wildland urban interface (WUI) and others. Fire protection attribute prices (IAPs) are then assessed by using pair-wise comparisons that best resonate with the respondent 7

Delphi also uses consensus. In future elicitation exercises each member of the planning team could be supplied with pre-meeting materials to better acquaint them with the valuation problem and the procedures required for a successful session. A booklet might include a description of the problem, rules for elicitation, definitions, etc. 8

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Fig. 1. Project area location.

until the full set of IAPs has been elicited. These comparisons produce a set of RAS that can then be used to address fire protection value for discrete areas (pixels) comprising the fire planning unit. By assessing the planning unit as a collection of fire protection attributes, the process breaks a potentially complex problem into manageable and relevant parts. These parts (fire protection attributes) when evaluated produce a proxy for prices (IAPs) that can be combined and used to estimate the importance of protecting areas from wildland fire across the planning unit in an initial attack context. Importantly, public officials evaluate the tradeoffs in protection attributes in physical units that they are familiar with and which they routinely address in planning and resource allocations through operational decision making. Also, our respondents consistently expressed the inability to directly address such tradeoffs in a monetized context. Like the attribute based methods in Table 1, this initial portion of the choice task included the steps of characterizing the decision problem and explaining the elicitation process. For elicitation purposes, we defined the good to be valued as “the importance to protect an acre from unwanted wildland fire at a specified intensity and seasonality.” The acre can contain one or more fire protection attributes. For example, an acre could be important to protect because it contains sequoia groves as well as urban interface values (and potentially attributes of other values at risk). Therefore, our valuation problem is characterized as a compositional attribute-based approach often used in

regression applications where the good to be valued is a function of independent variables describing the attributes that provide the good with value. Valuation of a recreation site as a function of its underlying attributes, such as proximity to water, number of camping sites etc., is a common example of the compositional approach to valuation used in many multi-site travel cost models. These models include the varying parameter TCM models of Vaughan and Russell (1982), the generalized TCM of Smith and Desvousges (1985), and the benefit function transfer (Loomis, 1992; Rosenberger and Loomis, 2001). We then established a set of procedures (see also Appendix A) to aid in attribute identification and definition. These included: • Each attribute is defined as a physical characteristic of the landscape that is measurable in acres (or other common area unit) such as an acre of commercial timber, rangeland or cultural sites. • Each attribute must be separate from the other attributes. If there is interaction between attributes for the purposes of fire protection, or if protecting one attribute changes the value of protecting another, a separate attribute would be defined to represent the interaction. • Every acre of land that receives fire protection must be represented by at least one attribute. • Any acre of land that does not receive fire suppression actions (water, rocky land, areas outside fire protection responsibilities, etc.) should not be included in the analysis. • Any acre of land may contain more than one attribute.

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6.2. Elicitation of fire protection attributes Once the public officials were comfortable with the context and theory of the fire protection choice task, we asked them to generate a list of fire protection attributes reflecting the key reasons why they would expect to engage in initial attack activities. We carefully reminded them that they were describing the reasons that it is important to protect resources from unwanted wildland fire at particular intensities and seasons; and that they were not estimating resource values. An example of this important difference would be assessing a resource, such as sequoia groves, which have a very high value to society but often benefit from low intensity fires implying a lesser fire protection value for fires at low intensities. We also reminded them that the attributes reflect the key reasons for strategic initial attack in the context of seasonal planning and budgeting and not how an individual fire incident might be managed. To facilitate this they focused on initial attack decisions at the planning unit scale rather than on particular fire events. Team members on the Southern Sierra proposed and discussed a wide set of attributes and, after approximately half a day of discussion, they identified six fire protection attributes for the fire management units used in this study. These included federally designated direct protection wildland-urban interface (DPA-WUI), sequoia groves, commercial timber under contract, forested areas defined by the team members as a proxy for non-commercial forest ecosystem values, rangeland, and unroaded natural areas which include wilderness and proposed wilderness. Before elicitation of the IAPs, we reviewed each attribute with the respondents to ensure that a clear definition was shared and recorded for each attribute. The spatial location of the elicited WUI, sequoia groves and roadless area attributes are illustrated in Fig. 2A–C. This set of attributes was chosen for illustration because it shows the contrasting locations of WUI and roadless with an intermixed sequoia attribute. The full set of six attributes was used in the calculation of the FPV that is illustrated subsequently. 6.3. Elicitation of implicit attribute prices (IAP) Elicitation of the IAPs, consistent with Eqs. (1)–(3), requires focus on the ratio of attribute prices under a given budget level. We began the IAP elicitation process by again defining the problem: we reviewed the meaning of rates of substitution among fire protection attributes and discussed how substitution differs from concepts of prioritization and ranking that are more familiar from established public planning processes. We next presented the team with procedures for elicitation of their IAPs in terms of proportions or ratios assuming current budget levels. We quickly found that planners needed a “benchmark” of comparison to engage in the attribute comparison process. We used wildland urban interface (DPA-WUI) under high fire intensity during the most active period of the fire season as the numeraire attribute (AN) with an assigned value of 1.00. While in principle this may be unnecessary, it was pragmatically essential in relating the “Expert” to the problem. It established an upper bound on the elicitation of IAPs and immediately

reveals the relative value of all IAPs to WUI. This also establishes a bounded interval for IAP elicitation. To focus on rates of substitution, we asked respondents to carefully consider their comparisons in terms of “How much more (or less) important is it to protect an acre of attribute “A” than it is to protect an acre of attribute “B” at a given intensity and seasonality”. For example, how much less important would it be to protect rangeland or commercial timber relative to protecting designated WUI? With this framework, the elicitation of implicit prices was conducted for the full range of fire intensity levels and seasonality. The Southern Sierra group elected to have four intensity categories: Fire Intensity Level (FIL) 1 corresponding to 0 to 2 ft flame lengths, FIL 2 corresponding to 2 to 4 foot flame lengths, FIL 3 corresponding to 4 to 6 ft flame lengths and FILs 4 to 6 corresponding to flame lengths greater than 6 ft. The initial elicitation exercise was performed using the combination of fire intensity and seasonality for which they could best converse as shown in Table 2. Because elicitation of the attribute prices requires detailed conversation and intricate exchange of knowledge we divided the “Expert” into three groups (Appendix A). Each group was assigned a separate table and tasked with estimating the remaining five IAPs. Once this task was completed, we kept half of each group at their table to retain the information generated while the other half visited another table thus creating three new groups. These new groups repeated the elicitation task and attempted to discuss and understand any differences in attribute estimation and to resolve those differences. The individuals staying at the table with the original information would be responsible for recording any changes to the IAP estimations as the process continued. The “movers” would travel, as a group and in turn, to each of the remaining tables. Once all tables had been visited by the “movers”, we reconvened the large group to record the three sets of estimated IAPs. In the large group we discussed any meaningful differences in implicit prices and established a consensus on the IAP price set. The initial phase of the implicit attribute price elicitation process in which attributes were established and initial prices were elicited took approximately half a day. The full elicitation process for all prices required about two days of meetings and facilitation. The final attribute list with implicit prices is shown in Table 2. Attribute prices estimated for sequoia groves and for non-commercial timber vary by intensity as expected. Sequoia represents a fire resistant species at low intensity levels and non-commercial timber sustains greater damage at higher intensity levels. Commercial timber under contract and DPA-WUI did not vary by intensity level because fire managers view their responsibility for protection of these assets as a constant regardless of intensity or season. 6.4. Mitigating potential strategic behavior and comparing planning units All stated preference methods for eliciting values are subject to the potential for strategic behavior: the deliberate stating of misleading, inaccurate values in an attempt to influence policy decisions (Boyle, 2003). Because MARS was originally

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Fig. 2. A. Presence of the sequoia grove attribute. B. Presence of the wildland urban interface attribute. C. Presence of the roadless attribute.

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developed to inform the federal annual wildland fire budgeting process, there is some concern that administrative units may engage in strategic behavior to attempt to bias the budgeting process in their favor. In national interagency fire planning there is potential motivation for two kinds of bias: 1) a biased allocation of resources within a fire planning unit or 2) a biased allocation of resources among planning units within a broader system. Such motivation could potentially apply to any valuation method used in this context. If MARS were to be applied only at the planning unit level, or if it were only used to inform the allocation of resources at the unit level, then the second motivation would not apply. Regardless of motivation, there are three potential mechanisms for bias: bias in the attribute list, bias in price estimates, and bias in the count of acres associated with a particular attribute. Mitigation measures are introduced to address each. Some are internal in the overall structure and design of the method and others were introduced through a rigorous set of elicitation rules and procedures provided in the Appendix A. These elicitation rules were arrived at in collaboration with fire officials charged with the responsibility for implementation of fire management and planning efforts at the U.S. National Interagency Fire Center located in Boise, ID. Establishing proper membership for the “Expert” is essential for accuracy in attribute identification and in pricing. Therefore, the first rule listed in the Appendix A is aimed at accuracy, as opposed to gaming, by insisting that the proper expertise is present to participate in the elicitation of attributes and of attribute pricing. We also introduced a rule to mitigate any potential for double counting of attributes by the participants. Because we define attributes as physical characteristics of the landscape (Appendix A: rules 1.2.3 and 1.2.4), they must be physically identified and located in the GIS system. Both the list of attributes and the acreage associated with them are testable for accuracy and this provides the opportunity for oversight and review. Therefore, the potential for “artificial” and double counting attributes can be directly addressed. In the pricing of attributes, we employ “closed interval” estimation as opposed to an open ended elicitation. By defining the numeraire attribute as the attribute of highest value and setting it equal to one (Appendix A rule 1.3.3), all other prices are constrained between zero and one. This also makes the price of each attribute proportionally obvious relative to the Table 2 Fire protection attributes and their implicit prices a Attribute List

Implicit attribute prices FIL 4–6

FIL 3

FIL 2

FIL 1

1.00 0.75 0.60 0.35 0.30 0.10

1.00 0.75 0.60 0.35 0.30 0.10

1.00 0.40 0.60 0.20 0.30 0.10

In fire season Federal DPA WUI Sequoia groves Commercial timber Forested area (non-commercial) Rangeland Roadless area

1.00 0.75 0.60 0.40 0.30 0.20

a Table abbreviations are fire intensity level (FIL), designated protection area (DPA), and wildland urban interface (WUI).

Fig. 3. Illustration of the calculation of fire protection (FPV) value from individual fire protection attributes and their implicit attribute prices (IAP) (cell entries for attribute pixel representation denote percentage of cell containing the attribute).9

numeraire attribute. This introduces the potential to immediately reveal certain unreasonable pricing to the elicitation team and to external observation and review. For inter-planning unit comparisons, discussed next, the process of eliciting attributes and rates of substitution with the high-intensity/peak fire season WUI as the numeraire provides some self-regulation by making it extremely difficult to raise the overall level of fire protection value for the planning unit relative to other planning units. Each of these potential schemes: biasing the attribute list, biasing the attribute prices, and biasing the acreage count by attribute, can be managed and their potential impact can be mitigated by the open process of eliciting the attribute list and the implicit prices. Through the open discussion and consensus required of the full group/small group elicitation process described in the Appendix A and with the potential for review and certification (Appendix A), potential gaming can be managed and mitigated. While structure, oversight, professionalism and peer pressure are able to curtail most of the worst attempts to bias the results within the structure of MARS, consistency between planning units, if needed, can be further addressed by employing a consistent facilitation team that is knowledgeable and capable of enforcing proper elicitation procedures when working with different planning units. MARS, in principle, can facilitate inter-planning unit planning and budgeting by consistent application of the numeraire attribute. For example, if two planning units rigorously define and apply identical numeraire attributes, such as a particular definition of WUI, then the set of attribute prices for both

9

WUI denotes wildland urban interface.

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planning units are comparable. This would make the computation of Fire Protection Values across planning units consistent to facilitate budget allocation decisions. Specifically, a causal change in budget allocation by planning unit can be compared with a change in the sum of FPVs by planning units to inform marginal decision making with respect to budgets. An important consideration is that while attributes other than the numeraire need to be consistently defined within the planning unit, differences between planning units are reconciled by differences in IAPs. For example, two planning units each define a rangeland attribute identically except that the vegetation in one unit incurs more damage at all intensity levels. This planning unit, cet. par., would price their rangeland attribute higher. To the extent that such differences are observable, they are potentially reviewable and enforceable. In this way meaningful differences in physical attributes are reflected in differences in IAPs. The immediate extension is that attributes, other than the numeraire attribute, need not be consistently defined across planning units and one would expect variation in attributes to reflect the extensive variation in ecosystem and fire conditions across the U.S. 6.5. Calculation and mapping of pixel fire protection value Returning to the Southern Sierra example, once the set of attributes and their prices were established, GIS specialists began the process of developing raster data layers to represent each of the six attributes. Raster data of this type produces a grid

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of 30 m by 30 m pixels. The fire protection value (FPV) was calculated, according to Eq. (5), for each pixel by summing the implicit attribute prices (times the percent of the pixel covered by the attribute) of every attribute present on the pixel and then multiplying the total implicit price by a conversion factor to make it a “per pixel” price. A stylized example is shown in Fig. 3 using the three attributes illustrated in Fig. 2A–C. In Fig. 3 the attribute panels show the presence of the fire protection attributes and their respective attribute prices. The WUI and sequoia grove attribute each only occupy half of the northwest pixel. Hence, they are represented as 50% present while the other attributes are represented as fully present (1.0) or not present (0). The FPV panel shows the computation of the fire protection value for each pixel. For example, the fire protection value for the northwest pixel is 0.875 (0.5 ⁎ 1.0 + 0.5 ⁎ 0.75). Note that we have preserved the importance of protecting a WUI acre or pixel from the most intensive fire at the most important time of the year as the numeraire attribute. The calculated FPV (Cumulative Weights) for the Southern Sierra study site using all six attributes is shown in Fig. 4. The base geo-spatial data layers used in this study were accessed and derived from GIS datasets within the Southern Sierra Geographic Information Cooperative (SS-GIC) project. The SS-GIC focused on developing and testing an approach to incorporate wildland fuels management information into an interagency, landscape-scale planning framework. The finer grain base GIS data layers were created from aerial photography source imagery geo-rectified into Digital Orthophoto Quarter

Fig. 4. Computation of fire protection value (FPV) for high fire intensity.

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Quadrangles (DOQQ's) originating at scales of 1:8000, 1:12000, and 1:15000. These in turn were digitized into polygonal vector data layers created at a generalized mapping accuracy of 1:24000. The base data sets were aggregated into fire protection weights by executing an ArcObjects routine to resample and aggregate the fire protection attribute layers. Cells with similar attributes were coalesced and dissolved to develop the attribute layers. Attributes were assigned the proper IAP and the prices for each pixel were added together to generate a fire protection value for each pixel. From these values a simple 30 by 30 meter grid layer was generated to represent the fire protection value of the pixels. Fire protection values can be converted into per acre weights by simple conversion from the per pixel (30 by 30 meter) value to a per acre value. 7. Conclusions Fire managers and planners need a system for assessing values at risk across the landscape that can be implemented annually at a reasonable cost and that will produce results that can be used for strategic planning and budgeting purposes. These conditions impose severe restrictions on the kinds of techniques that would be suitable. This problem, of providing credible pricing for planning purposes, has been a long-time challenge for economists and planners. Managers are constantly forced to make strategic resource allocation decisions without the aid of the customary valuation systems and they would benefit greatly by a structured system that can guide their analysis and assessment of values at risk. The approach presented in this paper (MARS) for estimating rates of substitution among fire protection attributes directly addresses the objectives and design criteria outlined above including the use of a non-monetized system. It also addresses the broad range of values important in today's fire management planning, it is an approach that can be completed annually with limited funds, and it can be used to inform broader planning efforts including inter-unit comparisons for budgeting purposes. A positive contribution and unintended consequence was that respondents became very skilled in their ability to discuss tradeoffs in physical terms for fire protection. Had we requested a similar discussion of total resource valuation, or if the discussion had been in monetized terms, the process would most likely have degenerated and no elicitation would have been possible. The recent philosophical change to ecosystem management requires increased consideration of non-market impacts making monetized elicitation potentially problematic. This is consistent with the statements of Gregory and Slovic (1997) that “…although people do hold strongly felt values for nonmonetary aspects of goods, such values often are not cognitively represented in monetary terms…” and further suggests that participants be allowed to select a familiar measure for valuation exercises. Perhaps one reason for a strong preference toward assessing rates of substitution in physical terms is that fire managers are constantly making resource tradeoff decisions quickly and in physical terms as part of their normal responsibilities. MARS provided them with a structured and formal process for complex considerations that were previously implicit.

MARS relies heavily on previous peer-reviewed and wellestablished valuation techniques and directly uses many of their procedures and approaches. For example, MARS focuses on rates of substitution as in conjoint analysis, employs the hedonic equation, employs a consensus result similar to the values jury approach, uses expert opinion as in the Delphi method, and effects a constructive approach to valuation. In each of these valuation techniques gaming or strategic behavior of the respondent is a potential concern. While such potential behavior can rarely be eliminated, measures can be taken as part of the process or in conjunction with the method to mitigate such behavior. Our application of MARS contained both, but the total elimination of this potential is unrealistic with any technique. There is also a direct “crosswalk” between net value change, rates of substitution, and the fire protection value (FPV) introduced here. From the crosswalk, it is clear that the results of MARS could be monetized and expressed in terms of NVC, but monetizing such expressions that have been designed to directly relate the full range of U.S. land management agency missions by reflecting both ecosystem services and traditional monetized values might have limited desirability. While there are direct relationships between MARS and traditional valuation approaches in natural resources and in fire management, perhaps the key contributions of MARS are in the ability to directly address a broad range of protection values in a way that has pragmatic appeal for fire management and planning at the planning unit level. Because MARS relies upon well known and heavily analyzed elements of previous valuation methods, its potential weaknesses are also well known. Perhaps chief among these is its reliance on a relatively small group of persons, the “Expert”, for elicitation thus producing a single, population observation (census). This and related concerns are well addressed by (Brown et al., 1995). Because MARS is new, there are extensive opportunities for future research including validation, refinement, assessments of robustness, and for exploring additional applications in wildland fire management and in other land management problems. Additional applications and research would enable refinement of the technique and a context for viewing the single set of IAPs developed in this study. Acknowledgments The authors gratefully acknowledge the members of the Fire Program Analysis team including Jeff Manley. Many thanks go to Craig Thompson who performed the GIS analysis and contributed the Figures 1, 2 and 4. We also thank J.R. Epps and Brian Eldredge who collaborated on the rule set. Thanks to the members of the Southern Sierra team, including Anne Birkholz, and to the members of the Southeast teams, including Mark Clere, for their facilitation of the testing of MARS. Appendix A. Elicitation rules to improve consistency and mitigate potential gaming Introductory information includes: Planning unit results of your elicitation are subject to review and certification by national analysts. You may be directed to

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revise and resubmit your analysis if your analysis is inconsistent with the following rules and suggestions. Attributes reflect the key reasons for initial response as opposed to tactical management of an individual fire. Attributes must be defined strategically to address the fire season in a preparedness context; not on particular events. You must have a thorough understanding of the process before beginning the elicitation process. This includes understanding the difference between implicit attribute prices and resources values. You must understand the role of FIL and sensitivity period in pricing of attributes. If you cannot explain each of these, you should not continue. The elicitation process is highly structured and it relies upon participants having developed a clear understanding of the method and adhering to the rules of elicitation. 1.0. The rules 1.1. Construction of the elicitation team 1.1.1. The team only includes FPU partner agency members. 1.1.2. Team membership must include representation of each agency that is part of the planning unit. The team consists of a mix of planners, fire managers, and resource specialists knowledgeable of the positive and negative effects of fire on resources. There should be enough membership to cover the full range of resources, fire effects, management concerns and agencies, but not so many that it is impossible to work efficiently. Our experience has shown that group size greater than 25 is difficult to manage, and group sizes less than 10 are problematic in obtaining proper representation. 1.2. Development of fire protection attributes 1.2.1. The entire team is present and participates in the development of the attribute list, definitions and price elicitation. 1.2.2. For application across planning units, a clearly defined definition of a numeraire attribute that can be consistently applied across planning units is required. At a minimum, precise definition of WUI, perhaps building on the SILVIS definition (http://silvis.forest.wisc.edu/projects/ WUI_Main.asp), should be considered. 1.2.3. Attributes are physical characteristics of the landscape that make the landscape important to protect from wildfire at a given fire intensity level (FIL) and time of year (sensitivity period). 1.2.4. Each fire protection attribute must be defined in and measurable in acres. Examples are: acres of fire sensitive endangered species habitat, acres of commercial timber, acres of rangeland, etc. 1.2.5. Do not double count attributes. Attributes must be mutually exclusive or separate from other attributes. Having both a general and a specific attribute definition including the same effect is not allowed. For example, using an

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attribute definition of sensitive habitat and another definition of a wildlife reserve would double count. It is best to only use the habitat based definition. The review process will look specifically for this type of error. 1.2.6. Each burnable acre that would receive fire protection must contain at least one attribute. For example, a single acre may include a recreation attribute; or a single acre may include multiple attributes such as commercial use and threatened and endangered habitat. 1.2.7. Any acre that would not receive fire suppression actions (water, rocky land, areas outside fire protection responsibilities, etc.) should not be included in the analysis. 1.2.8. In rare instances, interaction of attributes requires creation of a separate interaction attribute. If there is interaction between attributes for the purposes of fire protection, or if protecting one attribute changes the value of protecting another, a separate attribute needs to be defined to represent the interaction. Our experience suggests that this is unlikely. Only include interaction attributes if there is a clear and documented interaction between two or more fire protection attributes that would materially affect the analysis results. 1.2.9. Each team member must share a common understanding of the attribute definitions reached through consensus. This may require extensive discussions before the final attribute list and definitions are agreed upon. 1.2.10. After a common understanding of the attributes has been elicited, agreed to and confirmed, that list and its definitions is documented and kept with the planning package for certification and use by future planning teams. 1.2.11. Any changes to the attribute definitions require reeliciting implicit attribute prices and acreage estimations for the attributes. These changes will need to be reviewed and updated. This may also entail recalculation of fire protection values. 1.3. Elicitation of implicit attribute prices 1.3.1. All members must be present for and participate in the elicitation of attribute prices to ensure that proper knowledge is represented in the elicitation. 1.3.2. The team must agree to the list of attributes, the attribute definitions and to the final set of implicit attribute prices. 1.3.3. Set the WUI protection attribute at highest intensity level and the most severe fire sensitivity period to 1.00. 1.3.4. Using this WUI attribute as a reference point, start comparing the relative importance to protect other attributes during other periods and FILs. For instance, determine how much less important commercial uses are relative to protecting WUI during this time period and FIL. Continue this process for all attributes in all sensitivity periods and FILs. 1.3.5. Any changes to the attribute definitions require reeliciting implicit attribute prices with the entire team. Acreage counts for the attributes must be reviewed and updated periodically. For example, changes are required when land management plans are updated, when the

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planning unit boundaries have been modified, or when significant changes have occurred on the landscape. Changes to the number of acres assigned to an attribute will require re-calculations. 1.3.6. Estimate and record prices for all other attributes by asking the question: How much more (less) important is it to protect an acre of attribute A relative to protecting an acre of attribute B? The concept encompassed by the above question is crucial for producing credible prices. Each attribute must be compared on a per-acre basis.

planning unit fire protection values. DO NOT average the three sets of elicited prices — there may be important reasons that the estimates differ and these differences need to be directly addressed. Work through the reasons for differences in the large group and make sure that differences in pricing do not reflect different understandings of the attributes. If substantive differences in the attribute definitions emerge (this is not unusual), you must fix them. This may entail redefining an attribute or defining an additional attribute and then re-pricing. Often re-pricing at this stage can be accomplished with the entire group.

2.0.0. Suggestions 2.1.0. Development of fire protection attributes. Our experience has shown that a list of 12 or fewer attributes for fire protection will capture the main effects that drive initial response from a strategic perspective. Focus on establishing the key reasons that you engage in fire protection. The number of attributes will vary by FPU. You may receive a list of 10 predefined attribute categories for guidance. If so, you may tailor those that are relevant to your planning unit to directly reflect conditions on your planning unit. 2.2.0. Elicitation of attribute prices (very strongly recommended). To facilitate participation by all team members, to improve the quality of information flow, to reduce the impact of an individual with a strong personality, and to enable a detailed discussion of attribute prices, break the team into three groups of equal size. Assign a facilitator and time/note keeper to each group. A facilitator may be assigned to you to foster consistency in the process with other planning units. 2.2.1. Engage the four-step “split and rotate” process as follows. 2.2.2. Each small group will estimate a set of implicit attribute prices for the list of attributes. Make a list of attributes and estimated attribute prices. Stop after 20–25 min and begin step two. Do not allow additional time. 2.2.3. Half of each small group will stay and keep a list of attributes and estimated prices and half will “travel” to another group. These keepers of the prices must discuss their prices with each of the other two visiting 1/2 groups (travelers). Based upon improved understanding and additional information obtained from discussion with the travelers, they must attempt to reconcile any differences in estimated attribute pricing. It is also the responsibility of the travelers to improve their understanding of the attribute prices based upon discussion with the list keepers. 2.2.4. After another 20 min, the “travelers” visit the final small group and continue to reconcile any differences in the estimated attribute prices. This “split and-rotate” is repeated until the traveles have visited each group. 2.2.5. The entire team must reconvene as a single group to finish the set of implicit attribute prices. This is necessary because a single set of prices is required to calculate

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