A Framework for Quantitative Assessment of ImpactsRelated to Energy and Mineral Resource Development

Seth S. Haines,1,19 Jay E. Diffendorfer,2 Laurie Balistrieri,3 Byron Berger,4 Troy Cook,1,17

Don DeAngelis,5 Holly Doremus,6 Donald L. Gautier,7 Tanya Gallegos,1 Margot Gerritsen,8

Elisabeth Graffy,9 Sarah Hawkins,1 Kathleen M. Johnson,10 Jordan Macknick,11

Peter McMahon,12 Tim Modde,13 Brenda Pierce,10 John H. Schuenemeyer,14

Darius Semmens,2 Benjamin Simon,15 Jason Taylor,16,18 and Katie Walton-Day12

Received 22 February 2013; accepted 20 April 2013Published online: 15 May 2013

Natural resource planning at all scales demands methods for assessing the impacts of resourcedevelopment and use, and in particular it requires standardized methods that yield robust andunbiased results. Building from existing probabilistic methods for assessing the volumes of energyand mineral resources, we provide an algorithm for consistent, reproducible, quantitativeassessment of resource development impacts. The approach combines probabilistic input datawith Monte Carlo statistical methods to determine probabilistic outputs that convey the uncer-tainties inherent in the data. For example, one can utilize our algorithm to combine data from anatural gas resource assessment with maps of sage grouse leks and pinon-juniper woodlands in thesame area to estimate possible future habitat impacts due to possible future gas development. Asanother example: one could combine geochemical data and maps of lynx habitat with data from amineral deposit assessment in the same area to determinepossible future mining impacts on waterresources and lynx habitat. The approach can be applied to a broad range of positive and negativeresource development impacts, such as water quantity or quality, economic benefits, or airquality, limited only by the availability of necessary input data and quantified relationships amonggeologic resources, development alternatives, and impacts. The framework enables quantitativeevaluation of the trade-offs inherent in resource management decision-making, includingcumulative impacts, to address societal concerns and policy aspects of resource development.

KEY WORDS: Energy resources, mineral resources, impact assessment, integrated assessment,environmental.

1Central Energy Resources Science Center, U.S. Geological

Survey, Denver CO 80225, USA.2Geosciences and Environmental Change Science Center, U.S.

Geological Survey, Denver CO 80225, USA.3U.S. Geological Survey, Seattle WA 98195, USA.4Crustal Geophysics and Geochemistry Science Center, U.S.

Geological Survey, Denver CO 80225, USA.5Department of Biology, University of Miami, Miami FL, USA.6Berkeley Law, University of California, Berkeley CA, USA.7U.S. Geological Survey, 345 Middlefield Road, Menlo Park CA,

USA.8Department of Energy Resources Engineering, Stanford Uni-

versity, Stanford CA, USA.9Consortium for Science, Policy, and Outcomes, Arizona State

University, Tempe AZ, USA.10U.S. Geological Survey, 12201 Sunrise Valley Drive, Reston VA

20192, USA.

11National Renewable Energy Lab, Golden CO, USA.12Colorado Water Science Center, U.S. Geological Survey,

Denver CO 80225, USA.13U.S. Fish and Wildlife Service, Denver CO, USA.14Southwest Statistical Consulting, LLC, Cortez CO, USA.15Office of Policy Analysis, U.S. Department of the Interior,

Washington DC, USA.16National Operations Center, Bureau of Land Management,

Denver CO, USA.17Present address: Energy Information Administration, U.S.

Department of Energy, Washington DC, USA.18Present address: Cape Cod National Seashore, National Park

Service, Wellfleet MA, USA.19To whom correspondence should be addressed; e-mail:

shaines@usgs.gov

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1520-7439/14/0300-0003/0 � 2013

Natural Resources Research, Vol. 23, No. 1, March 2014 (� 2013)

DOI: 10.1007/s11053-013-9208-6

INTRODUCTION

Many critical questions facing society involvenatural resource availability, development, and use.Availability assessments of energy and mineralresources (e.g., Charpentier and Cook 2010; Singerand Menzie 2010) help decision makers, the privatesector, and the general public to make informeddecisions regarding these resources, but traditionallythese studies do not consider external costs and ben-efits (e.g., positive or negative impacts on ecologic,hydrologic, and socioeconomic systems) of resourcedevelopment and use. Similarly, assessments of waterand ecological/biological systems generally lackquantitative linkage to energy and mineral resourcedevelopment. Single-resource assessments are tre-mendously important, but development decisionsincreasingly require additional information to sup-port analysis of the trade-offs among diverse naturalresources. This information includes direct effects ofresource development (e.g., habitat lost or jobs cre-ated), resource co-dependencies (e.g., water requiredfor mining), and resource availability conflicts (e.g.,subsurface coal deposits made inaccessible by co-lo-cated gas well pads or protected areas). This type ofassessment fits into the broad category of study that isoften described as ‘‘integrated assessments.’’ In thismanuscript, we focus specifically on linking energy(mainly petroleum) and mineral (in particular, non-fuel minerals) resources with the potential impacts oftheir development.

A number of efforts have been made to linkassessments of related resources; they include vary-ing degrees of quantitative integration across thedisciplines and employ a variety of integrationmethods. Mulder and Hagens (2008) considerreturns on investment for energy technologies thatinclude impacts on natural and human systems,while Snyder and Kaiser (2009) and Thorhallsdottir(2007) compare costs and benefits of particulardevelopment strategies. Other approaches focus onthe environmental (Copeland et al. 2009; Okey andKuzemchak 2009), human-health (NationalResearch Council 2009), or water consumption(Nicot and Scalon 2012) implications of energydevelopment and use. Studies can produce site-spe-cific, geographically detailed information (Noble2008; Fargione et al. 2012), or they can producenational-scale information (International AtomicEnergy Agency 2005). None of these studies, how-ever, establish a broadly applicable methodology

that is based on robust assessments of the specificenergy or mineral resource being considered andquantitative assessments of the impacts of interest.

To address this need, we (the authors) met in aworkshop setting for three meetings of approxi-mately 3 days each, spread over 16 months. Inaddition, we held weekly telephone calls and webmeetings during the final 8 months of the effort. Ourassembled group includes diverse technical expertise(including geology, geophysics, geochemistry,hydrogeology, ecology, social science, economics,law, statistics, and more), employment (government,academia, and private industry), and experience.Due to its composition, the group initially struggledwith communication and managing diverse expec-tations about scope and objectives, but we foundcommon language and vision and were able toaccomplish what we set out to do. This manuscriptpresents the central findings of our work.

We propose a framework for quantitativeassessment of natural resource development impacts(positive and negative), which is based on the link-age of established energy and mineral resourceassessment methods with data and models thatdescribe the biologic, water, social/economic, andother effects of resource development. We linkenergy and mineral resources that are in the groundwith the potential surface impacts of their develop-ment and provide a means for comparing differentdevelopment options. The framework is intended toprovide consistent, reproducible, unbiased results toaddress a broad spectrum of resource managementquestions at a range of spatial scales (with primaryinitial focus on geologic-basin-scale assessment ofgeologically occurring resources). The frameworkcan accommodate any desired level of linkage acrossdisciplines, including both economic and non-eco-nomic valuation, while maintaining the standardi-zation of methods and results that is essential forwidespread utility. The objective is to enable better-informed resource management decision-making byquantifying the inherent trade-offs.

In this article, we provide background infor-mation on resource assessments in general, and onexisting methods for quantitative assessment ofenergy and mineral resources. We then describe theimpact assessment framework and aspects of itsimplementation. Finally, we provide two conceptualexamples, one involving the impacts of energyresource development and one involving the impactsof mineral resource development.

4 Haines et al.

BUILDING BLOCKS FOR ASSESSINGRESOURCE DEVELOPMENT IMPACTS

Resource development impact studies beginwith a question/problem, or a set of questions/problems, that motivates the work and definesbounds for the study. This motivation could be abroad resource availability question or a specificlocation-based concern, and the information may bedesired by decision makers in the public or privatesectors, and/or by other individuals. For example,federal policy analysts, technology companies, andenvironmental organizations might wish to contrastthe environmental cost of the United States filling itsneed for copper in the coming decades with thetechnological and economic benefits of this copperentering the marketplace. As another example,recreationalists, petroleum companies, and state andfederal land managers might wish to better under-stand the potential impact on sage grouse and localjob creation in western Colorado if the untappednatural gas is produced in that area.

Resource development impact studies alsobegin with the multi-disciplinary data required toanswer these questions, potentially including geo-logical information relevant to energy or mineralresource occurrence, biological information regard-ing vegetation types and species habitat, and waterinformation regarding the quantity and quality ofsurface and groundwater. Much of this informationmay be available as the outputs of separate disci-plinary resource assessments such as quantitativewater, biological, energy or mineral resourceassessments. Nearly all data, measurements, andassessments are estimates; the inherent uncertaintycan be, but is not always, quantified. A robust ap-proach for describing the uncertainty is the use ofprobability density functions (PDFs) to describe thepossible values for a given measurement or estimate,rather than incorrectly assuming a single value; thisapproach is scientifically sound and it is effective forcommunicating uncertainty.

Schmoker (2005) and Charpentier and Cook(2010) describe methods to estimate the quantity ofundiscovered ‘‘continuous’’ petroleum resourcesthat can be recovered with existing technology.Continuous resources (e.g., petroleum shale systemsand coalbed gas) are aerially large accumulations ofoil and/or gas, generally within low-permeabilityrock; in a separate category are conventionalresources, which are discrete accumulations ofpetroleum held in place by geologic traps and the

buoyancy of petroleum relative to water (Schmoker2005). The methods are peer-reviewed (AAPGCommittee on Resource Evaluation 1999) andwidely applied. Assessment of continuous petroleumresources begins with the definition of assessmentunits (AUs), which are the smallest explicit spatialsubdivisions of a geologic formation that can bemade based on available geologic and geophysicalinformation. For each AU several variables aredefined (as PDFs), including estimates of thedrainage area of each well (termed the ‘‘cell size’’),the number of untested cells that contain petroleum,and the estimated ultimate recovery of petroleumper cell (Fig. 1a). These PDFs are estimated basedon geologic, geophysical, geochemical, and petro-leum production data from the local area, or fromanalogous areas. Any existing wells within the AUrepresent cells that are ‘‘tested;’’ the assessmentestimates the quantity of undiscovered petroleum inthe untested cells. The input data are combined in aMonte Carlo simulation (Crovelli 2005) that deter-mines the total undiscovered technically recoverablepetroleum in the AU, as a PDF (Fig. 1a). Theresource quantity estimate informs a suite ofregional, national, and global planning questionsincluding possible economic analysis. The resourcequantity estimate, along with the input data, is alsosuitable for incorporation with diverse datasets asdescribed below.

Singer and Menzie (2010) and Singer (1993)describe an assessment method (Fig. 1b) for mineralresources. The method is based on the determina-tion of three assessment parts: (1) maps of permis-sive tracts (PTs) wherein the resource has aprobability of existing that is greater than 1:100,000,(2) grade-and-tonnage models that describe mineralcontent and deposit size, and (3) the estimatednumber of undiscovered deposits in the PT. PTs aredelineated based on geologic and geophysical datausing descriptive deposit models, and can be seen assomewhat analogous to the AUs of petroleumassessments. Grade-and-tonnage models consist ofcompiled data for well-studied deposits of a givendeposit type. The estimated number of undiscovereddeposits is combined with the grade-and-tonnagemodels in a Monte Carlo approach (Root et al.1992), yielding PDF estimates of the quantity ofeach assessed resource in the PT (Fig. 1b). Theseestimates are useful on their own, and for economicand other analyses to support decision making (e.g.,Spanski 1992; Gunther 1992). Furthermore, theinputs to the Monte Carlo approach and the wealth

5Framework for Quantitative Assessment

of geologic information that is assembled for theassessment are suitable for integration with otherdiverse datasets as described below.

Meaningful synthesis of varied but geographi-cally related data requires comparative approachesand metrics that are suitable to the question and

(a)

(b)

Figure 1. Fundamental elements of USGS assessment methodologies for (a) continuous petroleum resources and (b) mineral resources.

6 Haines et al.

resources considered. The field of economics pro-vides a suite of tools, but most environmental goodsand services (in contrast to energy and mineralresources) are not bought and sold in markets andthus have no established monetary values. Envi-ronmental and resource economists (e.g., Unsworthand Bishop 1994; Champ et al. 2003; Farber et al.2006) have developed an array of quantitative andqualitative techniques to estimate the benefits forwhat are characterized as ‘‘non market’’ environ-mental goods and services such as the benefits pro-vided by the existence of a particular outdoorrecreation area or the water filtration and storageprovided by a wetland. For cases where benefits canbe monetized, cost-benefit analysis provides an ap-proach for comparing alternate scenarios, whilehabitat equivalency analysis (e.g., Hufschmidt et al.1983; Dunford et al. 2004) and resource equivalencyanalysis (Zafonte and Hampton 2007) provide ap-proaches for non-monetary linkage between re-sources and habitat.

ASSESSMENT TERMINOLOGY

A broad continuum exists within the realm ofresource development impact assessments, extend-ing from tightly focused assessments of a singleresource to complex assessments of a suite ofresources and their interrelations, potentiallyincluding a large degree of stakeholder involvement.Our group defined three categories, or levels, ofassessment for our own use, and to provide structureand clarity for analysis and communication withstakeholders in future assessments (Table 1). Westress that these levels are not a series to be followedin order, nor are they mutually exclusive or tied to

spatial scale. The choice of which level of assessmentis appropriate in any given situation would be basedon policy and societal requirements for the studyand the availability of relevant data and funding.

A Level 1 assessment (Table 1) is the assemblyof data from different disciplines as appropriate tothe question being addressed, potentially includingrelevant and available mineral and energy assess-ment results and associated mapping and otherdatasets, as well as water quantity and quality data,habitat mapping and other ecological/biological data,and any desired human/social data such as populationinformation. The assembled data could include cal-culated indices that describe ecological health orvulnerability (e.g., habitat suitability maps) alongwith basic spatial analysis to identify areas of geo-graphic overlap between resources of interest, but noquantitative integration or modeling of developmentimpacts. At minimum, a Level 1 assessment providesa starting point for a more detailed study. In somecases, it will provide all of the desired information,such as a study of resource availability and habitatoverlap. Level 1 studies can take many forms,including the studies described by Okey andKuzemchak (2009), Randall et al. (2010), Latysh andBristol (2011), and Allen and Kauffman (2012).

A Level 2 assessment (Table 1) represents aconsistent and repeatable integration of data acrossdisciplines, employing standardized approaches(many of these approaches may not currently exist,at least not in a standardized form). These analysesare intended to provide quantitative estimates ofpotential development impacts (e.g., habitat loss orjob creation) and interdependencies betweenresources (e.g., water required for petroleumdevelopment). Level 2 assessments could be con-ducted in response to a wide range of questions,including national-scale resource availability studies

Table 1. Assessment Nomenclature

Level 1 Level 2 Level 3

Compiled available data Yes Yes Yes

Additional data acquisition No Possibly Yes

Stakeholder involvement None, or only during

project inception

and results

dissemination

Generally just project

inception and results

dissemination

Throughout project:

inception, execution,

results dissemination

Quantitative cross-disciplinary

integration

None Standardized methods Customized methods

for the case in

question

7Framework for Quantitative Assessment

and local-scale resource-conflict evaluation. Themain distinction from Level 1 assessments is thatLevel 2 assessments employ quantitative approachesto link processes across varied disciplines (e.g.,cause-and-effect relationships, etc.). The key char-acteristic of Level 2 assessments is that theseapproaches are standardized and robust. Due to thischaracteristic, stakeholder involvement is generallyonly necessary for the conception of projects and thepresentation of results. The primary objective of thismanuscript is to introduce a framework for con-ducting Level 2 assessments in a wide range ofapplications.

A Level 3 assessment (Table 1) is a fully cus-tomized study involving new data collection andanalyses, and often the development of analysismethods specific to the question being addressed.Stakeholder involvement is as extensive as war-ranted or desired, from project conception throughdata analysis and results dissemination. Theseassessments can include highly detailed processmodels that describe interactions and feedbacksbetween system components and drivers of change.Level 3 assessments could also include full life-cycleanalysis of resources or materials to more fullyquantify the impacts due to extraction, use, anddisposal or reuse. Due to its complexity, a Level 3assessment is undertaken to meet a specific need orin response to a request for highly specializedinformation. Many existing resource impact assess-ment studies (e.g., Mihalasky et al. 2006;Thorhallsdottir 2007; National Research Council2009) fit in the Level 3 category because they rep-resent highly detailed one-time studies using site- orstudy-specific evaluation methods.

QUANTIFYING RESOURCEDEVELOPMENT IMPACTS

A key component of Level 2 and 3 assessmentsis the quantitative linking of resource developmentwith impacts, and a key aspect of Level 2 studies isthat quantification methods be standardized. Toaddress this need, we present an approach forquantifying the positive and negative impacts ofenergy and mineral resource development based onestablished assessment approaches for the energy ormineral resource in question, building from ideaspresented by Hawkins et al. (2012). The algorithm(Fig. 2) combines input PDFs with the Monte Carlomethod, in which we randomly simulate a large

number (e.g., 10,000) of possible future scenarios todetermine PDFs that describe the range of possibleimpacts. Within each simulation there are two steps,detailed in the following paragraphs: (1) assigningthe quantity and geographic location of energy ormineral resource based on information in therespective resource assessment, and (2) quantifyingthe possible impacts of developing the simulatedenergy or mineral resource based on input impact-related data.

We simulate the quantity of energy or mineralresource in each Monte Carlo iteration baseddirectly on the information in the associatedenergy/mineral assessment. For continuous petro-leum, we use the PDFs describing the cell size andthe percentage of the untested AU area that haspotential to add to reserves to calculate a PDF thatidentifies the number of untested cells in the AUthat contain petroleum. For minerals, we use PDFsof the number of undiscovered deposits in a PT andof the grade and tonnage of those deposits tosimulate the number and size of undiscovereddeposits in the PT. We can filter these mineraldeposits with an economic algorithm (e.g., Robin-son and Menzie 2012) if desired to eliminate thosethat are uneconomic. We next simulate the specificlocations of the petroleum-containing cells or themineral deposits, in most cases by randomly dis-tributing the resource across the AU or PT. Insome cases, additional information may be avail-able, for example geologic mapping that constrainsthe likely location of minable mineral depositswithin the PT, or the assumed likelihood that infilldrilling of established oil/gas fields will bring AUwell density to what is predicted by the cell size,but it must be borne in mind that any such con-straints are uncertain. Regardless, the result is amap illustrating one possible scenario of thequantity and location of the energy/mineralresource. Any particular map from a given iterationis surely incorrect, but a sufficiently large numberof iterations will produce a large number of mapsthat cumulatively represent the range of possibili-ties indicated by the input data.

We assign surface infrastructure to each energyor mineral location as necessary to evaluate theparticular impact in question. Each petroleum-con-taining cell can be seen as representing one surfaceextraction point that requires a well pad and associ-ated infrastructure, or we can incorporate assump-tions regarding multiple wells per well pad. The exactlocation of the infrastructure within the cell can be

8 Haines et al.

important for some assessments, and for betteraccuracy the simulation can take into account real-istic constraints on well pad and road siting, such astopography and proximity to the existing road net-work. We can estimate the mine footprint for eachundiscovered mineral deposit based on the depositsize and on data for the specific deposit type. Thefootprint simulation can incorporate the existing roadnetwork via a spatially explicit system identifying themost efficient means of extending a road to the siteand its associated disturbance footprint, includingany desired logistical or environmental restrictions.The complexity of the incorporated developmentinfrastructure model (well or mine layout, design,and operations) can be tailored to the particularimpact being assessed. For example, quantifyinghabitat loss or increased sediment load entering astream requires assumptions regarding the exact wellor mine location along with road construction andother engineering choices. On the other hand,quantifying water used for gas well drilling or jobscreated to support copper mine operations does notrequire such specific information.

We calculate the impacts of interest for eachMonte Carlo iteration using the simulated locationsof wells or mines, the actual locations of existingwater, biological, or other resources (as relevant),and the quantified relationships that predict theimpacts due to the energy or mineral resourcedevelopment (Fig. 2). The data required will dependon the impacts being assessed. Examples of possibleinput data include per-well or per-mine PDFs of:water use or production (e.g., gallons of water perwell/mine), surface disturbance (e.g., acres of landper well/mine), job creation (e.g., number of jobs perwell/mine) and air quality disturbance (e.g., pollu-tant release per well/mine). These PDFs would beestimated based on data from local or analogousareas and those for mines will be dependent ondeposit size. Other types of development impactrelationships could also be included, such as thequantified relation between well pad presence andbehavior (mortality or avoidance) of a threatenedspecies (e.g., Walker et al. 2007). The input data willalso include whatever fundamental data are requiredto make use of the development impact relationships,

Figure 2. Fundamental elements of the proposed development impact quantification tool include the input PDFs and maps, the Monte

Carlo process, and the output distributions. Maps showing spatial aspects of the impacts may also be available, depending on the assessment

objectives and input data.

9Framework for Quantitative Assessment

such as habitat maps (for assessing possible habitatimpacts), water quantity/quality data (for assessingpossible water impacts), and human populationestimates (for assessing human-related impacts).Impacts are calculated for each extractive resourcedevelopment location (well or mine), and cumula-tive impacts are calculated across the study area; thisresult provides one possible scenario for impactsassociated with the extractive resource(s) of interest.By repeating this process through a sufficiently largenumber of iterations, the scenarios will span thelikely possibilities, and PDFs developed from thecombined output of all the iterations will provide aquantified, probabilistic estimate of potentialimpacts. Output data can also include maps showingthe probability of impacts within the AU or PT;these maps will primarily be useful in cases wherethe simulation of the resource locations and/or theinfrastructure is based on reasonable constraints andrealistic modeling.

As described, the Monte Carlo impact assess-ment algorithm is centered on a particular AU orPT. For studies that involve a larger number of AUsor PTs, we can calculate total impacts by assessingall of the AUs or PTs simultaneously; these cumu-lative impacts can be correctly determined onlywithin a single Monte Carlo framework. Whenappropriate, the algorithm can provide maps repre-senting the summation or compilation of the indi-vidual simulations produced in each Monte Carloiteration, with specific details dependent on thequestion being addressed. These maps will be mostrelevant in studies involving cumulative impacts forseveral AUs/PTs, or for cases where trusted spatialconstraint information is available to guide thesimulation of where the resource falls within theAU/PT or of where the development infrastructureexists within a petroleum-containing cell. For anydesired application of this assessment algorithm,consideration must be given to the availability of thenecessary input data, and/or to the acquisition of thedata needed to provide outputs with acceptableuncertainty. Many pressing questions may requireconsiderable augmentation of existing datasets.

CONCEPTUAL EXAMPLES OFQUANTIFYING DEVELOPMENT IMPACT

We illustrate the proposed assessment frame-work with two conceptual examples for which the

necessary data and quantified development impactrelationships are available. For each example theassembled input data constitute a Level 1 study inour three-level system. The studies themselveswould fit into Level 2 because the analyses areenvisioned as being standardized, and some of themore complex variations described later wouldconstitute Level 3 studies due to the need for highlycomplex data and analyses.

Piceance Basin Natural Gas, Pinon-Juniper Forest,and Sage Grouse Leks

The Uinta-Piceance Basin is a depositionalsedimentary basin in western Colorado and easternUtah (Fig. 3) assessed by the USGS for undiscov-ered oil and gas resources (Johnson and Roberts2003). We focus on the Piceance Basin ContinuousAU, which was estimated to contain a quantity ofgas described by a PDF that indicates a 95% chanceof at least 1.90 trillion cubic feet (TCF), a 50%chance of at least 2.96 TCF and a 5% chance of atleast 4.59 TCF. The AU consists mainly of theMesaverde Group, a geologic unit that is composedpredominantly of shale. Production of petroleumfrom such formations represents an increasinglylarge fraction of the US energy profile, and it gen-erally involves the drilling of a considerable numberof wells (thousands) spread over a large region. Theproposed Monte Carlo impact quantificationapproach (Fig. 4) requires the cell size and thepercent of the area of the AU that is expected tocontain technically recoverable resource, both pro-vided by Johnson and Roberts (2003).

The Uinta-Piceance Basin also includes pinon-juniper (PJ) woodlands (Fig. 4) that provide habitatfor a variety of wildlife including 24 bird species, 5insect species, 9 mammal species, and 6 reptile speciesof conservation concern in the state of Colorado(Colorado Division of Wildlife 2006). If we wish toquantify the area of PJ woodland potentially affectedby petroleum development, the inputs necessary forthe Monte Carlo approach include a map of the arealextent of PJ woodland and a quantified relationshipbetween gas development and habitat health. A morecomprehensive study could consider all infrastructureand transportation requirements, but for the purposesof this conceptual example, we simply assume thatany well pad that is placed in an area of PJ woodlandwill destroy the overlapping area of habitat. This

10 Haines et al.

provides a quantitative relationship between habitatacreage and gas development.

In addition, the Uinta-Piceance Basin is alsohome to greater sage grouse, currently a candidate(‘‘warranted but precluded’’) for listing under theEndangered Species Act (Fish and Wildlife Service2010). Sage grouse leks are established struttinggrounds where males congregate and mating occurs(Fig. 4); they, and adjacent nesting areas, are keycomponents of sage grouse habitat. Sage grouse leks

range in size from 0.4 to 40 ha (Fish and WildlifeService 2010) and are typically found in areas ofopen sagebrush in predominantly flat topography(Connelly et al. 2004). To estimate (via the MonteCarlo approach) the number of sage grouse leks thatcould potentially be lost due to petroleum develop-ment, necessary input data include mapped leklocations and the quantified relationship betweenpetroleum development and lek abandonment.Details of the petroleum development impact on

Figure 3. Study areas for the two conceptual assessment examples: the Uinta-Piceance Basin including the Piceance Continuous Gas

Assessment Unit (AU), and the San Juan volcanic zone (broadly, the mountains of southwest Colorado) and minerals permissive tract

CR26.

Figure 4. Inputs and outputs for the Uinta-Piceance Basin conceptual example. The assessment incorporates relevant inputs from existing

USGS assessment of continuous gas along with maps of the ‘‘tested’’ cell locations, the pinion-juniper (PJ) woodlands, and sage grouse leks

to determine the possible acreage of lost PJ woodland and the possible number of lost sage grouse leks corresponding with possible gas

development.

11Framework for Quantitative Assessment

sage grouse leks are the subject of on-going research(e.g., Lyon 2000; Holloran 2005; Walker et al. 2007);for the purpose of this example, we assume lekswithin 3.2 km of a well location will be abandoned,following Walker et al. (2007). Similar to PJ wood-land, a more comprehensive study could include roadsand other infrastructure, the impacts of which wouldbe calculated in much the same way as well pads.

The proposed Monte Carlo algorithm providesa robust approach to combine the input PDFs. EachMonte Carlo iteration (Fig. 4) begins with thedrawing of one value from the PDF of the number ofcells that contain petroleum resource, followed bythe distribution of the resource across the AU atrandom or using relevant (but inherently specula-tive) constraining information from the petroleumassessment report (Johnson and Roberts 2003). Thealgorithm then assigns well pads to each petroleum-containing cell according to whatever constraints areprovided for this step. The simplest approach is todo this randomly, but more accurate approacheswould follow local or standard development prac-tices to constrain the locations of the pads, such asavoiding steeply sloping terrain and restricted areas.We would then calculate the area of PJ woodlandand the number of sage grouse leks impacted by thesimulated development infrastructure and sum thesequantities across the AU or other areas of interest todetermine one possible scenario for PJ woodlandand habitat loss. By running tens of thousands ofMonte Carlo iterations we would build distributionsthat quantify the possible PJ woodland and lek lossassociated with petroleum resource development,including assessment of any cumulative effects suchas habitat fragmentation. The final output of theimpact assessment would be two PDFs: one showingthe area of PJ woodland lost and one showing thenumber of sage grouse leks impacted by petroleumextraction. A possible secondary product would bemaps showing probabilities of particular woodlandand lek areas being impacted, based on compilationof the individual simulation maps. Such maps wouldbe of value primarily if the assignment of petroleumresource across the AU can be constrained by validauxiliary information and/or if the siting of well padsand infrastructure within each petroleum-bearingcell is guided by realistic modeling.

The variations and extensions to this impactassessment are limited only by data availability. Forexample, we could quantify job creation or otherlocal or national economic benefits of gas develop-ment (e.g., Gunther 1992). We could quantify water

requirements for hydraulic fracturing associatedwith gas production, or (with appropriate data)possible water quality impacts. To the extent that itcan be quantified, we could assess the nationalsecurity benefits associated with producing PiceanceBasin gas relative to importing gas. Alternatively,we could compare development alternatives byquantifying the petroleum unavailable for produc-tion under a conservation strategy that protectsareas for PJ woodland or sage grouse leks. In thiscase, the simulation would include restrictions onthe placement of wells in critical habitat or otherprotected areas and we would calculate output dis-tributions that describe how much petroleum wouldbe available for extraction, and how much would beunavailable for extraction, if particular restrictionswere in place. Finally, the study could be carriedthrough to monetary valuation and a cost-benefitanalysis could be used to evaluate trade-offs betweenhabitat conservation and petroleum production.To the extent that these more complex analysesrequire customized, site-specific approaches, newdata acquisition, and extensive stakeholderinvolvement, such work would constitute Level 3studies.

Minerals, Lynx, and Water Quality in SouthwesternColorado

The San Juan volcanic field in southwesternColorado has long been an area of mining activitydue to its rich mineral endowment (Fig. 3). TheUSGS completed assessments for several types ofmineral deposits in this area in 1996. We focus onthe epithermal vein deposits of the quartz-adulariatype, identified as PT CR26 in southwestern Colo-rado (US Geological Survey National MineralResource Assessment Team 2002; Fig. 5). Thesedeposits contain gold, copper, silver, and othermetals. The US Geological Survey National MineralResource Assessment Team (2002) estimated thatCR26 has a 90% chance of containing one or moreundiscovered ore deposits, a 50% chance of con-taining three or more, a 10% chance of containingfive or more, a 5% chance of containing seven ormore, and a 1% chance of containing ten or moredeposits.

Reintroduction of lynx in Colorado between1999 and 2006 has led to a modest lynx popula-tion with a range that partially coincides with theareal distribution of CR26 (Devineau et al. 2010;

12 Haines et al.

Theobald and Shenk 2011). Quantifying potentiallynx habitat loss associated with development ofundiscovered mineral deposits requires a map oflynx habitat (Fig. 5) and a quantitative relationshipbetween mining development and habitat impact.For this example, we assume that any lynx habitatwithin the surface footprint of a mine is lost.

Water chemistry information is foundational tomany water- and species-related concerns in miningareas. Using statistics drawn from compilations ofmine-related water data such as those provided byPlumlee et al. (1999), we can generate PDFs thatlink applicable water quality metrics with deposittype, following Walton-Day et al. (2012). For ourassessment of CR26, we would create a PDFdescribing the pH of water associated with epither-mal vein deposits of quartz-adularia type and usethis PDF to predict water pH associated with eachdeposit simulated in each Monte Carlo iteration(indicative of groundwater within the deposit areaand water produced during mining). This informa-tion could be used by engineers planning mineoperations, or regionally by land managers studyingthe possible wildlife impacts in the event that mine-related water enters the ecosystem.

Each Monte Carlo iteration (Fig. 5) begins withthe drawing of a value from the PDF for the numberof undiscovered mineral deposits in the PT, along

with values from the appropriate grade and tonnagePDFs to determine the metal content and sizes ofthe undiscovered deposits. These deposits could befiltered with an economic algorithm (e.g., Robinsonand Menzie 2012) if desired. The algorithm wouldthen distribute these deposits across the PT to sim-ulate their spatial locations, either randomly or usingany relevant geologic constraints. For each of thesesimulated deposits, we would calculate the area ofdisturbed lynx habitat and we would draw a valuefrom the PDF of water pH, incorporating any localgeologic information that might constrain the pHrange. Repeating this process for tens of thousandsof Monte Carlo iterations would span the range ofpossibilities, and provide PDFs of the final outputs:area of lynx habitat potentially lost, and likely pHfor mining waters. Relevant location-specific infor-mation would be available if spatial aspects of theindividual Monte Carlo simulations are constrainedby geologic and/or infrastructure-related (e.g.,topographic constraints on road placement) infor-mation. Depending on specific study goals, the pHresults might be presented as a likelihood that minewater pH in any given location would be lower than6.5, which is an important threshold for aquatic life(US Environmental Protection Agency 1976) andthus a valuable piece of information for mine plan-ning.

Figure 5. Inputs and outputs for the San Juan minerals conceptual example. The assessment incorporates relevant inputs for existing

USGS assessment of minerals in tract CR26, along with maps of Canada lynx habitat and the locations of known quartz-adularia epithermal

vein deposits, as well as the probability distribution function for water pH associated with this deposit type, to determine ranges of possible

lost lynx habitat and of possible mine-related water pH.

13Framework for Quantitative Assessment

As with the Piceance Basin example, possibleextensions and modifications of this study are lim-ited only by available data. For example, benefits ofcopper availability for manufacturers and to thenational or global economy could be quantified. Asanother example, the assessment of water pH couldbe expanded to quantify potential impacts onaquatic life, or other impacts of interest, though thiswould require assumptions regarding the mineengineering and remediation. Such quantificationwould also likely requires estimates of the volume ofwater resulting from, or interacting with, miningoperations; these estimates would contain consider-able uncertainty due to the range of conditions thatcould exist at any given site. The assessment couldalso be extended to include cost-benefit analysis tocompare various development or protectionschemes, provided that the value of relevant eco-systems services can be adequately quantified.Inclusion of these more complicated aspects couldnecessitate new data collection and customized, site-specific analysis approaches and as such would leadto Level 3 classification.

DISCUSSION AND CONCLUSIONS

Our proposed assessment framework is intendedto provide a cohesive scientific foundation on which tobase resource management decisions for a broadrange of users in a wide range of contexts. Theframework is unique in that it provides a systematic,quantitative link between assessments of undiscov-ered energy and mineral resources and models for thepossible impacts of development. The frameworkrepresents a robust and reproducible approach forcombining the extensive datasets and studies thatexist in varied disciplines (e.g., geology, ecology,hydrology, and the social sciences). In addition, theframework provides probabilistic, quantitative esti-mates of the interrelations between these variedcomponents of the natural world based on establishedmethods for quantitative assessment of energy andmineral resources. Finally, it is applicable to a broadspectrum of questions regardless of scale, scope, orcomplexity, including the quantitative comparison ofvarious alternative development strategies.

The examples described here illustrate tworelatively straightforward applications of theassessment framework. The complexity of possibleapplications is limited only by the availability ofnecessary data and quantified relationships between

development and impacts. Inclusion of social values(e.g., disrupted viewsheds, lost recreational oppor-tunities, lifestyle improvement due to local eco-nomic growth) can readily be accomplished givenadequate data (e.g., Sherrouse et al. 2011; Bagstadet al. 2012). Inclusion of economic components andextension to full economic valuation are possible forany application, given adequate data and suitablevaluation schemes; methods and data related toecosystems services provide a means for mappingand valuing natural processes that benefit humans.Further-reaching effects of resource developmentand use (e.g., National Research Council 2009;Graedel and van der Voet 2010) can be readilyincluded, and relevant information is increasinglybecoming available in the form of life-cycle analysisfor various resources and commercial products.Given sufficient input data, our approach canincorporate full life-cycle analysis of any desiredresource. Contributions to climate change can bequantified much like any other impact (e.g., CO2

created from combustion of petroleum resources),and development impacts (e.g., habitat loss, waterconsumption) can be calculated relative to projectedfuture global conditions if desired. Similarly, otherdynamic aspects can be included as well (e.g., habi-tat change due to expansion of urban areas).

A more complicated extension of the approach isthe inclusion of low-probability, high-consequenceevents such as a mine tailings dam failure or a petro-leum well blowout. These can be quantified with ourapproach, but they present additional challenges.Quantifying the likelihood and the impact of such anevent is difficult due to the inherent dependence onspecific site conditions and engineering choices and itwould be critical that all assumptions be acceptable toa diverse group of stakeholders. Such studies willlikely require Level-3-type analysis (customized tothe specific question), at least until standardizedapproaches can be determined.

Another challenging extension of the approachis the inclusion of impacts that are linked to the rateof energy or mineral resource development. Forexample, the simultaneous mining of a number ofmineral deposits or drilling of a number of petro-leum wells in a particular area might result inunrecoverable habitat loss, whereas sequentialdevelopment of the same resources over a longerperiod could allow for progressive disturbance, res-toration, and recovery at each mine or well site andthus significantly reduce long-term impact. Quanti-fying this relationship would require time-dependent

14 Haines et al.

input data, but the end result would potentially behighly valuable to land managers as pre-develop-ment modeling and planning could lead to an opti-mized tradeoff between habitat preservation andenergy or mineral resource availability. Includingeconomic components in such work is possible andpotentially quite valuable (changing commodityprices play a key role in resource development, anddifferent resource production rates can impactdevelopment economics); this would require time-dependent economic valuation.

The presented conceptual examples representtwo cases where the necessary data and quantifiedimpact relationships exist, and thus where imple-mentation of this assessment framework can beachieved in the near future. Additional examplesexist, but a key challenge to widespread use of thisapproach will be the determination of standardizeddata types and development impact relationships forproblems of interest. A critical feature of ourapproach is the handling of data uncertainty, but toaccomplish this the uncertainty in the input datamust be accurately characterized. Broad inputuncertainty will simply result in broad outputuncertainty. We envision that the widespreadimplementation of this algorithm will begin with thestudies that can be conducted immediately and forwhich the output uncertainty is acceptable to theproblem being considered. In cases where finaluncertainty is greater than what is acceptable forstakeholders and/or for policy requirements, newdata acquisition or analysis will be required to min-imize uncertainty in the particular aspects of inputdata that require improvement. For undiscoveredenergy or mineral deposits the data are most oftenacquired by private industry, whereas for water andecological aspects the data may be acquired by pri-vate, governmental, or academic researchers.

The described assessment framework (includingthe Monte Carlo approach and the three-level ter-minology) is intended to provide results that arehelpful to a range of users, and that can be readilycommunicated to these users. The statistical nuancesof the methodology do not always need to be com-municated, but the significance of the output PDFsmust be communicated to users of the data. A com-mon way to convey the uncertainty represented by thePDFs is to present just a few key values such as thequantities that correspond with 5, 50, and 95% like-lihood. Another key aspect of the approach that mustbe communicated clearly is the set of assumptions thatunderlie the assessment methodology; all incorpo-

rated data, weighting schemes, and quantified impactrelationships must be clearly stated in order for thework to maintain the necessary transparency.

No one can predict the future, but our frameworkcan probabilistically quantify the possible future im-pacts (positive and negative) of energy and mineraldevelopment based on the quantity of energy/mineralresource thought to be present in a given area. Thoughit will not answer all possible questions, the providedinformation is intended to be accurate, standardized,and objective, such that it can be used and under-stood by diverse stakeholders including land man-agers, private companies, non-profit organizations,and policy makers. The information is intendedto be valuable in many decision-making con-texts, including any scale of land-use decision-mak-ing, resource availability versus environmentaldegradation concerns, and a suite of economicanalyses.

ACKNOWLEDGMENTS

This study was supported by the John WesleyPowell Center for Analysis and Synthesis, funded bythe U.S. Geological Survey. We very much appreciatethe opportunity to assemble this diverse group andtackle this challenging multi-disciplinary problem inthe setting of the Powell Center, and we are gratefulto Jill Baron, Marty Goldhaber, Lorie Hughes, andthe many others who helped make this possible. Weare grateful to Natasha Carr, Zack Bowen, and RonCharpentier for their contributions to various aspectsof this project. We thank Meryl Marshall-Daniels forher assistance. And we thank Jill Baron (USGS) andtwo anonymous journal reviewers who have helped toimprove this manuscript.

OPEN ACCESS This article is distributed under theterms of the Creative Commons Attribution Licensewhich permits any use, distribution, and reproduc-tion in any medium, provided the original author(s)and the source are credited.

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