Forest Service Northern Research Station General Technical Report NRS-170

Assessment and Valuation of 

Forest Ecosystem Services: 

State of the Science Review

Seth Binder, Robert G. Haight, Stephen Polasky, 

Travis Warziniack, Miranda H. Mockrin, 

Robert L. Deal, and Greg Arthaud

United States Department of Agriculture

May 2017
Published by

U.S. FOREST SERVICE

11 CAMPUS BLVD SUITE 200

NEWTOWN SQUARE PA 19073

May 2017

Manuscript received for publication 22 February 2016

Abstract

This review focuses on the assessment and economic valuation of ecosystem services 

from forest ecosystems—that is, our ability to predict changes in the quantity and 

value of ecosystem services as a result of specific forest management decisions. It is 

aimed at forest economists and managers and intended to provide a useful reference 

to those interested in developing the practice of integrated forest modeling and 

valuation. We review examples of ecosystem services associated with several broad 

classes of potentially competing forest uses—production of timber, sequestration 

of carbon, regulation of the quality and quantity of water, provision of residential 

and recreational amenities, and protection of endangered species. For each example 

considered, we briefly describe what is known about ecological production functions 

and economic benefits functions. We also highlight the challenges and best practices 

in the creation and use of this knowledge. In the final section, we discuss the process, 

strengths, pitfalls, and limitations of utilizing integrated models for benefit-cost 

analysis of proposed forest management activities.

Quality Assurance

This publication conforms to the Northern Research Station’s Quality Assurance 

Implementation Plan which requires technical and policy review for all scientific 

publications produced or funded by the Station. The process included a blind technical 

review by at least two reviewers, who were selected by the Assistant Director for 

Research and unknown to the author. This review policy promotes the Forest Service 

guiding principles of using the best scientific knowledge, striving for quality and 

excellence, maintaining high ethical and professional standards, and being responsible 

and accountable for what we do.
Assessment and Valuation of Forest 

Ecosystem Services: 

State of the Science Review

Seth Binder, Robert G. Haight, Stephen Polasky, Travis Warziniack, 

Miranda H. Mockrin, Robert L. Deal, and Greg Arthaud

The Authors

SETH BINDER is an Assistant Professor with St. Olaf College, Department of Economics, 

Department of Environmental Studies, Northfield, MN 55057, binder@stolaf.edu.

ROBERT G. HAIGHT is a Research Forester with the U.S. Forest Service, Northern Research 

Station, St. Paul, MN 55108, rhaight@fs.fed.us.

STEPHEN POLASKY is a Fesler-Lampert Professor of Ecological/Environmental Economics 

with the Department of Applied Economics, University of Minnesota, St. Paul, MN 55108.

TRAVIS WARZINIACK is a Research Economist with the U.S. Forest Service, Rocky Mountain 

Research Station, Fort Collins, CO 80526.

MIRANDA H. MOCKRIN is a Research Scientist with the U.S. Forest Service, Northern 

Research Station, Baltimore, MD 21228.

ROBERT L. DEAL is a Research Forester with the U.S. Forest Service, Pacific Northwest 

Research Station, Portland, OR 97205.

GREG ARTHAUD is a Research Social Scientist with the USDA Forest Service, 1400 

Independence Avenue, SW, Washington, DC 20250-0003.

General Technical Report NRS-170
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CONTENTS

Introduction...........................................................................................................................................................1

Framework for Identifying and Valuing Ecosystem Services ..............................................................................2

Timber Production ................................................................................................................................................4

Ecological Production Functions for Timber.......................................................................................................4

Economic Benefit Functions for Timber .............................................................................................................6

Carbon Storage......................................................................................................................................................6

Ecological Production Functions for Carbon Storage..........................................................................................8

Economic Benefit Functions for Carbon Storage................................................................................................9

Water Regulation................................................................................................................................................11

Flow Regime...................................................................................................................................................11

Thermal and Light Inputs................................................................................................................................14

Sediment Flux.................................................................................................................................................15

Chemicals, Nutrients, and Pathogens..............................................................................................................16

Aesthetic Amenities............................................................................................................................................17

Recreation ............................................................................................................................................................19

Wildlife..................................................................................................................................................................21

Ecological Production Functions to Predict Changes in Wildlife Abundance.....................................................21

Economic Benefit Functions for Wildlife Abundance........................................................................................23

Integrated Modeling and Benefit-Cost Analysis............................................................................................25

Static Benefits and Costs, and Selection, Among Discrete Alternatives............................................................25

Temporal Changes in Benefits and Costs, and Optimization, of a Continuous Choice Variable..........................26

Spatial Interdependence of Benefits and Costs................................................................................................27

Uncertainty.....................................................................................................................................................27

Benefits Transfer..............................................................................................................................................28

Non-use Values and Distributional Analysis.....................................................................................................29

Conclusions...........................................................................................................................................................30

Acknowledgments...............................................................................................................................................30

Literature Cited ...................................................................................................................................................31

Appendix: Recreation Valuation Methods.......................................................................................................45

Discrete Choice Random Utility Model.............................................................................................................45

Hedonic Travel Cost Method.............................................................................................................................45

Generalized Corner Solution............................................................................................................................46

Challenges.......................................................................................................................................................46
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Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 1

INTRODUCTION

Ecosystems provide many goods and services that 

enable and enrich human life, from traditional natural 

resources, such as timber, fish, and edible plants, to 

the aesthetic qualities and characteristics of a place, to 

clean water and air (Daily 1997). Human ingenuity has 

enabled people to refine, re-allocate, and intensify the 

production of many goods and services by combining 

natural processes with human-created tools and labor. 

This has led to extraordinary advances in longevity and 

material well-being. However, it has also led to declines 

in some forms of natural capital and many nonmarketed ecosystem services (Millennium Ecosystem 

Assessment 2005).

Scientists, policymakers, and land managers 

increasingly recognize the varied contributions of 

healthy, multi-functional ecosystems to human wellbeing and seek to develop the tools and knowledge 

necessary to manage these systems to best meet 

societal objectives. Within the last decade, several 

major academic and governmental initiatives related 

to ecosystem services have emerged. These include the 

publication of the Millenium Ecosystem Assessment 

(Millennium Ecosystem Assessment 2005), a National 

Research Council report on valuing ecosystem services 

(National Research Council 2004), a report from the 

EPA Science Advisory Board (U.S. Environmental 

Protection Agency 2009), a report on the economics 

of ecosystem and biodiversity (Kumar 2010), and the 

establishment of the new Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem 

Services (IPBES). The Executive Office of the 

President (EOP) recently released a memorandum 

directing Federal agencies to factor the value of 

ecosystem services into Federal planning and decisionmaking. An additional EOP memorandum outlines 

research needs to assess ecosystem services in coastal 

green infrastructure. Within the EOP Office of Science 

and Technology Policy, an interagency Ecosystem 

Service Working Group was formed to facilitate 

cooperation among relevant agencies.

A consistent finding among these publications is 

that economic valuation of ecosystem services and 

comprehensive benefit-cost analyses are important 

tools to help decisionmakers manage ecosystems. This 

review focuses on the assessment and valuation of 

ecosystem services from forest ecosystems—that is, our 

ability to predict changes in the quantity and economic 

value of ecosystem services as a result of specific 

forest management decisions. It is aimed at forest 

economists and managers of public and private forest 

land, with the intention of providing a useful reference 

to those interested in developing the practice of 

integrated forest modeling and valuation. To this end, 

we review examples of ecosystem services associated 

with several broad classes of potentially competing 

forest uses—production of timber, sequestration 

of carbon, regulation of the quality and quantity 

of water, provision of residential and recreational 

amenities, and protection of endangered species. 

For each ecosystem service, we review a selection of 

ecological and economic research related to ecological 

production functions and economic benefits functions, 

and highlight challenges and best practices in the 

creation and use of this knowledge. In the final section, 

we discuss the strengths, pitfalls, and limitations of 

utilizing integrated models for benefit-cost analysis of 

proposed forest management activities. We supplement 

this discussion with a more quantitative treatment for 

relatively simple decision problems of optimal land use 

(i.e., preserve, harvest, or develop a given forest area) 

and optimal rotation age.

The academic literature on ecosystem services is vast 

and we limit our scope to services of non-urban forests, 

public or private, that are amenable to economic 

valuation. We do not cover cultural ecosystem services 

such as cultural heritage or spiritual significance 

that are difficult to quantify and whose value is 

often thought to be antithetical to consideration in 

monetary terms. These cultural services have value in 

their own right, and they have played an important 

role in motivating public support for the protection 

of ecosystems. Daniel et al. (2012) review research 

on relationships between ecological structures/

functions and cultural values including landscape 

aesthetics, cultural heritage, outdoor recreation, and 

spiritual significance. We also do not cover ecosystem 

services provided by urban forests, wetlands, lakes, and 

undeveloped areas (e.g., McPhearson et al. 2014) where 

the beneficiaries are primarily urban residents.

Methods have been developed to estimate the 

economic value of urban forests based on their 

effects on air quality (Nowak et al. 2014), water 
2 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

quantity and quality (Hobbie et al. 2014, Keeler et 

al. 2012, McPherson et al. 2005), residential energy 

consumption (Akbari 2002), and aesthetic amenities 

(Sander et al. 2010). Non-urban forests may affect 

aesthetic amenities of nearby residents and so we do 

discuss hedonic property value studies (e.g., Sander et 

al. 2010) as a way to estimate the value of forests for 

the provision of aesthetic amenities.

Framework for Identifying and Valuing 

Ecosystem Services

Science-based decision support tools have the 

potential to provide information to Federal agencies, 

States, and private landholders about the benefits 

and tradeoffs among various forest uses. These tools 

require understanding—and quantitatively modeling 

of—the chain of relationships that link changes in 

forest policy or management to changes in human 

well-being (Fig. 1). Government agencies control some 

important management decisions directly (e.g., land 

use within National Forests). In this case, the agency 

would start the analysis by considering the effect of 

its management decisions on ecosystems (Link 2). 

In other cases, government agencies set policies that 

provide incentives to private landowners (e.g., the 

Conservation Reserve Program). Here the agency 

would need to predict how the policy would affect 

private landowner decisions (Link 1) and then what 

impact these decisions have on ecosystems (Link 2). 

From here, analysis must consider how changes in 

ecosystem structure and function translate into changes 

in ecosystem services (Link 4). Ecological production 

functions (NRC 2005, Polasky and Segerson 2009, 

Swallow 1990) capture these relationships. An example 

of an ecological production function is an empirically 

estimated equation that predicts the abundance of a 

wildlife species that people care about (the ecosystem 

service) as a function of the age, species composition, 

slope, and elevation of the forest stand in which 

the wildlife population lives. Ecological production 

functions can be used to estimate production potential 

and identify biophysical tradeoffs between alternative 

ecosystem services. Some analysts may prefer to base 

policy decisions on consideration of impacts on the 

flow of various ecosystem services, recognizing the 

potential for tradeoffs in those flows (Link 5) rather 

than assessing them in terms of the public’s preferences.

In many cases, inefficiencies in existing management 

imply the possibility of identifying alternative “winwin” management scenarios that increase all ecosystem 

Figure 1.—Conceptual diagram of the links among changes in ecosystem management decisions, the 

production of ecosystem services, and resulting benefits and costs to society. From Polasky and Segerson (2009). 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 3

services. In other cases, however, decisionmakers require 

additional information to help navigate tradeoffs 

among ecosystem services. Here, understanding the 

relative values of increases or decreases in different 

services can help managers or policymakers select 

the option that brings the greatest benefits to society. 

This requires the estimation and use of economic 

benefits functions, which quantify in monetary terms 

the relationships between changes in the provision of 

ecosystem services and changes in human well-being 

(Link 6). The benefits function for our example above 

would be an equation that translates changes in wildlife 

abundance into a dollar value, based perhaps on an 

economic valuation study of recreational demand for 

wildlife abundance.

Economic valuation of an ecosystem’s goods and 

services represents an attempt to estimate changes in 

people’s economic well-being—as measured by their 

own preferences—due to incremental (marginal)

changes in the ecosystem’s components. When 

ecosystem goods are traded in markets (e.g., timber), 

the market price (e.g., U.S. dollars/cubic meter) is a 

measure of the benefit people get from a unit of the 

good. Since most ecosystem services are not traded in 

markets, and therefore do not have observable prices, 

economists estimate the value of changes in ecosystem 

services by leveraging the information conveyed by 

individuals’ observable decisions. Information obtained 

from observable decisions in hypothetical markets 

created by the analyst is known as stated-preference 

data. In contrast, revealed preference data is obtained 

from observable decisions in actual markets for a weak 

complement to the non-market ecosystem service. In 

both cases, the choices and tradeoffs people make 

reflect their willingness to pay (WTP) to access or 

obtain ecosystem services or their willingness to accept

(WTA) some amount in exchange for a reduction in 

services. Their WTP or WTA is a monetary measure 

of the benefits they get from a change in the service. 

Economic benefits functions estimate WTP or 

WTA based on the nature and extent of changes in 

ecosystem components, the availability of substitute 

or complementary goods or services, and beneficiaries’ 

income and other demographic characteristics.

It is important to point out that methods for economic 

valuation of ecosystem services differ from survey 

methods, such as public participation geographic 

information systems (PPGIS), for assessing public 

preferences for ecosystem services. Analysts using 

PPGIS methods typically ask selected individuals to 

locate ecosystem services (e.g., aesthetic, recreation, 

economic, and ecological services) that they value 

within a given landscape (e.g., Brown and Reed 2009). 

The maps of landscape values are then analyzed to 

determine their relative importance as an estimate of 

people’s preferences (e.g., Brown and Donovan 2014). 

Economic valuation methods go further than PPGIS 

methods by estimating how much people are willing to 

pay for an incremental change in the level of any given 

service, based on their stated or revealed preferences in 

hypothetical or actual markets.

Together, ecological production functions and 

economic benefits functions form an integrated 

assessment model that links management decisions 

to their full costs and benefits (Daily et al. 2009, 

Nelson et al. 2009). This scientific approach to 

analysis, when transparent, will provide policymakers 

and managers with valuable information to support 

their decisions. However, working across disciplines 

and integrating models that were not necessarily 

designed to be compatible is no easy task. One of the 

biggest stumbling blocks can be an understanding and 

operationalization of the ecosystem service concept.

Ecosystem services have been variously defined as the 

benefits people obtain from nature (e.g., recreational 

fishing) (Millennium Ecosystem Assessment 2005), 

the end products of an ecosystem that are directly used 

or consumed by people (e.g., the fish anglers seek for 

their recreational benefit) (Boyd and Banzhaf 2007), or 

the processes by which ecosystems produce resources 

(e.g., nutrient cycling that enhances fish populations) 

(Ecological Society of America 2012). While all of 

these definitions make useful connections between 

ecology and human well-being, we use the definition 

advanced by Boyd and Banzhaf (2007) because it 

facilitates measurement, integrated modeling, and 

valuation. Ecosystem services are components of nature, 

directly enjoyed, consumed, or used by people for their 

well-being. As noted by Boyd and Banzhaf (2007), 

this definition has several important features. First, 

ecosystem services are end products of nature that are 

directly consumed, enjoyed, or used for human benefit. 

We distinguish between a benefit and an ecosystem 

service because benefits are often produced with 
4 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

capital and labor in addition to biophysical inputs. For 

example, flood control is a benefit that depends on the 

construction of levees, canals, and other engineering 

features in addition to the peak flow of water 

downstream from the forest. We think it is important 

to identify the end product of the ecosystem (peak 

flow of water) so that it can be measured and valued 

in the context of the benefit to which it contributes. 

Second, the distinction between end products and 

intermediate products or processes is important in 

welfare accounting. Because the value of intermediate 

goods or processes is embodied in the value of final 

goods, only the value of the final good need be counted. 

(However, it is still possible and may sometimes 

be desirable to estimate the value of a change in an 

intermediate ecosystem service.) Third, ecosystem 

services are components of ecosystems, which means 

they are ecological things or characteristics, not the 

functions or processes that support or produce the end 

products. Fourth, ecosystem services are measured by 

their quantities or physical units, which subsequently 

can be paired with estimates of the monetary value 

of changes in these quantities. We emphasize that 

other definitions and lists of ecosystem services, such 

as Daily’s (1997), were constructed to illustrate the 

connection between ecology and human well-being, 

not to facilitate measurement, integrated modeling, and 

valuation of services.

For each forest use, Table 1 provides a subset of forest 

benefits and associated ecosystem services that we 

illustrate in the following sections. Because a forest 

ecosystem consists of all the biological organisms in 

a woodland functioning together with all of the nonliving physical components of the woodland, goods 

and services may be biotic (e.g., timber, trout) or 

abiotic (e.g., stream water, carbon) components of the 

ecosystem. The examples given in the table and covered 

in this review are not exhaustive of either the benefits 

arising from different forest uses or the ecological 

services associated with a particular benefit. Rather, 

they are examples for which integrated biophysical 

and economic modeling techniques have been used 

for service valuation. Each row of the template 

represents a unique forest benefit and beneficiary. For 

each benefit, the template identifies: 1) the ecosystem 

service (i.e., the ecological end product) that can be 

measured or modeled in biophysical assessments and 

that directly affects human well-being; 2) the ecological 

production function that models how changes in 

ecosystem structure and function translate into changes 

in ecosystem services; and 3) the economic benefit 

function or method to estimate the monetary value 

of changes in the ecosystem service that result from 

changes in forest management.

TIMBER PRODUCTION

Forests provide timber for the wood products industry 

(Table 1), and timber production has long been an 

objective of public and private forest management. 

We begin with a description of timber management 

systems and ecological production functions that 

project timber yields. Then, we describe economic 

benefit functions, including the market processes that 

determine timber prices on public and private land 

and the net present value criterion to evaluate timber 

management systems. While we focus on timber, we 

emphasize that forest management is inherently a 

problem of joint production: decisions intended to 

produce timber affect the production of other forest 

ecosystem services. Here, we briefly note how ecological 

production functions and economic benefit functions 

for timber can be extended to assess changes in other 

ecosystem services and their values. In a later section, 

we give a detailed account of integrated modeling 

and benefit-cost analysis for the joint production of 

multiple ecosystem services.

Ecological Production Functions for 

Timber

Forests that are managed for timber production are 

subdivided into stands, which are administrative units 

bounded by physical and geographic features such as 

roads and property boundaries and by discontinuities 

in forest vegetation. Each stand is managed using 

a particular management system, typically evenage or uneven-age management (Smith 1962). A 

stand management system is a planned program of 

silvicultural treatments during the life of the stand. 

Treatments include regeneration cuttings that are used 

to establish a new stand by means of natural or artificial 

regeneration. Intermediate cuttings such as thinnings 

or selection harvests are used to control the density and 

growth of existing trees. Stand management systems are 

classified based on their regeneration method. The most 

well-known management system uses the clearcutting 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 5

Forest use Benefit Beneficiary

Ecosystem service

(valued end product of 

the forest ecosystem)

Ecological production 

function Economic benefits function

Timber 

production

Timber for wood 

products

Industrial wood 

producers

Merchantable timber 

(stumpage)

Stand simulation models 

(e.g., Forest Vegetation 

Simulator)

Forest landscape 

simulation models

Market price of timber 

(stumpage price)

Carbon 

storage

Climate regulation Everyone Sequestered carbon Carbon budget simulation 

models (e.g., FORCARB2)

Stand simulation models

Social cost of carbon

Water 

regulation

Irrigation water Farmers Flow of water 

downstream

Paired watershed studies

Forest hydrological 

models (e.g., Distributed 

Hydrology Soil Vegetation 

Model)

Market price for water

Shadow price of water

Hedonic price model for 

farmland

Flood control Homeowners Peak flow 

downstream

Paired watershed studies

Forest hydrological models

Avoided damage

Coldwater fishing Anglers Trout abundance Energy transfer models 

for stream temperature 

coupled with trout 

population models

Hedonic travel cost model

Discrete choice RUM

Clean drinking water Local water 

consumers 

Amount of sediment 

in water

Erosion prediction models 

(e.g., Water Erosion 

Prediction Project model)

Household demand model 

for water, avoided costs of 

treatment, replacement 

cost

Safe navigation Commercial 

navigators

Amount of sediment 

in water

Erosion prediction models Avoided costs of dredging

Clean drinking water Local water 

consumers 

Amounts nitrate and 

phosphorus in water

Nutrient and chemical 

movement models 

(e.g., Soil and Water 

Assessment Tool)

Household demand model 

for water, avoided costs of 

treatment, replacement 

cost 

Aesthetic 

amentity 

Aesthetic amenity Homeowners 

near forest

Forest cover in 

viewshed

Stand simulation models

Forest landscape 

simulation models

Hedonic property price 

model

Aesthetic amenity Leisure travelers 

and commuters

Forest cover in 

viewshed

Stand simulation models

Forest landscape 

simulation models

Recreational demand 

model

Recreation Recreational hiking, 

camping, and biking

Hikers, campers, 

bikers

Old growth area, 

forest density, 

burned area

Stand simulation models

Forest landscape 

simulation models

Recreational demand 

model

Discrete choice RUM

Wildlife Recreational hunting Hunters Game abundance Demographic models of 

wildlife abundance and 

viability (e.g., RAMAS)

Recreational demand 

model

Discrete choice RUM

Hedonic pricing of licenses

Protecting rare and 

endangered species

Everyone Species survival 

probability

Demographic models of 

wildlife abundance and 

viability

Contingent valuation 

method

Table 1.—Template for identifying and valuing ecosystem services from forests. For each forest use, we identify one or more 

benefits, beneficiaries, and ecosystem services. Ecosystem services are the valued end products of the forest ecosystem that 

contribute to the production of benefits and may be affected by forest management activities. The ecological production 

functions and economic benefits functions form an integrated assessment model that links management decisions to their full 

costs and benefits.
6 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

reproduction method and produces even-aged stands. 

In this case, even-aged management is a cycle of events 

(rotation period) that includes clearcutting a mature 

stand, planting a single cohort of trees, periodically 

thinning the new crop, and clearcutting the crop at a 

specified rotation age. Uneven-age management uses a 

selection harvesting method that involves the periodic 

harvest of trees in specified size classes. Selection 

harvests are conducted to control the spacing and 

growth of the remaining trees and to enhance natural 

regeneration from seeds produced by the remaining 

mature trees. Since selection harvest and regeneration 

take place simultaneously, uneven-aged stands include a 

mixture of trees in a wide range of size and age classes.

For both even-age and uneven-age management 

systems, timber yields are projected using stand 

simulation models (i.e., ecological production 

functions), which have been developed since the 1960s 

to provide forest managers with accurate growth and 

yield information for planning (see Munro 1974 for 

review). Stand simulation models include stage-class 

models or individual-tree simulators, both of which 

use discrete time difference equations to project tree 

growth, mortality, and regeneration. The models differ 

in their characterization of stand structure and their 

tractability for optimization.

In stage-class models, trees are classified into species 

and stem-diameter classes. Equations for tree growth, 

mortality, and regeneration project the changes in the 

number of trees in each class over time as a function of 

stand density (see Getz and Haight 1989 for review). 

Stage-class models are constructed by first estimating 

the parameters of each of the component equations 

separately and then inserting the equations into a 

matrix framework to project stand growth. This matrix 

framework facilitates the application of linear and nonlinear programming algorithms for optimization of 

stand treatments and harvests (Getz and Haight 1989, 

Haight 1987).

Individual-tree simulators describe the stand with 

a list of tree records where each record contains the 

current tree dimensions (e.g., stem diameter, height, 

and crown length) and an expansion factor representing 

the number of trees of its kind in the stand (see Liu 

and Ashton 1995 for review). Equations for diameter 

growth, height growth, and crown development 

predict changes in the tree attributes as a function 

of stand density. An equation for tree mortality 

adjusts the expansion factors, and an equation for 

regeneration creates new records. Like stage-class 

model construction, equations for changes in tree 

dimensions are estimated separately and then inserted 

in a simulation shell for projecting stand growth. 

Major modeling efforts in different regions of the 

United States have created individual-tree simulators, 

and many of those have been incorporated into the 

Forest Vegetation Simulator, which is a family of forest 

growth simulation models supported by the USDA 

Forest Service (see Crookston and Dixon 2005 for 

review).

An important property of individual-tree simulators is 

the detail in which a stand and its growth processes are 

described. Since individual-tree simulators explicitly 

project the dimensions of hundreds or even thousands 

of trees in a stand, they provide a detailed picture 

of stand structure and composition over time. This 

detail facilitates the simulation of many forest-related 

processes including wildlife population dynamics, insect 

and disease dynamics, wildfire intensity and spread, 

and carbon sequestration (Crookston and Dixon 

2005). Recently, progress has been made connecting 

individual-tree simulators with optimization algorithms 

to analyze stand treatment and harvest decisions in 

relation to the ecosystem services produced (e.g., 

Hyytiäinen et al. 2004, Rämö and Tahvonen 2014).

Forest landscape simulation models (FLSMs) 

have been developed to project the effects of forest 

management options on the spatial configuration, 

composition, and heterogeneity of vegetation, 

including community types, tree species age classes, 

and aboveground biomass (see Scheller and Mladenoff 

2007 for review). In contrast to stand simulation 

models, FLSMs are applied to extensive areas of forest. 

They subdivide the landscape into cells (or polygons) 

and project attributes of the vegetation in each cell. 

Projections are based on vegetation attributes within 

and across cells and disturbance processes that are 

endogenous (e.g., wildfire) or exogenous (e.g., land-use 

change or timber harvesting) to the model. The extent 

and detail of vegetation attributes that are projected 

with FLSMs allow for the projection and analysis 

of many forest benefits including carbon storage, 

recreation, wildlife abundance, and water yield.
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 7

Economic Benefit Functions for Timber

Wood products (e.g., lumber, paper, structural panels, 

and fuel) have well-defined markets and significant 

economic value, contributing $280 billion in 2007 to 

the U.S. economy (United States Census Bureau 2009). 

The economic benefit of timber for wood products is 

measured by stumpage value—the amount per unit 

area that a commercial wood cutter is willing to pay 

for an area of standing trees (Helms 1998). It is the 

product of the stumpage price and the amount of 

timber offered for sale. To understand how stumpage 

price is determined, it is useful to first describe the 

ownership of forest lands in the United States. Forest 

lands are primarily owned by private entities, including 

nonindustrial owners without processing facilities, 

forest industry owners with processing facilities, and 

various types of forest land investment organizations. 

Nonindustrial and forest land investors own over 

two-thirds of U.S. forest land; combined with the 

forest industry, the U.S. private sector owns roughly 

75 percent of U.S. forest lands and produces over 90 

percent of the industrial wood harvest. The remaining 

25 percent of forest lands is owned by government 

agencies—Federal, State, and local—which produce less 

than 10 percent of the industrial wood (Sedjo 2006).

Stumpage prices for private timber are marketdetermined (Sedjo 2006). They depend on the 

industry’s aggregate demand for trees (which is based 

in part on market-determined prices for wood products, 

which in turn depend on domestic and international 

trade) and the aggregate supply of industrial wood from 

private landowners (which is based on the current and 

expected future stumpage prices and age of the forest). 

Stumpage prices for a given ownership also depend on 

wood quality—itself dependent on timber age, species, 

and condition—and cost considerations associated with 

timber accessibility, mill distance, terrain, and other 

factors reflecting extraction, transport, and processing 

costs. Stumpage prices for most sales of public timber 

are determined through a competitive auction process, 

which reflects the market information on stumpage 

prices of private timber (Sedjo 2006).

The decision about which management system to use in 

a particular stand and the attributes of the management 

system is often made using a net present value criterion 

(e.g., Haight 1987). For even-age management, the 

problem is to determine: the timing and intensity 

of silvicultural treatments for the current stand; the 

time when the stand is clearcut and replaced with a 

plantation, if it is currently under natural forest cover; 

and the timing and intensity of silvicultural treatments 

and clearcut age for the plantation. For uneven-age 

management, the problem involves determining the 

sequence of selection harvests that converts the current 

stand to the desired uneven-age steady state. The net 

present value of each management system is calculated 

based on the discounted value of timber yields and 

costs of management (e.g., planting, weeding, and 

pruning) over all future harvests (Faustmann 1849, 

Samuelson 1976).

We emphasize that forest management systems 

intended to produce timber affect the production of 

other forest ecosystem services. For example, stands 

managed with selection harvests maintain plant 

understory species richness and abundance, which 

are important for wildlife habitat (Deal 2001), while 

maintaining stand growth and providing industrial 

wood (Deal and Tappeiner 2002). The stand structures 

that develop after selection harvests create structurally 

complex, multi-layered forest canopies that were much 

more similar to old-growth forests than the uniform 

young-growth stands that develop after clearcutting 

(Deal et al. 2010). The value of non-timber ecosystem 

services can be incorporated into the calculation of net 

present value of forest management systems. Integrated 

assessment models that account for the production 

and value of multiple ecosystem services have been 

developed and used in forest management since the 

1980s (e.g., Bowes and Krutilla 1989).

CARBON STORAGE

Forest ecosystems provide a climate regulation benefit 

(Table 1) because forests store carbon in the soil and 

in biomass that might otherwise be released into 

the atmosphere in the form of CO2. Carbon storage 

is a valuable ecosystem service because reducing 

atmospheric carbon reduces the intensity of future 

climate change. Reducing the likelihood of damage 

associated with more intense climate change will 

benefit everyone. In this section, we review biophysical 

modeling approaches to estimating forest carbon 

stocks and fluxes, and we discuss economic approaches 

to estimating the value of storing additional carbon 

through enhanced sequestration or release prevention.
8 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

Ecological Production Functions for 

Carbon Storage

When discussing regional, national, or global carbon 

stocks and fluxes, most papers report carbon in 

teragrams (Mt, megatons, million metric tonnes) or 

megagrams (Mg, one metric tonne). At the stand or 

forest levels, megagrams (Mg) per hectare of carbon are 

used. These measures are carbon mass, not CO2 mass, 

because carbon is a standard currency and can easily be 

converted to any other unit. Many reports give stocks 

and fluxes of the mass of CO2. To convert C mass to 

CO2 mass, multiply by 3.67 to account for the mass of 

the O2.

The evaluation of forestry opportunities for carbon 

storage received intense analysis beginning in 

the 1990s. Birdsey (1992) was the first to provide 

comprehensive estimates of carbon storage and 

accumulation in U.S. forests. Carbon was estimated 

separately for trees, soil, forest floor, and understory 

vegetation for major forest types and plantation 

species in eight geographic regions. For trees, carbon 

estimates were based on merchantable volumes in 

existing forest inventory data, which were converted 

to carbon based on conversion factors for total tree 

volume, total biomass, and carbon as percentage of 

dry mass. Estimates of carbon stored in soil, forest 

floor, and understory estimation were obtained 

from the ecological literature. Birdsey (1992) also 

estimated changes in carbon storage over time based 

on changes in live trees. The conversion factors and 

carbon estimates of Birdsey (1992) were incorporated 

into carbon budget models to examine the effects of 

forest management practices on carbon storage in U.S. 

timberlands (e.g., Adams et al. 1999, Alig et al. 1997).

In the early 2000s, work began to replace Birdsey’s 

(1992) conversion factors for merchantable tree 

volume with estimators for forest biomass and carbon. 

Jenkins et al. (2003) developed a consistent set of 

aboveground tree biomass equations as a function of 

tree diameter for over 100 species in the United States. 

These individual-tree equations were then applied to 

inventory plot data to estimate equations for biomass 

density (Mg/ha) of live and standing dead trees as a 

function of merchantable volume (m3/ha) for broad 

forest types and regions of the coterminous United 

States. (Smith et al. 2003a). Tree biomass is about 50 

percent carbon, so carbon estimates can be derived 

from estimates of biomass by multiplying by 0.5. The 

equations for forest biomass were incorporated in the 

U.S. Forest Service carbon budget simulation model 

(FORCARB2), which provides inventory-based 

estimates of U.S. forest carbon stocks (Smith et al. 

2004). The model includes separate, non-overlapping 

components of total forest ecosystem carbon pools, 

including live trees, standing dead trees, understory 

vegetation, down dead wood, forest floor, and organic 

carbon in soil. The model is applied to plot-level 

inventory data, where merchantable tree volume (m3/

ha) and age are used to estimate tree and forest floor 

carbon, respectively. FORCARB2 was used in U.S. 

Greenhouse Gas Inventory (United States Department 

of Agriculture 2008) and Forest Service studies (e.g., 

Heath et al. 2011), which provided managed forest 

carbon estimates for 253 million ha of U.S. forest land.

In addition to tools like FORCARB2 that estimate 

carbon storage at the county, state, and national 

levels, simulation models of forest growth have been 

developed to predict changes in carbon storage at the 

national, regional, and stand levels. Further, they are 

used to estimate net carbon storage under alternative 

forest policy or management scenarios relative to a 

baseline scenario to evaluate the carbon impacts of 

changes in policy (Richards and Stokes 2004). For 

example, Wear and Coulston (2015) develop a forest 

carbon projection model based on observations from 

over 350,000 permanent monitoring plots across 

the United States that are part of the USDA Forest 

Service’s Forest Inventory and Analysis Program. Their 

model, which is developed for regional or national 

projections, includes estimates of carbon densities by 

forest age class, forest sequestration rates by age class, 

areal extent of forest by age class, and age transition 

probabilities aggregated at the state or regional level, 

including disturbance and management effects. Forest 

dynamics are applied as transition probabilities to 

current estimates of areal extent by age and define 

changes in forest structure. Carbon sequestration is 

then estimated using observed carbon stock densities 

and sequestration rates applied to the new forest 

structure. Wear and Coulston (2015) project the effects 

of national-level policies intended to boost forest 

carbon sequestration, including reducing deforestation 

and development, increasing afforestation and 

reforestation, and reducing wildfire.
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 9

At the stand level, carbon projections can be made with 

the Forest Vegetation Simulator (FVS), an individualtree, distance-independent, growth and yield model 

that predicts changes in tree diameter, height, crown 

ratio, and crown width, as well as mortality, over time 

(Crookston and Dixon 2005). FVS has been calibrated 

for geographic areas of the United States and can 

simulate a wide range of silvicultural treatments for 

most major forest tree species, forest types, and stand 

conditions. The model includes equations to predict 

the biomass of live trees, dead trees, down dead wood, 

understory, and forest floor. Biomass, expressed as dry 

weight, is assumed to be 50 percent carbon. FVS is 

used to estimate the potential carbon consequences 

of forest management actions, including planting 

densities, thinning regimes, and rotation age (Hoover 

and Rebain 2011).

Economic Benefit Functions for Carbon 

Storage

Evaluating the economic benefits of carbon 

storage by U.S. forests depends on identifying a 

monetary value per ton of carbon removed from the 

atmosphere. Monetary units are especially helpful 

because they can then be compared with monetary 

costs of carbon policies and programs. The value to 

society of sequestering or preventing the release of 

additional carbon dioxide can be viewed as the avoided 

economic damages or costs of additional carbon in 

the atmosphere. The value of carbon is not fully (or 

even mostly) reflected in market prices. Although both 

voluntary and compulsory carbon markets exist, the 

prices in these markets reflect a demand for mitigation 

that falls far short of estimates of the actual economic 

damage avoided by preventing the release of additional 

carbon to the atmosphere. It is this value—the value of 

avoided damage—that forest managers should use in 

decision-making if they wish to maximize benefits to 

society and to treat the benefits of carbon storage on 

par with the benefits of other ecosystem services, for 

which valuation methods are designed to capture the 

full social marginal value.

Economists use the term social cost of carbon (dioxide 

emissions) (SCC) to describe the marginal damages 

from a ton of carbon emitted to the atmosphere (Tol 

2008). Storing an additional ton of carbon (above a 

given baseline) leads to less intense climate change 

and damage. The value of permanently storing that 

additional ton is equivalent to the SCC. This value 

depends on the specific trajectory of emissions, 

economic production, and climate change over time 

(Nordhaus 2008). Only if the world adopted an 

optimal incentive-based carbon policy would the 

resulting price of carbon exactly equal the SCC. In 

the absence of a complete, compulsory market for 

terrestrial carbon storage, a coherent approach to forest 

management that accounts for the full value of carbon 

storage will require coordination across agencies to 

choose a common methodology for the estimation of 

the SCC, and to jointly adopt revised estimates and 

management plans as economic conditions change.

The SCC depends fundamentally on estimates of total 

damages arising from a given change in climate over a 

specified period of time. As Tol (2009) notes, despite 

a proliferation of SCC estimates, there exist only 13 

different studies of total damage estimates upon which 

to base estimates of the SCC, of which only nine had 

been used at the time of Tol’s writing. Estimation of 

total damages remains an important and active area 

of research. Given the need for convergence in the 

selection of IAM approaches, features, parameter 

values, and underlying damage cost estimates, it is clear 

that estimates of the SCC will continue to evolve.

Several methods have been used to estimate the SCC, 

with important differences in the choice of model, 

scope, and parameterization. The academic literature 

lacks consensus in regard to these choices, and 

differences in approach have led to wide differences in 

SCC estimates. In a survey of the literature, Tol (2008) 

finds more than 200 different estimates of the SCC, 

with a wide and highly skewed distribution: mean, 

median, and mode values are 127, 74, and 35 $U.S. 

1995 per Mg CO2, respectively. The standard deviation 

in estimates is $243 per Mg CO2.

Perhaps the most widely referenced SCC estimates 

come from the Dynamic Integrated ClimateEconomy (DICE) model (Nordhaus 1993) and the 

Regional Integrated Climate-Economy (RICE) model 

(Nordhaus and Yang 1996) developed and continuously 

updated by William Nordhaus. Other integrated 

assessment models (IAMs) include FUND (Tol 1995), 

PAGE (Hope 2006), and WITCH (Bosetti et al. 

2007). Various scholars have adjusted the basic DICE/
10 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

RICE approach (though not all report changes in 

SCC): Sohngen & Mendelsohn (2003) incorporate the 

mitigation potential of forest carbon storage; Buonanno 

et al. (2003) and Popp (2004) include endogenous 

technical change; Sterner and Persson (2008) account 

for relative price changes and the consumption of 

non-market goods; de Bruin et al. (2009) integrate the 

costs and benefits of adaptation; Lemoine and Traeger 

(2012) incorporate tipping points and ambiguity 

aversion; and Cai et al. (2012) utilize a continuous-time 

framework to improve the temporal resolution and 

reliability of analysis.

Large differences in SCC estimates can be obtained 

even from the same model, depending on the choice of 

international equity weighting (Fankhauser et al. 1997) 

or intertemporal discount rate. Equity weighting is an 

attempt to correct differences in estimates of people’s 

WTP for reductions in damages from climate change 

(e.g., a reduction in human mortality risk). These 

differences in WTP may arise because of differences in 

income or other socio-economic conditions, which may 

be considered unfair. Equity weighting can significantly 

increase aggregate (global) damage figures, although 

some specifications of weighting functions also imply 

reduced estimates (Fankhauser et al. 1997).

Two camps have emerged with regard to the 

appropriate choice of the intertemporal discount 

rate. The first insists on a parameterization that is 

consistent with an ethical framework that values future 

generations on par with the present (Stern 2007), 

which implies a very small pure rate of time preference 

(PRTP) that exceeds zero only to reflect the very small 

probability of non-existence of future generations. 

The second camp insists on a parameterization that is 

consistent with observed behavior and other economic 

model parameters (Nordhaus 2007), which implies a 

much larger PRTP. Kaplow et al. (2010) and Goulder 

and Williams III (2012) argue that these approaches 

can be reconciled: the PRTP used for evaluative 

purposes (for example, in a planner’s social welfare 

function) need not be the same as the PRTP used for 

predictive purposes (i.e., in a positive economic model).

An important point of recent consensus among leading 

economists is that the discount rate should generally 

decline over time as a result of uncertainty, regardless 

of the choice of PRTP (Arrow et al. 2013). This means 

that the certainty-equivalent value of benefits and costs 

realized further out in time should be discounted at 

progressively lower rates. More concretely, in a threeperiod context, the value of consumption in the third 

period would be discounted back to the second period 

at a lower rate than that at which consumption in the 

second period is discounted back to the first. The “term 

structure” of declining discount rates should apply 

equally to all costs and benefits relevant to a given 

decision context, so the forest manager should ensure 

that the structure underlying any off-the-shelf estimate 

of the SCC to be incorporated into an integrated 

assessment model is consistent with the structure 

applied to other market and non-market benefits.

In 2009 the U.S. Government convened an interagency 

working group to estimate the SCC for use in 

regulatory analysis (Greenstone et al. 2013, U.S. 

Interagency Working Group 2010). They assumed 

a global perspective and used three IAMs (DICE, 

PAGE, and FUND). The estimates were subsequently 

updated in 2013 (U.S. Interagency Working Group 

2013). Using a 3 percent discount rate, the working 

group estimated that SCC increased from $44 to $72 

per Mg of CO2 from 2015 to 2044 measured in 2016 

U.S. dollars.

In addition to navigating the complications of SCC 

estimation and the alignment of discount rates, forest 

managers must also account for potential leakage 

and the impermanence of terrestrial carbon storage 

in calculating carbon values for large-scale policy 

decisions. Carbon leakage occurs when management 

actions that successfully lead to greater carbon storage 

locally indirectly create incentives for greater carbon 

emissions elsewhere. For example, a prohibition on 

harvest on all National Forest land would dramatically 

increase carbon storage in the National Forests. 

However, on a global timber market, the reduction in 

timber supply from National Forests may be partially 

or fully offset by increases in harvest on forest lands 

elsewhere, leading to additional carbon emissions 

that would partially, fully, or even more than offset 

the additional carbon stored locally. In contrast, an 

afforestation or reforestation project on marginal lands 

would produce carbon storage benefits with little risk of 

leakage.
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 11

For any given management plan, the value of carbon 

should be assessed on the carbon expected to be 

stored net of leakage. Murray et al. (2004) investigate 

the extent of leakage in the United States associated 

with forest set-asides (prohibitions on harvest), avoided 

deforestation for conversion to agriculture, inducements 

for afforestation, and an integrated afforestationavoided deforestation program. They find leakage rates 

that range from minimal (<10 percent) to enormous 

(>90 percent) depending on the activity and region. 

Further, for small projects, leakage is usually small in 

absolute terms but larger in proportion to the direct 

project benefits compared with the leakage rate of 

larger projects. They conclude that leakage effects 

should not be ignored in accounting for the net level 

of greenhouse gas offsets from land-use change and 

forestry mitigation activities.

Aside from the problem of leakage is the issue of 

impermanence in assessing the value of stored carbon. 

In the context of industrial production, the benefits 

arising from adopting a technology that decreases 

carbon emissions by one ton for a fixed amount of 

output are equivalent to the SCC. This equivalence 

stems from the fact that the ton of carbon that would 

otherwise have been emitted is permanently kept out 

of the atmosphere. However, in the context of forest 

and range land management, additional carbon storage 

may not be permanent. For instance, increasing timber 

rotation ages leads to greater carbon storage, but this 

carbon will eventually be re-emitted to the atmosphere 

after harvest.

Managers can account for impermanence in one of two 

ways. The first is to value both the carbon sequestered 

(i.e., new, additional carbon stored) and the carbon 

emitted using the SCC at the time of sequestration 

and emission, discounting all costs and benefits back 

to the present (van Kooten et al. 1995). The second 

approach is to calculate a carbon rental value (Sohngen 

and Mendelsohn 2003). Conceptually, the rental value 

is equal to the interest one could earn on the proceeds 

obtained by selling the asset (a ton of stored carbon) at 

its current price (the SCC), minus any expected capital 

gains due to changes in the SCC.

WATER REGULATION

Forest structure and composition affect the quality of 

aquatic ecosystems, which in turn affect many different 

benefits, from irrigation water to clean drinking 

water (Table 1). We focus on the effects of forest 

management within the riparian zone and surrounding 

hillsides of the riverine system. We divide the section 

according to four environmental drivers of aquatic 

ecosystem health: flow regime, thermal/light inputs, 

sediment flux, and chemicals, nutrients, and pathogens.

Flow Regime

Flow regime refers to the quantity, rate, timing, 

and pathways of water through the watershed. It is 

characterized by base flow, seasonal timing and annual 

variation, frequent (e.g., 2 year) floods, and rare or 

extreme (e.g., 100 year) floods. Floods and droughts 

create a patchiness of riparian landscape important for 

variation in species and age class of species. In semiarid regions, extreme floods bring large wood into 

riparian zones and rivers; in wet regions, large floods 

add wood to rivers by eroding banks and causing trees 

to fall into the channel (Naiman et al. 2008). After 

entering the channel, large wood helps retain organic 

matter, forms deep pools, and promotes nutrient uptake 

in the river. More frequent small floods serve to flush 

large wood and sediment down the river and eventually 

out of the system (Latterell and Naiman 2007). Flow 

regimes can be affected by diversions, dams, stream 

channelization, timber harvests, and wildfire.

Riparian and aquatic species have adapted to these 

natural flow regimes, creating locally distinct habitats 

(Lytle and Poff 2004, MacDougall and Turkington 

2005). In the Pacific Northwest, the lives of salmon are 

lockstep with the hydrograph: high flows in fall cue 

spawning and create the necessary spawning habitat; 

baseflows in the dry season maintain juvenile habitat; 

and elevated flows in spring improve emigration out 

of the river. In the snowmelt-dominated streams of 

the Rockies, willows and cottonwoods release their 

seeds during the recession of spring floods when seeds 

find scoured ground and moist substrate needed for 

germination (Scott et al. 1997). Maintaining natural 

flow regimes on forests is an effective means of 
12 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

managing invasive species, providing adequate habitat, 

and sustaining human uses of water on the forest and 

in downstream communities.

Two main methods are used for studying the 

relationship between changes in forest management 

and changes in flow regime: paired watershed studies 

and forest hydrology models. Paired watershed studies 

compare the flow regimes of two or more watersheds 

with similar physical characteristics (climate, soil, etc.). 

During the study, one set of watersheds undergoes 

a management action (e.g., clearcutting, prescribed 

fire) and one set of watersheds remains undisturbed 

to serve as a control. Ideally, comparisons of changes 

(or lack of changes) between watersheds allows one to 

tease out effects of land cover, management actions, 

and disturbances on changes in the flow regime. 

Such studies have shown a wide range of effects 

from clearcuts and partial cuts of forested watersheds 

on streamflow. Beschta et al. (2000) looked at three 

small watersheds and six large basins in the western 

Cascades (H.J. Andrews Experimental Forest). 

During the period of study, two of the three small 

watersheds experienced typical forest management 

actions, including road building, clearcutting, cable 

logging, and site preparation. The third watershed 

was left undisturbed and served as the control site. 

They found peak flow increased 13-16 percent in 

the treated watersheds for 1-year recurrence interval 

events and 6-9 percent for 5-year recurrence interval 

events. Swank et al. (2001) examined changes over a 

20-year period in a mixed hardwood covered watershed 

in the southern Appalachians (Coweeta Hydrologic 

Laboratory) following clearcutting and cable logging, 

compared to an untreated control watershed. They 

found annual flow increased 28 percent during the first 

year following logging but continued to decrease until 

virtually no effect was seen after the fifth year. Brown et 

al. (2005) reviews other paired watershed experiments 

that look at changes in water yield resulting from 

alterations in forest vegetation. Most studies find 

significant effects shortly after the vegetation change, 

but the effects are short-lived and dependent on the 

length of time it takes for the vegetation to grow back. 

Another review by Troendle et al. (2010) concluded 

that streamflow response varies by climate, species 

composition, and percentage change in vegetation 

density. Wetter regions like the southeast, northeast, 

north central, and northwest United States are likely 

to see increases in streamflow an order of magnitude 

larger than the arid southwest. Furthermore, due to 

variation in annual streamflow, about 20 percent of the 

basal area of the vegetation must be removed to see a 

statistically significant effect on streamflow (Bosch and 

Hewlett 1982, Hewlett and Hibbert 1967, Stednick 

1996).

When long run observations or control sites are 

not available, the potential effects of management 

actions can be evaluated using forest hydrological 

models. Forest hydrological models are computational 

representations of the watershed that allow managers to 

run counterfactual experiments. Typical models account 

for soil-vegetation-atmospheric transfer, canopy 

interception of precipitation, evapotranspiration, 

channel routing, and effects of management actions 

like roads and culverts. There is usually a complexity 

vs. usability tradeoff in these models. Simple models 

use straightforward applications of the U.S. Soil 

Conservation runoff curve number for calculating the 

relationship between precipitation and runoff (e.g., 

Mishra and Singh 2003). The runoff curve number is 

a relatively straightforward formula that gives surface 

runoff as a function of hydrologic soil type, land use, 

treatment, and hydrological condition. By comparison, 

the Distributed Hydrology Soil Vegetation Model 

(DHSVM), widely used in the Pacific Northwest, 

is capable of modeling the effects of vegetation 

change in watersheds up to 10,000 km2 in size at 

100 m resolution, sub-daily timescales, and multiyear simulations (Wigmosta et al. 1994). Models like 

DHSVM are feature rich but are often maintained as 

research models, with limited user support and without 

emphasis on user-friendly interfaces. Model choice 

also depends on the scale of the management decision 

being evaluated. The Soil and Water Assessment Tool 

(SWAT) is a widely used tool capable of modeling 

at the river basin scale but not useful for finer details 

like the effect of roads on an area’s hydrology. A good 

review of forest hydrological models is given in Beckers 

et al. (2009), and an inventory of forest hydrological 

models is maintained by Texas A&M University at 

http://hydrologicmodels.tamu.edu/models.htm.

A different set of tools may be more appropriate for 

evaluating effects of fire on flow. Hydrophobic soils and 

loss of ground cover following extreme fires increase 

runoff, with the potential for small rain events to cause 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 13

large-scale flooding. These effects are measured by 

comparing historic flow rates with gauges and weirs 

and by computer simulations, as shown in: Moody 

and Martin (2001) for postfire analysis of the Buffalo 

Creek Wildfire; Robichaud et al. (2002) for postfire 

analysis of the Hayman Wildfire; Rosgen et al. (2013) 

for postfire analysis of the Waldo Canyon Wildfire; 

and Jarrett (2009) more broadly for the Colorado 

Front Range. Moody and Martin observed 140 percent 

increases in peak flows following the Buffalo Creek 

Fire. Jarrett (2009), using physical measurements, 

observed up to 400 percent increases in peak flows on 

the Colorado Front Range following wildfires. Rosgen 

et al. (2013) used computer simulations with the 

WRENSS Water Yield Model to project the effect of 

lost vegetation on runoff following the Waldo Canyon 

Fire. They expect over 1.4-2.9 inch increases in water 

yield for watersheds affected by the fire.

The benefits associated with a change in flow regime 

could include increases in the provision of water for 

downstream irrigation, reductions in risk of floods, 

better control of invasive species and preservation 

of native species, improved maintenance of fish and 

wildlife habitat, and better maintenance of river 

corridors important for recreation. This wide range of 

benefits implies a wide range of valuation methods. 

Here we focus on the value of the flow regime for 

downstream irrigation and flood control. Estimating 

non-consumptive value of instream water to provide 

recreation opportunities or protect endangered species 

habitat requires the use of non-market valuation 

methods described in the sections on recreation and 

wildlife.

When they exist, prices charged in competitive water 

markets can provide signals of the value of increased 

flow. Brown (2006) reviews 1,380 transactions in 

Western water markets between 1990 and 2003. Over 

half the sales were to municipal areas to satisfy the 

needs of fast growing cities, such as along the Colorado 

Front Range, near Las Vegas, and near Reno, Nevada. 

Over half of the sellers were irrigators. The median sale 

prices were $2,120 per ML (mega liter or 1 million 

liters) for municipal uses and $1,917 for irrigation. 

Eleven percent of water rights purchases studied in 

Brown (2006) were for environmental purposes and 

sold for a median price of $706. Most of these (105 

of the 113) purchases were by government entities 

to maintain instream flows for the protection of 

aquatic species. Instream water is also valued for its 

contribution to recreation and for riparian and wetland 

restoration.

When water markets do not exist, benefits of water 

for commercial uses can be found by estimates of 

the shadow price of water, that is, the change in net 

profits from a small change in water use. Estimating 

the shadow price of water across producers is 

done frequently in production economics with 

mathematical programming (Scheierling et al. 2006), 

field experiments, and hedonic methods (Young 

2005). Estimating a full production function for 

the relevant beneficiaries typically requires a great 

deal of information. Pattanayak and Butry (2005) 

demonstrate that when changes in flow induce a 

change in the derived demand for a weak complement 

in the production process (e.g. labor), the value of the 

change in flow can be calculated as the difference in 

the firm’s surplus between the old and new derived 

demand curves. These demand curves may be simpler 

to estimate than the production function. In this and 

other methods it is important to account for spatial 

correlation among observations for downstream 

beneficiaries (Pattanayak and Butry 2005).

The value of reducing flood risk is often measured 

with avoided costs or avoided damages methods--

essentially an accounting of downstream values at risk 

and the change in probability of flooding that results 

from changes on the land. While such valuation is 

conceptually straightforward, implementation can 

be complicated. Watson et al. (2016) demonstrate 

how predicted changes in the hydrograph for a given 

location along a river or stream due to changes in 

upstream land use can be used to predict the extent 

and depth of flooding in a residential area. The authors 

estimate the difference in total damages using GIS 

data on the location, type, and value of structures in 

conjunction with a flooding depth-damage function 

estimated on the basis of historical insurance claims. 

To calculate the expected annual value of the difference 

in flood risk, they apply their comparative valuation 

method to a series of historical flooding events of 

known exceedance probabilities; estimate a continuous 

probability-damage function; and evaluate the 

expectation of that function.
14 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

Thermal and Light Inputs

Temperature of streams is closely related to flow 

regime, and it is a major concern in forest management. 

Volume of water determines the dispersal rate of 

thermal inputs, and vegetation in the riparian zone 

helps regulate water temperatures. At the most basic 

level, vegetation shades the stream, reducing solar 

radiation and the stream’s heat load (Davies and 

Nelson 1994; Hostetler 1991). Riparian vegetation 

also provides a number of buffering roles like trapping 

air next to the stream surface and regulating the 

temperature and flow of groundwater. These buffering 

roles are important in shallow saturated groundwater 

systems, where shade cools groundwater temperatures 

and as streams become larger (Hewlett and Fortson 

1982). When aquifer recharge and floodplain storage 

occurs during winter or spring snowmelt, new additions 

are made when stream water is coldest. The cold water 

is later released back to the stream during periods of 

low flow, when stream temperature is likely to be the 

highest. Permanent changes in stream temperature 

can make formerly suitable habitat unusable for native 

species (Holtby 1988). Species like bull trout that rely 

on cold waters for spawning are already seeing loss in 

habitat due to climate change and increased fire in the 

high elevation streams they occupy (Isaak et al. 2010).

Understanding stream temperatures is important to 

forest managers for two reasons. First, many streams 

in the United States are already impaired due to 

temperature. Restoring vegetation along streams is 

an important management action in such watersheds. 

Second, as atmospheric temperatures increase due to 

climate change, water temperatures will also increase. 

Many aquatic species will become temperaturelimited in their habitat, and threshold effects may 

cause dramatic changes in fish populations from 

small changes in stream temperature. In these cases, 

establishing refugia will be an important tool in 

preserving species and biodiversity (Isaak et al. 2015).

Models for projecting how stream temperatures 

change as a function of forest management fall into 

two broad categories, reviewed in Caissie (2006): 

statistical models using observed data on stream 

temperature, stream properties, and environmental 

variables; and models of energy transfer between the 

stream and the environment. Statistical models regress 

observed stream temperatures on a wide variety of 

stream and atmospheric variables to predict stream 

temperature and assess habitat suitability for aquatic 

species (Isaak et al. 2014). For example, Jeppesen and 

Iversen (1987) used air temperature, solar radiation, and 

depth of water in a statistical model to predict stream 

temperatures. A new class of spatial statistical models, 

called spatial statistical network models, account for 

spatial dependence between observations in a stream 

network (e.g., temperature in a downstream segment of 

river is highly dependent on temperature in upstream 

segments) and have been shown to significantly 

improve predictive power (Isaak et al. 2014). Energy 

transfer models are based on a system of physical 

relationships for heat transfer between streams and 

other factors in the environment. Models are calibrated 

by adjusting system parameters for variables like solar 

radiation and wind speed to minimize the errors 

between observed and predicted stream temperature.

Generally, regression models are very good at predicting 

changes in temperature at a given site within the 

historic range. Energy transfer models are more often 

used when evaluating impacts outside of the historic 

range or when historic data is not available, such as 

when evaluating the impact of thermal effluent from 

power plants, coldwater releases from reservoirs, and 

large-scale vegetation removal (Kim and Chapra 1997, 

Sinokrot and Stefan 1993, Vugts 1974, Younus et al. 

2000).

Stream temperature is not typically valued directly as an 

ecosystem attribute. Instead, changes in temperature are 

linked to changes in an attribute of ultimate interest to 

beneficiaries. For example, Isaak and Hubert (2004), in 

a study of trout populations in the Salt River, examined 

the effect of temperature on cutthroat, brown, and 

brook trout abundance. They found populations peaked 

near 12 degrees Celsius, with viable ranges between 3 

and 21 degrees Celsius. Similar findings were found 

for trout in Michigan and Wisconsin rivers (Wehrly 

et al. 2007) and for fish abundance in Michigan rivers 

(Creque et al. 2005). Changes in abundance of aquatic 

species, particularly species targeted for recreational 

fishing, can then be valued with non-market valuation 

methods, which are described in detail in the sections 

on recreation and wildlife and Appendix 1. McKean 

et al. (2010) use a travel cost method to estimate a 

demand curve for steelhead trout in the Snake River. 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 15

Dalton et al. (1998) use contingent valuation methods 

to value hypothetical increases in trout populations, and 

Sorg et al. (1985) use contingent valuation methods 

to value hypothetical increases in the likelihood of 

catching coldwater fish. Values from these studies range 

from $100 per trip in Dalton et al. to about $32 per trip 

in Sorg et al. (1985).

Wu and Skelton-Groth (2002) offer an interesting 

example of when restoration focused on decreasing 

stream temperatures does not make sense. They 

examine optimal investments in streamside vegetation 

in Oregon’s John Day River Basin, where flow 

alterations and reduced vegetation have produced 

water temperatures unsuitable for native rainbow 

trout and Chinook salmon. They concluded that four 

out of 10 streams studied should not be targeted for 

restoration either because their current temperatures 

were so high that achieving suitable temperatures 

was near impossible, or because other water quality 

conditions were too poor to sustain fish even if suitable 

temperatures were achieved.

Sediment Flux

River systems require sediment and organic matter 

to form habitat and provide nutrients to the stream. 

Runoff carries necessary sediment to the streams, and 

periodic flooding of rivers deposits sediment within 

the floodplain. Deposited sediment rebuilds wetlands 

and provides rich soil for vegetation, which in turn 

slows runoff and captures sediment, allowing the “right” 

amount of sediment to enter the stream (Cummins 

1974). In small streams, too much sediment often arises 

due to deforestation and roads that increase surface 

runoff. Increased rates of water and sediment delivered 

to the river accentuate high and low flow conditions 

(Bormann and Likens 1979), leading to the widening 

and shallowing of stream channels (Knapp and 

Matthews 1996, Richards et al. 1996, Sidle and Sharma 

1996). When stream bottoms accumulate too much 

sediment, they choke important plants and damage 

spawning grounds. About 15 percent of all impaired 

waters in the United States have sediment loads above 

EPA water quality standards (U.S. Environmental 

Protection Agency 2016).

Several models exist for projecting the effect of land 

management on sediment delivery. Most of these 

models use a variant of the Universal Soil Loss 

Equation (USLE). The USLE calculates the amount of 

long-term annual sheet and rill erosion, given by

A R = × K L × × S C× P

where A is the computed annual erosion, R is a rainfall 

and runoff factor, K is a soil erodibility factor, LS is a 

topographic factor, C is a vegetation and management 

factor, and P is a conservation factor. USLE, at least 

in its early forms, was limited to estimating annual 

averages of sediment from a hillslope and was not 

suitable for estimating sediment delivery to offsite 

streams or reservoirs via stream networks, nor was it 

capable of channel erosion. These shortcomings led 

to the development of the Water Erosion Prediction 

Project (WEPP) model. The WEPP model is a 

physically based model, meaning it uses sub-models to 

simulate physical processes tied to erosion, including 

infiltration, runoff, sediment transport, deposition, 

plant growth, and residue decomposition (Flanagan et 

al. 2007). The model is parametrized based on ongoing 

field studies and is constantly being integrated into 

new technologies and models. Soto and Diaz-Fierros 

(1998) found WEPP model predictions were very close 

to actual measured sediment in watersheds following 

a controlled burn and a wildfire. The Sediment 

Tool, similar to WEPP, uses USLE with a routing 

model to project total sediment yield in mediumsized watersheds. Riedel and Vose (2002) use the 

Sediment Tool to estimate reductions in sediment from 

restoration along the Conasuaga River in northern 

Georgia and southeastern Tennessee. The Conasuaga 

River is an important drinking water source, is 

extensively used for recreation, and has high sediment 

due to erosion from agricultural lands, streambanks, 

and forest roads.

For large watersheds, the SWAT may be more 

appropriate. SWAT has been used extensively to model 

sediment and agricultural chemical movement in large 

complex river basins, with several studies targeting 

transportation of nonpoint source pollutants (e.g., 

Santhi et al. 2001). In general, process models like 

WEPP and SWAT tend to underestimate erosion 

from large events and overestimate erosion from small 

events. Shen et al. (2009) compare WEPP and SWAT; 

they find the two models produce very similar results 

for sediment yield, so the choice between the two may 
16 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

depend on other uses of the model. SWAT, for example, 

may be preferable as nutrient or chemical transport is 

also of interest.

The Watershed Assessment of River Stability 

and Sediment Supply (WARSSS) is a procedure 

developed by the EPA (Rosgen 2007) meant to guide 

land managers in watershed scale assessments of 

erosions. WARSSS begins with an initial survey of 

the watershed to identify likely areas of erosion and to 

rule out watersheds that are not likely to be sources of 

erosion. Then, a more intense look at the watershed is 

aimed at matching the scale and scope of assessment 

and management activities with the resources available 

to the land manager. Final assessment is then done on 

the areas deemed high priority. The assessment phase 

could use any of the tools listed above, but WARSSS 

also recommends a few user-friendly spreadsheetlike tools including the Bank Erosion Hazard Index 

(BEHI) and the Near Bank Stress estimation tool for 

streambank erosion, the Hillslope Processes tool for 

sediment from hillslopes and roads, and the WRENSS 

model for mass wasting.

The value of reducing sedimentation, in places where 

there is too much, may be measured with avoided 

costs or damages. One of the key costs associated with 

too much sediment is the increased cost to municipal 

water suppliers for dredging reservoirs and removing 

sediment in drinking water. Moore and McCarl (1987) 

detail persistent costs of soil erosion in the Willamette 

Valley in northwestern Oregon. They found that a 1 

percent reduction in turbidity reduced water treatment 

costs by one-third of 1 percent, or about $3,385 

annually across the region. Additional costs of sediment 

in the Willamette Valley were $4.22 million annually to 

clean ditches and culverts and $0.85 per ton to dredge 

and remove accumulated sediment from the Port of 

Portland. Forster et al. (1987) found that a 10 percent 

reduction in soil erosion in Ohio’s corn belt would 

reduce water treatment costs by 4 percent. Dearmont et 

al. (1998) found that a 1 percent reduction in turbidity 

led to a one-fourth of 1 percent reduction in treatment 

costs across water suppliers in Texas. Using a dataset of 

430 of the largest water utilities in the United States, 

Holmes (1988) found a 1 percent increase in turbidity 

leads to seven hundredths of one percent increase in 

operating and maintenance costs.

Perhaps the largest collection of values for reductions in 

sediment is given in Hansen and Ribaudo (2008). They 

give values for reductions in erosion for 14 categories 

of economic and ecological benefits in each of the 

2,111 eight-digit hydrologic unit code watersheds in 

the contiguous United States. Included benefits range 

from improvement in catch rates for marine fisheries 

to avoidance of groundings by shipping fleets to better 

water for recreation. And though the estimates were 

intended for reductions in erosion from agricultural 

fields, they are easily transferable to other sources of 

erosion in the same watershed. The values are estimated 

at a variety of spatial scales using damage functions, 

replacement costs, travel costs, and averting behavior 

models.

Chemicals, Nutrients, and Pathogens

Nutrients used in agriculture are the leading cause 

of water impairments on rivers and lakes in the 

United States, and they are the second leading 

cause of impairments to wetlands. Pathogens such 

as Cryptosporidium, Giardia, and coliforms enter 

waterways from drainage of lands with animal feces or 

open sewage systems. Fertilizers and pesticides used 

on agricultural fields include phosphates, nitrogen, 

and potassium from manure, chemical fertilizers, 

and sludge. High nutrient levels cause algae blooms, 

lead to organic enrichment and oxygen depletion in 

water bodies (fourth leading cause of impairment), 

taint drinking water, and causes fish die-offs from too 

little oxygen. Drinking water with nitrates can cause 

methemoglobinemia, which can be fatal in infants.

Studies show riparian vegetation routinely removes 

as much as 90 percent of nitrates in the groundwater 

(Hill 1996). The USDA’s National Agroforestry Center 

(NAC) advocates the use of forested buffers along 

agricultural fields to diversify farm income, reduce soil 

erosion, improve water quality, and increase wildlife 

habitat. NAC’s tool Buffer$ (http://nac.unl.edu/tools/

buffer$.htm) provides site-specific cost and benefit 

measures of forested buffers compared to traditional 

farming methods. The SWAT model, described above, 

also models nutrient and chemical movement through 

the watershed. If both sediment and nutrients are 

of interest, using one model for both may make the 

most sense. The USGS’s SPARROW model models 

instream measurements of water quality as a function 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 17

of upstream landscape characteristics within the 

watershed. SPARROW is unique in that it models the 

source of nutrients in a given body of water rather than 

the flow of nutrients through a watershed. Applications 

of SPARROW have mostly been on very large 

watersheds. It has been used to identify the sources 

of nutrients in U.S. streams (Smith et al. 2003b), to 

identify sources of phosphorous and nitrogen entering 

the Gulf of Mexico from the Mississippi River basin 

(Alexander et al. 2007a), and to assess the role of 

headwater streams in downstream water quality in 

the Northeast (Alexander et al. 2007b). Because of 

the scale of many of these applications, SPARROW 

may be useful for large regional collaborative projects 

but may not be useful for most forest management 

activities.

Similar to valuing the benefits from reduced sediment 

in a water body, one way to value the contribution 

of forest and riparian vegetation to drinking water 

quality is to use an estimate of the cost avoided if 

the ecosystem service is preserved. That cost should 

include expenditures to avert or mitigate the increased 

pollution as well as any damages that arise from the 

portion of additional pollution that is not mitigated 

downstream. Many studies approximate avoided 

cost by estimating the cost of (fully) replacing the 

functional benefits of the ecosystem service. For 

example, Kapoor and Viraraghavan (1997) estimate 

the costs of various treatment processes for the removal 

of nitrates from drinking water. Replacement costs 

have been used to value the preservation of ecosystem 

services that result in filtration avoidance waivers, 

which allow water treatment plants to use lower cost 

treatment methods. The 1996 amendments to the Safe 

Drinking Water Act required all surface water systems 

to be filtered for Giardia, viruses, bacteria, and turbidity 

unless managers could control human activities in 

the watershed. Any water treatment plant capable 

of demonstrating high quality of raw water at their 

intakes could forgo many of these treatment measures. 

In the case of New York City’s Catskill/Delaware 

water supply system, the largest unfiltered water supply 

system in the county, the city’s investment in land 

purchases and conservation easements resulted in an 

avoided expenditure of $6-8 billion in capital costs 

plus operation and maintenance costs of $300 million 

annually (Chichilnisky and Heal 1998).

When achieving a standard mandated by law, 

replacement cost may be the most important factor for 

decisionmakers. However, in these (and other) cases, 

the replacement cost may exceed the economically 

efficient expenditure on averting or mitigating pollution 

downstream. If some policy or management alternatives 

that achieve the legal water quality standard involve 

changes in other ecosystem services, it may be useful 

to have estimates of the benefits per se of water quality 

changes in order to compare with the value of changes 

in other services. In this case, the so-called “value of 

statistical life” (VSL) can be applied to quantify the 

benefits of small changes in the probability of mortality 

due to changes in chemical or nutrient flux (e.g., 

Hanley 1990). The EPA uses a variety of approaches 

to estimate a VSL (currently at $7.4 million in 2006 

dollars) to evaluate costs and benefits of regulations. A 

review of VSL practices is given in Viscusi and Aldy 

(2003).

AESTHETIC AMENITIES

Forests provide aesthetic amenities to residents and 

visitors (Table 1). Attributes of individual trees and the 

forest itself, like color, size, texture, and shape, affect 

scenic quality as well as peoples’ feelings of shelter and 

security associated with trees and forests. There is a 

large literature on the assessment of forest amenities, 

which relates biophysical features of the landscape 

such as forest composition and structure to design- or 

perception-based parameters that are assumed to be 

measures of aesthetic quality (see Ribe 1989 and Daniel 

2001 for reviews). These design- or perception-based 

measures can then be used to define desired landscape 

features and cost-effective ways of attaining them 

(e.g., Brown 1987, Ribe et al. 2002). Rather than focus 

on assessment, here we focus on economic benefit 

functions to estimate people’s WTP for aesthetic 

amenities, as expressed through stated or revealed 

preference methods.

Stated preference methods are quantitative techniques 

for eliciting an individual’s preferences by asking 

the person to choose from different hypothetical 

alternatives. Different types of choice experiments have 

been used to estimate people’s WTP for programs that 

increase the scenic quality of forests (e.g., Biénabe and 

Hearne 2006, Haefele et al. 1992). One type of choice 

experiment is called the contingent valuation method, 
18 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

which presents a respondent with a simple yes/no 

decision for the provision of a particular environmental 

service at a particular price. A more general type of 

choice experiment presents the respondent with a 

menu of options of different environmental services 

at different prices (Adamowicz et al. 1998). Stated 

preference methods are discussed in detail in the 

section on wildlife valuation. Forest insect and disease 

control are examples of programs that aim to improve 

aesthetic and recreation quality. Several studies have 

used stated and revealed preference methods to 

estimate people’s WTP for pest control programs and 

are reviewed in Rosenberger et al. (2012).

Revealed preference methods attempt to estimate 

individual preferences based on observable decisions 

in actual markets for a good whose value depends on 

the non-market ecosystem service. Revealed preference 

methods have been used to estimate the value of 

aesthetic amenities in urban and rural forests based 

on home sales prices in different housing markets 

and analyses of how aesthetic amenities of forests 

affect those prices. Urban and rural forests improve 

the quality of life of nearby residents. Forests affect 

the scenic quality of a neighborhood, provide privacy, 

reduce stress, shelter residents from the negative effects 

of undesirable land uses, and improve retail areas by 

creating environments that are more attractive to 

consumers (Dwyer et al. 1992). The value of changes 

in the quality of residential amenities can be estimated 

by relating home sale value to forest characteristics 

in hedonic price models (Rosen 1974). The hedonic 

technique treats properties as bundles of amenities and 

statistically decomposes sale value into the measurable 

quantity of each amenity multiplied by its implicit 

price or marginal value. While economists continue 

to improve statistical methods of hedonic estimation 

(e.g., Bajari et al. 2012, Bayer et al. 2016, Bishop and 

Timmins 2011, Kuminoff et al. 2010), the method 

itself has a relatively long and robust tradition of use in 

economics.

For houses in Portland, Oregon, Netusil et al. (2010) 

find that increasing the acreage of forest patches with 

greater than 76 percent canopy closure within a quarter 

mile increases sales prices in neighborhoods with low 

levels of such cover but decreases the value of homes 

in neighborhoods where cover is already high. The 

authors hypothesize that in the latter neighborhoods, 

increasing cover offers little additional benefit and 

instead may block desirable views. Indeed, Sander and 

Polasky (2009) find no statistically significant evidence 

that increasing the view of forest cover increases 

home values, though Sander et al. (2010) find that 

marginal increases in canopy cover within 250 meters 

(approximately 0.15 miles), and especially within 100 

meters, of homes in the Minneapolis-St. Paul metro 

area lead to statistically significant but practically very 

small increases in sale value (at the mean, a 10 percent 

increase in cover increases sale value by 0.48 percent).

In these and other cases, it is difficult or impossible 

to disentangle the value of aesthetic amenities per se

from that of other benefits related to the same forest 

attributes. For example, Stetler et al. (2010) find that 

views of areas burned by wildfire depress home values 

in Montana by as much as 14 percent, depending on 

proximity to the burn. Because proximity mattered, and 

because the effect on home prices appeared persistent, 

the authors suggest that part of the loss in value may 

be attributed to the perceived risk of property damages 

from future wildfires. In this case, forest structure and 

composition regulate fire occurrence and behavior. 

Changes in home sales prices associated with changes 

in forest structure and composition may represent 

homeowners’ pricing of both aesthetic amenities and 

fire risk. Similarly, Kovacs et al. (2010) find that Marin 

County (California) homes near oak woodlands subject 

to infestation of Sudden Oak Death suffer declines in 

property value on the order of 3 to 6 percent, which 

may be attributable to a combination of diminished 

aesthetic amenities, higher risk of damage from dead 

trees, or reduced recreational opportunities. As a 

notable exception, Kim and Johnson (2002) identify 

effects that are likely exclusively aesthetic. They find 

lower sale prices for Oregon homes from which forest 

clearcuts (-$16,381) or pasture (-$25,994), rather than 

mixed-species standing timber, were visible at the time 

of purchase.1

Ambiguity regarding the services associated with 

a quantitative estimate of marginal value from 

hedonic price models can confound interpretation for 

management purposes. For example, Kim and Wells 

(2005) find that increasing the area of medium density 

forest within 0.5 km of homes near Flagstaff, Arizona, 

1 Numbers in parentheses correspond to 1995 USD; the 

authors do not report average sale prices.
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 19

increases their value, while increasing the area of high 

density forest decreases their value. The authors appeal 

to literature on the determinants of scenic beauty to 

argue that these values reflect the aesthetic benefits 

of lower density. Consequently, they argue that their 

estimates should be added to the value of reduced fire 

risk in assessing the total benefits of potential fuel 

treatments that reduce forest density. However, it is 

quite plausible that the estimated hedonic prices of 

forest density also (or even exclusively) capture the 

value of changes in fire risk, whether in whole or part.

Similarly, the value of recreation opportunities in 

nearby forests may or may not be capitalized into 

housing values, and this ambiguity can complicate 

benefit-cost analysis. To the extent that the value 

of recreation opportunities is reflected in property 

values, the marginal value of a change in forest 

quality will be captured by the hedonic method. For 

instance, Kovacs (2012) finds that improvements in 

the quality of regional parks near Portland, Oregon, 

increases home values within 5 to 10 miles. However, 

adding an estimate of the “amenity” value obtained 

through hedonic price analysis to an estimate of the 

“recreational” value obtained through one of the travel 

cost-based methods will then double-count some 

portion of the recreational value and thus overstate the 

benefits of management or policy interventions meant 

to enhance forest quality (McConnell 1990).

Another example of ambiguity comes from the 

literature on the hedonic valuation of landscape 

configurations. Several hedonic price studies have 

used forest landscape metrics such as forest patch size, 

density, and diversity. In pioneering work, Geoghegan 

et al. (1997) estimate the hedonic value of changes 

in indices of landscape diversity and fragmentation 

in Maryland’s Patuxent watershed. The authors argue 

that the value of changes in these landscape metrics 

obtained from hedonic values represent homeowners’ 

WTP for a suite of services. The problem is that the 

authors do not establish the relationship between the 

landscape metric and the quality of services, which 

may depend on forest attributes unrelated to the 

landscape metrics. As a result, the policy relevance of 

these landscape metrics for the management of forest 

ecosystem services is not clear. Even the use of more 

straightforward measures, such as canopy cover within 

a given radius of the home (e.g., Netusil et al. 2010, 

Sander et al. 2010), is subject to important ambiguity 

when it does not distinguish among forests with 

different uses and ownership types (Mansfield et al. 

2005).

These ambiguities could be reduced by using landscape 

metrics that are significant in perception-based 

measures of visual quality (e.g., Ribe et al. 2002).

While forest cover provides amenity benefits to nearby 

residents, forest cover along travel routes, particularly 

backcountry and scenic byways, provides aesthetic 

value to leisure travelers and commuters (Ben-Akiva 

et al. 1984). Despite the potential importance of these 

benefits and the mandate of Federal agencies, such as 

the BLM, to manage lands for their scenic value, little 

research has been done to estimate the economic value 

to travelers of changes in particular forest attributes. 

Alivand et al. (2015) estimate a route choice model that 

accounts for a variety of route and view characteristics. 

Though not a formal valuation exercise, they find that 

forest cover contributes significantly to travelers’ choice 

of scenic versus faster routes.

RECREATION

Forests provide many types of recreational benefits—

opportunities for hiking, camping, mountain biking, 

and more. A recent analysis suggests an average 

net economic value of access to National Forests of 

approximately $90 per person per trip (Bowker et al.

2009). Estimates such as these rely on the travel cost 

approach (Clawson and Knetch 1966), which estimates 

recreationists’ WTP for access to a site based on their 

opportunity cost of travel to the site. Relating data on 

trip frequency to the implicit price of a trip (the travel 

cost), the analyst estimates a demand curve for trips 

to the site in question. The demand curve can then be 

used to estimate the consumer surplus or net economic 

value of access to the site.

While forest management decisions may be as 

straightforward as allowing or prohibiting access to a 

given area, in many cases management decisions affect 

the quality—not the possibility—of the recreational 

experience. Altering attributes of the forest site, either 

directly or indirectly, can influence the attractiveness 

and value of a site for recreation. Assessing the 

recreational value of changes in site attributes requires 
20 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

a more sophisticated approach to valuation. In this 

section, we describe economic benefit functions that 

have been developed to estimate the monetary value of 

changes in forest attributes that affect recreation quality.

Economists have used a variety of valuation techniques 

to estimate the marginal values of forest attributes to 

recreationists (see Appendix 1 for descriptions and 

discussion of these methods). Studies have focused on 

several different attributes, including the size of trees 

(basal area), stand age, and species composition. For 

example, Englin and Mendelsohn (1991) estimate 

hikers’ WTP for trail attributes in Washington State 

wilderness areas using the hedonic travel cost method 

(HTCM) (Brown and Mendelsohn 1984). These 

attributes include clearcuts, old growth, and species 

type. The authors find that the marginal social value of 

old growth for recreationists varies substantially from 

site to site and can even be negative (presumably due to 

over-satiation). Similarly, they find that the social value 

of a clearcut along a trail is positive in some cases. The 

positive values of clearcuts may be due to enhancing the 

populations of certain wildlife species that hikers value 

for viewing, which were unobservable to the analysts; 

clearcuts per se may not necessarily have positive 

aesthetic benefits.

Pendleton et al. (1998) and Englin et al. (2006) 

both find that the marginal value of changes in 

forest attributes varies substantially from site to site. 

Pendleton et al. use HTCM to measure marginal social 

values of forest attributes along hiking trails in the 

southern Appalachian Mountains, including wilderness 

areas, state parks, and Great Smoky Mountain National 

Park. They find that average basal area has an average 

marginal social value ranging from $8.50 per m2/ha in 

North Carolina to $372 in Georgia. The marginal value 

varies from site to site depending on current basal area 

as well as the characteristics and origins of trail visitors. 

Englin et al. (2006) use the random utility model 

(RUM) to estimate the value of changes in the length 

of backcountry trail segments running through different 

age class and species combinations in Jasper National 

Park (Canada). They find that the average recreationist 

places additional value on “truly ancient” forest (i.e., 

greater than 300 years in age). Notably, the authors 

stress that perhaps the most important insight from 

their study is the fact that welfare effects due to changes 

in forest characteristics vary over sites and users.

A critical challenge for predicting the full impact of 

a policy or management intervention is the valuation 

of natural changes in forest characteristics associated 

with stand growth over time. Hilger and Englin (2009) 

account for dynamic effects on site choice of fire across 

multiple trails, allowing for correlation across sites and 

over time. Englin et al. (2001) estimate the change in 

recreational benefits as forest areas recover from fire 

in the Intermountain West. They find that the effect 

of fire on recreational benefits is non-linear, with 

early benefits that gradually decline and then increase 

again as the forest matures and returns to normal. The 

authors note that early benefits are likely due to the 

novelty of postfire vegetation and wildlife. Managers 

should be careful to consider how these novelty-based 

benefits change if they are substantial and if fires 

become more frequent as a result of fire management.

In many cases, insufficient information on stand ages 

or condition leads the analyst to define recreationists’ 

preferences for stand attributes simply as preferences 

over discrete age classes (e.g., 30 to 50 years versus 50 

to 100 years). The consequence of this for projecting 

out the impacts of a policy intervention over time 

is that the estimated value of a stand for recreation 

will suddenly jump in an unrealistic fashion when it 

crosses an age class threshold. Englin (1990) avoids 

this by employing a piece-wise linear interpolation to 

approximate the dynamic path of recreational value of 

a stand from clearcut to old growth, based on consumer 

surplus estimates associated with different age classes 

obtained from an HTCM study. However, when 

preferences are defined over, say, length of trail passing 

through forest of a certain age or type, as in Englin 

and Mendelsohn (1991), changes in individual stands 

cannot be evaluated in isolation: the marginal value of a 

mile of trail through a given stand type depends on the 

existing length of trail through that stand type.

What we are in the habit of measuring or can measure 

easily is not necessarily the valued forest attribute 

of a given recreational service. For example, stand 

age or basal area may not be the valued attributes of 

recreationists; however, they may be proxies for other 

characteristics that recreationists do value. Pendleton 

and Shonkwiler (2001) deal with the problem of 

highly collinear site attributes, which one might expect 

in comparing trails or sites associated with different 

ecosystems. These “bundled” attributes may not be 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 21

valued individually but rather combined in unknown 

ways to represent characteristics of value to the 

recreationist. Moreover, without sufficient independent 

variation, reliable estimation of preferences for these 

individual attributes may not be statistically possible 

or policy-relevant. The authors jointly estimate the 

latent relationship between site attributes (e.g., basal 

area, number of large trees, species type) and ecosystem 

characteristics (“riparian” and “upland”) along with 

recreationists’ preferences over these characteristics 

and other site attributes. This allows the indirect, but 

more reliable, estimation of the value of changes in site 

attributes, which are subject to management action.

Another important source of ambiguity is that what 

may be an ecosystem service at one level of provision 

may be a disservice at a different level. For example, 

increasing crown cover from low to moderate levels 

may be valuable to hikers, while increasing it from, 

say, 90 percent to 99 percent may actually decrease the 

value of the recreational experience. This potential for 

“over-satiation” can make it difficult to differentiate 

between results that faithfully capture such nuance 

and results that are simply implausible. A further 

and potentially related source of ambiguity is that 

some ecosystem characteristics may represent services 

to some recreationists while they are disservices or 

irrelevant for others. For instance, Hesseln et al. (2003) 

and Loomis et al. (2001) find that welfare effects of 

changes in forest characteristics due to fire differ across 

hikers and mountain bikers.

WILDLIFE

Forests provide habitat for wildlife populations, 

which contribute to a wide range of benefits (Table 

1). Many wildlife species provide benefits related to 

their recreational, aesthetic, or spiritual value. Many 

species also play an important role in disease control, 

pest control, pollination, nutrient cycling and soil 

formation, or maintenance of genetic diversity. Wildlife 

populations may also have costs, for example, if they 

are vectors of disease, damage native vegetation, prey 

on livestock, or are a threat to human safety. In this 

section, we describe approaches to projecting attributes 

of wildlife populations (i.e., ecological production 

functions) and to estimating the monetary value of 

changes in wildlife populations (i.e., economic benefit 

functions). Our examples focus on terrestrial fauna, 

which has received the most attention in the literature.

Ecological Production Functions to 

Predict Changes in Wildlife Abundance

The fields of wildlife management and conservation 

biology contain a long line of research on demographic 

models of population abundance and viability, which 

are commonly used to inform wildlife management 

decisions (see Akcakaya et al. 2004, Beissinger and 

Westphal 1998, and Beissinger et al. 2009 for reviews). 

This research has spawned commercial software 

packages such as RAMAS (Akcakaya 2005) and Vortex 

(Lacy 1993) that allow users to build, run, and analyze 

models for species with virtually any life history. Here, 

we briefly describe the major types of model structures 

and give some early examples of their use in wildlife 

management. We conclude with concerns about the 

reliability of model projections and some ways to 

address them.

Deterministic population models use information 

about the age, stage, or social structure of the wildlife 

population, age or stage of first reproduction, and 

estimates of reproductive success and survivorship for 

different ages or stages to create a matrix model that 

projects the distribution of individuals over age or 

stage classes in discrete time periods (Getz and Haight 

1989). When demographic parameters such as survival 

and fecundity are related to habitat attributes using 

resource selection functions (Boyce and McDonald 

1999), matrix models can be used to assess the impacts 

of habitat management activities. For example, 

Beissinger (1995) used matrix models to project 

trends in the population of the marbled murrelet, an 

endangered bird species in the western United States 

that depends on old growth forest habitat.

Stochastic population models are similar to matrix 

models except point estimates of demographic 

parameters are replaced with probability distributions. 

Monte Carlo methods are used to sample from the 

probability distributions and project a population by 

varying demographic rates in each time step. Each 

projection follows a unique trajectory and yields 

a different ending population size, and as a result, 

stochastic demographic models yield probabilistic 

results (e.g., probability of population exceeding some 
22 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

minimum level after 100 years). Model projections 

must be replicated many times with different random 

number seeds to adequately sample combinations 

of parameter values and obtain a precise estimate 

of the desired measure of population abundance. 

Effects of different management options can be built 

into stochastic population models. For example, 

Lindenmayer et al. (1993) projected how measures of 

population abundance of a threatened mammal differed 

under silvicultural practices that affected habitat 

carrying capacity.

Modeling approaches increasingly incorporate spatial 

structure, for both individuals and populations. 

Metapopulations are spatially structured groups of local 

breeding populations. Migration of individuals between 

habitat patches affects local population dynamics, 

including the possibility of re-establishing populations 

in a patch after local extinction. Metapopulation 

structure is incorporated into demographic models 

using the dispersal of individuals to link local habitat 

patches. Metapopulation models typically incorporate 

patch-specific demographic rates and dispersal rules 

that are based on patch size and interpatch distances. 

Patch quality can be represented by varying carrying 

capacity or reproductive output among patches. The 

advantage of metapopulation models over singlepopulation models is they partially incorporate spatial 

realism. Thus, the effects of landscape change can be 

modeled, including effects of corridors, patch-specific 

habitat destruction, quality alterations, and changes 

in interpatch distances. For example, Bevers et al. 

(1997) built a deterministic metapopulation model of 

a black-footed ferret population and examined how 

patch-specific habitat management strategies affect 

projected population size. Stochastic metapopulation 

models usually include demographic and environmental 

stochasticity and catastrophes, with the added 

dimension that an understanding of covariation of rates 

between patches may be important. Model outputs 

can be expressed as ending metapopulation size, the 

likelihood of extinction for the whole metapopulation, 

the percentage of patches occupied, or the minimum 

number of patches or area required for metapopulation. 

For example, Haight et al. (2004) use a stochastic 

metapopulation model of kit foxes to predict and 

compare measures of population abundance under 

different options for habitat protection. Spencer et 

al. (2011) and Scheller et al. (2011) modeled fisher 

population health under forest succession, treatment, 

and wildfire in stochastic metapopulation models.

Spatially explicit population models specify the location 

of the desired unit (e.g., individuals or populations) 

within a heterogeneous landscape and define spatial 

relations between habitat patches and the surrounding 

landscape matrix (Dunning et al. 1995). Grid-based 

models subdivide the landscape into cells and specify 

demographic parameters for the local population in 

each cell. Movement between patches is determined by 

immigration and emigration rates. Individually based 

models keep track of the location of each individual 

across the landscape, and the demographic attributes 

are assigned based on the patches they occupy. Thus, 

each individual marks a trajectory over the landscape 

during the course of a population projection. In both 

grid-based and individual-based models, demographic 

parameters are treated as random variables and 

movement rules explicitly incorporate an animal’s 

perception of the landscape. Because spatially explicit 

models provide a technique for modeling processes 

that operate from local to landscape scales, they 

can potentially predict population and community 

changes in response to land-use changes, climatic 

alterations, or various management strategies. For 

example, Lamberson et al. (1994) and Marcot et al. 

(2015) use individual-based models to project spotted 

owl populations under different forest management 

strategies in the Pacific Northwest. Cohen et al. (2014) 

analyze the effects of landscape composition and 

configuration on migrating songbird populations using 

an individual-based model.

There are several important concerns about the accuracy 

and interpretation of results from demographic models 

(Beissinger and Westphal 1998). Models use detailed 

demographic data, but information available may be 

inadequate, imprecise, and based on studies too limited 

in duration to properly estimate means and variances in 

vital rates. Long projections cannot be validated against 

short-term observations. Future changes in habitat 

quality or quantity are often unknown. Differences in 

model structure can have strong effects on management 

recommendations. Taken together, these concerns 

strongly suggest that one should place very limited 

confidence in the long-term projections generated 

by these models. Nevertheless, these concerns can 

be reduced by making short-term projections under 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 23

different scenarios of habitat quality or management 

to estimate changes in population abundance relative 

to a suitable baseline. These projections of marginal 

changes in population abundance can then be validated 

using field studies, experiments, and sensitivity analyses 

(e.g., Wood et al. 2015). Projections from demographic 

models have been successful in determining resource 

management decisions for spotted owls and grizzly 

bears because models were followed by comprehensive 

field studies and were thoroughly reviewed and revised 

(Boyce 1993, Lamberson et al. 1994, Marcot et al. 

2015).

Economic Benefit Functions for Wildlife 

Abundance

Estimating the monetary value of changes in 

wildlife abundance depends on whether the affected 

benefits are commercial or recreational in nature. 

Although market hunting of wildlife is largely illegal 

in the United States (Geist 1988), fur is a valuable 

commodity that is sold in markets. The harvest of 

furbearing animals in the United States has declined 

over the past several decades, with harvests averaging 

approximately 4.4 million pelts per year in the past 

decade, but harvests do increase when pelt prices rise 

(Flather et al. 2013). Valuing the commercial effects 

of changes in the abundance and spatial distribution 

of a furbearing animal population requires not only 

an appropriate ecological model but also an economic 

model to predict changes in the cost and quantity of 

harvest. The standard, information-intensive approach 

to valuation in this case is the specification and 

estimation of a full profit function for the affected 

producer(s), in which the wildlife species is an input to 

the production process. In some cases, however, it may 

be possible to estimate changes in producer surplus 

by estimating changes in the derived demand for a 

weakly complementary input (Huang and Smith 1998, 

Pattanayak and Butry 2005).

In addition to commercial effects, changing forest 

management practices may also have effects on the 

quality of recreational hunting. Sometimes the right 

to hunt on private land is sold in the market, and data 

on hunting leases can be used in hedonic analyses to 

estimate the monetary value of changes in the quality 

of hunting (Livengood 1983, Rhyne et al. 2009). 

Hunting leases include premiums or discounts based 

on density of wildlife, structure and composition of 

vegetation, distance to cities, degree of congestion, 

and size of the hunting area. The value of each site 

depends on the exact mix of characteristics as given 

by a hedonic price function. For example, Livengood 

(1983) estimates a hedonic price model for the value 

of hunting white-tailed deer in Texas from a survey 

of hunters during the 1978-79 hunting season. From 

this hedonic model, Livengood (1983) estimates the 

demand (marginal willingness to pay) for wildlife and 

finds that hunters are willing to pay $23 for a single 

deer while an additional deer was worth only $13. 

These values can then be used to estimate the effects 

of changes in management that affect deer density and 

hunting success. When information about hunting 

leases is not available, valuation of recreational hunting 

can be made using methods for valuing other types 

of recreation. For example, Knoche and Lupi (2007) 

use the random utility travel cost method to estimate 

demand for deer hunting sites on private farm land 

in southern lower Michigan and estimate the value of 

increasing public access for deer hunters to 10 percent 

of the private lands. The estimated aggregate WTP for 

this increased access was about $39 per acre, which is 

much higher than the average amount paid to farmers 

who enroll in Michigan’s Hunter Access Program.

When it comes to hunting on public land, hunters do 

not purchase hunting trips in a true market (much 

less do they purchase any particular forest attribute 

that determines the quality of the overall hunting 

experience); instead they purchase hunting licenses 

at fees that are set by the government and incur costs 

to partake in hunting (travel, supplies). Nevertheless, 

hunters’ choices of recreational sites reflect their 

implicit willingness to pay for more (or less) of 

particular attributes (e.g., wildlife population density), 

and researchers have used a variety of models to 

estimate the monetary value of changes in the quality 

of hunting experiences. These are the same models 

used to value changes in forest attributes for other 

types of recreationists, including the approximately 

2.3 million visitors to National Forests with the aim 

of viewing wildlife (Mockrin et al. 2012). Valuation 

approaches include the hedonic method (Livengood 

1983, Rosen 1974), HTCM (Brown and Mendelsohn 

1984, Mendelsohn 1987), the Kuhn-Tucker method 

(von Haefen and Phaneuf 2005), and both revealed 

and stated preference discrete choice RUMs (Boxall 
24 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

et al. 1996, Hanemann 1984, Morton et al. 1995). See 

Appendix 1 for a discussion of these methods.

Knoche et al. (2015) provide a notable application of 

integrated modeling and valuation to assess the benefits 

to pheasant hunters of spatially targeted habitat 

restoration efforts. Utilizing a previously developed, 

spatially explicit model of ring-necked pheasant 

sightings in conjunction with a RUM of hunter site 

choice, they find that the value restoration investments 

varies considerably across space, from near $0 to as 

much as $2.50 per acre (or more than twice the median 

value).

Beyond recreation, cultural values (aesthetics, spiritual 

value, and existence value) associated with wildlife 

populations are more challenging to quantify. Much 

of the research on non-market valuation has focused 

on threatened and endangered species as economists 

have long recognized that the total economic value 

of the majority of these species includes both 

recreational use and non-use (existence and bequest) 

values (Richardson and Loomis 2009). One way to 

estimate the total economic value is the contingent 

valuation method (CVM) (Mitchell and Carson 

1989). Applying CVM to a threatened or endangered 

species, researchers ask a carefully constructed sample 

of people questions about respondents’ WTP to protect 

the species. Estimates of individual WTP based on 

the sample group are then used to extrapolate to the 

population as a whole and estimate the total social 

value of protecting the species. The surveys are often 

designed in a yes or no referendum format put to 

the respondent as a vote on a specific management 

plan or policy to protect a particular species or set of 

species. Respondents are given detailed information 

on the species in question, the hypothetical protection 

measure, the expected outcome of the measure, and 

how the measure is to be financed (e.g. user fees, taxes, 

voluntary contributions). Results of CVM surveys 

suggest that people are willing to pay a small portion 

of their income toward the protection of endangered 

or rare species for a variety of reasons (Richardson and 

Loomis 2009).

Not surprisingly, methodological factors related 

to survey design lead to important differences in 

CVM estimates of the value of wildlife protection. 

To investigate these effects, Richardson and Loomis 

(2009) performed a meta-analysis of the results from 

49 studies that used CVM to estimate the value of 

threatened and endangered species in the United 

States. The authors found that several features of survey 

design, including type of species being valued, the 

change in the size of the species population, payment 

frequency, type of respondent, and survey mode, had 

an effect on respondents’ WTP for species existence. 

For instance, WTP is greater for marine mammals, 

fish, and birds than for land mammals and reptiles. 

Charismatic species, defined as those large vertebrates 

that are appealing to humans and gain support for 

conservation campaigns, had a WTP 115-180 percent 

higher than non-charismatic species. Surveys based on 

hypothetical management plans or policies with larger 

changes in the size of the species population lead to 

greater estimates of WTP for protection, although 

Jacobsen et al. (2012) did not see consistent patterns 

of paying more for larger population sizes. Surveys 

using a sample frame of visitors to a particular area 

result in higher WTP values than surveys that sample 

households, due to the fact that visitors have use as well 

as non-use values for threatened or endangered species. 

Surveys based on dichotomous choice, referendum 

format questions result in higher estimates than openended questions, all else constant (see also Balistreri et 

al. 2001andBrown et al. 1996). Richardson and Loomis 

(2009) conclude that the regression function from their 

meta-analysis provides a rough estimate of WTP for 

a particular threatened or endangered species under 

various circumstances. As an alternative to conducting 

an original CVM study, using the meta-analysis 

regression function as a benefits transfer method 

can play a significant role in estimating the benefits 

of threatened or endangered species protection. We 

discuss benefits transfer methods in the next section on 

integrated modeling and benefit-cost analysis.

Thousands of contingent valuation studies have 

been conducted in over 130 countries to estimate 

the existence value of public goods in cultural, 

environmental, health, transportation, and other sectors 

of the economy (Carson 2012). Despite advances in, 

and general agreement on, best practices in CVM 

methodology (see Hanemann 1994), it remains 

controversial (Mendelsohn and Olmstead 2009), with 

proponents asserting that reliable estimates can be 

generated with the use of properly structured survey 

instruments (e.g., Carson et al. 2001 and Carson 2012) 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 25

and critics suggesting that the method is fundamentally 

flawed (e.g., Hausman 2012). One of the issues is 

hypothetical bias, which arises when respondents 

answer a hypothetical question with which they have 

no market experience. When hypothetical questions 

are asked about WTP, the results tend to be upwardbiased (Hausman 2012). Another issue is the scope or 

embeddedness effect. Results of contingent valuation 

surveys have shown that the assessed value of a public 

good can vary widely depending on whether the good 

is assessed on its own or embedded as part of a larger, 

more inclusive package. Lastly, assessed values of a 

public good differ depending on whether respondents 

are asked how much they would be willing to pay 

to avoid a negative outcome (or achieve a positive 

outcome) or how much they would be willing to accept 

to allow a negative outcome (or not to receive a positive 

outcome). Economic theory says WTP and WTA 

should be approximately equal but CVM results point 

to persistent disparities (Hausman 2012).

INTEGRATED MODELING AND 

BENEFIT-COST ANALYSIS

Forest management is inherently a problem of joint 

production: Decisions intended to produce or preserve 

one type of forest ecosystem good or service affect 

the provision of other ecosystem services. Ideally, the 

value of changes in other ecosystem services is not 

an afterthought but rather proactively considered 

in decision-making. To this end, managers can 

utilize integrated models that account for the joint 

production of, and relative value of changes in, multiple 

ecosystem services (e.g., Bowes and Krutilla 1989, 

Calish et al. 1978, Creedy and Wurzbacher 2001, 

Grasso 1998, Nelson et al. 2009). Integrated models: 

1) predict changes in ecosystem structure as a result 

of a given management decision; 2) predict changes 

in the quantity or quality of ecosystem services as 

a result of changes in ecosystem structure; and 3) 

quantify the net benefits of all changes, including 

but not limited to changes in ecosystem services. In 

short, integrated models allow a decision-maker to 

identify the management alternative with the highest 

net benefit. This section discusses use of integrated 

modeling and benefit-cost analysis to inform forest 

policy and management. It introduces cases of 

increasing complexity to elucidate the integrated 

modeling approach and demonstrate the challenges and 

opportunities in applying it.

Static Benefits and Costs, and 

Selection, Among Discrete Alternatives

When land managers choose among distinct 

management alternatives and net benefits do 

not change through time, the analysis involves a 

straightforward comparison of annual net benefits 

among alternatives. For example, Polasky et al. (2011) 

quantify the changes in the values of annual flows of 

ecosystem services, including carbon storage, water 

quality, and agricultural and timber production, 

associated with actual and alternative land-use change 

scenarios during 1992-2001 in Minnesota. They find 

that including the value of carbon storage and water 

quality improvements dramatically changed the 

ranking of the alternatives.

To demonstrate the process of integrated modeling 

and benefit-cost analysis, we present a basic theoretical 

framework and simple numerical example comparing 

net benefits of developing or preserving a patch of 

mature forest, where preservation benefits include 

values of carbon storage and water availability for 

irrigators. Each management option is associated with 

a set of characteristics (S) that summarize the state of 

the system under that option, SD for development and 

SP for preservation. Ecological production functions 

(EPFs) translate from the state of system (and any 

additional management inputs) into the quantity of 

a given ecosystem service: xj = fj(Si). Denote by g(xj) 

the economic benefits function (EBF) that maps the 

quantity of ecosystem service j into a dollar value. 

For marginal changes in xj, dg/dxj = pj, where pj is the 

market or shadow price of xj. If management decisions 

only lead to marginal changes in the ecosystem service, 

then the EBF can be simplified to g(xj) = pjxj. However, 

this may give a poor approximation of the value of 

changes in ecosystem services when marginal WTP is 

sensitive to changes in the provision of services over 

the policy-relevant range of service quantities. In that 

case, the analyst should make use of an integrable 

demand curve (or demand system) in order to capture 

the surplus value corresponding to a given quantity of 

ecosystem services.
26 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

Given the relevant EPFs and EBFs, the manager 

can determine whether development or preservation 

maximizes the sum of market and non-market values:

max{i= D,P} BD1D + ∑gj(xj)

s.t. xj = fj(Si)

where BD denotes the annual benefits of development 

and 1D is an indicator function for the choice to 

develop.

For example, assume that developers estimate the 

value of development at $20,000 per year, so that BD = 

$20,000. Assuming the forest is in equilibrium, it will 

produce the same quantity of ecosystem services—

say, carbon storage and water yield for downstream 

irrigators—each year. Suppose that the forest’s 

aboveground biomass if preserved (SP) is 14,000 tons, 

and if developed (SD) would be 4,000 tons. If the 

carbon content of aboveground forest biomass in this 

region is 50 percent, then xc = fc(S) = 0.5S, and the 

preserved stand stores 7,000 tons of carbon while the 

land with residential development would store 2,000 

tons of carbon. The carbon rental value is $2.50 per ton 

per year. A hydrologic model predicts that

xw = fw(S) = 136 + 0.001S,

so that irrigators downstream will receive a 

summertime yield of 150 acre-feet per year with the 

standing forest and 140 acre-feet with the residential 

development. A hedonic study of farm prices in the 

area reveals a shadow price of $10 per acre-foot for 

rights to irrigation water. Using these EPFs and EBFs, 

development produces an estimated annual value of 

$26,400, while the current landscape produces an 

estimated annual value of $19,000. Based on this 

benefit-cost analysis alone, the land manager interested 

in maximizing net benefits would opt for development.

Temporal Changes in Benefits 

and Costs, and Optimization, of a 

Continuous Choice Variable

In the example above of development versus 

preservation, there are several reasons why benefits are 

not likely to be constant over time. First, the fate of the 

felled timber matters. If it has commercial value, this 

must be added to the overall value of the development 

alternative, but that value will only be realized once, not 

annually. Whether it will be burned, chipped, pulped, or 

used for long-lived wood products also matters, as this 

determines the timing and value of carbon emissions. 

Second, not all of the value of stored carbon is lost 

at once. Society continues to receive the benefits of 

some carbon storage even after the stand is gone, and 

this too must be added to the tally of benefits under 

the development alternative. Another complication 

arises if we no longer assume the marginal social value 

of ecosystem services stays constant over time. For 

instance, rising demand for irrigation water would 

increase its marginal value over time. The SCC is not 

fixed, but rather dependent on the concentration of 

greenhouse gases in the atmosphere, which will change 

over time. Even the annual value of the development 

itself may change over time with changes in the real 

estate market.

When the costs and benefits of forest management 

decisions vary over time, alternatives can be compared 

according to their net present value. This calculation 

requires projections of the consequences of current 

action on ecosystem processes, projections of the values 

of ecosystem services, and a discount rate that weighs 

present versus future benefits. Foresters are familiar 

with the well-known Faustmann (1849) formula for 

determining the net present value of an infinite series of 

timber plantations starting with bare land and accounting 

for the value of future timber yields and planting costs 

(see also Samuelson 1976). The Faustmann formula 

has been extended to include the discounted value of a 

stream of amenity benefits associated with the growing 

plantation (Hartman 1976).

Imagine that a land manager considers a sequence 

of timber harvests over time as an alternative to 

preservation or development. Then, a formula for the 

net present value (NVP) of timber management that 

includes the marginal values of services associated with 

water yield and carbon storage is:

NPV

p V T

r

p W t p C t

r

r

T

i

T t

t

T

T

i

i

= +

+

+

+





 





+

=

∑ 1 2 3

1 1 1

1

( )

( )

() ()

( )

( ) k

k

i

i

=

= ∑

∞

∑ 1

1

where r is the discount rate, Ti is the age of the stand 

at the time of the ith harvest, t is the index of stand 

age during the ith rotation, V(Ti) is the merchantable 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 27

volume at rotation age, W(t) and C(t) are water yield 

and carbon storage as a function of stand age, and 

p1, p2 , and p3 are prices or marginal values of timber, 

water, and carbon, respectively. Note that, for each 

rotation, the computation includes the values of timber 

harvest, water yield, and carbon storage discounted 

to the beginning of the rotation (numerator) and 

then discounted to the present (denominator). Given 

expected prices or marginal values and of the evolution 

of landscape characteristics over time, the land manager 

can evaluate and compare the NPV of the development, 

preservation, and timber management alternatives.

Notable applications of the Hartman-Faustmann 

model include: Calish et al. (1978), who investigate 

optimal rotation of Douglas-fir in the Pacific 

Northwest when accounting for the value of services 

associated with the provision of wildlife, water, and 

aesthetic amenities; Englin and Callaway (1993, 1995), 

who extend the analysis to include the value of carbon 

storage; and Creedy and Wurzbacher (2001), who 

consider the joint production and valuation of carbon 

storage and water yield services. None of these studies 

incorporates spatial interdependence among stands in 

the provision of services, which can greatly complicate 

the problem.

Spatial Interdependence of Benefits 

and Costs

Spatial interdependence arises when the provision 

or value of ecosystem services from one forest stand 

depends on the state of other managed lands in the 

vicinity. For example, a riparian buffer forest will filter 

out more nutrient and sediments if there are clearcuts 

upslope compared to having intact forest upslope. Hof 

and Bevers (2000) address the problem of scheduling 

the location of timber harvests over time to minimize 

sediment yield in stream segments while providing 

an even-flow of timber yield. Their model projects 

how sediment levels in different streams are affected 

by the timing and spatial arrangement of harvests in 

the vicinity of those streams. Their results suggest that 

maintaining forest cover adjacent to streams while 

clumping timber harvests in upper reaches of the 

watershed yields less sediment than dispersing small 

clearcuts across the watershed while producing the 

same amount of timber. While their model projects the 

tradeoffs between timber and sediment yield over time, 

it could be extended to maximize the discounted value 

of timber harvest and sediment reduction under given 

prices.

Another example of spatial interdependence involves 

the protection of habitat for wildlife populations 

whose dispersal and survival depend on the size and 

proximity of habitat patches. Polasky et al. (2008) 

utilize a spatially explicit biological model of species 

survival rates for 267 species that accounts for habitat 

preferences, area requirements, and dispersal ability 

between habitat patches. Their economic model 

incorporates site characteristics and location to predict 

market returns for a variety of potential land uses. 

Their results suggest that protecting and restoring 

contiguous blocks of low-elevation wetlands and oldgrowth conifer forests produce large increases in species 

survival relative to a land-use pattern that maximizes 

market value and converts these areas to agriculture or 

housing development.

Uncertainty

There is often considerable uncertainty about the 

provision of ecosystem services under different options 

or about the values of nonmarketed goods and services. 

Nearly every aspect of an ecological or economic 

model, from its specification to its parameters to the 

data to which it is applied, involves some uncertainty; 

and all of these uncertainties propagate through 

the modeling process to the final results. However, 

uncertainty does not preclude, nor even necessarily 

complicate, integrated modeling and benefit-cost 

analysis. When uncertainty (say, about a parameter 

value) can be bounded, the choice of high-end or 

low-end values might have little or no impact on the 

ranking of management alternatives. For example, 

returning to the stylized case above, suppose there was 

uncertainty about the shadow price of an acre-foot 

of water and estimates range anywhere from $0 to 

$100 per acre-foot. Over this entire range, the value 

of benefits under development exceeds the value of 

benefits under preservation. At $100 per acre-foot, the 

value of development is $39,000, while the value of 

preservation is $32,500.

Uncertainty really matters when it can change the 

ranking of alternatives. In many such cases, it is 

appropriate to use probability weights within an 
28 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

expected utility framework and select the management 

option with the greatest expected value. However, the 

irreversibility of some land-use transitions can change 

the calculus. If more or better information about the 

costs or benefits of alternative land uses is expected in 

the future, there is a “quasi-option” value to receiving 

this information--but the value can only be positive 

if irreversible decisions have not yet been made, 

otherwise the information is useless. Thus, the rational 

decisionmaker may wish to postpone irreversible landuse transitions though they appear to offer the greatest 

expected value according to current information (Arrow 

and Fisher 1974).

Benefits Transfer

Benefit-cost analysis requires the use of integrated 

ecological and economic models to predict the net 

social benefits of alternative management or policy 

options, and the decisionmaker needs to assess the 

availability and applicability of scientific and economic 

models for the problem at hand. It is best to use EPFs 

and EBFs that were derived for the contexts in which 

they will be applied. While estimates of the SCC 

apply everywhere and can be used “off the shelf,” the 

highly context-dependent nature of demand for water, 

recreational attributes, or residential amenities means 

that estimates from one place cannot be readily applied 

to another location.

Collecting the necessary data for a new, site-specific 

valuation study can be very costly (though technology 

has significantly lowered the cost of administering 

surveys and opened the door to innovative new 

approaches to data collection through web-scraping), 

and decisionmakers should take a common sense 

approach to the question of data collection. In many 

cases, sensitivity analysis of benefit-cost results using 

a range of simulated estimates for non-market values 

will shed light on the necessity of obtaining real 

estimates from new data: If the ranking of management 

alternatives does not appear to change within the 

range reasonable estimates for the ecosystem services 

in question, economic valuation is unnecessary. When 

rankings do change, the decisionmaker should compare 

the expected costs of data collection to the magnitude 

of potential benefits from more accurate information 

implied by the simulations.

If valuation would improve decision-making but the 

costs of new data are relatively high, the best option 

may be benefits transfer, the application of existing 

valuation studies from different locations to the focal 

site. Benefits transfer (BT) has garnered a great deal of 

attention among academic economists and practitioners 

(see Boyle et al. 2010 and Richardson et al. 2015 

for recent reviews). There is a strong consensus that 

practitioners of BT ought to utilize benefits functions

rather than point estimates of marginal value or 

consumer surplus in order to transfer information from 

the study context to the policy context. Simply taking a 

point estimate of the value of a change in an ecosystem 

service from one study site and using it in another (i.e., 

value transfer) ignores important differences and can 

introduce substantial error into valuation and decisionmaking. Averaging over multiple point estimates from 

different study sites generally will not alleviate this 

problem, since the values almost certainly will not come 

from a representative sample.

The methods of benefits function transfer fall into 

two categories: reduced-form meta-analysis and 

structural approaches relying on a particular utility 

function. Meta-analysis requires the availability of 

multiple studies that estimate the value of a change 

in a given ecosystem service using a common metric 

(i.e., Marshallian consumer surplus or Hicksian 

compensating variation). The analyst uses multiple 

regression to estimate a model that predicts these 

value estimates. In many cases, the analyst will not 

have a sufficient number of observations (i.e., valuation 

studies) to estimate a model that controls for all of the 

factors that drive differences in value across contexts. 

Boyle et al. (2010) note five dimensions along which 

differences between study site and policy site will 

contribute to differences in marginal value: 1) the 

studied and affected populations, 2) the physical 

conditions of the ecosystem service, 3) the availability 

of substitutes, 4) the institutional settings, and 5) other 

context-specific characteristics. Even when data are 

available, the analyst should keep in mind that the 

estimated model will not necessarily predict a value 

for the policy context that is consistent with economic 

theory.

Structural approaches, in contrast, produce value 

estimates consistent with economic theory. The most 

straightforward structural approach is the direct 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 29

transfer of an estimated demand or utility function from 

a study context to the policy context. This is known 

as preference function transfer. Given the estimated 

parameters of the demand or utility function from 

the study context, the analyst need only plug in values 

for the variables corresponding to the policy context. 

Ideally, parameters have been estimated not only for 

the shape of the demand or utility function, but also for 

“shift” variables, such as demographic and locational 

characteristics, the distributions of which are likely to 

differ between the study and policy contexts. Boyle et 

al. (2010) assert that stated-preference choice models, 

travel cost-based site choice models, and structural 

hedonic models that incorporate these shift variables 

are most amenable to preference function transfer.

When studies for more than one similar context are 

available, the analyst may wish to utilize preference 

calibration (Smith et al. 2002) instead of relying 

on preference transfer, which only leverages the 

information contained in a single study. Like reducedform meta-analysis, preference calibration incorporates 

value estimates from multiple studies, but it does 

so in a way that imposes consistency with standard 

economic theory. Using this method, the analyst 

assumes a particular form of the utility function and 

calibrates the parameters of this function to best fit 

existing value estimates from the literature, conditional 

on observed site and demographic characteristics 

if possible. Smith et al. demonstrate the calibration 

method for water quality preferences utilizing hedonic, 

contingent valuation and travel cost studies from quite 

different contexts. The multi-step process requires a 

strong understanding of consumer theory but provides 

a foundation for estimating the value marginal and 

non-marginal changes of the attribute of interest in the 

policy site.

No matter the choice of benefits function transfer 

method, an analyst’s data requirements for the policy 

site remain significant. Having the data necessary to 

account for demographic, institutional, and locational 

differences is critical. The increasing richness and 

availability of spatially explicit (i.e., GIS) data 

allow an unprecedented opportunity to account for 

such differences, but only to the extent that such 

characteristics have been described and incorporated 

in existing studies (Boyle et al. 2010, Troy and Wilson 

2006).

Non-use Values and Distributional 

Analysis

Not everything we value as a society fits neatly into 

benefit-cost analysis. Some non-market benefits are 

difficult (and some critics say impossible) to evaluate in 

monetary terms. Species preservation is a particularly 

salient example. As an alternative to incorporating 

endangered species into the CBA framework via 

valuation, decisionmakers can acknowledge an 

obligation to preserve a species for its own sake and 

build in preservation to the integrated model as a 

constraint. They can then analyze the tradeoffs between 

management alternatives that produce different 

levels of social benefits (as measured by the values of 

ecosystem services produced) and species abundancy or 

survival probability. For example, Polasky et al. (2008) 

trace out a two-dimensional production possibility 

frontier to illustrate the tradeoffs between biodiversity 

(vertebrate species richness) and landscape value under 

different land-use regimes within the Willamette River 

Basin. Armed with such information, decisionmakers 

can choose the regulatory or management regime to 

achieve an outcome most consistent with society’s 

commitment to other species.

Similarly, society may have a commitment to 

promote the well-being of particular stakeholders. 

In this case, benefit-cost analysis alone provides 

insufficient information to identify the most preferable 

management option because it ranks alternatives 

according to the Kaldor-Hicks potential compensation 

criterion, which maximizes net benefits irrespective 

of the actual distribution of those benefits among 

stakeholders. Distributional analysis can complement 

benefit-cost analysis in enabling a just, well-considered 

decision (Banzhaf 2011). It can help land managers 

avoid inequitable outcomes that would not otherwise 

be corrected through adjustments in tax or welfare 

policies or simply “net out” in the course of many 

different changes to forest management. The essence 

of distributional analysis is the disaggregation of net 

benefits by group. Whether groups are based race, 

income, or service beneficiary type, much of the 

information necessary for distributional analysis should 

already be on hand, as each of these variables plays 

an important role in recreation- and amenity-related 

ecosystem service valuation.
30 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

CONCLUSIONS

Forests provide an array of benefits beyond marketable 

commodities. The ecosystem service metaphor 

has brought greater attention to these benefits 

and encouraged a more nuanced understanding of 

the relationship between various forest attributes 

and their beneficiaries. Forest management and 

policymaking involves difficult decisions, with 

multiple and sometimes conflicting objectives and 

concerns about the distribution of benefits across time, 

space, and stakeholders. Integrating ecological and 

economic models to predict the benefits and costs of 

alternative management or policy options can assist 

decisionmakers in navigating these tradeoffs and make 

environmental decisionmaking more transparent, 

rational, and responsive to people’s preferences. Failing 

to properly quantify non-market benefits for decisionmaking may lead to substantial tradeoffs for society—

whether in the form of over-exploitation of commercial 

resources or overly cautious and costly restrictions on 

the use of forests. Despite this, economic valuation 

of forest ecosystem services and comprehensive 

benefit-cost analysis remain under-utilized in forest 

management. Though not without their limitations, 

these methods can provide decisionmakers with 

important information regarding the costs and benefits 

of management actions.

How should the Forest Service proceed given the 

current state of the literature on the assessment and 

economic valuation of ecosystem services? We have 

four specific suggestions:

1. Estimate the economic benefits of a given 

forest and associated management policy, 

using available methods for services related 

to timber, carbon, water, amenities, recreation, 

and wildlife. This practice is important to 

identifying and describing the range of benefits 

provided by the forest. It also provides a 

baseline for evaluating changes in management.

2. Estimate the change in economic benefits 

associated with a change in management, 

regulations, or incentives, or a natural 

disturbance. This practice is important to 

evaluating and prioritizing different policies, 

evaluating potential tradeoffs in management 

decisions, and assessing the damages caused by 

natural disturbances.

3. Enhance communication with stakeholders 

about the economic benefits and costs of 

potential changes in forest management. This 

practice is important because communities’ 

preferences for different ecosystem services 

may be affected by estimates of economic 

performance.

4. Monitor the performance of agency programs. 

This practice is important to tracking whether 

the actual economic benefits and costs of agency 

programs are consistent with projections.

It is probably premature for the Forest Service 

to integrate these suggestions into every forest 

management plan. The literature simply does not 

have enough evidence to show how to do it properly 

for the entire forest system. A more attainable goal 

would be to focus on a few forests in different regions 

of the country and develop and evaluate alternative 

management plans for those forests. What are the 

insights of ecosystem service valuation for actively 

managing timber, carbon, water, amenities, recreation, 

and wildlife? What is the response of stakeholders to 

economic information about a wide range of ecosystem 

services? Once some concrete experience is gained, then 

it would be time to consider extending the plans to the 

rest of the system. Finally, there is a continuing need to 

improve EPFs and economic valuation functions of all 

ecosystem services as well as methods of BT.

ACKNOWLEDGMENTS

We are grateful to Frank Casey, Jeff Englin, Lane 

Eskew, Paul Gobster, Robert Mendelsohn, Evan 

Mercer, and two anonymous reviewers for constructive 

comments.
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 31

LITERATURE CITED

Adamowicz, W.; Boxall, P.; Williams, M.; Louviere, 

J. 1998. Stated preference approaches for 

measurement of passive use values: choice 

experiments and contingent valuation. American 

Journal of Agricultural Economics. 80: 64-75.

Adams, D.; Alig, R.; McCarl, B.; Callaway, J.; Winnett, 

S. 1999. Minimum cost strategies for sequestering 

carbon in forests. Land Economics. 75(3): 360-374.

Akbari, H. 2002. Shade trees reduce building energy 

use and CO2 emissions from power plants.

Environmental Pollution. 116: S119-S126.

Akcakaya, H.R. 2005. RAMAS GIS: linking spatial 

data with population viability analysis. Version 5.

Setauket, NY: Applied Biomathematics.

Akcakaya, H.R.; Burgman, M.; Kindvall, O., eds. 

2004. Species conservation and management: case 

studies. New York: Oxford University Press.

Alexander, R.B.; Boyer, E.W.; Smith, R.A.; Schwarz, 

G.E. [et al.]. 2007a. The role of headwater streams 

in downstream water quality. Journal of the 

American Water Resources Association. 43(1): 41-

59.

Alexander, R.B.; Smith, R.A.; Schwarz, G.E.; Boyer, 

E.W. [et al.]. 2007b. Differences in phosphorus and 

nitrogen delivery to the Gulf of Mexico from the 

Mississippi River Basin. Environmental Science 

and Technology. 42(3): 822-830.

Alig, R.; Adams, D.; McCarl, B.; Callaway, J.M.; 

Winnett, S. 1997. Assessing effects of mitigation 

strategies for global climate change with an 

intertemporal model of the U.S. forest and 

agriculture sectors. Environmental and Resource 

Economics. 9:259-274.

Alivand, M.; Hochmair, H.; Srinivasan, S. 2015. 

Analyzing how travelers choose scenic routes using 

route choice models. Computers, Environment and 

Urban Systems. 50: 41-52.

Arrow, K.; Cropper, M.; Gollier, C.; Groom, B. [et al.]. 

2013. Determining benefits and costs for future 

generations. Science. 341(6144): 349-350.

Arrow, K. J.; Fisher, A.C. 1974. Environmental 

preservation, uncertainty, and irreversibility. The 

Quarterly Journal of Economics. 88(2): 312-319.

Baerenklau, K.A.; Provencher, B. 2005. Static 

modeling of dynamic recreation behavior: 

implications for prediction and welfare estimation.

Journal of Environmental Economics and 

Management. 50(3): 617-636.

Bajari, P.; Fruehwirth, J.C.; Kim, K.I.; Timmins, C. 

2012. A rational expectations approach to hedonic 

price regressions with time-varying unobserved 

product attributes: the price of pollution. The 

American Economic Review. 102(5): 1898-1926.

Balistreri, E.; McClelland, G.; Poe, G.; Schulze, W. 

2001. Can hypothetical questions reveal true 

values? A laboratory comparison of dichotomous 

choice and open-ended contingent values with 

auction values. Environmental and Resource 

Economics. 18: 275-292.

Banzhaf, H.S. 2011. Regulatory impact analyses of 

environmental justice effects. Journal of Land Use 

and Environmental Law. 27: 1-30.

Bayer, P.; McMillan, R.; Murphy, A.; Timmins, C. 

2016. A dynamic model of demand for houses and 

neighborhoods. Econometrica. 84: 893-942.

Beckers, J.; Smerdon, B.; Wilson, M. 2009. Review 

of hydrologic models for forest management and 

climate change applications in British Columbia 

and Alberta. FORREX Series No. 25. Kamloops, 

BC: FORREX Forum for Research and Extension 

in Natural Resources.

Beissinger, S.R. 1995. Population trends of the 

marbled murrelet projected from demographic 

analyses. In: Ralph, C.J.; Hunt, G.L.; Raphael Jr., 

M.G.; Piatt, J.G., eds. Ecology and conservation 

of the marbled murrelet. Gen. Tech. Rep. PSWGTR-152. Albany, CA: U.S. Department of 

Agriculture, Forest Service, Pacific Southwest 

Research Station: 385-393.

Beissinger, S.R.; Nicholson, E.; Possingham, H.P. 2009. 

Application of population viability analysis to 

landscape conservation planning. In: Millspaugh, 
32 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

J.J.; Thompson, F.R., III, eds. Models for planning 

wildlife conservation in large landscapes. 

Cambridge, MA: Academic Press/Elsevier: 33-49.

Beissinger, S.R.; Westphal, M.I. 1998. On the use of 

demographic models of population viability in 

endangered species management. The Journal of 

Wildlife Management. 62: 821-841.

Bell, K.; Strand, I. 2003. Reconciling models 

of recreational route and site choices. Land 

Economics. 79(3): 440-454.

Ben-Akiva, M.; Bergman, M.J.; Daly, A.J.; 

Ramaswamy, R. 1984. Modeling inter-urban 

route choice behaviour. In: Proceedings of the 9th 

international symposium on transportation and 

traffic theory. Utrecht, Netherlands: VNU Press: 

299-330.

Beschta, R.L.; Pyles, M.R.; Skaugset, A.E.; Surfleet, 

C.G. 2000. Peakflow responses to forest practices 

in the western cascades of Oregon, USA. Journal 

of Hydrology. 233(1): 102-120.

Bevers, M.; Hof, J.; Uresk, D.W.; Schenbeck, G.L. 

1997. Spatial optimization of prairie dog colonies 

for black-footed ferret recovery. Operations 

Research. 45: 495-507.

Biénabe, E.; Hearne, R.R. 2006. Public preferences 

for biodiversity conservation and scenic beauty 

within a framework of environmental services 

payments. Forest Policy and Economics. 9: 335-

348.

Birdsey, R.A. 1992. Carbon storage and accumulation 

in United States forest ecosystems. Gen. Tech. 

Rep. WO-59. Washington, DC: U.S. Department of 

Agriculture, Forest Service.

Bishop, K.C.; Timmins, C. 2011. Hedonic prices and 

implicit markets: estimating marginal willingness 

to pay for differentiated products without 

instrumental variables. Working Paper 17611. 

Cambridge, MA: National Bureau of Economic 

Research.

Bockstael, N.E.; McConnell, K.E. 2007. 

Environmental and resource valuation with 

revealed preferences: a theoretical guide to 

empirical models. Dordrecht, Netherlands: Springer 

Dordrecht. 376 p.

Bormann, F.H.; Likens, G.E. 1979. Pattern and 

process in a forested ecosystem. New York, NY: 

Springer-Verlag. 253 p.

Bosch, J.M.; Hewlett, J.D. 1982. A review of 

catchment experiments to determine the 

effect of vegetation changes on water yield and 

evapotranspiration. Journal of Hydrology. 55(1): 

3-23.

Bosetti, V.; Massetti, E.; Tavoni, M. 2007. The 

WITCH model: structure, baseline, solutions.

FEEM Working Paper. Milan, Italy: Fondazione 

Eni Enrico Mattei. http://www.feem.it/userfiles/

attach/Publication/NDL2007/NDL2007-010.pdf. 

(March 2017).

Bowes, M.D.; Krutilla, J.V. 1989. Multiple-use 

management: the economics of public forestlands.

Washington, DC: Resources for the Future. 357 p.

Bowker, J.M.; Starbuck, C.M.; English, D.B.K.; 

Bergstrom, J.C. [et al.]. 2009. Estimating the net 

economic value of national forest recreation: 

an application of the National Visitor Use 

Monitoring database. Faculty Series Working 

Paper, FS 09-02. Athens, GA: University of Georgia, 

Department of Agricultural and Applied Economics.

Boxall, P.C.; Adamowicz, W.L.; Swait, J.; Williams, 

M.; Louviere, J. 1996. A comparison of stated 

preference methods for environmental valuation.

Ecological Economics. 18: 243-253

Boyce, M.S. 1993. Population viability analysis: 

adaptive management for threatened and 

endangered species. In: Transactions of the North 

American wildlife and natural resources conference. 

Gardners, PA: Wildlife Management Institute: 520-

527.

Boyce, M.S.; McDonald, L.L. 1999. Relating 

populations to habitats using resource selection 

functions. Trends in Ecology and Evolution. 14: 

268-272.
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 33

Boyd, J.; Banzhaf, S. 2007. What are ecosystem 

services? The need for standardized environmental 

accounting units. Ecological Economics. 63(2-3): 

616-626.

Boyle, K.J.; Kuminoff, N.V.; Parmeter, C.F.; Pope, J.C. 

2010. The benefit-transfer challenges. Annual 

Review of Resource Economics. 2(1): 161-182.

Brown, G.; Donovan, S. 2014. Measuring change 

in place values for environmental and natural 

resource planning using public participation GIS 

(PPGIS): results and challenges for longitudinal 

research. Society and Natural Resources. 27:36-54.

Brown, A.E.; Zhang, L.; McMahon, T.A.; Western, 

A.W. [et al.]. 2005. A review of paired catchment 

studies for determining changes in water yield 

resulting from alterations in vegetation. Journal of 

Hydrology. 310(1): 28-61.

Brown, G.G.; Reed, P. 2009. Public participation GIS: 

A new method for use in National Forest planning.

Forest Science. 55(2): 166-182.

Brown, G., Jr.; Mendelsohn, R. 1984. The hedonic 

travel cost method. The Review of Economics and 

Statistics. 66(3): 427-433.

Brown, T.C. 1987. Production and cost of scenic 

beauty: examples for a ponderosa pine forest.

Forest Science. 33: 394-410.

Brown, T.C. 2006. Trends in water market activity 

and price in the western United States. Water 

Resources Research. 42(9): W09402. http://dx.doi.

org/10.1029/2005WR004180.

Brown, T.C.; Champ, P.A.; Bishop, R.C.; McCollum, 

D.W. 1996. Which response format reveals the 

truth about donations to a public good? Land 

Economics. 72(2): 152-166.

Buonanno, P.; Carraro, C.; Galeotti, M. 2003. 

Endogenous induced technical change and the 

costs of Kyoto. Resource and Energy Economics. 

25(1): 11-34.

Cai, Y.; Judd, K.L.; Lontzek, T.S. 2012. Continuoustime methods for integrated assessment models.

Working Paper 18365. Cambridge, MA: National 

Bureau of Economic Research.

Caissie, D. 2006. The thermal regime of rivers: a 

review. Freshwater Biology. 51(8): 1389-1406.

Calish, S.; Fight, R.; Teeguarden, D. 1978. How do 

nontimber values affect Douglas-fir rotations?

Journal of Forestry. 76(4): 217-222.

Carson, R.T. 2012. Contingent valuation: a practical 

alternative when prices aren’t available. Journal of 

Economic Perspectives. 26(4): 27-42

Carson, R.T.; Flores, N.E.; Meade, N.F. 2001. 

Contingent valuation: Controversies and evidence. 

Environmental and Resource Economics. 19(2): 

173-210.

Chichilnisky, G.; Heal, G. 1998. Economic returns 

from the biosphere. Nature. 391: 629-630.

Clawson, M.; Knetsch, J.L. 1966. Economics of 

outdoor recreation. Washington, DC: Resources for 

the Future. 328 p.

Cohen, E.B.; Pearson, S.M.; Moore, F.M. 2014. Effects 

of landscape composition and configuration on 

migrating songbirds: inference from an individualbased model. Ecological Applications. 24: 169-180.

Creedy, J.; Wurzbacher, A. 2001. The economic 

value of a forested catchment with timber, water, 

and carbon sequestration benefits. Ecological 

Economics. 38(1): 71-83.

Creque, S.M.; Rutherford, E.S.; Zorn, T.G. 2005. Use 

of GIS-derived landscape-scale habitat features to 

explain spatial patterns of fish density in Michigan 

rivers. North American Journal of Fisheries 

Management. 25(4): 1411-1425.

Crookston, N.L.; Dixon, G.E. 2005. The forest 

vegetation simulator: a review of its structure, 

content, and applications. Computers and 

Electronics in Agriculture. 49: 60-80.

Cropper, M.L.; Deck, L.; Kishor, N.; McConnell, 

K. 1993. Valuing product attributes using single 

market data: a comparison of hedonic and discrete 
34 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

choice approaches. The Review of Economics and 

Statistics. 75(2): 225-232.

Cummins, K.W. 1974. Structure and function of 

stream ecosystems. BioScience. 24(11): 631-41.

Daily, G., ed. 1997. Nature’s services: societal 

dependence on natural ecosystems. Washington, 

DC: Island Press. 392 p.

Daily, G.; Polasky, S.; Goldstein, J.; Kareiva, P.M. [et 

al.]. 2009. Ecosystem services in decision-making: 

time to deliver. Frontiers in Ecology and the 

Environment. 7(1): 21-28.

Dalton, R.S.; Bastian, C.T.; Jacobs, J.J.; Wesche, T.A. 

1998. Estimating the economic value of improved 

trout fishing on Wyoming streams. North 

American Journal of Fisheries Management. 18(4): 

786-797.

Daniel, T.C. 2001. Whither scenic beauty? Visual 

landscape quality assessment in the 21st century.

Landscape and Urban Planning. 54: 267-281.

Daniel, T.C.; Muhar, A.; Arnberger, A.; Aznar, O. [et 

al.]. 2012. Contributions of cultural services to 

the ecosystem services agenda. Proceedings of the 

National Academy of Sciences of the United States. 

109(23): 8812-8819.

Davies, P.E.; Nelson, M. 1994. Relationships between 

riparian buffer widths and the effects of logging 

on stream habitat, invertebrate community 

composition, and fish abundance. Marine and 

Freshwater Research. 45(7): 1289-1305.

Deal, R.L. 2001. The effects of partial cutting on 

forest plant communities of western hemlockSitka spruce stands of southeast Alaska. Canadian 

Journal of Forest Research. 31: 2067-2079.

Deal, R.L.; Heithecker, T.; Zenner, E. 2010. 

Comparison of tree size structure and growth 

for partially harvested and even-aged hemlock 

spruce stands in southeast Alaska. Journal of Forest 

Research. 15: 31-37.

Deal, R.L.; Tappeiner, J.C. 2002. The effects of partial 

cutting on stand structure and growth of western 

hemlock-Sitka spruce stands in southeast Alaska. 

Forest Ecology and Management. 159(3): 173-186.

Dearmont, D.; McCarl, B.A.; Tolman, D.A. 1998. 

Costs of water treatment due to diminished water 

quality: a case study in Texas. Water Resources 

Research. 34(4): 849-53.

de Bruin, K.C.; Dellink, R.B.; Tol, R.S. 2009. ADDICE: an implementation of adaptation in the 

DICE model. Climatic Change. 95(1-2): 63-81.

Dunning, J.B.; Danielson, B.J.; Noon, B.R.; Root, 

T.L. [et al.]. 1995. Spatially explicit population 

models: current forms and future uses. Ecological 

Applications. 5: 3-11

Dwyer, J.F.; McPherson, E.G.; Schroeder, H.W.; 

Rowntree, R.A. 1992. Assessing the benefits and 

costs of the urban forest. Journal of Arboriculture. 

18: 227-227.

Ecological Society of America. 2012. Ecosystem 

services fact sheet. http://www.esa.org/esa/wpcontent/uploads/2012/12/ecosystemservices.pdf. 

(August 2014).

Englin, J. 1990. Backcountry hiking and optimal 

timber rotation. Journal of Environmental 

Management. 31(2): 97-105.

Englin, J.; Callaway, J. 1993. Global climate change 

and optimal forest management. Natural Resource 

Modeling. 7: 191-202.

Englin, J.; Callaway, J. 1995. Environmental impacts 

of sequestering carbon through forestation.

Climatic Change. 31(1): 67-78.

Englin, J.; Cameron, T.A. 1996. Augmenting travel 

cost models with contingent behavior data.

Environmental and Resource Economics. 7(2): 133-

147.

Englin, J.E.; Loomis, J.B.; González-Cabán, A. 2001. 

The dynamic path of recreational values following 

a forest fire: a comparative analysis of states in the 

Intermountain West. Canadian Journal of Forest 

Research. 31(10): 1837-1844.
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 35

Englin, J.E.; McDonald, J.M.; Moeltner, K. 2006. 

Valuing ancient forest ecosystems: an analysis 

of backcountry hiking in Jasper National Park.

Ecological Economics. 57(4): 665-678.

Englin, J.; Mendelsohn, R. 1991. A hedonic travel 

cost analysis for valuation of multiple components 

of site quality: the recreation value of forest 

management. Journal of Environmental Economics 

and Management. 21(3): 275-290.

Englin, J.E.; Shonkwiler, J.S. 1995. Modeling 

recreation demand in the presence of unobservable 

travel costs: toward a travel price model. Journal of 

Environmental Economics and Management. 29(3): 

368-377.

Fankhauser, S.; Tol, R.S.; Pearce, D.W. 1997. The 

aggregation of climate change damages: a welfare 

theoretic approach. Environmental and Resource 

Economics. 10(3): 249-266.

Faustmann, M. 1849. On the determination of the 

value which forest land and immature stands 

possess for forestry. In: Gane, M., ed. Martin 

Faustmann and the evolution of discounted cash 

flow. University of Oxford: Oxford Institute: Paper 

32: 27-55.

Fezzi, C.; Bateman, I.J.; Ferrini, S. 2014. Using 

revealed preferences to estimate the value of travel 

time to recreation sites. Journal of Environmental 

Economics and Management. 67(1): 58-70.

Flanagan, D.C.; Gilley, J.E.; Franti, T.G. 2007. 

Water Erosion Prediction Project (WEPP): 

development history, model capabilities, and 

future enhancements. Transactions of the American 

Society of Agricultural and Biological Engineers. 50: 

1603-1612.

Flather, C.H.; Knowles, M.S.; Jones, M.F.; Schilli, 

C. 2013. Wildlife resource trends in the United 

States: a technical document supporting the 

2010 RPA Assessment. Gen. Tech. Rep. RMRSGTR-296. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain 

Research Station. 94 p.

Forster, D.L.; Bardos, C.P.; Southgate, D.D. 1987. Soil 

erosion and water treatment costs. Journal of Soil 

and Water Conservation. 42(5): 349-52.

Freeman, A.M.; Herriges, J.A.; Kling, C.L. 2014. The 

measurement of environmental and resource 

values: theory and methods. Washington, DC: 

Resources for the Future. 478 p.

Geist, V. 1988. How markets in wildlife meat and 

parts, and the sale of hunting privileges, jeopardize 

wildlife conservation. Conservation Biology. 2:15-

26.

Geoghegan, J.; Wainger, L.A.; Bockstael, N.E. 1997. 

Spatial landscape indices in a hedonic framework: 

an ecological economics analysis using GIS.

Ecological Economics. 23(3): 251-264.

Getz, W.M.; Haight, R.G. 1989. Population 

harvesting: demographic models of fish, forest, 

and animal resources. Monographs in population 

biology 27. Princeton, NJ: Princeton University 

Press. 391 p.

Goulder, L.H.; Williams III, R.C. 2012. The choice of 

discount rate for climate change policy evaluation.

Climate Change Economics. 3(04).

Grasso, M. 1998. Ecological-economic model for 

optimal mangrove trade-off between forestry 

and fishery production: comparing a dynamic 

optimization and a simulation model. Ecological 

Modelling. 112(2): 131-150.

Greenstone, M.; Kopits, E.; Wolverton, A. 2013. 

Developing a social cost of carbon for U.S. 

regulatory analysis: a methodology and 

interpretation. Review of Environmental 

Economics and Policy. 7(1): 23-46.

Haab, T.C.; Hicks, R.L. 1999. Choice set 

considerations in models of recreation demand: 

history and current state of the art. Marine 

Resource Economics. 14(4): 271-282.

Haab, T.C.; McConnell, K. 2002. Valuing 

environmental and natural resources: the 

econometrics of non-market valuation.

[Location of publisher unknown]: Edward Elgar 

Publishing. 352 p.
36 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

Haefele, M.; Kramer, R.A.; Holmes, T. 1992. 

Estimating the total value of forest quality in 

high-elevation spruce-fir forests. In: Payne, C.; 

Bowker, J.M.; Reed, P.C., comps. The economic 

value of wilderness: proceedings of the conference; 

1991 May 8-11. Gen. Tech. Rep. SE-78. Asheville, 

NC: U.S. Department of Agriculture, Forest Service, 

Southeastern Forest Experiment Station: 91-96.

Haight, R.G. 1987. Evaluating the efficiency of evenaged and uneven-aged stand management. Forest 

Science. 33: 116-134.

Haight, R.G.; Cypher, B.; Kelly, P.A.; Phillips, S.; Ralls, 

K.; Possingham, H.P. 2004. Optimizing reserve 

expansion for disjunct populations of San Joaquin 

kit fox. Biological Conservation. 117: 61-72.

Hanemann, W.M. 1984. Welfare evaluations in 

contingent valuation experiments with discrete 

responses. American Journal of Agricultural 

Economics. 66(3): 332-341.

Hanemann, W.M. 1994. Valuing the environment 

through contingent valuation. The Journal of 

Economic Perspectives. 8(4): 19-43.

Hanley, N. 1990. The economics of nitrate pollution.

European Review of Agricultural Economics. 17(2): 

129-151.

Hansen, L.; Ribaudo, M. 2008. Economic measures 

of soil conservation benefits: regional values 

for policy assessment. Technical Bulletin 1922. 

Washington, DC: U.S. Department of Agriculture, 

Economic Research Service.

Hartman, R. 1976. Harvesting decision when a 

standing forest has value. Economic Inquiry. 14: 

52-58.

Hausman, J. 2012. Contingent valuation: from 

dubious to hopeless. The Journal of Economic 

Perspectives. 26(4): 43-56.

Heath, L.S.; Smith, J.E.; Skog, K.E.; Nowak, D.J.; 

Woodall, C.W. 2011. Managed forest carbon 

estimates for the U.S. greenhouse gas inventory, 

1990-2008. Journal of Forestry. April/May: 167-173.

Helms, J.A. 1998. The dictionary of forestry. Bethesda, 

MD: Society of American Foresters. 210 p.

Hesseln, H.; Loomis, J.B.; González-Cabán, A.; 

Alexander, S. 2003. Wildfire effects on hiking and 

biking demand in New Mexico: A travel cost study.

Journal of Environmental Management. 69(4): 359-

368.

Hewlett, John D.; Fortson, J.C. 1982. Stream 

temperature under an inadequate buffer strip in 

the Southeast Piedmont. Journal of the American 

Water Resources Association. 18(6): 983-88.

Hewlett, J.D.; Hibbert, A.R. 1967. Factors affecting 

the response of small watersheds to precipitation 

in humid areas. Forest Hydrology. 1: 275-290.

Hilger, J.R.; Englin, J.E. 2009. Utility theoretic 

semi-logarithmic incomplete demand systems 

in a natural experiment: forest fire impacts on 

recreational values and use. Resource and Energy 

Economics. 31(4): 287-298.

Hill, Alan R. 1996. Nitrate removal in stream riparian 

zones. Journal of Environment Quality. 25(4): 743.

Hobbie, S.E.; Baker, L.A.; Buyarski, C.; Nidzgorski, 

D. [et al.]. 2014. Decomposition of tree leaf litter 

on pavement: implications for urban water quality.

Urban Ecosystems. 17: 369-385.

Hof, J.; Bevers, M. 2000. Optimal timber harvest 

scheduling with spatially defined sediment 

objectives. Canadian Journal of Forest Research. 30: 

1494-1500.

Holmes, T.P. 1988. The offsite impact of soil erosion 

on the water treatment industry. Land Economics. 

64(4): 356.

Holtby, L.B. 1988. Effects of logging on stream 

temperatures in Carnation Creek, British 

Columbia, and associated impacts on the coho 

salmon (Oncorhynchus kisutch). Canadian Journal of 

Fisheries and Aquatic Sciences. 45(3): 502-515.

Hoover, C.M.; Rebain, S.A. 2011. Forest carbon 

estimation using the forest vegetation 

simulator: seven things you need to know. 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 37

Gen. Tech. Rep. NRS-77. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 16 p.

Hope, C. 2006. The marginal impact of CO2 from 

PAGE2002: an integrated assessment model 

incorporating the IPCC’s five reasons for concern.

The Integrated Assessment Journal. 6(1): 19-56

Hostetler, S.W. 1991. Analysis and modeling of longterm stream temperatures on the Steamboat Creek 

Basin, Oregon: implications for land use and fish 

habitat. Journal of the American Water Resources 

Association. 27(4): 637-47.

Huang, J.; Smith, V.K. 1998. Weak complementarity 

and production. Economics Letters. 60: 329-33

Hyytiäinen, K.; Hari, P.; Kokkila, T.; Mäkelä, A. [et al.]. 

2004. Connecting a process-based forest growth 

model to stand-level economic optimization.

Canadian Journal of Forest Research. 34: 2060-2073.

Isaak, D.; Young, M.; Nagel, D.; Horan, D.; Groce, M. 

2015. The cold-water climate shield: Delineating 

refugia for preserving salmonid fishes through the 

21st Century. Global Change Biology. 21: 2540-

2553.

Isaak, D.J.; Hubert, W.A. 2004. Nonlinear response of 

trout abundance to summer stream temperatures 

across a thermally diverse montane landscape. 

Transactions of the American Fisheries Society. 

133(5): 1254-1259.

Isaak, D.J.; Luce, C.H.; Rieman, B.E.; Nagel, D.E. [et 

al.]. 2010. Effects of climate change and wildfire 

on stream temperatures and salmonid thermal 

habitat in a mountain river network. Ecological 

Applications. 20(5): 1350-71.

Isaak, D.J.; Peterson, E.E.; Ver Hoef, J.M.; Wenger, 

S.J. [et al.]. 2014. Applications of spatial 

statistical network models to stream data. Wiley 

Interdisciplinary Reviews: Water. 1(3): 277-294.

Jacobsen, J.B.; Lundhede, T.H.; Thorsen, B.J. 2012. 

Valuation of wildlife populations above survival.

Biodiversity and Conservation. 21: 543-563.

Jarrett, R.D. 2009. Hydrometeorology, river 

hydraulics, and flood hydrology in Colorado.

Denver, CO: U.S. Geological Survey, Paleohydrology 

and Climate Change. 28 p.

Jenkins, J.C.; Chojnacky, D.C.; Heath, L.S.; Birdsey, 

R.A. 2003. National-scale biomass estimators for 

United States tree species. Forest Science. 49(1): 

12-35.

Jeppesen, E.; Iversen, T.M. 1987. Two simple models 

for estimating daily mean water temperatures and 

diel variations in a Danish low gradient stream.

Oikos. 49: 149-155.

Juutinen, A.; Kosenius, A.-K.; Ovaskainen, V. 2014. 

Estimating the benefits of recreation-oriented 

management in state-owned commercial forests 

in Finland: a choice experiment. Journal of Forest 

Economics. 20: 396-412.

Kaplow, L.; Moyer, E.; Weisbach, D.A. 2010. The 

social evaluation of intergenerational policies and 

its application to integrated assessment models 

of climate change. The B.E. Journal of Economic 

Analysis and Policy. 10(2).

Kapoor, A.; Viraraghavan, T. 1997. Nitrate removal 

from drinking water—review. Journal of 

Environmental Engineering. 123(4): 371-380.

Keeler, B.L.; Polasky, S.; Brauman, K.A.; Johnson, K.A. 

[et al.]. 2012. Linking water quality and wellbeing for improved assessment and valuation of 

ecosystem services. Proceedings of the National 

Academy of Sciences of the United States. 109(45): 

18619-18624.

Kim, K.S.; Chapra, S.C. 1997. Temperature model 

for highly transient shallow streams. Journal of 

Hydraulic Engineering. 123(1): 30-40.

Kim, Y.-S.; Johnson, R.L. 2002. The impact of 

forests and forest management on neighboring 

property values. Society and Natural Resources. 

15(10): 887-901.

Kim, Y.-S.; Wells, A. 2005. The impact of forest 

density on property values. Journal of Forestry: 

103(3): 146-151.
38 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

Knapp, R.A.; Matthews, K.R. 1996. Livestock grazing, 

golden trout, and streams in the Golden Trout 

Wilderness, California: impacts and management 

implications. North American Journal of Fisheries 

Management. 16(4): 805-20.

Knoche, S.; Lupi, F. 2007. Valuing deer hunting 

ecosystem services from farm landscapes.

Ecological Economics. 64: 313-320.

Knoche, S.; Lupi, F.; Suiter, A. 2015. Harvesting 

benefits from habitat restoration: influence of 

landscape position on economic benefits to 

pheasant hunters. Ecological Economics. 113: 97-

105.

Kovacs, K.F. 2012. Integrating property value and 

local recreation models to value ecosystem 

services from regional parks. Landscape and Urban 

Planning. 108(2-4): 79-90.

Kovacs, K.F.; Holmes, T.P.; Englin, J.E.; Alexander, J. 

2010. The dynamic response of housing values to 

a forest invasive disease: evidence from a sudden 

oak death infestation. Environmental and Resource 

Economics. 49(3): 445-471.

Kumar, P., ed. 2010. The economics of ecosystems and 

biodiversity: ecological and economic foundations.

New York: Routledge. 410 p.

Kuminoff, N.V.; Parmeter, C.F.; Pope, J.C. 2010. 

Which hedonic models can we trust to recover 

the marginal willingness to pay for environmental 

amenities? Journal of Environmental Economics 

and Management. 60(3): 145-160.

Kuriyama, K.; Hanemann, W.M.; Hilger, J.R. 2010. 

A latent segmentation approach to a KuhnTucker model: an application to recreation 

demand. Journal of Environmental Economics and 

Management. 60(3): 209-220.

Lacy, R.C. 1993. Vortex: a computer simulation model 

for population viability analysis. Wildlife Research. 

20: 45-65.

Lamberson, R.H.; Noon, B.R.; Voss, C.; McKelvey, 

K.S. 1994. Reserve design for territorial species: 

the effects of patch size and spacing on the viability 

of the northern spotted owl. Conservation Biology. 

8: 185-195.

Larson, D.; Shaikh, S. 2001. Empirical specification 

requirements for two-constraint models of 

recreation choice. American Journal of Agricultural 

Economics. 83(2): 428-440.

Latterell, J.J.; Naiman, R.J. 2007. Sources and 

dynamics of large logs in a temperate floodplain 

river. Ecological Applications. 17(4): 1127-41.

Lemoine, D.M.; Traeger, C.P. 2012. Tipping points 

and ambiguity in the economics of climate change.

Working Paper 18230. Cambridge, MA: National 

Bureau of Economic Research. http://dx.doi.

org/10.3386/w18230.

Lindenmayer, D.B.; Burgman, M.A.; Thomas, V.C.; 

Clark, T.W. 1993. Predictions of the impacts of 

changes in population size and environmental 

variability on Leadbeater’s possum, Gymnobelideus 

leadbeateri McCoy (Marsupialia: Petauridae) using 

population viability analysis: an application of the 

computer program VORTEX. Wildlife Research. 

20: 67-86.

Liu, J.; Ashton, P.S. 1995. Individual-based simulation 

models for forest succession and management.

Forest Ecology and Management. 73: 157-175.

Livengood, K.R. 1983. Value of big game from 

markets for hunting leases: the hedonic approach.

Land Economics. 59(3): 287-291.

Loomis, J.; González-Cabán, A.; Englin, J. 2001. 

Testing for differential effects of forest fires on 

hiking and mountain biking demand and benefits.

Journal of Agricultural and Resource Economics. 

26(2): 508-522.

Lytle, D.A.; Poff, N.L. 2004. Adaptation to natural 

flow regimes. Trends in Ecology and Evolution. 

19(2): 94-100.

MacDougall, A.S.; Turkington, R. 2005. Are invasive 

species the drivers or passengers of change in 

degraded ecosystems? Ecology. 86(1): 42-55.

Mansfield, C.; Pattanayak, S.K.; McDow, W.; 

McDonald, R. [et al.]. 2005. Shades of green: 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 39

measuring the value of urban forests in the 

housing market. Journal of Forest Economics. 11(3): 

177-199.

Marcot, B.G.; Singleton, P.H.; Schumaker, N.H. 

2015. Analysis of sensitivity and uncertainty in an 

individual-based model of a threatened wildlife 

species. Natural Resource Modeling. 28: 37-58.

McConnell, K.E. 1990. Double counting in hedonic 

and travel cost models. Land Economics. 66(2): 

121-127.

McKean, J.R.; Johnson, D.; Taylor, R.G. 2010. 

Willingness-to-pay for steelhead trout fishing: 

implications of two-step consumer decisions with 

short-run endowments. Water Resources Research. 

46(9): W09523.

McPhearson, T.; Hamstead, Z.A.; Kremer, P. 2014. 

Urban ecosystem services for resilience planning 

and management in New York City. Ambio. 43: 

502-515.

McPherson, G.; Simpson, J.R.; Peper, P.J.; Maco, S.E. 

[et al.]. 2005. Municipal forest benefits and costs 

in five U.S. cities. Journal of Forestry. December: 

411-416.

Millennium Ecosystem Assessment. 2005. Ecosystems 

and human well-being: synthesis. Washington, DC: 

Island Press.

Mendelsohn, R. 1987. Modeling the demand for 

outdoor recreation. Water Resources Research. 

23(5): 961-967.

Mendelsohn, R.; Olmstead, S. 2009. The economic 

valuation of environmental amenities and 

disamenities: methods and applications. Annual 

Review of Environment and Resources. 34: 325-347.

Mishra, S.K.; Singh, V.P. 2003. Soil Conservation 

Service curve number (SCS-CN) methodology.

LOCATION: Springer Science and Business 

Media. Vol. 42.

Mitchell, R.C.; Carson, R.T. 1989. Using surveys 

to value public goods: the contingent valuation 

method. Washington, DC: Resources for the Future. 

471 p.

Mockrin, M.H.; Aiken, R.A.; Flather, C.H. 2012. 

Wildlife-associated recreation trends in the 

United States: a technical document supporting 

the Forest Service 2010 RPA Assessment. Fort 

Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 34 p.

Moody, J.A.; Martin, D.A. 2001. Initial hydrologic 

and geomorphic response following a wildfire in 

the Colorado Front Range. Earth Surface Processes 

and Landforms. 26(10): 1049-1070.

Moore, E.; Matalon, E.; Balazs, C.; Clary, J. [et al.]. 

2011. The human costs of nitrate-contaminated 

drinking water in the San Joaquin Valley. Oakland, 

CA: Pacific Institute.

Moore, W.B.; McCarl, B.A. 1987. Off-site costs of 

soil erosion: a case study in the Willamette Valley.

Western Journal of Agricultural Economics. 12(1): 

42-49.

Morton, K.M.; Adamowicz, W.L.; Boxall, P.C. 1995. 

Economic effects of environmental quality 

change on recreational hunting in northwestern 

Saskatchewan: a contingent behavior analysis.

Canadian Journal of Forest Research. 25: 912-920.

Munro, D.D. 1974. Forest growth models: a 

prognosis. In: Fries, J., ed. Growth models for tree 

and stand simulation. Res. Note 30. Stockholm: 

Department of Forest Yield Research, Royal College 

of Forestry: 7-21.

Murray, B.C.; McCarl, B.A.; Lee, H.-C. 2004. 

Estimating leakage from forest carbon 

sequestration programs. Land Economics. 80(1): 

109-124.

Naiman, R.J.; Latterell, J.J.; Pettit, N.E.; Olden, J.D. 

2008. Flow variability and the biophysical vitality 

of river systems. Comptes Rendus Geoscience. 340 

(9-10): 629-643.

National Research Council. 2004. Valuing ecosystem 

services: toward better environmental decisionmaking. Washington, DC: National Academies 

Press. 290 p.
40 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

Nelson, E.; Mendoza, G.; Regetz, J.; Polasky, S. [et 

al.]. 2009. Modeling multiple ecosystem services, 

biodiversity conservation, commodity production, 

and tradeoffs at landscape scales. Frontiers in 

Ecology and the Environment. 7(1): 4-11.

Netusil, N.R.; Chattopadhyay, S.; Kovacs, K.F. 2010. 

Estimating the demand for tree canopy: a secondstage hedonic price analysis in Portland, Oregon.

Land Economics. 86(2): 281-293.

Nordhaus, W. 1993. Optimal greenhouse-gas 

reductions and tax policy in the DICE model. The 

American Economic Review. 83(2): 313-317.

Nordhaus, W. 2008. A question of balance: economic 

modeling of global warming. New Haven, CT: Yale 

University Press. 256 p.

Nordhaus, W.D. 2007. A review of “The Stern review 

on the economics of climate change.” Journal of 

Economic Literature. 45(3): 686-702.

Nordhaus, W.D.; Yang, Z. 1996. A regional dynamic 

general-equilibrium model of alternative climatechange strategies. The American Economic Review. 

86(4): 741-765.

Nowak, D.M.; Hirabayashi, S.; Bodine, A.; Greenfield, 

E. 2014. Tree and forest effects on air quality and 

human health in the United States. Environmental 

Pollution. 193: 119-129.

Pattanayak, S.K.; Butry, D.T. 2005. Spatial 

complementarity of forests and farms: accounting 

for ecosystem services. American Journal of 

Agricultural Economics. 87(4): 995-1008.

Pendleton, L.H. 1999. Reconsidering the hedonic 

vs. RUM debate in the valuation of recreational 

environmental amenities. Resource and Energy 

Economics. 21(2): 167-189.

Pendleton, L.H.; Shonkwiler, J.S. 2001. Valuing 

bundled attributes: a latent characteristics 

approach. Land Economics. 77(1): 118-129.

Pendleton, L.H.; Sohngen, B.L.; Mendelsohn, R.; 

Holmes, T. 1998. Measuring environmental quality 

in the Southern Appalachian Mountains. Forest 

Science. 44(4): 603-609.

Phaneuf, D.J. 2011. Can consumption of convenience 

products reveal the opportunity cost of time?

Economics Letters. 113(1): 92-95.

Phaneuf, D.J.; Kling, C.L.; Herriges, J.A. 1998. Valuing 

water quality improvements using revealed 

preference methods when corner solutions 

are present. American Journal of Agricultural 

Economics. 80(5): 1025-1031.

Phaneuf, D.J.; Kling, C.L.; Herriges, J.A. 2000. 

Estimation and welfare calculations in a 

generalized corner solution model with an 

application to recreation demand. The Review of 

Economics and Statistics. 82: 83-92.

Phaneuf, D.J.; Smith, V.K. 2005. Recreation demand 

models. Handbook of Environmental Economics. 2: 

671-761.

Polasky, S.; Nelson, E.; Camm, J.; Csuti, B. [et 

al.]. 2008. Where to put things? Spatial land 

management to sustain biodiversity and economic 

returns. Biological Conservation. 141(6): 1505-

1524.

Polasky, S.; Nelson, E.; Pennington, D.; Johnson, 

K. 2011. The impact of land-use change on 

ecosystem services, biodiversity, and returns to 

landowners: a case study in the State of Minnesota.

Environmental and Resource Economics. 48(2): 

219-242.

Polasky, S.; Segerson, K. 2009. Integrating ecology 

and economics in the study of ecosystem services: 

some lessons learned. Annual Review of Resource 

Economics. 1: 409-434.

Popp, D. 2004. ENTICE: endogenous technological 

change in the DICE model of global warming.

Journal of Environmental Economics and 

Management. 48(1): 742-768.

Rämö, J.; Tahvonen, O. 2014. Economics of harvesting 

uneven-aged forest stands in Fennoscandia.

Scandinavian Journal of Forest Research. 29: 777-

792.

Rhyne, J.D.; Munn, I.A.; Hussain, A. 2009. Hedonic 

analysis of auctioned hunting leases: a case study 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 41

of Mississippi sixteenth section lands. Human 

Dimensions of Wildlife. 14(4): 227-239.

Ribe, R.G. 1989. The aesthetics of forestry: what 

has empirical preference research taught us?

Environmental Management. 13: 55-74.

Ribe, R.G.; Armstrong, E.T.; Gobster, P.H. 2002. 

Scenic vistas and the changing policy landscape: 

visualizing and testing the role of visual resources 

in ecosystem management. Landscape Journal. 21: 

42-66.

Richards, C.; Johnson, L.B.; Host, G.E. 1996. 

Landscape-scale influences on stream habitats and 

biota. Canadian Journal of Fisheries and Aquatic 

Sciences. 53(S1): 295-311.

Richards, K.R.; Stokes, C. 2004. A review of forest 

carbon sequestration cost studies: a dozen years of 

research. Climate Change. 63: 1-48.

Richardson, L.; Loomis, J. 2009. The total economic 

value of threatened, endangered, and rare species: 

an updated meta-analysis. Ecological Economics. 

68: 1535-1548.

Richardson, L.; Loomis, J.; Kroeger, T.; Casey, F. 2015. 

The role of benefits transfer in ecosystem service 

valuation. Ecological Economics. 115: 51-58.

Riedel, M.S.; Vose, J.M. 2002. Forest road erosion, 

sediment transport, and model validation in 

the Southern Appalachians. In: Second Federal 

interagency hydrologic modeling conference; 

2002 July 28-August 1; Las Vegas, NV. Available 

at https://www.treesearch.fs.fed.us/pubs/4542. 

( January 5, 2017).

Robichaud, P.; MacDonald, L.; Freeouf, J.; Neary, D. 

[et al.]. 2002. Hayman fire case study analysis: 

postfire rehabilitation, interim report. In: Graham, 

R.T., ed. Hayman Fire case study. Gen. Tech. Rep. 

RMRS-GTR-114. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain 

Research Station: 241-262.

Rolfe, J.; Windle, J. 2015. Multifunctional recreation 

and nouveau heritage values in plantation forests.

Journal of Forest Economics. 21: 131-151.

Rosen, S. 1974. Hedonic prices and implicit markets: 

product differentiation in pure competition. The 

Journal of Political Economy. 82(1): 34-55.

Rosenberger, R.S.; Bell, L.A.; Champ, P.A.; Smith, 

E.L. 2012. Nonmarket economic values of forest 

insect pests: an updated literature review. Gen. 

Tech. Rep. RMRS-GTR-275. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station: 1-46.

Rosgen, D.; Rosgen, B.; Collins, S.; Nankervis, J. [et. al.]. 

2013. Waldo Canyon Fire Watershed Assessment: 

the WARSSS results. Lake George, CO. Coalition 

for the Upper South Platte. 92 p. Unpublished report 

on file with Coalition for the Upper South Platte. 

http://www.uppersouthplatte.org/pdf/WARSSS/1.

WaldoCanyonFireAssessmentReport.pdf. (April 

2017).

Rosgen, D.L. 2007. Watershed assessment of river 

stability and sediment supply (WARSSS). Fort 

Collins, CO: Wildland Hydrology. 193p.

Samuelson, P. 1976. Economics of forestry in an 

evolving society. Economic Inquiry. 14(4): 466-492.

Sander, H.A.; Polasky, S. 2009. The value of views and 

open space: estimates from a hedonic pricing model 

for Ramsey County, MN, USA. Land Use Policy. 

26(3): 837-845.

Sander, H.; Polasky, S.; Haight, R.G. 2010. The value 

of urban tree cover: a hedonic property price model 

in Ramsey and Dakota Counties, Minnesota, USA.

Ecological Economics. 69(8): 1646-1656.

Santhi, C.; Arnold, J.G.; Williams, J.R.; Dugas, W.A. 

[et al.]. 2001. Validation of the SWAT model 

on a large river basin with point and nonpoint 

sources. Journal of the American Water Resources 

Association. 37(5): 1169-1188.

Scheierling, S.M.; Loomis, J.B.; Young, R.A. 2006. 

Irrigation water demand: a meta-analysis of price 

elasticities. Water Resources Research. 42(1): 

W01411.

Scheller, R.M.; Mladenoff, D.J. 2007. An ecological 

classification of forest landscape simulation 
42 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

models: tools and strategies for understanding 

broad-scale forested ecosystems. Landscape 

Ecology. 22: 491-505.

Scheller, R.M.; Spencer, W.D.; Rustigian-Romsos, 

H.; Syphard, A.D. [et al.]. 2011. Using stochastic 

simulation to evaluate competing risks of wildfires 

and fuels management on an isolated forest 

carnivore. Landscape Ecology. 26: 1491-1504.

Scott, M.L.; Auble, G.T.; Friedman, J.M. 1997. Flood 

dependency of cottonwood establishment along 

the Missouri River, Montana, USA. Ecological 

Applications. 7(2): 677-690.

Sedjo, R.A. 2006. Comparative views of different 

stumpage pricing systems: Canada and the United 

States. Forest Science. 52(4): 446-450.

Shen, Z.Y.; Gong, Y.W.; Li, Y.H.; Hong, Q. [et al.]. 

2009. A comparison of WEPP and SWAT for 

modeling soil erosion of the Zhangjiachong 

Watershed in the Three Gorges Reservoir Area. 

Agricultural Water Management. 96(10): 1435-

1442.

Sidle, R.C.; Sharma, A. 1996. Stream channel changes 

associated with mining and grazing in the Great 

Basin. Journal of Environmental Quality. 25(5): 

1111.

Simões, P.; Barata, E.; Cruz, L. 2013. Joint estimation 

using revealed and stated preference data: an 

application using a National Forest. Journal of 

Forest Economics. 19: 249-266.

Sinokrot, B.A.; Stefan, H.G. 1993. Stream 

temperature dynamics: measurements and 

modeling. Water Resources Research. 29(7): 2299-

2312.

Smith, D.M. 1962. The practice of silviculture. New 

York: John Wiley and Sons. 578 p.

Smith, J.E.; Heath, L.S.; Jenkins, J.C. 2003a. Forest 

volume-to-biomass models and estimates of mass 

for live and standing dead trees of U.S. forests.

Gen. Tech. Rep. NE-298. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northeastern Research Station. 57 p.

Smith, J.E.; Heath, L.S.; Woodbury, P.B. 2004. How 

to estimate forest carbon for large areas from 

inventory data. Journal of Forestry. 102: 25-31.

Smith, R.A.; Alexander, R.B.; Schwarz, G.E. 2003b. 

Natural background concentrations of nutrients 

in streams and rivers of the conterminous United 

States. Environmental Science and Technology. 

37(14): 3039-3047.

Smith, V.K.; Kaoru, Y. 1987. The hedonic travel cost 

model: a view from the trenches. Land Economics. 

63(2): 179-192.

Smith, V.K.; Van Houtven, G.; Pattanayak, S.K. 

2002. Benefit transfer via preference calibration: 

prudential algebra for policy. Land Economics. 

78(1): 132-152.

Sohngen, B.; Mendelsohn, R. 2003. An optimal 

control model of forest carbon sequestration.

American Journal of Agricultural Economics. 85(2): 

448-457.

Sorg, C.F.; Loomis, J.B.; Donnelly, D.M.; Peterson, 

G.L.; Nelson, L.J. 1985. Net economic value of 

cold and warm water fishing in Idaho. Resour. 

Bull. RM-11. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Forest 

and Range Experiment Station. 26 p.

Soto, B.; Díaz-Fierros, F. 1998. Runoff and soil 

erosion from areas of burnt scrub: comparison of 

experimental results with those predicted by the 

WEPP model. Catena. 31(4): 257-270.

Spencer, W.; Rustigian-Romsos, H.; Strittholt, J.; 

Scheller, R. [et al.]. 2011. Using occupancy and 

population models to assess habitat conservation 

opportunities for an isolated carnivore population.

Biological Conservation. 144: 788-803.

Stednick, J.D. 1996. Monitoring the effects of 

timber harvest on annual water yield. Journal of 

Hydrology. 176(1): 79-95.

Stern, N. 2007. The economics of climate change: 

the Stern review. Cambridge, UK: Cambridge 

University Press. 692 p.
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 43

Sterner, T.; Persson, U.M. 2008. An even sterner 

review: introducing relative prices into the 

discounting debate. Review of Environmental 

Economics and Policy. 2(1): 61-76.

Stetler, K.M.; Venn, T.J.; Calkin, D.E. 2010. The 

effects of wildfire and environmental amenities 

on property values in northwest Montana, USA.

Ecological Economics. 69(11): 2233-2243.

Swallow, S. 1990. Depletion of the environmental 

basis for renewable resources: the economics of 

interdependent renewable and nonrenewable 

resources. Journal of Environmental Economics and 

Management. 19(3): 281-296.

Swank, W.T.; Vose, J.M.; and Elliott, K.J. 2001. Longterm hydrologic and water quality responses 

following commercial clearcutting of mixed 

hardwoods on a southern Appalachian catchment.

Forest Ecology and Management. 143(1): 163-178.

Tol, R.S.J. 1995. The damage costs of climate change 

toward more comprehensive calculations.

Environmental and Resource Economics. 5(4): 353-

374.

Tol, R.S.J. 2008. The social cost of carbon: trends, 

outliers and catastrophes. Economics: The OpenAccess, Open-Assessment E-Journal. 2(25): 1-22. 

http://dx.doi.org/10.5018/economics-ejournal.

ja.2008-25. (April 2017).

Tol, R.S.J. 2009. The economic effects of climate 

change. The Journal of Economic Perspectives. 

23(2): 29-51.

Troendle, C.A.; MacDonald, L.H.; Luce, C.H.; Larsen, 

I.J. 2010. Fuel management and water yield. In: 

Eliot, W.J.; Miller, I.S.; Audin, L., eds. Cumulative 

watershed effects of fuel management in the western 

United States. Gen. Tech. Rep. RMRS-GTR-231. 

Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station: 

124-148.

Troy, A.; Wilson, M.A. 2006. Mapping ecosystem 

services: practical challenges and opportunities 

in linking GIS and value transfer. Ecological 

Economics. 60(2): 435-449. http://www.census.gov/

manufacturing. (March 2017).

U.S. Census Bureau. 2009. Annual survey of 

manufacturers, 2009. Washington, DC: U.S. 

Department of Commerce, Census Bureau. https://

www.census.gov/data/tables/2009/econ/asm/2009-

asm.html. (April 2017).

U.S. Department of Agriculture. 2008. U.S. agriculture 

and forestry greenhouse gas inventory: 1990-2005.

Technical Bulletin No. 1921. Washington, DC: U.S. 

Department of Agriculture, Office of the Chief 

Economist, Global Change Program Office. 161 p.

U.S. Environmental Protection Agency, Science 

Advisory Board. 2009. Valuing the protection of 

ecological systems and services. EPA-SAB-09-012. 

Washington, DC: U.S. Environmental Protection 

Agency. 138 p.

U.S. Environmental Protection Agency. 2016. 

National summary of impaired waters and TMDL 

information. https://iaspub.epa.gov/waters10/

attains_nation_cy.control?p_report_type=T. (March 

2017).

U.S. Interagency Working Group on Social Cost 

of Carbon. 2010. United States Government 

Technical Support Document: social cost of 

carbon for regulatory impact analysis under 

Executive Order 12866. 21 p. http://www.

whitehouse.gov/sites/default/files/omb/inforeg/

for-agencies/Social-Cost-of-Carbon-for-RIA.pdf. 

(March 2017).

U.S. Interagency Working Group on Social Cost 

of Carbon. 2013. United States Government 

Technical Support Document: technical update 

of the social cost of carbon for regulatory impact 

analysis under Executive Order 12866. 50 p.

https://www.whitehouse.gov/sites/default/files/omb/

inforeg/social_cost_of_carbon_for_ria_2013_update.

pdf. (March 2017).

van Kooten, G.; Binkley, C.; Delcourt, G. 1995. 

Effect of carbon taxes and subsidies on optimal 

forest rotation age and supply of carbon services.

American Journal of Agricultural Economics. 77(2): 

365-374.
44 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

Viscusi, W.K.; Aldy, J.E. 2003. The value of a 

statistical life: a critical review of market estimates 

throughout the world. Journal of Risk and 

Uncertainty. 27(1): 5-76.

von Haefen, R.; Phaneuf, D. 2005. Kuhn-Tucker 

demand system approaches to non-market 

valuation. In: Applications of simulation methods 

in environmental and resource economics. 

Dordrecht, Netherlands: Springer Dordrecht.

von Haefen, R.H.; Phaneuf, D.J. 2008. Identifying 

demand parameters in the presence of 

unobservables: a combined revealed and stated 

preference approach. Journal of Environmental 

Economics and Management. 56(1): 19-32.

Vugts, H.F. 1974. Calculation of temperature 

variations of small mountain streams. Journal of 

Hydrology. 23(3): 267-278.

Watson, K.B.; Ricketts, T.; Galford, G.; Polasky, S. [et 

al.]. 2016. Quantifying flood mitigation services: 

the economic value of Otter Creek wetlands 

and floodplains to Middlebury, VT. Ecological 

Economics. 130: 16-24.

Wear, D.N.; Coulston, J.W. 2015. From sink to source: 

regional variation in U.S. forest carbon futures.

Scientific Reports. 5: 16518.

Wehrly, K.E.; Wang, L.; Mitro, M. 2007. Field-based 

estimates of thermal tolerance limits for trout: 

incorporating exposure time and temperature 

fluctuation. Transactions of the American Fisheries 

Society. 136(2): 365-374.

Whitehead, J.C.; Haab, T.C.; Huang, J.-C. 2000. 

Measuring recreation benefits of quality 

improvements with revealed and stated behavior 

data. Resource and Energy Economics. 22(4): 339-

354.

Wigmosta, M.S.; Vail, L.W.; Lettenmaier, D.P. 1994. 

A distributed hydrology-vegetation model for 

complex terrain. Water Resources Research. 30(6): 

1665-1680.

Wood, K.A.; Stillman, R.A.; Goss-Custard, J.D. 

2015. Co-creation of individual-based models 

by practitioners and modellers to inform 

environmental decision-making. Journal of 

Applied Ecology. 52: 810-815.

Wu, J.; Skelton-Groth, K. 2002. Targeting 

conservation efforts in the presence of threshold 

effects and ecosystem linkages. Ecological 

Economics. 42(1-2): 313-31.

Young, R.A. 2005. Determining the economic value 

of water: concepts and methods. New York: 

Earthscan. 377 p.

Younus, M.; Hondzo, M.; Engel, B.A. 2000. Stream 

temperature dynamics in upland agricultural 

watersheds. Journal of Environmental Engineering. 

126(6): 518-526.
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 45

APPENDIX

Recreation Valuation Methods

Economists have utilized three primary revealed 

preference approaches to estimate the monetary value 

of changes in site attributes related recreation services: 

the discrete choice random utility model (Hanemann 

1984), the hedonic travel cost method (Brown Jr 

and Mendelsohn 1984), and the generalized corner 

solution or Kuhn-Tucker model (Phaneuf et al. 1998). 

A brief description of each follows; readers are referred 

to Haab & McConnell (2002), Phaneuf and Smith 

(2005), Freeman et al. (2014), and Bockstael and 

McConnell (2007) for more comprehensive treatment 

of the theory and econometric methods related to 

nonmarket valuation.

Discrete Choice Random Utility Model

The discrete choice random utility model (RUM) 

applies directly to recreationists’ decisions over which 

site to visit on a given occasion. (It is often paired with 

a count data model to estimate the demand for total 

number of trips.) Along with the implicit and explicit 

costs of travel to the various sites and a host of socioeconomic characteristics, forest attributes thought to 

influence people’s preferences among sites are included 

as explanatory factors in the site choice decision model. 

These explanatory factors are modeled as arguments 

in a utility function, which individuals are presumed 

to maximize in making their observed site choice 

decisions. In the RUM framework, the analyst specifies 

a utility function applicable to all individuals in the 

sample but recognizes that unobservable factors may 

differentially influence individuals’ preferences among 

sites; the analyst models this idiosyncrasy as individualspecific random error (hence the name, random utility 

model). Having estimated the parameters of the choice 

model, the analyst can calculate the average marginal 

value of each forest attribute based on its contribution 

to utility relative to that of travel costs. In addition, 

because the utility function is estimated directly, the 

RUM allows for relatively easy, exact calculation of 

the welfare effects of non-marginal changes in forest 

attributes. It is applicable to real-world observations 

(i.e., as a revealed preference method) or observations 

of hypothetical decision-making (i.e., as a stated 

preference method). Recent applications of discrete 

choice random utility models to value forest attributes 

for recreation include Juutinen et al. (2014), Rolfe and 

Windle (2015), and Simões et al. (2013).

A key limitation of the RUM is that it does not account 

for individuals’ preferences for variety in recreational site 

visits: observed choices are assumed to be independent 

of past or future trips. In addition, most RUM studies 

specify linear utility functions, despite the fact that 

this imposes strong assumptions on recreationists’ 

preferences (Hanemann 1984, Mendelsohn 1987). 

Most notably, linear models assume that recreationists 

place the same marginal value on forest attributes, 

regardless of the current level of those attributes. Such 

strong assumptions are particularly unlikely to hold for 

attributes such as average stocking density, for which 

increases may be “goods” at relatively low levels and 

“bads” at relatively high levels; and for studies spanning 

different recreational “markets,” in which visitors’ 

place of origin (ZIP code area, census block, town, 

etc.), choice set, and implicit prices differ (Pendleton 

1999). Future studies can and should explore more 

flexible utility specifications, as poor specifications can 

substantially bias estimates of ecosystem service values 

(Cropper et al. 1993, Pendleton et al. 1998).

Hedonic Travel Cost Method

As its name suggests, the hedonic travel cost method 

(HTCM) takes a hedonic approach to the question 

of recreation-related ecosystem service value by 

decomposing travel costs from each origin into the 

implicit prices of the characteristics of each chosen site. 

A different hedonic price equation is estimated for each 

origin or “market.” If the assumptions of the hedonic 

model are satisfied, these implicit prices reflect the value 

of a marginal change in each attribute across all sites to 

recreationists in each market. Recovering the welfare 

effects of discrete changes in a site attribute, multiple 

attributes, or an attribute only at a particular site 

requires derivation of an inverse demand system based 

on observed choices and estimated hedonic prices. The 

demand equations are then used to predict changes in 

site choice based on expected surplus. Although this 

requires quite a bit of additional analyses, estimation of 

the demand system can reveal important substitutability 

or complementarity between site attributes and allows 

for more accurate welfare calculation than the typical 

linear RUM to the extent that such non-zero crossprice coefficients exist.
46 Assessment and Valuation of Forest Ecosystem Services: State of the Science Review

The HTCM faces many limitations and criticisms. 

As with the RUM, it cannot explain or predict the 

choice of different sites in the same year or season by 

the same individual, and demand analysis is typically 

conditional on recreationists’ choice to make a trip. 

Unlike the RUM, it does not account for preference 

heterogeneity among demographically identical 

individuals. Moreover, it is not well suited for valuation 

when choice sets are very small and the assumption of 

a continuous price frontier is implausible. Although 

the HTCM has been criticized for producing negative 

implicit price estimates for some ecosystem services, 

this may be a feature rather than a flaw. While “too 

much of a good thing” rarely arises in markets, where 

producers have no incentive to provide costly attributes 

beyond the level at which consumers are willing to 

pay the marginal cost, recreationists may well be 

over-satiated by the natural supply of a particular site 

attribute. (Even when managers can alter the level 

of service provision, it may not be efficient to reduce 

natural supply to the level at which its marginal value 

would be non-negative.) However, the appeal to the 

natural supply of ecosystem services in justifying 

negative price estimates belies a more fundamental 

issue with the HTCM: it may not be appropriate to 

interpret the estimated cost function equation as a 

hedonic price equation at all (Smith and Kaoru 1987).

Generalized Corner Solution

The generalized corner solution or Kuhn-Tucker (KT) 

model is a relatively new and promising valuation 

method. Unlike the discrete choice RUM and HTCM, 

the KT model directly accounts for some recreationists’ 

choice to take multiple trips—possibly to different 

sites--in a given season. It also avoids potential bias 

that arises in the context of estimating preferences 

from single choice occasions when the process is a 

dynamic one (Baerenklau and Provencher 2005), 

though estimates may still be biased if such dynamics 

operate over longer time scales. At the same time, the 

KT model accounts in a statistically and theoretically 

rigorous way for the fact that the number of trips a 

given recreationist will make to most sites is zero. These 

zero values, or “corner solutions,” beget the name; the 

KT appellation refers to the (first-order) optimality 

conditions, which apply to both the extensive margin 

(site choice) and intensive margin (number of trips) 

and provide analytical rigor (von Haefen and Phaneuf 

2008). The KT model incorporates random utility 

parameters to account for individual preference 

heterogeneity, and parameters of the Kuhn-Tucker 

conditions are estimated using maximum likelihood. 

Estimation (and subsequent welfare analysis) is 

computationally intensive, but possible with the use of 

simulation techniques (von Haefen and Phaneuf 2008). 

The approach has been used to estimate preferences for 

freshwater and coastal/marine recreation (e.g., Phaneuf 

et al. 2000, Kuriyama et al. 2010), as well as moose 

hunting trips (von Haefen and Phaneuf 2005).

Challenges

Three important methodological or informational 

issues confront all of the major approaches to revealed 

preference valuation of recreation-related forest 

ecosystem services. The first complication relates to 

the identification or selection of the set of choices 

that recreationists actually consider in making their 

decisions. This information is not generally available to 

the analyst relying on visitor permit data or even brief 

on-site surveys. Including sites and travel routes that 

were not part of the true choice set can substantially 

bias the estimates of the relative value of different 

site characteristics (Bell and Strand 2003, Haab and 

Hicks 1999). Absent information on recreationists’ 

actual choice sets, Pendleton (1999) argues that the 

choice set should be restricted to sites visited by at 

least one person from each origin in the HTCM. It is 

unclear whether defining origins for this sole purpose 

in the context of other modeling approaches is most 

sensible, or whether the analyst should consider 

other characteristics, such as group or household size, 

demographic variables, and income in addition to or 

instead of grouping observations by proximity for the 

definition of choice sets.

Another challenge facing each of these valuation 

methods is quantification of the opportunity cost of 

travel time. For hourly workers free to choose as few 

or as many hours of work as they wish, the marginal 

value of their time is exactly equal to their wage rate. 

But recreationists rarely face labor-leisure tradeoffs 

of this sort. Nearly all are retired, salaried, or wage 

earners with limited discretion over their hours, making 

the identification of the marginal value of their time 

a much more complicated proposition (see Larson 

and Shaikh 2001). With few exceptions, however, 
Assessment and Valuation of Forest Ecosystem Services: State of the Science Review 47

analysts continue to utilize the wage rate or a fraction 

thereof—often one-third, as a rule of thumb. Englin 

and Shonkwiler (1995) treat unobserved travel costs as 

a latent variable, using distance, travel time, party size, 

and composition as indicators of this latent variable. 

Surprisingly, the authors find that respondents value 

the opportunity cost of time at 40 percent of wage rate, 

offering some validity to the standard, largely arbitrary 

assumption. Further application of this latent approach 

seems warranted. At the same time, other research 

efforts are underway to estimate the marginal value of 

time without appealing to the labor-leisure decision 

(e.g., Fezzi et al. 2014, Phaneuf 2011); these too seem 

promising.

A third challenge facing revealed preference valuation 

practitioners concerns the range of observation in 

quantity or quality of the ecosystem service of interest. 

Changes in environmental quality outside of the range 

currently observed cannot be evaluated reliably. For 

instance, if a policymaker wants to weigh the costs and 

benefits of opening some recreational area to harvest, 

but none of the sites within the choice set of local 

recreationists contain harvested stands, there is no way 

to evaluate the impact of the proposed change based 

on recreationists’ observed behavior. Welfare calculation 

requires either carefully applied benefits transfer from 

sites where such changes fall within the range of 

observation or supplementing revealed preference data 

with stated preference data (e.g., Englin and Cameron 

1996, Englin et al. 2001, von Haefen and Phaneuf 

2008, Whitehead et al. 2000). This allows wider 

application of the revealed preference approach, subject 

to validation of the contingent response data.

How the results of the valuation exercise are 

incorporated into the benefit-cost analysis depends 

on the valuation technique. The goal is to obtain a 

measure of equivalent or compensating variation 

associated with the expected changes in ecosystem 

attributes. Equivalent variation (EV) measures how 

much a consumer would be willing to pay or accept 

for the changes to occur; compensating variation 

(CV) measures how much money a consumer would 

have to lose or receive to be just as well off as she 

was before the change in attributes. In the context 

of recreation-related services, these measures should 

be nearly identical. Calculation of either measure 

requires the use of a utility function with the relevant 

ecosystem attributes among the arguments. The utility 

function may be specified directly, as in the case of 

the discrete choice random utility and KT models, or 

it may be “recovered” from the estimated parameters 

of the demand system, as in the case of the hedonic 

travel cost model. Applications of the discrete choice 

RUM typically assume a convenient distribution for 

the random term and a utility function that is linear 

in ecosystem attributes, which allows a closed-form 

solution for the CV or EV equations. In contrast, 

the KT model is inherently non-linear and requires 

computational integration via simulation to obtain 

numerical solutions (see Phaneuf et al. 2000 and von 

Haefen and Phaneuf 2005).
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Binder, Seth; Haight, Robert G.; Polasky, Stephen; Warziniack, Travis; Mockrin, 

Miranda H.; Deal, Robert L.; Arthaud, Greg. 2017. Assessment and valuation 

of forest ecosystem services: State of the science review. Gen. Tech. Rep. 

NRS-170. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 47 p.

This review focuses on the assessment and economic valuation of ecosystem 

services from forest ecosystems—that is, our ability to predict changes in the 

quantity and value of ecosystem services as a result of specific forest management 

decisions. It is aimed at forest economists and managers and intended to provide a 

useful reference to those interested in developing the practice of integrated forest 

modeling and valuation. We review examples of ecosystem services associated 

with several broad classes of potentially competing forest uses—production of 

timber, sequestration of carbon, regulation of the quality and quantity of water, 

provision of residential and recreational amenities, and protection of endangered 

species. For each example considered, we briefly describe what is known about 

ecological production functions and economic benefits functions. We also highlight 

the challenges and best practices in the creation and use of this knowledge. In the 

final section, we discuss the process, strengths, pitfalls, and limitations of utilizing 

integrated models for benefit-cost analysis of proposed forest management 

activities.

KEY WORDS: Ecological production function, non-market valuation, social cost 

of carbon, recreation demand model, hedonic travel cost model, discrete choice 

model, contingent valuation model, hedonic property price model

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