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This article appeared in a journal published by Elsevier. The... copy is furnished to the author for internal non-commercial research
This article appeared in a journal published by Elsevier. The attached
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Author's personal copy
Ecological Economics 71 (2011) 180–190
Contents lists available at SciVerse ScienceDirect
Ecological Economics
journal homepage: www.elsevier.com/locate/ecolecon
Analysis
Barriers to Massachusetts forest landowner participation in carbon markets
Marla Markowski-Lindsay a,⁎, Thomas Stevens b, David B. Kittredge a, Brett J. Butler c,
Paul Catanzaro a, Brenton J. Dickinson b
a
b
c
University of Massachusetts, Department of Environmental Conservation, 160 Holdsworth Way, Amherst MA 01003, USA
University of Massachusetts, Department of Resource Economics, 216 Stockbridge Hall, Amherst, MA 01003, USA
U.S. Forest Service-Northern Research Station, Family Forest Research Center, 160 Holdsworth Way, Amherst MA 01003, USA
a r t i c l e
i n f o
Article history:
Received 28 February 2011
Received in revised form 24 August 2011
Accepted 28 August 2011
Available online 30 September 2011
Keywords:
Family forest
Carbon sequestration
Ordered probit
a b s t r a c t
U.S. forests, including family-owned forests, are important carbon sinks and sources for carbon sequestration.
Family forest owners constitute a significant portion of the overall forestland in the U.S., but little is known
about their preferences for participating in carbon sequestration programs. The goal of this research is to understand what motivates Massachusetts family forest owners to participate in carbon markets. The study estimates the probability these landowners would engage in carbon sequestration programs using data from a
survey of 930 Massachusetts family forest owners. Results from a random effects ordered probit indicate that
under a carbon scenario similar to the current voluntary scheme, very few of these landowners would be interested in participating. Supply analysis indicates these landowners are more influenced to participate by
factors other than price. Regression analysis results suggest that survey respondents are concerned about
early withdrawal penalties, additionality requirements, and contract length. Forest owner harvesting plans,
opinions about forest usage, and beliefs about climate change all play a significant role in the decision to participate. The study suggests that policy makers should consider the reasons behind these low participation
rates, because private forest owners could play a pivotal role in the carbon sequestration potential of forests.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Forests and forest products are crucial for protecting existing carbon sinks and promoting additional carbon sequestration (NEFA,
2002). Forest management efforts can maintain or improve forest carbon stock through a variety of techniques (e.g., thinning, increasing
rotation length). Afforestation activities create new carbon sinks by
establishing forest on non-forested land. Reforestation increases carbon stocks by reestablishing forest cover following a timber harvest.
Avoided forestland conversion also helps to maintain or improve forest carbon stocks. (CAR, 2010; CCX, 2009; Helmes, 1998).
This article focuses on land with established forests. In these areas,
forest management efforts are the relevant activities for maintaining
and improving forest carbon stocks. In particular, we focus on established forests owned by families.
Family forest owners have the potential to play an important role
in carbon sequestration. Over half the forestland in the U.S. is privately owned, and nearly two-thirds of that land is in the hands of family
forest owners (Butler, 2008). In the Northeast, those percentages are
⁎ Corresponding author. Tel.: + 1 413 545 3589; fax: + 1 413 545 4358.
E-mail addresses: [email protected] (M. Markowski-Lindsay),
[email protected] (T. Stevens), [email protected] (D.B. Kittredge),
[email protected] (B.J. Butler), [email protected] (P. Catanzaro),
[email protected] (B.J. Dickinson).
0921-8009/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.ecolecon.2011.08.027
even greater. For example, in Massachusetts, over half of the forests
are family-forest owned, approximately 1.7 million acres (Butler,
2008). As trees grow on these private lands, they sequester considerable quantities of carbon each year. Forest owner participation in carbon markets could increase sequestration, but carbon markets are
currently in a state of flux, and few programs have direct provisions
for involvement from small-scale forest owners. For example, during
its existence (2000–2010), the Chicago Climate Exchange (CCX), a
voluntary greenhouse gas reduction and credit trading market, had
a detailed protocol for enrolling forestry projects as carbon offsets
(see CCX, 2009). Highlights of protocol requirements included:
• Establishment of baseline sequestration levels and subsequent annual verification;
• Forestry management plans that would lead to increased carbon sequestration over baseline levels, above and beyond standard business practice (i.e., additionality);
• Minimum enrollment amounts (all acreage unless an exemption is
explicitly granted);
• Regular reports of land disposition and harvesting information;
• 15 year time commitment; and
• Start-up fees and insurance retainers.
To enter this market, small-scale forest owners had to work with
an aggregator (i.e., carbon credit buyers who aggregate carbon credits
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M. Markowski-Lindsay et al. / Ecological Economics 71 (2011) 180–190
from multiple small landowners into lumps of tradable units (Birdsey,
2006)), because the market was geared towards large-scale transactions. Examples of U.S. aggregators included: Appalachian Carbon
Partnership; CarbonTree, LLC; Illinois Climate and Conservation Initiative; Michigan Climate and Conservation Initiative; Michigan Forest Carbon Offset and Trading Program; and Woodlands Carbon.
Although the aggregator market enabled the small-scale forest
owner to trade on the CCX, family forest owner participation was
low. For example, out of the roughly 12 million acres of privately
held forestland in Michigan, between 2007 and 2008, only 36 forest
owners and 72,972 acres were enrolled. Between 2009 and 2010,
Massachusetts offered a cost-share to forest owners to become involved in the offset market, but not a single one chose to enroll. For
policymakers to establish an effective climate change policy, it is important to understand, in detail, family forest owner decision-making
with respect to participation in carbon sequestration programs,
which is the focus of this paper.
2. Background
The existing carbon sequestration literature, although expansive
and very informative, does not fully address the question of what motivates a private forest owner to participate in a carbon sequestration
program. Previous carbon sequestration studies for forestland have
mainly focused on:
• The biophysical potential of carbon sequestration on forestland under
various policy and management regimes (e.g., Alig et al., 2006; Asante
et al., 2010; Davis et al., 2009; Hudiburg et al., 2009; Nunery and
Keeton, 2010; Stainback and Alavalapati, 2002). In addition, Uusivuori
and Laturi (2007) present a theoretical model and conduct a numerical analysis examining the impact of potential climate policy options
on the timber and carbon content of private forests.
• The financial costs of carbon sequestration on forestland. Systematic reviews of this literature include Richards and Stokes (2004),
Stavins and Richards (unpublished results) and van Kooten et al.
(2004). Other studies in this genre of literature include Huang and
Kronrad (2001), Han and Youn (2009), and Lubowski et al. (2006).
• Potential market instruments for encouraging the sequestration of
carbon (e.g., Bigsby, 2009; Cairns and Lasserre, 2004; Lippke and
Perez-Garcia, 2008; Wayburn and Passero, 2004).
• Theoretical models that measure the welfare impacts of carbon sequestration policies (e.g., Adams et al., 1999; Alig et al., 1997; and
Im et al., 2007).
Some studies have focused on the factors that influence participation in carbon sequestration (e.g., Shaikh et al., 2007a,b; van Kooten
et al., 2002), These studies consider farmer participation in afforestation efforts, but they do examine characteristics similar to those relevant for forest management activities (e.g., contract length, payment,
landowner characteristics); however, afforestation is not relevant for
the majority of Massachusetts forest owners,
A number of articles have been published on the broader topic of
participation in incentive programs for family forest owners. For example, Daniels et al. (2010) found that landowner purchase and management decisions were motivated, in part, by benefits to the owner
and doing the “right thing” (p. 49). Jacobson, et al. (2009a,b) surveyed
forestry officials responsible for forestry incentive programs and
found programs could be improved through increased visibility, availability, simplified administrative process and long term consistency.
Following an extensive literature review, a survey of program administrators, and focus groups with family forest owners, Kilgore et al.
(2007) concluded that “financial incentive programs have limited influence on forest owners' decisions regarding the management and
use of their land” (p. 184) and the most desired assistance was for a
forester to walk the woods with them. A study of Minnesota landowners found compensation, total acres owned, intention to obtain
181
a management plan, program awareness and other factors to influence enrollment in a forest stewardship plan (Kilgore et al., 2008).
Studying the preferences and attitudes towards carbon sequestration on family forest lands (i.e., the largest single segment of private
ownership nationwide) is the key to understanding participation
rates in carbon sequestration markets. A multitude of factors may influence forest owners' decisions to participate in carbon markets:
program characteristics (e.g., length of contract); current activities
occurring on their land; their own attitudes and opinions about the
use of their land, climate change, program implementer, and socioeconomic characteristics. Using the CCX protocol as a guide, program
characteristics most likely to affect forest owners may include: requirement of a management plan, required amount of enrolled acreage, time commitment, requirement of undertaking forest activity to
ensure carbon is sequestered above and beyond baseline levels (i.e.,
additionality), program revenue, and early withdrawal penalty. Land
characteristics and activities that might affect a forest owner's participation include whether or not the owner has previous experience
with harvesting trees from their property, whether the owner is
open to harvesting in the future, whether their forest land is currently
enrolled in another management program, and how much forested
acreage is owned. Forest owner beliefs that may affect participation
include whether timber production drives their management decisions, or whether they believe their land should be left unmanaged.
Opinions on climate change and program implementer may drive
participation in a carbon sequestration program. 1
Very few studies have examined the factors that motivate family
forest owners to participate in carbon sequestration programs for
established forests. A recent Master's Thesis by Dickinson (2010)
employed a survey to analyze Massachusetts forest owner participation in carbon sequestration programs. The carbon sequestration program question was part of a larger mail survey sent to private forest
owners in Massachusetts to gauge their use of land management information. Respondents were asked to rate three carbon sequestration programs in terms of their likelihood to participate. Four
characteristics varied by program: requirement of a written management plan to participate, length of time commitment, expected peracre net revenue, and existence of a penalty for early withdrawal. A
standard ordered logit analysis of the data from the 910 individuals
who rated all three programs indicates that the likelihood of carbon
sequestration program participation decreases with the requirement
of a management plan, an early withdrawal penalty, and a longer
time commitment, and increases with higher revenue amounts. The
likelihood of higher participation is associated with forest owners
who owned more than 100 acres of forestland and who had higher
levels of education. Dickinson estimates predicted probabilities of
choosing each rating for each program respondents were asked to
evaluate. The calculated probabilities for each survey version and program number provided to respondents indicate that the most popular
program receiving a rating of 10 (i.e., “definitely would enroll”) requires a management plan, has a 5 year time commitment, provides
$30 per acre annual revenue, and has no penalty for early withdrawal; this program has a probability of acceptance of 31.5%. The calculated probabilities also indicate that the program that could be deemed
the closest to CCX requirements (i.e., management plan required, 10
year time commitment, $5 per acre per year revenue, early withdrawal penalty) had an acceptance rate of only 4%. 2
Dickinson's study is helpful in understanding some of the elements
that motivate private forest owners about a carbon sequestration program, but the questions asked describe programs that fall short of some
important carbon sequestration program characteristics. The scenarios
1
Focus groups (discussed below) indicated forest owner concern about the entity
implementing the program.
2
It is worth noting that results from this study were similar to those derived in an
earlier pilot study (see Fletcher et al., 2009).
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M. Markowski-Lindsay et al. / Ecological Economics 71 (2011) 180–190
have the respondent assume a carbon program similar to one that
would trade on the CCX, however two key elements are missing:
proof that management plans would lead to increased carbon sequestration over baseline levels (i.e., additionality) and minimum enrollment amounts. Further, CCX protocol reflected a longer time
commitment (15 years) than described in the scenarios used in the
Dickinson analysis (5 and 10 years). One would expect that these
three important elements would influence participation levels. Dickinson's analysis also excludes key land characteristics and forest owner
beliefs that we believe would influence participation.
Maraseni et al. (2008) interviewed Australian family forest
owners to explore their perceptions of the barriers to participate in
a carbon trading scheme. In particular, Maraseni et al. (2008) are concerned with understanding landowner perspectives about expanding
forest land area from marginal pastures in Australia. This study acknowledges that the published literature does not consider whether
a carbon trading scheme would be successful from a landowner perspective. Interviewees indicate three general areas that could hinder
participation: uncertainty of the rules that would govern a carbon
trading policy in Australia; lack of knowledge of how to measure
and sell carbon credits; and uncertainty over whether such a program
would be profitable. While helpful, this study does not explicitly indicate what specific carbon program characteristics are likely to influence forest owner decisions.
Other studies have examined factors motivating family forest
owners to participate in carbon sequestration programs to varying degrees. Charnley et al. (2010) provide an overview of current and potential opportunities for family forest owners to contribute to carbon
sequestration in the U.S. This study discusses how various issues limit
participation in carbon markets, including: low carbon prices and
high market entry costs; market access difficulties for small landowners; management plan and certification requirements; and whether participation in carbon markets is consistent with other forest
management goals. Thompson (2010) uses the theory of planned behavior to measure the intentions of private forestland owners to participate in carbon markets and finds that 50% of respondents are
somewhat interested in exploring opportunities. This study does not
provide respondents with a description of carbon program details. It
excludes for example, program requirements and potential carbon
prices or offset income. Fischer and Charnley (2010) review the published literature to characterize the social and cultural influences on
family forest owners' willingness to manage their forest for carbon
and engage in carbon sequestration policies. Fischer and Charnley conclude that family forest owners may not be eager to participate in carbon sequestration programs because they place such high value on
privacy and autonomy, but not much on financial reward. While interesting, this study does not directly survey forest owners. Alig (2003)
synthesizes existing research findings to broadly consider how landowner behavior, forestry practices and socioeconomic conditions may
affect forest carbon stores through land use and land cover change.
It is the goal of this research to expand on the tentative, preliminary findings of Dickinson (2010) and to further explore what motivates the family forest owner in Massachusetts to participate in
carbon sequestration programs. As the eighth most forested state in
the country (by percent of land use), Massachusetts has the opportunity to contribute to climate change mitigation. The state has a large
population of family forest owners with diverse backgrounds, needs
and concerns. Massachusetts is the third most densely populated
state in the U.S. The residents of Massachusetts live in close proximity
to the forestland, resulting in development pressure, higher land
values, and smaller parcel size. Approximately 63% of the state is forested and 53% of that land base is owned by roughly 293,000 family
forest owners with an average parcel size of 6 acres (Butler, 2008;
Smith et al., 2009). A high number of these families place low priority
on timber income from their land, and family owner preferences are
primarily focused on passive benefits (e.g., aesthetics, recreation,
nature, and privacy) (Butler, 2008). While there is an increased likelihood of timber harvesting from east to west in the state (i.e., from
urban to rural), forest types, forestry activities and high land values
are largely homogeneous across this small state (D'Amato et al.,
2010). It is the collective decisions of these forest owners that will
shape the potential of carbon sequestration in Massachusetts' forests.
Our research suggests that it is possible to target certain groups of forest owners for carbon program enrollment, but given current requirements, participation rates are still likely to remain quite low.
3. Methods
We developed a mail survey for the purpose of investigating family forest owner participation in carbon sequestration programs in
Massachusetts. Four focus groups held across Massachusetts gauged
comprehension of the survey instrument questions that we were
testing and gained deeper insight into forest owner motivations.
Four additional focus groups provided forest owners' general impressions of carbon sequestration programs. The final survey asked questions about land ownership (e.g., size of landholdings, history and
future of land management activities, current land management program enrollment), owner beliefs (e.g., reasons for owning forestland,
beliefs about climate change), socioeconomic characteristics (e.g.,
age, gender, income, education), and carbon program options.
Conducted in May, 2010, the survey was mailed to a random sample of 930 individuals who own at least 10 acres of land in 152 cities
and towns reflecting the ecological diversity of forestland in Massachusetts. Property tax rolls provided the information on these individuals who may or may not reside within Massachusetts. We
developed the survey following Dillman's Tailored Design Method
(Dillman et al., 2009): pre-notice postcard sent three days prior to
the mail survey; mail survey including a detailed cover letter explaining the importance of responding; thank you post card sent one week
later expressing appreciation or reminding individuals to respond;
replacement mail survey and detailed cover letter sent 3 weeks
after previous survey mailing. The response rate was 43%.
The survey presented respondents with information defining and
describing carbon sequestration programs. Each respondent was
then asked to rate, on a scale of 1 to 5, three different hypothetical
carbon sequestration programs and one program described as the status quo (i.e., do not join any program, keep the status quo). The rating
question was worded such that a 5 indicates that the forest owner
definitely would enroll in the program given the opportunity, while
a 1 indicates that the forest owner definitely would not enroll in the
program. Any rating in the middle indicates varying levels of likelihood of enrollment on the part of the forest owner.
The programs varied according to: whether or not a management
plan is required of the forest owner, the amount of required enrolled
acreage, contract length, whether or not management of the land is
required to satisfy additionality requirements, expected net revenue
after paying enrollment costs, whether or not the program has a
withdrawal penalty, and whether the program is implemented by
the public or the private sector. The CCX required that all forested
acreage be enrolled unless an exemption is explicitly granted by the
CCX Forestry Committee. As such, we test 100% enrollment as well
as 50% enrollment. For contract length, we test two values: 15 years
and 30 years. The CCX protocol had a 15 year time commitment, but
we test a longer time commitment to determine if it affected participation, because other carbon markets have longer commitments (e.g.,
California Climate Action Reserve requires a 100 year commitment,
Voluntary Carbon Standard requires a commitment between 20 and
100 years). The net revenue values used in the survey ($10/acre/
year, $100/acre/year and $1,000/acre/year) were designed to test a
very wide range of values, including those well out of current market
range. There is considerable uncertainty about how the market might
evolve in the future given potential passage of a cap-and-trade or
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M. Markowski-Lindsay et al. / Ecological Economics 71 (2011) 180–190
183
Table 1
Means and definitions of variables for random effects ordered probit sample.
Variable
Mean
Definition
(Std. dev.)
Management plan
Enrolled acreage
Contract length
Additionality
Program revenue
Early withdrawal penalty
Implementer
Trees harvested in past
Plans to harvest in future
Forested acres owned
Owner not active with land
Chapter 61 enrolled
Manages for timber
Manages for nature
People cause climate change
Trees help climate change
Age 65 years or older
Lower education
Higher education
Income less than $50,000
Income more than $100,000
Gender
0.44
(0.50)
0.67
(0.36)
17.80
(10.90)
0.40
(0.49)
$273.66
(416.60)
0.47
(0.50)
0.47
(0.50)
0.43
(0.50)
0.56
(0.50)
48.38
(64.69)
0.56
(0.35)
0.41
(0.49)
0.20
(0.40)
0.35
(0.48)
0.48
(0.50)
0.61
(0.49)
0.31
(0.46)
0.16
(0.37)
0.59
(0.49)
0.23
(0.42)
0.39
(0.49)
0.77
(0.42)
Management plan required (1 if yes, 0 otherwise)
Percent of woodland required for enrollment (50 or 100)
Length of contract in years (15 or 30)
Required to manage land so that trees sequester more carbon than if nothing was done (1 if yes, 0 otherwise)
Expected net revenue per acre per year after paying enrollment costs in 2010 dollars ($10, $100, $1,000)
Must re-pay earnings plus a 20% fee (1 if yes, 0 otherwise)
Public sector (1), private sector (0)
Has had trees harvested and sold since acquisition (1 if yes, 0 otherwise)
Plans to harvest and sell trees from land in future (1 if yes, 0 otherwise)
Amount of forested acres owned
Not enrolled in any program, does not have a written forest management plan, does not farm (1 if yes, 0 otherwise)
Enrolled in a current use plan (1 if yes, 0 otherwise)
Land is important/very important for the production of timber products (1 if yes, 0 otherwise)
Important/very important to leave land unmanaged and let nature take its course on the land (1 if yes, 0 otherwise)
Owner strongly agrees that human activity causes climate change at unprecedented rates (1 if yes, 0 otherwise)
Owner strongly agrees that forests can help reduce the impact of climate change (1 if yes, 0 otherwise)
Respondent is 65 years or older (1 if yes, 0 otherwise)
Respondent has a high school diploma or less (1 if yes, 0 otherwise)
Respondent has Bachelor or Graduate degree (1 if yes, 0 otherwise)
Household annual income less than $50,000 (1 if yes, 0 otherwise)
Household annual income $100,000 or greater (1 if yes, 0 otherwise)
1 if male, 0 if female
other state/federal carbon legislation, potential coordinated efforts
between U.S. and other international markets, and the fact that the
CCX market itself closed in late 2010 after carbon traded close to $0
for months.
The 192 possible programs were reduced to 30 using the standard
fractional factorial design and then grouped into ten survey versions –
each version containing a distinct set of three carbon programs. Each
of the ten survey versions also contained the choice not to join any
program and keep the “status quo.” Appendix A contains the background information and an example choice set scenario for one version of the survey presented to respondents.
Although every respondent had the option not to join any program
and keep the status quo, the status quo could differ across respondents.
This choice set “program” was coded individually for each respondent,
using information provided in the survey. The survey information indicated that there were five different status quo program possibilities:
those who do not farm and are not enrolled in any current use program; those who are enrolled in a current use taxation program requiring a management plan, (e.g., Chapter 61, a program for owners of at
least 10 acres of contiguous forest land interested in keeping their
land in its current undeveloped use; Chapter 61 requires long-term,
sustainable timber management based on a state-approved management plan that must be renewed every 10 years. Enrolling in Chapter
61 reduces forest land valuation to reflect its value for growing timber
instead of houses); those who are not enrolled in a current use program but have a written management plan; those who have a farm
and are enrolled in a current use program that does not require a written management plan (Chapter 61a); and those who do not farm and
are enrolled in a current use program that does not require a written
management plan but offers a smaller reduction in assessed valuation
than Chapter 61 (Chapter 61b). Each of the five status quo possibilities
has a unique combination of values for the seven program characteristics. Thirteen individuals who could not be classified into one of the five
status quo options were removed from the sample.
The survey gathered forest ownership information including the
amount of forested acreage owned in Massachusetts, whether trees
have been harvested and sold from the Massachusetts land since it
was acquired, and whether the respondent has any plans to harvest
and sell trees from the Massachusetts land in the future. In addition,
two questions reflect how engaged the owner is in traditional forestry: is the respondent's land enrolled in any type of current use property tax program (Chapter 61, 61a, 61b), and does the respondent
have a written management plan.
The survey asks several questions about forest owner opinions and
beliefs. Respondents rated, on a scale of 1 to 5, how important ownership of their land was for production of saw logs, pulpwood or other
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M. Markowski-Lindsay et al. / Ecological Economics 71 (2011) 180–190
timber products, and how important the owner believes it is to leave
their land unmanaged and to let nature take its course. “Not important”
on the scale is reflected in a rating of 1 and “very important” in a rating
of 5.
The survey asks respondents to rate on a scale of 1 to 5 how much
they agree with four statements regarding climate change: “Human
activity is causing climate change at unprecedented rates”; “Forests
can help to reduce the impact of climate change”; “Cap-and-trade legislation would be the best way to reduce overall emissions from current polluters”; and “A carbon tax legislation would be the best way
to reduce overall emissions from current polluters.” A rating of 1
means the respondent strongly disagrees, while a rating of 5 means
the respondent strongly agrees, or the respondent could say “Don't
Know.” 3
Gathered socioeconomic characteristics include age, education
level, income, and gender. Respondents checked one of seven age categories to indicate their age. Based on results from previous research
(see LeVert et al., 2009 and Stevens et al., 2002), we are able to condense these into two categories for the analysis: those 65 years old
and older and those younger than 65 years old. Education is coded
as three categories: a high school diploma/GED or less, some college
or associate/technical degree, and bachelor/graduate degree. Three
categories describe household income: income less than $50,000
per year, income between $50,000 and $100,000 per year, and income
greater than or equal to $100,000 per year. Table 1 lists the variables
used in our analysis, their means, and their associated variable
definitions.
4. Model
ratings (see Greene, 2007). That is, if a respondent's utility is below
the first cutoff, he or she chooses a rating of 1. If utility is between
the first and second cutoffs, the rating is 2, etc. If utility is above the
fourth cutoff, the rating is 5. The relationship between ratings and
utility cutoffs are represented by the following equation, where the
first cutoff is normalized to zero, as is standard practice:
rij
rij
rij
rij
rij
¼ 1 if
¼ 2 if
¼ 3 if
¼ 4 if
¼ 5 if
Uij ≤ μ 1
μ 1 b Uij ≤ μ 2
μ 2 b Uij ≤ μ 3
μ 3 bUij ≤ μ 4
μ 4 b Uij :
ð3Þ
Because each individual provided ratings for four programs, we
expect to find correlation among the four responses from each individual. The random effects ordered probit model (see Greene and
Hensher, 2010) allows for this correlation in the unobserved error
term, ε. That is, the error term, originally presented in Eq. (1), is
now composed of a random, normally distributed portion, vij, and
an individual-specific disturbance, δi. This relationship is represented
by Eq. (4):
εij ¼ vij þ δi
2
2
2
Var εij ¼ σv þ σδ ¼ 1 þ σδ
Corr εij ; εis ; ¼ ρ ¼ σδ2
1 þ σδ2
ð4Þ
Our theoretical model considers the respondent facing the decision of how to rate each of the four choice set programs provided in
the survey. An appropriate way to understand these ordinal ratings
data is through the use of a latent variable that governs rating choices.
We assume that respondents make choices that increase their utility
or satisfaction, and that there is a continuous, unobservable variable
that represents opinion level or utility associated with rating each
carbon sequestration program (Train, 2003). We define the utility derived by the ith respondent from the jth carbon sequestration program (Uij) as:
The correlation among responses from the same individual accounts for a fraction of the total variance of the error term in the
model; this fraction is measured by the correlation coefficient, ρ.
The equations above provide the basis for the derivation of the
probit cumulative density function which is used to set up the probability of choosing any particular rating or lower (i.e., the cumulative
probabilities of the ratings). Probabilities for each marginal rating category are derived from these cumulative probabilities. The likelihood
function is the product of all the probabilities associated with each
observed rating and is maximized with respect to the unknown parameters. We use the Gauss–Hermite quadrature method in Stata
11 to evaluate the resulting integral.
Uij ¼ Zj βj þ Ci βi þ ε;
5. Results
ð1Þ
where Z is a vector of program attributes, C is a vector of individual
and land characteristics, the β's are associated unknown parameter
vectors, and ε is a normally distributed random component.
We do not directly observe respondents' utility for each program,
but rather the discrete rating they choose, varying from 1 (definitely
would not do) to 5 (definitely would do). Following Klosowski et al.
(2001), we assume that respondent i's observed rating for program
j (rij) is related to respondent utility through a transformation function, h:
rij ¼ h Uij :
ð2Þ
Eq. (2) provides the basis for each respondent's rating of a carbon
sequestration program to be dependent on the program, individual
and land characteristics described above.
From these relationships, we construct an ordered probit model,
wherein we define unknown utility cutoffs that delineate the five
3
As pointed out by an anonymous reviewer, these opinion-based questions may be
affecting the choice set results. As discussed in the literature (e.g., Dillman, et al., 2009),
the existence and ordering of opinion questions are likely to influence other survey responses. Our questions were presented before the choice set questions and were not
randomized.
Of all 402 survey respondents, 293 individuals provided ratings for
all four programs they were presented with in the survey. Of those,
249 individuals provided enough information to be included in the
analysis. This section (and Table 1) reflects statistics for respondents
who provided ratings for all four programs (n = 293). It is worth noting that the characteristics of the full sample (n = 402) and analysis
Table 2
Mean values for respondent agreement with climate change statements.
Climate change statement
Meana
(Std.
dev.)
Human activity is causing climate change at
unprecedented rates.
Forests can help to reduce the impact of
climate change.
Cap-and-trade legislation would be the best way
to reduce overall emissions from current polluters.
A carbon tax legislation would be the best way to
reduce overall emissions from current polluters.
3.83
(1.43)
4.43
(0.94)
2.34
(1.40)
2.78
(1.53)
Percent of sample
who responded
“Don't Know”
4%
5%
49%
36%
a
Mean and standard deviation reflects the sample of respondents who did not say
“Don't know” to the statement. That is, they provided a number on the Likert scale
ranging from 1 to 5 where 1 is “Strongly disagree” and 5 is “Strongly agree.”
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M. Markowski-Lindsay et al. / Ecological Economics 71 (2011) 180–190
185
Table 3
Comparison of random effects ordered probit, ordered probit and ordered logit results.
Variable
Random effects ordered probita
Ordered probita
Ordered logita
Management plan
Enrolled acreage
Contract length
Additionality
Program revenue
Early withdrawal penalty
Implementer
Trees harvested in past
Plans to harvest in future
Forested acres owned
Owner not active manager
Chapter 61 enrolled
Manages for timber
Manages for nature
People cause climate change
Trees help climate change
Age 65 years or older
Lower educationb
Higher educationb
Income less than $50,000b
Income more than $100,000b
Gender
Cutpoint 1
Cutpoint 2
Cutpoint 3
Cutpoint 4
Rho
− 0.0161
− 0.1212
− 0.0365***
− 0.3004***
0.0004***
− 0.3310***
0.1210
− 0.0248
0.2519**
0.0002
− 0.3458
0.0013
− 0.2420**
− 0.0239
0.0421
0.1659*
− 0.1618*
− 0.1534
0.2016**
− 0.0993
0.0563
− 0.1114
− 1.3966***
− 1.0593***
− 0.4474
0.1716
0.0898**
− 0.0255
− 0.0997
− 0.0339***
− 0.2753***
0.0003***
− 0.3172***
0.0905
− 0.0392
0.2760***
− 0.0005
− 0.3305*
0.0061
− 0.2327**
− 0.0513
0.0409
0.1593**
− 0.1347*
− 0.1824*
0.1814**
− 0.1133
0.0298
− 0.1118
− 1.3715***
− 1.0630***
− 0.4921*
0.1011
n/a
− 0.0441
− 0.1570
− 0.0557***
− 0.4666***
0.0005***
− 0.5295***
0.1269
− 0.0370
0.4809***
− 0.0009
− 0.5518*
0.0027
− 0.3626**
− 0.0565
0.0850
0.2675**
− 0.2615**
− 0.2981
0.3036**
− 0.1812
0.0127
− 0.2156
− 2.2823***
− 1.7701***
− 0.8277**
0.1751
n/a
a
b
Significance denoted as: ***1%, **5%, *10%.
To avoid the so-called “dummy variable trap” the analysis omits “Middle education” and “Income between $50,000 and $100,000 per year.”
sample (n = 249) do not deviate substantially from that sample; in
some cases, statistics are identical.
Approximately 31% of the sample are over the age of 65, 38% are
between 55 and 64 years old, and 31% are younger than 55. The majority (77%) of respondents are male. Over half the sample (59%) has
either a bachelor's or graduate degree, and nearly a quarter has some
college or an associate/technical degree. Over 75% of the sample has
household income greater than $50,000.
On average, respondents own 48 acres of forest, and 18% of the respondents are farmers. In each case, over half the sample said that the
following are very important reasons to own their land: to enjoy
beauty or scenery, for privacy, and as part of a home or vacation
home. Nearly 43% said they had experience with harvesting and selling timber in the past, while 44% said they would never harvest in the
future. Approximately 26% have a written management plan for their
land, 55% of the respondents are not involved in any forest management, do not farm, and do not have a written forest management
plan.
It is interesting to consider the responses to the four statements
that examine respondent opinion of climate change (See Table 2). Respondents generally agreed with the statements that “Human activity
is causing climate change at unprecedented rates” and “Forests can
help to reduce the impact of climate change.” However, many individuals responded “Don't Know” to the two statements about climate
change legislation. For those who provided an opinion on the legislation statements, respondents generally disagreed that either cap-andtrade or carbon tax legislation would be the best way to reduce overall emissions from current polluters. Because so many individuals
responded “Don't Know” to the legislation statements, these variables
were excluded from the analysis.
We tested for non-response bias by calling a random sample of
10% of non-respondents and obtaining answers to key survey questions. We selected survey questions that describe respondent landholdings (acreage), behavior (traditional forestry participant) and
attitudes (about climate change). A statistical comparison of the response and non-response groups (t-test) indicates that there is no
difference between them for any of the three questions asked:
acreage of forestland owned in Massachusetts; whether or not they
have a written forest management plan; and to what extent they
agree (on a scale of 1 to 5, where 1 is strongly disagree and 5 is
strongly agree) that “Human activity is causing climate change at unprecedented rates.” We might have expected greater acreages or existence of a management plan to influence the economic viability of
participation and climate change attitudes to affect willingness to respond, but this was not the case.
The estimation results from the random effects ordered probit
model indicate that several program characteristics, forest owner activities, beliefs and characteristics play a role in determining the likelihood of participation in a carbon sequestration program. Table 3
presents these regression results. 4
As expected, respondents preferred programs with greater net
revenue, no withdrawal penalty, shorter contract lengths, and no
additionality requirements (i.e., no requirement that forests must be
managed to sequester more carbon than if nothing was done).
Significant forest owner characteristic results conform to expectations. Respondents who plan on harvesting and selling trees from
their land in the future are more likely to rate the programs higher,
all else remaining equal. This is not unexpected, because individuals
who are willing to harvest in the future are open to outside individuals participating in the management of their land. In addition,
these individuals are actively planning to manage their land, and because carbon sequestration programs essentially are a type of management activity, they may be more open to participation, all else
equal. 5 Individuals who own forests for the production of saw logs,
pulpwood or other timber products are less likely to rate programs
higher. These respondents may be satisfied with the level of management they currently have on their property and may be uninterested
4
As shown in Table 3, ordered probit and ordered logit models were also estimated
to determine sensitivity of results to estimation technique. These results are discussed
later in this paper.
5
Owners who are not active managers are more likely to rate carbon programs lower in the ordered probit and ordered logit models (significant at the 10% level), but this
characteristic was insignificant in the preferred model.
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M. Markowski-Lindsay et al. / Ecological Economics 71 (2011) 180–190
Table 4
Estimated probabilities of participation across models.
Scenario 1a
Management plan
100% required enrollment
30 year commitment
Additionality
$10/acre/year
Penalty
Private implementer
Model
Status quo alternative varies by respondent (preferred specification)
Random effects ordered probitb
2.0%
Ordered probit
2.8%
Ordered logit
4.0%
Non-demanders removed from model
Random effects ordered probit
2.0%
Ordered probit
2.5%
Ordered logit
3.8%
All program characteristics set to 0 for status quo alternative
Random effects ordered probit
1.7%
vOrdered probit
2.4%
Ordered logit
3.8%
Status quo alternative dropped from the model
Random effects ordered probit
0.1%
Ordered probit
2.1%
Ordered logit
3.2%
a
b
Scenario 3a
No management plan
50% required enrollment
15 year commitment
No Additionality
$1000/acre/year
No penalty
Public implementer
6.1%
7.1%
7.9%
37.8%
37.5%
36.3%
5.5%
6.2%
7.0%
35.6%
36.1%
34.5%
5.4%
6.4%
7.4%
35.0%
35.2%
33.7%
0.8%
5.0%
5.8%
22.9%
28.0%
26.3%
All independent variables not specified are set to their mean value, see Table 1.
Preferred model.
0
200
400
600
800
1000
in additional levels of management. Also, as we learned from the
focus groups, these individuals may fear that carbon sequestration
programs would interfere with their harvesting practices. As
expected, respondents prefer carbon programs if they strongly believe that forests can help to reduce the impact of climate change. Respondents older than 65 years are less likely to rate programs higher.
As we learned from the focus groups, these individuals are likely considering legacy issues and could be unwilling to encumber their land
in management. Finally, respondents with higher education levels are
more likely to rate the programs higher.
The correlation coefficient, ρ, accounts for a fraction of the total
variance of the error term in the model and is statistically significant;
however, its value is only 0.09. We explore this result by also running
an ordered probit and an ordered logit model with the data (see
Table 3). These models remove the random effects component, treat
each observation as independent, including those responses for multiple programs by each respondent. The results, as presented in
Price per acre per year
Scenario 2a
Management plan
50% required enrollment
30 year commitment
No Additionality
$100/acre/year
Penalty
Public implementer
0
10
20
30
40
50
60
70
80
90
100
Participation rate
Average Respondent
1: Older, harvest plans, manages for timber
2: Older, no harvest plans, does not manage
for timber ,forests help climate change
3: Younger,no harvest plans, does not
manage for timber, high education
Fig. 1. Scenario 1 supply response. Supply response by landowner group for hypothetical
carbon program Scenario 1 (i.e., requires a management plan, 100% enrollment, 30-year
commitment, additionality, and an early withdrawal penalty; provides $10/acre/year;
and is implemented by the private sector).
Table 3, are not sensitive to these alternative specifications. The low
correlation coefficient and insensitivity to alternative specifications
suggest that the random effects model is probably not needed, nonetheless we maintain it as our preferred model because it is theoretically consistent with the data structure.
5.1. Probability Results
From a policy perspective, it is useful to explore whether respondents voted “for” programs with fewer requirements and greater revenue and “against” programs that were more onerous and less
profitable. We calculate the probability that respondents said they
“definitely would” join the program given the opportunity (i.e., respondent rated the program as a 5) for three types of programs:
those for which we would expect to have low, medium, and high
probabilities of participation. The program characteristics vary by scenario, and we evaluate the non-program variables at their sample
means. The program we would expect to have the lowest chance of
participation (Scenario 1) requires a management plan, 100% enrollment, 30-year commitment, additionality, and an early withdrawal
penalty; provides $10/acre/year; and is implemented by the private
sector. 6 This program most closely represents most current carbon
program characteristics. The random effects ordered probit model
has the lowest estimate of participation at 2%, and the ordered logit
the highest estimate at 4% (see Table 4). The program we would expect to have a mid-level of participation (Scenario 2) requires a management plan, 50% enrollment, 30-year time commitment, and an
early withdrawal penalty; does not require additionality; provides
$100/acre/year; and is implemented by the public sector. The range
of participation for this program is between 6% and 8%. Finally, the
program we would expect to have the highest level of participation
(Scenario 3) does not require a management plan, additionality or a
penalty; requires 50% enrollment and a 15-year time commitment;
provides $1000/acre/year; and is implemented by the public sector.
6
While we vary the managing entities for these three scenarios (i.e., public or private implementer), we do not have priors about how the managing entity affects participant burden or profitability; we include a random value for this characteristic as
part of each scenario simply because it is a program characteristic.
Author's personal copy
1000
800
600
400
200
0
10
20
30
40
50
60
70
80
90
100
Participation rate
Average respondent
1: Older, harvest plans, manages for timber
2: Older, no harvest plans, does not manage
for timber ,forests help climate change
3: Younger,no harvest plans, does not
manage for timber, high education
Fig. 2. Scenario 2 supply response. Supply response by landowner group for hypothetical carbon program Scenario 2 (i.e., requires a management plan, 50% enrollment,
30-year time commitment, and an early withdrawal penalty; does not require additionality; provides $100/acre/year; and is implemented by the public sector).
The range of participation is estimated between 36% and 38%. Table 4
shows that the probabilities across the three models are consistent.
5.2. Supply Response
0
200
400
600
800
1000
These probabilities enable us to construct a supply curve of participation, and consider how that supply curve shifts with different
types of forest owners. We identify groups of forest owners for illustrative purposes to show that policy makers may want to target specific groups when considering how to engage forest owners in carbon
sequestration. “Group 1” owners are older individuals who manage
their land for timber and have future harvest plans (i.e., Age 65
years or older = 1; Manages for timber = 1; Plans to harvest in future = 1). This group represents 7% of the sample. “Group 2” owners
are older individuals who do not manage their land for timber, do
not have future harvest plans, but believe that management of forests
can help to reduce the impact of climate change (i.e., Age 65 years or
older = 1; Manages for timber = 0; Plans to harvest in future = 0;
Trees help climate change = 1). This group represents 7% of the sample. “Group 3” owners are younger individuals who do not manage
their land for timber, do not have future harvest plans, and are highly
educated (i.e., Age 65 years or older = 0; Manages for timber = 0;
Plans to harvest in future = 0; Higher education = 1). This group
Price per acre per year
187
Table 5
Estimated price elasticity of supply by scenario and sample group.
0
Price per acre per year
M. Markowski-Lindsay et al. / Ecological Economics 71 (2011) 180–190
0
10
20
30
40
50
60
70
80
90
100
Participation rate
Average respondent
1: Older, harvest plans, manages for timber
2: Older, no harvest plans, does not manage
for timber ,forests help climate change
3: Younger,no harvest plans, does not
manage for timber, high education
Fig. 3. Scenario 3 supply response. Supply response by landowner group for hypothetical carbon program Scenario 1 (i.e., does not require a management plan, additionality
or a penalty; requires 50% enrollment and a 15-year time commitment; provides
$1000/acre/year; and is implemented by the public sector).
Sample group
Scenario Scenario Scenario
1a
2a
3a
Average respondent
Group 1: Older than 65 years, future harvest
plans, manages land for timber. 7% of sample
Group 2: Older than 65 years, no harvest plans,
does not manage for timber, believes forests can
help with climate change. 7% of sample
Group 3: Younger than 65 years, no harvest plans,
does not manage for timber, high education.
19% of sample
0.28
0.31
0.22
0.24
0.13
0.15
0.30
0.24
0.14
0.27
0.21
0.12
a
Preferred model used; elasticities estimated at mean revenue per acre per year
($273.66). For each group, all independent variables not specified are set to their
mean value (see Table 1).
represents 19% of the sample. These groupings are reasonably consistent with a principal components analysis of the independent variables describing respondent characteristics.
For each scenario, we show four supply curves: one associated
with the “average” respondent and one for each of the three groups
defined above. For all scenarios, we found that the group most likely
to participate is Group 3, the younger individuals who do not manage
for timber, followed by the average respondent (all variables set to
their mean value), Group 2 (older individuals who do not manage
for timber but have strong beliefs about climate change and forests),
and then Group 1 (older individuals who manage for timber). Figs. 1,
2 and 3 show the supply responses for these groups by scenario.
The supply curves for each group provide information on the price
elasticity of supply. Using the preferred model and calculating elasticity at the mean sample revenue of $273.66/acre/year, each supply
curve and scenario indicates that forest owner participation in a carbon sequestration program is not greatly influenced by a change in
price (i.e., revenue per acre per year). Across the board, the price elasticity of supply is inelastic (See Table 5). Group 3 (the group most
likely to participate) has the least inelastic supply curve of all the
samples; however, it is still very inelastic. In order to engage forest
owner participation in carbon sequestration markets, a policy would
clearly need to include other factors important to a landowner besides a payment mechanism.
5.3. Alternative Specifications
We are interested in testing the assumptions of our model as well
as some common issues raised in the literature. First, some respondents in our sample could be considered to be non-demanders, and
therefore not be willing to pay or accept anything for the scenario
presented (Carlsson and Kataria, 2008). Our test of non-demander
bias removes respondents who said they would never harvest any
of their forest. Previous research on Massachusetts forest owners
has identified a distinct segment that is quite averse to harvest (Finley
and Kittredge, 2006). Carbon programs have typically required some
harvesting, however because some of our scenarios do not require
harvesting, this test is a crude one. After removing this subsample,
for the most part, the variable significances in this model are the
same as for the preferred model, but two variables become insignificant (“Age 65 years or older” and “Trees help with climate change”)
and “Gender” becomes significant at the 5% level. The correlation coefficient for the random effects ordered probit is even lower at 0.06.
Table 4 shows that the estimated probabilities for these models are
not sensitive to this specification. We conclude that it is more important to leave these respondents in the sample because of the information their responses provide for the alternatives that do not require
harvesting.
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M. Markowski-Lindsay et al. / Ecological Economics 71 (2011) 180–190
Second, we re-specified the status quo alternative such that it was
the same for all respondents to test our specification that it differed
by respondent. The regression results were not sensitive to this alternative way of specifying status quo, and the correlation coefficient for
the random effects ordered probit remains at 0.09. Table 4 shows that
the estimated probabilities for this specification are slightly lower
than for the preferred specification, with differences in probabilities
ranging from only 0.2% to 2.8%. Interestingly enough, Massachusetts
policy makers did create a carbon credit program for private forest
owners, offering to underwrite the expense of enrollment and providing education for landowners, but after two years, none chose to participate. This is a rather unique validation of our research results,
whereby an analogous public policy was actually launched and promoted, and behaved as our results would predict (i.e., failure).
Third, we removed the status quo alternative completely from the
model to test for status quo bias (Samuelson and Zeckhauser, 1988).
Based on the literature, we would expect there to be a bias associated
with removing the status quo. The variable significances in the random
effects probit specification are the same as for the preferred model. The
correlation coefficient for the random effects ordered probit increases
to 0.55. Table 4 shows the decrease in probabilities for the different
program scenarios. The participation range drops from 2.0% to 0.1% at
the low end and 37.8% to 28.0% at the high end. As expected, the results
were sensitive to this alternative specification, and consistent with the
literature our preferred model contains the status quo option.
6. Conclusions
Much of the current U.S. policy discussion regarding climate
change has been on cap-and-trade mechanisms that would create a
carbon market similar to the voluntary CCX. This study considers
one potential player in this market – the family forest owners in Massachusetts who collectively own a significant portion of the forestland
in that state. We find that if a policy were to be established similar to
the current voluntary scheme, our model estimates that very few of
these landowners would be interested in participating. Policy makers
should understand the reasons behind these low participation rates,
because private forest owners could play a pivotal role in the carbon
sequestration potential of forests.
Pairing the supply response results with what we have learned
from the focus groups and the comments provided in the survey provides us with a glimpse as to why these probability estimates are low.
Group 1 represents the older individuals who manage their land for
timber and have harvest plans. These individuals have strong opinions about their ability to manage their land. Many of these people
mentioned that they were able to manage their land best and did
not want to be told what to do, because they believe their experience
enables them to manage. These individuals were the least likely to
participate in carbon sequestration programs in comparison to other
groups. Group 2 represents the older individuals who do not manage
for timber or have harvest plans, but strongly believe that forests
could help manage climate change. These individuals may have
strong opinions about the importance of leaving their forest unmanaged. It makes sense that some of these individuals would be willing
to participate because they believe that forests, as a natural resource,
could help with the climate change issue, but this willingness may be
tempered by their unwillingness to interfere with natural processes.
Our results indicate that these individuals are only slightly more likely to participate in a carbon program than Group 1, on average by less
than 1%. Relevant to both Groups 1 and 2, it is important to note that
many individuals mentioned legacy issues as a concern. Many older
individuals were hesitant about agreeing to a contract that could result in passing their land to their children with strings attached.
Group 3 represents younger individuals who do not manage for timber, do not have harvest plans, and are highly educated. This group
was the most open to participation, but participation rates are still
low: for scenario 1 (the scenario most similar to 2010 CCX protocol),
participation rates vary between 2% and 5% depending on the price
offered. As can be seen from Figs. 1, 2 and 3, differences in participation among groups is very small.
While the preferred model and preferred specification (i.e., random effects probit model that provides for 5 different status quo possibilities) produces the highest estimated probabilities for the carbon
sequestration program scenarios we considered, participation rates
may actually be higher than reported. Hypothetical choice experiment literature touches on the concept that respondents' willingness
to accept amount differs in reality from a hypothetical situation (i.e.,
hypothetical bias). Most of the hypothetical bias literature describes
differences in willingness to pay responses, but researchers have acknowledged the parallel situation in willingness to accept situations.
Nape et al. (2003) undertook experiments to test this bias and conclude that hypothetical bias in willingness to accept studies does
exist, and, based on their experimental analysis, the authors conclude
that people would actually accept a smaller payment than in a hypothetical situation. Thus, more people might participate in actual carbon programs than estimated from the hypothetical scenarios used
in this study, but we are unsure by how much.
The results of this study show similarities to earlier forest owner
studies of carbon sequestration programs (Dickinson, 2010; Fletcher
et al., 2009) in that withdrawal penalties, contract length, and revenue are significant program attributes, but it also offers more information for the policy maker. Additionality, a key factor in current
carbon sequestration programs, is a significant factor to forest
owners. Our study shows that the survey respondents are also concerned about early withdrawal penalties, and contract length. Supply
analysis indicates that forest owners are more influenced to participate by factors other than price; policymakers should consider this
result when designing a carbon sequestration policy.
Our results have relevance to the way landowners make decisions
about their land under conditions where it has considerable potential
development value. Owners may choose to sell, develop or change
land use and, in effect, voluntarily extinguish the carbon sequestration potential. In places where land use conversion of forest is prohibited by statute or regulation (e.g., zoning), land value is thus greatly
diminished and the decision making framework of landowners is very different. In the U.S., landowners generally have the right to convert
or develop, so our results could potentially be relevant in other U.S.
contexts. In Europe and Scandinavia, land use is much more tightly
controlled, and landowners generally do not have the choice to convert. Thus, the different decision framework or suite of choices suggests our results may be of lesser relevance.
Within the U.S., the National Woodland Owner Survey (NWOS)
data on forest owners suggests nationwide they are older, more affluent, well educated, and interested in the environment and amenity
values from their land (Butler, 2008); thus, they are not that different
from the Massachusetts participants in our study. Most owners have
no plans to sell timber in the future, which is characteristic of Groups
2 and 3 in our study. One important distinction is ownership size.
Massachusetts ownerships are relatively small compared to other
parts of the U.S., and there may be more interest in management
and carbon credits in areas of larger ownerships. Overall, though
this study's landownerships are in Massachusetts, in general, owners
share many characteristics with other owners nationwide.
While our results indicate what matters to forest owners in Massachusetts, it is clear that forest ownership trends and behavior are
variable across the U.S. Parcel size, socioeconomic characteristics,
timber harvesting behavior, climate change beliefs, and feelings
about government involvement, for example, are likely to differ
across the country. To design a viable voluntary or market-based
mechanism that engenders participation from family forest owners
throughout the U.S., it is crucial for policymakers to consider what
motivates family forest owners in other regions of the country.
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M. Markowski-Lindsay et al. / Ecological Economics 71 (2011) 180–190
Acknowledgements
This material is based upon work supported by the National Institute of Food and Agriculture, U. S. Department of Agriculture,
the Massachusetts Agricultural Experiment Station and the Department of Environmental Conservation, under Project No.
MAS009583. The U.S. Department of Agriculture, Massachusetts Agricultural Experiment Station and the Department of Environmental
189
Conservation assisted in the study design; data collection, analysis,
and interpretation; report writing and decision to submit for
publication.
The authors thank Dan Lass (University of Massachusetts, Department of Resource Economics) for his invaluable input on the econometric model which greatly served to improve this work. The
authors are, however, solely responsible for any errors that may be
contained in this manuscript.
Appendix A. Background information presented to respondents and an example choice set scenario for one version of the survey
15. Now we would like to ask you about how you might want to use your woodland in the future.
• Forests absorb greenhouse gases and store them in their tree trunks, branches, foliage and roots. This process is called carbon sequestration.
• Some people believe that carbon sequestration can help reduce the effects of climate change.
• As forest landowners, you could become involved by enrolling in a program that would pay you to ensure that your trees sequestered more
carbon than if you had left them as-is.
Suppose that you were approached about joining such a program:
Enrollment responsibilities:
•
•
•
•
A professional forester must inventory your enrolled woodland.
Every year the forester must verify that you are complying with the program.
If you plan to harvest timber from your enrolled woodland, the harvested area needs to be re-inventoried after cutting.
For the time that you are enrolled, no new development (such as creation or sale of building lots) would be allowed to happen.
We would like to know your interest in joining these types of programs. The next page gives you other aspects of the program to consider.
We would like you to do two things on the next page:
1) RATE each of the following four choices on a scale of 1 to 5, with 1 being a choice you definitely would not do and 5 being a choice you
definitely would do.
2) RANK the four choices using a letter scale: A, B, C and D.
• Rank A would be your first choice,
• Rank B would be your second choice,
• Rank C would be your third choice, and
• Rank D would be your last choice.
Question 15 continued
Choice #1
Management Plan Required
Enrolled Acreage
Yes. The cost of this is included below.
50% of your woodland.
Choice #2
No.
100% of your
woodland.
Contract Length
30 years.
15 years.
Woodland Activity
You must manage your land such that your None required.
trees sequester more carbon than if you had
done nothing. For example:
•Harvest slow-growing trees.
•Favor certain types of trees (like red oak).
•Grow trees until they are old.
Expected Net Income After Paying $100 per acre per year.
$10 per acre per
Enrollment Costs
year.
Withdrawal Penalty
None.
None.
Program Implemented By
Private sector.
Private sector.
Choice #3
Choice #4
No.
50% of your woodland.
Do not join any
program. Keep the
status quo.
15 years.
You must manage your land such that your
trees sequester more carbon than if you had
done nothing. For example:
•Harvest slow-growing trees.
•Favor certain types of trees (like red oak).
•Grow trees until they are old.
$1,000 per acre per year.
You must re-pay your earnings from the
program plus a 20% fee.
Public sector.
RATE each choice on a scale of 1 to 5, with 1 being a choice you definitely would not do and 5 being a choice you definitely would do.
Choice #1
Choice #2
Choice #3
WOULD……....WOULD
WOULD……....
WOULD……....WOULD
WOULD
NOT DO
DO
NOT DO
NOT DO
DO
DO
1......2......3......4……5
1......2......3......4……5
Choice #4
WOULD……....WOULD
NOT DO
DO
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