The Economics of Ecosystems & Biodiversity
Session 3:
Benefit transfer and
uncertainty
TEEB Training
The Economics of Ecosystems & Biodiversity
Outline
 Benefits transfer
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What is benefit transfer?
Approaches to benefit transfer
Challenges
Scaling-up values
 Uncertainty in valuation
TEEB Training
The Economics of Ecosystems & Biodiversity
What is Benefit Transfer?
 BT takes an existing value for an ecosystem or policy change (at the
‘study site’) and applies it to a new, similar, site (the ‘policy site’)
 Why transfer values?
– Ideally new ecological and economic studies would be
commissioned
– New valuation studies are expensive and time consuming
 There are four categories of BT:
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Unit BT
Adjusted unit BT
Value function transfer
Meta-analytic transfer
TEEB Training
The Economics of Ecosystems & Biodiversity
Unit BT
 Mean WTP value is taken from study site and directly
applied to policy site
 Value per person/household or per unit (e.g. hectare) is
multiplied by population or units at study site
 Quick and simple but ignores differences between study
and policy sites
 Adjusted unit BT
– Simple adjustments made for differences between study
and policy sites e.g. incomes or prices
TEEB Training
The Economics of Ecosystems & Biodiversity
Value function transfer
 Value functions (e.g. regression equations) are estimated
for study site
 Parameter (coefficient) estimates for explanatory
variables are applied to those variable for study site
– i.e. variables such as income, age, attitudes etc. have the
same effect on values at both study and policy sites
 Takes into account wider range of differences between
study and policy sites than simple adjustment of values
TEEB Training
The Economics of Ecosystems & Biodiversity
Meta-analytic function transfer
 Multiple valuation studies are collected and value
function is estimated
 Allows greater variation of site and socio-economic
characteristics to be considered
TEEB Training
The Economics of Ecosystems & Biodiversity
Complexity of BT approaches
 These approaches show increasing complexity
 Unit BT may provide the most accurate transfer value if
primary study was high quality, robust and site characteristics
have little variation
 Meta-analytic transfer can be time consuming and expensive
due to need to collect and code a database of primary studies
 Not all values are expressed in per household/person terms
(e.g. production function, net factor income) or benefiting
population cannot be identified, area based units (e.g. $ or
£/ha) are preferred
TEEB Training
The Economics of Ecosystems & Biodiversity
Challenges: Transfer errors
 Transferred values may differ significantly from actual
values
 Three sources of error:
– Errors in original measures of value at study site
– Errors arising during transfer
– Publication selection bias
 Value function and meta-analytic transfers typically give
lower transfer errors
TEEB Training
The Economics of Ecosystems & Biodiversity
Challenges: Aggregation
 Aggregation is the multiplication of unit values by the
quantity of those units to estimate total value
– Per household/person values – need to know size of the
‘market’, i.e. how many people hold values for the resource
– Per unit area values – need to know area of ecosystem to
transfer to
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The Economics of Ecosystems & Biodiversity
Challenges: Aggregation cont…
 Aggregation also refers to summing values of different
ES supplied by an ecosystem
– Potential double counting – ES may be jointly provided,
mutually exclusive, interacting or integral, i.e. not
independent
– Aggregating across multiple ES may give implausibly large
numbers (recall part-whole bias)
TEEB Training
The Economics of Ecosystems & Biodiversity
Challenges: Spatial scale
 Differing spatial scales of ES provision add complexity to BT
 Ecosystems differ in scale
 ES provided at different scales
 Demand arises at different scales – use and non-use values
 Issues arise over heterogeneity of site and context
characteristics, proximity of complimentary or substitute sites
 GIS can help account for some of these spatial issues
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The Economics of Ecosystems & Biodiversity
Challenges: Variations in characteristics and
context
 Values are influenced by:
– Characteristics of site: area, integrity, type of ecosystem
– Beneficiaries: distance to site, number of beneficiaries,
income, preferences, culture
– Context: availability of substitute and complimentary sites
and services
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The Economics of Ecosystems & Biodiversity
Challenges: Non-constant marginal values
 Some ES values exhibit diminishing returns to scale
 An additional hectare of forest added to a 10ha site may be
worth more than the same increase for a 100ha site
 Some ES may exhibit increasing returns to scale, e.g. area of
ecosystem needed to support a large predator
 Linear adjustments for changing ecosystem extent are likely to
be inadequate:
– non-linearities
– step changes
– thresholds
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The Economics of Ecosystems & Biodiversity
Challenges: Distance decay
 Distance decay
 Rate of distance decay likely to vary across ES
 Direct use values likely to have strong distance effect
 Non-use values may exhibit distance decay,
– cultural or political boundaries may be more important
 Some charismatic species (e.g. pandas, tigers, whales)
may have zero rate of spatial discounting
TEEB Training
The Economics of Ecosystems & Biodiversity
Challenges: Equity weighting
 Expectation that WTP is positively related to income
– Incomes vary across sites and beneficiaries, so some adjustment
is needed
 But poorer people (esp. developing countries) are more reliant
on ES and vulnerable to loss of ES
 Welfare losses are higher for the poor than for the wealthy
– Marginal utility of consumption is declining in consumption
– a wealthy person with high consumption gains less from an
additional unit than a poor person with lower overall
consumption
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The Economics of Ecosystems & Biodiversity
Challenges: Availability of primary studies
 Important to have sufficient primary studies of high
quality for all relevant ecosystem types, ES, and socioeconomic and cultural contexts
– well represented: wetlands and forest
– under-represented: marine, grassland, mountain
ecosystems
– well represented: recreation, environmental amenities
– under-represented: regulating services
 Few valuation studies in developing countries
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The Economics of Ecosystems & Biodiversity
Scaling-up values of ecosystem services
 We may be interested in
estimating the value of the
total stock of an ecosystem or
of all ES within a large region,
i.e. to scale-up values
 We cannot simply add up all
the estimated values from
smaller sites
 Appropriateness of marginal
values depends on our position
on the ES demand curve
TEEB Training
The Economics of Ecosystems & Biodiversity
Summary on Benefits Transfer
 BT seeks to use existing values to avoid expense and time
needed for primary valuation study
 Value function and meta-analytic BT preferred to unit BT as
accounts for differences between sites and beneficiaries – but
can be expensive and time consuming to do robustly
 BT is prone to errors due to poor primary studies,
generalisation during transfer, and publication bias
 A number of challenges arise due to complex nature of
ecosystems, ES provision, context and differing socioeconomic factors
 Scaling-up raises particular challenges due to changing
marginal values and critical thresholds
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The Economics of Ecosystems & Biodiversity
Uncertainty: Outline
1. Supply uncertainty
2. Preference uncertainty
3. Technical uncertainty
TEEB Training
The Economics of Ecosystems & Biodiversity
Supply uncertainty
 We might not have information on ecosystem functioning and
how biodiversity supports provision of ecosystem services
 What we can typically measure is quantity rather than quality:
– value is applied to quantity of biomass or extent of ecosystem
(e.g. hectares) rather than quality
– Valuation may also focus on more identifiable resources (e.g.
charismatic species)
 Extent to which this is a problem depends on motivation for
valuation
– If we value per hectare but there is large variability per hectare
then our assessments will be flawed
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The Economics of Ecosystems & Biodiversity
Supply uncertainty 1
Where states of nature are identifiable and probability
can be assigned then expected values may be
estimated
Potential outcomes are weighted by probability of
occurrence
For example: the expected level of carbon
sequestration of a set of forest tree species is related
to rainfall patterns (states of nature)
TEEB Training
The Economics of Ecosystems & Biodiversity
Supply uncertainty 2
 Barbier (2007) uses an estimated damage function (EDF)
approach to values the storm protection benefits of coastal
wetlands
 Changes in wetland area affect the probability and
severity of economically damaging storm events (states of
nature)
– WTP for expected damages resulting from changes in
ecosystem stocks
 Requires sufficient data on incidence of past events and
changes in wetland area
TEEB Training
The Economics of Ecosystems & Biodiversity
Preference uncertainty
 Valuation often assumes that preferences are known with
certainty, i.e. they are aware of their WTP for a given
ecosystem service
 Evidence suggests otherwise:
– Respondents adopt heuristic rather than systematic mode
when processing information
– Unfamiliar hypothetical market for an unfamiliar or
intangible good
 There are three methods for dealing with preference
uncertainty in CVM
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The Economics of Ecosystems & Biodiversity
Preference uncertainty
 Ask respondents to state how certain they are about their
WTP answer (e.g. Loomis & Ekstrand 1998)
– Does not solve problem of uncertainty per se
– Investigates whether respondents’ perceptions and attitudes
are correlated with self-reported ‘certainty scores’
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The Economics of Ecosystems & Biodiversity
Preference uncertainty
 Introduce uncertainty directly using multiple (bounded) WTP
questions
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The Economics of Ecosystems & Biodiversity
Preference uncertainty
 Ask respondents to give a range of values (e.g. Hanley et al.,
2009)
TEEB Training
The Economics of Ecosystems & Biodiversity
Technical uncertainty
 Uncertainty arises from conceptual, methodological and
technical aspects of all valuation methods
 Credibility of stated preferences
– SP methods assume that respondents answer truthfully
 Do respondents only answer truthfully if it is their
interests to do so?
 Surveys often do not have an mechanism to constrain strategic
behaviour
 Quality of the survey will affect credibility
 Sample size will affect margin of error
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