Evaluating multidimensional

biodiversity policy:what can we learn from contingent valuation studies of biological resources in the context

of rural amenities?

José Manuel Lima e Santos

Technical University of Lisbon Portugal

1. Multidimensional biodiversity policy

The living world — a hierarchy of levels of organisation:

biosphere level

landscape level

ecosystem level

population level

individual level

cell level

bio-molecule level

Each level:

• has a particular structurei.e.: the way the components are organised

ex: cells in an individual

• supports particular functions

ex: active transportation at the cell-membrane level;nutrient cycling at the ecosystem level;

has its particular dynamic processes

ex: vegetation succession at the landscape level

Living resources supply a huge variety of service-flows, crucial for people’s wellbeing:

• basic foodstuffs

• raw materials for industry

• medicines

• soil cover

• flood control

• nutrient recycling

• aesthetics

• recreation.

As these services become scarce, people are prepared to trade off other valuable things for them — that is: people have values for these services.

The valuable services of living resources can be:

identified with components themselves.

ex: particular genes for a plant breeder

not dependent on use but only on knowing about the continued existence of a living component

ex: people having existence values for whales

provided not directly by components, but by functions or dynamic processes at particular levels of organisation

ex.: the colonisation of burnt areas by seeds coming from remaining vegetation patches.

Public policies have an impact on living resources.

They can change:components

structure at different levels

function

dynamics

Thus, they also change:

services provided

wellbeing of those benefiting from this services.

such as:

• whether to go ahead with a biodiversity scheme?

• which lines of action to include in a biodiversity strategy when funds are scarce?

How should these welfare effects of policy affect policy decisions?

Relevant value concept for these decisions:

amount of other valued things (income) people are prepared to give up for the affected services of living resources

Task division:

Biologists, landscape ecologists and other scientists:

determine how the services of biological resources are changed by policy

Applied economists

measure how people’s wellbeing is affected by these changes in service levels, by measuring people’s willingness-to-pay (WTP) for either:

- avoiding degradation or - getting improvements in service levels

There are no markets for many services of living resources.

Hence the need for non-market valuation techniques, which use either:

value-eliciting surveys

stated-preference techniques

- contingent valuation,

- contingent ranking - choice experiments

value-traces implicit in observed behaviour and revealed by modelling this behaviour revealed-preference techniques

- hedonic-price models,

- travel-cost models

-averting-behaviour models

Most policy impacts on biological resources are multidimensional:

multiple services of these resources are affected.

Why?

more than one component (individual, species or ecosystem) supplying valuable services is directly affected.

the only affected component may deliver several services.

inter-component linkages lead to diverse components, functions or processes being affected.

This paper...

...empirical basis:

several contingent-valuation (CV) studies of biological resources in the context of rural amenities

…questions:

what can we learn from these studies about the shortcomings of policy recommendations based on usual valuation practices?

how can we improve these practices so as to eliminate those shortcomings?

…goal: to explore the issues raised by the multidimensional nature of biodiversity policies when evaluating these policies.

Scheme of presentation:

• Section 2: substitution effects between multiple impacts of a policy on biological resources

• Section 3: empirical evidence on the magnitude of substitution effects and the implied independent-valuation-and-summation (IVS) bias

• Section 4: implications for valuation studies of

multidimensional biodiversity policies

• Section 5: implications for benefit aggregation in policy

evaluation

• Section 6: implications for the selection of optimal policy-mix

2. Substitution effects in the valuation of multiple-service

changes I: theory

Are people’s values for a service of a biological resource dependent on the levels of other such services?

Economic theory provides a strong rationale for an affirmative answer:

substitution effectssubstitution effects

Population

Substituteswood A wood B

10 Euro

Population

Substituteswood A wood B

10 Euro

Population

wood A wood B+10 Euro

If substitution is less severe

. the sum of benefits for each wood in isolation is larger than the joint benefit for the two woods altogether

the benefit of conserving one wood depends on whether the other is to be conserved.

Why do substitution effects exist?1) both services may satisfy the same basic need; thus, they are substitutes in utility

2) even if they are not substitutes in utility, at least they compete for the same budget

AND

Conclusions from the examples of substitution effects:

1. the value for one service of biological resources is reduced if the level of a substitute service is increased

to value a change in one service level, other services should be held constant at known levels

For economics, as well as for biology, the whole is different from the sum of the parts.

2. the benefit of a policy providing two services that are substitutes for each other is smaller than the sum of the benefits of providing each service in isolation

This summing procedure is prone to independent valuation and summation (IVS) bias. AND

3. Substitution effects in the valuation of multiple-service changes II: empirical

evidence from contingent-valuation studies

What about the empirical evidence on the magnitude of substitution effects and the related IVS bias?

Evidence discussed here comes from a contingent-valuation (CV) study of the wildlife and landscape-conservation benefits of the Pennine Dales Environmentally Sensitive Area (ESA) scheme in the UK.

(1) the continuance of a specified ESA scheme at a given tax-rise cost

(2) giving up the scheme with no tax increase

Hypothetical policy-mixes were built by

combining three basic programmes:

P1, or the conservation of existing stone walls and field barns

P2, or the conservation of flower-rich hay meadows

P3, or the conservation of remaining small broad-leaved woods

The three programmes were expected to be complements in utility, at least in aesthetic

terms

the aesthetic impact of the whole experience would be larger than the sum of the partial impacts of the several attributes in isolation.

The model used to analyse survey results allowed for negative or positive substitution effects of any magnitude, which enabled us :

1. to observe the sign of substitution effects;

2. to test for statistical significance of these signs;

3. to measure their magnitude, and hence that of the associated IVS bias.

because

meadows and woods, both perceived as habitats for wildlife, could satisfy similar needs of visitors and could, thus, behave as substitutes in utility.

but

Table 1: Model of WTP for different alternative policy-mixes for the Pennine Dales ESA scheme

Dependent variable: WTPfor different conservationpolicy-mixes in thePennine Dales (£/year)

Prediction success (%)

Numb. of observat.: 2290 Actual TotalCorrect predictions: 78.0% would wouldn’t–2log-likeli. ratio: 978.84 Predicted would pay 50.6 15.9 66.6Deg. of freedom: 13 wouldn’t 6.0 27.4 33.4Level of signif.: P<0.0001 Total 56.7 43.3 100.0

Variables Parameterestimates

t-ratios sign.level

Labels

P1 32.62 3.259 0.01 Programme 1: stone walls and fieldbarns (0–1)

P2 21.80 2.274 0.05 Programme 2: flower-richmeadows (0–1)

P3 44.58 4.659 0.01 Programme 3: broad-leavedwoodland (0–1)

P1*P2 –10.33 –1.158 Interaction between programmes 1and 2 (0–1)

P1*P3 –20.08 –2.269 0.05 Interaction between programmes 1and 3 (0–1)

P2*P3 –30.47 –3.443 0.01 Interaction between programmes 2and 3 (0–1)

P1*INCOME 0.000755 2.516 0.01 Interaction between programme 1and income (£)

P2*INCOME 0.000896 2.930 0.01 Interaction between programme 2and income (£)

P3*INCOME 0.000394 1.312 0.10 Interaction between programme 3and income (£)

P1*FIRSTP1 8.96 1.192 Programme 1 when first inpreferences (0–1)

P2*FIRSTP2 38.35 4.640 0.01 Programme 2 when first inpreferences (0–1)

P3*FIRSTP3 20.84 2.328 0.01 Programme 3 when first inpreferences (0–1)

47.17 20.300 0.01 Dispersion parameter of the logisticrandom term

P1*P2 P1*P3 P2*P3

negative

This can be explained by the existence of a common budget for which WTP for all programmes compete.

As expected:

1) the strongest substitutes are meadows and woods, both habitat attributes

2) the substitution effects between aesthetic (walls and barns) and habitat (meadows and woods) attributes, that is P1*P3* and P1*P2, are weaker, with the last being insignificant.

What about the magnitude of the IVS bias

implied by substitution effects of these sizes? Table 2: Model-based point estimates of conditional means of WTP for different alternative policy-mixes for the Pennine Dales ESA scheme, as compared to the corresponding IVS results

But are they large enough to lead to recommend the wrong decision to policymakers?

To assess this, we compared:

1) the benefits implied by valid estimates

2) the benefits implied by IVS estimates,

Whatever the policy-mix, the right decision (given by comparison 1) was always to proceed with the policy.

Thus, an IVS-based decision is consistent with the right decision

So, the fact of IVS benefit estimates overestimating true benefits is, in this case, immaterial for policymaking purposes.

Estimated IVS biases are statistically significant.

with policy costs

However, in a similar case-study in Portugal, this was not the case. Likewise, for many other policies, costs do offset the valid (model-based) benefit estimates but not IVS (biased) estimates.

General theoretical models predict that the IVS bias increases with the number of components (programmes) in the policy-mix.

Adjusting for this bias is strongly advised.

The right decision was not to proceed with those policies, but based on IVS (biased) estimates we would recommend proceeding with them.

4. Implications for non-market valuation studies of services of

biological resourcesPeople’s values for one service depends on the levels of other services.

Consequences for the valuation of multiple-service biodiversity policies:

1) the best way to value multidimensional policy: valuing exactly the intended multiple-service change in one single step

‘Automatically’ adjusts for substitution effects, but implies carrying out an original study for each policy rarely possible in practice

2) Other services are also part of the relevant context for the valuation, even when the interest is valuing a single service

their levels need to be specified

(a) for CV applications — levels of those other services are to be included in the scenarios presented to CV survey respondents

(b) for revealed preference techniques — include other services’ levels in behavioural models used to infer peoples’ values

(c) for benefits transferred from past studies — making sure that levels of other services in the original study are similar to those levels in the policy context

Implications of (2):

5. Implications for benefit aggregation across multiple services of biological

resources

Usual steps in applied analysis of multidimensional policies:

1) searching for WTP estimates in past studies for each affected service (fishing days, aesthetics…)

2) transferring these estimates, possibly with adjustments

3) aggregating across services

procedure prone to IVS bias, potentially very large for multidimensional biodiversity policies

significant probability of recommending the wrong decision

to arrive at the appropriate adjustment factorsadjustment factors to correct for IVS bias

Is there a practical alternative to avoid this bias?Yes, to commission an original study to value, in one single step,

exactly the same multiple-service change as in the policy to be evaluated

an original study for each policy

often impracticable because of budget and time constraints.

Solution: recommend further research on substitution effectsfurther research on substitution effects

to gain a feeling for the magnitude of substitution effects, under different circumstances and with different types of biological resources

6. Sequential cost-benefit analysis for the selection of an optimal policy-mix for

biodiversity

Example: several lines of action need to be prioritised for inclusion in a general biodiversity strategy because funds are scarce as usual

The concern is not evaluating a proposed biodiversity policy

BUT

selecting an optimal policy-mix from a set of possible alternatives.

What is the proper technique to use for this purpose when substitution effects are expected to be significant?

sequential cost-benefit analysis

Briefly, the method proceeds as follows:

1) identify the most complete biodiversity policy-mix that is possible

2) divide this mix into a number of ‘programmes’ (components in different programmes should be independent in production)

Two alternative definitions for what ‘best’ means:

i) ‘best’ means welfare-maximising, and identifying it implies using a social cost-benefit analysis;

ii) best’ means making the most of available funds.

3) select the best programme mix

Sequentially solving the social cost-benefit problem implies:

evaluating the welfare effect of adding a particular programme to a previously constituted mix

the programme is added to the previous mix

the programme is not added to the previous mix

1programme ofcost social

programme of valuesequential ELSE

Proceed evaluating new programmes until there is no more programme left with benefit/cost ratio >1

Sequentially solving for the budget-constrained approach implies:

Ranking the programmes by ratio of sequential value to financial cost

Choosing the one with the highest ranking and add to the mix

Is the all budget already spent?

Yes

The optimal mix is already selected

No

Note that, as we are using sequential values for programmes, which depend on the programmes already included in the mix, this ranking can change each time we include a new programme in the mix.

Table 3 Point estimates of sequential values of programmes for the Pennine Dales ESA scheme when valued in different valuation sequences a

Back to the Pennine Dales ESA example

Table 4 Sequential benefit/cost ratios for conservation programmes in the Pennine Dales ESA, when evaluated in different valuation sequences (that is: with different initial policy-mixes)

Although in the Dales case IVS does not lead to recommend the wrong policy-mix, this is very often the case.

This wrong mix will be broader than optimum if programmes are substitutes, stricter if they are complements

A practical recommendation is, therefore, to use, when possible, the modelling approach proposed in this paper for benefit estimation, which accounts for substitution effects, and hence leads to select the right policy-mix.

When such a model cannot be estimated (e.g. when benefits are to be transferred from previous studies), it would be wise to adjust biased estimates for substitution effects.

Again, is recommended further research on substitution effects under the most diverse circumstances, using the proposed modelling approach, so that the proper adjustment factors can be developed for a broad range of valuation settings.