Measuring the productivity of threatened-species programs

Ecological Economics 39 (2001) 53 – 66 www.elsevier.com/locate/ecolecon

ANALYSIS

Measuring the productivity of threatened-species programs Ross Cullen a,*, Geoffrey A. Fairburn b,1, Kenneth F.D. Hughey c a

Commerce Di6ision, Lincoln Uni6ersity, PO Box 84, Lincoln, New Zealand b AERU, Lincoln Uni6ersity, PO Box 84, Lincoln, New Zealand c En6ironment Management and Design Di6ision, Lincoln Uni6ersity, PO Box 84, Lincoln, New Zealand Received 13 December 2000; received in revised form 20 March 2001; accepted 21 March 2001

Abstract Expenditures on threatened-species programs are increasing in many countries. Evaluation of the effectiveness and cost efficiency of these programs rarely occurs. An obstacle to evaluation of these programs is the need for a versatile unit of output. This paper reports how an output measure, COPY, and Cost– Utility Analysis methodology, are applied to evaluate New Zealand threatened-species programs. Program effectiveness, cost, and cost– utility ratios are presented, and the results show wide divergences occur between the programs studied. Cost– utility analysis provides a practical means to evaluate the productive efficiency of many threatened-species programs. © 2001 Elsevier Science B.V. All rights reserved. Keywords: Cost efficiency; Cost–utility analysis; Effectiveness; Expenditures; New Zealand; Threatened-species programs

1. Introduction There are believed to be 7 – 20 million species on the planet, and the expected loss of species over the next 25 years is projected to be in the range of 140000 –5 million. Between 2 and 25% of all species on the planet are at risk of extinction (UNEP quoted in World Bank, 2000). Species loss is widely recognised as one of the most serious environmental problems facing nations, and bio* Corresponding author. Tel.: + 64-3-3252811; fax: + 64-33253847. E-mail address: [email protected] (R. Cullen). 1 Present address: AMP Society, Sydney, Australia.

diversity protection expenditures are rapidly increasing in many countries. Success is not easily achieved in biodiversity protection, and hard decisions over where to use the available resources are inevitable. As Weitzman (1992, p. 363) has noted: ‘Preservation of diversity in one context can only be accomplished at some real opportunity cost … including a loss of diversity somewhere else.’ Resource scarcity combined with an urgent problem provides a powerful reason for wise selection of biodiversity protection projects and for rigorous evaluation of current projects. There is considerable debate about biodiversity project selection (Weitzman, 1992; Metrick and Weitzman, 1998; Moran and Pearce, 1998). How-

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R. Cullen et al. / Ecological Economics 39 (2001) 53–66

ever, economic literature on biodiversity protection is curiously unbalanced. Many studies have attempted to measure the benefits arising from existence of a species or ecosytstem (Carson, 1998), and some researchers have investigated hypothetical biodiversity protection programs (Montgomery et al., 1994, 2000; Ando et al., 1998). However, remarkably little economic research has been completed, which examines the delivery of real biodiversity projects and programs or which measures the cost effectiveness of those activities. Exceptions include the research by Whitby and Saunders (1996) and Hamilton et al. (2000). An obstacle to the completion of research on those topics is the challenge of quantifying the output produced by biodiversity projects. In this paper, we report on the application of cost– utility analysis (CUA) to determine the relative productivity of threatened-species recovery programs. CUA was developed more than 30 years ago to provide a means to evaluate health-care delivery programs and, with some modification, has considerable potential for use in conservation economics (Cullen et al., 1999).

2. Research focus Biodiversity protection may be pursued by several strategies. There is increasing emphasis in many countries on projects and programs that are focused on habitat protection and which, if successful, may simultaneously improve the conservation status of many species. There are also many projects and programs within countries that focus on the conservation status of individual species. The IUCN Red Data Book (IUCN, 1994) lists the endangered species in each country, and these species are often the focus of targeted effort with budgets dedicated to management of individual species. We focus on the development of a CUA framework for evaluating the effectiveness and efficiency of New Zealand threatened-species recovery programs. The Department of Conservation (DOC) is the government agency responsible for threatenedspecies management. New Zealand has approximately 1000 threatened species (DOC/MFE

2000), and 31 of those species have species recovery plans (DOC, 1999a). These species recovery plans outline the nature of the threats the species face, and the actions planned to tackle those threats and improve the conservation status of the species. In New Zealand, Australia, Hawaii and some other areas recently colonised by Europeans, predation and competition from introduced species are the most important threat to many indigenous species. Threatened species recovery projects and programs in New Zealand are often directed at controlling predator or competitor numbers. Possums (Trichosurus 6ulpecula), rats (Rattus spp), mice, mustelids, cats, dogs are targeted in many New Zealand threatened-species programs. Some remarkable successes have been achieved in developing low-cost techniques to exterminate or greatly reduce population densities of these predator and competitor species within conservation areas (Towns et al., 1997). Islands as large as 2500 ha have been secured for threatened species by the extermination of possums and rats (Cowan, 1992). Making the habitat secure is often the key action to improve the conservation status of New Zealand birds, invertebrates and reptiles (Towns et al., 1997). Assessing the productivity of threatened-species programs requires a method to quantify the conservation output produced by the expenditures. The goal of threatened-species programs is an improvement in conservation status of the species concerned. Conservation statuses of species are described by their position on ranking systems. Among the best-known ranking systems are the IUCN Red list criteria, which are used to place species in one of 11 categories, including: Extinct, Extinct in the wild, Critically endangered, Endangered, Vulnerable, and Lower risk (IUCN, 1994). A species initially categorised as being, say, Endangered may, following conservation action, be located in the Vulnerable category. The change in category of the species, and the date when the changes occur, provides data that can be used to quantify the output from conservation action. A technique is required to capture the changes in species’ status compared to some reference status level over a selected time period. CUA has been developed to tackle a similar task.

R. Cullen et al. / Ecological Economics 39 (2001) 53–66

3. Cost –utility analysis (CUA) CUA, a method first developed to evaluate health-care programs, can be used to make comparisons among a host of competing alternatives. The key idea of CUA is to measure the output of a program by way of utility, where utility refers to the worth of a health status, or improvement in health status following a treatment (Drummond et al., 1997). If the outcomes of any health-care program can be measured in units called ‘QualityAdjusted-Life-Years’ (or QALY), it is possible to compare different medical interventions. Comparisons can be made if the direct costs of each type of intervention are divided by the QALY they gain (Drummond et al., 1997). The QALY measure simultaneously captures health outcomes in terms of both reduced morbidity (quality gains) and reduced mortality (quantity gains). Indexes such as the EuroQol classification system or the health utilities index are used to gather data for the measurement of health quality. These systems typically break down the attributes of health (e.g. vision, hearing, speech, and emotion), develop indices to quantify the attributes and weight the attributes to reflect their importance (Petrou and Renton, 1993). A key requirement for CUA is data on final rather than immediate outputs. For example, data are needed on the lives saved from a cancer treatment program rather than the reported number of people with cancer in a region. If only intermediate data are available, it is not possible to evaluate healthcare programs using CUA and QALY. Drummond et al. (1997) provide several reasons why CUA was developed for health-care evaluation. These reasons also provide support for the use of CUA in conservation. “ Cost effectiveness analysis (CEA) is not useful for making comparisons across a broad set of medical interventions or health-care programs. This is because the measures of effect are not comparable. In CEA, the effectiveness measure is specifically related to the objective of the program (e.g. cases of disease averted) rather than the health improvement gained from a program.

“

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In any one health-care program, there is often more than one outcome of interest. Outcomes from medical interventions are often multi-dimensional and include life extension, improved long-term quality of life, side-effects from a treatment, short-term effects from a particular treatment. “ The outcomes of health-care programs are not of equal value. Some outcomes from medical treatments are more highly valued than others. To this, we add a fourth factor that has led to the adoption of CUA. “ Cost–benefit analysis (CBA) is too difficult to apply to health-care evaluation owing to the difficulty in assigning monetary values to health outcomes. Some rudimentary work has begun on applying non-market valuation methods such as contingent valuation to health care, and there is general agreement that CBA is not practical for comparing many medical interventions (Drummond et al., 1997). Cullen et al. (1999) note that the features of CUA that make it attractive to health-care evaluators are of relevance to conservation evaluators. Conservation work is often directed at goals that are achieved to varying degrees, the outcomes are not readily evaluated in monetary units, and the outcomes can persist (and decay) over time. Cullen et al. (1999) and Stephens and Lawless (1998) have applied CUA to conservation and have implicitly used the term ‘utility’ to refer to the change that occurs in an ecosystem. Cullen et al. (1999) developed the ‘Conservation Output Protection Year’ (or COPY) as means for evaluating the output gained from various species conservation projects. COPY serves the same function as QALY in health-care evaluation in the sense that it allows the effectiveness of unlike activities to be compared. In health care and conservation, it is recognised that some common metric or currency is required that allows unlike activities to be evaluated according to their relative cost effectiveness (Stephens and Lawless, 1998; Weitzman, 1998; Cullen et al., 1999). CUA and the QALY have allowed such efficiency evaluations for health-care programs to be completed, and the technique is used in many countries including the USA, UK,

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New Zealand, Australia, and Canada. CUA also holds considerable promise if it provides information that is useful to decision-makers striving to maximise conservation gains from a finite amount of resources.

4. Cost–utility analysis of threatened-species programs To quantify the effectiveness of threatened-species programs, we focus on several factors that must be combined in a measure of conservation payoff. Such a measure must account for: amount and timing of conservation achieved from a program when compared to the ‘without program’ case; and the value of the species or site that the program aims to protect. We argue that conservation output can be calculated by comparing a species conservation status in each period, ‘with management’ to the species conservation status ‘without management’ in each period. The sum of these gains in conservation status during the time studied gives the output for the program. If humans prefer conservation status gains sooner rather than later, conservation status gains can be discounted to make comparable gains from differing years. The species managed in these threatened-species programs can include fish, higher plants, mammals, birds, invertebrates. If humans prefer some taxa over others, then we can weight the conservation gains to reflect preferences for taxa. Charisma scores are used for that purpose. If some individual species are genetically unique and others relatively commonplace, we can weight programs’ conservation gains to reflect species’ genetic distinctiveness. Distinctiveness scores are used for that purpose. These elements can be combined to measure the payoff from a threatened-species program as shown in Eq. (1). Payoff= %To

(Stw − Stw/o) ×[DISi, CHARi] (1+ d)t

(1)

where: Stw is conservation status in year t with management; Stw/o is conservation status in year t without management; d is the discount rate; DISi

is the distinctiveness of species I; CHARi is the charisma of species i. Our evaluation approach requires a system to categorise the conservation status of threatened species, including species with naturally low population numbers. After studying the IUCN threat classification system and a New Zealand threat classification system, we have developed a categorisation system that is closely related to the IUCN system Table 1. This classification system contains seven categories, each of which is described in one or two sentences, and allows experts to place a threatened species in the appropriate unique category at any point in time. A classification system with more than seven categories we judged would pose problems in development of descriptive statements that were sufficiently precise to be used by species experts. The index was pre-tested before use with the help of two scientists from DOC and Landcare Research Ltd on one species, the North Island kokako. When estimation of the payoff is completed for a program, its cost–utility ratio can be calculated by dividing the present value of the program costs by the number of COPY achieved as specified in Eq. (2). %To Ct /(1+ d)t Cost per COPY =

COPYi

(2)

where: Ct is cost of the program in year t.

5. Cost–utility analysis for New Zealand species programs This section is in several parts. First, we report our criteria for selection of threatened-species programs to apply CUA. Second, we estimate the COPY gained from the programs and report on the sensitivity of the results to changes in some variables. Third, we report on the costs of the programs and their cost–utility ratios. To ensure that the methodology was tested in a range of situations, we selected threatened-species recovery programs using several criteria: Species type — fish, mammal, bird, reptile, invertebrate

R. Cullen et al. / Ecological Economics 39 (2001) 53–66

and plant; Environment — mainland, marine, and offshore island; Threat — introduced plants and animals (including humans); and Species PriTable 1 Conservation status index No concern (NC) A Taxon is of no concern when the population(s) are stable or increasing, there are no threats to the taxon, and no remedial conservation actions are required. Estimated, projected or inferred probability of extinction is less than 0.01% within the next 100 years. Little to some concern (LC) A Taxon is of little concern when there is a stable or increasing population(s) but minor uncertainties as to survival in the future (B100 years). Estimated, projected or inferred probability of extinction is less than 3% within the next 100 years. Near threatened or declining (NT/D) A Taxon is near threatened when the population(s) are stable but it is expected that threats will become a problem in less than 10 years if nothing is done. Estimated, projected or inferred probability of extinction is 3–10% in the next 100 years. Vulnerable (V) A Taxon is vulnerable when the population(s) are decreasing owing to threats, but the taxon has not been reduced to a critical level. Estimated, projected or inferred probability of extinction is at least 10% within the next 100 years. Endangered (E) A Taxon is endangered when population(s) have been reduced to a critical level or whose habitats have been so drastically reduced that the population(s) is deemed in immediate danger. Estimated, projected or inferred probability of extinction is at least 20% over the next 20 years or five generations, whichever is longer. Critically Endangered (CE) A Taxon is critically endangered when population(s) have been reduced to near catastrophic levels and extinction through stochastic events within a stated period is a distinct possibility. Estimated, projected or inferred probability of extinction is at least 50% within 10 years or three generations, whichever is longer. Presumed extinct (X) Taxa which are no longer known to exist in the wild or in cultivation after repeated searches of the type locality(s) and other known or likely places.

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ority — A to C categories in DOC species priority setting system (Molloy and Davis, 1994); length of time each species had been managed under a Recovery Plan. After discussion with two DOC staff and careful examination of the 31 Species Recovery Plans (some of which cover multiple species in the same genus), the following species recovery programs were selected. Cook Strait tuatara, Brothers island tuatara, Otago skink, Grand Skink, kakabeak, woodrose, shorttailed bat, long-tailed bat, brown teal, South Island saddleback, takahe, New Zealand dotterel, Campbell Island teal, North Island kokako, North Island brown kiwi, yellow-eyed penguin, Hector’s dolphin, parea, kea, kakapo, black stilt, Canterbury mudfish. The proportion of native bird programs in our sample is high at 60% but 15 out of the 31 (48.4%) species recovery plans as at December 1999 are for native birds. Recovery programs for these species have been in operation for varying lengths of time, Black stilt, Takahe, Hector’s dolphin and Kakapo dating to at least 1987 when DOC commenced operations. Species that had been managed under a Recovery Plan for less than 4 years were excluded from the study. To estimate the COPY gained by each species recovery program, it is necessary to obtain data on each species’ conservation status for each year between 1987 and 1998 and to apply this information in Eq. (1). To obtain ratings of the species status with and without management, using the criteria listed in Table 1, a survey form was mailed to each species recovery group leader or relevant expert within DOC. Respondents were asked to rate the species status with and without management for each year the recovery plan was in effect. They were also invited to comment on the conservation status index. Respondents’ assessments of the status of the species with and without management in 1987 and 1998 are shown in Table 2. We received a response rate of 79% to our survey.2 Respondents made a number of comments on the conservation status index. One sug2 In three instances a program manager is responsible for two species.

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Table 2 Conservation status rankings with and without management Species

Status with management in 1987

Status without management in 1987

Status with management in 1998

Status without management in 1998

Yellow-eyed penguin Black stilta Cook Strait tuatara Brothers Island Tuatara Hectors dolphina Canterbury mudfish Long-tailed bat Short-tailed bat woodrose South Island saddleback Kakapoa Takahea Otago skink Grand skink Campbell Island tea Kea Kokako Parea Brown kiwi Kakabeak New Zealand dotterel Brown teal

LC

V

LC

V

CE V

CE V

CE LC

X V

E

E

LC

E

V

V

LC

V

E

E

E

E

V E E V

V E E V

V E E V

V V E V

CE E V V E

CE E V V E

CE E V V V

CE CE V V E

V E No No No No

V E

V E

V E

a

response response response response

No response

Management of the species began at least as early as 1987.

gested that greater emphasis should be placed on population size rather than solely extinction risk, which in part is a function of estimated population size. Another noted that it relies on the availability of reliable knowledge of populations and habitats. Most comments were aimed at the broadness of the index criteria. Several argued that the categories were too broad to capture small-scale avoided losses and gains in the conservation status of a species. To allow us to calculate the output from each threatened-species program (COPY) using Eq. (1), we apply a numerical value between 0.00 and 1.00 to each of the conservation status categories in Table 1. These numbers are used to indicate the

relative utility of each of the conservation status categories. We have used a quadratic and a linear function to determine these values, and they are listed Tables 3 and 4, respectively. Use of the quadratic function awards decreasing amount of utility to conservation status changes the further away the species’ status is from extinction. The COPY values produced by each of the species recovery programs are presented in Tables 3 and 4, using a range of discount rates. Programs are ranked according to the quantity of COPY gained or their effectiveness. A zero score indicates that the program has remained at the same level of conservation status over time with and without management. A letter inside the brackets,

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Table 3 COPY gained from species recovery programs using a quadratic function for the conservation status indexa Species

COPY d=0%

PV COPY d =6%

PV COPY d =10%

PV COPY d = 15%

Black stilt Yellow-eyed penguin Short-tailed bat Takahe Hector’s dolphin Campbell Island teal Brother Island tuatara Cook Strait tuatara Long tailed bat Si saddleback Otago skink Grand skink Kea Woodrose Canterbury mudfish Kokako Kakapo

3.208 2.666 2.332 2.125 1.221 0.684 0.624 0.333 0 (V) 0 (V) 0 (V) 0 (V) 0 (V) 0 (E) 0 (E) 0 (E) 0 (CE)

2.265 1.974 1.727 1.408 0.738 0.387 0.335 0.179 0 (V) 0 (V) 0 (V) 0 (V) 0 (V) 0 (E) 0 (E) 0 (E) 0 (CE)

1.857 1.665 1.457 1.095 0.538 0.270 0.226 0.120 0 (V) 0 (V) 0 (V) 0 (V) 0 (V) 0 (E) 0 (E) 0 (E) 0 (CE)

1.466 1.385 1.212 0.819 0.370 0.176 0.141 0.075 0 (V) 0 (V) 0 (V) 0 (V) 0 (V) 0 (E) 0 (E) 0 (E) 0 (CE)

a d is a discount rate. For the quadratic function, the numbers assigned to each of the criteria were: No concern (NC) = 1, Little to some concern (LC) = 0.972, Near threatened or declining (NT/D)=0.888, Vulnerable (V) =0.75, Endangered (E) = 0.555, Critically Endangered (CE) =0.305 and Presumed Extinct (X) = 0. A quadratic function assumes that changes in conservation status (e.g. from V to E) are not of equal value. A higher value is attached to changes from a higher (e.g. E) to lower level of extinction risk (e.g. V) compared to changes from moderate to lower extinction risk (e.g. LC).

e.g. (E), refers to the status of species that have not changed during the period studied. The data in Tables 3 and 4 show that the choice of function for the index criteria changes the estimated COPY and ranking of programs. For example, with a linear function, the yellow-eyed penguin program is the most effective program (greatest number of COPY gained), whereas with a quadratic function, the black stilt is the most effective. Tables 3 and 4 indicate that discounting COPY does not change the effectiveness rank of these programs.3 Tables 3 and 4 show that nine of 17 species with evaluation data produced zero COPY or no conservation output. There are several possible reasons for zero COPY gained from a program including: index criteria are too broad to capture subtle gains and avoided losses in conservation status; the recovery program is at an experimental 3 Six percent approximates the real cost of borrowing for the New Zealand government and is a useful discount rate to use in this analysis where the timespan of the projects is at most eleven years and intergenerational issues do not arise.

phase and has a large research component; threatened species take a long time to respond to remedial management actions (i.e. greater than 15 years); the species is so close to extinction that improving its status in situ is very difficult, if not impossible, with current technology; the species conservation status has changed at the same rate with management as it would have without management; the recovery plan is poorly designed or implemented; respondents are unable to objectively evaluate the effectiveness of their programs. Further research is needed to determine the relative importance of those factors. The conservation status of four species, Hector’s dolphin, Cook Strait tuatara, Brothers Island tuatara, Campbell Island eal, improved with management during the period studied. Another four species have a COPY of greater than zero because the conservation program prevented a loss in their conservation status. In the case of the black stilt, a catastrophic loss has been avoided. This suggests that most of the conservation payoff

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Table 4 COPY gained from species recovery programs using a linear function for the conservation status indexa Species

COPY d= 0%

PVCOPY d =6%

PVCOPY d = 10%

PVCOPY d = 15%

Yellow-eyed penguin Black stilt Takahe Hectors dolphin Short-tailed bat Brothers Island tuatara Campbell Island teal Cook Strait tuatara Long-tailed bat Si saddleback Otago skink Grand skink Kea Woodrose Canterbury mudfish Kokako Kakapo

3.999 1.750 1.416 1.833 2.000 0.755 0.583 0.5 0 (V) 0 (V) 0 (V) 0 (V) 0 (V) 0 (E) 0 (E) 0 (E) 0 (CE)

2.962 1.235 0.938 1.107 1.481 0.405 0.331 0.268 0 (V) 0 (V) 0 (V) 0 (V) 0 (V) 0 (E) 0 (E) 0 (E) 0 (CE)

2.498 1.006 0.730 0.807 1.249 0.273 0.232 0.181 0 (V) 0 (V) 0 (V) 0 (V) 0 (V) 0 (E) 0 (E) 0 (E) 0 (CE)

2.077 0.799 0.546 0.555 1.038 0.170 0.151 0.112 0 (V) 0 (V) 0 (V) 0 (V) 0 (V) 0 (E) 0 (E) 0 (E) 0 (CE)

a For a linear function, the numbers assigned to each of the criteria on the conservation status index were: No concern (NC) = 1, Little to some concern (LC) =0.833, Near threatened or declining (NT/D)= 0.667, Vulnerable (V) = 0.5, Endangered (E) = 0.333, Critically Endangered (CE) =0.166 and Presumed Extinct (X) =0. A linear function assumes that all position changes in conservation status (e.g. from V to E) are of equal value. For example, a change from CE to E is the same value as a change from V to LC.

since 1987 has been by way of maintenance of species status rather than restoration. Support for this finding can be found in State of the Environment Report (Ministry for the Environment, 1997) and the draft Biodiversity Strategy (DOC, 1998), which report that New Zealand’s biodiversity protection efforts have slowed, but not reversed, the rate of biodiversity decline. Tables 3 and 4 report the output produced by threatened-species programs as measured by COPY. However, it is likely that some species are judged to be more valuable than others, and we explore the effects of using two systems to weight the COPY achieved. Biologists have attempted to determine the taxonomic uniqueness of species and to develop cardinal numbers to describe their uniqueness (May 1990). We investigate the effect of weighting COPY scores by species taxonomic distinctiveness ratings, by use of a simple rating system. We use the five-point ordinal scale derived from DOC species classification system (Molloy and Davis, 1994): 1 — one family in the order, or

one genus in the family; 0.8 — one species within the genus; 0.6 — recognised at species level, genetically and/or morphologically highly distinct from other members of the genus; 0.4 — recognised at species level and genetically quite similar to related species and 0.2 — recognised at subspecies level. Humans’ preferences for species can be investigated by surveys of the general population and by investigation of the basis of those preferences. Research by US psychologists has led to a ranking of phyla according to the species’ phylogenetic similarity to humans (DeKay and McClelland, 1996). We use the ratings of phyla from this US study and apply ratings for species’ charisma, which are evenly spaced between 1.000 and 0.125. The ratings are: 1.000 — mammal; 0.875 — bird; 0.750 — reptile; 0.625 — amphibian; 0.500 — fish; 0.375 — trees; 0.250 — other plants; 0.125 — invertebrates. The charisma weighting is a simple method to indicate how preferences for conservation can be used to weight output mea-

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Table 5 Weighting COPY by distinctiveness and charisma Species

Black stilt Yellow-eyed penguin Short-tailed bat Takahe Hector’s dolphin Campbell Island teal Brothers Island tuatara Cook Strait tuatara Long-tailed bat Kokako Kakapo Kea Canterbury mudfish South Island saddleback Otago skink Grand skink Woodrose

PV COPY d=6% (no weights)

Char

Dis

2.265 1.974 1.727 1.408 0.738 0.387 0.335

0.875 0.875 1 0.875 1 0.875 0.750

0.4 0.8 0.2 0.6 0.6 0.4 1

0.91 1.58 0.35 0.85 0.44 0.16 0.34

1.98 1.73 1.73 1.23 0.74 0.34 0.25

2.89 3.31 2.07 2.08 1.18 0.49 0.59

0.179 0 0 0 0 0 0

0.750 1 0.875 0.875 0.875 0.500 0.875

1 0.6 0.8 0.8 0.6 0.4 0.8

0.18 0 0 0 0 0 0

0.13 0 0 0 0 0 0

0.31 0 0 0 0 0 0

0 0 0

0.750 0.750 0.250

0.4 0.6 0.8

0 0 0

0 0 0

0 0 0

PV COPY×(Dis)

sures. It should be noted that DeKay and McClelland (1996) ratings are preferences for wildlife conservation in the United States, a country with many more native mammals than occur in New Zealand. The short-tailed bat, according to this scale, has more charisma than the kiwi, which is questionable given the prominence of the kiwi in New Zealand conservation. The effects of weighting the COPY gained by Distinctiveness and Charisma are shown in Table 5, and the data indicate that weighting COPY gained by the species’ charisma scores does not dramatically change the measured effectiveness of the programs. Of the programs with a COPY greater than zero, black stilt still remains the most effective with 1.98 COPY, while the Cook Strait tuatara remains the least effective with 0.13 COPY. There is some change in the effectiveness of the programs when distinctiveness is used as a weighting factor. The yellow-eyed penguin becomes the most effective program with 1.58 COPY, while the Campbell Island teal becomes the least effective of the programs with an unweighted COPY

PV COPY×(Char) PV COPY×(Dis+Char)

score of greater than zero. Takahe also becomes more effective than the short-tailed bat. Multiplying the COPY gained by ‘distinctiveness plus charisma’, as shown in the far right column, changes the size of the conservation output and the ranking of programs according to their effectiveness. For example, under the column COPY (no weighting), the black stilt program has produced 2.26 COPY, whereas the yellow-eyed penguin program produced 1.97 COPY. By contrast, weighting by distinctiveness plus charisma means that the yellow-eyed penguin program is the most effective of the two with 3.31 COPY compared to the black stilt with 2.89 COPY.

6. Expenditures and the cost effectiveness of programs Estimating the costs of programs and projects proved to be the most difficult part of our research. To maintain consistency, we requested of DOC financial managers the following cost components for each program: direct operational

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Table 6 Cumulative spending on selected species recovery programs (1998$)a Recovery program

Years

Total cost: December 1998 $, d=0%

Percentage of total expenditure spent on the programs evaluated

Kakapob Kokakob Takahe Black stilt NI brown kiwi Hector’s dolphin Short-/long-tailed bats Yellow-eyed penguin New Zealand dotterel Otago/Grand skinks Brown teal Woodrosec Canterbury mudfish Keac Campbell Island teal SI saddleback Tuatarac Parea

87–98 87–98 90–98 87–98 90–98 87–98 93–98

7 121 771 4 300 965 4 125 690 3 619 222 1 596 697 1 078 843 777 159

27.7 16.7 16.0 14.1 6.2 4.2 3.0

90–98

784 698

3.0

94–98

556 384

2.2

92–98

545 459

2.1

94–98 92–98 89–98

446 242 302 376 173 522

1.7 1.2 0.7

92–98 91–98

188 935 55 534

0.7 0.2

90–98 95–98 – Total

36 850 30 854 ? $25 741 207

0.1 0.1

a

Producer price index for central government outputs used to deflate costs. Data were obtained from the recovery group leader. c Data only cover one conservation zone. b

costs, organisational overhead component; staff salaries and; capital charge.4 This request was difficult to meet because some programs and projects are implemented together at the same site, expenditure data are unavailable for many programs before 1992/1993, the accounting system is designed to keep records on the output classes, not recovery programs or projects per se, and there is more than one output class that can be used for threatened-species 4

Our focus is on productive efficiency, not allocative efficiency; hence, we have not estimated opportunity costs of these programs. New Zealand threatened-species programs typically operate in national parks and reserves, and opportunity costs are negligible. There is an opportunity cost of foregone fishing associated with Hectors Dolphin (Hughey, 2000).

management. Many of the 31 published species recovery plans do not have transparent budgets, and the Department of Conservation standard operating procedures for preparing species recovery plans do not require detailed budget estimates (DOC, 1999b). DOC expenditure on species recovery programs is shown in Table 6. For recovery programs that cut across several conservancies or are implemented with other programs at a site, the expenditures reported should be viewed as a minimum. It is easier to assess how much has been spent on a species when only one species is managed at a site — black stilt and kakapo are good examples. The expenditure data on kokako recovery before 1992 were obtained from Cullen et al. (1999), while for kakapo, data were obtained from the recovery

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group leader. Expenditure data were available from only part of the range of three species. Expenditures on bat, skink, and tuatara are directed at both of their respective species, and 50% shares of the relevant expenditures have been allocated to each species of the pair. Based on the expenditure data in Table 6, approximately 75% of total expenditure goes to four species, kakapo, kokako, takahe and black stilt. These percentages may change if more reliable expenditure data could be obtained but still provide some useful insight. For kakapo and kokako, which together have cost over $11.4 million, the status of both these species with and without management has not changed, as illustrated by the zero COPY gained in Tables 3 and 4.

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Table 7 shows the cost–utility ratios of programs, ranked from lowest to highest cost per COPY. In this table, COPY values are calculated using the quadratic function. The cost– utility ratios of programs are also calculated after weighting by charisma, distinctiveness, and charisma+ distinctiveness. There is some slight change in cost–utility ratio rankings when distinctiveness is used as a weighting factor. The shorttailed bat recovery program becomes less cost-effective than the yellow-eyed penguin program, which has a discounted cost–utility ratio of $381653 per COPY.DIST. This compares to the short-tailed bat recovery program, which has a discounted cost–utility ratio of $911251 per COPY.DIST.

Table 7 Cost effectiveness of species recovery programs Species recovery program Brothers Island tuatara Cook Strait tuatara Campbell Island teal Short-tailed bat Yellow-eyed penguin Hector’s dolphin Black stilt Takahe South Island saddleback Canterbury mudfish Kea Woodrose Long-tailed bat Otago skink Grand skink Kokako Kakapo Parea Brown Kiwi Kakabeak New Zealand dotterel Brown teal

Total cost d =6%

PV cost per PV COPY (no weighting)

PV cost per PV COPY×(DIST)

PV cost per PV COPY×(CHAR)

PV cost per PV COPY×(DIS+CHAR)

13 694

40 780

40 276

54 776

23 210

13 694

76 457

76 078

105 338

44 174

39 940

103 178

249 625

117 470

81 510

318 938 603 013

184 570 305 344

911 251 381 653

184 570 348 562

154 076 182 179

773 844 2 441 822 3 278 178 31 965

1 048 245 1 077 724 2 327 560 Undefined

1 758 736 2 683 320 3 856 680 Undefined

1 045 735 1 233 243 2 665 185 Undefined

655 800 844 921 1 576 047 Undefined

115 925

Undefined

Undefined

Undefined

Undefined

164 827 263 719 318 938 241 865 241 865 2 857 275 4 495 632 ? 1 119 061 ? 484 632

Undefined Undefined Undefined Undefined Undefined Undefined Undefined ? ? ? ?

Undefined Undefined Undefined Undefined Undefined Undefined Undefined ? ? ? ?

Undefined Undefined Undefined Undefined Undefined Undefined Undefined ? ? ? ?

Undefined Undefined Undefined Undefined Undefined Undefined Undefined ? ? ? ?

?

?

?

?

367 067

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R. Cullen et al. / Ecological Economics 39 (2001) 53–66

Table 7 shows that there are nine programs with an undefined cost– utility ratio, as they have produced zero COPY. Evaluation of these programs is difficult. To judge how cost-effective these programs are, one practical option is to compare the direct cost of programs and the species’ conservation status. Programs that, at low cost, maintain a species’ conservation status are arguably more cost-effective than high-cost programs that maintain a species’ status. One means to increase the number of programs with defined cost-effectiveness values is to introduce subsidiary conservation status scores. A species may be evaluated as remaining within, say, CE during a decade, but some progress occurs during that time. In such cases, a move from CE to CE + might describe the species status more accurately, scores mid-way between CE and E could be applied, and COPY gained could be calculated. Considering the nine programs that at present achieve zero COPY, Kea, for example, might have some progress during 1987– 1998. If a score of V+ is applied from 1996– 1998, the COPY produced by this program is 0.2925, and its cost–utility ratio is $563511/COPY.

7. Judgment Our research indicates that CUA can provide valuable information on the productivity of threatened-species recovery programs and overcome a gaping weakness in our knowledge about these programs. The effectiveness of threatenedspecies programs can be measured in COPY and together with cost data, allow calculation of cost– utility ratios. The methodology makes light data demands, so long as species management costs are routinely and consistently recorded, and provides easily interpreted results that can be used to guide future investment decisions. There are, however, some limitations to the usefulness of the methodology as presently developed. The conservation scales are broad and may conceal subtle status changes occurring within a category. For evaluations that span only a few years, zero COPY may be produced, but some small change in the species status may have oc-

curred. It appears very difficult to capture such changes using a single index approach for determining status. One possible way to overcome the problem may be to use a multi-attribute approach to assessing conservation status where several variables are assessed and subtle or small changes in status are captured. Theoretically, in a multi-attribute approach one could break down the attributes or variables determining status (e.g. population size, number of locations, habitat availability) and develop suitable indices to quantity the attributes (Stephens, 1998). One possible issue with this approach would be developing suitable indices that are suitable for the full taxa (e.g. reptiles, birds, animals) and determining the relative importance or weighting of each attribute (e.g. is population size more or less important than number of locations?). Our approach to determining species’ conservation status relies upon experts’ assessments. There are two weaknesses to this approach. First, species experts may have limited amounts of information available on which to make their assessments. Their ranking of species status both with and without management should be treated with caution. Second, it is worth considering whether there are incentives that may lead species experts to provide biased responses on the conservation status of species. One possibility is that the species expert will understate the achievements made by a threatened-species program to ensure that program funding continues. A second possibility is that species experts will overstate program achievements to ensure that their program appears to have made good use of scarce conservation resources. Research is needed to determine if either of these possibilities occurs. The value weights used in CUA studies can be refined to capture more accurately each species’ genetic uniqueness and society’s relative preferences for New Zealand species. We have only applied CUA to individual species at present. Some recent conservation effort is applied to sites where multiple species are managed simultaneously. In those cases, program costs will need to be allocated across multiple species to allow calculation of cost–utility ratios. Our approach calculates the average cost per COPY over the study period, but information on marginal cost per

R. Cullen et al. / Ecological Economics 39 (2001) 53–66

COPY would be more illuminating for decisionmakers. This research improves upon earlier applications of CUA to threatened-species management and demonstrates how the technique can be developed to evaluate a range of threatened-species programs. The methodology can be further refined and tested, but it is clear that COPY and CUA already provide a low-cost means to measure the productivity of these programs. The results calculated on effectiveness and cost– utility ratios of threatened-species programs should provide important information for program managers and decision-makers intent on making wise use of scarce conservation resources.

Acknowledgements The authors thank the following persons for their assistance and advice: Jim Coleman, Graham Nugent, John Innes (all of Landcare Research New Zealand Ltd); John Cannell, Richard Aubrey, Ramesh Rajendra, Paul Thomas, Warren Murphy, Janice Molloy, Wayne Hutchison, Elaine Wright, Paul Janson, Jeff Hudson and the Recovery Group leaders and experts consulted within the Department of Conservation; David Norton (University of Canterbury); David Given (Lincoln University); Georgina Mace (IUCN); Michael Young (CSIRO) and three anonymous reviewers. Funding for this research was provided by the Foundation for Research Science and Technology, via sub-contract LIN 1156 with Landcare Research New Zealand Ltd.

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