Estimating the opportunity costs of biodiversity protection in the Brigalow Belt, New South Wales

Journal of Environmental Management 70 (2004) 351–362 www.elsevier.com/locate/jenvman

Estimating the opportunity costs of biodiversity protection in the Brigalow Belt, New South Wales J.A. Sinden* Agricultural and Resource Economics, School of Economics, University of New England, Armidale, NSW 2351, Australia Received 24 December 2002; revised 9 June 2003; accepted 8 December 2003

Abstract The New South Wales Government recently introduced the Native Vegetation Conservation Act to protect the native grassland and woodland of the state. The Act protects biodiversity by preventing farmers from clearing such vegetation on their properties but, as a consequence, reduces farm incomes and land values. An economic model of the relationship between land value and percentage of farm in native vegetation is integrated with an ecological model of the relationship between species lost and percentage of the farms in native vegetation. The integrated framework is applied to estimate the opportunity costs of the Act for one important agricultural area of the state, the northern part of the Brigalow Belt South Bio-Region. If all the vegetation were protected, the reduction in land value would be at least 14.3%, which is an opportunity cost of at least $148.5 m for the area. Both the benefits and costs of biodiversity protection must be accounted for, so risk simulations are then combined with benefit-cost analysis to compare the benefits of biodiversity protection to these costs. q 2004 Elsevier Ltd. All rights reserved. Keywords: Opportunity cost; Biodiversity protection; Unpriced values; Land values; Native vegetation conservation; New South Wales

1. Introduction Governments often regulate agricultural activities to provide environmental benefits for the community as a whole These regulations tend to reduce farm income and as a consequence to reduce land value as well. Where the restrictions protect native vegetation, these losses are a measure of the opportunity cost of biodiversity protection. On 1st January 1998, the New South Wales government introduced the Native Vegetation Conservation Act. These new regulations restricted agricultural activities in order to manage the remaining native vegetation sustainably, and to encourage landholder and community involvement in vegetation management (NSW Department of Land and Water Conservation, 1998). The Act removes the farmer’s rights to develop native woodland or native grassland. The Act then permits such development on application by the farmer and consent from the Department of Land and Water Conservation (DLWC). The balance between protection and clearing was to be determined through Regional Vegetation Management * Tel.: þ61-2-6773-2293; fax: þ 61-2-6773-3596. E-mail address: [email protected] (J.A. Sinden). 0301-4797/$ - see front matter q 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2003.12.013

Committees that were established under the Act (NSW Department of Land and Water Conservation, 1997). But the Committees did not have access to a framework to analyse the relevant economic effects on farmers so that they could address the balance between protection and clearing. The overall goal of this paper is to develop a framework to integrate the necessary economic and ecological relationships to estimate the effects of biodiversity protection on farmers. The specific objectives are then to apply the framework to (a) estimate the cost that the Act has imposed on farmers, (b) estimate the opportunity cost per species that is protected, and (c) compare the benefits and costs of the policy. The study area is a region where the necessary relationships have been modelled in a separate but compatible manner—the northern outwash province of the Brigalow Belt South Bio-region, New South Wales. The role of opportunity costs is reviewed Section 2. Section 3 and Fig. 1 present the framework and method to derive and integrate the necessary relationships. Data collection is described in Section 4, results are reported in Section 5, and discussion and conclusions are presented in Section 6.

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Fig. 1. A framework to relate regulations to conserve native vegetation, land values, and number of species preserved.

2. Role of opportunity costs Benefit-cost analysis provides the economic framework for debates about environmental management. A benefitcost assessment of biodiversity protection must include both direct and indirect benefits from the conservation of native vegetation. The direct on-farm gains include retention of shade for livestock, firewood and fencing material, and land and water conservation. Direct gains to others include medicinal benefits, eco-tourism, and land and water conservation down stream. Indirect benefits include the non-use gains of existence values (from knowing that the biodiversity still exists), bequest values (from bequeathing biodiversity to the future), and option values (from retaining the option of future use). Estimation of these benefits is hindered by uncertainty about their nature, size, and value, and is clouded by the irreversibility of species loss and the problems of very

long time horizons. A complete, precise, benefit-cost analysis is therefore beyond the current state of the art. But calculation of the costs is often more straightforward because losses of agricultural income and land value are easier to estimate. Assessment of protection issues from the benefit-cost perspective is therefore still worthwhile, and the estimation of opportunity costs provides useful information to do so. The opportunity cost of protecting biodiversity on farmland is the income foregone in the alternative agricultural use of land, and is the major cost of protection. Changes in farm income will lead to changes in land value, and so we can measure one or the other as the opportunity cost. The price paid indicates the value to the buyer of all the characteristics of the land, as he sees them and as affected by the market. The price paid will therefore be a measure of the market assessment of the long-term impacts as well as all the short-term impacts of the characteristics of

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the land—and so allows assessment of the contribution of the vegetation characteristic to opportunity costs. Middleton et al. (1999) measure the opportunity costs of the protection of woodland for several shires in New South Wales. They assess the loss in land value as: Loss ¼ ½ðvalue after clearing 2 value before clearingÞ 2 cost of clearing

ð1Þ

This standard procedure is now applied.

3. Method 3.1. A framework to estimate opportunity costs The Native Vegetation Conservation Act reduces the proportion of a farm that the farmer can clear and develop As a consequence, land values may be reduced but the number of protected species may be increased The two relationships to be modelled and integrated are therefore (a) the variation of land value with the percentage of the farm in native vegetation, and (b) the variation of number of species with the percentage of the farm in native vegetation. The framework to do this is shown in Fig. 1 and the two steps of the method are now detailed.

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The model to explain the formation and estimation of land value is the basic equation: LVALUEi ¼ a þ bKij þ ei

ð4Þ

where Kij is a vector of j characteristics of farm i; e is the error term, and a; and b are parameters to be estimated. One of the characteristics ðKÞ must of course be a measure of the amount of native vegetation that remains of the property. This kind of model has long been applied to identify the contribution of characteristics of the land to the value of land, and Rosen (1974) set out the basic theory. Relevant applications include Reynolds (1978) who estimated the contribution of live tree cover to land value, and Sinden et al. (1983) who determined the contribution of the death of eucalypt trees to pasture improvement and land value. King and Sinden (1988) estimated the contribution of soil conservation to land value and Irwin (2002) estimates the contribution of open space to urban property values. The difficulties with the method concern (a) whether the market is in equilibrium, because the values derived for each characteristic are assumed to be equilibrium prices, and (b) whether the participants in the market can perceive the differences in characteristics between properties. In the study area, the land market is very active with many buyers and sellers and the existence of native vegetation is well known and identifiable.

3.2. Estimate the loss in land value 3.3. Estimate the gain in number of species protected Following Eq (1), the opportunity cost (OC) of vegetation protection is the change in the market price (LVALUE) of farm i before and after the Act. When the clearing costs are deducted from land value after the Act: OCi ¼ LVALUEi before the Act 2 LVALUEi after the Act ð2Þ When a time series of land values is not available, data are collected at a given time for farms with different amounts of vegetation and the opportunity cost is estimated through comparisons of the different farms. The opportunity cost depends on the characteristics of both farm and farmer because it rests partly on how much native vegetation the farmer actually wants to clear. To a farmer who wants to retain Y percent, the value now of a farm that is cleared to Y percent is equivalent to the value of a farm before the Act that could be cleared to Y percent—if all the other characteristics are the same. So the opportunity cost may be estimated as: OCi ¼ LVALUEy 2 LVALUEi

ð3Þ

where i and y are farms with identical characteristics except for the percentage of native vegetation. Farm i has its current amount of native vegetation all of which must be retained under the Act, and y has the amount that the farmer would wish to retain.

The Act protects the existing native vegetation, so the gain in the number of species (GAINSP) is the difference between the loss in species without the Act and the loss with the Act The latter loss is the historical reduction in number in the particular environment up to the time the Act was introduced. Thus: GAINSPi ¼Percent that would be lost without the regulation 2 percent that are already lost now

ð5Þ

Without the regulation, the farmer would retain only Y percent of his native vegetation, whereas the current amount of V per cent would be retained under the Act. For farm i : GAINSPi ¼ PCSPLSy 2 PCSPLSv

ð6Þ

where PCSPLS is the percentage of species lost at the particular level of native vegetation. We now require a function to identify species loss from vegetation loss. Lambertson et al. (1992) address the general issues of estimating the rate of species survival as a function of forest landscape characteristics. Panetta and James (1999) gathered information on the relationship between the number of native species and the area occupied by weeds in natural areas. While they could only scale the percentage of weed area as low, medium or high,

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all their three cases (Mimosa pigra, Lonicera maacki, and exotic plants in eucalypt forest) showed a similar exponential relationship. The number of native species lost per hectare increases as the amount of weed cover increases, and at an exponential rate. Whalley (2000) suggested that the same kind of relationship might occur between area cleared and species lost in the study area. The loss of native species will increase with increases in the amount of clearance of native vegetation, and at an increasing rate. The following kind of exponential relationship models this situation. PCSPLS ¼ c þ ½ðPercent of native vegetation clearedÞ^ X ð7Þ where X is an exponent and c is a parameter to be estimated. The final step in the method is to calculate the cost per species protected by dividing the change in opportunity cost (OC) from Eq. (3) by the gain in species protected (GAINSP) from Eq. (6).

4. Data collection 4.1. Selection of a study area and farms Australia has been classified into bio-regions on the basis of substrate, climate and vegetation (Thackway and Cresswell, 1995). Bio-regions are then subdivided into provinces on the basis of topography and soils. All the necessary data were available for the northern outwash province of the Brigalow Belt South Bio-region in northwest New South Wales. The province occupies 1.227 m ha and comprises the eastern part of Moree Plains Shire and the western part of Yallaroi Shire. Data to estimate the variation of land value with native vegetation were available for Moree Plains Shire (Sinden, 2002). Data to estimate the relationship between native vegetation and species loss were available from McAlpine et al. (2002), and the number of threatened species of flora and fauna (1 – 25) were available for the province from the National Land and Water Resources Audit (2001). Sixty percent of the bio-region, as a whole, is cleared to agriculture (Benson, 1999) and 59% of the area of the sample of farms in Moree Plains Shire is cleared. But the values of agricultural land in Moree Plains Shire as a whole are rather lower than those for Yallaroi. The land value relationships, from Sinden (2002), were tested for systematic east-west variations but none were found. So the results for the Shire as a whole are applied to the study area and sensitivity tests were undertaken of the effects of variations in land value on the costs of protecting biodiversity. Information on price paid, area, name of buyer, and name of seller was obtained from the state Valuer—General for all exchanges of land in Moree Plains Shire since January 1991.

Corporate buyers and exchanges within a family were excluded from the potential sample. This left 180 exchanges from one farm family to another between January 1995 and December 2000. The first 52 of these buyers, with whom interviews could be arranged, were interviewed.1 The interviews were well scattered throughout the Shire. The author interviewed 46 of the farmers, one of the author’s students interviewed six, and one farmer was subsequently deleted because he was not using the property to earn income from agriculture.2 The final sample size was therefore 51, which comprises 9.3% of all landowners in the Shire. 4.2. Characteristics of the farms The price paid for land (LVALUE) was expressed per hectare in the dollars of December 2000 Prices had been rising steadily at 8% per year since January 1991, so inflation was dealt with by inflating the price paid at this annual rate to the common date of December 2000. The average land value in the province was estimated to be $844 per hectare. While land prices had been rising steadily over the long term, there were short-term cycles around this trend. For example, prices in three quarters toward the end of the period were higher than the trend. Accordingly, a variable TIME was specified as the number of quarters since December 1994, where the quarter for January to March 1995 was coded as 1. Increases in TIME should lead to increases in LVALUE. Gross margin per hectare (GMPH) is a standard measure of annual profitability and is defined as: GMPH ¼ Gross money revenue per ha 2 variable money costs per ha

ð8Þ

Gross margins were assessed with sustainable yields (the long-term Shire averages for crop production and for livestock carrying capacity), and conservative costs and prices as given by the farmer. The prices varied for the particular kinds and grades of output produced by each farmer, and increases in GMPH should increase land values. The area of the purchase was expressed in hectares (AREA). If the purchase was an addition to an existing farm, ADDN was coded as 1. If it was a whole farm, and was the farmer’s entire business, ADDN was coded as 0. The distance from the nearest large town, Moree Narrabri, or 1

The potential sample of 180 had been arranged into five strata by year of purchase, and telephone calls were made to 11 randomly-selected farmers within each strata. So 55 farmers were telephoned, and 52 of these agreed to be interviewed. 2 The student lived on a farm in the Shire and so knew the agriculture of the study area well. He was a fourth-year student in agricultural economics and was using the surveys for his final year thesis for his Bachelor of Agricultural Economics. He completed the surveys after careful preparation and under close supervision of the author.

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Goondiwindi, was a measure of the residential amenity of the purchase (DIST in kilometres). Negative relationships are expected for area and distance with land value, and a positive relationship is expected when the purchase is an addition. 4.3. Characteristics of the vegetation The farmers estimated the percentages of their land at the time of purchase in each of five classes—native forest, native woodland, native grassland which had not been cultivated in the last 10 years, native grassland that had been cultivated in the last 10 years, and land in cultivation. Following standard conventions, native forest was defined as woodland where the tree canopy covered more than 20% of the ground. Native woodland was defined as vegetation where tree canopy covered less than 20%. On average, native woods and forest occupied 19.2% of the farm and native grassland occupied 10.5%. In total then, native vegetation occupied 29.7% of each farm on average—a figure that is now rounded to 30%. The average loss in gross margin due to the Act (OCACT) was calculated per hectare over the whole area of the purchase. This opportunity cost occurs because the farmers cannot develop their native grassland that had not been cultivated in the last 10 years or their native woods and forest. It was calculated as follows. OCACT ¼½ðpercentage in native grassland £ AREA £ lost incomeÞ þ ðpercentage in native woods and forest £ AREA £ lost incomeÞ=AREA

ð9Þ

In this equation, income was gross margin per hectare calculated as above. To be conservative it was assumed that the farmer would crop (wheat/fallow/wheat/fallow) land that is now native grassland, and graze land that is now native woodland. Increases in OCACT should lead to decreases in land value. 4.4. Characteristics of the farmers The landholder’s knowledge of the application of the Native Vegetation Conservation Act in the Shire should affect the amount that he pays for land. The Moree Regional Vegetation Committee was a major source of this information so a rating of the quality of the landholder’s knowledge of the activities and proceedings of the Committee was devised. AWARENESS was coded as 1, if the level of knowledge was very low, to 5 if very high. It was coded by the interviewer from responses to questions on knowledge of the committee’s aims, members, and attendance at meetings. Increases in knowledge should lead to better decisions, and perhaps to higher prices paid so a positive sign was expected.

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The real loss to the farmer will depend on his plans to retain native vegetation anyway. For example, there would be no real loss if the farmer retained the existing 30% of his native vegetation anyway and the NVCA required him to retain that same amount. Several hundred farmers at a public meeting in Moree in 1999 were asked what proportion of their property they would retain in native vegetation and the great majority indicated that they would retain 15% (Andersen, 2001). In an effort to substantiate this value, or obtain a range for it, the 51 farmers in the field survey were asked whether they would protect 15% of their native vegetation and so set aside 6% of their farm. Fifty-nine percent agreed, so we take this as the minimum amount that would be set aside and round it down to 5% of the farm. We therefore have a range for the amount of native vegetation that the farmers would leave anyway (5 – 15%), and sensitivity analyses are undertaken on the effects of changes in this amount on the cost of protection. 4.5. Gains in species protected In an empirical study of the effects of vegetation clearing in a broad region embracing the study area, McAlpine et al. (2002) related animal extinctions to the percentage area cleared. They integrated data from the Darling Downs of Queensland, Northern Plains of Victoria, and the central west wheat belt of New South Wales. As they noted, their relationship provides a broad approximation of actual extinction rates and follows the curvilinear trend advanced by Koopowitz et al. (1994). The equation of their graphical relationship, with a logarithmic transformation instead of an exponential, is3:

LogðPCSPLSÞ ¼ 0:465 þ 0:03849 ðpercent area clearedÞ

ð10Þ

where PCSPLS is per cent species loss. The adjusted R squared for this equation is 0.956 and the t value on the variable for percent area cleared is 14.1. The relationship from McAlpine et al. (2002), modelled by Eq. (10), refers to losses of animal species. A relationship for loss of plant species, or a more resilient plant environment, would be less dramatic and be modelled by a lower coefficient to reflect lower losses of species for any given area cleared (Fensham and Sattler, 2002). To reflect this kind of situation, Robson (2002) suggested that the coefficient could be reduced to 0.03464 (90% of its value). A sensitivity analysis will therefore be undertaken on the effects of relaxing the loss rate in this way on the costs of protection. 3 The present author estimated this equation from the data graphed in McAlpine et al. (2002).

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4.6. The main items of data The values of the key items of data are now summarised. Variable

Minimum Mean Maximum

Area of province m ha Number of threatened species Land value $ per ha

* 1

1.227 * ** 25

$700

844

1500

30

80

30

80

**

15

% Farm still in native 0 vegetation (NV) % Farm the Act requires 0 kept in NV % Farm that farmers would 5 retain in NV

* Indicates not relevant, ** indicates not available.

The positive signs on GMPH and ADDN in Eq. (12) indicate that land values rise when gross margins rise and when the purchase is an addition to an existing property rather than a whole property. The time trend (TIME) is positive and significant suggesting that there are short-term price cycles that increase the price paid. The negative signs on AREA and OCACT indicate that land values fall when area increases and when OCACT increases. The opportunity cost of the Act was calculated from the per cent native woodland and percent native grassland, so increases in per cent native vegetation on the farm lead to increases in OCACT and so to decreases in land value. At present under the Act, 30% of the native vegetation on the average farm is retained and the average land value is $844 per ha. Land values at different levels of retention of native vegetation were calculated from Eq. (12). Starting with the observed means, they were:

5. Results 5.1. Opportunity costs per hectare The influence of farm and farmer characteristics on price paid was determined by estimating Eq (4) from data for the 51 farms Two versions of the equation were estimated and both are reported in Table 1. Eq. (11) is the full version, while Eq. (12) contains just the significant variables to concentrate on them and remove the ‘noise’ due to the insignificant variables. Tests of the econometric assumptions and specifications of this equation are reported in the appendix.

$844 when 30% vegetation, $868 when 25% vegetation, $916 when 15% vegetation, and $965 when 5% is

of the farm is retained in native of the farm is retained in native of the farm is retained in native retained.

Buyers were therefore paying $844 per hectare where 30% of the land was still in native vegetation. But they were paying $916 per hectare where only 15% remained in native vegetation. Equally, if they knew they could reduce

Table 1 Models to explain variations in land value Variables

Eq. (11)

Eq. (12)

Coefficient

t-statistics

Coefficients

t-statistics

Farm characteristics Profitability (GMPH) Area of purchase (AREA) Addition or whole farm (ADDN) Distance from town (DIST)

2.4845 2114.05 158.22 21.3214

6.6*** 2.8*** 2.1*** 1.0

2.5135 2127.94 163.61

6.9*** 3.3*** 2.3***

Farmer characteristic Awareness (AWARE)

7.7696

0.5

Native vegetation characteristics Opportunity cost (OCACT)

21.0502

2.1**

21.0270

2.1***

Market characteristic Cyclical price trend (TIME) Constant

6.5076 1272.1

1.1

8.2049 1279.7

1.5*

Properties of the model Coefficient of multiple determination Coefficient of determination Adjusted coefficient of determination

0.870 0.760 0.721

0.8681 0.7536 0.7262

* Indicates significant at 10% or better, ** indicates significant at 5% or better, *** indicates significant at 1% or better.

J.A. Sinden / Journal of Environmental Management 70 (2004) 351–362

the quantity of native vegetation from the existing 30 to 15%, they would also pay $916 per hectare (with an adjustment for the cost of clearing).4 The opportunity cost of protection is the loss in land value and is now calculated following Eq. (3). Land values were noted from above for the representative farm in situation (a) with the amount of vegetation that the farmer would wish to retain and in a situation (b) with the current amount of native vegetation all of which must be retained under the Act. For example if the farmer wished to retain 15% but was required to retain the current 30%, the loss is ($916 – $844) or $72 per ha. A range of the losses in land value is now shown for a range of farmer behaviours and legislative requirements. Farmer would retain

15% 5%

Loss ($per ha) because the farmer must Retain 30%

Retain 25%

916 2 844 ¼ 72 965 2 844 ¼ 121

916 2 868 ¼ 48 965 2 868 ¼ 97

. The losses per hectare vary widely ($48 –$121) according to the farmer’s plans to retain vegetation and the legislative requirement. They are recorded in Table 2, column 2, as the per-hectare cost to each farmer. The basic loss of $121 per ha, or 14.3%, refers to the Act’s requirement that all vegetation must be retained (an average of 30% of farm area in the study) and the farmer’s wish to retain only 5%. The losses for all farmers in the region are calculated as the loss per hectarepthe number of hectares in the region (1.227 mp column 2) and are shown in column 3. The opportunity cost for all the farmers is $148.5 m. 5.2. Opportunity costs per species protected The gain in number of species is estimated from the percentage species loss for the representative farm in a situation with the amount of vegetation that the farmer would wish to retain and in a situation with the current amount of native vegetation, all of which must be retained under the Act The percentage species lost in each case is calculated from Eq. (10). For example, if the farmer were going to clear 95% of his property and wished to retain 5% as native vegetation, 61.1% of the species would be lost. If 30% is the current amount retained, and 70% of the land is already cleared, 23.4% of species have already been lost. The gain in species protected is therefore: GAINSP ¼ 61:1 2 23:4 ¼ 37:7 percent:

ð13Þ

Again, if the farmer were going to leave 15% of his native vegetation, the gain in species conserved is 4

The extra costs of clearing and development are some $70 per ha. When spread over the entire area of the purchase, following the other financial variables, this extra cost is some 5 cents per ha.

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41.6 2 23.4 ¼ 18.2%. These gains in species protected are shown in Table 2, column 4. The two outcomes, real loss in land value (OC) and gain in species protected (GAINSP), can now be combined to estimate the cost to protect a species. The results for the base case have been summarised in Table 2. They refer to a land value of $844 per ha, to retention of 5% of native vegetation anyway, and to the existence of 30% of farm in native vegetation when the Act was introduced. The costs to all farmers in the area are $148.5 m (column 3 of Table 2). The opportunity costs for a 1% gain in number of species conserved (column 5) are calculated as costs to all farmers in the area divided by percentage gain in species protected (column 4). The opportunity costs for a one species gain (columns 6 and 7) are calculated from the cost for a 1% gain in number of species conserved (column 5) and the number of threatened species (1 or 25). For example, the cost of protecting a single species is $15.7 m if there are 25 threatened species present and all could be protected ($3.9 m/0.25). The current policy to retain the full 30% of each property in native vegetation therefore creates an average loss in land value of $121 per ha (or 14.3%), and a loss of $148.5 m to all farmers in the area. But this policy conserves 37.7% more species than would otherwise be left. If there were 25 threatened species and all could be protected, the cost would be $15.7 m per species. If there were less threatened species, the cost per species would of course be higher. The data used to calculate these values are not known with certainty, so the effects of these uncertainties on the costs of protection is now analysed. 5.3. An assessment of the uncertainties The opportunity cost of $15.7 m per species has been calculated with a high coefficient for Eq. (10) to reflect a rather more fragile environment than actually exists in the study area. It has also been calculated with the highest number of threatened species. The importance of changes in these and the other variables on the cost of protection was tested by calculating the percentage response in the cost due to a one per cent change in each variable. Higher responses indicate that the costs are more sensitive to changes in the particular variable. The results were as follows. (a) A 1% decrease in the coefficient to relate species loss to area cleared leads to a 3.9% decrease in the cost of protection. (b) A 1% increase in the number of threatened species leads to a 1.0 per decrease in the cost of protection. (c) A 1% decrease in the land value leads to a 1.0% decrease in the cost of protection. (d) A 1% decrease in the amount of native vegetation on the farm when the Act was introduced leads to a 0.4% decrease in the cost of protection.

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Table 2 Costs to protect a species Farmers retain native vegetation anyway at

% of farm 1

Cost to each farmer

$ per ha 2

Cost to all farmers in the area

$m 3

Gain in species conserved

Cost for a 1% gain in species conserved

Cost per threatened species conserved If 1 spp threatened

If 25 spp threatened

% 4

$m per 1% 5

$m per spp 6

7

For a policy to retain 30% of the native vegetation 5 121 148.5 15 72 88.3

37.7 18.2

3.9 4.8

393.6 484.6

15.7 19.4

For a policy to retain 25% of the native vegetation 5 97 119.0 15 48 58.9

32.8 13.3

3.6 4.4

363.2 443.5

14.5 17.7

(e) A 1% decrease in the amount of vegetation that must be retained on the farm leads to a 0.4% decrease in the cost of protection. (f) A 1% increase in the percentage of native vegetation, that the farmers will retain anyway, leads to a 0.2% decrease in the cost of protection. These responses in the cost of protection are all measured in a common yardstick (percentage change) and are all caused by a common one per cent change in a variable. They are all calculated for decreases in the cost of protection, so they can be compared to identify the sensitive variables. The most sensitive variables are the coefficient to relate species loss to area cleared (a), the number of threatened species in the region (b), and land value (c)—changes in them lead to the greatest changes in cost. The coefficient and the number of threatened species rest on published and peer-reviewed data. Even so, these two values should be clarified through further environmental research—not least because the threatened species may exist elsewhere. Land value is the variable with the best data and the land value models of Table 1 have good statistical properties (as described in the Appendix A). The least sensitive variables are the percentage native vegetation that farmers will retain anyway (f), the amount of native vegetation that must be left (e) and the amount that was left when the Act was introduced (d)—changes in these variables lead to small and perhaps inconsequential changes in the cost of protection. But how does the cost per species change with variations in number that are threatened and with variations in the coefficient (0.03849 in Eq. (10)) to model the percentage gain in species protected? To gain some insight into this question, a risk analysis was undertaken to simulate the potential variation in costs and to estimate the likelihood that the cost will be below certain levels. Because better data were not available, the number of threatened species was assumed to follow a triangular distribution skewed slightly to the upper end of the scale. The minimum number protected is 1, the most likely is taken as 18

and the maximum is 25. The distribution of the coefficients was also assumed to be a triangular distribution where the minimum is 0.03464, the most likely is 0.03655, and the maximum is 0.03849. Again these were judgements, because of a lack of better data. Five thousand costs were calculated, each with different values of number threatened and the coefficient. The cumulative distribution of costs was as follows. There was a 0% chance the cost is below $15.7 m, a 25% chance it is below $29.1 m, a 50% chance it is below $37.4 m, and a 75% chance it is below $51.1 m. The median is $37.4 m and so the likelihood of a cost above this value is just as likely as one below it—and the minimum was of course $15.7 m. 5.4. The benefits and costs of protection A more complete assessment of biodiversity protection requires that the benefits be compared to the costs so the following question can now usefully be addressed: do the direct and indirect benefits of protection exceed the opportunity cost of $148.5 m? An attempt to answer this question would offer better information for policy formation even though the benefits are hard to value. As Joubert et al. (1997) argue, some of the benefits of biodiversity protection can be valued through market prices but the remainder must be valued through stated preference survey methods so this mixed approach is now followed.5 A discount rate of 7% and an infinite time horizon are used. All values in the summary Table 3 are present values from this flow—hence equivalent to a land value. The results omit the relatively small government costs of administration. In the study area, the on-farm use benefits of fencing, shelter and shade are likely to be negligible. But the maximum value of the benefit, taken from a nearby 5 In contrast, Higgins et al. (1997) use market values for most of their benefits—for flowers that are harvested, genetic services supplied on the market, water sales, and revenues from fuel wood sales in a comprehensive assessment of the benefits of protecting the fynbos woodland of South Africa. Such a comprehensive set is unavailable for the study area so data from other areas is used.

J.A. Sinden / Journal of Environmental Management 70 (2004) 351–362 Table 3 A summary of estimated benefits from protection Benefit Use benefits On farm fencing, shelter and shade On farm land conservation Off farm land salinity reduction Firewood Non-use benefits Existence value etc Total a

Minimum Most likelya Maximum

0 0 6.513 2.694 0.948 10.155

14.007 45.5 29.3 5.386 64.1

28.014 60.601 39.082 8.082 84.500 220.279

As present values, $m. Estimates for the probability distribution.

region (Miles et al., 1998), could be $4.41 per ha per year or a present value of $28.014 m. The area is predominantly cropping so retention of woodland would provide little onfarm benefit to the agriculture, but again a maximum value from Walpole is $9.54 per ha pa or a present value of $60.601 m. The gains from reduction in off-farm land degradation will accrue in the immediate downstream area (all of which is in Moree Plains Shire). No problems from salinity occur there yet, but the trends of water table levels indicate that it may be problem in 15 years or so. If biodiversity protection in the study area prevents a loss of 5% (as a minimum) of annual net income, the present value of the gain is $6.513 m. If it prevents a loss of 30%, the gain is $39.082 m. The value of firewood that could be obtained on a managed sustainable basis is included. There are few ecotourism opportunities at present in the province, but more importantly, nearby localities would readily substitute the same benefits. Simpson et al. (1996) argue that, in general, pharmaceutical use benefits are also likely to be trivial. There are several reviews of methods to value the indirect non-use benefits of biodiversity, including OECD (2002), Sinden (1994), and Shortle and Abler (2001). A review of the methods is therefore unnecessary but their application to value of the indirect, non-use benefits of protection in the study area must be addressed. The difficulty of assessing these gains suggests that a strategy for valuation, which rests on the economic concept of aggregate willingness to pay, may be appropriate (Odom et al., 2003). The aggregate could be actual government expenditure as a measure of community willingness to pay, or the aggregate of individual willingness to pay elicited by contingent value surveys. The amount that the government would actually spend to buy habitat, or otherwise ensure protection, may be the minimum. The aggregate from contingent valuation surveys may be a maximum—to give a strategic range for the estimates of value. A range of values for indirect non-use benefits for the study area may therefore be derived as follows. In 2002, the Queensland state government agreed to contribute $200 m

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to protect similar areas of threatened but not endangered species (Morton et al., 2002). This action would save 5280 species so the cost per species is $37,900 as a lump sum payment. This could be a minimum for the strategic range. Lockwood and Carberry (1998) found households in southern New South Wales were willing to make a one-off payment of $1.69 for every extra threatened native plant or animal that is conserved. Hassall and Gillespie (2002) suggest that the number of households in a state should be multiplied by the per household value to obtain the total value of the species. The aggregate willingness to pay for protection of the indirect non-use benefits is then $3.38 m (1.69p2m) which could be the maximum non-use benefit of protecting a threatened species in New South Wales. The maximum value of biodiversity when all 25 species are protected is $84.5 m using the most optimistic contingent value (25p$3.38 m), and the minimum is $0.948 using the least optimistic value from government willingness to pay (25p$37,900). The results of Table 3 show that the minimum total benefit is less than, but the maximum exceeds, the opportunity cost of $148.5 m. So a risk simulation was undertaken to determine the likelihood that the benefit would exceed this cost. Triangular distributions were used again to define the probability distributions, and the minimum, most likely and maximum values are all shown in Table 3. The minima and maxima were estimated as above, but the most likely values were determined subjectively—and were slightly biased toward the benefits. The most likely values for fencing, shelter and shade, and firewood were their means, but values for the other three benefits were set at two thirds of their maxima. The results were as follows. The chances of the benefit exceeding $148.5 m were 15.5%, the chances of exceeding $140 m were 26.9%, the chances of exceeding $120 m were 59.3% and the chances of exceeding $100 m were 83.8%. While there is only a small chance (15.5%) that benefits exceed costs, there is a much larger chance (59.3%) that they will come within 81.1% ($120 m) of costs. There is therefore a 59.3% chance of a benefit-cost ratio of 0.8 (148.5/120) and that may be sufficient economic argument to justify protection in this area.

6. Discussion and conclusions 6.1. Problems and further research The framework to estimate opportunity costs consists of a statistical model to estimate the contribution of land characteristics to land value, and a statistical model to estimate the loss in number of species as a function of vegetation loss. Both are standard models in their respective disciplines, but the former are sometimes hard to estimate and the latter are often not available.

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Application of these two models rests on a set of farm data and an estimate of the number of threatened species. The farm data were collected by survey and this is sometimes a source of bias. But in New South Wales the prices, areas, buyer and seller of all property sales must be reported to the state Valuer General. Further, data on yield, costs and prices were well known to farmers and this objective information was cross-checked after each interview. The intention to retain native vegetation in the future is always a conjecture, so the effect of variations was addressed by sensitivity analysis. The tests for uncertainty suggested that variations in the farm data are relatively unimportant whereas variations in the number of threatened species are more important. If the species exist elsewhere or if the same biodiversity benefits are available from other species or environments, then the species of the study area may not be threatened or they may offer no particular biodiversity benefits. Clarification of the number of threatened species is therefore a most urgent requirement for further research. The problems of major, unpredictable, potential changes in data over time remain. For example, some of the protected vegetation may yet yield valuable medicinal products and provide income to the farmers. This income will be distributed widely where the vegetation type is distributed widely across the farms. Again, low profitability in currently unmanaged woodland is no proof that profitable management is impossible at some time in the future, as Paoli et al. (2001) demonstrate for non-timber forest products in Indonesia. In the study area, the occasional stands of cypress pine woodland can be grazed and yield timber outputs, and the low current incomes from both sources may rise. Another kind of issue concerns whether vegetation would actually be retained on private land. At present, the state of vegetation and the amount of clearing are regularly monitored because these on-farm ‘reserves’ of native vegetation are not officially-gazetted as Crown land. But as McKinney (2002) points out, even the present kind of private on-farm reserve is better than no reserve at all for reducing the loss of threatened species. 6.2. Conclusion A framework to integrate economic and ecological relationships to estimate the opportunity costs of biodiversity protection has been presented and applied to a particular region The opportunity costs were estimated to be $148.5 m for all farmers in the area, the minimum cost of protecting a species was $15.7 m and the median cost was $37.4 m per species, and there was a 59.3% chance that the benefit-cost ratio of protection would exceed 0.8. When such estimates are available, decision-makers can more readily address the economic and equity issues. At the very least, the opportunity costs should be estimated in a systematic manner and widely disseminated. The environmental goal

of biodiversity protection is not an issue, but the cost of achieving it should be discussed.

Acknowledgements I should like to thank the 51 farmers and their wives who gave their time so readily to complete the interviews and to show me around their properties. Andrew Yates, one of my students who lived in Moree Plains Shire, helped with six critically-located interviews. Mr L.F. Boland and Mr W.J. Yates, farmers in the Shire, helped throughout the project and particularly with comments on the results as they were obtained. Wal Whalley, Associate Professor of Botany at the University of New England, encouraged the work and helped to interpret the ecology. Rod Fensham and Paul Sattler provided helpful suggestions on a draft of this paper. I should also like to thank the two reviewers for their detailed suggestions.

Appendix A. Statistical properties of the land value model The model for the estimation of land values was presented as Eq. (4) and estimated as Eq. (12) by ordinary least squares regression (Table 1). This kind of regression is based on certain assumptions, so we now test these assumptions for this equation. There are several ways to specify the functional form of this equation, and the most appropriate was determined through econometric tests of goodness of fit. The equation was estimated with each specification and the explanatory powers of each were compared through the adjusted R squared, F; and t statistics. On this basis, a simple arithmetic function was used, but AREA was converted to natural logarithms to capture the curvilinear relationship of price with size of the purchase. The functional form of the model must also be correctly specified. That is, all the appropriate and important variables must be included and the appropriate form (arithmetic, logarithmic, etc) must be specified for each. Otherwise, the proportion of the variability in the dependent variable (land value), that is explained by the equation, is less than would be explained by the correct model. A good indication of mis-specification is a low adjusted R squared value. As explained in Section 3.2, the form of the land value model carefully follows theory and empirical evidence so should be the appropriate functional form. Further, the adjusted R squared of 0.7262 is high for data which captures both biological and management variation, so the land value equation appears to be correctly specified. The variances of the residuals for each value of an independent variable should be the same and so the equation should be homoscedastic. Violation of this requirement,

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through the existence of heteroscedasticity, can be detected by examining the residuals. Judge et al. (1980) discuss the forms of heteroscedasticity and the tests for it, and note that a major difficulty is the need to know which form is present. In the present study, the variation in land values is likely to depend on several variables, and these variables are unlikely to move in the same direction. In this situation, the BreuschPagan Test is useful. Greene (1997) argues that the three Glesjer Tests are more powerful in the specific context of regression models because they test the dispersion of residuals with respect to the independent variables and the constant. The Breusch-Pagan-Godfrey test and the Glesjer tests were therefore applied to Eq. (12). Following the Glesjer tests, the null hypothesis of homoscedasticity was not rejected. Following the Breusch-Pagan-Godfrey tests, this null hypothesis was not rejected either. Thus heteroscedasticity has no measurable influence on Eq. (12). The data are from a cross section of farms at a given time, so there should be no serial correlation (auto-correlation), and so the residuals should be independent of each other. If the data were a time-series, auto-correlation could occur. The t-statistics for each regression coefficient would then be overestimated and variables could be mistakenly taken as significant. As an initial test for this problem, the graph of the dependent variable on each independent variable was observed to see whether the residual on one observation tended to be accompanied by residuals with the same sign on preceding or subsequent observations. No such serial correlation could be observed. Next, the Durbin-Watson statistic was calculated as a more rigorous test for this autocorrelation. The statistic for Eq. (12) was 1.80, sufficiently near 2.0 to indicate no auto-correlation. The Brigalow soils in the outwash province of the eastern part of Moree Plains Shire are often described as more fertile than those in the west. The possibility of a systematic east-west variation in the effect of soils on yields and on land values was therefore tested by re-ordering the 51 farms by easting and using the Durbin-Watson statistic to test for autocorrelation. There appeared to be no autocorrelation and so no systematic east-west soil effect. The independent or explanatory variables must not be correlated. The existence of correlation between pairs of independent variables can be tested through their correlation coefficients. The highest three correlations between the independent explanatory variables are 0.298 (GMNORM and PARTW), 2 0.291 (GMNORM and AREA in logarithms), and 2 0.283 (GMNORM and OCACT). Griffiths et al. (1993) suggest the use of these correlations to diagnose a existence of a problem of multi-collinearity which would bias the coefficients of the model. They go further (p. 435) to offer the following rule of thumb. A correlation between two explanatory variables ‘greater than 0.80– 0.90 indicates a…potentially harmful collinear relationship’. On this basis, the correlations are not harmful. Klein (1962), supported by Huang (1970), had offered a further rule of thumb.

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Collinearity is ‘tolerable’ if the correlation coefficient is less than the coefficient of multiple determination ðRÞ: The coefficient of multiple determination in Eq. (12) is 0.8681 so the independent variables are clearly not correlated to an extent that will bias the coefficients. Gross margin per hectare across the whole property (GMPH) is not logically correlated with OCACT. The opportunity cost due to the Act was calculated from five variables (farm size, percentage of the property in native woodland, percentage in native grassland, and the gross margin per hectare of crop and gross margin per hectare of pasture)—none of which was GMPH. Further, the correlation coefficient between OCACT and GMPH was only— 0.283. The existence of correlation between more than two variables can be tested by comparing the R2 values for the full Eq. (12) with R2 values for models where each variable is deleted separately (Lewis et al., 1990). The variable, that is most correlated with the set of other remaining independent parameters, is the one that results in the smallest drop in R2 when it is deleted. This variable proved to be TIME, but deletion led to a reduction in R2 of only 0.0122. The highest correlation of TIME with other explanatory variables was 0.1853 with GMPH. This is too low to bias the coefficients of Eq. (12).

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