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Valuing Ex Ante Changes in Water Quality for the Marine Environment: A Hybrid Method Applied to Bass Straight, Victoria, Australia

Boyd Blackwell National Centre for the Marine Conservation and Resource Sustainability (NCMCRS),

Australian Maritime College (AMC), University of Tasmania Locked Bag 1370, Launceston, Tasmania, 7250 Australia

boydb@amc.edu.au 613 6324 3897, Fax: +613 6324 3801

Jeremy Willcox NCMCRS, AMC, University of Tasmania

Newnham, Tasmania, Australia

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Valuing Ex Ante Changes in Water Quality for the Marine Environment: A Hybrid Method Applied to

Bass Straight, Victoria, Australia

Abstract Water quality improvements from reduced wastewater outfall near popular recreational beach sites are likely to enhance the experience and health of users. An enhanced experience translates into improvements in the monetary values held by recreational beach users. This paper presents the results of a pilot test using the Contingent Travel Cost Method (CTCM), a combination of the travel cost method and contingent behaviour method, to capture changes in environmental quality ex ante. Monetary values are estimated for recreational gains at Gunnamatta beach on Bass Straight, Victoria, Australia from the closure of a nearby wastewater outfall at Boags Rocks. The recreational gains at Gunnamatta beach alone, not the entire southern Mornington Peninsula, are found to be in the order of tens to hundreds of millions of Australian dollars annually. These recreational benefits help to balance the public account for any planned closure. Key Words Ocean outfalls, recreational benefits, beaches, contingent travel cost method, contingent behaviour, upgrade benefits. 1 Introduction Many non-market valuation studies include the collection of travel cost and contingent valuation or behaviour data, primarily to combine estimation processes or make comparisons between estimates and estimation processes. Others collect both types because they provide different types of benefit measures: the former about current preferences and benefits and the latter about future preferences and benefits. Another reason for having the two in a study is that the methods can capture different values; the travel cost method is typically used to capture recreational or passive use values, while contingent valuation is used to capture non-use values such as existence (value in knowing a species exits) and bequest (passing on an example of a species to future generations). The focus of our paper relates to the last reason. While we can observe current visits to a site, we cannot observe future visits given a change in environmental quality – we need to ask a question about people’s contingent behaviour. In this paper, we take the simple approach of modifying the travel cost method with a respondent’s stated additional visits given an improvement in environmental quality, and call this approach the contingent travel cost method.

This approach, however, is not new and Whitehead et al. (2008) provides a timely review of combining methods. The first well known contingent behaviour study was undertaken by Cameron (1992) who supplemented the travel cost method by asking people whether particular cost rises would drive their fishing trips to zero.

Hanley, Bell and Alvarez-Farizo (2003) estimated the benefits of water quality improvements for beach users in Scotland as part of the European Union’s toughening of water quality legislation. Here the authors combined revealed preference data on actual and expected visits to beaches when ‘hypothetical quality improvements’ were offered to respondents.

An alternative but similar approach was taken by Kragt, Roebeling and Ruijs (2009). They asked people to estimate their decrease in diver and snorkling trips to the Great Barrier Reef, Australia, given a fall in coral and fish diversity. The distinction was valuing environmental degradation rather than improvement.

Our approach is most similar to Hanley, Bell and Alvarez-Farizo (2003) where we ask people whether their visits to a beach site change in response to an improvement in environmental quality. We believe, however, that our application of the method is unique in Australia involving an assessment of the recreational benefits to beach users from the possible closure of an ocean wastewater outfall located near the Gunnamatta Beach on the Mornington Peninsula, Victoria. Also, our assessment is much simpler than that of Hanley, Bell and Alvarez-Farizo (2003) because we estimate consumer surplus based on both actual and expected behaviour, then subtract the two to get the change in surplus from the closure. Our assessment is timely given an upgrade is planned by the Victorian Government for 2012 (Melbourne Water 2009) and given the problems for beach users associated with outfalls are not unique to Australia. Much attention has been given to the Gunnamatta outfall, as many have been critical about the negative effects it imposes (Iacovino and Iacovino 2007; www.cleanocean.org, www.greenleft.org.au, mps.vic.greens.org.au/node/1077).

For those readers not familiar with the non-market valuation literature, a number of definitions follow. Because there is typically no market for environmental goods and services, valuing them in monetary terms is a difficult task (Haab and McConnell 2002). Methods which attempt to value goods and service not traded in markets are called non-market valuation methods. There are many non-market valuation methods but the three of primary concern in this study are the travel cost, contingent valuation and contingent behaviour methods. In this study, the environmental service is water quality at Gunnamatta beach.

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The travel cost method estimates the “price” of a recreational site by establishing a relationship between the time and travel costs and the number of visits that users make to the site. The resulting trip generating function is then used to derive a demand curve for outdoor recreation. The area under the demand curve and above the price line is a measure of the surplus that users or consumers obtain through their passive use of the site.

The contingent valuation method uses a survey to directly asking people their willingness to pay for a particular environmental service, contingent on a certain scenario. This can also involve asking the amount people would be willing to be compensated given degradation or destruction of an environment. A close relative to this method is the contingent behaviour method.

The contingent behaviour method considers people’s stated changes in behaviour (quantity of a good consumed, for example number of trips taken to a beach) given changes in attributes of the good: either price or costs (Cameron 1992); or environmental quality (Eiswerth et al. 2000 as referred to by Hanley, Bell Alvarez-Farizo 2003). This method and the travel cost method are used in our study.

Most non-market valuation methods measure the economic benefits to passive users of the environment as consumer surplus. Consumer surplus is “the area under an income constant demand curve” (Haab and Connell 2002, pg.12) as depicted by the triangle, area X, in Figure 1. Consumer surplus is the amount that consumers benefit from paying prices lower than the maximum amounts they would be willing to pay. Changes in consumer surplus in this study are used to measure the benefits that recreational beach users will gain from improved environmental quality through closure of a waste water outfall at Gunnamatta beach, Victoria, Australia. Figure 1: Consumer surplus

This study is significant for three reasons. First, intact urban beach ecosystems are becoming scarce with the ecological integrities of many being degraded by:

• regional and global impacts such as marine pollution and sea level and temperature rises, manifestations of continued economic growth; and

• increased passive use and associated urbanisation and coastal hardening including coastal infrastructure that disposes of waste water at sea.

With rising scarcity of healthy urban beaches the value of beach recreation close to urban centres is likely to be substantial. For example, Blackwell (2007) found that urban beaches in Australia were valued at between approximately $200 million - $600 million annually, higher than values for terrestrial recreation at national parks.

Secondly, this study is important in predicting values for beach recreation prior to environmental improvement. Stated preference methods such as the travel cost method are useful in determining recreational behaviour, whereas revealed preference methods such as contingent valuation are useful in predicting future behavioural responses. To combine these 2 methods means that we can predict recreational behaviour of beach users in advance of a resource change, and therefore estimate an economic value for future improvements in environmental quality at popular beaches. This adds to the literature as an application of the contingent travel cost method to beach recreation.

Lastly, this study is the first example in the literature of an economic study which attempts to value the benefits to beach users from the closure of an outfall.

Demand

0 Quantity of beach visits (person visits)

Value of beach visit ($/visit)

X

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2 Material and Methods 2.1 General Approach The general approach for this study, contrary to most combined revealed and state preference studies (Whitehead et al. 2008), assumes that stated and revealed visits reflect two different states of demand for recreation at beaches. With environmental improvement, demand for beach recreation will change and does not necessarily reflect the same underlying preferences. This difference in two states of demand and resulting consumer surplus is depicted in Figure 2. With the closure of the outfall the quantity of beach visits demanded by users is expected to shift to the right given the improved environmental quality of the recreational experience. The demand shift results in the consumer surplus of users increasing in size by the area of trapezoid Y from the area of triangle X in Figure 2. Figure 2: Demand for beach visits

The area Y is estimated by this study. The shift in demand may not be parallel and may involve a change in slope as well. For simplicity, a possible shift in the slope of demand is not illustrated in Figure 2 - the main consequence of outfall closure would still remain: an increase in consumer surplus.

Once the additional consumer surplus per visit is calculated, it can be multiplied by the predicted increase in visits to Gunnamatta beach to obtain the total annual recreational benefit from closing the outfall. There will be some additional visitation by users not currently visiting beaches but Shaw’s (1988) correction should account for this.

2.2 Survey Administration and Implementation

Gunnamatta beach is located on the Mornington Peninsula of Victoria as depicted in Maps 1A and B. An on-site beach survey was undertaken of beach users of the Southern Mornington Peninsula at Gunnamatta and Sorrento beaches on Bass Straight, and Dromana, Rye, and Portsea beaches on Port Phillip Bay as depicted in Map 1B.

0

Demand with outfall closed

Seasonal marginal cost or price of entry to beach

Demand with outfall

Quantity of beach visits (person visits)

Value of beach visit ($/visit)

X

Y

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Map 1A: Ocean outfall locations, central Victoria

Source: www.cleanocean.org.au accessed 14/08/09.

Map 1B: Survey sites, Mornington Peninsula, Victoria

Source: www.visitmorningtonpeninsula.org, accessed 09/10/09

Gunnamatta beach lies immediately to the south east of the Boags Rock outfall, as depicted in Figure 2A, and emits the largest volume of waste water of any of Victoria’s ocean outfalls at 405ML per day (Iacovino 2009), the plumes of which can be seen in the figure. The outfall is very close to a number of marine recreation sites as can be seen in Map 2. Figure 2A: Ocean outfall, Boags Rocks and Gunnamatta surf beach located to south east

Source: www.cleanocean.org.au, accessed 9 October 2009

Figure 2B: Surfing wave at Boags Rock outfall

Source: www.cleanocean.org.au, accessed 9 October 2009

Melbourne

Boags Rocks outfall

Gunnamatta surf beach

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Map 2: Location of marine recreation activities in nearby vicinity to outfall

Source: Iacovino 2008

The survey was undertaken using selective random sampling with one person interviewed from every third group of people. The survey was conducted on the foreshore or in nearby facilities of the various beach sites. There were 112 respondents in the survey consisting of a diverse demographic. There were 45 questions in the questionnaire taking about 10 minutes to complete per respondent. 2.3 Data Collation, Analysis and Types Collected data were entered into a Microsoft excel database. Following collation, the data were loaded into Limdep (NLogit 3.0.6). Regression analyses were undertaken in Limdep in order to test how reliable and accurate the contingent travel cost method was with stated future visits as the dependent variable.

Because this study uses on-site sampling, ordinary least squares regression analysis suffers from 3 common problems (Shaw 1998):

• Truncation is likely to occur, which is where data for non-beach-users and non-locals is not included in

the study, which can lead to biased results. • People who visit the beaches regularly are more likely to be interviewed than those who don’t visit

often (endogenous stratification). • The presence of non-negative integers can also create bias.

Thus, this study uses maximum likelihood and limited dependent variable regressions to correct for these

biases as directed by Shaw (1988). The two different types of data used in this study were continuous (travel cost of beach users) and count (number of visits people make to the study sites). Because the dependent variable is a count data variable, count data regression models were used in the analysis. 2.4 A Priori Expectations

From the questionnaire thirteen variables were selected a priori (before regression analysis) to help explain beach visits. Table 1 shows the a priori expectations of the variables used to explain visits.

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Table 1: A priori expectations for variables used to explain individual current and new visits. Variable A Priori

Sign Description

Aware + Whether the respondent was aware of the Gunnamatta outfall or not (1,0). Solepurp + Whether the beach was the sole purpose of the respondent’s trip or not (1,0). TC - The amount in 2007 Australian dollars that respondents spent during their return trip

to the beach = per person ATO allowable running cost of a standard size car + 40% of the value of the respondent’s time spent in travel.

Surfer + Whether the respondent was a surfer or not (1,0). Age - The age (in decades) of the respondent. Age2 - The square root of the age of the respondent. Fem - Whether the respondent was female or not (1,0). Income + The total before tax household income (in tens of thousands of Australian dollars) of

the respondent. Educ + Number of years respondent has spent in formal education. Fullemp - Whether the respondent was employed full-time or not (1,0) (self employed=0). Visitor - Whether the respondent was a visitor (1) or resident (0) of Mornington Peninsula. Subvisit + or - Number of annual visits taken by respondent to their next preferred site (complement

or substitute site respectively) without closure of outfall. Newsubv + or - Number of annual visits taken by respondent to their next preferred site (complement

or substitute site respectively) with closure of outfall.

Some variables require further explanation. The travel cost variable (TC) is the variable of primary concern in this study because it is used to calculate changes in consumer surplus. A priori, travel costs should have an inverse relationship with visits and hence the requirement for a negative sign. As costs of any given trip to the beach increase fewer trips are likely to be taken by a person with a given level of disposable income.

Current visits (Curvisit) and the new level of visits (Newvisit) were used as the dependant variables. Newvisit was calculated by adding to current visits people’s stated change in visits contingent on closure of the outfall. Similarly, new alternative (complement or substitute) beach visits (Newsubv) were calculated by adding the respondent’s stated change in visits from closure of the outfall to their current level of visits (Subvisit) at the alternative site. Subvisit is used to account for substitute of complimentary site visits in explaining the current level of visits at the respondent’s currently visited site (Curvisit). Similarly, Newsubv is used in place of Subvist in the Newvisit model to account for the role of visits to alternative beach sites.

We expect people who are aware of the Gunnamatta outfall (Aware) to be more aware of beach issues and thus taking a higher number of visits than people who are not aware. Solepurp is used to trim the costs of travel to those primarily associated with visiting the site, as opposed to other less related activities. People whose sole purposes are to visit a given beach site are expected to take more visits to that site than those whose purpose for their travel trip is otherwise.

Surfers (Surfer) are expected to take more visits to beaches than the typical member of the population as are males given their generalised tendency to be willing to take more risks than compared to Females. Age is expected to have a curvi-linear relationship with visits given the human lifecycle and increased periods of spare time throughout this lifecycle, that is, when we are young and when we are elderly. These periods of spare time may mean we have a greater opportunity for visiting beaches. Generally as we get older (before retirement) we may have less spare time, particularly when we are raising a family and employed full time (Fullemp). However, when we are full time employed, we may also have greater financial capacity to spend our holidays near a beach. In this case Income provides a greater opportunity to undertake recreation, including at beaches. Household income is used here as a proxy for the wealth of the individual. For example, a youth from a wealthy family may have little personal income but have their University costs paid for by their parents, including an allowance for travel costs and incidentals associated with beach visits. People with higher levels of education may also partake of outdoor recreation appreciating the health benefits from doing so, relative to people with little education, income and thus restricted outdoor recreation opportunities.

A visitor to the nearby beach area is likely to take fewer annual visits than local beach goers. Some ambiguity arose when respondents were asked if they were a Visitor or not, i.e. a resident living locally. Respondents often had different perceptions of the term ‘resident’. For example there were two particular respondents who answered the question differently, who both said that their post code was 3939. There are also a number of locations with this post code, such as Boneo, Rosebud and Cape Schanck. In order to eliminate this confusion all respondents whose post codes were of towns on the Mornington Peninsula (including Frankston) were considered residents. Matching town locations were accessed using the Australia Post postcode search (www1.auspost.com.au/postcodes/ Accessed 30 September 2009).

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3 Results

3.1 Regression Analyses A number of regression analyses were undertaken. Poisson, negative binomial, and least squares regression techniques were used. There were 10 analyses undertaken in total as presented in Table 2 (Curvisit) and Table 3 (Newvisit).

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Table 2: Regression model results, dependent variable = Curvisit, n = 125 OLS Without truncation Truncation (Y=0) Poisson Negative Binomial Poisson Negative Binomial Independent Variables

Coefficient (std errors)

Coefficient (std errors)

Coefficient (std errors)

Coefficient (std errors)

Coefficient (std errors)

Constant 72.6*** (40.1)

3.93* (0.159)

2.90* (0.938)

3.92* (0.160)

2.47 (1.55)

Aware 9.77 (9.16)

0.601* (0.054)

0.723* (0.239)

0.604* (0.054)

0.896** (0.365)

Solepurp -6.08 (9.56)

-0.353* (0.050)

-0.322 (0.247)

-0.353* (0.050)

-0.545 (0.379)

TC -0.00154 (0.00138)

-0.000280* (0.00002)

-0.000165* (0.00005)

-0.000313* (0.00003)

-0.000315* (0.000106)

Surfer 41.0* (15.5)

0.472* (0.050)

0.497 (0.395)

0.476* (0.050)

0.479 (1.069)

Age -6.54 (16.0)

-0.005 (0.064)

0.366 (0.366)

-0.008 (0.065)

0.628 (0.733)

Age2 1.00 (1.82)

0.01 (0.007)

-0.029 (0.042)

0.011 (0.008)

-0.059 (0.084)

Fem -16.4*** (8.65)

-0.724* (0.046)

-0.433** (0.234)

-0.727* (0.046)

-0.544 (0.392)

Income 0.507 (0.983)

0.0207* (0.0039)

0.0255 (0.0253)

0.0217* (0.0039)

0.0289 (0.0476)

Educ -0.357 (1.55)

-0.008 (0.006)

-0.015 (0.042)

-0.008 (0.006)

-0.014 (0.071)

Fullemp -4.01 (9.12)

0.007 (0.042)

-0.020 (0.239)

0.018 (0.042)

0.059 (0.427)

Visitor -41.3* (12.1)

-1.28* (0.05)

-1.31* (0.32)

-1.27* (0.05)

-1.53* (0.65)

Subvisit 0.0217 (0.127)

-0.0003 (0.0004)

0.0010 (0.0035)

-0.0003 (0.0004)

-0.00001 (0.00669)

Alpha (Dispersion parameter)

- 1.08* (0.13)

- 2.08* (0.50)

R2 0.450 - Adj. R2 0.391 - F 7.64* - Log likelihood (Lg l) -642 -1870 -486 -1862 -463 Restricted Lg l -679 -4275 -1870 -4275 -1862 Chi squared 4810* 2768* 4826* 2798* Notes: * = significant at 1% level; ** = significant at 5% level; *** = significant at 10% level.

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Table 3: Regression model results, dependent variable = Newvisit, n = 125 OLS Without truncation Truncation (Y=0) Poisson Negative Binomial Poisson Negative Binomial Independent Variables

Coefficient (std errors)

Coefficient (std errors)

Coefficient (std errors)

Coefficient (std errors)

Coefficient (std errors)

Constant 58.1 (40.6)

3.60* (0.16)

2.73* (0.928)

3.59* (0.16)

2.31 (1.55)

Aware 10.4 (9.28)

0.647* (0.054)

0.772* (0.233)

0.650* (0.054)

0.950** (0.347)

Solepurp -6.39 (9.69)

-0.364* (0.049)

-0.316 (0.241)

-0.364* (0.049)

-0.487 (0.344)

TC -0.00152 (0.00140)

-0.000270* (0.000023)

-0.000158* (0.000048)

-0.000298* (0.000025)

-0.000285* (0.000092)

Surfer 53.5* (15.7)

0.589* (0.048)

0.5767 (0.3854)

0.591* (0.048)

0.550 (1.009)

Age -0.91 (16.3)

0.115 (0.062)

0.415 (0.366)

0.113*** (0.063)

0.649 (0.763)

Age2 0.41 (1.85)

-0.002 (0.007)

-0.036 (0.042)

-0.002 (0.007)

-0.063 (0.088)

Fem -17.8** (8.77)

-0.771* (0.045)

-0.469** (0.229)

-0.774* (0.045)

-0.568 (0.382)

Income 0.189 (0.996)

0.014* (0.004)

0.021 (0.025)

0.014* (0.004)

0.0214 (0.0434)

Educ 0.106 (1.57)

0.0022 (0.0057)

-0.0071 (0.0409)

0.0025 (0.0058)

-0.0046 (0.0657)

Fullemp -6.40 (9.24)

-0.061 (0.041)

-0.048 (0.234)

-0.052 (0.041)

0.032 (0.409)

Visitor -41.0* (12.3)

-1.24* (0.05)

-1.27* (0.31)

-1.24* (0.05)

-1.45** (0.60)

Newsubv 0.010 (0.127)

-0.0005 (0.0004)

0.0012 (0.0034)

-0.0005 (0.0004)

-0.0005 (0.0061)

Alpha (Dispersion parameter)

- 1.08* (0.13)

1.03* (0.13)

R2 0.487 Adj. R2 0.432 F 8.87* Log likelihood (Lg l) -643 -1859 -491 -1852 -470 Restricted Lg l -685 -4509 -1859 -4509 -1851 Chi squared - 5300* 2737* 5315* 2763* Notes: * = significant at 1% level; ** = significant at 5% level; *** = significant at 10% level.

The least squares regressions were included for comparison. Least squares has a tendency to create biased and inconsistent results for β (which is the travel cost coefficient in this study) as referred to previously by Shaw (1998). Truncated Poisson and truncated negative binomial regression techniques are favoured because they eliminate this bias (Shaw 1998). The truncated negative binomial model is to be favoured where over-dispersion is present (Dobbs 1993; Englin and Shonkwiler1995; and Offenbach and Goodwin1994). Over-dispersion is when a data set possesses greater variability than what was expected. 3.2 Consumer Surplus The following process was undertaken for all regressions, except least squares because of its inherent bias. Following Blackwell (2007) the consumer surplus values per person per visit can be calculated by using the following equation, where CS/q is the consumer surplus per person per visit and β is the travel cost coefficient.

1CSq β=

Next, the change in consumer surplus was calculated which related to the improvement of environmental quality from closing the Gunnamatta outfall. The measures of CS/q for current visits were subtracted from the measures of CS/q for new visits to find this measurement.

Finally, the differences that were generated from the previous step were multiplied by the most recent estimate of visits to Gunnamatta beach to assess the lost benefits for Gunnamatta beach alone, not all beaches on the southern Peninsula. One research area lacking funding and attention is that of estimating beach user numbers across Australia as discussed briefly by Blackwell (2007), because aggregated estimates rely heavily for their magnitude on the number of annual visits to a beach site. The annual visits for Gunnamatta were assumed to be 350,000 as reported by Wilmoth (2006).

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As can be seen from the calculations in Table 4 the total annual gain in consumer surplus (C) from closing the outfall varies depending on the preferred model used but appears to be a similar order of magnitude being tens to hundreds of millions of dollars in 2007, when the study was done. These values are likely to be 11% larger today in 2010. As explained previously, in cases of overdispersion, the truncated negative binomial model is typically preferred and had the highest log likelihood of all the models at -470 and -463 for current and new visits respectively. Table 4: Consumer surplus (2007 AUD) calculations Without Truncation Truncated Measure Regression model Calculation steps Poison Negative Binomial Poison Negative Binomial

New visits Coefficient of TC 0.00027 0.000158 0.000298 0.000285 - 1/coefficient 3.70E+03 6.33E+03 3.36E+03 3.51E+03 - In normal terms (A) 3704 6329 3356 3509 $/person/visit

Current visits Coefficient of TC 0.00028 0.000165 0.000313 0.000315 - 1/coefficient 3.57E+03 6.06E+03 3.19E+03 3.17E+03 - In normal terms (B) 3571 6061 3195 3175 $/person/visit

Difference C = A-B 132 269 161 334 $/person/visit Gunnamatta visits (D) 350,000 350,000 350,000 350,000 Site visits/yr Annual benefit (C*D) 46,296,296 93,977,752 56,285,782 116,959,064 $ Total

4 Discussion 4.1 Regression analysis The regression analyses are presented in Tables 2 and 3. All regressions models are overall statistically significant at the one percent level (F statistic in the linear model and Chi squared test in the negative binomial and Poisson models). The travel cost variable is the most important variable in this study because it is used to calculate consumer surplus. In all regressions, except the linear model (OLS), the travel cost variables are significant at a one percent level and all models have the correct sign. The sign confirms our expectations about the negative relationship between travel costs and visits and theoretical foundations for calculating consumer surplus. The insignificance of the TC coefficient confirms the likely bias from using least squares regression identified by Shaw (1988). The signs of various other co-efficients generally confirm our a priori expectations. In addition to travel costs, generally across the models, except the linear, awareness of the outfall and if a respondent was a visitor to the area were highly significant. Solepurp and income were significant only in the Poisson Models. Surfer appeared significant only in the OLS and Poisson models with or without truncation. Being a female also significantly explained visits in all models except the truncated negative binomial. Remaining variables: age, education, fulltime employed, and substitute or complementary site visits, were insignificant. The truncated Poisson and negative binomial models were thought to have been the most effective regression models, with the latter being more appropriate because over-dispersion was evident within the results (alpha dispersion parameters were found significant at the one percent level) and this model provides the highest log likelihoods (-463 for current visits and -470 for new visits). Using the truncated negative binomial model for new visits (Table 2 and 3), the following was found:

• People’s awareness of the outfall was significantly related to the amount of future visits they will make to a given beach site. The sign of the coefficient corresponds with the a priori expectations of the study.

• The cost to travel to a beach site is significant in determining the number of trips people will make in the future. Visits will increase by about 0.3 for each additional thousand dollars spent by an individual on travel costs. This is consistent with the a priori expectations.

• People’s resident or visitor status to the southern Mornington Peninsula is a significant factor in determining their future behaviour towards beach use. Visitors to the Peninsula will fewer visits than residents. This is consistent with a priori expectations.

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4.2 Consumer Surplus The measures of CS/q vary by which regression technique as shown in Table 4. Despite these differences, annual benefits do follow a similar trend of being tens to hundreds of millions of 2007 dollars per year with the truncated negative binomial model estimating about $56 million (2007 AUD) per year and the truncated Poisson estimating $120 million (2007 AUD) per year for visitors to Gunnamatta beach alone, not from all visitors to the southern Mornington Peninsula beaches. An estimate of visits to the Peninsula is required to make this further extension of the study and this currently forms part of the authors’ research program 5 Conclusion In conclusion, this study has discovered that closing the Gunnamatta outfall will provide significant benefits to the users of beaches on the Mornington Peninsula, not just those who recreate at Gunnamatta beach. Perhaps the most insightful and useful information, is the significant level of consumer surplus that is predicted to be of benefit, if the quality of the environment is improved by the closure of the outfall. This finding raises interest in current research to provide better estimates for visitors to beach sites in a regional sense to build on estimates contained in this paper. This study has also shown the usefulness of the hybrid method, the contingent travel cost method, for ascertaining gains from improvements in water quality at beach locations. A similar study to this one is needed for the impacts that dredging in Port Phillip Bay have had for users of the popular southern Peninsula beaches. Much attention has been given to the Gunnamatta outfall, with criticism about the negative effects it imposes. This study for the first time in Australia, provides robust estimates, based on statistically significant relationships, of the likely gains to beach and marine recreation from closing the outfall. Now, with studies such as this one, we may be able to provide sound arguments from an economic perspective for taking greater account of the broader impacts of human use of water, waste water creation, disposal at sea and opportunities for reuse. Acknowledgements The authors are grateful to the University of Tasmania for an Institutional Grant Scheme which helped in completing this research. The authors also wish to acknowledge the help Mr Jason Strugarek, Mr Carlo Iacavino, Mr Julian Reid and a number of visiting students to the National Centre in undertaking the work presented in this paper. Errors and representations lie with the authors.

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