Recreational beach use values with multiple activities

Ecological Economics 160 (2019) 137–144

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Analysis

Recreational beach use values with multiple activities Sean Pascoe

T

CSIRO Oceans and Atmosphere, 306 Carmody Road, St Lucia 4067, Australia

ARTICLE INFO

ABSTRACT

Keywords: Beach use Travel cost model Consumer surplus Multi-purpose activity

Beaches provide multiple recreational opportunities, such as swimming, surfing, fishing, walking or just enjoying being by the seaside. Most previous studies that have valued beach use have assumed a trip to the beach was a homogeneous activity. In this study, we estimate the non-market value of beach usage by New South Wales, Australia, residents using a travel cost modelling approach. Unlike previous studies, we develop the model based on the activities that visitors mostly undertake when visiting the beach, and allow for multiple activities to occur. We find that different uses of the beach attract different levels of consumer surplus. Activities such as surfing, fishing and swimming generate higher levels of consumer surplus than more passive activities such as just enjoying the natural environment. We also find that Sydney residents have different values to non-Sydney residents. From our analysis, a trip to the beach provides a base level of consumer surplus of around $10/trip for Sydney residents, with additional benefits derived from undertaking different activities. For example, surfing followed by a walk along the beach adds an additional $17 to the value of the visit. Understanding the pattern of use is therefore important when estimating the use values of beaches.

1. Introduction Beaches, as do most natural environments, offer multiple recreational opportunities (Maguire et al., 2011), and some beaches may be better suited to some uses than others. For example, one beach may be more appropriate for surfing, while another may be better for swimming. In most cases, beaches will have a mix of individuals undertaking a range of activities (i.e. some surfing, some swimming, some jogging etc.), and the value of these different activities will vary to the different participants. Most coastal recreational studies have either focused on a single activity (e.g. surfing (Lazarow and Nelsen, 2007) or recreational fishing (e.g. Pascoe et al., 2014a; Prayaga et al., 2010)), or assumed that beach recreation was itself a homogeneous activity with a single value (e.g. Zhang et al., 2015). Beach users also may take advantage of these multiple opportunities by gaining multiple benefits from the beach visit. For example, some beach users may visit the beach for the primary purposes of swimming, but while there also partake in beach walking and enjoying the scenery (Maguire et al., 2011; Pendleton et al., 2006). The recreational benefits associated with these activities are likely to be cumulative, with the combination determining the value derived. Further, a priori, it might be expected that an activity undertaken as a secondary activity may produce less value than if it was the primary activity. For example, it may be the case that undertaking recreational fishing at the end of a trip undertaken with a primary purpose of swimming may contribute fewer

additional benefits than if recreational fishing was the primary focus of the trip. Understanding how these values combine provides useful information to coastal and marine managers, as changes to beach and coastal management may change the types of activities that can occur there. For example, designation of a marine reserve adjacent to a beach may remove recreational fishing opportunities but not affect swimming or other uses. The welfare loss of this to the individuals affected will depend on the extent to which recreational fishing was a primary or secondary activity. In this paper, a travel cost model is developed to estimate the value of beach usage by coastal residents of New South Wales (NSW), Australia. Two variants of the model are developed: one estimating a generic value of beach use; and the other estimating the contribution of the different activities to these values. The model is developed using data collected from an online survey of NSW coastal residents. In the next section, an overview of the travel cost methodology is presented. This is followed by a description of the data used in the analysis. The estimated models are then presented, followed by a discussion of the key implications of their use. 2. Travel cost methodology The use of travel cost models for estimating the non-market value of environmental amenities is well established (Mendelsohn and Olmstead,

E-mail address: [email protected]. https://doi.org/10.1016/j.ecolecon.2019.02.018 Received 22 March 2018; Received in revised form 21 December 2018; Accepted 18 February 2019 0921-8009/ Crown Copyright © 2019 Published by Elsevier B.V. All rights reserved.

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2009). It has been applied to a wide range of areas, including tourism values of lakes and wetlands (Fleming and Cook, 2008; Gürlük and Rehber, 2008), coral reefs (Ahmed et al., 2007; Andersson, 2007), biodiversity and national parks (Chae et al., 2012; Heberling and Templeton, 2009; Larsen et al., 2008), recreational fishing (Alberini et al., 2007; Prayaga et al., 2010; Rolfe and Prayaga, 2007; Shrestha et al., 2002), and – most relevant to this study – beach visitation (Bin et al., 2005; Blackwell, 2007; Pendleton et al., 2006; Rolfe and Gregg, 2012; Windle et al., 2017; Yeh et al., 2006; Zhang et al., 2015). Underpinning the travel cost method is the estimation of the recreational demand function, from which consumer surplus estimates can be derived. Consumer surplus – the measure of non-market benefits to the beach user – is the difference between what the visitor would be (theoretically) willing to pay to go to the beach and what they are actually required to pay. The travel cost approach does not ask willingness to pay directly, but imputes it from the observed behaviour of other beach visitors through an estimated demand function, which relates the number of observed trips to the travel cost incurred. The demand function is estimated at an individual level,1 and assumes that individuals with similar characteristics will respond to the “price” of accessing the beach (i.e. the cost of the trip) in similar ways. The visitor's demand for the activity, represented by the number of trips undertaken, is assumed to be a function of the cost of travel (taken as a proxy measure of its price), the socioeconomic characteristics of the visitor (e.g. education level and income) and the characteristics of the location. The generic demand curve for an individual i can be given as

While several alternatives exist, a common approach is to use the negative binomial model.3 This has a variance var(xi|ziβ) = λi(1 + αλi), where α (the dispersion parameter) is a measure of the degree to which the conditional variance exceeds the conditional mean (Haab and McConnell, 2002). If α > 0, then overdispersion exists and the Poisson model should be rejected in favour of the negative binomial. The probability function for the negative binomial is given by:

Pr(x i ) =

where xi is the number of trips to the site undertaken, and zi is a vector of factors influencing demand, such as travel cost, individual characteristics such as income, site characteristics (if available), and ε is a random error term assumed to be independent and identically distributed (iid). The dependent variable – the number of trips – has a non-negative integer distribution and therefore ordinary least squares (OLS) regression methods are inappropriate for estimating the model (Haab and McConnell, 2002). Instead, count models are generally considered more appropriate (Creel and Loomis, 1990),2 which in principle estimate the probability of a visitor choosing to visit the beach. If the probability of visiting a beach on any particular day is small, constant and independent of previous decisions, and if the season length is relatively large, then the distribution of trips will approximate a Poisson distribution (Hellerstein, 1991), and the probability of a visit can be given by

Pr(x i = n) =

e

i

n!

n i

, n = 0, 1, 2, …

1

1 1

+

iv

xi

iv 1

+

iv

(3)

where Γ(⋅) is the gamma probability density function evaluated at (·), and v > 0 is an independently and identically distributed random variable with density g(v|α) (Martínez-Espiñeira and Amoako-Tuffour, 2008). This collapses to the standard Poisson distribution when α = 0. In most travel cost studies, data are collected on-site from individuals who have definitely undertaken at least one trip (as they were there). This introduces two potential biases; truncation, as the data are truncated at 1 (as no zeros are observed); and endogenous stratification, where individuals who visit the site frequently are more likely to be sampled than people who go to the site only occasionally (Shaw, 1988). Methods for correcting the bias this introduces are well established and have been validated empirically (Martínez-Espiñeira et al., 2008). For data collected from the general public, where a proportion of the sample may not have undertaken any beach visits, a different potential problem exists, the issue of zero-inflation. Two approaches are available to address zero-inflation, each with different underlying assumptions. The first approach assumes that the excess presence of zeros is a sampling issue, and estimates the probability of a true or false zero as well as the negative binomial model (the zero-inflated mixture model). The second approach (the hurdle count model) is a two stage model that first estimates the probability that a trip would be undertaken using a binary choice model (i.e. P(xi > 0) = F(zi), where zi is, again, the vector of explanatory variables), then estimates the truncated negative binomial for those trips that are undertaken, again using Eq. (3) (Rose et al., 2006; Zuur et al., 2009). The hurdle model has been used in several previous studies to estimate a travel cost model in the presence of excess zeros (e.g. Anderson, 2010; Bilgic and Florkowski, 2007; Carpio et al., 2008), and has also been found to perform better than other approaches when the data are characterised by the simultaneous presence of both excess zeros and overdispersion (Gurmu and Trivedi, 1996). The consumer surplus associated with undertaking the recreational activity is the area under the demand curve, which can be shown to reduce to −1/β1, where β1 is the estimated parameter associated with travel cost variable. Further, the marginal value of an attribute in the model (e.g. the value of an additional trip characteristic) can be estimated by −βi/β1 where βi is the coefficient relating to attribute i (i > 1) (Haab and McConnell, 2002). The standard errors associated with each consumer surplus estimate can be approximated using the Delta method (Casella and Berger, 2002). Implicit in the derivation of the above travel cost model is the assumption that individuals are travelling to a single destination to undertake a single activity. In many instances, individuals may undertake a trip for multiple purposes, with the activity of interest being just one of several activities undertaken. Several different approaches have been applied to separate out the different components of a multi-purpose trip, including allocating costs based on the proportion of total trip time spent at the destination (or activity) of interest (e.g. Farr et al., 2011) or a weighting based on the individual's perception of the importance of

(1)

x i = f (z i ) +

(x i + 1) (x i + 1) ( 1)

(2)

where n is the observed number of trips and the parameter λ is both the mean and variance of the distribution, commonly specified as an exponential function (Haab and McConnell, 2002) and given by λi = exp (ziβ) = E(xi|ziβ) where β is a vector of the unknown parameters associated with each explanatory variable z. A common problem experienced with travel cost models in practice, however, is that the data are not equidispersed, such that the observed variance and mean may differ. Where the variance exceeds the mean, as is often the case in recreational travel cost models, the data is said to be subject to overdispersion and an alternative distributional assumption may be required. Failure to account for overdispersion in models using count data can have serious consequences for estimation and inference (Grogger and Carson, 1991). 1 While zonal models have also been used to estimate consumer surplus associated with recreational travel (e.g. Fleming and Cook, 2008), the focus of this study is the individual travel cost model. 2 While count models are the most commonly employed, more recently travel cost analyses have also being undertaken using a random utility modelling framework, where choice of recreational location is also taken into consideration (e.g. Raguragavan et al., 2013; Yeh et al., 2006).

3

The negative binomial distribution derives from the Poisson distribution through the introduction of a parameter α that may vary randomly allowing for inter-person heterogeneity. If this random variable is assumed to have a gamma distribution, then integrating over this variable leads to the negative binomial model. For further details, see Cameron and Trivedi (1986). 138

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the activity/destination to their overall trip (e.g. Martínez-Espiñeira and Amoako-Tuffour, 2009; Pascoe et al., 2014b). Others include a dummy variable to differentiate multi-purpose and single purpose trips, allowing a different estimate of consumer surplus for each trip type (Loomis, 2006; Parsons and Wilson, 1997). A variation to the multi-purpose trip issue is that, in some cases, a destination may offer multiple recreational opportunities, the availability of which may affect the demand of individuals differently. Different individuals may undertake one or several of these. While weighting approaches and separate models could potentially be applied to each activity, several studies have treated these multiple activities in travel cost models through incorporating each activity as a dummy variable in the model, with respondents indicating which activities they participated in, and a single model estimated (e.g. Englin and Moeltner, 2004; Prayaga, 2017; Shrestha et al., 2007; Siderelis and Moore, 1995). In this study, we have adapted an approach similar to that of Parsons and Wilson (1997) and Shrestha et al. (2007), where we consider the multiple activities undertaken by visitors to the beach as joint consumption, and identify these through a series of dummy variables representing each activity. Unlike these previous studies, however, we also consider the relative importance of these activities to the decision to visit the beach. That is, we treat an activity undertaken as a primary activity separately to the same activity that is of secondary importance to the trip.

made by some individuals living within 25 km, while 50% of this group made up to 60 trips a year (Fig. 1). Most visits to the beach were for the purposes of swimming, with the second most common reason being to enjoy the natural environment (i.e. just being outdoors by the sea). The relationship between the primary and secondary reasons for the beach visit are shown in Table 1. The secondary activities were not evenly distributed, suggesting some degree of complementarity (e.g. swimming and sunbathing, walking and enjoying nature). 3.2. Estimation of travel costs and the value of travel time The survey did not explicitly collect travel cost data for several reasons. First, as the trips occurred over the year, there was the potential that recall bias would result in the costs of the most recent trip dominating the response. Travel costs (or perceptions of travel costs) of individuals can vary for a range of reasons not related to the benefits from the beach visit itself. For example, local expenditure (e.g. food, drinks) could vary substantially – some individuals may bring a picnic lunch, others may buy a takeaway meal while others may dine in a café or restaurant. Some may not eat at all while at the beach. While these may impact on the enjoyment of the day – and impact on the contribution of coastal tourism to the local economy – the beach experience is the same regardless of what was eaten (or where). Similarly, visitors on short breaks may stay in local accommodation, which may also vary substantially in cost. Such visitors gain utility from this in addition to the beach visitation per se, so attributing this to the visit may be misleading. For these reasons, estimates of the travel costs have been derived based on distance in several studies (e.g. Chae et al., 2012; Mkwara et al., 2015; Pascoe et al., 2014a; Windle et al., 2017). Survey respondents who visited a beach in the last 12 months were asked to estimate the distance to the beach they most frequently visited, as well as the total number of trips taken over the year (to all beaches). For visitors who did not visit a beach in the last 12 months (i.e. zero trips), information on home post codes was used to derive estimates of the distance to the nearest beach.5 Two methods for estimating travel cost for the round trip between home and the beach were considered. For the first approach, the average fuel consumption per kilometre of vehicles in NSW in 2016 was derived from ABS (2017). An average petrol price for 2016 in NSW ($1.17/l) was applied to this (Australian Institute of Petroleum, 2017). The second approach involved the use of the rate per kilometre that is tax deductable for car expenses from the Australian Tax Office6 of $0.66 per km. The first approach represents the marginal cost of travel ignoring the costs of wear and tear on the vehicle, while the second represents the estimated average total cost including a measure of fixed (e.g. insurance, registration, repairs, depreciation etc.) and variable costs (e.g. fuel and oil). Previous studies of recreational fishers suggest that their perceptions of their costs are more related to the fuel costs only rather than the full cost of travel (Rolfe and Prayaga, 2007), while other studies also suggest that depreciation costs have little impact on travel decisions, and are largely considered fixed rather than marginal costs (Hang et al., 2016). Other studies of commuter travel found that fuel price is the major factor influencing perceptions of travel cost (Henley et al., 1981). As a result, the number of beach trips may also be more related to these marginal fuel costs and only these are considered in the analysis. The cost was measured as the cost per trip, not per individual taking the trip. Group sizes varied, with an average of 2.99 people per trip.

3. Data 3.1. On-line survey Data for the analyses were collected through an on-line survey of NSW coastal residents, undertaken over December 2016 and January 2017. An online survey company was subcontracted to implement and run the survey, and provided an incentive program for respondents independent to the subject of the survey (i.e. there was no greater or lesser incentive for individuals who visited the beach, nor for any individual characteristic). The sample population frame was individuals (registered with the survey company) from the greater Sydney metropolitan region and from postcode areas adjacent to, or within 100 km, of the coast. In total, 1404 responses were collected by the survey company. Most respondents lived < 30 km from the coast, with the largest group living between 7 km and 20 km from the coast. The sample had more female than male respondents (55:45), a phenomenon common in online surveys (Smith, 2008). A quota was not imposed on gender, but the potential impact of gender on the response was examined in the econometric modelling. The age distribution was fairly uniformly distributed between the ages of 25 years and 60 years old. A minimum age of 18 years was imposed in the survey (to meet the survey ethics requirements), and the proportion of respondents under 25 years old was generally lower than the other age groups. Household size varied substantially, with around half the sample being single person households. Most households had no children under the age of 18 years. Most respondents had some form of tertiary education; either a trade certification or university degree. Around one half of the sample were employees, 10% self-employed and around 20% were retired. Further details on the data collection process and sample characteristics are presented in the supporting information. Around 80% of the sample visited at least one beach in the last 12 months. As expected, the total number of visits to the beach was generally related to the distance from the beach visited (Fig. 1). Around 97% of the total number of trips over the last 12 months were made by respondents living within 50 km from the beach, while 83% of total trips were made by respondents living < 25 km from the beach. High frequency visits4 were mostly made by people who lived relatively close to the beach. For example, > 120 trips over the last 12 months were

5 A similar approach was undertaken by Heberling and Templeton (2009) and Farr et al. (2011), and to a lesser extent by Rolfe and Prayaga (2007). The travel calculator used to estimate distance travelled can be found at www.racq.com. au/travel/Maps_and_Directions/trip_planner. 6 https://www.ato.gov.au/Individuals/Income-and-deductions/Deductionsyou-can-claim/Vehicle-and-travel-expenses/Car-expenses

4 Further details on the estimation of trip frequency are given in the supporting information.

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Fig. 1. Beach visitation frequency relative to distance from the beach. Table 1 Relationship between first and second reason for beach visitation. Secondary reason Primary reason Swimming Sun bathing Surfing Fishing Walking Enjoying nature Other Total

Swimming

Sun bathing

Surfing

Fishing

Walking

Enjoying nature

Other

– 3.3% 0.9% 1.4% 4.4% 9.6% 0.4% 19.9%

5.9% – 0.0% 0.2% 0.5% 1.7% 0.0% 8.3%

0.9% 0.2% – 0.1% 0.1% 0.8% 0.1% 2.1%

1.0% 0.0% 0.2% – 1.0% 1.3% 0.0% 3.6%

8.6% 0.7% 0.2% 0.4% – 16.5% 0.6% 27.1%

17.9% 1.0% 0.3% 1.0% 13.3% – 1.4% 35.1%

1.1% 0.1% 0.0% 0.1% 0.4% 2.3% – 4.0%

Around 30% the trips involved children, and 60% involved a spouse or partner. Given that the travel cost was estimated based on distance travelled and average fuel consumption rates, this cost is independent of the number travelling. Hence, there is an implicit assumption that the marginal cost associated with additional passengers is zero. Most Sydney beach councils also impose parking charges. In some cases, such as in the Northern Beaches Council area, local residents are exempt from charges within designated carparks within their council area provided they have applied for a parking permit, but may be charged for parking outside these designated areas (e.g. if the car park is full).7 In most other areas, however, charges are applied to local residents as well as visitors. These charges vary from beach to beach, and also from season to season. For example, average charges for a four hour visit range from roughly $30 (Bondi Beach during summer) to $14 (Bronte Beach during summer). In contrast, parking at a small number of beaches (e.g. Cronulla Beach) is free. To account for the effects of parking costs, four different average parking cost were tested: $0, $15, $20 and $25. These were applied to each trip undertaken by Sydney residents, with the exception of individuals living within the Northern Beaches Council area and Cronulla (identified by postcode), who were assumed to visit local beaches and were not subject to parking charges. The most appropriate value was determined using the model and assessing which assumption resulted in the best fit to the data based on the AIC (detailed in the supporting information). A key challenge for travel cost estimation is deriving an appropriate value for the opportunity cost of travel time (Farr et al., 2011). There is no generally agreed method for estimating the value of travel time, although it is often assumed to be proportional to income (Calfee and

Total 35.5% 5.3% 1.6% 3.2% 19.8% 32.1% 2.5% 100.0%

Winston, 1998; De Borger and Fosgerau, 2008; Douglas and Johnson, 2004). Estimates of the proportion of income applied to travel time in travel cost analysis ranges from zero (Alberini et al., 2007; Farr et al., 2011; Hanley et al., 2003; Rolfe and Prayaga, 2007; Shaw and Feather, 1999) to 100% (Ezzy et al., 2012; Whitehead et al., 2000). Most previous recreational fisheries studies use proportions < 50% (Shrestha et al., 2002), with several using a zero value on the basis that there is no utility or disutility from travelling to the site (Alberini et al., 2007; Rolfe and Prayaga, 2007). Several other studies estimated the appropriate proportion endogenously from the data itself (e.g. Bockstael et al., 1987; Lew and Larson, 2008; McConnell and Strand, 1981; McKean et al., 1995), avoiding the need for an ad-hoc assumption which may ultimately bias the estimated consumer surplus. In this study, we applied the approach proposed by McConnell and Strand (1981) to derive an appropriate opportunity cost of travel time. From this, we conclude that the most appropriate opportunity cost of travel time is zero. Full details of the analysis are provided in the supporting information. An analysis of the sensitivity of this to the estimates of consumer surplus is also presented in the supporting information. From these initial analyses, the base model for use in the main analysis was chosen to be the model with both intercept and slope interaction terms, $15 parking charge for Sydney residents, and zero opportunity cost of travel time. Further details of these analyses are presented in the supporting information. 4. Travel cost model estimation Initial model estimation using the Poisson distribution found significant overdispersion (a common problem with travel cost models), suggesting that a negative binomial model was more appropriate. As the proportion of zero trips in the data was high (i.e. 20%), zero

7 Waverly Council also provides the opportunity for local residents to purchase a parking permit for an annual cost of $130.

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Table 2 Zero inflated negative binomial travel cost model, main and secondary activities. Trip only

Trip plus activities Full model

β

SE

Count model coefficients (negative binomial with log link): Intercept 3.302 0.256 Travel cost −0.078 0.009 Sydney dummy 1.334 0.376 Cost*Sydney −0.032 0.018 Primary activity •Swim •Walk •Fish •Surf Secondary activity •Swim •Walk •Fish •Surf Cost ∗ primary activity •Cost ∗ swim •Cost ∗ walk •Cost ∗ fish •Cost ∗ surf Cost ∗ secondary activity •Cost ∗ swim •Cost ∗ walk •Cost ∗ fish •Cost ∗ surf Log(theta) −2.494 0.285 Hurdle model coefficients (binomial with logit link): Intercept 2.379 0.291 Distance −0.009 0.002 Age −0.022 0.004 Income 0.006 0.002 Log likelihood −5189 AIC 10,395.51

Sig *** *** *** .

*** *** *** *** ***

β

Reduced form SE

Sig

3.626 −0.141 1.638 −0.028

0.334 0.017 0.352 0.017

*** *** *** .

−0.473 0.220 0.042 0.983

0.316 0.356 0.597 1.000

−0.207 0.264 0.496 0.643

0.366 0.321 0.680 0.654

0.059 −0.001 0.093 0.035

0.017 0.020 0.032 0.057

0.058 0.023 −0.002 0.078 −1.954 2.379 −0.009 −0.022 0.006 −5139 10,328.2

β

SE

Sig

3.609 −0.133 1.635 −0.029

0.173 0.010 0.342 0.017

*** *** *** .

1.505

0.529

**

***

0.034

0.008

***

**

0.089

0.020

***

0.019 0.017 0.037 0.034 0.180

**

0.044 0.027

0.009 0.008

*** ***

* ***

0.100 −1.997

0.024 0.186

*** ***

0.291 0.002 0.004 0.002

*** *** *** ***

2.379 −0.009 −0.022 0.006 −5143 10,316.9

0.291 0.002 0.004 0.002

*** *** *** ***

Significance codes: ‘***’ 0.001; ‘**’ 0.01; ‘*’ 0.05;‘.’ 0.1;‘ ’ not significant.

inflation approaches were required. In all cases, the hurdle model performed substantially better than the zero-inflated mixture model (based on the AIC), so only the results from this are presented. The hurdle model involves a two stage process, one describing the likelihood of undertaking any recreational trip (the hurdle model (Cameron and Trivedi, 1998)) and a negative binomial count model that estimates the relationship between trip number, costs and other site attributes. All available individual characteristic information was initially included in the models, although many variables were found to be not significant, and their removal improved the model performance (as measured by the AIC). Two models were estimated; one measuring the benefits of the overall trip (i.e. value of a day at the beach) and the other including the reasons that the trips was undertaken – both primary and secondary. Passive activities, such as enjoying nature and sunbathing, were assumed to form part of the base of the model. More active pursuits, such as swimming, surfing, fishing or walking, were included as dummy variables, with a separate dummy variable for each activity as a primary or secondary reason. The final set of variables included in the models and their estimated parameters are given in Table 2. The respondents, who were distributed across the entire NSW coast, also visited a wide range of beaches, and the characteristics of these beaches would also have varied. Sydney beaches mostly include patrolled areas, adjacent parkland and close access to other facilities. Rural beaches may or may not have patrolled areas, and are often adjacent to bushland and more “natural” environments, and generally close to fewer other facilities. The characteristics of the beaches visited were not collected, but a Sydney

dummy variable was also incorporated into the model to capture the potential influence of the different beach types on visits. While there is no a priori reason to suspect that, ceteris paribus, Sydney residents would value a trip to a beach more or less than non-Sydney residents, urban beaches present a different experience to beaches in more rural regions and hence may attract a different value. As might be expected, in all cases, the likelihood of undertaking any trip to the beach decreased with distance from the beach and age of respondent, and increased with income level.8 Theta, a measure of dispersion related to α−1, was significantly different to zero in all cases indicating that the negative binomial specification was appropriate. From Table 2, the estimated value of a generic trip to the beach was approximately $24.87 (se 2.82) for Sydney residents and $12.78 (se 1.41) for non-Sydney residents. The marginal consumer surplus estimates associated with each activity derived from Table 2 are given in Table 3, while the benefits gained from the combined activities is given in Table 4. The base value of just being at the beach (“enjoying nature”) without undertaking any activity was $10.09 for Sydney residents. Undertaking an activity while at the beach provided additional benefits. For example, visiting a beach with a primary purpose of swimming, followed by a walk would result in around $25.29/trip (i.e. $10.09 + $7.79 + $7.41) for a Sydney resident. Fishing and surfing provided the greatest additional benefits as a primary purpose, although fishing was not statistically significant as a secondary purpose (Table 3) and is treated as zero in Table 4. 8 Other characteristic variables, such as group size and inclusion of children were also included in earlier models but found to be non-significant.

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Table 3 Derived marginal consumer surplus estimates, $/trip. Non-Sydney Primary reason Mean Passive use (base) Swim Walk Fish Surf

Secondary reason

Standard error

$7.51 $10.04 – $22.56 $11.31

Sydney

0.54 0.91 – 10.65 4.03

Mean

Standard error

– $11.18 $9.43 – $30.01

– 1.25 0.88 – 21.77

Secondary reason

Non-Sydney •Enjoying nature •Swimming •Surfing •Fishing •Walking Sydney •Enjoying nature •Swimming •Surfing •Fishing •Walking

Enjoy nature

Swimming

Surfing

Fishing

Walking

$7.51 $17.56 $18.82 $30.07 $7.51

$18.69 $17.56 $30.00 $41.25 $18.69

$7.51 $17.56 $18.82 $30.07 $7.51

$7.51 $17.56 $18.82 $30.07 $7.51

$16.94 $26.98 $28.25 $39.50 $7.51

$10.09 $17.88 $19.38 $23.75 $10.09

$17.88 $25.67 $27.17 $31.54 $17.88

$26.17 $33.96 $35.46 $39.83 $26.17

$10.09 $17.88 $19.38 $23.75 $10.09

$17.50 $25.29 $26.79 $31.16 $17.50

Mean $10.09 $7.79 – $13.66 $9.29

Secondary reason

Standard error 1.37 0.89 – 4.43 3.37

Mean – $8.45 $7.41 – $16.08

Standard error – 1.13 0.90 – 7.25

at the beach added to this value, and most respondents undertook more than one activity. From Table 1, around 28% of survey respondents were involved in some combination of swimming/enjoying nature, while 30% participated in some combination of walking/enjoying nature (either as primary or secondary activity), with fairly equal proportions undertaking each activity combination. From Table 5, the recreational fishing values estimated in this study are substantially lower than those estimated in the previous Australian studies, while the value of fishing as a secondary activity was found to be not significantly different to zero. These previous studies primarily targeted recreational fishers who were actively fishing at the time of the study (i.e. site based surveys, often boat ramp based, rather than broader community surveys). Consequently, the likelihood of contacting more serious recreational fishers was higher (the endogenous stratification problem), and it is reasonable to expect that these fishers would place a higher value on the activity. Although corrections can be made to allow for the effects of endogenous stratification, a high proportion of high frequency visitors (higher than the general population) in an on-site survey can still potentially lead to biased estimates of consumer surplus. In contrast, the estimates derived from our survey may be more representative of the “average” recreational fisher in the broader population, who may participate in fishing but not as a primary activity. The participation rate in recreational fishing from our survey – either as a primary or secondary reason for a beach visit – was 6.8%. In contrast, the most recent survey of recreational fishers in NSW found that 11.7% of NSW residents fished for at least one day during 2013–14 (West et al., 2015). Given that 80% of recreational fishers responding to the West et al. (2015) study only undertook between one and five trips a year, a higher proportion in our sample may have also undertaken some recreational fishing but not identified it as a primary or secondary reason for visiting the beach. Applying an estimate of the consumer surplus per trip for fishing as a primary reason for the trip to all people identified as participating in fishing at some point over the year will substantially overestimate the total consumer surplus generated by recreational fishing. From our results, the consumer surplus generated by the recreational fishing activity itself for almost three quarters of recreational fishers may be zero, with the benefits they received attributable only to the enjoyment of the natural environment or the other activities undertaken. Our study also found that surfing as a primary activity has a similar value to swimming, while surfing as a secondary activity has a higher marginal value than as a primary activity. Previous studies of surfing have focused on the “contribution” of the activity to local economies through expenditure (e.g. Lazarow and Nelsen, 2007; Mills and Cummins, 2015) rather than the benefits it provides to the surfers per se. While this study has focused on beach use, the approach is equally applicable in other areas, and a number of studies have included activities undertaken in their travel cost model. For example, Shrestha et al. (2007) included the key recreational activity undertaken in a travel cost model of nature based tourism in Florida, and found these had a significant impact on the number of trips. However, they did not use this information to derive values for the different activities. In the

Table 4 Derived combined consumer surplus estimates for multiple activities, $/trip.

Primary reason

Primary reason

The weighted average of these values, based on the proportion of visitors in each activity category in Table 1 was $16.28 for non-Sydney residents and $22.49 for Sydney residents. A likelihood ratio test of the models with activities specified against the generic beach trip models suggests a significant difference between the two ( 28DF = 50 ), with the lower AIC of the models with the activities specified being lower than the generic trip models suggesting the former is the more appropriate. The difference between the estimated consumer surplus from the generic model and that derived from the weighted average of the different activities may be a result of omitted variables bias in the generic model. 5. Discussion and conclusions Previous studies of recreational beach use in Australia have derived a wide range of estimates of recreational values (Table 5). These values reflect differences in assumptions about target population, as well as different assumptions about costs. For example Windle et al. (2017) and Raybould et al. (2013) included only local residents in the sample population, while Prayaga et al. (2010) and Rolfe and Dyack (2011) included visitors from a much broader population, many requiring overnight stays. Where overnight stays were included in the trip, assumptions were made about the importance of the beach visit to the overall trip, and travel costs were scaled accordingly. In our study, all trips were assumed to be a day trip, as most respondents lived within 30 km of the coast. While some of the more distant visitors may have stayed overnight, this would also have presumably provided benefits in addition to the beach visit. The value of beach usage estimated in this study – roughly $25/trip for Sydney residents and $13/trip for non-Sydney residents based on the weighted average values of the different activities – is largely consistent with the results from these other studies. A key feature of this study, however, is that recreational values are differentiated based on activities undertaken. A base value of trip to the beach – without taking advantage of any of the other activities – was around $10/trip for Sydney residents and around $7.50/trip for non-Sydney residents. Undertaking an activity 142

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Table 5 Willingness to pay estimates from other Australian travel-cost studies of recreational beach use. Region Beach use Queensland North Queensland Gladstone Sunshine Coast Gold Coast NSW Clarence Valley Sydney beaches Victoria Surf coast Western Australia Augusta-Margaret River Recreational fishing Queensland North Queensland Gladstone Moreton Bay South Australia Coorong a b

Year of data collection

Value per trip in year of study

Value in 2018 dollars

$35.09 $23.58 -$35.50 $35.01 $17.51 $89.60 $42.02a $19.47 $40.05 $3.36a

$39.55 $36.42 $26.81 $98.74 $128.58b $21.94 $44.14 $10.28b

2012 2010

$6.10a $20.63

$18.67b $23.79

Raybould et al. (2013) Anning (2012)

2012

$3.27a

$10.01b

Raybould et al. (2013)

2012

$3.28a

$14.47b

Raybould et al. (2013)

2007 2016 2010

$385.34 $143.16 $60.58a

$479.11 $148.94 $185.38b

Prayaga et al. (2010) Windle et al. (2017) Pascoe et al. (2014a)

2006

$953.26

$1208.97

Rolfe and Dyack (2011)

2011 (Not specified) 2016 2000 2012 2015 2011 2012 2012

Reference

Rolfe and Gregg (2012) Prayaga (2017) Windle et al. (2017) Blackwell (2007) Windle and Rolfe (2013) Andersson et al. (2015) Zhang et al. (2015) Windle and Rolfe (2013) Raybould et al. (2013)

Per person rather than per trip. Assuming three people per trip on average, consistent with the average in this study.

Appendix A. Supporting information

context of beach use, Prayaga (2017) included activities undertaken at the beach in her analysis, and found that these affect the number of trips undertaken, but also did not infer the additional consumer surplus they generated over the basic beach visit. The relationship between characteristics of the beach visited, seasonal conditions and the activities undertaken are areas for further study that were not considered in this study. These factors have been shown to be importance considerations in recreational site choice and use in other studies (e.g. Mkwara et al., 2015; Raguragavan et al., 2013; Yeh et al., 2006) and subsequently may affect the measure of consumer surplus associated with each activity. A final conclusion from our study is that beach use is not a homogeneous product, and the consumer surplus generated by beach use is dependent on what activities visitors undertake. Further, most beach users undertake more than one activity while at the beach, and the value they derive is a function of the different combinations of activities that they undertake. While this may seem obvious, the majority of travel cost analyses have assumed beach visitation to be a generic activity (e.g. Andersson et al., 2015; Bin et al., 2005; Lew and Larson, 2008; Pendleton et al., 2006; Windle and Rolfe, 2013). From our study, considering the beach as a homogeneous good resulted in an underestimate of the use value of the asset for some purposes and an overestimate for other activities. Hence, understanding the composition of beach users is important when assessing the use value of a beach.

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Acknowledgements Considerable input into survey design as well as feedback on earlier parts of the study were provided by staff from the NSW DPI Marine Estate, NSW Office of Environment and Heritage, the Sydney Coastal Council Group and Eurobodalla Shire Council. Assistance with survey design was also provided by Amar Doshi (CSIRO), and Mladen Kovac and Angelica Austin (NSW Office of Environment and Heritage). The comments and suggestions of the two anonymous reviewers were also greatly appreciated. Funding: This work was supported by the NSW Environment Trust Environmental Research Program, [grant number 2014/RD/0017] and CSIRO Oceans and Atmosphere. 143

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