Environmental Management and the Competitiveness of Nature ... · Environmental management and the competitiveness of a nature-based tourism destination. protected status.3 Both the

Environmental and Resource Economics 24: 213–233, 2003.© 2003 Kluwer Academic Publishers. Printed in the Netherlands.

213

Environmental Management and theCompetitiveness of Nature-Based TourismDestinations

TWAN HUYBERS1 and JEFF BENNETT2

1School of Economics and Management, The University of New South Wales, Australian DefenceForce Academy, Canberra ACT 2600, Australia (E-mail: [email protected]);2National Centre for Development Studies, The Australian National University

Accepted 15 August 2002

Abstract. Environmental management at nature-based tourism destinations includes a private anda public component. The former refers to voluntary environmental protection activities carried outby individual (and groups of) tourism operators. These activities are motivated by the dependenceof the product provided by the tourism industry on the quality of the natural environment. Publicsector management, on the other hand, refers to the environmental regulations imposed on tourismbusinesses. The overall environmental management structure has a dual effect on the competitive-ness of the tourism industry at nature-based tourism destinations. While the industry may benefitfrom environmental management through demand stimulation, the tourism businesses also incur theassociated management related costs. In this paper, the overall effect of environmental managementon competitiveness, as measured by aggregate tourism industry profitability, is estimated in a casestudy of Tropical North Queensland.

Key words: competitiveness, environmental management, region, tourism

JEL classifications: L83, Q21, Q26, Q28, R11

1. Introduction

Tourism activity has grown rapidly since the Second World War due to advances intechnology and increases in standards of living enjoyed across a large part of theworld. Nature-based tourism is a particularly fast growing type of tourism (WorldTravel and Tourism Council 1998). However, increases in the number of touristsat environmental attractions can put pressure on that environment (Departmentof Tourism 1994). This raises a number of issues. Firstly, since the environmentoften possesses the characteristics of a public good or a common access resource,governments have stepped in to impose regulations with respect to environmentalimpacts of tourism activity. This has caused concerns about compliance costswithin the tourism industry. Yet, since the environment provides the attractions fortourists and hence the livelihood for much of the tourism industry, the industryrecognises the importance of protecting the environment from tourism induced

214 TWAN HUYBERS AND JEFF BENNETT

damage. Indeed, in terms of competition with other destinations – both domesticand international – a superior condition of the environment might give the destin-ation’s industry a competitive edge. This, in turn, can provide the impetus fortourism operators to engage in voluntary protection of the environment on whichtheir businesses rely.

This implies a dual effect of environmental management activities on a tourismdestination’s competitiveness. On the one hand, there are cost implications fortourism businesses that affect negatively the destination’s competitive position.However, to the extent that visitor demand is determined by the quality of thedestination’s environment, the destination’s competitiveness is enhanced. Thecombination of these two effects is addressed in this paper. In particular, the costand demand implications of environmental management for competitiveness areinvestigated in a case study of Tropical North Queensland.1

Tropical North Queensland is an appropriate case study region for three relatedreasons. Firstly, the region is one of Australia’s prime nature-based destinationsfor both domestic and international tourists. The region’s tourism industry reliesheavily on the general quality of its environmental assets (Driml and Common1996; Far North Queensland Regional Planning Advisory Committee 1998). Thisincludes the two World Heritage listed natural attractions – the Great BarrierReef and Wet Tropics Rainforests – as well as the general scenic landscapes andnatural areas.2 Secondly, due to the development of Tropical North Queensland,there has been public concern about the quality of the region’s environment (FarNorth Queensland Regional Planning Advisory Committee 1996). As a result, theregion’s tourism businesses are subjected to various types of environmental regu-lations (Huybers and Bennett 2000b). The regulatory framework constitutes thepublic environmental management component with respect to the region’s tourismindustry. Finally, various private environmental management activities are carriedout in the region’s tourism industry. Huybers and Bennett (2000b) identify environ-mental protection as one of the two major areas of cooperation between tourismbusinesses in Tropical North Queensland.

The paper is structured as follows. The conceptual framework underlying theempirical investigation is developed in the next section. This is followed by asection in which the data and their sources are discussed. The assumptions under-lying the model are outlined in Section 4. The results of the simulations of themodel are presented in Section 5. In the final section, the implications of theinvestigation are discussed.

2. Conceptual Framework

The literature regarding the relationship between the environment and internationalcompetitiveness has traditionally focussed on the effects of environmental compli-ance costs (Jayadevappa and Chhatre 2000; UNEP 2000). In this conventionalview, firms’ environmental management measures are interpreted as reactive in

NATURE-BASED TOURISM DESTINATIONS 215

nature, i.e., businesses complying with externally imposed environmental regula-tions (Turner, Pearce and Bateman 1994; Altham and Guerin 1999). In that context,a comparatively high level of domestic environmental protection costs is hypothes-ised to reduce the internationally competitive position of domestic industries asreflected in reduced economic profits and/or smaller market shares.

The hypothesised relationship between environmental protection costs andinternational competitiveness has been subjected to empirical investigation (Kalt1988; Repetto 1995; Jaffe et al. 1995). The empirical studies focus on the macroe-conomic effects of environmental control costs on a country’s trade balance.The findings show no evidence of a significant impact of environment controlexpenditure on international trade flows. This is mainly attributed to the small shareof environment control costs in total business costs – approximately two percent,on average, across industries.

These studies are limited in two ways. They concentrate solely on the costeffects associated with environmental protection regulations imposed by govern-ments, and they are predominantly macro in focus. However, an analysis thatis restricted to the supply side effects across the whole economy ignores thepotential relevance of demand side factors on an industry by industry scale. Increas-ingly, firms and industries are becoming aware of the potential benefits of theirenvironmental protection activities with respect to consumer demand.

Acknowledging the potential demand effects of environmental management oncompetitiveness is particularly relevant to Australia’s tourism industry. Since theenvironment is an important component of Australia’s tourism product (Huybersand Bennett 1996, 1997), protection of the environment can be expected to bean important demand stimulator. The benefits that result from demand stimulationmay act as an incentive for voluntary environmental protection activities by tourismbusiness operators either individually or as a group.

The notion of the dual effect with respect to a nature-based destination’senvironment-competitiveness relationship is illustrated in Figure 1. It incorpor-ates the conventional view of tourism businesses’ regulatory compliance costsas one link between environmental management and competitiveness. However,in addition to government regulations, voluntary environmental protection activ-ities by tourism businesses constitute the other component of the environmentalmanagement framework.

The costs incurred by tourism businesses due to environmental managementreduce the competitiveness of the destination’s tourism industry compared withother destinations ceteris paribus. However, there may also be a link betweenenvironmental management and demand – both direct and indirect. The indirectlink is established via the positive effect of environmental management on thequality of the destination’s environmental assets. Tourists are drawn to the regionbecause of its superior environmental attractions compared to other destinations.The direct link pertains to the effect of prospective tourists’ preferences forvisiting destinations where the environmental attractions are known to have a


Figure 1. Environmental management and the competitiveness of a nature-based tourismdestination.

protected status.3 Both the direct and indirect demand side links imply an increasedwillingness by tourists to pay for a visit to the region.

The concept of competitiveness is often associated with an individual firm andits position in the business environment. For instance, in the strategic managementliterature, the emphasis is explicitly on a firm’s pursuit of positive economic profitssince firm strategy is essentially “. . . about how to position and manage a firmso that (efficiency) rents may be created, protected and possibly increased” (Foss1996: 1). In that context, competitiveness is defined as a firm’s ability to sustainits profitability in competition with its rivals. This firm-level definition of compet-itiveness is here applied to the aggregate tourism destination region that, as anentity, competes with domestic and international regions. Hence, the focus is onthe aggregate profitability of the nature-based tourism industry based in TropicalNorth Queensland and not on the individual tourism businesses in the region suchas accommodation and attraction operators.

Adopting the perspective of a region, or a country, as an aggregate entityis common in economic analysis. For instance, international trade flows areanalysed at the country level by considering aggregate country characteristics. Withrespect to tourism destinations, countries and lower level aggregate destinations arefrequently used as units of analysis (Findlay and Forsyth 1988; Ritchie and Crouch2000). For instance, with respect to nature-based destinations, Findlay and Forsyth(1988: 110) refer to “the foundation of market power at the national level. This isbecause any one country will have a unique natural resource endowment.”

Using a region as the unit of analysis is also consistent with the concept of thegeographically concentrated cluster (Pyke and Sengenberger 1992; Porter 1998;Schmitz 1999). In a cluster, firms within the region compete with each otherin certain areas on the basis of their individual strengths while they engage incollective inter-regional competition based on the region’s distinctive features. Fornature-based tourism destinations, cooperative behaviour is expected to be concen-


Figure 2. Tropical North Queensland: environmental management and profitability.

trated on the key natural attractions that are the major distinguishing destinationcharacteristics.

Huybers and Bennett (2000b) document evidence of the nature and extent ofcooperation between tourism businesses in Tropical North Queensland. The twomain areas are collective promotion of the destination, and environmental protec-tion activities. This is further developed in Huybers and Bennett (2001) in whichthe rationale for treating Tropical North Queensland as a collective entity andappropriate aggregate unit of analysis is established.

The conceptualisation of Figure 1 can be translated into the economic model ofFigure 2. This model is the basis for the empirical investigations reported in thepaper. It shows Tropical North Queensland as a destination that competes in animperfectly competitive international market for tourism destinations.4 The regionexerts a degree of market power due to the differentiation of its tourism productbased on the relatively high quality of its environmental assets. This is reflected inthe downward sloping demand curve. Applying the model of imperfect competitionto a tourism destination is consistent with Findlay and Forsyth (1988).

Figure 2 illustrates the impact of environmental management on the aggregateprofitability of Tropical North Queensland’s tourism industry. It shows the marketfor Tropical North Queensland’s composite tourism product in two situations: withand without environmental management. For simplicity, marginal costs (MC) areassumed constant and hence the MC and average cost (AC) curves are drawn as asingle horizontal line.5 Cost curves MC1 and AC1, the downward sloping demandcurve D1, and its associated marginal revenue curve MR1 refer to the situation


in which environmental management has been effective in achieving a “high”environmental quality. The profit-maximisation equilibrium is reached at quantityq1 and price p1, resulting in an aggregate economic profit accruing to the region’stourism industry equal to area abcp1.

The situation without environmental management is represented by a declinein both demand and in costs of Tropical North Queensland’s tourism productceteris paribus. These costs include firms’ costs of complying with imposedgovernment regulations, and any expenditure related to voluntary environmentalprotection activities. While tourism business operators’ costs fall due to the absenceof environmental management, the condition of the environment deteriorates andresults in a fall in demand. This situation is represented by the cost curvesMC2, AC2, demand curve D2 and marginal revenue curve MR2. The new levelof aggregate profit is equal to area defp2. A fall in aggregate industry profit, asdepicted in Figure 2, is interpreted as a decline in destination competitiveness.

The marginal perspective represented in Figure 2 implies that the directionand magnitude of the change in the region’s aggregate tourism industry profitdepends on the relative magnitudes of the cost and demand side effects ceterisparibus.6 The latter refers, for instance, to the situation at other destinations. It alsoinvolves the other conditions related to Tropical North Queensland itself such asthe region’s destination promotion. While Tropical North Queensland’s destinationpromotion emphasises the quality of its natural attractions, the extent and nature ofthat promotion and is assumed to be unaffected in the analysis.

The above model is used as the basis of an empirical assessment of the compet-itiveness effects of environmental management in Tropical North Queensland. Todetermine the shift in the cost curves in Figure 2, estimates of tourism businesses’costs of environmental management are required. Similarly, estimates of the effectof environmental quality on tourist demand are needed to assess the shift in thedemand curve in the figure. The nature of the relevant data and the methodologiesused to collect them are now briefly discussed.

3. Data

3.1. COSTS

Australian Bureau of Statistics (1999a) contains figures on environment protec-tion expenditure in Australian industries in 1996–97. Environment protectionexpenditure includes expenditure related to compliance with regulations as wellas voluntary activities associated with market forces. Using industry cost figures(Australian Bureau of Statistics 1999b), it can be shown that the costs of environ-mental management are less than or around one percent of total costs for Australianindustries.

However, environmental management costs may be more significant for indi-vidual industries or sectors within industries. For instance, some industries, sectors,


or individual firms depend more heavily on the environment than others and aremore heavily regulated with respect to their effects on the environment. This isreflected in higher environmental compliance costs. In that context, Huybers andBennett (1996, 1997) estimated the costs of environmental regulations to tourismoperators in Australia to be around five percent of total business costs.

To substantiate the industry estimates, the costs of environmental managementto tourism businesses in Tropical North Queensland were estimated using fieldstudy data (Huybers and Bennett 2000b). Out of 24 operators interviewed, 14operators gave a numerical estimate of the proportion of overall environmentalprotection costs and/or a separate estimate of the regulatory compliance costcomponent. From these responses, the extent of environmental management costswas found to be, on average, four percent of total business costs (ranging from1.2 percent to 10 percent).7 Of the overall environmental management costs, onaverage, 85 percent (or 3.4 percent of total business costs) was due to public sectorenvironmental management. Consequently, only 0.6 percent of total business costswas related to voluntary environmental protection activities. Four of the operatorsinterviewed indicated that the proportion of costs attributable to environmentalprotection costs was “minor”. This appears consistent with the above average offour percent. Six operators could not offer a response.

Business costs due to public environmental management are predominantlyrelated to the tourism operator permit system that applies to tourism businessesoperating in the region. These costs include fees associated with permit applica-tions and permit issuance, as well as per-client charges imposed on businesses. Therelatively low cost related to the businesses’ voluntary environmental protectionactivities is consistent with the nature of those activities which include operators’adherence to voluntary codes of conduct and the collection of information aboutreef quality. Such activities are associated with relatively low (opportunity) cost tooperators.

In the light of the small sample size, it is important to compare the above figureswith the findings from other studies. In terms of imposed regulatory compliancecosts, the proportion of 3.4 percent is comparable with information reported inHuybers and Bennett (1996, 1997). The relatively small proportion of 3.4 percentappears also to be consistent with Tourism Review Steering Committee (1997)findings regarding the tourism industry in the Great Barrier Reef World HeritageArea. In this report it is stated that the charges associated with operating within theGreat Barrier Reef Marine Park “were not especially significant beyond the generalfactor that any increases in operating cost could impact on cost-competitiveness”(Part 2, 28).

Overall, the survey results showed that environmental management is not cost-less to tourism operators. However, keeping in mind the small size of the sample,the survey findings suggest that tourism operators’ costs due to environmental


management constitute a relatively small proportion of total business costs atapproximately four percent.

3.2. DEMAND

As shown in Figure 2, the demand side effect in the model is the differencein demand due to environmental management and the associated quality of theregion’s environment. This effect can be assessed empirically using an analysisconsistent with Lancaster’s (1966, 1971) theory of consumer choice. It requires aninvestigation of the relative importance of Tropical North Queensland’s environ-mental quality among the general set of destination attributes that determinetourists’ destination choices.

Ideally, this would involve surveying potential tourists in several source marketsthat cover a large proportion of Tropical North Queensland’s visitor base. For thepurposes of this study, the holiday destination choices of prospective tourists fromthe UK were studied (Huybers and Bennett 2000a). The UK market was selectedfor three reasons: it constitutes Tropical North Queensland’s largest market in termsof visitor nights (Brand Far North Queensland 1996); it has been identified as akey future target market for the region (Regional Tourism Strategy ManagementTeam 1997: 26); and it represents the middle ground in terms of nature-basedtourism participation in Queensland (Blamey and Hatch 1996; Green, Moscardoand O’Leary 1998; Tourism Queensland 1999, 2000).

The data for the destination choice analysis were generated using the discretechoice modelling technique. Choice modelling is a stated-preference method inwhich information about decision makers’ preferences is elicited using specific-ally designed hypothetical situations. The method originated in the transport andmarketing literature and has been applied in many different fields (Louviere,Hensher and Swait 2000). Rugg (1973) and Morley (1994) have demonstrated thevalidity of applying a Lancaster style analysis to tourism demand in general anddestination choice in particular. However, only on a few occasions have statedchoice models been applied to holiday destination choices (Haider and Ewing1990; Morley 1994; Rahemtulla 1998).

A choice modelling survey employs a questionnaire in which respondents areasked to indicate their preferred option from a set of alternatives.8 Each alterna-tive option is described in terms of a number of key attributes that are specifiedat different levels. The configuration of attribute levels that describe the alter-natives varies between each question (known as a “choice set”) and follows anexperimental design. The response data from a sequence of choice sets enable theestimation of the relationships between attribute levels and the choice probabilities.They also allow the estimation of the trade-offs between the attributes.

Figures on outbound UK travel and the results from questionnaire testingduring focus groups yielded five destinations as alternative options to TropicalNorth Queensland in the questionnaire’s choice sets: California (USA), Cornwall


(Jamaica), Costa del Sol (Spain), Lycian Coast (Turkey) and Provence (France).9

The total market share of the six destinations in the survey was 7.7 percent ofall outbound UK visits in 1996. The relative shares among the six destinationswere similar to the relative shares among the six countries in which the chosendestinations are located. Those six countries made up approximately half of thetotal UK outbound travel market.

Feedback during focus group discussions resulted in the selection of sixdestination attributes: ‘Activities’, ‘Condition of the environment’, ‘Develop-ment/Crowdedness’, ‘Price of a 14-day holiday’, ‘Quality of facilities’, and ‘Rarityof natural attractions’. Each attribute was described by three different levels.10 Thelevels of the condition of the environment – the attribute of interest to this paper –were ‘unspoilt’, ‘somewhat spoilt’ and ‘very spoilt’.

The survey data were used to estimate a multinomial logit model of respondentdestination choice. Those results were used to estimate the implicit prices ofchanges in attributes from one level to another (see Huybers and Bennett 2000a).The model was also used to derive the demand curve of UK tourists for visitingTropical North Queensland. This was done by first selecting a base case thatincluded the assumed current attribute levels for each destination was selected. ForTropical North Queensland, this included an ‘unspoilt’ condition of the environ-ment. Subsequently, Tropical North Queensland’s market share was calculated atdifferent price levels. Finally, using the actual number of UK visitors as the refer-ence point, percentage changes in market shares were converted into changes invisitor numbers. The number of UK visitors that visited Tropical North Queenslandin 1998 (approximately 118,000) was used as the reference point.

Demand curves for UK visitors at the three different levels of environmentalquality were derived. Under the particular assumptions used to specify the basecase,11 the estimated demand schedules depicted in Table I were obtained. Thetable also shows the estimated inverse demand functions for the three differentlevels of environmental condition. These represent the shifts in the position of thedemand curve illustrated in Figure 2.

4. Model Assumptions

In the model simulations, the level of Tropical North Queensland’s aggregateregional profit is assessed for three levels of environmental condition as illustratedin Figure 3. It shows three possible paths of environmental quality associatedwith different levels of environmental management. Path A assumes the continu-ation of the current “unspoilt” environment in Tropical North Queensland underthe existing environmental management regime. It is associated with CostsEM

unsp,the environmental management costs to tourism operators as discussed in theprevious section. Paths B and C assume the deterioration over time to a “somewhatspoilt” and a “very spoilt” environment, respectively, due to diminished levels ofenvironmental management and, hence, lower business costs as indicated.12


Table I. Estimated demand schedules for different levels of environmentalcondition, Tropical North Queensland

Price (£) Environmental condition

‘Unspoilt’ ‘Somewhat spoilt’ ‘Very spoilt’

Number of visitors Number of visitors Number of visitors

2,400 101,959 68,945 37,420

2,300 108,190 73,613 40,191

2,200 114,670 78,528 43,146

2,150 118,000 81,079 44,694

2,100 121,390 83,694 46,292

2,000 128,343 89,114 49,638

1,900 135,520 94,788 53,192

Estimated inverse demand functions:

Punsp = 3,911 – 0.01488 Qunsp,

Psomesp = 3,723 – 0.01932 Qsomesp,

Pverysp = 3,572 - 0.03164 Qverysp.

Figure 3. Comparison of aggregate profit levels.

The aggregate tourism industry profit for the three different levels of environ-mental management is compared at the point in time indicated in Figure 3.13 Thisquantitative assessment involves a number of simulations of the model associatedwith Figure 2. That model is a general, conceptual representation of the changesin costs, demand and aggregate profit. A number of assumptions are requiredto estimate the changes in aggregate industry profit. These are now discussed.


The implications of the assumptions for the simulation results are considered inSection 5.2.

In the previous section, the tourism destination as a geographical cluster wasdiscussed. One of the characteristics of the cluster concept is that individual firms inthe cluster experience external economies of scale, i.e. their average costs decreaseas the industry output increases. In terms of the aggregate situation for the region’stourism industry, this implies internal economies of scale for the region overalland, hence, a downward sloping average cost curve. In the model simulations thatfollow, it is assumed that the industry as a whole operates on the downward slopingrange of the average cost curve and that the average cost curve is above the marginalcost curve.14

The demand curves in Figure 2 are based on the price that is charged by tourismoperators in the region. However, the price attribute used in the choice model-ling study was the “all-inclusive” price covering all holiday-related expenditureincluding airfares. Hence, the price associated with the demand curves derivedfrom the survey results in Chapter 6 includes a component that is not chargedby regional tourism businesses. As mentioned in the previous chapter, airfaresmake up approximately 40 percent of total visitor expenditure by UK visitors inAustralia. Assuming that this applies generally to Tropical North Queensland, thisimplies that the remaining 60 percent of tourists’ expenditure is revenue for theregion’s tourism industry. However, since separate demand curves for changesin airfares and the regional price component cannot be inferred from the choicemodelling results, tourism business costs due to environmental management areadjusted in the model simulations. To mirror the distribution of expenditure by tour-ists in the region, the proportion of environmental management costs is adjusted bythe same percentage. In other words, in the model simulations it is assumed that thecost effect due to environmental management is 60 percent of the total cost change.

As reported above, environmental management costs in Tropical North Queens-land were estimated at approximately 4 percent. However, this may be an under-estimate of the actual costs given that many respondents did not consider in theirestimates the full opportunity costs. Hence, a base assumption of environmentalmanagement costs of 5 percent is adopted. Following the rationale set out above,60 percent of the above costs, i.e., 3 percent, is taken into account in the modelsimulations. It is further assumed that the two levels of environmental managementare associated with equal incremental falls in environmental management costs totourism operators. The fall in environmental costs to tourism operators associatedwith a decline to a “somewhat spoilt” environment is 1.5 percent. A further fallin costs of 1.5 percent is assumed for the change to a “very spoilt” environmentalquality. These base assumptions apply to both marginal and average costs.

Given the estimated changes in demand and the assumed changes in costs,the simulated changes in aggregate industry profit were derived using the profit-maximisation condition that marginal costs and marginal revenue be equal. Theinitial level of average costs was calibrated to be consistent with the average


tourism industry profit margin and the base price level for Tropical North Queens-land used in the choice model. Based on the limited available data, the averageprofit margin for the tourism industry in Tropical North Queensland, is assumedto be ten percent in the model simulations below (Australian Bureau of Statistics1999b, 2000; British Airway 2000; Qantas 2000).

Two final comments regarding the simulated scenarios in the following sectionare warranted. Firstly, it is important to emphasise that the quantitative assessmentsof the various simulated scenarios are indicative only. The estimated changes arenot intended to be precise estimates. Rather, they are intended to give an indicationof the direction of changes and some indication of the magnitude of the changes. Inview of this, the simulations are subjected to a sensitivity analysis with respect tomodel assumptions. The second comment relates to the generalisation of the modelresults. As discussed above, the UK market may be seen to represent the medianvisitor in terms of nature-based activities of foreign tourists in Australia. Only tothe extent that the UK market is representative for all visitors can the quantitativeresults be extrapolated to Tropical North Queensland’s other source markets.

5. Simulation Results

In this section, the model simulation results are reported. The cost and demandeffects of Tropical North Queensland’s environmental management on the region’saggregate tourism industry profit are estimated. While the effect of environmentalmanagement on profit is emphasised, the estimated effect on visitor numbers andvisitor expenditure is also reported. The estimation results are presented first,followed by the results of the sensitivity analysis. Subsequently, the simulationresults are assessed.

5.1. ESTIMATED EFFECTS

Two broad scenarios of environmental change are simulated. Scenario I involvesa reduction in Tropical North Queensland’s environmental management activitiesand the resulting decline in the region’s environmental condition from “unspoilt” to“somewhat spoilt” as shown in Figure 3. Scenario II considers the deterioration ofthe environment to “very spoilt” due to a further diminished level of environmentalmanagement.

Table II shows the simulation results under the assumptions outlined inSection 4.15 In Scenario I, the estimated decline in visitor numbers is 27 percent.16

The table also shows that the estimated fall in total visitor expenditure is 30 percent.The greater decline in visitor expenditure is consistent with the notion that an entityoperating in an imperfectly competitive market such as a monopoly or monopolisticcompetition, operates on that range of the demand curve where demand is priceelastic. Finally, Table II shows that the estimated fall in aggregate industry profit is47 percent.


Table II. Simulation results

Change in Scenario I Scenario II

environmental “Unspoilt” to “Unspoilt” to

condition “somewhat spoilt” “very spoilt”

Average st. dev. Average st. dev.

Visitor numbers –27% (3%) –58% (3%)

Visitor expenditure –30% (2%) –61% (2%)

Aggregate profit –47% (8%) –80% (9%)

Table II also shows the estimated effects of Scenario II. The figures show thatthe resulting estimated decrease in both visitor numbers and visitor expenditure isapproximately twice the size of the fall in Scenario I. The pattern is similar withrespect to aggregate profit. However, for aggregate profit, the incremental changefrom Scenario I to Scenario II is smaller than the one for the initial change.

5.2. SENSITIVITY ANALYSIS

The sensitivity analysis comprises three dimensions: the demand side of the model,the assumed profit margin, and the assumed cost effects. Each of these dimensionsis now discussed and their sensitivity results are presented.

The sensitivity analysis with respect to the demand side of the model comprisesthe variation in model results due to the use of different choice model specifica-tions, and due to the choice of different base cases. The alternative multinomiallogit specification that was estimated included respondents’ socio-economic andattitudinal variables as co-variates in addition to the destination attributes. Thecalculation of market shares, and hence demand curves, for the alternative specific-ation was done in two different ways (using the sample means for the co-variates,and using individual responses). Hence, for the two specifications, three differentoutcomes were obtained.

The information required to select the appropriate attribute levels of the basecase was incomplete. Hence, the results for three different base cases were invest-igated. In combination with the above three cases due to different choice modelspecifications, this resulted in nine different demand side contingencies.

The average result shown in Table II refers to the average across these ninecontingencies. The sensitivity of the results is indicated by the standard deviation ofeach of the average simulation results. The low standard deviation of 3 percent forthe estimated fall in visitor numbers in both scenarios indicates that this result wastightly distributed across the nine demand side cases. Similarly, regarding visitorexpenditure, the standard deviations of 2 percent shows a consistent picture acrossthe demand side contingencies. However, the standard deviations of 8 percent and


9 percent with respect to aggregate industry profit are greater, both absolutelyand relatively. This indicates a lower level of robustness in estimated profit fallscompared with the estimated decreases in visitor numbers and expenditure. In theremainder of this section, the focus of attention is on the profit effects.

The sensitivity of simulation results to variations in the assumed aggregateindustry profit margin is the second sensitivity dimension. An investigation ofthis sensitivity parameter is required due to the limited empirical data on tourismindustry profit margins.17

The third sensitivity dimension relates to the changes in industry cost levels. Asdiscussed earlier, the region’s tourism industry is assumed to operate on the down-ward sloping part of the average cost curve. The downward shift of the marginaland average cost curves in combination with the fall in the optimum quantity ofvisitors gives rise to a range of possible changes in the levels of average costs andmarginal costs at that quantity. Hence, the sensitivity of the simulation results tothe assumed changes in the levels of average costs and marginal costs is assessed.This encompasses the sensitivity analysis with respect to the estimated costs ofenvironmental management derived from the survey of tourism operators.

An analysis was carried out to identify the effect of the sensitivity parameterson the estimated change in aggregate profit (PR). For each of the three sensitivityparameters, a number of levels was established across their respective ranges. Theassumed profit margin (PRM) comprised eleven levels between 5 and 15 percentwith increments equal to 1 percent. The range for the changes in the level of bothaverage cost (AC) and marginal cost (MC) was divided into seven equal levels with0.5 percent increments. The range differed for the two scenarios in accordancewith the discussion in Section 4. For Scenario I, the sensitivity range was from –3percent to 0 percent, while the changes for Scenario II ranged from –4.5 percentto –1.5 percent. Overall, for both scenarios, the effect of each of the 539 (11 ∗ 72)different combinations on average industry profit was calculated. The individualeffects of the three sensitivity parameters are shown in Table III.

The figures show that for each one-percent variation in the assumed profitmargin, the effect on the estimated change in aggregate industry profit is around 2.5percent. They also show that the sensitivity of the change in profit is relatively largewith respect to a one-percent variation in the assumed fall in average costs (7.46percent and 4.33 percent, respectively), while the effect of a change in marginalcosts is relatively small (less than one percent).

Finally, the sensitivity to the assumed profit margin and to the assumed changesin marginal costs and average costs was combined with the demand side sensi-tivity dimension. Hence, for both environmental change scenarios, the above 539combinations were investigated for each of the nine demand specifications. Theresults are included in Table IV. The overall estimated change in industry profit inScenario I is 49 percent on average. The variability around this estimate is reflectedin the 95 percent confidence interval for this decrease which ranges from –20 to –79


Table III. Effect of sensitivity parameters on estimated profitchange

Scenario I: Change from “unspoilt” to “somewhat spoilt”

PR = –82.3 + 2.31 ∗ PRM + 0.72 ∗ MC – 7.46 ∗ AC

(–126.01) (44.71) (4.53) (–45.70)

Scenario II: Change from “unspoilt” to “very spoilt”

PR = –120.53 + 2.61 ∗ PRM + 0.42 ∗ MC – 4.33 ∗ AC

(–162.04) (60.93) (3.21) (–31.95)

PR = estimated change in aggregate profit (%)

PRM = assumed aggregate profit margin (%)

MC = assumed change in marginal costs (%)

AC = assumed change in average costs (%)

Table IV. Overall estimated change in aggregate industry profit; incor-poration of sensitivity analysis results

Change in environmental Scenario I Scenario II

condition “Unspoilt” to “Unspoilt” to

“somewhat spoilt” “very spoilt”

Average change in profit –49% –83%

95% confidence interval [–20%, –79%] [–54%, –112%∗]

∗A reduction in profitability of more than 100 percent represents achange into a net aggregate industry loss (see discussion of results).

percent. Similarly, in the second scenario, the average fall is 83 percent with a 95confidence interval from –54 to –112 percent.

5.3. DISCUSSION OF SIMULATION RESULTS

The above scenarios simulate the effect of profitability, visitor numbers andexpenditure of the deterioration in the management of Tropical North Queensland’snatural environment and the resulting decline in the region’s environmental quality.The results suggest substantial negative effects on the region’s tourism industry.As shown in Table II, the estimated fall in visitor numbers and visitor expenditureis approximately 30 percent. This applies to the initial decline in environmentalquality as well as to the further deterioration. However, an assessment of thechange in visitor numbers or visitor expenditure presents an incomplete pictureof the effect on the competitiveness of that destination’s tourism industry. In termsof the more comprehensive measure of aggregate profitability, the simulation and


sensitivity results presented in Table IV show that the estimated effect on industryprofitability is substantial. Depending on the various contingencies, as reflected inthe confidence intervals, the effect on industry profit is likely to be greater than theeffect on visitor numbers and expenditure in both scenarios.

Two observations are warranted. Firstly, the estimated effects on visitornumbers, visitor expenditure and aggregate profit are relatively large. However,since these are the effects of large, discrete changes in the degree of environmentalmanagement and the resulting quality of the region’s main tourist attraction, aconsiderable impact on the region’s tourism industry appears plausible. As a relatedpoint, it should be noted that the analysis is comparative static in nature. It does notallow for an adjustment to the decline in demand by individual tourism businessesand/or by the region collectively.

Secondly, the estimated change in industry profitability is an aggregate measureacross the various tourism sectors and their businesses. It is expected that the effectsvary between the different sectors and individual businesses. A fall in aggregateprofitability is likely to represent a situation in which certain tourism businessesexit the industry with the remaining operators servicing a reduced number ofvisitors. This suggests that the businesses that continue their operations are eithercomparatively efficient and/or offer superior products. This interpretation of theestimated profit results is particularly relevant to the simulation result in Scenario IIwhich shows that industry profit could become negative. While this implies that,on average, the price that tourists are willing to pay does not cover unit costs, itdoes not suggest that the whole industry will disappear. Indeed, the estimated fallin visitors to the region in Scenario II is less than 100 percent.

Overall, the results indicate that the aggregate profitability of Tropical NorthQueensland’s tourism industry would decline as a result of the reduced degreeof management of the region’s natural environment. In other words, followingthe reverse argument, environmental management enhances the aggregate profit-ability of the region’s tourism industry, and, hence, contributes to the industry’scompetitiveness.

6. Conclusion

Environmental management of Tropical North Queensland’s natural environmenthas two effects on the competitiveness of the region’s tourism industry as measuredby the industry’s aggregate profitability. While it affects tourism businesses’ oper-ating costs, it also has an effect on tourists’ demand for visiting the destination.This conceptual structure is a departure from the traditional consideration of onlythe compliance cost effect of imposed environmental regulations. In addition to theincorporation of demand effects, it includes the associated voluntary environmentalmanagement activities by the region’s tourism industry.

The expected overall impact of the combined public and private environmentalmanagement framework in Tropical North Queensland has been estimated in


this paper. This comprises an empirical analysis of the region’s tourism industrythrough simulations of a model of imperfect competition. It has been shown that,taking into account cost and demand effects, environmental management has apositive impact on the industry’s aggregate profitability. The sensitivity analysiswith respect to assumed model parameters shows that the magnitude of the positiverelationship between environmental management and industry profitability variesdepending on the assumptions adopted. Hence, the simulation results are of anindicative nature only. Given the estimated direction of the change in aggregateprofitability, the evidence suggests the importance of the environment to thecompetitiveness of Tropical North Queensland’s tourism industry.

The unspoilt condition of the environment has a significant effect on tourists’demand. This implies that Tropical North Queensland’s environment and the asso-ciated management regime are strategic resources for the region’s tourism industry.Environmental management is a strategic investment in the region’s core tourismproduct. While environmental management results in business costs to tourismoperators, there are demand pay-offs that justify the investment. Alternatively,the consequences of not undertaking the investment would be adverse in terms oftourism profitability. Overall, for tourism businesses, environmental managementis more than a matter of complying with externally determined regulations. Thisestablishes the importance of cooperation with respect to the management of theregion’s environmental attractions – within the industry, and between industry andregulatory authorities.

It has been assumed that the existing environmental management frameworkcan protect the current condition of the region’s environment in an effective andefficient fashion. The protection of the environmental condition might be rela-tively easy to achieve, at reasonably low costs to tourism businesses, given currentvisitor numbers in the region. However, continued increases in the popularity of theregion as a tourist destination may require a change in the management frameworkand affect the region’s competitiveness. The absorption of greater visitor numbersis environmentally sustainable if the carrying capacity of the region’s environ-mental resources can be enhanced (see, for instance, Tisdell 1998). With respectto efficiency, the required measures to avoid increased damage from tourism mightinvolve higher environmental protection costs to tourism businesses and alter theregion’s cost/price competitiveness.

The findings presented in this paper have implications for other nature-basedtourist destination regions in Australia and elsewhere. The tourism industry inthese regions may also benefit from environmental management. Alternatively,the competitiveness of the industry could be harmed if the environment werenot protected. However, as was the case for Tropical North Queensland, thisdepends crucially on the effectiveness and efficiency with which the environ-mental attractions are managed at those destinations. In other words, environmentalmanagement does not necessarily lead to net benefits to the destination’s tourismindustry. The environmental management cost effect could outweigh the associated


demand benefits. It is recommended that case-specific studies be carried out todetermine the competitiveness impacts.

Finally, the findings in this paper have implications that reach beyond thetourism industry. For instance, similar to a “green and clean” tourism industry,agricultural industries could benefit from offering “clean”, organic food productson the world market. The production costs of organic products might be highercompared with other production processes involving, for instance, the use of partic-ular pesticides or the genetic modification of food. However, employing morecostly production methods does not necessarily constitute a competitive disad-vantage to those industries. That also depends on consumers’ concerns about thepotential health effects of alternative production methods. Consumers’ willingnessto pay a price premium for “clean” food products could override the production costdisadvantage. This, in combination with appropriate food labelling, could improvethe competitiveness of the organic food industry. To establish whether that is thecase, empirical case study analyses are required.18 The methodology presented inthis paper demonstrates a research approach through which such analyses can becarried out.

Notes

1. The region is generally known as ‘Far North Queensland’. The name ‘Tropical North Queens-land’ was adopted as the region’s tourism destination brand name (Brand Far North Queensland1996). Tropical North Queensland extends north to Cape York peninsula, west to the GulfSavannah, and south to Cardwell.

2. Further details about Tropical North Queensland and its tourism industry can be found inHuybers and Bennett (2000b).

3. The distinction between direct and indirect demand links is consistent with Mihalic (2000).4. Alternatively, the situation may be seen to represent the Marshallian “Representative firm”

(Borooah 1999).5. This assumption is relaxed in the model simulations below.6. This marginal perspective is consistent with Porter’s (1990) notion of competitiveness on the

basis of the generic differentiation strategy.7. This estimate does not necessarily reflect the full extent of business opportunity cost.8. The appropriate design of the questionnaire is of great importance. The stages of questionnaire

design are outlined in Huybers and Bennett (2000a).9. In each choice set, respondents were also given the option of not choosing any of the described

destinations.10. The attributes and attribute levels were described on an information sheet in the questionnaire.11. This is further addressed in the sensitivity analysis below.12. It should be noted that once the environmental quality has fallen below a certain level,

maintaining that level in the long run may require increasing environmental protection costs.13. The timeframe associated with the comparison point in Figure 3 is not specific.14. Empirical evidence of economies of scale at tourism destinations is noticeably absent in the

tourism economics literature.15. Figures are all rounded to the nearest integer.16. The relevance of the average result and its standard deviation in Table II is discussed in the

sensitivity analysis below.


17. An additional reason for this sensitivity analysis is that the available published figures pertain toaccounting profits and not economic profits.

18. We are grateful to an anonymous referee for pointing out that the feature of “clean” productionprocesses will have to be communicated to potential consumers through collective promotionactivities (by industry and/or government agencies). Contrary to the case of the Tropical NorthQueensland tourism destination, the associated promotion costs cannot be part of the ceterisparibus assumption in the analysis and would have to be taken into account explicitly.

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Environmental Management and the Competitiveness of Nature ... · Environmental management and the competitiveness of a nature-based tourism destination. protected status.3 Both the