The ecological footprint as a key indicator of sustainable tourism

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Tourism Management 28 (2007) 46–57 www.elsevier.com/locate/tourman

Research article

The ecological footprint as a key indicator of sustainable tourism Colin Hunter, Jon Shaw Department of Geography & Environment, University of Aberdeen, Elphinstone Road, Aberdeen, AB24 3UF, UK Received 31 December 2004; accepted 26 July 2005

Abstract This paper argues for ecological footprint (EF) analysis to become widely adopted as a key environmental indicator of sustainable tourism (ST). It is suggested that EF analysis provides a unique, global perspective on sustainability that is absent with the use of locally derived and contextualised ST indicators. A simple methodology to estimate indicative, minimum EF values for international tourism activities involving air travel is presented. Critically, the methodology accounts for the EF that would have been used by a tourist at home during the tourist trip, providing an estimate of the net, as well as the gross, tourism-related EF. Illustrations of the application of the methodology are provided, including the evaluation and comparison of specific tourism products. It is suggested that some (eco)tourism products may, potentially, make a positive contribution to resource conservation at the global scale. Areas for further research in applying EF analysis to tourism are outlined. r 2005 Elsevier Ltd. All rights reserved. Keywords: Sustainable tourism; Indicator; Ecological footprint

1. Introduction The importance of learning from related fields and disciplines is increasingly being recognised in the sustainable tourism (ST) literature, both as a means of advancing knowledge and understanding of ST, and as a means of avoiding ‘re-inventing the wheel’ in ST practice (e.g. Farrell & Twining-Ward, 2003; Hunter, 2002a; Ko, 2001). Potentially, one area where a great deal may be learned from the broader sustainable development (SD) and environmental management literature is in the development and use of suitable indicators of ST. This is explicitly recognised by Twining-Ward and Butler (2002), in one of the very few works to date specifically designed to formulate indicators of ST. Despite the undoubted demand for appropriate indicators of ST, these authors argue (p. 365) that research in this area is ‘‘still in its incipient stages’’, a view echoed by others (e.g. Li, 2004; Miller, 2001; Rebollo & Baidal, 2003). It is, perhaps, whilst the art/science of ST indicator research is still in its infancy that arguments for the Corresponding author. Tel.: +44 1224 272328; fax: +44 1224 272331.

E-mail address: [email protected] (C. Hunter). 0261-5177/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2005.07.016

adoption of a particular approach or type of indicator are best made. This paper argues for the widespread use of the ‘ecological footprint’ (EF) as a key environmental indicator of ST. The very act of proposing such adoption of the EF (or any single indicator) runs counter to some perceptions of progressive thinking in ST and SD research, where sustainability is regarded as an adaptive concept requiring that indicators of ST ‘‘reflect the space and time specific context of the locality under study’’ (TwiningWard & Butler, 2002, p. 367). Other studies, however, appear to assume that the development and use of a generic set of ST indicators is appropriate (Manning, Clifford, Dougherty, & Ernst, 1996; Miller, 2001). In proposing the widespread use of the EF technique in ST analyses, we acknowledge the importance of using indicators that reflect local circumstances, as suggested by Twining-Ward and Butler (2002), but do not take this position to mean that suitable indicators ought to be wholly determined by local conditions and attitudes. Indeed, it could be argued that, to date, ST indicators and indicators adopted to measure environmental conditions in tourism planning and management frameworks have been designed almost exclusively with the localised monitoring of destination-based impacts and resource

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demands in mind (e.g. Hughes, 2002; Li, 2004; Manning et al., 1996; Moore, Smith, & Newsome, 2003; Rebollo & Baidal, 2003; Smith & Newsome, 2002; Ward, Hughey, & Urlich, 2002). This parochialism may detract from the appreciation of tourism as an agent of global environmental change, and ignores the consequences of impacts generated in the transit region (Go¨ssling, 2002; Hunter, 1995). Uniquely, the EF is specifically designed to express aggregate environmental impact in terms of pressure on the global biosphere, and can account for travel-related impact components. The use of EF analysis in the context of ST remains almost completely unexplored with very limited work carried out to date. The aims of this paper are, therefore, to: provide an overview of the EF concept, including its use in tourism research; present a simple methodology for the rapid estimation of indicative net tourism EF values using existing secondary data sources; and, illustrate different applications of the net EF as a key environmental indicator of ST. With reference to the second of these aims, it will be argued that the notion of a net EF in the tourism context is particularly significant, as tourists when away from home are not generating the footprint that they normally would in the source country. The need to consider the EF of an international ecotourist in the context of the EF generated by her or him at home is explicitly recognised by Fennell (2002). Also, a desirable attribute of any potential indicator of SD or ST is that the necessary data be relatively easily available from existing, secondary sources (e.g. Bell & Morse, 2003), and this is reflected in the nature of the methodology developed in this paper. Finally, it is important to state that we recognise that the EF could only ever be one of a suite of indicators necessary for the holistic appraisal of ST. Locally based and derived indicators that also encompass economic and social activities and impacts would clearly also be required. 2. The EF concept The EF provides an aggregate estimate of demands upon the biophysical productivity and waste assimilation capacity of nature imposed by human lifestyles. Although a unique form of SD indicator, the EF technique draws upon older environmental impact appraisal approaches including net primary productivity accounting, energy and ‘Emergy’ accounting, carrying capacity assessment, and life-cycle analysis (Wackernagel & Yount, 2000). Published descriptions of EF analysis were first provided in the early to mid-1990s (Rees, 1992; Rees & Wackernagel, 1994), and their authors subsequently defined the EF as ‘‘an accounting tool that enables us to estimate the resource consumption and waste assimilation requirements of a defined human population or economy in terms of a corresponding productive land area’’ (Wackernagel & Rees, 1996, p. 9). The EF conceptualises a population or economy as having an ‘industrial metabolism’, consuming resources

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and producing wastes in order to sustain itself, thereby appropriating a portion of the planetary biosphere in the process (Wackernagel & Rees, 1996). EF analysis portrays these demands on natural resources in terms of an estimated hypothetical equivalent land/sea (biosphere) area, with the size of the footprint (sometimes also referred to as ‘appropriated carrying capacity’) for a given population and for a specified time period (normally a year) determined by the lifestyle of the population in question. The EF itself does not exist in real space, but rather can be viewed as the aggregation of myriad actual land and sea areas around the world appropriated by a given population relying on the global movement of raw materials and products. The unique attribute of EF analysis is the expression of demands upon natural resources in terms of an equivalent land/sea area (global hectares, or gha), thereby (it is claimed) facilitating comprehension of environmental impact and providing a powerful educational tool (e.g. Chambers, Simmons, & Wackernagel, 2000; Wackernagel & Yount, 2000). Detailed descriptions of the procedures involved in EF calculations are found elsewhere (e.g. Chambers et al., 2000; Wackernagel, Lewan, & Hansson, 1999), but, broadly speaking, they can be accomplished either by using the more traditional compound (‘top down’) approach, or with the component-based (‘bottom-up’) approach. The former takes the nation state as its primary unit of analysis, employing national trade flow and energy data to estimate the average per capita footprint, while the latter approach seeks to account for most consumption (e.g. for a region) by summing available life-cycle data across individual footprint components. Typically, EF calculations account for, and then combine, the use of energy, foodstuffs, raw materials and water, and also capture transport-related impacts, the production of wastes (including carbon dioxide from the burning of fossil fuels), and the loss of productive land associated with buildings, roads and other aspects of the built environment. Whichever way the EF is actually calculated, it is effectively an aggregate indicator of environmental impact or environmental sustainability that uses gha as the common currency to express impact magnitude across all components. Although it is relatively easy to conceive of an equivalent EF land area required to, for example, produce ‘x’ tonnes of a particular crop, or the equivalent sea space required to produce ‘y’ tonnes of a fish species, it is less easy to appreciate how other EF components, particularly the generation of carbon dioxide, can be converted into gha space. This is achieved in EF calculations by translating energy use/carbon dioxide emissions into an equivalent land (forest) area required to sequester carbon dioxide loading estimates. It is also important to appreciate that most advocates of the EF technique stress that EF calculations provide conservative estimates of global environmental impact, and the tradition in EF analysis is to consciously err on the side of caution when making estimates of resource use and waste production for use in

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calculations (e.g. Monfreda, Wackernagel, & Deumling, 2004; Wackernagel & Rees, 1996). Many applications of EF analysis have focused on nations and cities or groups of cities (e.g. Folke, Jansson, Larsson, & Costanza, 1997; Fricker, 1998; Parker, 1998; Wackernagel, 1998a; Wackernagel & Rees, 1996; Wackernagel, Lewan et al., 1999; Wackernagel, Onisto et al., 1999). A recent report provides details of the EF of 134 countries using the compound approach (Venetoulis, Chazan, & Gaudet, 2004). The per capita EF for the year 2000 ranged from 0.50 gha for Bangladesh to 9.57 gha for the USA. North America and Western Europe are the world regions with the highest average per capita footprints, followed by Central and Eastern Europe, the Middle East and Central Asia, the Asia–Pacific region, Latin America and the Caribbean, and Africa. Taking Western Europe in more detail, for example, national per capita EFs ranged from 3.26 gha for Italy, to 8.17 gha for Norway. A much used benchmark for comparison in EF studies is the so-called ‘fair earthshare’ value; i.e. the global average area of productive land/sea space available annually on a per capita basis. One recent estimate of this, which excludes land set aside for non-human species, is 1.8 gha/year (World Wildlife Fund, 2004). Other cited values are slightly higher (e.g. Chambers et al., 2000; Chambers, Griffiths, Lewis, & Jenkin, 2004), with some 2 gha/year regarded as a reasonable estimate (Venetoulis et al., 2004). As with other indicators of sustainable development and, indeed, environmental impact appraisal tools more generally, EF analysis has been closely scrutinised and subject to criticism. Debate on the utility of ecological footprinting encompasses a range of issues, including its application in policy-making (e.g. Hanley, Moffatt, Faichney, & Wilson, 1999; Moffatt, 2000; Opschoor, 2000), and its analytical soundness (e.g. van den Bergh & Verbruggen, 1999; Costanza, 2000; Ferguson, 1999, 2001; Levett, 1998; Rees, 2000; van Vuuren & Smeets, 2000; Wackernagel, 1998b, 1999; Wackernagel & Silverstein, 2000). It is clear, however, that EF calculations are becoming more frequent and better understood (e.g. Nijkamp, Rossi, & Vindigni, 2004), with many new applications proposed (Wackernagel & Yount, 2000). Recently, for example, EF analysis has been applied to situations as diverse as the examination of external debt relief (Torras, 2003), and passenger transport in Merseyside, England (Barrett & Scott, 2003). Moreover, the technique has now been widely used by many private sector organisations, NGOs, local authorities and educational establishments in order to ‘‘illustrate and inform many different audiences about sustainable development’’ (World Wildlife Fund-UK, 2002, p. 1).

3. EF applications in tourism Notwithstanding the obvious potential relevance of EF analysis to ST research and practice, very little attempt has been made to examine ecological footprinting in this context. In considering the ‘touristic EF’, Hunter (2002b,

p. 12) connects perceptions of ST with EF analysis, and argues that: ythe fundamental contribution of ecological footprinting, currently absent from the great majority of tourism impact studies, would be the ability to couch actual or potential tourism activities in terms of widely scoped ecological demand beyond the physical confines of any particular geographical setting (e.g. a destination area). Despite the fact that travel, for example, is an inherent part of the tourism industry, relatively little work has addressed the environmental impacts of tourist travel in the context of ST (Becken, 2002; Go¨ssling, 2000; Go¨ssling, 2002; Hoyer, 2000; Simmons & Becken, 2004), and wider demands upon natural resources, such as the implications of supplying energy, food and water to destination areas, are often excluded from studies of the sustainability of tourism products and destinations (e.g. Hunter, 2002a). Although purely theoretical in nature, Hunter’s (2002b) advocacy of the touristic EF as an important, global environmental indicator of ST would appear to be borne out by the (albeit very limited) available evidence where attempts have been made to calculate a tourism-related EF. As far as we are aware, only two such studies exist (Go¨ssling, Borgstrom Hansson, Horstmeier, & Saggel, 2002; World Wildlife Fund-UK, 2002). Go¨ssling et al. (2002) provide a component-based framework for the calculation of a leisure tourism EF for the Seychelles, using secondary data sources. These authors found that the per tourist EF to be some 1.9 gha/year (similar to the fair earthshare value of some 2 gha/capita/ year), with an average holiday in the Seychelles equivalent to 17–37% of the annual EF of a citizen of an industrialised country. Well over 90% of the total leisure tourism footprint was found to be due to air travel to and from the destination. Critically, the authors conclude that, in part, the Seychelles maintains a high quality local environment for tourists at the expense of a much larger hinterland, and that traditional approaches used to assess sustainability, such as limits of acceptable change or environmental impact assessment, would fail to provide the required global perspective on the sustainability of tourism activity in the Seychelles. Indeed, focusing on the air travel component, the authors argue (p. 210) that ‘‘[t]aking these results seriously, air travel should, from an ecological perspective, be actively discouraged.’’ The key point here, echoing that made by Go¨ssling et al. (2002), is that locally derived and based indicators of ST are not, by their very nature, capable of providing a global perspective on tourism’s resource demands and impacts. Using a mix of primary and secondary data, the World Wildlife Fund-UK (2002) study presents component-based EF analyses of two typical, 2-week UK package holiday products (flying from Gatwick airport) to the popular Mediterranean destinations of Majorca and Cyprus. The EF per bed night for Majorca was found to be 0.03 gha, giving a total EF per guest over the 2-week holiday of

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0.37 gha, while the corresponding values for Cyprus were 0.07 gha and 0.93 gha, respectively. Accounting for approximately 50% of the total EF in both cases, air travel was found to be by far the largest single component of the holiday EF, although a much smaller proportion than that reported by Go¨ssling et al. (2002) for the Seychelles, given the relatively short flights involved to the Mediterranean area. It should also be noted that the work of Go¨ssling et al. (2002) included an additional radiative forcing allowance for aircraft emissions other than carbon dioxide, an approach apparently not adopted in the World Wildlife Fund-UK (2002) study. Nevertheless, World Wildlife Fund-UK (2002) conclude that the Majorca and Cyprus holidays account for around 20% and 50%, respectively, of the annual fair earthshare value, analogous to people ‘‘spending 20 –50 % of their annual income in a 2-week period!’’ (World Wildlife Fund-UK, 2002, p. v). Although the report notes that judging the overall sustainability of specific holidays would require additional information on the state of the local environment at the destination area and the effects of tourism on the local community and on the local and national economy, the conclusion is drawn that whilst holidays involving air travel can be made more ‘responsible’, it is unlikely that such holidays will be wholly sustainable. Indeed, it is argued that: In certain respects, holidays abroad typify the unsustainable nature of current developed country consumption patterns. If everyone in the world took an annual holiday similar to the Cyprus break, an extra half-planet would be required to support the additional consumption involved in holidaying alone! (World Wildlife Fund-UK, 2002, p. 13). The two tourism-related calculations of the EF summarised above illustrate the potential benefits of adopting the EF as a key environmental indicator of ST: the EF provides a means of identifying and understanding globally expressed demands on the biosphere brought about by tourism activity. Although very valuable, however, these are isolated examples focusing on forms of mass tourism, and they did not set out to provide explicit guidance on how EF analysis might be rapidly and routinely adopted in ST assessments using existing, secondary data sources, and used in different contexts (e.g. to estimate the impact of different types of tourism product, or to examine the national implications of tourism policy). Moreover, both studies appear to focus on and highlight the gross holiday EF. As illustrated by the above quotation, the World Wildlife Fund-UK (2002) study uses the gross estimate of a holiday footprint to extrapolate impact in terms of additional, absolute, planetary space required. In other words, the holiday EF appears to be interpreted and presented as a wholly additional ‘burden’ on the global biosphere. In reality, however, whilst on holiday (and as recognised by World Wildlife Fund-UK (2002) in their study), the tourist is clearly not producing at home the footprint that would normally be created over the same

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period. The key indicator for any tourism product or destination area should therefore be the net, rather than the gross, EF generated. This distinction may appear rather obvious, and in some scenarios the EF that would have been generated at home will be small compared to the gross holiday EF, making the difference between gross and net EF values similarly small. Yet as we seek to demonstrate below, other, very different, outcomes for the apparent sustainability of tourism products are possible, and for this reason it is critical to distinguish between gross and net tourism EF values. Moreover, as the net estimate provides the more conservative basis for tourism EF accounting, it is more in keeping with the tradition in EF analysis of erring on the side of caution when calculating the magnitude of impact of a particular activity or group (e.g. Monfreda et al., 2004). As with the calculations summarised above, the context used for the methodology and examples provided in the remainder of this paper is international, holiday tourism. Secondary data, particularly for arrivals by air, are relatively easily available for international tourism, and the environmental costs of air travel are attracting increasing attention and concern in the literature (e.g. Simmons & Becken, 2004). 4. The rapid calculation of an indicative net EF for international tourism by air The gross tourism EF can be seen as having two broad components: that generated in the transit zone and that at the destination area. The net tourism EF is simply the sum of the transit and destination area components, less the source country EF for the period away from home. Potentially, there is the complication that a very small footprint will actually be generated by a tourist in the source country even when the tourist is away from home; for example, by leaving on some home heating or security lighting. The normal, major footprint components associated with energy use, transportation, food consumption and the consumption of other raw materials, etc. will be absent, however, so it would appear reasonable to assume the home-based footprint to be negligible during the international tourist trip. The overall procedure for obtaining indicative estimates of the annual equivalent net per capita EF for international tourism involving air travel is summarised below. Steps 1–5, relating to the air travel EF, draw from a number of sources, as indicated. Transit zone: (1) Determine the total, round trip flight distance (km). (2) Obtain energy use per tourist (megaJoules, MJ) by multiplying flight distance by an energy intensity conversion factor of 1.75–2.75 MJ/km (see below). (3) Obtain the equivalent land area (ha of forest) per tourist (per year), by dividing energy use per tourist by 73 GJ/ha (i.e. the number of gigaJoules that 1 ha of

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(4)

(5)

(6)

(7)

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forest will sequester, in carbon dioxide equivalent, per year when liquid fossil fuel is combusted) (World Wildlife Fund, 2000). Allow for the additional radiative forcing of aircraft emissions other than carbon dioxide emitted at altitude (IPCC, 1999; Schumann, 1994) by multiplying by a factor of 2.7 (Go¨ssling et al., 2002), giving a new estimate of required forest land (ha) (see below). Multiply by the appropriate ‘equivalence factor’ (in 2001 this was 1.38) to correct for forest land being more productive than average world space (World Wildlife Fund, 2004), giving a final estimate of the transit zone per tourist footprint in gha/year (see below). Destination area: Use either the host or source country average per capita EF as a proxy for the destination area EF of the tourist, reduced pro rata from an annualised value according to the length of stay (see below). Net EF: Use the average per capita EF of the source country and the length of stay away from home to calculate the per tourist EF that would have been generated at home for the period away (again reduced pro rata from an annualised value), and subtract this from the gross per tourist EF (the sum of steps 1–6).

Additional explanation is required for some of these steps. With reference to step 2, energy intensity is the energy use per passenger km, accounting for average load factors and an average freight-to-passenger ratio (Becken, 2002). Different conversion factors are suggested by different sources and vary according to trip length. For long haul flights, Lenzen (1999) estimates 1.75 MJ/km;1 Go¨ssling et al. (2002), drawing on a range of sources, suggest 2.0 MJ/km; and British Airways and Lufthansa cite overall energy intensities of 2.03 and 1.86 MJ/km, respectively (Green Globe, 2000, cited in Becken, 2002). For short haul flights, the Energy Efficiency Conservation Authority (1999) calculates a figure of 2.75 MJ/km in the context of New Zealand. The choice of figure to be applied will therefore depend upon the nature of the flight under consideration. Except where noted, EF calculations that appear later in this paper adopt an energy intensity value of 2.0 MJ/km, since this falls between the extremes noted above and would seem most appropriate to the medium and long haul flight scenarios presented. Clearly, accounting as above for the transit EF solely in terms of fuel/energy use by aircraft excludes other contributions to the transit EF such as land travel to and from the airport, a contribution to airport infrastructure and energy use, and in-flight food and beverage consumption by tourists. The size of these components relative to the fuel consumption footprint component of even the shortest of international flights is likely to be extremely 1 Lenzen’s conversion factor refers to secondary energy and thus excludes energy used in extracting, refining and transporting fuels.

small (e.g. Go¨ssling et al., 2002), and as such they are not considered further as part of the rapid EF estimation methodology presented here. Step 4 recognises the emission or formation of substances other than carbon dioxide, such as nitrogen oxides, methane, ozone and water vapour, at high altitude which contribute to radiative forcing (global climate change potential) by aircraft (IPCC, 1999). We have, therefore, adopted the approach of Go¨ssling (2002) and Go¨ssling et al. (2002) whereby the contribution to radiative forcing by other substances effectively increases the forest area required, in EF terms, to combat global climate change. The IPCC (1999) has estimated, from a range of values, that aviation’s carbon dioxide emission is only some 37% of its total radiative forcing effect, and the use of a 2.7 multiplier is therefore suggested in step 4 (100%/ 37% ¼ 2.7). With reference to step 5, it should be noted that equivalence factors are specific to each year for which national per capita EFs are produced. The latest national per capita EF estimates although produced in 2004 (World Wildlife Fund, 2004) were actually for the year 2001, and for this year the equivalence factor was determined to be 1.38. This highlights the broader point that care should be taken to ensure that a consistent reference year is used for all aspects of a tourism EF estimate, where applicable; i.e. if combining a per tourist EF estimate with actual international tourist arrival numbers and length of stay information in order to estimate the total EF of international tourism from a particular source country. With this important caveat in mind, the destination area per tourist footprint (step 6) can be estimated using compound national footprint data (e.g. Venetoulis et al., 2004; World Wildlife Fund, 2004) in one of two ways. It can be assumed either that, on average, tourists consume resources at the destination in approximately the same manner and extent as they would at home, or in the same manner and at the same rate as the average resident of the host country. In situations where the holiday product is of an up-market, luxury type but is situated in a comparatively poor country where the per capita EF is small, the former approach may be the most appropriate (Go¨ssling et al., 2002). However, in the first instance, we adopt the latter alternative (Hunter, 2002b), as this provides more conservative net EF estimates, in keeping with the general tradition of erring on the side of caution when undertaking EF analysis. This said, net EF estimates that follow would best be viewed as potential minimum values, and we return to the implications of assuming higher resource consumption by the tourist at the destination later in the paper. Whichever alternative is used, the average annual per capita footprint of source or host nation is reduced from the annualised value on a pro rata basis according to the length of stay. Data on average length of stay (bed nights) are commonly available from national tourism organisations, or are advertised as a matter of course for individual holiday products.

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As a partial check, it is interesting and informative to compare EF values obtained using the above methodology with those obtained in an actual study. In the case of the World Wildlife Fund-UK (2002) work, a quite detailed, component-based approach was employed in calculating the EF whilst at the destination area, using, for example, data obtained from hotels on the use of various natural resources. Whilst the precise basis of the air travel EF calculation is unclear, a check against the destination area EF is still possible. Majorca is used as an illustration since no national EF values are available for Cyprus. Given that the annual average per capita footprint of a Spanish citizen was some 4.80 gha in 2001 (World Wildlife Fund, 2004), Step 6 of our methodology suggests that the destination area per tourist EF for a two-week holiday in Majorca can be estimated as 0.18 gha (14/365 days
4.80 gha).2 This compares well to the World Wildlife Fund-UK (2002) destination area EF of 0.16 gha. 5. Examples of the application of the net tourism EF methodology There are very many potential illustrations of the EF applied to tourism. With per capita national EF data, flight data and length of stay information, it is possible to estimate—albeit crudely at this stage—a credible minimum value for the net tourism EF of an international tourist on any given holiday product. With enough additional information on the number and source of international tourist arrivals by air, the EF of a destination area (even host country) can be estimated. Additional primary data, not to mention the refinement and development of the methodology presented in this paper, will permit the generation of increasingly sophisticated EF estimates. In the meantime, the intention of the following paragraphs is merely to direct the reader to a range of possible uses of the tourism EF. 5.1. Calculating the net EF A useful starting point is to re-visit an existing tourism EF study and consider the importance of using the net, rather than the gross, EF as the more appropriate, conservative indicator of environmental sustainability. Of the two existing studies outlined above, the World Wildlife Fund-UK’s (2002) work provides the simpler basis for consideration as its focus is the individual product, involving one source country (the UK), rather than the destination as a whole. The gross per tourist EFs for a 2week holiday to Majorca and Cyprus were found to be 0.37 gha and 0.93 gha, respectively. Since the annual average per capita footprint of a UK citizen in 2001 was 5.40 gha (World Wildlife Fund, 2004), the average tourist 2

It is unclear from the World Wildlife Fund-UK (2002) report for which year data was collected. It seems reasonable to assume that EF data for the year 2001 is suitable.

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over a 2-week period at home would normally generate an EF of some 0.21 gha (14/365 days
5.40 gha). The net per tourist EFs over the 2-week holiday are thus 0.16 gha for Majorca and 0.72 gha for Cyprus. This reveals the net EF values as a percentage of gross values to be 43% for Majorca and 77% for Cyprus. In the case of Majorca, therefore, the additional impact of the holiday may potentially have been under half of that reported using the gross EF value. 5.2. Extending existing studies Another opportunity to apply the EF concept to tourism is to extend existing studies to encompass a tourism EF calculation. Becken (2002), for example, provides a very useful and detailed examination of the energy use and carbon dioxide emissions associated with international air tourist travel to New Zealand in 1999. Her data can be used to estimate in gross EF terms the impacts of New Zealand’s international tourism trade (Table 1). Becken’s calculation of energy used in the transit zone from each major source country can be taken in conjunction with total air arrivals data to estimate average per tourist energy use in transit, some 34.9 GJ. This translates to an average transit EF of around 1.76 gha per tourist, using the equivalence factor of 1.35 for 1999 (World Wildlife Fund, 2002). An estimate of the average per capita EF of a New Zealand citizen for the year 1999 is 8.68 gha (World Wildlife Fund, 2002). Using this value, and an average length of stay for all international tourists to New Zealand of 18 bed nights (Tourism Research Council New Zealand, 2004), it can be estimated that the average gross EF per international tourist to New Zealand around the turn of the century was 2.19 gha, higher than the fair earthshare value of some 2 gha/year. Approximately 80% of the gross per tourist EF can be attributed in this case to the flight component, reflecting New Zealand’s relative geographic isolation from many major international tourist markets. Extending this analysis to individual source countries also allows the calculation of net tourist EFs because national EF data can be used. Table 2 presents an estimate of the average net EF per UK tourist for a stay of 28 nights (Tourism Research Council New Zealand, 2004) in 1999. In this case, an estimated gross EF per tourist trip of 4.15 gha is obtained, with some 84% of this (3.48 gha) accounted for by the transit component. At home, a UK resident in 1999 would on average have generated an EF over the same time period of approximately 0.41 gha (World Wildlife Fund, 2002), producing a net per tourist EF of 3.74 gha. This value represents around 70% of the annual average per capita footprint of a UK resident at the time. From an environmental perspective, even allowing for widely differing interpretations of the meaning of sustainable tourism, it is difficult to see this outcome as anything other than indicative of a highly unsustainable aspect of New Zealand’s tourism industry.

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Table 1 Average gross EF per international tourist per year to New Zealand in 1999 for a stay of 18 nights, using the source study’s original energy intensity conversion factor of 1.75 MJ/km Step Transit zone 2 3 4 5

Energy use per tourist (55.6 PJ/1,591,650 visitors) Required forest land Air transport EF on forest land Air transport EF in world average space

34.9 GJ 0.48 ha 1.30 ha 1.76 gha

Destination area 6

Host country per capita EF for average length of stay (18 nights)

0.43 gha

Sum of (5) and (6) above

2.19 gha

Gross EF

Sources: Becken (2002), World Wildlife Fund (2000), and authors’ calculations.

Table 2 Average net EF per UK tourist per year to New Zealand in 1999 for a stay of 28 nights, using the source study’s original energy intensity conversion factor of 1.75 MJ/km Step Transit zone 1 2 3 4 5 Destination area 6 Net EF 7

Round trip flight distance Energy use per tourist (167,202 visitors) Required forest land Air transport EF on forest land Air transport EF in world average space

39,910 km 69,843 MJ (69.8 GJ) 0.96 ha 2.58 ha 3.48 gha

Host country per capita EF for average length of stay (28 nights) Gross per tourist EF

0.67 gha 4.15 gha

Deducting home country per capita EF for average length of stay (0.41 gha)

3.74 gha

Sources: Becken (2002), World Wildlife Fund (2000), and authors’ calculations.

Building on the claim that the EF is a useful educational tool (e.g. Chambers et al., 2000), there is also scope through the net tourist ecological footprint for policy makers to better understand the environmental implications of their decisions. To illustrate, it is not uncommon for national tourism organisations and governments to target key overseas markets in their promotion and development strategies (see Scottish Executive (2000) targets relating to encouraging visitors from the USA, Germany and France). In the New Zealand context, the EF implications of encouraging a (for the sake of argument) 10% increase in the number of UK tourists over a given time period can be estimated very quickly.3 UK tourist arrivals to New Zealand in 1999 were 167,202 (Becken, 2002), resulting in a total tourism EF for UK tourists of some 625, 335 gha (167,202
3.74 gha). The suggested increase in tourist numbers would thus generate an 3 Assuming roughly static parameters used in the methodology presented here, although changes in parameters could be built in to EF forecasts.

additional footprint of around 62,500 gha, although some allowance needs to be made for ‘opportunity cost’ trips which otherwise would have been taken by the extra visitors. This can be compared with, for example, the effects of aiming for a 10% increase over the same time period in Australian tourists. The 521,912 Australian tourists visiting New Zealand in 1999 (Becken, 2002), each of whose average stay of 12 nights (Tourism Research Council New Zealand, 2004) generated a net EF of around 0.66 gha (Table 3), were responsible for a total footprint of approximately 344,462 gha. The suggested increase in this case would result in an additional footprint (again excluding trips which otherwise would have been taken) of around 34,500 gha being generated. Such increases in footprint magnitude can easily be compared by interested parties seeking to evaluate the impacts of international tourism, and used to inform their marketing or lobbying activities. In this very simple example, it appears that, notwithstanding the longer average length of stay of UK

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visitors, achieving a 10% increase in Australian rather than British visitors would result in both economic benefit (more additional bed nights—626,292 as opposed to 468,160) and less additional global ecological impact (a smaller overall footprint). 5.3. Rethinking the impact of ecotourism? In the case of New Zealand, international tourism generally involves long flights and also occurs in the context of a country with a high per capita national footprint, resulting in large net and gross tourism EFs. Yet very different findings and sustainability implications occur in other situations. Table 4 considers international tourism from a developed (high footprint) country, the USA, to a developing (low footprint) country, Costa Rica, involving a relatively short flight from Florida, and using the latest national EF data for 2001. According to World Wildlife Fund, (2004), the USA has one of the highest per capita EFs in the world, 9.5 gha/year, whereas Costa Rica’s, at

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2.1 gha/year, is around the fair earthshare value. In this illustration, it is assumed (initially) that the tourist consumes resources at a similar rate to the average destination country resident, a not unreasonable scenario given that Costa Rica is a well-known eco-tourism destination (e.g. Weaver, 1999) and that low impact/ consumption eco-tourism products exist there (Buckley, 2003). For a stay of 14 nights, the average gross per tourist EF would equate to around 0.45 gha (Table 4) but, interestingly, the net value is potentially as low as 0.09 gha, a reduction of some 80%. If the holiday period is stretched to 3 weeks, the net per tourist EF potentially becomes negative (0.06 gha) suggesting, rather surprisingly, that this particular holiday scenario might actually reduce the consumption of global biological resources compared with that of an average USA citizen for the same time period at home. Arguably, this would in fact be the case for shorter lengths of stay if the tourists were more affluent and had generally higher individual EFs than the USA average.

Table 3 Average net EF per Australian tourist per year to New Zealand in 1999 for a stay of 12 nights, using the source study’s original energy intensity conversion factor of 1.75 MJ/km Step Transit zone 1 2 3 4 5 Destination area 6 Net EF 7

Round trip flight distance Energy use per tourist (521,912 visitors) Required forest land Air transport EF on forest land Air transport EF in world average space

6892 km 12061 MJ (12.06 GJ) 0.17 ha 0.46 ha 0.62 gha

Host country per capita EF for average length of stay (12 nights) Gross per tourist EF

0.29 gha 0.91 gha

Deducting home country per capita EF for average length of stay (0.25 gha)

0.66 gha

Sources: Becken (2002), World Wildlife Fund (2000), and authors’ calculations.

Table 4 Average net EF per American ‘eco-tourist’ per year travelling from Miami to Costa Rica for a stay of 14 nights Step Transit zone 1 2 3 4 5 Destination area 6 Net EF 7

Distance Energy use per tourist Required forest land Air transport EF on forest land Air transport EF in world average space

3604 km 7208 MJ (7.2 GJ) 0.1 ha 0.27 ha 0.37 gha

Host country per capita EF for average length of stay (14 nights) Gross per tourist EF

0.08 gha 0.45 gha

Deducting home country per capita EF for average length of stay (0.36 gha)

0.09 gha

Source: authors’ calculations using national EF data for 2001 from World Wildlife Fund (2004).

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Evidence suggests that eco-tourists are often better educated and more affluent than other tourist types (e.g. Page & Dowling, 2002). Clearly it is important not to overplay the significance of these illustrations because the transit zone EF—principally determined by the length of flight between the origin and destination countries—is critical to the overall net tourist EF. Recent critiques of eco-tourism (e.g. Mowforth & Munt, 2003; Simmons & Becken, 2004) have built upon earlier attacks focusing on the impacts of its frequent reliance on long haul air travel. Hall and Kinnaird (1994, cited in Mowforth & Munt, 2003) argue that ‘‘travel to ecotourism destinations undertaken in fuel-hungry aeroplanes is in itself incompatible with ecological sentimentsy. The extolling of eco-tourism developments in faraway lands... may be thus viewed as paradoxical.’’ Eco-tourists travelling from, say, London to the interior of Brazil would create a sizeable EF—broadly equivalent to the fair earthshare value—even if their activities within the host country created a negligible footprint (see, for example, Wolfe, 2004) and the length of their stay was a month or more (Table 5). Furthermore, it has so far been assumed that resource use by the eco-tourist at the destination is relatively resource-conservative, reflecting that of the host country. Yet, eco-tourism activities may also occur in much more up-market, resource-demanding contexts with tourists living much more luxurious lifestyles than locals (e.g. Kontogeorgopoulos, 2004), and tourists in general often exhibit rather hedonistic behaviour. It could be argued, therefore, that in many circumstances—particularly involving ‘popular’ or ‘soft’ (Page & Dowling, 2002) forms of eco-tourism—it would be more appropriate to adopt the average per capita EF of the source country as a proxy for the EF generated at the destination (Go¨ssling et al., 2002). Thus, the source country per capita EF would be used in both steps 6 and 7 of the above procedure, and the net tourism EF becomes the same as the transit zone EF

provided by steps 1–5, irrespective of the length of stay. Consequently, the net EF can never be negative, and is simply a function of distance travelled by air. Changing the assumption about the nature of resource demand by (eco)tourists at the destination in this way would obviously produce higher net EF estimates. The Miami to Costa Rica scenario, for example, would now give a net EF estimate of 0.37 gha, rather than 0.09 gha as previously (Table 4). With the longer flight, the difference for London to Manaus (2.46 gha, as compared to 2.05 gha; Table 5) is less marked. Potential differences associated with different assumptions about tourist consumption at the destination demonstrate the need for care in understanding the nature of the tourism product and in applying the EF methodology. Nevertheless, it is important to recognise that for some types of ‘adventure/hard’ eco-tourism product (e.g. Page & Dowling, 2002) resource use at the destination is akin to that of the host population and, in contrast to the earlier illustrations provided above, these may have the potential to offer a positive contribution to environmental sustainability in terms of global resource use. We would stress again, however, that EF estimates obtained using the procedure reported here are best regarded as providing potential minimum values, particularly if host country EF data are used to approximate resource use by tourists at the destination. Certainly, and despite the tradition in EF analysis to provide conservative estimates, there are circumstances where the use of the source country EF as a proxy for consumption at the destination provides the more realistic alternative. On the other hand, it may be rather too easily assumed that hedonistic behaviour by tourists will have a significant effect: host country national EF values, and therefore proxy consumption by tourists at the destination, reflect the nature of consumption as well as the magnitude of consumption. Eating and drinking more locally/nationally produced food and beverage than residents, for example, is likely to have a proportionately small impact on the destination EF. Where food and drink

Table 5 Average net EF per UK ‘eco-tourist’ per year travelling from London to Manaus, via Rio de Janeiro, for 28 nights Step Transit zone 1 2 3 4 5 Destination area 6 Net EF 7

Distance Energy use per tourist Required forest land Air transport EF on forest land Air transport EF in world average space

24,179 km 48,358 MJ (48.4 GJ) 0.66 ha 1.78 ha 2.46 gha

Host country per capita EF for average length of stay (28 nights) Gross per tourist EF

Negligible 2.46 gha

Deducting home country per capita EF for average length of stay (0.41 gha)

2.05 gha

Source: authors’ calculations using national EF data for 2001 from World Wildlife Fund (2004), and indicative resource consumption information from Wolfe (2004).

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are imported for tourists, however, and the energy embodied in these products is therefore high, tourism might generate a large additional EF at the destination. 6. Conclusions It is likely that the use of ST indicators that are wholly derived from a local perspective and through local processes of participation will underplay the recognition of tourism activity as a user of natural resources at the global scale. Furthermore, different sets of locally derived and contextualised indicators make it less easy to compare different areas or products in terms of environmental impact and sustainability. At the very least, therefore, it would appear appropriate to recognise the potential benefits of the widespread adoption of a unique indicator capable of providing a global perspective on tourism’s environmental impact. The use of EF analysis as an indicator of environmental sustainability allows quantitative comparison between different impact components (e.g. the transit zone and destination area footprints), and can provide an indication of the overall ecological impact of tourism products on global biological resources. The simple methodology outlined in this paper could be widely adopted for the environmental appraisal of international tourism products and destination areas. It should be stressed again, however, that the methodology is as yet rather crude, providing indicative estimates of the likely minimum potential tourism EF. This said, it would appear critical in any tourism EF analysis to determine the net tourism EF; i.e. to account for the EF that a tourist would normally produce at home whilst s/he is abroad. Otherwise, the additional burden on the planet’s resources created by the tourist trip/product may be greatly over-estimated, in contravention of the tradition in EF analysis work. Furthermore, as we sought to demonstrate above, it may be that some tourism products could actually alleviate the consumption of the world’s biological resources. The potential for this rather surprising outcome is greatest for some products to low EF countries, but involving tourists from high EF (generally developed) countries, and where short to medium haul flights are involved if the length of stay is of sufficient duration. By way of defining one avenue for future research, some types of ‘hard’ (Page & Dowling, 2002) eco-tourism product, at least on the face of it, exhibit the necessary characteristics for a ‘zero’ or ‘negative net EF’ outcome. However indicative it may be at this stage, EF analysis offers the prospect of more ‘rounded’ evaluations of eco-tourism (and other tourism) products, and suggests that any automatic dismissal of eco-tourism on environmental grounds—certainly if short to medium haul flights are involved—may be rather premature. Furthermore, it is by no means clear that even eco-tourism products involving long haul flights will, in net EF terms, tend to be more environmentally demanding than many mass tourism products. What is clear, however, is the danger of

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assessing the sustainability of tourism products without considering the transit zone. Avenues for further research in the application of EF analysis to tourism are many and varied. Many more simple estimates of the EF of different tourism products could be made using, for example, the methodology outlined in this paper. These might also attempt to incorporate different modes of transport to the destination. Ways of estimating the EF of domestic tourism activities could also be explored. Perhaps the greatest need, however, is to collect ‘real world’ primary data for the resources consumed during the life-cycle of a range of different tourism products, including low-impact, ‘genuine’ ecotourism holidays of various kinds, and very up-market, luxury hotel-type holiday resorts. Acknowledgements We would like to thank Martin Mowforth for commenting on an earlier draft of this paper, and Tristan Wolfe for his insights into adventure- and eco-tourism in South America. Thanks also to Pam Wight for a useful discussion of the ecological footprint applied to tourism, and to Dan Moran, Mathis Wackernagel and Thomas Wiedmann for their insights into air travel footprinting and carbon sequestration rates. Of course the usual disclaimer applies. References Barrett, J., & Scott, A. (2003). The application of the ecological footprint: a case of passenger transport in Merseyside. Local Environment, 8(2), 167–183. Becken, S. (2002). Analysing international tourist flows to estimate energy use associated with air travel. Journal of Sustainable Tourism, 10(2), 114–131. Bell, S., & Morse, S. (2003). Measuring sustainability: Learning from doing. London: Earthscan. van den Bergh, J., & Verbruggen, H. (1999). Spatial sustainability, trade and indicators: An evaluation of the ‘‘ecological footprint’’. Ecological Economics, 29, 61–72. Buckley, R. (2003). Case studies in ecotourism. Wallingford: CABI Publishing. Chambers, N., Griffiths, P., Lewis, K., & Jenkin, N. (2004). Scotland’s footprint: A resource flow and ecological footprint analysis of Scotland. Oxford, UK: Best Foot Forward. Chambers, N., Simmons, C., & Wackernagel, M. (2000). Sharing nature’s interest: Ecological footprints as an indicator of sustainability. London: Earthscan. Costanza, R. (2000). The dynamics of the ecological footprint concept. Ecological Economics, 32, 341–345. Energy Efficiency Conservation Authority. (1999). Energy-wise monitoring quarterly. New Zealand’s transport sector energy use: highlights, Issue 14. Wellington: EECA. Farrell, B. H., & Twining-Ward, L. (2003). Reconceptualizing tourism. Annals of Tourism Research, 31(2), 274–295. Fennell, D. A. (2002). Ecotourism programme planning. Wallingford, UK: CABI Publishing. Ferguson, A. (1999). The logical foundation of ecological footprints. Environment, Development and Sustainability, 1, 149–156. Ferguson, A. (2001). Comments on eco-footprinting. Ecological Economics, 37, 1–2.

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