Transportation Research Part A: Policy and Practice
The future tourism mobility of the world population: Emission growth versus climate policy
Introduction
The current development of tourism-related mobility is a serious challenge for global climate change mitigation. Tourist mobility contributes considerably to transport levels. Tourists include domestic, international, leisure and business travel involving at least one overnight stay (for the definition of tourism and a tourist see UNWTO–UNEP–WMO (2008, p. 121)). Globally, tourism’s emissions have been estimated at around 5% of overall CO2 emissions, with 75% of these the result of tourist mobility and 25% due to on-site consumption, including accommodation (21%) and tourist activities (4%) (UNWTO–UNEP–WMO, 2008).
While 5% of global emissions may appear insignificant when compared to other sectors such as agriculture, tourism is characterized by rapid growth. International tourist arrivals increased from 25 million in 1950 to 534 million in 1995, and 803 million in 2005. In the 2005–2007 period alone, international tourist arrivals grew by 100 million, reaching the 903 million mark. If this trend continues, tourism emissions will increase by over 150% by 2035 (UNWTO–UNEP–WMO, 2008). This growth in emissions must be considered in the context of emissions reduction targets as outlined by the IPCC, 2008a, IPCC, 2008b, which recommends to reduce global emissions by 50–80% by 2050. This is likely to lead to a situation of contraction and convergence, where growing emissions from what is currently a relatively small sector, tourism, will rapidly become more important both in relative and absolute terms. Overall emissions decline, while tourism emissions will continue to grow (see e.g., Bows et al., 2006b, Tight et al., 2005). Understanding and controlling emission growth in tourism will consequently become increasingly urgent.
Emission reduction targets are outlined in the Kyoto Agreement. Although there is a general consensus that global warming of more than ±2 °C is likely to lead to dangerous interference with the climate system (cf. Meinshausen et al., 2006), optimal emission reduction levels to be reached by 2050 are still under discussion. Until recently, it was thought that a 50% drop in emissions (compared to 1990 levels) by 2050 would be likely to prevent atmospheric CO2 concentration of 450 ppm, or what is considered to correspond to a warming of 2 °C. However, some recent publications have recommended that worldwide emissions be reduced by 80% by 2050 (Parry et al., 2008). Other authors have reasoned that the long-term CO2 concentration levels necessary to avoid dangerous interference with the climate system should not exceed 350 ppm (Hansen et al., 2008). In other words, they advocate a level lower than current atmospheric concentrations of CO2. If this goal were adopted, emissions would have to decline by about 3% per year after 2015 (Hansen et al., 2006).
Climate change is to a considerable extent addressed by modelling and scenario building techniques. With regard to transport, trend extrapolations or “business as usual” scenarios (Ceron and Dubois, 2006, Dubois and Ceron, 2007, Peeters et al., 2007, UNWTO–UNEP–WMO, 2008) all point to rapid growth in emissions in the order of a factor 2 to 3 over the next 30 years. Consequently, “avoiding dangerous climate change” objectives increase the need for backcasting techniques (Åkerman and Höjer, 2006, Anderson and Cavendish, 2001, van Notten et al., 2003, Swart et al., 2002) and normative scenarios (Coates and Glenn, 2003, van Notten et al., 2003, Prideaux et al., 2003) to identify pathways that could lead to emission reductions. Moreover, the time horizon involved (50–100 years) means that adaptive models that can capture changes in critical parameters must be built. Finally, ambitious emission reduction targets – both those proposed by the IPCC, 2008a, IPCC, 2008b as well as those adopted by governments – imply the need not only to consider developments in infrastructure and technology (quantitative changes relatively easily integrated into models), but also to explore the diversity of qualitative socio-cultural factors that shape, together with economic factors, current and future tourism demand. In future studies two cultures have emerged (Bradfield et al., 2005): a dominant quantitative culture, and a more qualitative culture (De Jouvenel, 1964, Godet, 1997, Hatem, 1993, Mermet, 2003, Mermet, 2005). The best way forward seems to integrate both, which presents a methodological challenge (Raskin et al., 2005). Regarding tourism futures, various attempts have been made in this direction by either academics (Buhalis and Costa, 2005, Bows et al., 2006a, Bows et al., 2007, Ceron and Dubois, 2005, Cooper and Hall, 2008, Hall, 2005a, Hall, 2005b, Dubois and Ceron, 2007, Laboratoire d’économie des transports and ENERDATA, 2008, Lyons et al., 2000, Peeters et al., 2004, Schafer and Victor, 1999, Timms et al., 2005, Yeoman, 2008, Yeoman et al., 2007) or by tourism and transport stakeholders (e.g., Conseil Général des Ponts et Chaussées, 2006, ENERDATA, 2004, Futuribles, 2005, KUONI and Gottlieb Duttweiler Institut, 2006, Shell, 2002, Thomson, xxxx, UNWTO–UNEP–WMO, 2008, WBCSD, 2004). An analysis of these studies reveals similar approaches, but also considerable differences and some potential shortcomings:
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The year 2050 is a time horizon frequently used, allowing consideration of long-term environmental issues, but avoiding the uncertainty of long-term societal change. For tourism, however, 2050 may already pose major challenges in making assumptions, for instance concerning consumption patterns.
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Many studies are hampered by a lack of data, which explains why tourism development scenarios are usually based on qualitative assumptions.
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National studies tend to focus on domestic transport, ignoring the development of international aviation. This is a major omission, given that air transport represents 40% of global tourism emissions, and implies a significant underestimation of transport volumes.
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Both backcasting and forecasting techniques are used, but seldom with an exclusive focus on tourism. Within tourism, transport is paid great attention due to expected growth patterns and the difficulty of using non-carbon energy sources.
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There are very few long-term scenarios that focus on both climate change and tourism.
Table 1 presents a sample of tourism and transport scenario-based surveys, illustrating the above summary.
Finally, very few long-term scenarios dealing with tourism and climate change exist. Of those that do, none consider “avoiding dangerous climate change” objectives. Existing works are also limited in scope. Some only assess the driving forces behind emission reductions (like technological progress or behavioral change). Others only deal with parts of the tourism sector, such as aviation (Åkerman, 2005, Henderson and Wickrama, 1999, Vedantham and Oppenheimer, 1998). Many use quantitative forecasting techniques based on more or less fixed relationships, for instance between GDP and mobility, even though these relationships may be affected by climate and socio-cultural changes (Joly, 2008, LET/LASURE, 2006, Schafer and Victor, 1999). Simple demographic effects like, for example, the effect of an ageing population on the number of trips, are not accounted for in these fixed relationships.
Section snippets
General framework
This article attempts to build three scenarios based on an analysis of tourism and transport, using backcasting techniques to explore ways of attaining a global objective to “avoid dangerous climate change”. Methodologically speaking, the innovation of this study is to integrate two sectors, transport and tourism, traditionally analysed using either mainly quantitative or qualitative approaches. While transport analyses depend heavily on quantitative (statistical) models like logit-models,
Developing three storylines
Overall, three scenarios like the “happy few” storyline mentioned above were developed (see Section 3.2). They were based on five megatrends: “international governance”, “technological development”, “climate policies”, “tourism and transport policies” and “lifestyles”.
International regulation and integration. Two main options were identified: intensified globalisation (economic, political and cultural cooperation) versus regional cooperation leading to diverging regional blocks.
Technological
Conclusions
The results indicate that in a situation where the tourism and tourism transport sectors are required to reduce emissions by a percentage as high as in all other economic sectors (even with the option of considerable trading and/or offsetting), only substantial changes in the way we travel will lead to (moderate) emission reductions. Clearly, none of the scenarios developed in this article achieves the level of emission reductions climate policy would necessitate. Avoiding dangerous climate
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