Are the current expectations for growing air travel demand realistic?

Annals of Tourism Research 80 (2020) 102840

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Are the current expectations for growing air travel demand realistic?

T

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Susanne Beckena,c, , Fabrizio Carmignanib a b c

Griffith Institute for Tourism (GIFT), Griffith University, Gold Coast Campus, Parkland Drive, Southport 4222, Australia Griffith Business School, Griffith University, Nathan Campus, Australia School of Hospitality and Tourism Management, University of Surrey, Stag Hill, Guildford GU2 7XH, United Kingdom

A R T IC LE I N F O

ABS TRA CT

Associate editor: Scott Daniel

Global air travel has reached 3.7 billion passengers in 2017 and is predicted to continue to grow at 4.7% per annum. Such forecasts fail to consider the rising cost of carbon and socio-economic declines due to climate change. Using three scenarios, this paper finds that air travel growth slows considerably, with the high mitigation scenario producing the relatively best outcome for the industry with 9.8 billion passengers in 2070. Low mitigation is the least favourable option in the long term, as emissions continue to grow rapidly, whilst demand turns negative in 2067, due to increasing economic damage and rising inequality. A counterfactual scenario reveals that only extremely optimistic assumptions produce high growth rates produced in the Boeing forecast.

Keywords: Air travel demand Elasticity Climate change Mitigation Scenario Carbon price

Introduction Global air passenger volumes amounted to over 3.7 billion passengers in 2017, with demand predicted to grow at rates of about 4.7% (Boeing, 2018) or 4.4% (Airbus, 2019) per annum for the next 20 years. The global tourism industry uses these numbers, alongside United Nations World Tourism Organisation projections for 2030 (UNWTO, 2011), in their planning decisions. The question is then whether these predictions are realistic considering possible long-term scenarios about the dynamics of some key determinants of air travel demand. The current global airline network is a complex system of various supply, demand and regulatory factors. Whilst additional supply, for example new point-to-point connections, can stimulate additional demand, especially for low-cost carriers, aviation growth is understood to be demand-led (Airbus, 2019). Thus, airlines carefully consider demand trends before they invest into new routes and partnerships. At a global scale, the desire to travel by air and reach far-away destinations has increased, amongst others, due to higher discretionary incomes, improved levels of education, population growth, and urbanisation. Because of historic trends and due to key socio-economic drivers pointing in this direction, continued growth might seem plausible and destination's investments into tourism warranted. One of the key predictor variables in tourism and air transport forecasts is Gross Domestic Product (GDP) (Martins, Gan, & Ferreira-Lopes, 2017; Song & Li, 2008). Established models consider that revenue-kilometres grow 20–40% faster than GDP (Smyth & Pearce, 2008). Given that most economic forecasts predict continued GDP growth, it is not surprising that aviation (and tourism, see UNWTO, 2011) forecasts also foresee growth. However, some contest the need and legitimacy of aviation's ongoing growth path (Peeters, Higham, Cohen, Eijgelaar, & Gössling, 2019), and there are several reasons why, indeed, forecasts might not materialise.

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Corresponding author. E-mail addresses: s.becken@griffith.edu.au (S. Becken), f.carmignani@griffith.edu.au (F. Carmignani).

https://doi.org/10.1016/j.annals.2019.102840 Received 15 July 2019; Received in revised form 7 November 2019; Accepted 13 November 2019 0160-7383/ © 2019 Elsevier Ltd. All rights reserved.

Annals of Tourism Research 80 (2020) 102840

S. Becken and F. Carmignani

This paper contributes to this discussion by exploring the impact of drivers that are typically not included in aviation projections. One important, yet overlooked, factor in predictions of air travel demand is climate change, even though both the rising cost of carbon and negative impacts on economic growth from climate change impacts could pose some significant limitations. Focusing on climate change as a potentially restraining factor on aviation, this paper explores the implications of three scenarios. The first scenario involves limited investment into climate change mitigation, resulting in rapidly accelerating climate change impacts that lead to a reduction in economic activity. In this case, discretionary incomes would decrease and demand for air travel be negatively affected. The second scenario assumes increasing efforts to reduce greenhouse gas (GHG) emissions, and this results in progressively higher prices for carbon. Demand for air travel would reduce due to higher airfares. A third scenario explores the impact of extreme climate change mitigation with associated high carbon prices and a high proportion of GDP dedicated to decarbonisation efforts. The aim of this research is to provide some assessment of how plausible long-term trends in key variables (GDP in particular) might affect air travel demand. The paper does not claim to deliver a definite alternative to existing quantitative projections (e.g. Boeing, 2018), but it is rather to be interpreted as a sensitivity analysis that provides boundaries for air travel growth that might currently remain unrecognised due to an overly optimistic approach. Considering the speed with which climate change is manifesting, we believe that examining the constraints posed by climate change is of high relevance to the tourism sector globally, and this paper is the first to provide such an assessment for the aviation component of tourism. Background The Paris Agreement sets out an ambitious path for rapidly reducing carbon emissions. To limit average warming well below 2 °C, substantial reductions are required and state Parties are now seeking to implement actions that achieve a balance between anthropogenic emissions and their removals through carbon sinks by 2050 (UNFCCC, 2015). The following provides some background on the demand for air travel, GHG emissions and carbon pricing. Demand for air travel Decades of research illustrate the complexity of factors that shape demand for air travel and a considerable body of literature has formed on forecasting techniques, choice of variables, and levels of scale and focus (Becken & Schiff, 2011; Peeters et al., 2019; Song & Li, 2008). For tourism more broadly, the Global Tourism Transport Model (GTTM) has been developed to assess the impacts of various policy strategies on related CO2 emissions (Peeters, 2017a). Two drivers stand out for their dominant influence on demand, namely the price of travel and people's income. Research on the role of these factors involves the estimation of own-price elasticity of demand and income elasticity. Demand elasticities measure the change in the quantity demanded of air travel services in response to changes in airfares, whereby elasticity depends on the focus of investigation. Responses to changes in different price classes (e.g. business versus economy class) are different to those of aggregated demand at the air carrier or route levels, national or even supranational levels (Smyth & Pearce, 2008) (Table 1). Brons, Pels, Nijkamp, and Rietveld (2002) provided a meta-analysis of 37 studies on the determinants of price elasticity of air travel. The authors found that the mean price elasticity was below unity at −1.146, indicating that price changes will result in more than a proportional change in demand. Several studies found that leisure travel is more price sensitive Table 1 Price elasticities of demand for air travel. Study

Type of demand

Elasticity

Notes

Smyth & Pearce, 2008

Route National Supra-National Long-haul international leisure Short-haul leisure Short-haul domestic leisure Short-haul domestic business Short-haul business Long-haul international business Australian visitors German visitors Business travel Leisure travel Median and mean for flights Outbound price elasticity

−1.4 −0.8 −0.6 −1.04

Econometric analysis based on > 500 regression models.

Gillen et al., 2008

Schiff & Becken, 2011 Granados et al., 2012 Mumbower, Garrow, & Higgins, 2014 Morlotti, Cattaneo, Malighetti, & Redondi, 2017

Review of 21 studies on empirically estimated own-price elasticities of demand for air travel.

−1.52 −1.1 −1.15 −0.7 −0.27 −0.26 −0.87 −0.34 −1.33 −1.32

to −1.05

Analysis of international markets to New Zealand.

to −1.29 to −2.28 and −1.973

Drawing on customer booking data.

From −0.535 for business route to Hamburg to −1.915 for leisure route to Split

2

Daily online pricing and seat map data. Internet fares for easyJet flights from Amsterdam to 21 European destinations.

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than business travel (Gillen, Morrison, & Stewart, 2008; Granados, Gupta, & Kauffman, 2012). Another comprehensive meta-analysis by Smyth and Pearce (2008) found that route-level demand for air travel is highly elastic (−1.4), and short-haul travel is more price-sensitive than long-haul air travel. Moreover, national and supra-national demand is relatively insensitive to price changes, because at those levels the choices for substitution are limited. This is particularly so when the price increase is due to a policy at the supra-national level that affects all players equally. Several studies have assessed the potential impact of carbon policies on the demand for air travel (e.g. scenarios by Vorster, Ungerer, & Volschenk, 2012); all concluding that the price of carbon would have to be substantial to lead to significant reductions (Mayor & Tol, 2007). Sgouridis, Bonnefoy, and Hansman (2011), for example, estimated that a price of $200 per metric ton of CO2 would be required to reduce aviation emissions by 8%. Using the British Air Passenger Duty as a surrogate for a carbon tax, Seetaram, Song, Yec, and Page (2018) found that elasticities are not linear and increase with higher price levels. The impact from changes in income can also be profound. Several studies indicate that demand for air travel is a luxury item and, accordingly, sensitive to reductions in disposable income (Gallet & Doucouliagos, 2014). Smyth and Pearce (2008) concluded that income elasticities are slightly lower for developed country markets (between 1.3 and 1.5), compared with lower income countries showing a short-haul income elasticity of between 2.0 (route level) and 1.8 (national level). Examining long-distance travel from Great Britain, Dargay and Clark (2012) established an income elasticity of 1.44, slightly higher than the 1.31 provided by Gelhausen, Berster, and Wilken (2018) for departures from German airports. In a comprehensive review, and using meta-regression techniques, Gallet and Doucouliagos (2014) arrived at a mean income elasticity of 1.19. Gunter and Smeral (2016) questioned the assumption that air travel is a luxury good. Using data on tourism export of six world regions, the authors established substantial differences in income elasticities for three different growth periods, namely 1977–1992, 1994–2003 and 2004–2013. The findings revealed a stepwise decline over the periods, whereby the average income elasticity for the last period was 0.34, with Asia being the only region showing a value close to 1. Structural breaks and reduced growth, including economic uncertainty in the last decade, were named as reasons for declining income elasticity, confirming similar findings by Culiuc (2014) who arrived as a value lower than 1 for the period between 2005 and 2010. In addition to economic variables, there is potential that consumer sentiment changes over time with the result that air travel is seen as an environmentally irresponsible form of mobility. In that case, demand might reduce in addition to price or income signals (Gössling, Scott, Hall, Ceron, & Dubois, 2012). Aviation emissions and the price of carbon In 2018, CO2 emissions from passenger aviation were in the order of 665 million tonnes (Global Sustainable Tourism Dashboard, 2019). The International Air Travel Association (IATA, 2018) estimated 2017 emissions to be 844 million tonnes, including both passenger and freight transport. Since fuel is a major operational cost to airlines, substantial investment goes into improving fuel efficiency and as a result carbon efficiency. International aviation emissions are not covered in the Paris Agreement but fall under the mandate of the International Civil Aviation Organisation (ICAO). In partnership with the International Air Transport Association (IATA), ICAO has developed a ‘bundle of measures’, centring on a market-based approach to compensate for emissions. The Carbon Offset and Reduction Scheme for International Aviation (CORSIA) seeks to deliver ‘carbon neutral growth’, which means that an increasing amount of carbon credits need to be purchased by airlines to compensate for aviation emissions beyond 2020 (IATA, 2013). CORSIA has been criticised for failing to lead to a meaningful reduction in aviation emissions (Peeters, 2017b).1 Carbon prices have fluctuated considerably and are generally low. About 20% of global CO2 emissions are covered by some form of pricing mechanism, with 75% of emissions being priced at less than US$10 (Boyce, 2018). A comparison with changing oil prices is revealing. The global peak in oil prices in 2007, for example, would have been equivalent to a price of $230 per tonne of CO2 (Boyce, 2018). Predictions of the carbon price, however, are challenging, especially when global estimates are sought. A recent report by the High-Level Commission on Carbon Prices (2017) concluded “a well-designed carbon price is an indispensable part of a strategy for reducing emissions in an efficient way” (p. 2). The report suggests that pricing levels consistent with the Paris targets should be at least US$40–80/tCO2 by 2020 and increase to US$50–100/tCO2 by 2030. These estimates are similar to those provided by integrated assessment models that estimate the social cost of carbon. A prominent example is Nordhaus' (2013) DICE (Dynamic Integrated Model of Climate and the Economy) that suggests $37/tCO2 in 2020 and $104 in 2050. In contrast, models that specify carbon concentrations (or temperatures) and calculate the cost of reaching these (Boyce, 2018) arrive at much higher estimates, for example $229 in 2030 and $1006 by 2050. Regardless, it seems highly likely that carbon prices will increase considerably, and this will affect aviation. Consequently, increasing exposure to carbon risk should be a high priority for air travel-dependent tourist destinations (Becken & Shuker, 2018). Economic impact of climate change and the cost of mitigation The projected impacts of climate change are well understood and the series of Intergovernmental Panel on Climate Change (IPCC) 1 The growing carbon footprint of the global travel and tourism industry is increasingly under scrutiny, leading the global industry body, the World Travel and Tourism Council, to announce the industry's aspiration to be carbon neutral by 2050. The announcement was made in September 2019 but details on how this is to be achieved are not available at this point.

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reports demonstrate a wide range of profound changes to climatic parameters and socio-ecological systems. The Synthesis Report of the Fifth Assessment Report (IPCC, 2014, p. 13–16) states that climate change “is projected to undermine food security”, “will impact human health”, “will increase risks for people, assets, economies and ecosystems” “increase displacement of people” and “can indirectly increase risks of violent conflicts”. There are already plenty of examples of climate change-related damages (including in relation to tourism, see Scott, Hall, & Gössling, 2019). To protect against recurring flooding and inundation, Miami Beach has invested over $400 million into adaptation measures such as storm drain upgrades, flood pumps and sea walls (Smiley, 2017). However, whilst the climate science is unequivocal and present-day examples are informative, the economics of future climate change damages is challenging. One of the first attempts to quantify the costs of climate change was presented by Sir Nicholas Stern in 2007. The Stern-Report concluded that early action will be more economical than the cost of not acting. Stern estimated that unmitigated climate change could equate to a cost of at least 5% of GDP each year. More recent models have produced damage functions that link global temperatures (and atmospheric CO2 concentrations) with the economic costs that arise from a wide range of climate change impacts, such as extreme weather events, sea level rise, ecosystem functions and productivity. The models then estimate future damages as a proportion of GDP (Revesz et al., 2014). There are many sources of uncertainty and damage curves have been criticised for their lack of both theoretical or empirical bases (Boyce, 2018; Pindyck, 2013), as well as other challenges such as the inadequate consideration of distributional effects or use of different discount rates to translate future damage into today's monetary value. The projected damage differs considerably between the existing models, ranging between 2 and 8% of GDP per annum for temperature increases of 4 °C (Revesz et al., 2014). Along similar lines, Kahn et al. (2019) use a stochastic growth model and panel data to document the adverse impact of persistent changes in temperature on real per-capita GDP over long periods of time. Their analysis suggests that if average global temperature increases by 0.04 °C per year until 2100, then in the absence of mitigation policies, per-capita GDP will decline by > 7%. In addition, several authors have attempted to estimate the cost of mitigating climate change. Stern (2007) suggested that global mitigation efforts would be in the order of 1% of GDP to avoid dangerous climate change. Others (e.g. Nordhaus, 2013) estimated that the annual cost of global mitigation policies could be up to 4% under conditions of less favourable international cooperation. Costs could be up to 20% (compared with a non-climate affected GDP) if inequalities amongst countries are fully considered.

Global GDP and long-term headwinds Earlier research has established that rising world GDP per capita positively impacts demand for travel (Martins et al., 2017). Over the last six decades, World GDP has grown at an average rate of 3.5% per annum (World Bank, 2018). Whether this growth rate can be maintained for the next sixty years is however questionable. In fact, there seems to be a growing consensus amongst economists that global growth will significantly slow-down in the future. Johansson et al. (2013) predict that global growth will decline to 2.6% a year by 2040 and then to 1.7% a year by 2060. Similarly, Guillemette and Turner (2018) presented a baseline scenario where world GDP growth progressively declines from the current rate to < 2% a year by 2060. Similar projections are reported by McKinsey and Company (2015), with average annual global GDP growth declining from 3.8% over the period 1950–2014 to 2.1% over the period 2014–64. Typically, the methodology used to generate these projections relies on a decomposition of the GDP growth rate into its proximate determinants: technological progress, labour and physical capital accumulation. Technological progress is modelled assuming that the rate of productivity growth will converge to some exogenous steady state value (e.g. 1.5% a year in the OECD projections). Labour accumulation is derived from the analysis of trends in working-age population and employment rate of different age/sex groups. Physical capital accumulation is obtained from a projection equation that incorporates convergence towards a stable capital to output rate after accounting for inertia, depreciation, and constraints arising from factors like current account imbalances or cyclical conditions. The decomposition however does not consider possible additional “headwinds” which might affect growth in the long-term. For instance, Gordon (2012, 2014) argues that even assuming that the rate of productivity growth continues into the future at the rate of the last three decades, economic growth in the US might fall to 0.5% by 2100 because of five headwinds: the plateau in educational attainment, rising income inequality, outsourcing and changing import patterns, global warming and restrictions on energy use, and the twin household and government deficits. Whilst these headwinds are specifically identified for the US and may not apply at global level, long-term GDP growth projections must account for possible factors that could hinder growth over and above the impact of technological progress and factors accumulation. Two such global headwinds are likely to be relevant, based on current assessment of future economic dynamics. The first one is the increase in income inequality, not just in the US, but also in other countries that are large contributors to global growth, China most notably (Piketty, Yang, & Zucman, 2017), and at global level (Bourguignon, 2017). The second is the risk of worsening fiscal imbalances, particularly in the form of increasing levels of debt to GDP ratios across advanced economies. In fact, up until now, the average debt to GDP ratio in OECD economies has remained between 90% and 100%. However, even in the presence of progressive consolidation (as represented by decreasing fiscal deficits over the medium-long term), the average debt to GDP ratio is expected to overshoot 100% and be close to 120% by 2025 (OECD, 2010).

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Fig. 1. Variables considered in the simulation. The oval shaped constructs determine the key drivers that influence GDP and Airfare. Orange text highlights variables adjusted for the three scenarios.

Methodology Overall approach The purpose of the quantitative exercise is to simulate how key climate change drivers might affect demand for air travel between 2020 and 2070. To that end, the following projection equation will be used:

ln(travel) = α ln(price ) + β ln(GDP ) where ln denotes the natural logarithm of travel (number of passengers), price (airfare) and GDP (global GDP). The equation embeds the basic idea that the cost of the airfare and global GDP are the two key determinants of global air travel demand. The parameters α and β are the elasticities of demand with respect to price and income, respectively. In operationalising the equation, the values of the elasticities will be drawn from the existing body of empirical evidence. The projections for price consider the dynamics of the cost of carbon and aircraft efficiency (excluding revolutionary changes in propulsion technology). Projections for income will be based on GDP figures provided by Johansson et al. (2013), net of the headwinds discussed in the previous section, namely income inequality and fiscal imbalances. In addition, the cost of mitigation and the damage associated with climate change will be accounted for by subtracting a certain percentage from the GDP figures. Fig. 1 summarises the conceptual framework underlying the projection equation. Three mitigation scenarios are considered: low, high, and extreme mitigation. Note that the different scenarios affect parameters related to mitigation costs, climate change damage, cost of carbon, and inequality. Instead, aircraft efficiency improvements and the impact of fiscal imbalances are the same across the scenarios. It is acknowledged that the above model is a gross simplification of the complexity of factors that drive demand for air travel. Further, there are interactions between the identified drivers, for example the price of carbon and mitigation cost (and potential reduction in tourism output, see Dwyer, Forsyth, Spurr, & Hoque, 2013), and higher amounts of GDP invested into mitigation, in turn, may accelerate improvements in the fuel efficiency of aircraft (leading, for example, to greater rates in reducing carbon intensity). These interactions are not specifically modelled, nor are structural details that determine the wider tourism transport system (as in GTTM, see Peeters, 2017a); however, changes to key variables for the purpose of this simulation are implemented in such a way that they provide a coherent and plausible picture. As such, our simulation complements related qualitative scenario building exercises as presented by Vorster et al. (2012). More detail on each variable and the underlying assumptions for the three scenarios is provided below.

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Estimating elasticity Two different types of elasticity are required for the simulation, price elasticity of demand and income elasticity. The literature on price elasticity (Table 1) demonstrates the challenge of deriving one single elasticity for all flights globally. Generally, research shows that business travel is less elastic than leisure travel, and long-haul flights are less elastic than short haul flights. Based on this classification, the price elasticities for the purpose of this paper are assumed as follows:

• Business travel: −0.7 • Short haul travel (non-business): −1.3 • Long haul travel (non-business): −1.1 Using travel data from Amadeus, relative market shares for the above segments were derived, whereby ‘business travellers’ were assumed to have booked either first or business class (an acknowledged simplification). Economy class passenger bookings were then divided into short-and long-haul travel, using an itinerary distance rule of over or equal/under 500 km (Scott et al., 2008). Using data from 2.54 million trips made in the month of January 2017, the share of business class travellers was 5.2%. Considering the remaining 94.8%, the data show that 80.5% of non-business travel was over 500 km, with the remaining 14.3% being short-haul. These shares imply a weighted elasticity of about −1.1, which is comparatively conservative relative to other studies, for example by Pentelow and Scott (2011) who applied an elasticity of −1.4 for travel to the Caribbean. Turning to income elasticity, recently published research by Gunter and Smeral (2016) and Gelhausen et al. (2018) suggests values close to unity or even below one. Whilst higher values might be appropriate under the assumption that future demand comes from developing countries (Smyth & Pearce, 2008), lower values would better fit a scenario where demand continues to be driven by more saturated markets such as Europe and/or if conditions of crisis negatively influence consumer choices (Gunter & Smeral, 2016). The final choice of elasticity parameters is informed by the following exercise. The elasticities are in practice the α and β parameters in the projection equation ln(travel) = α ln (price) + β ln (GDP). Historical data on number of passengers, airfare and GDP are available from IATA and the World Bank for the period 2004–2017. It is therefore possible to determine the values of α and β that, given the historical data on airfare and GDP, best approximate the sequence of historical data on passengers. These two values turn out to be α = −1.1 and β = 1.3. The actual growth rate in passengers from 2004 and 2017 is 5.6% a year on average. With α = −1.1 and β = 1.3, the projection equation generates a growth rate of 5.7%. This in turn validates the reduced-form model, in the sense that the projection equation on which the simulation is built reproduces past observed values of the relevant variables quite closely. Estimating the price of carbon and airfares Acknowledging that the price of carbon is very difficult to predict, especially at a global level given likely national or regional differences, the estimates provided by the High-Level Commission on Carbon Prices (2017) informed this research. More specifically, the bands for 2020 (US$30–50/tCO2), 2030 (US$80–100/tCO2), 2040 (US$110–130/tCO2) and 2050 (US$130–160/tCO2) were used as guidance, and years in-between were linearly interpolated. All three scenarios begin with a starting price of $20/tCO2. The Low Mitigation scenario assumes a price of $120/tCO2 in 2070, whereas the High Mitigation scenario used $200 t/CO2 as the 2070 point for interpolation. To test the impact of Boyce's suggestion, a carbon price of $1000/tCO2 in 2070 was assumed for the Extreme Mitigation scenario. To model the impact of rising carbon prices on airfares, information provided by IATA on 2017 total revenue (US$ 534 billion) and total passenger numbers (4.09 billion) was used to return an estimate average baseline airfare of $130. To verify this value, an alternative approach was taken that drew on Amadeus booking data to derive the average travel distance (2100 km) and combine this with IATA information on revenue per passenger-kilometre ($0.75/pkm). The average fare here was $156. As a result, a starting point of $150 for 2018 was used in the simulation. Using a carbon intensity of 0.1 kgCO2/pkm (see Global Sustainable Tourism Dashboard, 2019), the baseline carbon emissions for the average 2100 km journey (assuming travel distance patterns remain unchanged) were calculated to be 210 kg of CO2 per passenger, or 0.21 tonnes2. Airfares in the simulation increase over time in response to different carbon prices, with no other factors being considered (e.g. wages, other operational costs or taxes). Since airlines continually invest into fuel efficiency a conservative improvement of 1% in carbon efficiency per year (note: this is in addition to general improvements in productivity/efficiency captured in the GDP projections) was assumed (see Owen, Lee, & Lim, 2010), hence the carbon intensity is decreasing over time, for example to 150 kg of CO2 for an average passenger in 2050. It is understood that improvements could be higher, especially when decarbonisation pressure on airlines increases. Improvements in Air Traffic Management could add to future reductions in fuel use and this possibility is captured in Table 3 further below. Mitigation costs Mitigation costs as a proportion of GDP will vary depending on the scenario. All three scenarios begin with a global baseline cost 2 Note that the average travel distance is used as a single-point value of the whole distribution of travel distance; this is a simplification of the more varied spectrum of short to long haul travel. Using the average distance (and average fare and carbon content as a result) is appropriate when the distribution of travel distance is assumed to remain unchanged. The possible influence of change in distance is captured in Table 3.

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of 0.5% of GDP being spent on climate change mitigation. This baseline can be justified with the well-known McKinsey and Company (2013) carbon abatement cost curve and the estimate that if all measures that can be achieved at a maximum cost of Euro 60 per tonne of CO2 were implemented this would generate a global economic cost of between Euro 200 and 350 billion per year by 2030. This would represent < 1% of GDP, confirming Stern's (2007) earlier estimate. Since not all measures are being implemented (and the price of carbon is below the above threshold) the GDP cost of 0.5% seems plausible. Future costs of mitigation are difficult to estimate as they depend on climate change policies, price of carbon, technologies and many other factors. There are also likely considerable differences between countries. Despite these challenges, the IPCC (2014) estimated that, if all countries implemented measures immediately and assuming a uniform global price of carbon and available technology, then limiting warming to below 2 °C would equate to consumption losses of about 1.7% in 2030. The losses would rise to about 3.4% in 2050 and 4.8% in 2010. The assumption of a 2 °C world is most akin to the Extreme Mitigation scenario. Since there are co-benefits from climate mitigation (e.g. health improvements from reduced air pollution), which are not accounted for in the above losses, the extreme mitigation costs for 2070 are assumed to be slightly less than the IPCC estimates, namely 3.5% of GDP. Costs are then interpolated to obtain estimates for each year. The other two scenarios are associated with less investment in mitigation. The High Mitigation scenario assumes that by 2070, 2% of GDP is spent on mitigation, whereas the Low Mitigation scenario estimates only 1%, a doubling from current levels. Climate change damage The flipside of GDP spent on mitigation is the damage incurred due to increased impacts from climate change. The IPCC (2014) notes that global economic losses resulting from climate impacts, such as increasing temperatures and extreme climatic events, are difficult to estimate. This is further complicated by an increasing amount of money spent on adaptation to avoid the worst impacts. Hence, GDP will have to be allocated to invest into preventing climate impacts (or reducing them) and dealing with damage that could not be avoided. Those limits to adaptation are likely to increase during this century. Whilst somewhat contested, climate damage functions provide insight into the possible range of economic loss given modelled increases in temperature. Three models are commonly cited when estimating future climate change damage. Nordhaus (2013) provides the most conservative model with estimates of damage in the order of 1%, 2% and 4% of GDP for 2, 3 and 4 °C warming, respectively. The Weitzman (2012) model arrives at higher damage estimates than Nordhaus, but lower than Dietz and Stern (2014). The latter estimate 2%, 14% and 50% loss of GDP for the three levels of warming (Wade & Jennings, 2016). The Low Mitigation scenarios is logically linked to the highest increase in temperature and resulting climate damage. Considering a possible temperature increase of close to 2.5 °C by 2070 (IPCC, 2014), a damage cost of 3% of GDP is deemed plausible for this scenario. The High Mitigation scenario is linked with an estimated 2% damage, and the Extreme Mitigation scenario might lead to only 1.5% of GDP in damage by 2070. The reasoning for assuming a relatively high proportion of GDP loss even in the case of extreme investment into mitigation, is due to the lag effect of historically emitted GHG emissions. GDP, inequality and fiscal imbalances headwinds Following the discussion earlier, the value of GDP for the purpose of the simulation is obtained by subtracting headwinds from the baseline projections reported by Johansson et al. (2013). In turn, the headwinds are quantified from existing empirical evidence on the elasticity of growth with respect to inequality and debt, respectively. Starting from the former, the estimates reported by Carmignani (2011) suggest that a 1% increase in the Gini coefficient (as a measure of inequality) reduces average annual growth by approximately 0.1 percentage points. A 5% increase in inequality, which aligns with early estimates from the World Bank (2007), would therefore result in a headwind of 0.5 percentage points. This is in line with the headwind determined by Gordon for the case of US. Turning to debt, the results by Woo and Kumar (2015) show that a 10 percentage point increase in the debt to GDP ratio slows GDP growth down by approximately 0.2 percentage points. The previously mentioned forecasts of OECD (2010) indicate that the debt to GDP ratios on average might increase by approximately 20 percentage points by 2025, thus supporting the use of a headwind of 0.4. To clarify how headwinds work, consider the following example for the year 2030. The baseline growth rate projected by Johansson et al. (2013) is 3.2. The headwinds are −0.5 for inequality and −0.4 for debt, i.e. total −0.9. Therefore, our projected GDP growth rate for 2012 is 2.3%. A possible complication arises with respect to the treatment of inequality. As income inequality increases, poverty and other forms of inequality (e.g. health, education) also tend to increase, thus amplifying the disruptive effect on economic growth. This effect is exacerbated by climate change. As noted by IPCC (2014), climate change hinders poverty reduction because it undermines food security, leads to displacement and involuntary migration, and increases risk of disaster and conflict. It is the more vulnerable groups in society who are likely to be particularly affected by climate change. All of this suggests that the relationship between inequality and growth might be nonlinear. More specifically, for lower mitigation scenarios, it is plausible that climate change drives income inequality up, which in turn raises other forms of inequality, with an overall stronger (negative) effect on the economy's long-term growth prospects. To account for this, the inequality headwinds increase from 0.5 to 1 over the entire simulation period in the Low Mitigation scenario, and from 0.5 to 0.7 in the High Mitigation scenario. Conversely, in the Extreme Mitigation scenario the headwind stays at 0.5 throughout the simulation period, reflecting the minimal deterioration in climate and, hence, the absence of a feedback effect from climate change to income and other inequalities. 7

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Table 2 Parameters and values for three mitigation scenarios used for the simulation. Parameters

Mitigation scenarios

Price elasticity Income elasticity Inequality headwind (percentage points) - Start value 2020 - End value 2070 Debt headwind (percentage points) Climate change damage (% of GDP) - Start value 2020 - End value 2070 Cost of mitigation (% of GDP) - Start value 2020 - End value 2070 Carbon price (USD) - Start value 2020 - End vale 2070 Aircraft efficiency

Low

High

Extreme

−1.1 1.3

−1.1 1.3

−1.1 1.3

0.5 1.0 0.4

0.5 0.7 0.4

0.5 0.5 0.4

0.2 3

0.2 2

0.2 1.5

0.5 1

0.5 2

0.5 3.5

20 120 1% annual gain

20 200 1% annual gain

20 1000 1% annual gain

Summary of parameters and scenarios All in all, our simulation accounts for the effect of a rather large variety of variables, including economic growth, inequality, fiscal imbalances, climate change damage and mitigation, and the cost of travel. The projection equation summarises the dynamics and relationships between these variables in what can be regarded as a reduced-form model. The parameters used for the simulation exercise are summarised in Table 2 Note that when a start value and an end value are given, then values in between 2020 and 2070 are determined by linear interpolation. It is understood that this is a simplification. Many other factors could shape the demand for aviation. The literature indicates that the dominant drivers are captured in our model; however, Table 3 provides a qualitative assessment of other moderators. It appears that the additional drivers generally have the potential to reduce demand further compared with the assumptions in our simulation, with the exception of potentially faster improvements in technology due to increasing pressure on airlines.

Table 3 Additional factors that could influence demand for air travel. Driver

Explanation

Potential impact

Economic wealth per person

Global GDP normalised by world population shows wealth theoretically available per person.

The trend shows a declining GDP per person, likely leading to lesser demand.

Travel distance

Increase in the average travel distance.

Higher carbon cost per trip, leading to relatively greater demand reductions.

Higher elasticity of demand

Consumers may become more price sensitive.

More pronounced impact of higher carbon costs.

Income elasticity

Increasing income elasticity means a decreasing demand for air travel with declining GDP.

Accelerated gains in fuel efficiency

Higher income elasticity for low-income groups at increasing income disparity increases average income elasticity. Increasing pressure on airlines might result in greater technological leaps.

Substitution of transport modes

Greater pressure to decarbonise transport would favour land-based transport providers and innovations.

Extreme weather

Higher occurrence of extreme weather disruptions.

8

Average carbon intensity would decrease faster than assumed, leading to relatively lower carbon costs. Substitution options increase cross-elasticities, leading to reduced demand for (short/medium-haul) air travel Increased costs for airlines, leading to further price increases.

Trend of demand

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Table 4 Summary results for the three mitigation scenarios. 2030

2040

2050

2060

2070

Low mitigation GDP growth (%) Airfare price (USD) Air travel demand (% growth) Air travel demand (mil. passengers)

2.1 158 2.5 6433

1.4 160 1.7 7891

0.7 162 0.8 8898

0.3 164 0.3 9403

−0.1 165 −0.2 9463

High mitigation GDP growth (%) Airfare price (USD) Air travel demand (% growth) Air travel demand (mil. passengers)

2.2 161 2.4 6328

1.5 166 1.7 7730

0.9 170 1.0 8781

0.6 173 0.6 9449

0.2 175 0.2 9784

Extreme mitigation GDP growth (%) Airfare price (USD) Air travel demand (% growth) Air travel demand (mil. passengers)

2.2 191 0.9 5249

1.6 221 0.7 5702

1.0 244 0.3 6013

0.7 263 0.2 6190

0.4 277 0.04 6272

Results Simulation – three mitigation scenarios The key parameters influencing air travel demand are price and GDP. Fig. 2 shows the difference in the airfare for the three mitigation scenarios. Under Extreme Mitigation, airfares will increase by 80% between 2018 and 2070 due to the rising cost of carbon. The GDP growth rate is reduced by fiscal headwinds and growing inequality, but also – and of particular relevance to this paper – by the cost of mitigation and the damage cost of climate change impacts. Fig. 3 shows that the aggregate effect on GDP for the two climate related factors differs for the three scenarios. For the Low Mitigation scenario, most of the reduction in GDP is driven by increased climate damages, whereas for the Extreme Mitigation scenario, the biggest impact is from mitigation costs. The High Mitigation scenario is balanced, whereas the aggregate is highest for Extreme Mitigation, because of high mitigation costs, but still relatively (unavoidable) climate damage. Over the long term, that is well beyond 2070, the Extreme Mitigation scenario should be the most favourable, when mitigation cost stabilises, whilst climate damage costs might grow exponentially in the other two scenarios. These non-linear trends and longer time frames are not simulated here (Fig. 3). The core results of the simulation exercise are summarised in Table 3 below. Growth in air travel demand is fastest under the Low Mitigation scenario (but considerably below growth forecast by Boeing, 2018), primarily because of the very modest increase in airfare price (Fig. 4). Subsequently, from 2040 onwards, the effect of the slow-down in GDP growth due to the cost of climate change and growing inequality has an impact that more than offsets the relatively lower increase in airfares. Air travel demand turns negative

Fig. 2. Cost of an average airfare under different carbon price scenarios. 9

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Fig. 3. Percentage reduction of GDP due to climate change damage and the cost of reducing carbon for three scenarios.

from 2067, a trend that is only observed in the Low Mitigation scenario. The High Mitigation scenario delivers the best outcome for the air travel industry, whereby the difference to the Extreme Mitigation scenario is most prominent in the earlier periods where the rapid increase in the cost of the airfare under extreme mitigation investments implies that growth rates reduce sharply. In the Extreme Mitigation scenario, the number of passengers remains well below the other two scenarios. 10

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Fig. 4. Growth rates in air travel demand for the three scenarios.

The simulated aviation growth rate can be translated into CO2 emissions, starting at a proposed baseline of 700 million tonnes (Mt) in 2020 (Global Sustainable Tourism Dashboard, 2019; IATA, 2018). Fig. 5 shows that emissions continue to grow throughout the simulation period, and they stabilise for all scenarios at the end of the simulation period, albeit at different levels. Cumulative CO2 emissions between 2020 and 2070 for the three scenarios amount to 58,490 Mt. (Low), 58,886 Mt. (High) and 42,646 Mt. (Extreme). Counterfactual scenarios Whilst the model presented here is comparatively simple, it had been validated against historic data and it is therefore beneficial to compare it against a well-established and widely used industry projection, namely the one provided by Boeing up to 2037. The above simulations show that the average growth rate for GDP between 2020 and 2037 is 2.2% for Low Mitigation and 2.3% each in the High and Extreme Mitigation scenarios. These GDP projections are not so dissimilar from the 2.8% in the Boeing model. However, the average air travel demand growth rate in our scenarios is substantially lower, namely 0.9% under Extreme Mitigation and 2.5% and 2.6% in the High and Low Mitigation scenario, respectively. These are lower than the 4.7% projected by Boeing (2018) for the period 2018–2037. To better understand the sources of the difference, a counterfactual analysis is undertaken.

Fig. 5. Estimated CO2 emissions for the three scenarios. 11

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In the counterfactual, climate change damage, carbon price, and mitigation costs are kept constant at Low Mitigation scenario level. This scenario is the one that in the initial part of the simulation period already delivers the highest growth rate of air travel demand. Also, up to 2040 the difference with the High Mitigation scenario is minimal. The counterfactual then consists in determining the values of elasticities and/or GDP growth that yield a predicted growth rate of air travel demand of 4.7%. Thus, the counterfactual will establish which values of elasticities and GDP growth must be assumed to replicate the Boeing projection.3 Results can be summarised as follows: - When all the other parameters are kept at the Low Mitigation scenario level and GDP growth is 2.3% a year on average, then a price elasticity of −0.5 and an income elasticity of 2.8 have to be assumed in order to bring air travel demand growth to 4.7%. These elasticities rest on the assumption of a very high customer preference for air travel both in the face of high airfares and in relation to discretionary income. This is not completely implausible for some markets (Seetaram, Song, & Page, 2013); however, as a global average it might be optimistic. - If elasticities are kept at the level derived from the literature, then a 4.7% growth in air travel demand requires a 3.7% GDP growth per year on average. This is about one full percentage point higher than what is projected by Boeing. - If GDP growth is kept at the 2.8% rate predicted by Boeing, then a growth rate of 4.7% in air passengers demand can only be obtained if the income elasticity is 1.8 and the price elasticity decreased to −1. In other words, the growth predicted by Boeing, requires an income elasticity that is well above what is estimated in the literature. Overall, it would appear from the counterfactual exercise that achieving a growth rate in air travel demand as the one projected by Boeing for the period 2018–2036 would require either implausible elasticities or a rate of growth of GDP considerably higher than the 2.8% assumed by Boeing.4 Concluding discussion In the face of accelerating climate change coupled with demands for rapid decarbonisation, this paper is the first to explore the potential long-term impacts of climate change on aviation growth. Using a simulation approach, three scenarios depicted plausible combinations of key factors that determine the price of an airfare and GDP – the two key drivers of demand for air travel. The purpose was to provide a more nuanced approach to the critically important topic of tourism and aviation growth. An increasing number of destinations worldwide invest into tourism, and indeed depend on tourism as a key contributor to their national economies. Ignoring climate change as a key driver of future economies increases the risk of developing suboptimal or even counterproductive tourism policies, strategies, and management plans. We suggest that, based on this high-level simulation, destinations invest in a more detailed country-specific model that projects different growth paths for aviation demand. Such national or destination-level models might then consider changes in travel distance, market composition (and associated elasticities), and substitution potential (to name a few) to arrive at more nuanced scenarios, probably for shorter timeframes. This present research found that airfares will only rise significantly under extreme increases in the price of carbon, namely those levels that have been found to be necessary to achieve the 2 °C threshold of warning. These levels of over $1000 per tonne of CO2 are much higher than the more commonly used ‘social cost’ of carbon. Thus, whilst confirming earlier work on the limited impact of low levels of carbon taxes (Mayor & Tol, 2007), we note that carbon price trends indicate that extremely high taxes are unlikely. Changes in air travel demand in response to price are therefore less likely. Thus, current fears on the part of the industry about whether the CORSIA scheme might have a negative impact on demand are exaggerated, unless the international offsetting scheme is interpreted as a harbinger of future trends of rapidly increasing costs of carbon, which then might represent a risk to those destinations that disproportionally depend on air travel (Becken & Shuker, 2018). Consumer sentiment against air travel may also play an increasing role (Gössling et al., 2012), a trend that is now manifesting in the ‘flightshame’ movement (Airline Leader, 2019). These two trends might be reinforcing and demand policy responses way beyond tourism. The other determinant of demand for air travel, namely income approximated through GDP, is shaped by several non-climatic and climate-related headwinds. However, these are not independent from each other, as it is likely that the headwind of inequality is aggravated by higher levels of warming-induced climate change damage. The more direct climate related factors that erode mainstream predictions of GDP are the cost of mitigation (Extreme Mitigation) and the damage of climate change (Low Mitigation). Despite the combination of headwinds, and perhaps surprisingly, all three scenarios deliver positive growth rates for GDP beyond 3

It is worth remarking that the purpose of the paper is not to replicate the structure of the Boeing's model, but to use a reduced-form approach to see how air travel demand growth might significantly decline over the next few decades because of the expected trend in its macro level drivers. In this sense, the growth rate predicted by Boeing is taken as a benchmark. The counterfactual exercise then provides some understanding of how key parameters, such as elasticities, would have to change to reconcile Boeing's prediction with the one arising from this paper. 4 ARIMA and ARIMAX specifications have been used to model air travel demand with time series data at monthly or quarterly frequencies (e.g. Anvari, Tuna, Canci, & Turkay, 2016). The methodological contest of this paper is different: the historical data serve as a validation of the projection equation and are of limited time length (hence not ideal for ARIMA estimation). Nevertheless, as a further sensitivity test, an ARIMAX model was fitted on the historical data and the estimated parameters used as elasticities in a version of the projection equation that also includes an autoregressive term. The best fitting model turns out to be an ARIMA (1,1,0). Results are qualitatively similar to those reported in Table 4, although the projected air travel growth rates are generally lower than those shown in the table. Therefore, if anything, the ARIMAX specification reinforces the general finding of the paper. The full sets of ARIMAX results are available from the authors upon request. 12

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2060. The Low Mitigation shows a decline from 1968, partly because of rising inequality, and a contraction in aviation demand. For the other two scenarios, GDP declines but remains positive throughout the projection period. This means that destinations may expect future growth in air arrivals, but at lower rates. In summary, all three scenarios clearly show a slowing down in aviation growth, with the most drastic reduction being observed for Extreme Mitigation, largely because of a substantial increase in airfares in combination with considerable cost of mitigation. From a climate change perspective, all three scenarios lead to a continued, albeit slowed, increase in aviation emissions raising questions about the aviation industry's contribution to the goals agreed on in the Paris Agreement (Bows-Larkin, 2015; Peeters, 2017b). The Low Mitigation scenario provides the worst outcome for the climate. Unless a breakthrough in technology (e.g. hydrogen, hybrid or electric planes at large scale) can be achieved to reduce the need for fossil fuel, this should be of concern to both the industry and nations who ratified the accord. The comparison between the three scenarios presented in this paper with the Boeing forecast indicates that only optimistic assumptions could deliver the projected annual growth in air travel of 4.7%. Possibly, in their model, Boeing included a range of unknown factors, or assumed very high income elasticities for passengers originating from developing economies (Smyth & Pearce, 2008); an assumption that ignores possible shifts in social norms around a low-carbon paradigm (Becken, 2016). High income elasticities also contradict more recent research by Gunter and Smeral (2016) on air travel as a mature product. Whilst the emergence of new markets might justify high elasticities, the evidence that it is the less developed countries that will be disproportionally affected by climate change might render this assumption less plausible. Higher damage erodes GDP and the propensity to travel. Furthermore, recent tourism research on climate vulnerability showed that countries that are particularly dependent on tourism are often most vulnerable to climate impacts (Scott et al., 2019). Such observations highlight that further research at the national level is warranted to determine country-specific effects, both in terms of changing demand and deteriorating risk profiles. In conclusion, this research should be of great importance to anyone investing heavily into aviation-dependent tourism. Relying solely on industry-driven forecasts could be risky as it not only ignores the increasing exposure to carbon risk (Becken & Shuker, 2018), but also the very possible reduction in economic growth affecting tourism demand more broadly. Destinations that depend on air travel may want to consider the implications of possible reductions – or at least slowing down – in demand. References Airbus (2019). Growing horizons: 2018–2037. Global market forecast. Available (20/04/19) https://www.airbus.com/aircraft/market/global-market-forecast.html. Airline Leader (2019). “Flight shame” and the jet fuel tax threat. Vol. 51, Airline Leader13–16. Anvari, S., Tuna, S., Canci, M., & Turkay, M. (2016). Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems. Journal of Advanced Transportation, 50(1), 25–49. Becken, S. (2016). Evidence of a low-carbon tourism paradigm? Journal of Sustainable Tourism, 25(6), 832–850. Becken, S., & Schiff, A. (2011). 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Weitzman, M. (2012). GHG targets as insurance against catastrophic climate damages. Journal of Public Economic Theory, 14(2), 221–244. Woo, J., & Kumar, M. S. (2015). Public debt and growth. Economica, 82, 705–739. World Bank (2007). Global economic prospects: Managing the next wage of globalization. Available at (03/10/18) http://pubdocs.worldbank.org/en/ 135221443469728770/Global-Economic-Prospects-2007-Managing-the-next-wave-of-globalization.pdf. World Bank (2018). GDP growth. Available (12/04/19) https://data.worldbank.org/indicator/ny.gdp.mktp.kd.zg. Susanne Becken a Professor of Sustainable Tourism at Griffith University in Australia, and a VC Research Fellow at the University of Surrey, UK. She has published widely on sustainable tourism, climate change and tourism resource use. Susanne is a member of the Air New Zealand Sustainability Advisory Panel, PATA's Sustainability and Social Responsibility committee, and the Whitsunday Climate Change Innovation Hub. Fabrizio Carmignani is Dean (Academic) and Professor of Economics in the Griffith Business School. His research is in the field of applied macroeconomics and applied econometrics. His recent publications are in the areas of conflict economics, tourism economics, spatial econometrics, and the economics of natural resources. Between 2002 and 2009 he was the First Economist in the Trade, Finance and Economic Development Division of the UN Economic Commission for Africa.

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