1. Air Travel Demand and Income: Empirical Investigations and Future Scenarios Shoibal Chakravarty and Massimo Tavoni Princeton Environmental Institute, Princeton University International Energy Workshop Venice, Italy. 19 June 2009

2. Meeting transportation demand sustainably is an important future challenge Aviation: – Small compared to road transport – Growing fast (5.5%/yr over the past 20yrs), expected to grow at a rate twice that of road mobility – Concentrated in rich countries (OECD have ¾th share)‏ – Additional concerns (noise, safety, altitude emissions)‏ – Few alternatives (improve efficiency, alternative fuels, high speed rail)‏ Motivation * * ~4%,including contrails

3. Return flight emissions Economy class NYC-Paris1.4 tCO2/cap NYC-Tokyo2.6 tCO2/cap NYC-Chicago0.4 tCO2/cap Source: Climate Change and Tourism – Responding to Global Challenges (UNWTO Report)‏ Source: ICAO Typical Aviation Emissions

4. Improvements in Aircraft Fuel Efficiency

5. Empirical characterization of air travel demand/income relation – Travel demand literature points to high elasticity between air travel and income – About 2 for developing countries, 1.5 for developed ones – Other factors such as fares are important but are dominated by income – Essentially all studies assume constant elasticity, have not estimated an exact relation Use income distribution data to test hypothesis against panel of different countries – The idea of using income distribution in travel demand analysis is relatively new and has never been applied to aviation – Storchmann, Energy Econ. 2005: uses Gini in determining car ownership – Chamon et al., Economic Policy 2008: estimates an income threshold function for car ownership Generate long term BAU aviation demand forecasts Research Objective

6. Surveys Sparse survey data on flying (NHTS, MTC, UK CAA)‏ – Flying is largely undertaken by those in richer households and that most of the growth in flying is coming from people in these households flying more often (CAA, 2006)‏ – NHTS: Suggest a non linear relation with marked increase in elasticity after certain threshold – MTC: No neat evidence of saturation.

7. NHTS 2001 Survey

10. Price Sensitivity

12. Proportion of Airfare in Travel Expenses

13. Data Income Distribution – WDI 2007, WB PovCalNet, UN WIDER World Income Inequality Database (WIID 2b)‏ – Coverage: income/consumption shares in quintiles or deciles – Beta2 distribution fit, mean anchored at GDP/cap MER Air travel – ICAO: 1990-2004 passengers carried, passenger-km, (both national and international)‏ – UNData (World Bank): 1970-2005, passengers carried – Wide coverage of both developed and developing countries

14. Methodology S-shaped relation between income and flying, no country effects Weibull, (Logistic), (Gompertz)‏ Income distribution translates it into flights per capita Maximum Likelihood estimation on the S-shaped curve Scenarios to 2050 – Use EIU/GS/IPCC GDP/cap projections, UN population projections – Assume unchanged income distribution or use scenarios with higher /lower inequalities. – For given distance/trip and efficiency improvements, calculate energy and emissions

15. The Methodology in One Cartoon

16. Two groups of countries: 1. Early Fliers: USA, CAN, AUS, SWE, FIN, NOR, GBR, NLD, FRA, DEU, ITA, ISR, AUT, DNK, JPN 2. Catch up: BRA, CHL, CHN, CZE, HUN, KOR, MYS, ESP, GRC, PRT, IND, IDN, PHL, THA etc. 2 1 GDP per capita flights

19. Projected Growth in Passengers

20. Revenue Passenger Kilometer Projections WBCSD Boeing Airbus

21. Projections assuming 0%, 0.5% and 1.0% growth in average distance traveled per flight. Revenue Passenger Kilometer Projections

22. Projected Emissions Aviation could account for 5%-12% of CO 2 emissions in 2050.

23. Impact of change in inequality Aviation is a luxury good with a very nonlinear elasticity profile. Inequality could affect both rate of growth and possible saturation. Case study: Rapid increase in inequality in China in the last 25 years. China (1992) 0.35 China (2005) 0.46 Consider 3 scenarios: 1) Constant inequality, 2) Decreases to 1992 levels and 3) Increases to Brazil (2005) 0.56.

24. Constant Inequality Projection for China

25. Red: China (2005) 0.46 Blue: Brazil (2005) 0.56 Green: China (1992) 0.35

26. Conclusions Evidence of a sigmoid relationship between income and flying. Need to know more about saturation at high incomes (surveys), though it doesn’t seem to a significant factor yet OECD aviation demand will grow and the OECD will continue to lead in per capita flying. High growth from middle level developing countries like Brazil, China etc.

27. EXTRA SLIDES

28. PRICE ELASTICITIES Source: InterVISTAS Study for IATA (2007)‏ Note: Short series (3 years) except for the US.

29. INCOME ELASTICITES Source: InterVISTAS Study for IATA (2007)‏