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Air Travel Demand and Income: Empirical Investigations and Future Scenarios

This study explores the relationship between air travel demand and income, and presents future scenarios for aviation growth. It analyzes income distribution data to test hypotheses and provides insights into the impact of inequality on aviation. The study also examines price and income elasticities, and offers projections for passengers and emissions.

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Air Travel Demand and Income: Empirical Investigations and Future Scenarios

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

  2. Motivation Motivation • 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)‏ * * ~4%,including contrails

  3. Return flight emissions Economy class NYC-Paris 1.4 tCO2/cap NYC-Tokyo 2.6 tCO2/cap NYC-Chicago 0.4 tCO2/cap Typical Aviation Emissions Source: Climate Change and Tourism – Responding to Global Challenges (UNWTO Report)‏ Source: ICAO

  4. Improvements in Aircraft Fuel Efficiency

  5. Research Objective • 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

  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

  8. NHTS 2001 Survey

  9. Price Sensitivity

  10. Proportion of Airfare in Travel Expenses

  11. 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

  12. 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

  13. The Methodology in One Cartoon

  14. 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. 1 2 flights GDP per capita

  15. Projected Growth in Passengers

  16. Revenue Passenger Kilometer Projections Boeing Airbus WBCSD

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

  18. Projected Emissions Aviation could account for 5%-12% of CO2 emissions in 2050.

  19. 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.

  20. Constant Inequality Projection for China

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

  22. 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.

  23. EXTRA SLIDES

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

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

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