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Get Ready for Take-Off: A Two-Stage Model of Aircraft Market Diffusion

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  • Liu, Xueying

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

  • Madlener, Reinhard

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

Abstract

Over the past decades, the aviation sector has seen an unprecedented technology advancement as well as rise in global air traffic. In this paper, we establish a two-stage model, combining an epidemic diffusion model and a regression analysis to analyze and predict the diffusion process of modern commercial aviation aircraft models before they are launched into the market. For the estimation of the first-stage epidemic diffusion model, a total of 19,768 delivery records covering 52 widely used aircraft models are used and a non-linear least squares method is applied. For the second-stage regression analysis, we collect aircraft specific technical parameters including range, maximum take-off weight and emissions. We find that, at present, pollutant emissions are not of key significance in determining the success of market diffusion of aircraft models, while conventional parameters, such as range, takeoff weight, and bypass ratio of the engine, are comparably more significant. In terms of projection into the future, our model is relatively good at predicting the rate of diffusion but less so at predicting the market size. This naturally points to further research avenues in terms of the prediction of market potentials.

Suggested Citation

  • Liu, Xueying & Madlener, Reinhard, 2019. "Get Ready for Take-Off: A Two-Stage Model of Aircraft Market Diffusion," FCN Working Papers 15/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    • Handle: RePEc:ris:fcnwpa:2019_015
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    References listed on IDEAS

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    Cited by:

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    2. Liu, Xueying & Madlener, Reinhard, 2021. "The sky is the limit: Assessing aircraft market diffusion with agent-based modeling," Journal of Air Transport Management, Elsevier, vol. 96(C).
    3. Hellwig, Robert & Atasoy, Ayse Tugba & Madlener, Reinhard, 2020. "The Impact of Social Preferences and Information on the Willingness to Pay for Fairtrade Products," FCN Working Papers 6/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    4. Walter, Antonia & Held, Maximilian & Pareschi, Giacomo & Pengg, Hermann & Madlener, Reinhard, 2020. "Decarbonizing the European Automobile Fleet: Impacts of 1.5 °C-compliant Climate Policies in Germany and Norway," FCN Working Papers 18/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).

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    More about this item

    Keywords

    Transportation economics; Technological diffusion; Epidemic Diffusion Model; Aircraft;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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