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Airline Cost Methodology for Analyzing Biofuel Usage Feasibility

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Airline Cost Methodology for Analyzing Biofuel Usage Feasibility UNIVERSIDAD POLITÉCNICA DE MADRID ESCUELA TÉCNICA SUPERIOR DE INGENIERÍA AERONÁUTICA Y DEL ESPACIO DOCTORAL THESIS Airline cost methodology for analyzing biofuel usage feasibility Author Antonio López Lázaro Aeronautical Engineer Directors Arturo Benito Ruiz de Villa PhD. Aeronautical Engineer Gustavo Alonso PhD. Aeronautical Engineer May 15th, 2018 UNIVERSIDAD POLITÉCNICA DE MADRID ESCUELA TÉCNICA SUPERIOR DE INGENIERÍA AERONÁUTICA Y DEL ESPACIO DOCTORAL THESIS Airline cost methodology for analyzing biofuel usage feasibility Author Antonio López Lázaro Aeronautical Engineer Departament Aerospace systems, air transport and airports Mentored by PhD. Arturo Benito Ruiz De Villa PhD. Gustavo Alonso Rodrigo May 15th, 2018 This thesis was supported by LLM Aviation. Their cooperation is hereby gratefully acknowledged. Tribunal nombrado por el Sr. Rector Magfco. De la Universidad Politécnica de Madrid, el día ……… de ………………………………….. de …….. Presidente: ________________________________________________ Vocal: ____________________________________________________ Vocal: ____________________________________________________ Vocal: ____________________________________________________ Secretario: ________________________________________________ Secretario: ________________________________________________ Secretario: ________________________________________________ Realizado el acto de defense y lectura de la Tesis el día ….. de …………………….. de …….. en la E.T.S.I. / Facultad …………………………………………………. Calificación ……………………………………………. EL PRESIDENTE LOS VOCALES EL SECRETARIO Nada te turbe, nada te espante. S.T.J. A mi padre, Antonio Acknowledgements I would like to thank all my family members for encouraging and helping me to continue this project through complex familiar and professional situations during all these years. Deep acknowledge has to be done to my grandparents Joaquín and Amelia for their infinite confidence and support on my aeronautical studies. Special mention has to be done to my Thesis Directors, Dr. Arturo Benito and Dr. Gustavo Alonso, from the Air Transport Department at Universidad Politécnica de Madrid (UPM) for their permanent patience and confidence on me as well as for letting me get higher quality results. I am also absolutely grateful to Mr. Darío Campuzano, Aeronautical Engineer from UPM and fellow at Universidad Autónoma de Madrid, whose support and vision have been highly valuable for this thesis. Hopefully our academic and professional futures will be linked. I must also express my gratitude to LLM Aviation for the funding to perform this work as well as for the support provided during this research. Last but not the least i want to recognize both the inspiration and guidance of all my colleagues in Iberia Airlines, Airbus, Pullmantur/Wamos Air, Travelgenio.com, Euroairlines, LLM Aviation, UAM and UPM. Resumen Las emisiones de CO2 de la aviación están creciendo junto con el crecimiento del tráfico. Las organizaciones internacionales están preocupadas y están implementando reglas de incentivos para reducirlas. IATA ha declarado un Carbon Neutral Growth (CNG) de las emisiones a partir de 2020 dentro de su hoja de ruta. Este documento tiene como objetivo analizar las medidas adecuadas que podrían ayudar a alcanzar este objetivo y se centra en su impacto en las finanzas de 15 diversas aerolíneas españolas. Con estos objetivos, se diseña un modelo de estimación para realizar un pronóstico del mercado aéreo español 2017-2025. Está compuesto por tres submodelos: (i) el modelo de tráfico estima el rendimiento anual de los transportistas españoles en cada una de sus rutas, (ii) el modelo de biocombustible se encarga de estimar los precios, las emisiones y las regulaciones de los biocombustibles (con especial atención a porcentaje de mezcla) y (iii) el modelo de costo operativo estima la estructura de gastos del transportista. Los datos de varias fuentes (con respecto a las estadísticas de tráfico de 2016 y las previsiones de crecimiento y precios de los combustibles, por nombrar solo dos) se recopilan y fusionan para alimentar el modelo. Los resultados agregados muestran que se debe aplicar un aumento promedio de 3% / año del porcentaje obligatorio de mezcla para un GNC 2020 en el escenario base. Con respecto a las diferentes materias primas de biocombustibles investigadas, el rendimiento de camelina presenta un buen compromiso con respecto a los precios, las emisiones y los problemas de producción. Un estudio adicional sobre la estructura de costos de las aerolíneas muestra que las diferencias en el modelo operativo (tradicional, bajo precio...) pueden generar grandes diferencias en términos de impacto de la introducción de biocombustibles en los costos totales de las aerolíneas. Además, se observa una alta sensibilidad a los cambios en el precio del combustible común y el crecimiento del tráfico. Keywords: Carbon Neutral Growth (CNG); biofuel; tráfico; costs operativos; aerolíneas españolas; prognosis. Abstract Aviation CO2 emissions are growing along with traffic growth. International organizations are concerned and are implementing incentive rules in order to reduce them. IATA has stated a Carbon Neutral Growth (CNG) of emissions from 2020 forward within its roadmap. This paper aims to analyze suitable measures that could help to reach this target and focuses on their impact on the finances of 15 varied Spanish airlines. With these goals, an estimation model is designed in order to carry out a forecast of the 2017-2025 Spanish air market. This is comprised by three submodels: (i) the traffic model estimates the annual performance for Spanish carriers in each of their routes, (ii) the biofuel model is in charge of estimating the biofuel prices, emissions and regulations (with special attention to mandatory blending percentage) and (iii) the operating cost model estimates the carrier’s expenses structure. Data from several sources (regarding 2016 traffic statistics and forecasts of growth and fuel prices to name but two) is gathered and merged in order to feed the model. Aggregated results show that an average 3%/year increase of mandatory blending percentage should be applied for a 2020 CNG in the base scenario. Regarding the different biofuel feedstocks investigated, camelina’s performance presents a good compromise in respect to price, emissions and production issues. A further study on the airline’s cost structure show that differences in the operating model (legacy, low cost…) can lead to big differences in terms of impact of biofuels introduction on the total airline costs. In addition, high sensibility to changes in common fuel price and traffic growth is observed. Keywords: Carbon Neutral Growth (CNG); biofuel; traffic; operating cost; Spanish carriers; forecast. Analyzing CNG and biofuel impact Contents List of Figures .................................................................................................................. 19 List of Tables ................................................................................................................... 23 List of Acronyms .............................................................................................................. 25 Introduction ................................................................................................. 29 1.1. Background ....................................................................................................... 29 1.2. Aviation research framework ............................................................................. 31 1.3. Scope ................................................................................................................ 33 1.4. Arrangement ...................................................................................................... 34 Data analysis .............................................................................................. 37 2.1. Databases ......................................................................................................... 37 2.1.1. Traffic ......................................................................................................... 38 2.1.2. Aircraft and airports .................................................................................... 39 2.2. Spanish carriers ................................................................................................. 39 2.2.1. Data per airlines ......................................................................................... 41 2.2.2. Data per aircraft .......................................................................................... 44 2.2.3. Data per region ........................................................................................... 47 Methodology (model design) ....................................................................... 53 3.1. Traffic model ...................................................................................................... 54 3.1.1. Forecast ..................................................................................................... 54 3.1.2. Model ......................................................................................................... 59 3.2. Biofuel model ..................................................................................................... 61 3.2.1. Price forecast .............................................................................................. 62 3.2.2. Emissions ..................................................................................................
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