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Malte Schwoon

Malte Schwoon. University of Hamburg. International Max Planck Research School on EARTH SYSTEM MODELLING. Research Unit Sustainability and Global Change. Centre for Marine and Atmospheric Sciences. Learning-by-doing, Learning Spillovers and the Diffusion of Fuel Cell Vehicles.

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Malte Schwoon

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  1. Malte Schwoon University of Hamburg International Max Planck Research School on EARTH SYSTEM MODELLING Research Unit Sustainability and Global Change Centre for Marine and Atmospheric Sciences Learning-by-doing, Learning Spillovers and the Diffusion of Fuel Cell Vehicles Presentation at the International Conference on Computational Management Science May 17-18, 2006, Amsterdam

  2. Introduction Why fuel cell vehicles (FCVs)? Agent based technology diffusion model  Learning by doing (LBD) in fuel cell technologies LBD in energy technologies Calibration/scenarios Diffusion of FCVs depends on learning rate  Learning spillovers Increase speed of diffusion Asymmetric impact on car producers  Conclusion Outline

  3. Introduction Why FCVs? No local emissions, low noise Long term potential: Individual transport with low CO2 emissions (depending on energy mix of hydrogen production) Reduced dependency on oil New design options (low floor, low center of gravity)

  4. Honda FCX (2005) • Fuel: CGH2 • 100 kWe PEMFC (Honda) • 80 kW front + 2x 25 kW rear • Regenerative braking • Range > 500 km • Max speed: 160 km/h (limited) Introduction Mercedes Benz: NECAR 2 (1996) • Fuel: CGH2 • Two 25 kWe PEMFC (Ballard) • Cont. 33 kW, max. 45 kW • Range: 250km • Max speed: 110km/h • Acceleration: “quite good”

  5. Can we switch to an H2-economy? (1) Technological problems basically solved (RECENTLY!) : Fuel cell technology, H2-on-board storage, etc. The - problem of H2-infrastructure Introduction We can switch soon! (2) Economic start up problem for large scale introduction: No H2-infrastructure  nobody buys FCV Nobody buys FCV  no H2-infrastructure We will never switch!

  6. Introduction Scenarios/Projections of the diffusion of FCVs and/or H2-infrastructure: Schlecht (2003), Thomas et al. (1998), Moore and Raman (1998), Ogden (1999, 2002), Stromberger (2003), Mercuri et al. (2002), Sørensen et al. (2004), Oi and Wada (2004), Hart (2005), etc. Common approach 1. Develop scenarios of the number of hydrogen vehicles 2. Derive implied H2-demand/H2-infrastructure Implied assumption: smooth and successful introduction of both technologies Studies ignore dynamic interactions Technology driven studies ignore impact on producers/consumers or vice versa

  7. R&D funds Producers capital Car characteristics Credit availability Neighbors Investment decisions Driving patterns Savings Refueling worries Profits (Expected) LBD cost reductions Market sales Kwasnicki (1996) Janssen and Jager (2002) Introduction Government: Sets taxes and increases number of H2-outlets Tax Producers: Production and price decisions Consumers: Buying decisions Filling station owners: Increase share of stations with H2-outlet

  8. Learning by doing Electric Technologies in EU, 1980-1995 Progress ratio Source: Wene (2000) Learning rate = 1 – Progress ratio

  9. Learning by doing Observed learning rates Energy technologies (25 obs.) Various industries (>100 obs.) McDonald and Schrattenholzer (2001) Dutton and Thomas (1984) Learning rate for fuel cell technologies?

  10. Introduction • Calibration/scenario • Central case parameterization •  German compact car segment (1 mio sales per year) • - 12 producers • - 6400 different “representative” consumers •  Initial fuel cell cost of 13000€ per unit • for (mass) production of 1000 units •  Learning rate (LR) 15% (sens. 10-20%) •  Fuel cell cost  Internal combustion engine •  5% tax increase every year (tax  40%)

  11. Learning by doing Percentage share of FCVs within newly registered cars in the German compact car segment

  12. Learning spillovers • Learning spillovers due to • Reverse engineering • Inter-firm mobility of workers • Proximity (industry clusters) • Weak patent rights (government control) • Joint research projects • Learning on sub-contractor level • (Ballard Power Systems, International Fuel Cells) • (Opposite: proprietary learning)

  13. Learning spillovers Percentage share of FCVs within newly registered cars in the German compact car segment 10% spillover: 10 FCVs produced at competitor's plant  equivalent to 1 produced at own plant

  14. Change of NPV of profits (2010-2030) relative to “no spillover” case Learning spillovers

  15.  fast diffusion Conclusion Hydrogen/FCV individual transport system: Technological option, but requires governmental commitment Multi-agent simulation model helps understanding of dynamics (Standard sim-problems apply: parameters, functional forms, random events…) Modeling results High learning rates High spillovers High spillovers  2nd/3rd mover advantage Spillover policies? Environmentally concerned government: “High spillover policy”  fast diffusion  Asymmetric impact on producers  Resistance/appreciation of producers depends on their position in the switching-chain

  16. Thank you!

  17. Learning by doing Percentage share of FCVs within newly registered cars: Different lengths of the producers' decision horizons

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