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Malte Schwoon

Learning-by-doing, Learning Spillovers and the Diffusion of

Fuel Cell Vehicles

Research Unit Sustainability and Global ChangeCentre for Marine and Atmospheric Sciences

University of Hamburg

International Max Planck Research School onEARTH SYSTEM MODELLING

Presentation at theInternational Conference on Computational Management ScienceMay 17-18, 2006, Amsterdam

Research Unit Sustainability and Global ChangeCentre for Marine and Atmospheric Sciences 22

Introduction Why fuel cell vehicles (FCVs)?Agent based technology diffusion model

Learning by doing (LBD) in fuel cell technologiesLBD in energy technologiesCalibration/scenariosDiffusion of FCVs depends on learning rate

Learning spilloversIncrease speed of diffusionAsymmetric impact on car producers

Conclusion

Outline


Why FCVs?

No local emissions, low noiseLong 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)

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”

Introduction

• Fuel: CGH2 • 100 kWe PEMFC (Honda)• 80 kW front + 2x 25 kW rear • Regenerative braking• Range > 500 km• Max speed: 160 km/h (limited)

Honda FCX (2005)


Can we switch to an H2-economy?(1) Technological problems basically solved (RECENTLY!) :

Fuel cell technology, H2-on-board storage, etc.

We will never switch!

We can switch soon!

The - problem of H2-infrastructure

(2) Economic start up problem for large scale introduction:

No H2-infrastructure nobody buys FCV

Nobody buys FCV no H2-infrastructure

Introduction


or vice versa

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 approach1. Develop scenarios of the number of hydrogen vehicles 2. Derive implied H2-demand/H2-infrastructureImplied assumption: smooth and successful introduction of both technologies

Studies ignore dynamic interactionsTechnology driven studies ignore impact on producers/consumers


Introduction

Tax

Filling station owners:Increase share of stations with H2-outlet

Government: Sets taxes and increases number of H2-outlets

Producers:Production and price decisions

Consumers:Buying decisions

Credit availability

Producers capital

R&D funds

Investment decisions

Market sales

Profits

Savings

Car characteristics

(Expected) LBD cost reductions

Refueling worries

Driving patterns

Neighbors

Kwasnicki (1996)Janssen and Jager (2002)


Learning by doing

Electric Technologies in EU, 1980-1995

Source: Wene (2000)

Progress ratio

Learning rate = 1 – Progress ratio


Learning by doing

Energy technologies (25 obs.)

Various industries (>100 obs.)

Observed learning rates

McDonald and Schrattenholzer (2001) Dutton and Thomas (1984)

Learning rate for fuel cell technologies?


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%)

Introduction


Learning by doing

Percentage share of FCVs within newly registered cars in the German compact car segment

0%

10%

20%

30%

40%

50%

60%

70%

80%

2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Year

LR = 20% LR = 17.5% LR = 15% LR = 12.5% LR = 10%


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)

Learning spillovers


10% spillover: 10 FCVs produced at competitor's plant equivalent to 1 produced at own plant

Learning spillovers

Percentage share of FCVs within newly registered cars in the German compact car segment

0%

10%

20%

30%

40%

50%

60%

70%

80%

2010 2015 2020 2025 2030Year

100% 25% 10% 5% Proprietary learning


Learning spillovers

-10%

-5%

0%

5%

10%

15%

20%

1st 2nd 3rd 4th 5th 6th Sum 7th-12th

5% Spillover 10% Spillover 25% Spillover 100% Spillover

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


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 spilloversHigh 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

fast diffusion


Thank you!


Learning by doing

Percentage share of FCVs within newly registered cars: Different lengths of the producers' decision horizons

0%

10%

20%

30%

40%

50%

60%

70%

80%

2010 2015 2020 2025 2030Year

Time horizon 5 years Time horizon 4 yearsTime horizon 3 years Time horizon 2 yearsTime horizon 1 year

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