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A Consumer-Producer Model for Induced Technological Progress
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www.ecn.nl A Consumer-Producer Model for Induced Technological Progress IEW, University of Cape Town, South Africa, 19-21 June 2012 Francesco Ferioli and Bob van der Zwaan
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A Consumer-Producer Model for Induced Technological Progress. IEW, University of Cape Town, South Africa, 19-21 June 2012 Francesco Ferioli and Bob van der Zwaan. Learning curve. x : cumulative output C ( x ) : cost at cumulative output L : learning parameter - PowerPoint PPT Presentation
Transcript of A Consumer-Producer Model for Induced Technological Progress
www.ecn.nl
A Consumer-Producer Model for Induced Technological ProgressIEW, University of Cape Town, South Africa, 19-21 June 2012Francesco Ferioli and Bob van der Zwaan
Learning curve
L
xxxCxC
00 )()(
x : cumulative outputC(x) : cost at cumulative outputL : learning parameterLR = 1 – 2-L : learning rate
Learning rate
Ferioli, F., K. Schoots and B.C.C. van der Zwaan, “Use and Limitations of Learning Curves for Energy Technology Policy: a Component-Learning Hypothesis”, Energy Policy, 37, 2009, pp.2525-2535.
Offshore wind
1991
2005
2008
lr = 5%R² = 0.57
lr = 3%R² = 0.49
100
1000
10000
1 10 100 1000 10000
Spec
ific c
ost (
€(20
10)/
kW)
Cumulative capacity (MW)
Offshore windSpecific park cost Monopiles only
Costs corrected for commodity price fluctuationsData from 1991 to 2008Data from 1991 to 2005
van der Zwaan, B.C.C., R. Rivera-Tinoco, S. Lensink, P. van den Oosterkamp, “Cost Reductions for Offshore Wind Power: Exploring the Balance between Scaling, Learning and R&D”, Renewable Energy, 41, 2012, pp.389-393.
Learning curve caveats (I)
• Different analysts working on the same technology may get very different results (data, cost/price, currency, inflation, regression).
• R&D, economies-of-scale, automation: cost reduction mechanisms that are often included in learning curves and obscure true learning.
• ‘True’ learning-by-doing refers to acquisition of experience: seminal papers by Wright (1936) and Arrow (1962).
• Learning-by-manufacturing, learning-by-using, learning-by-copying, forgetting-by-not-doing…
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Learning curve caveats (II)
• Statistical significance of learning rates varies substantially over different technologies.
• Technology system boundaries may be different, which may explain differences between analysts.
• Technologies that do not learn, have ceased learning, have exited the market or perished, are not considered.
• For PV, the accumulation of experience only weakly explains overall cost reductions (plant size, module efficiency and cost of silicon).
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Work in progress
• Hence ongoing efforts to explore the caveats of learning curves and propose new models for better understanding them.
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Nordhaus
• (1) Learning curves are widely used to estimate cost functions in manufacturing models.
• (2) They have also been introduced in policy models of energy and climate change economics.
• Nordhaus argues that, while (1) may be useful, (2) may be a dangerous strategy, by elaborating three main points:– A fundamental identification problem exists in trying to separate learning from
exogenous technical change, so that learning rates are biased upwards;– Empirical tests illustrate the potential bias and show that learning rates are not
robust to alternative specifications;– Overestimating learning rates provides incorrect estimates of the marginal cost
of output and biases policy models towards the corresponding technologies.
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Nordhaus, W.D., 2008, The Perils of the Learning Model for Modeling Endogenous Technological Change, Yale University, 15 December 2008.
Wene
• Wene considers the learning system as a non-trivial machine and attempts to ground learning curves in cybernetic theory.
• He assumes operational closure and feedback regulation, which allows calculating eigenvalues for a self-reflecting system.
• The eigenvalues correspond to the learning rates of the system, with values of 20% (zero mode) and <8% (higher modes).
• This cybernetic approach thus reproduces the overall features of technology learning.
• It provides a framework for understanding gradual improvements and radical innovations for (energy) technologies.
• This approach towards technology learning systems needs complementary analysis.
9Wene C. O., 2007, “Technology learning system as non-trivial machines”, Kybernetes 36 (3/4), 348-363.
Component learning
)()1()()( 00
0 xCxxxCxC
L
x : cumulative outputC(x) : cost at cumulative outputL : learning parameterLR = 1 – 2-L : learning rateα : cost share of learning component at t=0
Ferioli, F., K. Schoots and B.C.C. van der Zwaan, “Use and Limitations of Learning Curves for Energy Technology Policy: a Component-Learning Hypothesis”, Energy Policy, 37, 2009, pp.2525-2535.
Growth and time
Ferioli, F. and B.C.C. van der Zwaan, “Learning in Times of Change: a Dynamic Explanation for Technological Progress”, Environmental Science and Technology, 43, 11, 2009, pp. 4002-4008.
Costs and time
Ferioli, F. and B.C.C. van der Zwaan, “Learning in Times of Change: a Dynamic Explanation for Technological Progress”, Environmental Science and Technology, 43, 11, 2009, pp. 4002-4008.
Alternative to learning curve
Exponential relations can be used to simulate the evolution over time of cumulative production and costs:
textx 0)( teCtC 0)(
The elimination of time gives a relation de facto equivalent to the learning curve:
00)(
xxCxC
Ferioli, F. and B.C.C. van der Zwaan, “Learning in Times of Change: a Dynamic Explanation for Technological Progress”, Environmental Science and Technology, 43, 11, 2009, pp. 4002-4008.
Stochastic learning curve model
Ferioli, F. and B.C.C. van der Zwaan, “Learning in Times of Change: a Dynamic Explanation for Technological Progress”, Environmental Science and Technology, 43, 11, 2009, pp. 4002-4008.
Unpacking the learning curve
• Goal: continue our work attempting to understand the economic dynamics behind technology learning curves.
• Observation: an expansion cycle appears to exist between: cost reductions & production growth, growth & market investments, and investments & cost reductions.
• Key parameters of our model: – investment-cost elasticity (producer side).– price-demand elasticity (consumer side)
• Aim: investigate the consequences for energy policy as well as induced technical change in energy scenario models.
New model (I)
Relation between costs C and investments I with elasticity Ec:
IIE
CC
c
CCE
yy
x
Relation between production y and costs C with (price-demand) elasticity Ex:
New model (II)
Let’s assume that:
gteItI 0)(
gtEceCC 0
Substituting in the elasticity equations and integrating, we get:
so that: gIdtdI
and gtEE cxeyy 0
New model (III)
For cumulative production then also (approximately) applies:
xE
xx
CC
1
00
From which it can then be shown that:
gtEE cxexx 0
which is the expression for the learning curve.
New Model (IV)
• Of course, the real world is more complex than one explanatory variable (investments I).
• It can easily be shown, however, that exogenous cost reducing factors – as long as they are exponential as well – can be added while the corresponding expressions for C and x still generate the learning curve (in line with Nordhaus’ suggestions).
• We have thus obtained a new model that introduces time in the learning curve methodology (like our previous paper) plus adds a level of deeper meaning of what learning really may be.
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Working paper
• Ferioli, F. and B.C.C van der Zwaan, “A Consumer-Producer Model for Induced Technological Progress: Market Investments, Cost Reductions and the Learning Curve”, in progress.
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Conclusions:
• For many technologies and under many different circumstances exponential growth and cost reduction appear the mechanisms behind learning curves.
• Further statistical testing of our model is required, allowing to determine the relative usefulness, practicality and ‘truthfulness’ of different approaches proposed to unpack learning.
