Energy Technology Learning - Key to Transform into a Low - Carbon Society

Wene, Clas‐Otto

doi:10.5772/24659

Cited by 6 publications

(15 citation statements)

References 28 publications

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“…Figure (c) shows decarbonization of the world GDP as a learning curve, indicating that learning at the highest level follows the same logic and has the same magnitude as, e.g. learning by the wind power construction team . Nakicenovic reports on carbon intensity of the US economy over the 140‐year period from 1850 to 1990.…”

Section: Pervasive Learning: Evidencementioning

confidence: 99%

“…the characteristic time for the system to return to steady state after perturbation. It was used in the investigation of learning rate distributions …”

Section: Learning System Moves In Time—but What Time?mentioning

confidence: 99%

“…The earlier derivations of learning rates focused on assessing and designing. Wene argues that the findings in these papers can be combined with the results for the learning equation to provide an equation of motion for the learning system.…”

Section: Learning System Moves In Time—but What Time?mentioning

confidence: 99%

“…learning by the wind power construction team. 28 Nakicenovic 35 reports on carbon intensity of the US economy over the 140-year period from 1850 to 1990. The results for US decarbonization indicate a learning rate of 18%.…”

Section: Learning Pervades Organizations At All Levelsmentioning

confidence: 99%

“…The inset diagram in Figure 2(e) implies that the radical innovations had a very deep effect on technology learning, resetting cumulative output to zero. 28 The success rate of the incumbent technology deteriorates before the implementation of the radical innovation, suggesting that the system has to experience failure of the old technology before accepting the new one. This behavior is investigated by Schwaninger and Groesser 38 for other industries.…”

Section: Learning Pervades Organizations At All Levelsmentioning

confidence: 99%

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Future energy system development depends on past learning opportunities

Wene¹

2015

WIREs Energy & Environment

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Learning curves pervade all levels of industry and suggest deployment programs to buy down the cost of presently too expensive environment-friendly technologies. However, the legitimate role of learning curves in a low-carbon strategy depends on the validity of extrapolations and forecasts of the curves. Moving toward a technology-led strategy for a low-carbon energy system requires understanding of the learning mechanisms and their stability. The first part of this review provides examples of pervasive learning and a brief literature survey of recent learning curve measurements for energy technologies and the use of learning curves for scenario and policy analysis. The second part reviews how authors from two different perspectives understand the learning curves and come to different conclusions about their stability and legitimate role in scenario analysis and policy making.

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Section: Pervasive Learning: Evidencementioning

confidence: 99%

“…the characteristic time for the system to return to steady state after perturbation. It was used in the investigation of learning rate distributions …”

Section: Learning System Moves In Time—but What Time?mentioning

confidence: 99%

Section: Learning System Moves In Time—but What Time?mentioning

confidence: 99%

Section: Learning Pervades Organizations At All Levelsmentioning

confidence: 99%

Section: Learning Pervades Organizations At All Levelsmentioning

confidence: 99%

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Future energy system development depends on past learning opportunities

Wene¹

2015

WIREs Energy & Environment

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Using the experience curve model to project carbon dioxide emissions through 2040

Chang

Jeon

2015

Carbon Management

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A cybernetic view on learning curves and energy policy

Wene

2015

Kybernetes

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Purpose – The purpose of this paper is to demonstrate that cybernetic theory explains learning curves and sets the curves as legitimate and efficient tools for a pro-active energy technology policy. Design/methodology/approach – The learning system is a non-trivial machine that is kept in non-equilibrium steady state at minimum entropy production by competitive, equilibrium markets. The system has operational closure and the learning curve expresses its eigenbehaviour. This eigenbehaviour is analysed not in calendar time but in the characteristic time of the system, i.e., its eigentime. Measured in eigentime, the minimum entropy production in the steady-state learning system is constant. The double closure mechanism described by Heinz von Förster makes it possible for the learning system to change (adapt) its eigenbehaviour without compromising its operational closure. Findings – By obeying basic laws of second order cybernetics and of non-equilibrium thermodynamics the learning system self-organises its learning to follow an optimal path described by the learning curve. The learning rates are obtained through an operator formalism and the results explain observed distributions. Application to solar cell (photo-voltaic) modules indicates that the silicon scarcity bubble 2005-2008 produced excess entropy corresponding to costs of the order of 100 billion US dollars. Research limitations/implications – Grounding technology learning and learning curves in cybernetics and non-equilibrium thermodynamics open up new possibilities to understand technology shifts through radical innovations or paradigm changes. Practical implications – Learning curves are legitimate and efficient tools for energy policy and industrial strategy. Originality/value – Grounding of technology learning and learning curves in cybernetic and thermodynamic theory provides a stable theoretical basis for applications in industry and policy.

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Energy Technology Learning - Key to Transform into a Low - Carbon Society

Cited by 6 publications

References 28 publications

Future energy system development depends on past learning opportunities

Future energy system development depends on past learning opportunities

Using the experience curve model to project carbon dioxide emissions through 2040

A cybernetic view on learning curves and energy policy

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