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

Experience enhances capacity for effective action. Exploiting markets to provide experience on new technologies is the key to a low-carbon society. As market actors in the whole chain from technology producer to technology operator and user accumulate experience, both cost and technical performance of the technology improves. This process is referred to as technology learning (IEA, 2000). Learning curves (Wright, 1936) and experience curves (BCG, 1968; Abell and Hammond, 1979) measure the results of the process.1 Understanding the process of technology learning is of fundamental importance for a costefficient technology-led transformation into a low-carbon society. The implications of the process for energy technology policy were discussed at a workshop convened by the International Energy Agency in 1999. The IEA Workshop recommended that experience and learning curves “are used to analyse the cost and benefits of programmes to promote environment friendly technologies” and “explicitly considered in exploring scenarios to reduce CO2 emissions and calculating the cost of reaching emissions targets” (IEA, 2000, Appendix B). The IEA Committee on Energy Research and Technology (CERT) supported the findings of the Workshop and initiated an international collaboration (IEA, 2000, Appendix C). Technology learning is a key process in the global scenario analysis within the IEA Energy Technology Perspectives bi-annual publications (IEA, 2006; 2008; 2010a). More importantly, the IEA work together with other recent high-level policy documents embrace the insights from experience and learning curves into the crucial role of government deployment programmes to make low-carbon energy technologies cost-efficient (Stern, 2006; EESC, 2009). The IEA 1999 Workshop and subsequent work pointed to two major areas where technology learning should inform and guide energy technology policy: exploring and calculating cost for CO2-reduction scenarios and designing efficient deployment programmes. Kahouli-Brahmi (2008) provides an overview of global scale models incorporating technology learning to investigate CO2-reduction scenarios. The first investigations by

[1]  J. Dutton,et al.  Treating Progress Functions as a Managerial Opportunity , 1984 .

[2]  L. Argote,et al.  Learning Curves in Manufacturing , 1990, Science.

[3]  Kim B. Clark,et al.  Behind the learning curve: a sketch of the learning process , 1991 .

[4]  Bruno Van Pottelsberghe,et al.  The impact of public R&D expenditure on business R&D* , 2003 .

[5]  A. Manne,et al.  Buying Greenhouse Insurance: The Economic Costs of CO2 Emission Limits , 1992 .

[6]  N. Stern.  The Economics of Climate Change: Implications of Climate Change for Development , 2007 .

[7]  Wim Turkenburg,et al.  Global experience curves for wind farms , 2005 .

[8]  Timothy J. Foxon,et al.  Innovation for a low carbon economy : economic, institutional and management approaches , 2008 .

[9]  Gregory F. Nemet,et al.  Beyond the learning curve: factors influencing cost reductions in photovoltaics , 2006 .

[10]  Martin Kumar Patel,et al.  A review of experience curve analyses for energy demand technologies , 2010 .

[11]  Leo Schrattenholzer,et al.  Learning rates for energy technologies , 2001 .

[12]  F. Varela.  Principles of biological autonomy , 1979 .

[13]  S. Messner,et al.  Endogenized technological learning in an energy systems model , 1997 .

[14]  Heinz von Foerster,et al.  For Niklas Luhmann: “How Recursive is Communication?” , 2003 .

[15]  Gregory F. Nemet,et al.  Interim monitoring of cost dynamics for publicly supported energy technologies , 2009 .

[16]  N. Nakicenovic.  Technological change and learning , 1996 .

[17]  Clas-Otto Wene,et al.  ASSESSING NEW ENERGY TECHNOLOGIES USING AN ENERGY SYSTEM MODEL WITH ENDOGENIZED EXPERIENCE CURVES , 1997 .

[18]  H. Foerster,et al.  Principles of Self-Organization — In a Socio-Managerial Context , 1984 .

[19]  F Ferioli,et al.  Learning in times of change: a dynamic explanation for technological progress. , 2009, Environmental science & technology.

[20]  Clas-Otto Wene,et al.  Technology learning systems as non-trivial machines , 2007, Kybernetes.

[21]  C. Wene.  Experience Curves for Energy Technology Policy , 2000 .

[22]  T. Martinsen.  Global technology learning and national policy--An incentive scheme for governments to assume the high cost of early deployment exemplified by Norway , 2010 .

[23]  Wilfried van Sark,et al.  Analysis of the use of experience curves for energy policy purposes: The case of photovoltaic power. Final report of the Photex project , 2004 .

[24]  Thomas Martinsen,et al.  Technology learning in a small open economy--The systems, modelling and exploiting the learning effect , 2011 .

[25]  C. Wene,et al.  Energy Technology Learning Through Deployment in Competitive Markets , 2008 .

[26]  S. Kahouli-Brahmi.  Technological learning in energy–environment–economy modelling: A survey , 2008 .

[27]  Lena Neij,et al.  Cost dynamics of wind power , 1999 .

[28]  Allan Gut,et al.  An intermediate course in probability , 1995 .

[29]  Raul Espejo,et al.  Organizational Transformation and Learning: A Cybernetic Approach to Management , 1996 .

[30]  K. Riahi,et al.  Importance of Technological Change and Spillovers in Long-Term Climate Policy , 2006 .

[31]  T. P. Wright,et al.  Factors affecting the cost of airplanes , 1936 .

[32]  A. Meyer.  Economics Of Climate Change , 1995, Nature.

[33]  Francisco J. Varela.  Two Principles for Self-Organization , 1984 .

[34]  Daniel H. Kim.  The Link between individual and organizational learning , 1997 .