Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies

By Tooraj Jamasb. 2007

211 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/6229c8392148c9c8562d0cc34ba7650796d9ce2e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Reinhard Madlener
2. R. Lohwasser
3. Sonia Yeh
4. Inês Lima Azevedo
5. Edward S. Rubin
6. A. V. Benthem
7. Steven Van Passel
8. P. Jaramillo

In Management Science and Operations Research

1. Edward S. Rubin
2. R. Lohwasser
3. A. V. Benthem
4. Inês Lima Azevedo
5. P. Jaramillo
6. Kenneth T. Gillingham
7. Daniel M. Kammen
8. Reinhard Madlener

Citations in the corpus

• The Role of R&D and Technology Diffusion in Climate Change Mitigation: New Perspectives Using the Witch Model (2009, unknown): “This formulation has received significant attention from the empirical and modelling literature in the recent past (see, for instance, Criqui, Klassen et al. 2000; Bahn and Kypreos, 2003; Söderholm and Sundqvist, 2003; Barreto and Klaassen, 2004; Barreto and Kypreos, 2004; Klassen, Miketa et al. 200”
• Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects (2011, unknown): “Jamasb [13] considers that the variability can be explained by the stage of technological development or the technology life cycle.”
• A review of learning rates for electricity supply technologies (2015, methodology): “The most prevalent multi-factor model for energy technologies is a “two-factor learning curve” where the key drivers of cost reduction are assumed to be the cumulative expenditure for R&D as well as the cumulative installed capacity or production of the technology (Jamasb, 2007).”
• Learning-by-Doing and the Optimal Solar Policy in California (2008, result): “This result corresponds with Jamasb (2007), who finds that mature technologies such as coal-fired electricity generation and large hydropower have had much lower learning rates than “evolving” technologies such as nuclear power, waste to electricity, and wind power.”
• An innovation-focused roadmap for a sustainable global photovoltaic industry (2014, background): “In a relatively mature industry, both forms of subsidies can contribute to the technological development (Jamasb, 2007; Bettencourt et al., 2013).”
• Relating R&D and Investment Policies to CCS Market Diffusion Through Two-Factor Learning (2013, background): “Jamasb (2007) provides an overview of 13 2FLC and groups technologies into the categories mature (e.”
• Towards a green energy economy? The EU Energy Union's transition to a low-carbon zero subsidy electricity system – Lessons from the UK's Electricity Market Reform (2016, background): “Jamasb and Pollitt (2015) have noted the adverse impact of liberalization on energy R&D, while Jamasb (2007) and Nordhaus (2014) note the danger of attributing too high a share of cost reductions to learning-by-doing and hence undervaluing R&D.”
• Historical construction costs of global nuclear power reactors (2016, unknown): “Despite these constraints, the single-factor learning curve methodology has been commonly and broadly applied in studies of nuclear cost trends, due to the availability of data and its ease of use (Jamasb, 2007).”
• The Future of Ex-Ante LCA? Lessons Learned and Practical Recommendations (2019, background): “Another drawback is that they have high data requirements, which may complicate the implementation of two-factor learning curves [30].”