Industrial dynamism and the creation of a “virtuous cycle” between R&D, market growth and price reduction: The case of photovoltaic power generation (PV) development in Japan

By C. Watanabe, K. Wakabayashi, Toshinori Miyazawa. 2000

213 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5c42f9e27c4b3a87ea128b95b845a42763095134

How researchers see this paper

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

In Environmental Engineering

1. Tobias S. Schmidt
2. V. H. Hoffmann
3. Gregory F. Nemet
4. Malte H. G. Schneider
5. P. Kobos
6. Inês Lima Azevedo
7. P. Jaramillo
8. J. Huenteler

In Global and Planetary Change

1. Gregory F. Nemet
2. P. Kobos
3. J. Huenteler
4. A. Sagar
5. V. H. Hoffmann
6. Tobias S. Schmidt
7. J. Erickson
8. T. Drennen

In Management Science and Operations Research

1. Tobias S. Schmidt
2. Edward S. Rubin
3. Gregory F. Nemet
4. Sonia Yeh
5. V. H. Hoffmann
6. Joern Hoppmann
7. M. Peters
8. Malte H. G. Schneider

Citations in the corpus

• Technological learning and renewable energy costs: implications for US renewable energy policy (2004, methodology): “Recent contributions by Miketa and Schrattenholzer (2004), McDonald and Schrattenholzer (2001), Schrattenholzer (2000), Wene (2000), Watanabe (1995b), Watanabe et al. (2000), Neij (1997, 1999), and others have applied the learning curve framework to energy technologies.”
• Learning curves for environmental technology and their importance for climate policy analysis (2004, background): “While some studies have sought to develop or propose such two-factor models of learning [8], in practice, such relationships are extremely difficult to develop and validate because of data limitations.”
• A review of learning rates for electricity supply technologies (2015, background): “…two-factor models endogenously tends to reduce the long-term cost of environmental policy measures while abating emissions more extensively compared to scenarios with no learning or with only one-factor models (Watanabe et al., 2003, 2000; Barreto and Kypreos, 2004; Fischer and Newell, 2008).”
• The two faces of market support—How deployment policies affect technological exploration and exploitation in the solar photovoltaic industry (2013, background): “8 Until 2004, mainly due to its sunshine program, Japan had been the biggest market for photovoltaic products (Algieri et al., 2011; Watanabe et al., 2000).”
• Energy-Technology Innovation (2006, background): “Watanabe C, Wakabayashi K, Miyazawa T. 2000.”
• Japan's post-Fukushima Challenge - Implications from the German Experience on Renewable Energy Policy (2012, background): “Early market support and research funding in Japan in the 1980s and 90s had spurred competitiveness (Watanabe et al., 2000), allowing Japanese firms to take leading positions in the global PV industry.”
• A review of uncertainties in technology experience curves (2012, background): “In general, studies found that incorporating these two factors tended to lower the cost of environmental policies and achieve higher emission abatement levels than with no learning model or with only one of the factors alone (Barreto and Klaassen, 2004; Fischer and Newell, 2008; Grubb et al., 2006; ”