Decade of PV industry R&amp;D advances in silicon module manufacturing

Symko-Davies, Martha; Mitchell, R.; Witt, C.E.; Thomas, H.; King, Richard J.; Ruby, D.S.

doi:10.1109/pvsc.2000.916168

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…Module efficiency (η) unitless 8% 13% 15.2% 1980: (Christensen, 1985), (Mints, 2015), (Maycock, 1995). 2001: (Rohatgi, 2003), (Symko-Davies et al, 2000), (Mints, 2015). 2012: (Powell et al, 2012), (Mints, 2015).…”

Section: Silicon Costsmentioning

confidence: 99%

Evaluating the causes of cost reduction in photovoltaic modules

2018

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Section: Silicon Costsmentioning

confidence: 99%

Evaluating the causes of cost reduction in photovoltaic modules

2018

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“…Improved crystal growing methods have increased the cross-sectional area of each wafer (WS) by a factor of four (Christensen, 1985;Symko-Davies et al, 2000;Rohatgi, 2003;Swanson, 2004). Larger wafers facilitate savings in the cell and module assembly processes where there are costs that are fixed per wafer, e.g.…”

Section: Wafer Sizementioning

confidence: 99%

Beyond the learning curve: factors influencing cost reductions in photovoltaics

2006

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“…Improved crystal growing methods have increased the cross-sectional area of each wafer (WS) by a factor of four (Christensen, 1985;Symko-Davies et al, 2000;Rohatgi, 2003;Swanson, 2006). Larger wafers facilitate savings in the cell and module assembly processes where there are costs that are fixed per wafer, e.g.…”

Section: Wafer Sizementioning

confidence: 99%

How Well Does Learning-By-Doing Explain Cost Reductions in a Carbon-Free Energy Technology?

Nemet

2006

SSRN Journal

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SummaryThe incorporation of experience curves has enhanced the treatment of technological change in models used to evaluate the cost of climate and energy policies. However, the set of activities that experience curves are assumed to capture is much broader than the set that can be characterized by learning-by-doing, the primary connection between experience curves and economic theory. How accurately do experience curves describe observed technological change? This study examines the case of photovoltaics (PV), a potentially important climate stabilization technology with robust technology dynamics. Empirical data are assembled to populate a simple engineering-based model identifying the most important factors affecting the cost of PV over the past three decades. The results indicate that learning from experience only weakly explains change in the most important cost-reducing factors-plant size, module efficiency, and the cost of silicon. They point to other explanatory variables to include in future models. Future work might also evaluate the potential for efficiency gains from policies that rely less on 'riding down the learning curve' and more on creating incentives for firms to make investments in the types of cost-reducing activities quantified in this study. Keywords: Learning-by-doing, Experience Curves, Learning Curves, Climate Policy JEL Classification: O31, Q42, Q48, Q55 Discussions with Arnulf Grübler at the International Institute for Applied SystemsAnalysis (IIASA) were extremely helpful in the early stages of this project. I also gratefully acknowledge Brian Arthur, Severin Borenstein, Martin Green, Bronwyn Hall, Daniel Kammen, Robert Margolis, Paul Maycock, David Mowery, and Chihiro Watanabe for providing valuable comments at various stages. I thank the U.S. National Academies for financial support while I was at IIASA. This paper was presented at the EAERE-FEEM-VIU Summer School on "Computable General Equilibrium Modeling in Environmental and Resource Economics", held in Venice from June 25th to July 1st, 2006 and supported by the Marie Curie Series of Conferences "European Summer School in Resource and Environmental Economics". Address for correspondence:Gregory F. Nemet Energy and Resources Group University of California 310 Barrows Hall 3050 Berkeley CA 94720-3050 USA E-mail: gnemet@berkeley.edu Technological change and learning curvesThe rate and direction of future technological change in energy technologies are important sources of uncertainty in models that assess the costs of stabilizing the climate (Edenhofer et al., 2006). 1 Treatment of technology dynamics in integrated assessment models has become increasingly sophisticated (Grubb et al., 2002) as models have incorporated lessons from the economics of innovation and as increased processing power and improved algorithms have enabled optimization of phenomena, such as increasing returns, which in the past had made computation unwieldy (Messner, 1997). Yet the representation of technological change in large energy-economic model rema...

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Decade of PV industry R&D advances in silicon module manufacturing

Cited by 10 publications

References 4 publications

Evaluating the causes of cost reduction in photovoltaic modules

Evaluating the causes of cost reduction in photovoltaic modules

Beyond the learning curve: factors influencing cost reductions in photovoltaics

How Well Does Learning-By-Doing Explain Cost Reductions in a Carbon-Free Energy Technology?

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