Abstract

The extent and timing of cost-reducing improvements in low-carbon energy systems are important sources of uncertainty in future levels of greenhouse-gas emissions. Models that assess the costs of climate change mitigation policy, and energy policy in general, rely heavily on learning curves to include technology dynamics. Historically, no energy technology has changed more dramatically than photovoltaics (PV), the cost of which has declined by a factor of nearly 100 since the 1950s. Which changes were most important in accounting for the cost reductions that have occurred over the past three decades? Are these results consistent with the notion that learning from experience drove technical change? In this paper, empirical data are assembled to populate a simple model identifying the most important factors affecting the cost of PV. The results indicate that learning from experience, the theoretical mechanism used to explain learning curves, only weakly explains change in the most important factors—plant size, module efficiency, and the cost of silicon. Ways in which the consideration of a broader set of influences, such as technical barriers, industry structure, and characteristics of demand, might be used to inform energy technology policy are discussed.

Keywords: Photovoltaics; Learning curves; Experience curves

Article Outline

1. Introduction

1.1. The learning curve model

1.2. Approach

2. Cost model methodology

2.1. Cost

2.2. Module efficiency

2.3. Plant size

2.4. Yield

2.5. Poly-crystalline share

2.6. Silicon cost

2.7. Silicon consumption

2.8. Wafer size

2.9. Full model

3. Model results: plant size, efficiency, and silicon cost

3.1. Period 1: 1975–79

3.1.1. Shift to lower quality reduces cost

3.1.2. Change in demand elasticity decreases margins

3.1.3. Increasing competition

3.1.4. Standardization

3.2. Period 2: 1980–2001

3.3. Sensitivity analysis

4. Roles of experience and learning

4.1. Experience and plant size

4.2. Experience and module efficiency

4.3. Silicon cost

4.4. Other factors

5. Conclusions

5.1. Addressing market dynamics

5.2. Technical factors and uncertainty

5.3. Scenarios of target costs

5.4. Summary

Acknowledgements

References