Interim monitoring of cost dynamics for publicly supported energy technologies

By Gregory F. Nemet. 2009

132 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Pollution, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f220cdf508f6c33074ba53bd13e8fc4b2b906f83

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. Sonia Yeh
2. Inês Lima Azevedo
3. P. Jaramillo
4. David J. Wright
5. Karin Hinzer
6. Mark Winskel
7. Hanan Anis
8. A. Gerlach

In Pollution

1. Christian Breyer
2. Inês Lima Azevedo
3. P. Jaramillo
4. David J. Wright
5. Karin Hinzer
6. Hanan Anis
7. A. Gerlach
8. Edward S. Rubin

In Sociology and Political Science

1. David J. Wright
2. Karin Hinzer
3. Hanan Anis
4. A. Gerlach
5. S. Samadi
6. Charlie Wilson
7. J. Haysom
8. Christian Breyer

Citations in the corpus

• The experience curve theory and its application in the field of electricity generation technologies – A literature review (2018, background): “Critics of this approach emphasise that it should not be taken for granted that past experience curves can simply be extrapolated [53,65,72].”
• Learning curve analysis of concentrated photovoltaic systems (2015, methodology): “We propose an approach based on maintaining the information contained in the raw data [41,42] and using regression analysis.”
• A review of learning rates for electricity supply technologies (2015, background): “Different markers (and colors) denote the different studies. and Schrattenholzer, 2001; Neij, 2008; Neij et al., 2003; Nemet, 2009; Priddle, 2000; Qiu and Anadon, 2012; Trappey et al., 2013; Weiss et al., 2010).”
• The dynamics of solar PV costs and prices as a challenge for technology forecasting (2013, background): “Indeed, although average historical PV learning rates are in the order of 20%, a closer look at the data shows high variability across time [4,26,27].”
• Up-scaling, formative phases, and learning in the historical diffusion of energy technologies (2012, background): “Expectations of future learning rates, particularly for low carbon energy technologies, are widely used to inform or rationalise technology policies (Wene, 2000; Nemet, 2009) and to model the diffusion of technologies under different scenario assumptions (Clarke et al., 2008).”
• Global overview on grid‐parity (2013, background): “Historically, no energy technology showed such a high learning rate over such a long period of time [23,24].”