Wind Power in Europe: A Simultaneous Innovation–Diffusion Model

By Patrik Söderholm, G. Klaassen. 2007

299 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4a6054fd8d2723d653f37bbb8d7feac090062865

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. Edward S. Rubin
3. Charlie Wilson
4. Massimo Tavoni
5. Carlo Carraro
6. Valentina Bosetti
7. Patrik Söderholm
8. Inês Lima Azevedo

In Finance

1. Edward S. Rubin
2. Sonia Yeh
3. T. Sundqvist
4. Patrik Söderholm
5. Tooraj Jamasb
6. Charlie Wilson
7. C. Fischer
8. Inês Lima Azevedo

In Management Science and Operations Research

1. Charlie Wilson
2. Edward S. Rubin
3. Sonia Yeh
4. T. Sundqvist
5. Inês Lima Azevedo
6. P. Jaramillo
7. Tooraj Jamasb
8. C. Fischer

Citations in the corpus

• A review of uncertainties in technology experience curves (2012, methodology): “There has been somework to validate this formulationwith empirical data (Jamasb, 2007; Klaassen et al., 2005; Söderholm and Klassen, 2007; Söderholm and Sundqvist, 2007).”
• What Should We Expect from Innovation? A Model-Based Assessment of the Environmental and Mitigation Cost Implications of Climate-Related R&D (2010, background): “…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. 2005; Kypreos, 2007; Jamasab, 2007; Söderholm and Klassen, 2007).”
• 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”
• Optimal Timing of Climate Change Policy: Interaction Between Carbon Taxes and Innovation Externalities (2009, unknown): “The first two arguments have taken firm root in the literature, thanks to—among others—the analysis by Wigley et al. (1996).1 The third argument, however, based on presumed technological advancements in abatement options, has raised a lively debate among economists studying technological change in r”
• Up-scaling, formative phases, and learning in the historical diffusion of energy technologies (2012, background): “Two-factor experience curves, for example, explicitly represent the influence of cumulative R&D expenditure and the resulting R&D-based knowledge stock on unit cost reductions (Söderholm and Klaassen, 2007; Ek and Soderholm, 2010).”
• Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies (2007, background): “To our knowledge, the only example of such approach is reported in Söderholm and Klassen (2007) which uses simultaneous learning-diffusion equations to estimate the effect of promotion policies for wind power in the UK, Spain, Denmark, and Germany.”
• Combining Policies for Renewable Energy: Is the Whole Less than the Sum of its Parts? (2010, background): “Söderholm and Klaassen (2007) reach a similar conclusion in favor of coupling R&D subsidies with price-based renewables support.”