Technical change dynamics: evidence from the emerging renewable energy technologies

By S. Isoard, Antonio Soria. 2001

179 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/d3c9438694cc905c6203afe4f97547133baa5d13

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Citations in the corpus

Optimal Timing of Climate Change Policy: Interaction Between Carbon Taxes and Innovation Externalities (2009, unknown): “2 See Lieberman (1984) for an early contribution focused on the chemical industry, and Isoard and Soria (2001) for a recent empirical analysis for energy technologies.”
Assessing the regional economic impacts of renewable energy sources – A literature review (2018, background): “When assessing deployment which occurs during the whole time, cost reductions of specific technologies because of learning effects, economies of scale (because of the market expansion of a technology) [76] should also be taken into account.”
Mitigation from a cross-sectoral perspective (2007, unknown): “It is difficult to separate the effects of time trends, economies of scale and R&D from those of LBD (Isoard and Soria, 2001) and different functional forms and data periods yield different estimates, so the learning rates may be more uncertain than suggested by their treatment in the models.”
Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects (2011, unknown): “(17) is simpliﬁed using the following deﬁnitions [30,31]: ; and P This leads to in which P i represents the prices of inputs required for producing and operating the technologies, and d i is the elasticity of the inputs.”
Power capacity expansion planning considering endogenous technology cost learning (vol 204, pg 831, 2017) (2017, background): “However, economic causality analyses provide evidence that technological change and diffusion are indeed dependent variables [24,36].”
Carbon Lock-Out: Advancing Renewable Energy Policy in Europe (2012, background): “Risk premiums required on capital markets for renewable energy projects are usually higher than those for fossil-fuel technologies due to higher uncertainties associated with relatively immature RES-E technologies and exogenous risks such as wind speed or sunshine duration [45].”
Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies (2007, methodology): “We here follow Berndt [24] and Isoard and Soria [25] and derive the learning curve model from a Cobb–Douglas cost function.”