Technical change dynamics: evidence from the emerging renewable energy technologies

Isoard, Stéphane; Soria, Antonio

doi:10.1016/s0140-9883(01)00072-x

Cited by 126 publications

(78 citation statements)

References 25 publications

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“…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]. Less experience on the lender side with RES-E technologies will also result in higher risk premiums.…”

Section: Definition Of Barriersmentioning

confidence: 99%

Carbon Lock-Out: Advancing Renewable Energy Policy in Europe

et al. 2012

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As part of its climate strategy, the EU aims at increasing the share of electricity from renewable energy sources (RES-E) in overall electricity generation. Attaining this target poses a considerable challenge as the electricity sector is "locked" into a carbon-intensive system, which hampers the adoption of RES-E technologies. Electricity generation, transmission and distribution grids as well as storage and demand response are subject to important path dependences, which put existing, non-renewable energy sources at an advantage. This paper examines how an EU framework for RES-E support policies should be designed to facilitate a carbon lock-out. For this purpose, we specify the major technological, economic and institutional barriers to RES-E. For each of the barriers, a policy review is carried out which assesses the performance of existing policy instruments and identifies needs for reform. The review reveals several shortcomings: while policies targeting generation are widely in place, measures to address barriers associated with OPEN ACCESSEnergies 2012, 5 324 electricity grids, storage and demand are still in their infancy and have to be extended. Moreover, the implementation of policies has been fragmented across EU Member States. In this respect, national policies should be embedded into an integrated EU-wide planning of the RES-E system with overarching energy scenarios and partially harmonized policy rules.

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Section: Definition Of Barriersmentioning

confidence: 99%

Carbon Lock-Out: Advancing Renewable Energy Policy in Europe

et al. 2012

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“…We here follow Berndt [24] and Isoard and Soria [25] and derive the learning curve model from a Cobb-Douglas cost function. This means that for our purposes the current unit cost of wind power capacity in country n during time period t, C C nt , is specified as:…”

Section: Deriving the Learning Curve From A Cobb-douglas Cost Functionmentioning

confidence: 99%

Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies

Söderholm

Sundqvist²

2007

Renewable Energy

177

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In bottom-up energy models endogenous technical change is introduced by implementing technology learning rates, which specify the quantitative relationship between the cumulative experiences of the technology on the one hand and cost reductions on the other. The main purpose of this paper is to critically analyze the choice of modeling and estimation strategies in learning curve analyses of power generation costs. We identify and discuss a number of theoretical and econometric issues involved in the estimation of learning curves. These include the presence of omitted variable bias and simultaneity, but also methodological problems related to the operationalization of theoretical concepts (i.e., learning-bydoing) and the associated use of data. We illustrate the importance of these issues by employing panel data for wind power installations in four western European countries, which are used to compare the results from different learning curve model specifications. The results illustrate that the estimates of learning rates may differ significantly across different model specifications and econometric approaches. The paper ends by outlining a number of recommendations for energy model analysts, who need to select appropriate energy technology learning rates from the empirical literature, or who choose to perform the empirical work themselves. r

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“…An important difference between LbD and R&D models is that the latter category of models does not assume from the outset that the technology needs to be used for its costs 1 They used these arguments to make the case that emission paths developed by the IPCC (1995) for ceiling atmospheric carbon dioxide concentrations tended to put too much effort up-front, while a delayed abatement response would be more cost-efficient. 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. 3 Manne and Richels (2004), however, find that LbD has almost no effect on the efficient timing of abatement.…”

Section: Introductionmentioning

confidence: 99%