Learning-by-Doing and the Optimal Solar Policy in California

By A. V. Benthem, Kenneth T. Gillingham, J. Sweeney. 2008

213 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/06f5c36f0799e68b01f11d486aeddafbfb052625

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Citations in the corpus

Drivers of cost reduction in solar photovoltaics (2015, background): “…systems during fornia state government has 7–2016 (see CPUC (2009)). lectricity (see http://www. test-conto-energiahe impact of subsidies to resee Hughes and Podolefsky static economies of scale in the industry (see Benthem et al. (2008), Algoso et al. (2005), and Shrimali and Baker (2011)).”
Feed-In Tariffs for Photovoltaics: Learning by Doing in Germany (2011, unknown): “Annual demand qt is modeled according to Benthem et al. (2008).”
Combining Policies for Renewable Energy: Is the Whole Less than the Sum of its Parts? (2010, background): “Elsewhere, van Benthem, Gillingham, and Sweeney (2008) find that learning spillovers provide sufficient justification for California’s solar subsidies, while the environmental externality rationale falls short.”
Consumer attitude and purchase intention toward rooftop photovoltaic installation: The roles of personal trait, psychological benefit, and government incentives (2020, background): “High costs are the primary barrier to installing PV systems, and a strong preference for capital incentives to reduce investment costs is needed.40 Subsidies and incentives are among the key drivers of global solar PV systems.41 The crucial factors for the diffusion of solar energy systems are finan”
The Impact of Demand Uncertainty on Consumer Subsidies for Green Technology Adoption (2016, unknown): “For the most part, the subsidy design literature in green technologies has not studied demand uncertainty (see for example, Benthem et al. (2008), Atasu et al. (2009), Lobel and Perakis (2013) and Alizamir et al. (2013)).”
Analysis of feed-in tariff policies for solar photovoltaic in China 2011–2016 (2017, methodology): “Also the tool of learning curve is usually adopted to represent the technology progress which is a key component in the optimal FIT models [22,23].”
A review of learning rates for electricity supply technologies (2015, background): “Van den Broek et al. (2009) also use a bottom-up approach to estimate future learning rates of capital and O&M cost for components of future PC and IGCC plants with CCS, but do not report composite results for an overall plant.”
Optimal subsidy estimation method using system dynamics and the real option model: Photovoltaic technology case (2015, methodology): “Some researchers have used a consumer choice model to forecast future PV demands and estimate the minimal subsidy for achieving the policy adoption of targets [7].”
Interim monitoring of cost dynamics for publicly supported energy technologies (2009, background): “Finally, empirically distinguishing local from global learning effects is needed to inform international coordination among government programs (Benthem et al., 2008; Shum andWatanabe, 2008).”