The costs of the French nuclear scale-up: A case of negative learning by doing

By Arnulf Grubler. 2010

358 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Materials Chemistry, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/0300ab8f31bb4b90d26c8149fe7b36db2af77ca7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Giorgio Locatelli
2. I. Kessides
3. J. Lovering
4. Yoshua Bengio
5. Daniel Nugent
6. C. Bingham
7. Iain Staffell
8. Alexandra Sasha Luccioni

In Materials Chemistry

1. I. Kessides
2. J. Lovering
3. Yoshua Bengio
4. Daniel Nugent
5. C. Bingham
6. Benjamin Sovacool
7. Arthur Yip
8. Mauro Mancini

In Safety, Risk, Reliability and Quality

1. Charlie Wilson
2. Edward S. Rubin
3. I. Kessides
4. J. Lovering
5. Claudia Kemfert
6. Yoshua Bengio
7. Daniel Nugent
8. C. Bingham

Citations in the corpus

• Tackling Climate Change with Machine Learning (2019, background): “Nuclear power plants Some argue that nuclear fission reactors (also known as nuclear power plants) are essential to meeting climate change goals [95], but these technologies face significant challenges including public safety, waste disposal, slow technological learning [96, 97], and high costs [95]”
• A review of learning rates for electricity supply technologies (2015, background): “NEMS also uses several other factors to estimate the future cost of power plant technologies, such as regional cost factors and “technological optimism” factors (Gumerman and Marnay, 2004).”
• European electricity sector decarbonization under different levels of foresight (2019, background): “Nuclear power investment costs have gone up systematically over the last decades, as observed by Joskow and Parsons [42]; Grubler [43]; Rangel and L evêque [44].”
• Historical construction costs of global nuclear power reactors (2016, positive): “Of particular note, Grubler (2010) analyzed the historical costs of nuclear power for France and the US, and concluded that nuclear power construction costs “ invariably exhibited negative learning ” and “ forgetting by doing, ” citing an increase in system complexity for nuclear power…”
• Economics and finance of Small Modular Reactors: A systematic review and research agenda (2020, positive): “For instance, the “ successful nuclear programme ” in South Korea is mostly due to an approach at “ programme level ” [80], instead of a “ plant-level ” like in USA or “ site-level ” like in France [85].”
• Power capacity expansion planning considering endogenous technology cost learning (vol 204, pg 831, 2017) (2017, background): “Famously, the cost of nuclear power plants have increased due to the system complexity and more stringent safety regulations [19].”
• Risk, innovation, electricity infrastructure and construction cost overruns: Testing six hypotheses (2014, methodology): “We initially searched the energy studies, electricity, transport, and infrastructure literature for reliable peer-reviewed data, which we did find in a few instances [1,13,22,39].”
• The Future of the Nuclear Industry Reconsidered: Risks, Uncertainties, and Continued Potential (2012, background): “Instead, these non-safety design changes were driven by the desire for larger scale, higher domestic value-added for the nuclear industry, and more output (Grubler, 2010). realization and time to build.”
• Up-scaling, formative phases, and learning in the historical diffusion of energy technologies (2012, background): “Second, learning is not a deterministic outcome of increasing production, but rather is contingent on a host of firm and industry-level innovation processes and efforts (Grubler, 2010).”