1. Introduction

2,3,5,6,7, Energy is the lifeblood of modern civilisation. Humans would still be hunter-gatherers if not for our ability to extract and use energy. Major advances in human wellbeing have been driven by transitions to cheaper and more plentiful energy. Examples include: the harnessing of fire, animals, wind and water power, and transitions from wood to coal, and from coal to oil and to gas [ 1 4 ]. A transition to cheaper, cleaner electricity globally would improve human wellbeing and reduce the environmental impacts of electricity generation [ 3 8 ].

People and businesses want cheap, reliable and secure energy. Globally, 1.2 billion people are still living without access to electricity [ 9 ]. According to the World Health Organisation (WHO) [ 8 ], “around 3 billion people cook and heat their homes using open fires and simple stoves burning biomass (wood, animal dung and crop waste) and coal”. WHO [ 7 ] estimated that 4.3 million deaths annually are attributable to indoor air pollution and 3.7 million to ambient (outdoor) air pollution. Gohlke et al. [ 6 ] found that increased electricity consumption per capita correlates with better health outcomes because of better access to clean water and sanitation, and reduced indoor and outdoor air pollution. They also found that access to a centralised power source is necessary to gain many of the benefits of clean power. Many of the deaths caused by indoor air pollution could be avoided if electricity replaced the burning of biomass and coal in homes, and many of the deaths attributable to outdoor air pollution could be avoided if clean technologies replaced fossil fuel for electricity generation.

Nuclear power produces comparatively little air or water pollution. Substituting nuclear for fossil fuel in electricity generation could prevent most of the deaths attributable to electricity generation. Cheap electricity increases productivity and economic growth, drives electrification for people without any electricity or with insufficient or unreliable electricity, and thereby more quickly raises living standards and human wellbeing. As the cost of electricity decreases, deployment rate increases. Transition takes place faster and the benefits are delivered sooner.

History is replete with examples of one technology replacing another [ 2 3 ]. Large infrastructure transitions have commonly taken around a century [ 10 ]. Examples are transitions to canals, railways, highways, oil and gas pipelines, telegraph, and electricity grids. Transitions typically follow an S-curve from 0 to 100% complete, with three phases: accelerating to about 20%, near-linear to about 80%, and decelerating to 100% [ 3 10 ]. Electricity grids reached 50% of world population in 1960 and 80% in 2010 [ 11 ].

The transition to nuclear power began in 1954 with the first reactor connected to the grid. Until the 1970s, it was envisaged that nuclear would emulate earlier energy transitions. For example, Wilson [ 12 ] projected that nuclear power would supply 14 to 21% of world primary energy by 2000. However, the transition to nuclear reached 4% by 1970, then stalled [ 3 ]. The deployment rate of nuclear capacity is currently less than in 1972; the transition has been stalled for 44 years.

The rate that technology transitions take place depends, in part, on the technologies being ‘fit-for-purpose’ and on the learning rates that occur during the transition period. To accelerate the transition to reliable, cheap, clean, safe and comparatively environmentally benign electricity generation, policies need to focus on ways to improve the learning rates and deployment rates of technologies that meet requirements. Historical learning rates provide insight into what rates may be achievable and what could be done to return to rapid rates.

[I], is widely used to quantify the rate at which costs reduce as experience is gained. Learning rate is the fractional reduction in cost per doubling of cumulative capacity or production. Rubin et al. [ The concept of learning rates, or cost experience curves, is widely used to quantify the rate at which costs reduce as experience is gained. Learning rate is the fractional reduction in cost per doubling of cumulative capacity or production. Rubin et al. [ 13 ] explain how to calculate learning rates, and summarise learning rates for selected electricity generation technologies. However, their paper has limited information on nuclear power learning rates, and none before 1972 or after 1996.

[II] provide a comprehensive analysis of nuclear power construction cost experience of early and recent reactors in seven countries; their analysis covers 58% of the reactors ever constructed for electricity generation, between 1954 and 2015. While there have been many studies of the cost escalation of nuclear power plants (e.g., [ Lovering et al. [ 14 provide a comprehensive analysis of nuclear power construction cost experience of early and recent reactors in seven countries; their analysis covers 58% of the reactors ever constructed for electricity generation, between 1954 and 2015. While there have been many studies of the cost escalation of nuclear power plants (e.g., [ 15 16 ], and others cited in Lovering et al. [ 14 ]), most are for the US and France only, and cover only periods since the 1970s. To the author’s knowledge, there are no comprehensive studies, other than Lovering et al. [ 14 ], that cover the full period of global commercial nuclear power reactor operation, nor any studies that provide the learning rates over the full period, and that highlight their reversal, which began in the late-1960s.

This study extends the literature by providing learning rates of nuclear power reactors for the seven countries analysed by Lovering et al. for the full period from 1954 through 2015. The aim is to answer two questions. What were the global benefits forgone as a consequence of the reversal of learning rates and the stalled deployment rates? What are the policy implications?

2 emissions between 1971 and 2009. The current analysis also uses a counterfactual approach. Lovering et al. data were re-analysed to calculate the historical learning rates and deployment rates of nuclear power, and to project the early rates to 2015. Evidence of disruption to the learning and deployment rates is presented and some of the benefits forgone are quantified. Estimates are presented for: Using counterfactual analysis, Kharecha and Hansen [ 17 ] estimated that electricity generated by nuclear power avoided 1.84 million air-pollution-related deaths and 64 Gt of COemissions between 1971 and 2009. The current analysis also uses a counterfactual approach. Lovering et al. data were re-analysed to calculate the historical learning rates and deployment rates of nuclear power, and to project the early rates to 2015. Evidence of disruption to the learning and deployment rates is presented and some of the benefits forgone are quantified. Estimates are presented for:

[III],[IV] of nuclear power could have been in 2015 if the early learning and deployment rates had continued what the Overnight Construction Cost (OCC)of nuclear power could have been in 2015 if the early learning and deployment rates had continued

the additional electricity that could have been generated by nuclear power if the early deployment rate trends had continued

the number of deaths and quantity of CO 2 emissions that could thereby have been avoided.

The analysis finds that the benefits forgone because of the disruption, and the resulting stalled transition from fossil fuels to nuclear power, are substantial.

The purpose of this paper is to publish the evidence and the consequences of the disruption, and to suggest an approach to removing the impediments that are delaying progress. It does not explore the causes of the disruption and cost escalations thereafter; that would require extensive studies beyond the scope of this paper.

To summarise, this study provides learning rates for a full set of reactors in seven countries, covering builds from 1954 through to projects that had been completed by the end of 2015, covering 58% of all power reactors ever built globally. It also provides global deployment rates for that period. It estimates the extra electricity that could have been generated by nuclear power since 1980 and what OCC would have been in 2015 if the learning and deployment rates had not been disrupted. It compares the projections to the actuals to estimate forgone benefits of the disruption. It suggests an approach to removing the impediments that are retarding the transition to nuclear power.