The submission by The Australia Institute (TAI) to the South Australian Nuclear Fuel Cycle Royal Commission includes discussion of time taken to build nuclear power plants. The mean time shown is 9.4 years. The conclusions drawn by TAI are:

“If Australia begins to develop a nuclear power industry, build times are likely to be long enough that renewables and storage will be well established long before completion, invalidating any realistic business case which is, if European nuclear power profitability and current levelised cost of energy is any guide, already tenuous”.

This analysis is flawed. The given mean, 9.4 years, is mathematically correct based on the data shown. However TAI have not interrogated and analysed the data adequately in order to provide a robust conclusion about potential nuclear build time for Australia.

The data set for this finding is shown by TAI in Table 1 on page 8 of the submission. I have reproduced it here.

Country Units Mean time Min. time Max. time China 18 5.7 4.4 11.2 India 7 7.3 5.1 11.6 South Korea 5 4.9 4 6.4 Japan 3 4.6 3.9 5.1 Russia 3 28.0 25.3 31.9 Argentina 1 32.9 32.9 32.9 Iran 1 36.3 36.3 36.3 Pakistan 1 5.3 5.3 5.3 Romania 1 24.1 24.1 24.1 Total 40.9 9.4 3.9 36.3

Useful research needs to clearly define the question under investigation and then both interrogate and analyse the available data set. Let’s assume the question here is “How long does it take to build nuclear reactors?” in the context of informing discussion in South Australia.

In this data set covering 40 reactor builds in nine nations there are six obvious outliers at the high end with builds of 24-years or greater. There is a gap of 13 years build time between the lowest of these outliers and the next longest reactor build (11.2 years). These outliers will skew the mean to the high end.

What should be done with them? They could be excluded. Given (a) the gap between the outliers and the rest of the data set (b) that they are relatively small in number and (c) three of the data points are the only build in a given nation, exclusion seems reasonable prima facie.

It would be better to interrogate the outliers, understand what caused them, and either exclude on a clear justification or attempt to correct the data for any identified confounding factors.

What are confounding factors? Borrowing a definition from epidemiology:

“Factors that can cause or prevent the outcome of interest, are not intermediate variables, and are not associated with the factor(s) under investigation. They give rise to situations in which the effects of two processes are not separated, or the contribution of causal factors cannot be separated, or the measure of the effect of exposure or risk is distorted because of its association with other factors influencing the outcome of the study“.

In this data, understanding the outcome of interest (nuclear build time) from the available data may well be confounded by factors that have nothing to do with nuclear build time (i.e. not associated with the factors under investigation). Could that be the case in this data? Let’s look at the outliers.

Argentina : Like many economies in South America, Argentina has experienced repeated economic and social upheaval. I discuss this example further below

: Like many economies in South America, Argentina has experienced repeated economic and social upheaval. I discuss this example further below Romania : Romania was part of the Warsaw Pact, under dictatorial rule for over forty years, which ended in violent revolution in 1989,

: Romania was part of the Warsaw Pact, under dictatorial rule for over forty years, which ended in violent revolution in 1989, Iran : No comment required. Including Iranian nuclear build in this dataset without question is clearly untenable.

: No comment required. Including Iranian nuclear build in this dataset without question is clearly untenable. Russia: The collapse of the Soviet Union in 1989 was probably the greatest social, political and economic upheaval of the second half of the 20th century

In all of the outliers, the metric of build time, measured from start of construction to end, has been grossly confounded by social, political and economic upheaval. These data points tell us very little about nuclear build time. They may tell us something about the impact of such upheaval on large infrastructure projects. It would be an interesting question to examine, separately.

One might try to correct this data for the confounding factors. One way might be by specifically measuring time in active construction as opposed to calendar time between start and finish. Take the Kirchner reactor in Argentina (Atucha 2). It was not a 33 year build as suggested by the data used by TAI. It started in 1981, in the last days of a military dictatorship. It proceeded slowly due to lack of funds until 1994 when it was suspended. The Argentine economy collapsed in default in 2001, began recovery a few years later and has been quite strong since then. Atucha 2 was revisited in 2006 as part of a strategic plan for the nuclear sector in Argentina, and reached full power in February 2015.

So, with all that interesting history one could attempt to apply a corrected figure for Atucha 2 and the other outliers. However for the sake of simplicity I will exclude these data points as too confounded to inform the question being asked. When these six data points (of 40) are excluded, the mean drops from 9.3 years to 5.8 years, a 3.5 year difference. With those exclusions and the relevant justification, the data set now appears to me to be more informative and representative of the potential Australian situation: a stable nation that may embark on a nuclear build program.

No one could seriously contend that a mean time of 5.8 years works against the argument for nuclear as a deployable source of clean energy. This is particularly true when considering the quantity and reliability of that supply.

In a metric pioneered by Geoff Russell and further developed at The Breakthrough Institute, nuclear has been decisively shown to be the fastest pathway to adding new energy anywhere, ever. For example South Australia’s wind sector (which I support) has incrementally (but, by most observations, quite aggressively) developed over a 12-year period to now provide a variable supply of about 3,000 GWh per annum. A single CANDU reactor would deliver about 5,500 GWh per year in a reliable, dispatchable form. They may deploy differently however one cannot form a “time to deploy” argument against nuclear without forming the same argument against wind.

In coming years there will be more data. Most of the builds coming from China and South Korea will pull that mean down. The current builds in Europe will pull it up. The current builds in the US will hover around the current 5.8 year mean.

The build program of the UAE will pull the mean down and that’s interesting for Australia. That is a new nuclear nation, not an established nuclear nation, delivering outcomes among the best in the world.

All of this information provides important guidance for Australia, provided we look not only at the numbers but also behind them.

Numbers are useful and they can also be misused. The role of the researcher and analyst in nuclear remains a crucial one. TAI need to apply more rigour to be taken seriously in this space.