Saturday, 29 October 2016

Lawrence, Sovacool, and Stirling paper controversy : timeline

I blogged this twice already. Once here and once at the sister blog.

Here is an exciting timeline for a paper published in July but, hopefully, withdrawn by October. So far, there is no official retraction or withdrawal. Someone should write to the journal's editor to confirm it.

Summary: The paper was published, got fair publicity in the anti-nuclear / 100%-renewables press (they are the same thing). It claimed that Pro-nuclear [European] countries [are] making slower progress on climate targets. It drew its data from open sources, but copied just about every value wrongly. So any conclusions it came to based on data would have to be revised. Several people took issue with it. Within 2 months the authors admitted their data had been transcribed wrongly. Despite the journal editor saying all that was needed were corrections to data and bits of the text. The day after I was told that, a blog by Nicholas Thompson demolished the paper with another refutation showing the conclusions could not be derived from the corrected data either. Finally, one of the authors admitted they may need to withdraw it. Better that than have it retracted lads. I'd withdraw it ASAP if I were them.


The timeline is for the Lawrence, Sovacool, and Stirling (LSS), paper controversy claiming nuclear power supporting countries do worse at reducing GHG emissions.

  1. July: The article is published in Climate Policy - a "peer reviewed journal".
  2. 22-Aug: James Hakner at Sussex Univ. finishes a press release and posts it to media outlets.
  3. On the same day, media reports begin rewriting the press release as a story dissing nuclear power.
  4. 23-Aug: I complain to the editor of Climate Policy by email (who is on leave anyway!)
  5. 24-Aug: One report in The Ecologist is by the press release's author!
  6. 25-Aug: I send out 10 emails to nuclear power supporting academics complaining about the paper. At least 3 of them reply to me: Jessica L, Ben H, and Nicholas T
  7. 26-Aug: Stephen Tindale and Suzanna Hinson at the Weinberg Foundation refute LSS paper.
  8. More media reports reprinting/rewriting their press release.
  9. 2-Sep: My blog outlining the article's faults. I notify the journal editor too by email.
  10. LSS notice my blog. Climate Policy editor discusses issues with authors and peer reviewers.
  11. LSS authors admit errors in their data, but refute my other 17 complaints about their paper.
  12. 11-Oct: I get an email from the journal editor saying the paper has been cleared as OK apart from the data which will be corrected and a few bits of the text. I tweet my annoyances.
  13. 12-Oct: Nicholas Thompson's blog refuting the conclusions they draw from their corrected data. Refuting the journal editor, the peer reviewers and the 3 authors.
  14. 27-Oct: Malcolm Grimston reports that Andy Stirling admitted the paper was rubbish and LSS have withdrawn it.
  15. 25-Nov: I hear the authors have retracted their article.


Press Release:

Green Media reports:

In most cases these were edited, sometimes whole republications of the Univ. of Sussex press release written by James Hakner.

Blogs & such:

Appendix - Corrected Data

In their original data (Table 2), emission reductions were shown as negative numbers. Emission increases as positive numbers. When presenting their corrected data LSS, reversed the number sign. They also made two arithmetic errors. I wanted to present LSS's corrected figure with the table the numbers were derived from: page 30 of Eurostat handbook (pdf). I calculated what the figures should be from the Eurostat data (heading: 2005-2012). My figures have same sign as the journal article, but opposite to LSS corrected numbers.

LSS correction
Group averages in parentheses
Emissions reductions
Index 100=199019901995200020052010201120122005-2012countryOLD DATACORRECT-ION
Group I-11.8GI(-6)-11.9
Latvia10047.738.242.546.744.742.90.4LV170.4LSS should be negative
Group II-13.8GII-11-12.9
Sweden100102.395.693.491.38680.7-12.7SE-177.3Should be 12.7
Group III-10.2GIII-3-10.2
Czech Republic1007774.574.470.468.467.3-7.1CZ97.1
United Kingdom10093.189.988.679.974.877.5-11.1UK-1611.1
Group IV-1.5GIV-15-1.5

Monday, 17 October 2016

Irish EPA grossly overestimate fatal cancer risk from radiation.


The Irish EPA grossly overestimate fatal cancer risk caused by radiation. By up to 6000-fold for age cohort 20-24. For example, if you are aged between 20 and 24, the Irish EPA overestimate your risk of dying of a fatal cancer due to radiation by 6000 times too much. The real risk is about 1 in a million. Irish EPA estimate it at 1 in 178.

In their fact sheet on radiation[1], The Irish Environment Protection Agency say:

  1. "we can estimate that a dose of 10 µSv may increase the lifetime risk of fatal cancer by about one in 2,000,000"
  2. Their estimate is based on real world risk assessments using:
    • Hiroshima and Nagasaki bomb survivor data
    • Patients exposed to external radiation for the treatment or diagnosis of certain diseases
    • Marshall Islanders exposed to severe fallout from atmospheric nuclear weapons tests
    • Miners exposed to radon and its decay products
    • Residents exposed to radon in the home
    • Workers exposed to radium-226 in luminous paint
    • Patients exposed to radium-224 for bone disease

This EPA leaflet looks convincing. What could be more believable than a risk assessment derived from real world cancer mortalities? Produced by a government agency called the Environment Protection Agency. You would have to take that seriously, or should you?

For a risk assessment to make any sense it should give some estimate of risk within reasonable error bounds. A risk assessment which is out by an order of magnitude (ten times too high or too low) is of little help. Surely an Environmental Protection Agency should be able to get their guesses right within an order of magnitude?

Reasonable explanation proposed for the Irish EPA statement

Joris van Dorp: If 10 muSv/yr gives 1 in 2 million chance of cancer, then assuming 70 years average life, dose is 2.7mSv × 70 yrs ~ 200.000 muSv, means chance of death is 20 thousand in 2 million ~ 1%.

1% is 1/40 of normal cancer incidence of 40%.

Joris' explanation looks good to me. But there's still a problem with this EPA handout. Taken literally it implies a far greater risk than the evidence shows. Irish EPA can should say each '10 µSv per year' if that's what they mean.

My model

I took what I know of regulations and cancer to make a model, which I compared to real world cancer data. Because I live in UK and have real world UK cancer mortality data, it was easiest to model this for the UK. However I can assure you, there's nothing special about UK with regard to cancer risk. If anything UK has a bad rep for stopping deaths from cancer. This is my final model compared to the real world.

Table 1: UK cancer mortalities. Real world compared to projected data
per 100,000
real numbersderivedprojections
Age RangeMale DeathsFemale DeathsMale RatesFemale RatesAverage (MF) ratesAverage (MF) rates - radiationinceptiondeathsOverestimate
0 to 0447392.322.150.12033.667 ×
05 to 0947402. ×
10 to 1438332.11.920.1878117.52349 ×
15 to 1964473.22.52.850.11,215299.54204 ×
20 to 24877543.53.750.11,553562.35998 ×
25 to 2913115166.96.450.21,890870.35397 ×
30 to 342102949.813.611.70.32,2281,197.74095 ×
35 to 393194601622.919.450.52,5651,531.83150 ×
40 to 4467898430.643.4370.92,9031,867.72019 ×
45 to 491428176961.974.668.251.73,2402,204.71292 ×
50 to 5425572853118.9129.9124.43.13,5782,542.2817 ×
55 to 5943604076234.7214.1224.45.63,9152,879.7513 ×
60 to 6473586070422.1334.3378.29.54,2533,217.2340 ×
65 to 69110898696658.1488.2573.1514.34,5903,554.7248 ×
70 to 741259997291043.6724.8884.222.14,9283,892.2176 ×
75 to 7914330112921501.6991.61246.631.25,2654,229.7136 ×
80 to 8414127121942169.51355.91762.744.15,6034,567.2104 ×
85 to 89104551025330311732.42381.759.55,9404,904.782 ×
All Ages8566776644271.6235.2253.46.3


Table 1 shows cancers grouped by age cohort give by UK Cancer Research[2] (yellow). Two columns are derived from real world data (green). The first simply averages male and female fatal cancer rates per 100k. The second derived column divides this by 40 on the assumption that 1 in 40 cancers are caused by radiation and that there's nothing exceptional about radiation cancers compared to others. Next we move to derived data (salmon). The inception column shows the number of cancers which are expected to eventually result in fatalities. The 'deaths' column applies a calculation based on the chart below which accounts for the time taken for a cancer to kill. This shows the number of fatalities expected in that age cohort. The final column (Overestimate) shows the ratio of column 9 (deaths) to column 7 (Average (MF) rates - radiation). Rates in the table are all give as per 100,000

Chart 1 : Cancer induction time distribution[3]


This part of the blog gives a detailed derivation of the previous model.

The Irish EPA say: "we can estimate that a dose of 10 µSv may increase the lifetime risk of fatal cancer by about one in 2,000,000". Nearly all regulatory agencies in the world assume a linear no-threshold effect due to radiation. [ I think the French[4] alone are different ]. So 10 µSv = 0.01 mSv. 1 in 2 million is 0.05 in 100,000. We will compare fatal cancers per 100,000 of the population. If we expect 0.01mSv to give a rate of 0.05, we can project 2.7mSv to cause a rate of 13.5. By simply scaling.

Table 2:
dose (mSv)Projected fatalities per 100k

2.7mSv is the average annual radiation exposure found in UK. Our real world cancer data comes from UK Cancer Research is also refers to the UK. I use this Irish EPA risk assessment to project the inception time of fatal cancers. I get the table below. It increases on a linear scale. Each extra year of life, adds an extra risk of contracting a fatal cancer from background radiation. A risk corresponding to an extra 13.5 per 100,000, per year.

Table 3: Project of fatal cancer inception times.
Year of lifeFatal cancer inception / 100k


Table 3 Notes
  • the table continues, ending at 89
  • Each 5 year period will be summed to make 5-year cohorts. The first cohort has 5 years labelled 0 - 4.

How very simple. I think this is one of the reasons why Linear No-threshold, LNT, is beloved of regulators. It is so very easy to math. Ref [4], has a good explanation of LNT. We can not just go from cancer inception to predict fatalities. There is a delay between inception and mortality which can be quite long (see Chart 1 above). At this point I simplified. I have real world data in 5 year cohorts, and inception-to-mortality data in 5 year cohorts. I grouped my inception data into 5 year cohorts too. By summing the 1-year cohort projections. Because I compare this with fatalities grouped into 5 year cohorts, I sum each 5 year band. The inception time for a fatal cancer differs from the time of death, according to a distribution shown in Chart 1: This chart was used to make a table. (Table 4). The distribution for the last 2 age ranges was smoothed. ( So 1 + 1, rather than 0 + 2). 281 is the estimated sample size. The estimated fatal cancer inceptions for each 5 year cohort were now multiplied to get the estimated time of death. These are the numbers seen under the age ranges (horizontally) These numbers were summed for each age range to arrive at final estimates of actual deaths per cohort.

Table 4: Cancer induction time distribution
total0 to 0405 to 0910 to 1415 to 1920 to 2425 to 2930 to 3435 to 3940 to 4445 to 49
Table 5: How induction time was added
Per 5 yearscancer inceptiondeaths
0 to 0420343.603
05 to 09540289.60918.737
10 to 1487811715.61449.96451.886
15 to 191,21530021.61981.192138.36358.372
20 to 241,55356227.625112.420224.840155.65841.797
25 to 291,89087033.630143.648311.317252.945111.45917.295
30 to 342,2281,19839.635174.875397.794350.231181.12146.1217.927
35 to 392,5651,53245.641206.103484.270447.518250.78374.94721.1391.441
40 to 442,9031,86851.646237.331570.747544.804320.445103.77234.3513.8430.721
45 to 493,2402,20557.651268.559657.224642.091390.107132.59847.5626.2461.9220.721
50 to 543,5782,54263.657299.786743.701739.377459.769161.42360.7748.6483.1231.922
55 to 593,9152,88069.662331.014830.178836.664529.431190.24973.98611.0504.3243.123
60 to 644,2533,21775.667362.242916.655933.950599.093219.07587.19813.4525.5254.324
65 to 694,5903,55581.673393.4701003.1321031.237668.754247.900100.40915.8546.7265.525
70 to 744,9283,89287.678424.6981089.6091128.523738.416276.726113.62118.2567.9276.726
75 to 795,2654,23093.683455.9251176.0851225.810808.078305.552126.83320.6589.1287.927
80 to 845,6034,56799.689487.1531262.5621323.096877.740334.377140.04423.06010.3299.128
85 to 895,9404,905105.694518.3811349.0391420.383947.402363.203153.25625.46311.53010.329

Model derivation explained

  • The cancer inception column in table 5 is derived by summing each consecutive 5 years from Table 3.
    E.g. 203 = 13.5 + 27 + 40.5 + 54 + 67.5
  • This inception data (e.g. 203) is distributed according to the frequency shown in Chart 1 (same as Table 4).
    E.g. 203 = 3.603 + 18.737 + 51.886 + 58.372 + 41.797 + 17.295 + 7.927 + 1.441 + 0.721 + 0.721
  • The 3rd column: deaths, is got by summing the delayed mortalities to the right of it
  • Data after the 85 to 89 column is ignored. It's just displayed to show the model.
  • Columns 2 and 3 of Table 5 go to make columns 8 and 9 of Table 1


  1. The document claiming 10 µSv exposure implies 1 in 2 million mortalies was authored by the Radiological Protection Institute of Ireland, RPII, which was established in 1992 and merged with the EPA in 2014. The EPA still use RPII fact sheets when dealing with radiation.
  2. UK Cancer Research. Download the spreadsheet data.
  3. Source: Chapter 2 - "Epidemiology Kept Simple: An Introduction to Traditional and Modern Epidemiology", by B. Burt Gerstman, Wiley, 2013, page 36
  4. Dose-effect relationship and estimation of the carcinogenic effects of low-doses of ionizing radiation, by Maurice Tubiana, André Aurengo, 2005

Thursday, 13 October 2016

All Change in Switzerland?

Switzerland has four nuclear plants supplying over a third of its electricity.

On the surface, it looks bad for nuclear power in Switzerland. In May 2011, following the accident at Fukushima Daiichi, the Swiss government passed "Energy Strategy 2050" programme, promoting renewables, but banning new nuclear power. Recently Swiss utilities Axpo, Alpiq and BKW withdrew their joint request to build nuclear plants. The Swiss Green Party, supported by Greenpeace, are behind a referendum on Nov 27 to close reactors early: Beznau I & II, and Muehleberg in 2017, with two remaining stations to follow in 2024 and 2029.

One the other hand: When put to a referendum the Swiss people consistently support nuclear power (apart from one 1990 vote). A 2014 poll showed the Swiss strongly in favour of keeping existing nuclear power plants, with 64% saying existing reactors are essential. The largest political party: Swiss People's Party (SVP), who are out of office, will push for a referendum via the Swiss system of direct democracy to dump the "Energy Strategy 2050". They delayed their referendum demand because they sought support from business which was not forthcoming. Despite this setback, the right-wing party says it is launching the referendum with the support of associations and businesses, including Swissmem who represent the machine, electrical and metal industries.

History of Swiss nuclear power referendums
1979A citizens' initiative for nuclear safetyrejected
1984"for a future without further nuclear power stations"55% to 45% against.
1990"stop the construction of nuclear power stations," proposing a 10-year moratorium on the construction of new nuclear power plantspassed with 54.5% to 45.5%
1990The initiative for a phase-outrejected by 53% to 47.1%
2000Green Tax for support of solar energyrejected by 67% to 31%.
2003"Electricity without Nuclear," asking for a decision on a nuclear power phase-outrejected 33.7% Yes, 66.3% No
2003"Moratorium Plus," for an extension of the earlier decided moratorium (in 1990) on the construction of new nuclear power plantsrejected 41.6% Yes, 58.4%

Swiss federal election, 2015
% vote
29.4%Swiss People's
18.8%Social Democrats
16.4%FDP (Liberals)
11.6%Christian Democrats
4.6%Green Liberals

Opinion Poll

Telephone interviews of 1,200 Swiss citizens, from Oct 3 to Oct 14, found the Swiss overwhelmingly in favour of the Greenpeace / Green Party proposal for a quick nuclear power phase out. 57% to 36%. With 7% undecided and a margin of error of 3%. Despite this, many analysts think the Swiss may reject Green Party proposals for an early nuclear power phase out on Nov 27! That's because participation in the national referendum isn't expected to be about 45%.

Notes, links

  1. Swiss utilities throw in towel over new nuclear plants
  2. SVP launch challenge to energy strategy
  3. Swiss government opposes campaign for quick nuclear exit
  4. Nuclear power in Switzerland (Wikipedia)
  5. 'Swiss want say on nuclear phase out'
  6. Telephone poll shows Swiss favour phase out by big majority.