Nukes pretty please: October 2016

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Here is an exciting timeline for a paper published in July but, hopefully, withdrawn by October.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.

Timeline

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

July: The article is published in Climate Policy - a "peer reviewed journal".
22-Aug: James Hakner at Sussex Univ. finishes a press release and posts it to media outlets.
On the same day, media reports begin rewriting the press release as a story dissing nuclear power.
23-Aug: I complain to the editor of Climate Policy by email (who is on leave anyway!)
24-Aug: One report in The Ecologist is by the press release's author!
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
26-Aug: Stephen Tindale and Suzanna Hinson at the Weinberg Foundation refute LSS paper.
More media reports reprinting/rewriting their press release.
2-Sep: My blog outlining the article's faults. I notify the journal editor too by email.
LSS notice my blog. Climate Policy editor discusses issues with authors and peer reviewers.
LSS authors admit errors in their data, but refute my other 17 complaints about their paper.
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.
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.
27-Oct: Malcolm Grimston reports that Andy Stirling admitted the paper was rubbish and LSS have withdrawn it.
25-Nov: I hear the authors have retracted their article.

Article:

Lawrence, Sovacool, and Stirling (Climate Policy) "Nuclear energy and path dependence in Europe’s ‘Energy union’: coherence or continued divergence?"

Press Release:

James Hakner, for Sussex Univ. 22-Aug: "Pro-nuclear countries making slower progress on climate targets"

Green Media and science reports:

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

Blogs & such:

Weinberg Foundation (blog by Stephen Tindale and Suzanna Hinson), 26-Aug: "Being pro-nuclear does not undermine climate and energy goals"
WUWT: Claim: Countries which favour Nuclear Power are Not Making Enough Effort to Install Renewables, by Eric Worrall / August 30
Mark Pawelek (blog), 2-Sep: "Refutation of recent Climate Policy paper written by Lawrence, Sovacool & Stirling"
Lawrence, Sovacool, and Stirling (prior to 11-Oct): "Response to Refutation of recent Climate Policy paper written by Lawrence, Sovacool & Stirling"
Nicholas Thompson (blog), 12-Oct: "A Response to Lawrence, Sovacool, and Stirling"
Malcolm Grimston, 17-Oct: "Another response to LSS"
Malcolm Grimston, 27-Oct: Comment: "I spoke to Andy Stirling about this last Saturday and he said ‘the paper was rubbish and we’ve withdrawn it’ (though the press release is still on the SPRU website)."
Me (again). 10 Nov: Summary of this saga, from my point of view, with explanation why this article should never have been published
The authors withdraw their article from publication.

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=1990	1990	1995	2000	2005	2010	2011	2012	2005-2012	country	OLD DATA	CORRECT-ION
Group I								-11.8	GI	(-6)	-11.9
Austria	100	102.6	103.6	120	110.2	107.6	104	-16.0	AT	-16	16
Cyprus	100	121.2	137.8	149.6	150.6	147.5	147.7	-1.9	CY	-5	1.9
Denmark	100	110.7	100.2	94.4	90.3	83.4	76.9	-17.5	DK	-20	17.5
Estonia	100	49.4	42.3	45.8	49.5	51.8	47.4	1.6	EE	11	-1.6
Greece	100	104.5	120.3	128.3	111.5	109.6	105.7	-22.6	EL	-4	22.6
Ireland	100	106.8	124.3	127.8	113.3	105.8	107	-20.8	IE	-20	20.8
Italy	100	102.4	106.9	111.6	97.5	95.3	89.7	-21.9	IT	-13	21.9
Latvia	100	47.7	38.2	42.5	46.7	44.7	42.9	0.4	LV	17	0.4	LSS should be negative
Luxembourg	100	80.8	80.7	108.3	101.9	100.2	97.5	-10.8	LU	-20	10.8
Malta	100	122.8	130.2	147.4	149.7	151.3	156.9	9.5	MT	5	-9.5
Portugal	100	117.3	138.2	144.7	118.6	116.5	114.9	-29.8	PT	1	29.8
Group II								-13.8	GII	-11	-12.9
Belgium	100	104.9	103.1	100.4	93	85.1	82.6	-17.8	BE	-15	17.8
Germany	100	89.8	84	80.9	76.7	74.5	76.6	-4.3	DE	-14	4.3
Netherlands	100	106.7	103	101.8	101.4	94.7	93.3	-8.5	NL	-16	8.5
Slovenia	100	100.5	102.7	110.2	105.8	105.9	102.6	-7.6	SI	4	7.6
Spain	100	110.9	134.8	154.2	125.4	126.4	122.5	-31.7	ES	-10	31.7
Sweden	100	102.3	95.6	93.4	91.3	86	80.7	-12.7	SE	-17	7.3	Should be 12.7
Group III								-10.2	GIII	-3	-10.2
Bulgaria	100	69.6	54.2	58.3	55.2	60.5	56	-2.3	BG	20	2.3
Czech Republic	100	77	74.5	74.4	70.4	68.4	67.3	-7.1	CZ	9	7.1
Finland	100	100.5	98.5	98	106.7	96.6	88.1	-9.9	FI	-16	9.9
France	100	99.5	101.5	101.6	93.9	88.9	89.5	-12.1	FR	-14	12.1
Hungary	100	81.3	79.5	80.6	69	67.2	63.7	-16.9	HU	10	16.9
Romania	100	70.7	54.6	57.9	47.8	50.5	48	-9.9	RO	19	9.9
Slovakia	100	74.1	68.7	70.6	64	63.2	58.4	-12.2	SK	13	12.2
United Kingdom	100	93.1	89.9	88.6	79.9	74.8	77.5	-11.1	UK	-16	11.1
Group IV								-1.5	GIV	-15	-1.5
Lithuania	100	45.1	40.1	47.8	43.3	44.3	44.4	-3.4	LT	15	3.4
Poland	100	94.7	84.4	85.5	88.1	87.6	85.9	0.4	PL	14	-0.4

Monday, 17 October 2016

Irish EPA grossly overestimate fatal cancer risk from radiation.

Introduction

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:

"we can estimate that a dose of 10 µSv may increase the lifetime risk of fatal cancer by about one in 2,000,000"
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 numbers				derived	projections
Age Range	Male Deaths	Female Deaths	Male Rates	Female Rates	Average (MF) rates	Average (MF) rates - radiation	inception	deaths	Overestimate
0 to 04	47	39	2.3	2	2.15	0.1	203	3.6	67 ×
05 to 09	47	40	2.4	2.2	2.3	0.1	540	28.3	493 ×
10 to 14	38	33	2.1	1.9	2	0.1	878	117.5	2349 ×
15 to 19	64	47	3.2	2.5	2.85	0.1	1,215	299.5	4204 ×
20 to 24	87	75	4	3.5	3.75	0.1	1,553	562.3	5998 ×
25 to 29	131	151	6	6.9	6.45	0.2	1,890	870.3	5397 ×
30 to 34	210	294	9.8	13.6	11.7	0.3	2,228	1,197.7	4095 ×
35 to 39	319	460	16	22.9	19.45	0.5	2,565	1,531.8	3150 ×
40 to 44	678	984	30.6	43.4	37	0.9	2,903	1,867.7	2019 ×
45 to 49	1428	1769	61.9	74.6	68.25	1.7	3,240	2,204.7	1292 ×
50 to 54	2557	2853	118.9	129.9	124.4	3.1	3,578	2,542.2	817 ×
55 to 59	4360	4076	234.7	214.1	224.4	5.6	3,915	2,879.7	513 ×
60 to 64	7358	6070	422.1	334.3	378.2	9.5	4,253	3,217.2	340 ×
65 to 69	11089	8696	658.1	488.2	573.15	14.3	4,590	3,554.7	248 ×
70 to 74	12599	9729	1043.6	724.8	884.2	22.1	4,928	3,892.2	176 ×
75 to 79	14330	11292	1501.6	991.6	1246.6	31.2	5,265	4,229.7	136 ×
80 to 84	14127	12194	2169.5	1355.9	1762.7	44.1	5,603	4,567.2	104 ×
85 to 89	10455	10253	3031	1732.4	2381.7	59.5	5,940	4,904.7	82 ×
90+	5742	7589	3853.3	1989.5	2921.4	73.0
All Ages	85667	76644	271.6	235.2	253.4	6.3

Explanation

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]

Derivation

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
0.01	0.05
1	5
2.7	13.5

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 life	Fatal cancer inception / 100k
0	13.5
1	27
2	40.5
3	54
4	67.5
5	81
6	94.5
7	108
8	121.5
9	135

etcetera

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
		total	0 to 04	05 to 09	10 to 14	15 to 19	20 to 24	25 to 29	30 to 34	35 to 39	40 to 44	45 to 49
		281	5	26	72	81	58	24	11	2	1	1
Table 5: How induction time was added
Per 5 years	cancer inception	deaths
0 to 04	203	4	3.603
05 to 09	540	28	9.609	18.737
10 to 14	878	117	15.614	49.964	51.886
15 to 19	1,215	300	21.619	81.192	138.363	58.372
20 to 24	1,553	562	27.625	112.420	224.840	155.658	41.797
25 to 29	1,890	870	33.630	143.648	311.317	252.945	111.459	17.295
30 to 34	2,228	1,198	39.635	174.875	397.794	350.231	181.121	46.121	7.927
35 to 39	2,565	1,532	45.641	206.103	484.270	447.518	250.783	74.947	21.139	1.441
40 to 44	2,903	1,868	51.646	237.331	570.747	544.804	320.445	103.772	34.351	3.843	0.721
45 to 49	3,240	2,205	57.651	268.559	657.224	642.091	390.107	132.598	47.562	6.246	1.922	0.721
50 to 54	3,578	2,542	63.657	299.786	743.701	739.377	459.769	161.423	60.774	8.648	3.123	1.922
55 to 59	3,915	2,880	69.662	331.014	830.178	836.664	529.431	190.249	73.986	11.050	4.324	3.123
60 to 64	4,253	3,217	75.667	362.242	916.655	933.950	599.093	219.075	87.198	13.452	5.525	4.324
65 to 69	4,590	3,555	81.673	393.470	1003.132	1031.237	668.754	247.900	100.409	15.854	6.726	5.525
70 to 74	4,928	3,892	87.678	424.698	1089.609	1128.523	738.416	276.726	113.621	18.256	7.927	6.726
75 to 79	5,265	4,230	93.683	455.925	1176.085	1225.810	808.078	305.552	126.833	20.658	9.128	7.927
80 to 84	5,603	4,567	99.689	487.153	1262.562	1323.096	877.740	334.377	140.044	23.060	10.329	9.128
85 to 89	5,940	4,905	105.694	518.381	1349.039	1420.383	947.402	363.203	153.256	25.463	11.530	10.329
				549.609	1435.516	1517.669	1017.064	392.028	166.468	27.865	12.731	11.530
					1521.993	1614.956	1086.726	420.854	179.680	30.267	13.932	12.731
						1712.242	1156.388	449.680	192.891	32.669	15.133	13.932
							1226.050	478.505	206.103	35.071	16.335	15.133
								507.331	219.315	37.473	17.536	16.335
									232.527	39.875	18.737	17.536
										42.278	19.938	18.737
											21.139	19.938
												21.139

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

References

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.
UK Cancer Research. Download the spreadsheet data.
Source: Chapter 2 - "Epidemiology Kept Simple: An Introduction to Traditional and Modern Epidemiology", by B. Burt Gerstman, Wiley, 2013, page 36
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
1979	A citizens' initiative for nuclear safety	rejected
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 plants	passed with 54.5% to 45.5%
1990	The initiative for a phase-out	rejected by 53% to 47.1%
2000	Green Tax for support of solar energy	rejected by 67% to 31%.
2003	"Electricity without Nuclear," asking for a decision on a nuclear power phase-out	rejected 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 plants	rejected 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
7.1%	Greens
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%.