climateetc--2020-11-18--Slower decay of landfalling Hurricanes in a warmer world — really?

A recent paper published in “ Nature ” made some excitement in the media , see here or here .
In the paper by Li & Chakraborty ( L & C 2020 thereafter ) , the authors find a statistically significant increase of the decay time when a North Atlantic hurricane makes a landfall due to warmer SST in a warming environment .
They also undertake some model-related research about the impact of this observations .
The key point of thepaper is the finding that warmer SSTs lengthen the decay time of hurricanes after landfalls .
In L & C @ @ @ @ @ @ @ 1f : Fig.1 : The reproduction of Fig.1 f in L & C 2020 .
The ordinate reflects the decay time τ in hours .
In the legend the authors state : “ We note that the τ time series echoes the SST time series with Pearson correlation r = 0.73.
” The authors describe the way they found the relation , which declares an increase of the decay time of more than 40 hours per 1K SST increase : “ We average τ for all the landfall events in a given year @ @ @ @ @ @ @ in a row , to this time series.
” They made a regression with strongly smoothed time series , a procedure that is normally frowned on .
In the supplementary data ( freely available ) one can download an Excel sheet where the raw data used can be found .
For the deduction of the increasing τ with increasing SST ( in the area 10°N…35°N ; 100°W…75°W , the authors take advantage of the data for 71 landfalling hurricanes during 1967 to 2018 .
The SST for each event is determined as follows @ @ @ @ @ @ @ time over the hurricane season , June–November , … ” The result “ R=0.73 ” , see Fig.1 , of the linear regression implies that 53 % of the variation of τ comes from the variation of the SST .
I had a look at the raw data and a few questions arose : • The use of the average SST of the whole hurricane season for a single event ?
The actual named hurricane develops over a few days in an actual environment , not the average SST of the actual whole @ @ @ @ @ @ @ the landfall happens during July or November , the average SST difference is 1.8 K in this case , which is much more than the range of the abscissa in Fig.1 .
The use of the seasonal average SST for all hurricanes during that season , rather than the actual SST applicable to each hurricane , has the potential to produce highly misleading results .
The average SST applicable to each hurricane might have little relationship with the average SST during the whole season • The use of the average of all τ @ @ @ @ @ @ @ hurricane is involved ?
Every hurricane event is a discrete event .
In the raw data many years have only one hurricane per year , these events are not averaged of course .
• Applying a double 3 years smoothing before making the regression shown in Fig.1 .
?
“ this approach lessens the effects of non-climatic factors and random noise ” .
However , the whole research is about the point : ” To what degree impact the actual SST the decay time of landfalling hurricanes ?
” There will be some @ @ @ @ @ @ @ appropriate to smooth over several years partly out to elicit a strong climate related signal .
Applying a 3-year smoothing to both decay time and SST data twice in a row is unjustifiable .
I decided to recalculate the regression shown in Fig.1 but I used the actual SST for every hurricane from the monthly ERSSTv5 data for the described area .
I included every hurricane because this is the physical approach : It ’ s not justified at all to use an average in some years and in some years not , @ @ @ @ @ @ @ hurricanes depending on how many are included in the raw data in each year .
I also use the unsmoothed data to avoid spurious correlations due to the applied smoothing .
This is the result : Fig.2 : The regression of the decay time on the SST without the data-preconditioning in L & C 2020 .
There is only a tiny non significant trend in the raw data- p=0.1 , so the slope does not reach the standard 95 % confidence level .
On twitter Ryan Maue questioned the raw data selection ; @ @ @ @ @ @ @ this post .
The outcome of L & C 2020 is very overconfident when it comes to the dependency of the decay time on the SST .
The R²=0.53 found in LC 2020 vanishes to an insignificant 0.04 if one uses the physical data , without the applying of unjustified averages and smoothing actions prior to the regression .
The SST impact on the decay time is negligible , other influences accounting for almost all variability in the decay time .
The peer review process of “ Nature ” for L & C @ @ @ @ @ @ @ it makes wonder if there was no reviewer with some fundamental skills in statistics involved .
However , this must be the case unfortunately : In the “ methods-statistical significance ” section the authors mention a test for autocorrelation and there is written : “ ( which we test using the Dublin–Watson test ) ” .
This must be a typo , the name of the test is “ Durbin– Watson ” .
One should hope that the peer review process of “ Nature ” would be improved soon to avoid overconfident , @ @ @ @ @ @ @ 2020 .

by Frank Bosse
A recent paper published in “Nature” made some excitement in the media, see here or here.
In the paper by Li & Chakraborty (L&C 2020 thereafter), the authors find a statistically significant increase of the decay time when a North Atlantic hurricane makes a landfall due to warmer SST in a warming environment. They also undertake some model-related research about the impact of this observations.
The key point of thepaper is the finding that warmer SSTs lengthen the decay time of hurricanes after landfalls.
In L&C 2020, this is shown by figure 1f:
Fig.1: The reproduction of Fig.1 f in L&C 2020. The ordinate reflects the decay time τ in hours.
In the legend the authors state: “We note that the τ time series echoes the SST time series with Pearson correlation r = 0.73.”
The authors describe the way they found the relation, which declares an increase of the decay time of more than 40 hours per 1K SST increase:
“We average τ for all the landfall events in a given year and apply a 3-year smoothing, twice in a row, to this time series.”
They made a regression with strongly smoothed time series, a procedure that is normally frowned on.
In the supplementary data (freely available) one can download an Excel sheet where the raw data used can be found.
For the deduction of the increasing τ with increasing SST (in the area 10°N…35°N ; 100°W…75°W , the authors take advantage of the data for 71 landfalling hurricanes during 1967 to 2018.
The SST for each event is determined as follows:
“We average the SST in time over the hurricane season, June–November, …”
The result “R=0.73”, see Fig.1, of the linear regression implies that 53% of the variation of τ comes from the variation of the SST.
I had a look at the raw data and a few questions arose:
The use of the average SST of the whole hurricane season for a single event?
The actual named hurricane develops over a few days in an actual environment, not the average SST of the actual whole season. It makes a difference if the landfall happens during July or November, the average SST difference is 1.8 K in this case, which is much more than the range of the abscissa in Fig.1 .
The use of the seasonal average SST for all hurricanes during that season, rather than the actual SST applicable to each hurricane, has the potential to produce highly misleading results. The average SST applicable to each hurricane might have little relationship with the average SST during the whole season
The use of the average of all τ in a year if more than one hurricane is involved?
Every hurricane event is a discrete event. In the raw data many years have only one hurricane per year, these events are not averaged of course.
Applying a double 3 years smoothing before making the regression shown in Fig.1. ?
The authors state:
“this approach lessens the effects of non-climatic factors and random noise”.
However, the whole research is about the point:”To what degree impact the actual SST the decay time of landfalling hurricanes?” There will be some other influences and it’s not appropriate to smooth over several years partly out to elicit a strong climate related signal. Applying a 3-year smoothing to both decay time and SST data twice in a row is unjustifiable.
I decided to recalculate the regression shown in Fig.1 but I used the actual SST for every hurricane from the monthly ERSSTv5 data for the described area. I included every hurricane because this is the physical approach: It’s not justified at all to use an average in some years and in some years not, as that gives radically different weightings to hurricanes depending on how many are included in the raw data in each year.
I also use the unsmoothed data to avoid spurious correlations due to the applied smoothing.
This is the result:
Fig.2: The regression of the decay time on the SST without the data-preconditioning in L&C 2020.
There is only a tiny non significant trend in the raw data- p=0.1, so the slope does not reach the standard 95% confidence level.
On twitter Ryan Maue questioned the raw data selection; that issue is beyond the scope of this post.
The outcome of L&C 2020 is very overconfident when it comes to the dependency of the decay time on the SST. The R²=0.53 found in LC 2020 vanishes to an insignificant 0.04 if one uses the physical data, without the applying of unjustified averages and smoothing actions prior to the regression.
This means:
The SST impact on the decay time is negligible, other influences accounting for almost all variability in the decay time.
The peer review process of “Nature” for L&C 2020 lasted more than 8 months, it makes wonder if there was no reviewer with some fundamental skills in statistics involved.
However, this must be the case unfortunately: In the “methods-statistical significance” section the authors mention a test for autocorrelation and there is written: “(which we test using the Dublin–Watson test)”. This must be a typo, the name of the test is “Durbin– Watson”.
One should hope that the peer review process of “Nature” would be improved soon to avoid overconfident, obviously flawed papers like L&C 2020.
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climateetc--2020-11-20--Cultural motivations for wind and solar renewables deployment

“ For me the question now is , now that we know that renewables can ’ t save the planet , are we going to keep letting them destroy it ?
” .
– Michael Schellenberger There have been many technical analyses of Wind and Solar energy , covering a raft of issues from energy density and efficiency , through subsidies and land usage , to maintenance , grid impacts , intermittency and more .
The angle examined here is in no way intended to replace such necessary views , whether they lean to the pessimistic or the optimistic or @ @ @ @ @ @ @ view is provided regarding an aspect that such technical analyses can not address , albeit it often features in the conclusions and questions these analyses raise at the end .
Right here at Climate Etc , the first of the excellent analyses by ‘ Planning Engineer ’ ( on his retirement revealed to be Russ Schussler , ex-VP of Transmission Planning at Georgia Transmission Corporations ) , laudably highlighted the limitation of technical analyses with his very first line : “ Power System Planners do not have the expertise or knowledge to say @ @ @ @ @ @ @ carbon emissions are worth the costs .
However they should be respected as experts for obtaining a better understanding of what the implications and costs of such programs are.
” So , who does have this critical knowledge and expertise regarding ultimate cost / benefit ?
The answer is highly likely to be no one at all , because not only is this issue deeply disputed , it is culturally ( as well as technically ) disputed .
Indeed , to the extent that the overwhelming public and authority narrative about climate change @ @ @ @ @ @ @ science , which also means that , especially in some nations such as the US , there are cultural counter-narratives too .
Hence debates about policy , including renewables deployment , aren ’ t legitimately resolved , because the potent cultural angle corrupts or polarizes or simply overrides any such debate .
Schussler acknowledges this cultural dispute , saying in the line following the above quote : “ Unfortunately many non-experts , driven by fear of AGW , have done much to cloud , distort and ignore critical issues around the cost and @ @ @ @ @ @ @ relating to the provision of electrical power.
” He also returns to this theme at the very end of his post : “ …I believe most planners and utilities recognize that the overall impacts to society ( unless needed to aver environmental disaster ) would be extremely harmful in the net analysis .
I hope that the voices of concerned utility experts are not drowned out by the noise of ‘ true believers ’ or disbelieved because of false accusations of self-interest.
” For sure ‘ true belief ’ is a cultural feature @ @ @ @ @ @ @ ’ .
Plus that caveat of ‘ unless needed ’ , or more subtly ‘ how much needed ’ , is a major issue within the dispute above .
An apparent need , could be partly or wholly the result of emotive belief , of which ‘ fear of [ C ] AGW ’ is a major component .
Schussler ’ s title , ‘ Myths and realities of renewable energy ’ , emphasis mine , is inclusive of the element that technical analysis can not ultimately probe regarding character and impact , @ @ @ @ @ @ @ cite myth as Renewables motivation , and David Archibald goes further in directly framing this element as ‘ religious ’ in nature ( religions being just one brand of cultural entities ) .
In a recent article on renewables ( and focusing on hydrogen ) at Jo Nova ’ s , he employs religiously orientated terms such as ‘ believers ’ and ‘ government encyclicals ’ : “ Briefly , the only reason solar and wind get a look-in is because solar panels and wind turbines are made using energy from coal at @ @ @ @ @ @ @ power at $ 0.20 per kWh… …You can ’ t use solar and wind power to make solar and wind power equipment ; as such they are neither renewable nor sustainable .
And they certainly won ’ t be replacing fossil fuels when the fossil fuels run out .
Even some lefties are figuring this out and thus the documentary Planet of the Humans .
So the global warming clerisy , headed by Alan Finkel in this country [ Australia ] , needs to keep coming up with new content to satisfy their @ @ @ @ @ @ @ spent on hydrogen .
The language of the Government encyclicals suggests that hydrogen is a new source of energy that just has to be tapped to guarantee a wonderful future.
” Needless to say , Archibald views this ‘ religious ’ influence as overwhelmingly negative , adding as part of his conclusion : “ Global warming doesn ’ t build orphanages or hospitals .
As a religion it doesn ’ t do any good at all.
” And others claim a cultish / ‘ religious ’ motivation too .
Yet whether viewing culture @ @ @ @ @ @ @ , or as an overwhelmingly negative force , technical analyses critical of renewables inevitably falter or get speculative when it comes demonstrating cultural sway ( for example , across nations invested in renewables ) .
An alternate approach is needed to progress this .
There are less critical / more optimistic analyses of Solar or Wind ‘ renewables ’ , which nevertheless tend to justify some amount of pain in achieving rosier end-scenarios , upon the premise that this is needed to avoid an otherwise inevitable and imminent global catastrophe ( apart from @ @ @ @ @ @ @ all changes are apparently painless ) .
Yet mainstream science says such a justification is unfounded ( notwithstanding the cultural dispute above ) .
Meanwhile the implementation of Solar and Wind is surging ahead , and has done so for decades in some countries .
So , is this global implementation largely due to pragmatic considerations that are consistent with mainstream science ?
After all , these things do actually produce power .
Or instead , largely due to cultural motivation that spreads solar-panels and wind-turbines as merely icons for the ‘ secular @ @ @ @ @ @ @ change ?
And no matter the costs , fiscal or environmental .
This question is resolvable , as hard social data on the cultural attitudes of many nations to climate-change , allows national motivations to be mapped to Renewables deployment .
Prior posts here , show that supportive attitudes to climate-change ( CC ) across national publics , have a dual and systemic strong relationship with religiosity .
CC supportive public responses to unconstrained climate survey questions correlate with national religiosity , while oppositely , CC supportive responses to reality-constrained questions anti-correlate with @ @ @ @ @ @ @ survey participants to consider and compare other reality issues in relation to climate change , typically by asking them to nominate the X ‘ most important ’ from a larger list of Y issues , of which one represents a climate-change issue , or literally just ‘ climate-change ’ .
Unconstrained questions don ’ t force such a comparative choice ] .
Using some data-series from the prior posts , Chart 1 below summarizes this dual relationship .
For many nations and plotted against national religiosity , it shows the CC supportive public @ @ @ @ @ @ @ lines , intuited ) for different strengths of both unconstrained and reality-constrained survey questions .
Strength for the unconstrained questions ( blue lines ) reflects how aligned these are to the emotive and existential values of catastrophic climate change culture ( CCCC ) , plus how much this is targeted at the personal .
Another way of thinking about this strength , is how biased the questions are towards CCCC , emotive content being a feature of bias and not of rationality .
Strength for reality-constrained questions ( orange lines ) reflects the @ @ @ @ @ @ @ ‘ most important ’ single issue of 12 , for example , is a stronger reality-constraint , a stronger clash with the other issues , than say picking the top 3 of 12 , which is stronger in turn than the top 5 of 12 .
Table 1 at the end of this post , shows which actual climate-change survey questions generated the data-series each line represents ( each is depicted as just the stand-alone trendline ) , along with the related r/r2/p values .
The most important conclusion to draw from these @ @ @ @ @ @ @ their strong relationships with religiosity ( an entirely cultural phenomenon ) , attitudes to climate-change across nations are likewise cultural .
In fact , they ’ re the net result of interaction between 2 cultures , i.e .
CCCC and religion .
So , this means they are not due to rationality ( cultural attitudes are emotively driven ) , or anything physical such as the characteristics of the climate-system , or indeed the particular climates or climate exposures of nations , or likewise any views on same as expressed by mainstream ( @ @ @ @ @ @ @ the exception of one historical coincidence ( annual sunshine duration – see later ) no matters encompassed by these categories would exhibit a systemic relationship with religiosity .
And for sure none can exhibit both correlation and anti-correlation simultaneously , depending only upon the type of question asked ; yet such is certainly possible for cultural responses .
This duality raises apparent contradictions .
For instance , maximum climate concerns ( RH ends of blue trends in Chart 1 ) occur in the same nations as minimum support for climate action / priority @ @ @ @ @ @ @ .
But such apparent contradictions aren ’ t unusual for cultural causation .
From the prior series here at Climate Etc , see : Apparent Paradoxes in the relationship of Climate ‘ Concerns , Skepticism , Activism , and Priority ’ explained by Religiosity .
In fact , the full situation is more complex than depicted by Chart 1 .
See the Summary File linked below ( and especially Chart 2 in there , not in the main post ) for more explanation , visualized via more series plus features added to the @ @ @ @ @ @ @ conclusion remains that climate-change attitudes across nations are mainly cultural , so not from either rationality or physical factors .
[ Note : the attached summary is more distilled and easier going than the 3 prior posts linked above .
Regarding the US , see the note at the very end of this post ] .
If a particular domain is dominated by cultural attitudes , spending and policy support within the domain should ultimately be rooted in those attitudes .
Hence , armed with the above cultural attitudes to climate-change , we @ @ @ @ @ @ @ nations , between these and actual Renewables deployment .
It isn ’ t that mass publics rush out and purchase Wind Turbines with their credit cards , in proportion to their national attitudes .
The actual commitment works via elites who enact policy .
Yet the limits on such policy should still be in proportion to the national public attitudes ( which doesn ’ t mean alignment in an absolute sense ) .
Culture works to this end whether or not a nation is a democracy , or in the latter case whether @ @ @ @ @ @ @ ( e.g .
in the UK , ‘ net-zero by 2050 ’ was nodded through without a parliamentary vote , and with essentially no costing or scrutiny or meaningful opposition ) .
As per the Introduction above , the primary justification for Renewables is indeed climate-change .
So , if culture does rule the roost for this particular policy area , Renewables deployment per nation should be governed by its ( cultural ) attitudes to climate-change .
In which case , we must see a very strong correlation between these two aspects .
@ @ @ @ @ @ @ climate-change are relevant , when comparing on a national basis with renewables commitment ?
As there is at least some reality-constraint on deploying renewables ( because all nations have budgets and competition for same ) , if indeed the expression of renewables policy is mainly cultural , this ought to align with a reality-constrained ( orange ) trend , not an unconstrained ( blue ) trend .
Plus , because public and also elite / authority knowledge about renewables is likely very poor on average , this makes the constraint weak at best @ @ @ @ @ @ @ better understood , then the constraint would be stronger ) .
So , we should be looking at a comparison with the attitude data from the ‘ WC ’ series in Chart 1 .
This comes from an enormous UN survey on policy priorities in many nations , the vote share for ‘ action on climate-change ’ forming the series Y-values .
Nations can be matched from this series to those with major Solar deployment ( 40 nations ) , Wind turbine deployment ( also 40 , with 5 nations different to the @ @ @ @ @ @ @ set ( of 35 ) deploying both .
Looking at the combined commitment for Wind and Solar should produce a more reliable result , as each may have technology / policy idiosyncrasies that could buck the trend in some way , but which are more likely to average out over the combination of both ( though we can start by examining each type in turn ) .
In practice , there ’ s highly unlikely to be zero or full correlation .
Policy decisions are very rarely 100 % free of cultural factors @ @ @ @ @ @ @ wholly cultural either .
So , we can set reasonable thresholds for the test .
If ‘ r ’ for the correlation of renewables commitment with weakly-constrained climate attitudes is say ~0.33 or less , then we can say culture ( per above inherent in those attitudes ) doesn ’ t dominate .
But if r~0.66 or above , then we can say that cultural motivation does dominate renewables commitment .
If the value lands in-between , I guess we ’ d have to see where and think more about dual modes .
@ @ @ @ @ @ @ impacts on very long-term social development , it is well-known that with some exceptions ( which aren ’ t an issue here ) GDP-per-capita across nations strongly negatively correlates with religiosity ( see Chart F1 in Xcel datafile ) .
2 ] Hence if above this , deploying Wind Turbine capacity is indeed motivated by a ‘ WC ’ type climate-attitude , which itself negatively correlates with religiosity ( see Chart 1 ) , then national religiosity plotted against each nation ’ s Wind Turbine capacity per-capita , should yield a power type @ @ @ @ @ @ @ F2 in Xcel datafile ) .
3 ] Power functions are more difficult to deal with or apply our test thresholds for ‘ r ’ to ( based on linear ) .
So , normalizing the Wind Turbine capacity per-capita wrt to GDP per-capita ( I used Spain ’ s GDP as the arbitrary standard ) , will remove the long-term effect of religiosity upon societies and any unequal fulfillment of motivation ( the same motivation , so ~fraction of GDP , will purchase more Wind Turbines if the GDP is bigger ) @ @ @ @ @ @ @ back to a linear function… The plot : Across the 40 nations , UN vote share for ‘ action on climate change ’ ( from ‘ WC ’ data ) , versus GDP-per-Capita-normalised Wind Turbine Capacity / Population ( to 2018 ) , shows a +ve correlation .
This suggests that Wind Turbines are significantly motivated by a ‘ WC ’ type cultural climate-change attitude : An ‘ r ’ of 0.64 just misses our test threshold for dominance ; but this is only half the story so far .
And it looks @ @ @ @ @ @ @ policies for Wind deployment .
E.g .
the Czech Republic has very little despite a high vote for ‘ action on climate-change ’ .
And with a vote share that is a little lower , Portugal nevertheless seems to have a huge Wind capacity .
In summary it seems that as motivation rises towards the right , especially beyond the green line , the range of expression in Wind Turbines , grows .
This likely reflects larger and more targeted Renewable Energy policies , which hence spread from gung-ho for Wind Turbines right @ @ @ @ @ @ @ Renewables are or aren ’ t more prioritized instead ( e.g .
Solar , Biomass , etc ) .
So , to better insulate against the effects of targeted policies , we can add into the mix the identical analysis for Solar deployment .
Preamble : 1 ] The equivalent charts for F1 and F2 in the Wind Turbine case , are F3 and F4 ( see the Excel datafile ) .
2 ] However , there ’ s an extra issue for Solar Power , which is that we need to adjust @ @ @ @ @ @ @ , according to its annual sunshine hours .
Otherwise the same spend ( where spend corresponds to motivation ) , hence the label MW , will produce different actual power per year in different nations .
So in Chart 4 , Solar Capacity deployed is normalized ( to Spain ) for Annual-Sunshine-DurationP1 .
The plot : Across the 40 nations ( 5 different to the Wind Turbine set ) UN vote share for ‘ action on climate change ’ ( from ‘ WC ’ data ) , versus GDP-per-Capita-normalised Annual-Sunshine-Adjusted Solar Capacity / @ @ @ @ @ @ @ a +ve correlation .
See Chart F7 in the Excel datafile .
This chart is very similar to Chart 2 above , with a greater range of expression in Solar Power to the right of the green line .
However , different nations than for Chart 2 are either high and low within this expression , because the idiosyncrasies associated with Solar Power are different to those for Wind Turbines .
An ‘ r ’ of 0.48 is somewhat lower than the case for Wind Turbines alone ; much of the reduction appears @ @ @ @ @ @ @ Solar despite only middling climate concerns , while Sweden has very modest Solar despite very high climate concerns .
But this is only half the story too .
Combining the results for both the technologies , will give us a more robust picture for Renewables motivation overall .
Across 35 nations , Chart 3 below shows UN vote share for ‘ action on climate change ’ ( from ‘ WC ’ data ) , versus GDP-per-Capita-normalised Combined Renewables Commitment / Population ( to 2018 ) .
The +ve correlation has improved .
Surpassing @ @ @ @ @ @ @ spare , ‘ r ’ is now at 0.73 .
The greater range of spread of nations at the RHS has considerably reduced ( excepting for Germany ) , this being consistent with expected total renewables motivation , which nevertheless may express much more in Wind Turbines than Solar for any particular single nation , or vice versa .
The lens we used to see in the first place that attitudes to climate-change are cultural , i.e .
the religiosity of nations , is color-coded onto Chart 3 .
This falls from left @ @ @ @ @ @ @ from some random noise , there ’ s a minor secondary variable at play which impacts religiosity as seen in this view ( see Postscript 2 ) .
Notwithstanding some utility , the main motivation for Wind and Solar Renewables is cultural .
Thus , their installations are more akin to churches than to power-stations .
This also means that , just like the cultural attitude it is rooted in , Renewables deployment per nation directly anti-correlates with religiosity ; see Postscript 2 .
While it seems highly intuitive that national attitudes for @ @ @ @ @ @ @ corresponding renewables deployments , going straight for the jugular might not result in understanding exactly how to compare ( or at least , there ’ d be more chance of making a mistake in so doing ) , or indeed what the result actually means .
For instance , without the benefit of the big picture in Chart 1 and what this is about in cultural terms , we wouldn ’ t have known that those attitudes are not due to rationality .
Plus , we may have ended up comparing the attitudes @ @ @ @ @ @ @ and still be wondering why more Renewables correlates with dramatically less concern about climate-change .
And the steps in the above preambles , usefully confirm the roles of GDP-per-capita and sunshine duration .
Note : widespread public knowledge of Renewables issues would increase constraints on policy , likely collapsing motivation from the ‘ WC ’ line in Chart1 , to the ‘ FC ’ line .
Note : This post says nothing about actual physical climate change , nor about the mainstream science of same ( which the CCCC narratives contradict ) .
@ @ @ @ @ @ @ and their expression in Renewables policy .
Michael Shellenberger , the environmentalist who tirelessly advocates for Nuclear Power as a solution to our energy needs , says this of Renewables ( and he ’ s talking largely about Solar and Wind deployments ) : “ For me the question now is , now that we know that renewables can ’ t save the planet , are we going to keep letting them destroy it ?
” An insight consistent with the fact that the ‘ purpose ’ of cultural narratives is to gain @ @ @ @ @ @ @ hold the cultural group together ; it ’ s irrelevant to this actual purpose that the resulting actions may undermine or even reverse their stated purpose ; this happens .
In the blurb for his book ‘ Apocalypse Never ’ Shellenberger also speaks about the character of modern environmentalism , the motivation beneath it , which along with much else drives Renewables deployment ( and also a net resistance to Nuclear Power as a ‘ solution ’ ) : “ What ’ s really behind the rise of apocalyptic environmentalism ?
There are @ @ @ @ @ @ @ for status and power .
But most of all there is a desire among supposedly secular people for transcendence .
This spiritual impulse can be natural and healthy .
But in preaching fear without love , and guilt without redemption , the new religion is failing to satisfy our deepest psychological and existential needs.
” Emphasis mine .
Shellenberger rightly identifies the overall motivation as cultural .
He uses the term ‘ religion ’ , as indeed do many others , simply because this is the most familiar example of a bounded cultural @ @ @ @ @ @ @ Regarding a ‘ climate catastrophe ’ generally , the social data completely agrees with him , as shown in Chart 1 ( and more fully elsewhere , see the Summary File below ) .
And as demonstrated above , this is exactly the case too for the specific motivation behind Renewables deployment .
However , I believe Shellenberger has one thing wrong .
Catastrophic climate culture is so pervasive exactly because it does satisfy deep psychological needs , which needs stem from signaling in-group identification via emotive , and preferentially existential , narratives @ @ @ @ @ @ @ mechanisms which bypass our rationality , be that the advice of Planning Engineer or any other mere reason , via whatever expertise , experience or analysis .
Anyone or any group that contradicts or even questions in-group narratives , is automatically out-group , and so passionately resisted .
Via the historical accident of atheism spreading outwards from NW Europe , and hence from mostly very cloudy ( annually averaged ) countries towards typically sunnier climes , the annual sunshine duration of countries has a pretty decent linear correlation with national religiosity , albeit @ @ @ @ @ @ @ F5 in the datafile , ‘ r ’ is 0.56 .
Note : annual sunshine duration per country is derived from the average measurements for between 2 cities ( smaller countries ) and 5 cities ( larger countries , excepting 10 for Russia ) ; see the Excel datafile for tables of same , plus original sources .
So , if national sunshine hours are substituted as a proxy for national religiosity in Chart F4 , this still shows a power type function , see Chart F6 in the datafile .
Similarly to @ @ @ @ @ @ @ wrt GDP-per-capita , thereby removing the simple effect of spending power ( and too its long-term relationship to religiosity ) .
This reveals the relative priority of Solar deployment ( and so relative motivation ) for each of the nations .
The result of this operation , shown in Chart 4 below , demonstrates a significant irony that stems from the cultural motivation behind Renewables .
Which is that , as the annual sunshine hours experienced by nations reduces , then more nations choose to deploy more Solar MW per capita – i.e @ @ @ @ @ @ @ least useful .
The increasing spread of nations right to left in the green triangle , represents the same increasing range of expression in Solar that is also seen in F7 ( see datafile ) , and likewise for Wind Turbines in Chart 2 above , which in both cases is more obvious to the right of the green lines .
That increasing range is due to an increasing cultural motivation for Renewables , coupled with targeted policies which for particular nations may preference other Renewables options over Solar , to some degree @ @ @ @ @ @ @ lens ’ in Chart 1 , which allows us to ‘ see ’ that national climate-change attitudes are indeed cultural .
The two strong cultures interact to produce the various trends , and in particular the correlation with religiosity of climate-change supportive answers to unconstrained questions , yet also anti-correlation with religiosity of climate-change supportive answers to reality-constrained questions , for sure is extremely hard to explain any other way .
One wouldn ’ t expect this lens or proxy itself to have quite such a robust correlation with national Renewables Commitment as @ @ @ @ @ @ @ but it ’ s a useful ‘ parity check ’ to graph this and demonstrate that it should still be pretty strong .
Chart F9 in the Excel datafile shows this .
‘ r ’ is ~0.65 against ranked Renewables Commitment ( compresses some of the outliers a bit , especially Germany ) , and ~0.6 against actual values .
In practice , there is more than just extra randomity via looking through this proxy .
The series of ‘ weaker strengths ’ in Chart 1 ( so ‘ WC ’ and ‘ @ @ @ @ @ @ @ about trend , due to the GDP-per-Capita of each nation relative to its religio-regional group .
This variability is faithfully reflected in Chart F9 , and is color-coded onto the chart .
See also Chart F10 and F11 , which show the consistency of the religio-regional-GDP-per-Capita group averages between the ‘ WC ’ and Renewables Commitment series .
See the Summary File for a full explanation , plus some ‘ weaker strength ’ climate attitude series as full-data visualizations , to better see the raw effect .
For a version of ‘ WC @ @ @ @ @ @ @ in Chart 3 , with hi / lo Renewables commitment and hi / lo GDP-per-religio-regional-group both encoded , see Chart F8 in the Datafile .
Note : Norway is excluded from above charts as policy bias to immense amounts of natural hydro-power available , denudes Solar and Wind Turbine motivations .
This could be accommodated by the inclusion of a third renewables string across all nations for hydro-power , but there ’ d be little value for this extra effort .
• Climate Survey Data-source : The huge 2015 UN ‘ My World @ @ @ @ @ @ @ many nations .
• Climate Survey Data-source : Climate questions in the Reuters / University of Oxford ‘ Digital News Report 2020 ’ survey .
• Original Excel chart : ‘ 1yx ’ here ( 22 nations , debiased X scale ) , ‘ 2xy ’ here ( 24 nations , x/y reversed , raw X scale , delete US & Vietnam rows ) .
• Original Excel chart : ‘ F1yx ’ here ( 22 nations , debiased X scale ) , ‘ 4xy ’ here ( 24 nations , x/y reversed @ @ @ @ @ @ @ & Vietnam rows ) .
• Original Excel chart : See ‘ 3yx ’ here ( red crosses , and just left of chart , column G and J for data ) .
• Original Excel chart : See ‘ F6 ’ here ( and superimposed on other series , chart 3yx just to the left ) .
• Original Excel chart : See ‘ 4yx ’ and ‘ 5yx ’ here ( 4yx faith color-coded , 5yx religio-regional color-coded ) .
• Original Excel chart : Not on the Internet yet , @ @ @ @ @ @ @ File below .
Link to Summary File regarding the generic relationship between national religiosities and attitudes to climate change : [ SUMMARY Religiosity Predicts CC Beliefs 2 ] Note : the above file includes a short separate section on the situation in the US , where there is a 4-way cultural dance ( the extra dancers being Rep / Con and Dem / Lib cultures ) .
See Footnote 14 in the file .
In the other 59 nations covered , there is a simpler 2-way cultural dance ( i.e .
religious faith @ @ @ @ @ @ @ same basic principles apply , however , which allows the framework derived from the 59 nations to produce some insight on the US situation .
I may expand on this in a future post .
Outside of the US , religiosity as a predictor of national climate-change attitudes dwarfs any net political considerations .
( And incidentally within nations on this issue , polarization due to political affiliation is much less than in the US ; various literature cites the US situation as exceptional ) .

by Andy West
“For me the question now is, now that we know that renewables can’t save the planet, are we going to keep letting them destroy it?”. – Michael Schellenberger
Introduction
There have been many technical analyses of Wind and Solar energy, covering a raft of issues from energy density and efficiency, through subsidies and land usage, to maintenance, grid impacts, intermittency and more. The angle examined here is in no way intended to replace such necessary views, whether they lean to the pessimistic or the optimistic or anywhere in-between. Rather, a complementary view is provided regarding an aspect that such technical analyses cannot address, albeit it often features in the conclusions and questions these analyses raise at the end. Right here at Climate Etc, the first of the excellent analyses by ‘Planning Engineer’ (on his retirement revealed to be Russ Schussler, ex-VP of Transmission Planning at Georgia Transmission Corporations), laudably highlighted the limitation of technical analyses with his very first line: “Power System Planners do not have the expertise or knowledge to say whether or not the benefits of reducing carbon emissions are worth the costs. However they should be respected as experts for obtaining a better understanding of what the implications and costs of such programs are.”
So, who does have this critical knowledge and expertise regarding ultimate cost / benefit? The answer is highly likely to be no one at all, because not only is this issue deeply disputed, it is culturally (as well as technically) disputed. Indeed, to the extent that the overwhelming public and authority narrative about climate change contradicts mainstream science let alone any skeptical science, which also means that, especially in some nations such as the US, there are cultural counter-narratives too. Hence debates about policy, including renewables deployment, aren’t legitimately resolved, because the potent cultural angle corrupts or polarizes or simply overrides any such debate.
Schussler acknowledges this cultural dispute, saying in the line following the above quote: “Unfortunately many non-experts, driven by fear of AGW, have done much to cloud, distort and ignore critical issues around the cost and capabilities of renewable energy and the realities relating to the provision of electrical power.” He also returns to this theme at the very end of his post: “…I believe most planners and utilities recognize that the overall impacts to society (unless needed to aver environmental disaster) would be extremely harmful in the net analysis. I hope that the voices of concerned utility experts are not drowned out by the noise of ‘true believers’ or disbelieved because of false accusations of self-interest.” For sure ‘true belief’ is a cultural feature and may often prompt ‘false accusations’. Plus that caveat of ‘unless needed’, or more subtly ‘how much needed’, is a major issue within the dispute above. An apparent need, could be partly or wholly the result of emotive belief, of which ‘fear of [C]AGW’ is a major component.
Schussler’s title, ‘Myths and realities of renewable energy’, emphasis mine, is inclusive of the element that technical analysis cannot ultimately probe regarding character and impact, because myths are cultural phenomena. Others cite myth as Renewables motivation, and David Archibald goes further in directly framing this element as ‘religious’ in nature (religions being just one brand of cultural entities). In a recent article on renewables (and focusing on hydrogen) at Jo Nova’s, he employs religiously orientated terms such as ‘believers’ and ‘government encyclicals’:
“Briefly, the only reason solar and wind get a look-in is because solar panels and wind turbines are made using energy from coal at $0.04 per kWh and turn out power at $0.20 per kWh… …You can’t use solar and wind power to make solar and wind power equipment; as such they are neither renewable nor sustainable. And they certainly won’t be replacing fossil fuels when the fossil fuels run out.
Even some lefties are figuring this out and thus the documentary Planet of the Humans. So the global warming clerisy, headed by Alan Finkel in this country [Australia], needs to keep coming up with new content to satisfy their simple-minded believers… …Vast sums are to be spent on hydrogen. The language of the Government encyclicals suggests that hydrogen is a new source of energy that just has to be tapped to guarantee a wonderful future.”
Needless to say, Archibald views this ‘religious’ influence as overwhelmingly negative, adding as part of his conclusion: “Global warming doesn’t build orphanages or hospitals. As a religion it doesn’t do any good at all.” And others claim a cultish / ‘religious’ motivation too. Yet whether viewing culture as only a clouding / distorting influence, or as an overwhelmingly negative force, technical analyses critical of renewables inevitably falter or get speculative when it comes demonstrating cultural sway (for example, across nations invested in renewables). An alternate approach is needed to progress this.
There are less critical / more optimistic analyses of Solar or Wind ‘renewables’, which nevertheless tend to justify some amount of pain in achieving rosier end-scenarios, upon the premise that this is needed to avoid an otherwise inevitable and imminent global catastrophe (apart from the most wildly optimistic, in which all changes are apparently painless). Yet mainstream science says such a justification is unfounded (notwithstanding the cultural dispute above).
Meanwhile the implementation of Solar and Wind is surging ahead, and has done so for decades in some countries. So, is this global implementation largely due to pragmatic considerations that are consistent with mainstream science? After all, these things do actually produce power. Or instead, largely due to cultural motivation that spreads solar-panels and wind-turbines as merely icons for the ‘secular religion’ of certain and catastrophic climate change? And no matter the costs, fiscal or environmental. This question is resolvable, as hard social data on the cultural attitudes of many nations to climate-change, allows national motivations to be mapped to Renewables deployment.
Cultural attitudes to climate-change across national publics
Prior posts here, show that supportive attitudes to climate-change (CC) across national publics, have a dual and systemic strong relationship with religiosity. CC supportive public responses to unconstrained climate survey questions correlate with national religiosity, while oppositely, CC supportive responses to reality-constrained questions anti-correlate with national religiosity. [Reality-constrained questions force survey participants to consider and compare other reality issues in relation to climate change, typically by asking them to nominate the X ‘most important’ from a larger list of Y issues, of which one represents a climate-change issue, or literally just ‘climate-change’. Unconstrained questions don’t force such a comparative choice]. Using some data-series from the prior posts, Chart 1 below summarizes this dual relationship. For many nations and plotted against national religiosity, it shows the CC supportive public responses measured (and for the dashed lines, intuited) for different strengths of both unconstrained and reality-constrained survey questions.
Strength for the unconstrained questions (blue lines) reflects how aligned these are to the emotive and existential values of catastrophic climate change culture (CCCC), plus how much this is targeted at the personal. Another way of thinking about this strength, is how biased the questions are towards CCCC, emotive content being a feature of bias and not of rationality. Strength for reality-constrained questions (orange lines) reflects the tightness of the constraint. Picking the ‘most important’ single issue of 12, for example, is a stronger reality-constraint, a stronger clash with the other issues, than say picking the top 3 of 12, which is stronger in turn than the top 5 of 12. Table 1 at the end of this post, shows which actual climate-change survey questions generated the data-series each line represents (each is depicted as just the stand-alone trendline), along with the related r/r2/p values.
The most important conclusion to draw from these trends is that, as revealed by their strong relationships with religiosity (an entirely cultural phenomenon), attitudes to climate-change across nations are likewise cultural. In fact, they’re the net result of interaction between 2 cultures, i.e. CCCC and religion. So, this means they are not due to rationality (cultural attitudes are emotively driven), or anything physical such as the characteristics of the climate-system, or indeed the particular climates or climate exposures of nations, or likewise any views on same as expressed by mainstream (or any) climate science.
With the exception of one historical coincidence (annual sunshine duration – see later) no matters encompassed by these categories would exhibit a systemic relationship with religiosity. And for sure none can exhibit both correlation and anti-correlation simultaneously, depending only upon the type of question asked; yet such is certainly possible for cultural responses. This duality raises apparent contradictions. For instance, maximum climate concerns (RH ends of blue trends in Chart 1) occur in the same nations as minimum support for climate action / priority (RH ends of orange trends). But such apparent contradictions aren’t unusual for cultural causation. From the prior series here at Climate Etc, see : Apparent Paradoxes in the relationship of Climate ‘Concerns, Skepticism, Activism, and Priority’ explained by Religiosity.
In fact, the full situation is more complex than depicted by Chart 1. See the Summary File linked below (and especially Chart 2 in there, not in the main post) for more explanation, visualized via more series plus features added to the above Chart 1. However, the conclusion remains that climate-change attitudes across nations are mainly cultural, so not from either rationality or physical factors. [Note: the attached summary is more distilled and easier going than the 3 prior posts linked above. Regarding the US, see the note at the very end of this post].
Probing motivations for Renewables Commitment
If a particular domain is dominated by cultural attitudes, spending and policy support within the domain should ultimately be rooted in those attitudes. Hence, armed with the above cultural attitudes to climate-change, we now need to explore the relationship across nations, between these and actual Renewables deployment. It isn’t that mass publics rush out and purchase Wind Turbines with their credit cards, in proportion to their national attitudes. The actual commitment works via elites who enact policy. Yet the limits on such policy should still be in proportion to the national public attitudes (which doesn’t mean alignment in an absolute sense).
Culture works to this end whether or not a nation is a democracy, or in the latter case whether or not democratic processes are actually involved (e.g. in the UK, ‘net-zero by 2050’ was nodded through without a parliamentary vote, and with essentially no costing or scrutiny or meaningful opposition). As per the Introduction above, the primary justification for Renewables is indeed climate-change. So, if culture does rule the roost for this particular policy area, Renewables deployment per nation should be governed by its (cultural) attitudes to climate-change. In which case, we must see a very strong correlation between these two aspects.
But which of the above attitudes to climate-change are relevant, when comparing on a national basis with renewables commitment? As there is at least some reality-constraint on deploying renewables (because all nations have budgets and competition for same), if indeed the expression of renewables policy is mainly cultural, this ought to align with a reality-constrained (orange) trend, not an unconstrained (blue) trend. Plus, because public and also elite / authority knowledge about renewables is likely very poor on average, this makes the constraint weak at best (if the downsides of deployment were better understood, then the constraint would be stronger).
So, we should be looking at a comparison with the attitude data from the ‘WC’ series in Chart 1. This comes from an enormous UN survey on policy priorities in many nations, the vote share for ‘action on climate-change’ forming the series Y-values. Nations can be matched from this series to those with major Solar deployment (40 nations), Wind turbine deployment (also 40, with 5 nations different to the Solar list), plus a common set (of 35) deploying both.
Looking at the combined commitment for Wind and Solar should produce a more reliable result, as each may have technology / policy idiosyncrasies that could buck the trend in some way, but which are more likely to average out over the combination of both (though we can start by examining each type in turn). In practice, there’s highly unlikely to be zero or full correlation. Policy decisions are very rarely 100% free of cultural factors, yet they’d rarely be wholly cultural either. So, we can set reasonable thresholds for the test. If ‘r’ for the correlation of renewables commitment with weakly-constrained climate attitudes is say ~0.33 or less, then we can say culture (per above inherent in those attitudes) doesn’t dominate. But if r~0.66 or above, then we can say that cultural motivation does dominate renewables commitment. If the value lands in-between, I guess we’d have to see where and think more about dual modes.
Commitment for Wind Turbines across 40 nations
Preamble: 1] Due to various impacts on very long-term social development, it is well-known that with some exceptions (which aren’t an issue here) GDP-per-capita across nations strongly negatively correlates with religiosity (see Chart F1 in Xcel datafile). 2] Hence if above this, deploying Wind Turbine capacity is indeed motivated by a ‘WC’ type climate-attitude, which itself negatively correlates with religiosity (see Chart 1), then national religiosity plotted against each nation’s Wind Turbine capacity per-capita, should yield a power type function. It does (see Chart F2 in Xcel datafile). 3] Power functions are more difficult to deal with or apply our test thresholds for ‘r’ to (based on linear).
So, normalizing the Wind Turbine capacity per-capita wrt to GDP per-capita (I used Spain’s GDP as the arbitrary standard), will remove the long-term effect of religiosity upon societies and any unequal fulfillment of motivation (the same motivation, so ~fraction of GDP, will purchase more Wind Turbines if the GDP is bigger). So, this resets our expectation back to a linear function…
The plot: Across the 40 nations, UN vote share for ‘action on climate change’ (from ‘WC’ data), versus GDP-per-Capita-normalised Wind Turbine Capacity / Population (to 2018), shows a +ve correlation. This suggests that Wind Turbines are significantly motivated by a ‘WC’ type cultural climate-change attitude:
An ‘r’ of 0.64 just misses our test threshold for dominance; but this is only half the story so far. And it looks like there are idiosyncrasies regarding individual national policies for Wind deployment. E.g. the Czech Republic has very little despite a high vote for ‘action on climate-change’. And with a vote share that is a little lower, Portugal nevertheless seems to have a huge Wind capacity. In summary it seems that as motivation rises towards the right, especially beyond the green line, the range of expression in Wind Turbines, grows. This likely reflects larger and more targeted Renewable Energy policies, which hence spread from gung-ho for Wind Turbines right down to minimal deployment, because other Renewables are or aren’t more prioritized instead (e.g. Solar, Biomass, etc). So, to better insulate against the effects of targeted policies, we can add into the mix the identical analysis for Solar deployment.
Commitment for Solar Power across 40 nations
Preamble: 1] The equivalent charts for F1 and F2 in the Wind Turbine case, are F3 and F4 (see the Excel datafile). 2] However, there’s an extra issue for Solar Power, which is that we need to adjust the MW that each nation has deployed, according to its annual sunshine hours. Otherwise the same spend (where spend corresponds to motivation), hence the label MW, will produce different actual power per year in different nations. So in Chart 4, Solar Capacity deployed is normalized (to Spain) for Annual-Sunshine-DurationP1.
The plot: Across the 40 nations (5 different to the Wind Turbine set) UN vote share for ‘action on climate change’ (from ‘WC’ data), versus GDP-per-Capita-normalised Annual-Sunshine-Adjusted Solar Capacity / Population (to 2018), shows a +ve correlation. See Chart F7 in the Excel datafile. This chart is very similar to Chart 2 above, with a greater range of expression in Solar Power to the right of the green line. However, different nations than for Chart 2 are either high and low within this expression, because the idiosyncrasies associated with Solar Power are different to those for Wind Turbines.
An ‘r’ of 0.48 is somewhat lower than the case for Wind Turbines alone; much of the reduction appears to stem from Japan going nuts on Solar despite only middling climate concerns, while Sweden has very modest Solar despite very high climate concerns. But this is only half the story too. Combining the results for both the technologies, will give us a more robust picture for Renewables motivation overall.
Commitment for combined Wind / Solar Renewables across 35 (common) nations
Across 35 nations, Chart 3 below shows UN vote share for ‘action on climate change’ (from ‘WC’ data), versus GDP-per-Capita-normalised Combined Renewables Commitment / Population (to 2018). The +ve correlation has improved. Surpassing the upper test threshold with some to spare, ‘r’ is now at 0.73.
The greater range of spread of nations at the RHS has considerably reduced (excepting for Germany), this being consistent with expected total renewables motivation, which nevertheless may express much more in Wind Turbines than Solar for any particular single nation, or vice versa. The lens we used to see in the first place that attitudes to climate-change are cultural, i.e. the religiosity of nations, is color-coded onto Chart 3. This falls from left to right, albeit fuzzily because aside from some random noise, there’s a minor secondary variable at play which impacts religiosity as seen in this view (see Postscript 2).
Discussion
Notwithstanding some utility, the main motivation for Wind and Solar Renewables is cultural. Thus, their installations are more akin to churches than to power-stations. This also means that, just like the cultural attitude it is rooted in, Renewables deployment per nation directly anti-correlates with religiosity; see Postscript 2.
While it seems highly intuitive that national attitudes for action on climate-change would align with the corresponding renewables deployments, going straight for the jugular might not result in understanding exactly how to compare (or at least, there’d be more chance of making a mistake in so doing), or indeed what the result actually means. For instance, without the benefit of the big picture in Chart 1 and what this is about in cultural terms, we wouldn’t have known that those attitudes are not due to rationality. Plus, we may have ended up comparing the attitudes from an unconstrained climate survey question, and still be wondering why more Renewables correlates with dramatically less concern about climate-change. And the steps in the above preambles, usefully confirm the roles of GDP-per-capita and sunshine duration.
Note: widespread public knowledge of Renewables issues would increase constraints on policy, likely collapsing motivation from the ‘WC’ line in Chart1, to the ‘FC’ line. Note: This post says nothing about actual physical climate change, nor about the mainstream science of same (which the CCCC narratives contradict). It’s only about public attitudes and their expression in Renewables policy.
Michael Shellenberger, the environmentalist who tirelessly advocates for Nuclear Power as a solution to our energy needs, says this of Renewables (and he’s talking largely about Solar and Wind deployments): “For me the question now is, now that we know that renewables can’t save the planet, are we going to keep letting them destroy it?” An insight consistent with the fact that the ‘purpose’ of cultural narratives is to gain emotive commitment, in turn only to hold the cultural group together; it’s irrelevant to this actual purpose that the resulting actions may undermine or even reverse their stated purpose; this happens.
In the blurb for his book ‘Apocalypse Never’ Shellenberger also speaks about the character of modern environmentalism, the motivation beneath it, which along with much else drives Renewables deployment (and also a net resistance to Nuclear Power as a ‘solution’): “What’s really behind the rise of apocalyptic environmentalism? There are powerful financial interests. There are desires for status and power. But most of all there is a desire among supposedly secular people for transcendence. This spiritual impulse can be natural and healthy. But in preaching fear without love, and guilt without redemption, the new religion is failing to satisfy our deepest psychological and existential needs.” Emphasis mine.
Shellenberger rightly identifies the overall motivation as cultural. He uses the term ‘religion’, as indeed do many others, simply because this is the most familiar example of a bounded cultural entity that people tend to have. Regarding a ‘climate catastrophe’ generally, the social data completely agrees with him, as shown in Chart 1 (and more fully elsewhere, see the Summary File below). And as demonstrated above, this is exactly the case too for the specific motivation behind Renewables deployment.
However, I believe Shellenberger has one thing wrong. Catastrophic climate culture is so pervasive exactly because it does satisfy deep psychological needs, which needs stem from signaling in-group identification via emotive, and preferentially existential, narratives. These in turn activate deep mental mechanisms which bypass our rationality, be that the advice of Planning Engineer or any other mere reason, via whatever expertise, experience or analysis. Anyone or any group that contradicts or even questions in-group narratives, is automatically out-group, and so passionately resisted.
Postscript 1: Historical accident and irony regarding Solar Deployment
Via the historical accident of atheism spreading outwards from NW Europe, and hence from mostly very cloudy (annually averaged) countries towards typically sunnier climes, the annual sunshine duration of countries has a pretty decent linear correlation with national religiosity, albeit some major individual exceptions. See Chart F5 in the datafile, ‘r’ is 0.56. Note: annual sunshine duration per country is derived from the average measurements for between 2 cities (smaller countries) and 5 cities (larger countries, excepting 10 for Russia); see the Excel datafile for tables of same, plus original sources. So, if national sunshine hours are substituted as a proxy for national religiosity in Chart F4, this still shows a power type function, see Chart F6 in the datafile. Similarly to above, we can then normalize this wrt GDP-per-capita, thereby removing the simple effect of spending power (and too its long-term relationship to religiosity). This reveals the relative priority of Solar deployment (and so relative motivation) for each of the nations.
The result of this operation, shown in Chart 4 below, demonstrates a significant irony that stems from the cultural motivation behind Renewables. Which is that, as the annual sunshine hours experienced by nations reduces, then more nations choose to deploy more Solar MW per capita – i.e. within exactly those geographies it is least useful.
The increasing spread of nations right to left in the green triangle, represents the same increasing range of expression in Solar that is also seen in F7 (see datafile), and likewise for Wind Turbines in Chart 2 above, which in both cases is more obvious to the right of the green lines. That increasing range is due to an increasing cultural motivation for Renewables, coupled with targeted policies which for particular nations may preference other Renewables options over Solar, to some degree.
Postscript 2: Renewables Commitment versus Religiosity
Religiosity is used as a ‘lens’ in Chart 1, which allows us to ‘see’ that national climate-change attitudes are indeed cultural. The two strong cultures interact to produce the various trends, and in particular the correlation with religiosity of climate-change supportive answers to unconstrained questions, yet also anti-correlation with religiosity of climate-change supportive answers to reality-constrained questions, for sure is extremely hard to explain any other way. One wouldn’t expect this lens or proxy itself to have quite such a robust correlation with national Renewables Commitment as the ‘WC’ climate attitude, but it’s a useful ‘parity check’ to graph this and demonstrate that it should still be pretty strong. Chart F9 in the Excel datafile shows this. ‘r’ is ~0.65 against ranked Renewables Commitment (compresses some of the outliers a bit, especially Germany), and ~0.6 against actual values. In practice, there is more than just extra randomity via looking through this proxy.
The series of ‘weaker strengths’ in Chart 1 (so ‘WC’ and ‘WA’) have some systemic variability about trend, due to the GDP-per-Capita of each nation relative to its religio-regional group. This variability is faithfully reflected in Chart F9, and is color-coded onto the chart. See also Chart F10 and F11, which show the consistency of the religio-regional-GDP-per-Capita group averages between the ‘WC’ and Renewables Commitment series. See the Summary File for a full explanation, plus some ‘weaker strength’ climate attitude series as full-data visualizations, to better see the raw effect. For a version of ‘WC’ using the same 35 nations as in Chart 3, with hi / lo Renewables commitment and hi / lo GDP-per-religio-regional-group both encoded, see Chart F8 in the Datafile.
Note: Norway is excluded from above charts as policy bias to immense amounts of natural hydro-power available, denudes Solar and Wind Turbine motivations. This could be accommodated by the inclusion of a third renewables string across all nations for hydro-power, but there’d be little value for this extra effort.
Link to Summary File regarding the generic relationship between national religiosities and attitudes to climate change: [SUMMARY Religiosity Predicts CC Beliefs 2]
Note: the above file includes a short separate section on the situation in the US, where there is a 4-way cultural dance (the extra dancers being Rep / Con and Dem / Lib cultures). See Footnote 14 in the file. In the other 59 nations covered, there is a simpler 2-way cultural dance (i.e. religious faith and catastrophic climate-change culture). The same basic principles apply, however, which allows the framework derived from the 59 nations to produce some insight on the US situation. I may expand on this in a future post. Outside of the US, religiosity as a predictor of national climate-change attitudes dwarfs any net political considerations. (And incidentally within nations on this issue, polarization due to political affiliation is much less than in the US; various literature cites the US situation as exceptional).
Link to Excel Datafile: [Wind and Solar motivations Data]
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climateetc--2020-11-21--Five rules for evidence communication

Five rules for evidence communication

“ Avoid unwarranted certainty , neat narratives and partisan presentation ; strive to inform , not persuade.
” I just spotted this Comment in Nature : Five rules for evidence communication .
Once I spotted co-author David Spiegenhalter , I knew this would be good .
I have definitely been in need of an antidote to the Covid-19 and global warming propaganda that I ’ ve come across lately .
I ’ m also working on a new climate change presentation ; this provides an excellent check list .
Here is a [ link ] to the article ( freely @ @ @ @ @ @ @ myriad examples from the current pandemic of which we might ask : have experts always been explicit in acknowledging unknowns ?
Complexity ?
Conflicts of interest ?
Inconvenient data ?
And , importantly , their own values ?
Our small , interdisciplinary group at the University of Cambridge , UK , collects empirical data on issues such as how to communicate uncertainty , how audiences decide what evidence to trust , and how narratives affect people ’ s decision-making .
Our aim is to design communications that do not lead people to a @ @ @ @ @ @ @ understand what is known about a topic and to make up their own minds on the basis of that evidence .
In our view , it is important to be clear about motivations , present data fully and clearly , and share sources .
We recognize that the world is in an ‘ infodemic ’ , with false information spreading virally on social media .
Therefore , many scientists feel they are in an arms race of communication techniques .
But consider the replication crisis , which has been blamed in part on @ @ @ @ @ @ @ and focus on a story rather than on full and neutral reporting of what they have done .
We worry that the urge to persuade or to tell a simple story can damage credibility and trustworthiness .
So how do we demonstrate good intentions ?
We have to be open about our motivations , conflicts and limitations .
Scientists whose objectives are perceived as prioritizing persuasion risk losing trust .
When zoologist John Krebs became chair of the UK Food Standards Agency in the 2000s , he faced a deluge of crises , @ @ @ @ @ @ @ cattle disease bovine spongiform encephalopathy .
He adopted the following strategy : • say what you know ; • what you don ’ t know ; • what you are doing to find out ; • what people can do in the meantime to be on the safe side ; and The aim is to ‘ inform but not persuade ’ , and — as the philosopher of trust Onora O ’ Neill says — “ to be accessible , comprehensible , usable and assessable ” .
• Address all the questions and @ @ @ @ @ @ @ Present potential benefits and possible harms in the same way so that they can be compared fairly .
• Avoid the biases inherent in any presentation format ( for example , use both ‘ positive ’ and ‘ negative ’ framing together ) .
• Use numbers alone , or both words and numbers .
• Demonstrate ‘ unapologetic uncertainty ’ : be open about a range of possible outcomes .
• When you don ’ t know , say so ; say what you are going to do to find out , @ @ @ @ @ @ @ quality and relevance of the underlying evidence ( for example , describe the data set ) .
• Use a carefully designed layout in a clear order , and include sources .
Trust is crucial .
Always aiming to ‘ sell the science ’ doesn ’ t help the scientific process or the scientific community in the long run , just as it doesn ’ t help people ( patients , the public or policymakers ) to make informed decisions in the short term .
That requires good evidence communication .
Ironically , @ @ @ @ @ @ @ of that .
The Supplementary Information is a longer version of this , well worth reading also .

by Judith Curry
“Avoid unwarranted certainty, neat narratives and partisan presentation; strive to inform, not persuade.”
I just spotted this Comment in Nature: Five rules for evidence communication. Once I spotted co-author David Spiegenhalter, I knew this would be good. I have definitely been in need of an antidote to the Covid-19 and global warming propaganda that I’ve come across lately. I’m also working on a new climate change presentation; this provides an excellent check list.
Here is a [link] to the article (freely accessible). Excerpts:
There are myriad examples from the current pandemic of which we might ask: have experts always been explicit in acknowledging unknowns? Complexity? Conflicts of interest? Inconvenient data? And, importantly, their own values?
Our small, interdisciplinary group at the University of Cambridge, UK, collects empirical data on issues such as how to communicate uncertainty, how audiences decide what evidence to trust, and how narratives affect people’s decision-making. Our aim is to design communications that do not lead people to a particular decision, but help them to understand what is known about a topic and to make up their own minds on the basis of that evidence. In our view, it is important to be clear about motivations, present data fully and clearly, and share sources.
We recognize that the world is in an ‘infodemic’, with false information spreading virally on social media. Therefore, many scientists feel they are in an arms race of communication techniques. But consider the replication crisis, which has been blamed in part on researchers being incentivized to sell their work and focus on a story rather than on full and neutral reporting of what they have done. We worry that the urge to persuade or to tell a simple story can damage credibility and trustworthiness.
So how do we demonstrate good intentions? We have to be open about our motivations, conflicts and limitations. Scientists whose objectives are perceived as prioritizing persuasion risk losing trust.
Inform, not persuade
Offer balance, not false balance
Disclose uncertainties
State evidence quality
Inoculate against misinformation
When zoologist John Krebs became chair of the UK Food Standards Agency in the 2000s, he faced a deluge of crises, including dioxins in milk and the infectious cattle disease bovine spongiform encephalopathy. He adopted the following strategy: say what you know;
what you don’t know;
what you are doing to find out;
what people can do in the meantime to be on the safe side; and
that advice will change. Quick tips for sharing evidence The aim is to ‘inform but not persuade’, and — as the philosopher of trust Onora O’Neill says — “to be accessible, comprehensible, usable and assessable”. Address all the questions and concerns of the target audience.
Anticipate misunderstandings; pre-emptively debunk or explain them.
Don’t cherry-pick findings.
Present potential benefits and possible harms in the same way so that they can be compared fairly.
Avoid the biases inherent in any presentation format (for example, use both ‘positive’ and ‘negative’ framing together).
Use numbers alone, or both words and numbers.
Demonstrate ‘unapologetic uncertainty’: be open about a range of possible outcomes.
When you don’t know, say so; say what you are going to do to find out, and by when.
Highlight the quality and relevance of the underlying evidence (for example, describe the data set).
Use a carefully designed layout in a clear order, and include sources. Trust is crucial. Always aiming to ‘sell the science’ doesn’t help the scientific process or the scientific community in the long run, just as it doesn’t help people (patients, the public or policymakers) to make informed decisions in the short term. That requires good evidence communication. Ironically, we hope we’ve persuaded you of that. The Supplementary Information is a longer version of this, well worth reading also.
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The blame game

How the ‘ blame game ’ gets in the way of solving complex societal problems .
An essay on how attempting to identify blame for complex societal problems can get in the way of finding solutions to these problems .
What the climate ‘ blame game ’ can learn from the Covid-19 ‘ blame game.
’ Manmade climate change is an emergent problem caused mainly by the abundance and usefulness of fossil fuels in providing cheap , reliable energy .
In his book The Moral Case for Fossil Fuels , energy theorist Alex Epstein outlines the benefits that the development @ @ @ @ @ @ @ gas have had on mankind , including improved health , increased lifespan , and expansion of material welfare .
Economist Richard Tol evaluated the private benefit of carbon , which is the value of energy services produced by fossil fuels .
He finds that the private benefit of carbon is much greater than the social cost of carbon that causes damage via climate change ; these benefits are related to the benefits of abundant and reliable energy .
So , who is to blame for fossil fuel emissions and manmade climate change ?
@ @ @ @ @ @ @ power , transportation , and steel , which are produced using fossil fuels ; or • electric utilities providers and manufacturers of the internal combustion and jet engines that use fossil fuels ; or • oil/gas and coal companies that produce fossil fuels ; or • governments who have the authority to regulate fossil fuel emissions .
The blame for manmade climate change is occasionally placed on national governments .
The Urgenda ruling ordered the Dutch government to step up its climate actions in reducing emissions .
In the Juliana civil lawsuit , @ @ @ @ @ @ @ declining to sign on to the Kyoto Protocol , pass a carbon tax and trade bill and withdrawing from the Paris Climate Agreement .
However , most often in civil litigation , the blame is placed on oil/gas and coal companies that produce the fuels .
The role of climate science in the carbon blame game is an interesting one .
As a basis of responsibility , a key element is the causal link between the actor and the harm .
Responsibility is also based on the ability to foresee the harm , @ @ @ @ @ @ @ finally , responsibility relates to the ability to prevent the harm .
Recent developments in attribution science are seeking to identify the culpability of individual or groups of oil/gas and coal companies as related to local sea level rise , ocean acidification and extreme weather events .
A new wave of private climate litigation has been motivated by publication of the Carbon Majors study by Richard Heede .
Heede ’ s research shows that nearly two-thirds of anthropogenic carbon emissions originated from just 90 companies and government-run industries .
Among them , the @ @ @ @ @ @ @ of world carbon emissions from fossil fuels and cement production since the Industrial Revolution .
Four of the eight companies are owned by national governments , whereas the other four are multinational corporations .
Heede ’ s research was a turning point in the debate about apportioning responsibility for climate change .
While Heede ’ s work helped identify individual defendants or groups of defendants related to climate change , it did not resolve the question of whether these emitters are responsible for specific climate change-related impacts and events .
Arriving at a @ @ @ @ @ @ @ based on increasing atmospheric CO and global mean surface temperature .
By tracing company emissions over time , Ekwurzel et al .
( 2017 ) attribute fractions of the accumulation of CO in the atmosphere , increases in atmospheric temperature and elevation of the sea level to the Carbon Major companies .
Ekwurzeil et al .
mentioned in the conclusions the idea of extending this attribution logic to extreme weather events .
A recent paper by Lickey et al .
( 2019 ) attempts to attribute ocean acidification to Carbon Majors .
The @ @ @ @ @ @ @ is not at all straightforward .
There are two specific issues here : whether climate models are valid sources of legal evidence for climate change attribution/cause ; and also the importance of determining partial causation in the context of natural climate variability .
Attribution of harm associated with the weather , climate change or sea level rise is complicated by the existence of multiple causes .
Assuming that some percentage of the harm can be justifiably attributed to fossil fuel emissions , does it make sense to attribute this harm in a legal @ @ @ @ @ @ @ , e.g .
coal and oil/gas companies ?
David Victor is a global thought leader on climate change policy and the energy-systems transformation that is required for a low-carbon future .
Victor dismissed Heede ’ s work on the Carbon Majors as part of a “ larger narrative of trying to create villains , ” seeking to distinguish between producers as being responsible for the problem and everyone else as victims .
Victor stated : “ Frankly we ’ re all the users and therefore we ’ re all guilty.
” [ link @ @ @ @ @ @ @ Heede ( author of the Carbon Majors report ) concedes that the responsibility is shared .
He stated : “ I as a consumer bear some responsibility for my own car , et cetera .
But we ’ re living an illusion if we think we ’ re making choices , because the infrastructure pretty much makes those choices for us.
” Heede makes a key point by saying that the infrastructure pretty much makes the choices for us .
The demand for fossil fuels is driven by electric utility and transportation infrastructures @ @ @ @ @ @ @ with a limited number of other options , unless they forego grid electricity and do not avail themselves of transportation systems that run on fossil fuels .
Individual consumers and companies are responsible for the demand for electric utilities and transportation , but are arguably indifferent to the source of electric power or transportation , provided that it is abundant , reliable , safe and economical .
If there were no demand for fossil fuels , then there would be nothing to blame on the Carbon Majors .
The fact that there is @ @ @ @ @ @ @ indicates that the issue of blame is not straightforward .
A change from fossil fuels to cleaner fuels is not simple or cheap , owing to infrastructure .
For electric power , this includes generation and transmission infrastructure .
For transportation , this includes vehicle engines and their manufacture plus refueling infrastructure .
David Victor states : “ To create a narrative that involves corporate guilt as opposed to problem-solving is not going solve anything.
” A problem-solving focus on infrastructure is needed for progress , but exactly what the infrastructure should look @ @ @ @ @ @ @ , economics and public policy .
Covid-19 provides an interesting case study regarding ‘ blame.
’ The origin of the virus is generally regarded to have occurred in Wuhan , China .
However , it is difficult to blame the worldwide spread of the virus on Wuhan .
While Covid-19 statistics coming from China are incomplete and have been judged to be not trustworthy , China appears to have done a better job at containing the internal spread of the virus than many other countries .
Currently , the ‘ blame ’ is @ @ @ @ @ @ @ to lockdown and mask wearing requirements plus the politicians who aren ’ t requiring them to do so .
With the advent of Covid-19 vaccines , the Covid-19 discussion is now dominated by the vaccine , with the origin of the disease receiving little attention .
The cure to the pandemic is technological , in the form of vaccines ; not worldwide behavioral change ( although behavioral change has worked in some smaller regions/countries ) .
In many countries , behavioral modifications to limit transmission that were associated with mandatory lockdowns simply didn @ @ @ @ @ @ @ economic infeasibility , concerns about psychological well being associated with isolation , and general political non-viability .
In context of the climate debate , the lesson from Covid-19 is this .
A technological solution ( analogous to development of the vaccine ) in terms of better electricity generation and transmission would quickly silence the climate ‘ blame game ’ by solving the problems to the environment caused by burning fossil fuels .
Suffering from insufficient electric power or electric power that is too expensive or unreliable ( analogous to the Covid lockdowns ) @ @ @ @ @ @ @ Again , the solution is problem solving and new technologies , not blame .
While isolation and austerity can be invoked for short time periods , they are not solutions .
The Covid-19 blame game didn ’ t get in the way of finding a solution ( i.e .
vaccine ) .
However , the rush to blame the fossil fuel companies and punish them is getting in the way of a sensible transition away from the worst impacts of fossil fuels on the environment .
A sensible transition involves continued use of @ @ @ @ @ @ @ avoids massive infrastructure investments in wind energy that have dubious net benefits over the life cycle of the wind turbines , and developing an improved energy infrastructure for the 21st century .
Abundant , secure , reliable , economical , and clean .
How do we prioritize among these , and to what extent should ‘ clean ’ trump the others ?
Do we define ‘ clean ’ only in terms of emissions , or do we also include mining/exploration , land use , life cycle issues , etc.
?
I am still @ @ @ @ @ @ @ , in the name of the ‘ climate crisis ’ , preventing the development of grid electricity in the poorest regions of Africa that can support development of an advanced economy .
I suspect that I will be waiting a long time for such a justification , because there isn ’ t one .
Playing the carbon ‘ blame game ’ is an excuse for punishing certain companies without actually solving societal problems .
The net effect is continued suffering in developing countries , failure to make much headway on reducing emissions and @ @ @ @ @ @ @ the climate in any way .

by Judith Curry
How the ‘blame game’ gets in the way of solving complex societal problems.
An essay on how attempting to identify blame for complex societal problems can get in the way of finding solutions to these problems. What the climate ‘blame game’ can learn from the Covid-19 ‘blame game.’
The blame for climate change
Manmade climate change is an emergent problem caused mainly by the abundance and usefulness of fossil fuels in providing cheap, reliable energy. In his book The Moral Case for Fossil Fuels, energy theorist Alex Epstein outlines the benefits that the development of coal, oil, and natural gas have had on mankind, including improved health, increased lifespan, and expansion of material welfare. Economist Richard Tol evaluated the private benefit of carbon, which is the value of energy services produced by fossil fuels. He finds that the private benefit of carbon is much greater than the social cost of carbon that causes damage via climate change; these benefits are related to the benefits of abundant and reliable energy.
So, who is to blame for fossil fuel emissions and manmade climate change?
consumers and industries who demand electric power, transportation, and steel, which are produced using fossil fuels; or
electric utilities providers and manufacturers of the internal combustion and jet engines that use fossil fuels; or
oil/gas and coal companies that produce fossil fuels; or
governments who have the authority to regulate fossil fuel emissions.
The blame for manmade climate change is occasionally placed on national governments. The Urgenda ruling ordered the Dutch government to step up its climate actions in reducing emissions. In the Juliana civil lawsuit, the U.S. federal government was blamed for declining to sign on to the Kyoto Protocol, pass a carbon tax and trade bill and withdrawing from the Paris Climate Agreement. However, most often in civil litigation, the blame is placed on oil/gas and coal companies that produce the fuels.
The role of climate science in the carbon blame game is an interesting one. As a basis of responsibility, a key element is the causal link between the actor and the harm. Responsibility is also based on the ability to foresee the harm, in terms of scientific understanding. And finally, responsibility relates to the ability to prevent the harm. Recent developments in attribution science are seeking to identify the culpability of individual or groups of oil/gas and coal companies as related to local sea level rise, ocean acidification and extreme weather events.
Carbon Majors
A new wave of private climate litigation has been motivated by publication of the Carbon Majors study by Richard Heede. Heede’s research shows that nearly two-thirds of anthropogenic carbon emissions originated from just 90 companies and government-run industries. Among them, the top eight companies account for 20 percent of world carbon emissions from fossil fuels and cement production since the Industrial Revolution. Four of the eight companies are owned by national governments, whereas the other four are multinational corporations.
Heede’s research was a turning point in the debate about apportioning responsibility for climate change. While Heede’s work helped identify individual defendants or groups of defendants related to climate change, it did not resolve the question of whether these emitters are responsible for specific climate change-related impacts and events.
Arriving at a dangerous climate outcome includes a causal chain based on increasing atmospheric CO 2 and global mean surface temperature. By tracing company emissions over time, Ekwurzel et al. (2017) attribute fractions of the accumulation of CO 2 in the atmosphere, increases in atmospheric temperature and elevation of the sea level to the Carbon Major companies. Ekwurzeil et al. mentioned in the conclusions the idea of extending this attribution logic to extreme weather events. A recent paper by Lickey et al. (2019) attempts to attribute ocean acidification to Carbon Majors.
The science of attribution, or causality, is not at all straightforward. There are two specific issues here: whether climate models are valid sources of legal evidence for climate change attribution/cause; and also the importance of determining partial causation in the context of natural climate variability.
Blame sharing
Attribution of harm associated with the weather, climate change or sea level rise is complicated by the existence of multiple causes. Assuming that some percentage of the harm can be justifiably attributed to fossil fuel emissions, does it make sense to attribute this harm in a legal sense to the producers of fossil fuels, e.g. coal and oil/gas companies?
David Victor is a global thought leader on climate change policy and the energy-systems transformation that is required for a low-carbon future. Victor dismissed Heede’s work on the Carbon Majors as part of a “larger narrative of trying to create villains,” seeking to distinguish between producers as being responsible for the problem and everyone else as victims. Victor stated: “Frankly we’re all the users and therefore we’re all guilty.” [link]
In the same article, Richard Heede (author of the Carbon Majors report) concedes that the responsibility is shared. He stated: “I as a consumer bear some responsibility for my own car, et cetera. But we’re living an illusion if we think we’re making choices, because the infrastructure pretty much makes those choices for us.”
Heede makes a key point by saying that the infrastructure pretty much makes the choices for us. The demand for fossil fuels is driven by electric utility and transportation infrastructures. Individual consumers and companies are faced with a limited number of other options, unless they forego grid electricity and do not avail themselves of transportation systems that run on fossil fuels. Individual consumers and companies are responsible for the demand for electric utilities and transportation, but are arguably indifferent to the source of electric power or transportation, provided that it is abundant, reliable, safe and economical.
If there were no demand for fossil fuels, then there would be nothing to blame on the Carbon Majors. The fact that there is continued and growing demand for fossil fuels indicates that the issue of blame is not straightforward. A change from fossil fuels to cleaner fuels is not simple or cheap, owing to infrastructure. For electric power, this includes generation and transmission infrastructure. For transportation, this includes vehicle engines and their manufacture plus refueling infrastructure.
David Victor states: “To create a narrative that involves corporate guilt as opposed to problem-solving is not going solve anything.” A problem-solving focus on infrastructure is needed for progress, but exactly what the infrastructure should look like depends on available and planned technologies, economics and public policy.
Covid-19 analogy
Covid-19 provides an interesting case study regarding ‘blame.’ The origin of the virus is generally regarded to have occurred in Wuhan, China. However, it is difficult to blame the worldwide spread of the virus on Wuhan. While Covid-19 statistics coming from China are incomplete and have been judged to be not trustworthy, China appears to have done a better job at containing the internal spread of the virus than many other countries. Currently, the ‘blame’ is focused on transmitters who are not adhering to lockdown and mask wearing requirements plus the politicians who aren’t requiring them to do so.
With the advent of Covid-19 vaccines, the Covid-19 discussion is now dominated by the vaccine, with the origin of the disease receiving little attention. The cure to the pandemic is technological, in the form of vaccines; not worldwide behavioral change (although behavioral change has worked in some smaller regions/countries). In many countries, behavioral modifications to limit transmission that were associated with mandatory lockdowns simply didn’t work, for reasons of economic infeasibility, concerns about psychological well being associated with isolation, and general political non-viability.
Conclusion
In context of the climate debate, the lesson from Covid-19 is this. A technological solution (analogous to development of the vaccine) in terms of better electricity generation and transmission would quickly silence the climate ‘blame game’ by solving the problems to the environment caused by burning fossil fuels. Suffering from insufficient electric power or electric power that is too expensive or unreliable (analogous to the Covid lockdowns) is economically damaging and politically unviable.
Again, the solution is problem solving and new technologies, not blame. While isolation and austerity can be invoked for short time periods, they are not solutions.
The Covid-19 blame game didn’t get in the way of finding a solution (i.e. vaccine). However, the rush to blame the fossil fuel companies and punish them is getting in the way of a sensible transition away from the worst impacts of fossil fuels on the environment.
A sensible transition involves continued use of relatively clean and dispatchable natural gas, avoids massive infrastructure investments in wind energy that have dubious net benefits over the life cycle of the wind turbines, and developing an improved energy infrastructure for the 21st century. Abundant, secure, reliable, economical, and clean. How do we prioritize among these, and to what extent should ‘clean’ trump the others? Do we define ‘clean’ only in terms of emissions, or do we also include mining/exploration, land use, life cycle issues, etc.?
I am still waiting for a moral argument that justifies, in the name of the ‘climate crisis’, preventing the development of grid electricity in the poorest regions of Africa that can support development of an advanced economy. I suspect that I will be waiting a long time for such a justification, because there isn’t one.
Playing the carbon ‘blame game’ is an excuse for punishing certain companies without actually solving societal problems. The net effect is continued suffering in developing countries, failure to make much headway on reducing emissions and certainly a failure to ‘improve’ the climate in any way.
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climateetc--2020-12-20--Asymptomatic spread(?) of Covid-19

Asymptomatic spread(?) of Covid-19

A new article in Nature [ link ] based on an extremely extensive and thorough analysis in Wuhan found no @ @ @ @ @ Cynically , comments on this paper question whether we can we trust anything coming out @ @ @ @ @ ?
This article motivated me to go to Google Scholar to look for papers related @ @ @ @ @ mostly found mathematical models , not careful analyses like the Wuhan study I have an @ @ @ @ @ of my employees in Colombia came down with Covid-19 , along with 11 family members @ @ @ @ @ no one had symptoms .
The first family member came down with symptoms two days @ @ @ @ @ , one family member died .
This paper [ link ] provides insights on aysmptomatic @ @ @ @ @ to make of this ?
Asymptomatic spread seems to be a key assumption under the @ @ @ @ @ In any event , we need a new Covid thread , I open this topic @ @ @ @ @ the denizens can identify some additional research on this .

by Judith Curry
I just finished reading an article entitled Asymptomatic Spread Revisited.
A new article in Nature [link] based on an extremely extensive and thorough analysis in Wuhan found no cases of asymptomatic transmission. Cynically, comments on this paper question whether we can we trust anything coming out of China on this subject?
This article motivated me to go to Google Scholar to look for papers related to asymptomatic spread. I mostly found mathematical models, not careful analyses like the Wuhan study
I have an anecdote on this. One of my employees in Colombia came down with Covid-19, along with 11 family members, after a gathering where no one had symptoms. The first family member came down with symptoms two days after the gathering. Unfortunately, one family member died.
This paper [link] provides insights on aysmptomatic versus pre-symptomatic.
So what to make of this? Asymptomatic spread seems to be a key assumption under the more extreme lockdown measures.
In any event, we need a new Covid thread, I open this topic up for discussion. Hopefully the denizens can identify some additional research on this.
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