iowaclimatescienceeducation--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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November 19, 2020 at 09:00PM


iowaclimatescienceeducation--2020-11-20--New footage reveals Netflix faked walrus climate deaths

Netflix faked ‘ Our Planet ’ walrus deaths in order to blame them on climate change – polar bears actually were the cause of walrus falling to their deaths from a Siberian cliff , independent video evidence from Russia shows .
A new video published by the Global Warming Policy Foundation on this new evidence .
h/t Mark .
London , 19 November : In a GWPF video released today , Dr. Susan Crockford , a Canadian wildlife expert , provides new evidence that the 2019 Netflix documentary film series , ‘ Our Planet ’ , withheld facts behind the @ @ @ @ @ @ @ of climate change .
If there was ever any doubt that polar bears , not climate change , were the cause of walrus falling to their deaths from a rocky cliff in Siberia a few years ago , new evidence presented here seals the deal : a Russian photographer has released independent video of the event that clearly shows polar bears driving walrus over the cliff to their deaths .
In 2019 , a sequence in the Netflix documentary ‘ Our Planet ’ showed a highly disturbing piece of footage of several walrus @ @ @ @ @ @ @ from a high cliff to their deaths .
It transpired this event happened in late September 2017 at a well-known walrus haulout at Cape Schmidt on the Chukchi Sea .
Narrator Sir David Attenborough blamed the tragedy on climate change , insisting that lack of summer sea ice due to climate change was to blame for the walrus falling to their deaths without provocation .
A few months later , however , using some of the same walrus footage , Attenborough ’ s BBC series called ‘ Seven Worlds , One Planet ’ @ @ @ @ @ @ @ walrus off the very same cliff .
It was damning evidence that the ‘ Our Planet ’ account of walrus deaths had been a false narrative constructed to elicit an emotional response from the public .
New independent video footage of the same event shot by Russian photographer Yevgeny Basov corroborates the BBC evidence that polar bears drove the walrus over the cliff .
Basov is a friend of Netflix ‘ science advisor ’ Anatoly Kochnev and was apparently invited to observe the commercial filming .
Like the original Netflix footage , this @ @ @ @ @ @ @ heart .
It captures a raw but natural encounter between predator and prey .
Walrus hauling out on land during the summer are natural events that happen even when sea ice is available .
Polar bears are known to stalk such herds until they stampede , leaving the weak or unwary crushed in their wake .
Cliffs are not essential to this polar bear hunting strategy but are especially efficient .
This brutal film footage of nature in action is not evidence of climate change or species on the brink of extinction .
@ @ @ @ @ @ @ the walrus narrative promoted by Sir David Attenborough in the Netflix documentary ‘ Our Planet ’ is a manipulative sham with no resemblance to reality .

Reposted from Polar Bear Science
Posted on November 19, 2020 |
Netflix faked ‘Our Planet’ walrus deaths in order to blame them on climate change – polar bears actually were the cause of walrus falling to their deaths from a Siberian cliff, independent video evidence from Russia shows.
A new video published by the Global Warming Policy Foundation on this new evidence. h/t Mark.
Press release 16 November 2020 from the Global Warming Policy Foundation:
London, 19 November: In a GWPF video released today, Dr. Susan Crockford, a Canadian wildlife expert, provides new evidence that the 2019 Netflix documentary film series, ‘Our Planet’, withheld facts behind the controversial walrus story it promoted as evidence of climate change. If there was ever any doubt that polar bears, not climate change, were the cause of walrus falling to their deaths from a rocky cliff in Siberia a few years ago, new evidence presented here seals the deal: a Russian photographer has released independent video of the event that clearly shows polar bears driving walrus over the cliff to their deaths. In 2019, a sequence in the Netflix documentary ‘Our Planet’ showed a highly disturbing piece of footage of several walrus bouncing off sharp rocks as they fell from a high cliff to their deaths. It transpired this event happened in late September 2017 at a well-known walrus haulout at Cape Schmidt on the Chukchi Sea. Narrator Sir David Attenborough blamed the tragedy on climate change, insisting that lack of summer sea ice due to climate change was to blame for the walrus falling to their deaths without provocation. A few months later, however, using some of the same walrus footage, Attenborough’s BBC series called ‘Seven Worlds, One Planet’ featured a number of polar bears driving walrus off the very same cliff. It was damning evidence that the ‘Our Planet’ account of walrus deaths had been a false narrative constructed to elicit an emotional response from the public. New independent video footage of the same event shot by Russian photographer Yevgeny Basov corroborates the BBC evidence that polar bears drove the walrus over the cliff. Basov is a friend of Netflix ‘science advisor’ Anatoly Kochnev and was apparently invited to observe the commercial filming. Like the original Netflix footage, this scene is not for the faint of heart. It captures a raw but natural encounter between predator and prey. Walrus hauling out on land during the summer are natural events that happen even when sea ice is available. Polar bears are known to stalk such herds until they stampede, leaving the weak or unwary crushed in their wake. Cliffs are not essential to this polar bear hunting strategy but are especially efficient. This brutal film footage of nature in action is not evidence of climate change or species on the brink of extinction. It does prove, however, that the walrus narrative promoted by Sir David Attenborough in the Netflix documentary ‘Our Planet’ is a manipulative sham with no resemblance to reality.
My video below published by the Global Warming Policy Foundation
Although the location is not specified in the original Russian video, by Russian photographer Yevgeny Basov, entitled simply ‘Walruses and polar bears of Chukotka’ (posted 17 May 2020, see below), it is clear that the location shown early in the film footage (up to the 4:00 mark) is of the cliff and walrus haulout at Cape Schmidt, which the author described in a photo essay published in November 2017 here.
See also:
The truth about Attenborough’s falling walruses (below):
Falling Walrus: Attenborough Tacitly Admits Netflix Deception (below)
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iowaclimatescienceeducation--2020-11-19--Climate Thinktank Slams Australia for Global Warming Inaction

Surging fossil fuel use and exports , no large scale carbon pricing scheme , no carbon target – almost as if the Australian government and the people who voted them into office think the climate crisis is a total joke .
Report highlights Australia ’ s performance on climate change compared to others in the G20 The weaknesses in Australia ’ s performance on a key issue compared to other G20 countries has been highlighted in a new report .
Australia ’ s record on climate change compared to other G20 countries has come under scrutiny in a new report @ @ @ @ @ @ @ an annual collaboration between 14 think tanks and non-governmental organisations , is aimed at encouraging ambitious climate action .
The report has highlighted Australia ’ s poor performance as having one of the highest rates of subsidies for fossil fuels , for being one of only two countries not implementing a carbon pricing scheme and being one of the worst performers when it comes to emissions reduction in transport , energy efficient buildings and deforestation .
“ Evidence suggests that COVID-19 recovery responses , thus far , have been disproportionately directed towards emissions-intensive @ @ @ @ @ @ @ notes .
“ This could contribute to emissions rebounding at a faster rate .
”

Guest essay by Eric Worrall
Surging fossil fuel use and exports, no large scale carbon pricing scheme, no carbon target – almost as if the Australian government and the people who voted them into office think the climate crisis is a total joke.
Report highlights Australia’s performance on climate change compared to others in the G20
The weaknesses in Australia’s performance on a key issue compared to other G20 countries has been highlighted in a new report.
Charis Chang
November 18, 2020
Australia’s record on climate change compared to other G20 countries has come under scrutiny in a new report.
The 2020 Climate Transparency Report, an annual collaboration between 14 think tanks and non-governmental organisations, is aimed at encouraging ambitious climate action.
The report has highlighted Australia’s poor performance as having one of the highest rates of subsidies for fossil fuels, for being one of only two countries not implementing a carbon pricing scheme and being one of the worst performers when it comes to emissions reduction in transport, energy efficient buildings and deforestation.
…
“Evidence suggests that COVID-19 recovery responses, thus far, have been disproportionately directed towards emissions-intensive and environmentally-damaging sectors,” the report notes. “This could contribute to emissions rebounding at a faster rate.”
…


iowaclimatescienceeducation--2020-11-19--Snow almost up to your waist in Russia

Snow almost up to your waist in Russia

Where are the biggest snowdrifts in Russia ?
In mid-November , the largest snow cover in Russia , according to @ @ @ @ @ in the northern Krasnoyarsk Territory .
The leader is the Istok Difficult Access Station ( @ @ @ @ @ the Snezhnogorsk region with a score of 76 cm ( 2½ feet ) , the @ @ @ @ @ the village of Svetlogorsk – 68 cm ( more than 2 ft ) .
snow @ @ @ @ @ Some territories in the Irkutsk and Magadan regions have not been left behind .
Here @ @ @ @ @ snow cover has also exceeded the 50 cm mark , as , for example , @ @ @ @ @ ( 58 cm ) .
Thanks to Martin Siebert for this link The post Snow @ @ @ @ @ in Russia appeared first on Ice Age Now .

Where are the biggest snowdrifts in Russia?
In mid-November, the largest snow cover in Russia, according to weather stations, was recorded in the northern Krasnoyarsk Territory. The leader is the Istok Difficult Access Station (TDS), located in the Snezhnogorsk region with a score of 76 cm (2½ feet), the second place is occupied by the village of Svetlogorsk – 68 cm (more than 2 ft). snow cover reached 55-57 cm.
Some territories in the Irkutsk and Magadan regions have not been left behind. Here, the height of the snow cover has also exceeded the 50 cm mark, as, for example, in Khamar-Daban, Irkutsk region (58 cm).
http://www.hmn.ru/index.php?index=1&ts=201118144007
Thanks to Martin Siebert for this link
The post Snow almost up to your waist in Russia appeared first on Ice Age Now.
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November 19, 2020 at 01:32PM


iowaclimatescienceeducation--2020-11-21--Anti-Lockdown Protests Across Europe

Anti-Lockdown Protests Across Europe

The mainstream reported “ hundreds ” of protesters , but as pictures plainly show it was more like tens of @ @ @ @ @ measures , economic destitution , and sweeping police powers are evaporating public trust and eroding @ @ @ @ @ Thousands of people protest in Berlin alone .
Police hose “ Corona Deniers ” w @ @ @ @ @ violent confrontations between riot police and protestors France : Fervent resistance in the shape of @ @ @ @ @ the country Italy : Extensive protest all across the country .
Media called it hundreds @ @ @ @ @ Marches occurred in dozens of cities across the country , including Rome , Naples , @ @ @ @ @ says the “ Mafia ” was responsible .
Denmark : Police to physically coerce vaccination @ @ @ @ @ protested for 9 days .
Media failed to mention the law or the protests .
@ @ @ @ @ link The post Anti-Lockdown Protests Across Europe appeared first on Ice Age Now .

The mainstream reported “hundreds” of protesters, but as pictures plainly show it was more like tens of thousands.
Increasingly draconian lockdown measures, economic destitution, and sweeping police powers are evaporating public trust and eroding public patience.
Protests follow “Second Wave” Repeat Lockdown
Germany: Thousands of people protest in Berlin alone. Police hose “Corona Deniers” w Water-cannons
Spain: Barcelona saw violent confrontations between riot police and protestors
France: Fervent resistance in the shape of angry marches through cities across the country
Italy: Extensive protest all across the country. Media called it hundreds, photos show more. Marches occurred in dozens of cities across the country, including Rome, Naples, Genoa and Bologna. Politico says the “Mafia” was responsible.
Slovakia: Huge march anti-covid/anti-govt
Denmark: Police to physically coerce vaccination through detainment. Parliament Danes protested for 9 days. Media failed to mention the law or the protests.
https://off-guardian.org/2020/11/19/anti-lockdown-protests-all-across-europe/
Thanks to Penelope for this link
The post Anti-Lockdown Protests Across Europe appeared first on Ice Age Now.
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November 20, 2020 at 07:04PM


iowaclimatescienceeducation--2020-11-20--EIA: “About 25% of U.S. power plants can start up within an hour”

In a world where mostly unreliable power plant ( solar & wind ) construction is on the rise , backup power generation becomes more important every day .
Unreliable generation capacity in the US has doubled since 2013 and now comprises 12 % of our generating capacity .
Backup power needs to be able to spin up quickly , ideally within minutes , certainly in less than 1 hour .
NOVEMBER 19 , 2020 About 25 % of U.S. power plants can start up within an hour About 25 % of U.S. power plants can start up—going from being shut @ @ @ @ @ @ @ based on data collected in EIA ’ s annual survey of electric generators .
Some power plants , especially those powered by coal and nuclear fuel , require more than half a day to reach full operations .
The time it takes a power plant to reach full operations can affect the reliability and operations of the electric grid .
Generator startup time differs across electricity-generating technologies because of the differences in the complexity of electricity generating processes , especially when starting again after all processes have been stopped ( cold shut down @ @ @ @ @ @ @ time is different from a generator ’ s ramp rate , which reflects how quickly that generator can modify its power output once it ’ s operating .
Most hydroelectric turbines , which use flowing water to spin a turbine , can go from cold start to full operations in less than 10 minutes .
Combustion turbines , which use a combusted fuel-air mixture to spin a turbine , are also relatively fast to start up .
Steam turbines often require more time .
A fuel heats up water to form steam , @ @ @ @ @ @ @ temperature , pressure , and moisture content thresholds before it can be directed to a turbine that can spin the electricity generator .
Nuclear power plants use steam turbines , but these plants have additional time-intensive processes that involve managing their nuclear fuel .
Almost all nuclear power plants require more than 12 hours to reach full operations .
Power plants that require more than 12 hours to start up are increasingly rare .
Only 4 % of the generating capacity that came online from 2010 to 2019 requires more than half a @ @ @ @ @ @ @ gas combined-cycle systems , which involve both a steam turbine and a combustion turbine , account for more capacity than any other generating technology in the United States .
Most of those systems can reach full operations in between 1 hour to 12 hours , although some can start up within an hour .
The percentage of the generator fleet that does not respond to this question in EIA ’ s survey has doubled—from 6 % in 2013 , when EIA first collected this data , to 12 % in 2019—as a result @ @ @ @ @ @ @ wind power plants added in recent years .
This question is not relevant for these types of plants .

Guess “Did you know?” by David Middleton
In a world where mostly unreliable power plant (solar & wind) construction is on the rise, backup power generation becomes more important every day. Unreliable generation capacity in the US has doubled since 2013 and now comprises 12% of our generating capacity. Backup power needs to be able to spin up quickly, ideally within minutes, certainly in less than 1 hour.
NOVEMBER 19, 2020
About 25% of U.S. power plants can start up within an hour
About 25% of U.S. power plants can start up—going from being shut down to fully operating—within one hour, based on data collected in EIA’s annual survey of electric generators. Some power plants, especially those powered by coal and nuclear fuel, require more than half a day to reach full operations. The time it takes a power plant to reach full operations can affect the reliability and operations of the electric grid.
Generator startup time differs across electricity-generating technologies because of the differences in the complexity of electricity generating processes, especially when starting again after all processes have been stopped (cold shut down). A generator’s startup time is different from a generator’s ramp rate, which reflects how quickly that generator can modify its power output once it’s operating.
Most hydroelectric turbines, which use flowing water to spin a turbine, can go from cold start to full operations in less than 10 minutes. Combustion turbines, which use a combusted fuel-air mixture to spin a turbine, are also relatively fast to start up.
Steam turbines often require more time. A fuel heats up water to form steam, and that steam needs to reach certain temperature, pressure, and moisture content thresholds before it can be directed to a turbine that can spin the electricity generator.
Nuclear power plants use steam turbines, but these plants have additional time-intensive processes that involve managing their nuclear fuel. Almost all nuclear power plants require more than 12 hours to reach full operations. Power plants that require more than 12 hours to start up are increasingly rare. Only 4% of the generating capacity that came online from 2010 to 2019 requires more than half a day to reach full load.
Natural gas combined-cycle systems, which involve both a steam turbine and a combustion turbine, account for more capacity than any other generating technology in the United States. Most of those systems can reach full operations in between 1 hour to 12 hours, although some can start up within an hour.
The percentage of the generator fleet that does not respond to this question in EIA’s survey has doubled—from 6% in 2013, when EIA first collected this data, to 12% in 2019—as a result of the number of utility-scale solar and wind power plants added in recent years. This question is not relevant for these types of plants.
Principal contributor: Owen Comstock


iowaclimatescienceeducation--2020-11-20--Lockdown had zero effect on the upward trend of CO2 emissions

“ Six months of lock down and I defy anyone to show ANY effect in NOAA ’ s graphs – ANY EFFECT AT ALL !
!
” – Rosco Mac __________ Lockdown had zero effect on the upward trend of CO2 emissions We have all been told how the lockdowns made a dramatic change to CO2 emissions by closing the airlines , cruising , domestic car travel etc etc .
But does any of this show up in the “ official ” NOAA CO2 emissions records ?
The answer is an emphatic NO !
!
!
Six months of lock @ @ @ @ @ @ @ ANY effect in NOAA ’ s graphs – ANY EFFECT AT ALL !
!
This shows how insignificant our emissions really are – no-one disputes we emitted far less during 2020 but it had no effect at all !
!
This should really terrify anyone who has the common sense to see that if COVID 19 lockdown had zero effect on the upward trend of CO2 emissions just what are we going to have to do to achieve the truly stupid claims of “ Net Zero ” by 2050 ?
Here is evidence @ @ @ @ @ @ @ ) is doomed to disappoint .
We can ’ t even all die because that means more emissions – seriously these people are so stupid .
The post Lockdown had zero effect on the upward trend of CO2 emissions appeared first on Ice Age Now .

“Six months of lock down and I defy anyone to show ANY effect in NOAA’s graphs – ANY EFFECT AT ALL !!”
– Rosco Mac
__________
Lockdown had zero effect on the upward trend of CO2 emissions
Rosco Mac
We have all been told how the lockdowns made a dramatic change to CO2 emissions by closing the airlines, cruising, domestic car travel etc etc.
But does any of this show up in the “official” NOAA CO2 emissions records ?
The answer is an emphatic NO !!!
https://www.esrl.noaa.gov/gmd/ccgg/trends/mlo.html
Six months of lock down and I defy anyone to show ANY effect in NOAA’s graphs – ANY EFFECT AT ALL !!
This shows how insignificant our emissions really are – no-one disputes we emitted far less during 2020 but it had no effect at all !!
This should really terrify anyone who has the common sense to see that if COVID 19 lockdown had zero effect on the upward trend of CO2 emissions just what are we going to have to do to achieve the truly stupid claims of “Net Zero” by 2050 ?
Here is evidence that Biden (if he should win) is doomed to disappoint.
We can’t even all die because that means more emissions – seriously these people are so stupid.
The post Lockdown had zero effect on the upward trend of CO2 emissions appeared first on Ice Age Now.
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November 20, 2020 at 01:17PM


iowaclimatescienceeducation--2020-11-20--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 .

Reposted from Dr. Judith Curry’s Climate Etc.
Posted on November 17, 2020 by curryja
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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iowaclimatescienceeducation--2020-11-20--Oddsmaker refuses to call presidential election – Video

This election reminds oddsmaker of the 2000 election when the media declared Al Gore the winner , only to lose @ @ @ @ @ Court finally stepped in .
.
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Mainstream media outlets may have called the presidential @ @ @ @ @ online betting sites agree .
One America ’ s Richard Pollock interviewed the head oddsmaker @ @ @ @ @ the presidential election , “ It was easily the largest bet event in wagering history @ @ @ @ @ super bowl.
” The betting company is waiting for certification of all states , and @ @ @ @ @ candidate , will wait until electors vote on December 14 .
The betting company spokesman @ @ @ @ @ does not select the president , rather , that is up to the electors .
@ @ @ @ @ call presidential election – Video appeared first on Ice Age Now .

This election reminds oddsmaker of the 2000 election when the media declared Al Gore the winner, only to lose to Bush when the Supreme Court finally stepped in.
.
.
Mainstream media outlets may have called the presidential election, but not all online betting sites agree.
One America’s Richard Pollock interviewed the head oddsmaker for mybookie.ag who says of the presidential election, “It was easily the largest bet event in wagering history. It surpassed the last super bowl.”
The betting company is waiting for certification of all states, and barring a concession by either candidate, will wait until electors vote on December 14.
The betting company spokesman reminds us that the media does not select the president, rather, that is up to the electors.
The post Oddsmaker refuses to call presidential election – Video appeared first on Ice Age Now.
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November 20, 2020 at 12:34PM


iowaclimatescienceeducation--2020-11-20--“Sinking” Maldives Clear Forests, Pave Beaches, To Construct Four New Airports For Future Tourism!

Despite all the money-generating gloomy predictions of sinking islands , we reported in 2013 on how the Maldives was planning to build 30 new luxury hotels for future tourists .
The resort island of Landaa Giraavaru ( Baa atoll ) , photo by : Frédéric Ducarme – CC BY-SA 4.0 .
We recall how in 2012 , the former President of the Maldives Islands , Mohamed , Nasheed said : “ If carbon emissions continue at the rate they are climbing today , my country will be underwater in seven years.
” Well , today the islands have not gone @ @ @ @ @ @ @ never before .
And to help with the job of ferrying the 1.7 million ( 2019 ) tourists to and from the resort islands , the Maldives have recently opened 4 new airports , according to German site Aero here !
No , the airports are not designed to evacuate tourists because sea levels are “ rapidly rising ” , as climate alarmists claim .
The airports are a long-term investment aimed at drawing in even more tourists and they are based on the projection that they ’ ll still be very much @ @ @ @ @ @ @ years from now .
Obviously the catastrophic climate warnings are not being heeded .
Most likely these warnings are not really serious at all .
The Maldives comprise 1200 islands , which were made accessible through a total of ten airports .
However , they are not enough to handle the expected traffic .
So four new domestic airports would open this year alone , announced Transportation and Aviation Minister Aishath Nahula , The new airports will feature 2200 meter runways , thus allowing commercial passenger jets to takeoff and land .
According @ @ @ @ @ @ @ news site , some 52 million dollars was financed by Abu Dhabi to cover the construction costs .
Environmentalists expressed anger at the project because “ forests had to be cut down and beaches concreted over ” .

Despite all the money-generating gloomy predictions of sinking islands, we reported in 2013 on how the Maldives was planning to build 30 new luxury hotels for future tourists.
The resort island of Landaa Giraavaru (Baa atoll), photo by: Frédéric Ducarme – CC BY-SA 4.0.
Underwater in 7 years?
We recall how in 2012, the former President of the Maldives Islands, Mohamed, Nasheed said: “If carbon emissions continue at the rate they are climbing today, my country will be underwater in seven years.”
4 new airports!
Well, today the islands have not gone underwater and remains popular with tourists like never before. And to help with the job of ferrying the 1.7 million (2019) tourists to and from the resort islands, the Maldives have recently opened 4 new airports, according to German site Aero here!
Long-term investment defies alarmist claims
No, the airports are not designed to evacuate tourists because sea levels are “rapidly rising”, as climate alarmists claim. The airports are a long-term investment aimed at drawing in even more tourists and they are based on the projection that they’ll still be very much in unhindered operation in 30 or 50 years from now. Obviously the catastrophic climate warnings are not being heeded. Most likely these warnings are not really serious at all.
The Maldives comprise 1200 islands, which were made accessible through a total of ten airports. However, they are not enough to handle the expected traffic. So four new domestic airports would open this year alone, announced Transportation and Aviation Minister Aishath Nahula,
Deforestation, paved beaches
The new airports will feature 2200 meter runways, thus allowing commercial passenger jets to takeoff and land. According to Aero, citing the Arabian Business news site, some 52 million dollars was financed by Abu Dhabi to cover the construction costs.
Environmentalists expressed anger at the project because “forests had to be cut down and beaches concreted over”.
Hat-tip: Die kalte Sonne
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November 20, 2020 at 11:24AM


iowaclimatescienceeducation--2020-11-20--Democrat asks “How do you deprogram 75 million people” Re-education camp?

A Democrats Regional Director suggested that Trump supporters should be deprogrammed in similar ways to post World War II tactics .
“ No seriously…how *do* you deprogram 75 million people , ” California Democrats regional director and Democratic National Convention member David Atkins tweeted Wednesday .
“ Where do you start ?
Fox ?
Facebook ?
We have to start thinking in terms of post-WWII Germany or Japan .
Or the failures of Reconstruction in the South.
” Alternately , they could try the old fashioned way : Talk to people .
Investigate the fears of election fraud and do @ @ @ @ @ @ @ .
Stop trying to dox , harass and intimidate lawyers so the Trump camp gets a fair hearing .
The Democrats can ’ t lose unless they were traitorous corruptocrats reliant on rampant ballot fraud .
There is always a way to rationalize anything , especially if you project your own flaws onto opponents : “ This is not your standard partisan policy disagreement , ” he continued .
“ This is a conspiracy theory fueled belligerent death cult against reality & basic decency .
The only actual policy debates of note are @ @ @ @ @ @ @ and center [ … ]

A Democrats Regional Director suggested that Trump supporters should be deprogrammed in similar ways to post World War II tactics.
‘How *Do* You Deprogram 75 Million People?’ —
Mary Margaret Olohan, Daily Caller
“No seriously…how *do* you deprogram 75 million people,” California Democrats regional director and Democratic National Convention member David Atkins tweeted Wednesday. “Where do you start? Fox? Facebook? We have to start thinking in terms of post-WWII Germany or Japan. Or the failures of Reconstruction in the South.”
Alternately, they could try the old fashioned way: Talk to people. Investigate the fears of election fraud and do honest actual recounts where signatures are checked. Stop trying to dox, harass and intimidate lawyers so the Trump camp gets a fair hearing.
The Democrats can’t lose unless they were traitorous corruptocrats reliant on rampant ballot fraud.
There is always a way to rationalize anything, especially if you project your own flaws onto opponents:
“This is not your standard partisan policy disagreement,” he continued. “This is a conspiracy theory fueled belligerent death cult against reality & basic decency. The only actual policy debates of note are happening within the dem coalition between left and center […]
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November 20, 2020 at 11:11AM