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Saturday, June 3, 2017

The Report from the Imperial Tailor

A morning newsitainment show with pretensions recently "interviewed" the Secretary of Energy over the President's decision to "withdraw" from the Paris Deal. Now, technically the US cannot withdraw because the agreement was never ratified with the advice and consent of the Senate, a requirement of that "scrap of paper" called the Constitution. (This document was once referred to as an "obstacle" to doing what was right by a constitutional law professor named Obama, who later won his way to higher office.) That's why it was called a "Deal" and not a "Treaty." The Constitution does not mention how Deals are to be handled, only how Treaties are to be handled. So, believing in Name-Magic, the handlers deemed that by calling it something else, it would become something else and the US could be committed to it by Executive Order alone. The other term for this sort of ruler is dictator, i.e., "he who dictates."

Now the pearl-clutching that commenced after the announcement of withdrawal was a wonder to behold. The NY Post ran the headline: Trump to World: Drop Dead, as if that would really, truly be the result of withdrawing from the agreement. But maybe not. Even if every jot and tittle of the agreement is carried out, even those things agreed to by China and Russia, the result might be a saving of 0.05°C by the year 2100. And that assumes that the models are correct. They haven't been yet; but who knows?
Anyway, on the TV show, the Sec Energy pointed out that the accord gives China, the world's biggest emitter of carbon (assuming CO2 to be a "pollutant") has promised to do exactly nothing while the US has pledged to reduce "greenhouse gas" emissions by 26-28% below 2005 levels by 2025 -- only 8 years away.  China, whose emissions are already about double those of the US, agrees to no reductions whatsoever, and only to try to reach "peak" emissions by 2030. At this point, the interviewer interrupted to point out that China had pledged to reduce emissions by 60% after 2030. He said this with a straight face, too, as if he believed a) China would follow through on that pledge; b) his network would remember by then to hold them to it; or c) it would be technologically feasible to accomplish. Well, TV personalities are seldom taught to think quantitatively.

Thanks to fracking, the US has already reduced CO2 by 7% below the 2005 baseline, but this success (which actually exceeds the more preening Europeans) frightened the activists so much that they started an anti-fracking campaign. The EPA's Clean Power Plan was to shutter cheap coal power plants and cover the landscape with wind and solar farms.  A version of the strategy that has led Germany to residential electricity prices about triple the U.S. average. This sort of thing can result in the collapse of industries dependent on cheap power: Paper down 12 percent.  Cement down 23 percent.  Iron and steel down 38 per cent.  Coal down 86 percent.

No wonder they wanted China and India to be exempt.

Never fear. California announced they would on their own try to abide by the Paris accords. In fact, in September 2016 California's legislature passed, and Governor Brown signed, SB-32 requiring a reduction of "greenhouse" emissions in California to 40% below 1990 levels by 2030.  That's about 12 1/2 years from now.

Now, California has been pushing to replace fossil fuels (boo) with "renewables" (yay) for 27 years, since 1990, covering the hillsides with wind turbines and the valleys with solar collectors.  So how much of the 40% reduction have they accomplished so far?  California's emissions for the latest year given (2014) were actually marginally above the 1990 level:

Does anyone really think California will accomplish this miracle in the next twelve years that it hasn't touched in the last 27? What will be the next strategy? Threats? Will household electricity become, as it is fast becoming in Germany, a luxury?

And will China, after doubling its emissions between now and 2030 really even try, let alone succeed in doing even more in the sixty years following?


20 comments:

  1. There are plenty of good reasons to try to get away from fossil fuels, where possible (I think you've mentioned the one about petrochemicals being much more useful as "feedstocks" for chemicals?), without the need for the apocalyptic alarmism. But, of course, something with a rational motive like that would likely limit its policy-effects to, at most, moderate increases of the regulations on certain industries. You need an emergency to justify a major power-grab.

    ReplyDelete
  2. As someone with some roots in Utah's coal country, I know all too well how California will meet those targets. They'll burn coal here in Utah as well as in Arizona and Nevada to power their "clean" cities.

    I'm not a huge fan of coal power; it's not the disaster it's made out to be, but it's a dangerous job and I'd prefer a cleaner method. I just think it's odd for California to brag about being "green" while outsourcing their "dirty" energy to poorer neighbors.

    ReplyDelete
    Replies
    1. I don't doubt that's what will happen, but that's giving too much credit to California for a capacity for sustained rational thought. It's just obvious that driving a car with an internal combustion engine generates pollutants. Much better to refuel by plugging it into the wall. Obviously, no pollution going on there.

      Delete
  3. Um...why are we using Lomborg's analysis as a reference?

    ReplyDelete
    Replies
    1. Why not? It's in line with IPCC and others. No one claims that Paris, even if everyone does what they say they'll do, will accomplish much in terms of practical effect.

      Delete
    2. Why not? Because Lomborg is a Denier Denier DENIER and a doubleplusungood crimethinker. Why, he probably voted for Trump and sleeps with Palin, even though he’s not American. Everyone who disagrees with any portion of the Party Line is a Denier, and all Deniers are exactly alike.

      Pardon me, I just rolled my eyes so hard they popped out of their sockets and now I have to find them. But this really is the mental process at work when the True Believers consign people to the void.

      Delete
    3. Well, he didn't vote for Trump, because the only non-Americans who get to vote in this country vote for Democrats.

      Delete
  4. I also call bull poopy on California. What little 'green energy' advance they have made so far doesn't take into account that a lot of their electrical power is shipped in from out of state.

    ReplyDelete
  5. I was directed to this post: http://www.realclimate.org/index.php/archives/2013/03/response-by-marcott-et-al/.

    ReplyDelete
  6. Brilliant post -- and bravo (bravi, more correctly) to those who commented! Such civility is rarer than hen's teeth!

    As for the post, Mr TOF has nailed it! No one wants a stinky coal plant in their yard, and renewables have their place (as well as their consequences -- e.g., desert environments destroyed by solar farms), but there's very, very little published on *how* anyone, anywhere will meet their (absurdly outlandish) targets. This is extraordinarily odd, particularly based on the money spent, the hillsides covered, and the like.

    Peace and Good on you all!

    ReplyDelete
  7. http://tofspot.blogspot.com.es/2017/06/the-report-from-imperial-tailor.html

    "And that assumes that the models are correct. They haven't been yet"

    Wrong. https://www.skepticalscience.com/climate-models-intermediate.htm

    "China, whose emissions are already about double those of the US"

    Half-truth. Total emissions are double for China, but per capita emissions are double for the US. And the (economic, social, etc.) effort to reduce emissions depend on the latter, not the former. https://en.wikipedia.org/wiki/List_of_countries_by_carbon_dioxide_emissions

    "the result might be a saving of 0.05°C by the year 2100. And that assumes that the models are correct. They haven't been yet; but who knows?"

    It's quite unpolite to not provide a proper cite, instead hiding it as a barely visible shortened url inside a graph. I will properly cite it:

    Impact of Current Climate Proposals. Bjorn Lomborg. Global policy, vol. 7, issue 1, feb. 2016, p. 109-118

    http://onlinelibrary.wiley.com/doi/10.1111/1758-5899.12295/full

    Let's take a look at the journal's description:

    Edited By: Professor David Held, Dr Eva-Maria Nag and Professor Dani Rodrik
    Impact Factor: 0.861
    ISI Journal Citation Reports © Ranking: 2016: 49/86 (International Relations); 94/165 (Political Science)
    Online ISSN: 1758-5899

    Oh really? Are you answering a climate science question with a reference to a politics journal?

    Now let's take a look at the only author:

    "Lomborg spent a year as an undergraduate at the University of Georgia, earned an M.A. degree in political science at the University of Aarhus in 1991, and a Ph.D. degree in political science at the University of Copenhagen in 1994."

    "Lomborg lectured in statistics in the Department of Political Science at the University of Aarhus as an assistant professor (1994–1996) and associate professor (1997–2005). He left the university in February 2005 and in May of that year became an adjunct professor in Policy-making, Scientific Knowledge and the Role of Experts at the Department of Management, Politics and Philosophy, Copenhagen Business School.[7]"

    https://en.wikipedia.org/wiki/Bj%C3%B8rn_Lomborg

    Wow! How impressive! Surely a M.A. in political science and teaching statistics to politics students gives you a huge expertise in climate science!

    ReplyDelete
    Replies
    1. I understand statisticians in general haven't been too impressed with the models. Or with models in general. They are always wrong, as in every single time; and one must assess how badly wrong and in what ways before leaning on them too strongly. This applies to models of any complex, interactive system: economic, demographic, et al. Especially when one has a "suite" of models, each a bit different, one can often find one that gives an ex post facto answer each time -- but it might not be the same model each time.

      One does wish someone would teach statistical inference to policy-makers. "Climate scientists" are just another groups of laymen when it comes to stats.
      +++
      As for China, a little close reading here is instructive:
      https://www.nytimes.com/2017/07/01/climate/china-energy-companies-coal-plants-climate-change.html

      And regarding the "boom" in "renewables" mentioned in the Times article, comments on the Energiewende in Germany, translated to English, are here: http://energypost.eu/end-energiewende/ and on problems in China, here: http://earthjournalism.net/stories/the-dark-side-of-renewable-energy

      "Much of the social history of the Western world over the past three decades has involved replacing what worked with what sounded good."
      --Thomas Sowell

      Delete
    2. "I understand statisticians in general haven't been too impressed with the models."

      What is your basis for that claim?

      "They are always wrong, as in every single time;"

      No, I already disproved that. Please read the data I provided instead of repeating your mantra.

      "and one must assess how badly wrong and in what ways before leaning on them too strongly."

      That is exactly what is quantified in the link I provided and that you seem to not have read.

      "Especially when one has a "suite" of models, each a bit different, one can often find one that gives an ex post facto answer each time -- but it might not be the same model each time."

      The models aren't generated that way. Again, please read the link.

      "One does wish someone would teach statistical inference to policy-makers. "Climate scientists" are just another groups of laymen when
      it comes to stats."

      What is your basis for that claim?

      "As for China, a little close reading here is instructive:
      https://www.nytimes.com/2017/07/01/climate/china-energy-companies-coal-plants-climate-change.html"

      That has nothing to do with what we are discussing, namely, your claim that China is producing twice CO2 than the US and thus must do much more effort to reduce it, and my reply that it's exactly the opposite, the US produce twice CO2 per capita, and thus the effort, such as taxes, that matters on a per capita basis and not globally (for example, voting decisions on the part of the citizens depend on the amount of taxes for each one of them, not globally), should be higher for the US, not lower.

      "And regarding the "boom" in "renewables" mentioned in the Times article, comments on the Energiewende in Germany, translated to English, are here: http://energypost.eu/end-energiewende/ and on problems in China, here: http://earthjournalism.net/stories/the-dark-side-of-renewable-energy"

      I didn't comment on the Germany case because I wanted to focus on the climate change and didn't want to start a renewables debate. My analysis of the Germany case is different than yours, but that doesn't really matter for the Paris discussion. The rise in CO2 production in Germany is due to the shutdown of the nuclear plants and the burning of more coal to make for the energy deficit. It hasn't much to do with renewables. But that's only a sideshow, not really important for the debate about the US withdrawal from the Paris deal. I would really prefer more nuclear than more renewables, but we are discussing is fossil fuels burning, not the other energy sources. I prefer to center the discussion on that instead of taking a distracting sidewalk.

      Delete
    3. What is your basis for that claim?

      It is well known that no model can contain as many variables as the real world. It doesn't matter what it is a model of. I have only seen good models on systems of organized simplicity, such as hydraulic bearings, where there are only a few variables. But when you have systems with many variable and the system depends not only on the variables but on how each variable is linked with every other variable, both the classical approach (mathematics) and the modern approach (statistics) break down and we have to resort to modeling, and that conjures up the dread uncertainty monster.

      For one thing, only a few of the uncertainties are quantifiable. The most critical ones are judgment calls (Scenario uncertainty).

      There is an extended discussion here:
      http://tofspot.blogspot.com/2014/02/americas-next-top-model-part-i.html

      Delete
    4. "It is well known that no model can contain as many variables as the real world. It doesn't matter what it is a model of. I have only seen good models on systems of organized simplicity, such as hydraulic bearings, where there are only a few variables. But when you have systems with many variable and the system depends not only on the variables but on how each variable is linked with every other variable, both the classical approach (mathematics) and the modern approach (statistics) break down and we have to resort to modeling, and that conjures up the dread uncertainty monster."

      As you said in the Galileo articles, that's only mystic woo woo. It's so vague that it can be applid to all science, thus rendering all science (and indedd all statistics) wrong.

      And how can you predict a 0.05ºC effect of the Paris deal when all statistics are unreliable?

      BTW, statistics is a part of mathematics, so "both the classical approach (mathematics) and the modern approach (statistics)" is nonsense.

      "For one thing, only a few of the uncertainties are quantifiable. The most critical ones are judgment calls (Scenario uncertainty). There is an extended discussion here: http://tofspot.blogspot.com/2014/02/americas-next-top-model-part-i.html"

      This again is so vague that it can be applied to everything or nothing, and every possibility in between. What are specifically those unquantifiable uncertainties in climate science? And why are they important, since the models make good predictions anyway?

      Delete
    5. Mathematics and statistics are not the same thing. Statistics makes some use of mathematics, specifically a bit of practical arithmetic, but you do not deduce actual relationships as you would in analytics or geometry. There is quite a bit of philosophy to it, though most do it poorly; so it might be construed a branch of logic.

      Part of the problem, as my cosmologist friend likes to say, is that any arbitrary set of data can be fitted with a model if you have enough parameters and you can play with the coefficients. Usually, we would say seven variables is enough to fit any data, as long as you can play with the coefficient. The well-known mathematician John von Neumann once wrote:
      "Give me four parameters, and I can fit an elephant. Give me five, and I can wiggle its trunk."

      There is nothing vague about any of this.

      And how can you predict a 0.05ºC effect of the Paris deal when all statistics are unreliable?

      Easy-peasy. It's what the proponents predict, and I take them at their word.

      But I did not say all statistics are unreliable. I made my living for forty years as an applied and consulting statistician, so I know a little bit about the subject. I know that sometimes people collect the wrong data, or they apply the wrong heuristic, or use models that are inappropriate or inapt or unskilled. When a model of illegal immigration predicts a negative rate of illegal immigration from Ireland to the United States, that model is likely inapt. Are Irish illegally leaving the US? No, it means someone is likely using in a thoughtless manner a symmetric normal model to a situation that is inherently bounded and skewed, and is taking tail values way too seriously. Cookbook statistics of the sort that all too many people get in a one- or two-semester survey course for non-specialists.

      The series I linked to has further links to articles in modeling and uncertainty that would pay closer attention. I'll draw your attention to two in particular:
      Dealing with the classification of kinds of uncertainties in general:
      Walker, W.E., et al. "Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support." Integrated Assessment (2003), Vol. 4, No. 1, pp. 5–17
      http://journals.sfu.ca/int_assess/index.php/iaj/article/viewFile/122/79
      and as applied to climate modeling in particular:
      Curry, Judith and Peter Webster. "Climate Science and the Uncertainty Monster" Bull. Am. Met. Soc., V. 92, Issue 12 (December 2011)
      http://journals.ametsoc.org/doi/pdf/10.1175/2011BAMS3139.1

      Delete
    6. If the model was (or rather could be, which it can't) as complex as the real world, it would be more efficient to just go look at the real world, which doesn't require you to construct a superhumanly complex model before you can investigate it.

      Delete
    7. "Mathematics and statistics are not the same thing."

      Don't try to teach mathematics to a mathematician. http://www.ams.org/msc/msc2010.html?t=62-XX&s=&btn=Search&ls=s

      "Part of the problem, as my cosmologist friend likes to say, is that any arbitrary set of data can be fitted with a model if you have enough parameters and you can play with the coefficients."

      AGAIN, THAT IS NOT HOW THE MODELS ARE GENERATED. READ THE @#$% LINK.

      "And how can you predict a 0.05ºC effect of the Paris deal when all statistics are unreliable? Easy-peasy. It's what the proponents predict, and I take them at their word."

      But you don't take climate change predictions at their word. HOW CHILDISH CHERRY PICKING.

      "But I did not say all statistics are unreliable. I made my living for forty years as an applied and consulting statistician, so I know a little bit about the subject. I know that sometimes people collect the wrong data, or they apply the wrong heuristic, or use models that are inappropriate or inapt or unskilled. When a model of illegal immigration predicts a negative rate of illegal immigration from Ireland to the United States, that model is likely inapt. Are Irish illegally leaving the US? No, it means someone is likely using in a thoughtless manner a symmetric normal model to a situation that is inherently bounded and skewed, and is taking tail values way too seriously. Cookbook statistics of the sort that all too many people get in a one- or two-semester survey course for non-specialists."

      More vague woo woo.

      "Curry, Judith and Peter Webster. "Climate Science and the Uncertainty Monster" Bull. Am. Met. Soc., V. 92, Issue 12 (December 2011)
      http://journals.ametsoc.org/doi/pdf/10.1175/2011BAMS3139.1"

      Can you be more specific? The paper seems to be about how to explain model uncertainty to non scientists. I don't see there any specific estimation or example of how unreliable climatic models are.

      Delete
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