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Models Hide the Shortcomings of Wind and Solar

Models Hide the Shortcomings of Wind and Solar

A major reason for the growth in the use of renewable energy is the fact that if a person looks at them narrowly enough–such as by using a model–wind and solar look to be useful. They don’t burn fossil fuels, so it appears that they might be helpful to the environment.

As I analyze the situation, I have reached the conclusion that energy modeling misses important points. I believe that profitability signals are much more important. In this post, I discuss some associated issues.

Overview of this Post

In Sections [1] through [4], I look at some issues that energy modelers in general, including economists, tend to miss when evaluating both fossil fuel energy and renewables, including wind and solar. The major issue in these sections is the connection between high energy prices and the need to increase government debt. To prevent the continued upward spiral of government debt, any replacement for fossil fuels must also be very inexpensive–perhaps as inexpensive as oil was prior to 1970. In fact, the real limit to fossil fuel extraction and to the building of new wind turbines and solar panels may be government debt that becomes unmanageable in an inflationary period.

In Section [5], I try to explain one reason why published Energy Return on Energy Investment (EROEI) indications give an overly favorable impression of the value of adding a huge amount of renewable energy to the electric grid. The basic issue is that the calculations were not set up for this purpose. These models were set up to evaluate the efficiency of generating a small amount of wind or solar energy, without consideration of broader issues. If these broader issues were included, EROEI indications would be much lower (less favorable).

…click on the above link to read the rest…

Why Economic Models Neglect Energy, and Why That’s a Problem 

The Empire of Uncertainty

The Empire of Uncertainty

Anyone claiming they can project the trajectory of the U.S. and global economy is deluding themselves.

Normalcy depends entirely on everyday life being predictable. To be predictable, life must be stable, which means that there is a high level of certainty in every aspect of life.

The world has entered an era of profound uncertainty, an uncertainty that will only increase as self-reinforcing feedbacks strengthen disrupting dynamics and perverse incentives drive unintended consequences.

It may be more accurate to say that we’ve entered the Empire of Uncertainty, an empire of ambiguous borders and treacherous topology.

A key driver of uncertainty is the Covid-19 virus, which is a slippery little beast. Nine months after its emergence on the world stage, discoveries are still being made about its fundamental nature.

Humans crave certainty, as ambiguity and uncertainty create unbearable anxiety. This desire to return to a predictable “normal” drives us to grasp onto whatever is being touted as a certainty: a cure, a vaccine, a fiscal policy to restore the “Old Normal” economy, etc.

But none of these proposed certainties is actually certain, and those touting these certainties are non-experts who latch onto an “expert” opinion that resolves their need for certainty and predictability.

What we want, of course, is a return to old certainties that we’re familiar with. In the context of pandemic, the model most people are working from is a conventional flu pandemic: a certain number of people get the virus and become ill, a certain number of then die, and those who survive resume their old life.

But there is mounting evidence that Covid-19 doesn’t follow this neat pattern of “the dead are gone and everyone else picks up where they left off.” Counting the dead as the key statistic completely ignores the long-term consequences of Covid-19 that include permanent organ damage.

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Do we Still have a Chance? The Challenge of Emergency Measures for the Survival of Humankind

Do we Still have a Chance? The Challenge of Emergency Measures for the Survival of Humankind


The epidemic of COVID-19 seems to have snuffed out all other subjects of debate. But there remain problems that we could define as a little more worrisome than the COVID pandemic, for instance, the possibility of the extinction of humankind and, perhaps, of the Earth’s biosphere. Here Dr. Ye Tao is giving an effective presentation that highlights that we are in a dire emergency. Perhaps, the pandemic can at least teach us what NOT to do in an emergency.

 Caution: highly catastrophistic post!

The Problem

The clip above shows a recent talk by Dr. Ye Tao, interesting for several reasons. One is how it goes to the core of the climate story with the typical approach of the physicists: based on data and on the laws of physics. This approach bypasses much of the ongoing debate, in large part hijacked by modelers and their opponents.

Unfortunately, the emphasis on models has generated the diffuse misunderstanding that climate change is mainly a question of models and that the future climate can be predicted by models. That resulted in an attempt by skeptics to show that models generated poor predictions in the past. From that, they maintain that if models can’t predict things right, then climate change doesn’t exist or is not a problem. One reason, although not the only one, why the debate remains stuck and leads to no decisions.

Instead, if you go to the basic physics of the issue, you’ll discover that models are certainly wrong as predictive tools simply because they can’t include the non-linear forces that push the system to change its state. But physics tells you that the problem is way worse than models can calculate. That’s what Dr. Tao does.

…click on the above link to read the rest of the article…

Bankers and Investors Finding Fracking Industry’s Underlying Models Prove Overly Optimistic

Bankers and Investors Finding Fracking Industry’s Underlying Models Prove Overly Optimistic

Dozens of drilling rigs are stacked at the Patterson-UTI yard in Midland, Texas after the oil price went negative on April 20, 2020. Midland, Texas. May 27, 2020.

Warren Buffet has a famous quote about investing: “Only when the tide goes out do you discover who’s been swimming naked.” 

When it comes to his $10 billion investment in Occidental Petroleum, Buffett will need to take that one to heart now that other investors have sued Occidental for the merger financed in part by Buffet’s stake, alleging that the amount of debt required for Occidental to merge with Anadarko left the company “precariously exposed” if oil prices went lower. They cited the billions that Buffett invested in the deal as compounding this risk. 

The fracking industry doesn’t care that you’re a world-famous investment sage: It destroys all capital. 

Even in 2019, when Buffett was investing in Occidental, we knew that the fracking industry had been losing hundreds of billions of dollars the past decade. However, with the industry’s staggering debt load, lack of ability to continue borrowing, and drops in oil demand due to the pandemic, the tide is now truly going out to reveal the fracking industry’s failing financial performance. That receding cover has also revealed that the industry has broken one of the most basic tenets of financing for oil and gas production: reserve based lending. 

Reserve based lending involves a firm estimating how much oil it has in the ground, and then assigning those reserves a value based on the most recent price of oil. A bank then lends the company money based on a percentage of this value. For lenders this has historically been a low-risk arrangement, because if a firm defaults on the loan, the bank can simply take possession of its oil field. So it has long been among the most reliable methods for smaller oil and gas companies to get financing. 

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The End of an Age: The Failure of Catastrophism

The End of an Age: The Failure of Catastrophism

Colin Campbell, the founder of the association for the study of peak oil and gas (ASPO) explaining the essence of oil depletion.

The considerations below originate from a post by Michael Krieger where he describes how he is so dismayed by the reaction of the public to the current epidemic that he is closing his blog to rethink the whole matter over. You can read of similar feelings in a post by Rob Slane of the “Blogmire” and of Chris Smaje on “Resilience.” Many others are dismayed at how badly the Covid-19 crisis was managed: a threat that was real but by all measures not so terrible as it was described. Nevertheless, it generated an overreaction, more division than unity, political sectarianism, counterproductive behaviors, and it ultimately led people to accept to be bullied and mistreated by their governments and even to be happy about that.

The “peak oil movement” was started by a group of retired geologists around the end of the 1990s. You could call us “catastrophists,” but catastrophe was not what we were aiming for. We were not revolutionaries, we never thought to storm the Bastille, to give power to the people, or to create a proletarian paradise. We were scientists, we just wanted society to get rid of fossil fuels as soon as possible, although we did think that the final result would have been a more just and peaceful society. 

But how to reach this goal? Of course, we understood that humankind is nothing homogeneous, but we saw no reason why the people in power shouldn’t have listened to our message. After all, it was in their best interest to keep the economy alive. So, the plan was to diffuse the message of resource depletion as a scientific message, not a political one.

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Too Much Faith in Models

Too Much Faith in Models

Too Much Faith in Models

Source: AP Photo/Jae C. Hong 

Between 2 million and 3 million Americans will die!

That was the prediction from “experts” at London’s Imperial College when COVID-19 began. They did also say if there was “social distancing of the whole population,” the death toll could be cut in half, but 1.1 million to 1.46 million Americans would still die by this summer.

Our actual death toll has been about one-tenth of that.

Nevertheless, Imperial College’s model was extremely influential.

Politicians issued stay-at-home orders. They said we must trust the “experts.”

“Follow the science. Listen to the experts. Do what they tell you,” said Joe Biden, laughing at what he considered an obvious truth.

But “there is no such thing as “the science!” replies science reporter Matt Ridley in my new video about “expert” predictions. “Science consists of people disagreeing with each other!”

The lockdowns, he adds, were “quite dangerously wrong.”

Because Imperial’s model predicted that COVID-19 would overwhelm hospitals, patients were moved to nursing homes. The coronavirus then spread in nursing homes.CARTOONS | STEVE BREENVIEW CARTOON

Ordering almost every worker to stay home led to an economic collapse that may have killed people, too.

“The main interventions that helped prevent people dying were stopping large gatherings, people washing their hands and wearing face masks, general social distancing — not forcing people to stay home,” says Ridley.

Even New York Governor Andrew Cuomo now admits: “We all failed at that business. All the early national experts: ‘Here’s my projection model.’ They were all wrong.”

If he and other politicians had just done just a little research, then they would have known that Imperial College researchers repeatedly predict great disasters that don’t happen. Their model predicted 65,000 deaths from swine flu, 136,000 from mad cow disease, and 200 million from bird flu.

The real numbers were in the hundreds.

…click on the above link to read the rest of the article…

Disease modelers gaze into their computers to see the future of Covid-19, and it isn’t good

Disease modelers gaze into their computers to see the future of Covid-19, and it isn’t good

SARS-CoV-2, the virus that causes Covid-19.COURTESY NIAID-RML

At least 550,000 cases. Maybe 4.4 million. Or something in between.

Like weather forecasters, researchers who use mathematical equations to project how bad a disease outbreak might become are used to uncertainties and incomplete data, and Covid-19, the disease caused by the new-to-humans coronavirus that began circulating in Wuhan, China, late last year, has those everywhere you look. That can make the mathematical models of outbreaks, with their wide range of forecasts, seem like guesswork gussied up with differential equations; the eightfold difference in projected Covid-19 cases in Wuhan, calculatedby a team from the U.S. and Canada, isn’t unusual for the early weeks of an outbreak of a never-before-seen illness.

But infectious-disease models have been approximating reality better and better in recent years, thanks to a better understanding of everything from how germs behave to how much time people spend on buses.

“Year by year there have been improvements in forecasting models and the way they are combined to provide forecasts,” said physicist Alessandro Vespignani of Northeastern University, a leading infectious-disease modeler.Related: 

Experts envision two scenarios if the new coronavirus isn’t contained

That’s not to say there’s not room for improvement. The key variables of most models are mostly the same ones epidemiologists have used for decades to predict the course of outbreaks. But with greater computer power now at their disposal, modelers are incorporating more fine-grained data to better reflect the reality of how people live their lives and interact in the modern world — from commuting to work to jetting around the world. These more detailed models can take weeks to spit out their conclusions, but they can better inform public health officials on the likely impact of disease-control measures.

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An Empirical Model For Oil Prices and Some Implications

An Empirical Model For Oil Prices and Some Implications


This work is preliminary. It is a preview of part of a paper I am writing with Aude Illig. There are three main reasons I am making this post. The first is as a public service. There are many people reading this blog who are directly affected by oil prices and who have to make decisions based on future oil prices. Having a model to understand the dynamics of oil prices is of use to them. The second reason is that some people reading this blog model oil extraction. These models either omit price considerations or make assumptions on them. Our model is a large improvement on these assumptions so it should improve their extraction models. The final reason is that I consider the quality of the comments on this blog to be high. I believe that the feedback I get from this post will improve the quality of the final paper. Indeed, Dennis Coyne has already provided valuable feedback after previewing the post. This study has been a humbling experience. Get ready to throw out everything you thought you knew about oil prices.

The model does not by any means explain all oil price variation. What is remarkable is that with only one data set, it explains so much. Many factors may affect the price of oil. This model provides a base to which other variables can be added to find what explains oil prices.

I was asked to write a chapter titled “Strategies for an Economy Facing Energy Constraints” for a book last year which I wrote with my daughter. I do not think the book will be published but the chapter may be of interest to some. I have posted the pdf file on line and will refer to it often [2].

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Olduvai IV: Courage
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Olduvai II: Exodus
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