Energy Forecasts are Contaminated

…Energy Forecasts are Contaminated…

There are few, if any forecasts of energy consumption that haven’t been contaminated by the CO2 climate change frenzy.

This includes forecasts made by the Energy Information Administration (EIA), the International Energy Agency (IEA), Exxon Mobile, BP etc.

Each has incorporated, in some manner, a price on carbon, actually a price on carbon dioxide. It’s almost always necessary to go to the fine print to learn that this is the case.

As a result, anything you read in any newspaper, or magazine, or hear on the news, about energy forecasts is probably contaminated by a price on carbon.

The average person for example, who is deciding on investments, must be very careful to analyze what he or she is reading, because it’s probably distorted by a price on carbon.

The same can be true when reading forecasts about the use of coal or natural gas, or renewables. They are all being distorted by assumptions about the price put on carbon.

For example, the EIA uses a $15-per-ton carbon price when it calculates the levelized cost of electricity (LCOE) generated by coal fired power plants. An EIA table lists the LCOE is $95 per MWh, without an asterisk to guide the reader to the note in the text that a price of carbon has been used when calculating the LCOE.

And, it doesn’t use a price on carbon for other LCOE calculations.

While it’s bad enough that these forecasts are contaminated by using a price on carbon; each organization making a long range energy forecast uses a different price for carbon.

Not only are energy forecasts distorted, they are distorted by varying degrees.

Here is a chart used in Exxon Mobile’s brochure recently sent to all its investors about the growth rate of energy consumption.

From Exxon publication.From left to right: Total, Non-OECD countries, OECD countries. From Exxon publication.From left to right: Total, Non-OECD countries, OECD countries.

Again, it’s necessary to read the fine print to find out that a carbon price was used when making these energy forecasts.

The social cost of carbon, which is the basis for establishing a price for carbon, uses different computer models when making the calculation

There are three basic programs for calculating the social cost of carbon, they are, the DICE, PAGE and FUND models.

Depending on the model chosen the price on carbon can be higher or lower.

These models are affected by three assumptions:

  • Discount rate
  • Time horizon
  • Equilibrium climate sensitivity

These three assumptions affect the outputs of the models.

For additional information on these programs see Fools Rush In.

As a result, nearly all the energy forecasts made by various organizations are distorted, and distorted by varying degrees.

Inevitably, each forecast supports an agenda.

  • A common agenda is in the support of electric vehicles, i.e., BEVs: For example, placing a price on carbon will make gasoline more expensive, making BEVs more competitive thereby increasing BEV sales.
  • Another purpose for establishing a price on carbon is to make wind and solar more competitive with natural gas and coal for generating electricity: For example, placing a price on carbon makes the LCOE of electricity produced by coal and natural gas greater than the LCOE for wind and solar power.
  • Using a price on carbon increases the cost of fertilizers used by farmers, especially fertilizers made from natural gas.

The average person cannot determine the truth about any energy forecast without doing extensive research.

Reading newspapers and magazines or listening to the news on radio or TV merely compounds confusion.

The purpose of putting a price on carbon is to eliminate the use of fossil fuels, which distorts the economy.

It also has the perverse effect of raising the price of virtually everything consumed by Americans, from food to electricity.

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4 Replies to “Energy Forecasts are Contaminated”

    • Thanks. It was a great article. Long but very good. I recommend it to anyone interested in what the IPCC actually said.