By Steve Goreham

A version of this article was recently published in The Wall Street Journal.

Alternative energy is exploding─literally. Lithium battery fires are breaking out on highways and in factories, home garages, and storage rooms. The rise in battery fires is amplified by government efforts to force adoption of electric vehicles and grid-scale batteries for electric power.

Lithium batteries have high energy density, making them valuable for phones and portable appliances. But when they catch fire, these batteries burn with high heat and can even explode. That’s why airlines prohibit lithium batteries in checked baggage.

On June 24, a battery factory in South Korea caught fire, triggering explosions and killing 22 workers. The fire broke out in Hwaseong at the Aricell plant, a maker of small lithium batteries for sensors and communications devices. Experts estimate that most workers were killed by toxic gases emitted by the burning batteries.

Scotland has suffered two major fires in battery recycling centers this year. On April 8, a large fire broke out at Fenix Battery Recycling in Kilwinning, North Ayrshire. More than 40 firefighters and personnel from six different agencies responded to the blaze, which burned for several days. The Scottish Fire and Rescue Service urged nearby residents to remain indoors with windows closed as long as two days after the fire started.

On June 23, a large fire broke out at the battery recycling treatment facility of WEEE Solutions in Glasgow. Eyewitnesses reported explosions, noises like gunshots, “steel flying everywhere,” and a huge plume of black smoke. Ten fire trucks were needed, and the blaze lasted four days.

E-bike battery fires are now the leading cause of fires in New York City, with 216 fires last year. E-bike fires have become a serious problem in Australia, Canada, and other nations as well. Low-quality bike batteries self-ignite in first-floor storerooms, destroying the buildings above. Even high-quality batteries are prone to self-ignition after damage or when connected to a faulty charging system.

Lithium batteries have been used for the last 30 years in phones and small appliances. But the introduction of electric cars (EVs) after the year 2000 provided a massive increase in battery size. Lithium batteries for cars and trucks are 10,000 times as large as phone batteries.

On August 19, a Tesla semi-truck crashed into trees along Interstate-80 in California. The crash ignited the truck’s large lithium battery. Firefighters tried to extinguish the fire with thousands of gallons of water but were forced to let the fire burn itself out. The interstate was shut down for 15 hours. The California Advanced Clean Fleets Regulation passed last year requires all new heavy trucks to be zero emissions vehicles, which practically means electric trucks with batteries prone to fire.

Automakers have been wrestling with lithium battery fires for more than a decade. Alfa Romeo, BMW, Ford, General Motors, Hyundai, Mercedes-Benz, Porche, Tesla, and other manufacturers have recalled millions of EVs because of battery fire problems. Batteries can self-ignite while the vehicle is in motion, when connected to a charger, or even when sitting idly in a parking lot. EVs prone to self-ignition have been prohibited from parking at West Coast parking lots.

In August, a Mercedes-Benz EQE that had been manufactured in China burst into flames in a parking garage in Inchon, Korea. The EV had been parked in the garage for several days and was not charging at the time. The resulting inferno destroyed or damaged 140 vehicles.

On August 24, a fire broke out in the outside parking lot of electric truck manufacturer Rivian in Normal, Illinois. More than 50 trucks were destroyed. The same plant also reportedly suffered three other battery fires in the last year and three more fires in 2021-2022.

How are governments responding to the rash of lithium battery fires? They are doubling down, promoting the use of even larger, grid-scale lithium batteries as part of efforts to transition from coal, oil, and natural gas to wind and solar energy.

Grid-scale batteries are viewed as the solution to wind and solar intermittency, meant to store excess electricity when wind and solar output is high, and then release electricity when wind and solar output is low. But the number of grid battery fires is growing, and grid batteries are hundreds of times larger than EV batteries.

On July 26, a grid battery carried on a truck ignited after a crash on Interstate 15 near Baker, California. The battery was 20 feet long and weighed 75,000 pounds. I-15 was shut down for 44 hours as firefighters worked to put out the blaze. Hundreds of motorists were stranded in the desert in 100-degree heat. Ambulances, medical teams, and fuel and water were dispatched to the site to help stranded motorists.

On May 15, the Gateway Energy Storage facility using lithium-ion batteries caught fire near San Diego, California. Firefighters managed to get the blaze under control in 24 hours, but it then re-ignited twice more and burned for a total of 11 days. Evacuation orders to residents were issued and cancelled periodically, depending on the fire status. The Gateway facility was the largest of its kind when constructed in 2020.

Vice President Kamala Harris recently announced $1 billion in grants for electric school buses. If a diesel bus engine catches fire, the driver can usually put it out with a fire extinguisher. But this is not possible with electric buses, which explode when they catch fire. Let’s hope we’re not headed for fire explosions in electric buses full of children.

The world faces an epidemic of lithium battery fires. If government leaders continue to push lithium batteries and the green energy transition, battery fires will soon be coming to a location near you.

Steve Goreham is a speaker on energy, the environment, and public policy and author of the bestselling book Green Breakdown: The Coming Renewable Energy Failure.

From Dr. Roy Spencer’s Global Warming Blog

by Roy W. Spencer, Ph. D.

Our paper (co-authored by John Christy and Danny Braswell) on computing the urban heat island (UHI) effect as a function of population density (PD) is now in the final stages of review after a 3rd round of edits, and I’m hopeful it will be accepted for publication soon. So far, I’ve only used Tavg data (the average of daily maximum and minimum temperatures) in developing and testing the method, and the paper uses only contiguous U.S. summertime data (June, July, August), which is what I will address here.

The method allows us to compute UHI trends using global gridded PD datasets that extend back to the 1800s. These UHI trends can then be compared to GHCN station temperature trends. If I do this for all U.S. GHCN stations having at least 120 years of complete monthly (June, July, or August) data out of 129 potential years during 1895-2023, the following plot shows some interesting results. (I begin with the “raw” data so we can then examine how homogenization changes the results.)

1. The greater a station’s population growth, the greater the observed warming trend. This is pretty convincing evidence that the raw GHCN data has substantial UHI effects impacting the computed trends (probably no surprise here). Note the UHI temperature trend averages 66% of the raw temperature trends.
2. The regression line fit to the data intercepting zero shows that those stations with no population growth have, on average, no warming trend. While this might lead some to conclude there has been no net warming in the U.S. during 1895-2023, it must be remembered these are raw data, with no adjustments for time-of-observation (TOBS) changes or instrumentation type changes which might have biased most or all of the stations toward lower temperature trends.

Since most of the rural stations (many of which have experienced little population growth) are in the western U.S., and there can be differences in actual temperature trends between the eastern and western U.S., let’s look at how things change if we just examine just the eastern U.S. (Ohio to the Florida peninsula, eastward):

This shows the Eastern U.S. has features similar to the U.S. as a whole, with a regression line intercept of zero (again) indicating those stations with no population growth have (on average) no warming trend in the raw GHCN data. But now, amazingly, the average UHI trend is over 95% of the raw station trends (!) This would seemingly suggest essentially all of the reported warming during 1895-2023 over the eastern U.S. has been due to the urbanization effect… if there are no systematic biases in the raw Tavg data that would cause those trends to be biased low. Also, as will be discussed below, this is the the period 1895-2023… the results for more recent decades are somewhat different.

Homogenization of the GHCN Data Produces Some Strange Effects

Next, let’s look at how the adjusted (homogenized) GHCN temperature trends compare to the UHI warming trends. Recall that the Pairwise Homogenization Algorithm (PHA) used by NOAA to create the “adjusted” GHCN dataset (which is the basis for official temperature statistics coming from the government) identifies and adjusts for step-changes in time at individual stations by comparing their temperature time series to the time series from surrounding stations. If we plot the adjusted data trend along with the raw data trends, the following figure shows some curious changes.

Here’s what homogenizations has done to the raw temperature data:

1. Stations with no population growth (that had on average no warming trend) now have a warming trend. I can’t explain this. It might be the “urban blending” artifact of the PHA algorithm discussed by Katata et al. (2023, and references therein) whereby homogenization doesn’t adjust urban stations to “look like” rural stations, but instead tends to smooth out the differences between neighboring stations, causing a “bleeding” of urban effects into the rural stations.
2. Stations with large population growth have had their warming trends reduced. This is the intended effect of homogenization.
3. There still exists a UHI effect in the homogenized trends, but it has been reduced by about 50% compared to the raw trends. This suggests the PHA algorithm is only partially removing spurious warming signals from increasing urbanization.
4. Homogenization has caused the all-station average warming trend to nearly double (+89%), from +0.036 to +0.067 deg. C per decade.I cannot explain this. It might be due to real effects from changes in instrumentation, the time-of-observation (TOBS) adjustment, an unintended artifact of the PHA algorithm, or some combination of all three.

Does This Mean Recent Warming In The U.S. Is Negligible?

Maybe not. While it does suggest problems with warming trends since 1895, if we examine the most recent period of warming (say, since 1961…a date I chose arbitrarily), we find considerably stronger warming trends.

Note that the GHCN trends since 1961 are nearly the same from raw (+0.192 C/decade) as from homogenized (+0.193 C/decade) data. The average UHI warming trend is only about 13% of the raw GHCN trend, and 10% of the homogenized trend, indicating little of the GHCN warming trend can be attributed to increases in population density.

But there still remains an urbanization signal in both the raw and adjusted data, as indicated by the non-zero regression slopes. One possible interpretation of these results is that, if the homogenization algorithm is distorting the station trends, and if we can use the raw GHCN data as a more accurate representation of reality, then the regression intercept of +0.10 deg. C/decade becomes the best estimate of the all-station average warming trend if NONE of the stations had any growth in population. That is little more than 50% of the homogenized data warming trend of +0.192 deg. C/decade.

What Does It All Mean?

First, there is evidence supporting the “urban blending” hypothesis of Katata et al., whereby the homogenization algorithm inadvertently blends urban station characteristics into rural temperature data. This appears to increase the all-station average temperature trend.

Second, homogenization appears to only remove about 50% of the UHI signal. Even after homogenization, GHCN temperature trends tend to be higher for stations with large population growth, lower for stations with little population growth. There is some evidence that truly rural stations would have only about 50% of the warming averaged across all U.S. stations, which is consistent with Anthony Watts’ estimates based upon restricting analysis to only those best-sited stations.

These results suggest there is now additional reason to distrust the official temperature trends reported for U.S. weather stations. They are, on average, too warm. By how much? That remains to be determined. Our method provides the first way (that I know of) to independently estimate the urban warming effect over time, albeit in an average sense (that is, it is accurate for the average across many stations, but its accuracy at individual stations is unknown). As my career winds down, I hope others in the future will extend this type of analysis.

[To see what the total UHI signal is in various calendar months around the world as of 2023, here are the hi-res images: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec. More details of our method, along with links to monthly ArcGIS-format files of global UHI grids since 1800 (Version 0) are contained in my blog post from November, 2023.]

The Heartland Institute

The whole world seems to be nervously awaiting the outcome of the presidential election, which is just 10 days away — perhaps no group more than the global climate cabal. Climate and energy policy is a topic barely mentioned on the campaign trail, and in the debates. But voters are going to decide in November whether America keeps spending billions to be a “leader” in global climate action schemes or pull out of the scam altogether … and maybe for good.

On Episode #132 of The Climate Realism Show our guest is Steve Milloy, publisher of Junkscience.com, senior fellow at the Energy & Environment Legal Institute, and member of the EPA transition team for the first Trump presidency. The Biden-Harris administration has pledged billions of US tax dollars for “global climate action.” Would it be too late to get a refund on that if Trump is elected? Would a second Trump administration get even more aggressive with producing oil and gas? Could he end the taxpayer-subsidized and failing “green energy” industry with the stroke of a pen? And what will the next four years of climate policy look like if Kamala Harris wins?

Heartland’s Anthony Watts, H. Sterling Burnett, Linnea Lueken, and Jim Lakely will also cover some of the Crazy Climate News of the week and take your questions in the chat. Join us LIVE at 1 p.m. ET this Friday and every Friday.

This show will be archived in WUWT Climate TV, a collection of over six hundred videos, featuring new interviews and analysis, and covering dozens of media sources discussing, debating and analyzing the latest in climate science, climate politics, and energy policy, including topics concerning temperature, sea level, polar bears, ocean acidification, extreme weather, censorship, wild fires, and more.