Tag Archives: motor gasoline

About That Tesla Roadster Flying Through Space– What Kind of Gas Mileage Is It Getting?

Elon Musk and his SpaceX team made huge news last week when they successfully completed the maiden launch of the Falcon Heavy on the afternoon of February 6, 2018. This launch was such a monumental accomplishment because the private company venture (the heaviest commercial rocket ever launched) could one day be used to take astronauts to the Moon and Mars, and it demonstrated the ability to do so with the ability to guide the rocket boosters back to Earth for reuse.

While all of this news was one of the most amazing accomplishments by a private sector company in terms of scale and implications for humanity, one of the most gripping aspects of the project ended up being the fact that the test payload Musk chose to attach to the rocket was his personal Tesla Roadster, painted cherry red to represent the launch’s step towards getting to Mars. The reason behind launching this $100,000 car into space (never to return) was purely to capture people’s attention and imagination, a goal that was undeniably achieved as Musk was able to give the world this image that mindbogglingly is real and not using any sort of Photoshop and was compelling enough to get everyone to take notice of this amazing accomplishment.

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Given that the mission statement of Tesla is “to accelerate the advent of sustainable transport by bringing compelling mass market electric cars to market as soon as possible,” I found it cheekily ironic that fossil fuel– rocket fuel, no less– had to be used to get this Tesla mobile. This not entirely serious thinking led me to the tongue-in-cheek line of questioning– how did the fuel economy of this space-bound Tesla compare with the fuel economies of cars that are restricted to a terrestrial existence? What about the relative carbon dioxide (CO2) emissions?

Let’s bust out that handy back-of-the-envelope to scratch out some (very) approximate estimates!



The Tesla Roadster

The car that was sent into an elliptical orbit around the Sun was Elon Musk’s personal 2008 Tesla Roadster, ‘piloted’ by a mannequin in a SpaceX flight suit named Starman. This model of Tesla electric cars weighs in at 2,723 pounds, went for a base price of $98,000, sold 2,400 units before production was stopped, and was notable as the first highway legal serial production all-electric car using lithium-ion batteries and the first all-electric car to travel more than 200 miles per charge.

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Fuel Economy

The official fuel economy rating of the Tesla Roadster from the Environmental Protection Agency (EPA) is 119 miles per gallon equivalent (MPGe), being able to travel 245 miles on an eight-hour charge (the MPGe value compares the amount of electricity needed to move an electric car with the amount of gasoline needed to move a gasoline-powered car using the energy equivalence of one gallon of gas matching 33.7 kilowatt-hours of electricity).

As a comparison for the fuel economy of a Tesla Roadster:

The following table summarizes this range of fuel economies of the Earth-restricted vehicles:

Carbon dioxide emissions

While the use of electricity when driving a Tesla (or any electric car) is indeed carbon neutral in that no CO2 is being emitted from a tailpipe, it is not entirely true to rate the CO2 emissions per mile driven as zero. The simple reason behind that is that the generation of electricity that ends up in the vehicles come tied to the CO2 emissions at the electric power generation plants. While the portion of the U.S. power sector that is driven by carbon neutral sources like wind, solar, and nuclear is growing, fossil fuels like coal, natural gas, and petroleum still accounted for over 60% of U.S. electricity generation in 2017. As such, whenever a Tesla gets plugged into the grid it is likely receiving electricity that comes from CO2-emitting sources (not to mention the inefficiencies that come from the transmission & distribution of the electricity, the charging losses of the batteries, and the ‘vampire losses’ of charge when the car is not plugged in and not in use). Because of this, the CO2 footprint of driving a Tesla, or any electric vehicle, is intrinsically tied with the energy makeup of the particular electricity supplier.

The Nissan Leaf, another all-electric vehicle, accounts for about 200 grams of CO2 per mile (g CO2/mile) on average across the United States, while California (with one of the highest proportions of clean electricity in the country) comes in at 100 g CO2/mile and Minnesota (a state that is very dependent on fossil fuel) comes in at 300 g CO2/mile. For the sake of this exercise we’ll use these readily available Nissan Leaf numbers as the benchmark CO2 emissions per mile of an electric car, even though the Tesla Roadster is likely slightly different due to different charging rates and battery technologies.

As a comparison for this rate of CO2 emissions of an electric car:

The following table summarizes this range of CO2 emissions for non-rocket fueled vehicles:

Launching Starman’s Roadster

At pre-launch, Musk noted that ultimately the payload (i.e., Starman’s Tesla Roadster) would get 400,000 million kilometers (almost 250,000 million miles) away from Earth, traveling at 11 kilometers per second (almost 7 miles per second), and would orbit for hundreds of millions, or even billions of years (see below graphic of the initial orbit that Musk tweeted out after the launch). To accomplish this, the Falcon Heavy generated 5 million pounds of thrust at liftoff (making it the most powerful liftoff since Nasa’s Saturn V). Generating this amount of power is no small feat.

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To estimate exactly how much fuel was used (and how much that would be in the equivalent gallons of motor gasoline) requires some estimates, but we have enough information to get at least in the ballpark.

When fueling its rockets, SpaceX uses a highly refined type of kerosene (also known as RP-1) because of its high energy per gallon, in addition to liquid oxygen (LOX) needed for combustion (the amount of LOX required is about double the amount of RP-1). The first stage of a Falcon 9 rocket (another type of rocket used by SpaceX) uses 119,100 kilograms (kg) of RP-1 and 276,600 kg of LOX, while the second stage uses 27,850 kg of RP-1 and 64,820 kg of LOX (see graphic below for what that multi-stage launch sequence looks like). A simplified explanation of the Falcon Heavy is really that it’s composed of three Falcon 9 rockets merged into the first stage and the second stage consisting of disconnecting from the three Falcon 9 rockets and a single stage 2 rocket (along with the payload) continuing on. Making rough estimates, this means the Falcon Heavy required three times the fuel of the first stage and one times to fuel of the second stage of the Falcon 9, or a total of 385,150 kg of RP-1 and 894,620 kg of LOX (this is admittedly a simplification of the fueling process, but I’m also admittedly not a rocket scientist. In attempting to keep these estimates as rigorous as possible, see the citations and links contained here and let me know in the comments if I got something wrong– particularly if you are a rocket scientist!).

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Musk, when discussing the potential dangers of the Falcon Heavy launch, noted that the fuel on board was 4 million pounds of TNT equivalent. In fact, the energy contained within looks like it could be over double that (whether this is a sign of Musk simplifying for the sake of giving the press a quote, speaking approximately without reference to the exact calculations beforehand, or missteps in my calculations, I’ll let you decide). While the total weight of the LOX is over double the weight of the RP-1, the LOX is simply there to allow for combustion and maximize the efficiency with which the rocket is burned. As such, the energy density of RP-1 is what we care about. Using an energy density of 43.2 Megajoules (MJ) per kg, we find that the energy contained in the Falcon Heavy’s fuel tanks was over 16.6 million MJ, which is equal to about 126,000 gallons of gasoline equivalent (or over 8.7 million pounds of TNT— so while our estimate is over double Musk’s offhand remark, we can take solace that we’re in the same order of magnitude!).

In terms of the CO2 released by burning this much fuel, we can use the “well to wake” emissions number of RP-1 of 85 grams of CO2-equivalent per MJ to estimate that the total CO2 emissions were over 1.4 million kg (or 1,400 metric tons) of CO2.

Comparing Starman’s Tesla with Earth vehicles

First things first– that’s definitely the most fossil fuel used and CO2 emitted ever in getting a car from point A to point B. But that doesn’t necessarily mean that Starman’s Tesla is the least efficient or most harmful to the environment. That’s because once the fuel is burned and Starman’s Tesla  was set into orbit in perpetual motion, logging millions of miles on the odometer while traveling 25,000 miles per hour, the rest of its journey was all without additional energy input.  Even the camera and communication equipment on board were attached to a battery with 12 hours of life with no other sources of energy, so after the 12 hours the equipment went dark and there was no more energy input to Starman’s Tesla– just momentum and gravity working their magic. So despite this initial abundance of fossil fuel and related CO2 emissions to set the Tesla in motion, on a per mile basis (which is how fuel economy and emissions are calculated) it will inevitably becomes the most efficient and clean car of all time!

But how long will it take for this to be true?

Fuel economy

In terms of fuel economy, the MPGe of Starman’s Tesla improves linearly with every mile traversed through space. After 1,200 miles, the Falcon Heavy and its payload of Starman and his Tesla left Low Earth Orbit, but the massive amount of fuel means it barely even registers as a blip on this graph at about 0.0095 MPGe.

After two days when Starman’s Tesla had traveled 450,000 miles, the fuel economy had risen to a little less than half that of the freight truck. You can also note in the graph that at the point of the 36,000 mile warranty of the Tesla Roadster the fuel economy aws still less than 0.3 MPGe– you’d certainly have a lot of angry Tesla owners if that’s all they were able to recoup on gasoline costs by the end of their warranty!

Lastly, after teasing out how far Starman’s Tesla would have to travel to become the most fuel efficient car (that is or ever was) on Earth, we find that it would take:

  • About 900,000 miles to beat the fuel economy of freight trucks;
  • About 2.9 million miles to beat the average of the U.S. light-duty stock fuel economy;
  • About 3.7 million miles to meet the 2018 light truck standards;
  • About 5.0 million miles to meet the 2018 car standards;
  • About 7.3 million miles to meet the most efficient gas powered car available;
  • About 15 million miles to meet the efficiency of an Earthly Tesla Roadster; and
  • About 17.2 million miles traveled to equal the 136 MPGe of the Hyundai Ioniq Electric, the most efficient car available.

As previously mentioned, the equipment on board Starman’s Tesla was attached to a battery that only had 12 hours of life, after which there was no functioning equipment on the Roadster. As such, there is no inherent tracking or communicating with Starman’s vehicle as it continues on its journey, making its exact tracking through space difficult.

But fear not– a great tool was launched after the Roadster was launched into orbit called ‘Where is Roadster?‘ Using the knowledge available regarding the position, orbit, and speed of the Tesla, this tool shows approximately where in its orbit the Roadster is and how far it has traveled in aggregate. This tool does not allow going back to see when exactly certain distances were passed, but from watching the site myself I can attest that Starman’s Roadster passed 17.2 million miles on the afternoon of February 14, 2018– meaning it only took eight days for this Tesla Roadster to become the most efficient car ever! Any distance it continues to travel will only increase the overall fuel economy (if you want to calculate this for yourself at any given moment, divide the current miles from ‘Where is Roadster?‘ by 126,279 gallons of gasoline equivalent).

CO2 emissions

In terms of CO2 emissions per mile, Starman’s Tesla improves according to a power equation– meaning in this case that there are drastic improvements in CO2 emissions per mile initially that flatten out over time. By the time Starman’s Tesla leaves Low Earth Orbit, not nearly enough miles have been traveled to offset the massive amount of CO2 emissions from the rocket launch, with Starman’s Tesla coming in at a mindblowing 1.2 million g CO2/mile at 1,200 miles– the equivalent of 182 freight trucks moving a mile at a time.

After two days and 450,000 miles traveled, the CO2 emissions per mile had dropped to 3,143 g CO2/mile, blowing way past the average freight truck emissions after about 219,000 miles. After the 36,000 mile warranty, the emissions still averaged over 39,000 g CO2/mile– another tidbit that would enrage an environmentally conscious electric car owner if it happened to them.

Again projecting out how far Starman’s Tesla would have to travel to become the cleanest car in existence, we find that it would take:

  • About 3.4 million miles to be cleaner than the average passenger vehicle;
  • About 4.7 million miles to be cleaner than an electric vehicle charged in fossil-fuel-dependent Minnesota;
  • About 5.0 million miles to meet the emissions standards for light trucks in 2018;
  • About 7.0 million miles to meet the emissions standards for cars in 2018;
  • About 7.1 million miles to be cleaner than the average electric vehicle in the United States; and
  • About 14.1 million miles to be cleaner than an electric vehicle charged in renewable-energy-heavy California.

Again by watching the ‘Where is the Roadster?‘ tool, I found that Starman’s Tesla also became the cleanest car ever (on a g CO2/mile basis) on February 14, only 8 days after launch. As with the fuel economy, this figure will only get better and better as Starman racks up the limitless miles circling the Sun for millions or billions of years (to calculate an updated emissions per mile, divide 1,414,270,800 grams of CO2 emissions by the updated miles traveled from ‘Where is Roadster?‘).

Conclusion

So there you have it, despite the massive amounts of fuel and resultant CO2 emissions required to launch the Tesla Roadster in space, it only took eight days of traveling faster than any car ever before to become the most fuel efficient and least CO2-emitting (on a per mile basis) ever made. But that fact was inevitable given that it’s in orbit around the Sun and will likely be for the rest of humanity’s existence– so what really is the point of crunching the numbers like this? Hopefully you’ll come away from this article with a handful of takeaways and topics/issues on which to do some more reading and learning:

  1. The impressiveness of this feat accomplished by Musk adn the whole team at SpaceX cannot be overstated. The Tesla Roadster weighs just 2,723 pounds, but this launch was testing a rocket system whose ultimate payload capacity extends to almost 141,000 pounds sent to Low Earth Orbit, 37,000 pounds sent to Mars, and 7,700 pounds sent to Pluto– all at decreased cost compared with historical launches that really opens up doors. That is the most important takeaway from the Falcon Heavy launch, a huge step towards what Musk hopes to be the next great space race.
  2. Beyond that, running through these tongue-in-cheek calculations should hopefully serve to pique your interest and give some information on the relative fuel efficiency electric cars are able to achieve, but also some of the current shortcomings in terms of using them as a way to reduce CO2 emissions. A lot of interesting pieces have been written on the true environmental impact of electric cars, as well as how that might evolve in the future. I’ll recommend a couple (from Green Car Reports, Wired, The Union of Concerned Scientists, and Scientific American, just to name a few), but it’s an important topic with much more out there to be read and debated.
  3. In addition, given the relative fuel economies and CO2 emissions of various vehicles (as wella s regulations covering these measurements), let that be a reason to look more into the efficiencies and emissions of your vehicles. In particular, you’ll note the average passenger vehicle has twice the emissions per mile as a new Model Year 2018 car that complies with EPA regulations, while the new cars will also get up to 74% more MPG compared with the average for the U.S. fleet of light-duty vehicles. Keep these types of figures in mind the next time you’re in the market for a vehicle, and consider how much fuel and emissions savings are being protected and increased by these existing regulations (both fuel economy and car emissions regulations are being considered for rollbacks by the Trump administration) as automotive regulations and policies continue to make the news.

Sources and additional reading

Can Driving a Tesla Offset the Impact Of A SpaceX Launch? Clean Technica

Electric Cars Are Not Necessarily Clean: Scientific American

Elon Musk says SpaceX has ‘done everything you can think of’ to prepare Falcon Heavy for launch today: Business Insider

Falcon 9 v1.1 & F9R Launch Vehicle Overview: Spaceflight 101

Falcon Heavy: SpaceX

Falcon Heavy: SpaceX stages an amazing launch — but what about the environmental impact? The Conversation

How Much Fuel Does It Take To Get To The Moon? Huffington Post

Musk’s Falcon Heavy Packs a Huge Payload: Forbes

SpaceX’s Falcon Heavy Rocket: By the Numbers: Space.com

SpaceX’s Falcon Heavy rocket nails its maiden test flight: NBC News

SpaceX launch: Why is there a Starman spacesuit in the Tesla Roadster? Express

The Falcon Heavy Packs A Huge Payload: Statista

Where is Elon Musk’s Tesla Roadster with Starman?

About the author: Matt Chester is an energy analyst in Washington DC, studied engineering and science & technology policy at the University of Virginia, and operates this blog and website to share news, insights, and advice in the fields of energy policy, energy technology, and more. For more quick hits in addition to posts on this blog, follow him on Twitter @ChesterEnergy.  

Correlating Energy Data Sets: The Right Way and the Wrong Way

Determining the correlation between multiple sets of data—a measure of whether data sets fluctuate with one another—is one of the most useful tools of statistical analysis. Correlating data sets can be the endgame itself, or it can be what cracks open the door on a full statistical investigation to determine the how and why of the correlation. No matter the reason, knowing what data correlation is, how to correlate data sets, what a confirmed correlation might mean are all necessary ideas to have in your tool belt.

 What is data correlation?

Generally speaking, correlation examines and quantifies the relationship between two variables, or sets of data. In statistics, data correlation is typically measured by the Pearson correlation coefficient (PCC), which ranges from -1 to +1. Whether the PCC is positive or negative indicates whether the relationship is a positive correlation (i.e., as one variable increases, the other variable generally increases as well) or a negative correlation (i.e., as one variable increases, the other variable generally decreases). The absolute value of the PCC indicates the strength of the relationship, where the closer it is to 1 the more strongly related the two variables are, while a PCC of 0 indicates no relationship whatsoever.

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How do you calculate data correlations?

The PCC of two variables can be easily calculated with a built-in function of Microsoft Excel (if you want to know how to calculate the PCC according to hand—first, kudos to you, scholar; second, see either this resource or this one for more detailed instructions on the calculation itself).



To start, list out your two variables in two columns of an excel sheet. For this example, we’ll pull the West Texas Intermediate (WTI) oil prices and the U.S. regular grade gasoline prices during a four-month period in the Fall of 2016 from the website for the Energy Information Administration (EIA) (for guidance on pulling data from EIA, see this previous blog post).

Link to Gasoline Price Data; Link to WTI Spot Price data

Note that the weekly prices here reflect the average price calculated for the week ending in the date listed. Also the Cushing, OK WTI spot price reflects the price of raw crude oil in Cushing, OK, a major trading hub for crude oil that is used as the price settlement point for WTI oil on the New York Mercantile Exchange (NYMEX).

Now to find the PCC, use the excel function CORREL. This function takes the form of the following:

=CORREL(ARRAY1, ARRAY2)

where ARRAY1 and ARRAY2 are the two data sets you are seeking to correlate.

Using this excel function, we get a PCC of 0.545. Remember that a positive PCC indicates that the two arrays tend to increase with each other,and that the closer the PCC is to 1 then the more closely related they are. This result of 0.545 would seem to indicate a fairly decent correlation between the price of WTI oil and the price of regular gasoline over these several months. Not only does a positive correlation between the prices of these two products make intuitive sense (because the price of crude oil is the largest factor in the retail price of gasoline), but we can confirm with a data visualization as well.

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Note that the first graph is showing the change in the two prices over time, with the date on the x-axis and the prices on the two y-axes. Visualizing the data this way, we can see that the prices are climbing and falling somewhat step-in-step. The second graph shows the relationship in a different way, with the price of oil on a given week on the x-axis and the price of gas on the same week on the y-axis. Visualizing it this way, and including a trendline for that data, you again see that as one variable rises, generally so too does the other variable. However, clearly it isn’t a direct one-to-one relationship—hence why the calculated PCC is 0.5455 and not closer to 1.

As a second example, let’s now find the correlation between gas prices during this same time period with the quantity of finished motor gasoline supplied to the market—as basic economic principles give us a sense that there should be a relationship between quantity sold and price. Below we again pull the relevant data sets from EIA and use the CORREL function

Link to Gasoline Price Data; Link to Gasoline Supplied Data

Note that the weekly prices here reflect the average price calculated for the week ending in the date listed.

For these two variables, we get a PCC of -0.173. Now that the PCC is negative, this implies a negative correlation—i.e., as the gasoline price increases, the amount of gasoline sold decreases. This conclusion again makes a degree of intuitive economic sense, as when the price of something increases ,the expected consumer response would be to purchase less of it. However, with PCC so far from -1 we don’t necessarily see this as a very strong correlation. We can look at the data visualization for these data sets as well:

Looking at the first graph, we can again see visually what the PCC was indicating in general. As the gasoline price reaches local peaks, the amount of product supplied tends to reach local valleys, and vice versa. The second graph indicates that with a negative trend line, though again it’s overall just a slight, general trend and not very rigid—as indicated by the PCC being closer to 0 than it is to -1.

There’s a data correlation—what now?

So the key to answering what happens next is to know why you were looking for a data correlation in the first place. Let’s say I was examining the correlation between gas prices and oil prices because I wanted to identify the factors that best predicted gas prices going forward. For each of the two variables tested with gas prices over the four month period in 2016, the expected generally correlation was confirmed with the data, though the PCC wasn’t strong enough to definitively declare victory at having found a correlation. What would I do in this scenario?

More data

The first course of action would be to gather more information. I’ve only looked at 16 weeks of data, but it has been enough to give me a correlative hypothesis (increased gas prices correlate with increased WTI oil price and decreased gasoline supplied). You might take this hypothesis and expand your analysis to include more historical data and see if the same correlation holds or if it moves in a different direction. Further, you might reason out that there are more subtle interactions of between the data that should be explored. Perhaps looking at the price of gas and the price of oil during the same week is too simplistic, and rather you should be looking at the price of oil compared with the price of gas the following week, two weeks, or even month to account for the time needed to refine crude oil into gasoline? Or if your goal is to really find the most influential correlating factors, then it would go without saying to test many more variables to figure out the ones with the closest correlation. For gas prices, you might consider also looking at general economic data, import/export data, production and refining production data, drilling data, and much more.

Test further

Once you have exhausted the data you are looking at and determine what correlates well based on that data, it is important to make sure to test it as well to make sure any conclusions you make are based on sound correlation. As with any type of hypothesis, a correlation is essentially meaningless unless it gets tested.

A couple methods for testing the correlation are available. First, as mentioned previously, expand your data set and put the correlation to the test on a wider set of data—either by looking further in the past to see if the correlation persists, or by using the correlation as a predictive model for future data and seeing if the relationship holds when the new data becomes available.

If you have not already done so, creating a visual representation of data, as done for the two sets of variables above, is a great way to gain understanding of your correlation (and has numerous other advantages for taking in data). As you conduct your data detective work, be sure to always check yourself by creating graphs and other visualizations to confirm suspicions and/or catch some new insights. Whenever possible, as well, work with the data yourself instead of referencing the visualizations of others. In the worlds of data and statistics, it is notoriously easy to ‘make’ the data appear to say whatever you want to say to a lesser informed audience (stay tuned for a future post on this topic).

Another important ‘test’ of sorts is one we already implicitly did when selecting our examples in the previous section—reason out why a correlation might exist. For the prices of crude oil and finished motor gasoline, the reason behind a correlation is somewhat self-evident. But if you’re looking at variables that are less obviously linked, this is where you can do research or consult with experts to determine if there exists any logical rationale to explain the correlation. Otherwise you could be grasping at straws, despite the apparent correlation—discussed in more detail next.

Recognize limitations

Being aware of the limitations of correlating data is the best defense against falling victim to the shortcomings of the technique. This idea is best illustrated in another example.

Let’s say I was continuing the above effort to find factors that I could use in the future to predict gas prices. As discussed, the spot price of WTI oil, with a PCC of 0.545, is determined to be great candidate for correlation with a reasonable PCC, data visualizations that illuminate the relationship, and a very logical and rational reason for the two variables to be correlated. So if oil demand at a PCC of 0.545 is counted—then I should be excited when I stumble upon a mystery variable with a PCC of 0.592!

Link to Gasoline Price Data; Source of Mystery Variable (Spoiler Alert!)

Note—Mystery Variable had no available data for the week of November 7, 2016

With a PCC of 0.592, I could feel great that I have another factor to add to my model. Looking at the data visualizations below does nothing to dispel that notion, either.

The issue is, however, not realizing that if you wade through large enough sets of data you are virtually guaranteed to find coincidental correlations. In this example, I was able to find just such a coincidental correlative data set by looking through the only other vast set of data I spend as much (or sometimes, shamefully, more) time with than energy-related data—fantasy football! Yes, the mystery variable that appeared to correlate decently well with U.S. gas prices from September to December of 2016 was actually the standard fantasy points scored by Washington player, Chris Thompson (missing data for the week of November 7 was due to his bye week).

The man that correlates with gas prices

After revealing the actual source of my mystery variable, you would obviously have me pump my brakes on any correlation. There is no possible explanation for why these two variables would be correlated (unless perhaps you would like to make the argument that when the price of gas goes up, Chris Thompson drives less and walks to and from practice—thus improving his cardiovascular endurance and improving his performance that subsequent week; I unfortunately could find no information on his in-season transportation habits).

The fallacy of connecting my mystery variable to gas prices would almost certainly have been exposed were you to test the correlation through expanding the data set and logical reasoning, as previously discussed. Unfortunately, other factors will not always be so obvious to rule out—which is why having as large of data sets as possible is key. Even then, however, you are bound to stumble upon these coincidental correlations (for some thoroughly entertaining and statistically vigorous examples, check out the Spurious Correlations blog) when casting a wide enough net. That fact is just one of the quirky statistical truths with very large sets of data (if interested on this topic specifically, I’d highly recommend reading either or both of these two fabulous books: The Drunkard’s Walk: How Randomness Rules Our Lives & The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day)

Beyond that, even if the correlation might seem sound, keep in one of the firs things taught in introductory statistics, and also one of the first things forgotten, that correlation is not causation (credit to Thomas Sowell). So while our fantasy football to gas prices comparison is a false correlation, even a true correlation does not automatically let you leap to the conclusion that one variable must be causing the other– a topic that this section of the blog will assuredly revisit in a future post. For now, though, I’ll leave it to America’s favorite statistician to summarize:

“Most of you will have heard the maxim “correlation does not imply causation.” Just because two variables have a statistical relationship with each other does not mean that one is responsible for the other. For instance, ice cream sales and forest fires are correlated because both occur more often in the summer heat. But there is no causation; you don’t light a patch of the Montana brush on fire when you buy a pint of Haagan-Dazs.”
― Nate Silver, The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

 

About the author: Matt Chester is an energy analyst in Washington DC, studied engineering and science & technology policy at the University of Virginia, and operates this blog and website to share news, insights, and advice in the fields of energy policy, energy technology, and more. For more quick hits in addition to posts on this blog, follow him on Twitter @ChesterEnergy.  

The Quest: Energy, Security, and the Remaking of the Modern World

To start out this review honestly, I finished reading The Quest: Energy, Security, and the Remaking of the Modern World by Daniel Yergin over a year ago so this is not a particularly ‘fresh’ review from me. However, I found that it was the perfect book with which to begin my book review series because it is considered by many in the energy industry to be the seminal book tracking the historical and geopolitical forces that shaped today’s landscape of energy markets and systems (and I was able to reference the notes I made to myself when reading through it for the first time).

This is book is incredibly rich with information about EVERYTHING related to energy. Obviously at over 800 pages, it’s not a light or quick read– but the depth of information and amount you can learn from it, regardless of it you’re learning about the state of world energy affairs for the first time or you’re a seasoned veteran of the industry, makes taking the time to read it more than worthwhile.



The first section of The Quest starts with a deep dive into the world of oil– the history and politics that have shaped today’s oil landscape, from the fall of the Soviet Union to the formation of the various nations in the Middle East. I really enjoyed learning more about this political and geographic background, as without proper historical context it can be difficult to fully understand the posturing, trade deals, and tensions that are found in the daily headlines regarding oil-rich countries and their conflicts. I also greatly enjoyed the background information on how the current ‘electric age’ came to be, detailing the genius of Thomas Edison and Nikola Tesla, the early rivalry and battles between their nascent companies in setting up an electric system, and how the legacy of those decisions in the early 20th century still affect how we use energy over a hundred years later.

The book continues on to detail the future of oil, as well as a vast amount of background on the technologies that went into discovering, trading, and utilizing non-oil energy sources such as natural gas, coal, nuclear, and renewable energy. Yergin finishes the story by relating the wealth of background information and historical context of the international energy landscape to how it will come shape our world in the future– politically, economically, socially, and technologically– by way of climate change, public policy, the future of transportation, the security of the energy grid, and continuing competition between nations for resources.

Rating:

  • Content—5/5: This book is nothing if not extremely informative. Yergin does a phenomenal job at shining a spotlight at the relation between state of the modern world and the allocation of various sources of energy and how the balances have shifted over time. If you are interested in learning a broad but in depth background on the state of worldwide energy affairs, you would be hard-pressed to find another book with this much information and analysis crammed into it.
  • Readability3/5: Be forewarned, this is not a book to be picked up lightly unless you’re ready to commit to a thorough read. Obviously the intent was not for this to be a poolside, pop science read, but rather a thorough volume that extensively covers the topic. That is, of course, a good thing as Yergin wrote this book to be studied moreso than consumed. However, at over 800 pages it did at times feel like a homework assignment to pick up again and slough through another dense chapter—and because of this it ended up taking me pretty much all of last summer to read.
  • Authority—5/5: Yergin is a renowned energy researcher, market analyst, economist, and many other accolades that there aren’t room to list here. Not only does his name itself carry enough weight to make this book an authority on the topic, but the research and analysis that went into it is plainly evident. You are reading from one of the authorities in modern energy markets.
  • FINAL RATING—4.3/5: Again, this book is by no means a light read– and I had to take a break from it at times so I didn’t get overwhelmed on the topic (which is saying something, given that the future of energy is the social/political topic about which I’m most passionate). But if you can commit the time and really want to contextualize the past, present, and future of energy– do yourself a favor and pick up this book.

 

If you’re interested in following what else I’m reading, even outside of energy-related topics, feel free to follow me on Goodreads. Should this review compel you to pick up The Quest by Daniel Yergin, please consider buying on Amazon through this link.

 

 

About the author: Matt Chester is an energy analyst in Washington DC, studied engineering and science & technology policy at the University of Virginia, and operates this blog and website to share news, insights, and advice in the fields of energy policy, energy technology, and more. For more quick hits in addition to posts on this blog, follow him on Twitter @ChesterEnergy.  

Navigating the Vast EIA Data Sets

The Energy Information Administration (EIA) is an independent arm the Department of Energy (DOE) that is tasked with surveying, analyzing, and disseminating all forms of data regarding energy in the United States. Further, EIA is a politically isolated wing of the DOE– meaning it is there to provide independent and factual data and analysis, completely independent from the partisan decision makers in Washington or the political inclinations of those in charge of at the top of DOE. Because that is the case, you can be confident the data put out by EIA is not driven by any agenda or censored in favor of a desired conclusion.

Thus for anyone with even a passing interest in the national production and use of energy, EIA really is a treasure trove of valuable information. However, those who are unfamiliar with navigating the EIA resources can easily get overwhelmed by the vastness of the data at their fingertips. Additionally, even seasoned veterans of the federal energy landscape might find it difficult to find the exact piece of data for which they are digging within the various reports and data sets made publicly available on the EIA website. So regardless of your experience level, what follows is a brief guide to what type of information is available as well as some advice as to how to make the best use of your time surfing around EIA.gov.



Types of data available

One of the really fabulous things about the EIA data sets is that they cover every kind of energy you can imagine. The energy categories you can focus into include, but are not limited to, the following:

Within these energy categories, you can look at the trends of production, consumption, imports/exports, and carbon dioxide emissions going back years (oftentimes even decades) and also modeled as a forecast into the coming years. Most data sets will have tools to automatically manipulate the data to change between units (e.g., total barrels of oil vs. barrels of oil per day), or even manipulate data trends (e.g., go from weekly data to 4-week moving averages to 10-year seasonal averages). Depending on the type of data, these numbers are regularly updated weekly, monthly, and/or yearly. If there’s a topic of particular interest, there’s a good chance there’s a report with the data on it being released at regular intervals– some of the more prominent reports are highlighted below.

Regularly updated reports

EIA releases a regular stream of reports that serve to update the publicly available data at given intervals. Some of the more prominent reports are listed below, and they are typically used to update all of the energy categories previously mentioned:

  • The Monthly Energy Review (MER) is a fairly comprehensive report on energy statistics, both from the past month and historically back a number of decades. Published during the last week of every month, the MER includes data on national energy production, consumption, and trade across petroleum, natural gas, coal, electricity, nuclear, renewables– as well as energy prices, carbon dioxide emissions, and international petroleum.
  • The Short-Term Energy Outlook (STEO) is another monthly EIA report, this one released on the first Tuesday following the first Thursday of the month. The STEO includes data on much the same topics as the MER, with the inclusion of some international energy data, and it also includes monthly and yearly projections for the rest of the current year and all of  the next year based on EIA’s predictive models. The inclusions of these forecasts makes for particularly useful data sets for anyone who might be trying to stay a step ahead of the energy markets. Also of particular interest for statistically-minded people out there is a regular comparison of numbers between the current STEO forecast and the previous month’s forecast. These comparisons show which way the model shows data to be trending, with the more significant ones called out in the report and noted with reasoning behind the changes.
  • The Annual Energy Outlook (AEO), like the STEO, provides modeled projections of energy markets– though the AEO focuses just on U.S. energy markets, models these annual forecasts long-term through the year 2050, and is released every January. The other aspect of the AEO that makes it particularly interesting is that its modeled forecasts, in addition to a reference case forecast, include different assumptions on economic, political, and technological conditions and calculate how those various assumptions might affect the outlook. For example, the 2017 AEO includes projections based on high economic growth vs. low economic growth, high oil price vs. low oil price, high investment in oil and gas resources and technology vs. low investment, and a projection that assumes a complete roll-back of the Clean Power Plan.
  • The International Energy Outlook (IEO) provides forecast energy market data consistent with the AEO, but regarding the international energy market through 2040.
    • With forecasts in both the STEO and the AEO, an understanding of exactly what is meant by the forecasts is imperative. The forecasts and projections do not necessarily reflect what a human prognosticator within EIA thinks could, should, or will happen– rather it demonstrates what the predictive models calculate given the best possible and unbiased inputs available. This difference is a subtle one, but if you ever find yourself questioning “does the person behind this report really think this is going to happen?”, recognize that some nuance exists and the reason you are skeptical might have not yet been able to be statistically included in the model.
  • The State Energy Data System (SEDS) is published once annually and breaks down national energy use, price, spending, and production by sector and by individual states. Within each of these categories, you can also break down the data by energy type (e.g., coal vs. natural gas) and by primary energy use vs. electric power generation. Having this granularity is useful to further dig into if certain energy trends are regional, restricted to certain climates, or are in response to specific state policies.

While they are not necessarily releasing new and specific data on a regular basis, two other EIA articles of note are worth pointing out because of the interesting stories and analyses they tell:

  • Today in Energy (TIE) comes out every weekday and gives a quick and readable article with energy news, analyses, and updates designed to educate the audience on the relevant energy issues. TIE frequently features graphs and charts that elegantly demonstrate the data in an easy to understand but also vastly elucidating way. One of the real advantages to reading TIE each day, though, is they often include tidbits from all the previously mentioned regularly updated reports, as well as other major releases or EIA conferences, enabling you to keep up with the newest information from EIA (click here for a post on the best TIE articles of 2017 to get you started).
  • This Week in Petroleum (TWIP) is an article that comes out every Wednesday that is very similar to the TIE articles, but focuses on the world of petroleum specifically and provides crucial insights on topics such as drilling, oil company investments, retail prices, inventories, transportation of crude and refined petroleum products, and more.

If any of these regular reports are of interest to you, you can sign up to get email alerts anytime these (or a number of other) reports are released by EIA by visiting this page. If you don’t know which reports you’d want but you want to keep an eye on what EIA is putting out, you can also simply subscribe to the “This Week at EIA” list that will once a week send you an email to notify you of ALL the new EIA productions from that week.

Finding specific data

While keeping up with all the regular reports from EIA is immensely useful, what brings many people to the EIA website is the search for a specific piece of data. You might want to see a history of average gasoline prices in a certain region of the country, find the projection of how much solar capacity is expected to be added in the next few years, track how much petroleum product is being refined in the Gulf Coast, or countless other facts and figures. Below you’ll find a few strategies you can employ to track down the information you seek.

Navigating the menus

EIA.gov has a useful menu interface through which you can usually navigate to your desired dataset easily.

Source: Homepage of EIA.gov
  • The “Sources & Uses” drop down will be where you can navigate to data sets about specific fuel sources and energy use;
  • The “Topics” drop down highlights the analysis on data by EIA as well as economic and environmental data; and
  • The “Geography” drop down is where you can navigate data by state or look at international data.
Source: Homepage of EIA.gov

Navigating from these menus is fairly self-explanatory, but let’s walk through the example of finding the recent history of gasoline prices in the Gulf Coast region of the United States. Gasoline is a petroleum product, so we would click on “Petroleum & Other Liquids” under the “Sources & Uses” menu.

Once on the “Petroleum & Other Liquids” page, the information we’re interested in would be under the data menu with the “Prices” link.

Source: Landing page for EIA.gov/petroleum

You’ll then see a listing of various regular releases of petroleum product price reports and data sets. Since we’re interested in Gulf Coast gasoline prices, we’ll click the third link for “Weekly retail gasoline and on-highway diesel prices.”

Source: EIA’s Petroleum and Other Liquids Prices

Clicking on this report will bring up the below interactive table. The default view will be to show U.S. prices averaged weekly. The time frame can be adjusted to monthly or annual prices (we’ll keep it at weekly). The location of the prices can be changed to allow viewing of data by region of the country or by select states and cities (we’ll change it to the Gulf Coast). The interactive table then displays the most recent week’s data as well as the previous five weeks (note: for ‘gas prices’ as is most often reported in the media and related to people filling up the gas tanks in their cars, we’re interested in the row titled ‘Regular’).

Source: EIA’s Weekly Retail Gasoline and Diesel Prices

If you’re interested in going further back in time then shown in the interactive table, the ‘View History’ links can be clicked to bring up an interactive table and graph going as far back as EIA has data (1992, in this case), shown below. Alternatively, if you want to have the raw data to manipulate yourself in Microsoft Excel, then click the ‘Download Series History’ link in the upper left (I’ll download and keep this data, perhaps handy for later in this post).

Source: EIA’s Weekly Gulf Coast Regular All Formulations Retail Gasoline Prices

Note in the above interactive chart there is the built-in abilities to view history by weekly/monthly/annual data, to download the source data, or the adjust the data to be a moving average or seasonal analysis.

If you find a page with the type of information you’ll want to reference regularly or check in on the data as they update, be sure to bookmark the URL for quick access!

STEO Custom Table Builder

Another useful tool is the STEO Custom Table Builder, which can be found here. The Custom Table Builder allows you to find all of the data that is included in the monthly STEO report (e.g., U.S. and international prices, production, and consumption for petroleum products, natural gas, electricity, coal, and renewable energy; CO2 emission data based on source fuel and sector; imports and exports of energy commodities; U.S. climate and economic data broken down by region; and more). This data can be tracked back to 1997 or projected forward two years on a monthly, quarterly, or annual basis. All you need to do is go to the Custom Table Builder, shown below, and select the options you wish to display.

Source: EIA’s Custom Table Builder

As an example, let’s use the STEO Custom Table Builder to determine the projected of how much solar power capacity in the near term. Solar would fall under the ‘U.S. Renewable Energy’ category, so click to expand that category, then expand the ‘Renewable Energy Capacity,’ and you’ll see the STEO has data for data for the capacity of large-scale solar for power generation, large-scale solar for other sectors, and small-scale solar for other sectors.

Source: EIA’s Custom Table Builder

Select all the data relevant to solar data, select the years you want (we’ll look at 2017 thus far through the end of 2018), and what frequency you want the data (we’ll look at monthly). Then hit submit, and the following will be the custom table built for you.

Source: EIA’s Custom Table Builder

Note: The forecast data is indicated in the Custom Table Builder with the numbers shown in italics. The above data was pulled before the September 2017 STEO was published, so the projections begin with the month of August 2017.

For this example, we’ll want to then download all the data to excel so the total solar capacity can be added up and analyzed. Click the ‘Download to Excel’ button at the upper right to get the raw data, and with a few minutes in Microsoft Excel you can get the below chart:

Source of Data: EIA.gov, pulled on September 10, 2017

This graph, made strictly from STEO Custom Table Builder data, shows the following:

  • As of July 2017, large-scale solar generation capacity was only 0.3 GW outside of the power sector and 23.7 GW, while small-scale solar generation capacity was 14.8 GW.
  • Together, solar power capacity in the United States added up to 39.1 GW as of July 2017.
  • By the end of 2018, total solar power capacity is projected to rise to 53.7 GW (an increase of 14.5 GW, or 37%), according to the EIA’s August 2017 STEO.

Search function

Using a search bar on some websites can be surprisingly frustrating, but luckily the EIA search function is very accurate and useful. So, I have found that, when in doubt, simply doing a search on EIA.gov is the best option.

Perhaps I want to track the amount of petroleum products in production on the Gulf Coast. This information is not in the STEO report, so the Custom Table Builder won’t be of use. And maybe I don’t immediately see how to navigate to this specific information on the menus. I would type into the search bar the data I’m seeking as specific as possible—‘weekly gulf coast refiner gasoline production’:

Source: Homepage of EIA.gov

Doing the above search yields the below results, of which the first one looks like just what we need.

Source: EIA.gov

Click on that first link, and ta-da! We’re taken to the weekly gasoline refinery report for the Gulf Coast (referred to as PADD 3). Again, you see the options here to look at the history back to 1994 both on a weekly and a 4-week average basis, use the chart tools to analyze moving averages or seasonal analyses, or download the data to utilize in your own way.

Source: Weekly Gulf Coast Refiner and Blender Net Production of Conventional Motor Gasoline

Contact experts

As a last resort, the EIA website offers resources to contact should you have questions or issues navigating the data. The people behind the EIA data are civil servants who are intelligent and very dedicated to their job and making sure you get the accurate and relevant information you need. So in a pinch, head to the Contact Us page and find the topic on which you need help from a subject matter expert.

If you want an alternative to going straight to the people at EIA, however, feel free to contact me as well and I’d be happy to try and help you track down information on EIA.gov as well. Use any of the contact methods mentioned in the Contact Page of this site, or leave a comment on this post.

Using the data

I have found that it is not at all an exaggeration to say that the world (of energy data, at least) is at your fingertips with EIA’s publicly available data. To demonstrate, I’ll walk through a quick example of what you can find.

If we take the previously gathered weekly data for Gulf Coast gasoline prices and gasoline production, we can plot them on the same graph:

Source of Data: EIA.gov, pulled on September 10, 2017

By taking advantage of the publicly data on EIA’s website, we can notice some trends on our own. In the above, there is a drastic increase in Gulf Coast gasoline prices, coincident with a large decrease in Gulf Coast refiner production of gasoline that bucks the month-long trend of production generally increasing. This is a curious change and would prompt investigation as to the reason why. Luckily, several of EIA’s Today in Energy articles already points out this trend and offers explanation—all related to the effects of Hurricane Harvey on the Gulf Coast petroleum systems (Article 1, Article 2, Article 3). Just goes to show that one of the best way to stay abreast of trends and information in the energy world is to follow EIA’s various reports and analyses.

 

Updated on September 28, 2017

 

 

 

About the author: Matt Chester is an energy analyst in Washington DC, studied engineering and science & technology policy at the University of Virginia, and operates this blog and website to share news, insights, and advice in the fields of energy policy, energy technology, and more. For more quick hits in addition to posts on this blog, follow him on Twitter @ChesterEnergy.