What’s better for the environment: driving or biking?
We’re going to assume that you chose biking. Your analysis probably went something like this: cars burn gasoline and emit exhaust. And gasoline is produced by oil companies who turn pristine pockets of nature into industrial sites. Bicycles, on the other hand, are much simpler. You just hop on and pedal away. No exhaust, no emissions, no environmental impact.
We’re also going to assume that this reasoning popped into your head quickly. As a result, you probably didn’t have to spend much time and energy drawing the conclusion that bikes are better for the environment. Easy peasy.
You’re probably not wrong. Biking may be better for the environment than driving. But the debate may be a little less clear-cut than you originally thought.
For starters, did you consider where the energy to power a bicycle comes from? Sure, it immediately comes from the person whose feet are on the pedals. But that isn’t the original source of the energy. The person riding the bike ate food at some point: he or she is just turning the chemical energy from food into kinetic energy of rotating pedals. It is the same job that an engine in a car does by turning the chemical energy from gasoline into kinetic energy of a rotating drive shaft. As it turns out, both the human body and a gasoline engine are equally efficient at converting the energy from their fuel sources into mechanical energy, in the neighbourhood of 20% to 25%.
But that’s only step one in the analysis. Step two is to consider where that fuel comes from. The oil industry gets a lot of attention for the negative impact its activities have on the environment. But do you know what also has a negative, albeit less visible impact on the environment? Food production.
Food doesn’t grow on trees. OK, well that’s not entirely true. Some food does grow on trees. The point is that the food you eat doesn’t grow in your backyard. A farmer in the country grows it. Or maybe the farmer grows food that he then feeds to another mammal that turns that gross food into delicious food that you then eat. In any event, there is a lot of work required to turn inedible seeds into your delicious meal.
In sum, a lot of energy must be expended to feed you, and this has a significant environmental impact. Estimates vary, but the consensus seems to be that for every unit of food energy that humans eat, ten times that energy is burned in fossil fuels to produce, transport, and prepare the food.
Land must also be used in both oil extraction and farming. Although pictures of drilling sites are much less visibly appealing than a cornfield, both have unnatural impacts on the local environment. Again, estimates vary, but cereal grains are thought to require at least 25 times more land per unit of energy produced than what oil extraction requires. Beef is even worse, given that both the animals and their food require space: estimate put their land use at least 500 times higher than oil extraction, per unit of energy produced.
Putting all these calculations together (which we didn’t do, but these guys did), it shows that biking and driving have similar environmental impacts on a per-kilometre basis. And if you have a meat-heavy diet, biking may be worse for the environment than carpooling in a hybrid car.
Important note: the above discussion is not intended to be an exhaustive discussion of environmental issues. DO NOT SEND US ANGRY EMAILS ACCUSING US OF HATING THE ENVIRONMENT. We love the environment and we love cyclists. Except Lance Armstrong. He really disappointed us.
Pause for a Moment
We aren’t out to make any points about how you should commute to work. You do you. What we are out to demonstrate is how your brain works. When you were initially presented with the question about biking versus driving, you likely took very little time to conclude that biking is obviously better. You thought about it briefly, came to what seemed like a reasonable answer, and then were comfortable that Cowan Asset Management decided to ask you dumb questions this month.
But when we started to talk-through the other considerations about energy conversion and production, you had to slow you brain down and start to use some of your mental horsepower to think through these additional, math-heavy considerations.
As another example about how your brain operates at two different speeds, think about how quickly your brain would make a decision if you were driving down the highway and a deer ran out in front of you. Compare this to how quickly your brain would make a decision if you were doing homework in a first-year calculus class.
Psychologists Daniel Kahneman and Amos Tversky did extensive research on these two different speeds at which the human brain operates. Their findings were summarized in Kahneman’s excellent book, Thinking Fast and Slow. He called the two speeds “System 1” and “System 2”. System 1 is instinctual, emotional, and fast. It requires very little energy and is the mode of thought people employ through most of their waking hours. System 2 is laborious, logical, and slow. Because System 2 is more laborious, people find it unpleasant and more exhausting to use than System 1. Humans’ preference to rely on System 1 versus System 2 often results in our judgement being rife with errors.
A classic example used to illustrate the difference between System 1 and System 2 is the following three questions. Go ahead and see if you can get a perfect score:
- A bat and a ball together cost $1.10. The bat costs $1 more than the ball. How much does the ball cost?
- If it takes five machines five minutes to make five widgets, how long would it take 100 machines to make 100 widgets?
- There is a patch of lily pads in a lake. Every day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half the lake?
For question one, the System 1 part of your brain wanted to quickly answer “$0.10”. But’s that’s not right, now is it? The correct answer is “$0.05” because a $0.05 ball plus a bat that’s $1.00 more expensive (at $1.05) adds up to $1.10. To get the correct answer, you would have had to slow down, used more energy, and engaged your System 2.
For question two, System 1 wanted you to answer “100 minutes”. But again, that’s not correct. If you paused to engage System 2, you would have realized that each machine makes one widget every five minutes. That means 100 machines would still take just five minutes to make 100 widgets.
Finally, for question three, System 1 wanted you to answer “24 days”: it seems intuitive that covering half the lake would take half the time. But since the area covered by the lily pads doubles every day, it would only take one less day – 47 in total – to cover half the lake.
We Try to Invest with System 2
When investing, we must be continually on guard against the biases that System 1 can introduce into our analysis. Just because a decision feels right and is easy to make, it doesn’t mean it’s the correct one. Furthermore, even after taking the time to analyze a problem using System 2, the resulting decision can still feel like the wrong one, causing us to emotionally second-guess our conclusions.
For example, we have written previously about the meteoric rise of technology stocks in the U.S. There was a point in time that whenever we saw a news report about the FANG stocks (Facebook, Amazon.com, Netflix, and Google) setting new highs, we would immediately write that news off as another sign that tech stocks were overvalued. We would look at the valuations of some of these companies – 165x price-to-earnings (“P/E”) ratio for Netflix or a $1 trillion market cap for Amazon – and scoff that there was no way the fundamentals supported how richly the market was valuing the companies.
But then we paused for a moment and began to think about just how dominant some of these companies are – specifically Google (or, more accurately, its parent company Alphabet Inc.). We realized we were making an emotional, knee-jerk reaction about Alphabet’s shares, and this reaction could be misguided. Although its trailing P/E ratio looked high at 32x, we knew there were several adjustments that had to be made to make this ratio a more accurate reflection of reality. So we took a couple of weeks and had a deeper look at the company, debated what its future growth was going to look like, and created a financial model that captured our estimates. The result? Alphabet isn’t nearly as expensive as we originally thought. In fact, it’s only a modestly-sized pullback away from looking downright cheap.
That said, people can make a lot of money by relying solely on System 1 decisions. Jumping on the Bitcoin euphoria this time last year or tech-boom enthusiasm in 1998 would have made anyone rich in the short-term. But the trick to monetizing these emotional gains is to head for the exits before everyone else does. And this requires anticipating when the rest of the market will stop relying on using their System 1 and begin using their System 2.
Until such time that we develop an accurate model for anticipating the market’s System 1 responses, we’re going to continue to rely on the laborious, logical, and slow System 2 mode of decision making. It may not result in as many fleeting moments of euphoria, but over the long-term we believe that it is the most repeatable way to make correct investment decisions.