Why setting carbon prices is such a complex challenge

Most economists agree that if we want to efficiently reduce CO2 emissions, we’ll need to put a price on carbon. But a nagging question remains. How are we supposed to figure out a price for an invisible, amorphous gas that underpins the economy and transforms the climate?

It’s relatively easy to put a price on a t-shirt or a pound of apples. Calculate the cost of all the inputs, from raw materials to labor to shipping. Then add a margin for profit, and the product is ready to be sold.

The costs of carbon, by contrast, are diffuse and diverse—which helps explain why the estimated prices of CO2 vary wildly across countries and companies. The US government uses an estimate of $33 per ton of CO2, while Sweden uses the strikingly high figure of $168. In internal calculations, Google Inc. uses $14 per ton, while (perhaps surprisingly) oil companies like BP and Exxon-Mobil use fairly high prices of $40 and $60, respectively.

So where do these carbon price tags come from? Whenever you see dollar amounts tacked on to tons of carbon, economists likely used one of two methods to calculate them.

The first method is like a very long addition problem. The strategy, according to World Bank senior economist Stephane Hallegate, “is to look at the damages on the environment and societies and people that one ton of carbon would create.” In other words, economists use computer models to tally up all the negative impacts of carbon, and set a carbon price high enough to offset those costs. “If the damages are going to be high, that justifies quite a high carbon price,” explains Niven Winchester, an MIT environmental economist.

In practice, however, adding up the costs of carbon can be extremely tedious. “You’re solving a puzzle,” he says—but the puzzle has a huge number of finicky pieces. A dizzying array of factors can affect the estimated cost of carbon. For example: How much will solar energy cost in 30 years? How much meat will humans consume, one century from now? How much CO2 would cause the Greenland ice sheet to melt? Because there are so many factors to estimate, MIT outsources its calculations to a massive computing center 100 miles west of Boston, the Massachusetts Green High Performance Computing Center (MGHPCC). Unlike simplified models of the economy, “integrated models” may generate datasets of many terabytes — a reflection of the many environmental and economic processes that researchers hope to capture.

To generate an accurate carbon price, models also need to incorporate new insights from economics and environmental science. As Stanford economist Michael Mastrandrea wrote in 2009, models “must contend with an ever-changing body of underlying literature.” On a scale from 1 to 10, Winchester estimates the difficulty of accurately linking models of the environment and the economy as “getting toward a 9 or 10.”

A simple example helps illustrate the challenge. Let’s say the cost of carbon increases, and causes reductions in agricultural output and increases in electricity costs. These shifts lead to second-order consequences: Farmers might choose different crops, while factories might relocate to countries that lack effective carbon taxes. In short, the economy affects the environment, which affects the economy—producing a feedback loop.

This helps explain why—at least for now—most economists favor a simpler strategy for pricing carbon: Choose a particular aim, and then calculate a price for carbon that would help achieve it. “First you assess the risk; you decide your goal,” says Hallegate. Right now, that goal is to limit global warming to 2 degrees by 2100. “Then you look at what you need to achieve this goal.”

Practically speaking, this means economists can rely on much simpler models when making carbon price calculations. Instead of trying to mimic the entire economy and environment in coupled models, they can rely solely on economic models that estimate the impact of carbon prices.

Arguably, both approaches are rooted in the same principles. Emissions figures like the 2 degree target are often tested by climate models. Integrated models like the ones used at MIT are just a more dynamic and precise way to capture the effects of not only carbon on society, but also also society on carbon.

Regardless of which method you choose, Hallegate says the cost of carbon contains more than the sum of the damage carbon causes. The price is sort of like home insurance against fire or flooding. Many homeowners end up paying more for insurance than they’ll ever receive from insurance companies—but they have protection if the house goes up in flames. “When we estimate the price of carbon, a lot of this value is really about the hedge. It’s insurance against things getting very, very nasty.”

In other words, the carbon price isn’t ever just a tally of damages, but a figure that covers a range of best- and worst-case scenarios. That’s why many carbon prices, like the U.S. Environmental Protection Agency’s “Social Cost of Carbon” (which was generated with the first method of adding up all the estimated burdens of CO2), include several staggered prices depending on the severity of climate change. A price range acknowledges the uncertainty in projecting our future climate.

And as climate scientists know, there is plenty of uncertainty. “A ton of carbon will stay in the atmosphere for hundreds or thousands of years,” Hallegate points out. It’s a tall order to try and predict the damage caused by CO2 in our lifetime—and the cumulative impacts it will have in the future.

Still, carbon prices have a simple advantage. Even when the underlying calculations lack clarity, price tags make sense to companies and consumers, and may be easier to grasp than new laws or regulations. Whether we’re buying a house or a handbag or a ton of carbon, prices pack an incredible amount of information into a few digits and a decimal point. When it comes to pricing CO2, the central challenge is starting with the right information, so we end up with the right price.

This article is published in collaboration with Road to Paris. Publication does not imply endorsement of views by the World Economic Forum.

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Author: Daniel Gross is a writer and public radio producer based in Boston.

Image: The sun is seen behind smoke billowing from a chimney of a heating plant in Taiyuan, Shanxi province December 9, 2013. REUTERS/Stringer.