Helping Consumers See the Green Behind Fuel Economy
By Jenna Zhang
When choosing your next family car, which is more important to you: saving money over the long term or reducing your carbon footprint? Are you concerned with fuel efficiency for economic or environmental reasons?
Fortunately, saving money and the environment can be one in the same. Vehicles that use less gas help cut fuel costs while also decreasing harmful emissions that contribute to climate change. But how do people actually make choices about fuel-efficient vehicles? How can policymakers communicate information about vehicle fuel economy that will help consumers save money and reduce their carbon emissions?
The new U.S. fuel economy labels (below) aim to help consumers better understand vehicle fuel economy so they can make the most educated choices when choosing a vehicle. These labels include not only the traditional miles per gallon (MPG) information, but also an annual fuel cost estimate, and greenhouse gas and smog ratings. The U.S. label is one of the first of its kind in that it reports not only gas consumption, but also gas cost (based on typical driving behavior). With these new labels in mind, a recent article entitled “Metric and Scale Design as Choice Architecture Tools” by Adrian Camilleri and Richard Larrick summarizes research on the best ways to report fuel economy information.
Vehicle fuel economy can be expressed in a variety of different metrics: for example, gas consumption, gas cost, or CO2 emissions released. These metrics can also be expressed over different scales: for example, over small or large distances or time periods. In the paper, the authors seek to understand how the manipulation of vehicle fuel economy labels’ metric and scale can shift consumers’ preferences, and what combination of metric and scale produces the strongest preferences for fuel-efficient vehicles. It also looks at ways policy makers can use fuel economy labels to encourage consumers to make more pro-environmental choices.
As the authors point out, choices are always made in a context. We often do not have stable, inherent preferences, but instead construct a current preference each time we’re asked. This preference construction can also be influenced by different contextual features of the situation. The “choice architecture” refers to all task and contextual features associated with a decision that can potentially influence what information is used or how that information is processed. For example, a consumer might read a product review before making a purchase. The person who writes the review decides which characteristics they will use to rate the product. The consumer’s choice will then be affected by the context which the product reviewer has created by using those specific characteristics to rate the product. In this example, the product characteristics chosen for the review are components of the “choice architecture,” and the reviewer is the “choice architect.”
Previous research (Larrick and Soll, 2008) shows consumers are not good at converting the MPG metric to gas consumption or gas cost. Miles per gallon is not a good fuel efficiency measurement because most people assume that MPG and gas consumption have a linear relationship. However, as the figure below indicates, this is not the case. The difference in gas consumption between a vehicle with a 10 MPG fuel economy and a 12 MPG fuel economy is much greater than the gas consumption difference between 40 MPG and 42 MPG. This misunderstanding often leads consumers to disregard the value of a small increase in fuel efficiency and assume that all vehicles with low gas mileage are equally as bad. Additionally the conversion from MPG to cost of gas is a difficult calculation for many people, and most do not attempt it or do it incorrectly. Because MPG has been shown to be difficult for consumers to understand, Camilleri and Larrick tested the use of different fuel economy metrics in an effort to find more useful ways of expressing fuel economy.
Two online studies, which tested a total of 908 participants, were conducted. Participants had to choose between two vehicles that traded off on price and fuel economy. When presenting the information on fuel economy, the researchers changed the metric (consumption of gas vs. cost of gas) and scale (100 miles vs. 15,000 miles vs. 100,000 miles) across the different trials. 100 miles is the current scale used on the U.S. fuel economy label, 15,000 roughly corresponds to annual miles driven, and 100,000 miles roughly corresponds to lifetime miles driven. In both studies, the fuel-efficient vehicle was more expensive. However, in the first study the fuel savings over a lifetime of use—approximately 100,000 miles—made up for the initially higher price; in other words, the fuel-efficient vehicle paid for itself over lifetime ownership. In the second study, the fuel-efficient vehicle paid for itself in half of the situations and did not pay for itself in the other half.
The article’s key finding is that consumers choose fuel-efficient vehicles more frequently when fuel economy is expressed in terms of the cost of gas on a long term, 100,000-mile scale. This is true even when the fuel-efficient vehicle is costly and the increased fuel economy does not pay for itself over the expected lifetime of the vehicle. The study also found that the 15,000-mile scale produced the weakest preference for fuel-efficient vehicles. This latter finding is significant because the new U.S. fuel economy label uses this 15,000 miles per year scale.
Energy labels are an important public policy tool as they allow governments to manage energy-efficiency policies and climate change mitigation programs. They can also encourage reductions in CO2 emissions by framing a pro-environmental behavior as an economically logical choice (buying the fuel-efficient vehicle saves money while also reducing CO2 emissions). There is currently no fuel economy label in the world that uses the combination of cost of gas over 100,000 miles. Using this metric and scale for fuel economy labels is an easy and effective way for policy makers to encourage consumers to choose fuel-efficient vehicles.
“Consumer’s decisions are influenced by how information is presented,” explains Camilleri, a post-doctoral research scientist at Duke University. “In the context of vehicle purchases, more pro-environmental choices can be encouraged by presenting the right combination of efficiency metric and scale. Our work indicates that expressing efficiency as the cost of gas over 100,000 miles produces the strongest preferences for efficient vehicles. This combination allows consumers to better appreciate long-term efficiency savings in terms of a familiar unit of measurement.”
Camilleri and Larrick’s article, “Metric and Scale Design as Choice Architecture Tools,” is featured in the Journal of Public Policy and Marketing. Full article text can be found here.
For more information, visit the Center for Research on Environmental Decisions website.
A short video explaining choice architecture and this study can be found here.
Jenna Zhang is a research assistant with the Center for Research on Environmental Decisions and a Columbia College student.