Are People Well Calibrated When It Comes to Emissions from Appliance Use and Food Consumption?
January 2, 2014
By Shajuti Hossain, Marie Komori, and Gabriel Goffman
Our team, which is guided by the project title “Goals and Collective Efficacy: Routes to Energy Saving”, is developing a number of exciting experiments that are described below. The general theme for all of our work is how to present information about energy savings from engaging in efficient actions.
Through my initial survey, I tested perceptions of carbon emissions from appliance usage and carbon emissions from food production. Given the assumption that using a light bulb for an hour and producing a tomato emit 100 units of carbon dioxide, people were asked to write how many units of carbon emissions they thought resulted from the usage of other appliances and production of other foods.
My survey results regarding appliances were consistent with my expectations, I saw that people underestimated the amount of carbon emissions from larger appliances. The same was true for food production: most people underestimated the amount of carbon emissions from producing high emitting foods, especially beef and shrimp. The figure below shows that as carbon dioxide emissions from appliance usage and food production increases, so does the difference between actual and median perceived emissions.
Moving forward, I want to focus on energy from food production, because while people can see a light bulb or computer using energy, people do not think about all the energy used to produce the piece of chicken on their plates. To analyze how we can convince individuals to eat fewer high emitting foods, I would like to test two things. The first is the perceptions of the amount of energy usage and carbon emissions from each step of the food production processes compared to the actual amounts. Testing this will show which aspects of food production need to be emphasized when asking individuals to consume fewer high emitting foods. The second is the effects meat and fish labeling have on consumers’ choices. This will help show how consumers respond to specific kinds of words and labels when they are compared side by side.
Through my research, I intend to answer the following questions: (1) Do people react more positively to one or to multiple reasons for taking an action? (2) Is it more effective for multiple reasons to be in same domains or different domains?
In my first study, people were given either one/two of the same/mixed egotistical/environmental reasons for changing their diet to a meat-free diet. Egotistical reasons included Health and Appearance reasons while environmental reasons included Animal Welfare and Climate Change. After these reasons were presented, the perceived persuasiveness of the reasons given and individuals’ behavioral intent to change their diet was measured.
The first set of results were insignificant in that the overall perceived persuasiveness was relatively low: on a scale of 1 – 6 (6 being the maximum perceived persuasiveness), on average, the persuasiveness of reasons given were around 3.5. As shown in the figure, none of the different permutations of reasons had a significant impact on change in future intended meat consumption as measured in number of days per week.
In order to improve this survey design, I intend to increase the perceived persuasiveness of reasons and make reasons within the same domain more similar to each other. Additionally, I will include a “cost” component, where statistical cost (in terms of money or CO2 emitted) is included in the reasonings given. I will also shift my study to a different domain, namely, light-bulbs.
I examined the effect of manipulating the size of two variables in relation to the environmental benefits of the vegetarian lifestyle. Previous studies have indicated that people are swayed by larger numbers more so than they are swayed by small numbers that demonstrate the same information. To explore this effect further, I described the benefit of being vegetarian while changing the size of the numbers I presented based on two variables: time and number of people.
I compared a normal diet to a vegetarian diet and then stated the number of emissions of GHG this would save in terms of gallons of gasoline. Specifically, each group received a specific statement with a different from frame: “if you (you and 999 people) were to be vegetarian for 1 day (1000 days) then food-related carbon emissions could be reduced from 15 (14,930) [14,930,000] to 10 (10,213) [10,213,000] pounds of CO2, which is the equivalent of saving .23 (230) [230,000] gallons of gasoline”. Note that the frames 1000 days/1 person and 1000 persons/1 day produced in the same numbers.
As shown in the figure, the frame based on differences in time did not have a statistically significant difference. However, the frame based on differences in number of people did have a statistically significant difference. The biggest effect was for the largest number (1000 days, 1000 people) showing the effect of large numbers . However, the effect for people was the only statistically significant difference.
One difficulty we encountered in the survey was that by taking the response variable in days we didn’t have a wide range of variations. Instead of days we could ask for number of meals. Another aspect that we would like to explore is people’s willingness to give up certain high carbon producing foods such as red meat or cheese. In addition, we would like to focus on the positive peer pressure effect that the effect of the “1000 persons frame” seems to show. We would like to see ways to use the idea of peer group vegetarian to encourage actual behavior change. Finally, the idea of the large number effect in relation to peer behavior could apply to other aspects of energy use. We plan to run another study focusing on willingness to drive less or use less electricity and to see whether there is also an effect for aggregating over potential collective behavior.