Whilst the environment, including our climate, came out as the main concern for Swiss people in 2022, a global survey by the market research firm IPSOS has found that misconceptions are rife about the most effective climate solutions.
In a bid to further understand how people perceive the efficacy of climate actions, two EPFL researchers Victor Kristof and Lucas Maystre, part of the lnformation and Network Dynamics Laboratory in the School of Computer and Communication Sciences (IC) have launched an interactive tool to measure and communicate how people view the impact of our daily choices.
“We wanted to apply some of the statistical models that we were developing in our research to study how people perceive the carbon footprint of their actions, for example, how much do you think is emitted by drinking from a plastic water bottle? It’s super difficult to answer, most people probably have no idea and what we’ve developed is a model that converts comparisons of the carbon footprint of different actions to an absolute scale. This enables us to compare perception to that of the true carbon footprint,” explained Kristof.
The data sets didn’t exist
In 2019, the pair started creating a data set of actions like flying, eating meat or heating our homes. They developed a small proof of concept app and collected 2000 answers from 250 students, creating a data set of 18 actions. Rather than collecting the survey data, running their model and developing the website, they found the most difficult task was finding the information on how many carbon emissions are generated by specific actions.
“What you find very often is a top-down approach where Switzerland’s 45 million tons of CO2 every year is divided by its population of 8-million people to obtain a per capita carbon number but this doesn’t tell you much about the details of what people do in their daily lives and how they can reduce their impact,” he continued.
Kristof and Maystre crossed departments and began working with Jérôme Payet and a team of Master’s students from EPFL’s School of Architecture, Civil and Environmental Engineering (ENAC) to createa more complete data set of the carbon footprint of 52 actions following a sound life-cycle analysis methodology. The data sets, Kristof says, just didn’t exist, so they had to create them from scratch, “We took a bottom-up approach and for action after action rigorously computed their carbon footprint. For example, rather than a general per capita number, for a train ride we took into consideration the energy source, the wagons, the railways, the train station, everything.” This data set, compiled by Blanche Dalimier, Alexis Barrou, and Edouard Cattin, obtained the Durabilis Award 2021.
As a result, they calculated that the average carbon footprint of a Swiss citizen is 11.6 tons of CO2, which corroborates results from other sources using different methodologies.
Tennis or tomatoes?
To build the data set further, and to share the knowledge collected, Climpact is now public and asks users to complete a survey and then discovers whether their perception of the impact of their actions is accurate and aligned with the carbon reality.
Everyday comparisons include things like buying tomatoes or eating meat for a year, skiing for a week, eating a fondue, playing tennis, commuting for a year by car, bus, or bike, or heating your house with an old boiler.
This last example is particularly poignant as next month Swiss voters go to the polls to decide on the Climate and Innovation Act, a Federal Government initiative that aims to reduce the country’s consumption of oil and gas, produce more energy locally and see the alpine nation become climate neutral by 2050.
Buildings are responsible for around a third of CO2 emissions in Switzerland through heating and general energy consumption and the bill provides financial assistance to those who replace old oil, gas or electric heating.
Kristof says that whilst so far, perception on the carbon impact of many actions is overall quite good, there are some interesting discrepancies. “What we hope to show policymakers is that many people hugely underestimate the impact of certain actions that actually have a large carbon footprint. Our early results suggest this is the case for example with heating our homes. We think it’s very important to raise awareness around this.”
Individual versus government and corporate responsibility
Visitors to Climpact can also browse through all of the 52 actions to see which emit the most and least, as well as calculate their individual actions. The researchers measured the impact of taking a train from Zurich to Lausanne but if users know the distance from other cities a calculator will automatically compute the carbon footprint of the journey. From an educational viewpoint this can be helpful but Kristof says just as interesting as the data sets that researchers were able to create was what they weren’t able to do.
“We often talk about individual responsibility and yes we should all be more careful of our impact on the climate, but there are a lot of things over which people just don't have a lever and governments and companies have a huge responsibility to move things in the right direction through their policies and practices.”
“For most actions we can use the bottom-up approach but for some sectors such as education, healthcare, and leisure it was very difficult to get accurate data. If you eat a meal in a restaurant or stay a night in a hotel you wouldn’t have a clue of the carbon emissions being generated. It’s not at all transparent and this is an indication that we need do more research into these sectors to get more accurate data.”
There are other things, Kristof says, that are very difficult to evaluate and that couldn’t be included in Climpact but that potentially have a large impact on how we tackle the climate crisis and he urged people to think about these, “Where do you put your money, for example, which bank do you choose, what are their climate policies? Do you vote and for whom? Do you participate in local actions or associations? These are soft actions that are next to impossible to measure from a carbon footprint perspective but that potentially have the most impact on reducing one's carbon footprint.”
The researchers used what is called a "discrete-choice model" and finds its root in the literature of psychometrics the early 1920's. It was adapted into an active learning algorithm, a machine learning method that enables the optimal selection of the next pair of actions to show to a user by maximizing the information that will be received from this comparison. This model then enables the transition from pairwise comparison data (i.e., one action relative to another) to perception on an absolute scale (i.e., the perception of each action individually). This helps shift the complexity for a user to answer difficult questions (how much does heating your house emit?) to a computing algorithm that will process simpler questions (does home heating emit more than flying?).