The Psychology of Saving Energy

Research Project, 2023 – 2024

This research project seeks to answer the following questions:
 What are the principal drivers of decisions on energy use in the laundry?
 How can we change laundry behaviour using cues, inhibitory control and reengineering?
 What barriers and drivers (physical and mental) should be considered in a policy/information strategy aimed at reducing Swedish laundry impacts?
 How much energy can be saved over the relevant life cycles by behavioural intervention?
We take a sophisticated approach to energy consumption, extending the scope outside the local use of energy in the household. This means including effects in trade‐linked systems upstream from the
household, which are affected by energy use within the household. This perspective is needed because consumers are faced not just with the question of whether to select a more energy‐efficient program on their laundry machines. They also decide how often to wash and dry their garments, actions which directly damage fibres and reduce garment lifespans in the face of expectations of the
apparent ‘newness’ of garments, which have risen over the last 20 years. Garment lifespans are half what they were before then, which drives up the rate of textile production and the corresponding energy demands in Asia. Reducing the demand for new garments, by lengthening their apparent lifespans, is therefore critical to reducing global energy demand.
However, changing consumer behaviour depends on overcoming habitual actions by finding “emotional reasons” for individuals to change. Previous interventions by others have acted on the assumption that consumers are merely uninformed. Unfortunately, these attempts have proved futile in actually changing consumer behaviour. Instead, we believe that laundering decisions are more driven by psychological and social aspects of belonging, a possibility that has been strengthened by a small pilot study at the lab (not currently published).
To validate these preliminary findings and continue our work we want to take a two‐pronged approach to data acquisition. We are currently able to collect an unusually rich dataset in the Living Lab, having connected the washers and dryers to the Internet of Things. Since the building is inhabited by regular tenants, we have the unique opportunity to ask people how they believe that they do their laundry and compare that with actual measured behaviour. The main limitation with this form of data collection is the modest number of inhabitants in the Living Lab (35). This means that we also need to do survey work outside the lab to corroborate the results.
We will survey the factors motivating the behaviour of residents in the Living Lab. To check whether the responses of residents in the Living Lab are representative of the wider population, we will repeat the surveys at a larger scale, e.g. two large surveys with approximately 1000 respondents each (N.B. final sample size will depend on presumed, and later stage observed, data quality and effect sizes). Like similar academic data collection activities on other topics, the larger surveys will be implemented by a commercial market research organization.
We will also intervene in the laundry programs at the Lab, changing the software in the washing machines. One example will be forcing the tenants to choose between programs labelled to reflect potential motivators for human behaviour (e.g. odour elimination; stain removal; environmental sustainability) to determine whether people are willing to change behaviours in real life. Quantitative data collection from the laundry devices in the Living Lab will indicate the extent to which interventions succeed.
The combination of the measured and self‐reported behaviour in the Living Lab will be used to calibrate the responses of the larger survey group to identify a reasonable scenario for the extent of energy savings inside the laundry and in the upstream supply chain. The results will be analysed both from a psychological perspective (i.e. what kinds of messaging are most likely to generate results) and in an environmental systems analysis (i.e. how much energy and emissions can be saved by the intervention, up and down the garment life cycle).