• "Knowledge is an unending adventure at the edge of uncertainty"
    Jacob Bronowski
  • "Knowledge is an unending adventure at the edge of uncertainty"
    Jacob Bronowski
  • "Knowledge is an unending adventure at the edge of uncertainty"
    Jacob Bronowski
  • "Knowledge is an unending adventure at the edge of uncertainty"
    Jacob Bronowski
  • "Knowledge is an unending adventure at the edge of uncertainty"
    Jacob Bronowski
  • "Knowledge is an unending adventure at the edge of uncertainty"
    Jacob Bronowski
  • "Knowledge is an unending adventure at the edge of uncertainty"
    Jacob Bronowski

Events

First RELIEF workshop held on the topic of “The influence of consumer behaviour on the environmental footprint of products”

The workshop on “The influence of consumer behaviour on the environmental footprint of products” held on the 30th of September 2016 in Nijmegen, the Netherlands brought together experts from the fields of life cycle assessment (LCA) and consumer behaviour. The purpose of this workshop was to discuss current and emerging approaches to understand and characterise consumer behaviour and practices in the context of assessing and improving the reliability of environmental footprints of consumer products.

The workshop began with a series of short presentations by invited experts. The table below gives a short overview of currently available techniques for consumer habits’ data collection that were presented. Other approaches were also discussed such as the use of modelling techniques (e.g. statistical models or neural networks), as well as the use of a set of values (hedonic, altruistic, egoistic, biospheric) to describe consumer behaviour.

Overview of methods available to collect consumer habits’ data

Method Advantages Drawbacks
Surveys Large population sampled, can monitor change over time Measures what people declare, not what they actually do
Diary keeping More accurate than surveys Requires goodwill of the participants
Observations Measures actual behaviour Hard to implement
Sensor technology Monitors actual consumer behaviour quantitatively Context of use difficult to assess, only small groups of consumers
Real-time text mining/big data Real-time answers, enables to identify trends Well-framed question necessary, representativeness of answers difficult to assess

Most presentations can be found in the links below:

Understanding Consumers Behaviour: A Behaviourally-Driven Approach to LCA And Eco-Design by Dr. Eugenia Polizzi Di Sorrentino

Effect of Occupants’ Behavior and Habits on Residential Energy Consumption by Dr. Merih Aydınalp Köksal

Drivers of Behaviour and Behavioural change by Dr. Ellen van der Werff

Challenges in Data Collection of Laundry Washing Behaviour visa Consumer Surveys by Dr. Kirsi Laitala

Sensor Technology for Measuring Behaviour by Dr. Hilde Hendrickx

The talks were followed by a breakout session in which workshop participants were asked to address a number of questions covering current challenges and emerging approaches to improve the quality, quantity and relevance of consumer habits data for product footprinting. Key recommendations from the group discussions were:

  • Need for greater cooperation and dialogue between consumer scientists and LCA experts to understand respective users’ data needs and better design data collection strategies (e.g. scope, type of data, descriptors)
  • Opportunity for LCA experts to utilise consumer science descriptors and classification of consumers’ types
  • Creation of open source database to share consumer study data and papers would be beneficial
  • New technologies (e.g. smart metering, data loggers) and approaches such as big data analysis will facilitate access to more and better quality data on behaviour of consumers

 

This one-day workshop has been very helpful for the RELIEF PhD students who are confident that the connections established with the invited researchers can be fruitful for future research. They were also very happy to see that all participants were enthusiastic to share knowledge and data.

RELIEF

Unilever Unilever

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641459

European Union