|Authors: ||A. Klein, K. Menrad|
|Keywords: ||food consumption, carbon footprint, segmentation|
In Germany, food consumption accounts for 14% of the average greenhouse gas (GHG) emissions of a citizen.
By changing personal food consumption patterns, it is possible for consumers to reduce their individual GHG-emissions.
Common recommendations concerning climate-friendly food choices are e.g. to eat seasonal or local fruit and vegetables or to avoid waste.
The aim of this research is to analyze how competent German consumers feel regarding these climate-friendly behavioral strategies and to investigate what measures could help consumers to make more climate-friendly food choices.
We use data from a focus group workshop as well as from a quantitative study based on 413 computer-assisted personal interviews with supermarket consumers conducted in 2014 in Southern Germany.
The predominantly female interviewees of this study state a high knowledge regarding waste reduction and the growing season of different produce.
However, it is not as easy for them to recognize produce imported by plane or locally-produced fruits and vegetables.
The results of a hierarchical cluster analysis (ward method) reveal three different consumer segments differing in their perceived competency regarding different climate-friendly strategies.
We find differences in socio-demographic and behavioral variables between the three clusters.
The focus group workshop and the quantitative study show that consumers evaluate a carbon label to be more suitable to support climate-friendly food choices than other instruments (e.g.
QR-Codes, general guidelines). The preference for specific measures differs between socio-demographic groups, but not between the identified consumer segments.
Our results show that a substantial part of consumers perceives a low to medium competency of specific climate-friendly food consumption instruments and thus needs some support regarding this issue.
Some suitable supporting strategies are discussed at the end of the manuscript.
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