Search Conferences

Type in any word, words or author name. This searchs through the abstract title, keywords and abstract text and authors. You may search all conferences or just select one conference.

 All Conferences
 EMAC 2019 Annual Conference
 EMAC 2020 Annual Conference
 EMAC 2020 Regional Conference
 EMAC 2021 Annual Conference
 EMAC 2021 Regional Conference
 EMAC 2022 Annual
 EMAC 2022 Regional Conference
 EMAC 2023 Annual
 EMAC 2023 Regional Conference

EMAC 2023 Annual

The Predictive Influence of a Climate Score Label on Real Purchase Behavior at the POS

Published: May 24, 2023


Jessica Mazurek, HHL Leipzig Graduate School of Management; Florian Skwara, HHL Leipzig Graduate School of Mangement; Stephanie Neidlinger, Helmut-Schmidt-University Hamburg


Due to the considerable impact of food production and consumption on the climate, the introduction of a uniform carbon labeling system is foreseeable. As of today, it is not widely researched if a carbon label can shift consumer behavior towards more climate-friendly consumption as empirical studies assessing real purchase behavior are scarce. In previous studies, carbon labels were found to predict climate-friendly purchase behavior. However, results mostly relate to purchase intention for labeled products which do not translate into greener purchases. Against the background of the attitude-behavior-gap, the hurdles associated with methodological bias make it difficult to conduct field experiments. Based on a field experiment in three German retail stores, we investigate whether a multilevel color-coded climate score label at the POS can nudge consumers to buy more climate-friendly food. For four fruits and vegetables, we computed and displayed climate scores for a duration of 24 days. Using hierarchical linear modeling, we examine whether purchases of less carbon-intensive products increased by comparing the experiment time period with the time period before the introduction of the label. We contribute to literature by analyzing real purchase data via a field experiment using a multilevel easy-to-understand climate score label, which can be applied across food categories.