4.7 Article

Appliance energy labels and consumer heterogeneity: A latent class approach based on a discrete choice experiment in China

Journal

ENERGY ECONOMICS
Volume 90, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.eneco.2020.104839

Keywords

Energy label; Energy efficient appliances; Discrete choice experiment; Latent class model; Willingness to pay

Categories

Funding

  1. National Natural Science Foundation of China [71673134, 71834003, 71922013]

Ask authors/readers for more resources

Given the growing concerns about environmental protection, a focus on energy label behavior is of particular public interest, as labels can communicate to consumers the sustainability of products. Based on a discrete choice experiment, we measure consumers' awareness and attitudes regarding refrigerators and washing machines. A mixed logit model is specified to quantify the attributes consumers look for when choosing the two electrical appliances. In the latent class model, four classes are observed. The results of the study reveal that the energy label program in China is effective. However, consumers do not always choose the energy efficient appliances, and their failure to do so is often related to an energy efficiency gap. Interestingly, both the largest groups for the refrigerator (33.17%) and washing machine (36.6%) tend to prefer the two electrical appliances with the lowest price. Both are foreign brands, with low energy label and larger overall capacity. Consumers are also willing to pay more for an improved energy grade label on the refrigerator (731.16 yuan) than on the washing machine (424.76 yuan). Suggestions regarding how to increase the probability of consumers choosing energy efficient appliances are also given in this paper. (c) 2020 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available