How effective is the European Union energy label? Evidence from a real-stakes experiment

Andor, Mark; Gerster, Andreas; Götte, Lorenz

doi:10.1088/1748-9326/ab05fe

Cited by 18 publications

(11 citation statements)

References 19 publications

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“…The relevance of such information substitution critically depends on the supply side and is moderate when producers mainly offer appliances that slightly exceed efficiency class thresholds, as is frequent practice. In addition, our findings on how class valuation and information substitu-tion effects can potentially offset each other help to rationalize why previous studies find only negligible effects of showing efficiency classes (Andor et al, 2019) and are thus important for understanding the effectiveness of energy labels.…”

Section: Discussionsupporting

confidence: 56%

“…A promising strategy to overcome such information substitution is the provision of operating cost information on the EU label, which has been suggested by some recent studies (Andor et al 2019, Andor et al 2017and Blasch et al 2017. By reducing cognitive effort, information on operating costs simplifies investment decisions and mitigates the attention crowding-out effect of energy efficiency classes.…”

Section: Discussionmentioning

confidence: 99%

“…Other studies have analyzed the implications of adding operating cost information to energy labels, yielding mixed results (Andor et al, 2017;Newell and Siikamäki, 2014;Stadelmann and Schubert, 2018). Using revealed rather than stated preferences, Andor et al (2019) demonstrate that providing lifetime energy cost information substantially increases the willingness-to-pay (WTP) for energy-efficient light bulbs, while the current EU label has no effect on WTP.…”

Section: Introductionmentioning

confidence: 98%

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Cognitive reflection and the valuation of energy efficiency

2019

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Based on a stated-choice experiment among about 3,600 German household heads on the purchase of electricity-using durables, this paper explores the impact of cognitive reflection on consumers' valuation of energy efficiency, as well as its interaction with consumers' response to the EU energy label. Using a standard cognitive reflection test, our results indicate that consumers with low cognitive reflection scores value energy efficiency less than those with high scores. Furthermore, we find that consumers with a low level of cognitive reflection respond more strongly to grade-like energy efficiency classes than to detailed information on annual energy use.

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Section: Discussionsupporting

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Cognitive reflection and the valuation of energy efficiency

2019

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“…So far, the use of labels that include a color scale has been implemented on light-bulb packages only regarding the energy-efficiency characteristics of the bulbs [11]. However, in the current study, our main objective is to reveal whether attaching health-related red/green traffic light icons (TLI) on light bulbs influences consumers' preferences towards choosing the healthier bulb and avoiding the energy-efficient bulb, which has been shown to be unhealthy [12].…”

Section: Introductionmentioning

confidence: 97%

‘To LED or Not to LED?’: Using Color Priming for Influencing Consumers’ Preferences of Light Bulbs

2021

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Recently, we have seen energy-efficient light-emitting diode (LED) light bulbs rapidly replace incandescent ones. However, results of new research are indicative of adverse health impacts of LED lighting, which is characterized by enriched blue light. Our study aims to reveal whether using color priming by attaching red/green traffic-light icons on light bulbs influences consumers’ preferences of light bulbs. We conducted a field study simulating the buying process, in which participants (N = 572) were presented with LED and carbon incandescent bulbs. We alternately displayed two pairs of bulbs: (1) in their original packaging and (2) in packages marked with traffic light icons (red = LED). Our results confirm that traffic light icons significantly (p < 0.01) increase the odds of choosing the healthier carbon bulb. The results highlight the benefits of attaching traffic light icons to light bulb packaging, helping consumers to make more health-conscientious purchasing decisions. Nowadays, this study’s contribution is more significant due to COVID-19 restrictions and stay-at-home policies, since people work or study remotely, which increases their exposure to household lighting. These results may incentivize policymakers to enforce adding traffic light icons to light bulb packaging, thus encouraging LED light bulb manufacturers to reduce the blue light component in order to improve the health aspect of their bulbs.

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“…Both in the European and American markets, it is mandatory to apply an Energy Label/Energy Star sticker on every product that indicates its energy efficiency so that customers can make an informed choice of the products their are buying. Of course, Energy Label/Energy Star influences consumer choice in making a purchase [3], making it extremely important for the manufacturers to get high scores on such rankings.…”

Section: Introductionmentioning

confidence: 99%

Laundry Fabric Classification in Vertical Axis Washing Machines Using Data-Driven Soft Sensors

et al. 2019

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Embedding household appliances with smart capabilities is becoming common practice among major fabric-care producers that seek competitiveness on the market by providing more efficient and easy-to-use products. In Vertical Axis Washing Machines (VA-WM), knowing the laundry composition is fundamental to setting the washing cycle properly with positive impact both on energy/water consumption and on washing performance. An indication of the load typology composition (cotton, silk, etc.) is typically provided by the user through a physical selector that, unfortunately, is often placed by the user on the most general setting due to the discomfort of manually changing configurations. An automated mechanism to determine such key information would thus provide increased user experience, better washing performance, and reduced consumption; for this reason, we present here a data-driven soft sensor that exploits physical measurements already available on board a commercial VA-WM to provide an estimate of the load typology through a machine-learning-based statistical model of the process. The proposed method is able to work in a resource-constrained environment such as the firmware of a VA-WM.

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How effective is the European Union energy label? Evidence from a real-stakes experiment

Cited by 18 publications

References 19 publications

Cognitive reflection and the valuation of energy efficiency

Cognitive reflection and the valuation of energy efficiency

‘To LED or Not to LED?’: Using Color Priming for Influencing Consumers’ Preferences of Light Bulbs

Laundry Fabric Classification in Vertical Axis Washing Machines Using Data-Driven Soft Sensors

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