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Cost analysis of screening methods to find non-compliant models using the example of tumble dryers
(2019)
The European energy label was established to reduce energy consumption in Europe. All classes and values on the energy label are stated by the supplier. The task of market surveillance is to ensure correct labels and to validate their values through compliance tests. However, this can only be done for a small fraction of all models on the market, since physical tests are expensive. Screening methods can be used to narrow down the number of critical products before compliance tests are done to decrease the costs of finding non-compliant models.
This study shows a mathematical approach to analyze the cost benefits of screening methods to find non-compliant models. Furthermore, the analysis presented has been applied to 18 screening methods for tumble dryers. The performance test for tumble dryers consists of seven test runs. All screening methods evaluated are composed of a reduced number of test runs. The most efficient screening method reduces the surveillance costs by 40 %.
The energy label is a tool that informs the consumer about the energy consumption of certain appliances. The tumble dryer is one of these appliances that were considered relevant for receiving an energy label in the European Union. All classes and values on the energy label are stated by the manufacturers. In order to guarantee that the manufacturers do not cheat and appliances are labeled correctly, the stated values are checked by the market surveillance of each country in compliance verification tests. However, in the case of tumble dryers these compliance verification tests are very expensive and can only be realized for a small sample size out of models which are available on the market. The fewer compliance verification tests are carried out, the higher is the chance that there are incorrectly labeled appliances on the market. Therefore, a modification of the test standard for tumble dryers has been analyzed which would reduce the costs of a performance test by at least 11 %.