National Workshops Bottom up example – Energy labelling for white appliances

1. National WorkshopsBottom up example – Energy labelling for white appliances Pascal LARSONNEUR Robert ANGIOLETTI 22nd January, 2007

2. Type of EEI activities covered Sector Residential Energy end-use White goods Cold appliances Washing machines Efficient solution A class appliances  A+ and A++ for cold appliances  < 0,17 kWh/kg for washing machines Harmonised bottom-up evaluation method for cold appliances and washing machines Name/Organisation etc.

3. Regulation: Minimum Equipment Energy Performance Standards Information: Focused information campaigns Labelling Training and Education Metering and informative billing Financial instruments for energy savings: Cash rebates Tax rebates and other taxes reducing energy end-use consumption Third-party financing Loans Voluntary agreements: Industrial companies (appliance manufacturers) Commercial (appliance trade) or industrial organisations Types of EEI facilitating measures Name/Organisation etc.

4. Cold appliances The energy label provides data on the annual unit consumption Unit gross annual energy savings of cold appliances = ( [annual energy consumption] *BL - [annual energy consumption]* BAT )* F Where: BL = baseline  In case of normal replacement, the baseline should be, either the second best energy class appliances (currently A+ ), which is a very conservative option, or the average of the A+ to C appliances on offer  In case of early replacement: average stock appliance BAT (best available technology): the best energy class on the market (currently A++) F: correction factor reflecting the ratio of the consumption under the standard test conditions and the measured consumption in real life Formula for unitary gross annual energy savings Name/Organisation etc.

5. Energy label annual unit consumption Cold appliances Name/Organisation etc.

6. Washing machines The energy label displays the standard cycle consumption It is therefore necessary to estimate the average number of cycles per year Unit gross annual energy savings of washing machines = ( [CC*AC]*BL - [CC*AC]*BAT )* F Where: CC: Cycle Consumption as stated on the energy label AC: Annual number of Cycles for the average household BL = baseline, depending on the level of effort  In case of normal replacement: second best energy class appliances (currently A );  In case of early replacement: average stock appliance; BAT (best available technology): the best energy class on the market (currently A+) F: correction factor reflecting the ratio of the consumption under the standard test conditions and the measured consumption in real life Formula for unitary gross annual energy savings Name/Organisation etc.

7. Energy label annual unit consumption Washing machines Name/Organisation etc.

8. Two approaches must be used:  Consumers willing to purchase a more efficient appliance than they would have done without the EEI promotion measure.  The measure may encourage early replacement. In the first case, a market modelling approach should define the baseline, since the savings refer to the appliance that would have been purchased if the measure was not implemented. Level 1: EU 27 not efficient appliances average unit consumption Level 2: country not efficient appliances average unit consumption In the latter case, a stock modelling approach should define the baseline: the savings are estimated with reference to an appliance that would have normally been kept in the stock, still functioning, if the measure not was applied (level 3). Baseline Name/Organisation etc.

9. Market modelling Market share (%), average EEI, average UC (kWh/y) Name/Organisation etc.

10. Stock UC of year N for saturated markets UCn = UCn-1 – S/V * (UCn-1 – UCsn) Where UCn is the UC of the consumers' stock at the end of year N UCn-1 is the UC of the consumers' stock at the end of year N-1 UCsn is the UC of the sales of year N, as calculated above S is the sales volume over one year (constant over the base period) V is the consumers' stock volume (constant over the base period) Stock modelling (1) Name/Organisation etc.

11. Stock UC of year N for non-saturated markets Sold appliances are obviously not only intended for replacement but also for first purchase, thereby increasing the equipment rate. The stock volume must therefore be calculated by estimating the ratio of sold appliances purchased for replacement and the ratio of first purchase. The average stock UC at the end of year N becomes: UCn = UCn-1 – Sn/Vn-1 . (UCn-1 – UCsn) Where Sn is now the sales volume of year N and Vn-1 the stock volume at the end of year N-1. Stock modelling (2) Name/Organisation etc.

12. There could be a rebound effect in the purchase of bigger cold appliances, knowing they are energy efficient. However, no information is available (this could be the subject of a model survey in a set of countries). The same is the case whether consumers are washing more often because they know that they purchased an energy-efficient washing machine. One direct rebound effect could also be linked to the fact that it is easier for larger washing machines (6, 7 or more kg of capacity) to achieve the threshold of 0.17 kWh/kg. So an EEI measure to promote energy-efficient washing machines could accelerate the shift towards bigger machines. Considering rebound effect Name/Organisation etc.

13. White goods are already included in the EU energy labelling scheme, and there is a minimum energy performance standard for cold appliances. Furthermore, the EcoDesign Directive is expected to lead to a new minimum energy performance standards for cold and wet appliances, and a revision of the EU energy label. Therefore, double-couting with these measures is likely to be an issue. It will be smaller, if the method presented here is only applied to facilitating measures that promote A++ cold appliances and very energy-efficient washing machines. In any case, two general recommendations can be made:  Try to evaluate the effect of the whole package of facilitating measures that promote energy-efficient cold appliances and washing machines;  Try to cross-check the results with a top-down evaluation using regression analysis of the diffusion indicators for the overall market share of the A++ cold appliances and the very energy-efficient washing machines, and/or for the average UC of all appliances sold in a Member State market. Requirements for double counting Name/Organisation etc.

14. Partial free-rider effect occurs for white appliances as a consequence of the self-sustained evolution of the market: higher demand for efficient appliances contributes to a larger and more competitive offer of best technologies. Free-rider effect is therefore due to those consumers who would have purchased the most efficient appliances, even if the measure were not implemented. A survey of participants will be needed to evaluate the share of free riders. As the free-rider effect and the multiplier effect work into the opposite direction, they partly cancel out each other. Furthermore, even with extensive surveys and evaluation efforts, the uncertainty for these effects can remain high. For EEI measures or measure packages that have limited gross annual energy savings (e.g., below 20 million kWh/year), it may therefore be considered to neglect both correction factors. Requirements for free-rider effect Name/Organisation etc.

15. Energy savings lifetime Efficient cold appliances : 15 years Efficient wet appliances : 12 years Source : CWA CEN Workshop 27 Saving lifetimes of energy efficient improvement measures in bottom up calculations Requirements for savings lifetime Name/Organisation etc.

16. Total gross annual energy savings = average annual energy savings per sold unit of equipment * number of sold equipment Total gross annual energy savings Name/Organisation etc.

17. Total ESD annual energy savings = total gross annual energy savings of all white appliances * double-counting factor * (1-free-rider coefficient + multiplier coefficient) Total ESD annual energy savings Name/Organisation etc.

18. Data collection (1) Name/Organisation etc.

19. Data collection (2) Name/Organisation etc.