LEARNING ENERGY EFFICIENCY: QUANTIFYING PRICE AND EFFICIENCY DYNAMICS OF HOUSEHOLD ENERGY DEMAND TECHNOLOGIES Martin Weiss Utrecht University, Copernicus Institute, Section of Science, Technology, and Society, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands Phone: +31-303-253-5144 Email: m.weiss@uu.nl Overview Martin Junginger University, Copernicus Institute, Households account for one-fourth of the final energy demand in industrialized countries. Utrecht Still, they offer large untapped Section of Technology, and Society, potentials for energy efficiency improvements. Utilizing these potentials requires the introduction ofScience, novel and efficient energy Heidelberglaan CS Utrecht, The Netherlands demand technologies. Introduction and market diffusion of such novel and efficient technologies2,is,3584 however, often hampered Phone: +31-303-253-7613 by high technology costs. Novel technologies are relatively expensive at the point of market introduction but eventually become Email: h.m.junginger@uu.nl cheaper due to mechanisms such as technological learning, innovation, and economies of scale. The combined effect of these mechanisms can be quantified by the so-called experience curve approach. This approach has been extensively applied to Martin renewable energy supply technologies. Its application to household energy demand technologies is, however, still scarce. Here,K. Patel Utrechtdemand University, Copernicus Institute, we apply the experience curve approach to analyze in first instance price dynamics household energy technologies. We Section of Science, Technology, and find a continuous and steady trend towards declining prices, albeit with differentiation of individual technologies. PricesSociety, Heidelberglaan 2, 3584 CS Utrecht, Netherlands decline with each doubling of cumulative production at rates of 6% for condensing gas boilers to 35% for heatThe pumps. Phone: +31-303-253-7601 Extending the conventional experience curve approach to specific energy consumption of large household appliances, we find a Email: m.k.patel@uu.nl trend towards improved energy efficiencies at rates of 20-35% for wet appliances and 13-17% for cold appliances with each doubling of cumulative production. Our analysis suggests that energy policy might be able to bend down the slope of energy experience curves, thereby highlighting the importance of energy policy as driver for efficiency improvements in household energy demand technologies. Methods The experience curve approach is an empirical concept hypothesizing that production costs of a technology decline at a constant rate with each doubling of cumulative production (BCG, 1968). The experience curve approach gained importance as management tool in manufacturing industries (Argote and Epple, 1990) and as instrument for technology forecasting in energy and CO2 emission scenarios (e.g., Wene et al., 2000). The experience curve approach has been extensively applied to and redefined for renewable energy supply technologies (e.g., Junginger et al., 2005). Its application to energy demand technologies for application in households is, however, still scarce. Here, we apply the experience curve approach to analyze price dynamics of condensing gas boilers, heat pumps, compact fluorescent light bulbs, and five large household appliances (washing machines, laundry dryers, dishwashers, refrigerators, and freezers). We model the specific price of household energy demand technologies as power-law function of cumulative production: Ccumi C0,i ( Pcumi )bi (1) where Ccumi [EUR2006/physical capacity] represents the specific price at Pcumi, C0,i [EUR2006/physical capacity] the specific price of the first unit produced, Pcumi [MW] the cumulative experience (i.e., the cumulative production), and bi the product-specific experience index of energy demand technology i. By applying the logarithmic function to Equation 1, we plot a linear experience curve with bi as slope parameter and log C0,i as intercept with the price axis. Based on the experience index bi, we calculate learning rates (LRi) [%] and progress ratios (PRi) [%] as rates, at which specific prices decline with each doubling of cumulative production: LRi 1 PRi 1 2 bi (2) We estimate the error interval of LRi and PRi based on the implicit error of the regression analysis, i.e., the 95% confidence interval of the slope parameter of the experience curve. In analogy, we extent the experience curve approach for large household appliances to specific energy consumption. We base our experience curve analysis on data reflecting specific prices and specific energy consumption of technologies sold in the Netherlands and Switzerland. Results Our experience curve analysis indicates a clear trend towards declining specific prices for the selected household energy demand technologies (Table 1). We explain the observed price reduction by technological learning, increased automation, and economies of scale in the manufacturing of components as well as semi-finished and finished products. Outsourcing of component production to low-wage regions such as Eastern Europe and China proved to be a major driver for declining production costs since the early 1990s. In addition, declining mark-ups in the wholesale and retail sector contributed substantially to the price decline observed in Table 1. Table 1: Learning rates for condensing gas boilers and large household appliances Technology Condensing gas space heating boilers Condensing gas combi boilers Compact fluorescent light bulbs Heat pumps Washing machines Laundry dyers Dishwashers Refrigerators Upright freezers Chest freezers Time period of analysis 1983-2006 1988-2006 1980-2004 1965-2008 1969-2003 1968-2007 1964-2008 1970-2003 1970-1998 LR in % (R2) 6 ± 2 (0.98) 14 ± 2 (0.98) 19 ± 5 (0.89) 35 ± 1 (0.99) 33 ± 9 (0.56) 28 ± 7 (0.80) 27 ± 7 (0.82) 9 ± 4 (0.43) 10 ± 5 (0.59) 8 ± 2 (0.87) Our methodological extension of the experience curve approach to specific energy consumption is new and provides interesting insights into the efficiency dynamics of large appliances. We find a continuous trend towards a decline of specific energy consumption, albeit with differentiation for wet appliances (LR of 18-35%) and cold appliances (LR of 13-17%). For dishwashers, refrigerators, and freezers, our data indicate an accelerated decline of specific energy consumption in recent years. This observation might be attributed to European and National energy policies (e.g., implementation of European energy labelling (EU, 1992) and Energy Premium Regulation in the Netherlands). Our findings suggest that energy policy is to some extent able to bend down the slope of energy experience curves. This finding is interesting because it contrasts the general observation that policies are only able to accelerate the riding-down of cost experiences by enabling faster market diffusion of technologies. Conclusions Experience curve analyses for energy demand technologies are still scarce to date. Here, we address this knowledge gap by developing experience curves for various household energy demand technologies. We regard the experience curve approach applicable and useful for analyzing price dynamics of these technologies. For the past decades, we identify a steady trend towards reducing technology prices due to mechanisms such as technological learning and economies of scale. We thus argue that introducing novel and initially expensive energy efficiency technologies must not permanently lead to high technology prices. Applying the experience curve approach to the specific energy consumption of large household appliances is new and reveals useful insights into the dynamics of energy efficiency improvements. Our results suggest that energy policy might be able to bend down the slope of energy experience curves, thereby accelerating energy efficiency improvements. References Argote, L., Epple, D. (1990): Learning curves in manufacturing. Science, 247, pp. 920-924. BCG (1968) Perspectives on experience. BCG - Boston Consulting Group Inc., Boston, USA. EU (1992): Council Directive 92/75/EEC of 22 September 1992 on the indication by labelling and standard product information of the consumption of energy and other resources by household appliances. EU - European Union. Official Journal L 297, 13 October 1992. pp. 16–19. Junginger, M. (2005): Learning in renewable energy technology development. Dissertation Utrecht University, Utrecht, The Netherlands. Wene, C.-O., Voß, A., Fried, T., 2000. Experience curves for policy making – the case of energy technologies. Band 67, Institut für Energiewirtschaft und Rationelle Energieanwendung. Universität Stuttgart, Germany.