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.