Large appliances are major power consumers in households of industrialized countries. Although their energy efficiency has been increasing substantially in past decades, still additional energy efficiency potentials exist. Energy policy that aims at realizing these potentials faces, however, growing concerns about possible adverse effects on commodity prices. Here, we address these concerns by applying the experience curve approach to analyze long-term price and energy efficiency trends of three wet appliances (washing machines, laundry dryers, and dishwashers) and two cold appliances (refrigerators and freezers). We identify a robust long-term decline in both specific price and specific energy consumption of large appliances. Specific prices of wet appliances decline at learning rates (LR) of 29±8% and thereby much faster than those of cold appliances (LR of 9±4%). Our results demonstrate that technological learning leads to substantial price decline, thus indicating that the introduction of novel and initially expensive energy efficiency technologies does not necessarily imply adverse price effects in the long term. By extending the conventional experience curve approach, we find a steady decline in the specific energy consumption of wet appliances (LR of 20-35%) and cold appliances (LR of 13-17%). Our analysis suggests that energy policy might be able to bend down energy experience curves.

@en

Transitioning towards a sustainable energy system requires the large-scale introduction of novel energy demand and supply technologies. Such novel technologies are often expensive at the point of their market introduction but eventually become cheaper due to technological learning. In order to quantify potentials for price and cost decline, the experience curve approach has been extensively applied to renewable and non-renewable energy supply technologies. However, its application to energy demand technologies is far less frequent. Here, we provide the first comprehensive review of experience curve analyses for energy demand technologies. We find a widespread trend towards declining prices and costs at an average learning rate of 18 ± 9%. This finding is consistent with the results for energy supply technologies and for manufacturing in general. Learning rates for individual energy demand technologies are symmetrically distributed around the arithmetic mean of the data sample. Absolute variation of learning rates within individual technology clusters of 7 ± 4%-points and between technology clusters of 7 ± 5%-points both contribute to the overall variability of learning rates. Our results show that technological learning is as important for energy demand technologies as it is for energy supply technologies. Applying the experience curve approach to forecast technology costs involves, however, unresolved uncertainties, as we demonstrate in a case study for the micro-cogeneration technology.

@en

High costs often prevent the market diffusion of novel and efficient energy technologies. Monitoring cost and price decline for these technologies is thus important in order to establish effective energy policy. Here, we present experience curves and cost-benefit analyses for condensing gas boilers produced and sold in the Netherlands between 1981 and 2006. For the most dominant boiler type on the Dutch market, i.e., condensing gas combi boilers, we identify learning rates of 14±1% for the average price and 16±8% for the additional price relative to non-condensing devices. Economies of scale, competitive sourcing of boiler components, and improvements in boiler assembly are among the main drivers behind the observed price decline. The net present value of condensing gas combi boilers shows an overall increasing trend. Purchasing in 2006 a gas boiler of this type instead of a non-condensing device generates a net present value of 970 EUR (Euro) and realizes CO₂ (carbon dioxide) emission savings at negative costs of -120 EUR per tonne CO₂. We attribute two-thirds of the improvements in the cost-benefit performance of condensing gas combi boilers to technological learning and one-third to a combination of external effects and governmental policies.

@en

We present and apply a simple bottom-up model for estimating non-energy use of fossil fuels and resulting CO2 (carbon dioxide) emissions. We apply this model for the year 2000: (1) to the world as a whole, (2) to the aggregate of Annex I countries and non-Annex I countries, and (3) to the ten non-Annex I countries with the highest consumption of fossil fuels for non-energy purposes. We find that worldwide non-energy use is equivalent to 1,670 ± 120 Mt (megatonnes) CO2 and leads to 700 ± 90 Mt CO2 emissions. Around 75% of non-energy use emissions is related to industrial processes. The remainder is attributed to the emission source categories of solvent and other product use, agriculture, and waste. Annex I countries account for 51% (360 ± 50 Mt CO2) and non-Annex I countries for 49% (340 ± 70 Mt CO2) of worldwide non-energy use emissions. Among non-Annex I countries, China is by far the largest emitter of non-energy use emissions (122 ± 18 Mt CO2). Our research deepens the understanding of non-energy use and related CO2 emissions in countries for which detailed emission inventories do not yet exist. Despite existing model uncertainties, we recommend NEAT-SIMP to inventory experts for preparing correct and complete non-energy use emission estimates for any country in the world.@en

Non-energy use of fossil fuels accounts for 7% of the Total Primary Energy Supply (TPES) of Germany and represents an important potential source of CO2 (carbon dioxide) emissions. To gain a better understanding of emissions associated with non-energy use in Germany, we conduct a bottom-up carbon flow analysis with the Non-energy use Emission Accounting Tables (NEAT) model for the period of 1990-2003. We calculate average yearly non-energy use emissions to be 25 ± 2 megatonnes (Mt) CO2, of which 77% are related to industrial processes, 17% to solvent and other product use, 2% to fertilizer use in agriculture, and 4% to wastewater treatment. The comparison of NEAT estimates and official data reveals gaps and errors in the German greenhouse gas (GHG) inventory. This research highlights the difficulties associated with non-energy use emissions accounting not only in Germany but in other countries as well. To ensure correct calculation of non-energy use emissions, we recommend that inventory experts (i) obtain detailed insight into the system boundaries of non-energy use data as stated in national energy statistics, (ii) allocate non-energy use emissions accordingly to the relevant emission source categories (i.e., energy, industrial processes, solvent and other product use, agriculture, or waste), (iii) ensure completeness of emission estimates, and (iv) be cautious with the use of default emission factors as given by the Intergovernmental Panel on Climate Change (IPCC).@en