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| 1 |
ID:
150901
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| Summary/Abstract |
This paper develops a model that explains the delay of decisions to adopt profitable energy-saving investments. This problem is known as the energy paradox. The model rationalizes the profitability requirements raised by the irreversibility, the uncertainty and the decrease of costs as a result of learning by doing. In this context, the wait gives investors more visibility and more lower investment costs, which gives them an option value. The representative agent has an interest to postpone its energy saving decision until future benefits increase and equalize its required option value. Formally, we internalize these explanatory factors in a stochastic model where the updated energy saving benefits follows a geometric Brownian motion. To affirm the capacity of the model, we generate simulation results for two equipments for electrical uses. Beyond that, we extend the model to simulate the effects of energy policy instruments to promote adoption of such equipments. Simulations prove that the taxation of energy prices is likely to be more effective than the subsidy for energy-saving equipments. It is also found that the combination of these instruments amplifies the adoption of energy-saving equipments and generates very favorable economic and environmental externalities.
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| 2 |
ID:
111089
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2012.
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| Summary/Abstract |
Increasing awareness on environmental damage and climate change has induced many nations to engage in green growth. South Korea, as one of the largest consumers of energy, is no exception and has shown its determination to pursue green growth in the future. In order to do so, South Korea plans to substitute fossil fuel with alternative sources in electricity generation. However, the key constraint to green growth is the high cost faced by renewable electricity generation. Fortunately, nuclear energy can serve as an economic alternative to fossil fuel. To achieve CO2 emission reduction and faster economic growth, it is wise to analyze prospects of alternatives using experience curve framework. The results and industry background are consistent for nuclear technology, and the results suggest that nuclear should serve as the main substitute. Consideration of policy risk inherent in renewable also strengthens the argument. Renewable technologies, on the other hand, showed overstated learning capacity that is partially inconsistent with technological background. Nevertheless, the renewable (photovoltaic and fuel cell2) should help nuclear marginally in substituting fossil fuel in South Korea's Electricity Generation.
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| 3 |
ID:
093528
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2010.
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High crude oil prices, uncertainties about the consequences of climate change and the eventual decline of conventional oil production raise the prospects of alternative fuels, such as biofuels. This paper describes a simple probabilistic model of the costs of energy crops, drawing on the user's degree of belief about a series of parameters as an input. This forward-looking analysis quantifies the effects of production constraints and experience on the costs of corn and sugar cane, which can then be converted to bioethanol. Land is a limited and heterogeneous resource: the crop cost model builds on the marginal land suitability, which is assumed to decrease as more land is taken into production, driving down the marginal crop yield. Also, the maximum achievable yield is increased over time by technological change, while the yield gap between the actual yield and the maximum yield decreases through improved management practices. The results show large uncertainties in the future costs of producing corn and sugar cane, with a 90% confidence interval of 2.9-7.2$/GJ in 2030 for marginal corn costs, and 1.5-2.5$/GJ in 2030 for marginal sugar cane costs. The influence of each parameter on these supply costs is examined.
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| 4 |
ID:
149915
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| Summary/Abstract |
Experience curves are useful for understanding technology development and can aid in the design and analysis of market transformation programs. Here, we employ a novel approach to create experience curves, to examine both global and North American compact fluorescent lamp (CFL) data for the years 1990–2007. We move away from the prevailing method of fitting a single, constant, exponential curve to data and instead search for break points where changes in the learning rate may have occurred. Our analysis suggests a learning rate of approximately 21% for the period of 1990–1997, and 51% and 79% in global and North American datasets, respectively, after 1998. We use price data for this analysis; therefore our learning rates encompass developments beyond typical “learning by doing”, including supply chain impacts such as market competition. We examine correlations between North American learning rates and the initiation of new programs, abrupt technological advances, and economic and political events, and find an increased learning rate associated with design advancements and federal standards programs. Our findings support the use of segmented experience curves for retrospective and prospective technology analysis, and may imply that investments in technology programs have contributed to an increase of the CFL learning rate.
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| 5 |
ID:
125463
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2013.
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| Summary/Abstract |
Minimum efficiency performance standards (MEPS) are an important policy instrument to raise the efficiency of products. In most schemes the concept of life cycle costs (LCC) is used to guide setting the MEPS levels. Although a large body of literature shows that product cost is decreasing with increasing cumulative production, the experience curve, this is currently not used for setting MEPS. This article shows how to integrate the concept of the experience curve into LCC calculations for setting MEPS in the European Union and applies this to household laundry driers, refrigerator-freezers and televisions. The results indicate that for driers and refrigerator-freezers at least twice the energy savings compared to the current approach can be achieved. These products also show that energy label classes can successfully be used for setting MEPS. For televisions an experience curve is provided, showing a learning rate of 29%. However, television prices do not show a relation with energy efficiency but are to a large extent determined by the time the product is placed on the market. This suggests to policy makers that for televisions and other products with a short (re)design and market cycle timing is more important than the MEPS levels itself.
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| 6 |
ID:
088043
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2009.
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| Summary/Abstract |
The US is currently the world's largest ethanol producer. An increasing percentage is used as transportation fuel, but debates continue on its costs competitiveness and energy balance. In this study, technological development of ethanol production and resulting cost reductions are investigated by using the experience curve approach, scrutinizing costs of dry grind ethanol production over the timeframe 1980-2005. Cost reductions are differentiated between feedstock (corn) production and industrial (ethanol) processing. Corn production costs in the US have declined by 62% over 30 years, down to 100$2005/tonne in 2005, while corn production volumes almost doubled since 1975. A progress ratio (PR) of 0.55 is calculated indicating a 45% cost decline over each doubling in cumulative production. Higher corn yields and increasing farm sizes are the most important drivers behind this cost decline. Industrial processing costs of ethanol have declined by 45% since 1983, to below 130$2005/m3 in 2005 (excluding costs for corn and capital), equivalent to a PR of 0.87. Total ethanol production costs (including capital and net corn costs) have declined approximately 60% from 800$2005/m3 in the early 1980s, to 300$2005/m3 in 2005. Higher ethanol yields, lower energy use and the replacement of beverage alcohol-based production technologies have mostly contributed to this substantial cost decline. In addition, the average size of dry grind ethanol plants increased by 235% since 1990. For the future it is estimated that solely due to technological learning, production costs of ethanol may decline 28-44%, though this excludes effects of the current rising corn and fossil fuel costs. It is also concluded that experience curves are a valuable tool to describe both past and potential future cost reductions in US corn-based ethanol production.
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| 7 |
ID:
088250
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2009.
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| Summary/Abstract |
In this paper, we investigate the use of learning curves for the description of observed cost reductions for a variety of energy technologies. Starting point of our analysis is the representation of energy processes and technologies as the sum of different components. While we recognize that in many cases "learning-by-doing" may improve the overall costs or efficiency of a technology, we argue that so far insufficient attention has been devoted to study the effects of single component improvements that together may explain an aggregated form of learning. Indeed, for an entire technology the phenomenon of learning-by-doing may well result from learning of one or a few individual components only. We analyze under what conditions it is possible to combine learning curves for single components to derive one comprehensive learning curve for the total product. The possibility that for certain technologies some components (e.g., the primary natural resources that serve as essential input) do not exhibit cost improvements might account for the apparent time dependence of learning rates reported in several studies (the learning rate might also change considerably over time depending on the data set considered, a crucial issue to be aware of when one uses the learning curve methodology). Such an explanation may have important consequences for the extent to which learning curves can be extrapolated into the future. This argumentation suggests that cost reductions may not continue indefinitely and that well-behaved learning curves do not necessarily exist for every product or technology. In addition, even for diffusing and maturing technologies that display clear learning effects, market and resource constraints can eventually significantly reduce the scope for further improvements in their fabrication or use. It appears likely that some technologies, such as wind turbines and photovoltaic cells, are significantly more amenable than others to industry-wide learning. For such technologies we assess the reliability of using learning curves at large to forecast energy technology cost reductions.
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