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| 1 |
ID:
112298
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2012.
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| Summary/Abstract |
The processing costs of US corn ethanol have declined by 45% since 1983 as production volumes have increased seventeen-fold. We investigate the role of various factors that could explain this, including economies of scale, cumulative experience, induced innovation in response to rising input prices, an autonomous technological change, and trade induced competition from imported ethanol. Using data on dry-mill ethanol processing costs over the 1983-2005 period, we find evidence to show that US corn ethanol production exhibited decreasing returns to scale, that learning by doing played an important role in reducing these processing costs with a learning rate of 0.25, and that sugarcane ethanol imports contributed to making the corn ethanol industry more competitive. Other factors such as the rising prices of energy and labor did induce lower processing costs, but the effect is not statistically significant. The inclusion of these competing explanations for the reduction in processing costs of US corn ethanol lead to a significantly higher learning rate than otherwise, and this learning rate is found to be robust across specifications.
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| 2 |
ID:
176846
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We analyze pricing in PV systems using data from 2008-2014 to identify the effects of knowledge spillovers in reducing the installed cost of PV. This paper estimates the size of the effects of learning by doing and knowledge spillovers using multiple formulations of spillover related variables. We found knowledge spillovers between firms within a county to be a significant and substantial factor in reducing the costs of PV. However, these spillovers reduce costs only for firms over a certain size threshold, and no cost-reducing spillovers were found for smaller installers. Geographic spillovers, within a firm from one county to another were also significant although not as large as the local between-firm effects. We ran 43 specifications on the data and generally found these main results to be robust, although not in every specification. One implication of these results is that policies that subsidize demand for PV are leading to the creation of new knowledge that would not exist at lower levels of demand. That the spillovers become stronger at higher levels of experience suggests that these subsidies would need to be substantial, particularly in new markets with many small firms.
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| 3 |
ID:
096217
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2010.
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Teachers of political science have increasingly recognized the utility of classroom simulations to provide students with an active-learning experience to enhance learning outcomes. Our article builds on this growing trend by proposing a congressional simulation to help students understand the complexities and nuances of the lawmaking process. Specifically, the simulation aids students in identifying the deliberative aspects of congressional policymaking, appreciating the complicated process involved in a bill becoming a law, understanding the multifaceted ways in which self-interest guides the decisions of congressional actors, and challenging student cynicism about Congress as an institution.
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| 4 |
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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