چشم انداز اقتصادی در منحنی تجربه و اقتصادهای پویا در فن آوری های انرژی های تجدید پذیر
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|23311||2006||11 صفحه PDF||سفارش دهید||6830 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Policy, Volume 34, Issue 4, March 2006, Pages 422–432
This paper analyzes dynamic economies in renewable energy technologies. The paper has two contributions. The first is to test the robustness of experience in solar photovoltaic, solar thermal and wind energy to the addition of an explicit time trend, which has been done in experience studies for other industries, but not for renewable energy technologies. Estimation is carried out on the assumption that cumulative capacity, industry production, average firm production, and electricity generation affect experience and thus the fall in price. The second contribution is to test the impact of R&D on price reduction. In general cumulative experience is found to be highly statistically significant when estimated alone, and highly statistically insignificant when time is added to the model. The effect of R&D is small and statistically significant in solar photovoltaic technology and statistically insignificant in solar thermal and wind technologies.
The success of the renewable energy industry's expansion can be measured both by the level of cost reduction and the extent of market penetration of renewable technologies. In the past twenty years, cost reduction has been more than expected, whereas market penetration has been markedly lower than expected (Darmstadter, 2000). Accordingly, many researchers have focused on evaluating the process of cost reduction over time, which has led to the application of ‘experience’ or ‘learning’ curve analysis to renewable energy technologies. Governments have supported renewable energy through policies such as R&D funding and price subsidisation. However, questions have surfaced in recent years concerning how much support a technology needs to become competitive.1 Experience curves, named after the inverse relationship observed between cost and cumulative output, have been adopted as a tool to help answer such questions because they provide a simple quantitative relationship between cost and the cumulative production of a technology. Recent work analyzing the long-run cost potential of renewable energy technologies using experience curves includes Matsson and Wene (1997), Neij, 1997 and Neij, 1999, IEA (2000), Neij et al. (2003), and Moor et al. (2003). The notion of experience as a driver of cost reduction is an attractive one. It refers to the process whereby people become better at doing what they do over time, leading to efficiency increases—and thus permanent cost reductions—at the firm level. The simplicity and universality of the experience, or learning, framework has led researchers to apply it to everything from airplane manufacturing to chemical processing, textiles production, and nuclear plant operation.2 Coined by the Boston Consulting Group in the 1960s as a tool to advise clients on competitive strategy, the experience curve concept was adapted from the ‘learning-by-doing’ literature in economics (Wright, 1936, Hirsch, 1952, Arrow, 1962 and Alchian, 1963). The popularity of the experience curve reached a peak in the mid-1970s, and firms were advised to expand output in order to deter entry and gain a long-term cost advantage over rivals. Unfortunately many of these strategies failed because firms did not consider the effect of knowledge diffusion, and the concept lost its favour (Lieberman, 1987). Newfound interest in experience curves has arisen in recent years as governments search for policies to address climate change. Unlike the previous generation of experience curves, when the focus was on production planning or strategic management, the focus in current energy technology applications has shifted to endogenous technical change and the use of reliable estimates of technological learning rates as inputs in energy forecasting models (McDonald and Schrattenholzer, 2001). The effect of R&D on an industry's capacity to decrease cost is analogous experience, in that it brings about dynamic economies, or downward shifts in the cost curve (see Spence, 1981 and Spence, 1986). R&D effects can also interact with experience curve effects to increase the pace of dynamic savings. It is thus desirable to test the impact of R&D on cost reduction in renewable energy technologies. This paper analyzes dynamic economies in renewable energy technologies. The paper has two contributions. The first is to test the robustness of experience curves to the addition of an explicit time trend, as has been done in other industry studies such as has been done by Sheshinski (1967), and Lieberman (1984), both of whom found time to be minor or insignificant factors in comparison to learning. A more detailed explanation for the addition of time is presented in Section 2. The second contribution is to test the impact of R&D. Results are presented for solar photovoltaic (PV), solar thermal, and wind technologies. The results obtained in the current study indicate that experience is not robust to the addition of a time trend, and that R&D has in general performed poorly in solar and wind technologies. More specifically, (a) experience estimates in the solar industry in the US and Europe are either substantially reduced or statistically insignificant, depending on the experience index; (b) experience estimates in the wind industry in Europe are statistically insignificant for all indices, however, when the effect of better wind capture is incorporated in prices, the estimate is borderline significant at the 10% level; (c) a panel of both solar and wind data yields insignificant experience estimates; and (d) the effect of R&D is small and statistically significant in solar PV but statistically insignificant in solar thermal and wind technologies. The rest of the paper is organized as follows. Section 2 presents the empirical framework for estimation, the empirical results, and the results discussion. Section 3 concludes.
نتیجه گیری انگلیسی
Studies conducted on experience in renewable energy, such as those cited in the previous section, have estimated PR s using the traditional methodology, as defined in Eqs. ( 2.3), when n=i=1n=i=1, and ( 2.4). However, the experience curve results in this paper indicate that although single experience indices may be highly statistically significant, whether estimated for individual technologies and countries or combined in panels, most of these estimates become insignificant when a time trend is included as an explanatory variable. These results support the introduction of imperfect foresight and stochastic uncertainty of learning rates in energy system models ( Mazzola and McCardle, 1996, Matsson, 2002 and Grubb et al., 2002). R&D funds are also cited as important tools in the advancement of renewables (IEA, 2000 and Department of Energy, 2001). However in comparison to other energy technologies, renewable energy R&D lags far behind, an empirical regularity supported by the results presented in Section 2. Thus a review of this paper's conclusions and recommendations are as follows. We are interested in the reliable estimation and forecasting of renewable energy technology costs, for the purpose of reducing the uncertainty surrounding policy decisions to help increase clean energy generation. Past renewable energy studies have used the experience curve methodology to estimate cost patterns on the basis of a price proxy. The results presented here caution against the estimation of experience on the basis of a single right-hand side variable as this may mask underlying statistical insignificance. One way to address this problem is to incorporate stochastic learning curve uncertainty into energy models. Another solution is to improve estimates by controlling for the biases that the estimation methodology and the use of price gives rise to. This latter solution requires data on government subsidisation rates and market concentration, as well as larger data sets. Cost estimation and forecasting must also go hand in hand with increased funds allocated to R&D and commercialization initiatives, because the impact of R&D and market penetration for renewables has been poor.