یادگیری فناوری در سیستم های اقتصاد کوچک باز ، مدل سازی و بهره برداری از اثر یادگیری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|29430||2011||12 صفحه PDF||سفارش دهید||9634 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Policy, Volume 39, Issue 5, May 2011, Pages 2361–2372
This paper reviews the characteristics of technology learning and discusses its application in energy system modelling in a global–local perspective. Its influence on the national energy system, exemplified by Norway, is investigated using a global and national Markal model. The dynamic nature of the learning system boundary and coupling between the national energy system and the global development and manufacturing system is elaborated. Some criteria important for modelling of spillover 1 are suggested. Particularly, to ensure balance in global energy demand and supply and accurately reflect alternative global pathways spillover for all technologies as well as energy carrier cost/prices should be estimated under the same global scenario. The technology composition, CO2 emissions and system cost in Norway up to 2050 exhibit sensitivity to spillover. Moreover, spillover may reduce both CO2 emissions and total system cost. National energy system analysis of low carbon society should therefore consider technology development paths in global policy scenarios. Without the spillover from international deployment a domestic technology relies only on endogenous national learning. However, with high but realistic learning rates offshore floating wind may become cost-efficient even if initially deployed only in Norwegian niche markets.
The development of the national energy system depends on a large number of factors external to the system but strongly influencing the choice of technology and energy carriers. A taxonomy of such factors used by Wene and Rydén (1988) is: the availability of domestic energy sources, cost of imported energy carriers, development of the energy technology, environmental constraints and energy demand. The taxonomy includes global, regional and national factors. A corresponding global–local perspective is thus called for national energy planning and analysis. For example, there may be a choice between local-renewable energy and fossil energy from the global market. While the price of fossil fuel is mainly determined by the balance of regional supply and demand, the exploitation of local renewable energy requires energy conversion technologies which costs and technical performances are largely determined in the global technology markets. From the national perspective the cost reductions of the nascent energy technologies are seen as spillover of technology learning from the global technology market. Understanding the forces of technological change and incorporating them in energy–economic–environmental (EEE) models have received increasing attention during the last decade. Different global EEE models provide a variety in their results with respect to future technology composition and total system cost, but concur that experience fosters technology learning (TL) and is an important factor affecting the cost of the transition to a sustainable energy system (Edenhofer et al., 2006). The starting point of the analysis presented is the assumption that in a small open economy spillover from the global market will in most cases be more important for the price of new energy technologies than the experience gained in the national market. However, in the very early stages of technology development learning in the national market may dominate. While TL reduces costs, national circumstances may require adaptation of a technology and thereby increase in costs. In the long run, though, it is also a source of learning and thus indirectly contributes to cost reductions. The aim of this paper is to contribute to the understanding and modelling of the effect of technological change on the national energy system of a small open economy. It is exemplified by Norway. In Norway, primary energy sources are abundant, particularly wind offshore and natural gas. There is also potential for storage of CO2 underneath the sea bed. There is thus ample potential for electricity generation with low or zero CO2 emissions. The development of the Norwegian energy system towards low emissions of CO2 may thus follow a variety of technology paths, depending on the cost development and performance of the nascent energy technologies available in the global market. Moreover, Norway's energy resources and engineering capacity offer possibilities as a cradle for offshore floating wind power, thus influencing the technology path through TL in a national niche market. We ask three questions: (1) How should spillover be included when modelling the energy system of a small open economy? (2) What is the potential influence of spillover on the Norwegian energy system? (3) What is the sensitivity of the national system to spillover and will learning in the national market give a similar result? The elucidation of the application of spillover on the national energy system analysis in a globalised energy technology market is novel. It contributes to the stock of knowledge on modelling TL with a focus on the national energy system. The first part of this paper reviews the properties of TL relevant to a small open economy and discusses it in a global–local perspective. From the discussion some criteria important for the parameterization and modelling are suggested. The criteria are subsequently applied to evaluate the influence of spillover on the Norwegian energy system up to 2050. Two national cases are analysed: (1) spillover of TL dominates and the local TL is assumed negligible, and (2) a special case where learning for offshore floating wind power (OFW) is dominated by the national niche market. While the other technologies benefit from spillover, TL for OFW is modelled endogenously and thus is dependent on national deployment only. The results presented focus on the overall system performance and technology composition of electricity conversion and light duty vehicle (LDV). Finally, some conclusions are drawn and suggestions for future work are offered.
نتیجه گیری انگلیسی
The review of the technology learning systems points at the importance of the feed forward and feed back link between the global energy technology development and manufacturing system and the national energy system. The review concludes that spillover should be estimated by a global model and should emanate from the same scenario and technology path. Moreover, adaptation to national circumstances may be required. The modelling results support the assumption that spillover may significantly influence the development of the national energy system of a small open economy. The analysis shows that spillover from different global scenarios, used as boundary condition for the national energy system analysis, significantly affects the system cost, CO2 emissions and technology composition of the energy system in Norway up to 2050. The results indicate substantial benefit for a small open economy like Norway from spillover under IEA's Energy technology Perspectives global policy scenarios. Contra-intuitive, spillover of global technology learning may reduce both CO2 emissions and total system cost, that is, if the industrial and electricity export opportunities, provided by the large offshore wind resources, are exploited. Assuming no spillover for offshore floating wind power but learning in the national niche market at the same learning rate as the global scenario yield, as expected, a very different result. However, offshore floating wind power may become competitive with a 20% learning rate and a CO2 incentive above 300 NOK/ton.17 Repeating the analysis with other global scenarios as boundary condition will map the uncertainty. Suitable data for the analysis is, however, not available in the literature. Development of a set of boundary conditions using a cross section of global models and making them available for national analysis is recommended. The approach described in this paper provides a method to include spillover of technology learning in national analysis consistently across technologies and energy carriers. The results illustrate the capacity of the methodology to highlight the interconnectedness within the energy systems and the technology development and manufacturing systems.