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| 1 |
ID:
097257
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2010.
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| Summary/Abstract |
In this paper it is argued that technology learning may be both a barrier and an incentive for technology change in the national energy system. The possibility to realize an ambitious global emission reduction scenario is enhanced by coordinated action between countries in national policy implementation. An indicator for coordinated action is suggested. Targeted measures to increase deployment of nascent energy technologies and increasing energy efficiency in a small open economy like Norway are examined. The measures are evaluated against a set of baselines with different levels of spillover of technology learning from the global market. It is found that implementation of technology subsidies increase the national contribution to early deployment independent of the level of spillover. In a special case with no spillover for offshore floating wind power and endogenous technology learning substantial subsidy or a learning rate of 20% is required. Combining the high learning rate and a national subsidy increases the contribution to early deployment. Enhanced building code on the other hand may reduce Norway's contribution to early deployment, and thus the realization of a global emission reduction scenario, unless sufficient electricity export capacity is assured.
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| 2 |
ID:
105751
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2011.
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| Summary/Abstract |
This paper describes a method to model the influence by global policy scenarios, particularly spillover of technology learning, on the energy service demand of the non-energy sectors of the national economy. It is exemplified by Norway. Spillover is obtained from the technology-rich global Energy Technology Perspective model operated by the International Energy Agency. It is provided to a national hybrid model where a national bottom-up Markal model carries forward spillover into a national top-down CGE1 model at a disaggregated demand category level. Spillover of technology learning from the global energy technology market will reduce national generation costs of energy carriers. This may in turn increase demand in the non-energy sectors of the economy because of the rebound effect. The influence of spillover on the Norwegian economy is most pronounced for the production level of industrial chemicals and for the demand for electricity for residential energy services. The influence is modest, however, because all existing electricity generating capacity is hydroelectric and thus compatible with the low emission policy scenario. In countries where most of the existing generating capacity must be replaced by nascent energy technologies or carbon captured and storage the influence on demand is expected to be more significant.
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| 3 |
ID:
092843
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2009.
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| Summary/Abstract |
Technology learning can make a significant difference to renewable energy as a mitigation option in South Africa's electricity sector. This article considers scenarios implemented in a Markal energy model used for mitigation analysis. It outlines the empirical evidence that unit costs of renewable energy technologies decline, considers the theoretical background and how this can be implemented in modeling. Two scenarios are modelled, assuming 27% and 50% of renewable electricity by 2050, respectively. The results show a dramatic shift in the mitigation costs. In the less ambitious scenario, instead of imposing a cost of Rand 52/t CO2-eq (at 10% discount rate), reduced costs due to technology learning turn renewables into negative cost option. Our results show that technology learning flips the costs, saving R143. At higher penetration rate, the incremental costs added beyond the base case decline from R92 per ton to R3. Including assumptions about technology learning turns renewable from a higher-cost mitigation option to one close to zero. We conclude that a future world in which global investment in renewables drives down unit costs makes it a much more cost-effective and sustainable mitigation option in South Africa.
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| 4 |
ID:
104892
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2011.
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| Summary/Abstract |
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 spillover1 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.
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| 5 |
ID:
177166
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| Summary/Abstract |
Wind energy technologies have seen a rapid decline in costs in the last two decades, but the drivers for these cost reductions are poorly understood. This paper addresses this knowledge gap by quantitatively investigating the drivers behind the cost reductions of onshore wind turbines between 2005 and 2017. Starting from a bottom-up cost model, the paper advances the methodology by identifying the techno-economic variables responsible for cost reductions of individual components (in $/kW) and linking them to drivers, specifically: learning by-deployment, learning-by-researching, supply-chain dynamics, and market dynamics. The analysis finds that changes in materials (copper, fiberglass, and iron), labour (employee productivity), legal and financial costs contributed over 30% to the cost reduction of wind turbine prices over the period 2005–2017. Moreover, learning-by-deployment was the most important innovation driver, being responsible for half of the cost reduction. The findings point to the importance of policies tailored to technology's stage of development. For onshore wind energy, which entered a mature phase in the period covered by this analysis, policy support for the needs of a growing industry such as stable support schemes together with appropriate regulatory and investment environments were more important than direct policy support for R&D which played a more important role in earlier periods.
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