Technological change in energy systems: Learning curves, logistic curves and input–output coefficients

Pan, Haoran; Köhler, Jonathan

doi:10.1016/j.ecolecon.2007.01.013

Cited by 61 publications

(42 citation statements)

References 13 publications

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“…This does not necessarily mean that such effects do not exist or are negligible; it probably points to the difficulties of operationalizing this factor in empirical studies. Many authors argue that differences in market structure can affect the actual costs of a technology by, for example, influencing a company's internal efficiency [10] or the level of innovation within an industry [11]. Specifically, it is sometimes argued that technological learning requires competitive markets [12,13], although this view is not shared universally by the theoretical and empirical literature on the relationship between an industry's degree of competition and its level of innovation [14,15].…”

Section: Factors Influencing the Market Costs Of Electricity Generatimentioning

confidence: 92%

A Review of Factors Influencing the Cost Development of Electricity Generation Technologies

Samadi

2016

Energies

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This article reviews the literature on the past cost dynamics of various renewable, fossil fuel and nuclear electricity generation technologies. It identifies 10 different factors which have played key roles in influencing past cost developments according to the literature. These 10 factors are: deployment-induced learning, research, development and demonstration (RD&D)-induced learning, knowledge spillovers from other technologies, upsizing, economies of manufacturing scale, economies of project scale, changes in material and labour costs, changes in fuel costs, regulatory changes, and limits to the availability of suitable sites. The article summarises the relevant literature findings for each of these 10 factors and provides an overview indicating which factors have impacted on which generation technologies. The article also discusses the insights gained from the review for a better understanding of possible future cost developments of electricity generation technologies. Finally, future research needs, which may support a better understanding of past and future cost developments, are identified.

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Section: Factors Influencing the Market Costs Of Electricity Generatimentioning

confidence: 92%

A Review of Factors Influencing the Cost Development of Electricity Generation Technologies

Samadi

2016

Energies

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“…Learning rates are derived based on the results of experience curve analysis. Experience curves and associated learning rates have been already adopted in bottom-up modelling studies; however, attention has focused heavily on energy supply technologies (91,92). The modelling exercise performed with the NEMS model for tax credits shows that it is possible to apply experience curve analysis for end-use technologies.…”

Section: The Techno-economic and Environmental Dimensionmentioning

confidence: 99%

Evaluating Energy Efficiency Policies with Energy-Economy Models

Mundaca

Neij

Worrell

et al. 2010

Annu. Rev. Environ. Resour.

118

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The growing complexities of energy systems, environmental problems, and technology markets are driving and testing most energy-economy models to their limits. To further advance bottom-up models from a multidisciplinary energy efficiency policy evaluation perspective, we review and critically analyze bottom-up energy-economy models and corresponding evaluation studies on energy efficiency policies to induce technological change. We use the household sector as a case study. Our analysis focuses on decision frameworks for technology choice, type of evaluation being carried out, treatment of market and behavioral failures, evaluated policy instruments, and key determinants used to mimic policy instruments. Although the review confirms criticism related to energy-economy models (e.g., unrealistic representation of decision making by consumers when choosing technologies), they provide valuable guidance for policy evaluation related to energy efficiency. Different areas to further advance models remain open, particularly related to modeling issues, techno-economic and environmental aspects, behavioral determinants, and policy considerations.

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“…Experience curves for building products and energy technologies have been calculated by many researchers (Grübler et al, 1999;International Energy Agency, 2000;Iwafune, 2000;Jakob andMadlener, 2003, 2004;Nemet, 2006;Papineau, 2006;Pan and Köhler, 2007;Weiss et al, 2010;de La Tour et al, 2013). Typically savings for building fabric technologies fall in the range of 9-27% per doubling of cumulative production, with 18% being average.…”

Section: Higher Construction and Lower Operating Costsmentioning

confidence: 99%