Non-Constant Learning Rates in Retrospective Experience Curve Analyses and their Correlation to Deployment Programs

Wei, Max; Smith, Sarah; Sohn, Michael D.

doi:10.2172/1237059

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Electronic ballasts began to replace magnetic ones in 1984, and accounted for 90% of CFL manufacturing costs throughout the late 1980s (Weiss). These ballasts demonstrated learning rates of 8% from 1986-1991and 23% from 1992-2005(Wei et al 2015a. Future work should further explore the relationship between these two technologies and determine the extent to which ballast cost reductions account for CFL cost reductions over time.…”

Section: Component Developmentmentioning

confidence: 98%

Retrospective North American CFL Experience Curve Analysis and Correlation to Deployment Programs

Smith

Wei

Sohn

2015

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Abstract:Retrospective experience curves are a useful tool for understanding historic technology development, and can contribute to investment program analysis and future cost estimation efforts. This work documents our development of an analysis approach for deriving retrospective experience curves with a variable learning rate, and its application to develop an experience curve for compact fluorescent lamps for the global and North American markets over the years [1990][1991][1992][1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006][2007]. Uncertainties and assumptions involved in interpreting data for our experience curve development are discussed, including the processing and transformation of empirical data, the selection of system boundaries, and the identification of historical changes in the learning rate over the course of 15 years. In the results that follow, we find that that the learning rate has changed at least once from 1990-2007. We also explore if, and to what degree, public deployment programs may have contributed to an increased technology learning rate in North America. We observe correlations between the changes in the learning rate and the initiation of new policies, abrupt technological advances, including improvements to ballast technology, and economic and political events such as trade tariffs and electricity prices. Finally, we discuss how the findings of this work (1) support the use of segmented experience curves for retrospective and prospective analysis and (2) may imply that investments in technological research and development have contributed to a change in market adoption and penetration.

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Section: Component Developmentmentioning

confidence: 98%

Retrospective North American CFL Experience Curve Analysis and Correlation to Deployment Programs

Smith

Wei

Sohn

2015

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“…Under these circumstances, traditional ECs can be modified to accommodate multiple experience slopes over a life-cycle. Such modified ECs, known as kinked ECs, with a kink (piecewise linear) in the slope, have been used in several studies [17,[31][32][33][34]. Further explanation and application of the kinked model can be found in a review article by Chang and Lee [35].…”

Section: Experience Curve Applications In Energymentioning

confidence: 99%

Dynamic Trends of Carbon Intensities among 127 Countries

2017

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Many countries in the world have been experiencing widely varying rates of change in their carbon intensity (CI) of economic output. The dynamic trend of CI in this research is measured by the progress ratio (PR) from an experience curve (EC) involving 127 countries during the period of 1980-2011. The overall average PR of 88.8% estimated for the total group of 127 indicates a decreasing trend of carbon intensity. This means that each doubling of the cumulative CO 2 emission by this group has reduced carbon intensity by 11.2%. While a majority of 83 countries experienced a decreasing trend with an average PR of 73.1%, the remaining 44 countries have experienced an increasing trend with an average PR of 114.5%. When two different types of EC, classical and kinked, were applied, 73 countries displayed a kinked slope with an average PR of 73.4%, and 54 countries displayed a classical slope with an average PR of 104.2%. Examination of the type of trend and slope of EC suggests the chance of a major improvement of the future CI in the following order: (1) the 35 countries with a classical slope and an increasing trend of CIs; (2) the nine countries with a kinked slope and an increasing trend of CIs; (3) the 19 countries with a classical slope and a decreasing trend of CIs; and (4) the 64 countries with a kinked slope and a decreasing trend of CIs. Further implications from these findings are discussed.

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“…Earlier work has highlighted the piecewise linear learning rate observed in many energy-related technologies ( [11] , [12]) and noted the correlation of deployment programs to downward bends or changes in the learning rate from an initial learning rate prior to the deployment program to a faster learning rate after the deployment program for several energy-related technologies.…”

Section: Fuel Cell Experience Curvesmentioning

confidence: 99%

“…12 A reduction in the number of cells corresponds to lower Pt content by a proportional amount, so most of the Pt reduction is assumed be achieved from cell count reduction overall system costs than the balance of plant, this yield improvement factor is a not a major contribution to overall costs under these assumptions. 11 Labor costs are well matched (see http://www.bls.gov/news.release/pdf/ichcc.pdf, accessed 20 March 2015.…”

Section: Cost Reduction From Reported Product Design Improvementsmentioning

confidence: 99%

Analysis of Fuel Cell Markets in Japan and the US: Experience Curve Development and Cost Reduction Disaggregation

Wei¹,

Smith²,

Sohn³

2016

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Fuel cells are both a longstanding and emerging technology for stationary and transportation applications, and their future use will likely be critical for the deep decarbonization of global energy systems. As we look into future applications, a key challenge for policy-makers and technology market forecasters who seek to track and/or accelerate their market adoption is the ability to forecast market costs of the fuel cells as technology innovations are incorporated into market products. Specifically, there is a need to estimate technology learning rates, which are rates of cost reduction versus production volume.Unfortunately, no literature exists for forecasting future learning rates for fuel cells. In this paper, we look retrospectively to estimate learning rates for two fuel cell deployment programs: (1) the micro-combined heat and power (CHP) program in Japan, and (2) the Self-Generation Incentive Program (SGIP) in California. These two examples have a relatively broad set of historical market data and thus provide an informative and international comparison of distinct fuel cell technologies and government deployment programs. We develop a generalized procedure for disaggregating experience-curve cost-reductions in order to disaggregate the Japanese fuel cell micro-CHP market into its constituent components, and we derive and present a range of learning rates that may explain observed market trends. Finally, we explore the differences in the technology development ecosystem and market conditions that may have contributed to the observed differences in cost reduction and draw policy observations for the market adoption of future fuel cell technologies. The scientific and policy contributions of this paper are the first comparative experience curve analysis of past fuel cell technologies in two distinct markets, and the first quantitative comparison of a detailed cost model of fuel cell systems with actual market data. The resulting approach is applicable to analyzing other fuel cell markets and other energy-related 2 technologies, and highlights the data needed for cost modeling and quantitative assessment of key cost reduction components.

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Non-Constant Learning Rates in Retrospective Experience Curve Analyses and their Correlation to Deployment Programs

Cited by 4 publications

References 12 publications

Retrospective North American CFL Experience Curve Analysis and Correlation to Deployment Programs

Retrospective North American CFL Experience Curve Analysis and Correlation to Deployment Programs

Dynamic Trends of Carbon Intensities among 127 Countries

Analysis of Fuel Cell Markets in Japan and the US: Experience Curve Development and Cost Reduction Disaggregation

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