Using learning curves on energy-efficient technologies to estimate future energy savings and emission reduction potentials in the U.S. iron and steel industry

Karalı, Nihan; Park, Won Young; McNeil, Michael A.

doi:10.2172/1372638

Cited by 7 publications

(8 citation statements)

References 39 publications

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“…More than 70 energy-efficiency measures are currently applied at different scales (e.g., low penetration or mature) in the U.S. iron and steel industry. About 60% of these technologies are used at their maximum potential or close to maximum [63]. The model calibration includes all the energy-efficient technologies.…”

Section: Assumptions and Scenariosmentioning

confidence: 99%

“…Basic parameters and assumptions for the U.S. iron and steel production processes can be found in Karali et al [61,62] and for 14 energy-efficient technologies in Table A1 in Appendix A. Technology-specific learning rates and maximum penetration levels used in this study for existing energy-efficient technologies come from an earlier study by the authors, which calculates the learning rates of energy-efficient technologies used in the U.S. iron and steel sector [63]. For emerging technologies, we use the average learning rate from the same study for energy-efficient technologies that have penetration levels of 20% or below (i.e., learning rate of 10%).…”

Section: Selection Of Energy-efficient Technologiesmentioning

confidence: 99%

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Modeling technological change and its impact on energy savings in the U.S. iron and steel sector

2017

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a b s t r a c tMarket penetration of energy-efficient technologies can be estimated using energy optimization models that minimize cost; however, such models typically estimate the minimum cost of optimal pathways under a certain set of non-dynamic assumptions, so technology penetrations determined for the longterm do not fully respond to changing circumstances or costs. In this study, investment costs of energy-efficient technologies are modeled dynamically in the Industrial Sector Energy-Efficiency Model (ISEEM) using a technological learning formula. Results from 24 energy-efficient technologies -14 existing, 10 emerging -selected from the United States (U.S.) iron and steel sector show that when technological learning is incorporated into the model, total energy consumption of this sector is expected to decrease by 13% (180 PJ) in 2050 compared to energy consumption in a non-learning scenario. Average energy intensity of the steel production improves from 12.3 GJ/t in the non-learning scenario to 10.7 GJ/t in the learning scenario in 2050. This decrease represents a cost savings of US$1.6 billion and a carbon dioxide emissions reduction potential of 14.9 billion tonnes. Results discussed in this paper focus on the U.S. iron and steel sector, but the proposed framework can be applied to study new technology development in any other industrial processes and regions.

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Section: Assumptions and Scenariosmentioning

confidence: 99%

Section: Selection Of Energy-efficient Technologiesmentioning

confidence: 99%

Modeling technological change and its impact on energy savings in the U.S. iron and steel sector

2017

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“…Until to the today, learning curve models have been widely used in different industries. Hartley (1965), , Benkard (2000), Bongers (2017) in the aircraft industry; Levitt et al (2013) in the automobile industry; Boston Consulting Group (1973), Dick (1991), and Chung (2001) in the semiconductor industry; , Kim et al (2019) in the ship industry; Lieberman (1984), Sinclair et al (2000) in the chemical industry; Karali et al (2015) in the iron and steel industry; Chen and Lu (2012), Xu et al (2017), Hayashi et al (2018) in the wind power and other power technologies; Tan and Elias (2000) in the construction industry; Pramongkit et al (2002), Franceschini and Galetto (2003), Karaoz and Albeni (2005) Both the learning and experience curves indicate that more experience will be gained and production costs will decrease with the production of a good or service (Louwen and Lacerda, 2020).The learning or experience curve illustrates the decrease in average cost as the cumulative total output of the firm increases (Church and Ware, 2000;Salvatore, 2008).Wright's model is referred to as the log-linear model has been widely used to estimate the linear learning curve.…”

Section: Methodsmentioning

confidence: 99%

Regional Technological Learning in Turkish Cement Industry

Çalmaşur¹,

Aysin²

2020

EJEF

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The learning curve reflects the reduction in average costs as the company's cumulative production increases. These curves are utilized when measuring company performance, managing production processes, and planning. In terms of cost reduction and profitability, the impact of learning is particularly important. The learning curves have been traditionally used in industries. In this study, the learning curves concerning the cement industry are examined. The cement sector inherits a high export potential in Turkey. Additionally, it is the industry branch that supplies the raw materials needed by countries' construction industries. On the other hand, the construction sector is a leading sector that mobilizes other markets. This sector is a major contributor to production, investment, and employment and plays a vital role in the development of the country. This paper aims to make a detailed analysis of the learning curves regarding the Turkish cement industry at the regional level covering the 2000-2018 period. In order to realize this aim, the linear and cubic learning models have been applied and the technological learning values for regions from 2000 to 2018 have been calculated. For the analysis, data of 68 factories operating in the Turkish cement industry obtained from Turkey Cement Manufacturers' Association have been used. The estimated results suggest that cubic models explain technological learning better than the linear models. The results indicated that learning levels differed across regions and times. While the highest learning level was observed in 2004, the highest level of forgetting was recorded in 2018. Finally, we can state that the learning curve of the Turkish cement industry between 2000 and 2018 is convex.

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“…The learning rate-the percentage at which costs decline after each doubling of cumulative production-is the commonly used metric for defining the rate of change in product costs derived from learning curves. As 'learning' continues the cost is bound to fall, although the exponential nature of the curves means that reductions will slowly level-off over time (Karali et al 2015). More importantly, ongoing learning effects reduce costs and enable a technology to reach broader markets and extend its range of applications.…”

Section: Learning Ratementioning

confidence: 99%

Overlooked factors in predicting the transition to clean electricity

Martin

Madrid‐López

Villalba

et al. 2022

Environ. Res.: Infrastruct. Sustain.

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The transition to clean energy will require significant increases in electricity sourced from renewable energy technologies. While wind and solar photovoltaic sources are generally expected to overtake hydropower to dominate the renewable electricity supply market, numerous other technologies vie for a share in this rapidly evolving arena. To date, predicting the emergence of different technologies has relied on large-scale energy models that employ simplified optimisations of economic and emissions reductions outcomes. This is problematic as many additional factors, largely underrepresented in current models, are likely to co-determine technological emergence storylines in the real world. Here, we present a summary of the best available information for five key factors as they apply to the seven most common renewable electricity technology categories. The findings suggest that wind and solar photovoltaic technologies remain the most likely to dominate the market going forward but could face considerable raw material supply risk issues. Other potentially more desirable alternatives exist but face their own geographic and environmental limitations. Ultimately, the study demonstrates the potential and importance of expanding the use of other relevant factors in the forecasting of energy transition pathways and in the field of energy modelling as a whole.

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Using learning curves on energy-efficient technologies to estimate future energy savings and emission reduction potentials in the U.S. iron and steel industry

Cited by 7 publications

References 39 publications

Modeling technological change and its impact on energy savings in the U.S. iron and steel sector

Modeling technological change and its impact on energy savings in the U.S. iron and steel sector

Regional Technological Learning in Turkish Cement Industry

Overlooked factors in predicting the transition to clean electricity

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