Modeling technological change and its impact on energy savings in the U.S. iron and steel sector
Abstract
We report market 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 long-term do not fully respond to changing circumstances or costs. Here, 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. In conclusion, 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 othermore »
- Authors:
-
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); Inha University; Greenhouse Gas Inventory and Research Center of Korea
- OSTI Identifier:
- 1532214
- Alternate Identifier(s):
- OSTI ID: 1416213
- Grant/Contract Number:
- AC02-05CH11231; AC02-05CH1131
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Applied Energy
- Additional Journal Information:
- Journal Volume: 202; Journal Issue: C; Journal ID: ISSN 0306-2619
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; energy optimization models; learning curve; dynamic cost; energy-efficient technologies; endogenous technological learning
Citation Formats
Karali, Nihan, Park, Won Young, and McNeil, Michael. Modeling technological change and its impact on energy savings in the U.S. iron and steel sector. United States: N. p., 2017.
Web. doi:10.1016/j.apenergy.2017.05.173.
Karali, Nihan, Park, Won Young, & McNeil, Michael. Modeling technological change and its impact on energy savings in the U.S. iron and steel sector. United States. https://doi.org/10.1016/j.apenergy.2017.05.173
Karali, Nihan, Park, Won Young, and McNeil, Michael. Sat .
"Modeling technological change and its impact on energy savings in the U.S. iron and steel sector". United States. https://doi.org/10.1016/j.apenergy.2017.05.173. https://www.osti.gov/servlets/purl/1532214.
@article{osti_1532214,
title = {Modeling technological change and its impact on energy savings in the U.S. iron and steel sector},
author = {Karali, Nihan and Park, Won Young and McNeil, Michael},
abstractNote = {We report market 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 long-term do not fully respond to changing circumstances or costs. Here, 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. In conclusion, 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.},
doi = {10.1016/j.apenergy.2017.05.173},
journal = {Applied Energy},
number = C,
volume = 202,
place = {United States},
year = {Sat Jun 03 00:00:00 EDT 2017},
month = {Sat Jun 03 00:00:00 EDT 2017}
}
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Works referenced in this record:
Energy Technologies Evaluated against Climate Targets Using a Cost and Carbon Trade-off Curve
journal, May 2013
- Trancik, Jessika E.; Cross-Call, Daniel
- Environmental Science & Technology, Vol. 47, Issue 12
Treating Progress Functions as a Managerial Opportunity
journal, April 1984
- Dutton, John M.; Thomas, Annie
- Academy of Management Review, Vol. 9, Issue 2
Learning Curves in Manufacturing
journal, February 1990
- Argote, L.; Epple, D.
- Science, Vol. 247, Issue 4945
Technological change and the timing of mitigation measures
journal, December 1998
- Grübler, Arnulf; Messner, Sabine
- Energy Economics, Vol. 20, Issue 5-6
The price evolution of wind turbines in China: A study based on the modified multi-factor learning curve
journal, April 2017
- Yu, Yang; Li, Hong; Che, Yuyuan
- Renewable Energy, Vol. 103
Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies
journal, July 2007
- Jamasb, Tooraj
- The Energy Journal, Vol. 28, Issue 3
Endogenizing R&D and market experience in the “bottom-up” energy-systems ERIS model
journal, August 2004
- Barreto, Leonardo; Kypreos, Socrates
- Technovation, Vol. 24, Issue 8
Wind Power in Europe: A Simultaneous Innovation–Diffusion Model
journal, October 2006
- Söderholm, Patrik; Klaassen, Ger
- Environmental and Resource Economics, Vol. 36, Issue 2
Use and limitations of learning curves for energy technology policy: A component-learning hypothesis
journal, July 2009
- Ferioli, F.; Schoots, K.; van der Zwaan, B. C. C.
- Energy Policy, Vol. 37, Issue 7
When will wind energy achieve grid parity in China? – Connecting technological learning and climate finance
journal, December 2015
- Yao, Xilong; Liu, Yang; Qu, Shiyou
- Applied Energy, Vol. 160
A framework for technological learning in the supply chain: A case study on CdTe photovoltaics
journal, May 2016
- Bergesen, Joseph D.; Suh, Sangwon
- Applied Energy, Vol. 169
Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600 MW retrofitted oxyfuel power plant as a case study
journal, July 2016
- Wu, X. D.; Yang, Q.; Chen, G. Q.
- Renewable and Sustainable Energy Reviews, Vol. 60
Feed-in tariffs for photovoltaics: Learning by doing in Germany?
journal, December 2011
- Wand, Robert; Leuthold, Florian
- Applied Energy, Vol. 88, Issue 12
Modeling technological learning and its application for clean coal technologies in Japan
journal, January 2011
- Nakata, Toshihiko; Sato, Takemi; Wang, Hao
- Applied Energy, Vol. 88, Issue 1
Factors Affecting the Cost of Airplanes
journal, February 1936
- Wright, T. P.
- Journal of the Aeronautical Sciences, Vol. 3, Issue 4
The Economic Implications of Learning by Doing
journal, June 1962
- Arrow, Kenneth J.
- The Review of Economic Studies, Vol. 29, Issue 3
Statistical Basis for Predicting Technological Progress
journal, February 2013
- Nagy, Béla; Farmer, J. Doyne; Bui, Quan M.
- PLoS ONE, Vol. 8, Issue 2
A dynamic process-based cost modeling approach to understand learning effects in manufacturing
journal, November 2010
- Nadeau, Marie-Claude; Kar, Ashish; Roth, Richard
- International Journal of Production Economics, Vol. 128, Issue 1
The Relationship Between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations
journal, January 1980
- Bass, Frank M.
- The Journal of Business, Vol. 53, Issue S3
Optimal strategies for general price-quality decision models of new products with learning production costs
journal, September 1996
- Teng, Jinn-Tsair; Thompson, Gerald L.
- European Journal of Operational Research, Vol. 93, Issue 3
Mitigation strategies and energy technology learning: An assessment with the POLES model
journal, January 2015
- Criqui, P.; Mima, S.; Menanteau, P.
- Technological Forecasting and Social Change, Vol. 90
Ethanol learning curve—the Brazilian experience
journal, March 2004
- Goldemberg, José; Coelho, Suani Teixeira; Nastari, Plinio Mário
- Biomass and Bioenergy, Vol. 26, Issue 3
Understanding the reductions in US corn ethanol production costs: An experience curve approach
journal, January 2009
- Hettinga, W. G.; Junginger, H. M.; Dekker, S. C.
- Energy Policy, Vol. 37, Issue 1
Scenario analysis for estimating the learning rate of photovoltaic power generation based on learning curve theory in South Korea
journal, January 2015
- Hong, Sungjun; Chung, Yanghon; Woo, Chungwon
- Energy, Vol. 79
Technological learning in bioenergy systems
journal, December 2006
- Junginger, Martin; de Visser, Erika; Hjort-Gregersen, Kurt
- Energy Policy, Vol. 34, Issue 18
Learning rates and future cost curves for fossil fuel energy systems with CO2 capture: Methodology and case studies
journal, May 2012
- Li, Sheng; Zhang, Xiaosong; Gao, Lin
- Applied Energy, Vol. 93
Learning rates for energy technologies
journal, March 2001
- McDonald, Alan; Schrattenholzer, Leo
- Energy Policy, Vol. 29, Issue 4
Use of experience curves to analyse the prospects for diffusion and adoption of renewable energy technology
journal, November 1997
- Neij, Lena
- Energy Policy, Vol. 25, Issue 13
Experience curves for wind power
journal, January 2004
- Neij, Lena; Andersen, Per Dannemand; Durstewitz, Michael
- International Journal of Energy Technology and Policy, Vol. 2, Issue 1/2
Beyond the learning curve: factors influencing cost reductions in photovoltaics
journal, November 2006
- Nemet, Gregory F.
- Energy Policy, Vol. 34, Issue 17
The Diffusion of New Technologies: Evidence from the Electric Utility Industry
journal, October 1990
- Rose, Nancy L.; Joskow, Paul L.
- The RAND Journal of Economics, Vol. 21, Issue 3
Learning curves for hydrogen production technology: An assessment of observed cost reductions
journal, June 2008
- Schoots, K.; Ferioli, F.; Kramer, G.
- International Journal of Hydrogen Energy, Vol. 33, Issue 11
Prospects for PV: a learning curve analysis
journal, January 2003
- van der Zwaan, Bob; Rabl, Ari
- Solar Energy, Vol. 74, Issue 1
Incorporating experience curves in appliance standards analysis
journal, January 2013
- Desroches, Louis-Benoit; Garbesi, Karina; Kantner, Colleen
- Energy Policy, Vol. 52
A review of experience curve analyses for energy demand technologies
journal, March 2010
- Weiss, Martin; Junginger, Martin; Patel, Martin K.
- Technological Forecasting and Social Change, Vol. 77, Issue 3
Experience curves for power plant emission control technologies
journal, January 2004
- Rubin, Edward S.; Yeh, Sonia; Hounshell, David A.
- International Journal of Energy Technology and Policy, Vol. 2, Issue 1/2
Technology Innovations and Experience Curves for Nitrogen Oxides Control Technologies
journal, December 2005
- Yeh, Sonia; Rubin, Edward S.; Taylor, Margaret R.
- Journal of the Air & Waste Management Association, Vol. 55, Issue 12
Formalizing best practice for energy system optimization modelling
journal, May 2017
- DeCarolis, Joseph; Daly, Hannah; Dodds, Paul
- Applied Energy, Vol. 194
Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models
journal, August 2006
- Berglund, Christer; Söderholm, Patrik
- Energy Policy, Vol. 34, Issue 12
Endogenized technological learning in an energy systems model
journal, September 1997
- Messner, Sabine
- Journal of Evolutionary Economics, Vol. 7, Issue 3
Technology evolution and energy modelling: overview of research and findings
journal, January 2000
- Capros, Pantelis; Vouyoukas, E. Lakis
- International Journal of Global Energy Issues, Vol. 14, Issue 1/2/3/4
Decarbonising road transport with hydrogen and electricity: Long term global technology learning scenarios
journal, March 2013
- Anandarajah, Gabrial; McDowall, Will; Ekins, Paul
- International Journal of Hydrogen Energy, Vol. 38, Issue 8
Internalisation of external cost in the power generation sector: Analysis with Global Multi-regional MARKAL model
journal, February 2007
- Rafaj, Peter; Kypreos, Socrates
- Energy Policy, Vol. 35, Issue 2
Technological learning for carbon capture and sequestration technologies
journal, July 2004
- Riahi, Keywan; Rubin, Edward S.; Taylor, Margaret R.
- Energy Economics, Vol. 26, Issue 4
Multi-regional evaluation of the U.S. electricity sector under technology and policy uncertainties: Findings from MARKAL EPA9rUS modeling
journal, June 2013
- Balash, Peter; Nichols, Christopher; Victor, Nadejda
- Socio-Economic Planning Sciences, Vol. 47, Issue 2
Riding down the experience curve for energy-efficient building envelopes: the Swiss case for 1970?2020
journal, January 2004
- Jakob, Martin; Madlener, Reinhard
- International Journal of Energy Technology and Policy, Vol. 2, Issue 1/2
Reducing energy consumption and CO2 emissions by energy efficiency measures and international trading: A bottom-up modeling for the U.S. iron and steel sector
journal, May 2014
- Karali, Nihan; Xu, Tengfang; Sathaye, Jayant
- Applied Energy, Vol. 120
Assessing new Energy Technologies Using an Energy System Model with Endogenized Experience Curves
journal, March 1997
- Mattsson, Niclas; Wene, Clas-Otto
- International Journal of Energy Research, Vol. 21, Issue 4
Modeling uncertainty of induced technological change
journal, November 2000
- Gritsevskyi, Andrii; Nakićenovi, Nebojša
- Energy Policy, Vol. 28, Issue 13
Endogenous learning and technology clustering: analysis with MARKAL model of the Western European energy system
journal, January 2000
- Seebregts, Ad; Kram, Tom; Schaeffer, Gerrit Jan
- International Journal of Global Energy Issues, Vol. 14, Issue 1/2/3/4
Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching
journal, January 2000
- Kouvaritakis, Nikolaos; Soria, Antonio; Isoard, Stephane
- International Journal of Global Energy Issues, Vol. 14, Issue 1/2/3/4
Endogenous learning in world post-Kyoto scenarios: application of the POLES model under adaptive expectations
journal, January 2000
- Kouvaritakis, Nikolaos; Soria, Antonio; Isoard, Stephane
- International Journal of Global Energy Issues, Vol. 14, Issue 1/2/3/4
The impact of learning-by-doing on the timing and costs of CO2 abatement
journal, July 2004
- Manne, Alan; Richels, Richard
- Energy Economics, Vol. 26, Issue 4
Analysis of Technological Portfolios for CO2 Stabilizations and Effects of Technological Changes
journal, January 2005
- Sano, Fuminori; Akimoto, Keigo; Homma, Takashi
- SSRN Electronic Journal
Induced Technological Change in a Limited Foresight Optimization Model
journal, January 2005
- Hedenus, Fredrik; Azar, Christian; Lindgren, Kristian
- SSRN Electronic Journal
Endogenous induced technical change and the costs of Kyoto
journal, February 2003
- Buonanno, Paolo; Carraro, Carlo; Galeotti, Marzio
- Resource and Energy Economics, Vol. 25, Issue 1
Learning-by-Doing vs. Learning by Researching in a model of climate change policy analysis
journal, August 2005
- Castelnuovo, Efrem; Galeotti, Marzio; Gambarelli, Gretel
- Ecological Economics, Vol. 54, Issue 2-3
Decarbonizing the Global Economy with Induced Technological Change: Scenarios to 2100 using E3MG
journal, September 2006
- Barker, Terry; Pan, Haoran; Kohler, Jonathan
- The Energy Journal, Vol. SI2006, Issue 01
Endogenous Structural Change and Climate Targets Modeling Experiments with Imaclim-R
journal, September 2006
- Crassous, Renaud; Hourcade, Jean-Charles; Sassi, Olivier
- The Energy Journal, Vol. SI2006, Issue 01