Undertaking high impact strategies: The role of national efficiency measures in long-term energy and emission reduction in steel making

In this paper, we applied bottom-up linear optimization modeling to analyze long-term national impacts of implementing energy efficiency measures on energy savings, CO2-emission reduction, production, and costs of steel making in China, India, and the U.S. We first established two base scenarios representing business-as-usual steel production for each country from 2010 to 2050; Base scenario (in which no efficiency measure is available) and Base-E scenario (in which efficiency measures are available), and model scenarios representing various emission-reduction targets that affects production, annual energy use and costs with the goal of cost minimization. A higher emission-reduction target generally induces larger structural changes and increased investments in nation-wide efficiency measures, in addition to autonomous improvement expected in the Base scenario. Given the same emission-reduction target compared to the base scenario, intensity of annual energy use and emissions exhibits declining trends in each country from year 2010 to 2050. While a higher emission-reduction target result in more energy reduction from the base scenario, such reduction can become more expensive to achieve. The results advance our understanding of long-term effects of national energy efficiency applications under different sets of emission-reduction targets for steel sectors in the three major economies, and provide useful implications for high impact strategies to manage production structures, production costs, energy use, and emission reduction in steel making.

[1]  T. T. Xu.  Development of Bottom-up Representation of Industrial Energy Efficiency Technologies in Integrated Assessment Models for the Iron and Steel Sector , 2011 .

[2]  Joris Flapper,et al.  Energy use and implications for efficiency strategies in global fluid-milk processing industry , 2009 .

[3]  Jinyue Yan,et al.  Improving policy making through government–industry policy learning: The case of a novel Swedish policy framework , 2009 .

[4]  Tengfang T. Xu,et al.  Sustainability options in pulp and paper making: Costs of conserved energy and carbon reduction in the US , 2013 .

[5]  G. Boyd,et al.  Estimating the linkage between energy efficiency and productivity , 2000 .

[6]  Germán Ferreira,et al.  Energy consumption analysis of Spanish food and drink, textile, chemical and non-metallic mineral products sectors , 2012 .

[7]  Tengfang T. Xu,et al.  Greenhouse Gas Mitigation Options in ISEEM Global Energy Model: 2010-2050 Scenario Analysis for Least-Cost Carbon Reduction in Iron and Steel Sector , 2014 .

[8]  Tengfang Xu,et al.  A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry , 2013 .

[9]  Yi-Ming Wei,et al.  Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis , 2013 .

[10]  B. W. Ang,et al.  Linear programming models for measuring economy-wide energy efficiency performance , 2008 .

[11]  Ernst Worrell,et al.  Productivity benefits of industrial energy efficiency measures , 2003 .

[12]  Tengfang Xu,et al.  Characterization of energy use and performance of global cheese processing. , 2009 .

[13]  Nan Zhou,et al.  China's Energy and Carbon Emissions Outlook to 2050 , 2011 .

[14]  Arif Hepbasli,et al.  A review and assessment of the energy utilization efficiency in the Turkish industrial sector using energy and exergy analysis method , 2007 .

[15]  Architrandi Priambodo,et al.  Energy use and carbon dioxide emission of Indonesian small and medium scale industries , 2001 .

[16]  Tengfang Xu,et al.  Assessment of energy efficiency improvement and CO2 emission reduction potentials in India's cement and iron & steel industries , 2014 .

[17]  C. Wang,et al.  Scenario analysis on CO2 emissions reduction potential in China's electricity sector , 2007 .

[18]  Marco Vannucci,et al.  Reducing the energy consumption and CO2 emissions of energy intensive industries through decision support systems – An example of application to the steel industry , 2013 .

[19]  Lukas H. Meyer,et al.  Summary for Policymakers , 2022, The Ocean and Cryosphere in a Changing Climate.

[20]  Joris Flapper,et al.  Reduce energy use and greenhouse gas emissions from global dairy processing facilities , 2011 .

[21]  Nihan Karali.  Industrial Sector Energy Efficiency Modeling (ISEEM) Framework Documentation , 2014 .

[22]  Stephane de la Rue du Can,et al.  Strategies for Low Carbon Growth In India: Industry and Non Residential Sectors , 2011 .

[23]  Siaw Kiang Chou,et al.  Research, development and innovations for sustainable future energy systems , 2013 .

[24]  Tengfang Xu,et al.  Reducing carbon footprint in cement material making: Characterizing costs of conserved energy and reduced carbon emissions , 2013 .

[25]  Rosemary Norman,et al.  Low grade thermal energy sources and uses from the process industry in the UK , 2012 .

[26]  Yukio Yanagisawa,et al.  Estimation of energy consumption for each process in the Japanese steel industry: a process analysis , 1999 .

[27]  Can Wang,et al.  Scenario analysis on CO2 emissions reduction potential in China's iron and steel industry , 2007 .

[28]  B. Sudhakara Reddy,et al.  Understanding industrial energy use - Physical energy intensity changes in Indian manufacturing sector , 2011 .

[29]  Tengfang T. Xu,et al.  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 , 2014 .