Promoting energy conservation in China's iron & steel sector

The iron & steel industry is one of the major energy-intensive sectors in China. In this paper, we define the variable of energy intensity to analyze the energy conservation potential in China's iron & steel sector using the co-integration method and scenario analysis. We find that there is a long-term relationship between energy intensity and factors such as R&D intensity, labor productivity, enterprise scale, and energy price. Monte Carlo simulation technique is further used to address uncertainty problem. The results show that under baseline scenario, the energy intensity of China's iron & steel sector will reach 17.09 tons of coal equivalents per 10,000 Yuan (Tce/10,000Yuan) in 2020. The energy saving potential in 2020 will be 344.05 Mtce (million tons of coal equivalents) and 579.43 Mtce under moderate energy-saving scenario and advanced energy-saving scenario respectively. Finally, based on the results of the elasticity coefficients of the long-term equation, we propose future policy for promoting energy conservation in China's iron & steel industry.

[1]  N. Metropolis,et al.  The Monte Carlo method. , 1949 .

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

[3]  P. Phillips,et al.  Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? , 1992 .

[4]  Jiuju Cai,et al.  Calculating Method for Influence of Material Flow on Energy Consumption in Steel Manufacturing Process , 2007 .

[5]  Feng He,et al.  Energy efficiency and productivity change of China’s iron and steel industry: Accounting for undesirable outputs , 2013 .

[6]  L. Hằng,et al.  The impacts of energy prices on energy intensity: Evidence from China , 2007 .

[7]  Mohamed Moubarak,et al.  Estimation of energy saving potential in China's paper industry , 2014 .

[8]  Yemane Wolde-Rufael,et al.  Bounds test approach to cointegration and causality between nuclear energy consumption and economic growth in India , 2010 .

[9]  David F. Hendry,et al.  Explaining Cointegration Analysis: Part II , 2000 .

[10]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[11]  Chen Mingsheng,et al.  The Mechanism and Measures of Adjustment of Industrial Organization Structure: the Perspective of Energy Saving and Emission Reduction , 2011 .

[12]  Li Li,et al.  Integrated technology selection for energy conservation and PAHs control in iron and steel industry: Methodology and case study , 2013 .

[13]  Stelios Rozakis,et al.  Micro-economic modelling of biofuel system in France to determine tax exemption policy under uncertainty , 2005 .

[14]  Alok Kumar Energy Intensity: A Quantitative Exploration for Indian Manufacturing , 2003 .

[15]  M. Patterson What is energy efficiency?: Concepts, indicators and methodological issues , 1996 .

[16]  Zhancheng Guo,et al.  Current situation of energy consumption and measures taken for energy saving in the iron and steel industry in China , 2010 .

[17]  Aie,et al.  Tracking Industrial Energy Efficiency and CO2 Emissions , 2007 .

[18]  Ali Hasanbeigi,et al.  Analysis of energy-efficiency opportunities for the cement industry in Shandong Province, China: A case study of 16 cement plants , 2010 .

[19]  Ernst Worrell,et al.  Energy efficiency and carbon dioxide emissions reduction opportunities in the US iron and steel sector , 2001 .

[20]  Iain MacGill,et al.  A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries , 2012 .

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

[22]  Li Zhang,et al.  Estimates of the potential for energy conservation in the Chinese steel industry , 2011 .

[23]  Peter J. Spinney,et al.  Monte Carlo simulation techniques and electric utility resource decisions , 1996 .

[24]  Wenjia Cai,et al.  Sectoral analysis for international technology development and transfer: Cases of coal-fired power generation, cement and aluminium in China , 2009 .

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

[26]  Ratna Choudhury,et al.  Energy inefficiency of indian steel industry --scope for energy conservation , 1997 .

[27]  S. Johansen Likelihood-Based Inference in Cointegrated Vector Autoregressive Models , 1996 .

[28]  Bruno Larue,et al.  The market efficiency hypothesis: The case of coffee and cocoa futures , 1997 .

[29]  G. Müller-Fürstenberger,et al.  Integrated assessment of global climate change with learning-by-doing and energy-related research and development , 2007 .

[30]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[31]  Boqiang Lin Electricity demand in the People's Republic of China : investment requirement and environmental impact , 2003 .

[32]  Kankana Mukherjee,et al.  Energy use efficiency in the Indian manufacturing sector: An interstate analysis , 2008 .

[33]  Zongguo Wen,et al.  Estimates of the potential for energy conservation and CO2 emissions mitigation based on Asian-Pacific Integrated Model (AIM): the case of the iron and steel industry in China , 2014 .

[34]  Jan Szargut,et al.  Exergy Analysis of Thermal, Chemical, and Metallurgical Processes , 1988 .

[35]  Jianling Zhang,et al.  Energy saving technologies and productive efficiency in the Chinese iron and steel sector , 2008 .

[36]  Robert K. Kaufmann,et al.  A biophysical analysis of the energy/real GDP ratio: implications for substitution and technical change , 1992 .

[37]  Mounir Belloumi,et al.  Energy consumption and GDP in Tunisia: Cointegration and causality analysis , 2009 .

[38]  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 .

[39]  Boqiang Lin,et al.  China's energy demand and its characteristics in the industrialization and urbanization process , 2012 .

[40]  P. Phillips Testing for a Unit Root in Time Series Regression , 1988 .

[41]  S. Johansen,et al.  MAXIMUM LIKELIHOOD ESTIMATION AND INFERENCE ON COINTEGRATION — WITH APPLICATIONS TO THE DEMAND FOR MONEY , 2009 .

[42]  Alan Meier,et al.  SUPPLY CURVES OF CONSERVED ENERGY FOR CALIFORNIA'S RESIDENTIAL SECTOR , 1982 .

[43]  D. F. Stewart,et al.  Technical efficiency and productivity change of China's iron and steel industry , 2002 .

[44]  P. Howie,et al.  Electricity demand in Kazakhstan , 2007 .

[45]  Michael Osterwald-Lenum A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics , 1992 .

[46]  Chaoqing Yuan,et al.  Research on energy-saving effect of technological progress based on Cobb-Douglas production function , 2009 .

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

[48]  Ernst Worrell,et al.  Potentials for energy efficiency improvement in the US cement industry , 2000 .