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Promoting energy conservation in China's iron & steel sector

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  • Lin, Boqiang
  • Wang, Xiaolei

Abstract

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.

Suggested Citation

  • Lin, Boqiang & Wang, Xiaolei, 2014. "Promoting energy conservation in China's iron & steel sector," Energy, Elsevier, vol. 73(C), pages 465-474.
  • Handle: RePEc:eee:energy:v:73:y:2014:i:c:p:465-474
    DOI: 10.1016/j.energy.2014.06.036
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    Cited by:

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    2. Dong, Kangyin & Sun, Renjin & Hochman, Gal & Li, Hui, 2018. "Energy intensity and energy conservation potential in China: A regional comparison perspective," Energy, Elsevier, vol. 155(C), pages 782-795.
    3. Zeng, Yujiao & Xiao, Xin & Li, Jie & Sun, Li & Floudas, Christodoulos A. & Li, Hechang, 2018. "A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant," Energy, Elsevier, vol. 143(C), pages 881-899.
    4. Chen, Demin & Lu, Biao & Dai, FangQin & Chen, Guang & Zhang, Xihe, 2018. "Bottleneck of slab thermal efficiency in reheating furnace based on energy apportionment model," Energy, Elsevier, vol. 150(C), pages 1058-1069.
    5. Ma, Ding & Chen, Wenying & Yin, Xiang & Wang, Lining, 2016. "Quantifying the co-benefits of decarbonisation in China’s steel sector: An integrated assessment approach," Applied Energy, Elsevier, vol. 162(C), pages 1225-1237.
    6. Lin, Boqiang & Du, Zhili, 2017. "Promoting energy conservation in China's metallurgy industry," Energy Policy, Elsevier, vol. 104(C), pages 285-294.
    7. Xu, Bin & Lin, Boqiang, 2016. "Regional differences in the CO2 emissions of China's iron and steel industry: Regional heterogeneity," Energy Policy, Elsevier, vol. 88(C), pages 422-434.
    8. Lin, Boqiang & Du, Kerui, 2014. "Measuring energy efficiency under heterogeneous technologies using a latent class stochastic frontier approach: An application to Chinese energy economy," Energy, Elsevier, vol. 76(C), pages 884-890.
    9. Liu, Xiong & Chen, Lingen & Feng, Huijun & Qin, Xiaoyong & Sun, Fengrui, 2016. "Constructal design of a blast furnace iron-making process based on multi-objective optimization," Energy, Elsevier, vol. 109(C), pages 137-151.
    10. Naser, Hanan, 2015. "Analysing the long-run relationship among oil market, nuclear energy consumption, and economic growth: An evidence from emerging economies," Energy, Elsevier, vol. 89(C), pages 421-434.
    11. Lin, Boqiang & Wang, Ailun, 2015. "Estimating energy conservation potential in China's commercial sector," Energy, Elsevier, vol. 82(C), pages 147-156.
    12. Huang, Junbing & Chen, Xiang, 2020. "Domestic R&D activities, technology absorption ability, and energy intensity in China," Energy Policy, Elsevier, vol. 138(C).
    13. Filippini, Massimo & Geissmann, Thomas & Karplus, Valerie J. & Zhang, Da, 2020. "The productivity impacts of energy efficiency programs in developing countries: Evidence from iron and steel firms in China," China Economic Review, Elsevier, vol. 59(C).
    14. Xu, Bin & Lin, Boqiang, 2016. "Reducing CO2 emissions in China's manufacturing industry: Evidence from nonparametric additive regression models," Energy, Elsevier, vol. 101(C), pages 161-173.
    15. Liu, Yan & Yang, Jian & Wang, Jing-yu & Ding, Xu-gang & Cheng, Zhi-long & Wang, Qiu-wang, 2015. "Prediction, parametric analysis and bi-objective optimization of waste heat utilization in sinter cooling bed using evolutionary algorithm," Energy, Elsevier, vol. 90(P1), pages 24-35.
    16. Lin, Boqiang & Chen, Yu, 2020. "Will land transport infrastructure affect the energy and carbon dioxide emissions performance of China’s manufacturing industry?," Applied Energy, Elsevier, vol. 260(C).
    17. Wen, Zongguo & Xu, Jinjing & Lee, Jason C.K. & Ren, Cuiping, 2017. "Symbiotic technology-based potential for energy saving: A case study in China's iron and steel industrial parks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1303-1311.

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