ISSN:2325-6192 (print)
ISSN:2325-6206 (online)
Journal of Environmental Accounting and Management
António Mendes Lopes (editor), Jiazhong Zhang(editor)
António Mendes Lopes (editor)

University of Porto, Portugal

Email: aml@fe.up.pt

Jiazhong Zhang (editor)

School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China

Fax: +86 29 82668723 Email: jzzhang@mail.xjtu.edu.cn

Analyzing Structure and Driving Force of Steel Consumption in China

Journal of Environmental Accounting and Management 6(1) (2018) 33--45 | DOI:10.5890/JEAM.2018.03.003

Chengkang Gao$^{1}$, Hongming Na$^{1}$, Mingyan Tian$^{2}$, Zhou Ye$^{1}$, Zhaoqian Qi$^{1}$

$^{1}$ State Environmental Protection (SEP) Key Laboratory of Eco-Industry, School of Metallurgy, Northeastern University, Shenyang, 110819, China

$^{2}$ Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, United States

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Abstract

As a big steel producer, China has a large number of steel consumption every year and it is still incredibly increasing now. The fast growth of steel consumption led to excessively production, which caused not only tons of material wastes but also seriously affect on economic and environment. Therefore, finding out and analyzing the driving force of steel consumption is an extremely importance research for reducing resource-consumption and pollutant-emission of steel industry. In this paper, the structure of steel consumption was established at first based on the bottom-up method. Further, the four factors closely related to steel consumption were identified by factor decomposition method. They are in-use stock of steel (Sn), average service life-span (Y), productivity per in-use stock of steel (H) and steel output per unit GDP (T). Next, the driving force of each factor was analyzed by their contribution rate to steel consumption of China. At the same time, the S-shaped model of growth, mathematical model and BP neural network model were used to extract four scenarios to simulate steel consumption. Results show that: (1) construction industry is the main industry of steel consumption, accounting for 50% of the total, and the rest of the industries is relatively low; (2) the driving force Sn of steel consumption gradually decreased while the rest factors (Y, H, T) on steel consumption increased every year within the research time. Especially, after 2009, the impact value of the four factors reached the same level of 25%; and (3) when the growth rate of GDP is keeping on 7%, steel output per unit GDP (T) will reach to 0.1248 t/104 RMB; the average service life-span of in-use stock of steel (Y) will increase to 8.4 years; and steel consumption will reach to 7.7×108 tons and the in-use stock of steel will be efficiently used.

Acknowledgments

The authors are grateful to the financial support provided by Research Project of Northeastern University (No.150204006), the China Scholarship Council (201606085050); the National Natural Science Foundation of China (NO.41301643) and (NO.71373003; NO.71403175).

References

1.  [1] Chyxx (2015), Demand analysis of China's iron and steel downstream industry. Available on: http://www.chyxx.com/industry/201507/330432.html (in Chinese).
2.  [2] Cooper, D.R., Skelton, A.C.H., Moynihan, M.C. and Allwood, J.M. (2014), Component level strategies for exploiting the lifespan of steel in products, Resources Conservation & Recycling 84(84), 24-34.
3.  [3] Döhrn, R. and Krätschell, K. (2014), Long-term trends in steel consumption, Mineral Economics 27(1), 43-49. Dahlström, K. and Ekins, P. (2007), Combining economic and environmental dimensions: value chain analysis of UK iron and steel flows, Ecological Economics 58(3), 507-519.
4.  [4] Geyer, R., Davis, J., Ley, J., He, J., Clift, R., Kwan, A., Sansome, M. and Jacksond, T. (2007), Time-dependent material ﬂow analysis of iron and steel in the UK: part 1: production and consumption trends 1970-2000, Resources Conservation & Recycling 51(1), 101-117.
5.  [5] Huh, K.S. (2011), Steel consumption and economic growth in Korea: long-term and short-term evidence, Resources Policy 36(2), 107-113.
6.  [6] Lu, Z., Yue, Q. and Gao, C. (2013), Study on steel output per unit GDP and steel production, energy consumption, materials consumption & wastes emission of steel industry, Engineering Sciences 28(6), 17-27 (in Chinese).
7.  [7] Lu, Z. and Yue, Q. (2010), Resolution of the mechanism of steel output growth and study on the reasons of the excessive growth of steel output in China in 2000-2007, Engineering Sciences 12(6), 4-11 (in Chinese).
8.  [8] Li, H., Wang, B. (2014), Analysis of environmental and economic benefits in iron and steel enterprises by entropy weight fuzzy comprehensive evaluation model, Environmental Engineering & Management Journal 13(5), 1213-1219.
9.  [9] Lu, Z. (2005), Crossing "environmental mountain" - on the increase and decrease of environment impact in the process of economic growth, Iron & Steel Scrap of China 06(2), 11-19.
10.  [10] Liu, L.J. and Xie, M.P. (2004), Prediction research of the recursive neural network of nonlinear dynamic system prediction analysis of the iron and steel output in China, Study of Finance & Economics 02(1), 7-11 (in Chinese).
11.  [11] Liu, L. (2012), The forecast of Qinhuangdao logistic based on BP neural network, International Journal of Digital Content Technology & Its Application 6(8), 8-16 (in Chinese).
12.  [12] MIIT. (2016), Planning for adjustment and upgrading of iron and steel industry. Available on: http://www.miit.gov.cn/n1146295/n1652858/n1653018/c5355576/content.html.
13.  [13] Park, J.A., Hong, S.J., Kim, I., Lee, J.Y. and Hur, T. (2011), Dynamic material flow analysis of steel resources in Korea, Resources Conservation & Recycling 55(4), 456-462.
14.  [14] The editorial board of China steel yearbook (2016), China Steel Yearbook 2016 (CSY 2016). Metallurgical industry press, 316-317 (in Chinese).
15.  [15] The editorial board of China steel yearbook (2001), China Steel Yearbook 2001(CSY 2011). Metallurgical industry press,224-225 (in Chinese)
16.  [16] Wang, L., Qi, Z.Y. and Pan, F. (2014), Economic output and social stock of steel: Evidence from dynamic material flow analysis and statistical time series analysis. International Conference on Management Science & Engineering (pp.719-725). IEEE.
17.  [17] Zhang, J.H. and Lu, Z.W. (2007), Average service life of zinc products in China, Journal of Northeastern University 28(9), 13091312 (in Chinese).