Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-20T03:23:33.908Z Has data issue: false hasContentIssue false
  • English
  • Français

Changes in environmentally sensitive productivity and technological modernization in China's iron and steel industry in the 1990s

Published online by Cambridge University Press:  24 June 2010

HIDEMICHI FUJII ,
SHINJI KANEKO  and
SHUNSUKE MANAGI
HIDEMICHI FUJII
Affiliation:
Graduate School of Environmental Studies, Tohoku University, 6-6-20 Aramaki-Aza Aoba, Aoba-Ku, Sendai 980-8579, Japan
SHINJI KANEKO
Affiliation:
Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8529, Japan. Tel: +81 (82) 424-6916. Email: [email protected]
SHUNSUKE MANAGI
Affiliation:
Graduate School of Environmental Studies, Tohoku University, 6-6-20 Aramaki-Aza Aoba, Aoba-Ku, Sendai 980-8579
Get access Rights & Permissions [Opens in a new window]

Abstract

Technological modernization is widely believed to contribute positively both to economic development and to environmental and resource conservation, through improvements in productivity and strengthening of business competitiveness. However, this may not always be true, particularly in the short term, as it requires substantial investments and may impose financial burdens on firms undertaking such investments. This study empirically examines the effects of technological modernization in China's iron and steel industry in the 1990s on conventional economic productivity (CEP) and environmentally sensitive productivities (ESPs). We employ a directional distance function that can handle multiple inputs and outputs to compute relative production efficiencies. We apply these models to the data covering 27 iron and steel firms in China between 1990 and 1999 – a period when the Chinese iron and steel industry modernized rapidly. We find that ESPs have continuously improved, even in the period when the CEP declined.

Type
Research Article
Information
Environment and Development Economics , Volume 15 , Issue 4 , August 2010 , pp. 485 - 504
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Asmild, M., Paradi, J.C., Aggarwall, V., and Schaffnit, C. (2004), ‘Combining DEA window analysis with the Malmquist Index approach in a study of the Canadian banking industry’, Journal of Productivity Analysis 21: 67–89.CrossRefGoogle Scholar
Chambers, R.G. (2002), ‘Exact nonradial input, output, and productivity measurement’, Economic Theory 20 (4): 751–765.CrossRefGoogle Scholar
Chambers, R.G., Chung, Y. H., and Färe, R. (1998), ‘Profit, directional distance functions, and Nerlovian efficiency’, Journal of Optimization Theory and Applications 98 (2): 351–364.CrossRefGoogle Scholar
Chambers, R.G. and Pope, R.D. (1996), ‘Aggregate productivity measures’, American Journal of Agricultural Economics 78 (5): 1360–1365.CrossRefGoogle Scholar
Charnes, A., Cooper, C.C., and Rhodes, E. (1978), ‘Measuring the efficiency of decision making units’, European Journal of Operational Research 2 (6): 429–444.CrossRefGoogle Scholar
China Environmental Yearbook Committee (eds) (1992–2005), China Environmental Yearbook, Beijing: China Environmental Yearbook Press.Google Scholar
China Water Power Press (2004), China Industrial Water Conservation Report, Beijing: China Water Power Press.Google Scholar
Chung, Y.H., Färe, R., and Grosskopf, S. (1997), ‘Productivity and undesirable output: a directional distance function approach’, Journal of Environmental Management 51: 229–240.CrossRefGoogle Scholar
De Groot, H.L.F., Withagen, C.A., and Minliang, Z. (2004), ‘Dynamics of China's regional development and pollution: an investigation into the Environmental Kuznets Curve’, Environment and Development Economics 9: 507–537.CrossRefGoogle Scholar
Färe, R. and Grosskopf, S. (1996), Intertemporal Production Frontiers: With Dynamic DEA, Boston: Kluwer Academic Publishers.CrossRefGoogle Scholar
Färe, R., Grosskopf, S., Lovell, C.A.K., and Pasurka, C.A. (1989), ‘Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach’, The Review of Economics and Statistics 71: 90–98.CrossRefGoogle Scholar
Färe, R., Grosskopf, S., Norris, M., and Zhang, Z. (1994), ‘Productivity growth, technical progress and efficiency change in industrialized countries’, American Economic Review 84 (1): 66–83.Google Scholar
Färe, R., Grosskopf, S., and Pasurka, C.A. (1986), ‘Effects on relative efficiency in electric power generation due to environmental controls’, Resources and Energy Economics 8 (2): 167–184.CrossRefGoogle Scholar
Färe, R., Grosskopf, S., and Pasurka, C.A. (2001), ‘Accounting for air pollution emissions in measures of state manufacturing productivity growth’, Journal of Regional Science 41 (3): 381–409.CrossRefGoogle Scholar
Färe, R., Grosskopf, S., and Pasurka, C. (2007), ‘Pollution abatement activities and traditional productivity’, Ecological Economics 62 (3–4): 673–682.CrossRefGoogle Scholar
Färe, R. and Primont, D. (1995), Multi-Output Production and Duality: Theory and Applications, Boston: Kluwer Academic Publishers.CrossRefGoogle Scholar
Fisher-Vanden, K., Jefferson, G., Liu, H., and Tao, Q. (2004), ‘What is driving China's decline in energy intensity?’, Resource and Energy Economics 26 (1): 77–97.CrossRefGoogle Scholar
Hailu, A. and Veeman, T.S. (2000), ‘Environmentally sensitive productivity analysis of the Canadian pulp and paper industry, 1959–1994: an input distance function approach’, Journal of Environmental Economics and Management 40: 251–274.CrossRefGoogle Scholar
Intergovernmental Panel on Climate Change (IPCC) (2006), 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Volume 1 General Guidance and Reporting, Japan: IPCC.Google Scholar
International Iron and Steel Institute (2005a), Steel Statistical Yearbook 2005, Brussels: International Iron and Steel Institute.Google Scholar
International Iron and Steel Institute (2005b), World Steel in Figures 2005, Brussels: International Iron and Steel Institute.Google Scholar
Kaneko, S., Yonamine, A., and Jung, T.Y. (2006), ‘Technology choice and CDM projects in China: case study of a small steel company in Shandong province’, Energy Policy 34 (10): 1139–1151.CrossRefGoogle Scholar
Luenberger, D.G. (1992), ‘Benefit function and duality’, Journal of Mathematical Economics 21: 461–481.CrossRefGoogle Scholar
Managi, S. and Kaneko, S. (2009), ‘Environmental performance and returns to pollution abatement in China’, Ecological Economics 68 (6): 1643–1651.CrossRefGoogle Scholar
Managi, S., Opaluch, J.J., Jin, D. and Grigalunas, T.A. (2005) ‘Environmental regulations and technological change in the offshore oil and gas industry’, Land Economics 81 (2): 303–319.CrossRefGoogle Scholar
Ministry of Metallurgical Industry (eds) (1989–2003), Yearbook of Iron and Steel Industry of China, Beijing: Metallurgical Industry Press.Google Scholar
Ministry of Metallurgical Industry (2003), China Iron and Steel Industry Fifty-Year Summary (Volumes 1 and 2), Beijing: Metallurgical Industry Press.Google Scholar
Price, L., Sinton, J., Worrell, E., Phylipsen, D., Xiulian, H., and Ji, L. (2002), ‘Energy use and carbon dioxide emissions from steel production in China’, Energy 27: 429–449.CrossRefGoogle Scholar
Schmidt, P. (1986), ‘Frontier production functions’, Econometric Reviews 4: 289–328.CrossRefGoogle Scholar
Shao, W., Yang, D., Hu, H., and Sanbongi, K. (2009), ‘Water resources allocation considering the water use flexible limit to water shortage – a case study in the Yellow River Basin of China’, Water Resources Management 23 (5): 869–880.CrossRefGoogle Scholar
Shephard, R.W. and Färe, R. (1974), ‘The law of diminishing returns’, Journal of Economics 34 (1): 69–90.Google Scholar
Shestalova, V. (2003), ‘Sequential Malmquist Indices of productivity growth: an application to OECD industrial activities’, Journal of Productivity Analysis 19: 211–226.CrossRefGoogle Scholar
State Statistical Bureau (eds) (1986–2006), China Statistical Yearbook, Beijing: China Statistical Press.Google Scholar
State Statistical Bureau (eds) (1991–1996, 1997–1999, 2005), China Energy Statistical Yearbook, Beijing: China Statistical Press.Google Scholar
Sugimoto, T. (1993), ‘The Chinese steel industry’, Resource Policy 19 (4): 264–286.CrossRefGoogle Scholar
Vardanyan, M. and Noh, D.-W. (2006), ‘Approximating pollution abatement costs via alternative specifications of a multi-output production technology: a case of the US electric utility industry’, Journal of Environmental Management 80 (2): 177–190.CrossRefGoogle ScholarPubMed
Wang, H. and Wheeler, D. (2003), ‘Equilibrium pollution and economic development in China’, Environment and Development Economics 8: 451–466.CrossRefGoogle Scholar
Woo, W. T. (2007), ‘What are the high-probability challenges to continued high growth in China?’ Hong Kong Institute of Economics and Business Strategy Working Paper no. 1168.Google Scholar
World Bank (2007), Cost of Pollution in China: Economic Estimates of Physical Damages, Washington, DC: World Bank.Google Scholar
World Bank (2008), Water Supply Pricing in China: Economic Efficiency, Environment, and Social Affordability, Washington, DC: World Bank.Google Scholar
Zhang, Y. (2002), ‘The impacts of economic reform on the efficiency of silviculture: a non-parametric approach’, Environment and Development Economics 7: 107–122.CrossRefGoogle Scholar
Zhou, P., Ang, B.W., and Poh, K.L. (2008), ‘A survey of data envelopment analysis in energy and environmental studies’, European Journal of Operational Research 189 (1): 1–18.CrossRefGoogle Scholar
Supplementary material: File

Fujii supplementary material

Appendix.doc

Download Fujii supplementary material(File)
File 63.5 KB