Regional technical efficiency of Chinese Iron and steel industry based on bootstrap network data envelopment analysis

[1]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[2]  B. Efron Bootstrap Methods: Another Look at the Jackknife , 1979 .

[3]  W. Baumol Productivity Growth, Convergence, and Welfare: What the Long-run Data Show , 1985 .

[4]  B. Silverman Density estimation for statistics and data analysis , 1986 .

[5]  S. Dowrick,et al.  OECD Comparative Economic Growth 1950-85: Catch-Up and Convergence , 1989 .

[6]  G. Jefferson,et al.  China's iron and steel industry: Sources of enterprise efficiency and the impact of reform , 1990 .

[7]  Cao Yong,et al.  Can Chinese state enterprises perform like market entities: Productive efficiency in the Chinese iron and steel industry , 1993 .

[8]  D. Quah Galton's Fallacy and Tests of the Convergence Hypothesis (Now published in Scandinavian Journal of Economics 95 (4), 1993, pp.427-443.) , 1993 .

[9]  X. Sala-i-Martin,et al.  The Classical Approach to Convergence Analysis , 1996 .

[10]  X. Sala-i-Martin,et al.  Regional cohesion: Evidence and theories of regional growth and convergence , 1996 .

[11]  Yanrui Wu,et al.  Technical efficiency and firm attributes in the Chinese iron and steel industry , 1996 .

[12]  P. W. Wilson,et al.  Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models , 1998 .

[13]  Francisco J. Arcelus,et al.  Convergence and productive efficiency in fourteen OECD countries: A non-parametric frontier approach , 2000 .

[14]  Xiao-guang Zhang,et al.  Technical Efficiency in China’s Iron and Steel Industry: Evidence from the new census data , 2001 .

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

[16]  David Hawdon,et al.  Efficiency, performance and regulation of the international gas industry—a bootstrap DEA approach , 2003 .

[17]  Oleksandr Movshuk,et al.  Restructuring, productivity and technical efficiency in China’s iron and steel industry, 1988–2000 , 2004 .

[18]  M. Flannery,et al.  Partial Adjustment Toward Target Capital Structures , 2004 .

[19]  Asami Miketa,et al.  Energy productivity across developed and developing countries in 10 manufacturing sectors: Patterns of growth and convergence , 2005 .

[20]  M. Fung,et al.  Scale economies, X-efficiency, and convergence of productivity among bank holding companies , 2006 .

[21]  Yi-Ming Wei,et al.  An empirical analysis of energy efficiency in China's iron and steel sector , 2007 .

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

[23]  Lawrence M. Seiford,et al.  Data envelopment analysis (DEA) - Thirty years on , 2009, Eur. J. Oper. Res..

[24]  Xiaogang Wang,et al.  Using the bootstrap method to detect influential DMUs in data envelopment analysis , 2010, Ann. Oper. Res..

[25]  B. Casu,et al.  Integration and efficiency convergence in EU banking markets , 2010 .

[26]  Claudia Curi,et al.  New evidence on the efficiency of Italian airports: A bootstrapped DEA analysis , 2011 .

[27]  Herbert F. Lewis,et al.  Resolving the deposit dilemma: A new DEA bank efficiency model , 2011 .

[28]  George Emm. Halkos,et al.  Industry performance evaluation with the use of financial ratios: An application of bootstrapped DEA , 2012, Expert Syst. Appl..

[29]  Chyan Yang,et al.  Managerial efficiency in Taiwan bank branches: A network DEA , 2012 .

[30]  P. V. Matos,et al.  Beta-convergence and sigma-convergence in corporate governance in Europe , 2012 .

[31]  A. Maghyereh,et al.  Financial integration of GCC banking markets: A non-parametric bootstrap DEA estimation approach , 2012 .

[32]  Liang Dong,et al.  Analysis of low-carbon industrial symbiosis technology for carbon mitigation in a Chinese iron/steel industrial park: A case study with carbon flow analysis , 2013 .

[33]  Thomas R. Sexton,et al.  Unoriented two-stage DEA: The case of the oscillating intermediate products , 2013, Eur. J. Oper. Res..

[34]  Tzu-Yu Lin,et al.  Using independent component analysis and network DEA to improve bank performance evaluation , 2013 .

[35]  Yu Sheng,et al.  Re-estimation of firms' total factor productivity in China's iron and steel industry , 2013 .

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

[37]  Madjid Tavana,et al.  A new network epsilon-based DEA model for supply chain performance evaluation , 2013, Comput. Ind. Eng..

[38]  Xiaolei Wang,et al.  Exploring energy efficiency in China׳s iron and steel industry: A stochastic frontier approach , 2014 .

[39]  Wei Yang,et al.  An Empirical Analysis on Regional Technical Efficiency of Chinese Steel Sector based on Network DEA Method , 2014, ITQM.

[40]  Zongguo Wen,et al.  Mode of circular economy in China's iron and steel industry: a case study in Wu'an city☆ , 2014 .

[41]  Chiang Kao,et al.  Network data envelopment analysis: A review , 2014, Eur. J. Oper. Res..

[42]  Sebastián Lozano,et al.  Alternative SBM Model for Network DEA , 2015, Comput. Ind. Eng..

[43]  Bin Xu,et al.  Regional differences in the CO2 emissions of China's iron and steel industry: Regional heterogeneity , 2016 .

[44]  Hana Nielsen,et al.  Productive efficiency in the iron and steel sector under state planning: The case of China and former Czechoslovakia in a comparative perspective , 2017 .