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Manufacturing productivity and energy efficiency: a stochastic efficiency frontier analysis

dc.contributor.authorShui, Huanyien_US
dc.contributor.authorJin, Xiaoningen_US
dc.contributor.authorNi, Junen_US
dc.date.accessioned2015-10-07T20:42:21Z
dc.date.available2016-12-01T14:33:05Zen
dc.date.issued2015-10-10en_US
dc.identifier.citationShui, Huanyi; Jin, Xiaoning; Ni, Jun (2015). "Manufacturing productivity and energy efficiency: a stochastic efficiency frontier analysis." International Journal of Energy Research 39(12): 1649-1663.en_US
dc.identifier.issn0363-907Xen_US
dc.identifier.issn1099-114Xen_US
dc.identifier.urihttps://hdl.handle.net/2027.42/113674
dc.publisherUniversity of Cambridge Presen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherbenchmarkingen_US
dc.subject.otherenergy efficiencyen_US
dc.subject.otherStochastic frontier analysisen_US
dc.titleManufacturing productivity and energy efficiency: a stochastic efficiency frontier analysisen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMechanical Engineeringen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/113674/1/er3368.pdf
dc.identifier.doi10.1002/er.3368en_US
dc.identifier.sourceInternational Journal of Energy Researchen_US
dc.identifier.citedreferenceBoyd G, Dutrow E, Tunnessen W. The evolution of the ENERGY STAR energy performance indicator for benchmarking industrial plant manufacturing energy use. Journal of Cleaner Production 2008; 16.6: 708 – 715.en_US
dc.identifier.citedreferenceYalcintas M. An energy benchmarking model based on artificial neural network method with a case example for tropical climates. International Journal of Energy Research 2006; 30.14: 1158 – 1174.en_US
dc.identifier.citedreferenceYalcintas M, Ozturk UA. An energy benchmarking model based on artificial neural network method utilizing US Commercial Buildings Energy Consumption Survey (CBECS) database. International Journal of Energy Research 2007; 31.4: 412 – 421.en_US
dc.identifier.citedreferenceBuck J, Young D. The potential for energy efficiency gains in the Canadian commercial building sector: a stochastic frontier study. Energy 2007; 32.9: 1769 – 1780.en_US
dc.identifier.citedreferenceConstantin PD, Martin DL, de Rivera y Rivera EBB. Cobb–Douglas, translog stochastic production function and data envelopment analysis in total factor productivity in Brazilian agribusiness. Journal of Operations and supply Chain Management 2009; 2.2: 20 – 34.en_US
dc.identifier.citedreferenceFilippini M, Hunt LC. US residential energy demand and energy efficiency: a stochastic demand frontier approach. Energy Economics 2012; 34.5: 1484 – 1491.en_US
dc.identifier.citedreferenceZhou P, Ang BW, Zhou DQ. Measuring economy‐wide energy efficiency performance: a parametric frontier approach. Applied Energy 2011; 90.1: 196 – 200.en_US
dc.identifier.citedreferenceAzadeh A, Asadzadeh SM, Nadimi V, Tajvidi A, Sheikalishahi M. A neuro fuzzy stochastic frontier analysis approach for long‐term natural gas consumption forecasting and behavior analysis: the cases of Bahrain. Saudi Arabia, Syria, and UAE, Applied Energy 2011; 88.11: 3850 – 3859.en_US
dc.identifier.citedreferenceAmornkitvikai Y, Harvie C. Measuring technical inefficiency factors for Thai listed manufacturing enterprises: a stochastic frontier (SFA) and data envelopment analysis (DEA). Australian Conference of Economists 2010: 1 – 29.en_US
dc.identifier.citedreferenceBoyd GA. A method for measuring the efficiency gap between average and best practice energy use. Journal of Industrial Ecology 2005; 9.3: 51 – 65.en_US
dc.identifier.citedreferenceBoyd GA. Estimating plant level energy efficiency with a stochastic frontier. The Energy Journal 2008; 29.2: 23 – 43.en_US
dc.identifier.citedreferenceBoyd GA. Estimating the changes in the distribution of energy efficiency in the U.S. automobile assembly industry. Energy Economics 2014; 42: 81 – 87.en_US
dc.identifier.citedreferenceCharles WC, Paul HD. A theory of production. The American Economic Review March 1928; 18.1: 139 – 165.en_US
dc.identifier.citedreferenceChristensen LR, Jorgenson DW, Lau LJ. Transcendental logarithmic production frontiers. The Review of Economics and Statistics 1973; 55.1: 28 – 45.en_US
dc.identifier.citedreferenceAigner D, Lovell CAK, Schmidt P. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 1976; 6.1: 21 – 37.en_US
dc.identifier.citedreferenceMeeusen W, Van den Broeck J. Efficiency estimation from Cobb–Douglas production function with composed error. International Economic Review 1977; 18.2: 435 – 444.en_US
dc.identifier.citedreferenceKumbhakar SC, Lovell CAK. Stochastic Frontier Analysis. University of Cambridge Pres: Cambridge, 2000.en_US
dc.identifier.citedreferenceBattese GE, Coelli TJ. A model of technical inefficiency effects in a stochastic production function for panel data. Empirical Economics 1995; 20.2: 325 – 332.en_US
dc.identifier.citedreferenceBattese GE, Coelli TJ. Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. Journal of Productivity Analysis 1992; 3: 153 – 169.en_US
dc.identifier.citedreferenceTechnology Roadmap for Energy Reduction in Automotive Manufacturing, U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy Industrial Technologies Program, U. S Council for Automotive Research 2008.en_US
dc.identifier.citedreferenceTrends in Selected Manufacturing Sectors. Opportunities and challenges for environmentally preferable energy outcomes. U. S. Environmental Protection Agency 2007.en_US
dc.identifier.citedreferenceLeven B, Weber C. Energy efficiency in innovative industries: application and benefits of energy indicators in the automobile industry, 2001 ACEEE Summer Study on Energy Efficiency in Industry Proceedings Volume 1. American Council for an Energy‐Efficient Economy ( ACEEE ), Washington, D.C. 2001; 67 – 75.en_US
dc.identifier.citedreferenceNeelis M, Ramirez A, Patel M, Farla J, Boonekamp P, Blok K. Energy efficiency developments in the Dutch energy‐intensive manufacturing industry 1980–2003. Energy Policy 2007; 35.12: 6112 – 6131.en_US
dc.identifier.citedreferenceAl‐Ghandoor A, Phelan PE, Villalobos R, Phelan BE. Modeling and forecasting the U.S. manufacturing aggregate energy intensity. International Journal of Energy Research 2008; 32.6: 50 – 513.en_US
dc.identifier.citedreferenceDincer I, Hussain MM, Al‐Zaharnah I. Analysis of sectoral energy and exergy use of Saudi Arabia. International Journal of Energy Research 2004; 28.3: 205 – 243.en_US
dc.identifier.citedreferenceBoyd G, Pang J. Estimating the linkage between energy efficiency and productivity. Energy Policy 2000; 28.5: 289 – 296.en_US
dc.identifier.citedreferenceGalitsky C, Worrell E. Energy efficiency improvement and cost saving opportunities for the vehicle assembly industry: an ENERGY STAR guide for energy and plant managers, Orlando Lawrence Berkeley National Laboratory 2008; [LBNL‐50939].en_US
dc.identifier.citedreferenceAguirre F, Villalobos JR, Phelan PE, Pacheco R. Assessing the relative efficiency of energy use among similar manufacturing industries. International Journal of Energy Research 2011; 35.6: 477 – 488.en_US
dc.identifier.citedreferenceFerrier JD, Hirschberg JG. Climate control efficiency. The Energy Journal 1992; 13.1: 37 – 54.en_US
dc.identifier.citedreferenceNassiri SM, Singh S. Study on energy use efficiency for paddy crop using data envelopment analysis (DEA) technique. Applied Energy 2009; 86.7–8: 1320 – 1325.en_US
dc.identifier.citedreferenceHu JL, Wang SC. Total‐factor energy efficiency of regions in China. Energy Policy 2006; 34.17: 3206 – 3217.en_US
dc.identifier.citedreferenceChang TP, Hu JL. Total‐factor energy productivity growth, technical progress, and efficiency change: an empirical study of China. Applied Energy 2010; 87.10: 3262 – 3270.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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