Assessing the relative efficiency of energy use among similar manufacturing industries

International Journal of Energy Research

Volumn 35, Issue 6, 2011, Pages 477-488

  Aguirre, Fernando ^a   Villalobos, J Rene ^b   Phelan, Patrick E ^c   Pacheco, Rafael ^d,e  

^a Arizona State University ^*  (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

^c Arizona State University   (United States)

^d ARIZONA STATE UNIVERSITY   (United States)

^e UNIVERSITY OF NOTRE DAME   (United States)

Author keywords

Aggregate efficiency decomposition model; Data envelopment analysis; Energy production signatures; Manufacturing

Indexed keywords

AGGREGATE EFFICIENCY; DEPARTMENT OF ENERGY; ENERGY CONSUMPTION; ENERGY USE; ENERGY-PRODUCTION SIGNATURES; INDUSTRIAL ENERGY EFFICIENCY; MANUFACTURING; MANUFACTURING INDUSTRIES; MANUFACTURING PLANT; MANUFACTURING SECTOR; PRODUCTION INEFFICIENCIES; REGRESSION METHOD; RELATIVE EFFICIENCY; SIMULATION MODEL;

BEHAVIORAL RESEARCH; BENCHMARKING; COMPUTER SIMULATION; DATA ENVELOPMENT ANALYSIS; ENERGY EFFICIENCY; ENERGY UTILIZATION; INDUSTRIAL APPLICATIONS; INDUSTRIAL PLANTS; LINEAR PROGRAMMING;

MANUFACTURE;

EID: 79953745459     PISSN: 0363907X     EISSN: 1099114X     Source Type: Journal    
DOI: 10.1002/er.1701     Document Type: Article

References (15)

1. Aguirre F. An effective diagnostic tool of energy and productivity efficiency of the manufacturing industry using limited data. Masters Thesis, Arizona State University, U.S.A., 2006.
2. Boyd GA, Pang JX. Estimating the linkage between energy efficiency and productivity. Energy Policy 2000; 28:289-296.
3. Worrell E, Laitner JA, Ruth M, Finman H. Productivity benefits of industrial energy efficiency measures. Energy 2003; 28:1081-1098.
4. Papadaratsakis K, Kasten DJ, Muller MR. On accounting for energy savings from industrial productivity improvements. Internal Report, Center for Advanced Energy Systems, Rutgers University, 2001.
5. Boyd GA, Pang JX. Estimating the linkage between energy efficiency and productivity. Energy Policy 2000; 28:289-296.
6. Murillo-Zamorano LR. The role of energy in productivity growth: a controversial issue? The Energy Journal 2005; 26(2):69-88.
7. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 2:429-444.
8. Fare R, Grosskopf S, Lovell CAK. The Measurement of Efficiency of Production. Kluwer-Nijhoff: Boston, 1985.
9. Winston WL. Operations Research: Applications and Algorithms. Duxbury Press: Belmont, 1994.
10. Seiford L, Thrall RM. Recent developments in DEA: the mathematical programming approach to frontier analysis. Journal of Econometrics 1990; 46:7-38.
11. Aigner D, Lovell CAK, Schmidt P. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 1997; 6:21-37.
12. Boyd GA. Estimating plant level energy efficiency with a stochastic frontier. The Energy Journal 2008; 29(2):23-43.
13. Alghandoor A, Phelan PE, Villalobos R, Phelan BE. U.S. manufacturing aggregate energy intensity decomposition: the application of multivariate regression analysis. International Journal of Energy Research, 2008; 32(2):91-106.
14. Alghandoor 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):501-513.
15. Montgomery DC, Peck EA, Vining GG. Introduction to Linear Regression Analysis (3rd edn). Wiley-Interscience: New York, 2001.