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Measuring Electricity Generation Efficiency

Measuring Electricity Generation Efficiency. Data Envelopment Analysis. versus. Fixed Proportion Technology Indicators. Darold Barnum, University of Illinois at Chicago Managerial Studies, College of Business Information & Decision Sciences, College of Business

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Measuring Electricity Generation Efficiency

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  1. Measuring Electricity Generation Efficiency Data Envelopment Analysis versus Fixed Proportion Technology Indicators

  2. Darold Barnum, University of Illinois at Chicago • Managerial Studies, College of Business • Information & Decision Sciences, College of Business • Pharmacy Administration, College of Pharmacy • John Gleason, Creighton University • Information Systems & Technology, College of Business

  3. DEA theory requires input substitutability

  4. Relationship between substitute inputs, holding output constant Negative Slope

  5. But, some inputs are not substitutable Output: 10 motorbus-miles (speed = 10 mph) Available motorbus hours (1, 1) Available driver hours

  6. Point frontier envelops the data Available motorbus hours (1, 1) Point frontier Available driver hours

  7. Relationship among fixed proportion inputs, holding output constant

  8. Rectilinear distances between target DMU and production frontier

  9. Models for measuring rectilinear distance • DEA Additive Model (ADD) • Fixed Proportion Additive Model (FPA) • Only difference is the location of the benchmark point on the production frontier

  10. Fixed Proportion Additive (FPA)

  11. How about Electricity Generation? • Capital – MW capacity • Labor – FTE employees • Energy – BTUs • Holding MWh output constant • Cannot substitute capacity for employees • Cannot substitute employees for BTUs • Cannot substitute BTUs for capacity

  12. Relationships among electricity generation inputs, holding MWh output constant, 70 Coal-fired plants P(z>9.1) = .000 P(z>4.1) = .000 P(z>.23) = .821

  13. Comparison of FPA and ADD estimates • 2007 data for 70 U.S. generation plants • Both models use the same metric, but measure efficiency from different points • ADD efficiencies averaged 42% greater than FPA efficiencies • ADD efficiencies of the DMUs ranged from 3.6% greater to 100% greater than FPA • ADD estimates were extremely biased and had strikingly low precision

  14. But . . . • None of the exigent published studies have used the ADD model • Most use the CCR or BCC radial models, which measure a DMU’s percentage of full efficiency when inputs are substitutable • We compare the CCR model with the Fixed Proportion Ratio (FPR), which measures a DMU’s percentage of full efficiency when inputs are not substitutable

  15. Fixed Proportion Ratio (FPR) Measure

  16. Results • For all 70 DMUs , R2(FPR, CCR) = 0.83 • For 24 DMUs with efficiency above 70%, R2(FPR,CCR) = 0.33 • Of more concern is the fact that the rank orders vary a lot • CCR ranks ranged from 29 higher to 24 lower than FPR ranks • Radial measures (like CCR or BCC) are unacceptable • Very large upward bias • Very low precision

  17. CONCLUSIONS . . . • Unfortunately, almost all published DEA efficiency studies of electricity generating plants have used radial measures • Thus, it is likely that most publications to date report electricity generating plant efficiency estimates that are significantly biased, imprecise, and report very inaccurate efficiency rankings. • Given the energy and environmental crises we are facing, this problem is of even greater concern if such studies are used for policy or operating decisions.

  18. Thank you!

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