IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v54y2018i1d10.1007_s00181-016-1217-y.html
   My bibliography  Save this article

Shadow prices of $$\hbox {CO}_{2}$$ CO 2 emissions at US electric utilities: a random-coefficient, random-directional-vector directional output distance function approach

Author

Listed:
  • Guohua Feng

    (University of North Texas)

  • Chuan Wang

    (Monash University)

  • Apostolos Serletis

    (University of Calgary)

Abstract

We estimate the shadow prices of $$\hbox {CO}_{2}$$ CO 2 emissions of electric utilities in the US over the period from 2001 to 2014, using a random-coefficient, random-directional-vector directional output distance function (DODF) model. The main feature of this model is that both its coefficients and directional vector are allowed to vary across firms, thus allowing different firms to have different production technologies and to follow different growth paths. Our Bayes factor analysis indicates that this model is strongly favored over the commonly used fixed-coefficient DODF model. Our results obtained from this model suggest that the average annual shadow price of $$\hbox {CO}_{2}$$ CO 2 emissions ranges from $61.62 to $105.72 (in 2001 dollars) with an average of $83.12. The results also suggest that the firm-specific average shadow price differs significantly across electric utilities. In addition, our estimates of the shadow price of $$\hbox {CO}_{2}$$ CO 2 emissions show an upward trend for both the sample electric utilities as a whole and the majority of the individual sample electric utilities.

Suggested Citation

  • Guohua Feng & Chuan Wang & Apostolos Serletis, 2018. "Shadow prices of $$\hbox {CO}_{2}$$ CO 2 emissions at US electric utilities: a random-coefficient, random-directional-vector directional output distance function approach," Empirical Economics, Springer, vol. 54(1), pages 231-258, February.
  • Handle: RePEc:spr:empeco:v:54:y:2018:i:1:d:10.1007_s00181-016-1217-y
    DOI: 10.1007/s00181-016-1217-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-016-1217-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00181-016-1217-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    2. Agee, Mark D. & Atkinson, Scott E. & Crocker, Thomas D. & Williams, Jonathan W., 2014. "Non-separable pollution control: Implications for a CO2 emissions cap and trade system," Resource and Energy Economics, Elsevier, vol. 36(1), pages 64-82.
    3. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    4. O'Donnell, Christopher J. & Coelli, Timothy J., 2005. "A Bayesian approach to imposing curvature on distance functions," Journal of Econometrics, Elsevier, vol. 126(2), pages 493-523, June.
    5. Atkinson, Scott E. & Dorfman, Jeffrey H., 2005. "Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution," Journal of Econometrics, Elsevier, vol. 126(2), pages 445-468, June.
    6. William L. Weber & Bruce Domazlicky, 2001. "Productivity Growth and Pollution in State Manufacturing," The Review of Economics and Statistics, MIT Press, vol. 83(1), pages 195-199, February.
    7. Fare, Rolf & Grosskopf, Shawna & Noh, Dong-Woon & Weber, William, 2005. "Characteristics of a polluting technology: theory and practice," Journal of Econometrics, Elsevier, vol. 126(2), pages 469-492, June.
    8. Färe, Rolf & Grosskopf, Shawna & Hayes, Kathy J. & Margaritis, Dimitris, 2008. "Estimating demand with distance functions: Parameterization in the primal and dual," Journal of Econometrics, Elsevier, vol. 147(2), pages 266-274, December.
    9. Carolyn Fischer & Richard D. Morgenstern, 2006. "Carbon Abatement Costs: Why the Wide Range of Estimates?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 73-86.
    10. Russell W. Pittman, 1981. "Issue in Pollution Control: Interplant Cost Differences and Economies of Scale," Land Economics, University of Wisconsin Press, vol. 57(1), pages 1-17.
    11. Limin Du & Aoife Hanley & Chu Wei, 2015. "Marginal Abatement Costs of Carbon Dioxide Emissions in China: A Parametric Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 61(2), pages 191-216, June.
    12. Fernandez, Carmen & Koop, Gary & Steel, Mark F.J., 2005. "Alternative efficiency measures for multiple-output production," Journal of Econometrics, Elsevier, vol. 126(2), pages 411-444, June.
    13. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    14. Henderson, Daniel J. & Kumbhakar, Subal C. & Li, Qi & Parmeter, Christopher F., 2015. "Smooth coefficient estimation of a seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 189(1), pages 148-162.
    15. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    16. Chien-Ming Chen & Magali A. Delmas, 2012. "Measuring Eco-Inefficiency: A New Frontier Approach," Operations Research, INFORMS, vol. 60(5), pages 1064-1079, October.
    17. Feng, Guohua & Zhang, Xiaohui, 2014. "Returns to scale at large banks in the US: A random coefficient stochastic frontier approach," Journal of Banking & Finance, Elsevier, vol. 39(C), pages 135-145.
    18. Zhou, P. & Zhou, X. & Fan, L.W., 2014. "On estimating shadow prices of undesirable outputs with efficiency models: A literature review," Applied Energy, Elsevier, vol. 130(C), pages 799-806.
    19. Weisser, Daniel, 2007. "A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies," Energy, Elsevier, vol. 32(9), pages 1543-1559.
    20. Nelson, Randy A, 1984. "Regulation, Capital Vintage, and Technical Change in the Electric Utility Industry," The Review of Economics and Statistics, MIT Press, vol. 66(1), pages 59-69, February.
    21. Edward R. Morey, 1986. "An Introduction to Checking, Testing, and Imposing Curvature Properties: The True Function and the Estimated Function," Canadian Journal of Economics, Canadian Economics Association, vol. 19(2), pages 207-235, May.
    22. Emir Malikov & Subal C. Kumbhakar & Mike G. Tsionas, 2016. "A Cost System Approach to the Stochastic Directional Technology Distance Function with Undesirable Outputs: The Case of us Banks in 2001–2010," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1407-1429, November.
    23. Ho-chuan Huang, 2004. "Estimation of Technical Inefficiencies with Heterogeneous Technologies," Journal of Productivity Analysis, Springer, vol. 21(3), pages 277-296, May.
    24. Pittman, Russell W, 1983. "Multilateral Productivity Comparisons with Undesirable Outputs," Economic Journal, Royal Economic Society, vol. 93(372), pages 883-891, December.
    25. Samuel Fankhauser, 1994. "The Social Costs of Greenhouse Gas Emissions: An Expected Value Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 157-184.
    26. Robert G. Chambers, 2002. "Exact nonradial input, output, and productivity measurement," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 20(4), pages 751-765.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Feng, Guohua & Serletis, Apostolos, 2014. "Undesirable outputs and a primal Divisia productivity index based on the directional output distance function," Journal of Econometrics, Elsevier, vol. 183(1), pages 135-146.
    2. Gary Koop & Lise Tole, 2008. "What is the environmental performance of firms overseas? An empirical investigation of the global gold mining industry," Journal of Productivity Analysis, Springer, vol. 30(2), pages 129-143, October.
    3. Holtkamp, A.M. & Brummer, B., 2018. "Environmental efficiency of smallholder rubber production," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277518, International Association of Agricultural Economists.
    4. Yu-Ying Lin, Eugene & Chen, Ping-Yu & Chen, Chi-Chung, 2013. "Measuring green productivity of country: A generlized metafrontier Malmquist productivity index approach," Energy, Elsevier, vol. 55(C), pages 340-353.
    5. Adewale Henry Adenuga & John Davis & George Hutchinson & Trevor Donnellan & Myles Patton, 2019. "Environmental Efficiency and Pollution Costs of Nitrogen Surplus in Dairy Farms: A Parametric Hyperbolic Technology Distance Function Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(3), pages 1273-1298, November.
    6. Tai-Hsin Huang & Yi-Huang Chiu & Chih-Ying Mao, 2021. "Imposing Regularity Conditions to Measure Banks’ Productivity Changes in Taiwan Using a Stochastic Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(2), pages 273-303, June.
    7. Kumbhakar, Subal C. & Tsionas, Efthymios G., 2016. "The good, the bad and the technology: Endogeneity in environmental production models," Journal of Econometrics, Elsevier, vol. 190(2), pages 315-327.
    8. Emir Malikov & Raushan Bokusheva & Subal C. Kumbhakar, 2018. "A hedonic-output-index-based approach to modeling polluting technologies," Empirical Economics, Springer, vol. 54(1), pages 287-308, February.
    9. Emir Malikov & Subal C. Kumbhakar & Efthymios G. Tsionas, 2015. "Bayesian Approach to Disentangling Technical and Environmental Productivity," Econometrics, MDPI, vol. 3(2), pages 1-23, June.
    10. Surender Kumar & Rakesh Kumar Jain, 2021. "Cost of CO2 emission mitigation and its decomposition: evidence from coal-fired thermal power sector in India," Empirical Economics, Springer, vol. 61(2), pages 693-717, August.
    11. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    12. Lee, Sang-choon & Oh, Dong-hyun & Lee, Jeong-dong, 2014. "A new approach to measuring shadow price: Reconciling engineering and economic perspectives," Energy Economics, Elsevier, vol. 46(C), pages 66-77.
    13. Atkinson, Scott E. & Tsionas, Mike G., 2016. "Directional distance functions: Optimal endogenous directions," Journal of Econometrics, Elsevier, vol. 190(2), pages 301-314.
    14. Guohua Feng & Bin Peng & Xiaohui Zhang, 2017. "Productivity and efficiency at bank holding companies in the U.S.: a time-varying heterogeneity approach," Journal of Productivity Analysis, Springer, vol. 48(2), pages 179-192, December.
    15. repec:rim:rimwps:26-07 is not listed on IDEAS
    16. Shixiong Cheng & Wei Liu & Kai Lu, 2018. "Economic Growth Effect and Optimal Carbon Emissions under China’s Carbon Emissions Reduction Policy: A Time Substitution DEA Approach," Sustainability, MDPI, vol. 10(5), pages 1-23, May.
    17. Yan, Jia & Sun, Xinyu & Liu, John J., 2009. "Assessing container operator efficiency with heterogeneous and time-varying production frontiers," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 172-185, January.
    18. Atkinson, Scott E. & Dorfman, Jeffrey H., 2005. "Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution," Journal of Econometrics, Elsevier, vol. 126(2), pages 445-468, June.
    19. Lambarraa, Fatima, 2011. "Dynamic Efficiency Analysis of Spanish Outdoor and Greenhouse Horticulture Sector," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114408, European Association of Agricultural Economists.
    20. Feng, Guohua & Zhang, Xiaohui, 2014. "Returns to scale at large banks in the US: A random coefficient stochastic frontier approach," Journal of Banking & Finance, Elsevier, vol. 39(C), pages 135-145.
    21. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Yan, Ming-Zhe & Wang, Jian-Lin & Xie, Bai-Chen, 2019. "Which provincial administrative regions in China should reduce their coal consumption? An environmental energy input requirement function based analysis," Energy Policy, Elsevier, vol. 127(C), pages 51-63.

    More about this item

    Keywords

    Shadow price of $$hbox {CO}_{2}$$ CO 2 emissions; Directional output distance function; Bayesian estimation; Electric utilities;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:empeco:v:54:y:2018:i:1:d:10.1007_s00181-016-1217-y. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.