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Energy Intensity and Long- and Short-Term Efficiency in US Manufacturing Industry

Author

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  • Oleg Badunenko

    (Department of Economics and Finance, College of Business, Arts and Social Sciences, Brunel University London, Uxbridge UB8 3PH, UK
    These authors contributed equally to this work.)

  • Subal C. Kumbhakar

    (Department of Economics, State University of New York at Binghamton, Binghamton, NY 13902-6000, USA
    Department of Innovation, Management and Marketing, University of Stavanger Business School, University of Stavanger, N-4036 Stavanger, Norway
    These authors contributed equally to this work.)

Abstract

We analyze energy use efficiency of manufacturing industries in US manufacturing over five decades from 1960 to 2011. We apply a 4-component stochastic frontier model, which allows disentangling efficiency into a short- and long-term efficiency as well as accounting for industry heterogeneity. The data come from NBER-CES Manufacturing Industry Database. We find that relative to decade-specific frontiers, the overall efficiency of manufacturing industries, which is a product of transient and persistent efficiencies has deteriorated greatly in the 1970s and rebounded only in the 2000s. The industries are very efficient in the short-term and this has not changed over five decades. The high level of overall inefficiency is almost completely due to the structural inefficiency which can be explained by what is referred to as the “energy paradox”. Finally, higher energy-intensive industries perform worse in terms of energy use efficiency than their low energy-intensity counterparts.

Suggested Citation

  • Oleg Badunenko & Subal C. Kumbhakar, 2020. "Energy Intensity and Long- and Short-Term Efficiency in US Manufacturing Industry," Energies, MDPI, vol. 13(15), pages 1-21, August.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3954-:d:393278
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    References listed on IDEAS

    as
    1. Roberto Colombi & Subal Kumbhakar & Gianmaria Martini & Giorgio Vittadini, 2014. "Closed-skew normality in stochastic frontiers with individual effects and long/short-run efficiency," Journal of Productivity Analysis, Springer, vol. 42(2), pages 123-136, October.
    2. Kanellakis, M. & Martinopoulos, G. & Zachariadis, T., 2013. "European energy policy—A review," Energy Policy, Elsevier, vol. 62(C), pages 1020-1030.
    3. Filippini, Massimo & Hunt, Lester C., 2012. "US residential energy demand and energy efficiency: A stochastic demand frontier approach," Energy Economics, Elsevier, vol. 34(5), pages 1484-1491.
    4. Gale A. Boyd, 2008. "Estimating Plant Level Energy Efficiency with a Stochastic Frontier," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 23-44.
    5. Filipović, Sanja & Verbič, Miroslav & Radovanović, Mirjana, 2015. "Determinants of energy intensity in the European Union: A panel data analysis," Energy, Elsevier, vol. 92(P3), pages 547-555.
    6. Chia-Nan Wang & Hong-Xuyen Thi Ho & Ming-Hsien Hsueh, 2017. "An Integrated Approach for Estimating the Energy Efficiency of Seventeen Countries," Energies, MDPI, vol. 10(10), pages 1-16, October.
    7. Zhou, P. & Ang, B.W., 2008. "Linear programming models for measuring economy-wide energy efficiency performance," Energy Policy, Elsevier, vol. 36(8), pages 2901-2906, August.
    8. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    9. Boyd, Gale A. & Lee, Jonathan M., 2019. "Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis," Energy Economics, Elsevier, vol. 81(C), pages 159-174.
    10. Todd D. Gerarden & Richard G. Newell & Robert N. Stavins, 2017. "Assessing the Energy-Efficiency Gap," Journal of Economic Literature, American Economic Association, vol. 55(4), pages 1486-1525, December.
    11. Mukherjee, Kankana, 2008. "Energy use efficiency in U.S. manufacturing: A nonparametric analysis," Energy Economics, Elsevier, vol. 30(1), pages 76-96, January.
    12. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    13. Mukherjee, Kankana, 2008. "Energy use efficiency in the Indian manufacturing sector: An interstate analysis," Energy Policy, Elsevier, vol. 36(2), pages 662-672, February.
    14. Eric J. Bartelsman & Wayne Gray, 1996. "The NBER Manufacturing Productivity Database," NBER Technical Working Papers 0205, National Bureau of Economic Research, Inc.
    15. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    16. Diewert, W E, 1974. "Functional Forms for Revenue and Factor Requirements Functions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 119-130, February.
    17. Saunders, Harry D., 2013. "Historical evidence for energy efficiency rebound in 30 US sectors and a toolkit for rebound analysts," Technological Forecasting and Social Change, Elsevier, vol. 80(7), pages 1317-1330.
    18. Thollander, Patrik & Danestig, Maria & Rohdin, Patrik, 2007. "Energy policies for increased industrial energy efficiency: Evaluation of a local energy programme for manufacturing SMEs," Energy Policy, Elsevier, vol. 35(11), pages 5774-5783, November.
    19. Mehdi Farsi & Massimo Filippini & William Greene, 2006. "Application Of Panel Data Models In Benchmarking Analysis Of The Electricity Distribution Sector," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 77(3), pages 271-290, September.
    20. Kumbhakar, Subal C., 2013. "Specification and estimation of multiple output technologies: A primal approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 465-473.
    21. Subal C. Kumbhakar & Gudbrand Lien, 2017. "Yardstick Regulation of Electricity Distribution Disentangling Short-run and Long-run Inefficiencies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    22. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    23. Mousavi-Avval, Seyed Hashem & Rafiee, Shahin & Jafari, Ali & Mohammadi, Ali, 2011. "Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach," Energy, Elsevier, vol. 36(5), pages 2765-2772.
    24. Thollander, Patrik & Backlund, Sandra & Trianni, Andrea & Cagno, Enrico, 2013. "Beyond barriers – A case study on driving forces for improved energy efficiency in the foundry industries in Finland, France, Germany, Italy, Poland, Spain, and Sweden," Applied Energy, Elsevier, vol. 111(C), pages 636-643.
    25. Subal C. Kumbhakar & Almas Heshmati, 1995. "Efficiency Measurement in Swedish Dairy Farms: An Application of Rotating Panel Data, 1976–88," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(3), pages 660-674.
    26. Bentzen, Jan, 2004. "Estimating the rebound effect in US manufacturing energy consumption," Energy Economics, Elsevier, vol. 26(1), pages 123-134, January.
    27. Badunenko, Oleg & Kumbhakar, Subal C., 2017. "Economies of scale, technical change and persistent and time-varying cost efficiency in Indian banking: Do ownership, regulation and heterogeneity matter?," European Journal of Operational Research, Elsevier, vol. 260(2), pages 789-803.
    28. Blass, Vered & Corbett, Charles J. & Delmas, Magali A. & Muthulingam, Suresh, 2014. "Top management and the adoption of energy efficiency practices: Evidence from small and medium-sized manufacturing firms in the US," Energy, Elsevier, vol. 65(C), pages 560-571.
    29. Kumbhakar, Subal C, 1991. "The Measurement and Decomposition of Cost-Inefficiency: The Translog Cost System," Oxford Economic Papers, Oxford University Press, vol. 43(4), pages 667-683, October.
    30. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    31. Wei, Taoyuan, 2007. "Impact of energy efficiency gains on output and energy use with Cobb-Douglas production function," Energy Policy, Elsevier, vol. 35(4), pages 2023-2030, April.
    32. Kumbhakar, Subal C & Hjalmarsson, Lennart, 1995. "Labour-Use Efficiency in Swedish Social Insurance Offices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(1), pages 33-47, Jan.-Marc.
    33. Parker, Steven & Liddle, Brantley, 2016. "Energy efficiency in the manufacturing sector of the OECD: Analysis of price elasticities," Energy Economics, Elsevier, vol. 58(C), pages 38-45.
    34. Mousavi-Avval, Seyed Hashem & Rafiee, Shahin & Jafari, Ali & Mohammadi, Ali, 2011. "Optimization of energy consumption for soybean production using Data Envelopment Analysis (DEA) approach," Applied Energy, Elsevier, vol. 88(11), pages 3765-3772.
    35. Hunt Allcott & Christopher Knittel & Dmitry Taubinsky, 2015. "Tagging and Targeting of Energy Efficiency Subsidies," American Economic Review, American Economic Association, vol. 105(5), pages 187-191, May.
    36. Trianni, Andrea & Cagno, Enrico & Worrell, Ernst & Pugliese, Giacomo, 2013. "Empirical investigation of energy efficiency barriers in Italian manufacturing SMEs," Energy, Elsevier, vol. 49(C), pages 444-458.
    37. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    38. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    39. Gale A. Boyd, 2005. "A Method for Measuring the Efficiency Gap between Average and Best Practice Energy Use: The ENERGY STAR Industrial Energy Performance Indicator," Journal of Industrial Ecology, Yale University, vol. 9(3), pages 51-65, July.
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    2. Badunenko, Oleg & Galeotti, Marzio & Hunt, Lester C., 2023. "Better to grow or better to improve? Measuring environmental efficiency in OECD countries with a stochastic environmental Kuznets frontier (SEKF)," Energy Economics, Elsevier, vol. 121(C).

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