IDEAS home Printed from https://ideas.repec.org/p/hhs/umnees/0982.html
   My bibliography  Save this paper

Essays on Energy Efficiency, Environmental Regulation and Labor Demand in Swedish Industry

Author

Listed:

Abstract

Paper [I] Energy efficiency improvement (EEI) benefits the climate and matters for energy security. The potential emission and energy savings due to EEI may however not fully materialize due to the rebound effect. In this study, we measure the size of the rebound effect for fuel and electricity within the four most energy intensive sectors in Sweden: Pulp and paper, Basic iron and steel, Chemical, and Mining. We use a detailed firm-level panel data set for 2000–2008 and apply a stochastic frontier analysis (SFA) for measuring the rebound effect. We find that neither fuel nor electricity rebound effects fully offset the potential energy and emission savings. Among the determinants, we find the CO2 intensity and the fuel/electricity shares to be useful indicators for identifying firms with higher or lower rebound effects within each sector. Paper [II] Energy efficiency improvement (EEI) is generally known to be a cost-effective measure for meeting energy, climate and sustainable growth targets. Unfortunately, behavioral responses to such improvements (called energy rebound effects) may reduce the expected savings in emissions and energy from EEI. Hence, the size of this effect should be considered to help set realistic energy and climate targets. Currently there are significant differences in approaches for measuring rebound effect. Here, we used a two-step procedure to measure both short- and long-term energy rebound effects in the Swedish manufacturing industry. In the first step, we used data envelopment analysis (DEA) to obtain energy efficiency scores. In the second step, we estimated energy rebound effects using a dynamic panel regression model. This approach was applied to a firm-level panel dataset covering all 14 sectors in the Swedish manufacturing industry over the period 1997–2008. We showed that, in the short run, partial rebound effects exist within most of manufacturing sectors, meaning that the rebound effect decreased, but did not totally offset, the energy and emission savings expected from EEI. The long-term rebound effect was smaller than the short-term effect, implying that within each sector, energy and emission savings due to EEI are larger in the long run compared to the short run. Paper [III] Energy inefficiency in production implies that the same level of goods and services could be produced using less energy. The potential energy inefficiency of a firm may be linked to long-term structural rigidities in the production process and/or systematic shortcomings in management (persistent inefficiency), or associated with temporary issues like misallocation of resources (transient inefficiency). Eliminating or mitigating different inefficiencies may require different policy measures. Studies measuring industrial energy inefficiency have mostly focused on overall inefficiencies and have paid little attention to distinctions between the types. The aim of this study was to assess whether energy inefficiency is transient and/or persistent in the Swedish manufacturing industry. I used a firm-level panel dataset covering fourteen industrial sectors from 1997–2008 and estimated a stochastic energy demand frontier model. The model included a four-component error term separating persistent and transient inefficiency from unobserved heterogeneity and random noise. I found that both transient and persistent energy inefficiencies exist in most sectors of the Swedish manufacturing industry. Overall, persistent energy inefficiency was larger than transient, but varied considerably in different manufacturing sectors. The results suggest that, generally, energy inefficiencies in the Swedish manufacturing industry were related to structural rigidities connected to technology and/or management practices. Paper [IV] The aim of this paper was to investigate whether the environment and employment compete with each other in Swedish manufacturing industry. The effect of a marginal increase in environmental expenditure and environmental investment costs on sector-level demand for labor (employment) was studied using a detailed firm-level panel dataset for the period 2001–2008. The results showed that the sign and magnitude of the net employment effects ultimately depend on the aggregate sector-level output demand elasticity. If the output demand is inelastic, these costs induce small net improvements in employment, while a more elastic output demand suggests negative, but in most sectors relatively small, net effects on demand for labor. Hence, the results did not generally indicate a substantial trade-off between jobs and the environment. The general policy recommendation that can be drawn from this study is that, in the absence of empirically estimated output demand elasticities, a careful attitude regarding national environmental initiatives for sectors exposed to world market competition should be adopted.

Suggested Citation

  • Amjadi, Golnaz, 2020. "Essays on Energy Efficiency, Environmental Regulation and Labor Demand in Swedish Industry," Umeå Economic Studies 982, Umeå University, Department of Economics.
  • Handle: RePEc:hhs:umnees:0982
    as

    Download full text from publisher

    File URL: http://www.usbe.umu.se/ues/ues982.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jurate Jaraite & Andrius Kazukauskas & Tommy Lundgren, 2014. "The effects of climate policy on environmental expenditure and investment: evidence from Sweden," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 3(2), pages 148-166, July.
    2. Brännlund, Runar & Lundgren, Tommy, 2008. "Environmental policy and profitability - Evidence from Swedish industry," Umeå Economic Studies 750, Umeå University, Department of Economics.
    3. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 59-80.
    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. Saunders, Harry D., 2000. "A view from the macro side: rebound, backfire, and Khazzoom-Brookes," Energy Policy, Elsevier, vol. 28(6-7), pages 439-449, June.
    6. Raymond J. Kopp, 1981. "The Measurement of Productive Efficiency: A Reconsideration," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 96(3), pages 477-503.
    7. Zhang, Shanshan & Lundgren, Tommy & Zhou, Wenchao, 2016. "Energy efficiency in Swedish industry," Energy Economics, Elsevier, vol. 55(C), pages 42-51.
    8. Morgenstern, Richard D. & Pizer, William A. & Shih, Jhih-Shyang, 2002. "Jobs Versus the Environment: An Industry-Level Perspective," Journal of Environmental Economics and Management, Elsevier, vol. 43(3), pages 412-436, May.
    9. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    10. Zhou, P. & Ang, B.W. & Zhou, D.Q., 2012. "Measuring economy-wide energy efficiency performance: A parametric frontier approach," Applied Energy, Elsevier, vol. 90(1), pages 196-200.
    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. Amjadi, Golnaz & Lundgren, Tommy & Persson, Lars, 2018. "The Rebound Effect in Swedish Heavy Industry," Energy Economics, Elsevier, vol. 71(C), pages 140-148.
    14. Mukherjee, Kankana, 2008. "Energy use efficiency in the Indian manufacturing sector: An interstate analysis," Energy Policy, Elsevier, vol. 36(2), pages 662-672, February.
    15. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    16. Blomberg, Jerry & Henriksson, Eva & Lundmark, Robert, 2012. "Energy efficiency and policy in Swedish pulp and paper mills: A data envelopment analysis approach," Energy Policy, Elsevier, vol. 42(C), pages 569-579.
    17. Buck, J. & Young, D., 2007. "The potential for energy efficiency gains in the Canadian commercial building sector: A stochastic frontier study," Energy, Elsevier, vol. 32(9), pages 1769-1780.
    18. Azadeh, A. & Amalnick, M.S. & Ghaderi, S.F. & Asadzadeh, S.M., 2007. "An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors," Energy Policy, Elsevier, vol. 35(7), pages 3792-3806, July.
    19. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    20. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107609464.
    21. Ramanathan, R., 2000. "A holistic approach to compare energy efficiencies of different transport modes," Energy Policy, Elsevier, vol. 28(11), pages 743-747, September.
    22. Lien, Gudbrand & Kumbhakar, Subal C. & Alem, Habtamu, 2018. "Endogeneity, heterogeneity, and determinants of inefficiency in Norwegian crop-producing farms," International Journal of Production Economics, Elsevier, vol. 201(C), pages 53-61.
    23. Greene, William H., 1980. "On the estimation of a flexible frontier production model," Journal of Econometrics, Elsevier, vol. 13(1), pages 101-115, May.
    24. Schmidt, Peter & Knox Lovell, C. A., 1979. "Estimating technical and allocative inefficiency relative to stochastic production and cost frontiers," Journal of Econometrics, Elsevier, vol. 9(3), pages 343-366, February.
    25. Michael Greenstone, 2002. "The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures," Journal of Political Economy, University of Chicago Press, vol. 110(6), pages 1175-1219, December.
    26. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    27. Shi, Guang-Ming & Bi, Jun & Wang, Jin-Nan, 2010. "Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs," Energy Policy, Elsevier, vol. 38(10), pages 6172-6179, October.
    28. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    29. Orea, Luis & Llorca, Manuel & Filippini, Massimo, 2015. "A new approach to measuring the rebound effect associated to energy efficiency improvements: An application to the US residential energy demand," Energy Economics, Elsevier, vol. 49(C), pages 599-609.
    30. Runar Brännlund & Tommy Lundgren, 2010. "Environmental policy and profitability: evidence from Swedish industry," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 12(1), pages 59-78, June.
    31. Peter Bogetoft & Lars Otto, 2011. "Benchmarking with DEA, SFA, and R," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7961-2, September.
    32. 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.
    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. Amjadi, Golnaz & Lundgren, Tommy, 2022. "Is industrial energy inefficiency transient or persistent? Evidence from Swedish manufacturing," Applied Energy, Elsevier, vol. 309(C).
    2. Zhang, Shanshan & Lundgren, Tommy & Zhou, Wenchao, 2016. "Energy efficiency in Swedish industry," Energy Economics, Elsevier, vol. 55(C), pages 42-51.
    3. Lundgren, Tommy & Marklund, Per-Olov & Zhang, Shanshan, 2016. "Industrial energy demand and energy efficiency – Evidence from Sweden," Resource and Energy Economics, Elsevier, vol. 43(C), pages 130-152.
    4. Mark A. Andor & David H. Bernstein & Stephan Sommer, 2021. "Determining the efficiency of residential electricity consumption," Empirical Economics, Springer, vol. 60(6), pages 2897-2923, June.
    5. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    6. Filippini, Massimo & Hunt, Lester C., 2015. "Measurement of energy efficiency based on economic foundations," Energy Economics, Elsevier, vol. 52(S1), pages 5-16.
    7. 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.
    8. Otsuka, Akihiro, 2023. "Industrial electricity consumption efficiency and energy policy in Japan," Utilities Policy, Elsevier, vol. 81(C).
    9. Wu, F. & Fan, L.W. & Zhou, P. & Zhou, D.Q., 2012. "Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis," Energy Policy, Elsevier, vol. 49(C), pages 164-172.
    10. Ouyang, Xiaoling & Chen, Jiaqi & Du, Kerui, 2021. "Energy efficiency performance of the industrial sector: From the perspective of technological gap in different regions in China," Energy, Elsevier, vol. 214(C).
    11. Resti, Andrea, 1997. "Evaluating the cost-efficiency of the Italian Banking System: What can be learned from the joint application of parametric and non-parametric techniques," Journal of Banking & Finance, Elsevier, vol. 21(2), pages 221-250, February.
    12. Honma, Satoshi & Hu, Jin-Li, 2014. "A panel data parametric frontier technique for measuring total-factor energy efficiency: An application to Japanese regions," Energy, Elsevier, vol. 78(C), pages 732-739.
    13. Makridou, Georgia & Andriosopoulos, Kostas & Doumpos, Michael & Zopounidis, Constantin, 2016. "Measuring the efficiency of energy-intensive industries across European countries," Energy Policy, Elsevier, vol. 88(C), pages 573-583.
    14. Xu, Xin & Cui, Qiang, 2017. "Evaluating airline energy efficiency: An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure," Energy, Elsevier, vol. 122(C), pages 274-286.
    15. Zangin Zeebari & Kristofer Månsson & Pär Sjölander & Magnus Söderberg, 2023. "Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market," Journal of Productivity Analysis, Springer, vol. 59(1), pages 79-97, February.
    16. Dolšak, Janez & Hrovatin, Nevenka & Zorić, Jelena, 2022. "Estimating the efficiency in overall energy consumption: Evidence from Slovenian household-level data," Energy Economics, Elsevier, vol. 114(C).
    17. Joanne Evans & Massimo Filippini & Lester C. Hunt, 2013. "The contribution of energy efficiency towards meeting CO2 targets," Chapters, in: Roger Fouquet (ed.), Handbook on Energy and Climate Change, chapter 8, pages 175-223, Edward Elgar Publishing.
    18. Akihiro Otsuka, 2020. "How do population agglomeration and interregional networks improve energy efficiency?," Asia-Pacific Journal of Regional Science, Springer, vol. 4(1), pages 1-25, February.
    19. Lin, Boqiang & Wang, Xiaolei, 2014. "Exploring energy efficiency in China׳s iron and steel industry: A stochastic frontier approach," Energy Policy, Elsevier, vol. 72(C), pages 87-96.
    20. Boogen, Nina, 2017. "Estimating the potential for electricity savings in households," Energy Economics, Elsevier, vol. 63(C), pages 288-300.

    More about this item

    Keywords

    Energy efficiency improvement; rebound effect; stochastic frontier analysis; data envelopment analysis; stochastic energy demand frontier model; persistent and transient energy inefficiency; energy inefficiency; environmental expenditure and environmental investment costs; output demand elasticity;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • K32 - Law and Economics - - Other Substantive Areas of Law - - - Energy, Environmental, Health, and Safety Law
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hhs:umnees:0982. See general information about how to correct material in RePEc.

    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: David Skog (email available below). General contact details of provider: https://edirc.repec.org/data/inumuse.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.