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Energy Intensity and Convergence in Swedish Industry: A Combined Econometric and Decomposition Analysis

Author

Listed:
  • Karimu, Amin

    (CERE and the Department of Economics, Umeå University)

  • Brännlund, Runar

    (CERE and the Department of Economics, Umeå University)

  • Lundgren, Tommy

    (CERE and the Department of Economics, Umeå University)

  • Söderholm, Patrik

    (Department of Business Administration, Technology and Social Sciences, Economics Unit, Luleå University of Technology)

Abstract

This paper analyzes the determinants of energy intensity and tests for convergence across 14 Swedish industrial sectors. The analysis builds on a non-parametric regression analysis of an intensity index constructed at the industry sector level as well as indexes constructed from a decomposition of this index. The latter isolates two key determinants of changes in energy intensity and convergence patterns: energy efficiency improvements and changes in economic output (activity). The empirical analysis relies on a detailed sectorial dataset covering the period 1990-2008. The findings indicate that input prices, including the price of energy, have been significant determinants of energy intensity in the Swedish industrial sectors. This effect can primarily be attributed to the efficiency channel and with a less profound influence from the activity channel. These results suggest that a well-designed tax system could be effective in delivering significant energy efficiency improvements in Swedish industry. We also find evidence of energy intensity convergence among the industrial sectors, and this primarily stems from the activity channel rather than from the efficiency channel. The above implies that during the studied time period Swedish industry shifted away from more to less energy-intensive production, in part perhaps driven by moving energy-intensive manufacturing abroad.

Suggested Citation

  • Karimu, Amin & Brännlund, Runar & Lundgren, Tommy & Söderholm, Patrik, 2016. "Energy Intensity and Convergence in Swedish Industry: A Combined Econometric and Decomposition Analysis," CERE Working Papers 2016:8, CERE - the Center for Environmental and Resource Economics.
  • Handle: RePEc:hhs:slucer:2016_008
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    2. Anna Dahlqvist & Patrik S derholm, 2019. "Industrial Energy Use, Management Practices and Price Signals: The Case of Swedish Process Industry," International Journal of Energy Economics and Policy, Econjournals, vol. 9(3), pages 30-45.
    3. Apergis, Nicholas & Ewing, Bradley T. & Payne, James E., 2017. "Introduction: Symposium on Energy Sector Convergence," Energy Economics, Elsevier, vol. 62(C), pages 335-337.
    4. Victor Ajayi & David Reiner, 2018. "European Industrial Energy Intensity: The Role of Innovation 1995-2009," Working Papers EPRG 1818, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    5. Tzen-Ying Ling & Wei-Kai Hung & Chun-Tsu Lin & Michael Lu, 2020. "Dealing with Green Gentrification and Vertical Green-Related Urban Well-Being: A Contextual-Based Design Framework," Sustainability, MDPI, vol. 12(23), pages 1-24, November.
    6. Payne, James E. & Vizek, Maruška & Lee, Junsoo, 2017. "Is there convergence in per capita renewable energy consumption across U.S. States? Evidence from LM and RALS-LM unit root tests with breaks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 715-728.
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    10. Wu, Jianxin & Wu, Yanrui & Se Cheong, Tsun & Yu, Yanni, 2018. "Distribution dynamics of energy intensity in Chinese cities," Applied Energy, Elsevier, vol. 211(C), pages 875-889.
    11. Bollino, Carlo Andrea & Galeotti, Marzio, "undated". "On the Water-Energy-Food Nexus: Is there Multivariate Convergence?," FEEM Working Papers 309919, Fondazione Eni Enrico Mattei (FEEM).
    12. Lin, Boqiang & Wang, Miao, 2021. "What drives energy intensity fall in China? Evidence from a meta-frontier approach," Applied Energy, Elsevier, vol. 281(C).
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    15. Payne, James E. & Saunoris, James W. & Nazlioglu, Saban & Smyth, Russell, 2024. "Renewable energy production across U.S. states: Convergence or divergence?," Energy Economics, Elsevier, vol. 140(C).
    16. Zha, Jianping & Tan, Ting & Fan, Rong & Xu, Han & Ma, Siqi, 2020. "How to reduce energy intensity to achieve sustainable development of China's transport sector? A cross-regional comparison analysis," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    17. Ivanovski, Kris & Awaworyi Churchill, Sefa & Smyth, Russell, 2018. "A club convergence analysis of per capita energy consumption across Australian regions and sectors," Energy Economics, Elsevier, vol. 76(C), pages 519-531.
    18. Nian Wang & Yingming Zhu & Yu Pei, 2021. "How does economic infrastructure affect industrial energy efficiency convergence? Empirical evidence from China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13973-13997, September.
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    20. Shemelis Kebede Hundie & Megersa Debela Daksa, 2019. "Does energy-environmental Kuznets curve hold for Ethiopia? The relationship between energy intensity and economic growth," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-21, December.
    21. Rafael Alvarado & Cristian Ortiz & Lizeth Cuesta & Brayan Tillaguango, 2023. "Spillovers impact of institutional and economic factors in energy intensity," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(3), pages 1805-1823, June.
    22. Wu, Ya & Su, JingRong & Li, Ke & Sun, Chuanwang, 2019. "Comparative study on power efficiency of China's provincial steel industry and its influencing factors," Energy, Elsevier, vol. 175(C), pages 1009-1020.
    23. Flavio R. Arroyo M. & Luis J. Miguel, 2019. "The Trends of the Energy Intensity and CO 2 Emissions Related to Final Energy Consumption in Ecuador: Scenarios of National and Worldwide Strategies," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    24. Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(C).
    25. Jain, Princy & Goswami, Binoy, 2021. "Energy efficiency in South Asia: Trends and determinants," Energy, Elsevier, vol. 221(C).

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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