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Drivers of energy efficiency in German manufacturing: A firm-level stochastic frontier analysis

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  • Lutz, Benjamin Johannes
  • Massier, Philipp
  • Sommerfeld, Katrin
  • Löschel, Andreas

Abstract

Increasing energy efficiency is one of the main goals in current German energy and climate policies. We study the determinants of energy efficiency in the German manufacturing sector based on official firm-level production census data. By means of a stochastic frontier analysis, we estimate the cost-minimizing energy demand function at the two-digit industry level using firm-level heterogeneity. Apart from the identification of the determinants of the energy demand function, we also analyze potential drivers of energy efficiency. Our results suggest that there is still potential to increase energy efficiency in most industries of the German manufacturing sector. Furthermore, we find that in most industries exporting and innovating firms as well as those investing in environmental protection measures are more energy efficient than their counterparts. In contrast, firms which are regulated by the European Union Emissions Trading System are mostly less energy efficient than non-regulated firms.

Suggested Citation

  • Lutz, Benjamin Johannes & Massier, Philipp & Sommerfeld, Katrin & Löschel, Andreas, 2017. "Drivers of energy efficiency in German manufacturing: A firm-level stochastic frontier analysis," CAWM Discussion Papers 99, University of Münster, Münster Center for Economic Policy (MEP).
    • Handle: RePEc:zbw:cawmdp:99
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    Cited by:

    1. Gale A. Boyd and Jonathan M. Lee, 2020. "Relative Effectiveness of Energy Efficiency Programs versus Market Based Climate Policies in the Chemical Industry," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 39-62.
    2. Axenbeck, Janna & Berner, Anne & Kneib, Thomas, 2022. "What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity," ZEW Discussion Papers 22-059, ZEW - Leibniz Centre for European Economic Research.
    3. Jan Stede, 2019. "Do Energy Efficiency Networks Save Energy? Evidence from German Plant-Level Data," Discussion Papers of DIW Berlin 1813, DIW Berlin, German Institute for Economic Research.
    4. Zarkovic, Maja, 2020. "Cap-and-trade and produce at least cost? Investigating firm behaviour in the EU ETS," Working papers 2020/12, Faculty of Business and Economics - University of Basel.
    5. Di Maria, Corrado & Zarkovic, Maja & Hintermann, Beat, 2020. "Are Emissions Trading Schemes Cost-effective?," Working papers 2020/13, Faculty of Business and Economics - University of Basel.
    6. Piao, Zhefan & Miao, Binbin & Zheng, Zihan & Xu, Feng, 2022. "Technological innovation efficiency and its impact factors: An investigation of China's listed energy companies," Energy Economics, Elsevier, vol. 112(C).
    7. Macharia, Kenneth Kigundu & Gathiaka, John Kamau & Ngui, Dianah, 2022. "Energy efficiency in the Kenyan manufacturing sector," Energy Policy, Elsevier, vol. 161(C).
    8. Djula Borozan & Luka Borozan, 2019. "Examining the Industrial Energy Consumption Determinants: A Panel Bayesian Model Averaging Approach," Energies, MDPI, vol. 13(1), pages 1-17, December.

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    More about this item

    Keywords

    Stochastic Frontier Analysis; Stochastic Demand Frontier; Energy Efficiency; Climate Policy; Manufacturing;
    All these keywords.

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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