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What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity

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  • Axenbeck, Janna
  • Berner, Anne
  • Kneib, Thomas

Abstract

The ongoing digital transformation has raised hopes for ICT-based climate protection within manufacturing industries, such as dematerialized products and energy efficiency gains. However, ICT also consume energy as well as resources, and detrimental effects on the environment are increasingly gaining attention. Accordingly, it is unclear whether trade-offs or synergies between the use of digital technologies and energy savings exist. Our analysis sheds light on the most important drivers of the relationship between ICT and energy use in manufacturing. We apply flexible tree-based machine learning to a German administrative panel data set including more than 25,000 firms. The results indicate firm-level heterogeneity, but suggest that digital technologies relate more frequently to an increase in energy use. Multiple characteristics, such as energy prices and firms' energy mix, explain differences in the effect.

Suggested Citation

  • 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.
    • Handle: RePEc:zbw:zewdip:22059
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    as
    1. Labandeira, Xavier & Labeaga, José M. & López-Otero, Xiral, 2017. "A meta-analysis on the price elasticity of energy demand," Energy Policy, Elsevier, vol. 102(C), pages 549-568.
    2. Astrid Kander & Paolo Malanima & Paul Warde, 2015. "Power to the People: Energy in Europe over the Last Five Centuries," Economics Books, Princeton University Press, edition 1, number 10138-2.
    3. Christopher R. Knittel & Samuel Stolper, 2021. "Machine Learning about Treatment Effect Heterogeneity: The Case of Household Energy Use," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 440-444, May.
    4. Ronald Bernstein & Reinhard Madlener, 2010. "Impact of disaggregated ICT capital on electricity intensity in European manufacturing," Applied Economics Letters, Taylor & Francis Journals, vol. 17(17), pages 1691-1695.
    5. Axenbeck, Janna & Niebel, Thomas, 2021. "Climate protection potentials of digitalized production processes: Microeconometric evidence?," ZEW Discussion Papers 21-105, ZEW - Leibniz Centre for European Economic Research.
    6. 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).
    7. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    8. Ren, Siyu & Hao, Yu & Xu, Lu & Wu, Haitao & Ba, Ning, 2021. "Digitalization and energy: How does internet development affect China's energy consumption?," Energy Economics, Elsevier, vol. 98(C).
    9. Eoghan O'Neill & Melvyn Weeks, 2019. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Working Papers EPRG 1906, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    10. Riccardo Brozzi & David Forti & Erwin Rauch & Dominik T. Matt, 2020. "The Advantages of Industry 4.0 Applications for Sustainability: Results from a Sample of Manufacturing Companies," Sustainability, MDPI, vol. 12(9), pages 1-19, May.
    11. X Nie & S Wager, 2021. "Quasi-oracle estimation of heterogeneous treatment effects [TensorFlow: A system for large-scale machine learning]," Biometrika, Biometrika Trust, vol. 108(2), pages 299-319.
    12. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    13. Griliches, Zvi, 1980. "R & D and the Productivity Slowdown," American Economic Review, American Economic Association, vol. 70(2), pages 343-348, May.
    14. Kenneth Gillingham & David Rapson & Gernot Wagner, 2016. "The Rebound Effect and Energy Efficiency Policy," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 10(1), pages 68-88.
    15. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    16. Bertschek, Irene & Erdsiek, Daniel & Niebel, Thomas & Schuck, Bettina & Seifried, Mareike & Ewald, Johannes & Lang, Thorsten & Hicking, Jan & Wenger, Lucas & Walter, Tim, 2020. "Schwerpunktstudie Digitalisierung und Energieeffizienz. Erkenntnisse aus Forschung und Praxis: 2020," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 230964.
    17. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    18. Rita K. Almeida & Ana M. Fernandes & Mariana Viollaz, 2020. "Software Adoption, Employment Composition, and the Skill Content of Occupations in Chilean Firms," Journal of Development Studies, Taylor & Francis Journals, vol. 56(1), pages 169-185, January.
    19. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    20. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández-Val, 2018. "Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," NBER Working Papers 24678, National Bureau of Economic Research, Inc.
    21. Kevin J. Stiroh, 2002. "Are ICT Spillovers Driving the New Economy?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 48(1), pages 33-57, March.
    22. Huang, Geng & He, Ling-Yun & Lin, Xi, 2022. "Robot adoption and energy performance: Evidence from Chinese industrial firms," Energy Economics, Elsevier, vol. 107(C).
    23. Miller, Steve, 2020. "Causal forest estimation of heterogeneous and time-varying environmental policy effects," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    24. Sadorsky, Perry, 2012. "Information communication technology and electricity consumption in emerging economies," Energy Policy, Elsevier, vol. 48(C), pages 130-136.
    25. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    26. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    27. Xu, Qiong & Zhong, Meirui & Li, Xin, 2022. "How does digitalization affect energy? International evidence," Energy Economics, Elsevier, vol. 107(C).
    28. Eric Williams, 2011. "Environmental effects of information and communications technologies," Nature, Nature, vol. 479(7373), pages 354-358, November.
    29. Jonathan M.V. Davis & Sara B. Heller, 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs," American Economic Review, American Economic Association, vol. 107(5), pages 546-550, May.
    30. Taneja, Shivani & Mandys, Filip, 2022. "The effect of disaggregated information and communication technologies on industrial energy demand," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    31. Ben Lahouel, Béchir & Taleb, Lotfi & Ben Zaied, Younes & Managi, Shunsuke, 2021. "Does ICT change the relationship between total factor productivity and CO2 emissions? Evidence based on a nonlinear model," Energy Economics, Elsevier, vol. 101(C).
    32. Erik Brynjolfsson & Lorin M. Hitt, 2000. "Beyond Computation: Information Technology, Organizational Transformation and Business Performance," Journal of Economic Perspectives, American Economic Association, vol. 14(4), pages 23-48, Fall.
    33. J. Daniel Khazzoom, 1980. "Economic Implications of Mandated Efficiency in Standards for Household Appliances," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 21-40.
    34. Brian C. Prest, 2020. "Peaking Interest: How Awareness Drives the Effectiveness of Time-of-Use Electricity Pricing," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 7(1), pages 103-143.
    35. Anders S. G. Andrae & Tomas Edler, 2015. "On Global Electricity Usage of Communication Technology: Trends to 2030," Challenges, MDPI, vol. 6(1), pages 1-41, April.
    36. Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
    37. Patrick Schulte & Heinz Welsch & Sascha Rexhäuser, 2016. "ICT and the Demand for Energy: Evidence from OECD Countries," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(1), pages 119-146, January.
    38. Eoghan O'Neill & Melvyn Weeks, 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Papers 1810.09179, arXiv.org, revised Oct 2019.
    39. Lange, Steffen & Pohl, Johanna & Santarius, Tilman, 2020. "Digitalization and energy consumption. Does ICT reduce energy demand?," Ecological Economics, Elsevier, vol. 176(C).
    40. Collard, Fabrice & Feve, Patrick & Portier, Franck, 2005. "Electricity consumption and ICT in the French service sector," Energy Economics, Elsevier, vol. 27(3), pages 541-550, May.
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    Cited by:

    1. Horbach, Jens, 2023. "Digitalisation and sustainability strategies at the firm level," Ruhr Economic Papers 1001, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, revised 2023.
    2. Kunkel, S. & Neuhäusler, P. & Matthess, M. & Dachrodt, M.F., 2023. "Industry 4.0 and energy in manufacturing sectors in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).

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    More about this item

    Keywords

    digital technologies; energy use; manufacturing; machine learning;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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