%0 Report %1 axenbeck2022drives %A Axenbeck, Janna %A Berner, Anne %A Kneib, Thomas %C Mannheim %D 2022 %K Digitalisierung Energieverbrauch Industrie digital_technologies energy_use machine_learning manufacturing %N 22-059 %T What Drives the Relationship Between Digitalization and Industrial Energy Demand? Exploring Firm-Level Heterogeneity %U https://www.zew.de/publikationen/what-drives-the-relationship-between-digitalization-and-industrial-energy-demand-exploring-firm-level-heterogeneity %X 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.

@techreport{axenbeck2022drives, 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.}, added-at = {2023-02-01T16:24:59.000+0100}, address = {Mannheim}, author = {Axenbeck, Janna and Berner, Anne and Kneib, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/2324e559a4a2a6eab0b82a80abb1ac04c/meneteqel}, institution = {ZEW – Leibniz-Zentrum für Europäische Wirtschaftsforschung}, interhash = {5154df04c4156a3267eb3eac67fa0b1c}, intrahash = {324e559a4a2a6eab0b82a80abb1ac04c}, keywords = {Digitalisierung Energieverbrauch Industrie digital_technologies energy_use machine_learning manufacturing}, language = {en}, month = nov, number = {22-059}, timestamp = {2023-02-01T16:24:59.000+0100}, title = {What Drives the Relationship Between Digitalization and Industrial Energy Demand? Exploring Firm-Level Heterogeneity}, type = {ZEW Discussion Paper}, url = {https://www.zew.de/publikationen/what-drives-the-relationship-between-digitalization-and-industrial-energy-demand-exploring-firm-level-heterogeneity}, year = 2022 }