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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.

Improving energy efficiency is often considered to be one of the keys to reducing greenhouse gas emissions. However, efficiency gains also reduce the cost of energy services and may even reduce the price of energy, resulting in energy use rebounding and potential energy use savings being eaten up. There is only limited empirical research quantifying the economy-wide rebound effect that takes the dynamic economic responses to energy efficiency improvements into account. We use a Structural Factor-Augmented Vector Autoregressive model (S-FAVAR) that allows us to track how energy use changes in response to an energy efficiency improvement while accounting for a vast range of potential confounders. Our findings point to economy-wide rebound effects of 78% to 101% after two years in France, Germany, Italy, the U.K., and the U.S. These findings imply that energy efficiency innovations alone may be of limited help in reducing future energy use and emphasize the importance of tackling carbon emissions directly.