2018
DOI: 10.5547/01956574.39.4.chaa
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Directed Technical Change and Energy Intensity Dynamics: Structural Change vs. Energy Efficiency

Abstract: Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in… Show more

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Cited by 14 publications
(2 citation statements)
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“…These expectations notwithstanding, AI also has some noneconomic consequences that have both positive and negative impacts on energy consumption (Vinuesa et al 2020), resulting in an uncertain impact on energy intensity. More specifically, by replacing and supplementing the physical and brain power of humans, AI may boost technological progress, and this is the primary driver of AI-related reductions in energy intensity (Haas and Kempa, 2016;Brynjolfsson et al, 2017). Nonetheless, compared to energy-consuming technologies such as machine learning and industrial robotics, the physical and brain power of humans is incredibly efficient and involves far less energy consumption, while AI research and applications such as deep learning platforms demand large amounts of energy (Lu et al, 2018;Vinuesa et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
“…These expectations notwithstanding, AI also has some noneconomic consequences that have both positive and negative impacts on energy consumption (Vinuesa et al 2020), resulting in an uncertain impact on energy intensity. More specifically, by replacing and supplementing the physical and brain power of humans, AI may boost technological progress, and this is the primary driver of AI-related reductions in energy intensity (Haas and Kempa, 2016;Brynjolfsson et al, 2017). Nonetheless, compared to energy-consuming technologies such as machine learning and industrial robotics, the physical and brain power of humans is incredibly efficient and involves far less energy consumption, while AI research and applications such as deep learning platforms demand large amounts of energy (Lu et al, 2018;Vinuesa et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
“…Previous literature has explored the determinants of energy consumption intensity. Scholars have investigated the influencing factors from energy structure [13], energy price [14][15][16], industrial structure [17][18][19], industrial intellectualization [20], urbanization [21][22][23][24], financial development [25,26], and technological progress [18,27,28]. Furthermore, from the environmental policy perspectives, some studies have investigated the influence of environmental regulations on energy consumption intensity at the macro and micro levels.…”
Section: Introductionmentioning
confidence: 99%