Identifying the technological knowledge depreciation rate using patent citation data: a case study of the solar photovol
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Identifying the technological knowledge depreciation rate using patent citation data: a case study of the solar photovoltaic industry Jie Liu1 · Arnulf Grubler2 · Tieju Ma1,2 · Dieter F. Kogler3 Received: 25 August 2019 © Akadémiai Kiadó, Budapest, Hungary 2020
Abstract Technological knowledge can be created via R&D investments, but it can also be eroded through depreciation. Knowing how fast knowledge depreciates is important for various reasons for practitioners and decision makers alike; especially if it comes to questions regarding how to “recharge” knowledge production processes within an ever changing global system. In this study, we use patent citation data to identify technological knowledge depreciation rates by adjusting for exogenous citation inflation and by disentangling the preferential-attachment dynamics of citation growth. Solar photovoltaic (PV) technology is employed as a case study. The rates calculated with our method are comparable to the few available estimates on technology depreciation rates in the PV industry. One of the advantages of the proposed method is that its underlying data are more readily available, and thus more replicable for the study of the knowledge depreciation rates in other relevant technology fields. Keywords Knowledge depreciation · Patent citation · Solar PV Mathematical subject classification 01-08 JEL classifications O31 · O34 · O39 * Tieju Ma [email protected] Jie Liu [email protected] Arnulf Grubler [email protected] Dieter F. Kogler [email protected] 1
School of Business, East China University of Science and Technology, Meilong Road 130, Shanghai 200237, China
2
Transition to New Technology Program, International Institute for Applied Systems Analysis, Schlossplatz 1, A‑2361 Laxenburg, Austria
3
Spatial Dynamics Lab, University College Dublin, Richview Campus, Belfield, Dublin 4 D04 V1W8, Ireland
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Vol.:(0123456789)
Scientometrics
Introduction Clean technologies have been generally regarded as the key solution to sustainable human development (Johnstone et al. 2012; Perruchas et al. 2020). Emerging technologies such as solar photovoltaics, wind turbines, and electric cars are at the “frontier” of economic development (Binz et al. 2017; Huenteler et al. 2016a; Rosenberg 1963). As technological change in such industries often necessitates long-term trial and error processes, understanding the evolutionary patterns of clean technologies’ development trajectories is thus critical to accelerating technological evolution overall, as well as to facilitate the transitions to sustainable systems in the context of climate change. Technological evolution has long been regarded as the upgrading of designs, that is, evolution in the artifact space (Huenteler et al. 2016a), while few research efforts have documented the vicissitude of technological knowledge aspects that actually underlie artifactual designs. Huenteler et al. (2016a) employ patent citation data in order to demonstrate the trajectory of technological knowledge (TK) genera
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