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Identifying the technological knowledge depreciation rate using patent citation data: a case study of the solar photovoltaic industry

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  • Jie Liu

    (East China University of Science and Technology)

  • Arnulf Grubler

    (International Institute for Applied Systems Analysis)

  • Tieju Ma

    (East China University of Science and Technology
    International Institute for Applied Systems Analysis)

  • Dieter F. Kogler

    (University College Dublin)

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.

Suggested Citation

  • Jie Liu & Arnulf Grubler & Tieju Ma & Dieter F. Kogler, 2021. "Identifying the technological knowledge depreciation rate using patent citation data: a case study of the solar photovoltaic industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 93-115, January.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:1:d:10.1007_s11192-020-03740-x
    DOI: 10.1007/s11192-020-03740-x
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