Jee, S.J. orcid.org/0000-0001-9582-8289 and Srivastav, S. (2024) Knowledge spillovers between clean and dirty technologies: Evidence from the patent citation network. Ecological Economics, 224. 108310. ISSN: 0921-8009
Abstract
Can dirty incumbents leverage their existing knowhow to transition to clean technologies? To address this question, we systematically measure direct and indirect knowledge spillovers between clean and dirty technologies using the patent citation network. We assume citations reflect pathways of learning and knowledge proximity. We first examine the proportion of citations in clean patents that directly refer to dirty technologies. Secondly, we investigate how clean and dirty technologies are indirectly linked in the citation network and which sectors most frequently bridge these two fields. We find that less than one-tenth of clean patents contain a direct citation to prior dirty patents, but nearly two-thirds are indirectly linked. Significant sectoral heterogeneity exists. Patents related to control technologies, data processing and optimization, and the management of heat and waste, frequently serve as bridges between clean and dirty technologies in the citation network. Our results have implications for: firm-level diversification strategies, green industrial policy, and the modelling of directed technical change, where lower knowledge spillovers between clean and dirty technologies correspond to higher path dependencies.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Knowledge spillovers; Directed technical change; Clean technology; Dirty technology; Bridge technologies; Green transition; Green industrial policy |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Analytics, Technology & Ops Department |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Jul 2025 14:40 |
Last Modified: | 25 Jul 2025 14:40 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ecolecon.2024.108310 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:229444 |