Identify the digitalization technology opportunity of low-carbon energy technologies: Using the patent data and collaborative filtering

The digitalization of low-carbon energy technologies (LCET) provides important technical support for the transition to a greener energy system. Digitalization addresses the phenomenon of the growing application of information and communications technologies (ICT) across the economy, which is regarded as the technology convergence between ICT and other technologies. Scholars have revealed the signs that LCET and ICT are becoming increasingly interlinked, which raises the challenges for predicting and identifying the technology opportunities for innovations in the converged technology area. To address the challenges, this paper proposes a collaborative filtering approach to identify the digitalization technology opportunity of low-carbon energy technologies using patent classification and patent citation information. We applied the proposed collaborative filtering approach using a large LCET patent dataset derived from the United States Patent and Trademark Office (USPTO). The results indicate that the proposed method can effectively identify digitalization technology opportunities of LCET, and the current LCET digitalization technology opportunities identified based on this approach are mainly concentrated in the Energy storage field. The advantages of the proposed approach are that its underlying data are more readily available and its technical complexity is relatively lower, and thus, more replicable for other technology fields.

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Publication Details

Journal
PLoS ONE
Published
2024-09-03
DOI
https://doi.org/10.1371/journal.pone.0309420
Primary Topic
Innovation Policy and R&D
Type
article
Field-Weighted Citation Impact
0.00

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article

Identify the digitalization technology opportunity of low-carbon energy technologies: Using the patent data and collaborative filtering

Jie Liu, Wanlin Cai
PLoS ONE
Innovation Policy and R&D
article

Identify the digitalization technology opportunity of low-carbon energy technologies: Using the patent data and collaborative filtering

Jie Liu, Wanlin Cai
article en

Abstract

The digitalization of low-carbon energy technologies (LCET) provides important technical support for the transition to a greener energy system. Digitalization addresses the phenomenon of the growing application of information and communications technologies (ICT) across the economy, which is regarded as the technology convergence between ICT and other technologies. Scholars have revealed the signs that LCET and ICT are becoming increasingly interlinked, which raises the challenges for predicting and identifying the technology opportunities for innovations in the converged technology area. To address the challenges, this paper proposes a collaborative filtering approach to identify the digitalization technology opportunity of low-carbon energy technologies using patent classification and patent citation information. We applied the proposed collaborative filtering approach using a large LCET patent dataset derived from the United States Patent and Trademark Office (USPTO). The results indicate that the proposed method can effectively identify digitalization technology opportunities of LCET, and the current LCET digitalization technology opportunities identified based on this approach are mainly concentrated in the Energy storage field. The advantages of the proposed approach are that its underlying data are more readily available and its technical complexity is relatively lower, and thus, more replicable for other technology fields.

PLoS ONEVol. 19(9)
Nanjing University of Science and Technology (CN), Zhejiang University of Finance and Economics (CN)
Department of Education of Zhejiang Province, Fundamental Research Funds for the Central Universities
Affordable and clean energy
Openalex Percentile: Top 82%
Innovation Policy and R&D
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Identify the digitalization technology opportunity of low-carbon energy technologies: Using the patent data and collaborative filtering — Jie Liu, Wanlin Cai · PLoS ONE (2024) | TGRS Research Map | TGRS