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

## TL;DR

This paper introduces a collaborative filtering method to identify digitalization opportunities in low-carbon energy technologies using patent data.

## Contribution

The novelty lies in using collaborative filtering with patent data to predict digitalization opportunities in low-carbon energy technologies.

## Key findings

- The proposed collaborative filtering approach effectively identifies digitalization technology opportunities in LCET.
- Current opportunities are mainly concentrated in the Energy storage field.
- The method is data-efficient and technically less complex, making it replicable for other technology areas.

## 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.