From peer influence to green cognition: How digital transformation fosters renewable energy innovation in manufacturing

Lang Wu and Junwei Shi

Energy Economics, 2025, vol. 148, issue C

Abstract: Using Python-based data mining and text analysis of corporate annual reports and patent databases, this study constructs a comprehensive dataset on renewable energy technology patents and digital transformation at the firm level for Chinese listed manufacturing enterprises. Our methodology employs the International Patent Classification system for renewable energy categorization and analyzes digital transformation through a customized keyword dictionary encompassing four dimensions. This novel dataset delivers critical micro-level evidence on renewable energy innovation pathways essential for accelerating manufacturing's energy transition—a sector responsible for approximately 30 % of global emissions—and achieving China's 2030/2060 carbon neutrality targets. Our findings reveal that: (1) digital transformation (DT) significantly promotes renewable energy technology innovation (RETI), with results robust across multiple tests; (2) mechanism analysis shows DT fosters RETI through “marketization compensation” and “industry competitive incentives” externally, while driving “information transparency” and “deepening green cognition” internally; and (3) heterogeneity analysis demonstrates stronger impacts in environmentally penalized firms and state-owned enterprises. This study empirically validates the DT-RETI relationship, providing policymakers with evidence for designing targeted incentives and offering corporate strategists a framework for integrating digital capabilities with sustainable innovation objectives.

Keywords: Digital transformation; Renewable energy technology innovation; Manufacturing firms; Peer effects; Green cognition (search for similar items in EconPapers)
JEL-codes: D22 L60 O32 Q55 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:148:y:2025:i:c:s0140988325005183

DOI: 10.1016/j.eneco.2025.108691

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