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Automatic synthesis of econometric empirical research results using large language model: A case study of digitalization-greening relationships

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  • Guo, Zitong
  • Yang, Guangfei
  • Wu, Wenjun

Abstract

Econometrics is widely used in economics and social sciences research, generating abundant research results. While meta-analysis, systematic literature review (SLR) and bibliometrics are widely used in hotspot identification and knowledge synthesizing, deeper knowledge extraction, integration and utilization, a broader range of topics, a higher degree of automation and labor demands are expected. To maximize the utility of empirical findings and meet the above expectations, a Large Language Model (LLM)-assisted protocol is proposed in this paper. This protocol utilizes LLM to screen literature records, discriminate econometric empirical studies, and extract structured information from abstracts. Neo4j is used for storage, visualization, and subsequent utilization and mining of the extracted structured information. An empirical case of relationships between digitalization and greening is used to validate the feasibility of the protocol. The findings indicate the protocol accomplishes the verification of the mediating paths in existing research, the exploration of potential mediating paths, and the identification of collider factors, which offer valuable insights for future empirical studies related to digitalization-greening relationships. Future research directions of LLM-driven econometric modeling and knowledge hypernetworks construction via SLR methodologies are finally proposed based on this protocol.

Suggested Citation

  • Guo, Zitong & Yang, Guangfei & Wu, Wenjun, 2026. "Automatic synthesis of econometric empirical research results using large language model: A case study of digitalization-greening relationships," Technological Forecasting and Social Change, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:tefoso:v:222:y:2026:i:c:s0040162525004019
    DOI: 10.1016/j.techfore.2025.124370
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