研究论文

“双碳”数据质量影响因素研究——基于Fuzzy-DEMATEL-ISM方法

  • 赵晓春 ,
  • 徐凌洋 ,
  • 周瑛
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  • 安徽大学管理学院 合肥 230601
赵晓春,教授,博士,E-mail:14006@ahu.edu.cn;徐凌洋,硕士研究生;周瑛,教授,博士。

收稿日期: 2024-12-31

  修回日期: 2025-02-17

  网络出版日期: 2026-02-12

基金资助

本文系国家社会科学基金项目“人工智能赋能低碳治理的信息处理模式创新研究”(项目编号:24BTQ064)研究成果之一。

Research on the Influencing Factors of “Dual-Carbon” Data Quality: Based on the Fuzzy-DEMATEL-ISM Method

  • Zhao Xiaochun ,
  • Xu Lingyang ,
  • Zhou Ying
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  • School of Management of Anhui University, Hefei 230601

Received date: 2024-12-31

  Revised date: 2025-02-17

  Online published: 2026-02-12

Supported by

This work is supported by the National Social Science Fund of China project titled “Research on Innovation in Information Processing Models of Low-carbon Governance Empowered by Artificial Intelligence” (Grant No. 24BTQ064).

摘要

[目的/意义]为确保国家“双碳”(碳达峰、碳中和)目标的顺利实现,聚焦于“双碳”数据质量,旨在识别并分析影响其质量的因素及其层级和结构关系,为基于“双碳”数据驱动的决策与应用提供坚实的理论支持。[方法/过程]采用全面质量管理(TQM)理论构建因素维度框架,并通过文献分析法确定影响“双碳”数据质量的具体因素。进一步地,利用模糊DEMATEL方法识别出关键影响因素,并结合解释结构模型(ISM)深入分析这些因素之间的层级关系及内在联系,从而系统性地揭示“双碳”数据质量的影响机制。[结果/结论]研究表明,“双碳”数据质量受到5个维度下共计17个因素的影响。其中,政治环境和公众环境是两个根源性的关键因素,其他6个关键影响因素为监管与管理、管理人员培训、数据处理技术、安全环境、市场环境以及规范,分布在深层和中层因素之中。从全面质量管理的角度出发,围绕环境、组织、标准、数据和技术5个方面进行系统分析,提出对“双碳”数据质量影响因素的新见解,展现了一定的创新性。

本文引用格式

赵晓春 , 徐凌洋 , 周瑛 . “双碳”数据质量影响因素研究——基于Fuzzy-DEMATEL-ISM方法[J]. 图书情报工作, 2026 , 70(4) : 27 -41 . DOI: 10.13266/j.issn.0252-3116.2026.04.003

Abstract

[Purpose/Significance] To ensure the successful achievement of the national “dual carbon” (carbon peaking and carbon neutrality) objectives, this study focuses on the quality of “dual carbon” data. Its aims to identify and scrutinize the factors that influences data quality, along with their hierarchical and structural interrelationships, to provide strong theoretical support for decisions and applications based on “dual carbon” data. [Method/Process] The study adopted total quality management (TQM) theory to construct a factor dimension framework and identified specific factors affecting the quality of “dual carbon” data through literature analysis. Furthermore, the fuzzy-DEMATEL method was employed to pinpoint key influencing factors. By integrating the Interpretive Structural Modeling (ISM), an in-depth analysis was conducted on the hierarchical relationships and intrinsic connections among these factors. This systematic approach revealed the influence mechanism of “dual carbon” data quality. [Result/Conclusion] The research has shown that “dual carbon” data quality is influenced by a total of 17 factors across 5 dimensions. Among them, the political environment and public environment are regarded as two fundamental key factors, while the other six key influencing factors, that is, regulation and management, management personnel training, data processing technology, security environment, market environment, and norms, are distributed among deep-level and middle-level factors. From the perspective of total quality management, this study focuses on five aspects: environment, organization, standards, data, and technology. It proposes new insights into the factors affecting “dual carbon” data quality, demonstrating a certain degree of innovation.

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