翟明洋,周长波,逯世泽,等.基于Delphi-AHP的钢铁行业碳排放数据质量评价研究[J].中国环境管理,2026,18(3):70-76.
ZHAI Mingyang,ZHOU Changbo,LU Shize,et al.Study on Carbon Emission Data Quality Evaluation in the Iron and Steel Industry Based on Delphi-AHP Method[J].Chinese Journal of Environmental Management,2026,18(3):70-76. |
| 基于Delphi-AHP的钢铁行业碳排放数据质量评价研究 |
| Study on Carbon Emission Data Quality Evaluation in the Iron and Steel Industry Based on Delphi-AHP Method |
| DOI：10.16868/j.cnki.1674-6252.2026.03.070 |
| 中文关键词: 钢铁行业 碳排放数据 指标体系 层次分析法 德尔菲法 |
| 英文关键词:steel industry carbon emissions data indicator system Analytic Hierarchy Process (AHP) Delphi method |
| 基金项目:生态环境部环境发展中心科技发展基金项目“钢铁行业碳排放数据质量风险管控及优化策略研究”（ZRZXJJ-202506）。 |
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| 中文摘要: |
| 钢铁行业作为高碳排放行业，其碳排放量约占全国碳排放总量的15%，于2025年3月正式纳入全国碳排放权交易市场。企业温室气体排放数据质量是保障碳市场有效运行的核心基础。2025年1月实施的《企业温室气体排放核算与报告指南钢铁行业（CETS—AG—03.01—V01—2024）》（以下简称《指南》）对数据质量管理提出更高要求，但现有的对钢铁企业报送的碳排放数据质量进行评价的工具适配性不足，亟须构建行业专用评价体系。本研究融合德尔菲法与层次分析法（Delphi-AHP），构建包含数据准确性、完整性、一致性、时效性及新增的“数据存证合规性”在内的5项准则层指标和17项指标层指标，并创新引入“静态指标评估+动态核算验证”双重机制，以弥补旧体系对《指南》工序存证要求的覆盖缺口。通过对华北某省重点钢铁集团开展实证评估，发现其数据质量综合得分为79.2分（中等偏上），数据时效性达标，但数据一致性存在显著短板。本研究从技术、管理、制度层面提出针对性改进建议，验证了评价体系的适用性与实践价值。本研究构建的数据质量评价模型，为企业数据管理优化和监管部门的精准施策提供了科学依据与实践支持。 |
| 英文摘要: |
| As a high-carbon emission industry, the iron and steel industry accounts for approximately 15% of China’s total carbon emissions and was officially included in the national carbon emissions trading market in March 2025. The quality of corporate greenhouse gas emission data is the core foundation for ensuring the effective operation of the carbon market. The “Guidelines for the Accounting and Reporting of Greenhouse Gas Emissions from Iron and Steel Enterprises (CETS—AG—03.01—V01—2024)”, implemented in January 2025, imposes higher requirements on data quality management. However, existing evaluation tools for the quality of carbon emission data submitted by iron and steel enterprises have insufficient adaptability, necessitating the development of an industry-specific evaluation system. This study integrates the Delphi method with the Analytic Hierarchy Process (Delphi-AHP) to construct an evaluation system comprising five criterionlevel indicators (including data accuracy, completeness, consistency, timeliness, and the newly added “data certification compliance”) and 17 indicator-level indicators. It also innovatively introduces a “static indicator assessment + dynamic accounting verification” dual mechanism to address the gap left by the old system in covering the process certification requirements of the new guidelines. An empirical evaluation conducted on a key steel group in a northern province of China shows that its comprehensive data quality score is 79.2 (above-average), with data timeliness meeting the standards but significant weaknesses in data consistency. Targeted improvement suggestions from technical, management, and institutional perspectives were proposed, verifying the applicability and practical value of the evaluation system. The data quality evaluation model constructed in this study provides a scientific basis and practical support for optimizing internal data management of enterprises and enabling precise policy-making by regulatory authorities. |
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