Li Xinbei, Liu Yu, Zhang Jing, Zhou Meifang, Meng Bo
School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, 100049, China.
Institutes of Science and Development, Chinese Academy of Sciences, Beijing, 100190, China.
Sci Data. 2025 Jan 8;12(1):30. doi: 10.1038/s41597-025-04366-5.
Carbon emission research based on input-output tables (IOTs) has received attention, but data quality issues persist due to inconsistencies between the sectoral scopes of energy statistics and IOTs. Specifically, China's official energy data are reported at the industry level, whereas IOTs are organized by product sectors. Valid IOT-based environmental models require consistent transformation from industry-level to product-level emissions. However, most existing studies overlook this necessary transformation, leading to substantial estimation errors. This study addresses this issue by developing a high-quality, product-level emissions dataset for China, grounded in robust product technology identification derived from IOTs. Our new emissions dataset, aligned with Chinese national IOTs, covers 29 to 34 product sectors across 7 benchmark years from 1997 to 2020. It includes data from 4 to 5 energy sectors and detailed emissions for 18 types of fossil fuels, using both IPCC-default and two China-specific emission factors. This inventory improves product-sector emission accounting and can be integrated into IOT-based climate and energy models, serving as a fundamental database for energy and emission analysis.
基于投入产出表（IOTs）的碳排放研究已受到关注，但由于能源统计与投入产出表的部门范围不一致，数据质量问题依然存在。具体而言，中国官方能源数据是按行业层面报告的，而投入产出表是按产品部门编制的。基于有效投入产出表的环境模型需要将行业层面的排放一致转换为产品层面的排放。然而，大多数现有研究忽略了这一必要的转换，导致大量估计误差。本研究通过利用基于投入产出表得出的可靠产品技术识别方法，为中国开发一个高质量的产品层面排放数据集来解决这一问题。我们新的排放数据集与中国国家投入产出表一致，涵盖了1997年至2020年7个基准年份的29至34个产品部门。它包括来自4至5个能源部门的数据以及18种化石燃料的详细排放数据，使用了IPCC默认排放因子和两个中国特定排放因子。这份清单改进了产品部门的排放核算，可纳入基于投入产出表的气候和能源模型，作为能源和排放分析的基础数据库。
