2000-2021年中国城市过程相关CO2排放量

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Sijia Cai, Jinghang Xu, Yuru Guan, Miaomaio Liu, Chang Tan, Jun Bi, Yuli Shan
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引用次数: 0

摘要

作为世界上最大的二氧化碳排放国,中国需要准确的城市二氧化碳排放数据来制定有效的低碳政策。然而,以往的研究主要考虑了化石燃料燃烧的排放,而忽视了工业生产(如矿物、化工、金属制品)过程相关的二氧化碳排放,这些排放约占中国总排放量的13%。本文构建了中国289个城市2000 - 2021年过程相关CO2排放的首个时间序列数据集。该数据集涵盖11种工业产品,并遵循政府间气候变化专门委员会(IPCC)推荐的方法。运用中国特色排放因子,编制城市统计年鉴和公报中的工业产出数据。缺失的输出数据使用misforest模型进行输入。在我们的数据集中,与过程相关的排放的估计不确定性在3.87%到3.91%之间。我们的数据集为分析城市层面的排放模式和设计有针对性的低碳政策提供了坚实的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
City-level process-related CO2 emissions in China 2000-2021.

As the world's largest CO2 emitter, China needs accurate city-level CO2 emission accounts to formulate effective low-carbon policies. However, previous studies mainly accounted for emissions from fossil fuel combustion and overlooked process-related CO2 emissions from industrial production (e.g., mineral, chemical, metal products), which account for approximately 13% of China's total emissions. In this study, we built the first time-series dataset of process-related CO2 emissions for 289 Chinese cities from 2000 to 2021. The dataset covers 11 industrial products and adheres to the methodology recommended by the Intergovernmental Panel on Climate Change (IPCC). We applied China-specific emission factors and compiled industrial output data from city statistical yearbooks and bulletins. Missing output data were imputed using missForest models. The estimated uncertainty of the process-related emissions in our dataset ranges from 3.87% to 3.91%. Our dataset provides a robust foundation for analyzing emission patterns at the city level and for designing targeted low-carbon policies.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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