Correlation Analysis and Monitoring Method of Carbon Emissions in the Steel Industry Based on Big Data

Q3 Environmental Science
Wang Yang, Gao Yi, Zou Zhiyu, Chen Yue, Xudong Wang, Luo Shuai, Liu Ning, Zhou Jin, Yan Dawei
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引用次数: 0

Abstract

Excessive carbon emissions will lead to catastrophic consequences such as global warming and rising oceans and will also have a serious negative impact on the human food supply and living environment. The steel industry is characterized by high pollution, and about 18% of China’s carbon emissions come from the steel industry. The ‘double carbon’ strategy has brought important tasks and severe challenges to China’s steel industry. With a view to evaluating the achievements of carbon emission control, carbon emission monitoring systems at home and abroad have been continuously established and improved. For the steel industry, accurate and efficient carbon monitoring technology has a guiding role in guiding energy conservation and carbon reduction. Traditional carbon emission accounting methods have some problems, such as long cycles and poor data quality, which restrict the improvement of the lean level of carbon emission monitoring management. Firstly, this paper investigates and analyzes the productive process and carbon emission process of the steel industry and constructs an entropy weight-grey correlation -TOPSIS analysis method for the correlation between carbon emissions and influencing factors. Based on the above content, a carbon emission monitoring method based on multiple influencing factors is put forward, and the high monitoring accuracy of the model is proved by taking the Tianjin steel industry as an example. The results show that information mining of relevant data can strikingly increase the accuracy of carbon emission monitoring in the steel industry.
基于大数据的钢铁行业碳排放关联分析与监测方法
过量的碳排放将导致全球变暖、海洋上升等灾难性后果,也将对人类的食物供应和生活环境造成严重的负面影响。钢铁行业具有高污染的特点,中国约 18% 的碳排放来自钢铁行业。双碳 "战略给中国钢铁工业带来了重要任务和严峻挑战。为评估碳排放控制成果,国内外碳排放监测体系不断建立和完善。对于钢铁行业来说,准确、高效的碳监测技术对节能减碳具有指导作用。传统的碳排放核算方法存在周期长、数据质量差等问题,制约了碳排放监测管理精益化水平的提高。本文首先对钢铁行业的生产过程和碳排放过程进行了调查分析,并构建了碳排放与影响因素相关性的熵权-灰色关联-TOPSIS分析方法。在上述内容的基础上,提出了一种基于多影响因素的碳排放监测方法,并以天津钢铁行业为例,证明了该模型具有较高的监测精度。结果表明,对相关数据进行信息挖掘可以显著提高钢铁行业碳排放监测的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Strategic Planning for Energy and the Environment
Strategic Planning for Energy and the Environment Environmental Science-Environmental Science (all)
CiteScore
1.50
自引率
0.00%
发文量
25
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