An estimation of future county-level cement production and associated air pollutant emissions in China through artificial neural networks.

IF 8.2 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Science of the Total Environment Pub Date : 2024-11-25 Epub Date: 2024-09-04 DOI:10.1016/j.scitotenv.2024.176036
Jiayu Xu, Jinya Yang, Jiaxin Dong, Siwei Li, Jia Xing, Yu Zhao
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Abstract

Cement production and its air pollutant and carbon dioxides (CO2) emissions in China will be relocated greatly as a joint effect of diverse development of industrial economy and implementation of environmental policies for different regions. The future pathway and spatial pattern of emissions are important for policy making of air quality improvement and CO2 emission abatement, as well as coordinating regional development. In this study, we developed an artificial neural network (ANN) model to predict cement production at the county level and to calculate the associated emissions of air pollutants and CO2 at the county level till 2060. Results show that the cement production will decline from 2327 million metric tons (Mt) in 2015 to 704 Mt. in 2060 under the Shared Socioeconomic Pathways 1 (SSP1). Counties closer to provincial capital will experience greater retirement of cement industry. Likewise, the emissions of air pollutants and CO2 will experience a steady downward trend driven by the declining cement production and the improvement of pollution control technologies. There will be a more significant regional heterogeneity in the reduction of production and emissions at city level compared to the province level. With the clearance for nearly two-thirds of counties, future cement production and emissions will be more intensively distributed in a few cities. The shares of emissions in southwestern regions will grow from 2015 to 2060 while those of eastern regions will continue decreasing. The comparison between the changing spatial distributions of emissions and gross domestic product (GDP) indicates a positive effect of existing policies in reconciling regional economic development and air pollution controls. The outcome could support the analyses on the impact of industrial development on air quality and public health, and the method can be applied widely for other industrial sectors for a more comprehensive understanding of future emission relocation.

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通过人工神经网络估算中国未来县级水泥产量及相关大气污染物排放。
在工业经济多元化发展和不同地区环境政策实施的共同作用下,中国的水泥生产及其大气污染物和二氧化碳(CO2)排放将发生巨大的迁移。未来的排放路径和空间格局对于制定空气质量改善和二氧化碳减排政策以及协调区域发展具有重要意义。在本研究中,我们建立了一个人工神经网络(ANN)模型来预测县一级的水泥产量,并计算直到 2060 年县一级的相关大气污染物和二氧化碳排放量。结果表明,在共享社会经济路径 1(SSP1)下,水泥产量将从 2015 年的 2.327 亿吨下降到 2060 年的 7.04 亿吨。离省会较近的县将会有更多的水泥行业退出。同样,由于水泥产量的下降和污染控制技术的改进,空气污染物和二氧化碳的排放量将呈稳步下降趋势。与省一级相比,市一级在减产减排方面的区域差异将更加明显。随着近三分之二的县被清理,未来水泥生产和排放将更集中地分布在少数城市。从 2015 年到 2060 年,西南地区的排放份额将增长,而东部地区的排放份额将继续下降。排放量和国内生产总值(GDP)空间分布变化的比较表明,现有政策在协调区域经济发展和空气污染控制方面具有积极作用。研究结果可为分析工业发展对空气质量和公众健康的影响提供支持,该方法还可广泛应用于其他工业部门,以更全面地了解未来的排放迁移情况。
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来源期刊
Science of the Total Environment
Science of the Total Environment 环境科学-环境科学
CiteScore
17.60
自引率
10.20%
发文量
8726
审稿时长
2.4 months
期刊介绍: The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere. The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.
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