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Journal of Environmental Accounting and Management 13(1) (2025) 77--90 | DOI:10.5890/JEAM.2025.03.007

Na Li$^{1}$, Huan You$^{1}$, Chengkang Gao$^{1}$, Zhenjiang Guo$^{2}$, Yanzheng Wu$^{1}$, Xinhong Zhang$^{1}$

$^1$ School of Metallurgy, Northeastern University, Shenyang, Liaoning 110819, China

$^2$ Liaoning Province Huludao Ecological environment Monitoring Center, Liaoning Province, Huludao 125000, China

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Abstract

The rapid growth of China's economy and industrialization has heightened pollutant emissions and environmental risks in the copper industry. Despite implementing emission standards to reduce pollution from the copper industry, issues remain. For instance, there is a lack of quantitative trajectory studies and systematic approaches to emission reduction. Therefore, this study adopted a bottom-up emission model to develop pollutant emission inventories for China's copper smelting industry in 2010, 2015, and 2020, followed by an analysis of their spatiotemporal characteristics. Additionally, a genetic algorithm-optimized neural network model and scenario analysis method were employed to quantify the pollutant reduction potential and environmental governance effects of comprehensive emission reduction measures. The results indicate that in 2020, the total emissions of key pollutants (SO${}_{2}$, As, Pb, and Cd) in the copper smelting industry were 183.67kt, 70.76t, 37.7t, and 17.15t, respectively. Smelting and converting processes were identified as the main pollution sources, accounting for over 95\% of the total emissions. Temporally, emissions of heavy metals As, Pb, and Cd showed a continuous increasing trend, while the growth rate of SO${}_{2}$ emissions slowed. Spatially, pollutant emissions presented an uneven distribution influenced by economic development and mineral resources. For instance, Jiangxi (18.38t), Jiangsu (9.48t), Guangdong (9.06t), Zhejiang (8.61t), and Anhui (6.64t) were the provinces with the highest emissions of heavy metal As. Scenario analysis indicates that by 2030, emissions of As are expected to decrease by 17.73t, 10.23t, and 24.87t under source reduction, process control, and end-of-pipe treatment measures, respectively.

Acknowledgments

The funding for this research was provided by the Based Research Projects of the National Natural Science Foundation of China (41871212), the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK1003), and the Fundamental Research Funds for the Central Universities (N2025008).

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