Influencing factors and trend prediction of PM 2.5 concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China

The government's development of eco-environmental policies can have a scientific foundation thanks to the fine particulate matter (PM ) medium- and long-term change forecast. This study develops a STRIPAT-Scenario analysis framework employing panel data from 11 cities in Zhejiang Province betwe...

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Veröffentlicht in:Environment, development and sustainability development and sustainability, 2022-09, p.1
Hauptverfasser: Zhang, Qiong, Ye, Shuangshuang, Ma, Tiancheng, Fang, Xuejuan, Shen, Yang, Ding, Lei
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Sprache:eng
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Zusammenfassung:The government's development of eco-environmental policies can have a scientific foundation thanks to the fine particulate matter (PM ) medium- and long-term change forecast. This study develops a STRIPAT-Scenario analysis framework employing panel data from 11 cities in Zhejiang Province between 2006 and 2020 to predict the changing trend of PM concentrations under five alternative scenarios. The results reveal that: (1) urbanization development ( ), economic development ( ), technological innovation investment ( ) and environmental regulation intensity have a significant inhibitory effect on PM concentration in Zhejiang Province, while industrial structure, industrial energy consumption and the number of motor vehicles ( ) have a significant increase on PM concentration. (2) Under any scenario, the PM concentration of 11 cities in Zhejiang Province can reach the constraint target set in the 14th Five-Year plan. The improvement in urban PM quality is most obviously impacted by the high-quality development scenario (S4). (3) Toward 2035, PM concentrations of 11 cities in Zhejiang Province can reach the National Class I level standard in most scenario models, among which Hangzhou, Jiaxing and Shaoxing are under high pressure to reduce emissions and are the key areas for PM management in Zhejiang Province. However, most cities cannot reach the 10 μg/m limit of WHO's AQG2005 version. Finally, this study makes recommendations for reducing PM in terms of enhancing industrial structure and funding science and technology innovation.
ISSN:1573-2975