Influencing factors and trend prediction of PM2.5 concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China

Qiong Zhang, Shuangshuang Ye, Tiancheng Ma, Xuejuan Fang, Yang Shen and Lei Ding ()
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Qiong Zhang: Ningbo Polytechnic
Shuangshuang Ye: Ningbo Polytechnic
Tiancheng Ma: Ningxia Art Vocational College
Xuejuan Fang: Chinese Academy of Sciences
Yang Shen: Ningbo Polytechnic
Lei Ding: Ningbo Polytechnic

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2023, vol. 25, issue 12, No 29, 14435 pages

Abstract: Abstract The government’s development of eco-environmental policies can have a scientific foundation thanks to the fine particulate matter (PM2.5) 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 PM2.5 concentrations under five alternative scenarios. The results reveal that: (1) urbanization development (P), economic development (A), technological innovation investment (T) and environmental regulation intensity have a significant inhibitory effect on PM2.5 concentration in Zhejiang Province, while industrial structure, industrial energy consumption and the number of motor vehicles (TR) have a significant increase on PM2.5 concentration. (2) Under any scenario, the PM2.5 concentration of 11 cities in Zhejiang Province can reach the constraint target set in the 14th Five-Year plan. The improvement in urban PM2.5 quality is most obviously impacted by the high-quality development scenario (S4). (3) Toward 2035, PM2.5 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 PM2.5 management in Zhejiang Province. However, most cities cannot reach the 10 μg/m3 limit of WHO’s AQG2005 version. Finally, this study makes recommendations for reducing PM2.5 in terms of enhancing industrial structure and funding science and technology innovation.

Keywords: PM2.5; STRIPAT model; Scenario analysis; Ridge regression; Influencing factors (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10668-022-02672-1

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