Analysis of PM2.5 Synergistic Governance Path from a Socio-Economic Perspective: A Case Study of Guangdong Province

• Published in: ISPRS international journal of geo-information Vol. 12; no. 8; p. 340
• Main Authors: Fan, Kunkun, Li, Daichao, Li, Cong, Jin, Xinlei, Ding, Fei, Zeng, Zhan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Air pollution; Cities; concentration prediction; Coordination models; Development strategies; Distribution patterns; Economic analysis; Economic development; Economics; Emissions; Energy consumption; Energy efficiency; Environmental impact; Environmental protection; Environmental quality; Freight transportation; GDP; Governance; Gross Domestic Product; Industrial emissions; Industrial pollution; influencing factor; Machine learning; Neural networks; Outdoor air quality; Particulate matter; PM2.5; Pollutants; Pollution control; Prediction models; Remote sensing; scenario simulation; Simulation; Social change; Socioeconomic aspects; Spatial distribution; Statistical analysis; Statistical methods; SVM; Urban areas; Urban development; Urban studies; Urban transportation; Urbanization; Guangdong China; China
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi12080340

Analyzing the influencing factors of PM2.5 concentration, scenario simulations, and countermeasure research to address the problem of PM2.5 pollution in Guangdong Province is of great significance for governments at all levels for formulating relevant policies. In this study, the ChinaHighPM2.5 dataset and economic and social statistics for Guangdong Province from 2010 to 2019 were selected, and a PM2.5 pollution management compliance path formulation method based on the multi-scenario simulation was proposed by combining the differences in city types and PM2.5 concentration prediction. Based on the prediction model of PM2.5 concentration constructed by the Ridge and SVM models and facing the PM2.5 pollution control target in 2025, the urban PM2.5 pollution control scenario considering the characteristics of urban development was constructed. According to the scenario simulation results of the PM2.5 prediction model, the PM2.5 pollution control path suitable for Guangdong Province during the 14th Five-Year Plan period was explored. The coupling coordination model was used to explore the spatial and temporal pattern evolution of PM2.5 pollution collaborative governance in various prefecture-level cities under the standard path, and the policy recommendations for PM2.5 pollution control during the 14th Five-Year Plan period are proposed. The results showed the following: ① in the case of small samples, the model can provide effective simulation predictions for the study of urban pollutant management compliance pathways. ② Under the scenario of PM2.5 management meeting the standard, in 2025, the annual average mass concentration of PM2.5 in all prefecture-level cities in Guangdong Province will be lower than 22 μg/m3, and the annual average concentration of PM2.5 in the whole province will drop from 25.91 μg/m3 to 21.04 μg/m3, which will fulfil the goal of reducing the annual average concentration of PM2.5 in the whole province to below 22 μg/m3, as set out in the 14th Five-Year Plan for the Ecological Environmental Protection of Guangdong Province. ③ Under the path of PM2.5 control and attainment, the regional coordination relationship among prefecture-level cities in Guangdong Province is gradually optimized, the number of intermediate-level coordinated cities will increase, and the overall spatial distribution pattern will be low in the middle and high in the surrounding area. Based on the characteristics of the four city types, it is recommended that a staggered development strategy be implemented to achieve synergy between economic development and environmental quality. Urban type I should focus on restructuring freight transportation to reduce urban pollutant emissions. City type II should focus on urban transportation and greening. For city type III, the focus should be on optimizing the industrial structure, adjusting the freight structure, and increasing the greening rate of the city. For city type IV, industrial upgrading, energy efficiency, freight structure, and management of industrial pollutant emissions should be strengthened.