This paper analyzes the dynamic impact of economic, social, and governance factors on PM2.5 concentrations in 89 countries from 2006 to 2019. Using the GMM-PVAR approach and Impulse-Response Functions, we examine how shocks to specific variables affect PM2.5 concentrations over a 10-year period. Our findings reveal that the influence of these factors on PM2.5 levels varies over time. For example, a shock in urbanization has no effect on PM2.5 concentrations in the first year, but in the second year, pollution increases significantly. In the third period, PM2.5 levels decrease, but they rise again in the fourth period, albeit not significantly. By the fifth period, pollution decreases until a new equilibrium is reached in the sixth period. Additionally, a shock in financial development, government effectiveness, industrialization, trade openness, or GDP has no effect on PM2.5 concentrations in the initial period. However, during the second period, air pollution decreases, followed by an increase in the third period and a decrease again in the fourth period. These dynamic patterns highlight the need for environmental policies that consider the evaluation time horizon. Our analysis is supplemented by the Granger causality test, guiding specific policy recommendations based on our findings.

中文翻译：

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驱动 PM2.5 浓度的动态因素：全球层面的新证据


本文分析了 2006 年至 2019 年经济、社会和治理因素对 89 个国家 PM2.5 浓度的动态影响。使用 GMM-PVAR 方法和脉冲响应函数，我们研究了对特定变量的冲击如何影响 PM2.5 浓度在 10 年内。我们的研究结果表明，这些因素对 PM2.5 水平的影响会随着时间的推移而变化。例如，城市化的冲击对第一年的 PM2.5 浓度没有影响，但在第二年，污染显著增加。在第三个阶段，PM2.5 水平下降，但在第四阶段再次上升，尽管并不显着。到第五个时期，污染减少，直到第六个时期达到新的平衡。此外，金融发展、政府效率、工业化、贸易开放或 GDP 的冲击对初始阶段的 PM2.5 浓度没有影响。然而，在第二个时期，空气污染减少，其次是第三个时期的增加，第四个时期再次减少。这些动态模式突出了考虑评估时间范围的环境策略的必要性。我们的分析由 Granger 因果关系测试补充，根据我们的研究结果指导具体的政策建议。