Ul-Haq Zia, Mehmood Usman, Tariq Salman, Mariam Ayesha
Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan.
Department of Political Science, University of Management and Technology, Lahore, Pakistan.
Environ Sci Pollut Res Int. 2023 Mar;30(14):40008-40017. doi: 10.1007/s11356-022-25046-6. Epub 2023 Jan 5.
Rapid industrialization and economic development in South Asia (SA) caused serious air pollution-related issues. Air pollutants, particularly fine particulate matter (PM), have negative effects on health, instigating widespread concern. The current study is an attempt to analyze the impact of non-renewable energy (NRE), globalization (GLO), GDP, renewable energy (RE), and population (POP) on PM concentration in SA from 1998 to 2020. In doing so, this study incorporated advanced and robust econometric techniques, i.e., Pesaran (Economet Rev 34(6-10), 1089-1117, 2015), to check the cross-sectional dependency, and the unit root presence checked through Cross-sectional Im, Pesaran, and Shin (CIPS) and Cross-sectionally Augmented Dickey-Fuller (CADF) unit root tests. Moreover, the long and short-run association among the selected variables was analyzed through Westerlund and Edgerton (Econ Lett 97(3), 185-190, 2007), cointegration test, and cross-sectional augmented ARDL (CS-ARDL). The empirical results indicate that the panel was cross-sectionally correlated, stationary at the first difference, and co-integrated in the long run. Moreover, the CS-ARDL model indicates a positive association between GDP and PM concentration. Similarly, NRE and POP contribute significantly to increasing the PM concentration in SA. However, RE and GLO play an important role to decrease the PM concentration in SA.
南亚的快速工业化和经济发展引发了严重的空气污染相关问题。空气污染物,尤其是细颗粒物(PM),对健康有负面影响,引发了广泛关注。当前的研究旨在分析1998年至2020年期间不可再生能源(NRE)、全球化(GLO)、国内生产总值(GDP)、可再生能源(RE)和人口(POP)对南亚PM浓度的影响。在此过程中,本研究采用了先进且稳健的计量经济学技术,即佩萨兰(《计量经济学评论》34(6 - 10),1089 - 1117,2015年)来检验横截面依赖性,并通过横截面Im、佩萨兰和申(CIPS)以及横截面增强迪基 - 富勒(CADF)单位根检验来检查单位根的存在情况。此外,通过韦斯特伦德和埃杰顿(《经济学通讯》97(3),185 - 190,2007年)、协整检验以及横截面增强自回归分布滞后(CS - ARDL)分析了所选变量之间的长期和短期关联。实证结果表明,该面板存在横截面相关性,在一阶差分下是平稳的,并且从长期来看是协整的。此外,CS - ARDL模型表明GDP与PM浓度之间存在正相关关系。同样,不可再生能源和人口对南亚PM浓度的增加有显著贡献。然而,可再生能源和全球化在降低南亚PM浓度方面发挥着重要作用。