Varying Vistas: Exploring the Uneven Impacts of World Society Integration on Cross-National Particulate Air Pollution Using Unconditional Quantile Regression

Abstract

Within the neo-institutional theoretical framework, numerous studies have underscored the pivotal role of international non-governmental organizations (EINGOs) in driving global environmental changes. This paper delves into the varying effects of world society connections on air pollution across distinct pollution levels, challenging the assumption that the efforts of EINGOs uniformly reduce air pollution across the world. Using unconditional quantile regression (UQR) analysis, I examine the role of EINGOs in shaping ambient particulate matter pollution across different pollution quantiles from a sample of 158 countries between 1990 and 2015. The findings from this analysis indicate that the impact of world society ties is significant in nations with moderately low (20th and 30th) and moderately high (70th and 80th) levels of air pollution, with no effect in the middle and extreme ends of the pollution spectrum. These results suggest the overestimation of INGOs’ ameliorative effects when applied globally. Identifying areas where EINGOs are effective allows for the development of targeted strategies tailored to the unique pollution challenges of different countries. By demonstrating that the impact of EINGOs on air pollution is heterogeneous and not equivalent across all nations, this study contributes to the ongoing debate surrounding their effectiveness on real-world environmental effectiveness.

Keywords:

• Particulate matter pollution (PM_2.5)
• climate change
• cross-national
• world society
• unconditional quantile regression (UQR)

Acknowledgements

The author would like to thank Kristen Shorette and Timothy P. Moran for their helpful comments, critiques, and support in developing this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, Kajol Patel, upon request.

Notes

1 Environmental organization membership data from the Yearbook of International Organizations was first assembled by Smith and Wiest (2005). In their data, INGOs are considered environmental if the yearbook indicates the organization is working on environment-related issues. Thank you to everyone who has contributed to updating this dataset.

2 World society scholars have utilized several measures to proxy world society and world polity effects such as the diffusion of all INGOs, INGO shaming events, international treaty ratification rates, and inter-governmental organization count.

3 Some scholars have used logged INGO counts to account for skew (Mejia 2020), in this model I use raw counts of the number of environmental organizations. For robustness, auxiliary analysis was conducted using logged counts, which proved consistent with the reported analysis.

4 Auxiliary analysis using an average of Freedom House’s 7-point political rights and civil liberties scores yields complementary results to the ones reported.

5 RIF estimation results in a dummy variable which assigns all cases either the value $q_{\tau }+\frac{\tau }{q_{\tau }(F_Y)}$ for country-years above the specified quantile and $q_{\tau }+\frac{\tau -1}{q_{\tau }(F_Y)}$ for country-years below.

Additional information

Notes on contributors

Kajol Patel

Kajol Patel is a Ph.D. student in the Department of Sociology at Stony Brook University. Their research explores the impacts of global institutions and local structures on cross-national environmental outcomes.