Abstract
The present study aims to ascertain the causes of environmental vulnerabilities in Pakistan, Bangladesh, and India. The countries are ranked top ten in the Global Climate Risk Index list in 1998–2017, 1999–2018, 2000–2019, 2018, and 2019. Hence, we determine whether the observing nations’ increasing GDP per capita, energy use, and population density intensify greenhouse gas emissions. This study also considers structural breaks in the time-series data from 1990 to 2019. Using the bounds-testing approach, we discover evidence of long-run cointegration among the variables. Furthermore, the long-run autoregressive distributed lag (ARDL) estimations disclose that GDP per capita positively correlates with greenhouse gas emissions, with this effect observed across all sample countries except India in the short run. Additionally, in the long and short run, energy use intensifies greenhouse gas emissions in all sample countries except Bangladesh. Population density also harms the environment in both time horizons in all the observed countries. The study also incorporates fully modified OLS (FMOLS) and dynamic OLS (DOLS) regressions for robust interpretations. Findings of FMOLS and DOLS also support the ARDL model’s outcomes. Moreover, diagnostic tests confirm the stability and reliability of ARDL models. Based on the varying findings on the impacts of energy use on greenhouse gas emissions across countries in the short and long run and the influence of GDP per capita in the short run, policymakers should tailor environmental policies according to nations’ energy and income structures.
Graphical Abstract
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Highlights
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This study sought to ascertain the causes of environmental vulnerabilities in Pakistan, Bangladesh, and India. The countries are ranked in the top ten list of environmentally vulnerable nations in the Global Climate Risk Index.
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The study examined the impact of increasing GDP per capita, energy use, and population density on greenhouse gas emissions.
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The authors utilized the bounds-testing approach to reveal long-run cointegration among variables in the study.
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In further assessment, the study discovered a positive correlation between GDP per capita and greenhouse gas emissions in all countries except for short-term results for India.
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The study also identified energy use as a contributor to greenhouse gas emissions in all countries except Bangladesh, both in the short and long run.
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Population density was also found to exacerbate environmental degradation in all observed countries, with a more significant impact in Pakistan compared to Bangladesh and India.
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Results suggested that GDP per capita, energy use, and population density contribute to environmental degradation in Pakistan in the long run.
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In Bangladesh, population density and GDP per capita were identified as dynamic factors contributing to environmental degradation in the long run.
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In India, GDP per capita, energy use, and population density positively affected greenhouse gas emissions.
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All data generated or analyzed during this study are included in the published article.
Notes
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This work was supported by Grant PID 2021-127893OB-I00 funded by MCIN/AEI/https://doi.org/10.13039/501100011033 and European Union Next Generation EU/PRTR.
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Golo, M.A., Han, D., Lorente, D.B. et al. Uncovering the Origins of Environmental Vulnerabilities: A Study of Three South Asian Nations. Int J Environ Res 18, 72 (2024). https://doi.org/10.1007/s41742-024-00626-1
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DOI: https://doi.org/10.1007/s41742-024-00626-1