Assessing human factors in environmental degradation: A panel data analysis of Asean countries using stirpat and cross-sectional dependency models
Many theories have been developed to analyse the nexus and solve environmental issues linked to human factors. In contributing to the existing literature, this study has two main objectives: (1) to examine the human factors that affect environmental degradation by considering the cross-section depe...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Human Resource Management Academic Research Society
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28519/2/02239311220241517321580.pdf http://eprints.utem.edu.my/id/eprint/28519/ https://hrmars.com/papers_submitted/24334/assessing-human-factors-in-environmental-degradation-a-panel-data-analysis-of-asean-countries-using-stirpat-and-cross-sectional-dependency-models.pdf |
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| Summary: | Many theories have been developed to analyse the nexus and solve environmental issues linked to human factors. In contributing to the existing literature, this study has two main
objectives: (1) to examine the human factors that affect environmental degradation by considering the cross-section dependency effect; and (2) to determine the appropriate model for explaining the relationship between the factors. Using panel data from 2000 to 2019, this study employs the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model to assess human environmental impacts, focusing on ASEAN countries. The
factors tested include population, GDP and energy intensity, with CO2 emissions as the dependent variable. To overcome the issue of ignoring the cross-section dependency effect
in panel data regression in past literature, the heterogeneous panel estimators of the mean group, common correlated effects mean group and augmented mean group (MG, CCEMG and AMG) are employed. The results reveal that CCEMG is the best estimator, with the smallest root mean square error (RMSE). The estimated GDP and energy intensity significantly contribute to higher CO2 emissions. The findings also show that cross-sectional dependency influences GDP. The results of this study may provide a perspective into how the economy should be developed without affecting the environment. |
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