The BVAR Model for Analyzing CO2 Emissions on Renewable Energy, Economic Growth, and Forest Area
This research investigates the management of CO₂ emissions, a significant factor in the climate change phenomenon, focusing on Indonesia. The objective is to examine the correlation between CO₂ emissions and their causal variables: economic growth (measured by gross domestic product), forest area, a...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ital Publication
2025-06-01
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| Series: | Emerging Science Journal |
| Subjects: | |
| Online Access: | https://ijournalse.org/index.php/ESJ/article/view/2789 |
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| Summary: | This research investigates the management of CO₂ emissions, a significant factor in the climate change phenomenon, focusing on Indonesia. The objective is to examine the correlation between CO₂ emissions and their causal variables: economic growth (measured by gross domestic product), forest area, and renewable energy (RE) consumption. The Bayesian vector autoregressive (BVAR) model was employed to address the complexity of multivariate interactions and overcome limitations associated with small datasets. The analysis revealed that economic growth and reduced forest area significantly contributed to high CO₂ emissions, while renewable energy consumption exhibited a mitigating effect. The BVAR model demonstrated substantial predictive accuracy, highlighting its suitability for analyzing environmental and economic data in resource-constrained scenarios. These findings emphasize the critical need for targeted policy actions in Indonesia, including safeguarding forest areas, addressing illegal logging and burning, and accelerating the transition to renewable energy. The study provides a novel application of the BVAR model in environmental research, showcasing its potential for generating actionable insights into emissions management. This study contributes to the understanding of sustainable development by proposing an innovative way to support evidence-based policies that reduce CO₂ emissions as well as mitigate climate change impacts. |
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| ISSN: | 2610-9182 |