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An EKC-based modelling of CO2 emissions, economic growth, electricity consumption and trade openness in Serbia.
- Source :
- Environmental Science & Pollution Research; Jan2024, Vol. 31 Issue 4, p5807-5825, 19p
- Publication Year :
- 2024
-
Abstract
- Understanding the complex interactions between the economy and the environment is crucial for promoting sustainable development and mitigating the negative impact of human activities on the Planet. The importance of this issue for Serbia is evident as the country strives to balance economic growth and environmental protection to ensure a sustainable and resilient future. Therefore, the main objective of this study is to investigate and model the relationship between CO<subscript>2</subscript> emissions, economic growth, electricity consumption, and trade openness in Serbia. Initially, an Autoregressive Distributed Lag (ARDL) model is used to characterize the Environmental Kuznets Curve (EKC) using data from the period from 1995 to 2019, followed by the construction of a bootstrap logistic regression model to predict environmental quality in Serbia. Long-term estimates of the model confirm an inverted U-shaped relationship, where all three variables exert a statistically significant influence on CO<subscript>2</subscript> emissions. In the short run, however, a causal relationship is only observed between electricity consumption and CO<subscript>2</subscript> emissions. The logistic regression results show that all three factors significantly influence environmental quality. The study proposes policy recommendations for Serbia, such as promoting sustainable economic growth, implementing long-term programs to reduce CO<subscript>2</subscript> emissions, reviewing trade policies to prioritize sustainable practices, and investing in renewable energy sources to reduce emissions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09441344
- Volume :
- 31
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Environmental Science & Pollution Research
- Publication Type :
- Academic Journal
- Accession number :
- 174915816
- Full Text :
- https://doi.org/10.1007/s11356-023-31617-y