Evaluating Enteric Fermentation-Driven Environmental Kuznets Curve Dynamics: A Bayesian Vector Autoregression Comparative Study of the EU and Least Developed Countries
Eleni Zafeiriou,
Spyros Galatsidas (),
Christina Moulogianni,
Spyridon Sofios and
Garyfallos Arabatzis
Additional contact information
Eleni Zafeiriou: School of Agricultural Development, Faculty of Agricultural and Forestry Sciences, Democritus University of Thrace, 68200 Orestiada, Greece
Spyros Galatsidas: School of Forestry and Management of the Environment and Natural Resources, Faculty of Agricultural and Forestry Sciences, Democritus University of Thrace, 68200 Orestiada, Greece
Christina Moulogianni: Department of Agricultural Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Spyridon Sofios: Independent Authority for Public Revenue, 54110 Thessaloniki, Greece
Garyfallos Arabatzis: School of Forestry and Management of the Environment and Natural Resources, Faculty of Agricultural and Forestry Sciences, Democritus University of Thrace, 68200 Orestiada, Greece
Agriculture, 2024, vol. 14, issue 11, 1-16
Abstract:
Global warming and climate change, primarily driven by human activities, with agriculture playing a significant role, have become central topics of scientific research. Livestock production, especially enteric fermentation, is a major source of greenhouse gas emissions, making it a focal point for both climate change adaptation and mitigation strategies. Both the European Union (EU) and Least Developed Countries (LDCs) are highly dependent on agriculture, particularly livestock, which plays a key role in their economic growth. In developing countries, livestock systems are evolving rapidly due to various factors, while in the EU, the livestock sector remains economically and socially significant, representing 36% of total agricultural activity. This study explores the environmental impact of enteric fermentation in livestock production, alongside the economic value it generates in both the EU and LDCs. The analysis utilizes a Bayesian Vector Autoregression (BVAR) methodology, which provides a more robust performance compared to traditional models like Vector Autoregression (VAR) and the Vector-error Correction Model (VECM). This research identifies significant relationships between the variables studied, with structural breaks quantified to reflect the impact of initiatives undertaken in both regions. Interestingly, the results challenge the environmental Kuznets curve, which hypothesizes an inverted U-shaped relationship between economic growth and environmental degradation, as proposed by Stern. This suggests that stronger economic incentives may be necessary to enhance policy effectiveness and promote eco-efficiency. The distinctive characteristics of livestock production in the EU and LDCs should be carefully considered when shaping agricultural policies, with a strong emphasis on farmer education as a critical factor for success. Additionally, corporate management practices must be tailored to address the unique needs, strengths, and challenges of livestock businesses in these two diverse regions.
Keywords: enteric fermentation; Kuznets; livestock; the European Union (EU); least developed countries (LDCs); Bayesian vector autoregression models (BVAR models) (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.mdpi.com/2077-0472/14/11/2036/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/11/2036/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:11:p:2036-:d:1519367
Access Statistics for this article
Agriculture is currently edited by Ms. Leda Xuan
More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().