Abstract
This study investigates the relationship between agricultural production and pollutant (CO2) emissions for the two panels, consisting respectively of 74 and 70 developed and develo** economies during the 1990–2018 period. The study tests for the presence of the environmental Kuznets curve (EKC) and experiments with alternative specifications of the EKC model (agriculture-induced EKC and EKC augmented by agricultural sector variables). It applies alternative panel data estimators and additionally considers cross-country heterogeneity and cross-sectional dependence. As per the EKC hypothesis, an inverted U-shaped relationship between emissions and agricultural and aggregate value added has been confirmed, both at the panel and at the individual economy level. The majority of the economies, with the exception of certain least developed ones, were found to be located on the downward segment of the EKC. The results were found to be robust with respect to employing different techniques. The findings demonstrate the absence of determinism in the ‘economy-environment’ relationship and the possibility of reducing emissions at higher stages of development. They imply the dual role of agriculture (as a source of emissions but also as a sector that potentially helps reduce emissions) and suggest the advantages of policies and measures that stimulate agricultural production. They also point to challenges associated with promoting ‘green agriculture’ and enacting relevant agri-environmental and economic policy reforms.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Notes
The succession of the stages is a stylized representation, while in reality the borderline between the stages may be blurred, with earlier stages contain the elements that characterize the later ones.
Fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), canonic cointegrating regression (CCR).
The agriculture emission-producing activities include crop residues, burning of crop residues, enteric fermentation, manure applied to soils and left on pasture, manure management, rice cultivation, application of synthetic fertilisers, savannah fires, drainage of organic soils, fires in organic soils, on-farm energy use and net forest conversion (FAO 2021).
In contrast to Breusch-Pagan test for cross-sectional dependence that requires that \(T > N\), both tests proposed by Pesaran are more flexible with the size of the dimensions.
The authors note that long-run relationship between income and environmental emission variable is likely, with no cointegration between two income variables (the level and the squared terms).
Stern and Common (2001), for instance, demonstrate that turning points may range for a global panel from as low as US$3000 to US$100,000.
In every specification, the coefficient values of GDP and agricultural value added per capita decreased from the lower to higher quantile and thus were smaller at higher quantile, reflecting the moderation of the negative effects of emissions, as the economy develops. In Specification 2a the pattern was observed as well; however, the coefficients of linear and quadratic terms both decreased by approximately the same magnitude from Quantile 1 to 9, hence giving the stable turning point across the quantiles.
Additional consideration, in both developed and develo** economies, would be the possibility of rising marginal abatement costs and foregone profits, due to adoption of new farming methods and potentially expensive (at least in the initial stages of introduction) ‘green’ technologies, and thus slow growth of sustainable agriculture or limited compliance with agri-environmental regulation (Zafeiriou and Azam 2017).
This dilemma of nurturing green environmentally friendly agriculture without at the same time blighting the growth of existing industrial and subsistence agriculture is a replication in modern times of the old problem mentioned by Kuznets (1964), namely how to extract surplus from agriculture to finance industrial development without undermining the former.
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Trofimov, I.D. Agriculture-induced environmental Kuznets curve: evidence from panel data. SN Bus Econ 4, 50 (2024). https://doi.org/10.1007/s43546-024-00644-3
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DOI: https://doi.org/10.1007/s43546-024-00644-3