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Testing the Agricultural Induced EKC Hypothesis: Fresh Empirical Evidence from the Top Ten Agricultural Countries
Within the scope of sustainable development goals and climate change mitigation, this study focuses on investigating the effects of energy consumption, agriculture, and economic growth on CO 2 emissions in the top ten agricultural countries for the period 1997-2016. By investigating the validity of the agricultural induced environmental Kuznets curve (EKC), the study mainly aims to explore how agricultural activities affect environmental quality. In doing so, this study utilizes the augmented mean group (AMG) estimator that allows for heterogeneity and cross-sectional dependence. The results of the AMG estimator suggest that the agricultural induced EKC hypothesis is valid for six out of the ten countries. The empirical results also indicate that agriculture reduces CO2 emissions, while energy consumption accelerates environmental degradation. All these results suggest that agricultural production and economic development can play an essential role in reducing environmental pollution.
EKC, panel data, Agricultural and Food Policy, energy consumption, Environmental Economics and Policy, heterogeneity, agriculture
EKC, panel data, Agricultural and Food Policy, energy consumption, Environmental Economics and Policy, heterogeneity, agriculture
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
