The Relationship between Agriculture and Carbon Dioxide Emission in Turkiye: A Non-Linear Evidence

Agricultural production has both increased and become more efficient with the development of technology. However, greenhouse gases such as CO2 released into the air during production cause climate change. This situation also affects agricultural productivity. Therefore, the main objective of this paper is the examine the interaction between agricultural sector activity and CO2 emissions in Turkiye in a non-linear framework. For this purpose, the Maki cointegration test and the Single Fourier frequency Toda & Yamamoto causality test were used to investigate the interplay between agricultural value added and CO2 using time series data covering the period from 1968 to 2018. In addition to the empirical analysis developed in the paper, our study adds to the literature by studying the relationship between CO2 and energy consumption in the agricultural sector, as opposed to studies that use aggregate CO2 emissions as an indicator of climate change. In addition, the short- and long-run interactions between CO2 and agricultural productivity were investigated by estimating two separate equations where agricultural productivity and CO2 emissions are used as dependent variables. The Maki cointegration test cointegration test shows the existence of a long-run relationship between agricultural value added and CO2 emissions under structural breaks. The detected significant breaks are associated with significant events affecting the Turkiye economy. For instance, when agricultural value added is the dependent variable, the break dates of 1971 and 1974 coincide with the oil crisis, while the breaking dates of 2002 and 2008 coincide with Turkiye's 2001 financial crisis and the 2008 global financial crisis. Similarly, the break dates of 1973 and 1977 obtained in the CO2 equation are associated with the 1970s' global oil crisis. Long-run parameter estimates derived from FMOLS and CCR estimators indicated that CO2 emissions have a long-run, positive and significant impact on agricultural productivity. In addition, the long-run results support the existence of a positive and significant impact of agricultural productivity on environmental degradation. The gradual shift causality test also supports the presence of one-way causality, running from agriculture output to CO2.