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

Year 2024, , 94 - 110, 30.01.2024

İbrahim Ürkmez , Ahmet Sevim , Abdurrahman Çatık

https://doi.org/10.33462/jotaf.1239615

Cited By: 1

Abstract

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 Türkiye 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 Türkiye 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 Türkiye’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.

Keywords

Agriculture Sector, Carbon Dioxide Emissions, Structural Break Analysis, Nonlinear analysis

Türkiye'de Tarım Sektörü ve Karbondioksit Emisyonu Arasındaki İlişki: Doğrusal Olmayan Bir Kanıt

Abstract

Tarımsal üretim, teknolojinin gelişmesi ile hem artmıştır hem de daha verimli hale geldiği gerçeği yadsınamaz. Ancak üretim esnasında havaya salınan CO2 gibi sera gazları iklim değişikliğine neden olmaktadır. Bu durum da tarımsal verimliliği etkilemektedir. Dolayısıyla bu çalışmada Türkiye’de tarım sektörü aktivitesi ile CO2 emisyonları arasındaki karşılıklı etkileşimi doğrusal olmayan bir çerçevede incelemeyi amaçlamaktadır. Bu amaçla, Maki eşbütünleşme testi ve Tek Fourier frekansı Toda & Yamamoto nedensellik testi, 1968'den 2018'e kadar olan dönemi kapsayan zaman serileri kullanarak tarımsal katma değer ile CO2 arasındaki etkileşimi araştırmak için kullanılmıştır. Makalede yapılan ampirik analize ek olarak, çalışmamız, toplam CO2 emisyonlarını iklim değişikliğinin bir göstergesi olarak kullanan çalışmaların aksine, tarım sektöründe CO2 ve enerji tüketimi arasındaki ilişkiyi inceleyerek literatüre katkıda bulunmaktadır. Ayrıca, CO2 ve tarımsal verimlilik arasındaki kısa ve uzun vadeli etkileşimler, tarımsal verimlilik ve CO2 emisyonlarının bağımlı değişkenler olarak kullanıldığı iki ayrı denklemin tahmini ile araştırılmaktadır. Maki eşbütünleşme testi, tarımsal katma değer ile yapısal kırılmalar altındaki CO2 emisyonları arasında uzun dönemli bir ilişkinin varlığını göstermektedir. Tespit edilen önemli kırılmalar, Türkiye ekonomisini etkileyen önemli olaylarla ilişkilidir. Örneğin, tarımsal katma değerin bağımlı değişken olduğu modelde, 1971 ve 1974 yıllarının kırılma tarihleri petrol kriziyle çakışırken, 2002 ve 2008 yıllarının kırılma tarihleri Türkiye'nin 2001 mali krizi ve 2008 küresel mali krizi ile çakışmaktadır. Benzer şekilde, CO2 denkleminde elde edilen 1973 ve 1977'nin kırılma tarihleri, 1970'lerin küresel petrol krizi ile ilişkilidir. FMOLS ve CCR tahmincilerinden türetilen uzun dönemli parametre tahminleri, CO2 emisyonlarının tarımsal verimlilik üzerinde uzun vadeli, olumlu ve önemli bir etkiye sahip olduğunu göstermektedir. Bunun yanında, uzun dönemli sonuçlar, tarımsal verimliliğin çevresel bozulma üzerinde olumlu ve önemli bir etkisinin varlığını desteklemektedir. Kademeli kayma nedensellik testi ise, tarımsal üretimden CO2’ye kadar uzanan tek yönlü nedenselliğin varlığını desteklemektedir. Bu bulgular Türkiye’de tarımsal verimlilik ve CO2’nin birbirini desteklediğini göstermektedir. Her ne kadar CO2’nin tarımsal verimliliği pozitif etkilemesi olumlu görünse de çevreci olmayan bir tarıma işaret etmektedir.

Keywords

Tarım sektörü, Karbondioksit emisyonları, Yapısal kırılma analizi, Doğrusal olmayan analiz

References

• Adams, R. M., Hurd, B. H., Lenhart, S. and Leary, N. (1998). Effects of global climate change on agriculture: An interpretative review. Climate Research, 11(1): 19–30. https://doi.org/10.3354/cr011019
• Başoğlu, A. and Telatar, O. M., (2013). İklim değişikliğinin etkileri: Tarım sektörü üzerine ekonometrik bir uygulama. Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi, 3(6): 7-25. (In Turkish)
• Bayraç, H. N. and Doğan, E. (2016). Impacts of climate change on agriculture sector in Turkey. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 11(1): 23–48.
• Ben Jebli, M. and Ben Youssef, S. (2017). Renewable energy consumption and agriculture: evidence for cointegration and Granger causality for Tunisian economy. International Journal of Sustainable Development and World Ecology, 24(2): 149–158. https://doi.org/10.1080/13504509.2016.1196467
• Brown, C., Meeks, R., Ghile, Y. and Hunu, K. (2010). An Empirical Analysis of the Effects of Climate Variables on National Level Economic Growth Background Paper to the 2010 World Development Report. http://econ.worldbank.org (Accessed dDate: 29.11.2022).
• Çetin, M., Saygin, S. and Demir, H. (2020). The impact of agricultural sector on environmental pollution: A cointegration and causality analysis for Turkish economy. Journal of Tekirdag Agricultural Faculty, 17(3): 329–345. https://doi.org/10.33462/jotaf.678764
• Dellal, D., Mccarl, B. A. and Butt, T. (2011). The economic assessment of climate change on Turkish agriculture. Journal of Environmental Protection and Ecology, 12(1): 376–385.
• Dickey, D. and Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4): 1057–1072.
• Dumrul, Y. and Kilicarslan, Z. (2017). Economic impacts of climate change on agriculture: Empirical evidence from the ARDL approach for Turkey. Pressacademia, 6(4): 336–347. https://doi.org/10.17261/pressacademia.2017.766
• Efron, G. (1979). Bootstrap methods: another look at the jackknife. The Annals of Statistics, 7(1): 1–26.
• Enders, W. and Jones, P. (2015). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics and Econometrics, 20(4): 399–419. https://doi.org/10.1515/snde-2014-0101
• Engle, R. F. and Granger, C. W. J. (1987). Co-integration and error-correction: Representation, estimation and testing. Econometrica, 55: 251-256.
• Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99–126. https://doi.org/10.1016/0304-4076(69)41685-7
• Hatemi-J., A. (2008). Tests for cointegration with two unknown regime shifts with an application to financial market integration. Empirical Economics, 35(3): 497–505. https://doi.org/10.1007/s00181-007-0175-9
• Hayaloğlu, P. (2018). İklim değişikliğinin tarım sektörü ve ekonomik büyüme üzerindeki etkileri. Gümüşhane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 9(25): 51–62.
• IEA (2022). International Energy Agency. https://www.iea.org/, (Accessed Date: 20.11.2022).
• Ike, G. N., Usman, O. and Sarkodie, S. A. (2020). Fiscal policy and CO2 emissions from heterogeneous fuel sources in Thailand: Evidence from multiple structural breaks cointegration test. Science of the Total Environment, 702: 134711 https://doi.org/10.1016/j.scitotenv.2019.134711
• Islam, Md. S., Tarique, K. Md. and Sohag, K. (2014). Co2 Emission and agricultural productivity in Southeast Asian Region: A pooled mean group estimation. A Scientific Journal of COMSATS-SCIENCE VISION, 18(1–2): 29–38.
• Jebli, B. M. and Youssef, S. B. (2019). Combustible renewables and waste consumption, agriculture, CO2 emissions and economic growth in Brazil. Carbon Management, 10(3): 309–321. https://doi.org/10.1080/17583004.2019.1605482
• Jebli, M. B. and Youssef, S. B. (2017). The role of renewable energy and agriculture in reducing CO2 emissions: Evidence for North Africa countries. Ecological Indicators, 74: 295-301.
• Johansen, S. and Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration — with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2): 169–210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x
• Khalid, A. A., Mahmood, F. and Rukh, G. (2016). Impact of climate changes on economic and agricultural value added share in GDP. Asian Management Research Journal, 1(1): 35–48.
• Khan, Z., Hussain, M., Shahbaz, M., Yang, S. and Jiao, Z. (2020). Natural resource abundance, technological innovation, and human capital nexus with financial development: A case study of China. Resources Policy, 65: 101585 https://doi.org/10.1016/j.resourpol.2020.101585
• Konukcu, F., Deveci, H. and Altürk, B. (2020). Modelling of the effect of climate change on wheat yield in Thrace Region with AquaCrop and WOFOST models. Journal of Tekirdag Agricultural Faculty, 17(1): 77-96. https://doi.org/10.33462/jotaf.593883
• Liu, H., Li, X., Fischer, G. and Sun, L. (2004). Study on the impacts of climate change on China’s agriculture. Climatic Change, 65(1–2): 125–148. https://doi.org/10.1023/B:CLIM.0000037490.17099.97
• Maki, D. (2012). Tests for cointegration allowing for an unknown number of breaks. Economic Modelling, 29(5): 2011–2015. https://doi.org/10.1016/j.econmod.2012.04.022
• Masud, M. M., Rahman, M. S., Al-Amin, A. Q., Kari, F. and Filho, W. L. (2014). Impact of climate change: An empirical investigation of Malaysian rice production. Mitigation and Adaptation Strategies for Global Change, 19(4): 431–444. https://doi.org/10.1007/s11027-012-9441-z
• Nazlioglu, S., Gormus, N. A. and Soytas, U. (2016). Oil prices and real estate investment trusts (REITs): Gradual-shift causality and volatility transmission analysis. Energy Economics, 60: 168–175. https://doi.org/10.1016/j.eneco.2016.09.009
• Ngarava, S., Zhou, L., Ayuk, J. and Tatsvarei, S. (2019). Achieving food security in a climate change environment: Considerations for environmental kuznets curve use in the South African agricultural sector. Climate, 7(9): 108. https://doi.org/10.3390/cli7090108
• Olanipekun, I. O., Olasehinde-Williams, G. O. and Alao, R. O. (2019). Agriculture and environmental degradation in Africa: The role of income. Science of the Total Environment, 692: 60–67. https://doi.org/10.1016/j.scitotenv.2019.07.129
• Pakdemirli, B. (2020). CO2 Impacts of CO2 emissions on agriculture: Empirical evidence from Turkey. Derim, 37(1): 33–43. https://doi.org/10.16882/derim.2020.700482
• Phillips, P. C. B. and Perron, P. (1988). Testing for unit roots in time series regression. Biometrika, 75(2): 335-346. https://doi.org/10.2307/2336182
• Qiao, H., Zheng, F., Jiang, H. and Dong, K. (2019). The greenhouse effect of the agriculture-economic growth-renewable energy nexus: Evidence from G20 countries. Science of the Total Environment, 671: 722–731. https://doi.org/10.1016/j.scitotenv.2019.03.336
• Rosenzweig, C. and Parry, M. L. (1994). Potential impact climate change on world food supply. Nature, 367: 133–138.
• Toda, H. Y. and Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2): 225-250. https://doi.org/10.1016/0304-4076(94)01616-8
• TSMS. (2022). Turkish State Meteorological Service. https://www.mgm.gov.tr/FILES/resmi-istatistikler/parametreAnalizi/2021-ortalama-sicaklik.pdf (Accessed Date: 10.12.2022).
• TURKSTAT. (2021). Turkish Statistical Institute. https://data.tuik.gov.tr/Bulten/Index?p=Greenhouse-Gas-Emissions-Statistics-1990-2019-37196 (Accessed Date: 10.12.2022).
• Ventosa-Santaulària, D. and Vera-Valdés, J. E. (2008). Granger-Causality in the presence of structural breaks. Economics Bulletin, 3(61): 1-14.
• Waheed, R., Chang, D., Sarwar, S. and Chen, W. (2018). Forest, agriculture, renewable energy, and CO2 emission. Journal of Cleaner Production, 172: 4231–4238. https://doi.org/10.1016/j.jclepro.2017.10.287
• Wang, H. (2022). Role of environmental degradation and energy use for agricultural economic growth: Sustainable implications based on ARDL estimation. Environmental Technology and Innovation, 25: 1–12. https://doi.org/10.1016/j.eti.2021.102028
• World Bank. (2022). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators (Accessed date: 20.11.2022).
• Zafeiriou, E. and Azam, M. (2017). CO2 emissions and economic performance in EU agriculture: Some evidence from Mediterranean countries. Ecological Indicators, 81: 104–114. https://doi.org/10.1016/j.ecolind.2017.05.039
• Zivot, E. and Andrews, D. W. K. (1992). Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis. Journal of Business & Economic Statistics 10(3): 251-270. https://doi.org/10.2307/1391541