The Triple Sustainability Dynamic in Türkiye's Agricultural Sector: Projections Until 2030 on Greenhouse Gas Emissions, Water Intensity, and Economic Dynamics

Abstract

This study aims to make predictions for the sustainability dynamics of Türkiye's agricultural sector until 2030, considering agricultural water use, greenhouse gas emissions, and agricultural economics from a three-pronged perspective. Predictions were made using data covering the years 1992-2022 based on greenhouse gas emissions and water intensity. Emissions were used in carbon equivalent (Mt CO2e), and water intensity in km³/billion USD. The analysis was conducted using the Prophet time series model across three scenarios. The scenarios were defined as Prophet estimate, international organization estimate, and linear decline. The relationships between variables were evaluated using correlation-based network analysis, and then regressive effects were tested using sensitivity analysis. Based on the findings, the international projections used in Scenario 2 (3.5%) showed that the share of agriculture in GDP would be 78.75 Mt CO2e in 2030. It was determined that this gave the most realistic value compared to the other two scenarios. The analysis findings also predicted that Türkiye's agricultural water intensity will decrease by 29.9% to 0.0379 km³/billion USD by 2030. Correlation analysis also revealed a strong positive correlation between water intensity and the share of agriculture, with a value of 0.87. Furthermore, it showed inverse relationships with greenhouse gas emissions, with values of -0.70 and -0.55, respectively. Subsequent sensitivity analysis confirmed that the impact of water intensity on the model is limited. The study, which offers a data-driven framework for Türkiye's agricultural policies, suggests focusing on drip irrigation, organic farming, and smart farming technologies as policy recommendations.

Keywords

• Greenhouse Gas Emissions
• Water Intensity
• Agricultural Economic Dynamics
• Sustainability

Türkiye Tarım Sektöründe Üçlü Sürdürülebilirlik Dinamiği: Sera Gazı Emisyonları, Su Yoğunluğu ve Ekonomik Dinamikler Üzerine 2030'a Kadar Projeksiyonlar

Abstract

Bu çalışma, tarımsal su kullanımı, sera gazı emisyonları ve tarımsal ekonomi olmak üzere üç yönlü bir bakış açısıyla, Türkiye'nin tarım sektörünün 2030 yılına kadar olan sürdürülebilirlik dinamikleri için tahminlerde bulunmayı amaçlamaktadır. Tahminler, sera gazı emisyonları ve su yoğunluğuna dayalı olarak 1992-2022 yıllarını kapsayan veriler kullanılarak yapılmıştır. Emisyonlar karbon eşdeğeri (Mt CO2e) cinsinden, su yoğunluğu ise km³/milyar USD cinsinden kullanılmıştır. Analiz, üç senaryo üzerinden Prophet zaman serisi modeli kullanılarak gerçekleştirilmiştir. Senaryolar, Prophet tahmini, uluslararası örgüt tahmini ve doğrusal düşüş olarak tanımlanmıştır. Değişkenler arasındaki ilişkiler korelasyon tabanlı ağ analizi kullanılarak değerlendirilmiş, ardından duyarlılık analizi kullanılarak regresif etkiler test edilmiştir. Bulgulara göre, Senaryo 2'de (%3,5) kullanılan uluslararası projeksiyonlar, 2030 yılında tarımın GSYİH'deki payının 78,75 Mt CO2e olacağını göstermiştir. Bu değerin diğer iki senaryoya kıyasla en gerçekçi değer olduğu belirlenmiştir. Analiz bulguları ayrıca, Türkiye'nin tarımsal su yoğunluğunun 2030 yılına kadar %29,9 azalarak 0,0379 km³/milyar USD'ye düşeceğini öngörmüştür. Korelasyon analizi ayrıca, su yoğunluğu ile tarımın payı arasında 0,87 değerinde güçlü bir pozitif korelasyon olduğunu ortaya koymuştur. Ayrıca, sera gazı emisyonlarıyla sırasıyla -0,70 ve -0,55 değerlerinde ters ilişkiler göstermiştir. Sonraki duyarlılık analizi, su yoğunluğunun model üzerindeki etkisinin sınırlı olduğunu doğrulamıştır. Türkiye'nin tarım politikaları için veri odaklı bir çerçeve sunan çalışma, politika önerileri olarak damla sulama, organik tarım ve akıllı tarım teknolojilerine odaklanılmasını önermektedir.

Keywords

• Sera Gazı Salınımları
• Su Yoğunluğu
• Tarım Ekonomisi Dinamikleri
• Sürdürülebilirlik

References

1. Alharbi, O., Sakai, M. & McClean, C. (2024). Analysis of future water demands in Al-Madinah (1990–2030) based on the modified IPAT model and sheared socio-economic pathways scenarios, Sustainable Water Resources Management. 10, 191. https://doi.org/10.1007/s40899-024-01169-1
2. Aquastat, (2025). AQUASTAT Dissemination System, 26/112025 https://data.apps.fao.org/aquastat/?lang=en
3. Cohen, J., Cohen, P., West, S. G., & Aiken, L.S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.). Routledge. https://doi.org/10.4324/9780203774441
4. Dang, C., Zhang, H., Yao, C., Mu, D., Lyu, F., Zhang, Yu. & Zhang, S. (2024). IWRAM: A hybrid model for irrigation water demand forecasting to quantify the impacts of climate change, Agricultural Water Management, 291, 108643. https://doi.org/10.1016/j.agwat.2023.108643
5. European Commission, EDGAR v8.0 Greenhouse Gas Emissions, (2025). https://data.jrc.ec.europa.eu/dataset/b54d8149-2864-4fb9-96b9-5fd3a020c224 26/11/2025
6. He, Z. Wang, R., Wang, J., Chen, H., Zhang, S., Wang, K., Lai, J., Ren, N. & Ho, S. (2025). Water conservation strategies reduce greenhouse gas emission from wastewater treatment plants: A domino effect, Environmental Science and Ecotechnology, 26, 100574. https://doi.org/10.1016/j.ese.2025.100574
7. IMF, GDP, current prices, (2025). 26/11/2025 https://www.imf.org/external/datamapper/NGDPD@WEO/OEMDC/ADVEC/WEOWORLD/TUR
8. Kozhagulov, S., Adambekova, A., Quadrado, J. C., Salnikov, V., Rysmagambetova, A. & Tanybayeva, A. (2025). Trends in Atmospheric Emissions in Central Asian Countries Since 1990 in the Context of Regional Development, Climate, 13(9), 176. https://doi.org/10.3390/cli13090176

9. Li, D.,Wang, Y., Liu, H., Li, C., Cheng, J., Zhang, X., Li, P., Wang, L., & Chang, R. (2025a). How Long Until Agricultural Carbon Peaks in the Three Gorges Reservoir? Insights from 18 Districts and Counties, Microorganisms, 13(6), 1217. https://doi.org/10.3390/microorganisms13061217
10. Li, J., Liu, Y., Tan, X. & Zhu, J. (2025b). County-level Agricultural Non-CO2 Greenhouse Gas Emissions and Scenario Simulation in Hunan Province, China. Chinese. Geographical. Science. 35, 914–928 (2025). https://doi.org/10.1007/s11769-025-1526-1
11. Liu, K., Bo, Y., Li, X., Wang, S. & Zhou, G. (2024). Uncovering Current and Future Variations of Irrigation Water Use Across China Using Machine Learning, Earth's Future, 12(3), e2023EF00356212. https://doi.org/10.1029/2023EF003562
12. Luo, Y., Yang, Y., Zhou, S., Meng, L. & Bate, B. (2024). Quantification and forecast of GHG emissions from municipal solid wastes by multi-expression programming method, Waste Management, 187, 225-234. https://doi.org/10.1016/j.wasman.2024.07.027
13. IMF World Economic Outlook (WEO), (2025). A Critical Juncture amid Policy Shifts, https://www.imf.org/en/publications/weo/issues/2025/04/22/world-economic-outlook-april-2025 26/11/2025
14. R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
15. Selman, E. E., Akol, A. M., Abdel Hamza, J. S. & Naje, A. S. (2024). Implementation of the AquaCrop Model for Forecasting the Effects of Climate Change on Water Consumption and Potato Yield Under Various Irrigation Techniques, Nature Environment and Pollution Technology, 23(2), 1201-1207. https://doi.org/10.46488/NEPT.2024.v23i02.056
16. Söller, L., Luetkemeier, R., Schmied, H. M. & Döll, P. (2024). Groundwater stress in Europe—assessing uncertainties in future groundwater discharge alterations due to water abstractions and climate change, Frontiers in Water, 6, 1448625. https://doi.org/10.3389/frwa.2024.1448625
17. Su, Q. & Karthikeyan, R. (2023). Regional Water Stress Forecasting: Effects of Climate Change, Socioeconomic Development, and Irrigated Agriculture—A Texas Case Study, Sustainability, 15(12), 9290. https://doi.org/10.3390/su15129290
18. Sun, M., Liu, T. L. X., Yan, L. & Yin, H. M. (2023a). Prediction of Carbon Emission From Cultivation in Eastern China Until 2035 Based on Analysis of Carbon Emission from 1998 to 2018 by STIRPAT Model, Applied Ecology and Environmental Research, 21(5), 4323-4341. http://dx.doi.org/10.15666/aeer/2105_43234341
19. Sun, Y., Nan, Z., Yang, W. & Li, L. (2023b). Projecting China’s future water footprints and water scarcity under socioeconomic and climate change pathways using an integrated simulation approach, Climate Services, 30, 100385. https://doi.org/10.1016/j.cliser.2023.100385
20. Taylor, S. J., & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72, 37-45. https://doi.org/10.1080/00031305.2017.1380080
21. Wang, L., Jia, B. & Xie, Z. (2024b). How climate change and groundwater extraction will shape the global terrestrial water cycle pattern, Journal of Hydrology, 641, 131780. https://doi.org/10.1016/j.jhydrol.2024.131780
22. Wang, M., He, G., Dou, P., Qin, C., Zhao, Y., Zhu, Y. & Jiang, S. (2024a). Greenhouse gas emissions and future forecast of the sewage treatment system in Beijing. Journal of Tsinghua University (Science and Technology), 64(2), 282-293. https://doi.org/10.16511/j.cnki.qhdxxb.2023.22.034 https://www.sciopen.com/article/10.16511/j.cnki.qhdxxb.2023.22.034
23. World Bank, (2025). Agriculture, forestry, and fishing, value added (annual % growth) – Turkiye, https://data.worldbank.org/indicator/NV.AGR.TOTL.KD.ZG?locations=TR, 26/11/2025
24. World Bank, (2025). Agriculture, forestry, and fishing, value added (% of GDP) – Turkiye, https://data.worldbank.org/indicator/NV.AGR.TOTL.ZS?locations=TR 26/11/2025
25. World Bank, (2025). GDP growth (annual %) – Turkiye, https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?locations=TR 26/11/2025
26. World Bank, (2025). GDP (current US$) – Turkiye, https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=TR 26/11/2025
27. World Bank, (2025). Türkiye - Country Partnership Framework for the Period FY24-FY28, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099031824111097800 26/11/2025
28. Xie, Z., Feng, M., Zhao, R., Xiao, L., Yao, S. & Gao, Y. (2026). Prediction of urban carbon peak by considering water-energy-carbon nexus of land use: The case of Zhengzhou, China, Sustainable Futures, 11, 101606. https://doi.org/10.1016/j.sftr.2025.101606
29. Yao, Y., Satoh, Y., van Maanen, N., Taranu, S., Keune, J., De Hertog, S. J., Lampe, S., Lawrence, D. M, Sacks, W. J., Wada, Y., Ducharne, A., Cook, B. I., Seneviratne, S. I., Liu, L., Buzan, J. R., Jägermeyr, J. & Thiery, W. (2025). Compounding future escalation of emissions -and irrigation-induced increases in humid-heat stress. Nature Communications, 16, 9326. https://doi.org/10.1038/s41467-025-64375-1
30. Yoo, J. H., & Hana, K. (2023). A new city's water–energy nexus implications: The case of Sejong City in South Korea, Energy & Environment, 35(6), 2975-2990. https://doi.org/10.1177/0958305X231155493
31. Yu, Z., Zhang, M., Zhan, Y., Guo, Y., Zhang, Y., Liang, X., Wang, C., Fan, Y., Shan, M., Guo, H. & Zhou, W. (2025). Analysis of Temporal and Spatial Evolution Characteristics and Peak Prediction of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang Province, Agriculture, 15(11), 1126. https://doi.org/10.3390/agriculture15111126
32. You, Y., Jiang, P., Wang, Y., Wang, W., Chen, D., & Hu, X. (2025). Growth in agricultural water demand aggravates water supply-demand risk in arid Northwest China: more a result of anthropogenic activities than climate change, Hydrology and Earth System Science, 29, 6373–6392. https://doi.org/10.5194/hess-29-6373-2025