Green Finance and Agricultural Sustainability: A Panel Data Analysis of Climate-Resilient Investment in Emerging Economies

Havane Tembelo; Mustafa Özyeşil

doi:10.18016/ksutarimdoga.vi.1727648

TR EN

Yeşil Finans ve Tarımsal Sürdürülebilirlik: Gelişmekte Olan Ekonomilerde İklim Dayanıklı Yatırımın Panel Veri Analizi

Öz

Bu çalışma, 2000-2022 döneminde 20 gelişmekte olan ekonomiden oluşan dengeli bir panelde yeşil finans ile tarımsal sürdürülebilirlik arasındaki uzun vadeli ilişkiyi araştırmaktadır. Tarımsal Toplam Faktör Verimliliği (TFP), sürdürülebilirliğin temel temsilcisi olarak kullanılırken, yeşil finans, yeşil tahvil ihraçları ve ESG ile ilgili yatırım akışları aracılığıyla ölçülmektedir. Analiz ayrıca, kişi başına GSYİH, enflasyon ve kırsal nüfus payı gibi makroekonomik kontrollerin yanı sıra, aracılık kanalı olarak iklime dayanıklı tarımsal yatırımı da içermektedir. Panel Dinamik Sıradan En Küçük Kareler (DOLS), içsellik, seri korelasyon ve eşbütünleşme dinamiklerini hesaba katmak için kullanılmıştır. Tamamlayıcı tahmin ediciler- Sabit Etkiler ve FMOLS - de sağlamlığı doğrulamak için uygulanmıştır. Bir dizi tanı testi, heteroskedastisite, seri korelasyon veya çoklu doğrusallığa dair hiçbir kanıt olmaksızın model varsayımlarının geçerliliğini doğrulamıştır. Sonuçlar, yeşil tahvil ihraçlarının ve ESG yatırımlarının tarımsal üretkenlik üzerinde istatistiksel olarak anlamlı ve pozitif bir etki yarattığını, enflasyon ve gelir düzeyi eşitsizliklerinin ise kısıtlayıcı faktörler olduğunu ortaya koymuştur. Bu bulgular, gelişmekte olan bölgelerde sürdürülebilir tarımsal sonuçları şekillendirmede yeşil finansın dönüştürücü rolünü vurgulamaktadır. Burada benimsenen ampirik strateji, Sürdürülebilir Kalkınma Hedefleri (SDG'ler) ile uyumlu iklime dayanıklı yatırım çerçevelerini teşvik etmek için politika açısından önemli içgörüler sunmaktadır

Anahtar Kelimeler

Green Finance and Agricultural Sustainability: A Panel Data Analysis of Climate-Resilient Investment in Emerging Economies

Abstract

This study investigates the long-run relationship between green finance and agricultural sustainability in a balanced panel of 20 emerging economies over the period 2000–2022. This empirical study aims to analyze the impact of green finance on the long-run agricultural sustainability in emerging markets having focus on the impact of green bond issuance and ESG investments on agricultural total factor productivity using the Panel DOLS method. Agricultural Total Factor Productivity (TFP) is employed as the principal proxy for sustainability, while green finance is measured through green bond issuance and ESG-related investment flows. The analysis further incorporates climate-resilient agricultural investment as a mediating channel, alongside macroeconomic controls including GDP per capita, inflation, and rural population share. Panel Dynamic Ordinary Least Squares (DOLS) was employed to account for endogeneity, serial correlation, and cointegration dynamics. Complementary estimators—Fixed Effects and FMOLS—were also applied to validate robustness. A series of diagnostic tests confirmed the validity of model assumptions, with no evidence of heteroskedasticity, serial correlation, or multicollinearity. Results revealed that green bond issuance and ESG investments exerted a statistically significant and positive impact on agricultural productivity, whereas inflation and income-level disparities were found to be constraining factors. These findings highlight the transformative role of green finance in shaping sustainable agricultural outcomes in developing regions. The empirical strategy adopted herein offers policy-relevant insights for promoting climate-resilient investment frameworks in alignment with the Sustainable Development Goals (SDGs).

Keywords

References

Albuquerque, R., Koskinen, Y., & Zhang, C. (2019). Corporate social responsibility and firm risk: Theory and empirical evidence. Management Science, 65(10), 4451–4469. https://doi.org/10.1287/mnsc.2018.3043
Alston, J. M., Marra, M. C., Pardey, P. G., & Wyatt, T. J. (2000). Research returns redux: A meta-analysis of the returns to agricultural R&D. Australian Journal of Agricultural and Resource Economics, 44(2), 185–215. https://doi.org/10.1111/1467-8489.00107
Ambec, S., Cohen, M. A., Elgie, S., & Lanoie, P. (2013). The Porter hypothesis at 20: Can environmental regulation enhance innovation and competitiveness? Review of Environmental Economics and Policy, 7(1), 2–22. https://doi.org/10.1093/reep/res016
Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. Annals of Statistics, 47(2), 1148–1178. https://doi.org/10.1214/18-AOS1709
Baltagi, B. H. (2021). Econometric analysis of panel data (6th ed.). Springer. https://doi.org/10.1007/978-3-030-53953-5
Barbier, E. B., & Hochard, J. P. (2018). Land degradation and poverty. Nature Sustainability, 1 (11), 623–631.
Barnett, B. J., & Mahul, O. (2007). Weather index insurance for agriculture and rural areas in lower-income countries. American Journal of Agricultural Economics, 89 (5), 1241–1247.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.

Bhattacharya, A., Kharas, H., Plant, M., & Prizzon, A. (2022). Financing a big investment push in emerging markets and developing economies for sustainable recovery and growth. Brookings Institution.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Carter, M. R., Cheng, L., & Sarris, A. (2016). Where and how index insurance can boost the adoption of improved agricultural technologies. Journal of Development Economics, 118, 59–71.
Carter, M. R., de Janvry, A., Sadoulet, E., & Sarris, A. (2017). Index insurance for developing country agriculture: A reassessment. Annual Review of Resource Economics, 9(1), 421–438.
Climate Bonds Initiative (CBI). (2023). Green bond database. (Accessed on 30.01.2026).
D’Orazio, P., & Popoyan, L. (2019). Fostering green investments and tackling climate-related financial risks: Which role for macroprudential policies? Ecological Economics, 160, 25–37.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74 (366a), 427–431.
Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80(4), 549–560.
Duflo, E., & Pande, R. (2007). Dams. The Quarterly Journal of Economics, 122(2), 601–646.
Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460.
Eccles, R. G., & Klimenko, S. (2019). The investor revolution. Harvard Business Review, 97(3), 106–116.
El Ghoul, S., Guedhami, O., Kwok, C. C. Y., & Mishra, D. R. (2011). Does corporate social responsibility affect the cost of capital? Journal of Banking & Finance, 35(9), 2388–2406.
FAO. (2023). FAOSTAT. Food and Agriculture Organization of the United Nations. (Erişim tarihi: 30.01.2026).
Flammer, C. (2021). Corporate green bonds. Journal of Financial Economics, 142(2), 499–516.
Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment, 5(4), 210–233.
Fuglie, K. O. (2018). R&D capital, knowledge spillovers, and productivity growth in world agriculture. Applied Economic Perspectives and Policy, 40(3), 421–444.
Hoechle, D. (2007). Robust standard errors for panel regressions with cross–sectional dependence. Stata Journal, 7(3), 281–312.
ICMA. (2025). Green Bond Principles (June 2025 update). International Capital Market Association. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74.
IMF. (2023). World Economic Outlook. International Monetary Fund. (Accessed on 30.01.2026).
Just, R. E., Calvin, L., & Quiggin, J. (1999). Adverse selection in crop insurance: Actuarial and asymmetric information incentives. American Journal of Agricultural Economics, 81(4), 834–849.
Kao, C., & Chiang, M. H. (2000). On the estimation and inference of a cointegrated regression in panel data. Advances in Econometrics, 15, 179–222.
Kose, M. A., Nagle, P., Ohnsorge, F., & Sugawara, N. (2021). Global waves of debt: Causes and consequences. World Bank Publications, Washington DC, 420 sy.
Levin, A., Lin, C.-F., & Chu, C.-S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24.
Li, Z., Wang, Y., & Zhang, W. (2023). ESG performance and agricultural investment efficiency: Evidence from global agribusiness firms. Agricultural Economics, 54(4), 345–362.
Miranda, M. J., & Farrin, K. (2012). Index insurance for developing countries. Applied Economic Perspectives and Policy, 34(3), 391–427.
O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690.
OECD. (2023). OECD. Stat agricultural investment indicators. (Accessed on 30.01.2026).
Pardey, P. G., Alston, J. M., & Chan-Kang, C. (2016). Public agricultural R&D over the past half century: An emerging new world order. Agronomy Journal, 108(6), 2389–2402.
Pedroni, P. (2000). Fully modified OLS for heterogeneous cointegrated panels. Advances in Econometrics, 15, 93–130.
Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests. Econometric Theory, 20(3), 597–625.
Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics 0435, University of Cambridge.
Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.
Pesaran, M. H. (2015). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6–10), 1089–1117.
Refinitiv. (2023). Eikon database. (Accessed on 30.01.2026).
Smith, V. H., & Goodwin, B. K. (1996). Crop insurance, moral hazard, and agricultural chemical use. American Journal of Agricultural Economics, 78(2), 428–438.
Şeyranlıoğlu, O., Han, A., & Aydınbaş, G. (2025). The impact of climate change and environmental degradation on agricultural credit usage: Evidence from Türkiye with Fourier approximations. KSU Journal of Agriculture and Nature, 28(6), 1516–1530.
USDA ERS. (2023). International agricultural productivity data. (Accessed on 30.01.2026).
Ward, F. A., & Pulido-Velazquez, M. (2021). Efficiency, equity, and sustainability in a water policy reform: A hydro-economic model of water recycling. Ecological Economics, 181, 106909.
Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics, 69(6), 709–748.
Wooldridge, J. M. (2016). Introductory econometrics: A modern approach (6th ed.). Cengage Learning, Boston, 789 sy.
World Bank. (2023a). CO2 emissions data. (Accessed on 18.06.2025).
World Bank. (2023b). GDP per capita (constant 2015 US$). (Accessed on 18.06.2025).
World Bank. (2023c). Rural population (% of total population). (Accessed on 18.06.2025).
Young, M. D., et al. (2021). Impacts of agronomic measures on crop, soil, and environmental parameters: A synthesis of 113 meta-analyses. Agriculture, Ecosystems & Environment, 322, 107658.

Details

Primary Language

English

Subjects

Sustainable Agricultural Development , Agricultural Economics (Other)

Journal Section

Research Article

Authors

Havane Tembelo
0000-0003-3394-4166
Türkiye

Mustafa Özyeşil ^*
0000-0002-4442-7087
Türkiye

Early Pub Date

February 8, 2026

Publication Date

February 8, 2026

Submission Date

June 27, 2025

Acceptance Date

October 23, 2025

Published in Issue

Year 2026 Volume: 29 Number: 3

DOI

https://doi.org/10.18016/ksutarimdoga.vi.1727648

IZ

https://izlik.org/JA78KB66LW

Cite

RIS / Bibtex

APA

Tembelo, H., & Özyeşil, M. (2026). Green Finance and Agricultural Sustainability: A Panel Data Analysis of Climate-Resilient Investment in Emerging Economies. Kahramanmaraş Sütçü İmam Üniversitesi Tarım Ve Doğa Dergisi, 29(3), 756-784. https://doi.org/10.18016/ksutarimdoga.vi.1727648