Journals / InTraders Uluslararası Ticaret Akademik Dergisi / 2026 / Volume: 9 Issue: 1

The Environmental Impacts of Capital Flows, Growth Rhythms, and Innovation: Findings from Türkiye

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Publication Date
2026-07-30
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⚡ TL;DR(Too Long; Didn't Read)

Using annual data for 1996–2023, the ARDL results indicate a stable long run relationship among the variables.

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Objective & Hypothesis:This study examines how foreign direct investment (FDI), economic growth, and R&D based innovation affect carbon dioxide (CO₂) emissions in Türkiye, motivated by the rising prominence of cross-border capital mobility, technological change, and scale effects in environmental economics.
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Key Findings & Data:Using annual data for 1996–2023, the ARDL results indicate a stable long run relationship among the variables.
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Conclusion & Impact:Policy implications emphasize steering FDI toward cleaner sectors, strengthening energy efficiency, and accelerating green technologies to align economic modernization with sustainable development goals.
🔬Design:Research Article
📍Scope:2023 Verileri
⚙️Variables:Foreign direct investment · economic growth · innovation

Abstract

This study examines how foreign direct investment (FDI), economic growth, and R&D based innovation affect carbon dioxide (CO₂) emissions in Türkiye, motivated by the rising prominence of cross-border capital mobility, technological change, and scale effects in environmental economics. It contributes to the literature by (i) estimating long-run equilibrium and short-run adjustment jointly within an ARDL bounds testing and error-correction (ECM) framework, rather than relying on single-period averages; (ii) showing that innovation, when coupled with energy-intensive production expansion, may raise emissions; and (iii) linking the environmental impact of FDI to its qualitative and sectoral composition, informing the pollution haven pollution halo debate for Türkiye. Using annual data for 1996–2023, the ARDL results indicate a stable long run relationship among the variables. Long-run estimates reveal that higher FDI inflows, faster economic growth, and greater R&D expenditures are associated with higher CO₂ emissions. These findings are corroborated by alternative robustness estimators. Short-run dynamics from the ECM suggest that deviations from the long-run path are corrected relatively quickly, with the system returning to equilibrium in about 16 months. To clarify directionality, the Toda–Yamamoto causality test detects unidirectional causality running from FDI, economic growth, and innovation to CO₂ emissions. Overall, the evidence implies that Türkiye’s development trajectory has remained carbon intensive and that growth and investment strategies designed without explicit sustainability constraints can increase carbon intensity. Policy implications emphasize steering FDI toward cleaner sectors, strengthening energy efficiency, and accelerating green technologies to align economic modernization with sustainable development goals.

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Cite This Article

Polat, İ. H., Tosunoğlu, M., Doğan, M. S. (2026). The Environmental Impacts of Capital Flows, Growth Rhythms, and Innovation: Findings from Türkiye. InTraders Uluslararası Ticaret Akademik Dergisi, Volume: 9 Issue: 1. https://doi.org/10.55065/intraders.1880290

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References (77)

  1. Adebayo, T. S., & Kirikkaleli, D. (2021). Impact of renewable energy consumption, globalisation, and technological innovation on environmental degradation in Japan: Application of wavelet tools. Environment, Development and Sustainability, 23(11), 16057-16082. https://doi.org/10.1007/s10668-021-01322-2
  2. Adebayo, T. S., Oladipupo, S. D., Adeshola, I., & Rjoub, H. (2022). Wavelet analysis of the impact of renewable energy consumption and technological innovation on CO2 emissions: Evidence from Portugal. Environmental Science and Pollution Research, 29(16), 23887–23904. https://doi.org/10.1007/s11356-021-17708-8
  3. Aghion, P., Dechezleprêtre, A., Hemous, D., Martin, R., & Van Reenen, J. (2016). Carbon taxes, path dependency, and directed technical change: Evidence from the auto industry. Journal of Political Economy, 124(1), 1–51. https://doi.org/10.1086/684581
  4. Akpınar, A., & Çelik, N. (2025). OECD ülkelerinde cari açık sorunu ve çözüm önerileri: DFE-ARDL panel model bulguları. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 26(1), 46–66. https://doi.org/10.24889/ifede.1515316
  5. Alam, I., & Quazi, R. (2003). Determinants of capital flight: An econometric case study of Bangladesh. International Review of Applied Economics, 17(1), 85–103. https://doi.org/10.1080/713673164
  6. Atılgan, D. (2025). Çevresel teknolojik inovasyonun küresel ısınma üzerine etkisi: BRIC ve Türkiye ülkeleri üzerine ampirik bir inceleme. Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (52), 1-14. https://doi.org/10.61904/sbe.1591309
  7. Ayaydın, H., Ergün, T., Barut, A., Erdem, A., & Pilatin, A. (2025). FinTech-driven green energy transition, foreign direct investment, and ecological footprint in BRICST countries. Scientific Reports, 15(1), 34838. https://doi.org/10.1038/s41598-025-18898-8
  8. Bahmani-Oskooee, M. M., & Goswami, G. G. (2003). A disaggregated approach to test the J-curve phenomenon: Japan versus her major trading partners. Journal of Economics and Finance, 27(1), 102–113. https://doi.org/10.1007/bf02751593
  9. Balsalobre-Lorente, D., Shahbaz, M., Roubaud, D., & Farhani, S. (2018). How economic growth, renewable electricity and natural resources contribute to CO2 emissions? Energy Policy, 113, 356–367. https://doi.org/10.1016/j.enpol.2017.10.050
  10. Banerjee, A., Dolado, J., & Mestre, R. (1998). Error-correction mechanism tests for cointegration in a single-equation framework. Journal of Time Series Analysis, 19(3), 267–283. https://doi.org/10.1111/1467-9892.00091
  11. Bekun, F. V., Gyamfi, B. A., Olasehinde-Williams, G., & Yadav, A. (2024). Revisiting the foreign direct investment-CO2 emissions nexus within the N-EKC framework: Evidence from South Asian countries. Sustainable Futures, 8, 100357. https://doi.org/10.1016/j.sftr.2024.100357
  12. Benli, M. (2020). Türkiye’de doğrudan yabancı yatırımlar, karbon emisyonu ve iktisadi büyüme: Veriye dayalı bir analiz. Uluslararası Ekonomi ve Yenilik Dergisi, 6(1), 35–59. https://doi.org/10.20979/ueyd.617746
  13. Boateng, E., Annor, C. B., Amponsah, M., & Ayibor, R. E. (2024). Does FDI mitigate CO2 emissions intensity? Not when institutional quality is weak. Journal of Environmental Management, 354, 120386. https://doi.org/10.1016/j.jenvman.2024.120386
  14. Bozatlı, O. (2026). The relationship between environmental taxes and ecological footprint in Türkiye: An analysis in the context of the EKC hypothesis. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 27(1), 119-132. https://doi.org/10.37880/cumuiibf.1820332
  15. Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 37(2), 149–163. https://doi.org/10.1111/j.2517-6161.1975.tb01532.x
  16. Çamkaya, S. (2024). Yenilenebilir enerji ve sanayileşmenin çevre üzerindeki etkisinin STIRPAT-Kaya-EKC hipotezi çerçevesinde analizi: AARDL Modelinden kanıtlar. Bingöl Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(1), 107-125. https://doi.org/10.33399/biibfad.1359874
  17. Çamkaya, S., Polat, İ. H., & Polat, Ü. (2022). Are foreign direct investments effective on environmental quality in Turkey? An approach with non-linear ardl method. İktisadi İdari ve Siyasal Araştırmalar Dergisi, 7(17), 30-46. https://doi.org/10.25204/iktisad.1023839
  18. Coşkun, M. F., Konat, G., & Yilanci, V. (2025). Investigation of the role of technological innovation in reducing carbon dioxide damage in Turkey with Fourier tests: Testing the Kuznets curve hypothesis. Environment, Development and Sustainability, 1-25. https://doi.org/10.1007/s10668-025-06137-z
  19. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 1057–1072. https://doi.org/10.2307/1912517
  20. Dolado, J. J., & Lütkepohl, H. (1996). Making Wald tests work for cointegrated VAR systems. Econometric Reviews, 15(4), 369–386. https://doi.org/10.1080/07474939608800362
  21. Doran, N. M., Bădîrcea, R. M., Jianu, E., Antoniu, M. E., Ciobanu, R. M., & Ciobanu, Ș. C. F. (2025). Unveiling CO2 Emission Dynamics Under Innovation Drivers in the European Union. Sustainability, 17(8), 3463. https://doi.org/10.3390/su17083463
  22. Erdoğan, L., Tiryaki, A., & Ceylan, R. (2018). Türkiye'de uzun dönem ekonomik büyümenin belirleyicilerinin ARDL, FMOLS, DOLS ve CCR yöntemleriyle tahmini. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 36(4), 39–57. https://doi.org/10.17065/huniibf.336371
  23. Eriçok, R. E., & Yılancı, V. (2013). Eğitim harcamaları ve ekonomik büyüme ilişkisi: Sınır testi yaklaşımı. Bilgi Ekonomisi ve Yönetimi Dergisi, 8(1), 87–101. https://izlik.org/JA94TN69FR
  24. Fernández, Y. F., López, M. F., & Blanco, B. O. (2018). Innovation for sustainability: the impact of R&D spending on CO2 emissions. Journal of Cleaner Production, 172, 3459–3467. https://doi.org/10.1016/j.jclepro.2017.11.001
  25. Gökmenoğlu, K., & Taspinar, N. (2016). The relationship between CO2 emissions, energy consumption, economic growth and FDI: The case of Turkey. The Journal of International Trade & Economic Development, 25(5), 706–723. https://doi.org/10.1080/09638199.2015.1119876
  26. Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2, 111–120. https://doi.org/10.1016/0304-4076(74)90034-7
  27. Gujarati, D. N. (2004). Basic econometrics (4th ed.). McGraw-Hill.
  28. Gültekin, H. (2023). Finansal gelişme, inovasyon ve CO2 emisyonları: ARDL sınır testi yaklaşımı. Econder Uluslararası Akademik Dergi, 7(1), 25–39. https://doi.org/10.35342/econder.1269394
  29. Hansen, B. E., & Phillips, P. C. B. (1990). Estimation and inference in models of cointegration: A simulation study. Advances in Econometrics, 8, 225–248.
  30. Haug, A. A., & Ucal, M. (2019). The role of trade and FDI for CO2 emissions in Turkey: Nonlinear relationships. Energy Economics, 81, 297-307. https://doi.org/10.1016/j.eneco.2019.04.006
  31. IEA (2023). International Energy Agency. Emissions. Available at https://www.iea.org/world/emissions
  32. IPCC (2023). Intergovernmental Panel on Climate Change. Climate change 2023: Synthesis report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Available at https://www.ipcc.ch/report/ar6/syr/
  33. Iqbal, M., Chand, S., & Ul Haq, Z. (2023). Economic policy uncertainty and CO2 emissions: A comparative analysis of developed and developing nations. Environmental Science and Pollution Research, 30(6), 15034–15043. https://doi.org/10.1007/s11356-022-23115-4
  34. Jiang, Y., Zhou, Z., & Liu, C. (2019). Does economic policy uncertainty matter for carbon emissions? Evidence from US sector level data. Environmental Science and Pollution Research, 26(24), 24380–24394. https://doi.org/10.1007/s11356-019-05627-8
  35. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52, 169–210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x
  36. Khan, Z., Ali, M., Kirikkaleli, D., Wahab, S., & Jiao, Z. (2020). The impact of technological innovation and public‐private partnership investment on sustainable environment in China: Consumption‐based carbon emissions analysis. Sustainable Development, 28(5), 1317–1330. https://doi.org/10.1002/sd.2086
  37. Kılıçarslan, Z., & Dumrul, Y. (2017). Foreign direct investments and CO2 emissions relationship: the case of Turkey. Business and Economics Research Journal, 8(4), 647-660. https://doi.org/10.20409/berj.2017.73
  38. Kızılkaya, O., Sofuoğlu, E., & Çoban, O. (2016). Ekonomik büyüme, enerji tüketimi ve çevre kirliliği analizi: Türkiye örneği. Kırıkkale Üniversitesi Sosyal Bilimler Dergisi, 6(2), 255–272. https://izlik.org/JA92RT65HT
  39. Kivyiro, P., & Arminen, H. (2014). Carbon dioxide emissions, energy consumption, economic growth, and foreign direct investment: Causality analysis for Sub-Saharan Africa. Energy, 74, 595–606. https://doi.org/10.1016/j.energy.2014.07.025
  40. Koçak, E. (2024). Çevresel teknolojik inovasyonun küresel isınma üzerindeki etkisine ilişkin bir inceleme: Türkiye’den kanıtlar. Fiscaoeconomia, 8(2), 478-494. https://doi.org/10.25295/fsecon.1405227
  41. Li, L., & Zhou, H. (2025). The impact of foreign direct investment on carbon emissions in economies along the belt and road. Sustainability, 17(13), 5905. https://doi.org/10.3390/su17135905
  42. Mehmood, B., Feliceo, A., & Shahid, A. (2014). What causes what? Aviation demand and economic growth in Romania: Cointegration estimation and causality analysis. Romanian Economic and Business Review, 9(1), 21–34.
  43. Mensah, C. N., Long, X., Boamah, K. B., Bediako, I. A., Dauda, L., & Salman, M. (2018). The effect of innovation on CO2 emissions of OCED countries from 1990 to 2014. Environmental Science and Pollution Research, 25(29), 29678-29698. https://doi.org/10.1007/s11356-018-2968-0
  44. Narayan, P. K. (2005). The saving and investment nexus for China: Evidence from cointegration tests. Applied Economics, 37(17), 1979–1990. https://doi.org/10.1080/00036840500278103
  45. Narayan, P. K., & Narayan, S. (2005). Estimating income and price elasticities of imports for Fiji in a cointegration framework. Economic Modelling, 22, 423–438. https://doi.org/10.1016/j.econmod.2004.06.004
  46. Narayan, P. K., & Smyth, R. (2005a). Electricity consumption, employment and real income in Australia: Evidence from multivariate Granger causality tests. Energy Policy, 33(9), 1109–1116. https://doi.org/10.1016/j.enpol.2003.11.010
  47. Narayan, P. K., & Smyth, R. (2005b). Trade liberalisation and economic growth in Fiji: An empirical assessment using the ARDL approach. Journal of the Asia Pacific Economy, 10(1), 96–115. https://doi.org/10.1080/1354786042000309099
  48. Narayan, P. K., & Smyth, R. (2006). What determines migration flows from low-income to high-income countries? An empirical investigation of Fiji–U.S. migration, 1972–2001. Contemporary Economic Policy, 24(2), 332–342. https://doi.org/10.1093/cep/byj019
  49. Nguyen, T. T., Pham, T. A. T., & Tram, H. T. X. (2020). Role of information and communication technologies and innovation in driving carbon emissions and economic growth in selected G-20 countries. Journal of Environmental Management, 261, 110162. https://doi.org/10.1016/j.jenvman.2020.110162
  50. Osinusi, K. B., Ajayi, E. G., & Sokunbi, A. R. (2024). Foreign direct investment and carbon emissions nexus in Nigeria: What role does fiscal policy play? Alvan Journal of Social Sciences, 1(1), 1–16. https://doi.org/10.32065/fucjef
  51. Park, J. Y. (1992). Canonical cointegrating regressions. Econometrica, 60(1), 119–143. https://doi.org/10.2307/2951679
  52. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616
  53. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
  54. Polat, İ. H. (2026). The New Drivers of CO₂ Emissions in BRIC Countries: Assessing the Role of Economic Policy Uncertainty, Clean Energy Consumption and Financial Development. İktisadi İdari ve Siyasal Araştırmalar Dergisi, 29, 292-315. https://doi.org/10.25204/iktisad.1835684
  55. Polat, İ. H., Tosunoğlu, M., & Gümüş, M. (2025). The balancing act: Can corruption control and accountability save public efficiency in India? Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 26(2), 464–490. https://doi.org/10.17494/ogusbd.1682596
  56. Qoyash, F. K., & Eren, M. (2022). Türkiye’de teknolojik inovasyon ve yenilenebilir enerji tüketiminin çevre kirliliği üzerine etkisi. Ardahan Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 4(2), 110-118. https://izlik.org/JA76KM94WX
  57. Rambaldi, A. N., & Doran, H. E. (1996). Testing for Granger non-causality in cointegrated systems made easy (Working paper). Department of Econometrics, University of New England.
  58. Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002–1037. https://doi.org/10.1086/261420
  59. Shahbaz, M., Nasir, M. A., & Roubaud, D. (2018). Environmental degradation in France: The effects of FDI, financial development, and energy innovations. Energy Economics, 74, 843–857. https://doi.org/10.1016/j.eneco.2018.07.020
  60. Shahbaz, M., Nasreen, S., Abbas, F., & Anis, O. (2015). Does foreign direct investment impede environmental quality in high-, middle-, and low-income countries? Energy Economics, 51, 275–287. https://doi.org/10.1016/j.eneco.2015.06.014
  61. Siripi, H., Kuuwill, A., & Tamakloe, M. D. (2024). Economic growth and environmental sustainability: The role of foreign direct investment and technological innovation on carbon emissions in Ghana. Research in Globalisation, 9, 100260. https://doi.org/10.1016/j.resglo.2024.100260
  62. Stock, J. H., & Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica, 61(4), 783–820. https://doi.org/10.2307/2951763
  63. Syed, Q. R., & Bouri, E. (2022). Impact of economic policy uncertainty on CO2 emissions in the US: Evidence from bootstrap ARDL approach. Journal of Public Affairs, 22(3), e2595. https://doi.org/10.1002/pa.2595
  64. Tarı, R., & Yıldırım, D. Ç. (2009). Döviz kuru belirsizliğinin ihracata etkisi: Türkiye için bir uygulama. Yönetim ve Ekonomi Dergisi, 16(2), 95–105. https://izlik.org/JA22SB33TP
  65. Tekbaş, M., & Yıldırım, M. (2023). Gelişmekte olan ülkelerde inovasyon ve ekonomik büyümenin CO2 emisyonu üzerine etkisi. Journal of Emerging Economies and Policy, 8(2), 507–516. https://izlik.org/JA69YJ86EW
  66. Toda, H. Y., & 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
  67. Topal, S. (2024). LCC hipotezi çerçevesinde türkiye’de kirlilik siğinaği ve kirlilik hale hipotezlerinin sinanmasi. Uluslararası Yönetim İktisat ve İşletme Dergisi, 20(2), 418-436. https://doi.org/10.17130/ijmeb.1414228
  68. Tosunoğlu, M. (2024). The relationship between technological innovation and economic growth in EU countries: A system GMM approach. Verimlilik Dergisi, 58(4), 651–662. https://doi.org/10.51551/verimlilik.1440264
  69. Tripathy, P., Brahmi, M., Pallayil, B., & Mishra, B. R. (2025). Mediating Effects of Foreign Direct Investment Inflows on Carbon Dioxide Emissions. Economies, 13(1), 18. https://doi.org/10.3390/economies13010018
  70. Uğur, M. S. (2022). The relationship between foreign direct investment, economic growth, energy consumption and CO2 emissions: Evidence from ARDL model with a structural break for Turkey. Ege Academic Review, 22(3), 337-352. https://doi.org/10.21121/eab.1100759
  71. World Bank. (2024a). World Bank Metadata Glossary: World Development Indicators (NE.CON.GOVT.ZS). https://databank.worldbank.org/metadataglossary/world-development-indicators/series/NE.CON.GOVT.ZS
  72. World Bank. (2024b). World Development Indicators. World Bank. https://databank.worldbank.org/source/world-development-indicators
  73. World Bank. (2025a). Carbon dioxide (CO₂) emissions (total) excluding LULUCF (Mt CO₂e) – Türkiye (EN.GHG.CO2.MT.CE.AR5). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators
  74. World Bank. (2025b). Foreign direct investment, net inflows (% of GDP) – Türkiye (BX.KLT.DINV.WD.GD.ZS). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators
  75. World Bank. (2025c). GDP (constant 2015 US$) – Türkiye (1996–2023). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators
  76. World Bank. (2025d). Research and development expenditure (% of GDP) – Türkiye (GB.XPD.RSDV.GD.ZS). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators
  77. Zhu, X., Che, J., Niu, X., Cao, N., & Liu, M. (2025). Study on the effect of technological innovation on carbon emission intensity in 278 prefecture-level cities in China. Scientific Reports, 15(1), 1–17. https://doi.org/10.1038/s41598-025-99370-5