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Türkiye’nin 2030 CO2 Emisyon Hedefinin Holt-Winters Üstel Düzeltme Modeli ile Tahmini

Yıl 2025, Sayı: Özel Sayı 3, 109 - 127, 31.12.2025
https://doi.org/10.33203/mfy.1834216

Öz

Bu çalışma Paris Anlaşması ile taahhüt edilen, Türkiye’nin karbon emisyon hedefinin gerçekleşebilme durumunun orta vadeli olarak tahmin edilmesini amaçlamaktadır. Bu kapsamda Holt-Winter Üstel Düzeltme modeli ile aşırı uyum riski azaltılarak Türkiye'nin 2024-2030 dönemi karbon emisyon düzeyi %95 güven aralığında tahmin edilmiştir. Elde edilen bulgular, emisyonların 2030 yılına kadar artmaya devam ederek yaklaşık 600 MtCO₂e düzeyine ulaşacağını göstermektedir. Bu tahmin ise Türkiye'nin referans senaryoya göre %41 azaltım taahhüdü içeren Güncellenmiş Ulusal Katkı Beyanı ile paralel bir sonuçtur. Zira katkı beyanı ile oldukça eleştiri alan bir hedef olarak; mutlak bir emisyon azalışından ziyade, artıştan azaltım mantığı kurgulanmıştır. Referans senaryo baz alınarak emisyon azaltımı yerine, emisyon artış hızının azaltılması hedeflenmiştir. Çalışma bulguları da Paris Anlaşması kapsamındaki resmi taahhüt ile uyumludur. Türkiye’nin 2053 net sıfır emisyon hedefine ulaşmak için çok daha katı iklim ve enerji politikalarına ihtiyaç duyduğunu ortaya koymaktadır.

Kaynakça

  • Abdullah, L., & Pauzi, H. M. (2015). Methods in forecasting carbon dioxide emissions: A decade review. Jurnal Teknologi (Sciences & Engineering), 75(1), 67–82. https://doi.org/10.11113/jt.v75.2603
  • Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 8(1), 69–80. https://doi.org/10.1016/0169-2070(92)90008-W
  • Auffhammer, M., & Carson, R. T. (2008). Forecasting the path of China’s CO₂ emissions using province-level information. Journal of Environmental Economics and Management, 55(3), 229–247. https://doi.org/10.1016/j.jeem.2007.10.002
  • Ayaz, İ. (2024). Forecasting CO₂ emissions with machine learning methods: Türkiye example and future trends. Naturengs, 5(2), 82–87. https://doi.org/10.46572/naturengs.1595329
  • Aydın, S., & Aydoğdu, G. (2022). Makine öğrenmesi algoritmaları kullanılarak Türkiye ve AB ülkelerinin CO₂ emisyonlarının tahmini. Avrupa Bilim ve Teknoloji Dergisi, (37), 42–46. https://doi.org/10.31590/ejosat.1129958
  • Ayvaz, B., Kuşakcı, A. O., & Temur, G. T. (2017). Energy-related CO₂ emission forecast for Turkey and Europe and Eurasia: A discrete grey model approach. Grey Systems: Theory and Application, 7(3), 436–452. https://doi.org/10.1108/GS-08-2017-0031
  • Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014 GMD+1
  • Chatfield, C. (1978). The Holt–Winters forecasting procedure. Journal of the Royal Statistical Society: Series C (Applied Statistics), 27(3), 264–279. https://doi.org/10.2307/2347162
  • Chatfield, C. (2004). The analysis of time series: An introduction (6th ed.). Chapman & Hall/CRC.
  • Çoşgun, A. (2024). Estimation of Turkey’s carbon dioxide emission with machine learning. International Journal of Computational and Experimental Science and Engineering, 10(1), 95–101. https://doi.org/10.22399/ijcesen.302
  • Diebold, F. X. (2015). Forecasting in economics, business, finance and beyond. Penn Arts & Sciences.
  • Gardner, E. S., Jr. (2006). Exponential smoothing: The state of the art—Part II. International Journal of Forecasting, 22(4), 637–666. https://doi.org/10.1016/j.ijforecast.2006.03.005
  • Garip, E., & Oktay, A. B. (2018). Forecasting CO₂ emission with machine learning methods. In International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1–4). IEEE. https://doi.org/10.1109/IDAP.2018.8620767
  • Hamdan, A., Al-Salaymeh, A., AlHamad, I. M., et al. (2023). Predicting future global temperature and greenhouse gas emissions via LSTM model. Sustainable Energy Research, 10, 21. https://doi.org/10.1186/s40807-023-00092-x
  • Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted averages (ONR Research Memorandum No. 52). Carnegie Institute of Technology.
  • Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5–10. https://doi.org/10.1016/j.ijforecast.2003.09.015
  • Hou, L., & Chen, H. (2024). The prediction of medium- and long-term trends in urban carbon emissions based on an ARIMA–BPNN combination model. Energies, 17, 1856.
  • Hu, Y.-C., Jiang, P., Tsai, J.-F., & Yu, C.-Y. (2021). An optimized fractional grey prediction model for carbon dioxide emissions forecasting. International Journal of Environmental Research and Public Health, 18(2), 587. https://doi.org/10.3390/ijerph18020587
  • Huang, J., Zhang, X., Lv, X., & Xia, Z. (2025). Carbon emissions prediction based on the Informer combination forecasting model: A case study of Sichuan. Carbon Neutral Systems, 1, 17. https://doi.org/10.1007/s44438-025-00014-y
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts.
  • IPCC. (2022). Climate change 2022: Mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
  • Jansen, A. G. S. M., Teunis, P. F. M., & van der Heijden, P. G. M. (2003). Measuring prediction error in censored data. Statistics in Medicine, 22(21), 3365–3377.
  • Kıratoğlu, E. (2024). Bütçenin Yüzyılı: Cumhuriyetten Günümüze Bütçe Politikaları. JOEEP: Journal of Emerging Economies and Policy, 9(1), 21-34.
  • Kong, D., Dai, Z., Tang, J., & Zhang, H. (2023). Forecasting urban carbon emissions using an Adaboost–STIRPAT model. Frontiers in Environmental Science, 11, 1284028. https://doi.org/10.3389/fenvs.2023.1284028
  • Lewis, C. D. (1982). Industrial and business forecasting methods. Butterworths.
  • Lin, C. S., Liou, F., & Huang, C. P. (2011). Grey forecasting model for CO₂ emissions: A Taiwan study. Applied Energy, 88(11), 3816–3820. https://doi.org/10.1016/j.apenergy.2011.05.040
  • Lotfalipour, M. R., Falahi, M. A., & Bastam, M. (2013). Prediction of CO₂ emissions in Iran using grey and ARIMA models. International Journal of Energy Economics and Policy, 3(3), 229–237. https://doi.org/10.5547/2160-5890.3.3.12
  • Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and applications (3rd ed.). John Wiley & Sons.
  • Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2008). Introduction to time series analysis and forecasting. John Wiley & Sons.
  • Arık, O. A., & Hamamcıoğlu, E. (2024). Türkiye’nin CO₂ emisyonunun ANFIS ile tahmin edilmesi. Researcher, 4(2), 141–156.
  • OECD, & UNDB. (2025). Investing in climate for growth and development: The case for enhanced nationally determined contributions (NDCs). OECD Publishing. (Rapor; çoğu durumda URL/ISBN kullanılmakta, DOI isteğe bağlıdır.)
  • OECD. Air and GHG emissions https://www.oecd.org/en/data/indicators/air-and-ghg-emissions.html Erişim Tarihi: 21.09.2025
  • Ord, J. K., & Fildes, R. (2013). Principles of business forecasting. South-Western Cengage Learning.
  • Öztürk, S., & Emir, A. (2024). Estimations of greenhouse gases emissions of Turkey by statistical methods. Konya Journal of Engineering Sciences, 12(1), 138–149. https://doi.org/10.36306/konjes.1267008
  • Pabuçcu, H., & Bayramoğlu, T. (2016). Yapay sinir ağları ile CO₂ emisyonu tahmini: Türkiye örneği. Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 18(3), 762–778.
  • Pao, H. T., & Tsai, C. M. (2011). Modeling and forecasting the CO₂ emissions, energy consumption, and economic growth in Brazil. Energy, 36(5), 2450–2458. https://doi.org/10.1016/j.energy.2011.01.032
  • Sel, A., & Tekgün, B. (2022). ANFIS yöntemi ile Türkiye karbondioksit salınımı tahmini. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 13(34), 486–504. https://doi.org/10.21076/vizyoner.990380
  • Shih, S. H., & Tsokos, C. P. (2008). Prediction models for carbon dioxide emissions and the atmosphere. Neural, Parallel and Scientific Computations, 16(1), 165–180.
  • T.C. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı (2023). Ulusal sera gazı emisyon envanteri.
  • TÜİK. (2023). Sera Gazı Emisyon İstatistikleri, 1990–2021. Erişim adresi: https://data.tuik.gov.tr TÜİK Veri Portalı
  • Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/cr030079
  • Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324–342. https://doi.org/10.1287/mnsc.6.3.324
  • Wu, L., Liu, S., Liu, D., Fang, Z., & Xu, H. (2015). Modelling and forecasting CO₂ emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model. Energy, 79, 489–495. https://doi.org/10.1016/j.energy.2014.11.026
  • Zuo, Z., Guo, H., & Cheng, J. (2020). An LSTM–STIRPAT model analysis of China’s 2030 CO₂ emissions peak. Carbon Management, 11(6), 577–592. https://doi.org/10.1080/17583004.2020.1849415

Forecasting of Türkiye's 2030 CO₂ Emission Target Using The Holt–Winters Exponential Smoothing Model

Yıl 2025, Sayı: Özel Sayı 3, 109 - 127, 31.12.2025
https://doi.org/10.33203/mfy.1834216

Öz

This study aims to estimate, in the medium term, the feasibility of achieving Turkey's carbon emission target as committed under the Paris Agreement.. Using the Holt–Winters exponential smoothing model, which mitigates overfitting in short annual series, it estimates Türkiye’s CO₂ emissions for 2024–2030 within a 95% confidence interval. The findings indicate that emissions will continue to rise until 2030, reaching roughly 600 MtCO₂e. This forecast is consistent with Türkiye’s Updated Nationally Determined Contribution, which envisages a 41% reduction relative to a reference scenario. However, the pledge has been widely criticised, as it is framed not as an absolute reduction in emissions, but as a “reduction from increase”, targeting a slower growth rate in emissions rather than a decline relative to current levels. The results confirm this logic and show that, under existing commitments, Türkiye will fall short of a decarbonisation path, implying a need for stricter climate and energy policies to meet the 2053 net-zero goal.

Kaynakça

  • Abdullah, L., & Pauzi, H. M. (2015). Methods in forecasting carbon dioxide emissions: A decade review. Jurnal Teknologi (Sciences & Engineering), 75(1), 67–82. https://doi.org/10.11113/jt.v75.2603
  • Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 8(1), 69–80. https://doi.org/10.1016/0169-2070(92)90008-W
  • Auffhammer, M., & Carson, R. T. (2008). Forecasting the path of China’s CO₂ emissions using province-level information. Journal of Environmental Economics and Management, 55(3), 229–247. https://doi.org/10.1016/j.jeem.2007.10.002
  • Ayaz, İ. (2024). Forecasting CO₂ emissions with machine learning methods: Türkiye example and future trends. Naturengs, 5(2), 82–87. https://doi.org/10.46572/naturengs.1595329
  • Aydın, S., & Aydoğdu, G. (2022). Makine öğrenmesi algoritmaları kullanılarak Türkiye ve AB ülkelerinin CO₂ emisyonlarının tahmini. Avrupa Bilim ve Teknoloji Dergisi, (37), 42–46. https://doi.org/10.31590/ejosat.1129958
  • Ayvaz, B., Kuşakcı, A. O., & Temur, G. T. (2017). Energy-related CO₂ emission forecast for Turkey and Europe and Eurasia: A discrete grey model approach. Grey Systems: Theory and Application, 7(3), 436–452. https://doi.org/10.1108/GS-08-2017-0031
  • Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014 GMD+1
  • Chatfield, C. (1978). The Holt–Winters forecasting procedure. Journal of the Royal Statistical Society: Series C (Applied Statistics), 27(3), 264–279. https://doi.org/10.2307/2347162
  • Chatfield, C. (2004). The analysis of time series: An introduction (6th ed.). Chapman & Hall/CRC.
  • Çoşgun, A. (2024). Estimation of Turkey’s carbon dioxide emission with machine learning. International Journal of Computational and Experimental Science and Engineering, 10(1), 95–101. https://doi.org/10.22399/ijcesen.302
  • Diebold, F. X. (2015). Forecasting in economics, business, finance and beyond. Penn Arts & Sciences.
  • Gardner, E. S., Jr. (2006). Exponential smoothing: The state of the art—Part II. International Journal of Forecasting, 22(4), 637–666. https://doi.org/10.1016/j.ijforecast.2006.03.005
  • Garip, E., & Oktay, A. B. (2018). Forecasting CO₂ emission with machine learning methods. In International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1–4). IEEE. https://doi.org/10.1109/IDAP.2018.8620767
  • Hamdan, A., Al-Salaymeh, A., AlHamad, I. M., et al. (2023). Predicting future global temperature and greenhouse gas emissions via LSTM model. Sustainable Energy Research, 10, 21. https://doi.org/10.1186/s40807-023-00092-x
  • Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted averages (ONR Research Memorandum No. 52). Carnegie Institute of Technology.
  • Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5–10. https://doi.org/10.1016/j.ijforecast.2003.09.015
  • Hou, L., & Chen, H. (2024). The prediction of medium- and long-term trends in urban carbon emissions based on an ARIMA–BPNN combination model. Energies, 17, 1856.
  • Hu, Y.-C., Jiang, P., Tsai, J.-F., & Yu, C.-Y. (2021). An optimized fractional grey prediction model for carbon dioxide emissions forecasting. International Journal of Environmental Research and Public Health, 18(2), 587. https://doi.org/10.3390/ijerph18020587
  • Huang, J., Zhang, X., Lv, X., & Xia, Z. (2025). Carbon emissions prediction based on the Informer combination forecasting model: A case study of Sichuan. Carbon Neutral Systems, 1, 17. https://doi.org/10.1007/s44438-025-00014-y
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts.
  • IPCC. (2022). Climate change 2022: Mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
  • Jansen, A. G. S. M., Teunis, P. F. M., & van der Heijden, P. G. M. (2003). Measuring prediction error in censored data. Statistics in Medicine, 22(21), 3365–3377.
  • Kıratoğlu, E. (2024). Bütçenin Yüzyılı: Cumhuriyetten Günümüze Bütçe Politikaları. JOEEP: Journal of Emerging Economies and Policy, 9(1), 21-34.
  • Kong, D., Dai, Z., Tang, J., & Zhang, H. (2023). Forecasting urban carbon emissions using an Adaboost–STIRPAT model. Frontiers in Environmental Science, 11, 1284028. https://doi.org/10.3389/fenvs.2023.1284028
  • Lewis, C. D. (1982). Industrial and business forecasting methods. Butterworths.
  • Lin, C. S., Liou, F., & Huang, C. P. (2011). Grey forecasting model for CO₂ emissions: A Taiwan study. Applied Energy, 88(11), 3816–3820. https://doi.org/10.1016/j.apenergy.2011.05.040
  • Lotfalipour, M. R., Falahi, M. A., & Bastam, M. (2013). Prediction of CO₂ emissions in Iran using grey and ARIMA models. International Journal of Energy Economics and Policy, 3(3), 229–237. https://doi.org/10.5547/2160-5890.3.3.12
  • Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and applications (3rd ed.). John Wiley & Sons.
  • Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2008). Introduction to time series analysis and forecasting. John Wiley & Sons.
  • Arık, O. A., & Hamamcıoğlu, E. (2024). Türkiye’nin CO₂ emisyonunun ANFIS ile tahmin edilmesi. Researcher, 4(2), 141–156.
  • OECD, & UNDB. (2025). Investing in climate for growth and development: The case for enhanced nationally determined contributions (NDCs). OECD Publishing. (Rapor; çoğu durumda URL/ISBN kullanılmakta, DOI isteğe bağlıdır.)
  • OECD. Air and GHG emissions https://www.oecd.org/en/data/indicators/air-and-ghg-emissions.html Erişim Tarihi: 21.09.2025
  • Ord, J. K., & Fildes, R. (2013). Principles of business forecasting. South-Western Cengage Learning.
  • Öztürk, S., & Emir, A. (2024). Estimations of greenhouse gases emissions of Turkey by statistical methods. Konya Journal of Engineering Sciences, 12(1), 138–149. https://doi.org/10.36306/konjes.1267008
  • Pabuçcu, H., & Bayramoğlu, T. (2016). Yapay sinir ağları ile CO₂ emisyonu tahmini: Türkiye örneği. Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 18(3), 762–778.
  • Pao, H. T., & Tsai, C. M. (2011). Modeling and forecasting the CO₂ emissions, energy consumption, and economic growth in Brazil. Energy, 36(5), 2450–2458. https://doi.org/10.1016/j.energy.2011.01.032
  • Sel, A., & Tekgün, B. (2022). ANFIS yöntemi ile Türkiye karbondioksit salınımı tahmini. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 13(34), 486–504. https://doi.org/10.21076/vizyoner.990380
  • Shih, S. H., & Tsokos, C. P. (2008). Prediction models for carbon dioxide emissions and the atmosphere. Neural, Parallel and Scientific Computations, 16(1), 165–180.
  • T.C. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı (2023). Ulusal sera gazı emisyon envanteri.
  • TÜİK. (2023). Sera Gazı Emisyon İstatistikleri, 1990–2021. Erişim adresi: https://data.tuik.gov.tr TÜİK Veri Portalı
  • Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/cr030079
  • Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324–342. https://doi.org/10.1287/mnsc.6.3.324
  • Wu, L., Liu, S., Liu, D., Fang, Z., & Xu, H. (2015). Modelling and forecasting CO₂ emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model. Energy, 79, 489–495. https://doi.org/10.1016/j.energy.2014.11.026
  • Zuo, Z., Guo, H., & Cheng, J. (2020). An LSTM–STIRPAT model analysis of China’s 2030 CO₂ emissions peak. Carbon Management, 11(6), 577–592. https://doi.org/10.1080/17583004.2020.1849415
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ekonomik Modeller ve Öngörü, Büyüme
Bölüm Araştırma Makalesi
Yazarlar

İlknur Yeşim Dinçel Kıratoğlu 0000-0001-6367-7949

Gönderilme Tarihi 1 Aralık 2025
Kabul Tarihi 28 Aralık 2025
Yayımlanma Tarihi 31 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Sayı: Özel Sayı 3

Kaynak Göster

APA Dinçel Kıratoğlu, İ. Y. (2025). Türkiye’nin 2030 CO2 Emisyon Hedefinin Holt-Winters Üstel Düzeltme Modeli ile Tahmini. Maliye ve Finans Yazıları(Özel Sayı 3), 109-127. https://doi.org/10.33203/mfy.1834216
AMA Dinçel Kıratoğlu İY. Türkiye’nin 2030 CO2 Emisyon Hedefinin Holt-Winters Üstel Düzeltme Modeli ile Tahmini. Maliye ve Finans Yazıları. Aralık 2025;(Özel Sayı 3):109-127. doi:10.33203/mfy.1834216
Chicago Dinçel Kıratoğlu, İlknur Yeşim. “Türkiye’nin 2030 CO2 Emisyon Hedefinin Holt-Winters Üstel Düzeltme Modeli ile Tahmini”. Maliye ve Finans Yazıları, sy. Özel Sayı 3 (Aralık 2025): 109-27. https://doi.org/10.33203/mfy.1834216.
EndNote Dinçel Kıratoğlu İY (01 Aralık 2025) Türkiye’nin 2030 CO2 Emisyon Hedefinin Holt-Winters Üstel Düzeltme Modeli ile Tahmini. Maliye ve Finans Yazıları Özel Sayı 3 109–127.
IEEE İ. Y. Dinçel Kıratoğlu, “Türkiye’nin 2030 CO2 Emisyon Hedefinin Holt-Winters Üstel Düzeltme Modeli ile Tahmini”, Maliye ve Finans Yazıları, sy. Özel Sayı 3, ss. 109–127, Aralık2025, doi: 10.33203/mfy.1834216.
ISNAD Dinçel Kıratoğlu, İlknur Yeşim. “Türkiye’nin 2030 CO2 Emisyon Hedefinin Holt-Winters Üstel Düzeltme Modeli ile Tahmini”. Maliye ve Finans Yazıları Özel Sayı 3 (Aralık2025), 109-127. https://doi.org/10.33203/mfy.1834216.
JAMA Dinçel Kıratoğlu İY. Türkiye’nin 2030 CO2 Emisyon Hedefinin Holt-Winters Üstel Düzeltme Modeli ile Tahmini. Maliye ve Finans Yazıları. 2025;:109–127.
MLA Dinçel Kıratoğlu, İlknur Yeşim. “Türkiye’nin 2030 CO2 Emisyon Hedefinin Holt-Winters Üstel Düzeltme Modeli ile Tahmini”. Maliye ve Finans Yazıları, sy. Özel Sayı 3, 2025, ss. 109-27, doi:10.33203/mfy.1834216.
Vancouver Dinçel Kıratoğlu İY. Türkiye’nin 2030 CO2 Emisyon Hedefinin Holt-Winters Üstel Düzeltme Modeli ile Tahmini. Maliye ve Finans Yazıları. 2025(Özel Sayı 3):109-27.

Maliye ve Finans Yazıları dergisinin kapsamını ekonomi, maliye, finans ve bankacılık alanlarındaki çalışmalar oluşturmaktadır.