Investigating the Dynamic Relationship Between Greenhouse Gas Emissions and Gross Domestic Product in Türkiye

Year 2023, Volume: 25 Issue: Özel, 101 - 134, 30.10.2023

Göktuğ Şahin , Savaş Gayaker

https://doi.org/10.26745/ahbvuibfd.1363770

Abstract

This study aims to investigate the causal relationship between Gross Domestic Product and greenhouse gas emissions in Türkiye from 1951 to 2018, using the Causal Decomposition Method that integrates Ensemble Empirical Mode Decomposition, Hilbert-Huang Transform, and Phase Coherence Methods. The primary focus is on identifying the key sectors contributing significantly to greenhouse gas emissions, particularly those connected to industrial production. The analysis reveals a one-way, short-term causal relationship from Gross Domestic Product to greenhouse gas emissions, spanning approximately 3 years. This finding suggests that changes in Gross Domestic Product have short-term effects on emissions, but not vice versa. Special emphasis is placed on the gases Cardon Dioxide, Methane and Nitrous Oxide, as they demonstrate a strong, consistent causal connection with Gross Domestic Product. The significance of this study lies in its utilization of the Ensemble Empirical Mode Decomposition approach to investigate this dynamic causality and address a notable gap in the existing literature. Empirical results indicate a complex yet observable association between Gross Domestic Product growth and greenhouse gas emissions in Türkiye, and that this relationship becomes more important, especially in the short and long term, with periodic fluctuations.

Keywords

Greenhouse Gas Emissions, Gross Domestic Product, Casual Decomposition Method, Macroeconomics, Türkiye

Thanks

We sincerely thank the Esteemed Editors and Reviewers who contributed to our work by sparing their valuable time.

Türkiye'de Sera Gazı Emisyonları ile Gayrisafi Yurtiçi Hasıla Arasındaki Dinamik İlişkinin İncelenmesi

Abstract

Bu çalışma, 1951 ile 2018 yıllarını kapsayan dönemde Türkiye'deki Gayrisafi Yurtiçi Hasıla ile sera gazı emisyonları arasındaki nedensel ilişkinin incelenmesini amaçlamaktadır. Bu inceleme, Topluluk Ampirik Kip Ayrıştırma, Hilbert-Huang Dönüşümü ve Faz Uyumluluk Yöntemlerinin entegre edilmesiyle birlikte Nedensel Ayrıştırma Yöntemi kullanılarak gerçekleştirilmiştir. Çalışmanın odak noktası, sanayi üretimiyle bağlantılı olan ve sera gazı emisyonlarına önemli ölçüde katkıda bulunan birincil sektörlerin belirlenmesidir. Analiz, Gayrisafi Yurtiçi Hasıladan sera gazı emisyonlarına doğru tek yönlü ve kısa vadeli bir nedensel ilişkiyi, yaklaşık olarak 3 yıllık bir dönemi kapsayacak şekilde ortaya koymaktadır. Bu bulgu, Gayrisafi Yurtiçi Hasıladaki değişikliklerin emisyonlar üzerinde kısa vadeli etkilere sahip olduğunu, ancak tersinin geçerli olmadığını öne sürmektedir. Çalışmada Karbondioksit, Metan ve Nitröz Oksit gazlarına özel önem verilmekte olup bu gazların Gayrisafi Yurtiçi Hasıla ile güçlü ve tutarlı bir nedensel ilişkisi olduğu gözlenmektedir. Çalışmanın önemi, ilgili dinamik nedenselliği incelemek için Topluluk Ampirik Kip Ayrıştırma yaklaşımının kullanılması ve mevcut literatürde önemli bir boşluğu doldurmasıdır. Ampirik sonuçlar, Türkiye'de Gayrisafi Yurtiçi Hasıla büyümesi ile sera gazı emisyonları arasında karmaşık ancak gözlemlenebilir bir ilişkinin olduğunu ve bu ilişkinin dönemsel dalgalanmalar ile birlikte özellikle kısa ve uzun vadede daha da önem kazandığını göstermektedir.

Keywords

Sera Gazı Emisyonları, Gayrisafi Yurtiçi Hasıla, Nedensel Ayrıştırma Yöntemi, Makroekonomi, Türkiye

Thanks

Değerli zamanlarını ayırmak suretiyle çalışmamıza katkıda bulunan Sayın Editör ve Hakemlere en içten teşekkürlerimizi sunarız.

References

• Abbass, K., Qasim, M. Z., Song, H., Murshed, M., Mahmood, H., & Younis, I. (2022). A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environmental Science and Pollution Research, 29(28), 42539-42559.
• Ahmad, W., Aamir, M., Khalil, U., Ishaq, M., Iqbal, N., & Khan, M. (2021). A new approach for forecasting crude oil prices using median ensemble empirical mode decomposition and group method of data handling. Mathematical Problems in Engineering, 1-12.
• Ai, L., Wang, J., & Yao, R. (2011). Classification of parkinsonian and essential tremor using Empirical Mode Decomposition and support vector machine. Digital Signal Processing, 21(4), 543-550.
• Allwood, J. M., Bosetti, N. K., Gomez-Echeverri, L., von Stechow, C., & Smith, P. (2014). Annex I: Glossary, Acronyms and Chemical Symbols. In Climate Change 2014: Mitigation of Climate Change.: Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1249-1279). Cambridge University Press. (Access Date: 22.07.2023)
• Amar, A., & Guennoun Z.E.A. (2012). Contribution of wavelet transformation and empirical mode decomposition to measurement of US core inflation. Applied Mathematical Sciences, 6(135), 6739-6752.
• An, N., Zhao, W., Wang, J., Shang, D., & Zhao, E. (2013). Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy, 49, 279-288. Awajan, A. M., Ismail, M. T., & Al Wadi, S. (2018). Improving forecasting accuracy for stock market data using EMD-HW bagging. PloS one, 13(7), e0199582.
• Bokde, N. D., Tranberg, B., & Andresen, G. B. (2021). Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling. Applied Energy, 281, 116061.
• Callen, T. (2014). Gross Domestic Product: An Economy’s All. https://www.imf.org/en/Publications/fandd/issues/Series/Back-to-Basics/gross-domestic-product-GDP (Access Date: 21.07.2023)
• CBO (2003). The Economics of Climate Change: A Primer. https://www.cbo.gov/sites/default/files/108th-congress-2003-2004/reports/04-25-climatechange.pdf (Access Date: 21.07.2023)
• Cheng, C. H., & Wei, L. Y. (2014). A novel time-series model based on Empirical Mode Decomposition for forecasting TAIEX. Economic Modelling, 36, 136-141.
• Cheng, J., Yang, Y., & Yang, Y. (2012). A rotating machinery fault diagnosis method based on Local Mean Decomposition. Digital Signal Processing, 22(2), 356-366.
• Cho, J. H., Kim, D. K., & Kim, E. J. (2022). Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble Empirical Mode Decomposition and Causal Decomposition. Physica A: Statistical Mechanics and its Applications, 600, 127488.
• Climate Watch (2023). Greenhouse Gas (GHG) Emissions. https://www.climatewatchdata.org/ghg-emissions (Access Date: 21.06.2023)
• Das, P., Jha, G. K., Lama, A., Parsad, R., & Mishra, D. (2020). Empirical Mode Decomposition based support vector regression for agricultural price forecasting. Indian Journal of Extension Education, 56(2), 7-12.
• DCC (2023a). United Nations Framework Convention on Climate Change. https://iklim.gov.tr/en/un-framework-convention-on-climate-change-i-114 (Access Date: 12.06.2023).
• DCC (2023b). Vienna Convention. https://iklim.gov.tr/en/vienna-convention-i-119 (Access Date: 12.06.2023).
• DCC (2023c). Montreal Protocol. https://iklim.gov.tr/en/montreal-protocol-i-120 (Access Date: 12.06.2023).
• DCC (2023d). Kyoto Protocol. https://iklim.gov.tr/en/kyoto-protocol-i-118 (Access Date: 12.06.2023).
• DCC (2023e). Paris Agreement. https://iklim.gov.tr/en/paris-agreement-i-117 (Access Date: 12.06.2023).
• Deyle, E. R., & Sugihara, G. (2011). Generalized theorems for nonlinear state space reconstruction. Plos one, 6(3), e18295.
• Doll, C. H., Muller, J. P., & Elvidge, C. D. (2000). Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions. AMBIO: A Journal of the Human Environment, 29(3), 157-162. EPA (2023a). Basics of Climate Change. https://www.epa.gov/climatechange-science/basics-climate-change (Access Date: 21.06.2023)
• EPA (2023b). Sources of Greenhouse Gas Emissions. https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions (Access Date: 21.06.2023)
• Eskander, S. M. S. U., & Fankhauser, S. (2020). Reduction in greenhouse gas emissions from national climate legislation. Nature Climate Change, 10(8), 750-756.
• European Parliament (2023). Climate Change: The Greenhouse Gases Causing Global Warming. https://www.europarl.europa.eu/news/en/headlines/society/20230316STO77629/climate-change-the-greenhouse-gases-causing-global-warming (Access Date: 15.05.2023).
• Eurostat (2023a). Glossary: Greenhouse Gas (GHG) – Statistics Explained. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Greenhouse_gas_(GHG) (Access Date: 15.05.2023).
• Eurostat (2023b). National Accounts and GDP – Statistics Explained. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=National_accounts_and_GDP&oldid=571240 (Access Date: 15.05.2023).
• Fang, Y., Guan, B., Wu, S., & Heravi, S. (2020). Optimal forecast combination based on ensemble Empirical Mode Decomposition for agricultural commodity futures prices. Journal of Forecasting, 39(6), 877-886.
• FRED (2023). Real GDP at Constant National Prices for Turkey (RGDPNATRA666NRUG). https://fred.stlouisfed.org (Access Date: 21.06.2023).
• Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 37(3), 424-438.
• Granger, C. W. (1988). Some recent development in a concept of causality. Journal of Econometrics, 39(1-2), 199-211.
• Granger, C. W., & Lin, J. L. (1995). Causality in the long run. Econometric Theory, 11(3), 530-536.
• Gyamfi, E. N., Sarpong, F. A., & Adam, A. M. (2021). Drivers of stock prices in Ghana: an Empirical Mode Decomposition approach. Mathematical Problems in Engineering, 2021, 1-7.
• Haberl, H., Wiedenhofer, D., Virág, D., Kalt, G., Plank, B., Brockway, P., Fishman, T., Hausknost, D., Krausmann, F., Leon-Gruchalski, B., Mayer, A., Pichler, M., Schaffartzik, A., Sousa, T., Streeck, J., & Creutzig, F. (2020). A systematic review of the evidence on decoupling of GDP, resource use and GHG emissions, part II: synthesizing the insights. Environmental Research Letters, 15(6), 065003.
• Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., & Liu, H. H. (1998). The Empirical Mode Decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995.
• Huang, W. M., Lee, G. W., & Wu, C. C. (2008). GHG emissions, GDP growth and the Kyoto Protocol: A revisit of Environmental Kuznets Curve hypothesis. Energy Policy, 36(1), 239-247.
• IPCC (2007). AR4 WGI Chapter 2: Changes in Atmospheric Constituents and in Radiative Forcing. https://archive.ipcc.ch/publications_and_data/ar4/wg1/en/ch2.html (Access Date: 21.05.2023).
• IPCC (2021). AR6 Climate Change 2021: The Physical Science Basis. https://www.ipcc.ch/report/sixth-assessment-report-working-group-i (Access Date: 21.05.2023).
• IPCC (2022). Climate Change 2022: Mitigation of Climate Change. https://www.ipcc.ch/report/ar6/wg3 (Access Date: 21.05.2023).
• Kaygusuz, K. (2009). Energy and environmental issues relating to greenhouse gas emissions for sustainable development in Turkey. Renewable and Sustainable Energy Reviews, 13(1), 253-270.
• Kong, F., Song, J., & Yang, Z. (2022). A daily carbon emission prediction model combining two-stage feature selection and optimized extreme learning machine. Environmental Science and Pollution Research, 29(58), 87983-87997.
• Lahmiri, S. (2014a). Comparative study of ECG signal denoising by wavelet thresholding in Empirical and Variational Mode Decomposition domains. Healthcare Technology Letters, 1(3), 104-109.
• Lahmiri, S., & Boukadoum, M. (2014b). Automated detection of circinate exudates in retina digital images using Empirical Mode Decomposition and the entropy and uniformity of the intrinsic mode functions. Biomedical Engineering/Biomedizinische Technik, 59(4), 357-366.
• Lahmiri, S., & Boukadoum, M. (2015a). A weighted bio-signal denoising approach using Empirical Mode Decomposition. Biomedical Engineering Letters, 5, 131-139.
• Lahmiri, S., & Boukadoum, M. (2015b). Pathology grading in retina digital images using Student-adjusted Empirical Mode Decomposition and power law statistics. In 2015 IEEE 6th Latin American Symposium on Circuits & Systems (LASCAS), 1-4.
• Lemmens, A., Croux, C., & Dekimpe, M. G. (2008). Measuring and testing Granger Causality over the spectrum: An application to European production expectation surveys. International Journal of Forecasting, 24(3), 414-431.
• Li, C., Wang, X., Tao, Z., Wang, Q., & Du, S. (2011). Extraction of time varying information from noisy signals: An approach based on the Empirical Mode Decomposition. Mechanical Systems and Signal Processing, 25(3), 812-820.
• Lin, C. S., Chiu, S. H., & Lin, T. Y. (2012). Empirical Mode Decomposition–based least squares support vector regression for foreign exchange rate forecasting. Economic Modelling, 29(6), 2583-2590.
• Lin, S. L. (2022). Application of Empirical Mode Decomposition to improve deep learning for US GDP data forecasting. Heliyon, 8(1), e08748.
• Lisi, F., & Nan, F. (2014). Component estimation for electricity prices: Procedures and comparisons. Energy Economics, 44, 143-159.
• Mao, X., Yang, A. C., Peng, C. K., & Shang, P. (2020). Analysis of economic growth fluctuations based on EEMD and causal decomposition. Physica A: Statistical Mechanics and its Applications, 553, 124661.
• Marrero, G. A. (2010). Greenhouse gases emissions, growth and the energy mix in Europe. Energy Economics, 32(6), 1356-1363.
• MFA. (2023). Türkiye'nin Çevre Politikası. https://www.mfa.gov.tr/sub.tr.mfa?c74e3b4e-02fc-45fb-b019-384acb992538 (Access Date: 20.06.2023)
• Ministry of Trade (2021). Yeşil Mutabakat Eylem Planı. https://ticaret.gov.tr/data/60f1200013b876eb28421b23/MUTABAKAT%20YEŞİL.pdf (Access Date: 20.06.2023)
• OECD (2014). Understanding National Accounts: Second Edition. https://www.oecd-ilibrary.org/economics/understanding-national-accounts_9789264214637-en (Access Date: 20.06.2023)
• Öztürk, I., & Acaravcı, A. (2010). CO2 emissions, energy consumption and economic growth in Turkey. Renewable and Sustainable Energy Reviews, 14(9), 3220-3225.
• Premanode, B., & Toumazou, C. (2013). Improving prediction of exchange rates using differential EMD. Expert systems with applications, 40(1), 377-384.
• Rashid, N. I. A., Samsudin, R., & Shabri, A. (2016). Exchange rate forecasting using modified empirical mode decomposition and least squares support vector machine. Int. J. Advance Soft Compu. Appl, 8(3), 31-47.
• Ricci, R., & Pennacchi, P. (2011). Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions. Mechanical Systems and Signal Processing, 25(3), 821-838.
• Şahin, G., Taksim, M. A., & Yitgin, B. (2021). Effects of the European Green Deal on Turkey’s electricity market. İşletme Ekonomi ve Yönetim Araştırmaları Dergisi, 4(1), 40-58.
• Sözen, A., Gülseven, Z., & Arcaklıoğlu, E. (2009). Estimation of GHG emissions in Turkey using energy and economic indicators. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 31(13), 1141-1159.
• Sugihara, G., May, R., Ye, H., Hsieh, C. H., Deyle, E., Fogarty, M., & Munch, S. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496-500.
• Takens, F. (1981). Detecting strange attractors in turbulence. Lecture Notes in Mathematics, 898, 366-38.
• Tucker, M. (1995). Carbon dioxide emissions and global GDP. Ecological Economics, 15(3), 215-223.
• UN (2023). Emissions of Greenhouse Gases. https://www.un.org/esa/sustdev/natlinfo/indicators/methodology_sheets/atmosphere/ghg_emissions.pdf (Access Date: 21.07.2023)
• UNFCCC (1992). United Nations Framework Convention on Climate Change. https://unfccc.int/resource/docs/convkp/conveng.pdf (Access Date: 12.05.2023)
• UNFCCC (2023). What is the United Nations Framework Convention on Climate Change?. https://unfccc.int/process-and-meetings/what-is-the-united-nations-framework-convention-on-climate-change (Access Date: 12.05.2023)
• WHO (2021). Climate Change and Health. https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health (Access Date: 11.04.2023)
• World Bank (2022). Climate Change and Air Pollution. World Bank. https://www.worldbank.org/en/news/feature/2022/09/01/what-you-need-to-know-about-climate-change-and-air-pollution (Access Date: 11.04.2023)
• WRI (2020). Greenhouse Gas Emissions by Countries and Sectors. https://www.wri.org/insights/4-charts-explain-greenhouse-gas-emissions-countries-and-sectors (12.05.2023)
• Wu, J. D., & Tsai, Y. J. (2011). Speaker identification system using empirical mode decomposition and an artificial neural network. Expert Systems with Applications, 38(5), 6112-6117.
• Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(01), 1-41.
• Xia, C., & Huang, J. (2022). Construction of inflation forecasting model based on Ensemble Empirical Mode Decomposition and Bayesian Model. Journal of Sensors, 2022.
• Xu, C., Zhao, X., & Wang, Y. (2022). Causal decomposition on multiple time scales: Evidence from stock price-volume time series. Chaos, Solitons & Fractals, 159, 112137.
• Yang, A. C., Peng, C. K., & Huang, N. E. (2018). Causal decomposition in the mutual causation system. Nature Communications, 9(1), 3378.
• Zhang, L., Xu, M., Chen, H., Li, Y., & Chen, S. (2022). Globalization, green economy and environmental challenges: State of the art review for practical implications. Frontiers in Environmental Science, 10, 870271.
• Zhang, T., & Tang, Z. (2023). Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach. Frontiers in Energy Research, 10, 991570.
• Zhang, W. F., & Yan, H. (2012). Exon prediction using empirical mode decomposition and Fourier transform of structural profiles of DNA sequences. Pattern Recognition, 45(3), 947-955.
• Zhang, X., Lai, K. K., & Wang, S. Y. (2008). A new approach for crude oil price analysis based on empirical mode decomposition. Energy Economics, 30(3), 905-918.
• Zhang, X., Yu, L., Wang, S., & Lai, K. K. (2009). Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method. Energy Economics, 31(5), 768-778.
• Zhou, J., & Wang, S. (2021). A carbon price prediction model based on the secondary decomposition algorithm and influencing factors. Energies, 14(5), 1328.
• Zhu, B., Wang, P., Chevallier, J., & Wei, Y. (2015). Carbon price analysis using empirical mode decomposition. Computational Economics, 45, 195-206.