IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i17p13025-d1228231.html
   My bibliography  Save this article

European Union 2030 Carbon Emission Target: The Case of Turkey

Author

Listed:
  • Mehmet Kayakuş

    (Department of Management Information Systems, Faculty of Manavgat Social Sciences and Humanities, Akdeniz University, 07070 Antalya, Türkiye)

  • Mustafa Terzioğlu

    (Accounting and Tax Department, Korkuteli Vocational School, Akdeniz University, 07070 Antalya, Türkiye)

  • Dilşad Erdoğan

    (Department of Finance, Banking and Insurance, Korkuteli Vocational School, Akdeniz University, 07070 Antalya, Türkiye)

  • Selin Aygen Zetter

    (Department of Office Services and Secretariat, Social Sciences Vocational School, Akdeniz University, 07070 Antalya, Türkiye)

  • Onder Kabas

    (Department of Machine, Technical Science Vocational School, Akdeniz University, 07070 Antalya, Türkiye)

  • Georgiana Moiceanu

    (Department of Entrepreneurship and Management, Faculty of Entrepreneurship, Business Engineering and Management, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania)

Abstract

Climate awareness caused by the threat of global warming is the number one agenda item for developed and developing economies. Plans developed in this context, environmentally friendly trends in economic activities, and countries’ efforts to adapt to sustainable development have enabled new road maps. The most important of these efforts is the Paris Climate Agreement signed in 2015 and the Green Deal implemented by the European Union (EU) within the framework of this agreement. In this study, the carbon emissions of Turkey, which has important trade relations with the EU, were estimated using machine learning techniques, and a prediction was made for 2030 based on the results obtained. These results were evaluated in line with the targets of the Green Deal. The R 2 of support vector regression (SVR), which was applied in the model as one of the machine learning techniques, was found to be 98.4%, and it was found to have the highest predictive power. This technique was followed by multiple linear regression (MLR) with a 97.6% success rate and artificial neural networks (ANN) with a 95.8% success rate, respectively. According to the estimates achieved with the most successful model, SVR, Turkey’s carbon emissions are expected to be 723.97 million metric tons (mt) of carbon dioxide (CO 2 ) in 2030, the target year set by the EU. This level is 42% higher than the target that needs to be achieved given the data existing in 2019. According to these results, Turkey will not be able to reach the targets set by the EU unless it increases its coal-based energy consumption and provides incentives for renewable energy sources.

Suggested Citation

  • Mehmet Kayakuş & Mustafa Terzioğlu & Dilşad Erdoğan & Selin Aygen Zetter & Onder Kabas & Georgiana Moiceanu, 2023. "European Union 2030 Carbon Emission Target: The Case of Turkey," Sustainability, MDPI, vol. 15(17), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13025-:d:1228231
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/17/13025/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/17/13025/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    2. Udemba, Edmund Ntom & Emir, Firat & Philip, Lucy Davou, 2022. "Mitigating poor environmental quality with technology, renewable and entrepreneur policies: A symmetric and asymmetric approaches," Renewable Energy, Elsevier, vol. 189(C), pages 997-1006.
    3. Sandip Garai & Ranjit Kumar Paul & Debopam Rakshit & Md Yeasin & Walid Emam & Yusra Tashkandy & Christophe Chesneau, 2023. "Wavelets in Combination with Stochastic and Machine Learning Models to Predict Agricultural Prices," Mathematics, MDPI, vol. 11(13), pages 1-18, June.
    4. Pradyot Ranjan Jena & Shunsuke Managi & Babita Majhi, 2021. "Forecasting the CO 2 Emissions at the Global Level: A Multilayer Artificial Neural Network Modelling," Energies, MDPI, vol. 14(19), pages 1-23, October.
    5. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mehmet Balcilar & Daberechi Chikezie Ekwueme & Hakki Ciftci, 2023. "Assessing the Effects of Natural Resource Extraction on Carbon Emissions and Energy Consumption in Sub-Saharan Africa: A STIRPAT Model Approach," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    2. Monika Zimmermann & Florian Ziel, 2024. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Papers 2405.17070, arXiv.org.
    3. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    4. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    5. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    6. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    7. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    8. Lessmann, Stefan & Voß, Stefan, 2017. "Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy," International Journal of Forecasting, Elsevier, vol. 33(4), pages 864-877.
    9. Maria Tzitiridou-Chatzopoulou & Georgia Zournatzidou & Michael Kourakos, 2024. "Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland," IJERPH, MDPI, vol. 21(7), pages 1-13, June.
    10. Wang, Jianzhou & Niu, Xinsong & Zhang, Lifang & Liu, Zhenkun & Wei, Danxiang, 2022. "The influence of international oil prices on the exchange rates of oil exporting countries: Based on the hybrid copula function," Resources Policy, Elsevier, vol. 77(C).
    11. Bunn, Derek W. & Taylor, James W., 2001. "Setting accuracy targets for short-term judgemental sales forecasting," International Journal of Forecasting, Elsevier, vol. 17(2), pages 159-169.
    12. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2019. "Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors," International Journal of Production Economics, Elsevier, vol. 209(C), pages 92-102.
    13. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E., 2010. "Judging the judges through accuracy-implication metrics: The case of inventory forecasting," International Journal of Forecasting, Elsevier, vol. 26(1), pages 134-143, January.
    14. Balazs Pager & Zsuzsanna Zsibókb, 2020. "Regionalizing National-Level Growth Projections in the Visegrad Countries – The Issue Of Ex-Post Rescaling," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 14(1), pages 1-24, JUNE.
    15. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.
    16. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    17. Nerea Portillo Juan & Vicente Negro Valdecantos & José María del Campo, 2022. "A New Climate Change Analysis Parameter: A Global or a National Approach Dilemma," Energies, MDPI, vol. 15(4), pages 1-24, February.
    18. Zeng, Qingshun & Shi, Changfeng & Zhu, Wenjun & Zhi, Jiaqi & Na, Xiaohong, 2023. "Sequential data-driven carbon peaking path simulation research of the Yangtze River Delta urban agglomeration based on semantic mining and heuristic algorithm optimization," Energy, Elsevier, vol. 285(C).
    19. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    20. McKenzie, Jordi, 2011. "Mean absolute percentage error and bias in economic forecasting," Economics Letters, Elsevier, vol. 113(3), pages 259-262.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13025-:d:1228231. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.