European Union 2030 Carbon Emission Target: The Case of Turkey

Mehmet Kayakuş, Mustafa Terzioğlu, Dilşad Erdoğan, Selin Aygen Zetter, Onder Kabas () and Georgiana Moiceanu ()
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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

Sustainability, 2023, vol. 15, issue 17, 1-23

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.

Keywords: carbon dioxide emission; environment; Turkey; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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