Estimation of CO2 Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach

ENE YALÇIN S.

Systems, cilt.13, sa.3, 2025 (SSCI, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 3
  • Basım Tarihi: 2025
  • Doi Numarası: 10.3390/systems13030194
  • Dergi Adı: Systems
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus
  • Anahtar Kelimeler: artificial neural networks, CO2 emissions, deep learning, forecasting, machine learning, transport systems
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

This study focuses on estimating transportation system-related emissions in CO2 eq., considering several socioeconomic and energy- and transportation-related input variables. The proposed approach incorporates artificial neural networks, machine learning, and deep learning algorithms. The case of Turkey was considered as an example. Model performance was evaluated using a dataset of Turkey, and future projections were made based on scenario analysis compatible with Turkey’s climate change mitigation strategies. This study also adopted a transportation type-based analysis, exploring the role of Turkey’s road, air, marine, and rail transportation systems. The findings of this study indicate that the aforementioned models can be effectively implemented to predict transport emissions, concluding that they have valuable and practical applications in this field.