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Journals / Ekonomi Politika ve Finans Araştırmaları Dergisi / 2026 / Cilt: 11 Sayı: 2

Modeling the Energy Transition in Türkiye’s Transportation Sector: A Machine Learning Approach

Türkiye’nin Ulaşım Sektöründe Enerji Dönüşümünün Modellenmesi: Bir Makine Öğrenmesi Yaklaşımı

Mustafa Çağrı Peker(TOBB EKONOMİ VE TEKNOLOJİ ÜNİVERSİTESİ)0000-0001-7191-2646
Pages
480–505
Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results show entrenched fossil fuel infrastructures, taxation structures, and supply chain dependencies hinder EV diffusion, while niche innovations—domestic production (e.g., TOGG) and expanding charging networks—transform the sector.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The global shift toward clean energy is reshaping supply chains, with road transportation, a carbon-emitting sector, at the center.
📊
Key Findings & Data:In Türkiye, cleaner road transportation aligns with national energy strategies, the Paris Agreement, and the EU Green Deal.
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Conclusion & Impact:Results show entrenched fossil fuel infrastructures, taxation structures, and supply chain dependencies hinder EV diffusion, while niche innovations—domestic production (e.g., TOGG) and expanding charging networks—transform the sector.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Enerji Dönüşümü · Çok Düzeyli Perspektif · Elektrikli Araçlar

Abstract

The global shift toward clean energy is reshaping supply chains, with road transportation, a carbon-emitting sector, at the center. Electric vehicles (EVs) offer a decarbonization pathway with minimal consumer disruption. In Türkiye, cleaner road transportation aligns with national energy strategies, the Paris Agreement, and the EU Green Deal. Although fossil-fuel mobility dominates, rising EV adoption reflects technological progress, regulatory incentives, and market dynamics. The study applies the Multi-Level Perspective (MLP) and machine learning (ML) to examine Türkiye's road-transport energy transition. Results show entrenched fossil fuel infrastructures, taxation structures, and supply chain dependencies hinder EV diffusion, while niche innovations—domestic production (e.g., TOGG) and expanding charging networks—transform the sector. Perceptron and Decision Tree models identify adoption drivers: charging availability, fuel prices, and macroeconomic conditions. GDP per capita and diesel, gasoline, and LPG price increases boost EV sales, whereas inflation, interest rates, and exchange rates reduce them. Accelerating the transition requires coordinated governance, tax incentives, infrastructure investment, and decarbonized electricity generation.

Özet

Küresel temiz enerji geçişi, tedarik zincirlerini yeniden şekillendirirken, yüksek karbon emisyonlu sektörlerden biri olan karayolu taşımacılığı dönüşümün merkezindedir. Elektrikli araçlar (EA'lar), tüketici davranışını değiştirmeden karbon azaltımı için bir fırsat sunmaktadır. Türkiye'de karayolu taşımacılığının temiz enerjiye yönelmesi; ulusal enerji politikaları, Paris Anlaşması ve AB Yeşil Mutabakatı hedefleriyle uyumludur. Fosil yakıtlı ulaşım baskın olsa da, artan EA kullanımı teknolojik gelişmeler, düzenleyici teşvikler ve piyasa dinamikleriyle şekillenmektedir. Çalışma, Türkiye'nin karayolu taşımacılığındaki enerji dönüşümünü incelemek için Çok Düzeyli Perspektif (MLP) ve makine öğrenmesi (MÖ) tekniklerini kullanır. Bulgular, fosil yakıt altyapıları, vergi yapıları ve tedarik zinciri bağımlılıklarının EA yayılımını sınırladığını; TOGG gibi yerli üretim ve genişleyen şarj ağları gibi yeniliklerin sektörü dönüştürdüğünü gösterir. Perceptron ve Karar Ağacı modelleri, benimsemenin temel etkenlerini şarj erişimi, akaryakıt fiyatları ve makroekonomik koşullar olarak belirler. Kişi başı gelir ile dizel, benzin ve LPG fiyatlarındaki artış EA satışlarını olumlu; enflasyon, faiz oranı ve döviz kuru olumsuz etkiler. Dönüşümün hızlanması; eşgüdümlü yönetişim, vergi teşvikleri, altyapı yatırımı ve elektrik üretiminde karbon azaltımını gerektirir.

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Cite This Article

Peker, M. Ç. (2026). Modeling the Energy Transition in Türkiye’s Transportation Sector: A Machine Learning Approach. Ekonomi Politika ve Finans Araştırmaları Dergisi, Cilt: 11 Sayı: 2, 480–505. https://doi.org/10.30784/epfad.1839090

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