Modeling the Energy Transition in Türkiye’s Transportation Sector: A Machine Learning Approach
トルコ運輸部門のエネルギー転換のモデリング：機械学習アプローチ (AI 翻訳)
Mustafa Çağrı Peker
🤖 gxceed AI 要約
日本語
機械学習を用いてトルコの道路運輸におけるエネルギー転換を分析。電気自動車（EV）普及の障壁として化石燃料インフラや課税構造、サプライチェーン依存を特定。一方、国内生産（TOGG）や充電網整備が転換を促進。パーセプトロンと決定木モデルにより、充電利用可能性、燃料価格、マクロ経済要因がEV販売に影響することを示す。政策協調、税制優遇、インフラ投資、脱炭素電力が必要。
English
This study applies machine learning (Perceptron and Decision Tree) and the Multi-Level Perspective framework to model the energy transition in Türkiye's road transport sector. It identifies entrenched fossil fuel infrastructure, taxation, and supply chain dependencies as key barriers to EV diffusion, while niche innovations like domestic production (TOGG) and charging network expansion drive change. Key drivers of EV adoption include charging availability, fuel prices, and GDP per capita, while inflation and interest rates hinder it. The findings call for coordinated governance, tax incentives, infrastructure investment, and decarbonized electricity.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
トルコはEUとの関税同盟やグリーンディールへの適合が求められ、運輸部門の脱炭素化は日本企業にもサプライチェーン上の影響を与える。トルコのEV普及政策と日本との比較が可能。
In the global GX context
As Türkiye aligns with the EU Green Deal and Paris Agreement, this paper provides empirical evidence on EV adoption drivers relevant to emerging economies. The use of ML to identify macro-financial barriers offers insights for transition finance strategies globally.
👥 読者別の含意
🔬研究者:Methodological contribution combining ML and MLP for energy transition analysis.
🏢実務担当者:Insights on EV adoption factors for market entry or supply chain planning in Turkey.
🏛政策担当者:Evidence for designing tax incentives, charging infrastructure, and macro-stabilization policies.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.30784/epfad.1839090first seen 2026-07-23 04:58:33
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