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Journals / Verimlilik Dergisi / 2026 / Cilt: 60 Sayı: 3

Assessing The Effects of Circular Economy and Energy Transition on Environmental Performance: Insights from Panel Data and Machine Learning Approaches

Döngüsel Ekonomi ve Enerji Dönüşümünün Çevresel Performans Üzerindeki Etkisi: Panel Veri ve Makine Öğrenmesi Tabanlı Bir Analiz

Ali Kemal Başbuğ0000-0002-0830-5816
Pages
965–984
Publication Date
2026-07-16
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AI Summary & Key Takeaways

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

Findings: The results show that renewable energy significantly reduces CO₂ emissions.

📌 3 Key Takeaways

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Objective & Hypothesis:Purpose: This study examines the effects of circular economy indicators and energy transition on environmental performance in 22 OECD countries over the period 2004–2023.
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Key Findings & Data:Environmental performance is measured by per capita CO₂ emissions, while circular economy is proxied by material productivity, recycling rate, and municipal waste.
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Conclusion & Impact:Findings: The results show that renewable energy significantly reduces CO₂ emissions.
🔬Design:Modeling & Simulation
📍Scope:2004–2023 Verileri
⚙️Variables:Döngüsel Ekonomi · Enerji Dönüşümü · CO₂ Emisyonları

Abstract

Purpose: This study examines the effects of circular economy indicators and energy transition on environmental performance in 22 OECD countries over the period 2004–2023. Environmental performance is measured by per capita CO₂ emissions, while circular economy is proxied by material productivity, recycling rate, and municipal waste. Methodology: A hybrid approach combining second-generation panel data econometrics and machine learning is employed. The analysis uses the CIPS unit root test, Westerlund cointegration test, and CCEMG estimator for long-run coefficients. Additionally, Random Forest, XGBoost, and SHAP analysis are applied to capture nonlinear relationships and assess variable importance. Findings: The results show that renewable energy significantly reduces CO₂ emissions. The Environmental Kuznets Curve (EKC) hypothesis is supported, indicating an inverted-U relationship between income and emissions. Among circular economy indicators, only recycling has a significant negative effect, while others operate through indirect and nonlinear channels. Originality: The study contributes by integrating panel data econometrics and machine learning to jointly assess circular economy and energy transition, providing novel insights into nonlinear and heterogeneous environmental effects.

Özet

Amaç: Bu çalışma, 2004–2023 dönemi için 22 OECD ülkesinde döngüsel ekonomi göstergeleri ile enerji dönüşümünün çevresel performans üzerindeki etkilerini incelemektedir. Çevresel performans kişi başına CO₂ emisyonları ile ölçülürken, döngüsel ekonomi malzeme verimliliği, geri dönüşüm oranı ve belediye atıkları ile temsil edilmektedir. Yöntem: Çalışmada ikinci nesil panel veri ekonometrisi ile makine öğrenmesi yöntemlerini birleştiren hibrit bir yaklaşım kullanılmaktadır. Analizde CIPS birim kök testi, Westerlund eşbütünleşme testi ve uzun dönem katsayıları için CCEMG tahmincisi uygulanmıştır. Ayrıca doğrusal olmayan ilişkileri incelemek ve değişken önemini belirlemek amacıyla Random Forest, XGBoost ve SHAP analizi kullanılmıştır. Bulgular: Sonuçlar, yenilenebilir enerji kullanımının CO₂ emisyonlarını anlamlı biçimde azalttığını göstermektedir. Çevresel Kuznets Eğrisi (EKC) hipotezi desteklenmekte olup gelir ile emisyonlar arasında ters-U şeklinde bir ilişki tespit edilmiştir. Döngüsel ekonomi göstergeleri arasında yalnızca geri dönüşüm oranının anlamlı ve negatif etkisi bulunurken, diğer göstergelerin dolaylı ve doğrusal olmayan etkiler yoluyla işlediği belirlenmiştir. Özgünlük: Çalışma, döngüsel ekonomi ve enerji dönüşümünü panel veri ekonometrisi ile makine öğrenmesi yöntemlerini birlikte kullanarak analiz etmekte ve çevresel performans üzerindeki doğrusal olmayan ve heterojen etkilere ilişkin özgün bulgular sunmaktadır.

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Cite This Article

Başbuğ, A. K. (2026). Assessing The Effects of Circular Economy and Energy Transition on Environmental Performance: Insights from Panel Data and Machine Learning Approaches. Verimlilik Dergisi, Cilt: 60 Sayı: 3, 965–984. https://doi.org/10.51551/verimlilik.1854527

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